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# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import logging
import torch
from fairseq import optim
from omegaconf import DictConfig
logger = logging.getLogger(__name__)
class AMPOptimizer(optim.FairseqOptimizer):
"""
Wrap an *optimizer* to support AMP (automatic mixed precision) training.
"""
def __init__(self, cfg: DictConfig, params, fp32_optimizer, **kwargs):
super().__init__(cfg.optimizer)
self.fp32_optimizer = fp32_optimizer
amp_kwargs = {"init_scale": cfg.common.fp16_init_scale}
if getattr(cfg.common, "amp_scale_window", None) is not None:
amp_kwargs["growth_interval"] = cfg.common.amp_init_scale
self._grad_scaler = torch.cuda.amp.GradScaler(**amp_kwargs)
self.min_loss_scale = cfg.common.min_loss_scale
@classmethod
def build_optimizer(cls, cfg: DictConfig, params, **kwargs):
"""
Args:
cfg (omegaconf.DictConfig): fairseq args
params (iterable): iterable of parameters to optimize
"""
fp32_optimizer = optim.build_optimizer(cfg.optimizer, params)
return cls(cfg, params, fp32_optimizer, **kwargs)
def backward(self, loss):
"""Computes the sum of gradients of the given tensor w.r.t. graph leaves.
Compared to :func:`fairseq.optim.FairseqOptimizer.backward`, this
function additionally dynamically scales the loss to avoid gradient
underflow.
"""
self._grad_scaler.scale(loss).backward()
def step(self):
self.scaler.step(self.fp32_optimizer)
self.scaler.update()
def clip_grad_norm(self, max_norm, aggregate_norm_fn=None):
"""Clips gradient norm."""
self.scaler.unscale_(self.optimizer)
grad_norm = self.fp32_optimizer.clip_grad_norm(max_norm, aggregate_norm_fn)
if not torch.isfinite(grad_norm).all():
new_loss_scale = self.next_loss_scale
if new_loss_scale <= self.min_loss_scale:
raise FloatingPointError(
(
"AMP: Minimum loss scale reached ({}). Your loss is probably exploding. "
"Try restarting training or use fp32. {}"
).format(self.min_loss_scale, new_loss_scale)
)
else:
logger.info("AMP: overflow detected, setting scale to "
f"to {new_loss_scale}")
return grad_norm
@property
def scaler(self):
return self._grad_scaler
@property
def next_loss_scale(self):
return self.scaler.get_scale() * self.scaler.get_backoff_factor()
@property
def optimizer(self):
return self.fp32_optimizer.optimizer
@optimizer.setter
def optimizer(self, optimizer):
self.fp32_optimizer.optimizer = optimizer
@property
def lr_scheduler(self):
return getattr(self.fp32_optimizer, "lr_scheduler", None)
@property
def optimizer_config(self):
return self.fp32_optimizer.optimizer_config
def get_lr(self):
return self.fp32_optimizer.get_lr()
def set_lr(self, lr):
self.fp32_optimizer.set_lr(lr)
def all_reduce_grads(self, module):
self.fp32_optimizer.all_reduce_grads(module)
@property
def supports_flat_params(self):
return self.fp32_optimizer.supports_flat_params
|
bart_ls-main
|
fairseq-py/fairseq/optim/amp_optimizer.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from collections.abc import Collection
from dataclasses import dataclass, field
from typing import List
import torch
from fairseq.dataclass import FairseqDataclass
from omegaconf import II, DictConfig
from torch.optim.optimizer import Optimizer, required
from . import FairseqOptimizer, register_optimizer
@dataclass
class FairseqNAGConfig(FairseqDataclass):
momentum: float = field(default=0.99, metadata={"help": "momentum factor"})
weight_decay: float = field(default=0.0, metadata={"help": "weight decay"})
# TODO common vars in parent class
lr: List[float] = II("optimization.lr")
@register_optimizer("nag", dataclass=FairseqNAGConfig)
class FairseqNAG(FairseqOptimizer):
def __init__(self, cfg: DictConfig, params):
super().__init__(cfg)
self._optimizer = NAG(params, **self.optimizer_config)
@property
def optimizer_config(self):
"""
Return a kwarg dictionary that will be used to override optimizer
args stored in checkpoints. This allows us to load a checkpoint and
resume training using a different set of optimizer args, e.g., with a
different learning rate.
"""
return {
"lr": self.cfg.lr[0]
if isinstance(self.cfg.lr, Collection)
else self.cfg.lr,
"momentum": self.cfg.momentum,
"weight_decay": self.cfg.weight_decay,
}
class NAG(Optimizer):
def __init__(self, params, lr=required, momentum=0, weight_decay=0):
defaults = dict(lr=lr, lr_old=lr, momentum=momentum, weight_decay=weight_decay)
super(NAG, self).__init__(params, defaults)
@property
def supports_memory_efficient_fp16(self):
return True
@property
def supports_flat_params(self):
return True
def step(self, closure=None):
"""Performs a single optimization step.
Args:
closure (callable, optional): A closure that reevaluates the model
and returns the loss.
"""
loss = None
if closure is not None:
loss = closure()
for group in self.param_groups:
weight_decay = group["weight_decay"]
momentum = group["momentum"]
lr = group["lr"]
lr_old = group.get("lr_old", lr)
lr_correct = lr / lr_old if lr_old > 0 else lr
for p in group["params"]:
if p.grad is None:
continue
p_data_fp32 = p.data
if p_data_fp32.dtype in {torch.float16, torch.bfloat16}:
p_data_fp32 = p_data_fp32.float()
d_p = p.grad.data.float()
param_state = self.state[p]
if "momentum_buffer" not in param_state:
param_state["momentum_buffer"] = torch.zeros_like(d_p)
else:
param_state["momentum_buffer"] = param_state["momentum_buffer"].to(
d_p
)
buf = param_state["momentum_buffer"]
if weight_decay != 0:
p_data_fp32.mul_(1 - lr * weight_decay)
p_data_fp32.add_(buf, alpha=momentum * momentum * lr_correct)
p_data_fp32.add_(d_p, alpha=-(1 + momentum) * lr)
buf.mul_(momentum * lr_correct).add_(d_p, alpha=-lr)
if p.data.dtype in {torch.float16, torch.bfloat16}:
p.data.copy_(p_data_fp32)
group["lr_old"] = lr
return loss
|
bart_ls-main
|
fairseq-py/fairseq/optim/nag.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import types
import torch
def get_fused_adam_class():
"""
Look for the FusedAdam optimizer from apex. We first try to load the
"contrib" interface, which is a bit faster than the main interface,
but is technically deprecated.
"""
try:
# The "deprecated" interface in recent versions of apex is a bit
# faster than the main interface, since we don't use the apex
# optimizer. This can be installed by passing the
# `--deprecated_fused_adam` option when building apex.
global fused_adam_cuda
import importlib
fused_adam_cuda = importlib.import_module("fused_adam_cuda")
return FusedAdamV1
except ImportError:
try:
# fallback to the newer interface
from apex.optimizers import FusedAdam as _FusedAdam # noqa
from apex.multi_tensor_apply import multi_tensor_applier
if multi_tensor_applier.available:
return FusedAdamV2
except ImportError:
pass
return None
class FusedAdamV1(torch.optim.Optimizer):
"""
Implements Adam algorithm. Currently GPU-only. Requires Apex to be installed via
``python setup.py install --cuda_ext --cpp_ext``.
It has been proposed in `Adam: A Method for Stochastic Optimization`_.
Compared to the original version in Apex, the fairseq version casts grads
and params to FP32 internally to support ``--memory-efficient-fp16``.
Args:
params (iterable): iterable of parameters to optimize or dicts defining
parameter groups.
lr (float, optional): learning rate. (default: 1e-3)
betas (Tuple[float, float], optional): coefficients used for computing
running averages of gradient and its square. (default: (0.9, 0.999))
eps (float, optional): term added to the denominator to improve
numerical stability. (default: 1e-8)
weight_decay (float, optional): weight decay (L2 penalty) (default: 0)
amsgrad (boolean, optional): whether to use the AMSGrad variant of this
algorithm from the paper `On the Convergence of Adam and Beyond`_
(default: False) NOT SUPPORTED in FusedAdam!
eps_inside_sqrt (boolean, optional): in the 'update parameters' step,
adds eps to the bias-corrected second moment estimate before
evaluating square root instead of adding it to the square root of
second moment estimate as in the original paper. (default: False)
.. _Adam: A Method for Stochastic Optimization:
https://arxiv.org/abs/1412.6980
.. _On the Convergence of Adam and Beyond:
https://openreview.net/forum?id=ryQu7f-RZ
"""
def __init__(
self,
params,
lr=1e-3,
bias_correction=True,
betas=(0.9, 0.999),
eps=1e-8,
eps_inside_sqrt=False,
weight_decay=0.0,
max_grad_norm=0.0,
amsgrad=False,
use_fp16_stats=False,
):
global fused_adam_cuda
import importlib
fused_adam_cuda = importlib.import_module("fused_adam_cuda")
if amsgrad:
raise RuntimeError("FusedAdam does not support the AMSGrad variant.")
defaults = {
"lr": lr,
"bias_correction": bias_correction,
"betas": betas,
"eps": eps,
"weight_decay": weight_decay,
"max_grad_norm": max_grad_norm,
}
super().__init__(params, defaults)
self.eps_mode = 0 if eps_inside_sqrt else 1
self.use_fp16_stats = use_fp16_stats
self.FLOAT16_MAX = 65504.0
@property
def supports_memory_efficient_fp16(self):
return True
@property
def supports_flat_params(self):
return True
@property
def supports_step_with_scale(self):
return True
def step(self, closure=None, grads=None, scale=1.0, grad_norms=None):
"""Performs a single optimization step.
Args:
closure (callable, optional): A closure that reevaluates the model
and returns the loss.
grads (list of tensors, optional): weight gradient to use for the
optimizer update. If gradients have type torch.half, parameters
are expected to be in type torch.float. (default: None)
output params (list of tensors, optional): A reduced precision copy
of the updated weights written out in addition to the regular
updated weights. Have to be of same type as gradients. (default: None)
scale (float, optional): factor to divide gradient tensor values
by before applying to weights. (default: 1)
"""
loss = None
if closure is not None:
loss = closure()
if grads is None:
grads_group = [None] * len(self.param_groups)
# backward compatibility
# assuming a list/generator of parameter means single group
elif isinstance(grads, types.GeneratorType):
grads_group = [grads]
elif type(grads[0]) != list:
grads_group = [grads]
else:
grads_group = grads
if grad_norms is None:
grad_norms = [None] * len(self.param_groups)
for group, grads_this_group, grad_norm in zip(
self.param_groups, grads_group, grad_norms
):
if grads_this_group is None:
grads_this_group = [None] * len(group["params"])
# compute combined scale factor for this group
combined_scale = scale
if group.get("max_grad_norm", 0) > 0:
# norm is in fact norm*scale
clip = ((grad_norm / scale) + 1e-6) / group["max_grad_norm"]
if clip > 1:
combined_scale = clip * scale
bias_correction = 1 if group.get("bias_correction", 1) else 0
for p, grad in zip(group["params"], grads_this_group):
# note: p.grad should not ever be set for correct
# operation of mixed precision optimizer that sometimes
# sends None gradients
if p.grad is None and grad is None:
continue
if grad is None:
grad = p.grad.data
if grad.is_sparse:
raise RuntimeError(
"FusedAdam does not support sparse gradients, "
"please consider SparseAdam instead"
)
if p.device.type == "cpu":
p_data_fp32 = p.data.cuda(non_blocking=True).float()
out_p = torch.tensor([], dtype = torch.float)
else:
p_data_fp32 = p.data.float()
out_p = p.data
state = self.state[p]
# State initialization
dtype = torch.float16 if self.use_fp16_stats else p_data_fp32.dtype
if len(state) == 0:
state["step"] = 0
# Exponential moving average of gradient values
state["exp_avg"] = torch.zeros_like(p_data_fp32, dtype=dtype)
# Exponential moving average of squared gradient values
state["exp_avg_sq"] = torch.zeros_like(p_data_fp32, dtype=dtype)
if self.use_fp16_stats:
state["exp_avg_scale"] = 1.0
state["exp_avg_sq_scale"] = 1.0
else:
device = p_data_fp32.device
state["exp_avg"] = state["exp_avg"].to(device, dtype)
state["exp_avg_sq"] = state["exp_avg_sq"].to(device, dtype)
exp_avg = state["exp_avg"]
exp_avg_sq = state["exp_avg_sq"]
if self.use_fp16_stats:
assert exp_avg.dtype == torch.float16
exp_avg = exp_avg.float() * state["exp_avg_scale"]
exp_avg_sq = exp_avg_sq.float() * state["exp_avg_sq_scale"]
beta1, beta2 = group["betas"]
state["step"] += 1
with torch.cuda.device(p_data_fp32.device):
fused_adam_cuda.adam(
p_data_fp32,
out_p,
exp_avg,
exp_avg_sq,
grad,
group["lr"],
beta1,
beta2,
group["eps"],
combined_scale,
state["step"],
self.eps_mode,
bias_correction,
group["weight_decay"],
)
if p.device.type == "cpu":
p.data.copy_(p_data_fp32, non_blocking=True)
if self.use_fp16_stats:
def inf_norm(t):
return torch.norm(t, float("inf"))
# from github.com/openai/jukebox/blob/master/jukebox/utils/fp16.py
state["exp_avg_scale"], state["exp_avg_sq_scale"] = (
1e-8 + inf_norm(exp_avg) / self.FLOAT16_MAX,
1e-8 + inf_norm(exp_avg_sq) / self.FLOAT16_MAX,
)
state["exp_avg"], state["exp_avg_sq"] = (
(exp_avg / state["exp_avg_scale"]).half(),
(exp_avg_sq / state["exp_avg_sq_scale"]).half(),
)
return loss
try:
from apex.optimizers import FusedAdam
from apex.multi_tensor_apply import multi_tensor_applier
class FusedAdamV2(FusedAdam):
"""
Compared to the original version in Apex, the fairseq version casts grads
and params to FP32 internally to support ``--memory-efficient-fp16``.
"""
def __init__(self, *args, use_fp16_stats=False, **kwargs):
if use_fp16_stats:
raise NotImplementedError("--fp16-adam-stats is only supported with FusedAdamV1")
super().__init__(*args, **kwargs)
if not hasattr(self, "multi_tensor_adam"):
raise Exception(
"Apex installation is outdated. Please install an updated version of apex."
)
@property
def supports_memory_efficient_fp16(self):
return True
@property
def supports_flat_params(self):
return True
def step(
self,
closure=None,
grads=None,
output_params=None,
scale=None,
grad_norms=None,
):
"""Performs a single optimization step."""
loss = None
if closure is not None:
loss = closure()
for group in self.param_groups:
bias_correction = 1 if group["bias_correction"] else 0
beta1, beta2 = group["betas"]
# assume same step across group now to simplify things
# per parameter step can be easily support by making it tensor, or pass list into kernel
if "step" in group:
group["step"] += 1
else:
group["step"] = 1
# create lists for multi-tensor apply
g_16, p_16, orig_p_16, m_16, v_16 = [], [], [], [], []
g_32, p_32, m_32, v_32 = [], [], [], []
for p in group["params"]:
if p.grad is None:
continue
if p.grad.data.is_sparse:
raise RuntimeError(
"FusedAdam does not support sparse gradients, "
"please consider SparseAdam instead"
)
state = self.state[p]
# State initialization
if len(state) == 0:
# Exponential moving average of gradient values
state["exp_avg"] = torch.zeros_like(p.data, dtype=torch.float)
# Exponential moving average of squared gradient values
state["exp_avg_sq"] = torch.zeros_like(
p.data, dtype=torch.float
)
else:
state["exp_avg"] = state["exp_avg"].to(
device=p.data.device, dtype=torch.float
)
state["exp_avg_sq"] = state["exp_avg_sq"].to(
device=p.data.device, dtype=torch.float
)
if p.dtype == torch.float16:
g_16.append(p.grad.data.float())
p_16.append(p.data.float())
orig_p_16.append(p.data)
m_16.append(state["exp_avg"])
v_16.append(state["exp_avg_sq"])
elif p.dtype == torch.float32:
g_32.append(p.grad.data)
p_32.append(p.data)
m_32.append(state["exp_avg"])
v_32.append(state["exp_avg_sq"])
else:
raise RuntimeError("FusedAdam only support fp16 and fp32.")
with torch.cuda.device(p.device):
if len(g_16) > 0:
multi_tensor_applier(
self.multi_tensor_adam,
self._dummy_overflow_buf,
[g_16, p_16, m_16, v_16],
group["lr"],
beta1,
beta2,
group["eps"],
group["step"],
self.adam_w_mode,
bias_correction,
group["weight_decay"],
)
for orig_p, p in zip(orig_p_16, p_16):
orig_p.copy_(p.data)
if len(g_32) > 0:
multi_tensor_applier(
self.multi_tensor_adam,
self._dummy_overflow_buf,
[g_32, p_32, m_32, v_32],
group["lr"],
beta1,
beta2,
group["eps"],
group["step"],
self.adam_w_mode,
bias_correction,
group["weight_decay"],
)
return loss
except ImportError:
pass
|
bart_ls-main
|
fairseq-py/fairseq/optim/fused_adam.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from dataclasses import dataclass, field
import torch
import torch.distributed as dist
from fairseq.dataclass.configs import FairseqBMUFConfig
from fairseq.dataclass.utils import gen_parser_from_dataclass
from fairseq.optim.fairseq_optimizer import FairseqOptimizer
class FairseqBMUF(FairseqOptimizer):
"""
Implements incremental block distributed data parallelism similar to
https://ieeexplore.ieee.org/document/7472805
Paper title: Scalable training of deep learning machines by incremental
block training with intra-block parallel optimization and blockwise
model-update filtering
"""
def __init__(self, cfg: FairseqBMUFConfig, optimizer):
super().__init__(cfg)
self._optimizer = optimizer
self._num_updates = 0
self.sync_iter = cfg.global_sync_iter
self.block_momentum = cfg.block_momentum
self.block_lr = cfg.block_lr
self._reset_local_data()
self.warmup_iteration = cfg.warmup_iterations
self.use_nbm = cfg.use_nbm
self.initial_state = self._optimizer.state_dict()
self.average_sync = self.cfg.average_sync
self.world_size = self.cfg.distributed_world_size
@staticmethod
def add_args(parser):
"""Add optimizer-specific arguments to the parser."""
gen_parser_from_dataclass(parser, FairseqBMUFConfig())
@property
def optimizer(self):
return self._optimizer.optimizer
@property
def optimizer_config(self):
return self._optimizer.optimizer_config
def get_lr(self):
return self._optimizer.get_lr()
def set_lr(self, lr):
self._optimizer.set_lr(lr)
def state_dict(self):
return self._optimizer.state_dict()
def load_state_dict(self, state_dict, optimizer_overrides=None):
self._optimizer.load_state_dict(state_dict, optimizer_overrides)
self.initial_state = self._optimizer.state_dict()
def multiply_grads(self, c):
"""Multiplies grads by a constant *c*."""
self._optimizer.multiply_grads(c)
def clip_grad_norm(self, max_norm, aggregate_norm_fn=None):
"""Clips gradient norm."""
return self._optimizer.clip_grad_norm(max_norm, aggregate_norm_fn)
def average_params(self):
self._optimizer.average_params()
def _block_sync(self):
if self.world_size <= 1:
return
# Update the global model using local models from all GPUs
# (Step-1) Calculate grad between previously synced model and
# currrent local model
if self.block_momentum != 0:
self._calc_grad()
# (Step-2) Average gradient from all GPUs
self._avg_grad_from_all_gpus()
# (Step-3) Calculate global momentum and update the global model
if self.block_momentum != 0:
self._update_global_model()
# (Step-4) Average local optimizer params
if self.average_sync:
self.average_params()
def _is_warmup_end(self):
# Check whether train iterations is equal to warmup iter
if self.get_num_updates() == self.warmup_iteration:
return True
return False
def _is_bmuf_iter(self):
# Check whether train iterations is equal to bmuf sync iter
if (self.get_num_updates() > self.warmup_iteration) and (
self.get_num_updates() % self.sync_iter == 0
):
return True
return False
def _warmup_sync(self, root_rank=0):
if self.world_size <= 1:
return
# Broadcast the local model to all gpus
for param in self.params:
dist.broadcast(param.data, src=root_rank)
# Update local optimizer state
if self.average_sync:
self._optimizer.average_params()
else:
self._optimizer.load_state_dict(self.initial_state)
self._reset_local_data()
def step(self, closure=None):
"""Performs a single optimization step."""
self._optimizer.step(closure)
self.set_num_updates(self.get_num_updates() + 1)
if self._is_warmup_end():
self._warmup_sync()
elif self._is_bmuf_iter():
self._block_sync()
def zero_grad(self):
"""Clears the gradients of all optimized parameters."""
self._optimizer.zero_grad()
def get_num_updates(self):
"""Get the number of parameters updates."""
return self._num_updates
def set_num_updates(self, num_updates):
"""Set the number of parameters updates."""
self._num_updates = num_updates
@torch.no_grad()
def _reset_local_data(self):
# (Step-0) Initialize global momentum parameters and store global copy on each gpu
self.global_params = [torch.zeros_like(p.data) for p in self.params]
self.smoothed_grads = [p.data.new_zeros(p.data.size()) for p in self.params]
self.grads = [p.data.new_zeros(p.data.size()) for p in self.params]
# saving the global model locally for calculating gradient during bmuf sync
for param, global_param in zip(self.params, self.global_params):
global_param.copy_(param.data)
@torch.no_grad()
def _calc_grad(self):
# global_params is basically the global copy from the previously finished
# synchronisation. param.data is local parameter after block_sync_freq
# for the local gpu. so grad is difference between previously synced
# model and currrent local model.
for index, (param, global_param) in enumerate(
zip(self.params, self.global_params)
):
self.grads[index] = global_param - param.data
def _avg_grad_from_all_gpus(self):
for index, param in enumerate(self.params):
sync_para = param.data if self.block_momentum == 0 else self.grads[index]
sync_para /= float(dist.get_world_size())
dist.all_reduce(sync_para, op=dist.ReduceOp.SUM)
@torch.no_grad()
def _update_global_model(self):
for index, (param, global_param, smoothed_grad, grad) in enumerate(
zip(
self.params,
self.global_params,
self.smoothed_grads,
# all gpus would share the same value of smoothed_grad, since it is
# always computed on synchronized gradients.
self.grads,
)
):
# global_param is basically last syncrhornized parameter. though
# smoothed_grad is local, all processes will have same value of
# smoothed_grad and hence param is globally synchronized copy.
# smoothed_grad(t) = BM * smoothed_grad(t-1) + BM_lr * grad(t)
smoothed_grad = self.block_momentum * smoothed_grad + self.block_lr * grad
param.data.copy_(global_param - smoothed_grad)
# A Nesterov momentum here is to do a partial weight update before
# calculating the gradient
if self.use_nbm:
param.data.copy_(param.data - self.block_momentum * smoothed_grad)
# backup for the next synchronization.
self.smoothed_grads[index] = smoothed_grad
global_param.copy_(param.data)
|
bart_ls-main
|
fairseq-py/fairseq/optim/bmuf.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
class DynamicLossScaler(object):
def __init__(
self,
init_scale=2.0 ** 15,
scale_factor=2.0,
scale_window=2000,
tolerance=0.0,
threshold=None,
min_loss_scale=1e-4,
):
self.loss_scale = init_scale
self.scale_factor = scale_factor
self.scale_window = scale_window
self.tolerance = tolerance
self.threshold = threshold
self._iter = 0
self._last_overflow_iter = -1
self._last_rescale_iter = -1
self._overflows_since_rescale = 0
self.min_loss_scale = min_loss_scale
def scale(self, outputs):
return self.loss_scale * outputs
def update(self):
if (self._iter - self._last_overflow_iter) % self.scale_window == 0:
self.loss_scale *= self.scale_factor
self._last_rescale_iter = self._iter
self._iter += 1
def _decrease_loss_scale(self):
self.loss_scale /= self.scale_factor
if self.threshold is not None:
self.loss_scale = max(self.loss_scale, self.threshold)
def check_overflow(self, grad_norm):
# detect inf and nan
if grad_norm == float("inf") or grad_norm != grad_norm:
# overflow has occured
prev_scale = self.loss_scale
iter_since_rescale = self._iter - self._last_rescale_iter
self._last_overflow_iter = self._iter
self._overflows_since_rescale += 1
pct_overflow = self._overflows_since_rescale / float(iter_since_rescale)
if pct_overflow >= self.tolerance:
self._decrease_loss_scale()
self._last_rescale_iter = self._iter
self._overflows_since_rescale = 0
if self.loss_scale <= self.min_loss_scale:
# Use FloatingPointError as an uncommon error that parent
# functions can safely catch to stop training.
self.loss_scale = prev_scale
raise FloatingPointError(
(
"Minimum loss scale reached ({}). Your loss is probably exploding. "
"Try lowering the learning rate, using gradient clipping or "
"increasing the batch size."
).format(self.min_loss_scale)
)
self._iter += 1
raise OverflowError("setting loss scale to: " + str(self.loss_scale))
|
bart_ls-main
|
fairseq-py/fairseq/optim/dynamic_loss_scaler.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import math
import torch
import torch.optim
from . import LegacyFairseqOptimizer, register_optimizer
@register_optimizer("adafactor")
class FairseqAdafactor(LegacyFairseqOptimizer):
def __init__(self, args, params):
super().__init__(args)
self._optimizer = Adafactor(params, **self.optimizer_config)
@staticmethod
def add_args(parser):
"""Add optimizer-specific arguments to the parser."""
# fmt: off
parser.add_argument('--adafactor-eps', default='(1e-30, 1e-3)', metavar="E",
help='epsilons for Adafactor optimizer')
parser.add_argument('--clip-threshold', type=float, default=1.0, metavar="C",
help='threshold for clipping update root mean square')
parser.add_argument('--decay-rate', type=float, default=-0.8, metavar="D",
help='decay rate of the second moment estimator')
parser.add_argument('--beta1', type=float, default=None, metavar="B",
help='beta for first moment estimator. Optional')
parser.add_argument('--weight-decay', '--wd', default=0.0, type=float, metavar='WD',
help='weight decay')
parser.add_argument('--scale-parameter', action='store_true',
help='scale learning rate by root mean square of parameter')
parser.add_argument('--relative-step', action='store_true',
help='set learning rate to inverse square root of timestep,'
'otherwise use external learning rate')
parser.add_argument('--warmup-init', action='store_true',
help='use relative step for warm-up learning rate schedule')
# fmt: on
@property
def optimizer_config(self):
"""
Return a kwarg dictionary that will be used to override optimizer
args stored in checkpoints. This allows us to load a checkpoint and
resume training using a different set of optimizer args, e.g., with a
different learning rate.
Note : Convergence issues empirically observed with fp16 on.
Might require search for appropriate configuration.
"""
return {
"lr": self.args.lr[0],
"eps": eval(self.args.adafactor_eps),
"clip_threshold": self.args.clip_threshold,
"decay_rate": self.args.decay_rate,
"beta1": self.args.beta1,
"weight_decay": self.args.weight_decay,
"scale_parameter": self.args.scale_parameter, # defaults to False
"relative_step": self.args.relative_step, # defaults to False
"warmup_init": self.args.warmup_init,
}
class Adafactor(torch.optim.Optimizer):
"""Implements Adafactor algorithm.
This implementation is based on:
`Adafactor: Adaptive Learning Rates with Sublinear Memory Cost`
(see https://arxiv.org/abs/1804.04235)
Note that this optimizer internally adjusts the learning rate
depending on the *scale_parameter*, *relative_step* and
*warmup_init* options. To use a manual (external) learning rate
schedule you should set `scale_parameter=False` and
`relative_step=False`.
Args:
params (iterable): iterable of parameters to optimize or dicts defining
parameter groups
lr (float, optional): external learning rate (default: None)
eps (tuple[float, float]): regularization constans for square gradient
and parameter scale respectively (default: (1e-30, 1e-3))
clip_threshold (float): threshold of root mean square of
final gradient update (default: 1.0)
decay_rate (float): coefficient used to compute running averages of square
gradient (default: -0.8)
beta1 (float): coefficient used for computing running averages of gradient
(default: None)
weight_decay (float, optional): weight decay (L2 penalty) (default: 0)
scale_parameter (bool): if True, learning rate is scaled by root mean square of
parameter (default: True)
relative_step (bool): if True, time-dependent learning rate is computed
instead of external learning rate (default: True)
warmup_init (bool): time-dependent learning rate computation depends on
whether warm-up initialization is being used (default: False)
"""
def __init__(
self,
params,
lr=None,
eps=(1e-30, 1e-3),
clip_threshold=1.0,
decay_rate=-0.8,
beta1=None,
weight_decay=0.0,
scale_parameter=True,
relative_step=True,
warmup_init=False,
):
if lr is not None and relative_step:
raise ValueError("Cannot combine manual lr and relative_step options")
if warmup_init and not relative_step:
raise ValueError("warmup_init requires relative_step=True")
defaults = dict(
lr=lr,
eps=eps,
clip_threshold=clip_threshold,
decay_rate=decay_rate,
beta1=beta1,
weight_decay=weight_decay,
scale_parameter=scale_parameter,
relative_step=relative_step,
warmup_init=warmup_init,
)
super(Adafactor, self).__init__(params, defaults)
@property
def supports_memory_efficient_fp16(self):
return True
@property
def supports_flat_params(self):
return False
def _get_lr(self, param_group, param_state):
rel_step_sz = param_group["lr"]
if param_group["relative_step"]:
min_step = (
1e-6 * param_state["step"] if param_group["warmup_init"] else 1e-2
)
rel_step_sz = min(min_step, 1.0 / math.sqrt(param_state["step"]))
param_scale = 1.0
if param_group["scale_parameter"]:
param_scale = max(param_group["eps"][1], param_state["RMS"])
return param_scale * rel_step_sz
def _get_options(self, param_group, param_shape):
factored = len(param_shape) >= 2
use_first_moment = param_group["beta1"] is not None
return factored, use_first_moment
def _rms(self, tensor):
return tensor.norm(2) / (tensor.numel() ** 0.5)
def _approx_sq_grad(self, exp_avg_sq_row, exp_avg_sq_col):
r_factor = (
(exp_avg_sq_row / exp_avg_sq_row.mean(dim=-1, keepdim=True))
.rsqrt_()
.unsqueeze(-1)
)
c_factor = exp_avg_sq_col.unsqueeze(-2).rsqrt()
return torch.mul(r_factor, c_factor)
def step(self, closure=None):
"""Performs a single optimization step.
Args:
closure (callable, optional): A closure that reevaluates the model
and returns the loss.
"""
loss = None
if closure is not None:
loss = closure()
for group in self.param_groups:
for p in group["params"]:
if p.grad is None:
continue
grad = p.grad.data
if grad.dtype in {torch.float16, torch.bfloat16}:
grad = grad.float()
if grad.is_sparse:
raise RuntimeError("Adafactor does not support sparse gradients.")
state = self.state[p]
grad_shape = grad.shape
factored, use_first_moment = self._get_options(group, grad_shape)
# State Initialization
if len(state) == 0:
state["step"] = 0
if use_first_moment:
# Exponential moving average of gradient values
state["exp_avg"] = torch.zeros_like(grad)
if factored:
state["exp_avg_sq_row"] = torch.zeros(grad_shape[:-1]).to(grad)
state["exp_avg_sq_col"] = torch.zeros(
grad_shape[:-2] + grad_shape[-1:]
).to(grad)
else:
state["exp_avg_sq"] = torch.zeros_like(grad)
state["RMS"] = 0
else:
if use_first_moment:
state["exp_avg"] = state["exp_avg"].to(grad)
if factored:
state["exp_avg_sq_row"] = state["exp_avg_sq_row"].to(grad)
state["exp_avg_sq_col"] = state["exp_avg_sq_col"].to(grad)
else:
state["exp_avg_sq"] = state["exp_avg_sq"].to(grad)
p_data_fp32 = p.data
if p.data.dtype in {torch.float16, torch.bfloat16}:
p_data_fp32 = p_data_fp32.float()
state["step"] += 1
state["RMS"] = self._rms(p_data_fp32)
group["lr"] = self._get_lr(group, state)
beta2t = 1.0 - math.pow(state["step"], group["decay_rate"])
update = (grad ** 2) + group["eps"][0]
if factored:
exp_avg_sq_row = state["exp_avg_sq_row"]
exp_avg_sq_col = state["exp_avg_sq_col"]
exp_avg_sq_row.mul_(beta2t).add_(
update.mean(dim=-1), alpha=1.0 - beta2t
)
exp_avg_sq_col.mul_(beta2t).add_(
update.mean(dim=-2), alpha=1.0 - beta2t
)
# Approximation of exponential moving average of square of gradient
update = self._approx_sq_grad(exp_avg_sq_row, exp_avg_sq_col)
update.mul_(grad)
else:
exp_avg_sq = state["exp_avg_sq"]
exp_avg_sq.mul_(beta2t).add_(update, alpha=1.0 - beta2t)
update = exp_avg_sq.rsqrt().mul_(grad)
update.div_(
(self._rms(update) / group["clip_threshold"]).clamp_(min=1.0)
)
update.mul_(group["lr"])
if use_first_moment:
exp_avg = state["exp_avg"]
exp_avg.mul_(group["beta1"]).add_(update, alpha=1 - group["beta1"])
update = exp_avg
if group["weight_decay"] != 0:
p_data_fp32.add_(
p_data_fp32, alpha=-group["weight_decay"] * group["lr"]
)
p_data_fp32.add_(-update)
if p.data.dtype in {torch.float16, torch.bfloat16}:
p.data.copy_(p_data_fp32)
return loss
|
bart_ls-main
|
fairseq-py/fairseq/optim/adafactor.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch.optim
from . import LegacyFairseqOptimizer, register_optimizer
@register_optimizer("sgd")
class SGD(LegacyFairseqOptimizer):
def __init__(self, args, params):
super().__init__(args)
self._optimizer = torch.optim.SGD(params, **self.optimizer_config)
@staticmethod
def add_args(parser):
"""Add optimizer-specific arguments to the parser."""
# fmt: off
parser.add_argument('--momentum', default=0.0, type=float, metavar='M',
help='momentum factor')
parser.add_argument('--weight-decay', '--wd', default=0.0, type=float, metavar='WD',
help='weight decay')
# fmt: on
@property
def optimizer_config(self):
"""
Return a kwarg dictionary that will be used to override optimizer
args stored in checkpoints. This allows us to load a checkpoint and
resume training using a different set of optimizer args, e.g., with a
different learning rate.
"""
return {
"lr": self.args.lr[0],
"momentum": self.args.momentum,
"weight_decay": self.args.weight_decay,
}
@property
def supports_flat_params(self):
return True
|
bart_ls-main
|
fairseq-py/fairseq/optim/sgd.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
from fairseq import utils
from fairseq.dataclass.utils import gen_parser_from_dataclass
class FairseqOptimizer(object):
def __init__(self, cfg):
super().__init__()
self.cfg = cfg
@classmethod
def add_args(cls, parser):
"""Add optimizer-specific arguments to the parser."""
dc = getattr(cls, "__dataclass", None)
if dc is not None:
gen_parser_from_dataclass(parser, dc())
@property
def optimizer(self):
"""Return a torch.optim.optimizer.Optimizer instance."""
if not hasattr(self, "_optimizer"):
raise NotImplementedError
if not isinstance(self._optimizer, torch.optim.Optimizer):
raise ValueError("_optimizer must be an instance of torch.optim.Optimizer")
return self._optimizer
@optimizer.setter
def optimizer(self, optimizer):
"""Reset optimizer instance."""
if not hasattr(self, "_optimizer"):
raise NotImplementedError
if not isinstance(self._optimizer, torch.optim.Optimizer):
raise ValueError("_optimizer must be an instance of torch.optim.Optimizer")
self._optimizer = optimizer
@property
def optimizer_config(self):
"""
Return a kwarg dictionary that will be used to override optimizer
args stored in checkpoints. This allows us to load a checkpoint and
resume training using a different set of optimizer args, e.g., with a
different learning rate.
"""
raise NotImplementedError
@property
def params(self):
"""Return an iterable of the parameters held by the optimizer."""
for param_group in self.param_groups:
for p in param_group["params"]:
yield p
@property
def param_groups(self):
return self.optimizer.param_groups
def __getstate__(self):
return self._optimizer.__getstate__()
def get_lr(self):
"""Return the current learning rate."""
return self.param_groups[0]["lr"]
def set_lr(self, lr):
"""Set the learning rate."""
for param_group in self.param_groups:
param_group["lr"] = lr
def state_dict(self):
"""Return the optimizer's state dict."""
return self.optimizer.state_dict()
def load_state_dict(self, state_dict, optimizer_overrides=None):
"""Load an optimizer state dict.
In general we should prefer the configuration of the existing optimizer
instance (e.g., learning rate) over that found in the state_dict. This
allows us to resume training from a checkpoint using a new set of
optimizer args.
"""
self.optimizer.load_state_dict(state_dict)
if optimizer_overrides is not None and len(optimizer_overrides) > 0:
# override learning rate, momentum, etc. with latest values
for group in self.param_groups:
group.update(optimizer_overrides)
def backward(self, loss):
"""Computes the sum of gradients of the given tensor w.r.t. graph leaves."""
loss.backward()
def all_reduce_grads(self, module):
"""Manually all-reduce gradients (if required)."""
if hasattr(module, "all_reduce_grads"):
module.all_reduce_grads()
def multiply_grads(self, c):
"""Multiplies grads by a constant *c*."""
for p in self.params:
if p.grad is not None:
if torch.is_tensor(c):
c = c.to(p.grad.device)
p.grad.data.mul_(c)
def clip_grad_norm(self, max_norm, aggregate_norm_fn=None):
"""Clips gradient norm."""
return utils.clip_grad_norm_(self.params, max_norm, aggregate_norm_fn)
def step(self, closure=None, scale=1.0, groups=None):
"""Performs a single optimization step."""
if self.supports_step_with_scale:
if self.supports_groups:
self.optimizer.step(closure, scale=scale, groups=groups)
else:
self.optimizer.step(closure, scale=scale)
else:
if scale != 1.0:
self.multiply_grads(1.0 / scale)
if self.supports_groups:
self.optimizer.step(closure, groups=groups)
else:
self.optimizer.step(closure)
def zero_grad(self):
"""Clears the gradients of all optimized parameters."""
for p in self.params:
p.grad = None
self.optimizer.zero_grad()
@property
def supports_memory_efficient_fp16(self):
if hasattr(self.optimizer, "supports_memory_efficient_fp16"):
return self.optimizer.supports_memory_efficient_fp16
return False
@property
def supports_step_with_scale(self):
if hasattr(self.optimizer, "supports_step_with_scale"):
return self.optimizer.supports_step_with_scale
return False
@property
def supports_groups(self):
if hasattr(self.optimizer, "supports_groups"):
return self.optimizer.supports_groups
return False
@property
def supports_flat_params(self):
"""
Whether the optimizer supports collapsing of the model
parameters/gradients into a single contiguous Tensor.
"""
if hasattr(self.optimizer, "supports_flat_params"):
return self.optimizer.supports_flat_params
return False
def average_params(self):
pass
def broadcast_global_state_dict(self, state_dict):
"""
Broadcasts a global state dict to all ranks.
Useful for optimizers that shard state between ranks.
"""
if hasattr(self.optimizer, "broadcast_global_state_dict"):
return self.optimizer.broadcast_global_state_dict(state_dict)
else:
return state_dict
class LegacyFairseqOptimizer(FairseqOptimizer):
def __init__(self, args):
self.args = args
|
bart_ls-main
|
fairseq-py/fairseq/optim/fairseq_optimizer.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""isort:skip_file"""
import importlib
import os
from fairseq import registry
from fairseq.optim.bmuf import FairseqBMUF # noqa
from fairseq.optim.fairseq_optimizer import ( # noqa
FairseqOptimizer,
LegacyFairseqOptimizer,
)
from fairseq.optim.amp_optimizer import AMPOptimizer
from fairseq.optim.fp16_optimizer import FP16Optimizer, MemoryEfficientFP16Optimizer
from fairseq.optim.shard import shard_
from omegaconf import DictConfig
__all__ = [
"AMPOptimizer",
"FairseqOptimizer",
"FP16Optimizer",
"MemoryEfficientFP16Optimizer",
"shard_",
]
(
_build_optimizer,
register_optimizer,
OPTIMIZER_REGISTRY,
OPTIMIZER_DATACLASS_REGISTRY,
) = registry.setup_registry("--optimizer", base_class=FairseqOptimizer, required=True)
def build_optimizer(cfg: DictConfig, params, *extra_args, **extra_kwargs):
if all(isinstance(p, dict) for p in params):
params = [t for p in params for t in p.values()]
params = list(filter(lambda p: p.requires_grad, params))
return _build_optimizer(cfg, params, *extra_args, **extra_kwargs)
# automatically import any Python files in the optim/ directory
for file in sorted(os.listdir(os.path.dirname(__file__))):
if file.endswith(".py") and not file.startswith("_"):
file_name = file[: file.find(".py")]
importlib.import_module("fairseq.optim." + file_name)
|
bart_ls-main
|
fairseq-py/fairseq/optim/__init__.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
import torch.optim
from . import LegacyFairseqOptimizer, register_optimizer
@register_optimizer("adamax")
class FairseqAdamax(LegacyFairseqOptimizer):
def __init__(self, args, params):
super().__init__(args)
self._optimizer = Adamax(params, **self.optimizer_config)
@staticmethod
def add_args(parser):
"""Add optimizer-specific arguments to the parser."""
# fmt: off
parser.add_argument('--adamax-betas', default='(0.9, 0.999)', metavar='B',
help='betas for Adam optimizer')
parser.add_argument('--adamax-eps', type=float, default=1e-8, metavar='D',
help='epsilon for Adam optimizer')
parser.add_argument('--weight-decay', '--wd', default=0.0, type=float, metavar='WD',
help='weight decay')
parser.add_argument('--no-bias-correction', default=False, action='store_true',
help='disable bias correction')
# fmt: on
@property
def optimizer_config(self):
"""
Return a kwarg dictionary that will be used to override optimizer
args stored in checkpoints. This allows us to load a checkpoint and
resume training using a different set of optimizer args, e.g., with a
different learning rate.
"""
return {
"lr": self.args.lr[0],
"betas": eval(self.args.adamax_betas),
"eps": self.args.adamax_eps,
"weight_decay": self.args.weight_decay,
"bias_correction": not self.args.no_bias_correction,
}
class Adamax(torch.optim.Optimizer):
"""Implements Adamax algorithm (a variant of Adam based on infinity norm).
It has been proposed in `Adam: A Method for Stochastic Optimization`__.
Compared to the version in PyTorch, this version implements a fix for weight decay.
Args:
params (iterable): iterable of parameters to optimize or dicts defining
parameter groups
lr (float, optional): learning rate (default: 2e-3)
betas (Tuple[float, float], optional): coefficients used for computing
running averages of gradient and its square
eps (float, optional): term added to the denominator to improve
numerical stability (default: 1e-8)
weight_decay (float, optional): weight decay (L2 penalty) (default: 0)
bias_correction (bool, optional): enable bias correction (default: True)
__ https://arxiv.org/abs/1412.6980
"""
def __init__(
self,
params,
lr=2e-3,
betas=(0.9, 0.999),
eps=1e-8,
weight_decay=0,
bias_correction=True,
):
if not 0.0 <= lr:
raise ValueError("Invalid learning rate: {}".format(lr))
if not 0.0 <= eps:
raise ValueError("Invalid epsilon value: {}".format(eps))
if not 0.0 <= betas[0] < 1.0:
raise ValueError("Invalid beta parameter at index 0: {}".format(betas[0]))
if not 0.0 <= betas[1] < 1.0:
raise ValueError("Invalid beta parameter at index 1: {}".format(betas[1]))
if not 0.0 <= weight_decay:
raise ValueError("Invalid weight_decay value: {}".format(weight_decay))
defaults = dict(
lr=lr,
betas=betas,
eps=eps,
weight_decay=weight_decay,
bias_correction=bias_correction,
)
super(Adamax, self).__init__(params, defaults)
@property
def supports_memory_efficient_fp16(self):
return True
@property
def supports_flat_params(self):
return True
def step(self, closure=None):
"""Performs a single optimization step.
Args:
closure (callable, optional): A closure that reevaluates the model
and returns the loss.
"""
loss = None
if closure is not None:
loss = closure()
for group in self.param_groups:
for p in group["params"]:
if p.grad is None:
continue
grad = p.grad.data.float()
if grad.is_sparse:
raise RuntimeError("Adamax does not support sparse gradients")
p_data_fp32 = p.data
if p.data.dtype in {torch.float16, torch.bfloat16}:
p_data_fp32 = p_data_fp32.float()
state = self.state[p]
# State initialization
if len(state) == 0:
state["step"] = 0
state["exp_avg"] = torch.zeros_like(p_data_fp32)
state["exp_inf"] = torch.zeros_like(p_data_fp32)
else:
state["exp_avg"] = state["exp_avg"].to(p_data_fp32)
state["exp_inf"] = state["exp_inf"].to(p_data_fp32)
exp_avg, exp_inf = state["exp_avg"], state["exp_inf"]
beta1, beta2 = group["betas"]
eps = group["eps"]
state["step"] += 1
# Update biased first moment estimate.
exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1)
# Update the exponentially weighted infinity norm.
torch.max(
exp_inf.mul_(beta2),
grad.abs_(),
out=exp_inf,
)
step_size = group["lr"]
if group["bias_correction"]:
bias_correction = 1 - beta1 ** state["step"]
step_size /= bias_correction
if group["weight_decay"] != 0:
p_data_fp32.add_(
p_data_fp32, alpha=-group["weight_decay"] * group["lr"]
)
p_data_fp32.addcdiv_(exp_avg, exp_inf.add(eps), value=-step_size)
if p.data.dtype in {torch.float16, torch.bfloat16}:
p.data.copy_(p_data_fp32)
return loss
|
bart_ls-main
|
fairseq-py/fairseq/optim/adamax.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from collections import defaultdict
from itertools import chain
import torch
from fairseq import optim
from omegaconf import DictConfig
from .dynamic_loss_scaler import DynamicLossScaler
class _FP16OptimizerMixin(object):
def __init__(self, *args, **kwargs):
# forward __init__ call to the next class in mro(method resolution order)
super().__init__(*args, **kwargs)
self._multiply_factor = 1.0
@property
def has_flat_params(self):
return torch.is_tensor(self.fp32_params) or (
isinstance(self.fp32_params, dict)
and all(torch.is_tensor(t) for t in self.fp32_params.values())
)
@classmethod
def build_fp32_params(cls, args, params, flatten=True):
# create FP32 copy of parameters and grads
if flatten:
is_pipeline_parallel = getattr(
args, "pipeline_model_parallel", False
) and getattr(args, "distributed_no_spawn", False)
total_param_size = sum(p.data.numel() for p in params)
devices = [torch.cuda.current_device()]
if is_pipeline_parallel:
devices = list(set(args.pipeline_devices))
fp32_params = {}
for device in devices:
if is_pipeline_parallel:
device_param_size = sum(
p.data.numel() for p in params if p.device.index == device
)
device_params = [p for p in params if p.device.index == device]
else:
device_param_size = total_param_size
device_params = params
fp32_params[device] = (
device_params[0].new(0).float().new(device_param_size)
)
offset = 0
for p in device_params:
numel = p.data.numel()
fp32_params[device][offset : offset + numel].copy_(p.data.view(-1))
offset += numel
fp32_params[device] = torch.nn.Parameter(fp32_params[device])
fp32_params[device].grad = fp32_params[device].data.new(
device_param_size
)
return fp32_params
else:
fp32_params = []
for p in params:
p32 = torch.nn.Parameter(p.data.float())
if hasattr(p, 'expert'):
p32.expert = True
elif hasattr(p, 'base_expert'):
p32.base_expert = True
p32.grad = torch.zeros_like(p32.data)
if hasattr(p, "param_group"):
p32.param_group = p.param_group
fp32_params.append(p32)
return fp32_params
def state_dict(self):
"""Return the optimizer's state dict."""
state_dict = self.fp32_optimizer.state_dict()
if self.scaler is not None:
state_dict["loss_scale"] = self.scaler.loss_scale
return state_dict
def load_state_dict(self, state_dict, optimizer_overrides=None):
"""Load an optimizer state dict.
In general we should prefer the configuration of the existing optimizer
instance (e.g., learning rate) over that found in the state_dict. This
allows us to resume training from a checkpoint using a new set of
optimizer args.
"""
if "loss_scale" in state_dict and self.scaler is not None:
self.scaler.loss_scale = state_dict["loss_scale"]
self.fp32_optimizer.load_state_dict(state_dict, optimizer_overrides)
def backward(self, loss):
"""Computes the sum of gradients of the given tensor w.r.t. graph leaves.
Compared to :func:`fairseq.optim.FairseqOptimizer.backward`, this
function additionally dynamically scales the loss to avoid gradient
underflow.
"""
if self.scaler is not None:
loss = self.scaler.scale(loss)
loss.backward()
self._needs_sync = True
def _sync_fp16_grads_to_fp32(self):
if self._needs_sync:
# copy FP16 grads to FP32
if self.has_flat_params:
devices = list(self.fp32_params.keys())
device_params_dict = defaultdict(list)
for p in self.fp16_params:
if p.requires_grad:
device_params_dict[p.device.index].append(p)
for device in devices:
device_params = device_params_dict[device]
offset = 0
for p in device_params:
grad_data = (
p.grad.data
if p.grad is not None
else p.data.new_zeros(p.data.shape)
)
numel = grad_data.numel()
self.fp32_params[device].grad.data[
offset : offset + numel
].copy_(grad_data.view(-1))
offset += numel
else:
for p, p32 in zip(self.fp16_params, self.fp32_params):
if not p.requires_grad:
continue
if p.grad is not None:
if p32.grad is None:
p32.grad = p.grad.data.float()
else:
p32.grad.data.copy_(p.grad.data)
else:
p32.grad = torch.zeros_like(p.data, dtype=torch.float)
self._needs_sync = False
def _sync_fp32_params_to_fp16(self):
# copy FP32 params back into FP16 model
if self.has_flat_params:
devices = list(self.fp32_params.keys())
device_params_dict = defaultdict(list)
for p in self.fp16_params:
device_params_dict[p.device.index].append(p)
for device in devices:
device_params = device_params_dict[device]
offset = 0
for p in device_params:
numel = p.data.numel()
p.data.copy_(
self.fp32_params[device]
.data[offset : offset + numel]
.view_as(p.data)
)
offset += numel
else:
for p, p32 in zip(self.fp16_params, self.fp32_params):
if not p.requires_grad:
continue
p.data.copy_(p32.data)
def _unscale_grads(self):
self._sync_fp16_grads_to_fp32()
if (
# Skip the multiplication if it's a no-op (i.e., if _multiply_factor
# is 1.0). At the same time, we want to avoid the device-to-host
# transfer by comparing it to 1.0. Since _multiply_factor starts as
# a Python float, we roughly assume that if it's a tensor then it's
# probably not =1.0 anymore and we do the multiplication. Otherwise
# we can safely check the value without a D2H transfer.
torch.is_tensor(self._multiply_factor)
or self._multiply_factor != 1.0
):
self.fp32_optimizer.multiply_grads(self._multiply_factor)
self._multiply_factor = 1.0
def multiply_grads(self, c):
"""Multiplies grads by a constant ``c``."""
self._multiply_factor *= c
def clip_grad_norm(self, max_norm, aggregate_norm_fn=None):
"""Clips gradient norm and updates dynamic loss scaler."""
self._sync_fp16_grads_to_fp32()
grad_norm = self._multiply_factor * self.fp32_optimizer.clip_grad_norm(
0, aggregate_norm_fn
)
if self.scaler is not None:
if grad_norm > max_norm > 0.0:
self._multiply_factor *= max_norm / grad_norm
self.scaler.check_overflow(grad_norm)
elif max_norm > 0.0:
clip_coef = (max_norm / (grad_norm + 1e-6)).clamp_(max=1)
self._multiply_factor *= clip_coef
return grad_norm
def step(self, closure=None, groups=None):
"""Performs a single optimization step."""
self._sync_fp16_grads_to_fp32()
if getattr(self, "supports_step_with_scale", False):
self.fp32_optimizer.step(closure, scale=(1.0 / self._multiply_factor), groups=groups)
else:
self._unscale_grads()
self.fp32_optimizer.step(closure, groups=groups)
if self.scaler is not None:
self.scaler.update()
self._sync_fp32_params_to_fp16()
def zero_grad(self):
"""Clears the gradients of all optimized parameters."""
for p in self.fp16_params:
p.grad = None
if self.has_flat_params:
if torch.is_tensor(self.fp32_params):
self.fp32_params.grad.zero_()
elif isinstance(self.fp32_params, dict):
for fp32_params in self.fp32_params.values():
fp32_params.grad.zero_()
else:
raise RuntimeError("self.fp32_params must be a tensor or dict")
else:
for p32 in self.fp32_params:
if p32.grad is not None:
p32.grad.zero_()
self._needs_sync = False
if self.scaler is not None:
self._multiply_factor = 1.0 / float(self.scaler.loss_scale)
class FP16Optimizer(_FP16OptimizerMixin, optim.FairseqOptimizer):
"""
Wrap an *optimizer* to support FP16 (mixed precision) training.
"""
def __init__(self, cfg: DictConfig, params, fp32_optimizer, fp32_params, **kwargs):
super().__init__(cfg.optimizer)
self.fp16_params = params
self.fp32_optimizer = fp32_optimizer
self.fp32_params = fp32_params
if getattr(cfg.common, "fp16_scale_window", None) is None:
if len(cfg.optimization.update_freq) > 1:
raise ValueError(
"--fp16-scale-window must be given explicitly when using a "
"custom --update-freq schedule"
)
data_parallel_size = int(
cfg.distributed_training.distributed_world_size
/ cfg.common.model_parallel_size
)
scale_window = int(
2 ** 14 / data_parallel_size / cfg.optimization.update_freq[0]
)
else:
scale_window = cfg.common.fp16_scale_window
if not getattr(cfg.common, "bf16", False):
self.scaler = DynamicLossScaler(
init_scale=cfg.common.fp16_init_scale,
scale_window=scale_window,
tolerance=cfg.common.fp16_scale_tolerance,
threshold=cfg.common.threshold_loss_scale,
min_loss_scale=cfg.common.min_loss_scale,
)
else:
# disable loss scaling for bfloat16
self.scaler = None
@classmethod
def build_optimizer(cls, cfg: DictConfig, params, **kwargs):
"""
Args:
cfg (omegaconf.DictConfig): fairseq args
params (iterable): iterable of parameters to optimize
"""
flatten = not getattr(cfg.common, "fp16_no_flatten_grads", False)
if getattr(cfg.common, "bf16", False):
flatten = False # mixed precision is faster on TPUs without flat grads
fp32_params = cls.build_fp32_params(cfg.optimizer, params, flatten=flatten)
if flatten:
fp32_optimizer = optim.build_optimizer(cfg.optimizer, [fp32_params])
else:
fp32_optimizer = optim.build_optimizer(cfg.optimizer, fp32_params)
if flatten and not fp32_optimizer.supports_flat_params:
raise RuntimeError(
f"chosen optimizer {fp32_optimizer.__class__.__name__} does not support flat params, please set --fp16-no-flatten-grads"
)
return cls(cfg, params, fp32_optimizer, fp32_params, **kwargs)
@property
def optimizer(self):
return self.fp32_optimizer.optimizer
@optimizer.setter
def optimizer(self, optimizer):
self.fp32_optimizer.optimizer = optimizer
@property
def lr_scheduler(self):
return getattr(self.fp32_optimizer, "lr_scheduler", None)
@property
def optimizer_config(self):
return self.fp32_optimizer.optimizer_config
def get_lr(self):
return self.fp32_optimizer.get_lr()
def set_lr(self, lr):
self.fp32_optimizer.set_lr(lr)
def all_reduce_grads(self, module):
self.fp32_optimizer.all_reduce_grads(module)
@property
def supports_flat_params(self):
return self.fp32_optimizer.supports_flat_params
class _MemoryEfficientFP16OptimizerMixin(object):
def __init__(self, *args, **kwargs):
# forward __init__ call to the next class in MRO (method resolution order)
super().__init__(*args, **kwargs)
self._multiply_factor = 1.0
@property
def has_flat_params(self):
return False
def state_dict(self):
"""Return the optimizer's state dict."""
state_dict = self.wrapped_optimizer.state_dict()
if self.scaler is not None:
state_dict["loss_scale"] = self.scaler.loss_scale
return state_dict
def load_state_dict(self, state_dict, optimizer_overrides=None):
"""Load an optimizer state dict.
In general we should prefer the configuration of the existing optimizer
instance (e.g., learning rate) over that found in the state_dict. This
allows us to resume training from a checkpoint using a new set of
optimizer args.
"""
if "loss_scale" in state_dict and self.scaler is not None:
self.scaler.loss_scale = state_dict["loss_scale"]
self.wrapped_optimizer.load_state_dict(state_dict, optimizer_overrides)
# Hack: PyTorch automatically casts the optimizer state to match the
# type of the current parameters. But with --memory-efficient-fp16 the
# params are FP16 while the optimizer state is FP32 and we don't want
# to cast. A workaround is to manually copy back the original state
# after the optimizer has been loaded.
if not getattr(self.optimizer, "disable_mem_eff_fp16_loading_hack", False):
groups = self.optimizer.param_groups
saved_groups = state_dict["param_groups"]
id_map = {
old_id: p
for old_id, p in zip(
chain(*(g["params"] for g in saved_groups)),
chain(*(g["params"] for g in groups)),
)
}
for k, v in state_dict["state"].items():
if k in id_map:
param = id_map[k]
self.optimizer.state[param] = v
def backward(self, loss):
"""Computes the sum of gradients of the given tensor w.r.t. graph leaves.
Compared to :func:`fairseq.optim.FairseqOptimizer.backward`, this
function additionally dynamically scales the loss to avoid gradient
underflow.
"""
if self.scaler is not None:
loss = self.scaler.scale(loss)
loss.backward()
def _unscale_grads(self):
if (
# Skip the multiplication if it's a no-op (i.e., if _multiply_factor
# is 1.0). At the same time, we want to avoid the device-to-host
# transfer by comparing it to 1.0. Since _multiply_factor starts as
# a Python float, we roughly assume that if it's a tensor then it's
# probably not =1.0 anymore and we do the multiplication. Otherwise
# we can safely check the value without a D2H transfer.
torch.is_tensor(self._multiply_factor)
or self._multiply_factor != 1.0
):
self.wrapped_optimizer.multiply_grads(self._multiply_factor)
self._multiply_factor = 1.0
def multiply_grads(self, c):
"""Multiplies grads by a constant *c*."""
self._multiply_factor *= c
def clip_grad_norm(self, max_norm, aggregate_norm_fn=None):
"""Clips gradient norm and updates dynamic loss scaler."""
max_norm = float(max_norm)
grad_norm = self._multiply_factor * self.wrapped_optimizer.clip_grad_norm(
0, aggregate_norm_fn
)
if self.scaler is not None:
grad_norm_cpu = float(grad_norm)
if grad_norm_cpu > max_norm > 0.0:
self._multiply_factor *= max_norm / grad_norm_cpu
# detect overflow and adjust loss scale
self.scaler.check_overflow(grad_norm_cpu)
elif max_norm > 0.0:
clip_coef = (max_norm / (grad_norm + 1e-6)).clamp_(max=1)
self._multiply_factor *= clip_coef
return grad_norm
def step(self, closure=None, groups=None):
"""Performs a single optimization step."""
if getattr(self, "supports_step_with_scale", False):
# NOTE(msb) optimizer divides by scale factor
self.wrapped_optimizer.step(closure, scale=(1.0 / self._multiply_factor), groups=groups)
else:
self._unscale_grads()
self.wrapped_optimizer.step(closure, groups=groups)
if self.scaler is not None:
self.scaler.update()
def zero_grad(self):
"""Clears the gradients of all optimized parameters."""
self.wrapped_optimizer.zero_grad()
if self.scaler is not None:
self._multiply_factor = 1.0 / float(self.scaler.loss_scale)
else:
self._multiply_factor = 1.0
@property
def supports_flat_params(self):
return self.wrapped_optimizer.supports_flat_params
class MemoryEfficientFP16Optimizer(
_MemoryEfficientFP16OptimizerMixin, optim.FairseqOptimizer
):
"""
Wrap an *optimizer* to support FP16 (mixed precision) training.
Compared to :class:`fairseq.optim.FP16Optimizer`, this version does not
maintain an FP32 copy of the model. We instead expect the optimizer to
convert the gradients to FP32 internally and sync the results back to the
FP16 model params. This significantly reduces memory usage but slightly
increases the time spent in the optimizer.
Since this wrapper depends on specific functionality in the wrapped
optimizer (i.e., on-the-fly conversion of grads to FP32), only certain
optimizers can be wrapped. This is determined by the
*supports_memory_efficient_fp16* property.
"""
def __init__(
self, cfg: DictConfig, params, optimizer, allow_unsupported=False, **kwargs
):
if not allow_unsupported and not optimizer.supports_memory_efficient_fp16:
raise ValueError(
"Unsupported optimizer: {}".format(optimizer.__class__.__name__)
)
super().__init__(getattr(cfg, "optimizer", None))
self.wrapped_optimizer = optimizer
if getattr(cfg.common, "fp16_scale_window", None) is None:
if len(cfg.optimization.update_freq) > 1:
raise ValueError(
"--fp16-scale-window must be given explicitly when using a "
"custom --update-freq schedule"
)
data_parallel_size = int(
cfg.distributed_training.distributed_world_size
/ cfg.common.model_parallel_size
)
scale_window = int(
2 ** 14 / data_parallel_size / cfg.optimization.update_freq[0]
)
else:
scale_window = cfg.common.fp16_scale_window
if not getattr(cfg.common, "bf16", False):
self.scaler = DynamicLossScaler(
init_scale=cfg.common.fp16_init_scale,
scale_window=scale_window,
tolerance=cfg.common.fp16_scale_tolerance,
threshold=cfg.common.threshold_loss_scale,
min_loss_scale=cfg.common.min_loss_scale,
)
else:
# disable loss scaling for bfloat16
self.scaler = None
@classmethod
def build_optimizer(cls, cfg: DictConfig, params, **kwargs):
"""
Args:
args (argparse.Namespace): fairseq args
params (iterable): iterable of parameters to optimize
"""
fp16_optimizer = optim.build_optimizer(cfg.optimizer, params)
return cls(cfg, params, fp16_optimizer, **kwargs)
@property
def optimizer(self):
return self.wrapped_optimizer.optimizer
@optimizer.setter
def optimizer(self, optimizer):
self.wrapped_optimizer.optimizer = optimizer
@property
def optimizer_config(self):
return self.wrapped_optimizer.optimizer_config
@property
def lr_scheduler(self):
return getattr(self.wrapped_optimizer, "lr_scheduler", None)
def get_lr(self):
return self.wrapped_optimizer.get_lr()
def set_lr(self, lr):
self.wrapped_optimizer.set_lr(lr)
def all_reduce_grads(self, module):
self.wrapped_optimizer.all_reduce_grads(module)
|
bart_ls-main
|
fairseq-py/fairseq/optim/fp16_optimizer.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch.optim
from . import LegacyFairseqOptimizer, register_optimizer
@register_optimizer("adagrad")
class Adagrad(LegacyFairseqOptimizer):
def __init__(self, args, params):
super().__init__(args)
self._optimizer = torch.optim.Adagrad(params, **self.optimizer_config)
@staticmethod
def add_args(parser):
"""Add optimizer-specific arguments to the parser."""
# fmt: off
parser.add_argument('--weight-decay', '--wd', default=0.0, type=float, metavar='WD',
help='weight decay')
# fmt: on
@property
def optimizer_config(self):
"""
Return a kwarg dictionary that will be used to override optimizer
args stored in checkpoints. This allows us to load a checkpoint and
resume training using a different set of optimizer args, e.g., with a
different learning rate.
"""
return {
"lr": self.args.lr[0],
"weight_decay": self.args.weight_decay,
}
@property
def supports_flat_params(self):
return False
|
bart_ls-main
|
fairseq-py/fairseq/optim/adagrad.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from typing import Any, Dict
from fairseq.distributed import utils
try:
from fairscale.optim import OSS
_has_fairscale = True
except ImportError:
_has_fairscale = False
def shard_(optimizer, group):
if not _has_fairscale:
raise ImportError(
"\n\nPlease install the fairscale package:" "\n\n pip install fairscale"
)
class FairseqOSS(OSS):
@property
def disable_mem_eff_fp16_loading_hack(self):
return True
def __getattr__(self, name):
if name.startswith("supports") and hasattr(self.optim, name):
return getattr(self.optim, name)
raise AttributeError(
"'FairseqOSS' object has no attribute {0!r}".format(name)
)
def broadcast_global_state_dict(
self, state_dict: Dict[str, Any]
) -> Dict[str, Any]:
"""
Broadcasts the entire state_dict to all other ranks
each rank is responsible to load their own partition of data
"""
return utils.broadcast_object(
state_dict,
src_rank=0,
group=self.group,
)
torch_optimizer = optimizer.optimizer
optim_cls = type(torch_optimizer)
optimizer.optimizer = FairseqOSS(
torch_optimizer.param_groups,
optim_cls,
group=group,
**optimizer.optimizer_config
)
|
bart_ls-main
|
fairseq-py/fairseq/optim/shard.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import importlib
from collections.abc import Collection
from dataclasses import dataclass, field
from typing import List
import torch
from fairseq.dataclass import FairseqDataclass
from fairseq.optim import FairseqOptimizer, register_optimizer
from omegaconf import II, DictConfig
try:
import deepspeed
has_deepspeed = True
except ImportError as e:
has_deepspeed = False
def _get_cpu_adam():
try:
from deepspeed.ops.op_builder import CPUAdamBuilder
return CPUAdamBuilder().load()
except ImportError:
# fbcode
from deepspeed.ops.adam import DeepSpeedCPUAdam as ds_opt_adam
return ds_opt_adam
@dataclass
class FairseqCPUAdamConfig(FairseqDataclass):
adam_betas: str = field(
default="(0.9, 0.999)", metadata={"help": "betas for Adam optimizer"}
)
adam_eps: float = field(
default=1e-8, metadata={"help": "epsilon for Adam optimizer"}
)
weight_decay: float = field(default=0.0, metadata={"help": "weight decay"})
fp16_adam_stats: bool = field(
default=False, metadata={"help": "use FP16 stats (with automatic scaling)"}
)
# TODO common vars below in parent
lr: List[float] = II("optimization.lr")
@register_optimizer("cpu_adam", dataclass=FairseqCPUAdamConfig)
class FairseqCPUAdam(FairseqOptimizer):
"""Adam optimizer for fairseq, optimized for CPU tensors.
Important note: this optimizer corresponds to the "AdamW" variant of
Adam in its weight decay behavior. As such, it is most closely
analogous to torch.optim.AdamW from PyTorch.
"""
def __init__(self, cfg: DictConfig, params):
super().__init__(cfg)
self._optimizer = CPUAdam(params, **self.optimizer_config)
@property
def optimizer_config(self):
"""
Return a kwarg dictionary that will be used to override optimizer
args stored in checkpoints. This allows us to load a checkpoint and
resume training using a different set of optimizer args, e.g., with a
different learning rate.
"""
return {
"lr": self.cfg.lr[0]
if isinstance(self.cfg.lr, Collection)
else self.cfg.lr,
"betas": eval(self.cfg.adam_betas),
"eps": self.cfg.adam_eps,
"weight_decay": self.cfg.weight_decay,
"use_fp16_stats": self.cfg.fp16_adam_stats,
}
class CPUAdam(torch.optim.Optimizer):
optimizer_id = 0
def __init__(
self,
params,
lr=1e-3,
bias_correction=True,
betas=(0.9, 0.999),
eps=1e-8,
weight_decay=0,
use_fp16_stats=False,
):
defaults = {
"lr": lr,
"bias_correction": bias_correction,
"betas": betas,
"eps": eps,
"weight_decay": weight_decay,
}
super().__init__(params, defaults)
self.use_fp16_stats = use_fp16_stats
self.FLOAT16_MAX = 65504.0
if not has_deepspeed:
raise ImportError("Please install DeepSpeed: pip install deepspeed")
self.opt_id = CPUAdam.optimizer_id
CPUAdam.optimizer_id = CPUAdam.optimizer_id + 1
self.ds_opt_adam = _get_cpu_adam()
adamw_mode = True
self.ds_opt_adam.create_adam(
self.opt_id, lr, betas[0], betas[1], eps, weight_decay, adamw_mode
)
@property
def supports_memory_efficient_fp16(self):
return True
@property
def supports_flat_params(self):
return True
@torch.no_grad()
def step(self, closure=None):
loss = None
if closure is not None:
with torch.enable_grad():
loss = closure()
torch.cuda.synchronize()
for group_id, group in enumerate(self.param_groups):
for param_id, p in enumerate(group["params"]):
if p.grad is None:
continue
state = self.state[p]
if len(state) == 0:
state["step"] = 0
dtype = torch.float16 if self.use_fp16_stats else p.data.dtype
# gradient momentums
state["exp_avg"] = torch.zeros_like(
p.data, dtype=dtype, device="cpu"
)
# gradient variances
state["exp_avg_sq"] = torch.zeros_like(
p.data, dtype=dtype, device="cpu"
)
if self.use_fp16_stats:
assert torch.is_floating_point(p.data)
state["exp_avg_scale"] = 1.0
state["exp_avg_sq_scale"] = 1.0
exp_avg, exp_avg_sq = state["exp_avg"], state["exp_avg_sq"]
p_data_bak = p.data # backup of the original data pointer
p.data = p.data.to(dtype=torch.float32, device="cpu")
p.grad.data = p.grad.data.to(dtype=torch.float32, device="cpu")
if self.use_fp16_stats:
exp_avg = exp_avg.float() * state["exp_avg_scale"]
exp_avg_sq = exp_avg_sq.float() * state["exp_avg_sq_scale"]
state["step"] += 1
beta1, beta2 = group["betas"]
self.ds_opt_adam.adam_update(
self.opt_id,
state["step"],
group["lr"],
beta1,
beta2,
group["eps"],
group["weight_decay"],
group["bias_correction"],
p.data,
p.grad.data,
exp_avg,
exp_avg_sq,
)
if p_data_bak.data_ptr() != p.data.data_ptr():
p_data_bak.copy_(p.data)
p.data = p_data_bak
if self.use_fp16_stats:
def inf_norm(t):
return torch.norm(t, float("inf"))
# from github.com/openai/jukebox/blob/master/jukebox/utils/fp16.py
state["exp_avg_scale"], state["exp_avg_sq_scale"] = (
1e-8 + inf_norm(exp_avg) / self.FLOAT16_MAX,
1e-8 + inf_norm(exp_avg_sq) / self.FLOAT16_MAX,
)
state["exp_avg"], state["exp_avg_sq"] = (
(exp_avg / state["exp_avg_scale"]).half(),
(exp_avg_sq / state["exp_avg_sq_scale"]).half(),
)
return loss
|
bart_ls-main
|
fairseq-py/fairseq/optim/cpu_adam.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import logging
import math
from collections.abc import Collection
from dataclasses import dataclass, field
from typing import Any, List
import torch
import torch.distributed as dist
import torch.optim
from fairseq.dataclass import FairseqDataclass
from fairseq.optim import FairseqOptimizer, register_optimizer
from fairseq.optim.fused_adam import get_fused_adam_class
from omegaconf import II, OmegaConf
logger = logging.getLogger(__name__)
@dataclass
class FairseqAdamConfig(FairseqDataclass):
adam_betas: Any = field(
default=(0.9, 0.999), metadata={"help": "betas for Adam optimizer"}
)
adam_eps: float = field(
default=1e-8, metadata={"help": "epsilon for Adam optimizer"}
)
weight_decay: float = field(default=0.0, metadata={"help": "weight decay"})
use_old_adam: bool = field(
default=False, metadata={"help": "Use fairseq.optim.adam.Adam"}
)
fp16_adam_stats: bool = field(
default=False, metadata={"help": "use FP16 stats (with automatic scaling)"}
)
# TODO common vars below in parent
tpu: bool = II("common.tpu")
lr: List[float] = II("optimization.lr")
@register_optimizer("adam", dataclass=FairseqAdamConfig)
class FairseqAdam(FairseqOptimizer):
"""Adam optimizer for fairseq.
Important note: this optimizer corresponds to the "AdamW" variant of
Adam in its weight decay behavior. As such, it is most closely
analogous to torch.optim.AdamW from PyTorch.
"""
def __init__(self, cfg: FairseqAdamConfig, params):
super().__init__(cfg)
fused_adam_cls = get_fused_adam_class()
use_fused_adam = (
not getattr(cfg, "use_old_adam", False)
and fused_adam_cls is not None
and torch.cuda.is_available()
)
if getattr(cfg, "tpu", False):
if self.cfg.fp16_adam_stats:
raise NotImplementedError("--fp16-adam-stats is only supported on GPU")
# on TPUs we use the Adam defined here, since it
# automatically casts gradients to FP32
self._optimizer = Adam(params, **self.optimizer_config)
elif use_fused_adam:
logger.info("using FusedAdam")
self._optimizer = fused_adam_cls(
params,
use_fp16_stats=self.cfg.fp16_adam_stats,
**self.optimizer_config
)
else:
if self.cfg.fp16_adam_stats:
raise NotImplementedError("--fp16-adam-stats is only supported with FusedAdamV1")
self._optimizer = Adam(params, **self.optimizer_config)
@property
def optimizer_config(self):
"""
Return a kwarg dictionary that will be used to override optimizer
args stored in checkpoints. This allows us to load a checkpoint and
resume training using a different set of optimizer args, e.g., with a
different learning rate.
"""
return {
"lr": self.cfg.lr[0]
if isinstance(self.cfg.lr, Collection)
else self.cfg.lr,
"betas": eval(self.cfg.adam_betas)
if isinstance(self.cfg.adam_betas, str)
else OmegaConf.to_container(self.cfg.adam_betas),
"eps": self.cfg.adam_eps,
"weight_decay": self.cfg.weight_decay,
}
def average_params(self):
"""Reduce Params is only used during BMUF distributed training."""
state_dict = self.optimizer.state_dict()
total_gpus = float(dist.get_world_size())
for _, value in state_dict["state"].items():
value["exp_avg"] /= total_gpus
value["exp_avg_sq"] /= total_gpus
dist.all_reduce(value["exp_avg"], op=dist.ReduceOp.SUM)
dist.all_reduce(value["exp_avg_sq"], op=dist.ReduceOp.SUM)
class Adam(torch.optim.Optimizer):
r"""Implements Adam algorithm.
This implementation is modified from torch.optim.Adam based on:
`Fixed Weight Decay Regularization in Adam`
(see https://arxiv.org/abs/1711.05101)
It has been proposed in `Adam: A Method for Stochastic Optimization`_.
Args:
params (iterable): iterable of parameters to optimize or dicts defining
parameter groups
lr (float, optional): learning rate (default: 1e-3)
betas (Tuple[float, float], optional): coefficients used for computing
running averages of gradient and its square (default: (0.9, 0.999))
eps (float, optional): term added to the denominator to improve
numerical stability (default: 1e-8)
weight_decay (float, optional): weight decay (L2 penalty) (default: 0)
amsgrad (boolean, optional): whether to use the AMSGrad variant of this
algorithm from the paper `On the Convergence of Adam and Beyond`_
.. _Adam\: A Method for Stochastic Optimization:
https://arxiv.org/abs/1412.6980
.. _On the Convergence of Adam and Beyond:
https://openreview.net/forum?id=ryQu7f-RZ
"""
def __init__(
self,
params,
lr=1e-3,
betas=(0.9, 0.999),
eps=1e-8,
weight_decay=0,
amsgrad=False,
):
defaults = dict(
lr=lr, betas=betas, eps=eps, weight_decay=weight_decay, amsgrad=amsgrad
)
super(Adam, self).__init__(params, defaults)
@property
def supports_memory_efficient_fp16(self):
return True
@property
def supports_flat_params(self):
return True
def step(self, closure=None):
"""Performs a single optimization step.
Args:
closure (callable, optional): A closure that reevaluates the model
and returns the loss.
"""
loss = None
if closure is not None:
loss = closure()
for group in self.param_groups:
for p in group["params"]:
if p.grad is None:
continue
grad = p.grad.data
if grad.dtype in {torch.float16, torch.bfloat16}:
grad = grad.float()
if grad.is_sparse:
raise RuntimeError(
"Adam does not support sparse gradients, please consider SparseAdam instead"
)
amsgrad = group.get("amsgrad", False)
p_data_fp32 = p.data
if p.data.dtype in {torch.float16, torch.bfloat16}:
p_data_fp32 = p_data_fp32.float()
state = self.state[p]
# State initialization
if len(state) == 0:
state["step"] = 0
# Exponential moving average of gradient values
state["exp_avg"] = torch.zeros_like(p_data_fp32)
# Exponential moving average of squared gradient values
state["exp_avg_sq"] = torch.zeros_like(p_data_fp32)
if amsgrad:
# Maintains max of all exp. moving avg. of sq. grad. values
state["max_exp_avg_sq"] = torch.zeros_like(p_data_fp32)
else:
state["exp_avg"] = state["exp_avg"].to(p_data_fp32)
state["exp_avg_sq"] = state["exp_avg_sq"].to(p_data_fp32)
if amsgrad:
state["max_exp_avg_sq"] = state["max_exp_avg_sq"].to(
p_data_fp32
)
exp_avg, exp_avg_sq = state["exp_avg"], state["exp_avg_sq"]
if amsgrad:
max_exp_avg_sq = state["max_exp_avg_sq"]
beta1, beta2 = group["betas"]
state["step"] += 1
# Decay the first and second moment running average coefficient
exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1)
exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1 - beta2)
if amsgrad:
# Maintains the maximum of all 2nd moment running avg. till now
torch.max(max_exp_avg_sq, exp_avg_sq, out=max_exp_avg_sq)
# Use the max. for normalizing running avg. of gradient
denom = max_exp_avg_sq.sqrt().add_(group["eps"])
else:
denom = exp_avg_sq.sqrt().add_(group["eps"])
bias_correction1 = 1 - beta1 ** state["step"]
bias_correction2 = 1 - beta2 ** state["step"]
step_size = group["lr"] * math.sqrt(bias_correction2) / bias_correction1
if group["weight_decay"] != 0:
p_data_fp32.add_(
p_data_fp32, alpha=-group["weight_decay"] * group["lr"]
)
p_data_fp32.addcdiv_(exp_avg, denom, value=-step_size)
if p.data.dtype in {torch.float16, torch.bfloat16}:
p.data.copy_(p_data_fp32)
return loss
|
bart_ls-main
|
fairseq-py/fairseq/optim/adam.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import logging
from collections import defaultdict
from dataclasses import dataclass, field
from typing import Dict, Any, List, Optional
import torch.optim
from fairseq.dataclass import FairseqDataclass
from fairseq.optim import FairseqOptimizer, register_optimizer, _build_optimizer
from fairseq.optim.lr_scheduler import FairseqLRScheduler, build_lr_scheduler
from omegaconf import II, open_dict
logger = logging.getLogger(__name__)
@dataclass
class OptimizerAndSchedulerConfig(FairseqDataclass):
optimizer: Any = None
lr_scheduler: Optional[Any] = None
lr: List = II("optimization.lr")
lr_float: Optional[float] = None # this makes it easier to sweep on learning rate with auto sweepers
@dataclass
class CompositeOptimizerConfig(FairseqDataclass):
groups: Dict[str, Any] = field(
default_factory=lambda: {},
metadata={
"help": "optimizer name -> optimizer OptimizerAndSchedulerConfig. "
"Configures a different optimizer and (optionally) lr scheduler for each parameter group"
},
)
@register_optimizer("composite", dataclass=CompositeOptimizerConfig)
class FairseqCompositeOptimizer(FairseqOptimizer):
optimizers: Dict[str, FairseqOptimizer] = {}
lr_schedulers: Dict[str, FairseqLRScheduler] = {}
lr_scheduler: FairseqLRScheduler = None
_optimizer: torch.optim.Optimizer
def __init__(self, cfg: CompositeOptimizerConfig, params):
super().__init__(cfg)
assert (
len(params) > 1
), "Composite optimizer only works when there are multiple parameter groups (try fp16_no_flatten_grads: true)"
groupped_params = defaultdict(list)
for p in params:
group = getattr(p, "param_group", "default")
groupped_params[group].append(p)
assert groupped_params.keys() == cfg.groups.keys(), (
f"Parameter groups {groupped_params.keys()} and optimizer groups {cfg.groups.keys()} are not the same! "
"Try setting 'param_group' on your parameters in the model."
)
for group, group_params in groupped_params.items():
group_cfg = cfg.groups[group]
with open_dict(group_cfg):
if group_cfg.lr_float is not None:
group_cfg.optimizer.lr = [group_cfg.lr_float]
group_cfg.lr_scheduler.lr = [group_cfg.lr_float]
else:
group_cfg.optimizer.lr = group_cfg.lr
group_cfg.lr_scheduler.lr = group_cfg.lr
self.optimizers[group] = _build_optimizer(group_cfg.optimizer, group_params)
if group_cfg.lr_scheduler is not None:
self.lr_schedulers[group] = build_lr_scheduler(
group_cfg.lr_scheduler, self.optimizers[group]
)
if len(self.lr_schedulers) > 0:
assert len(self.lr_schedulers) == len(self.optimizers), (
f"Please provide an lr scheduler for each optimizer to use pass_through scheduler. "
f"Optimizers: {self.optimizers}; Lr scheds: {self.lr_schedulers}"
)
self.lr_scheduler = CompositeLRScheduler(self.lr_schedulers)
self._optimizer = CompositeOptimizer(self.optimizers)
@property
def supports_groups(self):
return True
@property
def param_groups(self):
for opt in self.optimizers.values():
for group in opt.param_groups:
yield group
def get_lr(self):
"""Return the current learning rate."""
k = (
"default"
if "default" in self.optimizers
else next(iter(self.optimizers.keys()))
)
return self.optimizers[k].param_groups[0]["lr"]
def state_dict(self):
"""Return the LR scheduler state dict."""
return {k: s.state_dict() for k, s in self.optimizers.items()}
def load_state_dict(self, state_dict, optimizer_overrides=None):
"""Load an LR scheduler state dict."""
for k, state in state_dict.items():
if k not in self.optimizers:
# skip extra keys like "loss_scale" added by fp16 optimizer
continue
overrides = (
optimizer_overrides[k]
if isinstance(optimizer_overrides, dict) and k in optimizer_overrides
else None
)
self.optimizers[k].load_state_dict(state, optimizer_overrides=overrides)
class CompositeOptimizer(torch.optim.Optimizer):
def __init__(self, optimizers: Dict[str, FairseqOptimizer]):
self.optimizers = optimizers
@property
def supports_memory_efficient_fp16(self):
return all(o.supports_memory_efficient_fp16 for o in self.optimizers.values())
@property
def supports_flat_params(self):
return all(o.supports_flat_params for o in self.optimizers.values())
def step(self, closure=None, groups=None):
"""Performs a single optimization step.
Args:
closure (callable, optional): A closure that reevaluates the model
and returns the loss.
"""
loss = None
if closure is not None:
loss = closure()
for k, opt in self.optimizers.items():
if groups is None or k in groups:
opt.step()
return loss
def zero_grad(self):
for opt in self.optimizers.values():
opt.zero_grad()
class CompositeLRScheduler(FairseqLRScheduler):
def __init__(self, lr_schedulers):
super().__init__(None, None)
self.lr_schedulers = lr_schedulers
def state_dict(self):
"""Return the LR scheduler state dict."""
return {k: s.state_dict() for k, s in self.lr_schedulers.items()}
def load_state_dict(self, state_dict):
"""Load an LR scheduler state dict."""
for k, state in state_dict.items():
self.lr_schedulers[k].load_state_dict(state)
def step_begin_epoch(self, epoch):
"""Update the learning rate at the beginning of the given epoch."""
for s in self.lr_schedulers.values():
s.step_begin_epoch(epoch)
def step(self, epoch, val_loss=None):
"""Update the learning rate at the end of the given epoch."""
for s in self.lr_schedulers.values():
s.step(epoch)
def step_update(self, num_updates):
"""Update the learning rate after each update."""
return {k: s.step_update(num_updates) for k, s in self.lr_schedulers.items()}
|
bart_ls-main
|
fairseq-py/fairseq/optim/composite.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from fairseq.optim import LegacyFairseqOptimizer, register_optimizer
@register_optimizer("lamb")
class FairseqLAMB(LegacyFairseqOptimizer):
"""LAMB optimizer."""
def __init__(self, args, params):
super().__init__(args)
try:
from apex.optimizers import FusedLAMB
self._optimizer = FusedLAMB(params, **self.optimizer_config)
except ImportError:
raise ImportError("Please install apex to use LAMB optimizer")
@staticmethod
def add_args(parser):
"""Add optimizer-specific arguments to the parser."""
# fmt: off
parser.add_argument('--lamb-betas', default='(0.9, 0.999)', metavar='B',
help='betas for LAMB optimizer')
parser.add_argument('--lamb-eps', type=float, default=1e-8, metavar='D',
help='epsilon for LAMB optimizer')
parser.add_argument('--weight-decay', '--wd', default=0.0, type=float, metavar='WD',
help='weight decay')
# fmt: on
@property
def optimizer_config(self):
"""
Return a kwarg dictionary that will be used to override optimizer
args stored in checkpoints. This allows us to load a checkpoint and
resume training using a different set of optimizer args, e.g., with a
different learning rate.
"""
return {
"lr": self.args.lr[0],
"betas": eval(self.args.lamb_betas),
"eps": self.args.lamb_eps,
"weight_decay": self.args.weight_decay,
}
@property
def supports_flat_params(self):
return False
|
bart_ls-main
|
fairseq-py/fairseq/optim/fused_lamb.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch.optim
from . import LegacyFairseqOptimizer, register_optimizer
@register_optimizer("adadelta")
class Adadelta(LegacyFairseqOptimizer):
def __init__(self, args, params):
super().__init__(args)
self._optimizer = torch.optim.Adadelta(params, **self.optimizer_config)
@staticmethod
def add_args(parser):
"""Add optimizer-specific arguments to the parser."""
# fmt: off
parser.add_argument('--adadelta-rho', type=float, default=0.9, metavar='RHO',
help='coefficient used for computing a running average of squared gradients')
parser.add_argument('--adadelta-eps', type=float, default=1e-6, metavar='EPS',
help='term added to the denominator to improve numerical stability')
parser.add_argument('--weight-decay', '--wd', default=0.0, type=float, metavar='WD',
help='weight decay')
parser.add_argument('--anneal-eps', action='store_true', help='flag to anneal eps')
# fmt: on
@property
def optimizer_config(self):
"""
Return a kwarg dictionary that will be used to override optimizer
args stored in checkpoints. This allows us to load a checkpoint and
resume training using a different set of optimizer args, e.g., with a
different learning rate.
"""
return {
"lr": self.args.lr[0],
"rho": self.args.adadelta_rho,
"eps": self.args.adadelta_eps,
"weight_decay": self.args.weight_decay,
}
@property
def supports_flat_params(self):
return True
|
bart_ls-main
|
fairseq-py/fairseq/optim/adadelta.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from dataclasses import dataclass
from fairseq.dataclass import FairseqDataclass
from fairseq.optim.lr_scheduler import FairseqLRScheduler, register_lr_scheduler
@dataclass
class PassThroughScheduleConfig(FairseqDataclass):
pass
@register_lr_scheduler("pass_through", dataclass=PassThroughScheduleConfig)
class PassThroughScheduleSchedule(FairseqLRScheduler):
"""Delegate lr scheduling to the optimizer."""
def __init__(self, cfg: PassThroughScheduleConfig, optimizer):
super().__init__(cfg, optimizer)
assert (
hasattr(optimizer, "lr_scheduler") and optimizer.lr_scheduler is not None
), "Pass-through schedule can only be used with optimizers with their own schedulers"
def state_dict(self):
return self.optimizer.lr_scheduler.state_dict()
def load_state_dict(self, state_dict):
self.optimizer.lr_scheduler.load_state_dict(state_dict)
def step_begin_epoch(self, epoch):
"""Update the learning rate at the beginning of the given epoch."""
return self.optimizer.lr_scheduler.step_begin_epoch(epoch)
def step_update(self, num_updates):
"""Update the learning rate after each update."""
return self.optimizer.lr_scheduler.step_update(num_updates)
|
bart_ls-main
|
fairseq-py/fairseq/optim/lr_scheduler/pass_through.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from . import LegacyFairseqLRScheduler, register_lr_scheduler
import logging
import ast
logger = logging.getLogger(__name__)
logger.setLevel(logging.WARNING)
@register_lr_scheduler("manual")
class ManualSchedule(LegacyFairseqLRScheduler):
"""Decay the LR on a manual schedule."""
def __init__(self, args, optimizer):
super().__init__(args, optimizer)
self.epoch2lr = self.parse_manuallr_args(args.epoch2lr)
self.update2lr = self.parse_manuallr_args(args.update2lr)
logger.info("@@@ ManualSchedule epoch2lr={}".format(self.epoch2lr))
logger.info("@@@ ManualSchedule update2lr={}".format(self.update2lr))
if 1 in self.epoch2lr:
self.lr = self.epoch2lr[1]
elif 1 in self.update2lr:
self.lr = self.update2lr[1]
else:
self.lr = args.lr[0]
self.optimizer.set_lr(self.lr) # Set the beginning of the epoch.
def parse_manuallr_args(self, lr_args_str):
lr_dict = ast.literal_eval(lr_args_str.replace(' ', ''))
if not isinstance(lr_dict, dict):
raise ValueError("epoch2lr/update2lr must be abel to evaluated to a dict")
lr_args = {}
logger.info("@@@ after parsing input dictionary lr_dict = {}".format(lr_dict))
for key, val in lr_dict.items():
if "," in key:
for k in key.split(","):
lr_args[int(k)] = float(val)
elif "-" in key:
s = int(key.split("-")[0])
e = int(key.split("-")[1])
for k in range(s, e + 1, 1):
lr_args[k] = float(val)
else:
lr_args[int(key)] = float(val)
return lr_args
@staticmethod
def add_args(parser):
"""Add arguments to the parser for this LR scheduler."""
# fmt: off
parser.add_argument(
"--epoch2lr",
type=str,
metavar="DICT",
default="{}",
help="a dictionary used to set lr for each epoch manually",
)
parser.add_argument(
"--update2lr",
type=str,
metavar="DICT",
default="{}",
help="a dictionary used to set lr for each update manually",
)
# fmt: on
def state_dict(self):
return {"lr": self.lr}
def load_state_dict(self, state_dict):
if "lr" in state_dict:
self.lr = state_dict["lr"]
def get_next_lr(self, epoch):
manual_keys = [k for k in self.epoch2lr if k <= epoch]
if manual_keys:
manual_lr = self.epoch2lr[max(manual_keys)]
else:
logger.warning("@@@ epoch={} does not exist in manual lr input. epoch2lr={}...".format(
epoch, list(self.epoch2lr.items())[:min(10, len(self.epoch2lr.keys())-1)]
))
manual_lr = self.optimizer.get_lr()
return manual_lr
def step_begin_epoch(self, epoch):
"""Update the learning rate at the beginning of the given epoch."""
self.lr = self.get_next_lr(epoch)
self.optimizer.set_lr(self.lr)
return self.optimizer.get_lr()
def step_update(self, num_updates):
"""Update the learning rate after each update."""
manual_keys = [k for k in self.update2lr if k <= num_updates]
if manual_keys:
manual_lr = self.update2lr[max(manual_keys)]
else:
logger.warning("epoch={} does not exist in manual lr input update2lr={}...".format(
num_updates, list(self.update2lr.items())[:min(10, len(self.update2lr.keys())-1)]))
manual_lr = self.optimizer.get_lr()
self.optimizer.set_lr(manual_lr)
return self.optimizer.get_lr()
|
bart_ls-main
|
fairseq-py/fairseq/optim/lr_scheduler/manual_lr_scheduler.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from dataclasses import dataclass, field
from typing import Optional, List
from omegaconf import II
from fairseq.dataclass import FairseqDataclass
from fairseq.optim.lr_scheduler import FairseqLRScheduler, register_lr_scheduler
@dataclass
class FixedLRScheduleConfig(FairseqDataclass):
force_anneal: Optional[int] = field(
default=None,
metadata={"help": "force annealing at specified epoch"},
)
lr_shrink: float = field(
default=0.1,
metadata={"help": "shrink factor for annealing, lr_new = (lr * lr_shrink)"},
)
warmup_updates: int = field(
default=0,
metadata={"help": "warmup the learning rate linearly for the first N updates"},
)
lr: List[float] = II("optimization.lr")
@register_lr_scheduler("fixed", dataclass=FixedLRScheduleConfig)
class FixedLRSchedule(FairseqLRScheduler):
"""Decay the LR on a fixed schedule."""
def __init__(self, cfg: FixedLRScheduleConfig, optimizer):
super().__init__(cfg, optimizer)
self.lr = cfg.lr[0]
if cfg.warmup_updates > 0:
self.warmup_factor = 1.0 / cfg.warmup_updates
else:
self.warmup_factor = 1
def state_dict(self):
return {"lr": self.lr}
def load_state_dict(self, state_dict):
if "lr" in state_dict:
self.lr = state_dict["lr"]
def get_next_lr(self, epoch):
lrs = self.cfg.lr
if self.cfg.force_anneal is None or epoch < self.cfg.force_anneal:
# use fixed LR schedule
next_lr = lrs[min(epoch - 1, len(lrs) - 1)]
else:
# annneal based on lr_shrink
next_lr = lrs[-1] * self.cfg.lr_shrink ** (
epoch + 1 - self.cfg.force_anneal
)
return next_lr
def step_begin_epoch(self, epoch):
"""Update the learning rate at the beginning of the given epoch."""
self.lr = self.get_next_lr(epoch)
self.optimizer.set_lr(self.warmup_factor * self.lr)
return self.optimizer.get_lr()
def step_update(self, num_updates):
"""Update the learning rate after each update."""
if self.cfg.warmup_updates > 0 and num_updates < self.cfg.warmup_updates:
self.warmup_factor = (num_updates + 1) / float(self.cfg.warmup_updates)
self.optimizer.set_lr(self.warmup_factor * self.lr)
else:
self.optimizer.set_lr(self.lr)
return self.optimizer.get_lr()
|
bart_ls-main
|
fairseq-py/fairseq/optim/lr_scheduler/fixed_schedule.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from dataclasses import dataclass, field
from typing import List
import torch.optim.lr_scheduler
from omegaconf import II
from fairseq.dataclass import FairseqDataclass
from fairseq.optim.lr_scheduler import FairseqLRScheduler, register_lr_scheduler
@dataclass
class ReduceLROnPlateauLRScheduleConfig(FairseqDataclass):
lr_shrink: float = field(
default=0.1, metadata={"help": "shrink factor for annealing"}
)
lr_threshold: float = field(
default=1e-4,
metadata={
"help": (
"threshold for measuring the new optimum, to only focus on "
"significant changes"
)
},
)
lr_patience: int = field(
default=0,
metadata={
"help": (
"number of epochs with no improvement after which learning rate will "
"be reduced"
)
},
)
warmup_updates: int = field(
default=0,
metadata={"help": "warmup the learning rate linearly for the first N updates"},
)
warmup_init_lr: float = field(
default=-1,
metadata={
"help": "initial learning rate during warmup phase; default is cfg.lr"
},
)
lr: List[float] = II("optimization.lr")
maximize_best_checkpoint_metric: bool = II(
"checkpoint.maximize_best_checkpoint_metric"
)
@register_lr_scheduler(
"reduce_lr_on_plateau", dataclass=ReduceLROnPlateauLRScheduleConfig
)
class ReduceLROnPlateauLRSchedule(FairseqLRScheduler):
"""
Decay the LR by a factor every time the validation loss plateaus.
Also comes with optional warmup phase, where we linearly increase
the learning rate from some initial learning rate
(``--warmup-init-lr``) until the configured learning rate
(``--lr``). Thereafter the lr is adjusted according to original
reduce_on_plateau scheme.
During warmup::
lrs = torch.linspace(
cfg.warmup_init_lr, cfg.lr, cfg.warmup_updates
)
lr = lrs[update_num]
"""
def __init__(self, cfg: ReduceLROnPlateauLRScheduleConfig, optimizer):
super().__init__(cfg, optimizer)
if len(cfg.lr) > 1:
raise ValueError(
"Cannot use a fixed learning rate schedule with reduce_lr_on_plateau."
" Consider --lr-scheduler=fixed instead."
)
self.lr_scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(
self.optimizer.optimizer,
patience=cfg.lr_patience,
factor=cfg.lr_shrink,
mode="max" if cfg.maximize_best_checkpoint_metric else "min",
threshold=cfg.lr_threshold,
)
warmup_end_lr = cfg.lr[0]
# if no warm up, sets initial lr to be cfg.lr[0]
if cfg.warmup_init_lr < 0:
cfg.warmup_init_lr = 0 if cfg.warmup_updates > 0 else warmup_end_lr
# linearly warmup for the first cfg.warmup_updates
if cfg.warmup_updates > 0:
self.lr_step = (warmup_end_lr - cfg.warmup_init_lr) / cfg.warmup_updates
# this flag is either set from arg when no warm up, or set by
# step_update() when warmup finishes
self.warmup_end = True if cfg.warmup_updates <= 0 else False
# initial learning rate
# this self.lr is used only during init and/or warm up period
self.lr = warmup_end_lr if self.warmup_end else cfg.warmup_init_lr
self.optimizer.set_lr(self.lr)
def state_dict(self):
"""Return the LR scheduler state dict."""
return {
"best": self.lr_scheduler.best,
"last_epoch": self.lr_scheduler.last_epoch,
}
def load_state_dict(self, state_dict):
"""Load an LR scheduler state dict."""
self.lr_scheduler.best = state_dict["best"]
if "last_epoch" in state_dict:
self.lr_scheduler.last_epoch = state_dict["last_epoch"]
def step(self, epoch, val_loss=None):
"""
Update the learning rate at the end of the given epoch if warmup
finishes otherwise no update of lr on epoch boundaries
"""
if val_loss is not None and self.warmup_end is True:
self.lr_scheduler.step(val_loss)
else:
self.lr_scheduler.last_epoch = epoch
return self.optimizer.get_lr()
def step_update(self, num_updates):
"""
Update the learning rate after each update."""
# if there is warmup
if self.cfg.warmup_updates > 0:
if num_updates <= self.cfg.warmup_updates:
self.lr = self.cfg.warmup_init_lr + num_updates * self.lr_step
self.optimizer.set_lr(self.lr)
else:
if self.warmup_end is False:
self.warmup_end = True
# else do nothing
return self.optimizer.get_lr()
|
bart_ls-main
|
fairseq-py/fairseq/optim/lr_scheduler/reduce_lr_on_plateau.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""isort:skip_file"""
import importlib
import os
from fairseq import registry
from fairseq.optim.lr_scheduler.fairseq_lr_scheduler import ( # noqa
FairseqLRScheduler,
LegacyFairseqLRScheduler,
)
from omegaconf import DictConfig
(
build_lr_scheduler_,
register_lr_scheduler,
LR_SCHEDULER_REGISTRY,
LR_SCHEDULER_DATACLASS_REGISTRY,
) = registry.setup_registry(
"--lr-scheduler", base_class=FairseqLRScheduler, default="fixed"
)
def build_lr_scheduler(cfg: DictConfig, optimizer):
return build_lr_scheduler_(cfg, optimizer)
# automatically import any Python files in the optim/lr_scheduler/ directory
for file in sorted(os.listdir(os.path.dirname(__file__))):
if file.endswith(".py") and not file.startswith("_"):
file_name = file[: file.find(".py")]
importlib.import_module("fairseq.optim.lr_scheduler." + file_name)
|
bart_ls-main
|
fairseq-py/fairseq/optim/lr_scheduler/__init__.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from dataclasses import dataclass, field
from typing import Optional, List
from omegaconf import II
from fairseq.dataclass import FairseqDataclass
from fairseq.optim.lr_scheduler import FairseqLRScheduler, register_lr_scheduler
@dataclass
class PolynomialDecayLRScheduleConfig(FairseqDataclass):
warmup_updates: int = field(
default=0,
metadata={"help": "warmup the learning rate linearly for the first N updates"},
)
force_anneal: Optional[int] = field(
default=None,
metadata={"help": "force annealing at specified epoch"},
)
end_learning_rate: float = field(
default=0.0,
metadata={"help": "learning rate to decay to"},
)
power: float = field(
default=1.0,
metadata={"help": "decay exponent"},
)
total_num_update: float = field(
default=II("optimization.max_update"),
metadata={"help": "total number of updates over which to decay learning rate"},
)
lr: List[float] = II("optimization.lr")
@register_lr_scheduler("polynomial_decay", dataclass=PolynomialDecayLRScheduleConfig)
class PolynomialDecayLRSchedule(FairseqLRScheduler):
"""Decay the LR on a fixed schedule."""
def __init__(self, cfg: PolynomialDecayLRScheduleConfig, optimizer):
super().__init__(cfg, optimizer)
assert cfg.total_num_update > 0
self.lr = cfg.lr[0]
if cfg.warmup_updates > 0:
self.warmup_factor = 1.0 / cfg.warmup_updates
else:
self.warmup_factor = 1
self.end_learning_rate = cfg.end_learning_rate
self.total_num_update = cfg.total_num_update
self.power = cfg.power
self.optimizer.set_lr(self.warmup_factor * self.lr)
def get_next_lr(self, epoch):
lrs = self.cfg.lr
if self.cfg.force_anneal is None or epoch < self.cfg.force_anneal:
# use fixed LR schedule
next_lr = lrs[min(epoch, len(lrs) - 1)]
else:
# annneal based on lr_shrink
next_lr = self.optimizer.get_lr()
return next_lr
def step_begin_epoch(self, epoch):
"""Update the learning rate at the beginning of the given epoch."""
self.lr = self.get_next_lr(epoch)
self.optimizer.set_lr(self.warmup_factor * self.lr)
return self.optimizer.get_lr()
def step_update(self, num_updates):
"""Update the learning rate after each update."""
if self.cfg.warmup_updates > 0 and num_updates <= self.cfg.warmup_updates:
self.warmup_factor = num_updates / float(self.cfg.warmup_updates)
lr = self.warmup_factor * self.lr
elif num_updates >= self.total_num_update:
lr = self.end_learning_rate
else:
warmup = self.cfg.warmup_updates
lr_range = self.lr - self.end_learning_rate
pct_remaining = 1 - (num_updates - warmup) / (
self.total_num_update - warmup
)
lr = lr_range * pct_remaining ** (self.power) + self.end_learning_rate
self.optimizer.set_lr(lr)
return self.optimizer.get_lr()
|
bart_ls-main
|
fairseq-py/fairseq/optim/lr_scheduler/polynomial_decay_schedule.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from collections.abc import Collection
from dataclasses import dataclass, field
from typing import List
from omegaconf import II
from fairseq.dataclass import FairseqDataclass
from fairseq.optim.lr_scheduler import FairseqLRScheduler, register_lr_scheduler
@dataclass
class InverseSquareRootLRScheduleConfig(FairseqDataclass):
warmup_updates: int = field(
default=4000,
metadata={"help": "warmup the learning rate linearly for the first N updates"},
)
warmup_init_lr: float = field(
default=-1,
metadata={
"help": "initial learning rate during warmup phase; default is cfg.lr"
},
)
lr: List[float] = II("optimization.lr")
@register_lr_scheduler("inverse_sqrt", dataclass=InverseSquareRootLRScheduleConfig)
class InverseSquareRootSchedule(FairseqLRScheduler):
"""Decay the LR based on the inverse square root of the update number.
We also support a warmup phase where we linearly increase the learning rate
from some initial learning rate (``--warmup-init-lr``) until the configured
learning rate (``--lr``). Thereafter we decay proportional to the number of
updates, with a decay factor set to align with the configured learning rate.
During warmup::
lrs = torch.linspace(cfg.warmup_init_lr, cfg.lr, cfg.warmup_updates)
lr = lrs[update_num]
After warmup::
decay_factor = cfg.lr * sqrt(cfg.warmup_updates)
lr = decay_factor / sqrt(update_num)
"""
def __init__(self, cfg: InverseSquareRootLRScheduleConfig, optimizer):
super().__init__(cfg, optimizer)
if isinstance(cfg.lr, Collection) and len(cfg.lr) > 1:
raise ValueError(
"Cannot use a fixed learning rate schedule with inverse_sqrt."
" Consider --lr-scheduler=fixed instead."
)
warmup_end_lr = cfg.lr[0] if isinstance(cfg.lr, Collection) else cfg.lr
if cfg.warmup_init_lr < 0:
cfg.warmup_init_lr = 0 if cfg.warmup_updates > 0 else warmup_end_lr
# linearly warmup for the first cfg.warmup_updates
self.lr_step = (warmup_end_lr - cfg.warmup_init_lr) / cfg.warmup_updates
# then, decay prop. to the inverse square root of the update number
self.decay_factor = warmup_end_lr * cfg.warmup_updates ** 0.5
# initial learning rate
self.lr = cfg.warmup_init_lr
self.optimizer.set_lr(self.lr)
def step(self, epoch, val_loss=None):
"""Update the learning rate at the end of the given epoch."""
super().step(epoch, val_loss)
# we don't change the learning rate at epoch boundaries
return self.optimizer.get_lr()
def step_update(self, num_updates):
"""Update the learning rate after each update."""
if num_updates < self.cfg.warmup_updates:
self.lr = self.cfg.warmup_init_lr + num_updates * self.lr_step
else:
self.lr = self.decay_factor * num_updates ** -0.5
self.optimizer.set_lr(self.lr)
return self.lr
|
bart_ls-main
|
fairseq-py/fairseq/optim/lr_scheduler/inverse_square_root_schedule.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from argparse import Namespace
from fairseq.dataclass.utils import gen_parser_from_dataclass
from fairseq.optim import FairseqOptimizer
class FairseqLRScheduler(object):
def __init__(self, cfg, optimizer):
super().__init__()
if optimizer is not None and not isinstance(optimizer, FairseqOptimizer):
raise ValueError("optimizer must be an instance of FairseqOptimizer")
self.cfg = cfg
self.optimizer = optimizer
self.best = None
@classmethod
def add_args(cls, parser):
"""Add arguments to the parser for this LR scheduler."""
dc = getattr(cls, "__dataclass", None)
if dc is not None:
gen_parser_from_dataclass(parser, dc())
def state_dict(self):
"""Return the LR scheduler state dict."""
return {"best": self.best}
def load_state_dict(self, state_dict):
"""Load an LR scheduler state dict."""
self.best = state_dict["best"]
def step_begin_epoch(self, epoch):
"""Update the learning rate at the beginning of the given epoch."""
pass
def step(self, epoch, val_loss=None):
"""Update the learning rate at the end of the given epoch."""
if val_loss is not None:
if self.best is None:
self.best = val_loss
else:
self.best = min(self.best, val_loss)
def step_update(self, num_updates):
"""Update the learning rate after each update."""
return self.optimizer.get_lr()
class LegacyFairseqLRScheduler(FairseqLRScheduler):
def __init__(self, args: Namespace, optimizer):
if not isinstance(optimizer, FairseqOptimizer):
raise ValueError("optimizer must be an instance of FairseqOptimizer")
self.args = args
self.optimizer = optimizer
self.best = None
|
bart_ls-main
|
fairseq-py/fairseq/optim/lr_scheduler/fairseq_lr_scheduler.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import math
from dataclasses import dataclass, field
from typing import Optional, List, Tuple
from omegaconf import II
from fairseq.dataclass import FairseqDataclass
from fairseq.optim.lr_scheduler import FairseqLRScheduler, register_lr_scheduler
@dataclass
class TriStageLRScheduleConfig(FairseqDataclass):
warmup_steps: int = field(
default=0,
metadata={"help": "warmup the learning rate linearly for the first N updates"},
)
hold_steps: int = field(
default=0,
metadata={"help": "steps in hold stage"},
)
decay_steps: int = field(
default=0,
metadata={"help": "steps in decay stages"},
)
phase_ratio: Optional[Tuple[float, float, float]] = field(
default=None,
metadata={
"help": (
"if set, automatically sets warmup/hold/decay steps to the ratio "
"specified here from max_updates. the ratios must add up to 1.0"
)
},
)
init_lr_scale: float = field(
default=0.01,
metadata={"help": "initial learning rate scale during warmup phase"},
)
final_lr_scale: float = field(
default=0.01,
metadata={"help": "final learning rate scale"},
)
max_update: float = II("optimization.max_update")
lr: List[float] = II("optimization.lr")
@register_lr_scheduler("tri_stage", dataclass=TriStageLRScheduleConfig)
class TriStageLRSchedule(FairseqLRScheduler):
"""Tristage learning rate schedulr
Implement the learning rate scheduler in https://arxiv.org/pdf/1904.08779.pdf
Similar to inverse_squre_root scheduler, but tri_stage learning rate employs
three stages LR scheduling:
- warmup stage, starting from `lr` * `init_lr_scale`, linearly
increased to `lr` in `warmup_steps` iterations
- hold stage, after `warmup_steps`, keep the LR as `lr` for `hold_steps`
iterations
- decay stage, after hold stage, decay LR exponetially to
`lr` * `final_lr_scale` in `decay_steps`;
after that LR is keep as `final_lr_scale` * `lr`
During warmup::
init_lr = cfg.init_lr_scale * cfg.lr
lrs = torch.linspace(init_lr, cfg.lr, cfg.warmup_steps)
lr = lrs[update_num]
During hold::
lr = cfg.lr
During decay::
decay_factor = - math.log(cfg.final_lr_scale) / cfg.decay_steps
lr = cfg.lr * exp(- (update_num - warmup_steps - decay_steps) * decay_factor)
After that::
lr = cfg.lr * cfg.final_lr_scale
"""
def __init__(self, cfg: TriStageLRScheduleConfig, optimizer):
super().__init__(cfg, optimizer)
if len(cfg.lr) > 1:
raise ValueError(
"Cannot use a fixed learning rate schedule with tri-stage lr."
" Consider --lr-scheduler=fixed instead."
)
# calculate LR at each point
self.peak_lr = cfg.lr[0]
self.init_lr = cfg.init_lr_scale * cfg.lr[0]
self.final_lr = cfg.final_lr_scale * cfg.lr[0]
if cfg.phase_ratio is not None:
assert cfg.max_update > 0
assert sum(cfg.phase_ratio) == 1, "phase ratios must add up to 1"
self.warmup_steps = int(cfg.max_update * cfg.phase_ratio[0])
self.hold_steps = int(cfg.max_update * cfg.phase_ratio[1])
self.decay_steps = int(cfg.max_update * cfg.phase_ratio[2])
else:
self.warmup_steps = cfg.warmup_steps
self.hold_steps = cfg.hold_steps
self.decay_steps = cfg.decay_steps
assert (
self.warmup_steps + self.hold_steps + self.decay_steps > 0
), "please specify steps or phase_ratio"
self.warmup_rate = (
(self.peak_lr - self.init_lr) / self.warmup_steps
if self.warmup_steps != 0
else 0
)
self.decay_factor = -math.log(cfg.final_lr_scale) / self.decay_steps
# initial learning rate
self.lr = self.init_lr
self.optimizer.set_lr(self.lr)
def _decide_stage(self, update_step):
"""
return stage, and the corresponding steps within the current stage
"""
if update_step < self.warmup_steps:
# warmup state
return 0, update_step
offset = self.warmup_steps
if update_step < offset + self.hold_steps:
# hold stage
return 1, update_step - offset
offset += self.hold_steps
if update_step <= offset + self.decay_steps:
# decay stage
return 2, update_step - offset
offset += self.decay_steps
# still here ? constant lr stage
return 3, update_step - offset
def step(self, epoch, val_loss=None):
"""Update the learning rate at the end of the given epoch."""
super().step(epoch, val_loss)
# we don't change the learning rate at epoch boundaries
return self.optimizer.get_lr()
def step_update(self, num_updates):
"""Update the learning rate after each update."""
stage, steps_in_stage = self._decide_stage(num_updates)
if stage == 0:
self.lr = self.init_lr + self.warmup_rate * steps_in_stage
elif stage == 1:
self.lr = self.peak_lr
elif stage == 2:
self.lr = self.peak_lr * math.exp(-self.decay_factor * steps_in_stage)
elif stage == 3:
self.lr = self.final_lr
else:
raise ValueError("Undefined stage")
self.optimizer.set_lr(self.lr)
return self.lr
|
bart_ls-main
|
fairseq-py/fairseq/optim/lr_scheduler/tri_stage_lr_scheduler.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import math
from collections.abc import Collection
from dataclasses import dataclass, field
from typing import List
from omegaconf import II
from fairseq.dataclass import FairseqDataclass
from fairseq.optim.lr_scheduler import FairseqLRScheduler, register_lr_scheduler
@dataclass
class CosineLRScheduleConfig(FairseqDataclass):
warmup_updates: int = field(
default=0,
metadata={"help": "warmup the learning rate linearly for the first N updates"},
)
warmup_init_lr: float = field(
default=-1,
metadata={
"help": "initial learning rate during warmup phase; default is cfg.lr"
},
)
lr: List[float] = field(
default=II("optimization.lr"),
metadata={"help": "max learning rate, must be more than cfg.min_lr"},
)
min_lr: float = field(default=0.0, metadata={"help": "min learning rate"})
t_mult: float = field(
default=1.0, metadata={"help": "factor to grow the length of each period"}
)
lr_period_updates: float = field(
default=-1, metadata={"help": "initial number of updates per period"}
)
lr_shrink: float = field(
default=0.1, metadata={"help": "shrink factor for annealing"}
)
# This is not required, but is for convenience in inferring lr_period_updates
max_update: int = II("optimization.max_update")
@register_lr_scheduler("cosine", dataclass=CosineLRScheduleConfig)
class CosineLRSchedule(FairseqLRScheduler):
"""Assign LR based on a cyclical schedule that follows the cosine function.
See https://arxiv.org/pdf/1608.03983.pdf for details.
We also support a warmup phase where we linearly increase the learning rate
from some initial learning rate (``--warmup-init-lr``) until the configured
max learning rate (``--lr``).
During warmup::
lrs = torch.linspace(cfg.warmup_init_lr, cfg.lr, cfg.warmup_updates)
lr = lrs[update_num]
After warmup::
lr = cfg.min_lr + 0.5*(cfg.lr - cfg.min_lr)*(1 + cos(t_curr / t_i))
where ``t_curr`` is current percentage of updates within the current period
range and ``t_i`` is the current period range, which is scaled by ``t_mul``
after every iteration.
"""
def __init__(self, cfg: CosineLRScheduleConfig, fairseq_optimizer):
super().__init__(cfg, fairseq_optimizer)
if isinstance(cfg.lr, Collection) and len(cfg.lr) > 1:
raise ValueError(
"Cannot use a fixed learning rate schedule with cosine."
f" Consider --lr-scheduler=fixed instead. ({cfg.lr})"
)
self.max_lr = cfg.lr[0] if isinstance(cfg.lr, Collection) else cfg.lr
assert (
self.max_lr > cfg.min_lr
), f"max_lr (={cfg.lr}) must be more than min_lr (={cfg.min_lr})"
warmup_end_lr = self.max_lr
if cfg.warmup_init_lr < 0:
cfg.warmup_init_lr = cfg.min_lr
self.t_mult = cfg.t_mult
self.period = cfg.lr_period_updates
if self.period <= 0:
assert (
cfg.max_update > 0
), "Either --max_update or --lr-period-updates must be set"
self.period = cfg.max_update - cfg.warmup_updates
if cfg.warmup_updates > 0:
# linearly warmup for the first cfg.warmup_updates
self.lr_step = (warmup_end_lr - cfg.warmup_init_lr) / cfg.warmup_updates
else:
self.lr_step = 1
self.warmup_updates = cfg.warmup_updates
self.lr_shrink = cfg.lr_shrink
# initial learning rate
self.lr = cfg.warmup_init_lr
self.optimizer.set_lr(self.lr)
def step(self, epoch, val_loss=None):
"""Update the learning rate at the end of the given epoch."""
super().step(epoch, val_loss)
# we don't change the learning rate at epoch boundaries
return self.optimizer.get_lr()
def step_update(self, num_updates):
"""Update the learning rate after each update."""
if num_updates < self.cfg.warmup_updates:
self.lr = self.cfg.warmup_init_lr + num_updates * self.lr_step
else:
curr_updates = num_updates - self.cfg.warmup_updates
if self.t_mult != 1:
i = math.floor(
math.log(
1 - curr_updates / self.period * (1 - self.t_mult), self.t_mult
)
)
t_i = self.t_mult ** i * self.period
t_curr = (
curr_updates
- (1 - self.t_mult ** i) / (1 - self.t_mult) * self.period
)
else:
i = math.floor(curr_updates / self.period)
t_i = self.period
t_curr = curr_updates - (self.period * i)
lr_shrink = self.lr_shrink ** i
min_lr = self.cfg.min_lr * lr_shrink
max_lr = self.max_lr * lr_shrink
self.lr = min_lr + 0.5 * (max_lr - min_lr) * (
1 + math.cos(math.pi * t_curr / t_i)
)
self.optimizer.set_lr(self.lr)
return self.lr
|
bart_ls-main
|
fairseq-py/fairseq/optim/lr_scheduler/cosine_lr_scheduler.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from collections.abc import Collection
from dataclasses import dataclass, field
from typing import List
from omegaconf import II
from fairseq.dataclass import FairseqDataclass
from fairseq.optim.lr_scheduler import FairseqLRScheduler, register_lr_scheduler
@dataclass
class StepLRScheduleConfig(FairseqDataclass):
warmup_updates: int = field(
default=0,
metadata={"help": "warmup the learning rate linearly for the first N updates"},
)
warmup_init_lr: float = field(
default=-1,
metadata={
"help": "initial learning rate during warmup phase; default is cfg.lr"
},
)
lr: List[float] = field(
default=II("optimization.lr"),
metadata={"help": "max learning rate, must be more than cfg.min_lr"},
)
min_lr: float = field(default=0.0, metadata={"help": "min learning rate"})
lr_deacy_period: int = field(default=25000, metadata={"help": "decay period"})
lr_decay: float = field(default=0.5, metadata={"help": "decay factor"})
@register_lr_scheduler("step", dataclass=StepLRScheduleConfig)
class StepLRSchedule(FairseqLRScheduler):
"""Decay learning rate every k updates by a fixed factor
"""
def __init__(self, cfg: StepLRScheduleConfig, fairseq_optimizer):
super().__init__(cfg, fairseq_optimizer)
self.max_lr = cfg.lr[0] if isinstance(cfg.lr, Collection) else cfg.lr
self.min_lr = cfg.min_lr
self.lr_deacy_period = cfg.lr_deacy_period
self.lr_decay = cfg.lr_decay
self.warmup_updates = cfg.warmup_updates
self.warmup_init_lr = (
cfg.warmup_init_lr if cfg.warmup_init_lr >= 0 else self.min_lr
)
assert(self.lr_deacy_period > 0)
assert(self.lr_decay <= 1)
assert(self.min_lr >= 0)
assert(self.max_lr > self.min_lr)
if cfg.warmup_updates > 0:
# linearly warmup for the first cfg.warmup_updates
self.warmup_lr_step = (
(self.max_lr - self.warmup_init_lr) / self.warmup_updates
)
else:
self.warmup_lr_step = 1
# initial learning rate
self.lr = self.warmup_init_lr
self.optimizer.set_lr(self.lr)
def step(self, epoch, val_loss=None):
"""Update the learning rate at the end of the given epoch."""
super().step(epoch, val_loss)
# we don't change the learning rate at epoch boundaries
return self.optimizer.get_lr()
def step_update(self, num_updates):
"""Update the learning rate after each update."""
if num_updates < self.cfg.warmup_updates:
self.lr = self.warmup_init_lr + num_updates * self.warmup_lr_step
else:
curr_updates = num_updates - self.cfg.warmup_updates
lr_mult = self.lr_decay ** (curr_updates // self.lr_deacy_period)
self.lr = max(self.max_lr * lr_mult, self.min_lr)
self.optimizer.set_lr(self.lr)
return self.lr
|
bart_ls-main
|
fairseq-py/fairseq/optim/lr_scheduler/step_lr_scheduler.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import math
from dataclasses import dataclass, field
from typing import List
from omegaconf import II
from fairseq.dataclass import FairseqDataclass
from fairseq.optim.lr_scheduler import FairseqLRScheduler, register_lr_scheduler
@dataclass
class TriangularLRScheduleConfig(FairseqDataclass):
max_lr: float = field(
default="???", metadata={"help": "max learning rate, must be more than cfg.lr"}
)
lr_period_updates: float = field(
default=5000,
metadata={"help": "initial number of updates per period (cycle length)"},
)
lr_shrink: float = field(
default=0.1, metadata={"help": "shrink factor for annealing"}
)
shrink_min: bool = field(
default=False, metadata={"help": "if set, also shrinks min lr"}
)
lr: List[float] = II("optimization.lr")
@register_lr_scheduler("triangular", dataclass=TriangularLRScheduleConfig)
class TriangularLRSchedule(FairseqLRScheduler):
"""Assign LR based on a triangular cyclical schedule.
See https://arxiv.org/pdf/1506.01186.pdf for details.
"""
def __init__(self, cfg: TriangularLRScheduleConfig, optimizer):
super().__init__(cfg, optimizer)
if len(cfg.lr) > 1:
raise ValueError(
"Cannot use a fixed learning rate schedule with triangular."
" Consider --lr-scheduler=fixed instead."
)
lr = cfg.lr[0]
assert cfg.max_lr > lr, "max_lr must be more than lr"
self.min_lr = lr
self.max_lr = cfg.max_lr
self.stepsize = cfg.lr_period_updates // 2
self.lr_shrink = cfg.lr_shrink
self.shrink_min = cfg.shrink_min
# initial learning rate
self.lr = self.min_lr
self.optimizer.set_lr(self.lr)
def step(self, epoch, val_loss=None):
"""Update the learning rate at the end of the given epoch."""
super().step(epoch, val_loss)
# we don't change the learning rate at epoch boundaries
return self.optimizer.get_lr()
def step_update(self, num_updates):
"""Update the learning rate after each update."""
cycle = math.floor(num_updates / (2 * self.stepsize))
lr_shrink = self.lr_shrink ** cycle
max_lr = self.max_lr * lr_shrink
if self.shrink_min:
min_lr = self.min_lr * lr_shrink
else:
min_lr = self.min_lr
x = abs(num_updates / self.stepsize - 2 * (cycle + 1) + 1)
self.lr = min_lr + (max_lr - min_lr) * max(0, (1 - x))
self.optimizer.set_lr(self.lr)
return self.lr
|
bart_ls-main
|
fairseq-py/fairseq/optim/lr_scheduler/triangular_lr_scheduler.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from typing import List, Optional
import torch
from torch import Tensor
@torch.jit.script
def script_skip_tensor_list(x: List[Tensor], mask):
res = [xi[mask] if xi.size(0) == mask.size(0) else xi[:, mask] for xi in x]
outputs = []
for i, t in enumerate(res):
if t.numel() != 0:
outputs.append(t)
else:
outputs.append(x[i])
return outputs
@torch.jit.script
def script_skip_tensor(x: Tensor, mask):
# None case
if x.size(0) == 0:
return x
res = x[mask] if x.size(0) == mask.size(0) else x[:, mask]
if res.numel() == 0:
return x
else:
return res
@torch.jit.script
def expand_2d_or_3d_tensor(x, trg_dim: int, padding_idx: int):
"""
Expand 2D/3D tensor on dim=1
"""
if x is None:
return None
assert x.dim() == 2 or x.dim() == 3
assert trg_dim >= x.size(1), (trg_dim, x.size())
if trg_dim == x.size(1):
return x
dims = [x.size(0), trg_dim - x.size(1)]
if x.dim() == 3:
dims.append(x.size(2))
x = torch.cat([x, torch.zeros(dims).to(x).fill_(padding_idx)], 1)
return x
@torch.jit.script
def coalesce(x: Optional[Tensor], y: Tensor) -> Tensor:
return x if x is not None else y
@torch.jit.script
def fill_tensors(
x: Optional[Tensor], mask, y: Optional[Tensor], padding_idx: int
) -> Optional[Tensor]:
"""
Filling tensor x with y at masked positions (dim=0).
"""
if x is None or x.size()[0] == 0 or y is None:
return x
assert x.dim() == y.dim() and mask.size(0) == x.size(0)
assert x.dim() == 2 or (x.dim() == 3 and x.size(2) == y.size(2))
n_selected = mask.sum()
if n_selected == 0:
return x
assert n_selected == y.size(0)
if n_selected == x.size(0):
return y
if x.size(1) < y.size(1):
x = expand_2d_or_3d_tensor(x, y.size(1), padding_idx)
x[mask] = y
elif x.size(1) > y.size(1):
x[mask] = torch.tensor(padding_idx).type_as(x)
if x.dim() == 2:
x[mask, : y.size(1)] = y
else:
x[mask, : y.size(1), :] = y
else:
x[mask] = y
return x
|
bart_ls-main
|
fairseq-py/fairseq/models/model_utils.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""
Base classes for various fairseq models.
"""
import logging
from argparse import Namespace
from typing import Dict, List, Optional, Tuple
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import utils
from fairseq.data import Dictionary
from fairseq.dataclass.utils import (
convert_namespace_to_omegaconf,
gen_parser_from_dataclass,
)
from fairseq.models import FairseqDecoder, FairseqEncoder
from omegaconf import DictConfig
from torch import Tensor
logger = logging.getLogger(__name__)
def check_type(module, expected_type):
if hasattr(module, "unwrapped_module"):
assert isinstance(module.unwrapped_module, expected_type), \
f"{type(module.unwrapped_module)} != {expected_type}"
else:
assert isinstance(module, expected_type), f"{type(module)} != {expected_type}"
class BaseFairseqModel(nn.Module):
"""Base class for fairseq models."""
def __init__(self):
super().__init__()
self._is_generation_fast = False
@classmethod
def add_args(cls, parser):
"""Add model-specific arguments to the parser."""
dc = getattr(cls, "__dataclass", None)
if dc is not None:
# do not set defaults so that settings defaults from various architectures still works
gen_parser_from_dataclass(parser, dc(), delete_default=True)
@classmethod
def build_model(cls, args, task):
"""Build a new model instance."""
raise NotImplementedError("Model must implement the build_model method")
def get_targets(self, sample, net_output):
"""Get targets from either the sample or the net's output."""
return sample["target"]
def get_normalized_probs(
self,
net_output: Tuple[Tensor, Optional[Dict[str, List[Optional[Tensor]]]]],
log_probs: bool,
sample: Optional[Dict[str, Tensor]] = None,
):
"""Get normalized probabilities (or log probs) from a net's output."""
return self.get_normalized_probs_scriptable(net_output, log_probs, sample)
# TorchScript doesn't support super() method so that the scriptable Subclass
# can't access the base class model in Torchscript.
# Current workaround is to add a helper function with different name and
# call the helper function from scriptable Subclass.
def get_normalized_probs_scriptable(
self,
net_output: Tuple[Tensor, Optional[Dict[str, List[Optional[Tensor]]]]],
log_probs: bool,
sample: Optional[Dict[str, Tensor]] = None,
):
"""Scriptable helper function for get_normalized_probs in ~BaseFairseqModel"""
if hasattr(self, "decoder"):
return self.decoder.get_normalized_probs(net_output, log_probs, sample)
elif torch.is_tensor(net_output):
# syntactic sugar for simple models which don't have a decoder
# (e.g., the classification tutorial)
logits = net_output.float()
if log_probs:
return F.log_softmax(logits, dim=-1)
else:
return F.softmax(logits, dim=-1)
raise NotImplementedError
def extract_features(self, *args, **kwargs):
"""Similar to *forward* but only return features."""
return self(*args, **kwargs)
def max_positions(self):
"""Maximum length supported by the model."""
return None
def load_state_dict(
self,
state_dict,
strict=True,
model_cfg: Optional[DictConfig] = None,
args: Optional[Namespace] = None,
):
"""Copies parameters and buffers from *state_dict* into this module and
its descendants.
Overrides the method in :class:`nn.Module`. Compared with that method
this additionally "upgrades" *state_dicts* from old checkpoints.
"""
if model_cfg is None and args is not None:
logger.warn("using 'args' is deprecated, please update your code to use dataclass config")
model_cfg = convert_namespace_to_omegaconf(args).model
self.upgrade_state_dict(state_dict)
from fairseq.checkpoint_utils import prune_state_dict
new_state_dict = prune_state_dict(state_dict, model_cfg)
return super().load_state_dict(new_state_dict, strict)
def upgrade_state_dict(self, state_dict):
"""Upgrade old state dicts to work with newer code."""
self.upgrade_state_dict_named(state_dict, "")
def upgrade_state_dict_named(self, state_dict, name):
"""Upgrade old state dicts to work with newer code.
Args:
state_dict (dict): state dictionary to upgrade, in place
name (str): the state dict key corresponding to the current module
"""
assert state_dict is not None
def do_upgrade(m, prefix):
if len(prefix) > 0:
prefix += "."
for n, c in m.named_children():
name = prefix + n
if hasattr(c, "upgrade_state_dict_named"):
c.upgrade_state_dict_named(state_dict, name)
elif hasattr(c, "upgrade_state_dict"):
c.upgrade_state_dict(state_dict)
do_upgrade(c, name)
do_upgrade(self, name)
def set_num_updates(self, num_updates):
"""State from trainer to pass along to model at every update."""
for m in self.modules():
if hasattr(m, "set_num_updates") and m != self:
m.set_num_updates(num_updates)
def prepare_for_inference_(self, cfg: DictConfig):
"""Prepare model for inference."""
kwargs = {}
kwargs["beamable_mm_beam_size"] = (
None
if getattr(cfg.generation, "no_beamable_mm", False)
else getattr(cfg.generation, "beam", 5)
)
kwargs["need_attn"] = getattr(cfg.generation, "print_alignment", False)
if getattr(cfg.generation, "retain_dropout", False):
kwargs["retain_dropout"] = cfg.generation.retain_dropout
kwargs["retain_dropout_modules"] = cfg.generation.retain_dropout_modules
self.make_generation_fast_(**kwargs)
def make_generation_fast_(self, **kwargs):
"""
Legacy entry point to optimize model for faster generation.
Prefer prepare_for_inference_.
"""
if self._is_generation_fast:
return # only apply once
self._is_generation_fast = True
# remove weight norm from all modules in the network
def apply_remove_weight_norm(module):
try:
nn.utils.remove_weight_norm(module)
except (AttributeError, ValueError): # this module didn't have weight norm
return
self.apply(apply_remove_weight_norm)
def apply_make_generation_fast_(module, prefix):
if len(prefix) > 0:
prefix += "."
base_func = BaseFairseqModel.make_generation_fast_
for n, m in module.named_modules():
if (
m != self
and hasattr(m, "make_generation_fast_")
# don't call this implementation again, e.g., if
# children modules also inherit from BaseFairseqModel
and m.make_generation_fast_.__func__ is not base_func
):
name = prefix + n
m.make_generation_fast_(name=name, **kwargs)
apply_make_generation_fast_(self, "")
def train(mode=True):
if mode:
raise RuntimeError("cannot train after make_generation_fast")
# this model should no longer be used for training
self.eval()
self.train = train
def prepare_for_onnx_export_(self, **kwargs):
"""Make model exportable via ONNX trace."""
seen = set()
def apply_prepare_for_onnx_export_(module):
if (
module != self
and hasattr(module, "prepare_for_onnx_export_")
and module not in seen
):
seen.add(module)
module.prepare_for_onnx_export_(**kwargs)
self.apply(apply_prepare_for_onnx_export_)
@classmethod
def from_pretrained(
cls,
model_name_or_path,
checkpoint_file="model.pt",
data_name_or_path=".",
**kwargs,
):
"""
Load a :class:`~fairseq.models.FairseqModel` from a pre-trained model
file. Downloads and caches the pre-trained model file if needed.
The base implementation returns a
:class:`~fairseq.hub_utils.GeneratorHubInterface`, which can be used to
generate translations or sample from language models. The underlying
:class:`~fairseq.models.FairseqModel` can be accessed via the
*generator.models* attribute.
Other models may override this to implement custom hub interfaces.
Args:
model_name_or_path (str): either the name of a pre-trained model to
load or a path/URL to a pre-trained model state dict
checkpoint_file (str, optional): colon-separated list of checkpoint
files in the model archive to ensemble (default: 'model.pt')
data_name_or_path (str, optional): point args.data to the archive
at the given path/URL. Can start with '.' or './' to reuse the
model archive path.
"""
from fairseq import hub_utils
x = hub_utils.from_pretrained(
model_name_or_path,
checkpoint_file,
data_name_or_path,
archive_map=cls.hub_models(),
**kwargs,
)
logger.info(x["args"])
return hub_utils.GeneratorHubInterface(x["args"], x["task"], x["models"])
@classmethod
def hub_models(cls):
return {}
class FairseqEncoderDecoderModel(BaseFairseqModel):
"""Base class for encoder-decoder models.
Args:
encoder (FairseqEncoder): the encoder
decoder (FairseqDecoder): the decoder
"""
def __init__(self, encoder, decoder):
super().__init__()
self.encoder = encoder
self.decoder = decoder
check_type(self.encoder, FairseqEncoder)
check_type(self.decoder, FairseqDecoder)
def forward(self, src_tokens, src_lengths, prev_output_tokens, **kwargs):
"""
Run the forward pass for an encoder-decoder model.
First feed a batch of source tokens through the encoder. Then, feed the
encoder output and previous decoder outputs (i.e., teacher forcing) to
the decoder to produce the next outputs::
encoder_out = self.encoder(src_tokens, src_lengths)
return self.decoder(prev_output_tokens, encoder_out)
Args:
src_tokens (LongTensor): tokens in the source language of shape
`(batch, src_len)`
src_lengths (LongTensor): source sentence lengths of shape `(batch)`
prev_output_tokens (LongTensor): previous decoder outputs of shape
`(batch, tgt_len)`, for teacher forcing
Returns:
tuple:
- the decoder's output of shape `(batch, tgt_len, vocab)`
- a dictionary with any model-specific outputs
"""
encoder_out = self.encoder(src_tokens, src_lengths=src_lengths, **kwargs)
decoder_out = self.decoder(
prev_output_tokens, encoder_out=encoder_out, **kwargs
)
return decoder_out
def forward_decoder(self, prev_output_tokens, **kwargs):
return self.decoder(prev_output_tokens, **kwargs)
def extract_features(self, src_tokens, src_lengths, prev_output_tokens, **kwargs):
"""
Similar to *forward* but only return features.
Returns:
tuple:
- the decoder's features of shape `(batch, tgt_len, embed_dim)`
- a dictionary with any model-specific outputs
"""
encoder_out = self.encoder(src_tokens, src_lengths=src_lengths, **kwargs)
features = self.decoder.extract_features(
prev_output_tokens, encoder_out=encoder_out, **kwargs
)
return features
def output_layer(self, features, **kwargs):
"""Project features to the default output size (typically vocabulary size)."""
return self.decoder.output_layer(features, **kwargs)
def max_positions(self):
"""Maximum length supported by the model."""
return (self.encoder.max_positions(), self.decoder.max_positions())
def max_decoder_positions(self):
"""Maximum length supported by the decoder."""
return self.decoder.max_positions()
class FairseqModel(FairseqEncoderDecoderModel):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
utils.deprecation_warning(
"FairseqModel is deprecated, please use FairseqEncoderDecoderModel "
"or BaseFairseqModel instead",
stacklevel=4,
)
class FairseqMultiModel(BaseFairseqModel):
"""Base class for combining multiple encoder-decoder models."""
def __init__(self, encoders, decoders):
super().__init__()
assert encoders.keys() == decoders.keys()
self.keys = list(encoders.keys())
for key in self.keys:
check_type(encoders[key], FairseqEncoder)
check_type(decoders[key], FairseqDecoder)
self.models = nn.ModuleDict(
{
key: FairseqEncoderDecoderModel(encoders[key], decoders[key])
for key in self.keys
}
)
@staticmethod
def build_shared_embeddings(
dicts: Dict[str, Dictionary],
langs: List[str],
embed_dim: int,
build_embedding: callable,
pretrained_embed_path: Optional[str] = None,
):
"""
Helper function to build shared embeddings for a set of languages after
checking that all dicts corresponding to those languages are equivalent.
Args:
dicts: Dict of lang_id to its corresponding Dictionary
langs: languages that we want to share embeddings for
embed_dim: embedding dimension
build_embedding: callable function to actually build the embedding
pretrained_embed_path: Optional path to load pretrained embeddings
"""
shared_dict = dicts[langs[0]]
if any(dicts[lang] != shared_dict for lang in langs):
raise ValueError(
"--share-*-embeddings requires a joined dictionary: "
"--share-encoder-embeddings requires a joined source "
"dictionary, --share-decoder-embeddings requires a joined "
"target dictionary, and --share-all-embeddings requires a "
"joint source + target dictionary."
)
return build_embedding(shared_dict, embed_dim, pretrained_embed_path)
def forward(self, src_tokens, src_lengths, prev_output_tokens, **kwargs):
raise NotImplementedError
def max_positions(self):
"""Maximum length supported by the model."""
return {
key: (
self.models[key].encoder.max_positions(),
self.models[key].decoder.max_positions(),
)
for key in self.keys
}
def max_decoder_positions(self):
"""Maximum length supported by the decoder."""
return min(model.decoder.max_positions() for model in self.models.values())
@property
def encoder(self):
return self.models[self.keys[0]].encoder
@property
def decoder(self):
return self.models[self.keys[0]].decoder
def forward_decoder(self, prev_output_tokens, **kwargs):
return self.decoder(prev_output_tokens, **kwargs)
def load_state_dict(
self,
state_dict,
strict=True,
model_cfg=None,
args: Optional[Namespace] = None,
):
"""Copies parameters and buffers from *state_dict* into this module and
its descendants.
Overrides the method in :class:`nn.Module`. Compared with that method
this additionally "upgrades" *state_dicts* from old checkpoints.
"""
if model_cfg is None and args is not None:
logger.warn("using 'args' is deprecated, please update your code to use dataclass config")
model_cfg = convert_namespace_to_omegaconf(args).model
self.upgrade_state_dict(state_dict)
from fairseq.checkpoint_utils import prune_state_dict
new_state_dict = prune_state_dict(state_dict, model_cfg)
return super().load_state_dict(new_state_dict, strict)
class FairseqLanguageModel(BaseFairseqModel):
"""Base class for decoder-only models.
Args:
decoder (FairseqDecoder): the decoder
"""
def __init__(self, decoder):
super().__init__()
self.decoder = decoder
check_type(self.decoder, FairseqDecoder)
def forward(self, src_tokens, **kwargs):
"""
Run the forward pass for a decoder-only model.
Feeds a batch of tokens through the decoder to predict the next tokens.
Args:
src_tokens (LongTensor): tokens on which to condition the decoder,
of shape `(batch, tgt_len)`
src_lengths (LongTensor): source sentence lengths of shape `(batch)`
Returns:
tuple:
- the decoder's output of shape `(batch, seq_len, vocab)`
- a dictionary with any model-specific outputs
"""
return self.decoder(src_tokens, **kwargs)
def forward_decoder(self, prev_output_tokens, **kwargs):
return self.decoder(prev_output_tokens, **kwargs)
def extract_features(self, src_tokens, **kwargs):
"""
Similar to *forward* but only return features.
Returns:
tuple:
- the decoder's features of shape `(batch, seq_len, embed_dim)`
- a dictionary with any model-specific outputs
"""
return self.decoder.extract_features(src_tokens, **kwargs)
def output_layer(self, features, **kwargs):
"""Project features to the default output size (typically vocabulary size)."""
return self.decoder.output_layer(features, **kwargs)
def max_positions(self):
"""Maximum length supported by the model."""
return self.decoder.max_positions()
def max_decoder_positions(self):
"""Maximum length supported by the decoder."""
return self.decoder.max_positions()
@property
def supported_targets(self):
return {"future"}
class FairseqEncoderModel(BaseFairseqModel):
"""Base class for encoder-only models.
Args:
encoder (FairseqEncoder): the encoder
"""
def __init__(self, encoder):
super().__init__()
self.encoder = encoder
check_type(self.encoder, FairseqEncoder)
def forward(self, src_tokens, src_lengths, **kwargs):
"""
Run the forward pass for a encoder-only model.
Feeds a batch of tokens through the encoder to generate features.
Args:
src_tokens (LongTensor): input tokens of shape `(batch, src_len)`
src_lengths (LongTensor): source sentence lengths of shape `(batch)`
Returns:
the encoder's output, typically of shape `(batch, src_len, features)`
"""
return self.encoder(src_tokens, src_lengths, **kwargs)
def get_normalized_probs(self, net_output, log_probs, sample=None):
"""Get normalized probabilities (or log probs) from a net's output."""
encoder_out = net_output["encoder_out"]
if torch.is_tensor(encoder_out):
logits = encoder_out.float()
if log_probs:
return F.log_softmax(logits, dim=-1)
else:
return F.softmax(logits, dim=-1)
raise NotImplementedError
def max_positions(self):
"""Maximum length supported by the model."""
return self.encoder.max_positions()
|
bart_ls-main
|
fairseq-py/fairseq/models/fairseq_model.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from typing import Optional, Tuple
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq.iterative_refinement_generator import DecoderOut
from fairseq.models import register_model, register_model_architecture
from fairseq.models.model_utils import (
coalesce,
expand_2d_or_3d_tensor,
script_skip_tensor,
)
from fairseq.models.transformer import (
Embedding,
TransformerDecoder,
TransformerEncoder,
TransformerModel,
)
from fairseq.modules import TransformerDecoderLayer
from fairseq.modules.transformer_sentence_encoder import init_bert_params
from torch import Tensor
@torch.jit.script
def _fill(x: Optional[Tensor], mask, y: Optional[Tensor], padding_idx: int):
"""
Filling tensor x with y at masked positions (dim=0).
"""
if x is None or x.size()[0] == 0 or y is None:
return torch.empty([0])
assert x.dim() == y.dim() and mask.size(0) == x.size(0)
assert x.dim() == 2 or (x.dim() == 3 and x.size(2) == y.size(2))
n_selected = mask.sum()
if n_selected == 0:
return x
assert n_selected == y.size(0)
if n_selected == x.size(0):
return y
y = y.to(x)
if x.size(1) < y.size(1):
x = expand_2d_or_3d_tensor(x, y.size(1), padding_idx)
x[mask] = y
elif x.size(1) > y.size(1):
x[mask] = torch.tensor(padding_idx).type_as(x)
if x.dim() == 2:
x[mask, : y.size(1)] = y
else:
x[mask, : y.size(1), :] = y
else:
x[mask] = y
return x
def _get_ins_targets(in_tokens, out_tokens, padding_idx, unk_idx):
try:
from fairseq import libnat
except ImportError as e:
import sys
sys.stderr.write("ERROR: missing libnat. run `pip install --editable .`\n")
raise e
in_seq_len, out_seq_len = in_tokens.size(1), out_tokens.size(1)
in_tokens_list = [
[t for t in s if t != padding_idx] for i, s in enumerate(in_tokens.tolist())
]
out_tokens_list = [
[t for t in s if t != padding_idx] for i, s in enumerate(out_tokens.tolist())
]
full_labels = libnat.suggested_ed2_path(
in_tokens_list, out_tokens_list, padding_idx
)
mask_inputs = [
[len(c) if c[0] != padding_idx else 0 for c in a[:-1]] for a in full_labels
]
# generate labels
masked_tgt_masks = []
for mask_input in mask_inputs:
mask_label = []
for beam_size in mask_input[1:-1]: # HACK 1:-1
mask_label += [0] + [1 for _ in range(beam_size)]
masked_tgt_masks.append(
mask_label + [0 for _ in range(out_seq_len - len(mask_label))]
)
mask_ins_targets = [
mask_input[1:-1] + [0 for _ in range(in_seq_len - 1 - len(mask_input[1:-1]))]
for mask_input in mask_inputs
]
# transform to tensor
masked_tgt_masks = torch.tensor(masked_tgt_masks, device=out_tokens.device).bool()
mask_ins_targets = torch.tensor(mask_ins_targets, device=in_tokens.device)
masked_tgt_tokens = out_tokens.masked_fill(masked_tgt_masks, unk_idx)
return masked_tgt_masks, masked_tgt_tokens, mask_ins_targets
def _get_del_targets(in_tokens, out_tokens, padding_idx):
try:
from fairseq import libnat
except ImportError as e:
import sys
sys.stderr.write("ERROR: missing libnat. run `pip install --editable .`\n")
raise e
out_seq_len = out_tokens.size(1)
in_tokens_list = [
[t for t in s if t != padding_idx] for i, s in enumerate(in_tokens.tolist())
]
out_tokens_list = [
[t for t in s if t != padding_idx] for i, s in enumerate(out_tokens.tolist())
]
full_labels = libnat.suggested_ed2_path(
in_tokens_list, out_tokens_list, padding_idx
)
word_del_targets = [b[-1] for b in full_labels]
word_del_targets = [
labels + [0 for _ in range(out_seq_len - len(labels))]
for labels in word_del_targets
]
# transform to tensor
word_del_targets = torch.tensor(word_del_targets)
return word_del_targets
def _get_del_ins_targets(in_tokens, out_tokens, padding_idx):
try:
from fairseq import libnat
except ImportError as e:
import sys
sys.stderr.write("ERROR: missing libnat. run `pip install --editable .`\n")
raise e
in_seq_len, out_seq_len = in_tokens.size(1), out_tokens.size(1)
in_tokens_list = [
[t for t in s if t != padding_idx] for i, s in enumerate(in_tokens.tolist())
]
out_tokens_list = [
[t for t in s if t != padding_idx] for i, s in enumerate(out_tokens.tolist())
]
full_labels = libnat.suggested_ed2_path(
in_tokens_list, out_tokens_list, padding_idx
)
word_del_targets = [b[-1] for b in full_labels]
word_del_targets = [
labels + [0 for _ in range(out_seq_len - len(labels))]
for labels in word_del_targets
]
mask_inputs = [
[len(c) if c[0] != padding_idx else 0 for c in a[:-1]] for a in full_labels
]
mask_ins_targets = [
mask_input[1:-1] + [0 for _ in range(in_seq_len - 1 - len(mask_input[1:-1]))]
for mask_input in mask_inputs
]
# transform to tensor
mask_ins_targets = torch.tensor(mask_ins_targets)
word_del_targets = torch.tensor(word_del_targets)
return word_del_targets, mask_ins_targets
@register_model("fb_levenshtein_transformer")
class LevenshteinTransformerModel(TransformerModel):
def __init__(self, args, encoder, decoder):
super().__init__(args, encoder, decoder)
self.tgt_dict = decoder.dictionary
self.bos = decoder.dictionary.bos()
self.eos = decoder.dictionary.eos()
self.pad = decoder.dictionary.pad()
self.unk = decoder.dictionary.unk()
@staticmethod
def add_args(parser):
TransformerModel.add_args(parser)
parser.add_argument(
"--apply-bert-init",
action="store_true",
help="use custom param initialization for BERT",
)
parser.add_argument(
"--early-exit",
default="6,6,6",
type=str,
help="number of decoder layers for del_word, ins_mask, ins_word",
)
parser.add_argument(
"--no-share-discriminator",
action="store_true",
help="additional decoder-layers to learn deletion",
)
parser.add_argument(
"--no-share-maskpredictor",
action="store_true",
help="additional decoder-layers to learn predicting masks",
)
parser.add_argument(
"--sampling-for-deletion",
action="store_true",
help="instead of argmax, use sampling to predict the tokens",
)
# Added for compatibility
parser.add_argument(
"--decoder-out-embed-dim",
default=None,
type=int,
metavar="N",
help="decoder output embedding dimension (bottleneck layer before"
"output layer if specified.)",
)
@property
def validate(self):
return {"length-beam": False}
@classmethod
def build_decoder(cls, args, tgt_dict, embed_tokens):
decoder = LevenshteinTransformerDecoder(args, tgt_dict, embed_tokens)
if getattr(args, "apply_bert_init", False):
decoder.apply(init_bert_params)
return decoder
@classmethod
def build_encoder(cls, args, src_dict, embed_tokens):
encoder = TransformerEncoder(args, src_dict, embed_tokens)
if getattr(args, "apply_bert_init", False):
encoder.apply(init_bert_params)
return encoder
def forward(
self, src_tokens, src_lengths, prev_output_tokens, tgt_tokens, **kwargs
):
assert tgt_tokens is not None, "forward function only supports training."
# encoding
encoder_out = self.encoder(src_tokens, src_lengths=src_lengths, **kwargs)
# generate training labels for insertion
masked_tgt_masks, masked_tgt_tokens, mask_ins_targets = _get_ins_targets(
prev_output_tokens, tgt_tokens, self.pad, self.unk
)
mask_ins_targets = mask_ins_targets.clamp(min=0, max=255) # for safe prediction
mask_ins_masks = prev_output_tokens[:, 1:].ne(self.pad)
mask_ins_out, _ = self.decoder.forward_mask_ins(
prev_output_tokens, encoder_out=encoder_out
)
word_ins_out, _ = self.decoder.forward_word_ins(
masked_tgt_tokens, encoder_out=encoder_out
)
# make online prediction
if self.decoder.sampling_for_deletion:
word_predictions = torch.multinomial(
F.softmax(word_ins_out, -1).view(-1, word_ins_out.size(-1)), 1
).view(word_ins_out.size(0), -1)
else:
word_predictions = F.log_softmax(word_ins_out, dim=-1).max(2)[1]
word_predictions.masked_scatter_(
~masked_tgt_masks, tgt_tokens[~masked_tgt_masks]
)
# generate training labels for deletion
word_del_targets = _get_del_targets(word_predictions, tgt_tokens, self.pad)
word_del_out, _ = self.decoder.forward_word_del(word_predictions, encoder_out)
return {
"mask_ins": {
"out": mask_ins_out,
"tgt": mask_ins_targets,
"mask": mask_ins_masks,
"ls": 0.01,
},
"word_ins": {
"out": word_ins_out,
"tgt": tgt_tokens,
"mask": masked_tgt_masks,
"ls": self.args.label_smoothing,
"nll_loss": True,
},
"word_del": {
"out": word_del_out,
"tgt": word_del_targets,
"mask": word_predictions.ne(self.pad),
},
}
def forward_encoder(self, encoder_inputs):
return self.encoder(*encoder_inputs)
def forward_decoder(
self, decoder_out, encoder_out, eos_penalty=0.0, max_ratio=None, **kwargs
):
output_tokens = decoder_out.output_tokens
output_scores = decoder_out.output_scores
attn = decoder_out.attn
if max_ratio is not None and encoder_out["encoder_padding_mask"]:
max_lengths = (
(~encoder_out["encoder_padding_mask"][0]).sum(1) * max_ratio
).clamp(min=10)
else:
max_lengths = output_tokens.new_full(output_tokens.size()[:1], 255)
def skip_encoder_out(encoder_out, mask):
if not mask.any():
return encoder_out
else:
return self.encoder.reorder_encoder_out(
encoder_out, mask.nonzero(as_tuple=False).squeeze()
)
@torch.jit.script
def del_word(
output_tokens,
output_scores,
attn: Optional[Tensor],
word_del_attn: Optional[Tensor],
word_del_out,
can_del_word,
pad_idx: int,
bos_idx: int,
eos_idx: int,
):
# delete words
# do not delete tokens if it is <s> </s>
if can_del_word.sum() != 0: # we cannot delete, skip
word_del_score = F.log_softmax(word_del_out, 2)
word_del_pred = word_del_score.max(-1)[1].to(torch.bool)
in_tokens = output_tokens[can_del_word]
in_scores = output_scores[can_del_word]
# apply deletion to a tensor
in_masks = in_tokens.ne(pad_idx)
bos_eos_masks = in_tokens.eq(bos_idx) + in_tokens.eq(eos_idx)
max_len = in_tokens.size(1)
word_del_pred.masked_fill_(torch.bitwise_not(in_masks), 1)
word_del_pred.masked_fill_(bos_eos_masks, 0)
reordering = (
torch.arange(max_len, device=in_tokens.device)[None, :]
.expand_as(in_tokens)
.contiguous()
.masked_fill(word_del_pred, max_len)
.sort(1)[1]
)
_tokens = in_tokens.masked_fill(word_del_pred, pad_idx).gather(
1, reordering
)
_scores = in_scores.masked_fill(word_del_pred, 0).gather(1, reordering)
if word_del_attn is not None:
_mask = word_del_pred[:, :, None].expand_as(word_del_attn)
_reordering = reordering[:, :, None].expand_as(word_del_attn)
_attn = word_del_attn.masked_fill(_mask, 0.0).gather(1, _reordering)
attn = _fill(attn, can_del_word, _attn, 0)
output_tokens = coalesce(
_fill(output_tokens, can_del_word, _tokens, pad_idx), output_tokens
)
output_scores = coalesce(
_fill(output_scores, can_del_word, _scores, 0), output_scores
)
return output_tokens, output_scores, attn
@torch.jit.script
def ins_placeholders(
output_tokens,
output_scores,
mask_ins_out,
can_ins_mask,
pad_idx: int,
unk_idx: int,
eos_idx: int,
eos_penalty: float,
max_lengths: Optional[Tensor],
) -> Tuple[Tensor, Tensor]:
# insert placeholders
if can_ins_mask.sum() != 0:
mask_ins_score = F.log_softmax(mask_ins_out, 2)
if eos_penalty > 0.0:
mask_ins_score[:, :, 0] -= eos_penalty
mask_ins_pred = mask_ins_score.max(-1)[1]
if max_lengths is not None:
mask_ins_pred = torch.min(
mask_ins_pred,
max_lengths[can_ins_mask, None].expand_as(mask_ins_pred),
)
in_tokens = output_tokens[can_ins_mask]
in_scores = output_scores[can_ins_mask]
in_masks = in_tokens.ne(pad_idx)
in_lengths = in_masks.sum(1)
# HACK: hacky way to shift all the paddings to eos first.
in_tokens.masked_fill_(torch.bitwise_not(in_masks), eos_idx)
mask_ins_pred.masked_fill_(torch.bitwise_not(in_masks[:, 1:]), 0)
out_lengths = in_lengths + mask_ins_pred.sum(1)
out_max_len = out_lengths.max()
out_masks = (
torch.arange(out_max_len, device=out_lengths.device)[None, :].long()
< out_lengths[:, None]
)
reordering = (mask_ins_pred + in_masks[:, 1:].long()).cumsum(1)
out_tokens = (
torch.zeros(
in_tokens.size()[0],
out_max_len,
device=in_tokens.device,
dtype=in_tokens.dtype,
)
.fill_(pad_idx)
.masked_fill_(out_masks, unk_idx)
)
out_tokens = torch.cat([in_tokens[:, :1], out_tokens[:, 1:]], 1)
out_tokens.scatter_(1, reordering, in_tokens[:, 1:])
if in_scores is not None:
in_scores.masked_fill_(torch.bitwise_not(in_masks), 0)
out_scores = torch.zeros_like(out_tokens).to(in_scores)
out_tokens = torch.cat([in_tokens[:, :1], out_tokens[:, 1:]], 1)
out_scores.scatter_(1, reordering, in_scores[:, 1:])
else:
out_scores = None
output_tokens = coalesce(
_fill(output_tokens, can_ins_mask, out_tokens, pad_idx),
output_tokens,
)
output_scores = coalesce(
_fill(output_scores, can_ins_mask, out_scores, 0), output_scores
)
return output_tokens, output_scores
@torch.jit.script
def ins_words(
output_tokens,
output_scores,
attn: Optional[Tensor],
word_ins_attn: Optional[Tensor],
word_ins_out,
can_ins_word,
pad_idx: int,
unk_idx: int,
) -> Tuple[Tensor, Tensor, Optional[Tensor]]:
# insert words
if can_ins_word.sum() != 0:
word_ins_scores = F.log_softmax(word_ins_out, 2)
word_ins_pred = word_ins_scores.max(-1)[1]
in_tokens = output_tokens[can_ins_word]
in_scores = output_scores[can_ins_word]
word_ins_masks = in_tokens.eq(unk_idx)
out_tokens = in_tokens.masked_scatter(
word_ins_masks, word_ins_pred[word_ins_masks]
)
if in_scores is not None:
out_scores = in_scores.masked_scatter(
word_ins_masks, word_ins_scores[word_ins_masks]
)
else:
out_scores = None
output_tokens = coalesce(
_fill(output_tokens, can_ins_word, out_tokens, pad_idx),
output_tokens,
)
output_scores = coalesce(
_fill(output_scores, can_ins_word, out_scores, 0), output_scores
)
if attn is not None:
attn = coalesce(_fill(attn, can_ins_word, word_ins_attn, 0), attn)
return output_tokens, output_scores, attn
can_del_word = output_tokens.ne(self.pad).sum(1) > 2
word_del_out, word_del_attn = self.decoder.forward_word_del(
script_skip_tensor(output_tokens, can_del_word),
skip_encoder_out(encoder_out, can_del_word),
)
output_tokens, output_scores, attn = del_word(
output_tokens,
output_scores,
attn,
word_del_attn,
word_del_out,
can_del_word,
self.pad,
self.bos,
self.eos,
)
can_ins_mask = output_tokens.ne(self.pad).sum(1) < max_lengths
mask_ins_out, _ = self.decoder.forward_mask_ins(
script_skip_tensor(output_tokens, can_ins_mask),
skip_encoder_out(encoder_out, can_ins_mask),
)
output_tokens, output_scores = ins_placeholders(
output_tokens,
output_scores,
mask_ins_out,
can_ins_mask,
self.pad,
self.unk,
self.eos,
eos_penalty,
max_lengths=(
max_lengths
if max_ratio is not None and encoder_out["encoder_padding_mask"]
else None
),
)
can_ins_word = output_tokens.eq(self.unk).sum(1) > 0
word_ins_out, word_ins_attn = self.decoder.forward_word_ins(
script_skip_tensor(output_tokens, can_ins_word),
skip_encoder_out(encoder_out, can_ins_word),
)
output_tokens, output_scores, attn = ins_words(
output_tokens,
output_scores,
attn,
word_ins_attn,
word_ins_out,
can_ins_word,
self.pad,
self.unk,
)
# delete some unnecessary paddings
cut_off = output_tokens.ne(self.pad).sum(1).max()
@torch.jit.script
def slice_wrap(x, l):
return x[:, :l]
@torch.jit.script
def slice_wrap_attn(x: Optional[Tensor], l):
if x is None or x.size()[0] == 0:
return torch.empty([0])
else:
return x[:, :l, :]
output_tokens = slice_wrap(output_tokens, cut_off)
output_scores = slice_wrap(output_scores, cut_off)
attn = slice_wrap_attn(attn, cut_off)
return decoder_out._replace(
output_tokens=output_tokens,
output_scores=output_scores,
attn=attn,
step=0,
max_step=0,
)
def initialize_output_tokens(self, encoder_out, src_tokens):
initial_output_tokens = torch.cat(
[
src_tokens.new_zeros(src_tokens.size(0), 1).fill_(self.bos),
src_tokens.new_zeros(src_tokens.size(0), 1).fill_(self.eos),
],
1,
)
initial_output_scores = torch.zeros_like(initial_output_tokens).to(
encoder_out["encoder_out"][0]
)
initial_attn = torch.empty([0])
if getattr(self.decoder.layers[-1], "need_attn", True):
initial_attn = torch.zeros([src_tokens.size(0), 2, src_tokens.size(1)]).to(
initial_output_tokens
)
return DecoderOut(
output_tokens=initial_output_tokens,
output_scores=initial_output_scores,
attn=initial_attn,
step=0,
max_step=0,
history=None,
)
class LevenshteinTransformerDecoder(TransformerDecoder):
def __init__(self, args, dictionary, embed_tokens, no_encoder_attn=False):
super().__init__(
args, dictionary, embed_tokens, no_encoder_attn=no_encoder_attn
)
self.dictionary = dictionary
self.bos = dictionary.bos()
self.unk = dictionary.unk()
self.eos = dictionary.eos()
self.sampling_for_deletion = getattr(args, "sampling_for_deletion", False)
self.embed_mask_ins = Embedding(256, self.output_embed_dim * 2, None)
self.embed_word_del = Embedding(2, self.output_embed_dim, None)
# del_word, ins_mask, ins_word
self.early_exit = [int(i) for i in args.early_exit.split(",")]
assert len(self.early_exit) == 3
# copy layers for mask-predict/deletion
self.layers_msk = None
if getattr(args, "no_share_maskpredictor", False):
self.layers_msk = nn.ModuleList(
[
TransformerDecoderLayer(args, no_encoder_attn)
for _ in range(self.early_exit[1])
]
)
self.layers_del = None
if getattr(args, "no_share_discriminator", False):
self.layers_del = nn.ModuleList(
[
TransformerDecoderLayer(args, no_encoder_attn)
for _ in range(self.early_exit[0])
]
)
def extract_features(
self,
prev_output_tokens,
encoder_out=None,
early_exit=None,
layers=None,
**unused
):
"""
Similar to *forward* but only return features.
Inputs:
prev_output_tokens: Tensor(B, T)
encoder_out: a dictionary of hidden states and masks
Returns:
tuple:
- the decoder's features of shape `(batch, tgt_len, embed_dim)`
- a dictionary with any model-specific outputs
the LevenshteinTransformer decoder has full-attention to all generated tokens
"""
# embed positions
positions = (
self.embed_positions(prev_output_tokens)
if self.embed_positions is not None
else None
)
# embed tokens and positions
x = self.embed_scale * self.embed_tokens(prev_output_tokens.long())
if self.project_in_dim is not None:
x = self.project_in_dim(x)
if positions is not None:
x += positions
x = self.dropout_module(x)
# B x T x C -> T x B x C
x = x.transpose(0, 1)
attn = None
inner_states = [x]
# decoder layers
decoder_padding_mask = prev_output_tokens.eq(self.padding_idx)
layers = self.layers if layers is None else layers
early_exit = len(layers) if early_exit is None else early_exit
for _, layer in enumerate(layers[:early_exit]):
x, attn, _ = layer(
x,
encoder_out["encoder_out"][0]
if (encoder_out is not None and len(encoder_out["encoder_out"]) > 0)
else None,
encoder_out["encoder_padding_mask"][0]
if (
encoder_out is not None
and len(encoder_out["encoder_padding_mask"]) > 0
)
else None,
self_attn_mask=None,
self_attn_padding_mask=decoder_padding_mask,
)
inner_states.append(x)
if self.layer_norm:
x = self.layer_norm(x)
# T x B x C -> B x T x C
x = x.transpose(0, 1)
if self.project_out_dim is not None:
x = self.project_out_dim(x)
return x, attn, inner_states
def forward_mask_ins(self, prev_output_tokens, encoder_out=None, **unused):
features, attn, _ = self.extract_features(
prev_output_tokens,
encoder_out=encoder_out,
early_exit=self.early_exit[1],
layers=self.layers_msk,
**unused
)
features_cat = torch.cat([features[:, :-1, :], features[:, 1:, :]], 2)
return F.linear(features_cat, self.embed_mask_ins.weight), attn
def forward_word_ins(self, prev_output_tokens, encoder_out=None, **unused):
features, attn, _ = self.extract_features(
prev_output_tokens,
encoder_out=encoder_out,
early_exit=self.early_exit[2],
layers=self.layers,
**unused
)
return self.output_layer(features), attn
def forward_word_del(self, prev_output_tokens, encoder_out=None, **unused):
features, attn, _ = self.extract_features(
prev_output_tokens,
encoder_out=encoder_out,
early_exit=self.early_exit[0],
layers=self.layers_del,
**unused
)
return F.linear(features, self.embed_word_del.weight), attn
@register_model_architecture("fb_levenshtein_transformer", "fb_levenshtein_transformer")
def base_architecture(args):
args.encoder_embed_path = getattr(args, "encoder_embed_path", None)
args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512)
args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 2048)
args.encoder_layers = getattr(args, "encoder_layers", 6)
args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 8)
args.encoder_normalize_before = getattr(args, "encoder_normalize_before", False)
args.encoder_learned_pos = getattr(args, "encoder_learned_pos", False)
args.decoder_embed_path = getattr(args, "decoder_embed_path", None)
args.decoder_embed_dim = getattr(args, "decoder_embed_dim", args.encoder_embed_dim)
args.decoder_ffn_embed_dim = getattr(
args, "decoder_ffn_embed_dim", args.encoder_ffn_embed_dim
)
args.decoder_layers = getattr(args, "decoder_layers", 6)
args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 8)
args.decoder_normalize_before = getattr(args, "decoder_normalize_before", False)
args.decoder_learned_pos = getattr(args, "decoder_learned_pos", False)
args.attention_dropout = getattr(args, "attention_dropout", 0.0)
args.activation_dropout = getattr(args, "activation_dropout", 0.0)
args.activation_fn = getattr(args, "activation_fn", "relu")
args.dropout = getattr(args, "dropout", 0.1)
args.adaptive_softmax_cutoff = getattr(args, "adaptive_softmax_cutoff", None)
args.adaptive_softmax_dropout = getattr(args, "adaptive_softmax_dropout", 0)
args.share_decoder_input_output_embed = getattr(
args, "share_decoder_input_output_embed", False
)
args.share_all_embeddings = getattr(args, "share_all_embeddings", False)
args.no_token_positional_embeddings = getattr(
args, "no_token_positional_embeddings", False
)
args.adaptive_input = getattr(args, "adaptive_input", False)
args.apply_bert_init = getattr(args, "apply_bert_init", False)
args.decoder_output_dim = getattr(
args, "decoder_output_dim", args.decoder_embed_dim
)
args.sampling_for_deletion = getattr(args, "sampling_for_deletion", False)
args.decoder_input_dim = getattr(args, "decoder_input_dim", args.decoder_embed_dim)
args.early_exit = getattr(args, "early_exit", "6,6,6")
args.no_share_discriminator = getattr(args, "no_share_discriminator", False)
args.no_share_maskpredictor = getattr(args, "no_share_maskpredictor", False)
@register_model_architecture(
"fb_levenshtein_transformer", "fb_levenshtein_transformer_wmt_en_de"
)
def levenshtein_transformer_wmt_en_de(args):
base_architecture(args)
# similar parameters used in the "Attention Is All You Need" paper (Vaswani et al., 2017)
@register_model_architecture(
"fb_levenshtein_transformer", "fb_levenshtein_transformer_vaswani_wmt_en_de_big"
)
def levenshtein_transformer_vaswani_wmt_en_de_big(args):
args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 1024)
args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 4096)
args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 16)
args.encoder_normalize_before = getattr(args, "encoder_normalize_before", False)
args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 1024)
args.decoder_ffn_embed_dim = getattr(args, "decoder_ffn_embed_dim", 4096)
args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 16)
args.dropout = getattr(args, "dropout", 0.3)
base_architecture(args)
# default parameters used in tensor2tensor implementation
@register_model_architecture(
"fb_levenshtein_transformer", "fb_levenshtein_transformer_wmt_en_de_big"
)
def levenshtein_transformer_wmt_en_de_big_t2t(args):
args.encoder_normalize_before = getattr(args, "encoder_normalize_before", True)
args.decoder_normalize_before = getattr(args, "decoder_normalize_before", True)
args.attention_dropout = getattr(args, "attention_dropout", 0.1)
args.activation_dropout = getattr(args, "activation_dropout", 0.1)
levenshtein_transformer_vaswani_wmt_en_de_big(args)
|
bart_ls-main
|
fairseq-py/fairseq/models/fb_levenshtein_transformer.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from typing import Dict, List, NamedTuple, Optional
import torch
import torch.nn as nn
from torch import Tensor
EncoderOut = NamedTuple(
"EncoderOut",
[
("encoder_out", Tensor), # T x B x C
("encoder_padding_mask", Optional[Tensor]), # B x T
("encoder_embedding", Optional[Tensor]), # B x T x C
("encoder_states", Optional[List[Tensor]]), # List[T x B x C]
("src_tokens", Optional[Tensor]), # B x T
("src_lengths", Optional[Tensor]), # B x 1
],
)
class FairseqEncoder(nn.Module):
"""Base class for encoders."""
def __init__(self, dictionary):
super().__init__()
self.dictionary = dictionary
def forward(self, src_tokens, src_lengths=None, **kwargs):
"""
Args:
src_tokens (LongTensor): tokens in the source language of shape
`(batch, src_len)`
src_lengths (LongTensor): lengths of each source sentence of shape
`(batch)`
"""
raise NotImplementedError
def forward_torchscript(self, net_input: Dict[str, Tensor]):
"""A TorchScript-compatible version of forward.
Encoders which use additional arguments may want to override
this method for TorchScript compatibility.
"""
if torch.jit.is_scripting():
return self.forward(
src_tokens=net_input["src_tokens"],
src_lengths=net_input["src_lengths"],
)
else:
return self.forward_non_torchscript(net_input)
@torch.jit.unused
def forward_non_torchscript(self, net_input: Dict[str, Tensor]):
encoder_input = {
k: v for k, v in net_input.items() if k != "prev_output_tokens"
}
return self.forward(**encoder_input)
def reorder_encoder_out(self, encoder_out, new_order):
"""
Reorder encoder output according to `new_order`.
Args:
encoder_out: output from the ``forward()`` method
new_order (LongTensor): desired order
Returns:
`encoder_out` rearranged according to `new_order`
"""
raise NotImplementedError
def max_positions(self):
"""Maximum input length supported by the encoder."""
return 1e6 # an arbitrary large number
def upgrade_state_dict_named(self, state_dict, name):
"""Upgrade old state dicts to work with newer code."""
return state_dict
def set_num_updates(self, num_updates):
"""State from trainer to pass along to model at every update."""
def _apply(m):
if hasattr(m, "set_num_updates") and m != self:
m.set_num_updates(num_updates)
self.apply(_apply)
|
bart_ls-main
|
fairseq-py/fairseq/models/fairseq_encoder.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import logging
import math
import os
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import checkpoint_utils
from fairseq.incremental_decoding_utils import with_incremental_state
from fairseq.models import (
CompositeEncoder,
FairseqDecoder,
FairseqEncoder,
FairseqEncoderDecoderModel,
register_model,
register_model_architecture,
)
from fairseq.modules import (
DownsampledMultiHeadAttention,
FairseqDropout,
GradMultiply,
LayerNorm,
LearnedPositionalEmbedding,
LinearizedConvolution,
)
logger = logging.getLogger(__name__)
@register_model("fconv_self_att")
class FConvModelSelfAtt(FairseqEncoderDecoderModel):
@classmethod
def hub_models(cls):
return {
"conv.stories.pretrained": {
"path": "https://dl.fbaipublicfiles.com/fairseq/models/stories_checkpoint.tar.gz",
"checkpoint_file": "pretrained_checkpoint.pt",
"tokenizer": "nltk",
},
"conv.stories": {
"path": "https://dl.fbaipublicfiles.com/fairseq/models/stories_checkpoint.tar.gz",
"checkpoint_file": "fusion_checkpoint.pt",
"tokenizer": "nltk",
"pretrained": "True",
"pretrained_checkpoint": "./pretrained_checkpoint.pt",
},
# Test set containing dictionaries
"data.stories": "https://dl.fbaipublicfiles.com/fairseq/data/stories_test.tar.bz2",
}
def __init__(self, encoder, decoder, pretrained_encoder=None):
super().__init__(encoder, decoder)
self.encoder.num_attention_layers = sum(
layer is not None for layer in decoder.attention
)
self.pretrained_encoder = pretrained_encoder
if self.pretrained_encoder is None:
encoders = {"encoder": encoder}
else:
encoders = {"encoder": encoder, "pretrained": self.pretrained_encoder}
# for fusion model, CompositeEncoder contains both pretrained and training encoders
# these are forwarded and then combined in the decoder
self.encoder = CompositeEncoder(encoders)
@staticmethod
def add_args(parser):
"""Add model-specific arguments to the parser."""
# fmt: off
parser.add_argument('--dropout', type=float, metavar='D',
help='dropout probability')
parser.add_argument('--encoder-embed-dim', type=int, metavar='N',
help='encoder embedding dimension')
parser.add_argument('--encoder-layers', type=str, metavar='EXPR',
help='encoder layers [(dim, kernel_size), ...]')
parser.add_argument('--decoder-embed-dim', type=int, metavar='N',
help='decoder embedding dimension')
parser.add_argument('--decoder-layers', type=str, metavar='EXPR',
help='decoder layers [(dim, kernel_size), ...]')
parser.add_argument('--decoder-out-embed-dim', type=int, metavar='N',
help='decoder output embedding dimension')
parser.add_argument('--decoder-attention', type=str, metavar='EXPR',
help='decoder attention [True, ...]')
parser.add_argument('--self-attention', type=str, metavar='EXPR',
help='decoder self-attention layers, ex: [True] + [False]*5')
parser.add_argument('--multihead-attention-nheads', type=int,
help='Number of heads to use in attention')
parser.add_argument('--multihead-self-attention-nheads', type=int,
help='Number of heads to use in self-attention')
parser.add_argument('--encoder-attention', type=str, metavar='EXPR',
help='encoder attention [True, ...]')
parser.add_argument('--encoder-attention-nheads', type=int,
help='Number of heads to use in encoder attention')
parser.add_argument('--project-input', type=str, metavar='EXPR',
help='Use projections in self-attention [True, ...]')
parser.add_argument('--gated-attention', type=str, metavar='EXPR',
help='Use GLU layers in self-attention projections [True, ...]')
parser.add_argument('--downsample', type=str, metavar='EXPR',
help='Use downsampling in self-attention [True, ...]')
parser.add_argument('--pretrained-checkpoint', metavar='DIR',
help='path to load checkpoint from pretrained model')
parser.add_argument('--pretrained', type=str, metavar='EXPR',
help='use pretrained model when training [True, ...]')
# fmt: on
@classmethod
def build_model(cls, args, task):
"""Build a new model instance."""
trained_encoder, trained_decoder = None, None
pretrained = eval(args.pretrained)
if pretrained:
logger.info("loading pretrained model")
if not os.path.exists(args.pretrained_checkpoint):
new_pretrained_checkpoint = os.path.join(
args.data, args.pretrained_checkpoint
)
if os.path.exists(new_pretrained_checkpoint):
args.pretrained_checkpoint = new_pretrained_checkpoint
trained_model = checkpoint_utils.load_model_ensemble(
filenames=[args.pretrained_checkpoint],
task=task,
)[0][0]
trained_decoder = list(trained_model.children())[1]
trained_encoder = list(trained_model.children())[0]
# freeze pretrained model
for param in trained_decoder.parameters():
param.requires_grad = False
for param in trained_encoder.parameters():
param.requires_grad = False
encoder = FConvEncoder(
task.source_dictionary,
embed_dim=args.encoder_embed_dim,
convolutions=eval(args.encoder_layers),
dropout=args.dropout,
max_positions=args.max_source_positions,
attention=eval(args.encoder_attention),
attention_nheads=args.encoder_attention_nheads,
)
decoder = FConvDecoder(
task.target_dictionary,
embed_dim=args.decoder_embed_dim,
convolutions=eval(args.decoder_layers),
out_embed_dim=args.decoder_out_embed_dim,
attention=eval(args.decoder_attention),
dropout=args.dropout,
max_positions=args.max_target_positions,
selfattention=eval(args.self_attention),
attention_nheads=args.multihead_attention_nheads,
selfattention_nheads=args.multihead_self_attention_nheads,
project_input=eval(args.project_input),
gated_attention=eval(args.gated_attention),
downsample=eval(args.downsample),
pretrained=pretrained,
trained_decoder=trained_decoder,
)
model = FConvModelSelfAtt(encoder, decoder, trained_encoder)
return model
@property
def pretrained(self):
return self.pretrained_encoder is not None
class FConvEncoder(FairseqEncoder):
"""Convolutional encoder"""
def __init__(
self,
dictionary,
embed_dim=512,
max_positions=1024,
convolutions=((512, 3),) * 20,
dropout=0.1,
attention=False,
attention_nheads=1,
):
super().__init__(dictionary)
self.dropout_module = FairseqDropout(
dropout, module_name=self.__class__.__name__
)
self.num_attention_layers = None
num_embeddings = len(dictionary)
self.padding_idx = dictionary.pad()
self.embed_tokens = Embedding(num_embeddings, embed_dim, self.padding_idx)
self.embed_positions = PositionalEmbedding(
max_positions,
embed_dim,
self.padding_idx,
)
def expand_bool_array(val):
if isinstance(val, bool):
# expand True into [True, True, ...] and do the same with False
return [val] * len(convolutions)
return val
attention = expand_bool_array(attention)
in_channels = convolutions[0][0]
self.fc1 = Linear(embed_dim, in_channels, dropout=dropout)
self.projections = nn.ModuleList()
self.convolutions = nn.ModuleList()
self.attention = nn.ModuleList()
self.attproj = nn.ModuleList()
for i, (out_channels, kernel_size) in enumerate(convolutions):
self.projections.append(
Linear(in_channels, out_channels)
if in_channels != out_channels
else None
)
self.convolutions.append(
ConvTBC(in_channels, out_channels * 2, kernel_size, dropout=dropout)
)
self.attention.append(
SelfAttention(out_channels, embed_dim, attention_nheads)
if attention[i]
else None
)
in_channels = out_channels
self.fc2 = Linear(in_channels, embed_dim)
def forward(self, src_tokens, src_lengths):
# embed tokens and positions
x = self.embed_tokens(src_tokens) + self.embed_positions(src_tokens)
x = self.dropout_module(x)
input_embedding = x.transpose(0, 1)
# project to size of convolution
x = self.fc1(x)
encoder_padding_mask = src_tokens.eq(self.padding_idx).t() # -> T x B
if not encoder_padding_mask.any():
encoder_padding_mask = None
# B x T x C -> T x B x C
x = x.transpose(0, 1)
# temporal convolutions
for proj, conv, attention in zip(
self.projections, self.convolutions, self.attention
):
residual = x if proj is None else proj(x)
if encoder_padding_mask is not None:
x = x.masked_fill(encoder_padding_mask.unsqueeze(-1), 0)
x = self.dropout_module(x)
padding_l = (conv.kernel_size[0] - 1) // 2
padding_r = conv.kernel_size[0] // 2
x = F.pad(x, (0, 0, 0, 0, padding_l, padding_r))
x = conv(x)
x = F.glu(x, dim=2)
if attention is not None:
x = attention(x)
x = (x + residual) * math.sqrt(0.5)
# T x B x C -> B x T x C
x = x.transpose(1, 0)
# project back to size of embedding
x = self.fc2(x)
if encoder_padding_mask is not None:
encoder_padding_mask = encoder_padding_mask.t() # -> B x T
x = x.masked_fill(encoder_padding_mask.unsqueeze(-1), 0)
# scale gradients (this only affects backward, not forward)
x = GradMultiply.apply(x, 1.0 / (2.0 * self.num_attention_layers))
# add output to input embedding for attention
y = (x + input_embedding.transpose(0, 1)) * math.sqrt(0.5)
return {
"encoder_out": (x, y),
"encoder_padding_mask": encoder_padding_mask, # B x T
}
def reorder_encoder_out(self, encoder_out, new_order):
encoder_out["encoder_out"] = tuple(
eo.index_select(0, new_order) for eo in encoder_out["encoder_out"]
)
if encoder_out["encoder_padding_mask"] is not None:
encoder_out["encoder_padding_mask"] = encoder_out[
"encoder_padding_mask"
].index_select(0, new_order)
if "pretrained" in encoder_out:
encoder_out["pretrained"]["encoder_out"] = tuple(
eo.index_select(0, new_order)
for eo in encoder_out["pretrained"]["encoder_out"]
)
return encoder_out
def max_positions(self):
"""Maximum input length supported by the encoder."""
return self.embed_positions.max_positions
@with_incremental_state
class FConvDecoder(FairseqDecoder):
"""Convolutional decoder"""
def __init__(
self,
dictionary,
embed_dim=512,
out_embed_dim=256,
max_positions=1024,
convolutions=((512, 3),) * 8,
attention=True,
dropout=0.1,
selfattention=False,
attention_nheads=1,
selfattention_nheads=1,
project_input=False,
gated_attention=False,
downsample=False,
pretrained=False,
trained_decoder=None,
):
super().__init__(dictionary)
self.register_buffer("version", torch.Tensor([2]))
self.pretrained = pretrained
self.pretrained_decoder = trained_decoder
self.dropout_module = FairseqDropout(
dropout, module_name=self.__class__.__name__
)
self.need_attn = True
in_channels = convolutions[0][0]
def expand_bool_array(val):
if isinstance(val, bool):
# expand True into [True, True, ...] and do the same with False
return [val] * len(convolutions)
return val
attention = expand_bool_array(attention)
selfattention = expand_bool_array(selfattention)
if not isinstance(attention, list) or len(attention) != len(convolutions):
raise ValueError(
"Attention is expected to be a list of booleans of "
"length equal to the number of layers."
)
num_embeddings = len(dictionary)
padding_idx = dictionary.pad()
self.embed_tokens = Embedding(num_embeddings, embed_dim, padding_idx)
self.embed_positions = PositionalEmbedding(
max_positions,
embed_dim,
padding_idx,
)
self.fc1 = Linear(embed_dim, in_channels, dropout=dropout)
self.projections = nn.ModuleList()
self.convolutions = nn.ModuleList()
self.attention = nn.ModuleList()
self.selfattention = nn.ModuleList()
self.attproj = nn.ModuleList()
for i, (out_channels, kernel_size) in enumerate(convolutions):
self.projections.append(
Linear(in_channels, out_channels)
if in_channels != out_channels
else None
)
self.convolutions.append(
LinearizedConv1d(
in_channels,
out_channels * 2,
kernel_size,
padding=(kernel_size - 1),
dropout=dropout,
)
)
self.attention.append(
DownsampledMultiHeadAttention(
out_channels,
embed_dim,
attention_nheads,
project_input=project_input,
gated=False,
downsample=False,
)
if attention[i]
else None
)
self.attproj.append(
Linear(out_channels, embed_dim, dropout=dropout)
if attention[i]
else None
)
self.selfattention.append(
SelfAttention(
out_channels,
embed_dim,
selfattention_nheads,
project_input=project_input,
gated=gated_attention,
downsample=downsample,
)
if selfattention[i]
else None
)
in_channels = out_channels
self.fc2 = Linear(in_channels, out_embed_dim)
self.fc3 = Linear(out_embed_dim, num_embeddings, dropout=dropout)
# model fusion
if self.pretrained:
# independent gates are learned from the concatenated input
self.gate1 = nn.Sequential(
Linear(out_embed_dim * 2, out_embed_dim), nn.Sigmoid()
)
self.gate2 = nn.Sequential(
Linear(out_embed_dim * 2, out_embed_dim), nn.Sigmoid()
)
# pretrained and trained models are joined
self.joining = nn.Sequential(
Linear(out_embed_dim * 2, out_embed_dim * 2),
LayerNorm(out_embed_dim * 2),
nn.GLU(),
Linear(out_embed_dim, out_embed_dim * 2),
LayerNorm(out_embed_dim * 2),
nn.GLU(),
Linear(out_embed_dim, out_embed_dim),
LayerNorm(out_embed_dim),
)
# pretrained model contains an output layer that is nhid -> vocab size
# but the models are combined in their hidden state
# the hook stores the output of the pretrained model forward
self.pretrained_outputs = {}
def save_output():
def hook(a, b, output):
self.pretrained_outputs["out"] = output
return hook
self.pretrained_decoder.fc2.register_forward_hook(save_output())
def forward(self, prev_output_tokens, encoder_out):
trained_encoder_out = encoder_out["pretrained"] if self.pretrained else None
encoder_out = encoder_out["encoder"]["encoder_out"]
encoder_a, encoder_b = self._split_encoder_out(encoder_out)
# embed positions
positions = self.embed_positions(prev_output_tokens)
# embed tokens and positions
x = self.embed_tokens(prev_output_tokens) + positions
x = self.dropout_module(x)
target_embedding = x.transpose(0, 1)
# project to size of convolution
x = self.fc1(x)
# B x T x C -> T x B x C
x = x.transpose(0, 1)
# temporal convolutions
avg_attn_scores = None
for proj, conv, attention, selfattention, attproj in zip(
self.projections,
self.convolutions,
self.attention,
self.selfattention,
self.attproj,
):
residual = x if proj is None else proj(x)
x = self.dropout_module(x)
x = conv(x)
x = F.glu(x, dim=2)
# attention
if attention is not None:
r = x
x, attn_scores = attention(
attproj(x) + target_embedding, encoder_a, encoder_b
)
x = x + r
if not self.training and self.need_attn:
if avg_attn_scores is None:
avg_attn_scores = attn_scores
else:
avg_attn_scores.add_(attn_scores)
if selfattention is not None:
x = selfattention(x)
x = (x + residual) * math.sqrt(0.5)
# T x B x C -> B x T x C
x = x.transpose(0, 1)
# project back to size of vocabulary
x = self.fc2(x)
x = self.dropout_module(x)
if not self.pretrained:
x = self.fc3(x)
# fusion gating
if self.pretrained:
trained_x, _ = self.pretrained_decoder.forward(
prev_output_tokens, trained_encoder_out
)
y = torch.cat([x, self.pretrained_outputs["out"]], dim=-1)
gate1 = self.gate1(y)
gate2 = self.gate2(y)
gated_x1 = gate1 * x
gated_x2 = gate2 * self.pretrained_outputs["out"]
fusion = torch.cat([gated_x1, gated_x2], dim=-1)
fusion = self.joining(fusion)
fusion_output = self.fc3(fusion)
return fusion_output, avg_attn_scores
else:
return x, avg_attn_scores
def max_positions(self):
"""Maximum output length supported by the decoder."""
return self.embed_positions.max_positions
def make_generation_fast_(self, need_attn=False, **kwargs):
self.need_attn = need_attn
def _split_encoder_out(self, encoder_out):
"""Split and transpose encoder outputs."""
# transpose only once to speed up attention layers
encoder_a, encoder_b = encoder_out
encoder_a = encoder_a.transpose(0, 1).contiguous()
encoder_b = encoder_b.transpose(0, 1).contiguous()
result = (encoder_a, encoder_b)
return result
class SelfAttention(nn.Module):
def __init__(
self,
out_channels,
embed_dim,
num_heads,
project_input=False,
gated=False,
downsample=False,
):
super().__init__()
self.attention = DownsampledMultiHeadAttention(
out_channels,
embed_dim,
num_heads,
dropout=0,
bias=True,
project_input=project_input,
gated=gated,
downsample=downsample,
)
self.in_proj_q = Linear(out_channels, embed_dim)
self.in_proj_k = Linear(out_channels, embed_dim)
self.in_proj_v = Linear(out_channels, embed_dim)
self.ln = LayerNorm(out_channels)
def forward(self, x):
residual = x
query = self.in_proj_q(x)
key = self.in_proj_k(x)
value = self.in_proj_v(x)
x, _ = self.attention(
query, key, value, mask_future_timesteps=True, use_scalar_bias=True
)
return self.ln(x + residual)
def Embedding(num_embeddings, embedding_dim, padding_idx):
m = nn.Embedding(num_embeddings, embedding_dim, padding_idx=padding_idx)
m.weight.data.normal_(0, 0.1)
return m
def PositionalEmbedding(num_embeddings, embedding_dim, padding_idx):
m = LearnedPositionalEmbedding(num_embeddings, embedding_dim, padding_idx)
m.weight.data.normal_(0, 0.1)
return m
def Linear(in_features, out_features, dropout=0.0):
"""Weight-normalized Linear layer (input: N x T x C)"""
m = nn.Linear(in_features, out_features)
m.weight.data.normal_(mean=0, std=math.sqrt((1 - dropout) / in_features))
m.bias.data.zero_()
return m
def LinearizedConv1d(in_channels, out_channels, kernel_size, dropout=0.0, **kwargs):
"""Weight-normalized Conv1d layer optimized for decoding"""
m = LinearizedConvolution(in_channels, out_channels, kernel_size, **kwargs)
std = math.sqrt((4 * (1.0 - dropout)) / (m.kernel_size[0] * in_channels))
m.weight.data.normal_(mean=0, std=std)
m.bias.data.zero_()
return m
def ConvTBC(in_channels, out_channels, kernel_size, dropout=0.0, **kwargs):
"""Weight-normalized Conv1d layer"""
from fairseq.modules import ConvTBC
m = ConvTBC(in_channels, out_channels, kernel_size, **kwargs)
std = math.sqrt((4 * (1.0 - dropout)) / (m.kernel_size[0] * in_channels))
m.weight.data.normal_(mean=0, std=std)
m.bias.data.zero_()
return m
@register_model_architecture("fconv_self_att", "fconv_self_att")
def base_architecture(args):
args.dropout = getattr(args, "dropout", 0.1)
args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512)
args.encoder_layers = getattr(args, "encoder_layers", "[(512, 3)] * 3")
args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 512)
args.decoder_layers = getattr(args, "decoder_layers", "[(512, 3)] * 8")
args.decoder_out_embed_dim = getattr(args, "decoder_out_embed_dim", 256)
args.decoder_attention = getattr(args, "decoder_attention", "True")
args.self_attention = getattr(args, "self_attention", "False")
args.encoder_attention = getattr(args, "encoder_attention", "False")
args.multihead_attention_nheads = getattr(args, "multihead_attention_nheads", 1)
args.multihead_self_attention_nheads = getattr(
args, "multihead_self_attention_nheads", 1
)
args.encoder_attention_nheads = getattr(args, "encoder_attention_nheads", 1)
args.project_input = getattr(args, "project_input", "False")
args.gated_attention = getattr(args, "gated_attention", "False")
args.downsample = getattr(args, "downsample", "False")
args.pretrained_checkpoint = getattr(args, "pretrained_checkpoint", "")
args.pretrained = getattr(args, "pretrained", "False")
@register_model_architecture("fconv_self_att", "fconv_self_att_wp")
def fconv_self_att_wp(args):
args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 256)
args.encoder_layers = getattr(
args, "encoder_layers", "[(128, 3)] * 2 + [(512,3)] * 1"
)
args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 256)
args.decoder_layers = getattr(
args, "decoder_layers", "[(512, 4)] * 4 + [(768, 4)] * 2 + [(1024, 4)] * 1"
)
args.decoder_out_embed_dim = getattr(args, "decoder_out_embed_dim", 256)
args.self_attention = getattr(args, "self_attention", "True")
args.multihead_self_attention_nheads = getattr(
args, "multihead_self_attention_nheads", 4
)
args.project_input = getattr(args, "project_input", "True")
args.gated_attention = getattr(args, "gated_attention", "True")
args.downsample = getattr(args, "downsample", "True")
base_architecture(args)
|
bart_ls-main
|
fairseq-py/fairseq/models/fconv_self_att.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from fairseq import utils
from fairseq.models import (
FairseqLanguageModel,
register_model,
register_model_architecture,
)
from fairseq.models.fconv import FConvDecoder
from fairseq.utils import safe_hasattr
@register_model("fconv_lm")
class FConvLanguageModel(FairseqLanguageModel):
def __init__(self, decoder):
super().__init__(decoder)
@staticmethod
def add_args(parser):
"""Add model-specific arguments to the parser."""
parser.add_argument(
"--dropout", type=float, metavar="D", help="dropout probability"
)
parser.add_argument(
"--decoder-embed-dim",
type=int,
metavar="N",
help="decoder embedding dimension",
)
parser.add_argument(
"--decoder-layers",
type=str,
metavar="EXPR",
help="decoder layers [(dim, kernel_size), ...]",
)
parser.add_argument(
"--decoder-out-embed-dim",
type=int,
metavar="N",
help="decoder output embedding dimension",
)
parser.add_argument(
"--adaptive-softmax-cutoff",
metavar="EXPR",
help="comma separated list of adaptive softmax cutoff points. "
"Must be used with adaptive_loss criterion",
)
parser.add_argument(
"--adaptive-softmax-dropout",
type=float,
metavar="D",
help="sets adaptive softmax dropout for the tail projections",
)
parser.add_argument(
"--decoder-attention",
type=str,
metavar="EXPR",
help="decoder attention [True, ...]",
)
@classmethod
def build_model(cls, args, task):
"""Build a new model instance."""
# make sure all arguments are present in older models
base_lm_architecture(args)
if safe_hasattr(args, "max_target_positions") and not safe_hasattr(
args, "tokens_per_sample"
):
args.tokens_per_sample = args.max_target_positions
decoder = FConvDecoder(
dictionary=task.target_dictionary,
embed_dim=args.decoder_embed_dim,
convolutions=eval(args.decoder_layers),
out_embed_dim=args.decoder_embed_dim,
attention=eval(args.decoder_attention),
dropout=args.dropout,
max_positions=args.tokens_per_sample,
share_embed=False,
positional_embeddings=False,
adaptive_softmax_cutoff=(
utils.eval_str_list(args.adaptive_softmax_cutoff, type=int)
if args.criterion == "adaptive_loss"
else None
),
adaptive_softmax_dropout=args.adaptive_softmax_dropout,
)
return FConvLanguageModel(decoder)
@register_model_architecture("fconv_lm", "fconv_lm")
def base_lm_architecture(args):
args.dropout = getattr(args, "dropout", 0.1)
args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 128)
args.decoder_layers = getattr(args, "decoder_layers", "[(1268, 4)] * 13")
args.decoder_attention = getattr(args, "decoder_attention", "False")
args.adaptive_softmax_cutoff = getattr(args, "adaptive_softmax_cutoff", None)
args.adaptive_softmax_dropout = getattr(args, "adaptive_softmax_dropout", 0)
@register_model_architecture("fconv_lm", "fconv_lm_dauphin_wikitext103")
def fconv_lm_dauphin_wikitext103(args):
layers = "[(850, 6)] * 3"
layers += " + [(850, 1)] * 1"
layers += " + [(850, 5)] * 4"
layers += " + [(850, 1)] * 1"
layers += " + [(850, 4)] * 3"
layers += " + [(1024, 4)] * 1"
layers += " + [(2048, 4)] * 1"
args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 280)
args.decoder_layers = getattr(args, "decoder_layers", layers)
args.decoder_attention = getattr(args, "decoder_attention", "False")
args.adaptive_softmax_cutoff = getattr(
args, "adaptive_softmax_cutoff", "10000,20000,200000"
)
base_lm_architecture(args)
@register_model_architecture("fconv_lm", "fconv_lm_dauphin_gbw")
def fconv_lm_dauphin_gbw(args):
layers = "[(512, 5)]"
layers += " + [(128, 1, 0), (128, 5, 0), (512, 1, 3)] * 3"
layers += " + [(512, 1, 0), (512, 5, 0), (1024, 1, 3)] * 3"
layers += " + [(1024, 1, 0), (1024, 5, 0), (2048, 1, 3)] * 6"
layers += " + [(1024, 1, 0), (1024, 5, 0), (4096, 1, 3)]"
args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 128)
args.decoder_layers = getattr(args, "decoder_layers", layers)
args.decoder_attention = getattr(args, "decoder_attention", "False")
args.adaptive_softmax_cutoff = getattr(
args, "adaptive_softmax_cutoff", "10000,50000,200000"
)
base_lm_architecture(args)
|
bart_ls-main
|
fairseq-py/fairseq/models/fconv_lm.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from fairseq import utils
from fairseq.models import (
FairseqLanguageModel,
register_model,
register_model_architecture,
)
from fairseq.models.lstm import Embedding, LSTMDecoder
DEFAULT_MAX_TARGET_POSITIONS = 1e5
@register_model("lstm_lm")
class LSTMLanguageModel(FairseqLanguageModel):
def __init__(self, decoder):
super().__init__(decoder)
@staticmethod
def add_args(parser):
"""Add model-specific arguments to the parser."""
# fmt: off
parser.add_argument('--dropout', type=float, metavar='D',
help='dropout probability')
parser.add_argument('--decoder-embed-dim', type=int, metavar='N',
help='decoder embedding dimension')
parser.add_argument('--decoder-embed-path', type=str, metavar='STR',
help='path to pre-trained decoder embedding')
parser.add_argument('--decoder-hidden-size', type=int, metavar='N',
help='decoder hidden size')
parser.add_argument('--decoder-layers', type=int, metavar='N',
help='number of decoder layers')
parser.add_argument('--decoder-out-embed-dim', type=int, metavar='N',
help='decoder output embedding dimension')
parser.add_argument('--decoder-attention', type=str, metavar='BOOL',
help='decoder attention')
parser.add_argument('--adaptive-softmax-cutoff', metavar='EXPR',
help='comma separated list of adaptive softmax cutoff points. '
'Must be used with adaptive_loss criterion')
parser.add_argument('--residuals', default=False,
action='store_true',
help='applying residuals between LSTM layers')
# Granular dropout settings (if not specified these default to --dropout)
parser.add_argument('--decoder-dropout-in', type=float, metavar='D',
help='dropout probability for decoder input embedding')
parser.add_argument('--decoder-dropout-out', type=float, metavar='D',
help='dropout probability for decoder output')
parser.add_argument('--share-decoder-input-output-embed', default=False,
action='store_true',
help='share decoder input and output embeddings')
# fmt: on
@classmethod
def build_model(cls, args, task):
"""Build a new model instance."""
# make sure all arguments are present in older models
base_architecture(args)
if getattr(args, "max_target_positions", None) is not None:
max_target_positions = args.max_target_positions
else:
max_target_positions = getattr(
args, "tokens_per_sample", DEFAULT_MAX_TARGET_POSITIONS
)
def load_pretrained_embedding_from_file(embed_path, dictionary, embed_dim):
num_embeddings = len(dictionary)
padding_idx = dictionary.pad()
embed_tokens = Embedding(num_embeddings, embed_dim, padding_idx)
embed_dict = utils.parse_embedding(embed_path)
utils.print_embed_overlap(embed_dict, dictionary)
return utils.load_embedding(embed_dict, dictionary, embed_tokens)
pretrained_decoder_embed = None
if args.decoder_embed_path:
pretrained_decoder_embed = load_pretrained_embedding_from_file(
args.decoder_embed_path, task.target_dictionary, args.decoder_embed_dim
)
if args.share_decoder_input_output_embed:
# double check all parameters combinations are valid
if task.source_dictionary != task.target_dictionary:
raise ValueError(
"--share-decoder-input-output-embeddings requires a joint dictionary"
)
if args.decoder_embed_dim != args.decoder_out_embed_dim:
raise ValueError(
"--share-decoder-input-output-embeddings requires "
"--decoder-embed-dim to match --decoder-out-embed-dim"
)
decoder = LSTMDecoder(
dictionary=task.dictionary,
embed_dim=args.decoder_embed_dim,
hidden_size=args.decoder_hidden_size,
out_embed_dim=args.decoder_out_embed_dim,
num_layers=args.decoder_layers,
dropout_in=args.decoder_dropout_in,
dropout_out=args.decoder_dropout_out,
attention=False, # decoder-only language model doesn't support attention
encoder_output_units=0,
pretrained_embed=pretrained_decoder_embed,
share_input_output_embed=args.share_decoder_input_output_embed,
adaptive_softmax_cutoff=(
utils.eval_str_list(args.adaptive_softmax_cutoff, type=int)
if args.criterion == "adaptive_loss"
else None
),
max_target_positions=max_target_positions,
residuals=args.residuals,
)
return cls(decoder)
@register_model_architecture("lstm_lm", "lstm_lm")
def base_architecture(args):
args.dropout = getattr(args, "dropout", 0.1)
args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 512)
args.decoder_embed_path = getattr(args, "decoder_embed_path", None)
args.decoder_hidden_size = getattr(
args, "decoder_hidden_size", args.decoder_embed_dim
)
args.decoder_layers = getattr(args, "decoder_layers", 1)
args.decoder_out_embed_dim = getattr(args, "decoder_out_embed_dim", 512)
args.decoder_attention = getattr(args, "decoder_attention", "0")
args.decoder_dropout_in = getattr(args, "decoder_dropout_in", args.dropout)
args.decoder_dropout_out = getattr(args, "decoder_dropout_out", args.dropout)
args.share_decoder_input_output_embed = getattr(
args, "share_decoder_input_output_embed", False
)
args.adaptive_softmax_cutoff = getattr(
args, "adaptive_softmax_cutoff", "10000,50000,200000"
)
args.residuals = getattr(args, "residuals", False)
|
bart_ls-main
|
fairseq-py/fairseq/models/lstm_lm.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from collections import OrderedDict
from fairseq import utils
from fairseq.models import (
FairseqMultiModel,
register_model,
register_model_architecture,
)
from fairseq.models.transformer import (
Embedding,
TransformerDecoder,
TransformerEncoder,
TransformerModel,
base_architecture,
)
from fairseq.utils import safe_hasattr
@register_model("multilingual_transformer")
class MultilingualTransformerModel(FairseqMultiModel):
"""Train Transformer models for multiple language pairs simultaneously.
Requires `--task multilingual_translation`.
We inherit all arguments from TransformerModel and assume that all language
pairs use a single Transformer architecture. In addition, we provide several
options that are specific to the multilingual setting.
Args:
--share-encoder-embeddings: share encoder embeddings across all source languages
--share-decoder-embeddings: share decoder embeddings across all target languages
--share-encoders: share all encoder params (incl. embeddings) across all source languages
--share-decoders: share all decoder params (incl. embeddings) across all target languages
"""
def __init__(self, encoders, decoders):
super().__init__(encoders, decoders)
@staticmethod
def add_args(parser):
"""Add model-specific arguments to the parser."""
TransformerModel.add_args(parser)
parser.add_argument(
"--share-encoder-embeddings",
action="store_true",
help="share encoder embeddings across languages",
)
parser.add_argument(
"--share-decoder-embeddings",
action="store_true",
help="share decoder embeddings across languages",
)
parser.add_argument(
"--share-encoders",
action="store_true",
help="share encoders across languages",
)
parser.add_argument(
"--share-decoders",
action="store_true",
help="share decoders across languages",
)
@classmethod
def build_model(cls, args, task):
"""Build a new model instance."""
from fairseq.tasks.multilingual_translation import MultilingualTranslationTask
assert isinstance(task, MultilingualTranslationTask)
# make sure all arguments are present in older models
base_multilingual_architecture(args)
if not safe_hasattr(args, "max_source_positions"):
args.max_source_positions = 1024
if not safe_hasattr(args, "max_target_positions"):
args.max_target_positions = 1024
src_langs = [lang_pair.split("-")[0] for lang_pair in task.model_lang_pairs]
tgt_langs = [lang_pair.split("-")[1] for lang_pair in task.model_lang_pairs]
if args.share_encoders:
args.share_encoder_embeddings = True
if args.share_decoders:
args.share_decoder_embeddings = True
def build_embedding(dictionary, embed_dim, path=None):
num_embeddings = len(dictionary)
padding_idx = dictionary.pad()
emb = Embedding(num_embeddings, embed_dim, padding_idx)
# if provided, load from preloaded dictionaries
if path:
embed_dict = utils.parse_embedding(path)
utils.load_embedding(embed_dict, dictionary, emb)
return emb
# build shared embeddings (if applicable)
shared_encoder_embed_tokens, shared_decoder_embed_tokens = None, None
if args.share_all_embeddings:
if args.encoder_embed_dim != args.decoder_embed_dim:
raise ValueError(
"--share-all-embeddings requires --encoder-embed-dim to match --decoder-embed-dim"
)
if args.decoder_embed_path and (
args.decoder_embed_path != args.encoder_embed_path
):
raise ValueError(
"--share-all-embeddings not compatible with --decoder-embed-path"
)
shared_encoder_embed_tokens = FairseqMultiModel.build_shared_embeddings(
dicts=task.dicts,
langs=task.langs,
embed_dim=args.encoder_embed_dim,
build_embedding=build_embedding,
pretrained_embed_path=args.encoder_embed_path,
)
shared_decoder_embed_tokens = shared_encoder_embed_tokens
args.share_decoder_input_output_embed = True
else:
if args.share_encoder_embeddings:
shared_encoder_embed_tokens = FairseqMultiModel.build_shared_embeddings(
dicts=task.dicts,
langs=src_langs,
embed_dim=args.encoder_embed_dim,
build_embedding=build_embedding,
pretrained_embed_path=args.encoder_embed_path,
)
if args.share_decoder_embeddings:
shared_decoder_embed_tokens = FairseqMultiModel.build_shared_embeddings(
dicts=task.dicts,
langs=tgt_langs,
embed_dim=args.decoder_embed_dim,
build_embedding=build_embedding,
pretrained_embed_path=args.decoder_embed_path,
)
# encoders/decoders for each language
lang_encoders, lang_decoders = {}, {}
def get_encoder(lang):
if lang not in lang_encoders:
if shared_encoder_embed_tokens is not None:
encoder_embed_tokens = shared_encoder_embed_tokens
else:
encoder_embed_tokens = build_embedding(
task.dicts[lang],
args.encoder_embed_dim,
args.encoder_embed_path,
)
lang_encoders[lang] = cls._get_module_class(
True, args, task.dicts[lang], encoder_embed_tokens, src_langs
)
return lang_encoders[lang]
def get_decoder(lang):
if lang not in lang_decoders:
if shared_decoder_embed_tokens is not None:
decoder_embed_tokens = shared_decoder_embed_tokens
else:
decoder_embed_tokens = build_embedding(
task.dicts[lang],
args.decoder_embed_dim,
args.decoder_embed_path,
)
lang_decoders[lang] = cls._get_module_class(
False, args, task.dicts[lang], decoder_embed_tokens, tgt_langs
)
return lang_decoders[lang]
# shared encoders/decoders (if applicable)
shared_encoder, shared_decoder = None, None
if args.share_encoders:
shared_encoder = get_encoder(src_langs[0])
if args.share_decoders:
shared_decoder = get_decoder(tgt_langs[0])
encoders, decoders = OrderedDict(), OrderedDict()
for lang_pair, src, tgt in zip(task.model_lang_pairs, src_langs, tgt_langs):
encoders[lang_pair] = (
shared_encoder if shared_encoder is not None else get_encoder(src)
)
decoders[lang_pair] = (
shared_decoder if shared_decoder is not None else get_decoder(tgt)
)
return MultilingualTransformerModel(encoders, decoders)
@classmethod
def _get_module_class(cls, is_encoder, args, lang_dict, embed_tokens, langs):
module_class = TransformerEncoder if is_encoder else TransformerDecoder
return module_class(args, lang_dict, embed_tokens)
def load_state_dict(self, state_dict, strict=True, model_cfg=None):
state_dict_subset = state_dict.copy()
for k, _ in state_dict.items():
assert k.startswith("models.")
lang_pair = k.split(".")[1]
if lang_pair not in self.models:
del state_dict_subset[k]
super().load_state_dict(state_dict_subset, strict=strict, model_cfg=model_cfg)
@register_model_architecture("multilingual_transformer", "multilingual_transformer")
def base_multilingual_architecture(args):
base_architecture(args)
args.share_encoder_embeddings = getattr(args, "share_encoder_embeddings", False)
args.share_decoder_embeddings = getattr(args, "share_decoder_embeddings", False)
args.share_encoders = getattr(args, "share_encoders", False)
args.share_decoders = getattr(args, "share_decoders", False)
@register_model_architecture(
"multilingual_transformer", "multilingual_transformer_iwslt_de_en"
)
def multilingual_transformer_iwslt_de_en(args):
args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512)
args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 1024)
args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 4)
args.encoder_layers = getattr(args, "encoder_layers", 6)
args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 512)
args.decoder_ffn_embed_dim = getattr(args, "decoder_ffn_embed_dim", 1024)
args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 4)
args.decoder_layers = getattr(args, "decoder_layers", 6)
base_multilingual_architecture(args)
|
bart_ls-main
|
fairseq-py/fairseq/models/multilingual_transformer.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import logging
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import utils
from fairseq.models import (
FairseqDecoder,
FairseqLanguageModel,
register_model,
register_model_architecture,
)
from fairseq.models.transformer import Embedding, Linear
from fairseq.modules import (
AdaptiveSoftmax,
CharacterTokenEmbedder,
FairseqDropout,
LayerNorm,
PositionalEmbedding,
SinusoidalPositionalEmbedding,
TransformerDecoderLayer,
)
from fairseq.modules.character_token_embedder import CHAR_PAD_IDX
from fairseq.modules.fb_bidirectional_multihead_attention import (
BidirectionalMultiheadSelfAttention,
)
logger = logging.getLogger(__name__)
@register_model("bi_transformer_lm")
class BiTransformerLanguageModel(FairseqLanguageModel):
def __init__(self, decoder):
super().__init__(decoder)
@staticmethod
def add_args(parser):
"""Add model-specific arguments to the parser."""
parser.add_argument(
"--activation-fn",
choices=utils.get_available_activation_fns(),
help="activation function to use",
)
parser.add_argument(
"--dropout",
default=0.1,
type=float,
metavar="D",
help="dropout probability",
)
parser.add_argument(
"--attention-dropout",
default=0.0,
type=float,
metavar="D",
help="dropout probability for attention weights",
)
parser.add_argument(
"--activation-dropout",
"--relu-dropout",
type=float,
metavar="D",
help="dropout probability after activation in FFN.",
)
parser.add_argument(
"--decoder-embed-dim",
type=int,
metavar="N",
help="decoder embedding dimension",
)
parser.add_argument(
"--decoder-ffn-embed-dim",
type=int,
metavar="N",
help="decoder embedding dimension for FFN",
)
parser.add_argument(
"--decoder-layers", type=int, metavar="N", help="num decoder layers"
)
parser.add_argument(
"--decoder-attention-heads",
type=int,
metavar="N",
help="num decoder attention heads",
)
parser.add_argument(
"--adaptive-softmax-cutoff",
metavar="EXPR",
help="comma separated list of adaptive softmax cutoff points. "
"Must be used with adaptive_loss criterion",
)
parser.add_argument(
"--adaptive-softmax-dropout",
type=float,
metavar="D",
help="sets adaptive softmax dropout for the tail projections",
)
parser.add_argument(
"--share-decoder-input-output-embed",
action="store_true",
help="share decoder input and output embeddings",
)
parser.add_argument(
"--no-token-positional-embeddings",
action="store_true",
help="if set, disables positional embeddings (outside self attention)",
)
parser.add_argument(
"--character-embeddings",
action="store_true",
help="if set, uses character embedding convolutions to produce token embeddings",
)
parser.add_argument(
"--character-filters",
type=str,
metavar="LIST",
default="[(1, 64), (2, 128), (3, 192), (4, 256), (5, 256), (6, 256), (7, 256)]",
help="size of character embeddings",
)
parser.add_argument(
"--character-embedding-dim",
type=int,
metavar="N",
default=4,
help="size of character embeddings",
)
parser.add_argument(
"--char-embedder-highway-layers",
type=int,
metavar="N",
default=2,
help="number of highway layers for character token embeddder",
)
parser.add_argument(
"--linear-final-layer",
action="store_true",
help="if set, uses a simple linear layer for the final prediction that combines the "
"forward and backward tower instead of an attentional layer",
)
parser.add_argument(
"--linear-final-layer-bias",
action="store_true",
help="if set, has a bias on the final linear layer",
)
parser.add_argument(
"--no-bias-kv",
action="store_true",
help="if set, pads attn with zero instead of adding a learnable bias kv",
)
parser.add_argument(
"--max-char-len",
type=int,
metavar="N",
default=50,
help="if set and char_inputs, max characters to use per token",
)
# below two arguments are only used during inference / finetuning
parser.add_argument(
"--char-inputs",
action="store_true",
help="if set, model takes character ids as input",
)
parser.add_argument(
"--unmask-curr-state",
action="store_true",
help="if set, there will be no mask for current state",
)
@classmethod
def build_model(cls, args, task):
"""Build a new model instance."""
# make sure all arguments are present in older models
base_bi_lm_architecture(args)
if not hasattr(args, "max_source_positions"):
args.max_source_positions = args.tokens_per_sample
if not getattr(args, "max_target_positions", None):
args.max_target_positions = args.tokens_per_sample
if args.character_embeddings:
embed_tokens = CharacterTokenEmbedder(
task.dictionary,
eval(args.character_filters),
args.character_embedding_dim,
args.decoder_embed_dim,
args.char_embedder_highway_layers,
max_char_len=args.max_char_len,
char_inputs=args.char_inputs,
)
else:
embed_tokens = Embedding(
len(task.dictionary), args.decoder_embed_dim, task.dictionary.pad()
)
logger.info(args)
decoder = BiTransformerDecoder(args, task.output_dictionary, embed_tokens)
return BiTransformerLanguageModel(decoder)
@property
def supported_targets(self):
return {"self", "past", "future"}
def get_layers_by_depth_for_fine_tuning(self):
decoder_layers = self.decoder.get_layers_by_depth_for_fine_tuning()
return [
{"decoder.%s" % name: layer for name, layer in layers.items()}
for layers in decoder_layers
]
class BiTransformerClassificationHead(nn.Module):
def __init__(self, embed_dim, num_classes):
super().__init__()
self.proj = Linear(2 * embed_dim, num_classes)
def forward(self, features, padding_mask=None, **kwargs):
assert features.size(1) >= 2 # B x T x C
# extract endpoints for classification
x = features
if x.size(1) == 2:
x = x.view(x.size(0), -1)
else:
left = x[:, 0, :]
if padding_mask is None:
right = x[:, -1, :]
else:
eos_idx = (~padding_mask).int().sum(dim=1) - 1
eos_idx += (torch.arange(eos_idx.size(0)) * x.size(1)).type_as(eos_idx)
right = x.contiguous().view(-1, x.size(-1))[eos_idx]
x = torch.cat([left, right], dim=1)
return self.proj(x)
class BiTransformerDecoder(FairseqDecoder):
"""Transformer decoder."""
def __init__(self, args, dictionary, embed_tokens, classification_head=None):
super().__init__(dictionary)
self.onnx_trace = False
self.dropout_module = FairseqDropout(
args.dropout, module_name=self.__class__.__name__
)
self.share_input_output_embed = args.share_decoder_input_output_embed
self.embed_dim = embed_tokens.embedding_dim
self.padding_idx = embed_tokens.padding_idx
self.max_target_positions = args.max_target_positions
self.self_target = args.self_target
self.future_target = args.future_target
self.past_target = args.past_target
self.char_inputs = args.char_inputs
self.embed_tokens = embed_tokens
self.embed_scale = math.sqrt(self.embed_dim)
self.embed_positions = (
PositionalEmbedding(
args.max_target_positions,
self.embed_dim,
self.padding_idx,
learned=args.decoder_learned_pos,
)
if not args.no_token_positional_embeddings
else None
)
self.forward_layers = nn.ModuleList(
[
TransformerDecoderLayer(
args,
no_encoder_attn=True,
add_bias_kv=not args.no_bias_kv,
add_zero_attn=args.no_bias_kv,
)
for _ in range(args.decoder_layers)
]
)
self.backward_layers = nn.ModuleList(
[
TransformerDecoderLayer(
args,
no_encoder_attn=True,
add_bias_kv=not args.no_bias_kv,
add_zero_attn=args.no_bias_kv,
)
for _ in range(args.decoder_layers)
]
)
self.full_attn_layer = None
self.full_linear_layer = None
if self.self_target:
if args.linear_final_layer:
self.full_linear_layer = Linear(
self.embed_dim * 2, self.embed_dim, args.linear_final_layer_bias
)
else:
self.full_attn_layer = BidirectionalTransformerDecoderLayer(args)
self.load_softmax = not getattr(args, "remove_head", False)
self.embed_out = None
self.adaptive_softmax = None
self.classification_head = classification_head
if self.load_softmax:
if args.adaptive_softmax_cutoff is not None:
self.adaptive_softmax = AdaptiveSoftmax(
len(dictionary),
args.decoder_embed_dim,
utils.eval_str_list(args.adaptive_softmax_cutoff, type=int),
dropout=args.adaptive_softmax_dropout,
)
elif not self.share_input_output_embed:
self.embed_out = nn.Parameter(
torch.Tensor(len(dictionary), self.embed_dim)
)
nn.init.normal_(self.embed_out, mean=0, std=self.embed_dim ** -0.5)
def prepare_for_onnx_export_(self):
self.onnx_trace = True
def forward(self, src_tokens, **kwargs):
x, extra = self.extract_features(src_tokens, **kwargs)
x = self.output_layer(x)
return x, extra
def extract_features(self, src_tokens, **kwargs):
"""
Similar to *forward* but only return features.
Returns:
tuple:
- the decoder's features of shape `(batch, seq_len, embed_dim)`
- a dictionary of additional data, where 'attn' contains the attention over the final
states (concatenated from forward and backward towers) and 'inner_states' is a list
of internal model states used to compute the predictions (for example to use in ELMO).
The first element is the token embeddings (with the positional embeddings added).
The next n elements are tuples of the hidden states for the forward and backward towers.
The last element is the output of the final full layer on top of the towers and would be
equivalent to the logits if adaptive softmax is used.
NOTE: unlike the logits, the format for all hidden states is T x B x C
"""
# compute padding mask
if self.char_inputs:
# casting to byte for onnx
padding_mask = src_tokens[:, :, 0].eq(CHAR_PAD_IDX).bool()
else:
padding_mask = src_tokens.eq(self.padding_idx).bool()
# embed positions
positional_input = self.padding_idx * padding_mask.long()
positions = (
self.embed_positions(positional_input)
if self.embed_positions is not None
else None
)
# embed tokens and positions
x = self.embed_scale * self.embed_tokens(src_tokens)
if positions is not None:
x += positions
x = self.dropout_module(x)
# B x T x C -> T x B x C
fwd_x = bwd_x = x.transpose(0, 1)
inner_states = [fwd_x]
future_mask = self.buffered_future_mask(fwd_x)
past_mask = self.buffered_past_mask(bwd_x)
if not padding_mask.any():
padding_mask = None
# decoder layers
for fwd, back in zip(self.forward_layers, self.backward_layers):
fwd_x, _, _ = fwd(
fwd_x,
self_attn_mask=future_mask,
self_attn_padding_mask=padding_mask,
)
bwd_x, _, _ = back(
bwd_x,
self_attn_mask=past_mask,
self_attn_padding_mask=padding_mask,
)
inner_states.extend((fwd_x, bwd_x))
if self.self_target:
if self.full_attn_layer is not None:
x, attn = self.full_attn_layer(
fwd_x,
bwd_x,
padding_mask,
)
inner_states.append(x)
elif self.full_linear_layer is not None:
zeros = x.new_zeros(1, fwd_x.size(1), fwd_x.size(2))
fwd_x = torch.cat([zeros, fwd_x[:-1]], dim=0)
bwd_x = torch.cat([bwd_x[1:], zeros], dim=0)
x = torch.cat([fwd_x, bwd_x], dim=-1)
x = self.full_linear_layer(x)
attn = None
inner_states.append(x)
x = [x]
else:
x = []
attn = None
if self.future_target:
x.append(fwd_x)
if self.past_target:
x.append(bwd_x)
# T x B x C -> B x T x C
x = [z.transpose(0, 1) for z in x]
if len(x) == 1:
x = x[0]
return x, {"attn": attn, "inner_states": inner_states}
def output_layer(self, features, **kwargs):
"""Project features to the vocabulary size."""
if self.classification_head:
return self.classification_head(features, **kwargs)
x = features
if not isinstance(x, list):
x = [x]
if self.adaptive_softmax is None:
# project back to size of vocabulary
if self.share_input_output_embed and hasattr(self.embed_tokens, "weight"):
x = [F.linear(x, self.embed_tokens.weight) for x in x]
elif self.embed_out is not None:
x = [F.linear(x, self.embed_out) for x in x]
if len(x) == 1:
x = x[0]
return x
def buffered_future_mask(self, tensor):
dim = tensor.size(0)
if self.onnx_trace:
a = torch._dim_arange(tensor, 0).unsqueeze(0).repeat(dim, 1)
b = torch._dim_arange(tensor, 0).unsqueeze(1).repeat(1, dim)
future_mask = a > b
future_mask_neg_inf = torch.where(
future_mask, torch.Tensor([float("-Inf")]), torch.Tensor([0])
).type_as(tensor)
return future_mask_neg_inf
if (
not hasattr(self, "_future_mask")
or self._future_mask is None
or self._future_mask.device != tensor.device
):
self._future_mask = torch.triu(
utils.fill_with_neg_inf(tensor.new(dim, dim)), 1
)
if self._future_mask.size(0) < dim:
self._future_mask = torch.triu(
utils.fill_with_neg_inf(self._future_mask.resize_(dim, dim)), 1
)
return self._future_mask[:dim, :dim]
def buffered_past_mask(self, tensor):
dim = tensor.size(0)
if self.onnx_trace:
a = torch._dim_arange(tensor, 0).unsqueeze(0).repeat(dim, 1)
b = torch._dim_arange(tensor, 0).unsqueeze(1).repeat(1, dim)
past_mask = a < b
past_mask_neg_inf = torch.where(
past_mask, torch.Tensor([float("-Inf")]), torch.Tensor([0])
).type_as(tensor)
return past_mask_neg_inf
if (
not hasattr(self, "_past_mask")
or self._past_mask is None
or self._past_mask.device != tensor.device
):
self._past_mask = torch.tril(
utils.fill_with_neg_inf(tensor.new(dim, dim)), -1
)
if self._past_mask.size(0) < dim:
self._past_mask = torch.tril(
utils.fill_with_neg_inf(self._past_mask.resize_(dim, dim)), -1
)
return self._past_mask[:dim, :dim]
def max_positions(self):
"""Maximum output length supported by the decoder."""
if self.embed_positions is None:
return self.max_target_positions
return min(self.max_target_positions, self.embed_positions.max_positions)
def upgrade_state_dict_named(self, state_dict, name):
if isinstance(self.embed_positions, SinusoidalPositionalEmbedding):
state_dict[name + ".embed_positions._float_tensor"] = torch.FloatTensor(1)
if not self.load_softmax:
for k in list(state_dict.keys()):
if k.startswith(name + ".adaptive_softmax.") or k.startswith(
name + ".embed_out"
):
del state_dict[k]
return state_dict
def get_layers_by_depth_for_fine_tuning(self):
"""
Returns a list of module dictionaries, where each module dictionary
(name -> module) contains modules at the same "depth" in the model.
The first module dictionary corresponds to the lowest level layer (embeddings)
and the last corresponds to the highest level layer.
"""
emb_layers = self._module_dict(("embed_tokens", "embed_positions"))
fwd_bwd_layers = [
{"forward_layers.%d" % i: fwd, "backward_layers.%d" % i: bwd}
for i, (fwd, bwd) in enumerate(
zip(self.forward_layers, self.backward_layers)
)
]
top_layers = self._module_dict(("full_attn_layer", "full_linear_layer"))
return [emb_layers] + fwd_bwd_layers + [top_layers]
def _module_dict(self, attributes):
return {
attr: getattr(self, attr)
for attr in attributes
if getattr(self, attr, None) is not None
}
class BidirectionalTransformerDecoderLayer(nn.Module):
"""Decoder layer block."""
def __init__(self, args):
super().__init__()
self.embed_dim = args.decoder_embed_dim
self.self_attn = BidirectionalMultiheadSelfAttention(
self.embed_dim,
args.decoder_attention_heads,
dropout=args.attention_dropout,
mask_curr_state=not args.unmask_curr_state,
)
self.dropout_module = FairseqDropout(
args.dropout, module_name=self.__class__.__name__
)
self.activation_fn = utils.get_activation_fn(
activation=getattr(args, "activation_fn", "relu")
)
activation_dropout_p = getattr(args, "activation_dropout", 0)
if activation_dropout_p == 0:
# for backwards compatibility with models that use args.relu_dropout
activation_dropout_p = getattr(args, "relu_dropout", 0)
self.activation_dropout_module = FairseqDropout(
float(activation_dropout_p), module_name=self.__class__.__name__
)
self.normalize_before = args.decoder_normalize_before
self.fwd_layer_norm = LayerNorm(self.embed_dim, export=args.char_inputs)
self.bwd_layer_norm = LayerNorm(self.embed_dim, export=args.char_inputs)
self.fc1 = Linear(self.embed_dim, args.decoder_ffn_embed_dim)
self.fc2 = Linear(args.decoder_ffn_embed_dim, self.embed_dim)
self.final_layer_norm = LayerNorm(self.embed_dim, export=args.char_inputs)
def forward(self, fwd_x, bwd_x, key_padding_mask):
fwd_x = self.maybe_layer_norm(self.fwd_layer_norm, fwd_x, before=True)
bwd_x = self.maybe_layer_norm(self.bwd_layer_norm, bwd_x, before=True)
x, attn = self.self_attn(
fwd_x=fwd_x,
bwd_x=bwd_x,
key_padding_mask=key_padding_mask,
)
x = self.dropout_module(x)
x = self.maybe_layer_norm(self.fwd_layer_norm, x, after=True)
residual = x
x = self.maybe_layer_norm(self.final_layer_norm, x, before=True)
x = self.activation_fn(self.fc1(x))
x = self.activation_dropout_module(x)
x = self.fc2(x)
x = self.dropout_module(x)
x = residual + x
x = self.maybe_layer_norm(self.final_layer_norm, x, after=True)
return x, attn
def maybe_layer_norm(self, layer_norm, x, before=False, after=False):
assert before ^ after
if after ^ self.normalize_before:
return layer_norm(x)
else:
return x
@register_model_architecture("bi_transformer_lm", "bi_transformer_lm")
def base_bi_lm_architecture(args):
# by default bi-directional language models predict the current token (self)
args.self_target = getattr(
args, "self_target", not getattr(args, "exclude_self_target", False)
)
args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 512)
args.decoder_ffn_embed_dim = getattr(args, "decoder_ffn_embed_dim", 2048)
args.decoder_layers = getattr(args, "decoder_layers", 6)
args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 8)
args.decoder_learned_pos = getattr(args, "decoder_learned_pos", False)
args.adaptive_softmax_cutoff = getattr(args, "adaptive_softmax_cutoff", None)
args.adaptive_softmax_dropout = getattr(
args, "adaptive_softmax_dropout", args.dropout
)
args.share_decoder_input_output_embed = getattr(
args, "share_decoder_input_output_embed", False
)
args.no_token_positional_embeddings = getattr(
args, "no_token_positional_embeddings", False
)
args.character_embeddings = getattr(args, "character_embeddings", False)
args.character_filters = getattr(
args,
"character_filters",
"[(1, 64), (2, 128), (3, 192), (4, 256), (5, 256), (6, 256), (7, 256)]",
)
args.character_embedding_dim = getattr(args, "character_embedding_dim", 128)
args.char_embedder_highway_layers = getattr(args, "char_embedder_highway_layers", 2)
args.linear_final_layer = getattr(args, "linear_final_layer", False)
args.linear_final_layer_bias = getattr(args, "linear_final_layer_bias", False)
args.future_target = getattr(args, "future_target", False)
args.past_target = getattr(args, "past_target", False)
args.no_bias_kv = getattr(args, "no_bias_kv", False)
args.char_inputs = getattr(args, "char_inputs", False)
args.unmask_curr_state = getattr(args, "unmask_curr_state", False)
args.max_char_len = getattr(args, "max_char_len", 50)
# otherwise model training is unstable
args.decoder_normalize_before = True
@register_model_architecture("bi_transformer_lm", "bi_transformer_lm_big")
def bi_transformer_lm_big(args):
args.self_target = True
args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 1024)
args.decoder_ffn_embed_dim = getattr(args, "decoder_ffn_embed_dim", 4096)
args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 16)
base_bi_lm_architecture(args)
@register_model_architecture("bi_transformer_lm", "bi_transformer_lm_bpe_large")
def bi_transformer_lm_bpe_large(args):
args.self_target = True
# TODO support query formulation
args.decoder_layers = getattr(args, "decoder_layers", 12)
args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 1024)
args.decoder_ffn_embed_dim = getattr(args, "decoder_ffn_embed_dim", 4096)
args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 32)
base_bi_lm_architecture(args)
@register_model_architecture("bi_transformer_lm", "bi_transformer_lm_big_non_cloze")
def bi_transformer_lm_big_non_cloze(args):
bi_transformer_lm_big(args)
args.self_target = False
args.future_target = True
args.past_target = True
@register_model_architecture("bi_transformer_lm", "bi_transformer_lm_huge")
def bi_transformer_lm_huge(args):
args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 2048) # 2.6B params
args.decoder_ffn_embed_dim = getattr(args, "decoder_ffn_embed_dim", 8192)
args.decoder_layers = getattr(args, "decoder_layers", 24)
args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 32)
args.activation_fn = getattr(args, "activation_fn", "gelu_fast")
base_bi_lm_architecture(args)
@register_model_architecture("bi_transformer_lm", "bi_transformer_lm_huge_relu")
def bi_transformer_lm_huge_relu(args):
args.activation_fn = getattr(args, "activation_fn", "relu")
bi_transformer_lm_huge(args)
|
bart_ls-main
|
fairseq-py/fairseq/models/fb_bidirectional_transformer.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from typing import Dict, List, Optional, Tuple
import torch.nn as nn
from fairseq import utils
from torch import Tensor
class FairseqDecoder(nn.Module):
"""Base class for decoders."""
def __init__(self, dictionary):
super().__init__()
self.dictionary = dictionary
self.onnx_trace = False
self.adaptive_softmax = None
def forward(self, prev_output_tokens, encoder_out=None, **kwargs):
"""
Args:
prev_output_tokens (LongTensor): shifted output tokens of shape
`(batch, tgt_len)`, for teacher forcing
encoder_out (dict, optional): output from the encoder, used for
encoder-side attention
Returns:
tuple:
- the decoder's output of shape `(batch, tgt_len, vocab)`
- a dictionary with any model-specific outputs
"""
x, extra = self.extract_features(
prev_output_tokens, encoder_out=encoder_out, **kwargs
)
x = self.output_layer(x)
return x, extra
def extract_features(self, prev_output_tokens, encoder_out=None, **kwargs):
"""
Returns:
tuple:
- the decoder's features of shape `(batch, tgt_len, embed_dim)`
- a dictionary with any model-specific outputs
"""
raise NotImplementedError
def output_layer(self, features, **kwargs):
"""
Project features to the default output size, e.g., vocabulary size.
Args:
features (Tensor): features returned by *extract_features*.
"""
raise NotImplementedError
def get_normalized_probs(
self,
net_output: Tuple[Tensor, Optional[Dict[str, List[Optional[Tensor]]]]],
log_probs: bool,
sample: Optional[Dict[str, Tensor]] = None,
):
"""Get normalized probabilities (or log probs) from a net's output."""
return self.get_normalized_probs_scriptable(net_output, log_probs, sample)
# TorchScript doesn't support super() method so that the scriptable Subclass
# can't access the base class model in Torchscript.
# Current workaround is to add a helper function with different name and
# call the helper function from scriptable Subclass.
def get_normalized_probs_scriptable(
self,
net_output: Tuple[Tensor, Optional[Dict[str, List[Optional[Tensor]]]]],
log_probs: bool,
sample: Optional[Dict[str, Tensor]] = None,
):
"""Get normalized probabilities (or log probs) from a net's output."""
if hasattr(self, "adaptive_softmax") and self.adaptive_softmax is not None:
if sample is not None:
assert "target" in sample
target = sample["target"]
else:
target = None
out = self.adaptive_softmax.get_log_prob(net_output[0], target=target)
return out.exp_() if not log_probs else out
logits = net_output[0]
if log_probs:
return utils.log_softmax(logits, dim=-1, onnx_trace=self.onnx_trace)
else:
return utils.softmax(logits, dim=-1, onnx_trace=self.onnx_trace)
def max_positions(self):
"""Maximum input length supported by the decoder."""
return 1e6 # an arbitrary large number
def upgrade_state_dict_named(self, state_dict, name):
"""Upgrade old state dicts to work with newer code."""
return state_dict
def prepare_for_onnx_export_(self):
self.onnx_trace = True
|
bart_ls-main
|
fairseq-py/fairseq/models/fairseq_decoder.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""isort:skip_file"""
import argparse
import importlib
import os
from contextlib import ExitStack
from fairseq.dataclass import FairseqDataclass
from fairseq.dataclass.utils import merge_with_parent
from hydra.core.config_store import ConfigStore
from omegaconf import open_dict, OmegaConf
from .composite_encoder import CompositeEncoder
from .distributed_fairseq_model import DistributedFairseqModel
from .fairseq_decoder import FairseqDecoder
from .fairseq_encoder import FairseqEncoder
from .fairseq_incremental_decoder import FairseqIncrementalDecoder
from .fairseq_model import (
BaseFairseqModel,
FairseqEncoderDecoderModel,
FairseqEncoderModel,
FairseqLanguageModel,
FairseqModel,
FairseqMultiModel,
)
MODEL_REGISTRY = {}
MODEL_DATACLASS_REGISTRY = {}
ARCH_MODEL_REGISTRY = {}
ARCH_MODEL_NAME_REGISTRY = {}
ARCH_MODEL_INV_REGISTRY = {}
ARCH_CONFIG_REGISTRY = {}
__all__ = [
"BaseFairseqModel",
"CompositeEncoder",
"DistributedFairseqModel",
"FairseqDecoder",
"FairseqEncoder",
"FairseqEncoderDecoderModel",
"FairseqEncoderModel",
"FairseqIncrementalDecoder",
"FairseqLanguageModel",
"FairseqModel",
"FairseqMultiModel",
]
def build_model(cfg: FairseqDataclass, task):
model = None
model_type = getattr(cfg, "_name", None) or getattr(cfg, "arch", None)
if not model_type and len(cfg) == 1:
# this is hit if config object is nested in directory that is named after model type
model_type = next(iter(cfg))
if model_type in MODEL_DATACLASS_REGISTRY:
cfg = cfg[model_type]
else:
raise Exception(
"Could not infer model type from directory. Please add _name field to indicate model type. "
"Available models: "
+ str(MODEL_DATACLASS_REGISTRY.keys())
+ " Requested model type: "
+ model_type
)
if model_type in ARCH_MODEL_REGISTRY:
# case 1: legacy models
model = ARCH_MODEL_REGISTRY[model_type]
elif model_type in MODEL_DATACLASS_REGISTRY:
# case 2: config-driven models
model = MODEL_REGISTRY[model_type]
if model_type in MODEL_DATACLASS_REGISTRY:
# set defaults from dataclass. note that arch name and model name can be the same
dc = MODEL_DATACLASS_REGISTRY[model_type]
if isinstance(cfg, argparse.Namespace):
cfg = dc.from_namespace(cfg)
else:
cfg = merge_with_parent(dc(), cfg)
else:
if model_type in ARCH_CONFIG_REGISTRY:
with open_dict(cfg) if OmegaConf.is_config(cfg) else ExitStack():
# this calls the different "arch" functions (like base_architecture()) that you indicate
# if you specify --arch on the command line. this is only applicable to the old argparse based models
# hydra models should expose different architectures via different config files
# it will modify the cfg object and default parameters according to the arch
ARCH_CONFIG_REGISTRY[model_type](cfg)
assert model is not None, (
f"Could not infer model type from {cfg}. "
"Available models: {}".format(
MODEL_DATACLASS_REGISTRY.keys()
)
+ f" Requested model type: {model_type}"
)
return model.build_model(cfg, task)
def register_model(name, dataclass=None):
"""
New model types can be added to fairseq with the :func:`register_model`
function decorator.
For example::
@register_model('lstm')
class LSTM(FairseqEncoderDecoderModel):
(...)
.. note:: All models must implement the :class:`BaseFairseqModel` interface.
Typically you will extend :class:`FairseqEncoderDecoderModel` for
sequence-to-sequence tasks or :class:`FairseqLanguageModel` for
language modeling tasks.
Args:
name (str): the name of the model
"""
def register_model_cls(cls):
if name in MODEL_REGISTRY:
raise ValueError("Cannot register duplicate model ({})".format(name))
if not issubclass(cls, BaseFairseqModel):
raise ValueError(
"Model ({}: {}) must extend BaseFairseqModel".format(name, cls.__name__)
)
MODEL_REGISTRY[name] = cls
if dataclass is not None and not issubclass(dataclass, FairseqDataclass):
raise ValueError(
"Dataclass {} must extend FairseqDataclass".format(dataclass)
)
cls.__dataclass = dataclass
if dataclass is not None:
MODEL_DATACLASS_REGISTRY[name] = dataclass
cs = ConfigStore.instance()
node = dataclass()
node._name = name
cs.store(name=name, group="model", node=node, provider="fairseq")
@register_model_architecture(name, name)
def noop(_):
pass
return cls
return register_model_cls
def register_model_architecture(model_name, arch_name):
"""
New model architectures can be added to fairseq with the
:func:`register_model_architecture` function decorator. After registration,
model architectures can be selected with the ``--arch`` command-line
argument.
For example::
@register_model_architecture('lstm', 'lstm_luong_wmt_en_de')
def lstm_luong_wmt_en_de(cfg):
args.encoder_embed_dim = getattr(cfg.model, 'encoder_embed_dim', 1000)
(...)
The decorated function should take a single argument *cfg*, which is a
:class:`omegaconf.DictConfig`. The decorated function should modify these
arguments in-place to match the desired architecture.
Args:
model_name (str): the name of the Model (Model must already be
registered)
arch_name (str): the name of the model architecture (``--arch``)
"""
def register_model_arch_fn(fn):
if model_name not in MODEL_REGISTRY:
raise ValueError(
"Cannot register model architecture for unknown model type ({})".format(
model_name
)
)
if arch_name in ARCH_MODEL_REGISTRY:
raise ValueError(
"Cannot register duplicate model architecture ({})".format(arch_name)
)
if not callable(fn):
raise ValueError(
"Model architecture must be callable ({})".format(arch_name)
)
ARCH_MODEL_REGISTRY[arch_name] = MODEL_REGISTRY[model_name]
ARCH_MODEL_NAME_REGISTRY[arch_name] = model_name
ARCH_MODEL_INV_REGISTRY.setdefault(model_name, []).append(arch_name)
ARCH_CONFIG_REGISTRY[arch_name] = fn
return fn
return register_model_arch_fn
def import_models(models_dir, namespace):
for file in os.listdir(models_dir):
path = os.path.join(models_dir, file)
if (
not file.startswith("_")
and not file.startswith(".")
and (file.endswith(".py") or os.path.isdir(path))
):
model_name = file[: file.find(".py")] if file.endswith(".py") else file
importlib.import_module(namespace + "." + model_name)
# extra `model_parser` for sphinx
if model_name in MODEL_REGISTRY:
parser = argparse.ArgumentParser(add_help=False)
group_archs = parser.add_argument_group("Named architectures")
group_archs.add_argument(
"--arch", choices=ARCH_MODEL_INV_REGISTRY[model_name]
)
group_args = parser.add_argument_group(
"Additional command-line arguments"
)
MODEL_REGISTRY[model_name].add_args(group_args)
globals()[model_name + "_parser"] = parser
# automatically import any Python files in the models/ directory
models_dir = os.path.dirname(__file__)
import_models(models_dir, "fairseq.models")
|
bart_ls-main
|
fairseq-py/fairseq/models/__init__.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import os
from typing import Any, Dict
from fairseq import checkpoint_utils
from fairseq.data.legacy.masked_lm_dictionary import MaskedLMDictionary
from fairseq.models import register_model, register_model_architecture
from fairseq.models.transformer import (
TransformerDecoder,
TransformerEncoder,
TransformerModel,
base_architecture as transformer_base_architecture,
)
@register_model("transformer_from_pretrained_xlm")
class TransformerFromPretrainedXLMModel(TransformerModel):
@staticmethod
def add_args(parser):
"""Add model-specific arguments to the parser."""
TransformerModel.add_args(parser)
parser.add_argument(
"--pretrained-xlm-checkpoint",
type=str,
metavar="STR",
help="XLM model to use for initializing transformer encoder and/or decoder",
)
parser.add_argument(
"--init-encoder-only",
action="store_true",
help="if set, don't load the XLM weights and embeddings into decoder",
)
parser.add_argument(
"--init-decoder-only",
action="store_true",
help="if set, don't load the XLM weights and embeddings into encoder",
)
@classmethod
def build_model(self, args, task, cls_dictionary=MaskedLMDictionary):
assert hasattr(args, "pretrained_xlm_checkpoint"), (
"You must specify a path for --pretrained-xlm-checkpoint to use "
"--arch transformer_from_pretrained_xlm"
)
assert isinstance(task.source_dictionary, cls_dictionary) and isinstance(
task.target_dictionary, cls_dictionary
), (
"You should use a MaskedLMDictionary when using --arch "
"transformer_from_pretrained_xlm because the pretrained XLM model "
"was trained using data binarized with MaskedLMDictionary. "
"For translation, you may want to use --task "
"translation_from_pretrained_xlm"
)
assert not (
getattr(args, "init_encoder_only", False)
and getattr(args, "init_decoder_only", False)
), "Only one of --init-encoder-only and --init-decoder-only can be set."
return super().build_model(args, task)
@classmethod
def build_encoder(cls, args, src_dict, embed_tokens):
return TransformerEncoderFromPretrainedXLM(args, src_dict, embed_tokens)
@classmethod
def build_decoder(cls, args, tgt_dict, embed_tokens):
return TransformerDecoderFromPretrainedXLM(args, tgt_dict, embed_tokens)
def upgrade_state_dict_with_xlm_weights(
state_dict: Dict[str, Any], pretrained_xlm_checkpoint: str
) -> Dict[str, Any]:
"""
Load XLM weights into a Transformer encoder or decoder model.
Args:
state_dict: state dict for either TransformerEncoder or
TransformerDecoder
pretrained_xlm_checkpoint: checkpoint to load XLM weights from
Raises:
AssertionError: If architecture (num layers, attention heads, etc.)
does not match between the current Transformer encoder or
decoder and the pretrained_xlm_checkpoint
"""
if not os.path.exists(pretrained_xlm_checkpoint):
raise IOError("Model file not found: {}".format(pretrained_xlm_checkpoint))
state = checkpoint_utils.load_checkpoint_to_cpu(pretrained_xlm_checkpoint)
xlm_state_dict = state["model"]
for key in xlm_state_dict.keys():
for search_key in ["embed_tokens", "embed_positions", "layers"]:
if search_key in key:
subkey = key[key.find(search_key) :]
assert subkey in state_dict, (
"{} Transformer encoder / decoder "
"state_dict does not contain {}. Cannot "
"load {} from pretrained XLM checkpoint "
"{} into Transformer.".format(
str(state_dict.keys()), subkey, key, pretrained_xlm_checkpoint
)
)
state_dict[subkey] = xlm_state_dict[key]
return state_dict
class TransformerEncoderFromPretrainedXLM(TransformerEncoder):
def __init__(self, args, dictionary, embed_tokens):
super().__init__(args, dictionary, embed_tokens)
if getattr(args, "init_decoder_only", False):
# Don't load XLM weights for encoder if --init-decoder-only
return
assert hasattr(args, "pretrained_xlm_checkpoint"), (
"--pretrained-xlm-checkpoint must be specified to load Transformer "
"encoder from pretrained XLM"
)
xlm_loaded_state_dict = upgrade_state_dict_with_xlm_weights(
state_dict=self.state_dict(),
pretrained_xlm_checkpoint=args.pretrained_xlm_checkpoint,
)
self.load_state_dict(xlm_loaded_state_dict, strict=True)
class TransformerDecoderFromPretrainedXLM(TransformerDecoder):
def __init__(self, args, dictionary, embed_tokens, no_encoder_attn=False):
super().__init__(args, dictionary, embed_tokens, no_encoder_attn)
if getattr(args, "init_encoder_only", False):
# Don't load XLM weights for decoder if --init-encoder-only
return
assert hasattr(args, "pretrained_xlm_checkpoint"), (
"--pretrained-xlm-checkpoint must be specified to load Transformer "
"decoder from pretrained XLM"
)
xlm_loaded_state_dict = upgrade_state_dict_with_xlm_weights(
state_dict=self.state_dict(),
pretrained_xlm_checkpoint=args.pretrained_xlm_checkpoint,
)
self.load_state_dict(xlm_loaded_state_dict, strict=True)
@register_model_architecture(
"transformer_from_pretrained_xlm", "transformer_from_pretrained_xlm"
)
def base_architecture(args):
transformer_base_architecture(args)
|
bart_ls-main
|
fairseq-py/fairseq/models/transformer_from_pretrained_xlm.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import logging
import os
import signal
import threading
import torch
import torch.nn as nn
from torch.nn.parallel import DistributedDataParallel
from fairseq.distributed import (
DistributedTimeoutWrapper,
LegacyDistributedDataParallel,
ModuleProxyWrapper,
TPUDistributedDataParallel,
)
logger = logging.getLogger(__name__)
_GOSSIP_DISABLED = False
try:
import gossip
except ImportError:
_GOSSIP_DISABLED = True
def DistributedFairseqModel(args, model, process_group, device):
"""
Wrap a *model* to support distributed data parallel training.
This is similar to the built-in DistributedDataParallel, but allows
additional configuration of the DistributedDataParallel class to
use, and also provides easier access to the wrapped model by
forwarding requests for missing attributes to the wrapped model.
Args:
args (argparse.Namespace): fairseq args
model (BaseFairseqModel): model to wrap
process_group: the c10d process group to be used for distributed data
parallel all-reduction.
device: device to move model to
"""
assert isinstance(model, nn.Module)
if args.tpu:
wrapped_model = TPUDistributedDataParallel(
module=model.to(device),
process_group=process_group,
)
# forward missing getattr and state_dict/load_state_dict to orig model
wrapped_model = ModuleProxyWrapper(wrapped_model)
elif args.ddp_backend in {"c10d", "pytorch_ddp"}:
wrapped_model = DistributedDataParallel(
module=model.to(device),
device_ids=[args.device_id],
output_device=args.device_id,
broadcast_buffers=args.broadcast_buffers,
bucket_cap_mb=args.bucket_cap_mb,
process_group=process_group,
find_unused_parameters=args.find_unused_parameters,
gradient_as_bucket_view=args.gradient_as_bucket_view,
)
if args.ddp_comm_hook == "fp16":
logger.info("enable fp16 communication hook in DDP")
try:
from torch.distributed.algorithms.ddp_comm_hooks import (
register_ddp_comm_hook,
DDPCommHookType,
)
except:
logger.error(
"Could not import from torch.distributed.algorithms.ddp_comm_hooks; you may need to update your pytorch version"
)
raise
register_ddp_comm_hook(DDPCommHookType.FP16_COMPRESS, wrapped_model)
# forward missing getattr and state_dict/load_state_dict to orig model
wrapped_model = ModuleProxyWrapper(wrapped_model)
elif args.ddp_backend in {"no_c10d", "legacy_ddp"}:
wrapped_model = LegacyDistributedDataParallel(
module=model.to(device),
buffer_size=2 ** 28,
process_group=process_group,
)
# forward missing getattr and state_dict/load_state_dict to orig model
wrapped_model = ModuleProxyWrapper(wrapped_model)
elif args.ddp_backend == "slow_mo":
if _GOSSIP_DISABLED:
raise ImportError(
"Cannot find gossip library. Please install from: "
"github.com/facebookresearch/stochastic_gradient_push"
)
# The values of slowmo_momentum below were obtained by tuning on the
# En-De 16 dataset by training the transformer_wmt_en_de_large model
if args.slowmo_momentum is None:
if args.distributed_world_size <= 16:
args.slowmo_momentum = 0.0
elif args.distributed_world_size <= 32:
args.slowmo_momentum = 0.2
elif args.distributed_world_size <= 64:
args.slowmo_momentum = 0.5
else:
args.slowmo_momentum = 0.6
wrapped_model = gossip.GossipDataParallel(
module=model.to(device),
device_ids=[args.device_id],
output_device=args.device_id,
broadcast_buffers=args.broadcast_buffers,
nprocs_per_node=args.nprocs_per_node,
slowmo_momentum=args.slowmo_momentum,
localsgd=(args.slowmo_algorithm == "LocalSGD"),
localsgd_frequency=args.localsgd_frequency,
)
# forward missing getattr and state_dict/load_state_dict to orig model
wrapped_model = ModuleProxyWrapper(wrapped_model)
elif args.ddp_backend == "fully_sharded":
try:
from fairscale.nn.data_parallel import FullyShardedDataParallel as FSDP
except ImportError:
raise ImportError(
"Cannot find FullyShardedDataParallel. "
"Please install fairscale with: pip install fairscale"
)
assert isinstance(model, FSDP), "expected model to already be wrapped in FSDP"
wrapped_model = model
if args.memory_efficient_fp16:
wrapped_model = wrapped_model.half()
if not args.cpu_offload:
wrapped_model = wrapped_model.to(device=device)
else:
raise ValueError("Unknown --ddp-backend: " + args.ddp_backend)
# kill hung distributed jobs after a timeout
if getattr(args, "heartbeat_timeout", -1) > 0:
wrapped_model = DistributedTimeoutWrapper(
wrapped_model, timeout=getattr(args, "heartbeat_timeout", -1)
)
return wrapped_model
|
bart_ls-main
|
fairseq-py/fairseq/models/distributed_fairseq_model.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from dataclasses import dataclass, field
from typing import Optional
from fairseq import options, utils
from fairseq.dataclass import ChoiceEnum, FairseqDataclass
from fairseq.models import (
FairseqLanguageModel,
register_model,
register_model_architecture,
)
from fairseq.models.transformer import (
DEFAULT_MIN_PARAMS_TO_WRAP, Embedding, TransformerDecoder
)
from fairseq.modules import AdaptiveInput, CharacterTokenEmbedder
from fairseq.utils import safe_getattr, safe_hasattr
from omegaconf import II
DEFAULT_MAX_TARGET_POSITIONS = 1024
@dataclass
class TransformerLanguageModelConfig(FairseqDataclass):
activation_fn: ChoiceEnum(utils.get_available_activation_fns()) = field(
default="relu", metadata={"help": "activation function to use"}
)
dropout: float = field(default=0.1, metadata={"help": "dropout probability"})
attention_dropout: float = field(
default=0.0, metadata={"help": "dropout probability for attention weights"}
)
activation_dropout: float = field(
default=0.0, metadata={"help": "dropout probability after activation in FFN."}
)
relu_dropout: float = field(
default=0.0, metadata={"help": "dropout probability after activation in FFN."}
)
decoder_embed_dim: int = field(
default=512, metadata={"help": "decoder embedding dimension"}
)
decoder_output_dim: int = field(
default=512, metadata={"help": "decoder output dimension"}
)
decoder_input_dim: int = field(
default=512, metadata={"help": "decoder input dimension"}
)
decoder_ffn_embed_dim: int = field(
default=2048, metadata={"help": "decoder embedding dimension for FFN"}
)
decoder_layers: int = field(default=6, metadata={"help": "num decoder layers"})
decoder_attention_heads: int = field(
default=8, metadata={"help": "num decoder attention heads"}
)
decoder_normalize_before: bool = field(
default=False, metadata={"help": "apply layernorm before each decoder block"}
)
no_decoder_final_norm: bool = field(
default=False,
metadata={"help": "don't add an extra layernorm after the last decoder block"},
)
adaptive_softmax_cutoff: Optional[str] = field(
default=None,
metadata={
"help": "comma separated list of adaptive softmax cutoff points. "
"Must be used with adaptive_loss criterion"
},
)
adaptive_softmax_dropout: float = field(
default=0,
metadata={"help": "sets adaptive softmax dropout for the tail projections"},
)
adaptive_softmax_factor: float = field(
default=4, metadata={"help": "adaptive input factor"}
)
no_token_positional_embeddings: bool = field(
default=False,
metadata={
"help": "if set, disables positional embeddings (outside self attention)"
},
)
share_decoder_input_output_embed: bool = field(
default=False, metadata={"help": "share decoder input and output embeddings"}
)
character_embeddings: bool = field(
default=False,
metadata={
"help": "if set, uses character embedding convolutions to produce token embeddings"
},
)
character_filters: str = field(
default="[(1, 64), (2, 128), (3, 192), (4, 256), (5, 256), (6, 256), (7, 256)]",
metadata={"help": "size of character embeddings"},
)
character_embedding_dim: int = field(
default=4, metadata={"help": "size of character embeddings"}
)
char_embedder_highway_layers: int = field(
default=2,
metadata={"help": "number of highway layers for character token embeddder"},
)
adaptive_input: bool = field(
default=False, metadata={"help": "if set, uses adaptive input"}
)
adaptive_input_factor: float = field(
default=4, metadata={"help": "adaptive input factor"}
)
adaptive_input_cutoff: Optional[str] = field(
default=None,
metadata={"help": "comma separated list of adaptive input cutoff points."},
)
tie_adaptive_weights: bool = field(
default=False,
metadata={
"help": "if set, ties the weights of adaptive softmax and adaptive input"
},
)
tie_adaptive_proj: bool = field(
default=False,
metadata={
"help": "if set, ties the projection weights of adaptive softmax and adaptive input"
},
)
decoder_learned_pos: bool = field(
default=False,
metadata={"help": "use learned positional embeddings in the decoder"},
)
layernorm_embedding: bool = field(
default=False, metadata={"help": "add layernorm to embedding"}
)
no_scale_embedding: bool = field(
default=False, metadata={"help": "if True, dont scale embeddings"}
)
checkpoint_activations: bool = field(
default=False, metadata={"help": "checkpoint activations at each layer"}
)
offload_activations: bool = field(
default=False,
metadata={"help": "move checkpointed activations to CPU after they are used."},
)
# config for "Reducing Transformer Depth on Demand with Structured Dropout" (Fan et al., 2019)
decoder_layerdrop: float = field(
default=0.0, metadata={"help": "LayerDrop probability for decoder"}
)
decoder_layers_to_keep: Optional[str] = field(
default=None,
metadata={
"help": "which layers to *keep* when pruning as a comma-separated list"
},
)
# config for Training with Quantization Noise for Extreme Model Compression ({Fan*, Stock*} et al., 2020)
quant_noise_pq: float = field(
default=0.0,
metadata={"help": "iterative PQ quantization noise at training time"},
)
quant_noise_pq_block_size: int = field(
default=8,
metadata={"help": "block size of quantization noise at training time"},
)
quant_noise_scalar: float = field(
default=0.0,
metadata={
"help": "scalar quantization noise and scalar quantization at training time"
},
)
# config for Fully Sharded Data Parallel (FSDP) training
min_params_to_wrap: int = field(
default=DEFAULT_MIN_PARAMS_TO_WRAP,
metadata={
"help": (
"minimum number of params for a layer to be wrapped with FSDP() when "
"training with --ddp-backend=fully_sharded. Smaller values will "
"improve memory efficiency, but may make torch.distributed "
"communication less efficient due to smaller input sizes. This option "
"is set to 0 (i.e., always wrap) when --checkpoint-activations or "
"--offload-activations are passed."
)
}
)
# config for "BASE Layers: Simplifying Training of Large, Sparse Models"
base_layers: Optional[int] = field(
default=0, metadata={"help": "number of BASE layers in total"}
)
base_sublayers: Optional[int] = field(
default=1, metadata={"help": "number of sublayers in each BASE layer"}
)
base_shuffle: Optional[int] = field(
default=1, metadata={"help": "shuffle tokens between workers before computing assignment"}
)
# options from other parts of the config
add_bos_token: bool = II("task.add_bos_token")
tokens_per_sample: int = II("task.tokens_per_sample")
max_target_positions: Optional[int] = II("task.max_target_positions")
tpu: bool = II("common.tpu")
@register_model("transformer_lm", dataclass=TransformerLanguageModelConfig)
class TransformerLanguageModel(FairseqLanguageModel):
@classmethod
def hub_models(cls):
def moses_fastbpe(path):
return {"path": path, "tokenizer": "moses", "bpe": "fastbpe"}
def spm(path):
return {"path": path, "tokenizer": "space", "bpe": "sentencepiece"}
return {
"transformer_lm.gbw.adaptive_huge": "https://dl.fbaipublicfiles.com/fairseq/models/lm/adaptive_lm_gbw_huge.tar.bz2",
"transformer_lm.wiki103.adaptive": "https://dl.fbaipublicfiles.com/fairseq/models/lm/adaptive_lm_wiki103.v2.tar.bz2",
"transformer_lm.wmt19.en": moses_fastbpe(
"https://dl.fbaipublicfiles.com/fairseq/models/lm/wmt19.en.tar.bz2"
),
"transformer_lm.wmt19.de": moses_fastbpe(
"https://dl.fbaipublicfiles.com/fairseq/models/lm/wmt19.de.tar.bz2"
),
"transformer_lm.wmt19.ru": moses_fastbpe(
"https://dl.fbaipublicfiles.com/fairseq/models/lm/wmt19.ru.tar.bz2"
),
"transformer_lm.wmt20.en": spm(
"https://dl.fbaipublicfiles.com/fairseq/models/lm/wmt20.en.tar.gz"
),
"transformer_lm.wmt20.ta": spm(
"https://dl.fbaipublicfiles.com/fairseq/models/lm/wmt20.ta.tar.gz"
),
"transformer_lm.wmt20.iu.news": spm(
"https://dl.fbaipublicfiles.com/fairseq/models/lm/wmt20.iu.news.tar.gz"
),
"transformer_lm.wmt20.iu.nh": spm(
"https://dl.fbaipublicfiles.com/fairseq/models/lm/wmt20.iu.nh.tar.gz"
),
}
def __init__(self, decoder):
super().__init__(decoder)
@classmethod
def build_model(cls, args, task):
"""Build a new model instance."""
if args.decoder_layers_to_keep:
args.decoder_layers = len(args.decoder_layers_to_keep.split(","))
if safe_getattr(args, "max_target_positions", None) is None:
args.max_target_positions = safe_getattr(
args, "tokens_per_sample", DEFAULT_MAX_TARGET_POSITIONS
)
if args.character_embeddings:
embed_tokens = CharacterTokenEmbedder(
task.source_dictionary,
eval(args.character_filters),
args.character_embedding_dim,
args.decoder_embed_dim,
args.char_embedder_highway_layers,
)
elif args.adaptive_input:
embed_tokens = AdaptiveInput(
len(task.source_dictionary),
task.source_dictionary.pad(),
args.decoder_input_dim,
args.adaptive_input_factor,
args.decoder_embed_dim,
options.eval_str_list(args.adaptive_input_cutoff, type=int),
args.quant_noise_pq,
args.quant_noise_pq_block_size,
)
else:
embed_tokens = cls.build_embedding(
args, task.source_dictionary, args.decoder_input_dim
)
if args.tie_adaptive_weights:
assert args.adaptive_input
assert args.adaptive_input_factor == args.adaptive_softmax_factor
assert (
args.adaptive_softmax_cutoff == args.adaptive_input_cutoff
), "{} != {}".format(
args.adaptive_softmax_cutoff, args.adaptive_input_cutoff
)
assert args.decoder_input_dim == args.decoder_output_dim
decoder = TransformerDecoder(
args, task.target_dictionary, embed_tokens, no_encoder_attn=True
)
return cls(decoder)
@classmethod
def build_embedding(cls, args, dictionary, embed_dim, path=None):
embed_tokens = Embedding(len(dictionary), embed_dim, dictionary.pad())
return embed_tokens
def base_lm_architecture(args):
# backward compatibility for older model checkpoints
if safe_hasattr(args, "no_tie_adaptive_proj"):
# previous models defined --no-tie-adaptive-proj, so use the existence of
# that option to determine if this is an "old" model checkpoint
args.no_decoder_final_norm = True # old models always set this to True
if args.no_tie_adaptive_proj is False:
args.tie_adaptive_proj = True
if safe_hasattr(args, "decoder_final_norm"):
args.no_decoder_final_norm = not args.decoder_final_norm
args.dropout = safe_getattr(args, "dropout", 0.1)
args.attention_dropout = safe_getattr(args, "attention_dropout", 0.0)
args.decoder_embed_dim = safe_getattr(args, "decoder_embed_dim", 512)
args.decoder_ffn_embed_dim = safe_getattr(args, "decoder_ffn_embed_dim", 2048)
args.decoder_layers = safe_getattr(args, "decoder_layers", 6)
args.decoder_attention_heads = safe_getattr(args, "decoder_attention_heads", 8)
args.adaptive_softmax_cutoff = safe_getattr(args, "adaptive_softmax_cutoff", None)
args.adaptive_softmax_dropout = safe_getattr(args, "adaptive_softmax_dropout", 0)
args.adaptive_softmax_factor = safe_getattr(args, "adaptive_softmax_factor", 4)
args.decoder_learned_pos = safe_getattr(args, "decoder_learned_pos", False)
args.activation_fn = safe_getattr(args, "activation_fn", "relu")
args.decoder_layerdrop = safe_getattr(args, "decoder_layerdrop", 0)
args.decoder_layers_to_keep = safe_getattr(args, "decoder_layers_to_keep", None)
args.quant_noise_pq = safe_getattr(args, "quant_noise_pq", 0)
args.quant_noise_pq_block_size = safe_getattr(args, "quant_noise_pq_block_size", 8)
args.quant_noise_scalar = safe_getattr(args, "quant_noise_scalar", 0)
args.base_layers = safe_getattr(args, "base_layers", 0)
args.base_sublayers = safe_getattr(args, "base_sublayers", 1)
args.base_shuffle = safe_getattr(args, "base_shuffle", False)
args.add_bos_token = safe_getattr(args, "add_bos_token", False)
args.no_token_positional_embeddings = safe_getattr(
args, "no_token_positional_embeddings", False
)
args.share_decoder_input_output_embed = safe_getattr(
args, "share_decoder_input_output_embed", False
)
args.character_embeddings = safe_getattr(args, "character_embeddings", False)
args.decoder_output_dim = safe_getattr(
args, "decoder_output_dim", args.decoder_embed_dim
)
args.decoder_input_dim = safe_getattr(args, "decoder_input_dim", args.decoder_embed_dim)
# Model training is not stable without this
args.decoder_normalize_before = True
args.no_decoder_final_norm = safe_getattr(args, "no_decoder_final_norm", False)
args.adaptive_input = safe_getattr(args, "adaptive_input", False)
args.adaptive_input_factor = safe_getattr(args, "adaptive_input_factor", 4)
args.adaptive_input_cutoff = safe_getattr(args, "adaptive_input_cutoff", None)
args.tie_adaptive_weights = safe_getattr(args, "tie_adaptive_weights", False)
args.tie_adaptive_proj = safe_getattr(args, "tie_adaptive_proj", False)
args.no_scale_embedding = safe_getattr(args, "no_scale_embedding", False)
args.layernorm_embedding = safe_getattr(args, "layernorm_embedding", False)
args.checkpoint_activations = safe_getattr(args, "checkpoint_activations", False)
args.offload_activations = safe_getattr(args, "offload_activations", False)
if args.offload_activations:
args.checkpoint_activations = True
@register_model_architecture("transformer_lm", "transformer_lm_big")
def transformer_lm_big(args):
args.decoder_layers = safe_getattr(args, "decoder_layers", 12)
args.decoder_embed_dim = safe_getattr(args, "decoder_embed_dim", 1024)
args.decoder_ffn_embed_dim = safe_getattr(args, "decoder_ffn_embed_dim", 4096)
args.decoder_attention_heads = safe_getattr(args, "decoder_attention_heads", 16)
base_lm_architecture(args)
@register_model_architecture("transformer_lm", "transformer_lm_wiki103")
@register_model_architecture("transformer_lm", "transformer_lm_baevski_wiki103")
def transformer_lm_baevski_wiki103(args):
args.decoder_layers = safe_getattr(args, "decoder_layers", 16)
args.decoder_attention_heads = safe_getattr(args, "decoder_attention_heads", 8)
args.dropout = safe_getattr(args, "dropout", 0.3)
args.adaptive_input = safe_getattr(args, "adaptive_input", True)
args.tie_adaptive_weights = safe_getattr(args, "tie_adaptive_weights", True)
args.adaptive_input_cutoff = safe_getattr(args, "adaptive_input_cutoff", "20000,60000")
args.adaptive_softmax_cutoff = safe_getattr(
args, "adaptive_softmax_cutoff", "20000,60000"
)
args.adaptive_softmax_dropout = safe_getattr(args, "adaptive_softmax_dropout", 0.2)
args.attention_dropout = safe_getattr(args, "attention_dropout", 0.1)
args.activation_dropout = safe_getattr(args, "activation_dropout", 0.1)
args.no_decoder_final_norm = safe_getattr(args, "no_decoder_final_norm", True)
args.tie_adaptive_proj = safe_getattr(args, "tie_adaptive_proj", True)
transformer_lm_big(args)
@register_model_architecture("transformer_lm", "transformer_lm_gbw")
@register_model_architecture("transformer_lm", "transformer_lm_baevski_gbw")
def transformer_lm_baevski_gbw(args):
args.decoder_embed_dim = safe_getattr(args, "decoder_embed_dim", 512)
args.dropout = safe_getattr(args, "dropout", 0.1)
args.attention_dropout = safe_getattr(args, "attention_dropout", 0.1)
args.no_decoder_final_norm = safe_getattr(args, "no_decoder_final_norm", True)
transformer_lm_big(args)
@register_model_architecture("transformer_lm", "transformer_lm_gpt")
def transformer_lm_gpt(args):
args.decoder_embed_dim = safe_getattr(args, "decoder_embed_dim", 768)
args.decoder_ffn_embed_dim = safe_getattr(args, "decoder_ffn_embed_dim", 3072)
args.decoder_layers = safe_getattr(args, "decoder_layers", 12)
args.decoder_attention_heads = safe_getattr(args, "decoder_attention_heads", 12)
args.dropout = safe_getattr(args, "dropout", 0.1)
args.attention_dropout = safe_getattr(args, "attention_dropout", 0.1)
args.activation_fn = safe_getattr(args, "activation_fn", "gelu")
base_lm_architecture(args)
@register_model_architecture("transformer_lm", "transformer_lm_gpt2_small")
def transformer_lm_gpt2_small(args):
args.decoder_embed_dim = safe_getattr(args, "decoder_embed_dim", 1024)
args.decoder_ffn_embed_dim = safe_getattr(args, "decoder_ffn_embed_dim", 4096)
args.decoder_layers = safe_getattr(args, "decoder_layers", 24)
args.decoder_attention_heads = safe_getattr(args, "decoder_attention_heads", 16)
args.dropout = safe_getattr(args, "dropout", 0.1)
args.attention_dropout = safe_getattr(args, "attention_dropout", 0.1)
args.activation_fn = safe_getattr(args, "activation_fn", "gelu")
base_lm_architecture(args)
@register_model_architecture("transformer_lm", "transformer_lm_gpt2_tiny")
def transformer_lm_gpt2_tiny(args):
args.decoder_embed_dim = safe_getattr(args, "decoder_embed_dim", 64)
args.decoder_ffn_embed_dim = safe_getattr(args, "decoder_ffn_embed_dim", 64)
args.decoder_layers = safe_getattr(args, "decoder_layers", 2)
args.decoder_attention_heads = safe_getattr(args, "decoder_attention_heads", 1)
args.dropout = safe_getattr(args, "dropout", 0.1)
args.attention_dropout = safe_getattr(args, "attention_dropout", 0.1)
args.activation_fn = safe_getattr(args, "activation_fn", "gelu")
base_lm_architecture(args)
@register_model_architecture("transformer_lm", "transformer_lm_gpt2_medium")
def transformer_lm_gpt2_medium(args):
args.decoder_embed_dim = safe_getattr(args, "decoder_embed_dim", 1280)
args.decoder_ffn_embed_dim = safe_getattr(args, "decoder_ffn_embed_dim", 5120)
args.decoder_layers = safe_getattr(args, "decoder_layers", 36)
args.decoder_attention_heads = safe_getattr(args, "decoder_attention_heads", 20)
args.dropout = safe_getattr(args, "dropout", 0.1)
args.attention_dropout = safe_getattr(args, "attention_dropout", 0.1)
args.activation_fn = safe_getattr(args, "activation_fn", "gelu")
base_lm_architecture(args)
@register_model_architecture("transformer_lm", "transformer_lm_gpt2_big")
def transformer_lm_gpt2_big(args):
args.decoder_embed_dim = safe_getattr(args, "decoder_embed_dim", 1600)
args.decoder_ffn_embed_dim = safe_getattr(args, "decoder_ffn_embed_dim", 6400)
args.decoder_layers = safe_getattr(args, "decoder_layers", 48)
args.decoder_attention_heads = safe_getattr(args, "decoder_attention_heads", 25)
args.dropout = safe_getattr(args, "dropout", 0.1)
args.attention_dropout = safe_getattr(args, "attention_dropout", 0.1)
args.activation_fn = safe_getattr(args, "activation_fn", "gelu")
base_lm_architecture(args)
def base_gpt3_architecture(args):
args.decoder_input_dim = args.decoder_embed_dim
args.decoder_output_dim = args.decoder_embed_dim
args.decoder_ffn_embed_dim = safe_getattr(args, "decoder_ffn_embed_dim", args.decoder_embed_dim * 4)
# GPT-3 used learned positional embeddings, rather than sinusoidal
args.decoder_learned_pos = safe_getattr(args, "decoder_learned_pos", True)
args.dropout = safe_getattr(args, "dropout", 0.0)
args.attention_dropout = safe_getattr(args, "attention_dropout", 0.0)
args.activation_fn = safe_getattr(args, "activation_fn", "gelu")
args.share_decoder_input_output_embed = True
base_lm_architecture(args)
@register_model_architecture("transformer_lm", "transformer_lm_gpt3_small")
def transformer_lm_gpt3_small(args):
# 125M params
args.decoder_layers = safe_getattr(args, "decoder_layers", 12)
args.decoder_embed_dim = safe_getattr(args, "decoder_embed_dim", 768)
args.decoder_attention_heads = safe_getattr(args, "decoder_attention_heads", 12)
base_gpt3_architecture(args)
@register_model_architecture("transformer_lm", "transformer_lm_gpt3_medium")
def transformer_lm_gpt3_medium(args):
# 350M params
args.decoder_layers = safe_getattr(args, "decoder_layers", 24)
args.decoder_embed_dim = safe_getattr(args, "decoder_embed_dim", 1024)
args.decoder_attention_heads = safe_getattr(args, "decoder_attention_heads", 16)
base_gpt3_architecture(args)
@register_model_architecture("transformer_lm", "transformer_lm_gpt3_large")
def transformer_lm_gpt3_large(args):
# 760M params
args.decoder_layers = safe_getattr(args, "decoder_layers", 24)
args.decoder_embed_dim = safe_getattr(args, "decoder_embed_dim", 1536)
args.decoder_attention_heads = safe_getattr(args, "decoder_attention_heads", 16)
base_gpt3_architecture(args)
@register_model_architecture("transformer_lm", "transformer_lm_gpt3_xl")
def transformer_lm_gpt3_xl(args):
# 1.3B params
args.decoder_layers = safe_getattr(args, "decoder_layers", 24)
args.decoder_embed_dim = safe_getattr(args, "decoder_embed_dim", 2048)
args.decoder_attention_heads = safe_getattr(args, "decoder_attention_heads", 32)
base_gpt3_architecture(args)
@register_model_architecture("transformer_lm", "transformer_lm_gpt3_2_7")
def transformer_lm_gpt3_2_7(args):
# 2.7B params
args.decoder_layers = safe_getattr(args, "decoder_layers", 32)
args.decoder_embed_dim = safe_getattr(args, "decoder_embed_dim", 2560)
args.decoder_attention_heads = safe_getattr(args, "decoder_attention_heads", 32)
base_gpt3_architecture(args)
@register_model_architecture("transformer_lm", "transformer_lm_gpt3_6_7")
def transformer_lm_gpt3_6_7(args):
# 6.7B params
args.decoder_layers = safe_getattr(args, "decoder_layers", 32)
args.decoder_embed_dim = safe_getattr(args, "decoder_embed_dim", 4096)
args.decoder_attention_heads = safe_getattr(args, "decoder_attention_heads", 32)
base_gpt3_architecture(args)
@register_model_architecture("transformer_lm", "transformer_lm_gpt3_13")
def transformer_lm_gpt3_13(args):
# 13B params
args.decoder_layers = safe_getattr(args, "decoder_layers", 40)
args.decoder_embed_dim = safe_getattr(args, "decoder_embed_dim", 5120)
args.decoder_attention_heads = safe_getattr(args, "decoder_attention_heads", 40)
base_gpt3_architecture(args)
@register_model_architecture("transformer_lm", "transformer_lm_gpt3_175")
def transformer_lm_gpt3_175(args):
# 175B params
args.decoder_layers = safe_getattr(args, "decoder_layers", 96)
args.decoder_embed_dim = safe_getattr(args, "decoder_embed_dim", 12288)
args.decoder_attention_heads = safe_getattr(args, "decoder_attention_heads", 96)
base_gpt3_architecture(args)
|
bart_ls-main
|
fairseq-py/fairseq/models/transformer_lm.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import utils
from fairseq.models import (
FairseqEncoder,
FairseqEncoderDecoderModel,
FairseqIncrementalDecoder,
register_model,
register_model_architecture,
)
from fairseq.modules import (
AdaptiveSoftmax,
BeamableMM,
FairseqDropout,
GradMultiply,
LearnedPositionalEmbedding,
LinearizedConvolution,
)
@register_model("fconv")
class FConvModel(FairseqEncoderDecoderModel):
"""
A fully convolutional model, i.e. a convolutional encoder and a
convolutional decoder, as described in `"Convolutional Sequence to Sequence
Learning" (Gehring et al., 2017) <https://arxiv.org/abs/1705.03122>`_.
Args:
encoder (FConvEncoder): the encoder
decoder (FConvDecoder): the decoder
The Convolutional model provides the following named architectures and
command-line arguments:
.. argparse::
:ref: fairseq.models.fconv_parser
:prog:
"""
@classmethod
def hub_models(cls):
def moses_subword(path):
return {
"path": path,
"tokenizer": "moses",
"bpe": "subword_nmt",
}
return {
"conv.wmt14.en-fr": moses_subword(
"https://dl.fbaipublicfiles.com/fairseq/models/wmt14.v2.en-fr.fconv-py.tar.bz2"
),
"conv.wmt14.en-de": moses_subword(
"https://dl.fbaipublicfiles.com/fairseq/models/wmt14.en-de.fconv-py.tar.bz2"
),
"conv.wmt17.en-de": moses_subword(
"https://dl.fbaipublicfiles.com/fairseq/models/wmt17.v2.en-de.fconv-py.tar.bz2"
),
}
def __init__(self, encoder, decoder):
super().__init__(encoder, decoder)
self.encoder.num_attention_layers = sum(
layer is not None for layer in decoder.attention
)
@staticmethod
def add_args(parser):
"""Add model-specific arguments to the parser."""
# fmt: off
parser.add_argument('--dropout', type=float, metavar='D',
help='dropout probability')
parser.add_argument('--encoder-embed-dim', type=int, metavar='N',
help='encoder embedding dimension')
parser.add_argument('--encoder-embed-path', type=str, metavar='STR',
help='path to pre-trained encoder embedding')
parser.add_argument('--encoder-layers', type=str, metavar='EXPR',
help='encoder layers [(dim, kernel_size), ...]')
parser.add_argument('--decoder-embed-dim', type=int, metavar='N',
help='decoder embedding dimension')
parser.add_argument('--decoder-embed-path', type=str, metavar='STR',
help='path to pre-trained decoder embedding')
parser.add_argument('--decoder-layers', type=str, metavar='EXPR',
help='decoder layers [(dim, kernel_size), ...]')
parser.add_argument('--decoder-out-embed-dim', type=int, metavar='N',
help='decoder output embedding dimension')
parser.add_argument('--decoder-attention', type=str, metavar='EXPR',
help='decoder attention [True, ...]')
parser.add_argument('--share-input-output-embed', action='store_true',
help='share input and output embeddings (requires'
' --decoder-out-embed-dim and --decoder-embed-dim'
' to be equal)')
# fmt: on
@classmethod
def build_model(cls, args, task):
"""Build a new model instance."""
# make sure that all args are properly defaulted (in case there are any new ones)
base_architecture(args)
encoder_embed_dict = None
if args.encoder_embed_path:
encoder_embed_dict = utils.parse_embedding(args.encoder_embed_path)
utils.print_embed_overlap(encoder_embed_dict, task.source_dictionary)
decoder_embed_dict = None
if args.decoder_embed_path:
decoder_embed_dict = utils.parse_embedding(args.decoder_embed_path)
utils.print_embed_overlap(decoder_embed_dict, task.target_dictionary)
encoder = FConvEncoder(
dictionary=task.source_dictionary,
embed_dim=args.encoder_embed_dim,
embed_dict=encoder_embed_dict,
convolutions=eval(args.encoder_layers),
dropout=args.dropout,
max_positions=args.max_source_positions,
)
decoder = FConvDecoder(
dictionary=task.target_dictionary,
embed_dim=args.decoder_embed_dim,
embed_dict=decoder_embed_dict,
convolutions=eval(args.decoder_layers),
out_embed_dim=args.decoder_out_embed_dim,
attention=eval(args.decoder_attention),
dropout=args.dropout,
max_positions=args.max_target_positions,
share_embed=args.share_input_output_embed,
)
return FConvModel(encoder, decoder)
class FConvEncoder(FairseqEncoder):
"""
Convolutional encoder consisting of `len(convolutions)` layers.
Args:
dictionary (~fairseq.data.Dictionary): encoding dictionary
embed_dim (int, optional): embedding dimension
embed_dict (str, optional): filename from which to load pre-trained
embeddings
max_positions (int, optional): maximum supported input sequence length
convolutions (list, optional): the convolutional layer structure. Each
list item `i` corresponds to convolutional layer `i`. Layers are
given as ``(out_channels, kernel_width, [residual])``. Residual
connections are added between layers when ``residual=1`` (which is
the default behavior).
dropout (float, optional): dropout to be applied before each conv layer
"""
def __init__(
self,
dictionary,
embed_dim=512,
embed_dict=None,
max_positions=1024,
convolutions=((512, 3),) * 20,
dropout=0.1,
):
super().__init__(dictionary)
self.dropout_module = FairseqDropout(
dropout, module_name=self.__class__.__name__
)
self.num_attention_layers = None
num_embeddings = len(dictionary)
self.padding_idx = dictionary.pad()
self.embed_tokens = Embedding(num_embeddings, embed_dim, self.padding_idx)
if embed_dict:
self.embed_tokens = utils.load_embedding(
embed_dict, self.dictionary, self.embed_tokens
)
self.embed_positions = PositionalEmbedding(
max_positions,
embed_dim,
self.padding_idx,
)
convolutions = extend_conv_spec(convolutions)
in_channels = convolutions[0][0]
self.fc1 = Linear(embed_dim, in_channels, dropout=dropout)
self.projections = nn.ModuleList()
self.convolutions = nn.ModuleList()
self.residuals = []
layer_in_channels = [in_channels]
for _, (out_channels, kernel_size, residual) in enumerate(convolutions):
if residual == 0:
residual_dim = out_channels
else:
residual_dim = layer_in_channels[-residual]
self.projections.append(
Linear(residual_dim, out_channels)
if residual_dim != out_channels
else None
)
if kernel_size % 2 == 1:
padding = kernel_size // 2
else:
padding = 0
self.convolutions.append(
ConvTBC(
in_channels,
out_channels * 2,
kernel_size,
dropout=dropout,
padding=padding,
)
)
self.residuals.append(residual)
in_channels = out_channels
layer_in_channels.append(out_channels)
self.fc2 = Linear(in_channels, embed_dim)
def forward(self, src_tokens, src_lengths):
"""
Args:
src_tokens (LongTensor): tokens in the source language of shape
`(batch, src_len)`
src_lengths (LongTensor): lengths of each source sentence of shape
`(batch)`
Returns:
dict:
- **encoder_out** (tuple): a tuple with two elements, where the
first element is the last encoder layer's output and the
second element is the same quantity summed with the input
embedding (used for attention). The shape of both tensors is
`(batch, src_len, embed_dim)`.
- **encoder_padding_mask** (ByteTensor): the positions of
padding elements of shape `(batch, src_len)`
"""
# embed tokens and positions
x = self.embed_tokens(src_tokens) + self.embed_positions(src_tokens)
x = self.dropout_module(x)
input_embedding = x
# project to size of convolution
x = self.fc1(x)
# used to mask padding in input
encoder_padding_mask = src_tokens.eq(self.padding_idx).t() # -> T x B
if not encoder_padding_mask.any():
encoder_padding_mask = None
# B x T x C -> T x B x C
x = x.transpose(0, 1)
residuals = [x]
# temporal convolutions
for proj, conv, res_layer in zip(
self.projections, self.convolutions, self.residuals
):
if res_layer > 0:
residual = residuals[-res_layer]
residual = residual if proj is None else proj(residual)
else:
residual = None
if encoder_padding_mask is not None:
x = x.masked_fill(encoder_padding_mask.unsqueeze(-1), 0)
x = self.dropout_module(x)
if conv.kernel_size[0] % 2 == 1:
# padding is implicit in the conv
x = conv(x)
else:
padding_l = (conv.kernel_size[0] - 1) // 2
padding_r = conv.kernel_size[0] // 2
x = F.pad(x, (0, 0, 0, 0, padding_l, padding_r))
x = conv(x)
x = F.glu(x, dim=2)
if residual is not None:
x = (x + residual) * math.sqrt(0.5)
residuals.append(x)
# T x B x C -> B x T x C
x = x.transpose(1, 0)
# project back to size of embedding
x = self.fc2(x)
if encoder_padding_mask is not None:
encoder_padding_mask = encoder_padding_mask.t() # -> B x T
x = x.masked_fill(encoder_padding_mask.unsqueeze(-1), 0)
# scale gradients (this only affects backward, not forward)
x = GradMultiply.apply(x, 1.0 / (2.0 * self.num_attention_layers))
# add output to input embedding for attention
y = (x + input_embedding) * math.sqrt(0.5)
return {
"encoder_out": (x, y),
"encoder_padding_mask": encoder_padding_mask, # B x T
}
def reorder_encoder_out(self, encoder_out, new_order):
if encoder_out["encoder_out"] is not None:
encoder_out["encoder_out"] = (
encoder_out["encoder_out"][0].index_select(0, new_order),
encoder_out["encoder_out"][1].index_select(0, new_order),
)
if encoder_out["encoder_padding_mask"] is not None:
encoder_out["encoder_padding_mask"] = encoder_out[
"encoder_padding_mask"
].index_select(0, new_order)
return encoder_out
def max_positions(self):
"""Maximum input length supported by the encoder."""
return self.embed_positions.max_positions
class AttentionLayer(nn.Module):
def __init__(self, conv_channels, embed_dim, bmm=None):
super().__init__()
# projects from output of convolution to embedding dimension
self.in_projection = Linear(conv_channels, embed_dim)
# projects from embedding dimension to convolution size
self.out_projection = Linear(embed_dim, conv_channels)
self.bmm = bmm if bmm is not None else torch.bmm
def forward(self, x, target_embedding, encoder_out, encoder_padding_mask):
residual = x
# attention
x = (self.in_projection(x) + target_embedding) * math.sqrt(0.5)
x = self.bmm(x, encoder_out[0])
# don't attend over padding
if encoder_padding_mask is not None:
x = (
x.float()
.masked_fill(encoder_padding_mask.unsqueeze(1), float("-inf"))
.type_as(x)
) # FP16 support: cast to float and back
# softmax over last dim
sz = x.size()
x = F.softmax(x.view(sz[0] * sz[1], sz[2]), dim=1)
x = x.view(sz)
attn_scores = x
x = self.bmm(x, encoder_out[1])
# scale attention output (respecting potentially different lengths)
s = encoder_out[1].size(1)
if encoder_padding_mask is None:
x = x * (s * math.sqrt(1.0 / s))
else:
s = s - encoder_padding_mask.type_as(x).sum(
dim=1, keepdim=True
) # exclude padding
s = s.unsqueeze(-1)
x = x * (s * s.rsqrt())
# project back
x = (self.out_projection(x) + residual) * math.sqrt(0.5)
return x, attn_scores
def make_generation_fast_(self, beamable_mm_beam_size=None, **kwargs):
"""Replace torch.bmm with BeamableMM."""
if beamable_mm_beam_size is not None:
del self.bmm
self.add_module("bmm", BeamableMM(beamable_mm_beam_size))
class FConvDecoder(FairseqIncrementalDecoder):
"""Convolutional decoder"""
def __init__(
self,
dictionary,
embed_dim=512,
embed_dict=None,
out_embed_dim=256,
max_positions=1024,
convolutions=((512, 3),) * 20,
attention=True,
dropout=0.1,
share_embed=False,
positional_embeddings=True,
adaptive_softmax_cutoff=None,
adaptive_softmax_dropout=0.0,
):
super().__init__(dictionary)
self.register_buffer("version", torch.Tensor([2]))
self.dropout_module = FairseqDropout(
dropout, module_name=self.__class__.__name__
)
self.need_attn = True
convolutions = extend_conv_spec(convolutions)
in_channels = convolutions[0][0]
if isinstance(attention, bool):
# expand True into [True, True, ...] and do the same with False
attention = [attention] * len(convolutions)
if not isinstance(attention, list) or len(attention) != len(convolutions):
raise ValueError(
"Attention is expected to be a list of booleans of "
"length equal to the number of layers."
)
num_embeddings = len(dictionary)
padding_idx = dictionary.pad()
self.embed_tokens = Embedding(num_embeddings, embed_dim, padding_idx)
if embed_dict:
self.embed_tokens = utils.load_embedding(
embed_dict, self.dictionary, self.embed_tokens
)
self.embed_positions = (
PositionalEmbedding(
max_positions,
embed_dim,
padding_idx,
)
if positional_embeddings
else None
)
self.fc1 = Linear(embed_dim, in_channels, dropout=dropout)
self.projections = nn.ModuleList()
self.convolutions = nn.ModuleList()
self.attention = nn.ModuleList()
self.residuals = []
layer_in_channels = [in_channels]
for i, (out_channels, kernel_size, residual) in enumerate(convolutions):
if residual == 0:
residual_dim = out_channels
else:
residual_dim = layer_in_channels[-residual]
self.projections.append(
Linear(residual_dim, out_channels)
if residual_dim != out_channels
else None
)
self.convolutions.append(
LinearizedConv1d(
in_channels,
out_channels * 2,
kernel_size,
padding=(kernel_size - 1),
dropout=dropout,
)
)
self.attention.append(
AttentionLayer(out_channels, embed_dim) if attention[i] else None
)
self.residuals.append(residual)
in_channels = out_channels
layer_in_channels.append(out_channels)
self.adaptive_softmax = None
self.fc2 = self.fc3 = None
if adaptive_softmax_cutoff is not None:
assert not share_embed
self.adaptive_softmax = AdaptiveSoftmax(
num_embeddings,
in_channels,
adaptive_softmax_cutoff,
dropout=adaptive_softmax_dropout,
)
else:
self.fc2 = Linear(in_channels, out_embed_dim)
if share_embed:
assert out_embed_dim == embed_dim, (
"Shared embed weights implies same dimensions "
" out_embed_dim={} vs embed_dim={}".format(out_embed_dim, embed_dim)
)
self.fc3 = nn.Linear(out_embed_dim, num_embeddings)
self.fc3.weight = self.embed_tokens.weight
else:
self.fc3 = Linear(out_embed_dim, num_embeddings, dropout=dropout)
def forward(
self, prev_output_tokens, encoder_out=None, incremental_state=None, **unused
):
if encoder_out is not None:
encoder_padding_mask = encoder_out["encoder_padding_mask"]
encoder_out = encoder_out["encoder_out"]
# split and transpose encoder outputs
encoder_a, encoder_b = self._split_encoder_out(
encoder_out, incremental_state
)
if self.embed_positions is not None:
pos_embed = self.embed_positions(prev_output_tokens, incremental_state)
else:
pos_embed = 0
if incremental_state is not None:
prev_output_tokens = prev_output_tokens[:, -1:]
x = self._embed_tokens(prev_output_tokens, incremental_state)
# embed tokens and combine with positional embeddings
x += pos_embed
x = self.dropout_module(x)
target_embedding = x
# project to size of convolution
x = self.fc1(x)
# B x T x C -> T x B x C
x = self._transpose_if_training(x, incremental_state)
# temporal convolutions
avg_attn_scores = None
num_attn_layers = len(self.attention)
residuals = [x]
for proj, conv, attention, res_layer in zip(
self.projections, self.convolutions, self.attention, self.residuals
):
if res_layer > 0:
residual = residuals[-res_layer]
residual = residual if proj is None else proj(residual)
else:
residual = None
x = self.dropout_module(x)
x = conv(x, incremental_state)
x = F.glu(x, dim=2)
# attention
if attention is not None:
x = self._transpose_if_training(x, incremental_state)
x, attn_scores = attention(
x, target_embedding, (encoder_a, encoder_b), encoder_padding_mask
)
if not self.training and self.need_attn:
attn_scores = attn_scores / num_attn_layers
if avg_attn_scores is None:
avg_attn_scores = attn_scores
else:
avg_attn_scores.add_(attn_scores)
x = self._transpose_if_training(x, incremental_state)
# residual
if residual is not None:
x = (x + residual) * math.sqrt(0.5)
residuals.append(x)
# T x B x C -> B x T x C
x = self._transpose_if_training(x, incremental_state)
# project back to size of vocabulary if not using adaptive softmax
if self.fc2 is not None and self.fc3 is not None:
x = self.fc2(x)
x = self.dropout_module(x)
x = self.fc3(x)
return x, avg_attn_scores
def reorder_incremental_state(self, incremental_state, new_order):
super().reorder_incremental_state(incremental_state, new_order)
encoder_out = utils.get_incremental_state(
self, incremental_state, "encoder_out"
)
if encoder_out is not None:
encoder_out = tuple(eo.index_select(0, new_order) for eo in encoder_out)
utils.set_incremental_state(
self, incremental_state, "encoder_out", encoder_out
)
def max_positions(self):
"""Maximum output length supported by the decoder."""
return (
self.embed_positions.max_positions
if self.embed_positions is not None
else float("inf")
)
def upgrade_state_dict(self, state_dict):
if utils.item(state_dict.get("decoder.version", torch.Tensor([1]))[0]) < 2:
# old models use incorrect weight norm dimension
for i, conv in enumerate(self.convolutions):
# reconfigure weight norm
nn.utils.remove_weight_norm(conv)
self.convolutions[i] = nn.utils.weight_norm(conv, dim=0)
state_dict["decoder.version"] = torch.Tensor([1])
return state_dict
def make_generation_fast_(self, need_attn=False, **kwargs):
self.need_attn = need_attn
def _embed_tokens(self, tokens, incremental_state):
if incremental_state is not None:
# keep only the last token for incremental forward pass
tokens = tokens[:, -1:]
return self.embed_tokens(tokens)
def _split_encoder_out(self, encoder_out, incremental_state):
"""Split and transpose encoder outputs.
This is cached when doing incremental inference.
"""
cached_result = utils.get_incremental_state(
self, incremental_state, "encoder_out"
)
if cached_result is not None:
return cached_result
# transpose only once to speed up attention layers
encoder_a, encoder_b = encoder_out
encoder_a = encoder_a.transpose(1, 2).contiguous()
result = (encoder_a, encoder_b)
if incremental_state is not None:
utils.set_incremental_state(self, incremental_state, "encoder_out", result)
return result
def _transpose_if_training(self, x, incremental_state):
if incremental_state is None:
x = x.transpose(0, 1)
return x
def extend_conv_spec(convolutions):
"""
Extends convolutional spec that is a list of tuples of 2 or 3 parameters
(kernel size, dim size and optionally how many layers behind to look for residual)
to default the residual propagation param if it is not specified
"""
extended = []
for spec in convolutions:
if len(spec) == 3:
extended.append(spec)
elif len(spec) == 2:
extended.append(spec + (1,))
else:
raise Exception(
"invalid number of parameters in convolution spec "
+ str(spec)
+ ". expected 2 or 3"
)
return tuple(extended)
def Embedding(num_embeddings, embedding_dim, padding_idx):
m = nn.Embedding(num_embeddings, embedding_dim, padding_idx=padding_idx)
nn.init.normal_(m.weight, 0, 0.1)
nn.init.constant_(m.weight[padding_idx], 0)
return m
def PositionalEmbedding(num_embeddings, embedding_dim, padding_idx):
m = LearnedPositionalEmbedding(num_embeddings, embedding_dim, padding_idx)
nn.init.normal_(m.weight, 0, 0.1)
nn.init.constant_(m.weight[padding_idx], 0)
return m
def Linear(in_features, out_features, dropout=0.0):
"""Weight-normalized Linear layer (input: N x T x C)"""
m = nn.Linear(in_features, out_features)
nn.init.normal_(m.weight, mean=0, std=math.sqrt((1 - dropout) / in_features))
nn.init.constant_(m.bias, 0)
return nn.utils.weight_norm(m)
def LinearizedConv1d(in_channels, out_channels, kernel_size, dropout=0.0, **kwargs):
"""Weight-normalized Conv1d layer optimized for decoding"""
m = LinearizedConvolution(in_channels, out_channels, kernel_size, **kwargs)
std = math.sqrt((4 * (1.0 - dropout)) / (m.kernel_size[0] * in_channels))
nn.init.normal_(m.weight, mean=0, std=std)
nn.init.constant_(m.bias, 0)
return nn.utils.weight_norm(m, dim=2)
def ConvTBC(in_channels, out_channels, kernel_size, dropout=0.0, **kwargs):
"""Weight-normalized Conv1d layer"""
from fairseq.modules import ConvTBC
m = ConvTBC(in_channels, out_channels, kernel_size, **kwargs)
std = math.sqrt((4 * (1.0 - dropout)) / (m.kernel_size[0] * in_channels))
nn.init.normal_(m.weight, mean=0, std=std)
nn.init.constant_(m.bias, 0)
return nn.utils.weight_norm(m, dim=2)
@register_model_architecture("fconv", "fconv")
def base_architecture(args):
args.dropout = getattr(args, "dropout", 0.1)
args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512)
args.encoder_embed_path = getattr(args, "encoder_embed_path", None)
args.encoder_layers = getattr(args, "encoder_layers", "[(512, 3)] * 20")
args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 512)
args.decoder_embed_path = getattr(args, "decoder_embed_path", None)
args.decoder_layers = getattr(args, "decoder_layers", "[(512, 3)] * 20")
args.decoder_out_embed_dim = getattr(args, "decoder_out_embed_dim", 256)
args.decoder_attention = getattr(args, "decoder_attention", "True")
args.share_input_output_embed = getattr(args, "share_input_output_embed", False)
@register_model_architecture("fconv", "fconv_iwslt_de_en")
def fconv_iwslt_de_en(args):
args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 256)
args.encoder_layers = getattr(args, "encoder_layers", "[(256, 3)] * 4")
args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 256)
args.decoder_layers = getattr(args, "decoder_layers", "[(256, 3)] * 3")
args.decoder_out_embed_dim = getattr(args, "decoder_out_embed_dim", 256)
base_architecture(args)
@register_model_architecture("fconv", "fconv_wmt_en_ro")
def fconv_wmt_en_ro(args):
args.decoder_out_embed_dim = getattr(args, "decoder_out_embed_dim", 512)
base_architecture(args)
@register_model_architecture("fconv", "fconv_wmt_en_de")
def fconv_wmt_en_de(args):
convs = "[(512, 3)] * 9" # first 9 layers have 512 units
convs += " + [(1024, 3)] * 4" # next 4 layers have 1024 units
convs += " + [(2048, 1)] * 2" # final 2 layers use 1x1 convolutions
args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 768)
args.encoder_layers = getattr(args, "encoder_layers", convs)
args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 768)
args.decoder_layers = getattr(args, "decoder_layers", convs)
args.decoder_out_embed_dim = getattr(args, "decoder_out_embed_dim", 512)
base_architecture(args)
@register_model_architecture("fconv", "fconv_wmt_en_fr")
def fconv_wmt_en_fr(args):
convs = "[(512, 3)] * 6" # first 6 layers have 512 units
convs += " + [(768, 3)] * 4" # next 4 layers have 768 units
convs += " + [(1024, 3)] * 3" # next 3 layers have 1024 units
convs += " + [(2048, 1)] * 1" # next 1 layer uses 1x1 convolutions
convs += " + [(4096, 1)] * 1" # final 1 layer uses 1x1 convolutions
args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 768)
args.encoder_layers = getattr(args, "encoder_layers", convs)
args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 768)
args.decoder_layers = getattr(args, "decoder_layers", convs)
args.decoder_out_embed_dim = getattr(args, "decoder_out_embed_dim", 512)
base_architecture(args)
|
bart_ls-main
|
fairseq-py/fairseq/models/fconv.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from typing import Dict, List, Optional, Tuple
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import utils
from fairseq.models import (
FairseqEncoder,
FairseqEncoderDecoderModel,
FairseqIncrementalDecoder,
register_model,
register_model_architecture,
)
from fairseq.modules import AdaptiveSoftmax, FairseqDropout
from torch import Tensor
DEFAULT_MAX_SOURCE_POSITIONS = 1e5
DEFAULT_MAX_TARGET_POSITIONS = 1e5
@register_model("lstm")
class LSTMModel(FairseqEncoderDecoderModel):
def __init__(self, encoder, decoder):
super().__init__(encoder, decoder)
@staticmethod
def add_args(parser):
"""Add model-specific arguments to the parser."""
# fmt: off
parser.add_argument('--dropout', type=float, metavar='D',
help='dropout probability')
parser.add_argument('--encoder-embed-dim', type=int, metavar='N',
help='encoder embedding dimension')
parser.add_argument('--encoder-embed-path', type=str, metavar='STR',
help='path to pre-trained encoder embedding')
parser.add_argument('--encoder-freeze-embed', action='store_true',
help='freeze encoder embeddings')
parser.add_argument('--encoder-hidden-size', type=int, metavar='N',
help='encoder hidden size')
parser.add_argument('--encoder-layers', type=int, metavar='N',
help='number of encoder layers')
parser.add_argument('--encoder-bidirectional', action='store_true',
help='make all layers of encoder bidirectional')
parser.add_argument('--decoder-embed-dim', type=int, metavar='N',
help='decoder embedding dimension')
parser.add_argument('--decoder-embed-path', type=str, metavar='STR',
help='path to pre-trained decoder embedding')
parser.add_argument('--decoder-freeze-embed', action='store_true',
help='freeze decoder embeddings')
parser.add_argument('--decoder-hidden-size', type=int, metavar='N',
help='decoder hidden size')
parser.add_argument('--decoder-layers', type=int, metavar='N',
help='number of decoder layers')
parser.add_argument('--decoder-out-embed-dim', type=int, metavar='N',
help='decoder output embedding dimension')
parser.add_argument('--decoder-attention', type=str, metavar='BOOL',
help='decoder attention')
parser.add_argument('--adaptive-softmax-cutoff', metavar='EXPR',
help='comma separated list of adaptive softmax cutoff points. '
'Must be used with adaptive_loss criterion')
parser.add_argument('--share-decoder-input-output-embed', default=False,
action='store_true',
help='share decoder input and output embeddings')
parser.add_argument('--share-all-embeddings', default=False, action='store_true',
help='share encoder, decoder and output embeddings'
' (requires shared dictionary and embed dim)')
# Granular dropout settings (if not specified these default to --dropout)
parser.add_argument('--encoder-dropout-in', type=float, metavar='D',
help='dropout probability for encoder input embedding')
parser.add_argument('--encoder-dropout-out', type=float, metavar='D',
help='dropout probability for encoder output')
parser.add_argument('--decoder-dropout-in', type=float, metavar='D',
help='dropout probability for decoder input embedding')
parser.add_argument('--decoder-dropout-out', type=float, metavar='D',
help='dropout probability for decoder output')
# fmt: on
@classmethod
def build_model(cls, args, task):
"""Build a new model instance."""
# make sure that all args are properly defaulted (in case there are any new ones)
base_architecture(args)
if args.encoder_layers != args.decoder_layers:
raise ValueError("--encoder-layers must match --decoder-layers")
max_source_positions = getattr(
args, "max_source_positions", DEFAULT_MAX_SOURCE_POSITIONS
)
max_target_positions = getattr(
args, "max_target_positions", DEFAULT_MAX_TARGET_POSITIONS
)
def load_pretrained_embedding_from_file(embed_path, dictionary, embed_dim):
num_embeddings = len(dictionary)
padding_idx = dictionary.pad()
embed_tokens = Embedding(num_embeddings, embed_dim, padding_idx)
embed_dict = utils.parse_embedding(embed_path)
utils.print_embed_overlap(embed_dict, dictionary)
return utils.load_embedding(embed_dict, dictionary, embed_tokens)
if args.encoder_embed_path:
pretrained_encoder_embed = load_pretrained_embedding_from_file(
args.encoder_embed_path, task.source_dictionary, args.encoder_embed_dim
)
else:
num_embeddings = len(task.source_dictionary)
pretrained_encoder_embed = Embedding(
num_embeddings, args.encoder_embed_dim, task.source_dictionary.pad()
)
if args.share_all_embeddings:
# double check all parameters combinations are valid
if task.source_dictionary != task.target_dictionary:
raise ValueError("--share-all-embeddings requires a joint dictionary")
if args.decoder_embed_path and (
args.decoder_embed_path != args.encoder_embed_path
):
raise ValueError(
"--share-all-embed not compatible with --decoder-embed-path"
)
if args.encoder_embed_dim != args.decoder_embed_dim:
raise ValueError(
"--share-all-embeddings requires --encoder-embed-dim to "
"match --decoder-embed-dim"
)
pretrained_decoder_embed = pretrained_encoder_embed
args.share_decoder_input_output_embed = True
else:
# separate decoder input embeddings
pretrained_decoder_embed = None
if args.decoder_embed_path:
pretrained_decoder_embed = load_pretrained_embedding_from_file(
args.decoder_embed_path,
task.target_dictionary,
args.decoder_embed_dim,
)
# one last double check of parameter combinations
if args.share_decoder_input_output_embed and (
args.decoder_embed_dim != args.decoder_out_embed_dim
):
raise ValueError(
"--share-decoder-input-output-embeddings requires "
"--decoder-embed-dim to match --decoder-out-embed-dim"
)
if args.encoder_freeze_embed:
pretrained_encoder_embed.weight.requires_grad = False
if args.decoder_freeze_embed:
pretrained_decoder_embed.weight.requires_grad = False
encoder = LSTMEncoder(
dictionary=task.source_dictionary,
embed_dim=args.encoder_embed_dim,
hidden_size=args.encoder_hidden_size,
num_layers=args.encoder_layers,
dropout_in=args.encoder_dropout_in,
dropout_out=args.encoder_dropout_out,
bidirectional=args.encoder_bidirectional,
pretrained_embed=pretrained_encoder_embed,
max_source_positions=max_source_positions,
)
decoder = LSTMDecoder(
dictionary=task.target_dictionary,
embed_dim=args.decoder_embed_dim,
hidden_size=args.decoder_hidden_size,
out_embed_dim=args.decoder_out_embed_dim,
num_layers=args.decoder_layers,
dropout_in=args.decoder_dropout_in,
dropout_out=args.decoder_dropout_out,
attention=utils.eval_bool(args.decoder_attention),
encoder_output_units=encoder.output_units,
pretrained_embed=pretrained_decoder_embed,
share_input_output_embed=args.share_decoder_input_output_embed,
adaptive_softmax_cutoff=(
utils.eval_str_list(args.adaptive_softmax_cutoff, type=int)
if args.criterion == "adaptive_loss"
else None
),
max_target_positions=max_target_positions,
residuals=False,
)
return cls(encoder, decoder)
def forward(
self,
src_tokens,
src_lengths,
prev_output_tokens,
incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None,
):
encoder_out = self.encoder(src_tokens, src_lengths=src_lengths)
decoder_out = self.decoder(
prev_output_tokens,
encoder_out=encoder_out,
incremental_state=incremental_state,
)
return decoder_out
class LSTMEncoder(FairseqEncoder):
"""LSTM encoder."""
def __init__(
self,
dictionary,
embed_dim=512,
hidden_size=512,
num_layers=1,
dropout_in=0.1,
dropout_out=0.1,
bidirectional=False,
left_pad=True,
pretrained_embed=None,
padding_idx=None,
max_source_positions=DEFAULT_MAX_SOURCE_POSITIONS,
):
super().__init__(dictionary)
self.num_layers = num_layers
self.dropout_in_module = FairseqDropout(
dropout_in*1.0, module_name=self.__class__.__name__
)
self.dropout_out_module = FairseqDropout(
dropout_out*1.0, module_name=self.__class__.__name__
)
self.bidirectional = bidirectional
self.hidden_size = hidden_size
self.max_source_positions = max_source_positions
num_embeddings = len(dictionary)
self.padding_idx = padding_idx if padding_idx is not None else dictionary.pad()
if pretrained_embed is None:
self.embed_tokens = Embedding(num_embeddings, embed_dim, self.padding_idx)
else:
self.embed_tokens = pretrained_embed
self.lstm = LSTM(
input_size=embed_dim,
hidden_size=hidden_size,
num_layers=num_layers,
dropout=self.dropout_out_module.p if num_layers > 1 else 0.0,
bidirectional=bidirectional,
)
self.left_pad = left_pad
self.output_units = hidden_size
if bidirectional:
self.output_units *= 2
def forward(
self,
src_tokens: Tensor,
src_lengths: Tensor,
enforce_sorted: bool = True,
):
"""
Args:
src_tokens (LongTensor): tokens in the source language of
shape `(batch, src_len)`
src_lengths (LongTensor): lengths of each source sentence of
shape `(batch)`
enforce_sorted (bool, optional): if True, `src_tokens` is
expected to contain sequences sorted by length in a
decreasing order. If False, this condition is not
required. Default: True.
"""
if self.left_pad:
# nn.utils.rnn.pack_padded_sequence requires right-padding;
# convert left-padding to right-padding
src_tokens = utils.convert_padding_direction(
src_tokens,
torch.zeros_like(src_tokens).fill_(self.padding_idx),
left_to_right=True,
)
bsz, seqlen = src_tokens.size()
# embed tokens
x = self.embed_tokens(src_tokens)
x = self.dropout_in_module(x)
# B x T x C -> T x B x C
x = x.transpose(0, 1)
# pack embedded source tokens into a PackedSequence
packed_x = nn.utils.rnn.pack_padded_sequence(
x, src_lengths.cpu(), enforce_sorted=enforce_sorted
)
# apply LSTM
if self.bidirectional:
state_size = 2 * self.num_layers, bsz, self.hidden_size
else:
state_size = self.num_layers, bsz, self.hidden_size
h0 = x.new_zeros(*state_size)
c0 = x.new_zeros(*state_size)
packed_outs, (final_hiddens, final_cells) = self.lstm(packed_x, (h0, c0))
# unpack outputs and apply dropout
x, _ = nn.utils.rnn.pad_packed_sequence(
packed_outs, padding_value=self.padding_idx * 1.0
)
x = self.dropout_out_module(x)
assert list(x.size()) == [seqlen, bsz, self.output_units]
if self.bidirectional:
final_hiddens = self.combine_bidir(final_hiddens, bsz)
final_cells = self.combine_bidir(final_cells, bsz)
encoder_padding_mask = src_tokens.eq(self.padding_idx).t()
return tuple(
(
x, # seq_len x batch x hidden
final_hiddens, # num_layers x batch x num_directions*hidden
final_cells, # num_layers x batch x num_directions*hidden
encoder_padding_mask, # seq_len x batch
)
)
def combine_bidir(self, outs, bsz: int):
out = outs.view(self.num_layers, 2, bsz, -1).transpose(1, 2).contiguous()
return out.view(self.num_layers, bsz, -1)
def reorder_encoder_out(self, encoder_out: Tuple[Tensor, Tensor, Tensor, Tensor], new_order):
return tuple(
(
encoder_out[0].index_select(1, new_order),
encoder_out[1].index_select(1, new_order),
encoder_out[2].index_select(1, new_order),
encoder_out[3].index_select(1, new_order),
)
)
def max_positions(self):
"""Maximum input length supported by the encoder."""
return self.max_source_positions
class AttentionLayer(nn.Module):
def __init__(self, input_embed_dim, source_embed_dim, output_embed_dim, bias=False):
super().__init__()
self.input_proj = Linear(input_embed_dim, source_embed_dim, bias=bias)
self.output_proj = Linear(
input_embed_dim + source_embed_dim, output_embed_dim, bias=bias
)
def forward(self, input, source_hids, encoder_padding_mask):
# input: bsz x input_embed_dim
# source_hids: srclen x bsz x source_embed_dim
# x: bsz x source_embed_dim
x = self.input_proj(input)
# compute attention
attn_scores = (source_hids * x.unsqueeze(0)).sum(dim=2)
# don't attend over padding
if encoder_padding_mask is not None:
attn_scores = (
attn_scores.float()
.masked_fill_(encoder_padding_mask, float("-inf"))
.type_as(attn_scores)
) # FP16 support: cast to float and back
attn_scores = F.softmax(attn_scores, dim=0) # srclen x bsz
# sum weighted sources
x = (attn_scores.unsqueeze(2) * source_hids).sum(dim=0)
x = torch.tanh(self.output_proj(torch.cat((x, input), dim=1)))
return x, attn_scores
class LSTMDecoder(FairseqIncrementalDecoder):
"""LSTM decoder."""
def __init__(
self,
dictionary,
embed_dim=512,
hidden_size=512,
out_embed_dim=512,
num_layers=1,
dropout_in=0.1,
dropout_out=0.1,
attention=True,
encoder_output_units=512,
pretrained_embed=None,
share_input_output_embed=False,
adaptive_softmax_cutoff=None,
max_target_positions=DEFAULT_MAX_TARGET_POSITIONS,
residuals=False,
):
super().__init__(dictionary)
self.dropout_in_module = FairseqDropout(
dropout_in*1.0, module_name=self.__class__.__name__
)
self.dropout_out_module = FairseqDropout(
dropout_out*1.0, module_name=self.__class__.__name__
)
self.hidden_size = hidden_size
self.share_input_output_embed = share_input_output_embed
self.need_attn = True
self.max_target_positions = max_target_positions
self.residuals = residuals
self.num_layers = num_layers
self.adaptive_softmax = None
num_embeddings = len(dictionary)
padding_idx = dictionary.pad()
if pretrained_embed is None:
self.embed_tokens = Embedding(num_embeddings, embed_dim, padding_idx)
else:
self.embed_tokens = pretrained_embed
self.encoder_output_units = encoder_output_units
if encoder_output_units != hidden_size and encoder_output_units != 0:
self.encoder_hidden_proj = Linear(encoder_output_units, hidden_size)
self.encoder_cell_proj = Linear(encoder_output_units, hidden_size)
else:
self.encoder_hidden_proj = self.encoder_cell_proj = None
# disable input feeding if there is no encoder
# input feeding is described in arxiv.org/abs/1508.04025
input_feed_size = 0 if encoder_output_units == 0 else hidden_size
self.layers = nn.ModuleList(
[
LSTMCell(
input_size=input_feed_size + embed_dim
if layer == 0
else hidden_size,
hidden_size=hidden_size,
)
for layer in range(num_layers)
]
)
if attention:
# TODO make bias configurable
self.attention = AttentionLayer(
hidden_size, encoder_output_units, hidden_size, bias=False
)
else:
self.attention = None
if hidden_size != out_embed_dim:
self.additional_fc = Linear(hidden_size, out_embed_dim)
if adaptive_softmax_cutoff is not None:
# setting adaptive_softmax dropout to dropout_out for now but can be redefined
self.adaptive_softmax = AdaptiveSoftmax(
num_embeddings,
hidden_size,
adaptive_softmax_cutoff,
dropout=dropout_out,
)
elif not self.share_input_output_embed:
self.fc_out = Linear(out_embed_dim, num_embeddings, dropout=dropout_out)
def forward(
self,
prev_output_tokens,
encoder_out: Optional[Tuple[Tensor, Tensor, Tensor, Tensor]] = None,
incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None,
src_lengths: Optional[Tensor] = None,
):
x, attn_scores = self.extract_features(
prev_output_tokens, encoder_out, incremental_state
)
return self.output_layer(x), attn_scores
def extract_features(
self,
prev_output_tokens,
encoder_out: Optional[Tuple[Tensor, Tensor, Tensor, Tensor]] = None,
incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None,
):
"""
Similar to *forward* but only return features.
"""
# get outputs from encoder
if encoder_out is not None:
encoder_outs = encoder_out[0]
encoder_hiddens = encoder_out[1]
encoder_cells = encoder_out[2]
encoder_padding_mask = encoder_out[3]
else:
encoder_outs = torch.empty(0)
encoder_hiddens = torch.empty(0)
encoder_cells = torch.empty(0)
encoder_padding_mask = torch.empty(0)
srclen = encoder_outs.size(0)
if incremental_state is not None and len(incremental_state) > 0:
prev_output_tokens = prev_output_tokens[:, -1:]
bsz, seqlen = prev_output_tokens.size()
# embed tokens
x = self.embed_tokens(prev_output_tokens)
x = self.dropout_in_module(x)
# B x T x C -> T x B x C
x = x.transpose(0, 1)
# initialize previous states (or get from cache during incremental generation)
if incremental_state is not None and len(incremental_state) > 0:
prev_hiddens, prev_cells, input_feed = self.get_cached_state(
incremental_state
)
elif encoder_out is not None:
# setup recurrent cells
prev_hiddens = [encoder_hiddens[i] for i in range(self.num_layers)]
prev_cells = [encoder_cells[i] for i in range(self.num_layers)]
if self.encoder_hidden_proj is not None:
prev_hiddens = [self.encoder_hidden_proj(y) for y in prev_hiddens]
prev_cells = [self.encoder_cell_proj(y) for y in prev_cells]
input_feed = x.new_zeros(bsz, self.hidden_size)
else:
# setup zero cells, since there is no encoder
zero_state = x.new_zeros(bsz, self.hidden_size)
prev_hiddens = [zero_state for i in range(self.num_layers)]
prev_cells = [zero_state for i in range(self.num_layers)]
input_feed = None
assert (
srclen > 0 or self.attention is None
), "attention is not supported if there are no encoder outputs"
attn_scores: Optional[Tensor] = (
x.new_zeros(srclen, seqlen, bsz) if self.attention is not None else None
)
outs = []
for j in range(seqlen):
# input feeding: concatenate context vector from previous time step
if input_feed is not None:
input = torch.cat((x[j, :, :], input_feed), dim=1)
else:
input = x[j]
for i, rnn in enumerate(self.layers):
# recurrent cell
hidden, cell = rnn(input, (prev_hiddens[i], prev_cells[i]))
# hidden state becomes the input to the next layer
input = self.dropout_out_module(hidden)
if self.residuals:
input = input + prev_hiddens[i]
# save state for next time step
prev_hiddens[i] = hidden
prev_cells[i] = cell
# apply attention using the last layer's hidden state
if self.attention is not None:
assert attn_scores is not None
out, attn_scores[:, j, :] = self.attention(
hidden, encoder_outs, encoder_padding_mask
)
else:
out = hidden
out = self.dropout_out_module(out)
# input feeding
if input_feed is not None:
input_feed = out
# save final output
outs.append(out)
# Stack all the necessary tensors together and store
prev_hiddens_tensor = torch.stack(prev_hiddens)
prev_cells_tensor = torch.stack(prev_cells)
cache_state = torch.jit.annotate(
Dict[str, Optional[Tensor]],
{
"prev_hiddens": prev_hiddens_tensor,
"prev_cells": prev_cells_tensor,
"input_feed": input_feed,
},
)
self.set_incremental_state(incremental_state, "cached_state", cache_state)
# collect outputs across time steps
x = torch.cat(outs, dim=0).view(seqlen, bsz, self.hidden_size)
# T x B x C -> B x T x C
x = x.transpose(1, 0)
if hasattr(self, "additional_fc") and self.adaptive_softmax is None:
x = self.additional_fc(x)
x = self.dropout_out_module(x)
# srclen x tgtlen x bsz -> bsz x tgtlen x srclen
if not self.training and self.need_attn and self.attention is not None:
assert attn_scores is not None
attn_scores = attn_scores.transpose(0, 2)
else:
attn_scores = None
return x, attn_scores
def output_layer(self, x):
"""Project features to the vocabulary size."""
if self.adaptive_softmax is None:
if self.share_input_output_embed:
x = F.linear(x, self.embed_tokens.weight)
else:
x = self.fc_out(x)
return x
def get_cached_state(
self,
incremental_state: Dict[str, Dict[str, Optional[Tensor]]],
) -> Tuple[List[Tensor], List[Tensor], Optional[Tensor]]:
cached_state = self.get_incremental_state(incremental_state, "cached_state")
assert cached_state is not None
prev_hiddens_ = cached_state["prev_hiddens"]
assert prev_hiddens_ is not None
prev_cells_ = cached_state["prev_cells"]
assert prev_cells_ is not None
prev_hiddens = [prev_hiddens_[i] for i in range(self.num_layers)]
prev_cells = [prev_cells_[j] for j in range(self.num_layers)]
input_feed = cached_state[
"input_feed"
] # can be None for decoder-only language models
return prev_hiddens, prev_cells, input_feed
def reorder_incremental_state(
self,
incremental_state: Dict[str, Dict[str, Optional[Tensor]]],
new_order: Tensor,
):
if incremental_state is None or len(incremental_state) == 0:
return
prev_hiddens, prev_cells, input_feed = self.get_cached_state(incremental_state)
prev_hiddens = [p.index_select(0, new_order) for p in prev_hiddens]
prev_cells = [p.index_select(0, new_order) for p in prev_cells]
if input_feed is not None:
input_feed = input_feed.index_select(0, new_order)
cached_state_new = torch.jit.annotate(
Dict[str, Optional[Tensor]],
{
"prev_hiddens": torch.stack(prev_hiddens),
"prev_cells": torch.stack(prev_cells),
"input_feed": input_feed,
},
)
self.set_incremental_state(incremental_state, "cached_state", cached_state_new),
return
def max_positions(self):
"""Maximum output length supported by the decoder."""
return self.max_target_positions
def make_generation_fast_(self, need_attn=False, **kwargs):
self.need_attn = need_attn
def Embedding(num_embeddings, embedding_dim, padding_idx):
m = nn.Embedding(num_embeddings, embedding_dim, padding_idx=padding_idx)
nn.init.uniform_(m.weight, -0.1, 0.1)
nn.init.constant_(m.weight[padding_idx], 0)
return m
def LSTM(input_size, hidden_size, **kwargs):
m = nn.LSTM(input_size, hidden_size, **kwargs)
for name, param in m.named_parameters():
if "weight" in name or "bias" in name:
param.data.uniform_(-0.1, 0.1)
return m
def LSTMCell(input_size, hidden_size, **kwargs):
m = nn.LSTMCell(input_size, hidden_size, **kwargs)
for name, param in m.named_parameters():
if "weight" in name or "bias" in name:
param.data.uniform_(-0.1, 0.1)
return m
def Linear(in_features, out_features, bias=True, dropout=0.0):
"""Linear layer (input: N x T x C)"""
m = nn.Linear(in_features, out_features, bias=bias)
m.weight.data.uniform_(-0.1, 0.1)
if bias:
m.bias.data.uniform_(-0.1, 0.1)
return m
@register_model_architecture("lstm", "lstm")
def base_architecture(args):
args.dropout = getattr(args, "dropout", 0.1)
args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512)
args.encoder_embed_path = getattr(args, "encoder_embed_path", None)
args.encoder_freeze_embed = getattr(args, "encoder_freeze_embed", False)
args.encoder_hidden_size = getattr(
args, "encoder_hidden_size", args.encoder_embed_dim
)
args.encoder_layers = getattr(args, "encoder_layers", 1)
args.encoder_bidirectional = getattr(args, "encoder_bidirectional", False)
args.encoder_dropout_in = getattr(args, "encoder_dropout_in", args.dropout)
args.encoder_dropout_out = getattr(args, "encoder_dropout_out", args.dropout)
args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 512)
args.decoder_embed_path = getattr(args, "decoder_embed_path", None)
args.decoder_freeze_embed = getattr(args, "decoder_freeze_embed", False)
args.decoder_hidden_size = getattr(
args, "decoder_hidden_size", args.decoder_embed_dim
)
args.decoder_layers = getattr(args, "decoder_layers", 1)
args.decoder_out_embed_dim = getattr(args, "decoder_out_embed_dim", 512)
args.decoder_attention = getattr(args, "decoder_attention", "1")
args.decoder_dropout_in = getattr(args, "decoder_dropout_in", args.dropout)
args.decoder_dropout_out = getattr(args, "decoder_dropout_out", args.dropout)
args.share_decoder_input_output_embed = getattr(
args, "share_decoder_input_output_embed", False
)
args.share_all_embeddings = getattr(args, "share_all_embeddings", False)
args.adaptive_softmax_cutoff = getattr(
args, "adaptive_softmax_cutoff", "10000,50000,200000"
)
@register_model_architecture("lstm", "lstm_wiseman_iwslt_de_en")
def lstm_wiseman_iwslt_de_en(args):
args.dropout = getattr(args, "dropout", 0.1)
args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 256)
args.encoder_dropout_in = getattr(args, "encoder_dropout_in", 0)
args.encoder_dropout_out = getattr(args, "encoder_dropout_out", 0)
args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 256)
args.decoder_out_embed_dim = getattr(args, "decoder_out_embed_dim", 256)
args.decoder_dropout_in = getattr(args, "decoder_dropout_in", 0)
args.decoder_dropout_out = getattr(args, "decoder_dropout_out", args.dropout)
base_architecture(args)
@register_model_architecture("lstm", "lstm_luong_wmt_en_de")
def lstm_luong_wmt_en_de(args):
args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 1000)
args.encoder_layers = getattr(args, "encoder_layers", 4)
args.encoder_dropout_out = getattr(args, "encoder_dropout_out", 0)
args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 1000)
args.decoder_layers = getattr(args, "decoder_layers", 4)
args.decoder_out_embed_dim = getattr(args, "decoder_out_embed_dim", 1000)
args.decoder_dropout_out = getattr(args, "decoder_dropout_out", 0)
base_architecture(args)
|
bart_ls-main
|
fairseq-py/fairseq/models/lstm.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from fairseq.models import register_model, register_model_architecture
from fairseq.models.transformer import (
TransformerModel,
base_architecture,
transformer_wmt_en_de_big,
)
@register_model("transformer_align")
class TransformerAlignModel(TransformerModel):
"""
See "Jointly Learning to Align and Translate with Transformer
Models" (Garg et al., EMNLP 2019).
"""
def __init__(self, encoder, decoder, args):
super().__init__(args, encoder, decoder)
self.alignment_heads = args.alignment_heads
self.alignment_layer = args.alignment_layer
self.full_context_alignment = args.full_context_alignment
@staticmethod
def add_args(parser):
# fmt: off
super(TransformerAlignModel, TransformerAlignModel).add_args(parser)
parser.add_argument('--alignment-heads', type=int, metavar='D',
help='Number of cross attention heads per layer to supervised with alignments')
parser.add_argument('--alignment-layer', type=int, metavar='D',
help='Layer number which has to be supervised. 0 corresponding to the bottommost layer.')
parser.add_argument('--full-context-alignment', action='store_true',
help='Whether or not alignment is supervised conditioned on the full target context.')
# fmt: on
@classmethod
def build_model(cls, args, task):
# set any default arguments
transformer_align(args)
transformer_model = TransformerModel.build_model(args, task)
return TransformerAlignModel(
transformer_model.encoder, transformer_model.decoder, args
)
def forward(self, src_tokens, src_lengths, prev_output_tokens):
encoder_out = self.encoder(src_tokens, src_lengths)
return self.forward_decoder(prev_output_tokens, encoder_out)
def forward_decoder(
self,
prev_output_tokens,
encoder_out=None,
incremental_state=None,
features_only=False,
**extra_args,
):
attn_args = {
"alignment_layer": self.alignment_layer,
"alignment_heads": self.alignment_heads,
}
decoder_out = self.decoder(prev_output_tokens, encoder_out, **attn_args)
if self.full_context_alignment:
attn_args["full_context_alignment"] = self.full_context_alignment
_, alignment_out = self.decoder(
prev_output_tokens,
encoder_out,
features_only=True,
**attn_args,
**extra_args,
)
decoder_out[1]["attn"] = alignment_out["attn"]
return decoder_out
@register_model_architecture("transformer_align", "transformer_align")
def transformer_align(args):
args.alignment_heads = getattr(args, "alignment_heads", 1)
args.alignment_layer = getattr(args, "alignment_layer", 4)
args.full_context_alignment = getattr(args, "full_context_alignment", False)
base_architecture(args)
@register_model_architecture("transformer_align", "transformer_wmt_en_de_big_align")
def transformer_wmt_en_de_big_align(args):
args.alignment_heads = getattr(args, "alignment_heads", 1)
args.alignment_layer = getattr(args, "alignment_layer", 4)
transformer_wmt_en_de_big(args)
|
bart_ls-main
|
fairseq-py/fairseq/models/transformer_align.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from .fairseq_encoder import FairseqEncoder
class CompositeEncoder(FairseqEncoder):
"""
A wrapper around a dictionary of :class:`FairseqEncoder` objects.
We run forward on each encoder and return a dictionary of outputs. The first
encoder's dictionary is used for initialization.
Args:
encoders (dict): a dictionary of :class:`FairseqEncoder` objects.
"""
def __init__(self, encoders):
super().__init__(next(iter(encoders.values())).dictionary)
self.encoders = encoders
for key in self.encoders:
self.add_module(key, self.encoders[key])
def forward(self, src_tokens, src_lengths):
"""
Args:
src_tokens (LongTensor): tokens in the source language of shape
`(batch, src_len)`
src_lengths (LongTensor): lengths of each source sentence of shape
`(batch)`
Returns:
dict:
the outputs from each Encoder
"""
encoder_out = {}
for key in self.encoders:
encoder_out[key] = self.encoders[key](src_tokens, src_lengths)
return encoder_out
def reorder_encoder_out(self, encoder_out, new_order):
"""Reorder encoder output according to new_order."""
for key in self.encoders:
encoder_out[key] = self.encoders[key].reorder_encoder_out(
encoder_out[key], new_order
)
return encoder_out
def max_positions(self):
return min(self.encoders[key].max_positions() for key in self.encoders)
def upgrade_state_dict(self, state_dict):
for key in self.encoders:
self.encoders[key].upgrade_state_dict(state_dict)
return state_dict
|
bart_ls-main
|
fairseq-py/fairseq/models/composite_encoder.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import logging
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import utils
from fairseq.models import (
FairseqEncoder,
FairseqEncoderModel,
register_model,
register_model_architecture,
)
from fairseq.modules import (
LayerNorm,
SinusoidalPositionalEmbedding,
TransformerSentenceEncoder,
)
from fairseq.modules.transformer_sentence_encoder import init_bert_params
from fairseq.utils import safe_hasattr
logger = logging.getLogger(__name__)
@register_model("masked_lm")
class MaskedLMModel(FairseqEncoderModel):
"""
Class for training a Masked Language Model. It also supports an
additional sentence level prediction if the sent-loss argument is set.
"""
def __init__(self, args, encoder):
super().__init__(encoder)
self.args = args
# if specified then apply bert initialization on the model. We need
# to explictly call this to make sure that the output embeddings
# and projection layers are also correctly initialized
if getattr(args, "apply_bert_init", False):
self.apply(init_bert_params)
@staticmethod
def add_args(parser):
"""Add model-specific arguments to the parser."""
# Arguments related to dropout
parser.add_argument(
"--dropout", type=float, metavar="D", help="dropout probability"
)
parser.add_argument(
"--attention-dropout",
type=float,
metavar="D",
help="dropout probability for" " attention weights",
)
parser.add_argument(
"--act-dropout",
type=float,
metavar="D",
help="dropout probability after" " activation in FFN",
)
# Arguments related to hidden states and self-attention
parser.add_argument(
"--encoder-ffn-embed-dim",
type=int,
metavar="N",
help="encoder embedding dimension for FFN",
)
parser.add_argument(
"--encoder-layers", type=int, metavar="N", help="num encoder layers"
)
parser.add_argument(
"--encoder-attention-heads",
type=int,
metavar="N",
help="num encoder attention heads",
)
# Arguments related to input and output embeddings
parser.add_argument(
"--encoder-embed-dim",
type=int,
metavar="N",
help="encoder embedding dimension",
)
parser.add_argument(
"--share-encoder-input-output-embed",
action="store_true",
help="share encoder input" " and output embeddings",
)
parser.add_argument(
"--encoder-learned-pos",
action="store_true",
help="use learned positional embeddings in the encoder",
)
parser.add_argument(
"--no-token-positional-embeddings",
action="store_true",
help="if set, disables positional embeddings" " (outside self attention)",
)
parser.add_argument(
"--num-segment", type=int, metavar="N", help="num segment in the input"
)
parser.add_argument(
"--max-positions", type=int, help="number of positional embeddings to learn"
)
# Arguments related to sentence level prediction
parser.add_argument(
"--sentence-class-num",
type=int,
metavar="N",
help="number of classes for sentence task",
)
parser.add_argument(
"--sent-loss",
action="store_true",
help="if set," " calculate sentence level predictions",
)
# Arguments related to parameter initialization
parser.add_argument(
"--apply-bert-init",
action="store_true",
help="use custom param initialization for BERT",
)
# misc params
parser.add_argument(
"--activation-fn",
choices=utils.get_available_activation_fns(),
help="activation function to use",
)
parser.add_argument(
"--pooler-activation-fn",
choices=utils.get_available_activation_fns(),
help="Which activation function to use for pooler layer.",
)
parser.add_argument(
"--encoder-normalize-before",
action="store_true",
help="apply layernorm before each encoder block",
)
def forward(self, src_tokens, segment_labels=None, **kwargs):
return self.encoder(src_tokens, segment_labels=segment_labels, **kwargs)
def max_positions(self):
return self.encoder.max_positions
@classmethod
def build_model(cls, args, task):
"""Build a new model instance."""
# make sure all arguments are present in older models
base_architecture(args)
if not safe_hasattr(args, "max_positions"):
args.max_positions = args.tokens_per_sample
logger.info(args)
encoder = MaskedLMEncoder(args, task.dictionary)
return cls(args, encoder)
class MaskedLMEncoder(FairseqEncoder):
"""
Encoder for Masked Language Modelling.
"""
def __init__(self, args, dictionary):
super().__init__(dictionary)
self.padding_idx = dictionary.pad()
self.vocab_size = dictionary.__len__()
self.max_positions = args.max_positions
self.sentence_encoder = TransformerSentenceEncoder(
padding_idx=self.padding_idx,
vocab_size=self.vocab_size,
num_encoder_layers=args.encoder_layers,
embedding_dim=args.encoder_embed_dim,
ffn_embedding_dim=args.encoder_ffn_embed_dim,
num_attention_heads=args.encoder_attention_heads,
dropout=args.dropout,
attention_dropout=args.attention_dropout,
activation_dropout=args.act_dropout,
max_seq_len=self.max_positions,
num_segments=args.num_segment,
use_position_embeddings=not args.no_token_positional_embeddings,
encoder_normalize_before=args.encoder_normalize_before,
apply_bert_init=args.apply_bert_init,
activation_fn=args.activation_fn,
learned_pos_embedding=args.encoder_learned_pos,
)
self.share_input_output_embed = args.share_encoder_input_output_embed
self.embed_out = None
self.sentence_projection_layer = None
self.sentence_out_dim = args.sentence_class_num
self.lm_output_learned_bias = None
# Remove head is set to true during fine-tuning
self.load_softmax = not getattr(args, "remove_head", False)
self.masked_lm_pooler = nn.Linear(
args.encoder_embed_dim, args.encoder_embed_dim
)
self.pooler_activation = utils.get_activation_fn(args.pooler_activation_fn)
self.lm_head_transform_weight = nn.Linear(
args.encoder_embed_dim, args.encoder_embed_dim
)
self.activation_fn = utils.get_activation_fn(args.activation_fn)
self.layer_norm = LayerNorm(args.encoder_embed_dim)
self.lm_output_learned_bias = None
if self.load_softmax:
self.lm_output_learned_bias = nn.Parameter(torch.zeros(self.vocab_size))
if not self.share_input_output_embed:
self.embed_out = nn.Linear(
args.encoder_embed_dim, self.vocab_size, bias=False
)
if args.sent_loss:
self.sentence_projection_layer = nn.Linear(
args.encoder_embed_dim, self.sentence_out_dim, bias=False
)
def forward(self, src_tokens, segment_labels=None, masked_tokens=None, **unused):
"""
Forward pass for Masked LM encoder. This first computes the token
embedding using the token embedding matrix, position embeddings (if
specified) and segment embeddings (if specified).
Here we assume that the sentence representation corresponds to the
output of the classification_token (see bert_task or cross_lingual_lm
task for more details).
Args:
- src_tokens: B x T matrix representing sentences
- segment_labels: B x T matrix representing segment label for tokens
Returns:
- a tuple of the following:
- logits for predictions in format B x T x C to be used in
softmax afterwards
- a dictionary of additional data, where 'pooled_output' contains
the representation for classification_token and 'inner_states'
is a list of internal model states used to compute the
predictions (similar in ELMO). 'sentence_logits'
is the prediction logit for NSP task and is only computed if
this is specified in the input arguments.
"""
inner_states, sentence_rep = self.sentence_encoder(
src_tokens,
segment_labels=segment_labels,
)
x = inner_states[-1].transpose(0, 1)
# project masked tokens only
if masked_tokens is not None:
x = x[masked_tokens, :]
x = self.layer_norm(self.activation_fn(self.lm_head_transform_weight(x)))
pooled_output = self.pooler_activation(self.masked_lm_pooler(sentence_rep))
# project back to size of vocabulary
if self.share_input_output_embed and hasattr(
self.sentence_encoder.embed_tokens, "weight"
):
x = F.linear(x, self.sentence_encoder.embed_tokens.weight)
elif self.embed_out is not None:
x = self.embed_out(x)
if self.lm_output_learned_bias is not None:
x = x + self.lm_output_learned_bias
sentence_logits = None
if self.sentence_projection_layer:
sentence_logits = self.sentence_projection_layer(pooled_output)
return x, {
"inner_states": inner_states,
"pooled_output": pooled_output,
"sentence_logits": sentence_logits,
}
def max_positions(self):
"""Maximum output length supported by the encoder."""
return self.max_positions
def upgrade_state_dict_named(self, state_dict, name):
if isinstance(
self.sentence_encoder.embed_positions, SinusoidalPositionalEmbedding
):
state_dict[
name + ".sentence_encoder.embed_positions._float_tensor"
] = torch.FloatTensor(1)
if not self.load_softmax:
for k in list(state_dict.keys()):
if (
"embed_out.weight" in k
or "sentence_projection_layer.weight" in k
or "lm_output_learned_bias" in k
):
del state_dict[k]
return state_dict
@register_model_architecture("masked_lm", "masked_lm")
def base_architecture(args):
args.dropout = getattr(args, "dropout", 0.1)
args.attention_dropout = getattr(args, "attention_dropout", 0.1)
args.act_dropout = getattr(args, "act_dropout", 0.0)
args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 4096)
args.encoder_layers = getattr(args, "encoder_layers", 6)
args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 8)
args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 1024)
args.share_encoder_input_output_embed = getattr(
args, "share_encoder_input_output_embed", False
)
args.encoder_learned_pos = getattr(args, "encoder_learned_pos", False)
args.no_token_positional_embeddings = getattr(
args, "no_token_positional_embeddings", False
)
args.num_segment = getattr(args, "num_segment", 2)
args.sentence_class_num = getattr(args, "sentence_class_num", 2)
args.sent_loss = getattr(args, "sent_loss", False)
args.apply_bert_init = getattr(args, "apply_bert_init", False)
args.activation_fn = getattr(args, "activation_fn", "relu")
args.pooler_activation_fn = getattr(args, "pooler_activation_fn", "tanh")
args.encoder_normalize_before = getattr(args, "encoder_normalize_before", False)
@register_model_architecture("masked_lm", "bert_base")
def bert_base_architecture(args):
args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 768)
args.share_encoder_input_output_embed = getattr(
args, "share_encoder_input_output_embed", True
)
args.no_token_positional_embeddings = getattr(
args, "no_token_positional_embeddings", False
)
args.encoder_learned_pos = getattr(args, "encoder_learned_pos", True)
args.num_segment = getattr(args, "num_segment", 2)
args.encoder_layers = getattr(args, "encoder_layers", 12)
args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 12)
args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 3072)
args.sentence_class_num = getattr(args, "sentence_class_num", 2)
args.sent_loss = getattr(args, "sent_loss", True)
args.apply_bert_init = getattr(args, "apply_bert_init", True)
args.activation_fn = getattr(args, "activation_fn", "gelu")
args.pooler_activation_fn = getattr(args, "pooler_activation_fn", "tanh")
args.encoder_normalize_before = getattr(args, "encoder_normalize_before", True)
base_architecture(args)
@register_model_architecture("masked_lm", "bert_large")
def bert_large_architecture(args):
args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 1024)
args.encoder_layers = getattr(args, "encoder_layers", 24)
args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 16)
args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 4096)
bert_base_architecture(args)
@register_model_architecture("masked_lm", "xlm_base")
def xlm_architecture(args):
args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 1024)
args.share_encoder_input_output_embed = getattr(
args, "share_encoder_input_output_embed", True
)
args.no_token_positional_embeddings = getattr(
args, "no_token_positional_embeddings", False
)
args.encoder_learned_pos = getattr(args, "encoder_learned_pos", True)
args.num_segment = getattr(args, "num_segment", 1)
args.encoder_layers = getattr(args, "encoder_layers", 6)
args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 8)
args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 4096)
args.sent_loss = getattr(args, "sent_loss", False)
args.activation_fn = getattr(args, "activation_fn", "gelu")
args.encoder_normalize_before = getattr(args, "encoder_normalize_before", False)
args.pooler_activation_fn = getattr(args, "pooler_activation_fn", "tanh")
args.apply_bert_init = getattr(args, "apply_bert_init", True)
base_architecture(args)
|
bart_ls-main
|
fairseq-py/fairseq/models/masked_lm.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import logging
from typing import Dict, Optional
from fairseq.incremental_decoding_utils import with_incremental_state
from fairseq.models import FairseqDecoder
from torch import Tensor
logger = logging.getLogger(__name__)
@with_incremental_state
class FairseqIncrementalDecoder(FairseqDecoder):
"""Base class for incremental decoders.
Incremental decoding is a special mode at inference time where the Model
only receives a single timestep of input corresponding to the previous
output token (for teacher forcing) and must produce the next output
*incrementally*. Thus the model must cache any long-term state that is
needed about the sequence, e.g., hidden states, convolutional states, etc.
Compared to the standard :class:`FairseqDecoder` interface, the incremental
decoder interface allows :func:`forward` functions to take an extra keyword
argument (*incremental_state*) that can be used to cache state across
time-steps.
The :class:`FairseqIncrementalDecoder` interface also defines the
:func:`reorder_incremental_state` method, which is used during beam search
to select and reorder the incremental state based on the selection of beams.
To learn more about how incremental decoding works, refer to `this blog
<http://www.telesens.co/2019/04/21/understanding-incremental-decoding-in-fairseq/>`_.
"""
def __init__(self, dictionary):
super().__init__(dictionary)
def forward(
self, prev_output_tokens, encoder_out=None, incremental_state=None, **kwargs
):
"""
Args:
prev_output_tokens (LongTensor): shifted output tokens of shape
`(batch, tgt_len)`, for teacher forcing
encoder_out (dict, optional): output from the encoder, used for
encoder-side attention
incremental_state (dict, optional): dictionary used for storing
state during :ref:`Incremental decoding`
Returns:
tuple:
- the decoder's output of shape `(batch, tgt_len, vocab)`
- a dictionary with any model-specific outputs
"""
raise NotImplementedError
def extract_features(
self, prev_output_tokens, encoder_out=None, incremental_state=None, **kwargs
):
"""
Returns:
tuple:
- the decoder's features of shape `(batch, tgt_len, embed_dim)`
- a dictionary with any model-specific outputs
"""
raise NotImplementedError
def reorder_incremental_state(
self,
incremental_state: Dict[str, Dict[str, Optional[Tensor]]],
new_order: Tensor,
):
"""Reorder incremental state.
This will be called when the order of the input has changed from the
previous time step. A typical use case is beam search, where the input
order changes between time steps based on the selection of beams.
"""
pass
def reorder_incremental_state_scripting(
self,
incremental_state: Dict[str, Dict[str, Optional[Tensor]]],
new_order: Tensor,
):
"""Main entry point for reordering the incremental state.
Due to limitations in TorchScript, we call this function in
:class:`fairseq.sequence_generator.SequenceGenerator` instead of
calling :func:`reorder_incremental_state` directly.
"""
for module in self.modules():
if hasattr(module, "reorder_incremental_state"):
result = module.reorder_incremental_state(incremental_state, new_order)
if result is not None:
incremental_state = result
def set_beam_size(self, beam_size):
"""Sets the beam size in the decoder and all children."""
if getattr(self, "_beam_size", -1) != beam_size:
seen = set()
def apply_set_beam_size(module):
if (
module != self
and hasattr(module, "set_beam_size")
and module not in seen
):
seen.add(module)
module.set_beam_size(beam_size)
self.apply(apply_set_beam_size)
self._beam_size = beam_size
|
bart_ls-main
|
fairseq-py/fairseq/models/fairseq_incremental_decoder.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import utils
from fairseq.models import (
FairseqEncoder,
FairseqEncoderDecoderModel,
FairseqIncrementalDecoder,
register_model,
register_model_architecture,
)
from fairseq.modules import (
AdaptiveSoftmax,
DynamicConv,
FairseqDropout,
LayerNorm,
LightweightConv,
MultiheadAttention,
PositionalEmbedding,
)
from fairseq.utils import safe_hasattr
@register_model("lightconv")
class LightConvModel(FairseqEncoderDecoderModel):
"""
LightConv and DynamicConv model from `"Pay Less Attention with Lightweight and Dynamic Convolutions" (Wu, et al, 2019)
<https://openreview.net/pdf?id=SkVhlh09tX>`_.
To use LightConv please set ``--encoder-conv-type lightweight --decoder-conv-type lightweight``
To use DynamicConv please set ``--encoder-conv-type dynamic --decoder-conv-type dynamic``
Args:
encoder (LightConvEncoder): the encoder
decoder (LightConvDecoder): the decoder
The LightConv model provides the following named architectures and
command-line arguments:
.. argparse::
:ref: fairseq.models.lightconv_parser
:prog:
"""
@classmethod
def hub_models(cls):
# fmt: off
def moses_subword(path):
return {
'path': path,
'tokenizer': 'moses',
'bpe': 'subword_nmt',
}
return {
'lightconv.no_glu.iwslt14.de-en': moses_subword('https://dl.fbaipublicfiles.com/fairseq/models/dynamicconv/iwslt14.de-en.lightconv.tar.gz'),
'dynamicconv.no_glu.iwslt14.de-en': moses_subword('https://dl.fbaipublicfiles.com/fairseq/models/dynamicconv/iwslt14.de-en.dynamicconv.tar.gz'),
'lightconv.no_glu.wmt16.en-de': moses_subword('https://dl.fbaipublicfiles.com/fairseq/models/dynamicconv/wmt16.en-de.joined-dict.lightconv.tar.gz'),
'dynamicconv.no_glu.wmt16.en-de': moses_subword('https://dl.fbaipublicfiles.com/fairseq/models/dynamicconv/wmt16.en-de.joined-dict.dynamicconv.tar.gz'),
'lightconv.glu.wmt16.en-de': moses_subword('https://dl.fbaipublicfiles.com/fairseq/models/dynamicconv/wmt16.en-de.joined-dict.lightconv-glu.tar.gz'),
'dynamicconv.glu.wmt16.en-de': moses_subword('https://dl.fbaipublicfiles.com/fairseq/models/dynamicconv/wmt16.en-de.joined-dict.dynamicconv-glu.tar.gz'),
'lightconv.glu.wmt17.en-de': moses_subword('https://dl.fbaipublicfiles.com/fairseq/models/dynamicconv/wmt16.en-de.joined-dict.lightconv-glu.tar.gz'),
'dynamicconv.glu.wmt17.en-de': moses_subword('https://dl.fbaipublicfiles.com/fairseq/models/dynamicconv/wmt16.en-de.joined-dict.dynamicconv-glu.tar.gz'),
'lightconv.glu.wmt14.en-fr': moses_subword('https://dl.fbaipublicfiles.com/fairseq/models/dynamicconv/wmt14.en-fr.joined-dict.lightconv-glu.tar.gz'),
'dynamicconv.glu.wmt14.en-fr': moses_subword('https://dl.fbaipublicfiles.com/fairseq/models/dynamicconv/wmt14.en-fr.joined-dict.dynamicconv-glu.tar.gz'),
'lightconv.glu.wmt17.zh-en': moses_subword('https://dl.fbaipublicfiles.com/fairseq/models/dynamicconv/wmt17.zh-en.lightconv-glu.tar.gz'),
'dynamicconv.glu.wmt17.zh-en': moses_subword('https://dl.fbaipublicfiles.com/fairseq/models/dynamicconv/wmt17.zh-en.dynamicconv-glu.tar.gz'),
}
# fmt: on
def __init__(self, encoder, decoder):
super().__init__(encoder, decoder)
@staticmethod
def add_args(parser):
"""Add model-specific arguments to the parser."""
parser.add_argument(
"--dropout", type=float, metavar="D", help="dropout probability"
)
parser.add_argument(
"--attention-dropout",
type=float,
metavar="D",
help="dropout probability for attention weights",
)
parser.add_argument(
"--relu-dropout",
type=float,
metavar="D",
help="dropout probability after ReLU in FFN",
)
parser.add_argument(
"--input-dropout",
type=float,
metavar="D",
help="dropout probability of the inputs",
)
parser.add_argument(
"--encoder-embed-path",
type=str,
metavar="STR",
help="path to pre-trained encoder embedding",
)
parser.add_argument(
"--encoder-embed-dim",
type=int,
metavar="N",
help="encoder embedding dimension",
)
parser.add_argument(
"--encoder-conv-dim",
type=int,
metavar="N",
help="encoder embedding dimension",
)
parser.add_argument(
"--encoder-ffn-embed-dim",
type=int,
metavar="N",
help="encoder embedding dimension for FFN",
)
parser.add_argument(
"--encoder-layers", type=int, metavar="N", help="num encoder layers"
)
parser.add_argument(
"--encoder-attention-heads",
type=int,
metavar="N",
help="num encoder attention heads or LightConv/DynamicConv heads",
)
parser.add_argument(
"--encoder-normalize-before",
action="store_true",
help="apply layernorm before each encoder block",
)
parser.add_argument(
"--encoder-learned-pos",
action="store_true",
help="use learned positional embeddings in the encoder",
)
parser.add_argument(
"--decoder-embed-path",
type=str,
metavar="STR",
help="path to pre-trained decoder embedding",
)
parser.add_argument(
"--decoder-embed-dim",
type=int,
metavar="N",
help="decoder embedding dimension",
)
parser.add_argument(
"--decoder-conv-dim",
type=int,
metavar="N",
help="decoder embedding dimension",
)
parser.add_argument(
"--decoder-ffn-embed-dim",
type=int,
metavar="N",
help="decoder embedding dimension for FFN",
)
parser.add_argument(
"--decoder-layers", type=int, metavar="N", help="num decoder layers"
)
parser.add_argument(
"--decoder-attention-heads",
type=int,
metavar="N",
help="num decoder attention heads or LightConv/DynamicConv heads",
)
parser.add_argument(
"--decoder-learned-pos",
action="store_true",
help="use learned positional embeddings in the decoder",
)
parser.add_argument(
"--decoder-normalize-before",
action="store_true",
help="apply layernorm before each decoder block",
)
parser.add_argument(
"--share-decoder-input-output-embed",
action="store_true",
help="share decoder input and output embeddings",
)
parser.add_argument(
"--share-all-embeddings",
action="store_true",
help="share encoder, decoder and output embeddings"
" (requires shared dictionary and embed dim)",
)
parser.add_argument(
"--adaptive-softmax-cutoff",
metavar="EXPR",
help="comma separated list of adaptive softmax cutoff points. "
"Must be used with adaptive_loss criterion",
),
parser.add_argument(
"--adaptive-softmax-dropout",
type=float,
metavar="D",
help="sets adaptive softmax dropout for the tail projections",
)
"""LightConv and DynamicConv arguments"""
parser.add_argument(
"--encoder-kernel-size-list",
type=lambda x: utils.eval_str_list(x, int),
help='list of kernel size (default: "[3,7,15,31,31,31,31]")',
)
parser.add_argument(
"--decoder-kernel-size-list",
type=lambda x: utils.eval_str_list(x, int),
help='list of kernel size (default: "[3,7,15,31,31,31]")',
)
parser.add_argument(
"--encoder-glu", type=utils.eval_bool, help="glu after in proj"
)
parser.add_argument(
"--decoder-glu", type=utils.eval_bool, help="glu after in proj"
)
parser.add_argument(
"--encoder-conv-type",
default="dynamic",
type=str,
choices=["dynamic", "lightweight"],
help="type of convolution",
)
parser.add_argument(
"--decoder-conv-type",
default="dynamic",
type=str,
choices=["dynamic", "lightweight"],
help="type of convolution",
)
parser.add_argument("--weight-softmax", default=True, type=utils.eval_bool)
parser.add_argument(
"--weight-dropout",
type=float,
metavar="D",
help="dropout probability for conv weights",
)
@classmethod
def build_model(cls, args, task):
"""Build a new model instance."""
# make sure all arguments are present in older models
base_architecture(args)
if not safe_hasattr(args, "max_source_positions"):
args.max_source_positions = 1024
if not safe_hasattr(args, "max_target_positions"):
args.max_target_positions = 1024
src_dict, tgt_dict = task.source_dictionary, task.target_dictionary
def build_embedding(dictionary, embed_dim, path=None):
num_embeddings = len(dictionary)
padding_idx = dictionary.pad()
emb = Embedding(num_embeddings, embed_dim, padding_idx)
# if provided, load from preloaded dictionaries
if path:
embed_dict = utils.parse_embedding(path)
utils.load_embedding(embed_dict, dictionary, emb)
return emb
if args.share_all_embeddings:
if src_dict != tgt_dict:
raise RuntimeError(
"--share-all-embeddings requires a joined dictionary"
)
if args.encoder_embed_dim != args.decoder_embed_dim:
raise RuntimeError(
"--share-all-embeddings requires --encoder-embed-dim to match --decoder-embed-dim"
)
if args.decoder_embed_path and (
args.decoder_embed_path != args.encoder_embed_path
):
raise RuntimeError(
"--share-all-embeddings not compatible with --decoder-embed-path"
)
encoder_embed_tokens = build_embedding(
src_dict, args.encoder_embed_dim, args.encoder_embed_path
)
decoder_embed_tokens = encoder_embed_tokens
args.share_decoder_input_output_embed = True
else:
encoder_embed_tokens = build_embedding(
src_dict, args.encoder_embed_dim, args.encoder_embed_path
)
decoder_embed_tokens = build_embedding(
tgt_dict, args.decoder_embed_dim, args.decoder_embed_path
)
encoder = LightConvEncoder(args, src_dict, encoder_embed_tokens)
decoder = LightConvDecoder(args, tgt_dict, decoder_embed_tokens)
return LightConvModel(encoder, decoder)
class LightConvEncoder(FairseqEncoder):
"""
LightConv encoder consisting of *args.encoder_layers* layers. Each layer
is a :class:`LightConvEncoderLayer`.
Args:
args (argparse.Namespace): parsed command-line arguments
dictionary (~fairseq.data.Dictionary): encoding dictionary
embed_tokens (torch.nn.Embedding): input embedding
"""
def __init__(self, args, dictionary, embed_tokens):
super().__init__(dictionary)
self.dropout_module = FairseqDropout(
args.dropout, module_name=self.__class__.__name__
)
embed_dim = embed_tokens.embedding_dim
self.padding_idx = embed_tokens.padding_idx
self.max_source_positions = args.max_source_positions
self.embed_tokens = embed_tokens
self.embed_scale = math.sqrt(embed_dim)
self.embed_positions = (
PositionalEmbedding(
args.max_source_positions,
embed_dim,
self.padding_idx,
learned=args.encoder_learned_pos,
)
if not args.no_token_positional_embeddings
else None
)
self.layers = nn.ModuleList([])
self.layers.extend(
[
LightConvEncoderLayer(
args, kernel_size=args.encoder_kernel_size_list[i]
)
for i in range(args.encoder_layers)
]
)
self.register_buffer("version", torch.Tensor([2]))
self.normalize = args.encoder_normalize_before
if self.normalize:
self.layer_norm = LayerNorm(embed_dim)
def forward(self, src_tokens, **unused):
"""
Args:
src_tokens (LongTensor): tokens in the source language of shape
`(batch, src_len)`
Returns:
dict:
- **encoder_out** (Tensor): the last encoder layer's output of
shape `(src_len, batch, embed_dim)`
- **encoder_padding_mask** (ByteTensor): the positions of
padding elements of shape `(batch, src_len)`
"""
# embed tokens and positions
x = self.embed_scale * self.embed_tokens(src_tokens)
if self.embed_positions is not None:
x += self.embed_positions(src_tokens)
x = self.dropout_module(x)
# B x T x C -> T x B x C
x = x.transpose(0, 1)
# compute padding mask
encoder_padding_mask = src_tokens.eq(self.padding_idx)
if not encoder_padding_mask.any():
encoder_padding_mask = None
# encoder layers
for layer in self.layers:
x = layer(x, encoder_padding_mask)
if self.normalize:
x = self.layer_norm(x)
return {
"encoder_out": x, # T x B x C
"encoder_padding_mask": encoder_padding_mask, # B x T
}
def reorder_encoder_out(self, encoder_out, new_order):
"""
Reorder encoder output according to *new_order*.
Args:
encoder_out: output from the ``forward()`` method
new_order (LongTensor): desired order
Returns:
*encoder_out* rearranged according to *new_order*
"""
if encoder_out["encoder_out"] is not None:
encoder_out["encoder_out"] = encoder_out["encoder_out"].index_select(
1, new_order
)
if encoder_out["encoder_padding_mask"] is not None:
encoder_out["encoder_padding_mask"] = encoder_out[
"encoder_padding_mask"
].index_select(0, new_order)
return encoder_out
def max_positions(self):
"""Maximum input length supported by the encoder."""
if self.embed_positions is None:
return self.max_source_positions
return min(self.max_source_positions, self.embed_positions.max_positions)
class LightConvDecoder(FairseqIncrementalDecoder):
"""
LightConv decoder consisting of *args.decoder_layers* layers. Each layer
is a :class:`LightConvDecoderLayer`.
Args:
args (argparse.Namespace): parsed command-line arguments
dictionary (~fairseq.data.Dictionary): decoding dictionary
embed_tokens (torch.nn.Embedding): output embedding
no_encoder_attn (bool, optional): whether to attend to encoder outputs.
Default: ``False``
"""
def __init__(
self, args, dictionary, embed_tokens, no_encoder_attn=False, final_norm=True
):
super().__init__(dictionary)
self.dropout_module = FairseqDropout(
args.dropout, module_name=self.__class__.__name__
)
self.share_input_output_embed = args.share_decoder_input_output_embed
input_embed_dim = embed_tokens.embedding_dim
embed_dim = args.decoder_embed_dim
output_embed_dim = args.decoder_output_dim
padding_idx = embed_tokens.padding_idx
self.max_target_positions = args.max_target_positions
self.embed_tokens = embed_tokens
self.embed_scale = math.sqrt(embed_dim) # todo: try with input_embed_dim
self.project_in_dim = (
Linear(input_embed_dim, embed_dim, bias=False)
if embed_dim != input_embed_dim
else None
)
self.embed_positions = (
PositionalEmbedding(
args.max_target_positions,
embed_dim,
padding_idx,
learned=args.decoder_learned_pos,
)
if not args.no_token_positional_embeddings
else None
)
self.layers = nn.ModuleList([])
self.layers.extend(
[
LightConvDecoderLayer(
args, no_encoder_attn, kernel_size=args.decoder_kernel_size_list[i]
)
for i in range(args.decoder_layers)
]
)
self.adaptive_softmax = None
self.project_out_dim = (
Linear(embed_dim, output_embed_dim, bias=False)
if embed_dim != output_embed_dim and not args.tie_adaptive_weights
else None
)
if args.adaptive_softmax_cutoff is not None:
self.adaptive_softmax = AdaptiveSoftmax(
len(dictionary),
output_embed_dim,
utils.eval_str_list(args.adaptive_softmax_cutoff, type=int),
dropout=args.adaptive_softmax_dropout,
adaptive_inputs=embed_tokens if args.tie_adaptive_weights else None,
factor=args.adaptive_softmax_factor,
tie_proj=args.tie_adaptive_proj,
)
elif not self.share_input_output_embed:
self.embed_out = nn.Parameter(
torch.Tensor(len(dictionary), output_embed_dim)
)
nn.init.normal_(self.embed_out, mean=0, std=output_embed_dim ** -0.5)
self.register_buffer("version", torch.Tensor([2]))
self.normalize = args.decoder_normalize_before and final_norm
if self.normalize:
self.layer_norm = LayerNorm(embed_dim)
def forward(
self, prev_output_tokens, encoder_out=None, incremental_state=None, **kwargs
):
"""
Args:
prev_output_tokens (LongTensor): previous decoder outputs of shape
`(batch, tgt_len)`, for teacher forcing
encoder_out (Tensor, optional): output from the encoder, used for
encoder-side attention
incremental_state (dict): dictionary used for storing state during
:ref:`Incremental decoding`
Returns:
tuple:
- the last decoder layer's output of shape `(batch, tgt_len,
vocab)`
- the last decoder layer's attention weights of shape `(batch,
tgt_len, src_len)`
"""
# embed positions
positions = (
self.embed_positions(
prev_output_tokens,
incremental_state=incremental_state,
)
if self.embed_positions is not None
else None
)
if incremental_state is not None:
prev_output_tokens = prev_output_tokens[:, -1:]
if positions is not None:
positions = positions[:, -1:]
# embed tokens and positions
x = self.embed_scale * self.embed_tokens(prev_output_tokens)
if self.project_in_dim is not None:
x = self.project_in_dim(x)
if positions is not None:
x += positions
x = self.dropout_module(x)
# B x T x C -> T x B x C
x = x.transpose(0, 1)
attn = None
inner_states = [x]
# decoder layers
for layer in self.layers:
x, attn = layer(
x,
encoder_out["encoder_out"] if encoder_out is not None else None,
encoder_out["encoder_padding_mask"]
if encoder_out is not None
else None,
incremental_state,
)
inner_states.append(x)
if self.normalize:
x = self.layer_norm(x)
# T x B x C -> B x T x C
x = x.transpose(0, 1)
if self.project_out_dim is not None:
x = self.project_out_dim(x)
if self.adaptive_softmax is None:
# project back to size of vocabulary
if self.share_input_output_embed:
x = F.linear(x, self.embed_tokens.weight)
else:
x = F.linear(x, self.embed_out)
return x, {"attn": attn, "inner_states": inner_states}
def max_positions(self):
"""Maximum output length supported by the decoder."""
if self.embed_positions is None:
return self.max_target_positions
return min(self.max_target_positions, self.embed_positions.max_positions)
def buffered_future_mask(self, tensor):
dim = tensor.size(0)
if (
not hasattr(self, "_future_mask")
or self._future_mask is None
or self._future_mask.device != tensor.device
):
self._future_mask = torch.triu(
utils.fill_with_neg_inf(tensor.new(dim, dim)), 1
)
if self._future_mask.size(0) < dim:
self._future_mask = torch.triu(
utils.fill_with_neg_inf(self._future_mask.resize_(dim, dim)), 1
)
return self._future_mask[:dim, :dim]
class LightConvEncoderLayer(nn.Module):
"""Encoder layer block.
Args:
args (argparse.Namespace): parsed command-line arguments
kernel_size: kernel size of the convolution
"""
def __init__(self, args, kernel_size=0):
super().__init__()
self.embed_dim = args.encoder_embed_dim
self.conv_dim = args.encoder_conv_dim
padding_l = (
kernel_size // 2
if kernel_size % 2 == 1
else ((kernel_size - 1) // 2, kernel_size // 2)
)
if args.encoder_glu:
self.linear1 = Linear(self.embed_dim, 2 * self.conv_dim)
self.act = nn.GLU()
else:
self.linear1 = Linear(self.embed_dim, self.conv_dim)
self.act = None
if args.encoder_conv_type == "lightweight":
self.conv = LightweightConv(
self.conv_dim,
kernel_size,
padding_l=padding_l,
weight_softmax=args.weight_softmax,
num_heads=args.encoder_attention_heads,
weight_dropout=args.weight_dropout,
)
elif args.encoder_conv_type == "dynamic":
self.conv = DynamicConv(
self.conv_dim,
kernel_size,
padding_l=padding_l,
weight_softmax=args.weight_softmax,
num_heads=args.encoder_attention_heads,
weight_dropout=args.weight_dropout,
)
else:
raise NotImplementedError
self.linear2 = Linear(self.conv_dim, self.embed_dim)
self.dropout_module = FairseqDropout(
args.dropout, module_name=self.__class__.__name__
)
self.relu_dropout_module = FairseqDropout(
args.relu_dropout, module_name=self.__class__.__name__
)
self.input_dropout_module = FairseqDropout(
args.input_dropout, module_name=self.__class__.__name__
)
self.normalize_before = args.encoder_normalize_before
self.fc1 = Linear(self.embed_dim, args.encoder_ffn_embed_dim)
self.fc2 = Linear(args.encoder_ffn_embed_dim, self.embed_dim)
self.layer_norms = nn.ModuleList([LayerNorm(self.embed_dim) for _ in range(2)])
def forward(self, x, encoder_padding_mask):
"""
Args:
x (Tensor): input to the layer of shape `(seq_len, batch, embed_dim)`
encoder_padding_mask (ByteTensor): binary ByteTensor of shape
`(batch, src_len)` where padding elements are indicated by ``1``.
Returns:
encoded output of shape `(batch, src_len, embed_dim)`
"""
residual = x
x = self.maybe_layer_norm(0, x, before=True)
x = self.input_dropout_module(x)
x = self.linear1(x)
if self.act is not None:
x = self.act(x)
if encoder_padding_mask is not None:
x = x.masked_fill(encoder_padding_mask.transpose(0, 1).unsqueeze(2), 0)
x = self.conv(x)
x = self.linear2(x)
x = self.dropout_module(x)
x = residual + x
x = self.maybe_layer_norm(0, x, after=True)
residual = x
x = self.maybe_layer_norm(1, x, before=True)
x = F.relu(self.fc1(x))
x = self.relu_dropout_module(x)
x = self.fc2(x)
x = self.dropout_module(x)
x = residual + x
x = self.maybe_layer_norm(1, x, after=True)
return x
def maybe_layer_norm(self, i, x, before=False, after=False):
assert before ^ after
if after ^ self.normalize_before:
return self.layer_norms[i](x)
else:
return x
def extra_repr(self):
return (
"dropout={}, relu_dropout={}, input_dropout={}, normalize_before={}".format(
self.dropout_module.p,
self.relu_dropout_module.p,
self.input_dropout_module.p,
self.normalize_before,
)
)
class LightConvDecoderLayer(nn.Module):
"""Decoder layer block.
Args:
args (argparse.Namespace): parsed command-line arguments
no_encoder_attn (bool, optional): whether to attend to encoder outputs.
Default: ``False``
kernel_size: kernel size of the convolution
"""
def __init__(self, args, no_encoder_attn=False, kernel_size=0):
super().__init__()
self.embed_dim = args.decoder_embed_dim
self.conv_dim = args.decoder_conv_dim
if args.decoder_glu:
self.linear1 = Linear(self.embed_dim, 2 * self.conv_dim)
self.act = nn.GLU()
else:
self.linear1 = Linear(self.embed_dim, self.conv_dim)
self.act = None
if args.decoder_conv_type == "lightweight":
self.conv = LightweightConv(
self.conv_dim,
kernel_size,
padding_l=kernel_size - 1,
weight_softmax=args.weight_softmax,
num_heads=args.decoder_attention_heads,
weight_dropout=args.weight_dropout,
)
elif args.decoder_conv_type == "dynamic":
self.conv = DynamicConv(
self.conv_dim,
kernel_size,
padding_l=kernel_size - 1,
weight_softmax=args.weight_softmax,
num_heads=args.decoder_attention_heads,
weight_dropout=args.weight_dropout,
)
else:
raise NotImplementedError
self.linear2 = Linear(self.conv_dim, self.embed_dim)
self.dropout_module = FairseqDropout(
args.dropout, module_name=self.__class__.__name__
)
self.relu_dropout_module = FairseqDropout(
args.relu_dropout, module_name=self.__class__.__name__
)
self.input_dropout_module = FairseqDropout(
args.input_dropout, module_name=self.__class__.__name__
)
self.normalize_before = args.decoder_normalize_before
self.conv_layer_norm = LayerNorm(self.embed_dim)
if no_encoder_attn:
self.encoder_attn = None
self.encoder_attn_layer_norm = None
else:
self.encoder_attn = MultiheadAttention(
self.embed_dim,
args.decoder_attention_heads,
dropout=args.attention_dropout,
encoder_decoder_attention=True,
)
self.encoder_attn_layer_norm = LayerNorm(self.embed_dim)
self.fc1 = Linear(self.embed_dim, args.decoder_ffn_embed_dim)
self.fc2 = Linear(args.decoder_ffn_embed_dim, self.embed_dim)
self.final_layer_norm = LayerNorm(self.embed_dim)
self.need_attn = True
def forward(
self,
x,
encoder_out,
encoder_padding_mask,
incremental_state,
prev_conv_state=None,
prev_attn_state=None,
conv_mask=None,
conv_padding_mask=None,
):
"""
Args:
x (Tensor): input to the layer of shape `(seq_len, batch, embed_dim)`
encoder_padding_mask (ByteTensor): binary ByteTensor of shape
`(batch, src_len)` where padding elements are indicated by ``1``.
Returns:
encoded output of shape `(batch, src_len, embed_dim)`
"""
residual = x
x = self.maybe_layer_norm(self.conv_layer_norm, x, before=True)
if prev_conv_state is not None:
if incremental_state is None:
incremental_state = {}
self.conv._set_input_buffer(incremental_state, prev_conv_state)
x = self.input_dropout_module(x)
x = self.linear1(x)
if self.act is not None:
x = self.act(x)
x = self.conv(x, incremental_state=incremental_state)
x = self.linear2(x)
x = self.dropout_module(x)
x = residual + x
x = self.maybe_layer_norm(self.conv_layer_norm, x, after=True)
attn = None
if self.encoder_attn is not None:
residual = x
x = self.maybe_layer_norm(self.encoder_attn_layer_norm, x, before=True)
if prev_attn_state is not None:
if incremental_state is None:
incremental_state = {}
prev_key, prev_value = prev_attn_state
saved_state = {"prev_key": prev_key, "prev_value": prev_value}
self.encoder_attn._set_input_buffer(incremental_state, saved_state)
x, attn = self.encoder_attn(
query=x,
key=encoder_out,
value=encoder_out,
key_padding_mask=encoder_padding_mask,
incremental_state=incremental_state,
static_kv=True,
need_weights=(not self.training and self.need_attn),
)
x = self.dropout_module(x)
x = residual + x
x = self.maybe_layer_norm(self.encoder_attn_layer_norm, x, after=True)
residual = x
x = self.maybe_layer_norm(self.final_layer_norm, x, before=True)
x = F.relu(self.fc1(x))
x = self.relu_dropout_module(x)
x = self.fc2(x)
x = self.dropout_module(x)
x = residual + x
x = self.maybe_layer_norm(self.final_layer_norm, x, after=True)
return x, attn
def maybe_layer_norm(self, layer_norm, x, before=False, after=False):
assert before ^ after
if after ^ self.normalize_before:
return layer_norm(x)
else:
return x
def make_generation_fast_(self, need_attn=False, **kwargs):
self.need_attn = need_attn
def extra_repr(self):
return (
"dropout={}, relu_dropout={}, input_dropout={}, normalize_before={}".format(
self.dropout_module.p,
self.relu_dropout_module.p,
self.input_dropout_module.p,
self.normalize_before,
)
)
def Embedding(num_embeddings, embedding_dim, padding_idx):
m = nn.Embedding(num_embeddings, embedding_dim, padding_idx=padding_idx)
nn.init.normal_(m.weight, mean=0, std=embedding_dim ** -0.5)
nn.init.constant_(m.weight[padding_idx], 0)
return m
def Linear(in_features, out_features, bias=True):
m = nn.Linear(in_features, out_features, bias)
nn.init.xavier_uniform_(m.weight)
if bias:
nn.init.constant_(m.bias, 0.0)
return m
@register_model_architecture("lightconv", "lightconv")
def base_architecture(args):
args.encoder_embed_path = getattr(args, "encoder_embed_path", None)
args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512)
args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 2048)
args.encoder_layers = getattr(args, "encoder_layers", 7)
args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 8)
args.encoder_normalize_before = getattr(args, "encoder_normalize_before", False)
args.encoder_learned_pos = getattr(args, "encoder_learned_pos", False)
args.decoder_embed_path = getattr(args, "decoder_embed_path", None)
args.decoder_embed_dim = getattr(args, "decoder_embed_dim", args.encoder_embed_dim)
args.decoder_ffn_embed_dim = getattr(
args, "decoder_ffn_embed_dim", args.encoder_ffn_embed_dim
)
args.decoder_layers = getattr(args, "decoder_layers", 6)
args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 8)
args.decoder_normalize_before = getattr(args, "decoder_normalize_before", False)
args.decoder_learned_pos = getattr(args, "decoder_learned_pos", False)
args.attention_dropout = getattr(args, "attention_dropout", 0.0)
args.relu_dropout = getattr(args, "relu_dropout", 0.0)
args.dropout = getattr(args, "dropout", 0.1)
args.adaptive_softmax_cutoff = getattr(args, "adaptive_softmax_cutoff", None)
args.adaptive_softmax_dropout = getattr(args, "adaptive_softmax_dropout", 0)
args.share_decoder_input_output_embed = getattr(
args, "share_decoder_input_output_embed", False
)
args.share_all_embeddings = getattr(args, "share_all_embeddings", False)
args.no_token_positional_embeddings = getattr(
args, "no_token_positional_embeddings", False
)
args.decoder_output_dim = getattr(
args, "decoder_output_dim", args.decoder_embed_dim
)
args.decoder_input_dim = getattr(args, "decoder_input_dim", args.decoder_embed_dim)
args.encoder_conv_dim = getattr(args, "encoder_conv_dim", args.encoder_embed_dim)
args.decoder_conv_dim = getattr(args, "decoder_conv_dim", args.decoder_embed_dim)
args.encoder_kernel_size_list = getattr(
args, "encoder_kernel_size_list", [3, 7, 15, 31, 31, 31, 31]
)
args.decoder_kernel_size_list = getattr(
args, "decoder_kernel_size_list", [3, 7, 15, 31, 31, 31]
)
if len(args.encoder_kernel_size_list) == 1:
args.encoder_kernel_size_list = (
args.encoder_kernel_size_list * args.encoder_layers
)
if len(args.decoder_kernel_size_list) == 1:
args.decoder_kernel_size_list = (
args.decoder_kernel_size_list * args.decoder_layers
)
assert (
len(args.encoder_kernel_size_list) == args.encoder_layers
), "encoder_kernel_size_list doesn't match encoder_layers"
assert (
len(args.decoder_kernel_size_list) == args.decoder_layers
), "decoder_kernel_size_list doesn't match decoder_layers"
args.encoder_glu = getattr(args, "encoder_glu", True)
args.decoder_glu = getattr(args, "decoder_glu", True)
args.input_dropout = getattr(args, "input_dropout", 0.1)
args.weight_dropout = getattr(args, "weight_dropout", args.attention_dropout)
@register_model_architecture("lightconv", "lightconv_iwslt_de_en")
def lightconv_iwslt_de_en(args):
args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512)
args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 1024)
args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 4)
args.encoder_layers = getattr(args, "encoder_layers", 7)
args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 512)
args.decoder_ffn_embed_dim = getattr(args, "decoder_ffn_embed_dim", 1024)
args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 4)
args.decoder_layers = getattr(args, "decoder_layers", 6)
args.attention_dropout = getattr(args, "attention_dropout", 0.1)
args.weight_dropout = getattr(args, "weight_dropout", 0.1)
args.encoder_glu = getattr(args, "encoder_glu", False)
args.decoder_glu = getattr(args, "decoder_glu", False)
args.input_dropout = getattr(args, "input_dropout", 0.0)
base_architecture(args)
@register_model_architecture("lightconv", "lightconv_wmt_en_de")
def lightconv_wmt_en_de(args):
base_architecture(args)
@register_model_architecture("lightconv", "lightconv_wmt_en_de_big")
def lightconv_wmt_en_de_big(args):
args.attention_dropout = getattr(args, "attention_dropout", 0.1)
args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 1024)
args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 4096)
args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 16)
args.encoder_normalize_before = getattr(args, "encoder_normalize_before", False)
args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 1024)
args.decoder_ffn_embed_dim = getattr(args, "decoder_ffn_embed_dim", 4096)
args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 16)
args.dropout = getattr(args, "dropout", 0.3)
base_architecture(args)
@register_model_architecture("lightconv", "lightconv_wmt_en_fr_big")
def lightconv_wmt_en_fr_big(args):
args.dropout = getattr(args, "dropout", 0.1)
lightconv_wmt_en_de_big(args)
@register_model_architecture("lightconv", "lightconv_wmt_zh_en_big")
def lightconv_wmt_zh_en_big(args):
args.dropout = getattr(args, "dropout", 0.2)
args.attention_dropout = getattr(args, "attention_dropout", 0.2)
args.weight_dropout = getattr(args, "weight_dropout", 0.2)
lightconv_wmt_en_de_big(args)
|
bart_ls-main
|
fairseq-py/fairseq/models/lightconv.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from fairseq import utils
from fairseq.models import (
FairseqLanguageModel,
register_model,
register_model_architecture,
)
from fairseq.models.lightconv import Embedding, LightConvDecoder
from fairseq.modules import AdaptiveInput, CharacterTokenEmbedder
@register_model("lightconv_lm")
class LightConvLanguageModel(FairseqLanguageModel):
def __init__(self, decoder):
super().__init__(decoder)
@staticmethod
def add_args(parser):
"""Add model-specific arguments to the parser."""
parser.add_argument(
"--dropout",
default=0.1,
type=float,
metavar="D",
help="dropout probability",
)
parser.add_argument(
"--attention-dropout",
default=0.0,
type=float,
metavar="D",
help="dropout probability for attention weights",
)
parser.add_argument(
"--relu-dropout",
default=0.0,
type=float,
metavar="D",
help="dropout probability after ReLU in FFN",
)
parser.add_argument(
"--input-dropout",
type=float,
metavar="D",
help="dropout probability of the inputs",
)
parser.add_argument(
"--decoder-embed-dim",
type=int,
metavar="N",
help="decoder embedding dimension",
)
parser.add_argument(
"--decoder-output-dim",
type=int,
metavar="N",
help="decoder output dimension",
)
parser.add_argument(
"--decoder-input-dim", type=int, metavar="N", help="decoder input dimension"
)
parser.add_argument(
"--decoder-ffn-embed-dim",
type=int,
metavar="N",
help="decoder embedding dimension for FFN",
)
parser.add_argument(
"--decoder-layers", type=int, metavar="N", help="num decoder layers"
)
parser.add_argument(
"--decoder-attention-heads",
type=int,
metavar="N",
help="num decoder attention heads or LightConv/DynamicConv heads",
)
parser.add_argument(
"--decoder-normalize-before",
default=False,
action="store_true",
help="apply layernorm before each decoder block",
)
parser.add_argument(
"--adaptive-softmax-cutoff",
metavar="EXPR",
help="comma separated list of adaptive softmax cutoff points. "
"Must be used with adaptive_loss criterion",
)
parser.add_argument(
"--adaptive-softmax-dropout",
type=float,
metavar="D",
help="sets adaptive softmax dropout for the tail projections",
)
parser.add_argument(
"--adaptive-softmax-factor",
type=float,
metavar="N",
help="adaptive input factor",
)
parser.add_argument(
"--no-token-positional-embeddings",
default=False,
action="store_true",
help="if set, disables positional embeddings (outside self attention)",
)
parser.add_argument(
"--share-decoder-input-output-embed",
default=False,
action="store_true",
help="share decoder input and output embeddings",
)
parser.add_argument(
"--character-embeddings",
default=False,
action="store_true",
help="if set, uses character embedding convolutions to produce token embeddings",
)
parser.add_argument(
"--character-filters",
type=str,
metavar="LIST",
default="[(1, 64), (2, 128), (3, 192), (4, 256), (5, 256), (6, 256), (7, 256)]",
help="size of character embeddings",
)
parser.add_argument(
"--character-embedding-dim",
type=int,
metavar="N",
default=4,
help="size of character embeddings",
)
parser.add_argument(
"--char-embedder-highway-layers",
type=int,
metavar="N",
default=2,
help="number of highway layers for character token embeddder",
)
parser.add_argument(
"--adaptive-input",
default=False,
action="store_true",
help="if set, uses adaptive input",
)
parser.add_argument(
"--adaptive-input-factor",
type=float,
metavar="N",
help="adaptive input factor",
)
parser.add_argument(
"--adaptive-input-cutoff",
metavar="EXPR",
help="comma separated list of adaptive input cutoff points.",
)
parser.add_argument(
"--tie-adaptive-weights",
action="store_true",
help="if set, ties the weights of adaptive softmax and adaptive input",
)
parser.add_argument(
"--tie-adaptive-proj",
action="store_true",
help="if set, ties the projection weights of adaptive softmax and adaptive input",
)
parser.add_argument(
"--decoder-learned-pos",
action="store_true",
help="use learned positional embeddings in the decoder",
)
"""LightConv and DynamicConv arguments"""
parser.add_argument(
"--decoder-kernel-size-list",
type=lambda x: utils.eval_str_list(x, int),
help='list of kernel size (default: "[3,7,15,31,31,31]")',
)
parser.add_argument(
"--decoder-glu", type=utils.eval_bool, help="glu after in proj"
)
parser.add_argument(
"--decoder-conv-type",
default="dynamic",
type=str,
choices=["dynamic", "lightweight"],
help="type of convolution",
)
parser.add_argument("--weight-softmax", default=True, type=utils.eval_bool)
parser.add_argument(
"--weight-dropout",
type=float,
metavar="D",
help="dropout probability for conv weights",
)
@classmethod
def build_model(cls, args, task):
"""Build a new model instance."""
# make sure all arguments are present in older models
base_lm_architecture(args)
if getattr(args, "max_source_positions", None) is None:
args.max_source_positions = args.tokens_per_sample
if getattr(args, "max_target_positions", None) is None:
args.max_target_positions = args.tokens_per_sample
if args.character_embeddings:
embed_tokens = CharacterTokenEmbedder(
task.dictionary,
eval(args.character_filters),
args.character_embedding_dim,
args.decoder_embed_dim,
args.char_embedder_highway_layers,
)
elif args.adaptive_input:
embed_tokens = AdaptiveInput(
len(task.dictionary),
task.dictionary.pad(),
args.decoder_input_dim,
args.adaptive_input_factor,
args.decoder_embed_dim,
utils.eval_str_list(args.adaptive_input_cutoff, type=int),
)
else:
embed_tokens = Embedding(
len(task.dictionary), args.decoder_input_dim, task.dictionary.pad()
)
if args.tie_adaptive_weights:
assert args.adaptive_input
assert args.adaptive_input_factor == args.adaptive_softmax_factor
assert (
args.adaptive_softmax_cutoff == args.adaptive_input_cutoff
), "{} != {}".format(
args.adaptive_softmax_cutoff, args.adaptive_input_cutoff
)
assert args.decoder_input_dim == args.decoder_output_dim
decoder = LightConvDecoder(
args,
task.output_dictionary,
embed_tokens,
no_encoder_attn=True,
final_norm=False,
)
return LightConvLanguageModel(decoder)
@register_model_architecture("lightconv_lm", "lightconv_lm")
def base_lm_architecture(args):
args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 512)
args.decoder_ffn_embed_dim = getattr(args, "decoder_ffn_embed_dim", 2048)
args.decoder_layers = getattr(args, "decoder_layers", 6)
args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 8)
args.adaptive_softmax_cutoff = getattr(args, "adaptive_softmax_cutoff", None)
args.adaptive_softmax_dropout = getattr(args, "adaptive_softmax_dropout", 0)
args.adaptive_softmax_factor = getattr(args, "adaptive_softmax_factor", 4)
args.decoder_learned_pos = getattr(args, "decoder_learned_pos", False)
args.character_embeddings = getattr(args, "character_embeddings", False)
args.decoder_output_dim = getattr(
args, "decoder_output_dim", args.decoder_embed_dim
)
args.decoder_input_dim = getattr(args, "decoder_input_dim", args.decoder_embed_dim)
args.decoder_conv_dim = getattr(args, "decoder_conv_dim", args.decoder_embed_dim)
# The model training is not stable without this
args.decoder_normalize_before = True
args.adaptive_input = getattr(args, "adaptive_input", False)
args.adaptive_input_factor = getattr(args, "adaptive_input_factor", 4)
args.adaptive_input_cutoff = getattr(args, "adaptive_input_cutoff", None)
args.tie_adaptive_weights = getattr(args, "tie_adaptive_weights", False)
args.tie_adaptive_proj = getattr(args, "tie_adaptive_proj", False)
args.decoder_kernel_size_list = getattr(
args, "decoder_kernel_size_list", [3, 7, 15, 31, 31, 31]
)
if len(args.decoder_kernel_size_list) == 1:
args.decoder_kernel_size_list = (
args.decoder_kernel_size_list * args.decoder_layers
)
assert (
len(args.decoder_kernel_size_list) == args.decoder_layers
), "decoder_kernel_size_list doesn't match decoder_layers"
args.decoder_glu = getattr(args, "decoder_glu", True)
args.input_dropout = getattr(args, "input_dropout", 0.1)
args.weight_dropout = getattr(args, "weight_dropout", args.attention_dropout)
@register_model_architecture("lightconv_lm", "lightconv_lm_gbw")
def lightconv_lm_gbw(args):
args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 512)
args.dropout = getattr(args, "dropout", 0.1)
args.attention_dropout = getattr(args, "attention_dropout", 0.1)
args.decoder_ffn_embed_dim = getattr(args, "decoder_ffn_embed_dim", 4096)
args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 16)
base_lm_architecture(args)
|
bart_ls-main
|
fairseq-py/fairseq/models/lightconv_lm.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from typing import Dict, List, Optional
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import utils
from fairseq.incremental_decoding_utils import with_incremental_state
from fairseq.models import register_model, register_model_architecture
from fairseq.models.transformer import TransformerDecoder, TransformerModel
from fairseq.modules import FairseqDropout, LayerNorm, MultiheadAttention
from torch import Tensor
DEFAULT_MAX_SOURCE_POSITIONS = 1024
DEFAULT_MAX_TARGET_POSITIONS = 1024
@register_model("aan_transformer")
class AANTransformerModel(TransformerModel):
"""
Implements a variant of the model described in "Accelerating Neural
Transformer via an Average Attention Network" (Zhang et al., 2018)
<https://arxiv.org/abs/1805.00631`_.
Different from paper, we use a single gate for AAN gating function (mixing
AAN and residual via sigmoid(z) and 1-sigmoid(z) rather than sigmoid(z_1)
and sigmoid (z_2).
Fixed configuration for FB production: No additional FFN for AAN block.
Args:
encoder (TransformerEncoder): the encoder
decoder (TransformerDecoder): the decoder
"""
@classmethod
def build_decoder(cls, args, tgt_dict, embed_tokens):
return AANTransformerDecoder(
args,
tgt_dict,
embed_tokens,
no_encoder_attn=getattr(args, "no_cross_attention", False),
)
@with_incremental_state
class AverageAttention(nn.Module):
def __init__(self, embed_dim, dropout=0.0, bias=True):
super().__init__()
self.embed_dim = embed_dim
self.dropout = dropout
def forward(
self,
value,
mask_trick: bool = False,
mask_future_timesteps: bool = False,
incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None,
):
"""Input shape: Time x Batch x Channel
` mask_trick` is to use matrix multiplication instead of cumulative sum
to average the inputs.
Future timesteps can be masked with the
`mask_future_timesteps` argument. Padding elements can be excluded from
the key by passing a binary ByteTensor (`key_padding_mask`) with shape:
batch x src_len, where padding elements are indicated by 1s.
"""
assert mask_future_timesteps or incremental_state is None
if incremental_state is None:
return self._forward(value, mask_trick, mask_future_timesteps)
else:
return self._forward_incremental(
value, mask_trick, mask_future_timesteps, incremental_state
)
def _forward(self, value, mask_trick: bool, mask_future_timesteps: bool):
length, batch_size = value.size()[:2]
if not mask_future_timesteps:
attn = value.mean(dim=0, keepdim=True).repeat(length, 1, 1)
attn_weights = None
elif mask_trick:
v = value.transpose(0, 1)
# no TorchScript support for specifying start in arange()
attn_weights = torch.arange(length, out=torch.zeros([0]).to(v)) + 1
attn_weights = (
attn_weights.reciprocal_().unsqueeze_(1).repeat(1, length).tril(0)
)
attn_weights = attn_weights.unsqueeze_(0).repeat(batch_size, 1, 1)
attn_weights = F.dropout(
attn_weights, p=self.dropout, training=self.training
)
attn = torch.bmm(attn_weights, v)
attn = attn.transpose(0, 1).contiguous()
else:
# no TorchScript support for specifying start in arange()
attn_weights = (
torch.arange(length, out=torch.zeros([0]).to(value)) + 1
).view(length, 1, 1)
attn = value.cumsum(0) / attn_weights
attn_weights = None
return attn, attn_weights
def _forward_incremental(
self,
value,
mask_trick: bool,
mask_future_timesteps: bool,
incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]],
):
if mask_trick:
saved_state = self._get_input_buffer(incremental_state)
if "prev_vec" in saved_state:
prev_vec = saved_state["prev_vec"]
assert prev_vec is not None
value = torch.cat([prev_vec, value], dim=0)
saved_state["prev_vec"] = value
assert incremental_state is not None
incremental_state = self._set_input_buffer(incremental_state, saved_state)
attn_weights = None
attn = value.mean(0, keepdim=True)
else:
saved_state = self._get_input_buffer(incremental_state)
if "prev_sum" in saved_state:
prev_sum = saved_state["prev_sum"]
assert prev_sum is not None
curr_sum = prev_sum + value
prev_pos = saved_state["prev_pos"]
assert prev_pos is not None
pos = prev_pos + 1
attn = curr_sum / pos
else:
curr_sum = value
attn = value
pos = torch.ones([1]).int()
saved_state["prev_sum"] = curr_sum
saved_state["prev_pos"] = pos
assert incremental_state is not None
incremental_state = self._set_input_buffer(incremental_state, saved_state)
attn_weights = None
return attn, attn_weights
def extra_repr(self):
return "embed_dim={}, dropout={}".format(self.embed_dim, self.dropout)
def reorder_incremental_state(
self,
incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]],
new_order,
):
"""Reorder buffered internal state (for incremental generation)."""
input_buffer = self._get_input_buffer(incremental_state)
if input_buffer is not None:
for k in ("prev_vec", "prev_sum"):
if k in input_buffer:
input_buffer_k = input_buffer[k]
if input_buffer_k is not None and input_buffer_k.size(1) > 1:
input_buffer[k] = input_buffer_k.index_select(1, new_order)
if incremental_state is not None:
incremental_state = self._set_input_buffer(incremental_state, input_buffer)
return incremental_state
def _get_input_buffer(
self, incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]]
) -> Dict[str, Optional[Tensor]]:
result = self.get_incremental_state(incremental_state, "attn_state")
if result is not None:
return result
else:
empty_result: Dict[str, Optional[Tensor]] = {}
return empty_result
def _set_input_buffer(
self,
incremental_state: Dict[str, Dict[str, Optional[Tensor]]],
buffer: Dict[str, Optional[Tensor]],
):
return self.set_incremental_state(incremental_state, "attn_state", buffer)
class AANTransformerDecoderLayer(nn.Module):
"""Decoder layer block.
In the original paper each operation (multi-head attention, encoder
attention or FFN) is postprocessed with: `dropout -> add residual ->
layernorm`. In the tensor2tensor code they suggest that learning is more
robust when preprocessing each layer with layernorm and postprocessing with:
`dropout -> add residual`. We default to the approach in the paper, but the
tensor2tensor approach can be enabled by setting
*args.decoder_normalize_before* to ``True``.
Args:
args (argparse.Namespace): parsed command-line arguments
no_encoder_attn (bool, optional): whether to attend to encoder outputs
(default: False).
"""
def __init__(
self, args, no_encoder_attn=False, add_bias_kv=False, add_zero_attn=False
):
super().__init__()
self.embed_dim = args.decoder_embed_dim
self.cross_self_attention = getattr(args, "cross_self_attention", False)
self.avg_attn = AverageAttention(self.embed_dim, dropout=args.attention_dropout)
# differently than original paper, we use a single gate
self.aan_gating_fc = Linear(self.embed_dim * 2, self.embed_dim)
self.dropout_module = FairseqDropout(
args.dropout, module_name=self.__class__.__name__
)
self.activation_fn = utils.get_activation_fn(
activation=getattr(args, "activation_fn", "relu")
)
activation_dropout_p = getattr(args, "activation_dropout", 0)
if activation_dropout_p == 0:
# for backwards compatibility with models that use args.relu_dropout
activation_dropout_p = getattr(args, "relu_dropout", 0)
self.activation_dropout_module = FairseqDropout(
float(activation_dropout_p), module_name=self.__class__.__name__
)
self.normalize_before = args.decoder_normalize_before
# use layerNorm rather than FusedLayerNorm for exporting.
# char_inputs can be used to determint this.
# TODO remove this once we update apex with the fix
export = getattr(args, "char_inputs", False)
self.self_attn_layer_norm = LayerNorm(self.embed_dim, export=export)
if no_encoder_attn:
self.encoder_attn = None
self.encoder_attn_layer_norm = None
else:
self.encoder_attn = self.build_encoder_attention(self.embed_dim, args)
self.encoder_attn_layer_norm = LayerNorm(self.embed_dim, export=export)
self.fc1 = self.build_fc1(self.embed_dim, args.decoder_ffn_embed_dim)
self.fc2 = self.build_fc2(args.decoder_ffn_embed_dim, self.embed_dim)
self.final_layer_norm = LayerNorm(self.embed_dim, export=export)
self.need_attn = True
self.onnx_trace = False
def build_fc1(self, input_dim, output_dim):
return nn.Linear(input_dim, output_dim)
def build_fc2(self, input_dim, output_dim):
return nn.Linear(input_dim, output_dim)
def build_self_attention(
self, embed_dim, args, add_bias_kv=False, add_zero_attn=False
):
return MultiheadAttention(
embed_dim,
args.decoder_attention_heads,
dropout=args.attention_dropout,
add_bias_kv=add_bias_kv,
add_zero_attn=add_zero_attn,
self_attention=not getattr(args, "cross_self_attention", False),
)
def build_encoder_attention(self, embed_dim, args):
return MultiheadAttention(
embed_dim,
args.decoder_attention_heads,
kdim=getattr(args, "encoder_embed_dim", None),
vdim=getattr(args, "encoder_embed_dim", None),
dropout=args.attention_dropout,
encoder_decoder_attention=True,
)
def prepare_for_onnx_export_(self):
self.onnx_trace = True
def forward(
self,
x,
encoder_out: Optional[torch.Tensor] = None,
encoder_padding_mask: Optional[torch.Tensor] = None,
incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None,
prev_self_attn_state: Optional[List[torch.Tensor]] = None,
prev_attn_state: Optional[List[torch.Tensor]] = None,
self_attn_mask: Optional[torch.Tensor] = None,
self_attn_padding_mask: Optional[torch.Tensor] = None,
need_attn: bool = False,
need_head_weights: bool = False,
):
"""
Args:
x (Tensor): input to the layer of shape `(seq_len, batch, embed_dim)`
encoder_padding_mask (ByteTensor, optional): binary
ByteTensor of shape `(batch, src_len)` where padding
elements are indicated by ``1``.
need_attn (bool, optional): return attention weights
need_head_weights (bool, optional): return attention weights
for each head (default: return average over heads).
Returns:
encoded output of shape `(seq_len, batch, embed_dim)`
"""
if need_head_weights:
need_attn = True
residual = x
if self.normalize_before:
x = self.self_attn_layer_norm(x)
x, _ = self.avg_attn(
value=x,
mask_trick=self.training,
mask_future_timesteps=True,
incremental_state=incremental_state,
)
# differently than original paper, we use a single gate
gate = torch.sigmoid(self.aan_gating_fc(torch.cat([residual, x], dim=-1)))
x = gate * x + (1 - gate) * residual
x = self.dropout_module(x)
x = residual + x
if not self.normalize_before:
x = self.self_attn_layer_norm(x)
if self.encoder_attn is not None:
residual = x
if self.normalize_before:
x = self.encoder_attn_layer_norm(x)
x, attn = self.encoder_attn(
query=x,
key=encoder_out,
value=encoder_out,
key_padding_mask=encoder_padding_mask,
incremental_state=incremental_state,
static_kv=True,
need_weights=need_attn or (not self.training and self.need_attn),
need_head_weights=need_head_weights,
)
x = self.dropout_module(x)
x = residual + x
if not self.normalize_before:
x = self.encoder_attn_layer_norm(x)
residual = x
if self.normalize_before:
x = self.final_layer_norm(x)
x = self.activation_fn(self.fc1(x))
x = self.activation_dropout_module(x)
x = self.fc2(x)
x = self.dropout_module(x)
x = residual + x
if not self.normalize_before:
x = self.final_layer_norm(x)
return x, attn, None
def make_generation_fast_(self, need_attn: bool = False, **kwargs):
self.need_attn = need_attn
class AANTransformerDecoder(TransformerDecoder):
def build_decoder_layer(self, args, no_encoder_attn=False):
return AANTransformerDecoderLayer(args, no_encoder_attn)
def Linear(in_features, out_features, bias=True):
m = nn.Linear(in_features, out_features, bias)
nn.init.xavier_uniform_(m.weight)
if bias:
nn.init.constant_(m.bias, 0.0)
return m
@register_model_architecture("aan_transformer", "aan_transformer")
def base_architecture(args):
args.encoder_embed_path = getattr(args, "encoder_embed_path", None)
args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512)
args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 2048)
args.encoder_layers = getattr(args, "encoder_layers", 6)
args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 8)
args.encoder_normalize_before = getattr(args, "encoder_normalize_before", False)
args.encoder_learned_pos = getattr(args, "encoder_learned_pos", False)
args.decoder_embed_path = getattr(args, "decoder_embed_path", None)
args.decoder_embed_dim = getattr(args, "decoder_embed_dim", args.encoder_embed_dim)
args.decoder_ffn_embed_dim = getattr(
args, "decoder_ffn_embed_dim", args.encoder_ffn_embed_dim
)
args.decoder_layers = getattr(args, "decoder_layers", 6)
args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 8)
args.decoder_normalize_before = getattr(args, "decoder_normalize_before", False)
args.decoder_learned_pos = getattr(args, "decoder_learned_pos", False)
args.attention_dropout = getattr(args, "attention_dropout", 0.0)
args.activation_dropout = getattr(args, "activation_dropout", 0.0)
args.activation_fn = getattr(args, "activation_fn", "relu")
args.dropout = getattr(args, "dropout", 0.1)
args.adaptive_softmax_cutoff = getattr(args, "adaptive_softmax_cutoff", None)
args.adaptive_softmax_dropout = getattr(args, "adaptive_softmax_dropout", 0)
args.share_decoder_input_output_embed = getattr(
args, "share_decoder_input_output_embed", False
)
args.share_all_embeddings = getattr(args, "share_all_embeddings", False)
args.no_token_positional_embeddings = getattr(
args, "no_token_positional_embeddings", False
)
args.adaptive_input = getattr(args, "adaptive_input", False)
args.no_cross_attention = getattr(args, "no_cross_attention", False)
args.cross_self_attention = getattr(args, "cross_self_attention", False)
args.decoder_output_dim = getattr(
args, "decoder_output_dim", args.decoder_embed_dim
)
args.decoder_input_dim = getattr(args, "decoder_input_dim", args.decoder_embed_dim)
args.no_scale_embedding = getattr(args, "no_scale_embedding", False)
args.layernorm_embedding = getattr(args, "layernorm_embedding", False)
|
bart_ls-main
|
fairseq-py/fairseq/models/fb_aan_transformer.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
#!/usr/bin/env python3
"""
This module has the EMA class used to store a copy of the exponentially decayed
model params.
Typical usage of EMA class involves initializing an object using an existing
model (random or from a seed model) and setting the config like ema_decay,
ema_start_update which determine how the EMA model is updated. After every
update of the model i.e. at the end of the train_step, the EMA should be updated
by passing the new model to the EMA.step function. The EMA model state dict
can be stored in the extra state under the key of "ema" and dumped
into a checkpoint and loaded. The EMA object can be passed to tasks
by setting task.uses_ema property.
EMA is a smoothed/ensemble model which might have better performance
when used for inference or further fine-tuning. EMA class has a
reverse function to load the EMA params into a model and use it
like a regular model.
"""
import copy
import logging
import torch
from fairseq import checkpoint_utils
class EMA(object):
"""Exponential Moving Average of Fairseq Models
EMA keeps a copy of the exponentially decayed model params.
The set of params should include both gradient-descent and
non-gradient descent params, such as batch mean/var and buffers.
This is a modified implementation of
the open source code in https://github.com/zhawe01/fairseq-gec.git,
and internal source code in
fbcode/mobile-vision/projects/classification_pytorch/lib/utils/model_ema.py.
Similar to TF EMA.
https://www.tensorflow.org/api_docs/python/tf/train/ExponentialMovingAverage.
EMA provides a averaged and smoothed set of model weights, and has been shown to
improve vision models. EMA class does all necessary functions to update, reload,
or init EMA methods.
EMA object is initialized from an arbitrary model. By default, it is stored in
the same device (unless device specified at initialization) and with the
same precision as the model (unless ema_fp32 is True). ema_fp32 is recommended.
This stores the EMA parameters in fp32 only for the EMA update step, and
is used at the default precision otherwise.
EMA is usually enabled using EMAConfig with store_ema=True. Some important
parameters to configure EMA are
1) ema_decay - The decay of EMA
2) ema_update_freq - EMA is updated every this many model updates.
3) ema_start_update - Start EMA update after this many model updates [default 0]
Key methods:
1) step - One update of EMA using new model
2) restore - Update EMA from a state dict
3) reverse - Load EMA into a model
4) get_decay, _set_decay - Used to get or set the decay. Note _set_decay is
called from step.
5) build_fp32_params - Used to initialize or update the fp32 copy of EMA params.
Note this is enabled only when ema_fp32=True
"""
def __init__(self, model, config, device=None):
"""
@param model model to initialize the EMA with
@param config EMAConfig object with configuration like
ema_decay, ema_update_freq, ema_fp32
@param device If provided, copy EMA to this device (e.g. gpu).
Otherwise EMA is in the same device as the model.
"""
self.decay = config.ema_decay
self.model = copy.deepcopy(model)
self.model.requires_grad_(False)
self.config = config
self.fp32_params = {}
if self.config.ema_seed_model is not None:
state = checkpoint_utils.load_ema_from_checkpoint(self.config.ema_seed_model)
self.model.load_state_dict(state["model"], strict=True)
if device is not None:
logging.info(f"Copying EMA model to device {device}")
self.model = self.model.to(device=device)
if self.config.ema_fp32:
self.build_fp32_params()
self.update_freq_counter = 0
def get_model(self):
return self.model
def build_fp32_params(self, state_dict=None):
"""
Store a copy of the EMA params in fp32.
If state dict is passed, the EMA params is copied from
the provided state dict. Otherwise, it is copied from the
current EMA model parameters.
"""
if not self.config.ema_fp32:
raise RuntimeError(
"build_fp32_params should not be called if ema_fp32=False. "
"Use ema_fp32=True if this is really intended."
)
if state_dict is None:
state_dict = self.model.state_dict()
def _to_float(t):
return t.float() if torch.is_floating_point(t) else t
for param_key in state_dict:
if param_key in self.fp32_params:
self.fp32_params[param_key].copy_(state_dict[param_key])
else:
self.fp32_params[param_key] = _to_float(state_dict[param_key])
def restore(self, state_dict, build_fp32_params=False):
""" Load data from a model spec into EMA model """
self.model.load_state_dict(state_dict, strict=False)
if build_fp32_params:
self.build_fp32_params(state_dict)
def _set_decay(self, decay):
self.decay = decay
def get_decay(self):
return self.decay
def _step_internal(self, new_model, updates=None):
""" One update of the EMA model based on new model weights """
decay = self.decay
ema_state_dict = {}
ema_params = self.fp32_params if self.config.ema_fp32 else self.model.state_dict()
for key, param in new_model.state_dict().items():
try:
ema_param = ema_params[key]
except KeyError:
ema_param = param.float().clone() if param.ndim == 1 else copy.deepcopy(param)
if param.shape != ema_param.shape:
raise ValueError(
"incompatible tensor shapes between model param and ema param"
+ "{} vs. {}".format(param.shape, ema_param.shape)
)
if "version" in key:
# Do not decay a model.version pytorch param
continue
ema_param.mul_(decay)
ema_param.add_(param.to(dtype=ema_param.dtype), alpha=1-decay)
ema_state_dict[key] = ema_param
self.restore(ema_state_dict, build_fp32_params=False)
def step(self, new_model, updates=None):
"""
One update of EMA which is done every self.config.ema_update_freq
updates of the model.
@param updates The current number of model updates done.
Decay is set of 0 if model updates < ema_start_update, which means
the model will be simply copied over to the EMA.
When model updates >= ema_start_updates, then EMA is updated with
a decay of self.config.ema_decay.
"""
self._set_decay(
0
if updates is not None
and updates < self.config.ema_start_update
else self.config.ema_decay
)
if updates is not None and self.config.ema_update_freq > 1:
self.update_freq_counter += 1
if self.update_freq_counter >= self.config.ema_update_freq:
self._step_internal(new_model, updates)
self.update_freq_counter = 0
else:
self._step_internal(new_model, updates)
def reverse(self, model):
"""
Load the model parameters from EMA model.
Useful for inference or fine-tuning from the EMA model.
"""
model.load_state_dict(self.model.state_dict(), strict=False)
return model
|
bart_ls-main
|
fairseq-py/fairseq/models/ema/ema.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import importlib
import os
from .ema import EMA
def build_ema(model, cfg, device):
return EMA(model, cfg, device)
# automatically import any Python files in the models/ema/ directory
for file in sorted(os.listdir(os.path.dirname(__file__))):
if file.endswith(".py") and not file.startswith("_"):
file_name = file[: file.find(".py")]
importlib.import_module("fairseq.models.ema." + file_name)
|
bart_ls-main
|
fairseq-py/fairseq/models/ema/__init__.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
#!/usr/bin/env python3
import logging
import math
from typing import Dict, List, Optional, Tuple
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import checkpoint_utils, utils
from fairseq.data.data_utils import lengths_to_padding_mask
from fairseq.models import (
FairseqEncoder,
FairseqEncoderDecoderModel,
register_model,
register_model_architecture,
)
from fairseq.models.transformer import Embedding, TransformerDecoder
from fairseq.modules import LayerNorm, PositionalEmbedding, TransformerEncoderLayer
from torch import Tensor
logger = logging.getLogger(__name__)
@register_model("convtransformer")
class ConvTransformerModel(FairseqEncoderDecoderModel):
"""
Transformer-based Speech translation model from ESPNet-ST
https://arxiv.org/abs/2004.10234
"""
def __init__(self, encoder, decoder):
super().__init__(encoder, decoder)
@staticmethod
def add_args(parser):
"""Add model-specific arguments to the parser."""
parser.add_argument(
"--input-feat-per-channel",
type=int,
metavar="N",
help="encoder input dimension per input channel",
)
parser.add_argument(
"--activation-fn",
choices=utils.get_available_activation_fns(),
help="activation function to use",
)
parser.add_argument(
"--dropout", type=float, metavar="D", help="dropout probability"
)
parser.add_argument(
"--attention-dropout",
type=float,
metavar="D",
help="dropout probability for attention weights",
)
parser.add_argument(
"--activation-dropout",
"--relu-dropout",
type=float,
metavar="D",
help="dropout probability after activation in FFN.",
)
parser.add_argument(
"--encoder-embed-dim",
type=int,
metavar="N",
help="encoder embedding dimension",
)
parser.add_argument(
"--encoder-ffn-embed-dim",
type=int,
metavar="N",
help="encoder embedding dimension for FFN",
)
parser.add_argument(
"--encoder-layers", type=int, metavar="N", help="num encoder layers"
)
parser.add_argument(
"--encoder-attention-heads",
type=int,
metavar="N",
help="num encoder attention heads",
)
parser.add_argument(
"--encoder-normalize-before",
action="store_true",
help="apply layernorm before each encoder block",
)
parser.add_argument(
"--decoder-embed-dim",
type=int,
metavar="N",
help="decoder embedding dimension",
)
parser.add_argument(
"--decoder-ffn-embed-dim",
type=int,
metavar="N",
help="decoder embedding dimension for FFN",
)
parser.add_argument(
"--decoder-layers", type=int, metavar="N", help="num decoder layers"
)
parser.add_argument(
"--decoder-attention-heads",
type=int,
metavar="N",
help="num decoder attention heads",
)
parser.add_argument(
"--decoder-normalize-before",
action="store_true",
help="apply layernorm before each decoder block",
)
parser.add_argument(
"--decoder-output-dim",
type=int,
metavar="N",
help="decoder output dimension (extra linear layer if different from decoder embed dim)",
)
parser.add_argument(
"--share-decoder-input-output-embed",
action="store_true",
help="share decoder input and output embeddings",
)
parser.add_argument(
"--layernorm-embedding",
action="store_true",
help="add layernorm to embedding",
)
parser.add_argument(
"--no-scale-embedding",
action="store_true",
help="if True, dont scale embeddings",
)
parser.add_argument(
"--load-pretrained-encoder-from",
type=str,
metavar="STR",
help="model to take encoder weights from (for initialization)",
)
parser.add_argument(
"--load-pretrained-decoder-from",
type=str,
metavar="STR",
help="model to take decoder weights from (for initialization)",
)
parser.add_argument(
"--conv-out-channels",
type=int,
metavar="INT",
help="the number of output channels of conv layer",
)
@classmethod
def build_encoder(cls, args):
encoder = ConvTransformerEncoder(args)
if getattr(args, "load_pretrained_encoder_from", None):
encoder = checkpoint_utils.load_pretrained_component_from_model(
component=encoder, checkpoint=args.load_pretrained_encoder_from
)
return encoder
@classmethod
def build_decoder(cls, args, task, embed_tokens):
decoder = TransformerDecoderNoExtra(args, task.target_dictionary, embed_tokens)
if getattr(args, "load_pretrained_decoder_from", None):
decoder = checkpoint_utils.load_pretrained_component_from_model(
component=decoder, checkpoint=args.load_pretrained_decoder_from
)
return decoder
@classmethod
def build_model(cls, args, task):
"""Build a new model instance."""
# make sure all arguments are present in older models
base_architecture(args)
def build_embedding(dictionary, embed_dim):
num_embeddings = len(dictionary)
padding_idx = dictionary.pad()
return Embedding(num_embeddings, embed_dim, padding_idx)
decoder_embed_tokens = build_embedding(
task.target_dictionary, args.decoder_embed_dim
)
encoder = cls.build_encoder(args)
decoder = cls.build_decoder(args, task, decoder_embed_tokens)
return cls(encoder, decoder)
@staticmethod
@torch.jit.unused
def set_batch_first(lprobs):
lprobs.batch_first = True
def get_normalized_probs(
self,
net_output: Tuple[Tensor, Optional[Dict[str, List[Optional[Tensor]]]]],
log_probs: bool,
sample: Optional[Dict[str, Tensor]] = None,
):
# net_output['encoder_out'] is a (B, T, D) tensor
lprobs = self.get_normalized_probs_scriptable(net_output, log_probs, sample)
if self.training:
self.set_batch_first(lprobs)
return lprobs
def output_layout(self):
return "BTD"
"""
The forward method inherited from the base class has a **kwargs argument in
its input, which is not supported in torchscript. This method overrites the forward
method definition without **kwargs.
"""
def forward(self, src_tokens, src_lengths, prev_output_tokens):
encoder_out = self.encoder(src_tokens=src_tokens, src_lengths=src_lengths)
decoder_out = self.decoder(
prev_output_tokens=prev_output_tokens, encoder_out=encoder_out
)
return decoder_out
class ConvTransformerEncoder(FairseqEncoder):
"""Conv + Transformer encoder"""
def __init__(self, args):
"""Construct an Encoder object."""
super().__init__(None)
self.dropout = args.dropout
self.embed_scale = (
1.0 if args.no_scale_embedding else math.sqrt(args.encoder_embed_dim)
)
self.padding_idx = 1
self.in_channels = 1
self.input_dim = args.input_feat_per_channel
self.conv = torch.nn.Sequential(
torch.nn.Conv2d(1, args.conv_out_channels, 3, stride=2, padding=3 // 2),
torch.nn.ReLU(),
torch.nn.Conv2d(
args.conv_out_channels,
args.conv_out_channels,
3,
stride=2,
padding=3 // 2,
),
torch.nn.ReLU(),
)
transformer_input_dim = self.infer_conv_output_dim(
self.in_channels, self.input_dim, args.conv_out_channels
)
self.out = torch.nn.Linear(transformer_input_dim, args.encoder_embed_dim)
self.embed_positions = PositionalEmbedding(
args.max_source_positions,
args.encoder_embed_dim,
self.padding_idx,
learned=False,
)
self.transformer_layers = nn.ModuleList([])
self.transformer_layers.extend(
[TransformerEncoderLayer(args) for i in range(args.encoder_layers)]
)
if args.encoder_normalize_before:
self.layer_norm = LayerNorm(args.encoder_embed_dim)
else:
self.layer_norm = None
def pooling_ratio(self):
return 4
def infer_conv_output_dim(self, in_channels, input_dim, out_channels):
sample_seq_len = 200
sample_bsz = 10
x = torch.randn(sample_bsz, in_channels, sample_seq_len, input_dim)
x = torch.nn.Conv2d(1, out_channels, 3, stride=2, padding=3 // 2)(x)
x = torch.nn.Conv2d(out_channels, out_channels, 3, stride=2, padding=3 // 2)(x)
x = x.transpose(1, 2)
mb, seq = x.size()[:2]
return x.contiguous().view(mb, seq, -1).size(-1)
def forward(self, src_tokens, src_lengths):
"""Encode input sequence.
:param torch.Tensor xs: input tensor
:param torch.Tensor masks: input mask
:return: position embedded tensor and mask
:rtype Tuple[torch.Tensor, torch.Tensor]:
"""
bsz, max_seq_len, _ = src_tokens.size()
x = (
src_tokens.view(bsz, max_seq_len, self.in_channels, self.input_dim)
.transpose(1, 2)
.contiguous()
)
x = self.conv(x)
bsz, _, output_seq_len, _ = x.size()
x = x.transpose(1, 2).transpose(0, 1).contiguous().view(output_seq_len, bsz, -1)
x = self.out(x)
x = self.embed_scale * x
subsampling_factor = int(max_seq_len * 1.0 / output_seq_len + 0.5)
input_len_0 = (src_lengths.float() / subsampling_factor).ceil().long()
input_len_1 = x.size(0) * torch.ones([src_lengths.size(0)]).long().to(
input_len_0.device
)
input_lengths = torch.min(input_len_0, input_len_1)
encoder_padding_mask = lengths_to_padding_mask(input_lengths)
positions = self.embed_positions(encoder_padding_mask).transpose(0, 1)
x += positions
x = F.dropout(x, p=self.dropout, training=self.training)
for layer in self.transformer_layers:
x = layer(x, encoder_padding_mask)
if not encoder_padding_mask.any():
maybe_encoder_padding_mask = None
else:
maybe_encoder_padding_mask = encoder_padding_mask
return {
"encoder_out": [x],
"encoder_padding_mask": [maybe_encoder_padding_mask]
if maybe_encoder_padding_mask is not None
else [],
"encoder_embedding": [],
"encoder_states": [],
"src_tokens": [],
"src_lengths": [],
}
@torch.jit.export
def reorder_encoder_out(self, encoder_out: Dict[str, List[Tensor]], new_order):
"""
Reorder encoder output according to *new_order*.
Args:
encoder_out: output from the ``forward()`` method
new_order (LongTensor): desired order
Returns:
*encoder_out* rearranged according to *new_order*
"""
new_encoder_out = [encoder_out["encoder_out"][0].index_select(1, new_order)]
if len(encoder_out["encoder_padding_mask"]) == 0:
new_encoder_padding_mask = []
else:
new_encoder_padding_mask = [
(encoder_out["encoder_padding_mask"][0]).index_select(0, new_order)
]
if len(encoder_out["encoder_embedding"]) == 0:
new_encoder_embedding = []
else:
new_encoder_embedding = [
(encoder_out["encoder_embedding"][0]).index_select(0, new_order)
]
encoder_states = encoder_out["encoder_states"]
if len(encoder_states) > 0:
for idx, state in enumerate(encoder_states):
encoder_states[idx] = state.index_select(1, new_order)
return {
"encoder_out": new_encoder_out,
"encoder_padding_mask": new_encoder_padding_mask,
"encoder_embedding": new_encoder_embedding,
"encoder_states": encoder_states,
"src_tokens": [],
"src_lengths": [],
}
class TransformerDecoderNoExtra(TransformerDecoder):
def extract_features(
self,
prev_output_tokens,
encoder_out: Optional[Dict[str, List[Tensor]]],
incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None,
full_context_alignment: bool = False,
alignment_layer: Optional[int] = None,
alignment_heads: Optional[int] = None,
):
# call scriptable method from parent class
x, _ = self.extract_features_scriptable(
prev_output_tokens,
encoder_out,
incremental_state,
full_context_alignment,
alignment_layer,
alignment_heads,
)
return x, None
@register_model_architecture(model_name="convtransformer", arch_name="convtransformer")
def base_architecture(args):
args.input_feat_per_channel = getattr(args, "input_feat_per_channel", 80)
args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512)
args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 2048)
args.encoder_layers = getattr(args, "encoder_layers", 6)
args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 8)
args.encoder_normalize_before = getattr(args, "encoder_normalize_before", False)
args.decoder_embed_dim = getattr(args, "decoder_embed_dim", args.encoder_embed_dim)
args.decoder_ffn_embed_dim = getattr(
args, "decoder_ffn_embed_dim", args.encoder_ffn_embed_dim
)
args.decoder_layers = getattr(args, "decoder_layers", 6)
args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 8)
args.decoder_normalize_before = getattr(args, "decoder_normalize_before", False)
args.decoder_learned_pos = getattr(args, "decoder_learned_pos", False)
args.attention_dropout = getattr(args, "attention_dropout", 0.0)
args.activation_dropout = getattr(args, "activation_dropout", 0.0)
args.activation_fn = getattr(args, "activation_fn", "relu")
args.dropout = getattr(args, "dropout", 0.1)
args.adaptive_softmax_cutoff = getattr(args, "adaptive_softmax_cutoff", None)
args.adaptive_softmax_dropout = getattr(args, "adaptive_softmax_dropout", 0)
args.share_decoder_input_output_embed = getattr(
args, "share_decoder_input_output_embed", False
)
args.no_token_positional_embeddings = getattr(
args, "no_token_positional_embeddings", False
)
args.adaptive_input = getattr(args, "adaptive_input", False)
args.decoder_layerdrop = getattr(args, "decoder_layerdrop", 0.0)
args.decoder_output_dim = getattr(
args, "decoder_output_dim", args.decoder_embed_dim
)
args.decoder_input_dim = getattr(args, "decoder_input_dim", args.decoder_embed_dim)
args.no_scale_embedding = getattr(args, "no_scale_embedding", False)
args.quant_noise_pq = getattr(args, "quant_noise_pq", 0)
args.max_source_positions = getattr(args, "max_source_positions", 3000)
args.max_target_positions = getattr(args, "max_target_positions", 1024)
args.tie_adaptive_weights = getattr(args, "tie_adaptive_weights", False)
args.conv_out_channels = getattr(args, "conv_out_channels", args.encoder_embed_dim)
@register_model_architecture("convtransformer", "convtransformer_espnet")
def convtransformer_espnet(args):
args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 256)
args.encoder_layers = getattr(args, "encoder_layers", 12)
args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 4)
args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 4)
|
bart_ls-main
|
fairseq-py/fairseq/models/speech_to_text/convtransformer.py
|
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import logging
import copy
from typing import Dict, List, Optional, Tuple
from fairseq import utils, checkpoint_utils
from fairseq.models import (FairseqEncoderDecoderModel, FairseqEncoder,
register_model, register_model_architecture)
from fairseq.models.transformer import Embedding, TransformerDecoder
from fairseq.models.wav2vec import Wav2VecEncoder
from fairseq.modules.layer_norm import LayerNorm
from fairseq.data.data_utils import lengths_to_padding_mask
from fairseq.utils import safe_hasattr
from torch import Tensor
import torch.nn as nn
logger = logging.getLogger(__name__)
class Conv1dAdaptor(nn.Module):
def __init__(self, in_dim, out_dim, n_layers=3, kernel_size=3, stride=2,
add_layernorm=False):
super().__init__()
self.layers = nn.ModuleList(
nn.Conv1d(in_dim if i == 0 else out_dim, out_dim * 2, kernel_size,
stride=stride, padding=kernel_size // 2)
for i in range(n_layers)
)
self.layernorms = None
if add_layernorm:
self.layernorms = nn.ModuleList(LayerNorm(out_dim)
for _ in range(n_layers))
self.stride = stride
@classmethod
def add_args(cls, parser):
parser.add_argument("--adaptor-n-layers", type=int)
parser.add_argument("--adaptor-kernel-size", type=int)
parser.add_argument("--adaptor-stride", type=int)
parser.add_argument("--adaptor-layernorm", action='store_true')
def get_out_seq_lens_tensor(self, in_seq_lens_tensor):
out = in_seq_lens_tensor.clone()
for _ in self.layers:
out = ((out.float() - 1) / self.stride + 1).floor().long()
return out
def forward(self, x, padding_mask):
# T x B x C -> B x C x T
x = x.transpose(0, 1).transpose(1, 2)
for i, layer in enumerate(self.layers):
x = nn.functional.glu(layer(x), dim=1)
if self.layernorms is not None:
x = self.layernorms[i](x.transpose(1, 2)).transpose(1, 2)
# B x C x T -> T x B x C
x = x.transpose(1, 2).transpose(0, 1)
if padding_mask is None:
out_padding_mask = None
else:
out_lengths = self.get_out_seq_lens_tensor((~padding_mask).sum(1))
out_padding_mask = lengths_to_padding_mask(out_lengths)
return x, out_padding_mask
def add_wav2vec_asr_args(parser):
parser.add_argument("--w2v-path", help="path to wav2vec 2.0 model")
parser.add_argument(
"--no-pretrained-weights",
action="store_true",
help="if true, does not load pretrained weights",
)
parser.add_argument(
"--dropout-input",
type=float,
metavar="D",
help="dropout to apply to the input (after feat extr)",
)
parser.add_argument(
"--final-dropout",
type=float,
metavar="D",
help="dropout after transformer and before final projection",
)
parser.add_argument(
"--apply-mask", action="store_true", help="apply masking during fine-tuning"
)
parser.add_argument(
"--dropout",
type=float,
metavar="D",
help="dropout probability inside wav2vec 2.0 model",
)
parser.add_argument(
"--attention-dropout",
type=float,
metavar="D",
help="dropout probability for attention weights inside wav2vec 2.0 model",
)
parser.add_argument(
"--activation-dropout",
"--relu-dropout",
type=float,
metavar="D",
help="dropout probability after activation in FFN inside wav2vec 2.0 model",
)
parser.add_argument(
"--mask-length", type=int, help="repeat the mask indices multiple times"
)
parser.add_argument(
"--mask-prob", type=float, help="probability of replacing a token with mask"
)
parser.add_argument(
"--mask-selection",
type=str,
choices=["static", "uniform", "normal", "poisson"],
help="how to choose masks",
)
parser.add_argument(
"--mask-other",
type=float,
help="stdev of the mask length in case of 'normal' selection strategy",
)
parser.add_argument(
"--no-mask-overlap",
action="store_true",
help="whether to allow masks to overlap",
)
parser.add_argument(
"--mask-channel-length", type=int, help="repeat the mask indices multiple times"
)
parser.add_argument(
"--mask-channel-prob",
type=float,
help="probability of replacing a token with mask",
)
parser.add_argument(
"--mask-channel-selection",
type=str,
choices=["static", "uniform", "normal", "poisson"],
help="how to choose masks",
)
parser.add_argument(
"--mask-channel-other",
type=float,
help="stdev of the mask length in case of 'normal' selection strategy",
)
parser.add_argument(
"--no-mask-channel-overlap",
action="store_true",
help="whether to allow masks to overlap",
)
parser.add_argument(
"--freeze-finetune-updates",
default=0,
type=int,
help="dont finetune wav2vec for this many updates",
)
parser.add_argument(
"--feature-grad-mult",
default=None,
type=float,
help="reset feature grad mult in wav2vec 2.0 to this",
)
parser.add_argument(
"--layerdrop",
default=0.0,
type=float,
help="probability of dropping a layer in wav2vec 2.0",
)
parser.add_argument("--w2v-args", default=None)
class Wav2VecEncoderWithAdaptor(FairseqEncoder):
def __init__(self, args):
super().__init__(None)
self.w2v_encoder = Wav2VecEncoder(args)
encoder_out_dim = self.w2v_encoder.w2v_model.encoder.embedding_dim
# Projection + 8x shrinking
self.adaptor = Conv1dAdaptor(
encoder_out_dim, args.decoder_embed_dim,
n_layers=args.adaptor_n_layers,
kernel_size=args.adaptor_kernel_size, stride=args.adaptor_stride,
add_layernorm=args.adaptor_layernorm
)
for k, p in self.w2v_encoder.w2v_model.named_parameters():
# Freeze pretrained models by default
if safe_hasattr(args, 'finetune_w2v_params') and XMTransformerModel.finetune_params(
args.finetune_w2v_params, k):
p.requires_grad = True
else:
p.requires_grad = False
@classmethod
def add_args(cls, parser):
add_wav2vec_asr_args(parser)
parser.add_argument(
"--normalize", action="store_true",
help="if set, normalizes input to have 0 mean and unit variance",
)
parser.add_argument("--finetune-w2v-params", type=str, metavar="STR",
help="comma-separated param strings to finetune.")
Conv1dAdaptor.add_args(parser)
def forward(self, src_tokens, src_lengths=None, **kwargs):
padding_mask = lengths_to_padding_mask(src_lengths)
out = self.w2v_encoder.forward(src_tokens, padding_mask, tbc=True)
x = out["encoder_out"]
enc_padding_mask = None
if out["encoder_padding_mask"] is not None:
enc_padding_mask = out["encoder_padding_mask"].transpose(0, 1) # T X B --> B X T
x, enc_padding_mask = self.adaptor(x, enc_padding_mask)
return {
"encoder_out": [x], # T x B x C
"encoder_padding_mask": [enc_padding_mask] if enc_padding_mask.any() else [], # B x T
"encoder_embedding": [], # B x T x C
"encoder_states": [], # List[T x B x C]
"src_tokens": [],
"src_lengths": [],
}
def reorder_encoder_out(self, encoder_out, new_order):
new_encoder_out = (
[] if len(encoder_out["encoder_out"]) == 0
else [x.index_select(1, new_order) for x in encoder_out["encoder_out"]]
)
new_encoder_padding_mask = (
[] if len(encoder_out["encoder_padding_mask"]) == 0
else [x.index_select(0, new_order) for x in
encoder_out["encoder_padding_mask"]]
)
new_encoder_embedding = (
[] if len(encoder_out["encoder_embedding"]) == 0
else [x.index_select(0, new_order) for x in
encoder_out["encoder_embedding"]]
)
encoder_states = encoder_out["encoder_states"]
if len(encoder_states) > 0:
for idx, state in enumerate(encoder_states):
encoder_states[idx] = state.index_select(1, new_order)
return {
"encoder_out": new_encoder_out, # T x B x C
"encoder_padding_mask": new_encoder_padding_mask, # B x T
"encoder_embedding": new_encoder_embedding, # B x T x C
"encoder_states": encoder_states, # List[T x B x C]
"src_tokens": [], # B x T
"src_lengths": [], # B x 1
}
def add_decoder_args(parser):
parser.add_argument("--activation-fn", type=str, default='relu',
choices=utils.get_available_activation_fns(),
help="activation function to use")
parser.add_argument("--decoder-dropout", type=float, metavar="D",
help="dropout probability")
parser.add_argument("--decoder-attention-dropout", type=float,
metavar="D",
help="dropout probability for attention weights")
parser.add_argument("--decoder-activation-dropout", type=float,
metavar="D",
help="dropout probability after activation in FFN.")
parser.add_argument("--decoder-embed-dim", type=int, metavar="N",
help="decoder embedding dimension")
parser.add_argument("--decoder-ffn-embed-dim", type=int, metavar="N",
help="decoder embedding dimension for FFN")
parser.add_argument("--decoder-layers", type=int, metavar="N",
help="num decoder layers")
parser.add_argument("--decoder-attention-heads", type=int, metavar="N",
help="num decoder attention heads")
parser.add_argument("--decoder-normalize-before", action="store_true",
help="apply layernorm before each decoder block")
parser.add_argument("--layernorm-embedding", action="store_true",
help="add layernorm to embedding")
parser.add_argument("--no-scale-embedding", action="store_true",
help="if True, dont scale embeddings")
parser.add_argument(
"--load-pretrained-decoder-from", type=str, metavar="STR",
help="model to take decoder weights from (for initialization)"
)
parser.add_argument("--finetune-decoder-params", type=str,
metavar="STR",
help="comma-separated param strings to finetune.")
parser.add_argument("--checkpoint-activations", action="store_true")
@register_model("xm_transformer")
class XMTransformerModel(FairseqEncoderDecoderModel):
def __init__(self, encoder, decoder):
super().__init__(encoder, decoder)
@classmethod
def add_args(cls, parser):
"""Add model-specific arguments to the parser."""
Wav2VecEncoderWithAdaptor.add_args(parser)
add_decoder_args(parser)
@classmethod
def build_encoder(cls, args):
_args = copy.deepcopy(args)
state = checkpoint_utils.load_checkpoint_to_cpu(args.w2v_path)
if state.get("cfg") is not None:
encoder_embed_dim = state["cfg"]._content["model"]["encoder_embed_dim"]
elif state.get("args") is not None:
encoder_embed_dim = state["args"].encoder_embed_dim
else:
raise ValueError(f"Invalid config in {args.w2v_path}")
_args.decoder_embed_dim = encoder_embed_dim
encoder = Wav2VecEncoderWithAdaptor(_args)
return encoder
@classmethod
def build_decoder(cls, args, task, embed_tokens):
_args = copy.deepcopy(args)
_args.dropout = args.decoder_dropout
_args.attention_dropout = args.decoder_attention_dropout
_args.activation_dropout = args.decoder_activation_dropout
_args.max_target_positions = 1024
decoder = TransformerDecoder(_args, task.target_dictionary,
embed_tokens)
if getattr(args, "load_pretrained_decoder_from", None):
decoder = checkpoint_utils.load_pretrained_component_from_model(
component=decoder, checkpoint=args.load_pretrained_decoder_from
)
for k, p in decoder.named_parameters():
# Freeze pretrained models by default
if safe_hasattr(args, 'finetune_decoder_params') and XMTransformerModel.finetune_params(
args.finetune_decoder_params, k):
p.requires_grad = True
else:
p.requires_grad = False
return decoder
@classmethod
def build_model(cls, args, task):
"""Build a new model instance."""
# make sure all arguments are present in older models
base_architecture(args)
def build_embedding(dictionary, embed_dim):
num_embeddings = len(dictionary)
padding_idx = dictionary.pad()
return Embedding(num_embeddings, embed_dim, padding_idx)
decoder_embed_tokens = build_embedding(task.target_dictionary,
args.decoder_embed_dim)
encoder = cls.build_encoder(args)
decoder = cls.build_decoder(args, task, decoder_embed_tokens)
return cls(encoder, decoder)
def get_normalized_probs(
self,
net_output: Tuple[Tensor, Optional[Dict[str, List[Optional[Tensor]]]]],
log_probs: bool,
sample: Optional[Dict[str, Tensor]] = None,
):
# net_output['encoder_out'] is a (B, T, D) tensor
lprobs = self.get_normalized_probs_scriptable(net_output, log_probs,
sample)
lprobs.batch_first = True
return lprobs
def forward(self, src_tokens, src_lengths, prev_output_tokens, **kwargs):
"""
The forward method inherited from the base class has a **kwargs
argument in its input, which is not supported in torchscript. This
method overrites the forward method definition without **kwargs.
"""
encoder_out = self.encoder(src_tokens=src_tokens,
src_lengths=src_lengths, **kwargs)
decoder_out = self.decoder(prev_output_tokens=prev_output_tokens,
encoder_out=encoder_out)
return decoder_out
def upgrade_state_dict(self, state_dict):
for k, _ in state_dict.items():
if 'adaptor.layers' in state_dict:
print(k)
new = k.replace('adaptor.layers', 'adaptor_layers')
state_dict[new] = state_dict[k]
del state_dict[k]
@staticmethod
def finetune_params(finetune_params, param_name):
if finetune_params == "all":
return True
finetune_params_list = finetune_params.split(",")
for finetune_param in finetune_params_list:
if finetune_param in param_name:
return True
return False
def set_default_w2v_encoder_args(args):
args.no_pretrained_weights = getattr(args, "no_pretrained_weights", False)
args.dropout_input = getattr(args, "dropout_input", 0)
args.final_dropout = getattr(args, "final_dropout", 0)
args.apply_mask = getattr(args, "apply_mask", False)
args.dropout = getattr(args, "dropout", 0)
args.attention_dropout = getattr(args, "attention_dropout", 0)
args.activation_dropout = getattr(args, "activation_dropout", 0)
args.mask_length = getattr(args, "mask_length", 10)
args.mask_prob = getattr(args, "mask_prob", 0.5)
args.mask_selection = getattr(args, "mask_selection", "static")
args.mask_other = getattr(args, "mask_other", 0)
args.no_mask_overlap = getattr(args, "no_mask_overlap", False)
args.mask_channel_length = getattr(args, "mask_channel_length", 10)
args.mask_channel_prob = getattr(args, "mask_channel_prob", 0.5)
args.mask_channel_before = getattr(args, "mask_channel_before", False)
args.mask_channel_selection = getattr(args, "mask_channel_selection",
"static")
args.mask_channel_other = getattr(args, "mask_channel_other", 0)
args.no_mask_channel_overlap = getattr(args, "no_mask_channel_overlap",
False)
args.freeze_finetune_updates = getattr(args, "freeze_finetune_updates", 0)
args.feature_grad_mult = 0.1
args.layerdrop = getattr(args, "layerdrop", 0.0)
args.normalize = getattr(args, "normalize", False)
def set_default_adaptor_args(args):
args.adaptor_n_layers = getattr(args, "adaptor_n_layers", 3)
args.adaptor_kernel_size = getattr(args, "adaptor_kernel_size", 3)
args.adaptor_stride = getattr(args, "adaptor_stride", 2)
args.adaptor_layernorm = getattr(args, "adaptor_layernorm", False)
def set_default_mbart_decoder_args(args):
args.decoder_embed_path = getattr(args, 'decoder_embed_path', None)
args.decoder_embed_dim = getattr(args, 'decoder_embed_dim', 1024)
args.decoder_ffn_embed_dim = getattr(args, 'decoder_ffn_embed_dim',
4 * 1024)
args.decoder_layers = getattr(args, 'decoder_layers', 12)
args.decoder_attention_heads = getattr(args, 'decoder_attention_heads', 16)
args.decoder_normalize_before = getattr(args, 'decoder_normalize_before',
True)
args.decoder_learned_pos = getattr(args, 'decoder_learned_pos', True)
args.decoder_layerdrop = getattr(args, "decoder_layerdrop", 0.0)
args.adaptive_input = getattr(args, "adaptive_input", False)
args.decoder_attention_dropout = getattr(args, 'decoder_attention_dropout',
0.)
args.decoder_activation_dropout = getattr(args,
'decoder_activation_dropout', 0.)
args.decoder_dropout = getattr(args, 'decoder_dropout', 0.1)
args.adaptive_softmax_cutoff = getattr(args, 'adaptive_softmax_cutoff',
None)
args.adaptive_softmax_dropout = getattr(args, 'adaptive_softmax_dropout', 0)
args.share_decoder_input_output_embed = getattr(
args, 'share_decoder_input_output_embed', True
)
args.no_token_positional_embeddings = getattr(
args, "no_token_positional_embeddings", False
)
args.decoder_output_dim = getattr(args, 'decoder_output_dim',
args.decoder_embed_dim)
args.decoder_input_dim = getattr(args, 'decoder_input_dim',
args.decoder_embed_dim)
args.no_scale_embedding = getattr(args, 'no_scale_embedding', False)
args.quant_noise_pq = getattr(args, "quant_noise_pq", 0)
args.layernorm_embedding = getattr(args, 'layernorm_embedding', True)
args.activation_fn = getattr(args, 'activation_fn', 'gelu')
args.pooler_activation_fn = getattr(args, 'pooler_activation_fn', 'tanh')
args.pooler_dropout = getattr(args, 'pooler_dropout', 0.0)
args.checkpoint_activations = getattr(args, "checkpoint_activations", False)
@register_model_architecture(model_name="xm_transformer",
arch_name="xm_transformer")
def base_architecture(args):
set_default_w2v_encoder_args(args)
set_default_adaptor_args(args)
set_default_mbart_decoder_args(args)
|
bart_ls-main
|
fairseq-py/fairseq/models/speech_to_text/xm_transformer.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
#!/usr/bin/env python3
from ast import literal_eval
from typing import List, Tuple
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import checkpoint_utils, utils
from fairseq.data.data_utils import lengths_to_padding_mask
from fairseq.models import (
FairseqEncoder,
FairseqEncoderDecoderModel,
FairseqIncrementalDecoder,
register_model,
register_model_architecture,
)
@register_model("s2t_berard")
class BerardModel(FairseqEncoderDecoderModel):
"""Implementation of a model similar to https://arxiv.org/abs/1802.04200
Paper title: End-to-End Automatic Speech Translation of Audiobooks
An implementation is available in tensorflow at
https://github.com/eske/seq2seq
Relevant files in this implementation are the config
(https://github.com/eske/seq2seq/blob/master/config/LibriSpeech/AST.yaml)
and the model code
(https://github.com/eske/seq2seq/blob/master/translate/models.py).
The encoder and decoder try to be close to the original implementation.
The attention is an MLP as in Bahdanau et al.
(https://arxiv.org/abs/1409.0473).
There is no state initialization by averaging the encoder outputs.
"""
def __init__(self, encoder, decoder):
super().__init__(encoder, decoder)
@staticmethod
def add_args(parser):
parser.add_argument(
"--input-layers",
type=str,
metavar="EXPR",
help="List of linear layer dimensions. These "
"layers are applied to the input features and "
"are followed by tanh and possibly dropout.",
)
parser.add_argument(
"--dropout",
type=float,
metavar="D",
help="Dropout probability to use in the encoder/decoder. "
"Note that this parameters control dropout in various places, "
"there is no fine-grained control for dropout for embeddings "
"vs LSTM layers for example.",
)
parser.add_argument(
"--in-channels",
type=int,
metavar="N",
help="Number of encoder input channels. " "Typically value is 1.",
)
parser.add_argument(
"--conv-layers",
type=str,
metavar="EXPR",
help="List of conv layers " "(format: (channels, kernel, stride)).",
)
parser.add_argument(
"--num-blstm-layers",
type=int,
metavar="N",
help="Number of encoder bi-LSTM layers.",
)
parser.add_argument(
"--lstm-size", type=int, metavar="N", help="LSTM hidden size."
)
parser.add_argument(
"--decoder-embed-dim",
type=int,
metavar="N",
help="Embedding dimension of the decoder target tokens.",
)
parser.add_argument(
"--decoder-hidden-dim",
type=int,
metavar="N",
help="Decoder LSTM hidden dimension.",
)
parser.add_argument(
"--decoder-num-layers",
type=int,
metavar="N",
help="Number of decoder LSTM layers.",
)
parser.add_argument(
"--attention-dim",
type=int,
metavar="N",
help="Hidden layer dimension in MLP attention.",
)
parser.add_argument(
"--output-layer-dim",
type=int,
metavar="N",
help="Hidden layer dim for linear layer prior to output projection.",
)
parser.add_argument(
"--load-pretrained-encoder-from",
type=str,
metavar="STR",
help="model to take encoder weights from (for initialization)",
)
parser.add_argument(
"--load-pretrained-decoder-from",
type=str,
metavar="STR",
help="model to take decoder weights from (for initialization)",
)
@classmethod
def build_encoder(cls, args, task):
encoder = BerardEncoder(
input_layers=literal_eval(args.input_layers),
conv_layers=literal_eval(args.conv_layers),
in_channels=args.input_channels,
input_feat_per_channel=args.input_feat_per_channel,
num_blstm_layers=args.num_blstm_layers,
lstm_size=args.lstm_size,
dropout=args.dropout,
)
if getattr(args, "load_pretrained_encoder_from", None):
encoder = checkpoint_utils.load_pretrained_component_from_model(
component=encoder, checkpoint=args.load_pretrained_encoder_from
)
return encoder
@classmethod
def build_decoder(cls, args, task):
decoder = LSTMDecoder(
dictionary=task.target_dictionary,
embed_dim=args.decoder_embed_dim,
num_layers=args.decoder_num_layers,
hidden_size=args.decoder_hidden_dim,
dropout=args.dropout,
encoder_output_dim=2 * args.lstm_size, # bidirectional
attention_dim=args.attention_dim,
output_layer_dim=args.output_layer_dim,
)
if getattr(args, "load_pretrained_decoder_from", None):
decoder = checkpoint_utils.load_pretrained_component_from_model(
component=decoder, checkpoint=args.load_pretrained_decoder_from
)
return decoder
@classmethod
def build_model(cls, args, task):
"""Build a new model instance."""
encoder = cls.build_encoder(args, task)
decoder = cls.build_decoder(args, task)
return cls(encoder, decoder)
def get_normalized_probs(self, net_output, log_probs, sample=None):
# net_output['encoder_out'] is a (B, T, D) tensor
lprobs = super().get_normalized_probs(net_output, log_probs, sample)
# lprobs is a (B, T, D) tensor
lprobs.batch_first = True
return lprobs
class BerardEncoder(FairseqEncoder):
def __init__(
self,
input_layers: List[int],
conv_layers: List[Tuple[int]],
in_channels: int,
input_feat_per_channel: int,
num_blstm_layers: int,
lstm_size: int,
dropout: float,
):
"""
Args:
input_layers: list of linear layer dimensions. These layers are
applied to the input features and are followed by tanh and
possibly dropout.
conv_layers: list of conv2d layer configurations. A configuration is
a tuple (out_channels, conv_kernel_size, stride).
in_channels: number of input channels.
input_feat_per_channel: number of input features per channel. These
are speech features, typically 40 or 80.
num_blstm_layers: number of bidirectional LSTM layers.
lstm_size: size of the LSTM hidden (and cell) size.
dropout: dropout probability. Dropout can be applied after the
linear layers and LSTM layers but not to the convolutional
layers.
"""
super().__init__(None)
self.input_layers = nn.ModuleList()
in_features = input_feat_per_channel
for out_features in input_layers:
if dropout > 0:
self.input_layers.append(
nn.Sequential(
nn.Linear(in_features, out_features), nn.Dropout(p=dropout)
)
)
else:
self.input_layers.append(nn.Linear(in_features, out_features))
in_features = out_features
self.in_channels = in_channels
self.input_dim = input_feat_per_channel
self.conv_kernel_sizes_and_strides = []
self.conv_layers = nn.ModuleList()
lstm_input_dim = input_layers[-1]
for conv_layer in conv_layers:
out_channels, conv_kernel_size, conv_stride = conv_layer
self.conv_layers.append(
nn.Conv2d(
in_channels,
out_channels,
conv_kernel_size,
stride=conv_stride,
padding=conv_kernel_size // 2,
)
)
self.conv_kernel_sizes_and_strides.append((conv_kernel_size, conv_stride))
in_channels = out_channels
lstm_input_dim //= conv_stride
lstm_input_dim *= conv_layers[-1][0]
self.lstm_size = lstm_size
self.num_blstm_layers = num_blstm_layers
self.lstm = nn.LSTM(
input_size=lstm_input_dim,
hidden_size=lstm_size,
num_layers=num_blstm_layers,
dropout=dropout,
bidirectional=True,
)
self.output_dim = 2 * lstm_size # bidirectional
if dropout > 0:
self.dropout = nn.Dropout(p=dropout)
else:
self.dropout = None
def forward(self, src_tokens, src_lengths=None, **kwargs):
"""
Args
src_tokens: padded tensor (B, T, C * feat)
src_lengths: tensor of original lengths of input utterances (B,)
"""
bsz, max_seq_len, _ = src_tokens.size()
# (B, C, T, feat)
x = (
src_tokens.view(bsz, max_seq_len, self.in_channels, self.input_dim)
.transpose(1, 2)
.contiguous()
)
for input_layer in self.input_layers:
x = input_layer(x)
x = torch.tanh(x)
for conv_layer in self.conv_layers:
x = conv_layer(x)
bsz, _, output_seq_len, _ = x.size()
# (B, C, T, feat) -> (B, T, C, feat) -> (T, B, C, feat) ->
# (T, B, C * feat)
x = x.transpose(1, 2).transpose(0, 1).contiguous().view(output_seq_len, bsz, -1)
input_lengths = src_lengths.clone()
for k, s in self.conv_kernel_sizes_and_strides:
p = k // 2
input_lengths = (input_lengths.float() + 2 * p - k) / s + 1
input_lengths = input_lengths.floor().long()
packed_x = nn.utils.rnn.pack_padded_sequence(x, input_lengths)
h0 = x.new(2 * self.num_blstm_layers, bsz, self.lstm_size).zero_()
c0 = x.new(2 * self.num_blstm_layers, bsz, self.lstm_size).zero_()
packed_outs, _ = self.lstm(packed_x, (h0, c0))
# unpack outputs and apply dropout
x, output_lengths = nn.utils.rnn.pad_packed_sequence(packed_outs)
if self.dropout is not None:
x = self.dropout(x)
encoder_padding_mask = (
lengths_to_padding_mask(output_lengths).to(src_tokens.device).t()
)
return {
"encoder_out": x, # (T, B, C)
"encoder_padding_mask": encoder_padding_mask, # (T, B)
}
def reorder_encoder_out(self, encoder_out, new_order):
encoder_out["encoder_out"] = encoder_out["encoder_out"].index_select(
1, new_order
)
encoder_out["encoder_padding_mask"] = encoder_out[
"encoder_padding_mask"
].index_select(1, new_order)
return encoder_out
class MLPAttention(nn.Module):
"""The original attention from Badhanau et al. (2014)
https://arxiv.org/abs/1409.0473, based on a Multi-Layer Perceptron.
The attention score between position i in the encoder and position j in the
decoder is: alpha_ij = V_a * tanh(W_ae * enc_i + W_ad * dec_j + b_a)
"""
def __init__(self, decoder_hidden_state_dim, context_dim, attention_dim):
super().__init__()
self.context_dim = context_dim
self.attention_dim = attention_dim
# W_ae and b_a
self.encoder_proj = nn.Linear(context_dim, self.attention_dim, bias=True)
# W_ad
self.decoder_proj = nn.Linear(
decoder_hidden_state_dim, self.attention_dim, bias=False
)
# V_a
self.to_scores = nn.Linear(self.attention_dim, 1, bias=False)
def forward(self, decoder_state, source_hids, encoder_padding_mask):
"""The expected input dimensions are:
decoder_state: bsz x decoder_hidden_state_dim
source_hids: src_len x bsz x context_dim
encoder_padding_mask: src_len x bsz
"""
src_len, bsz, _ = source_hids.size()
# (src_len*bsz) x context_dim (to feed through linear)
flat_source_hids = source_hids.view(-1, self.context_dim)
# (src_len*bsz) x attention_dim
encoder_component = self.encoder_proj(flat_source_hids)
# src_len x bsz x attention_dim
encoder_component = encoder_component.view(src_len, bsz, self.attention_dim)
# 1 x bsz x attention_dim
decoder_component = self.decoder_proj(decoder_state).unsqueeze(0)
# Sum with broadcasting and apply the non linearity
# src_len x bsz x attention_dim
hidden_att = torch.tanh(
(decoder_component + encoder_component).view(-1, self.attention_dim)
)
# Project onto the reals to get attentions scores (src_len x bsz)
attn_scores = self.to_scores(hidden_att).view(src_len, bsz)
# Mask + softmax (src_len x bsz)
if encoder_padding_mask is not None:
attn_scores = (
attn_scores.float()
.masked_fill_(encoder_padding_mask, float("-inf"))
.type_as(attn_scores)
) # FP16 support: cast to float and back
# srclen x bsz
normalized_masked_attn_scores = F.softmax(attn_scores, dim=0)
# Sum weighted sources (bsz x context_dim)
attn_weighted_context = (
source_hids * normalized_masked_attn_scores.unsqueeze(2)
).sum(dim=0)
return attn_weighted_context, normalized_masked_attn_scores
class LSTMDecoder(FairseqIncrementalDecoder):
def __init__(
self,
dictionary,
embed_dim,
num_layers,
hidden_size,
dropout,
encoder_output_dim,
attention_dim,
output_layer_dim,
):
"""
Args:
dictionary: target text dictionary.
embed_dim: embedding dimension for target tokens.
num_layers: number of LSTM layers.
hidden_size: hidden size for LSTM layers.
dropout: dropout probability. Dropout can be applied to the
embeddings, the LSTM layers, and the context vector.
encoder_output_dim: encoder output dimension (hidden size of
encoder LSTM).
attention_dim: attention dimension for MLP attention.
output_layer_dim: size of the linear layer prior to output
projection.
"""
super().__init__(dictionary)
self.num_layers = num_layers
self.hidden_size = hidden_size
num_embeddings = len(dictionary)
padding_idx = dictionary.pad()
self.embed_tokens = nn.Embedding(num_embeddings, embed_dim, padding_idx)
if dropout > 0:
self.dropout = nn.Dropout(p=dropout)
else:
self.dropout = None
self.layers = nn.ModuleList()
for layer_id in range(num_layers):
input_size = embed_dim if layer_id == 0 else encoder_output_dim
self.layers.append(
nn.LSTMCell(input_size=input_size, hidden_size=hidden_size)
)
self.context_dim = encoder_output_dim
self.attention = MLPAttention(
decoder_hidden_state_dim=hidden_size,
context_dim=encoder_output_dim,
attention_dim=attention_dim,
)
self.deep_output_layer = nn.Linear(
hidden_size + encoder_output_dim + embed_dim, output_layer_dim
)
self.output_projection = nn.Linear(output_layer_dim, num_embeddings)
def forward(
self, prev_output_tokens, encoder_out=None, incremental_state=None, **kwargs
):
encoder_padding_mask = encoder_out["encoder_padding_mask"]
encoder_outs = encoder_out["encoder_out"]
if incremental_state is not None:
prev_output_tokens = prev_output_tokens[:, -1:]
bsz, seqlen = prev_output_tokens.size()
srclen = encoder_outs.size(0)
# embed tokens
embeddings = self.embed_tokens(prev_output_tokens)
x = embeddings
if self.dropout is not None:
x = self.dropout(x)
# B x T x C -> T x B x C
x = x.transpose(0, 1)
# initialize previous states (or get from cache during incremental
# generation)
cached_state = utils.get_incremental_state(
self, incremental_state, "cached_state"
)
if cached_state is not None:
prev_hiddens, prev_cells = cached_state
else:
prev_hiddens = [encoder_out["encoder_out"].mean(dim=0)] * self.num_layers
prev_cells = [x.new_zeros(bsz, self.hidden_size)] * self.num_layers
attn_scores = x.new_zeros(bsz, srclen)
attention_outs = []
outs = []
for j in range(seqlen):
input = x[j, :, :]
attention_out = None
for i, layer in enumerate(self.layers):
# the previous state is one layer below except for the bottom
# layer where the previous state is the state emitted by the
# top layer
hidden, cell = layer(
input,
(
prev_hiddens[(i - 1) % self.num_layers],
prev_cells[(i - 1) % self.num_layers],
),
)
if self.dropout is not None:
hidden = self.dropout(hidden)
prev_hiddens[i] = hidden
prev_cells[i] = cell
if attention_out is None:
attention_out, attn_scores = self.attention(
hidden, encoder_outs, encoder_padding_mask
)
if self.dropout is not None:
attention_out = self.dropout(attention_out)
attention_outs.append(attention_out)
input = attention_out
# collect the output of the top layer
outs.append(hidden)
# cache previous states (no-op except during incremental generation)
utils.set_incremental_state(
self, incremental_state, "cached_state", (prev_hiddens, prev_cells)
)
# collect outputs across time steps
x = torch.cat(outs, dim=0).view(seqlen, bsz, self.hidden_size)
attention_outs_concat = torch.cat(attention_outs, dim=0).view(
seqlen, bsz, self.context_dim
)
# T x B x C -> B x T x C
x = x.transpose(0, 1)
attention_outs_concat = attention_outs_concat.transpose(0, 1)
# concat LSTM output, attention output and embedding
# before output projection
x = torch.cat((x, attention_outs_concat, embeddings), dim=2)
x = self.deep_output_layer(x)
x = torch.tanh(x)
if self.dropout is not None:
x = self.dropout(x)
# project back to size of vocabulary
x = self.output_projection(x)
# to return the full attn_scores tensor, we need to fix the decoder
# to account for subsampling input frames
# return x, attn_scores
return x, None
def reorder_incremental_state(self, incremental_state, new_order):
super().reorder_incremental_state(incremental_state, new_order)
cached_state = utils.get_incremental_state(
self, incremental_state, "cached_state"
)
if cached_state is None:
return
def reorder_state(state):
if isinstance(state, list):
return [reorder_state(state_i) for state_i in state]
return state.index_select(0, new_order)
new_state = tuple(map(reorder_state, cached_state))
utils.set_incremental_state(self, incremental_state, "cached_state", new_state)
@register_model_architecture(model_name="s2t_berard", arch_name="s2t_berard")
def berard(args):
"""The original version: "End-to-End Automatic Speech Translation of
Audiobooks" (https://arxiv.org/abs/1802.04200)
"""
args.input_layers = getattr(args, "input_layers", "[256, 128]")
args.conv_layers = getattr(args, "conv_layers", "[(16, 3, 2), (16, 3, 2)]")
args.num_blstm_layers = getattr(args, "num_blstm_layers", 3)
args.lstm_size = getattr(args, "lstm_size", 256)
args.dropout = getattr(args, "dropout", 0.2)
args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 128)
args.decoder_num_layers = getattr(args, "decoder_num_layers", 2)
args.decoder_hidden_dim = getattr(args, "decoder_hidden_dim", 512)
args.attention_dim = getattr(args, "attention_dim", 512)
args.output_layer_dim = getattr(args, "output_layer_dim", 128)
args.load_pretrained_encoder_from = getattr(
args, "load_pretrained_encoder_from", None
)
args.load_pretrained_decoder_from = getattr(
args, "load_pretrained_decoder_from", None
)
@register_model_architecture(model_name="s2t_berard", arch_name="s2t_berard_256_3_3")
def berard_256_3_3(args):
"""Used in
* "Harnessing Indirect Training Data for End-to-End Automatic Speech
Translation: Tricks of the Trade" (https://arxiv.org/abs/1909.06515)
* "CoVoST: A Diverse Multilingual Speech-To-Text Translation Corpus"
(https://arxiv.org/pdf/2002.01320.pdf)
* "Self-Supervised Representations Improve End-to-End Speech Translation"
(https://arxiv.org/abs/2006.12124)
"""
args.decoder_num_layers = getattr(args, "decoder_num_layers", 3)
berard(args)
@register_model_architecture(model_name="s2t_berard", arch_name="s2t_berard_512_3_2")
def berard_512_3_2(args):
args.num_blstm_layers = getattr(args, "num_blstm_layers", 3)
args.lstm_size = getattr(args, "lstm_size", 512)
args.dropout = getattr(args, "dropout", 0.3)
args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 256)
args.decoder_num_layers = getattr(args, "decoder_num_layers", 2)
args.decoder_hidden_dim = getattr(args, "decoder_hidden_dim", 1024)
args.attention_dim = getattr(args, "attention_dim", 512)
args.output_layer_dim = getattr(args, "output_layer_dim", 256)
berard(args)
@register_model_architecture(model_name="s2t_berard", arch_name="s2t_berard_512_5_3")
def berard_512_5_3(args):
args.num_blstm_layers = getattr(args, "num_blstm_layers", 5)
args.lstm_size = getattr(args, "lstm_size", 512)
args.dropout = getattr(args, "dropout", 0.3)
args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 256)
args.decoder_num_layers = getattr(args, "decoder_num_layers", 3)
args.decoder_hidden_dim = getattr(args, "decoder_hidden_dim", 1024)
args.attention_dim = getattr(args, "attention_dim", 512)
args.output_layer_dim = getattr(args, "output_layer_dim", 256)
berard(args)
|
bart_ls-main
|
fairseq-py/fairseq/models/speech_to_text/berard.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from .berard import * # noqa
from .convtransformer import * # noqa
from .s2t_transformer import * # noqa
from .xm_transformer import * # noqa
|
bart_ls-main
|
fairseq-py/fairseq/models/speech_to_text/__init__.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
#!/usr/bin/env python3
import logging
import math
from typing import Dict, List, Optional, Tuple
from pathlib import Path
import torch
import torch.nn as nn
from fairseq import checkpoint_utils, utils
from fairseq.data.data_utils import lengths_to_padding_mask
from fairseq.models import (
FairseqEncoder,
FairseqEncoderDecoderModel,
register_model,
register_model_architecture,
)
from fairseq.models.transformer import Embedding, TransformerDecoder
from fairseq.modules import (
FairseqDropout,
LayerNorm,
PositionalEmbedding,
TransformerEncoderLayer,
)
from torch import Tensor
logger = logging.getLogger(__name__)
class Conv1dSubsampler(nn.Module):
"""Convolutional subsampler: a stack of 1D convolution (along temporal
dimension) followed by non-linear activation via gated linear units
(https://arxiv.org/abs/1911.08460)
Args:
in_channels (int): the number of input channels
mid_channels (int): the number of intermediate channels
out_channels (int): the number of output channels
kernel_sizes (List[int]): the kernel size for each convolutional layer
"""
def __init__(
self,
in_channels: int,
mid_channels: int,
out_channels: int,
kernel_sizes: List[int] = (3, 3),
):
super(Conv1dSubsampler, self).__init__()
self.n_layers = len(kernel_sizes)
self.conv_layers = nn.ModuleList(
nn.Conv1d(
in_channels if i == 0 else mid_channels // 2,
mid_channels if i < self.n_layers - 1 else out_channels * 2,
k,
stride=2,
padding=k // 2,
)
for i, k in enumerate(kernel_sizes)
)
def get_out_seq_lens_tensor(self, in_seq_lens_tensor):
out = in_seq_lens_tensor.clone()
for _ in range(self.n_layers):
out = ((out.float() - 1) / 2 + 1).floor().long()
return out
def forward(self, src_tokens, src_lengths):
bsz, in_seq_len, _ = src_tokens.size() # B x T x (C x D)
x = src_tokens.transpose(1, 2).contiguous() # -> B x (C x D) x T
for conv in self.conv_layers:
x = conv(x)
x = nn.functional.glu(x, dim=1)
_, _, out_seq_len = x.size()
x = x.transpose(1, 2).transpose(0, 1).contiguous() # -> T x B x (C x D)
return x, self.get_out_seq_lens_tensor(src_lengths)
@register_model("s2t_transformer")
class S2TTransformerModel(FairseqEncoderDecoderModel):
"""Adapted Transformer model (https://arxiv.org/abs/1706.03762) for
speech-to-text tasks. The Transformer encoder/decoder remains the same.
A trainable input subsampler is prepended to the Transformer encoder to
project inputs into the encoder dimension as well as downsample input
sequence for computational efficiency."""
def __init__(self, encoder, decoder):
super().__init__(encoder, decoder)
@staticmethod
def add_args(parser):
"""Add model-specific arguments to the parser."""
# input
parser.add_argument(
"--conv-kernel-sizes",
type=str,
metavar="N",
help="kernel sizes of Conv1d subsampling layers",
)
parser.add_argument(
"--conv-channels",
type=int,
metavar="N",
help="# of channels in Conv1d subsampling layers",
)
# Transformer
parser.add_argument(
"--activation-fn",
type=str,
default="relu",
choices=utils.get_available_activation_fns(),
help="activation function to use",
)
parser.add_argument(
"--dropout", type=float, metavar="D", help="dropout probability"
)
parser.add_argument(
"--attention-dropout",
type=float,
metavar="D",
help="dropout probability for attention weights",
)
parser.add_argument(
"--activation-dropout",
"--relu-dropout",
type=float,
metavar="D",
help="dropout probability after activation in FFN.",
)
parser.add_argument(
"--encoder-embed-dim",
type=int,
metavar="N",
help="encoder embedding dimension",
)
parser.add_argument(
"--encoder-ffn-embed-dim",
type=int,
metavar="N",
help="encoder embedding dimension for FFN",
)
parser.add_argument(
"--encoder-layers", type=int, metavar="N", help="num encoder layers"
)
parser.add_argument(
"--encoder-attention-heads",
type=int,
metavar="N",
help="num encoder attention heads",
)
parser.add_argument(
"--encoder-normalize-before",
action="store_true",
help="apply layernorm before each encoder block",
)
parser.add_argument(
"--decoder-embed-dim",
type=int,
metavar="N",
help="decoder embedding dimension",
)
parser.add_argument(
"--decoder-ffn-embed-dim",
type=int,
metavar="N",
help="decoder embedding dimension for FFN",
)
parser.add_argument(
"--decoder-layers", type=int, metavar="N", help="num decoder layers"
)
parser.add_argument(
"--decoder-attention-heads",
type=int,
metavar="N",
help="num decoder attention heads",
)
parser.add_argument(
"--decoder-normalize-before",
action="store_true",
help="apply layernorm before each decoder block",
)
parser.add_argument(
"--share-decoder-input-output-embed",
action="store_true",
help="share decoder input and output embeddings",
)
parser.add_argument(
"--layernorm-embedding",
action="store_true",
help="add layernorm to embedding",
)
parser.add_argument(
"--no-scale-embedding",
action="store_true",
help="if True, dont scale embeddings",
)
parser.add_argument(
"--load-pretrained-encoder-from",
type=str,
metavar="STR",
help="model to take encoder weights from (for initialization)",
)
parser.add_argument(
'--encoder-freezing-updates',
type=int,
metavar='N',
help='freeze encoder for first N updates'
)
@classmethod
def build_encoder(cls, args):
encoder = S2TTransformerEncoder(args)
pretraining_path = getattr(args, "load_pretrained_encoder_from", None)
if pretraining_path is not None:
if not Path(pretraining_path).exists():
logger.warning(
f"skipped pretraining because {pretraining_path} does not exist"
)
else:
encoder = checkpoint_utils.load_pretrained_component_from_model(
component=encoder, checkpoint=pretraining_path
)
logger.info(f"loaded pretrained encoder from: {pretraining_path}")
return encoder
@classmethod
def build_decoder(cls, args, task, embed_tokens):
return TransformerDecoderScriptable(args, task.target_dictionary, embed_tokens)
@classmethod
def build_model(cls, args, task):
"""Build a new model instance."""
# make sure all arguments are present in older models
base_architecture(args)
def build_embedding(dictionary, embed_dim):
num_embeddings = len(dictionary)
padding_idx = dictionary.pad()
return Embedding(num_embeddings, embed_dim, padding_idx)
decoder_embed_tokens = build_embedding(
task.target_dictionary, args.decoder_embed_dim
)
encoder = cls.build_encoder(args)
decoder = cls.build_decoder(args, task, decoder_embed_tokens)
return cls(encoder, decoder)
def get_normalized_probs(
self,
net_output: Tuple[Tensor, Optional[Dict[str, List[Optional[Tensor]]]]],
log_probs: bool,
sample: Optional[Dict[str, Tensor]] = None,
):
# net_output['encoder_out'] is a (B, T, D) tensor
lprobs = self.get_normalized_probs_scriptable(net_output, log_probs, sample)
lprobs.batch_first = True
return lprobs
def forward(self, src_tokens, src_lengths, prev_output_tokens):
"""
The forward method inherited from the base class has a **kwargs
argument in its input, which is not supported in torchscript. This
method overwrites the forward method definition without **kwargs.
"""
encoder_out = self.encoder(src_tokens=src_tokens, src_lengths=src_lengths)
decoder_out = self.decoder(
prev_output_tokens=prev_output_tokens, encoder_out=encoder_out
)
return decoder_out
class S2TTransformerEncoder(FairseqEncoder):
"""Speech-to-text Transformer encoder that consists of input subsampler and
Transformer encoder."""
def __init__(self, args):
super().__init__(None)
self.encoder_freezing_updates = args.encoder_freezing_updates
self.num_updates = 0
self.dropout_module = FairseqDropout(
p=args.dropout, module_name=self.__class__.__name__
)
self.embed_scale = math.sqrt(args.encoder_embed_dim)
if args.no_scale_embedding:
self.embed_scale = 1.0
self.padding_idx = 1
self.subsample = Conv1dSubsampler(
args.input_feat_per_channel * args.input_channels,
args.conv_channels,
args.encoder_embed_dim,
[int(k) for k in args.conv_kernel_sizes.split(",")],
)
self.embed_positions = PositionalEmbedding(
args.max_source_positions, args.encoder_embed_dim, self.padding_idx
)
self.transformer_layers = nn.ModuleList(
[TransformerEncoderLayer(args) for _ in range(args.encoder_layers)]
)
if args.encoder_normalize_before:
self.layer_norm = LayerNorm(args.encoder_embed_dim)
else:
self.layer_norm = None
def _forward(self, src_tokens, src_lengths, return_all_hiddens=False):
x, input_lengths = self.subsample(src_tokens, src_lengths)
x = self.embed_scale * x
encoder_padding_mask = lengths_to_padding_mask(input_lengths)
positions = self.embed_positions(encoder_padding_mask).transpose(0, 1)
x += positions
x = self.dropout_module(x)
encoder_states = []
for layer in self.transformer_layers:
x = layer(x, encoder_padding_mask)
if return_all_hiddens:
encoder_states.append(x)
if self.layer_norm is not None:
x = self.layer_norm(x)
return {
"encoder_out": [x], # T x B x C
"encoder_padding_mask": [encoder_padding_mask] if encoder_padding_mask.any() else [], # B x T
"encoder_embedding": [], # B x T x C
"encoder_states": encoder_states, # List[T x B x C]
"src_tokens": [],
"src_lengths": [],
}
def forward(self, src_tokens, src_lengths, return_all_hiddens=False):
if self.num_updates < self.encoder_freezing_updates:
with torch.no_grad():
x = self._forward(src_tokens, src_lengths,
return_all_hiddens=return_all_hiddens)
else:
x = self._forward(src_tokens, src_lengths,
return_all_hiddens=return_all_hiddens)
return x
def reorder_encoder_out(self, encoder_out, new_order):
new_encoder_out = (
[] if len(encoder_out["encoder_out"]) == 0
else [x.index_select(1, new_order) for x in encoder_out["encoder_out"]]
)
new_encoder_padding_mask = (
[] if len(encoder_out["encoder_padding_mask"]) == 0
else [x.index_select(0, new_order) for x in encoder_out["encoder_padding_mask"]]
)
new_encoder_embedding = (
[] if len(encoder_out["encoder_embedding"]) == 0
else [x.index_select(0, new_order) for x in encoder_out["encoder_embedding"]]
)
encoder_states = encoder_out["encoder_states"]
if len(encoder_states) > 0:
for idx, state in enumerate(encoder_states):
encoder_states[idx] = state.index_select(1, new_order)
return {
"encoder_out": new_encoder_out, # T x B x C
"encoder_padding_mask": new_encoder_padding_mask, # B x T
"encoder_embedding": new_encoder_embedding, # B x T x C
"encoder_states": encoder_states, # List[T x B x C]
"src_tokens": [], # B x T
"src_lengths": [], # B x 1
}
def set_num_updates(self, num_updates):
super().set_num_updates(num_updates)
self.num_updates = num_updates
class TransformerDecoderScriptable(TransformerDecoder):
def extract_features(
self,
prev_output_tokens,
encoder_out: Optional[Dict[str, List[Tensor]]] = None,
incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None,
full_context_alignment: bool = False,
alignment_layer: Optional[int] = None,
alignment_heads: Optional[int] = None,
):
# call scriptable method from parent class
x, _ = self.extract_features_scriptable(
prev_output_tokens,
encoder_out,
incremental_state,
full_context_alignment,
alignment_layer,
alignment_heads,
)
return x, None
@register_model_architecture(model_name="s2t_transformer", arch_name="s2t_transformer")
def base_architecture(args):
args.encoder_freezing_updates = getattr(args, "encoder_freezing_updates", 0)
# Convolutional subsampler
args.conv_kernel_sizes = getattr(args, "conv_kernel_sizes", "5,5")
args.conv_channels = getattr(args, "conv_channels", 1024)
# Transformer
args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512)
args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 2048)
args.encoder_layers = getattr(args, "encoder_layers", 12)
args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 8)
args.encoder_normalize_before = getattr(args, "encoder_normalize_before", True)
args.decoder_embed_dim = getattr(args, "decoder_embed_dim", args.encoder_embed_dim)
args.decoder_ffn_embed_dim = getattr(
args, "decoder_ffn_embed_dim", args.encoder_ffn_embed_dim
)
args.decoder_layers = getattr(args, "decoder_layers", 6)
args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 8)
args.decoder_normalize_before = getattr(args, "decoder_normalize_before", True)
args.decoder_learned_pos = getattr(args, "decoder_learned_pos", False)
args.dropout = getattr(args, "dropout", 0.1)
args.attention_dropout = getattr(args, "attention_dropout", args.dropout)
args.activation_dropout = getattr(args, "activation_dropout", args.dropout)
args.activation_fn = getattr(args, "activation_fn", "relu")
args.adaptive_softmax_cutoff = getattr(args, "adaptive_softmax_cutoff", None)
args.adaptive_softmax_dropout = getattr(args, "adaptive_softmax_dropout", 0)
args.share_decoder_input_output_embed = getattr(
args, "share_decoder_input_output_embed", False
)
args.no_token_positional_embeddings = getattr(
args, "no_token_positional_embeddings", False
)
args.adaptive_input = getattr(args, "adaptive_input", False)
args.decoder_layerdrop = getattr(args, "decoder_layerdrop", 0.0)
args.decoder_output_dim = getattr(
args, "decoder_output_dim", args.decoder_embed_dim
)
args.decoder_input_dim = getattr(args, "decoder_input_dim", args.decoder_embed_dim)
args.no_scale_embedding = getattr(args, "no_scale_embedding", False)
args.quant_noise_pq = getattr(args, "quant_noise_pq", 0)
@register_model_architecture("s2t_transformer", "s2t_transformer_s")
def s2t_transformer_s(args):
args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 256)
args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 256 * 8)
args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 4)
args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 4)
args.dropout = getattr(args, "dropout", 0.1)
base_architecture(args)
@register_model_architecture("s2t_transformer", "s2t_transformer_xs")
def s2t_transformer_xs(args):
args.encoder_layers = getattr(args, "encoder_layers", 6)
args.decoder_layers = getattr(args, "decoder_layers", 3)
args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 256 * 4)
args.dropout = getattr(args, "dropout", 0.3)
s2t_transformer_s(args)
@register_model_architecture("s2t_transformer", "s2t_transformer_sp")
def s2t_transformer_sp(args):
args.encoder_layers = getattr(args, "encoder_layers", 16)
s2t_transformer_s(args)
@register_model_architecture("s2t_transformer", "s2t_transformer_m")
def s2t_transformer_m(args):
args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512)
args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 512 * 4)
args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 8)
args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 8)
args.dropout = getattr(args, "dropout", 0.15)
base_architecture(args)
@register_model_architecture("s2t_transformer", "s2t_transformer_mp")
def s2t_transformer_mp(args):
args.encoder_layers = getattr(args, "encoder_layers", 16)
s2t_transformer_m(args)
@register_model_architecture("s2t_transformer", "s2t_transformer_l")
def s2t_transformer_l(args):
args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 1024)
args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 1024 * 4)
args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 16)
args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 16)
args.dropout = getattr(args, "dropout", 0.2)
base_architecture(args)
@register_model_architecture("s2t_transformer", "s2t_transformer_lp")
def s2t_transformer_lp(args):
args.encoder_layers = getattr(args, "encoder_layers", 16)
s2t_transformer_l(args)
|
bart_ls-main
|
fairseq-py/fairseq/models/speech_to_text/s2t_transformer.py
|
# Copyright (c) 2017-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the LICENSE file in
# the root directory of this source tree. An additional grant of patent rights
# can be found in the PATENTS file in the same directory.
import logging
from collections.abc import Iterable
from itertools import repeat
from typing import List, Optional, Tuple
import torch
from torch import Tensor
# ------------------------------------------------------------------------------
# assert_equal()
# ------------------------------------------------------------------------------
def assert_equal(value1, value2, name1=None, name2=None):
"""Asserts two values are equal otherwise raise an error."""
str_name1 = "" if name1 is None else "{} ".format(name1)
str_name2 = "" if name2 is None else "{} ".format(name2)
if value1 != value2:
str_value1 = "{}" if name1 is None else "({})"
str_value1 = str_value1.format(value1)
str_value2 = "{}" if name2 is None else "({})"
str_value2 = str_value2.format(value2)
raise ValueError(
"Expected {}{} == {}{}".format(str_name1, str_value1, str_name2, str_value2)
)
def fill_config(config, key, value):
if value is not None:
if key not in config or config[key] is None:
config[key] = value
assert_equal(value, config[key], "value", f'config["{key}"]')
# ------------------------------------------------------------------------------
# check_and_return_expected()
# ------------------------------------------------------------------------------
def check_and_return_expected(value, undefined_value, expected_value, name=None):
"""
Return the expected value while checking if the given value is undefined or
equal to the expected value.
"""
if (undefined_value is None and value is None) or (undefined_value == value):
return expected_value
if value != expected_value:
str_name = "" if name is None else "{} ".format(name)
str_value = "{}" if name is None else "({})"
str_value = str_value.format(value)
raise ValueError(
"Expected {}{} == {}".format(str_name, str_value, expected_value)
)
return expected_value
# ------------------------------------------------------------------------------
# get_time_axis()
# ------------------------------------------------------------------------------
def get_time_axis(layout):
"""
Extract the time axis from the layout, for example for breaking sequence into
segments.
"""
if layout in ["TB", "TBD"]:
return 0
if layout in ["BT", "BTD"]:
return 1
if layout in ["BCTD"]:
return 2
raise ValueError("Unsupported layout = {}".format(layout))
# ------------------------------------------------------------------------------
# get_batch_axis()
# ------------------------------------------------------------------------------
def get_batch_axis(layout):
"""
Extract the batch axis from the layout
"""
if layout in ["TB", "TBD"]:
return 1
if layout in ["BT", "BTD", "BCTD"]:
return 0
raise ValueError("Unsupported layout = {}".format(layout))
# ------------------------------------------------------------------------------
# monotonically_increasing_and_bounded()
# ------------------------------------------------------------------------------
def monotonically_increasing_and_bounded(iterable, min=None, max=None):
"""
Check if the elements in the given iterable are monotonically increasing and
bounded by upper/lower bounds.
"""
if not isinstance(iterable, Iterable):
raise TypeError(
"Expected iterable to be of type Iterable, got ({})".format(
iterable.__class__.__name__
)
)
for i in range(len(iterable)):
if min is not None and iterable[i] < min:
return False
if max is not None and iterable[i] > max:
return False
if i > 0 and iterable[i] <= iterable[i - 1]:
return False
return True
# ------------------------------------------------------------------------------
# to_pair()
# ------------------------------------------------------------------------------
def to_pair(value, name):
"""Make a pair (of type tuple) of given value."""
if isinstance(value, Iterable):
if len(value) != 2:
raise ValueError(
"Expected `{}` to have exactly 2 elements, got: ({})".format(
name, value
)
)
return value
return tuple(repeat(value, 2))
# ------------------------------------------------------------------------------
# infer_conv_output_attrs()
# ------------------------------------------------------------------------------
# TODO(cfyeh): figure out if we can get `output_dim` without calling the module.
def infer_conv_output_attrs(
module, input_channels, input_dim, batch_size=1, max_length=8
):
"""Get output attributes of a module with input."""
input = torch.randn(batch_size, input_channels, max_length, input_dim)
output = module(input)
output_channels = output.shape[1]
output_dim = output.shape[-1]
return output_channels, output_dim
# ------------------------------------------------------------------------------
# NoOp
# ------------------------------------------------------------------------------
class NoOp(torch.nn.Module):
"""
NoOp simply passes the input as the output.
"""
def __init__(self):
super().__init__()
def forward(self, input: Tensor) -> Tensor:
return input
# ------------------------------------------------------------------------------
# Permute: a torch.nn.Module applies permutation on the input tensor.
# ------------------------------------------------------------------------------
class Permute(torch.nn.Module):
def __init__(self, dims):
super().__init__()
self.dims = dims
def forward(self, input: Tensor) -> Tensor:
return input.permute(self.dims).contiguous()
# ------------------------------------------------------------------------------
# lengths_to_padding_mask()
# ------------------------------------------------------------------------------
def lengths_to_padding_mask(lengths: Tensor) -> Tensor:
"""Convert lengths of shape (B, ) to padding mask."""
batch_size = lengths.shape[0]
max_length = int(torch.max(lengths).item())
padding_mask = torch.arange( # [0, ..., T-1]
max_length, device=lengths.device, dtype=lengths.dtype
).expand(batch_size, max_length) >= lengths.unsqueeze(1)
return padding_mask
# ------------------------------------------------------------------------------
# lengths_to_attention_mask()
# ------------------------------------------------------------------------------
def lengths_to_attention_mask(
lengths: Tensor,
left_context: Optional[int] = None,
right_context: Optional[int] = None,
) -> Optional[Tensor]:
"""
Generate attention mask based on (lengths, left_context, right_context).
left_context is None means unlimited left context.
right_context is None means unlimited right context.
"""
if left_context is None and right_context is None:
return None
max_length = int(torch.max(lengths).item())
# For example, with `max_length` == 5,
# indices = tensor([
# [ 0, 1, 2, 3, 4, 5],
# [-1, 0, 1, 2, 3, 4],
# [-2, -1, 0, 1, 2, 3],
# [-3, -2, -1, 0, 1, 2],
# [-4, -3, -2, -1, 0, 1],
# [-5, -4, -3, -2, -1, 0],
# ])
# In some cases the second torch.arange is created on cpu which causes a
# failure. Adding the device option to guard against it.
indices = torch.arange(
max_length, device=lengths.device, dtype=lengths.dtype
).expand(max_length, max_length) - torch.arange(
max_length, device=lengths.device
).view(
max_length, -1
)
# For example, with `max_length` == 5,
# bool_mask = tensor([
# [True, True, True, True, True],
# [True, True, True, True, True],
# [True, True, True, True, True],
# [True, True, True, True, True],
# [True, True, True, True, True],
# ])
bool_mask = (
torch.tensor([True]).to(device=lengths.device).expand(max_length, max_length)
)
# For example, with `max_length` == 5, left_context == 2
# left_mask = tensor([
# [ True, True, True, True, True],
# [ True, True, True, True, True],
# [ True, True, True, True, True],
# [False, True, True, True, True],
# [False, False, True, True, True],
# ])
if left_context is not None:
left_mask = indices >= -left_context
bool_mask = bool_mask & left_mask
# For example, with `max_length` == 5, right_context == 1
# right_mask = tensor([
# [True, True, False, False, False],
# [True, True, True, False, False],
# [True, True, True, True, False],
# [True, True, True, True, True],
# [True, True, True, True, True],
# ])
if right_context is not None:
right_mask = indices <= right_context
bool_mask = bool_mask & right_mask
bool_mask = (~bool_mask).to(device=lengths.device)
return bool_mask
# ------------------------------------------------------------------------------
# infer_output_norm()
# ------------------------------------------------------------------------------
def infer_output_norm(module, output_norm=None):
"""
Infer the output norm (string and module) needed on the module gvien desired
output normalization.
"""
if output_norm == module.output_norm():
# output_norm already matches module.output_norm().
return (None, NoOp())
if output_norm is None and module.output_norm() is not None:
logger = logging.getLogger("infer_output_norm()")
logger.warning(
"trying to set output_norm ({}) ".format(output_norm)
+ "but got module.output_norm() ({}), ".format(module.output_norm())
+ "the combined output_norm() will be ({})".format(module.output_norm())
)
return (None, NoOp())
if output_norm == "log_softmax":
if module.output_norm() is not None:
raise ValueError(
"incompatible output_norm ({}) ".format(output_norm)
+ "and module.output_norm() ({})".format(module.output_norm())
)
else:
return ("log_softmax", torch.nn.LogSoftmax(dim=-1))
if output_norm == "softmax":
if module.output_norm() is not None:
raise ValueError(
"incompatible output_norm ({}) ".format(output_norm)
+ "and module.output_norm() ({})".format(module.output_norm())
)
else:
return ("softmax", torch.nn.Softmax(dim=-1))
raise ValueError(
"output_norm ({}) not in ".format(output_norm)
+ "supported list = [None, softmax, log_softmax]"
)
# ------------------------------------------------------------------------------
# infer_channels_from_layout()
# ------------------------------------------------------------------------------
def infer_channels_from_layout(layout, channels):
"""Extract the number of channels from the layout."""
if layout in ("TBD", "BTD"):
if channels is not None and channels != 1:
raise ValueError(
"Expected channels ({}) to be 1 for layout = {}".format(
channels, layout
)
)
if channels is None:
return 1
return channels
# ------------------------------------------------------------------------------
# pad_sequence()
# ------------------------------------------------------------------------------
@torch.jit.export
def pad_sequence(
sequence: Tensor,
time_axis: int,
extra_left_context: int = 0,
extra_right_context: int = 0,
) -> Tensor:
"""Pad extra left/right contexts to the sequence."""
if extra_left_context == 0 and extra_right_context == 0:
return sequence
tensors_to_concat = []
if extra_left_context:
size = (extra_left_context,)
fill_value = 0
indices = torch.full(
size=size,
fill_value=fill_value,
dtype=torch.long,
device=sequence.device,
)
left_padding = torch.index_select(sequence, time_axis, indices)
tensors_to_concat.append(left_padding)
tensors_to_concat.append(sequence)
# NOTE(cfyeh): for efficiency reason we pad 0 instead of the last frame for
# extra right contexts.
if extra_right_context:
size = list(sequence.shape)
size[time_axis] = extra_right_context
right_padding = torch.zeros(size, dtype=sequence.dtype, device=sequence.device)
tensors_to_concat.append(right_padding)
padded_sequence = torch.cat(tensors_to_concat, dim=time_axis)
return padded_sequence
# ------------------------------------------------------------------------------
# sequence_to_segments()
# ------------------------------------------------------------------------------
@torch.jit.export
def sequence_to_segments(
sequence: Tensor,
time_axis: int,
lengths: Tensor,
segment_size: Optional[int] = None,
extra_left_context: int = 0,
extra_right_context: int = 0,
) -> List[Tuple[Tensor, Tensor]]:
"""Breaks sequence into segments."""
sequence = pad_sequence(
sequence=sequence,
time_axis=time_axis,
extra_left_context=extra_left_context,
extra_right_context=extra_right_context,
)
lengths = lengths + extra_left_context + extra_right_context
segments: List[Tuple[Tensor, Tensor]] = []
if segment_size is None:
segments.append((sequence, lengths))
return segments
offset = 0
end = sequence.shape[time_axis]
step = segment_size
size = extra_left_context + segment_size + extra_right_context
while offset + extra_left_context + extra_right_context < end:
clamped_size = min(size, end - offset)
segment_lengths = torch.clamp(lengths - offset, min=0, max=clamped_size)
indices = torch.arange(
start=offset,
end=(offset + clamped_size),
step=1,
dtype=torch.long,
device=sequence.device,
)
segment_tensor = torch.index_select(sequence, time_axis, indices)
segments.append((segment_tensor, segment_lengths))
offset = offset + step
return segments
# ------------------------------------------------------------------------------
# segments_to_sequence()
# ------------------------------------------------------------------------------
@torch.jit.export
def segments_to_sequence(
segments: List[Tuple[Tensor, Tensor]], time_axis: int
) -> Tuple[Tensor, Tensor]:
"""Concatenate segments into a full sequence."""
if len(segments) == 1:
return segments[0]
tensors_to_concat: List[Tensor] = []
lengths_to_stack: List[Tensor] = []
for tensor, lengths in segments:
tensors_to_concat.append(tensor)
lengths_to_stack.append(lengths)
sequence = torch.cat(tensors_to_concat, dim=time_axis)
lengths = torch.stack(lengths_to_stack, dim=0)
lengths = torch.sum(lengths, dim=0)
return sequence, lengths
def lengths_to_encoder_padding_mask(lengths, batch_first: bool = False):
"""
convert lengths (a 1-D Long/Int tensor) to 2-D binary tensor
Args:
lengths: a (B, )-shaped tensor
batch_first: whether to return a (B, T) tensor
Return:
max_length: maximum length of B sequences
encoder_padding_mask: a (max_length, B) binary mask, where
[t, b] = False for t < lengths[b] and True otherwise
TODO:
kernelize this function if benchmarking shows this function is slow
"""
max_lengths = torch.max(lengths).item()
bsz = lengths.size(0)
encoder_padding_mask = torch.arange(
max_lengths
).to( # a (T, ) tensor with [0, ..., T-1]
lengths.device
).view( # move to the right device
1, max_lengths
).expand( # reshape to (1, T)-shaped tensor
bsz, -1
) > lengths.view( # expand to (B, T)-shaped tensor
bsz, 1
).expand(
-1, max_lengths
)
if not batch_first:
return encoder_padding_mask.t(), max_lengths
else:
return encoder_padding_mask, max_lengths
# ------------------------------------------------------------------------------
# attention suppression
# ------------------------------------------------------------------------------
def attention_suppression(attention_weights: Tensor, scale: float):
# B, H, qlen, klen -> B, H, qlen, 1
attention_prob = torch.nn.functional.softmax(attention_weights.float(), dim=-1)
attention_nozeros = attention_prob.to(torch.bool)
nozeros_sum = torch.sum(attention_nozeros.to(torch.float), dim=-1, keepdim=True)
# For very sparse situation, we need get round about 0s
key_sum = torch.sum(attention_prob, dim=-1, keepdim=True)
# nozeros_sum should > 1
key_mean = key_sum / (nozeros_sum + 1e-8)
# std calculation
dis = (attention_prob - key_mean) * (attention_prob - key_mean)
# if attention_prob[i] < threshold, then dis_masked[i] = 0; for all i
dis_masked = torch.where(
attention_nozeros, dis, attention_prob.new_zeros(attention_prob.size())
)
key_var = torch.sum(dis_masked, dim=-1, keepdim=True)
key_var = key_var / (nozeros_sum - 1.0 + 1e-8)
key_std = torch.sqrt(key_var)
key_thread = key_mean - scale * key_std
# if attention_prob[i] >= key_thread, then attention_prob[i]
# , otherwise "-inf"
inf_tensor = attention_prob.new_zeros(attention_prob.size()).detach()
inf_tensor[:] = float("-inf")
attention_weights_float = torch.where(
attention_prob < key_thread,
inf_tensor,
attention_weights.float(),
)
return attention_weights_float.type_as(attention_weights)
def layer_norm_backward_hook(module, grad_input, grad_output, clamp_value):
return tuple(torch.clamp(v, min=-clamp_value, max=clamp_value) for v in grad_input)
|
bart_ls-main
|
fairseq-py/fairseq/models/speech_to_text/utils.py
|
#!/usr/bin/env python3
# Copyright (c) 2017-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the LICENSE file in
# the root directory of this source tree. An additional grant of patent rights
# can be found in the PATENTS file in the same directory.
import math
import re
from functools import partial
from typing import List, Optional, Tuple
import torch
import torch.nn as nn
from fairseq.models import (
FairseqEncoder,
)
from fairseq.models.speech_to_text.utils import (
NoOp,
lengths_to_padding_mask,
segments_to_sequence,
)
from fairseq.models.speech_to_text.utils import (
attention_suppression,
layer_norm_backward_hook,
)
from torch import Tensor, device as Device
from torch.quantization.qconfig import (
default_dynamic_qconfig,
per_channel_dynamic_qconfig,
)
class RelativePositionEmbedding(nn.Module):
"""
Implementation according to https://arxiv.org/abs/1803.02155
"""
def __init__(self, head_dim, max_position, norm_init=True):
super().__init__()
self.head_dim = head_dim
self.max_position = max_position
self.embeddings = nn.Parameter(torch.Tensor(max_position * 2 + 1, head_dim))
if norm_init:
nn.init.xavier_normal_(self.embeddings)
else:
nn.init.xavier_uniform_(self.embeddings)
def forward(self, input: Tensor):
output = nn.functional.embedding(input.long(), self.embeddings)
return output
class Fp32LayerNorm(nn.Module):
def __init__(
self,
input_dim,
clamp_grad=True,
max_grad_value=256,
eps=1e-5,
elementwise_affine=True,
):
super().__init__()
self.torch_module = torch.nn.LayerNorm(
input_dim, eps=eps, elementwise_affine=elementwise_affine
)
if clamp_grad:
hook = partial(layer_norm_backward_hook, clamp_value=max_grad_value)
self.torch_module.register_backward_hook(hook)
def forward(self, input):
output = torch.nn.functional.layer_norm(
input.float(),
self.torch_module.normalized_shape,
self.torch_module.weight.float()
if self.torch_module.weight is not None
else None,
self.torch_module.bias.float()
if self.torch_module.bias is not None
else None,
self.torch_module.eps,
).type_as(input)
return output
# ------------------------------------------------------------------------------
# PositionwiseFF
# ------------------------------------------------------------------------------
class PositionwiseFF(nn.Module):
"""
FFN layer in transformer.
Args:
input_dim: input embedding dimension
ffn_dim: FFN layer inner dimension
dropout_on_fc1: dropout for first linear layer
dropout_on_fc2: dropout fr second linear layer
activation_fn: activation function used after first linear layer. \
Only relu or gelu is supported.
"""
def __init__(
self, input_dim, ffn_dim, dropout_on_fc1, dropout_on_fc2, activation_fn
):
super(PositionwiseFF, self).__init__()
self.input_dim = input_dim
self.ffn_dim = ffn_dim
if activation_fn == "relu":
ac = nn.ReLU()
elif activation_fn == "gelu":
ac = nn.GELU()
else:
raise ValueError("Unsupported activation_fn = ({})".format(activation_fn))
# fc1 -> ac -> dropout -> fc2 -> dropout
self.module = nn.Sequential(
nn.Linear(input_dim, ffn_dim),
ac,
nn.Dropout(dropout_on_fc1),
nn.Linear(ffn_dim, input_dim),
nn.Dropout(dropout_on_fc2),
)
self.layer_norm = Fp32LayerNorm(input_dim)
def forward(self, input):
module_out = self.module(self.layer_norm(input))
output = module_out + input
return output
def quantize_(self, params=None):
if params and "per_channel" in params and params["per_channel"]:
qconfig = per_channel_dynamic_qconfig
else:
qconfig = default_dynamic_qconfig
torch.quantization.quantize_dynamic(
self, {torch.nn.Linear: qconfig}, dtype=torch.qint8, inplace=True
)
return self
# ------------------------------------------------------------------------------
# SummarizationLayer
# ------------------------------------------------------------------------------
class SummarizationLayer(nn.Module):
def __init__(self, method, segment_size, embedding_dim):
super(SummarizationLayer, self).__init__()
self.segment_size = segment_size
self.embedding_dim = embedding_dim
nonlin_match = re.match(r"nonlinear\((?P<act>[a-z]+),(?P<dim>[0-9]+)\)", method)
self.method = method
if method == "mean":
self.module = nn.AvgPool1d(
kernel_size=segment_size,
stride=segment_size,
ceil_mode=True,
)
elif method == "max":
self.module = nn.MaxPool1d(
kernel_size=segment_size,
stride=segment_size,
ceil_mode=True,
)
elif method == "linear":
self.module = nn.Linear(segment_size, 1)
elif nonlin_match:
nonlin_args = nonlin_match.groupdict()
act_type = nonlin_args["act"]
hid_dim = int(nonlin_args["dim"])
if act_type == "relu":
act = nn.ReLU()
elif act_type == "gelu":
act = nn.GELU()
else:
raise ValueError("Unsupported activation_fn = ({})".format(act_type))
self.module = nn.Sequential(
nn.Linear(segment_size, hid_dim),
act,
nn.Linear(hid_dim, 1),
)
else:
raise ValueError("Unsupported summarization method = ({})".format(method))
def forward(self, input):
# T, B, D -> B, D, T
input = input.permute(1, 2, 0)
if self.method == "mean" or self.method == "max":
output = self.module(input)
output = output.permute(2, 0, 1)
return output
full_seg_length = input.size(2) // self.segment_size * self.segment_size
if full_seg_length > 0:
# at least one seg is full
B = input.size(0)
D = input.size(1)
input_todo = (
input[:, :, :full_seg_length]
.contiguous()
.view(B, -1, self.segment_size)
)
output = self.module(input_todo)
output = output.view(B, D, -1)
else:
output = input.new_zeros(input.size(0), input.size(1), 0)
left = input.size(2) - full_seg_length
if left > 0:
# when last seg is not full, use zeros as last memory placeholder
zeros = input.new_zeros(input.size(0), input.size(1), 1)
output = torch.cat([output, zeros], dim=2)
output = output.permute(2, 0, 1)
return output
# ------------------------------------------------------------------------------
# NoSegAugmentedMemoryMultiheadAttentionBmm
# ------------------------------------------------------------------------------
class NoSegAugmentedMemoryMultiheadAttentionBmm(nn.Module):
"""
Whole utterance augmented memory multihead attention using BMM.
Different with previous augmented memory multihead attention where
the utterance is chunked into segments. Here we use attention mask
achieve so. The input embedding [right_context, utterance, summary]
is a concatenation of right context, utterance and summary.
Right context block is the concatenation of all the right context for
each segments. [right_context_0, right_context_1, ..., right_context_n]
For example, if we have utterance = [v0, v1, v2, ...., v20]. segment
size 8, right_context size 4. Then the right context blocks =
[v8, v9, v10, v11, v16, v17, v18, v19, 0, 0, 0, 0], where v8, v9, v10,
and v11 are the right context for first segment. v16, v17, v18 and v19
are the right context for second segment. 0, 0, 0 and 0 are right context
for the last segment.
utterance is corresponding to input embedding sequence
summary is concatenation of average of each segments. [summary_0,
summary_1, ..., ].
In augmented memory multihead attention, the query is [right_context,
utterance, summary], key is [memory, right_context, utterance]. Different
with AugmentedMemoryMultiheadAttentionBmm, memory here is passed from
previous attention layer. For the first attention layer, memory is average
of each segment.
Memory is a concatenation of memory from each segments in previous attention
layer. For example, current layer is i, then memory is [m_0, m_1, ..., m_n].
Each m_k is the output from seg_k in layer i-1.
args:
input_dim: input embedding dimension
num_heads: number of heads in multihead self-attention
dropout: attention dropout
std_scale: if std_scale is not None. The weak attention suppression is
turned on. For std_scale = 0.5, all the attention smaller than
mean + 0.5 * std will be suppressed.
scaled_init: whether to use scaled init for linear weight
tanh_on_mem: whether to use tanh on memory output
use_mem: whether to use memory or not. When max_memory_size is 0, then
we don't have memory anymore.
layer_index: current self-attention layer index that is used in depth
initialization
max_relative_position: max relative position used in relative position
embedding
rpe_old_option: To be compatible with previous model. The previous model
was trained with attention += attention + rpe. The correct equation
should be attention = attention + rpe
"""
def __init__(
self,
input_dim,
num_heads,
dropout=0.0,
std_scale=None,
scaled_init=False,
tanh_on_mem=False,
use_mem=True,
mini_batches=False,
negative_inf="-inf",
layer_index=-1,
max_relative_position=0,
rpe_old_option=True,
):
if input_dim % num_heads:
raise ValueError(
"input_dim ({}) must be divisible by num_heads ({})".format(
input_dim, num_heads
)
)
super().__init__()
embed_dim = input_dim
self.e2h_kv = torch.nn.Linear(input_dim, 2 * input_dim, bias=True)
self.e2h_q = torch.nn.Linear(input_dim, input_dim, bias=True)
self.rpe_old_option = rpe_old_option
if max_relative_position > 0:
self.use_rpe = True
self.rpe_k = RelativePositionEmbedding(
head_dim=input_dim // num_heads,
max_position=max_relative_position,
)
self.rpe_v = RelativePositionEmbedding(
head_dim=input_dim // num_heads,
max_position=max_relative_position,
)
else:
self.use_rpe = False
self.rpe_k = None
self.rpe_v = None
if scaled_init:
if layer_index == -1:
gain = 1.0 / math.sqrt(2)
else:
# https://arxiv.org/abs/2005.09684 depthwise initialization
# stablize the training greatly. Use depthwise initialization to
# replace incremental loss.
gain = 1.0 / math.sqrt(layer_index + 1)
torch.nn.init.xavier_uniform_(self.e2h_kv.weight, gain=gain)
torch.nn.init.xavier_uniform_(self.e2h_q.weight, gain=gain)
self.out_proj = torch.nn.Linear(embed_dim, embed_dim, bias=True)
self.embed_dim = embed_dim
self.num_heads = num_heads
self.dropout = dropout
self.head_dim = embed_dim // num_heads
self.scaling = self.head_dim ** -0.5
self.std_scale = std_scale
self.use_mem = use_mem
self.mini_batches = mini_batches
self.negative_inf = negative_inf
if tanh_on_mem:
self.squash_mem = torch.tanh
self.nonlinear_squash_mem = True
else:
self.squash_mem = NoOp()
self.nonlinear_squash_mem = False
def prepare_qkv(
self,
input: Tensor,
mems: Tensor,
lengths: Tensor,
summary_length: int,
lc_length: int,
):
# T: right_context length + utterance_length + summary_length
T, B, D = input.shape
mem_length = mems.size(0)
utterance_length = torch.max(lengths)
right_context_blocks_length = T - utterance_length - summary_length
rc_block = input[:right_context_blocks_length, :, :]
utterance_block = input[right_context_blocks_length : T - summary_length, :, :]
if B == 1:
padding_mask = None
else:
klengths = lengths + mem_length + right_context_blocks_length + lc_length
padding_mask = lengths_to_padding_mask(lengths=klengths)
mem_rc_input = torch.cat([mems, rc_block, utterance_block], dim=0)
# In training lc_length = 0
key_length = mem_rc_input.size(0) + lc_length
rc_input_sum = input
q = self.e2h_q(rc_input_sum)
kv = self.e2h_kv(mem_rc_input)
k, v = kv.chunk(chunks=2, dim=2)
result_qkv = (q, k, v)
input_shape = (T, B, D)
result_lengths_info = (
mem_length,
utterance_length,
right_context_blocks_length,
key_length,
)
if padding_mask is not None:
assert padding_mask.size(0) == B
assert padding_mask.size(1) == key_length
return result_qkv, input_shape, result_lengths_info, padding_mask
def prepare_attention_weights(
self,
q: Tensor,
new_k: Tensor,
new_v: Tensor,
input_shape: Tuple[int, int, int],
rpe: Optional[Tensor],
) -> Tuple[Tensor, Tensor, Tensor]:
T, B, D = input_shape
q = (
q.contiguous().view(-1, B * self.num_heads, self.head_dim).transpose(0, 1)
* self.scaling
)
k = (
new_k.contiguous()
.view(-1, B * self.num_heads, self.head_dim)
.transpose(0, 1)
)
v = (
new_v.contiguous()
.view(-1, B * self.num_heads, self.head_dim)
.transpose(0, 1)
)
attention_weights = torch.bmm(q, k.transpose(1, 2))
if self.use_rpe and rpe is not None and self.rpe_v is not None:
r_k = self.rpe_k(rpe)
# [q, B*h, d] * [q, k, d] -> [B*h, q, k]
attention_weights_rpe = torch.matmul(
q.transpose(0, 1), r_k.transpose(1, 2)
).transpose(0, 1)
attention_weights = attention_weights + attention_weights_rpe
attention_weights_float = attention_weights.float()
return attention_weights, attention_weights_float, v
def prepare_attention_output(
self,
attention_weights: Tensor,
attention_weights_float: Tensor,
v: Tensor,
input_shape: Tuple[int, int, int],
key_length: int,
padding_mask: Optional[Tensor],
rpe: Optional[Tensor],
) -> Tensor:
T, B, D = input_shape
if padding_mask is not None:
attention_weights_float = attention_weights_float.view(
B, self.num_heads, T, key_length
)
attention_weights_float = attention_weights_float.masked_fill(
padding_mask.unsqueeze(1).unsqueeze(2).to(torch.bool), float("-inf")
)
attention_weights_float = attention_weights_float.view(
B * self.num_heads, T, key_length
)
if self.std_scale is not None:
attention_weights_float = attention_suppression(
attention_weights_float, self.std_scale
)
attention_weights_float = torch.nn.functional.softmax(
attention_weights_float, dim=-1
)
attention_weights = attention_weights_float.type_as(attention_weights)
attention_probs = torch.nn.functional.dropout(
attention_weights, p=self.dropout, training=self.training
)
# [T, key_length, B, n_head]+ [key_length, B, n_head, d_head]
# -> [T, B, n_head, d_head]
attention = torch.bmm(attention_probs, v)
if self.use_rpe and rpe is not None and self.rpe_v is not None:
r_v = self.rpe_v(rpe)
attention_rpe = torch.matmul(
attention_probs.transpose(0, 1), r_v
).transpose(0, 1)
if self.rpe_old_option:
attention += attention + attention_rpe
else:
attention = attention + attention_rpe
assert list(attention.shape) == [B * self.num_heads, T, self.head_dim]
attention = attention.transpose(0, 1).contiguous().view(T, B, self.embed_dim)
rc_output_memory = self.out_proj(attention)
return rc_output_memory
@torch.jit.unused
def forward(
self,
input: Tensor,
lengths: Tensor,
mems: Tensor,
attention_mask: Tensor,
pre_mems: Optional[Tensor] = None,
left_context_key: Optional[Tensor] = None,
left_context_val: Optional[Tensor] = None,
rpe: Optional[Tensor] = None,
) -> Tuple[Tensor, Tensor, Tensor, Tensor]:
"""
forward function for NoSegAugmentedMemoryMultiheadAttentionBmm in training.
args:
input: formed in the following way
[right_context_0, right_contex_1, ..., seg_0, seg_1,
..., summary_0, summary_1,..]
lengths: the length of query which is [seg_0, seg_1, ....]
mems: [mem_0, mem_1, ...].
attention_mask: attention mask for query = [right_context, query, summary]
key = [mem, right_context, query]. This is only used for traing.
"""
if self.use_mem:
mem_length = mems.size(0)
summary_length = mem_length + 1
if pre_mems is not None:
mems = torch.cat([pre_mems, mems], dim=0)
else:
mem_length = 0
summary_length = 0
# In training, lc_length = 0
if left_context_key is not None:
lc_length = left_context_key.size(0)
else:
lc_length = 0
results = self.prepare_qkv(
input=input,
mems=mems,
lengths=lengths,
summary_length=summary_length,
lc_length=lc_length,
)
result_qkv, input_shape, result_lengths_info, padding_mask = results
q, k, v = result_qkv
(
mem_length,
utterance_length,
right_context_blocks_length,
key_length,
) = result_lengths_info
if left_context_key is not None:
# add the cache key and value
new_k = torch.cat(
[
k[: mem_length + right_context_blocks_length, :, :],
left_context_key,
k[-utterance_length:, :, :],
],
dim=0,
)
new_v = torch.cat(
[
v[: mem_length + right_context_blocks_length, :, :],
left_context_val,
v[-utterance_length:, :, :],
],
dim=0,
)
next_k = new_k[mem_length + right_context_blocks_length :, :, :]
next_v = new_v[mem_length + right_context_blocks_length :, :, :]
else:
new_k = k
new_v = v
next_k = None
next_v = None
attention_weights, attention_weights_float, v = self.prepare_attention_weights(
q=q,
new_k=new_k,
new_v=new_v,
input_shape=input_shape,
rpe=rpe,
)
# mask attention
attention_mask = attention_mask.unsqueeze(0)
attention_weights_float = attention_weights_float.masked_fill(
attention_mask, float(self.negative_inf)
)
rc_output_memory = self.prepare_attention_output(
attention_weights=attention_weights,
attention_weights_float=attention_weights_float,
v=v,
input_shape=input_shape,
key_length=key_length,
padding_mask=padding_mask,
rpe=rpe,
)
if self.use_mem:
# next_m length equals to summary length - 1
# last memory is ignored
if self.mini_batches:
next_m = rc_output_memory[-summary_length:]
else:
next_m = rc_output_memory[-summary_length:-1]
next_m = self.squash_mem(next_m)
# rc and output
rc_output = rc_output_memory[:-summary_length]
if not self.nonlinear_squash_mem:
next_m = torch.clamp(next_m, min=-10, max=10)
else:
next_m = mems
rc_output = rc_output_memory
return rc_output, next_m, next_k, next_v
@torch.jit.export
def forward_jit(
self,
input: Tensor,
lengths: Tensor,
mems: Tensor,
left_context_key: Tensor,
left_context_val: Tensor,
rpe: Optional[Tensor],
) -> Tuple[Tensor, Tensor, Tensor, Tensor]:
"""
forward function for NoSegAugmentedMemoryMultiheadAttentionBmm in decoding.
args:
input: formed in the following way
[right_context_0, right_contex_1, ..., seg_0, seg_1,
..., summary_0, summary_1,..]
lengths: the length of query which is [seg_0, seg_1, ....]
mems: [mem_0, mem_1, ...].
left_context_key: left_context for key part. This is only used for online
decoding. In training, this is empty tensor
left_context_val: left_context for value part. This is only used for online
decoding. In training, this is empty tensor
"""
lc_length = left_context_key.size(0)
# In decoding, summary_length = 1 or 0
if self.use_mem:
summary_length = 1
else:
summary_length = 0
results = self.prepare_qkv(
input=input,
mems=mems,
lengths=lengths,
summary_length=summary_length,
lc_length=lc_length,
)
result_qkv, input_shape, result_lengths_info, padding_mask = results
q, k, v = result_qkv
(
mem_length,
utterance_length,
right_context_blocks_length,
key_length,
) = result_lengths_info
# add the cache key and value
new_k = torch.cat(
[
k[: mem_length + right_context_blocks_length, :, :],
left_context_key,
k[-utterance_length:, :, :],
],
dim=0,
)
new_v = torch.cat(
[
v[: mem_length + right_context_blocks_length, :, :],
left_context_val,
v[-utterance_length:, :, :],
],
dim=0,
)
next_k = new_k[mem_length + right_context_blocks_length :, :, :]
next_v = new_v[mem_length + right_context_blocks_length :, :, :]
attention_weights, attention_weights_float, v = self.prepare_attention_weights(
q=q,
new_k=new_k,
new_v=new_v,
input_shape=input_shape,
rpe=rpe,
)
# In online decoding, we don't have attention mask. But we still need
# to disable the attention from summary query to memory
attention_weights_float[:, -1, :mem_length] = float(self.negative_inf)
rc_output_memory = self.prepare_attention_output(
attention_weights=attention_weights,
attention_weights_float=attention_weights_float,
v=v,
input_shape=input_shape,
key_length=key_length,
padding_mask=padding_mask,
rpe=rpe,
)
# In decoding, summary length is 1
if self.use_mem:
next_m = rc_output_memory[-1:]
next_m = self.squash_mem(next_m)
# rc and output
rc_output = rc_output_memory[:-1]
if not self.nonlinear_squash_mem:
next_m = torch.clamp(next_m, min=-10, max=10)
else:
rc_output = rc_output_memory
# empty tensor as input mems
next_m = mems
return rc_output, next_m, next_k, next_v
def quantize_(self, params=None):
if params and "per_channel" in params and params["per_channel"]:
qconfig = per_channel_dynamic_qconfig
else:
qconfig = default_dynamic_qconfig
torch.quantization.quantize_dynamic(
self, {torch.nn.Linear: qconfig}, dtype=torch.qint8, inplace=True
)
return self
class NoSegAugmentedMemoryTransformer(nn.Module):
"""
Whole utterance augmented memory transformer.
This is not pyspeech nn layer. It is used as a module in a master layer where
multiple transformers is used.
"""
def __init__(
self,
input_dim,
num_heads,
ffn_dim,
dropout_in_attn=0.0,
dropout_on_attn=None,
dropout_on_fc1=None,
dropout_on_fc2=None,
activation_fn="relu",
tanh_on_mem=False,
std_scale=None,
scaled_init=False,
segment_size=128,
use_mem=True,
mini_batches=False,
negative_inf="-inf",
layer_index=-1,
summarization_method="mean",
max_relative_position=0,
rpe_old_option=True,
):
super(NoSegAugmentedMemoryTransformer, self).__init__()
self.attention = NoSegAugmentedMemoryMultiheadAttentionBmm(
input_dim=input_dim,
num_heads=num_heads,
dropout=dropout_in_attn,
scaled_init=scaled_init,
tanh_on_mem=tanh_on_mem,
std_scale=std_scale,
use_mem=use_mem,
mini_batches=mini_batches,
negative_inf=negative_inf,
layer_index=layer_index,
max_relative_position=max_relative_position,
)
self.dropout = nn.Dropout(dropout_on_attn)
self.pos_ff = PositionwiseFF(
input_dim=input_dim,
ffn_dim=ffn_dim,
dropout_on_fc1=dropout_on_fc1,
dropout_on_fc2=dropout_on_fc2,
activation_fn=activation_fn,
)
self.layer_norm_pre = Fp32LayerNorm(input_dim)
self.layer_norm = Fp32LayerNorm(input_dim)
self.segment_size = segment_size
self.use_mem = use_mem
self.memory_op = SummarizationLayer(
summarization_method, segment_size, input_dim
)
def set_mini_batches(self, mini_batches):
self.attention.mini_batches = mini_batches
def gen_summary_queries(self, input):
sum_input = self.memory_op(input)
return sum_input
def pre_attention_ops(self, input, right_context_blocks):
rc_length = right_context_blocks.size(0)
input_length = input.size(0)
rc_and_input = torch.cat([right_context_blocks, input], dim=0)
residual_input = rc_and_input
rc_and_input = self.layer_norm_pre(rc_and_input)
query_input = rc_and_input[-input_length:, :, :]
return rc_length, input_length, residual_input, query_input, rc_and_input
def after_attention_ops(self, attention_output, residual_input):
output = self.dropout(attention_output)
output = output + residual_input
output = self.pos_ff(output)
output = self.layer_norm(output)
return output
@torch.jit.export
def forward_jit(
self,
input: Tensor,
lengths: Tensor,
mems: Tensor,
left_context_key: Tensor,
left_context_val: Tensor,
right_context_blocks: Tensor,
rpe: Optional[Tensor],
) -> Tuple[Tensor, Tensor, Tensor, Tensor, Tensor]:
results = self.pre_attention_ops(input, right_context_blocks)
rc_length, input_length, residual_input, query_input, rc_and_input = results
# In online decoding, the summary query size is always 1 or 0
if self.use_mem:
summary_query = self.gen_summary_queries(query_input)
summary_query = summary_query[0:1, :, :]
rc_qu_su = torch.cat([rc_and_input, summary_query], dim=0)
else:
rc_qu_su = rc_and_input
rc_output, next_m, next_k, next_v = self.attention.forward_jit(
input=rc_qu_su,
lengths=lengths,
mems=mems,
left_context_key=left_context_key,
left_context_val=left_context_val,
rpe=rpe,
)
rc_output = self.after_attention_ops(rc_output, residual_input)
results = (
rc_output[-input_length:, :, :],
next_m,
rc_output[0:rc_length, :, :],
next_k,
next_v,
)
return results
@torch.jit.unused
def forward(
self,
input,
lengths,
mems,
right_context_blocks,
attention_mask,
pre_mems,
left_context_key,
left_context_val,
rpe,
):
results = self.pre_attention_ops(input, right_context_blocks)
rc_length, input_length, residual_input, query_input, rc_and_input = results
if self.use_mem:
summary_query = self.gen_summary_queries(query_input)
rc_qu_su = torch.cat([rc_and_input, summary_query], dim=0)
else:
rc_qu_su = rc_and_input
rc_output, next_m, next_k, next_v = self.attention(
input=rc_qu_su,
lengths=lengths,
mems=mems,
attention_mask=attention_mask,
pre_mems=pre_mems,
left_context_key=left_context_key,
left_context_val=left_context_val,
rpe=rpe,
)
# [TODO] Note memory did not go through pos_ff. What happen if we pass
# memory through the pos_ff as well?
rc_output = self.after_attention_ops(rc_output, residual_input)
results = (
rc_output[-input_length:, :, :],
next_m,
rc_output[0:rc_length, :, :],
next_k,
next_v,
)
return results
class NoSegAugmentedMemoryTransformerEncoderLayer(FairseqEncoder):
"""
Whole utterance augmented memory transformer encoder layer. This is a master layer
where we can define multiple augmented memory transformers. There are two reasons
to setup the master layer.
1. We only need to define once about the attention mask. All the layers in the master
layer share the same mask.
2. pyspeech nn layer has special input and output format. Defining one master layer is
easier to passing memory between different layes inside the master layer
args:
input_dim: input embedding dimension
num_heads: number of heads in multihead self-attention
ffn_dim: ffn dimension in FFN layer
num_layers: number of augmented memory transformer layers
dropout_in_attn: dropout used in multi-head self-attention
dropout_on_attn: dropout used for output from te multihead self-attention
dropout_on_fc1: dropout used in FFN layer for the first linear layer
dropout_on_fc2: dropout used in FFN layer for the second linear layer
segment_size: segment size for each segment
context_config: (left_context_size, right_context_size) defines the surround context size
for each segment
max_memory_size: maximum memory size used for each segment
scaled_init: whether use scaled init for weight initialization in attention layer
std_scale: if std_scale is not None. The weak attention suppression is
turned on. For std_scale = 0.5, all the attention smaller than
mean + 0.5 * std will be suppressed.
activation_fn: activation function used in FFN layer. [ReLU, GELU] supported
tanh_on_mem: whether use tanh on memory
mini_batches: use mini-btach training
negative_inf: the negative infinity value used in attention masking. default is "-inf".
For some situation, e.g. LM. it is better to use "-1e8" to avoid nan issue.
summarization_method: method to generate segment summrization embedding
max_relative_position: max relatie position for relative position embedding
rpe_old_option: To be compatible with previous model. The previous model
was trained with attention += attention + rpe. The correct equation
should be attention = attention + rpe
[TODO]: remove the rpe_old_option by the end of 2021 Q1.
"""
def __init__(
self,
input_dim,
num_heads,
ffn_dim,
num_layers=1,
dropout_in_attn=0.0,
dropout_on_attn=0.0,
dropout_on_fc1=0.0,
dropout_on_fc2=0.0,
segment_size=128,
context_config=(0, 0),
max_memory_size=0,
scaled_init=True,
std_scale=None,
activation_fn="relu",
tanh_on_mem=False,
mini_batches=False,
negative_inf="-inf",
deep_init=True,
summarization_method="mean",
max_relative_position=0,
rpe_old_option=True,
):
super().__init__(None)
if input_dim % num_heads:
raise ValueError(
"input_dim ({}) must be divisible by num_heads ({})".format(
input_dim, num_heads
)
)
# we used to support growing memory size. However, it will cause
# cross stream batching failure. Now we need to have exact max memory size
if max_memory_size < 0:
raise ValueError("max_memory_size must be >= 0")
# Only assign right_context. In decoding, left context will be cached.
# No need to let the online decoder to re-assign the left context
self.left_context, self.right_context = context_config
self.segment_size = segment_size
self.memory_dim = input_dim
self.max_memory_size = max_memory_size
self.mini_batches = mini_batches
if self.max_memory_size != 0:
self.use_mem = True
else:
self.use_mem = False
self.memory_op = SummarizationLayer(
summarization_method, segment_size, input_dim
)
self.layers = torch.nn.ModuleList()
self.num_layers = num_layers
self.max_relative_position = max_relative_position
if self.max_relative_position > 0:
self.use_rpe = True
else:
self.use_rpe = False
for i in range(self.num_layers):
if deep_init:
layer_index = i
else:
layer_index = -1
self.layers.append(
NoSegAugmentedMemoryTransformer(
num_heads=num_heads,
input_dim=input_dim,
ffn_dim=ffn_dim,
dropout_in_attn=dropout_in_attn,
dropout_on_attn=dropout_on_attn,
dropout_on_fc1=dropout_on_fc1,
dropout_on_fc2=dropout_on_fc2,
segment_size=segment_size,
std_scale=std_scale,
activation_fn=activation_fn,
tanh_on_mem=tanh_on_mem,
scaled_init=scaled_init,
use_mem=self.use_mem,
mini_batches=mini_batches,
negative_inf=negative_inf,
layer_index=layer_index,
summarization_method=summarization_method,
max_relative_position=max_relative_position,
rpe_old_option=rpe_old_option,
)
)
def set_mini_batches(self, mini_batches):
# handy function only used for unit test
self.mini_batches = mini_batches
for layer in self.layers:
layer.set_mini_batches(mini_batches)
def _get_relative_position(
self,
input: Tensor,
max_relative_position: int,
left_context_length: int,
past_length: int,
is_decoding: bool,
):
# For training, we copy the right context to the start of the utterance
# First dimension in distance is corresponding to query.
# [right context, utterance, summary vector]
# Second dimension in distance is corresponding to key.
# [Memory bank, right context, utterance]
# For summary vector in query part, the distance with
# all other position is 2*max_position. For memory bank in key,
# the distance with all other positions is 0.
T, B, D = input.shape
num_segs = math.ceil((T - self.right_context) / self.segment_size)
# utterance
u_st = past_length * self.segment_size
u_ed = u_st + T
utterance_ranges = torch.arange(u_st, u_ed - self.right_context)
# left context. Only in minibatch or decoding
left_context_ranges = torch.arange(u_st - left_context_length, u_st)
# Right context block
# right context + utterance
right_context_blocks = []
for i in range(0, num_segs - 1):
st = (i + 1) * self.segment_size + u_st
ed = st + self.right_context
assert ed < u_ed
temp = torch.arange(st, ed)
right_context_blocks.append(temp)
right_context_blocks.append(torch.arange(u_ed - self.right_context, u_ed))
right_context_ranges = torch.cat(right_context_blocks)
if self.use_mem:
# Memory bank
# The position for memory -n, .., -1
if is_decoding:
memory_size = min(past_length, self.max_memory_size)
else:
memory_size = num_segs + past_length - 1
memory_bank_ranges = torch.arange(
-max_relative_position - 1, -max_relative_position - 1 - memory_size, -1
)
# summary vector
# The position for summary vector as the T+max_relative_position+1.
# After the clamping, the relative position is max_relative_position
summary_pos_st = u_ed + max_relative_position + 1
summary_vector_ranges = torch.arange(
summary_pos_st, summary_pos_st + num_segs
)
key_ranges = torch.cat(
[
memory_bank_ranges,
right_context_ranges,
left_context_ranges,
utterance_ranges,
]
)
query_ranges = torch.cat(
[right_context_ranges, utterance_ranges, summary_vector_ranges]
)
else:
key_ranges = torch.cat(
[right_context_ranges, left_context_ranges, utterance_ranges]
)
query_ranges = torch.cat([right_context_ranges, utterance_ranges])
distance = key_ranges[None, :] - query_ranges[:, None]
distance_clamp = (
torch.clamp(distance, -max_relative_position, max_relative_position)
+ max_relative_position
)
distance_clamp = distance_clamp.to(input.device).long().detach()
return distance_clamp
def _get_attention_mask(self, input, past_length=0, left_context_cache=0):
# attention mask for each query contains three parts:
# 1. memory part
# 2. left_context + segment
# 3. right_context_block
# so for each segment and its correspoinding right context block,
# the attention matrix is formed by 9 parts:
# [0, m, 0, 0, right_context, 0, 0, seg, 0]
# [before memory, memory, after memory, before right context, right_context,
# after right context, before seg, seg, after seg]
#
# Query is formed in the way as [right_context_blocks, utterance, summary]
#
# Note: put m and right_context before segment is convenient
# for padding_mask operation.
# Key lengths = m_length + right_context_block_length + lengths
utterance_length, batch_size, _ = input.shape
summary_length = math.ceil(utterance_length / self.segment_size)
num_segs = summary_length
rc_length = self.right_context * num_segs
rc = self.right_context
lc = self.left_context
# using mini-batches, there is left context cache available for current
# sequence.
lcc = left_context_cache
# max_memory_size is 0 then we don't have memory and summary
# past_length is the memory carry from previous sequence
if self.use_mem:
mem_length = num_segs - 1 + past_length
else:
mem_length = 0
rc_mask = []
query_mask = []
summary_mask = []
for j in range(0, num_segs):
ssize = min(self.segment_size, utterance_length - j * self.segment_size)
rc_size = rc
rc_mat = []
q_mat = []
s_mat = []
m_start = max(j + past_length - self.max_memory_size, 0)
# max_memory_size is 0, then we don't use memory
if self.use_mem:
# part 0: before memory
rc_mat.append(input.new_zeros(rc_size, m_start))
q_mat.append(input.new_zeros(ssize, m_start))
s_mat.append(input.new_zeros(1, m_start))
# part 1: memory
col_1 = j + past_length - m_start
rc_mat.append(torch.ones(rc_size, col_1, device=input.device))
q_mat.append(torch.ones(ssize, col_1, device=input.device))
# based on D22875746, disable summary query attention
# on memeory is better for long form utterance
s_mat.append(input.new_zeros(1, col_1))
# part 2: after memory
col_2 = mem_length - (j + past_length)
rc_mat.append(input.new_zeros(rc_size, col_2))
q_mat.append(input.new_zeros(ssize, col_2))
s_mat.append(input.new_zeros(1, col_2))
# part 3: before right context
rc_start = j * rc
rc_mat.append(input.new_zeros(rc_size, rc_start))
q_mat.append(input.new_zeros(ssize, rc_start))
s_mat.append(input.new_zeros(1, rc_start))
# part 4: right context
rc_end = rc_start + rc
col_4 = rc
rc_mat.append(torch.ones(rc_size, col_4, device=input.device))
q_mat.append(torch.ones(ssize, col_4, device=input.device))
s_mat.append(torch.ones(1, col_4, device=input.device))
# part 5: after right context
col_5 = rc_length - rc_end
rc_mat.append(input.new_zeros(rc_size, col_5))
q_mat.append(input.new_zeros(ssize, col_5))
s_mat.append(input.new_zeros(1, col_5))
# part 6: before query segment
seg_start = max(j * self.segment_size + lcc - lc, 0)
rc_mat.append(input.new_zeros(rc_size, seg_start))
q_mat.append(input.new_zeros(ssize, seg_start))
s_mat.append(input.new_zeros(1, seg_start))
# part 7: query segment
# note: right context is put in right context block
# here we only need to consider about left context
seg_end = min((j + 1) * self.segment_size + lcc, utterance_length + lcc)
col_7 = seg_end - seg_start
rc_mat.append(torch.ones(rc_size, col_7, device=input.device))
q_mat.append(torch.ones(ssize, col_7, device=input.device))
s_mat.append(torch.ones(1, col_7, device=input.device))
# part 8: after query segment
col_8 = utterance_length + lcc - seg_end
rc_mat.append(input.new_zeros(rc_size, col_8))
q_mat.append(input.new_zeros(ssize, col_8))
s_mat.append(input.new_zeros(1, col_8))
rc_mask.append(torch.cat(rc_mat, dim=1))
query_mask.append(torch.cat(q_mat, dim=1))
summary_mask.append(torch.cat(s_mat, dim=1))
# no memory, then we don't need summary either
if self.use_mem:
attention_mask = (
1
- torch.cat(
[
torch.cat(rc_mask, dim=0),
torch.cat(query_mask, dim=0),
torch.cat(summary_mask, dim=0),
],
dim=0,
)
).to(torch.bool)
else:
attention_mask = (
1
- torch.cat(
[torch.cat(rc_mask, dim=0), torch.cat(query_mask, dim=0)], dim=0
)
).to(torch.bool)
return attention_mask
@torch.jit.export
def init_state(
self, batch_size: int, device: Optional[Device] = None
) -> List[Tensor]:
empty_memory = torch.zeros(
self.num_layers,
self.max_memory_size,
batch_size,
self.memory_dim,
device=device,
)
left_context_key = torch.zeros(
self.num_layers,
self.left_context,
batch_size,
self.memory_dim,
device=device,
)
left_context_val = torch.zeros(
self.num_layers,
self.left_context,
batch_size,
self.memory_dim,
device=device,
)
past_length = torch.zeros(1, batch_size, dtype=torch.int32, device=device)
return [empty_memory, left_context_key, left_context_val, past_length]
@torch.jit.export
def batch_state(self, states: List[List[Tensor]]) -> List[Tensor]:
if len(states) == 0:
return []
batched_m = []
batched_lc_key = []
batched_lc_val = []
batched_past_length = []
for state in states:
if len(state) == 0:
continue
m, lc_key, lc_val, past_length = state
batched_m.append(m)
batched_lc_key.append(lc_key)
batched_lc_val.append(lc_val)
batched_past_length.append(past_length)
if (
(len(batched_m) == 0)
or (len(batched_lc_key) == 0)
or (len(batched_lc_val) == 0)
or (len(batched_past_length) == 0)
):
return [
torch.tensor([]),
torch.tensor([]),
torch.tensor([]),
torch.tensor([]),
]
batched_m = torch.cat(batched_m, dim=2)
batched_lc_key = torch.cat(batched_lc_key, dim=2)
batched_lc_val = torch.cat(batched_lc_val, dim=2)
batched_past_length = torch.cat(batched_past_length, dim=1)
return [batched_m, batched_lc_key, batched_lc_val, batched_past_length]
@torch.jit.export
def reorder_state(self, state: List[Tensor], indices: Tensor) -> List[Tensor]:
if len(state) == 0:
return []
m, lc_key, lc_val, past_length = state
indices = indices.to(device=m.device)
reord_m = torch.index_select(m, 2, indices)
reord_lc_key = torch.index_select(lc_key, 2, indices)
reord_lc_val = torch.index_select(lc_val, 2, indices)
reord_past_length = torch.index_select(past_length, 1, indices)
return [reord_m, reord_lc_key, reord_lc_val, reord_past_length]
@torch.jit.export
def reset_state(self, state: List[Tensor], indices: Tensor) -> List[Tensor]:
m, lc_key, lc_val, past_length = state
m = m.index_fill(dim=2, index=indices, value=0.0)
lc_key = lc_key.index_fill(dim=2, index=indices, value=0.0)
lc_val = lc_val.index_fill(dim=2, index=indices, value=0.0)
past_length = past_length.index_fill(dim=1, index=indices, value=0)
return [m, lc_key, lc_val, past_length]
@torch.jit.export
def state_size(self) -> int:
return 4
@torch.jit.export
def batch_size_in_state(
self, state: Optional[List[Tensor]], sloppy: bool = True
) -> Optional[int]:
if state is None:
return None
return state[0].size(2)
def gen_summary_queries(self, input):
sum_input = self.memory_op(input)
return sum_input
def _gen_right_context_padded_input(self, input):
# This function deals with input that is already
# padded with right context (e.g. minibatch training)
right_context_blocks = []
T, B, D = input.shape
num_segs = math.ceil((T - self.right_context) / self.segment_size)
for i in range(0, num_segs - 1):
st = (i + 1) * self.segment_size
ed = st + self.right_context
assert ed < T
temp = input[st:ed, :, :]
right_context_blocks.append(temp)
# last segment right context is already available
right_context_blocks.append(input[T - self.right_context :, :, :])
return torch.cat(right_context_blocks, dim=0)
def _gen_segs_right_context(self, input, lengths):
segments = []
T, B, D = input.size()
nT = T - self.right_context
# assume input is right context padded
num_segs = math.ceil(nT / self.segment_size)
# pad zeros to the utterance to make sure each
# segment has the same right context. For the
for i in range(0, num_segs - 1):
st = i * self.segment_size
ed = min(T, st + self.segment_size + self.right_context)
temp = input[st:ed, :, :]
rest_lengths = torch.clamp(
lengths - self.segment_size, min=0, max=nT - (i + 1) * self.segment_size
)
segments.append((temp, lengths - rest_lengths + self.right_context))
lengths = rest_lengths
last_seg = input[st + self.segment_size :, :, :]
segments.append((last_seg, rest_lengths + self.right_context))
return segments
@torch.jit.unused
def forward(
self, input: Tensor, padding_masks: Tensor, state: Optional[List[Tensor]] = None
) -> Tuple[Tensor, Tensor, List[Tensor], List[Tensor]]:
# Xutai: originally the second argument is lengths.
lengths = (~padding_masks).sum(dim=1).long()
# mini batch training.
if self.mini_batches:
return self.forward_mini_batches(input, lengths, state)
# regular full sequence training. Note, assume the right context in provided
# in the input.
T, B, D = input.size()
right_context_blocks = self._gen_right_context_padded_input(input)
# generate the relative positional embedding
if self.use_rpe:
rpe = self._get_relative_position(
input=input,
max_relative_position=self.max_relative_position,
left_context_length=0,
past_length=0,
is_decoding=False,
)
else:
rpe = None
input = input[: T - self.right_context, :, :]
attention_mask = self._get_attention_mask(input)
# firt layer use each segment mean as memory
# ignore the last one seg average
if self.use_mem:
mems = self.gen_summary_queries(input)[:-1, :, :]
else:
mems = torch.zeros(0, input.size(1), input.size(2), device=input.device)
mems = mems.type_as(input)
output = input
all_outputs = []
for layer in self.layers:
output, mems, right_context_blocks, _, _ = layer(
input=output,
lengths=lengths,
attention_mask=attention_mask,
mems=mems,
right_context_blocks=right_context_blocks,
pre_mems=None,
left_context_key=None,
left_context_val=None,
rpe=rpe,
)
all_outputs.append(output)
return output, padding_masks, [], all_outputs
def forward_jit_mini_batch_init(
self,
seg: Tensor,
state: Optional[List[Tensor]] = None,
is_decoding: bool = False,
):
# Prepare state. In whole sequence training, state is ignored.
# For minibatch training, we need to prepare state
if state is None:
state = self.init_state(batch_size=seg.size(1), device=seg.device)
if seg.dtype == torch.half:
state = [state[0].half(), state[1].half(), state[2].half(), state[3]]
if self.use_mem:
# note input average only on seg, not on right context
# first layer use each segmetn mean as memory. the last
# one segment average is used in state
full_mems = self.gen_summary_queries(seg)
if is_decoding:
mems = full_mems[0:1, :, :]
state_mems = torch.cat([state[0][0], mems], dim=0)
else:
mems = full_mems[:-1, :, :]
state_mems = torch.cat([state[0][0], full_mems], dim=0)
else:
mems = state[0][0]
state_mems = mems
# track processed segment number or memory number
# the same batch as the same bumber of past length
past_length = state[3][0][0].item()
past_left_context = min(past_length * self.segment_size, self.left_context)
past_length = min(self.max_memory_size, past_length)
return state, mems, state_mems, past_length, past_left_context
def state_update_before(
self, layer: int, state: List[Tensor], past_length: int, past_left_context: int
):
pre_mems = state[0][layer][self.max_memory_size - past_length :, :, :]
lc_key = state[1][layer][self.left_context - past_left_context :, :, :]
lc_val = state[2][layer][self.left_context - past_left_context :, :, :]
return pre_mems, lc_key, lc_val
def state_update_after(
self,
layer: int,
state: List[Tensor],
mems: Tensor,
next_key: Tensor,
next_val: Tensor,
mems_list: List[Tensor],
lc_key_list: List[Tensor],
lc_val_list: List[Tensor],
):
# mems is used for next layer
if layer < self.num_layers - 1:
state_mems = torch.cat([state[0][layer + 1], mems], dim=0)
mems_list.append(state_mems[-self.max_memory_size :, :, :])
# when mems pass to next sequence, we need the last memory. when mems
# use for the next layer, we can ignore the last memory
mems = mems[:-1, :, :]
# note state[1][i] and state[2][i] original length equals to self.left_context
new_k = torch.cat([state[1][layer], next_key], dim=0)
new_v = torch.cat([state[2][layer], next_val], dim=0)
lc_key_list.append(new_k[-self.left_context :, :, :])
lc_val_list.append(new_v[-self.left_context :, :, :])
return mems_list, lc_key_list, lc_val_list, mems
def state_update_after_loop(
self,
state: List[Tensor],
mems_list: List[Tensor],
lc_key_list: List[Tensor],
lc_val_list: List[Tensor],
update_length: int,
):
state[0] = torch.stack(mems_list, dim=0)
state[1] = torch.stack(lc_key_list, dim=0)
state[2] = torch.stack(lc_val_list, dim=0)
state[3] = state[3] + update_length
return state
@torch.jit.unused
def forward_mini_batches(
self, input: Tensor, lengths: Tensor, state: Optional[List[Tensor]] = None
) -> Tuple[Tensor, Tensor, List[Tensor], List[Tensor]]:
T, B, D = input.size()
# input without right context
seg = input[: T - self.right_context, :, :]
# get right context blocks
right_context_blocks = self._gen_right_context_padded_input(input)
mems_list = []
lc_key_list = []
lc_val_list = []
results = self.forward_jit_mini_batch_init(seg, state, False)
state, mems, state_mems, past_length, past_left_context = results
# relative position embedding
if self.use_rpe:
rpe = self._get_relative_position(
input=input,
max_relative_position=self.max_relative_position,
left_context_length=past_left_context,
past_length=past_length,
is_decoding=False,
)
else:
rpe = None
# get attention mask based on seg (not include right context) and available
# left context
attention_mask = self._get_attention_mask(seg, past_length, past_left_context)
mems_list.append(state_mems[-self.max_memory_size :, :, :])
output = seg
i = 0
all_outputs = []
for layer in self.layers:
# In order to make cross stream batching work, mem, left context key
# and left context value in the state should always be the same shape.
# We use the past length to track the processed segment number. In this
# way, we take out the essential memory, left context key and left
# context val from the state. After finish the forward for current segment
# we add the new memory, left context key and left context value into the
# staate and trim out the oldest part to keep the shape consistent.
pre_mems, lc_key, lc_val = self.state_update_before(
i, state, past_length, past_left_context
)
output, mems, right_context_blocks, next_key, next_val = layer.forward(
input=output,
lengths=lengths,
attention_mask=attention_mask,
mems=mems,
right_context_blocks=right_context_blocks,
pre_mems=pre_mems,
left_context_key=lc_key,
left_context_val=lc_val,
rpe=rpe,
)
all_outputs.append(output)
mems_list, lc_key_list, lc_val_list, mems = self.state_update_after(
layer=i,
state=state,
mems=mems,
next_key=next_key,
next_val=next_val,
mems_list=mems_list,
lc_key_list=lc_key_list,
lc_val_list=lc_val_list,
)
i += 1
# update state
update_length = math.ceil((T - self.right_context) / self.segment_size)
state = self.state_update_after_loop(
state=state,
mems_list=mems_list,
lc_key_list=lc_key_list,
lc_val_list=lc_val_list,
update_length=update_length,
)
return output, lengths, state, all_outputs
def forward_jit_test(
self, input: Tensor, lengths: Tensor, state: Optional[List[Tensor]] = None
) -> Tuple[Tensor, Tensor, List[Tensor]]:
"""
This one simulate sequence encoder forward jit. This is for unit test purpose.
It is not used in training or decoding. Note, extra_right_context is set in
the model. In unit test, input = [utterance, right_context], lengths =
[utterance_length].
args:
input: input utterance
lengths: utterance input length
state: None here. input is whole utterance
"""
# [TODO] sequence_to_segment has bug in lengths.
seg_src_tokens_lengths = self._gen_segs_right_context(input, lengths)
seg_enc_tokens_lengths: List[Tuple[Tensor, Tensor]] = []
state: Optional[List[Tensor]] = None
for seg_src_tokens, seg_src_lengths in seg_src_tokens_lengths:
seg_enc_tokens, seg_enc_lengths, state = self.forward_jit(
input=seg_src_tokens, lengths=seg_src_lengths, state=state
)
seg_enc_tokens_lengths.append((seg_enc_tokens, seg_enc_lengths))
enc_tokens, enc_lengths = segments_to_sequence(
segments=seg_enc_tokens_lengths, time_axis=0
)
state = [] # returns trivial state
return enc_tokens, enc_lengths, state
@torch.jit.export
def forward_jit(
self, input: Tensor, lengths: Tensor, state: Optional[List[Tensor]] = None
) -> Tuple[Tensor, Tensor, List[Tensor]]:
"""
Forward helper for online decoding.
args:
input: [seg, right_context]. We assume in online we
always padding the right context to the preset right context size.
For the last segment, we may have short segment size, but right
context size is the same as other segments
lengths: utterance input length is the utterance segment length and
right context size
state: [memory, left_context_key, left_context_val]. To improve throughput,
in addition to memory, we also cache key and value for left_context in
multihead self-attention
"""
# In online decoding, input = [segment, right_context]
# Lengths = [segment_length, right_context_length]
# so we need strip right context in output
T, B, D = input.size()
rc_str = T - self.right_context
rc_end = T
right_context_blocks = input[rc_str:rc_end, :, :]
seg = input[:rc_str, :, :]
lengths = torch.clamp(lengths - self.right_context, min=0)
mems_list = []
lc_key_list = []
lc_val_list = []
results = self.forward_jit_mini_batch_init(seg, state, True)
state, mems, state_mems, past_length, past_left_context = results
# relative position embedding
if self.use_rpe:
rpe = self._get_relative_position(
input=input,
max_relative_position=self.max_relative_position,
left_context_length=past_left_context,
past_length=past_length,
is_decoding=True,
)
else:
rpe = None
# memory for first layer.
mems_list.append(state_mems[-self.max_memory_size :, :, :])
output = seg
i = 0
for layer in self.layers:
# In order to make cross stream batching work, mem, left context key
# and left context value in the state should always be the same shape.
# We use the past length to track the processed segment number. In this
# way, we take out the essential memory, left context key and left
# context val from the state. After finish the forward for current segment
# we add the new memory, left context key and left context value into the
# staate and trim out the oldest part to keep the shape consistent.
true_mems, lc_key, lc_val = self.state_update_before(
layer=i,
state=state,
past_length=past_length,
past_left_context=past_left_context,
)
output, mems, right_context_blocks, next_key, next_val = layer.forward_jit(
input=output,
lengths=lengths,
mems=true_mems,
right_context_blocks=right_context_blocks,
left_context_key=lc_key,
left_context_val=lc_val,
rpe=rpe,
)
# mems is used for next layer
mems_list, lc_key_list, lc_val_list, _ = self.state_update_after(
layer=i,
state=state,
mems_list=mems_list,
mems=mems,
next_key=next_key,
next_val=next_val,
lc_key_list=lc_key_list,
lc_val_list=lc_val_list,
)
i += 1
# update state
state = self.state_update_after_loop(
state=state,
mems_list=mems_list,
lc_key_list=lc_key_list,
lc_val_list=lc_val_list,
update_length=1,
)
return output, lengths, state
def quantize_(self, params=None):
if params and "per_channel" in params and params["per_channel"]:
qconfig = per_channel_dynamic_qconfig
else:
qconfig = default_dynamic_qconfig
torch.quantization.quantize_dynamic(
self, {torch.nn.Linear: qconfig}, dtype=torch.qint8, inplace=True
)
return self
# ------------------------------------------------------------------------------
# Emformer encoder for seq2seq model
# This is a wrapper over the original emformer
# ------------------------------------------------------------------------------
def emformer_encoder(klass):
class SpeechEncoder(klass):
def __init__(self, args):
super().__init__(args)
stride = SpeechEncoder.conv_layer_stride(args)
trf_left_context = args.segment_left_context // stride
trf_right_context = args.segment_right_context // stride
context_config = [trf_left_context, trf_right_context]
self.transformer_layers = nn.ModuleList(
[
NoSegAugmentedMemoryTransformerEncoderLayer(
input_dim=args.encoder_embed_dim,
num_heads=args.encoder_attention_heads,
ffn_dim=args.encoder_ffn_embed_dim,
num_layers=args.encoder_layers,
dropout_in_attn=args.dropout,
dropout_on_attn=args.dropout,
dropout_on_fc1=args.dropout,
dropout_on_fc2=args.dropout,
activation_fn=args.activation_fn,
context_config=context_config,
segment_size=args.segment_length,
max_memory_size=args.max_memory_size,
scaled_init=True, # TODO: use constant for now.
tanh_on_mem=args.amtrf_tanh_on_mem,
)
]
)
def forward(self, src_tokens, src_lengths):
encoder_out = super().forward(src_tokens, src_lengths)
output = encoder_out["encoder_out"][0]
encoder_padding_masks = encoder_out["encoder_padding_mask"][0]
# This is because that in the original implementation
# the output didn't consider the last segment as right context.
encoder_padding_masks = encoder_padding_masks[:, : output.size(0)]
return {
"encoder_out": [output],
"encoder_padding_mask": [encoder_padding_masks],
"encoder_embedding": [],
"encoder_states": [],
"src_tokens": [],
"src_lengths": [],
}
@staticmethod
def conv_layer_stride(args):
# TODO: make it configurable from the args
return 4
SpeechEncoder.__name__ = klass.__name__
return SpeechEncoder
|
bart_ls-main
|
fairseq-py/fairseq/models/speech_to_text/modules/emformer.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from typing import Tuple, List
import torch
import torch.nn.functional as F
from fairseq.models import FairseqEncoder
from fairseq.models.speech_to_text import (
ConvTransformerEncoder,
)
from fairseq.models.speech_to_text.utils import attention_suppression
from fairseq.models.speech_to_text.utils import (
lengths_to_encoder_padding_mask,
segments_to_sequence,
sequence_to_segments,
)
from fairseq.modules import MultiheadAttention, TransformerEncoderLayer
from torch import nn, Tensor
# ------------------------------------------------------------------------------
# AugmentedMemoryConvTransformerEncoder
# ------------------------------------------------------------------------------
class AugmentedMemoryConvTransformerEncoder(ConvTransformerEncoder):
def __init__(self, args):
super().__init__(args)
args.encoder_stride = self.stride()
self.left_context = args.left_context // args.encoder_stride
self.right_context = args.right_context // args.encoder_stride
self.left_context_after_stride = args.left_context // args.encoder_stride
self.right_context_after_stride = args.right_context // args.encoder_stride
self.transformer_layers = nn.ModuleList([])
self.transformer_layers.extend(
[
AugmentedMemoryTransformerEncoderLayer(args)
for i in range(args.encoder_layers)
]
)
def stride(self):
# Hard coded here. Should infer from convs in future
stride = 4
return stride
def forward(self, src_tokens, src_lengths, states=None):
"""Encode input sequence.
:param torch.Tensor xs: input tensor
:param torch.Tensor masks: input mask
:return: position embedded tensor and mask
:rtype Tuple[torch.Tensor, torch.Tensor]:
"""
bsz, max_seq_len, _ = src_tokens.size()
x = (
src_tokens.view(bsz, max_seq_len, self.in_channels, self.input_dim)
.transpose(1, 2)
.contiguous()
)
x = self.conv(x)
bsz, _, output_seq_len, _ = x.size()
x = x.transpose(1, 2).transpose(0, 1).contiguous().view(output_seq_len, bsz, -1)
x = self.out(x)
x = self.embed_scale * x
subsampling_factor = 1.0 * max_seq_len / output_seq_len
input_lengths = torch.max(
(src_lengths.float() / subsampling_factor).ceil().long(),
x.size(0) * src_lengths.new_ones([src_lengths.size(0)]).long(),
)
encoder_padding_mask, _ = lengths_to_encoder_padding_mask(
input_lengths, batch_first=True
)
# TODO: fix positional embedding
positions = self.embed_positions(encoder_padding_mask).transpose(0, 1)
x += positions
x = F.dropout(x, p=self.dropout, training=self.training)
# State to store memory banks etc.
if states is None:
states = [
{"memory_banks": None, "encoder_states": None}
for i in range(len(self.transformer_layers))
]
for i, layer in enumerate(self.transformer_layers):
# x size:
# (self.left_size + self.segment_size + self.right_size)
# / self.stride, num_heads, dim
# TODO: Consider mask here
x = layer(x, states[i])
states[i]["encoder_states"] = x[
self.left_context_after_stride : -self.right_context_after_stride
]
lengths = (
(
~encoder_padding_mask[
:, self.left_context_after_stride : -self.right_context_after_stride
]
)
.sum(dim=1, keepdim=True)
.long()
)
return states[-1]["encoder_states"], lengths, states
# ------------------------------------------------------------------------------
# AugmentedMemoryTransformerEncoderLayer
# ------------------------------------------------------------------------------
class AugmentedMemoryTransformerEncoderLayer(TransformerEncoderLayer):
def __init__(self, args):
super().__init__(args)
self.left_context = args.left_context // args.encoder_stride
self.right_context = args.right_context // args.encoder_stride
def forward(self, x, state):
length, batch_size, x_dim = x.size()
residual = x
if self.normalize_before:
x = self.self_attn_layer_norm(x)
# init_state
if state.get("memory_banks", None) is None:
state["memory_banks"] = []
# TODO reseach new sum_query method
seg_start = self.left_context
seg_end = length - self.right_context
if seg_start < seg_end:
summarization_query = torch.mean(x[seg_start:seg_end], keepdim=True, dim=0)
else:
summarization_query = x.new_zeros(1, batch_size, x_dim)
x = torch.cat([x, summarization_query], dim=0)
x = self.self_attn(input_and_summary=x, state=state)
x = self.dropout_module(x)
x = residual + x
if not self.normalize_before:
x = self.self_attn_layer_norm(x)
residual = x
if self.normalize_before:
x = self.final_layer_norm(x)
x = self.activation_fn(self.fc1(x))
x = self.activation_dropout_module(x)
x = self.fc2(x)
x = self.dropout_module(x)
x = residual + x
if not self.normalize_before:
x = self.final_layer_norm(x)
return x
def build_self_attention(self, embed_dim, args):
return AugmentedMemoryMultiheadAttention(
embed_dim=embed_dim,
num_heads=args.encoder_attention_heads,
dropout=args.attention_dropout,
self_attention=True,
q_noise=self.quant_noise,
qn_block_size=self.quant_noise_block_size,
tanh_on_mem=True,
max_memory_size=args.max_memory_size,
)
# ------------------------------------------------------------------------------
# AugmentedMemoryMultiheadAttention
# ------------------------------------------------------------------------------
class AugmentedMemoryMultiheadAttention(MultiheadAttention):
"""
Augmented Memory Attention from
Streaming Transformer-based Acoustic Models
Using Self-attention with Augmented Memory
https://arxiv.org/abs/2005.08042
"""
def __init__(
self,
embed_dim,
num_heads,
kdim=None,
vdim=None,
dropout=0.0,
bias=True,
add_bias_kv=False,
add_zero_attn=False,
self_attention=False,
encoder_decoder_attention=False,
q_noise=0.0,
qn_block_size=8,
tanh_on_mem=False,
memory_dim=None,
std_scale=0.5, # 0.5 based on https://arxiv.org/abs/2005.09137
max_memory_size=-1,
disable_mem_on_mem_attn=True,
):
super().__init__(
embed_dim,
num_heads,
kdim,
vdim,
dropout,
bias,
add_bias_kv,
add_zero_attn,
self_attention,
encoder_decoder_attention,
q_noise,
qn_block_size,
)
self.memory_dim = memory_dim if memory_dim is not None else embed_dim
self.std_scale = std_scale
self.disable_mem_on_mem_attn = disable_mem_on_mem_attn
# This Operator was used for factorization in PySpeech
self.v2e = lambda x: x
if tanh_on_mem:
self.squash_mem = torch.tanh
self.nonlinear_squash_mem = True
else:
self.squash_mem = lambda x: x
self.nonlinear_squash_mem = False
self.max_memory_size = max_memory_size
def forward(self, input_and_summary, state):
"""
input: Encoder states of current segment with left or right context,
plus one summarization query
"""
length, batch_size, _ = input_and_summary.shape
length = length - 1 # not include sum_query, last index
memory = state["memory_banks"]
# TODO: positional embedding on memory
if self.max_memory_size > -1 and len(memory) > self.max_memory_size:
# TODO: need to fix here
if self.max_memory_size == 0:
memory = memory.new_zeros(1, memory.size(1), self.memory_dim)
else:
memory = memory[-self.max_memory_size :]
memory_and_input = torch.cat(memory + [input_and_summary[:-1]], dim=0)
input_and_sum_query = input_and_summary
q = self.q_proj(self.v2e(input_and_sum_query))
k = self.k_proj(self.v2e(memory_and_input))
v = self.v_proj(self.v2e(memory_and_input))
q = (
q.contiguous()
.view(-1, batch_size * self.num_heads, self.head_dim)
.transpose(0, 1)
* self.scaling
)
k = (
k.contiguous()
.view(-1, batch_size * self.num_heads, self.head_dim)
.transpose(0, 1)
)
v = (
v.contiguous()
.view(-1, batch_size * self.num_heads, self.head_dim)
.transpose(0, 1)
)
attention_weights = torch.bmm(q, k.transpose(1, 2))
if self.disable_mem_on_mem_attn:
attention_weights = self.suppress_mem_on_mem_attention(
batch_size, self.num_heads, len(memory), attention_weights
)
if self.std_scale is not None:
attention_weights = attention_suppression(attention_weights, self.std_scale)
assert list(attention_weights.shape) == [
batch_size * self.num_heads,
length + 1,
length + len(memory),
]
attention_weights = torch.nn.functional.softmax(
attention_weights.float(), dim=-1
).type_as(attention_weights)
attention_probs = self.dropout_module(attention_weights)
# [T, T, B, n_head] + [T, B, n_head, d_head] -> [T, B, n_head, d_head]
attention = torch.bmm(attention_probs, v)
assert list(attention.shape) == [
batch_size * self.num_heads,
length + 1,
self.head_dim,
]
attention = (
attention.transpose(0, 1)
.contiguous()
.view(length + 1, batch_size, self.embed_dim)
)
output_and_memory = self.out_proj(attention)
next_m = output_and_memory[-1:]
next_m = self.squash_mem(next_m)
output = output_and_memory[:-1]
state["memory_banks"].append(next_m)
return output
def suppress_mem_on_mem_attention(
self, B: int, num_heads: int, mem_size: int, attention_weight: Tensor
):
"""
Arguments:
- B: batch size
- num_heads: number of attention heads
- mem_size: size of memory bank
- attention_weight: a [B*num_heads, T + 1, T + mem_size] vector
Return:
modified attention_weight with [B*num_heads, -1, :mem_size] = -inf
"""
attention_weight[:, -1, :mem_size] = float("-inf")
return attention_weight
# ------------------------------------------------------------------------------
# SequenceEncoder
# ------------------------------------------------------------------------------
class SequenceEncoder(FairseqEncoder):
"""
SequenceEncoder encodes sequences.
More specifically, `src_tokens` and `src_lengths` in `forward()` should
describe a batch of "complete" sequences rather than segments.
Segment-by-segment inference can be triggered by `segment_size`:
1) `segment_size` is None:
SequenceEncoder treats the input sequence as one single segment.
2) `segment_size` is not None (some int instead):
SequenceEncoder does the following:
1. breaks the input sequence into several segments
2. inference on each segment and collect the outputs
3. concatanete segment outputs into the output sequence.
Note that `segment_size` here shouldn't include additional left/right
contexts needed, for example if we wish to infer with LC-BLSTM where the
middle chunk size is 100 and right context is 20, `segment_size` should be
100.
"""
def __init__(self, args, module):
super().__init__(None)
self.module = module
self.input_time_axis = 1
self.output_time_axis = 0
self.segment_size = args.segment_size
self.left_context = args.left_context
self.right_context = args.right_context
def forward(
self,
src_tokens: Tensor,
src_lengths: Tensor,
states=None,
):
seg_src_tokens_lengths = sequence_to_segments(
sequence=src_tokens,
time_axis=self.input_time_axis,
lengths=src_lengths,
segment_size=self.segment_size,
extra_left_context=self.left_context,
extra_right_context=self.right_context,
)
seg_encoder_states_lengths: List[Tuple[Tensor, Tensor]] = []
for seg_src_tokens, seg_src_lengths in seg_src_tokens_lengths:
(seg_encoder_states, seg_enc_lengths, states) = self.module(
seg_src_tokens,
seg_src_lengths,
states=states,
)
seg_encoder_states_lengths.append((seg_encoder_states, seg_enc_lengths))
encoder_out, enc_lengths = segments_to_sequence(
segments=seg_encoder_states_lengths, time_axis=self.output_time_axis
)
encoder_padding_mask, _ = lengths_to_encoder_padding_mask(
enc_lengths, batch_first=True
)
if not encoder_padding_mask.any():
encoder_padding_mask = None
return {
"encoder_out": [encoder_out],
"encoder_padding_mask": [encoder_padding_mask],
"encoder_embedding": [],
"encoder_states": [states],
"src_tokens": [],
"src_lengths": [],
}
def incremental_encode(
self,
seg_src_tokens: Tensor,
seg_src_lengths: Tensor,
states=None,
):
"""
Different from forward function, this function takes segmented speech
as input, and append encoder states to previous states
"""
(seg_encoder_states, seg_enc_lengths, states) = self.module(
seg_src_tokens,
seg_src_lengths,
states=states,
)
return seg_encoder_states, seg_enc_lengths, states
# ------------------------------------------------------------------------------
# Augmented memory model decorator
# ------------------------------------------------------------------------------
def augmented_memory(klass):
class StreamSeq2SeqModel(klass):
@staticmethod
def add_args(parser):
super(StreamSeq2SeqModel, StreamSeq2SeqModel).add_args(parser)
parser.add_argument(
"--segment-size", type=int, required=True, help="Length of the segment."
)
parser.add_argument(
"--left-context",
type=int,
default=0,
help="Left context for the segment.",
)
parser.add_argument(
"--right-context",
type=int,
default=0,
help="Right context for the segment.",
)
parser.add_argument(
"--max-memory-size",
type=int,
default=-1,
help="Right context for the segment.",
)
StreamSeq2SeqModel.__name__ = klass.__name__
return StreamSeq2SeqModel
|
bart_ls-main
|
fairseq-py/fairseq/models/speech_to_text/modules/augmented_memory_attention.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from .hubert import * # noqa
from .hubert_asr import * # noqa
|
bart_ls-main
|
fairseq-py/fairseq/models/hubert/__init__.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import contextlib
from argparse import Namespace
from typing import Any
import torch
import torch.nn as nn
from dataclasses import dataclass, field
from fairseq import checkpoint_utils, tasks, utils
from fairseq.dataclass import FairseqDataclass
from fairseq.dataclass.utils import convert_namespace_to_omegaconf
from fairseq.models import BaseFairseqModel, FairseqEncoder, register_model
from fairseq.models.hubert.hubert import MASKING_DISTRIBUTION_CHOICES
from fairseq.tasks import FairseqTask
from omegaconf import II, MISSING
@dataclass
class HubertAsrConfig(FairseqDataclass):
w2v_path: str = field(
default=MISSING, metadata={"help": "path to hubert model"}
)
no_pretrained_weights: bool = field(
default=False,
metadata={"help": "if true, does not load pretrained weights"},
)
dropout_input: float = field(
default=0.0,
metadata={"help": "dropout to apply to the input (after feat extr)"},
)
final_dropout: float = field(
default=0.0,
metadata={
"help": "dropout after transformer and before final projection"
},
)
dropout: float = field(
default=0.0,
metadata={"help": "dropout probability inside hubert model"},
)
attention_dropout: float = field(
default=0.0,
metadata={
"help": "dropout probability for attention weights "
"inside hubert model"
},
)
activation_dropout: float = field(
default=0.0,
metadata={
"help": "dropout probability after activation in FFN "
"inside hubert model"
},
)
# masking
apply_mask: bool = field(
default=False, metadata={"help": "apply masking during fine-tuning"}
)
mask_length: int = field(
default=10, metadata={"help": "repeat the mask indices multiple times"}
)
mask_prob: float = field(
default=0.5,
metadata={
"help": "probability of replacing a token with mask "
"(normalized by length)"
},
)
mask_selection: MASKING_DISTRIBUTION_CHOICES = field(
default="static", metadata={"help": "how to choose masks"}
)
mask_other: float = field(
default=0,
metadata={
"help": "secondary mask argument "
"(used for more complex distributions), "
"see help in compute_mask_indices"
},
)
no_mask_overlap: bool = field(
default=False, metadata={"help": "whether to allow masks to overlap"}
)
# channel masking
mask_channel_length: int = field(
default=10,
metadata={"help": "length of the mask for features (channels)"},
)
mask_channel_prob: float = field(
default=0.0,
metadata={"help": "probability of replacing a feature with 0"},
)
mask_channel_selection: MASKING_DISTRIBUTION_CHOICES = field(
default="static",
metadata={"help": "how to choose mask length for channel masking"},
)
mask_channel_other: float = field(
default=0,
metadata={
"help": "secondary mask argument "
"(used for more complex distributions), "
"see help in compute_mask_indices"
},
)
no_mask_channel_overlap: bool = field(
default=False,
metadata={"help": "whether to allow channel masks to overlap"},
)
freeze_finetune_updates: int = field(
default=0,
metadata={"help": "dont finetune hubert for this many updates"},
)
feature_grad_mult: float = field(
default=0.0,
metadata={"help": "reset feature grad mult in hubert to this"},
)
layerdrop: float = field(
default=0.0,
metadata={"help": "probability of dropping a layer in hubert"},
)
normalize: bool = II("task.normalize")
data: str = II("task.data")
# this holds the loaded hubert args
w2v_args: Any = None
@dataclass
class HubertCtcConfig(HubertAsrConfig):
pass
@register_model("hubert_ctc", dataclass=HubertCtcConfig)
class HubertCtc(BaseFairseqModel):
def __init__(self, cfg: HubertCtcConfig, w2v_encoder: BaseFairseqModel):
super().__init__()
self.cfg = cfg
self.w2v_encoder = w2v_encoder
def upgrade_state_dict_named(self, state_dict, name):
super().upgrade_state_dict_named(state_dict, name)
return state_dict
@classmethod
def build_model(cls, cfg: HubertCtcConfig, task: FairseqTask):
"""Build a new model instance."""
w2v_encoder = HubertEncoder(cfg, task.target_dictionary)
return cls(cfg, w2v_encoder)
def get_normalized_probs(self, net_output, log_probs):
"""Get normalized probabilities (or log probs) from a net's output."""
logits = net_output["encoder_out"]
if log_probs:
return utils.log_softmax(logits.float(), dim=-1)
else:
return utils.softmax(logits.float(), dim=-1)
def get_logits(self, net_output):
logits = net_output["encoder_out"]
padding = net_output["encoder_padding_mask"]
if padding is not None and padding.any():
padding = padding.T
logits[padding][..., 0] = 0
logits[padding][..., 1:] = float("-inf")
return logits
def forward(self, **kwargs):
x = self.w2v_encoder(**kwargs)
return x
@dataclass
class HubertSeq2SeqConfig(HubertAsrConfig):
decoder_embed_dim: int = field(
default=768, metadata={"help": "decoder embedding dimension"}
)
decoder_ffn_embed_dim: int = field(
default=3072, metadata={"help": "decoder embedding dimension for FFN"}
)
decoder_layers: int = field(
default=6, metadata={"help": "num of decoder layers"}
)
decoder_layerdrop: float = field(
default=0.0, metadata={"help": "decoder layerdrop chance"}
)
decoder_attention_heads: int = field(
default=4, metadata={"help": "num decoder attention heads"}
)
decoder_learned_pos: bool = field(
default=False,
metadata={"help": "use learned positional embeddings in the decoder"},
)
decoder_normalize_before: bool = field(
default=False,
metadata={"help": "apply layernorm before each decoder block"},
)
no_token_positional_embeddings: bool = field(
default=False,
metadata={
"help": "if set, disables positional embeddings "
"(outside self attention)"
},
)
decoder_dropout: float = field(
default=0.0, metadata={"help": "dropout probability in the decoder"}
)
decoder_attention_dropout: float = field(
default=0.0,
metadata={
"help": "dropout probability for attention weights "
"inside the decoder"
},
)
decoder_activation_dropout: float = field(
default=0.0,
metadata={
"help": "dropout probability after activation in FFN "
"inside the decoder"
},
)
max_target_positions: int = field(
default=2048, metadata={"help": "max target positions"}
)
share_decoder_input_output_embed: bool = field(
default=False,
metadata={"help": "share decoder input and output embeddings"},
)
class HubertEncoder(FairseqEncoder):
def __init__(self, cfg: HubertAsrConfig, tgt_dict=None):
self.apply_mask = cfg.apply_mask
arg_overrides = {
"dropout": cfg.dropout,
"activation_dropout": cfg.activation_dropout,
"dropout_input": cfg.dropout_input,
"attention_dropout": cfg.attention_dropout,
"mask_length": cfg.mask_length,
"mask_prob": cfg.mask_prob,
"mask_selection": cfg.mask_selection,
"mask_other": cfg.mask_other,
"no_mask_overlap": cfg.no_mask_overlap,
"mask_channel_length": cfg.mask_channel_length,
"mask_channel_prob": cfg.mask_channel_prob,
"mask_channel_selection": cfg.mask_channel_selection,
"mask_channel_other": cfg.mask_channel_other,
"no_mask_channel_overlap": cfg.no_mask_channel_overlap,
"encoder_layerdrop": cfg.layerdrop,
"feature_grad_mult": cfg.feature_grad_mult,
}
if cfg.w2v_args is None:
state = checkpoint_utils.load_checkpoint_to_cpu(
cfg.w2v_path, arg_overrides
)
w2v_args = state.get("cfg", None)
if w2v_args is None:
w2v_args = convert_namespace_to_omegaconf(state["args"])
cfg.w2v_args = w2v_args
else:
state = None
w2v_args = cfg.w2v_args
if isinstance(w2v_args, Namespace):
cfg.w2v_args = w2v_args = convert_namespace_to_omegaconf(
w2v_args
)
assert cfg.normalize == w2v_args.task.normalize, (
"Fine-tuning works best when data normalization is the same. "
"Please check that --normalize is set or unset for "
"both pre-training and here"
)
w2v_args.task.data = cfg.data
task = tasks.setup_task(w2v_args.task)
if state is not None and "task_state" in state:
# This will load the stored "dictionaries" object
task.load_state_dict(state["task_state"])
model = task.build_model(w2v_args.model)
if state is not None and not cfg.no_pretrained_weights:
# set strict=False because we omit some modules
model.load_state_dict(state["model"], strict=False)
model.remove_pretraining_modules()
super().__init__(task.source_dictionary)
d = w2v_args.model.encoder_embed_dim
self.w2v_model = model
self.final_dropout = nn.Dropout(cfg.final_dropout)
self.freeze_finetune_updates = cfg.freeze_finetune_updates
self.num_updates = 0
if tgt_dict is not None:
self.proj = Linear(d, len(tgt_dict))
elif getattr(cfg, "decoder_embed_dim", d) != d:
self.proj = Linear(d, cfg.decoder_embed_dim)
else:
self.proj = None
def set_num_updates(self, num_updates):
"""Set the number of parameters updates."""
super().set_num_updates(num_updates)
self.num_updates = num_updates
def forward(self, source, padding_mask, tbc=True, **kwargs):
w2v_args = {
"source": source,
"padding_mask": padding_mask,
"mask": self.apply_mask and self.training,
}
ft = self.freeze_finetune_updates <= self.num_updates
with torch.no_grad() if not ft else contextlib.ExitStack():
x, padding_mask = self.w2v_model.extract_features(**w2v_args)
if tbc:
# B x T x C -> T x B x C
x = x.transpose(0, 1)
x = self.final_dropout(x)
if self.proj:
x = self.proj(x)
return {
"encoder_out": x, # T x B x C
"encoder_padding_mask": padding_mask, # B x T
"padding_mask": padding_mask,
}
def reorder_encoder_out(self, encoder_out, new_order):
if encoder_out["encoder_out"] is not None:
encoder_out["encoder_out"] = encoder_out[
"encoder_out"
].index_select(1, new_order)
if encoder_out["encoder_padding_mask"] is not None:
encoder_out["encoder_padding_mask"] = encoder_out[
"encoder_padding_mask"
].index_select(0, new_order)
return encoder_out
def max_positions(self):
"""Maximum input length supported by the encoder."""
return None
def upgrade_state_dict_named(self, state_dict, name):
return state_dict
def Embedding(num_embeddings, embedding_dim, padding_idx):
m = nn.Embedding(num_embeddings, embedding_dim, padding_idx=padding_idx)
nn.init.normal_(m.weight, mean=0, std=embedding_dim ** -0.5)
nn.init.constant_(m.weight[padding_idx], 0)
return m
def Linear(in_features, out_features, bias=True):
m = nn.Linear(in_features, out_features, bias)
nn.init.xavier_uniform_(m.weight)
if bias:
nn.init.constant_(m.bias, 0.0)
return m
|
bart_ls-main
|
fairseq-py/fairseq/models/hubert/hubert_asr.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import logging
from typing import Dict, List, Optional, Tuple
import numpy as np
import torch
import torch.nn as nn
from dataclasses import dataclass, field
from fairseq import utils
from fairseq.data.data_utils import compute_mask_indices
from fairseq.data.dictionary import Dictionary
from fairseq.dataclass import ChoiceEnum, FairseqDataclass
from fairseq.models import BaseFairseqModel, register_model
from fairseq.models.wav2vec.wav2vec2 import (
ConvFeatureExtractionModel,
TransformerEncoder,
)
from fairseq.modules import GradMultiply, LayerNorm
from fairseq.tasks.hubert_pretraining import (
HubertPretrainingConfig,
HubertPretrainingTask,
)
from omegaconf import II
logger = logging.getLogger(__name__)
EXTRACTOR_MODE_CHOICES = ChoiceEnum(["default", "layer_norm"])
MASKING_DISTRIBUTION_CHOICES = ChoiceEnum(
["static", "uniform", "normal", "poisson"]
)
@dataclass
class HubertConfig(FairseqDataclass):
label_rate: int = II("task.label_rate")
extractor_mode: EXTRACTOR_MODE_CHOICES = field(
default="default",
metadata={
"help": "mode for feature extractor. default has a single group "
"norm with d groups in the first conv block, whereas layer_norm "
"has layer norms in every block (meant to use with normalize=True)"
},
)
encoder_layers: int = field(
default=12, metadata={"help": "num encoder layers in the transformer"}
)
encoder_embed_dim: int = field(
default=768, metadata={"help": "encoder embedding dimension"}
)
encoder_ffn_embed_dim: int = field(
default=3072, metadata={"help": "encoder embedding dimension for FFN"}
)
encoder_attention_heads: int = field(
default=12, metadata={"help": "num encoder attention heads"}
)
activation_fn: ChoiceEnum(utils.get_available_activation_fns()) = field(
default="gelu", metadata={"help": "activation function to use"}
)
# dropouts
dropout: float = field(
default=0.1,
metadata={"help": "dropout probability for the transformer"},
)
attention_dropout: float = field(
default=0.1,
metadata={"help": "dropout probability for attention weights"},
)
activation_dropout: float = field(
default=0.0,
metadata={"help": "dropout probability after activation in FFN"},
)
encoder_layerdrop: float = field(
default=0.0,
metadata={"help": "probability of dropping a tarnsformer layer"},
)
dropout_input: float = field(
default=0.0,
metadata={"help": "dropout to apply to the input (after feat extr)"},
)
dropout_features: float = field(
default=0.0,
metadata={
"help": "dropout to apply to the features (after feat extr)"
},
)
final_dim: int = field(
default=0,
metadata={
"help": "project final representations and targets to this many "
"dimensions. set to encoder_embed_dim is <= 0"
},
)
untie_final_proj: bool = field(
default=False,
metadata={"help": "use separate projection for each target"},
)
layer_norm_first: bool = field(
default=False,
metadata={"help": "apply layernorm first in the transformer"},
)
conv_feature_layers: str = field(
default="[(512,10,5)] + [(512,3,2)] * 4 + [(512,2,2)] * 2",
metadata={
"help": "string describing convolutional feature extraction "
"layers in form of a python list that contains "
"[(dim, kernel_size, stride), ...]"
},
)
conv_bias: bool = field(
default=False, metadata={"help": "include bias in conv encoder"}
)
logit_temp: float = field(
default=0.1, metadata={"help": "temperature to divide logits by"}
)
target_glu: bool = field(
default=False, metadata={"help": "adds projection + glu to targets"}
)
feature_grad_mult: float = field(
default=1.0,
metadata={"help": "multiply feature extractor var grads by this"},
)
# masking
mask_length: int = field(default=10, metadata={"help": "mask length"})
mask_prob: float = field(
default=0.65,
metadata={"help": "probability of replacing a token with mask"},
)
mask_selection: MASKING_DISTRIBUTION_CHOICES = field(
default="static", metadata={"help": "how to choose mask length"}
)
mask_other: float = field(
default=0,
metadata={
"help": "secondary mask argument "
"(used for more complex distributions), "
"see help in compute_mask_indicesh"
},
)
no_mask_overlap: bool = field(
default=False, metadata={"help": "whether to allow masks to overlap"}
)
mask_min_space: int = field(
default=1,
metadata={
"help": "min space between spans (if no overlap is enabled)"
},
)
# channel masking
mask_channel_length: int = field(
default=10,
metadata={"help": "length of the mask for features (channels)"},
)
mask_channel_prob: float = field(
default=0.0,
metadata={"help": "probability of replacing a feature with 0"},
)
mask_channel_selection: MASKING_DISTRIBUTION_CHOICES = field(
default="static",
metadata={"help": "how to choose mask length for channel masking"},
)
mask_channel_other: float = field(
default=0,
metadata={
"help": "secondary mask argument "
"(used for more complex distributions), "
"see help in compute_mask_indicesh"
},
)
no_mask_channel_overlap: bool = field(
default=False,
metadata={"help": "whether to allow channel masks to overlap"},
)
mask_channel_min_space: int = field(
default=1,
metadata={
"help": "min space between spans (if no overlap is enabled)"
},
)
# positional embeddings
conv_pos: int = field(
default=128,
metadata={
"help": "number of filters for convolutional positional embeddings"
},
)
conv_pos_groups: int = field(
default=16,
metadata={
"help": "number of groups for convolutional positional embedding"
},
)
latent_temp: Tuple[float, float, float] = field(
default=(2, 0.5, 0.999995),
metadata={"help": "legacy (to be removed)"},
)
# loss computation
skip_masked: bool = field(
default=False,
metadata={"help": "skip computing losses over masked frames"},
)
skip_nomask: bool = field(
default=False,
metadata={"help": "skip computing losses over unmasked frames"},
)
@register_model("hubert", dataclass=HubertConfig)
class HubertModel(BaseFairseqModel):
def __init__(
self,
cfg: HubertConfig,
task_cfg: HubertPretrainingConfig,
dictionaries: List[Dictionary],
) -> None:
super().__init__()
logger.info(f"HubertModel Config: {cfg}")
feature_enc_layers = eval(cfg.conv_feature_layers) # noqa
self.embed = feature_enc_layers[-1][0]
self.feature_extractor = ConvFeatureExtractionModel(
conv_layers=feature_enc_layers,
dropout=0.0,
mode=cfg.extractor_mode,
conv_bias=cfg.conv_bias,
)
feature_ds_rate = np.prod([s for _, _, s in feature_enc_layers])
self.feat2tar_ratio = (
cfg.label_rate * feature_ds_rate / task_cfg.sample_rate
)
self.post_extract_proj = (
nn.Linear(self.embed, cfg.encoder_embed_dim)
if self.embed != cfg.encoder_embed_dim
else None
)
self.mask_prob = cfg.mask_prob
self.mask_selection = cfg.mask_selection
self.mask_other = cfg.mask_other
self.mask_length = cfg.mask_length
self.no_mask_overlap = cfg.no_mask_overlap
self.mask_min_space = cfg.mask_min_space
self.mask_channel_prob = cfg.mask_channel_prob
self.mask_channel_selection = cfg.mask_channel_selection
self.mask_channel_other = cfg.mask_channel_other
self.mask_channel_length = cfg.mask_channel_length
self.no_mask_channel_overlap = cfg.no_mask_channel_overlap
self.mask_channel_min_space = cfg.mask_channel_min_space
self.dropout_input = nn.Dropout(cfg.dropout_input)
self.dropout_features = nn.Dropout(cfg.dropout_features)
self.feature_grad_mult = cfg.feature_grad_mult
self.logit_temp = cfg.logit_temp
self.skip_masked = cfg.skip_masked
self.skip_nomask = cfg.skip_nomask
final_dim = (
cfg.final_dim if cfg.final_dim > 0 else cfg.encoder_embed_dim
)
self.mask_emb = nn.Parameter(
torch.FloatTensor(cfg.encoder_embed_dim).uniform_()
)
self.encoder = TransformerEncoder(cfg)
self.layer_norm = LayerNorm(self.embed)
self.target_glu = None
if cfg.target_glu:
self.target_glu = nn.Sequential(
nn.Linear(final_dim, final_dim * 2), nn.GLU()
)
self.untie_final_proj = cfg.untie_final_proj
if self.untie_final_proj:
self.final_proj = nn.Linear(
cfg.encoder_embed_dim, final_dim * len(dictionaries)
)
else:
self.final_proj = nn.Linear(cfg.encoder_embed_dim, final_dim)
# modules below are not needed during fine-tuning
if any([d is None for d in dictionaries]):
logger.info(
"cannot find dictionary. assume will be used for fine-tuning"
)
else:
self.num_classes = [len(d) for d in dictionaries]
self.label_embs_concat = nn.Parameter(
torch.FloatTensor(sum(self.num_classes), final_dim)
)
nn.init.uniform_(self.label_embs_concat)
def upgrade_state_dict_named(self, state_dict, name):
"""Upgrade a (possibly old) state dict for new versions of fairseq."""
super().upgrade_state_dict_named(state_dict, name)
return state_dict
@classmethod
def build_model(cls, cfg: HubertConfig, task: HubertPretrainingTask):
"""Build a new model instance."""
model = HubertModel(cfg, task.cfg, task.dictionaries)
return model
def apply_mask(self, x, padding_mask, target_list):
B, T, C = x.shape
if self.mask_prob > 0:
mask_indices = compute_mask_indices(
(B, T),
padding_mask,
self.mask_prob,
self.mask_length,
self.mask_selection,
self.mask_other,
min_masks=2,
no_overlap=self.no_mask_overlap,
min_space=self.mask_min_space,
)
mask_indices = torch.from_numpy(mask_indices).to(x.device)
x[mask_indices] = self.mask_emb
else:
mask_indices = None
if self.mask_channel_prob > 0:
mask_channel_indices = compute_mask_indices(
(B, C),
None,
self.mask_channel_prob,
self.mask_channel_length,
self.mask_channel_selection,
self.mask_channel_other,
no_overlap=self.no_mask_channel_overlap,
min_space=self.mask_channel_min_space,
)
mask_channel_indices = (
torch.from_numpy(mask_channel_indices)
.to(x.device)
.unsqueeze(1)
.expand(-1, T, -1)
)
x[mask_channel_indices] = 0
return x, mask_indices
def compute_nce(self, x, pos, negs):
neg_is_pos = (pos == negs).all(-1)
pos = pos.unsqueeze(0)
targets = torch.cat([pos, negs], dim=0)
logits = torch.cosine_similarity(
x.float(), targets.float(), dim=-1
).type_as(x)
logits /= self.logit_temp
if neg_is_pos.any():
logits[1:][neg_is_pos] = float("-inf")
logits = logits.transpose(0, 1) # (num_x, num_cls+1)
return logits
def forward_features(self, source: torch.Tensor) -> torch.Tensor:
if self.feature_grad_mult > 0:
features = self.feature_extractor(source)
if self.feature_grad_mult != 1.0:
features = GradMultiply.apply(features, self.feature_grad_mult)
else:
with torch.no_grad():
features = self.feature_extractor(source)
return features
def forward_targets(
self, features: torch.Tensor, target_list: List[torch.Tensor],
) -> Tuple[torch.Tensor, torch.Tensor]:
# Trim features to ensure labels exist and then get aligned labels
feat_tsz = features.size(2)
targ_tsz = min([t.size(1) for t in target_list])
if self.feat2tar_ratio * feat_tsz > targ_tsz:
feat_tsz = int(targ_tsz / self.feat2tar_ratio)
features = features[..., :feat_tsz]
target_inds = torch.arange(feat_tsz).float() * self.feat2tar_ratio
target_list = [t[:, target_inds.long()] for t in target_list]
return features, target_list
def forward_padding_mask(
self, features: torch.Tensor, padding_mask: torch.Tensor,
) -> torch.Tensor:
extra = padding_mask.size(1) % features.size(1)
if extra > 0:
padding_mask = padding_mask[:, :-extra]
padding_mask = padding_mask.view(
padding_mask.size(0), features.size(1), -1
)
padding_mask = padding_mask.all(-1)
return padding_mask
def forward(
self,
source: torch.Tensor,
target_list: Optional[List[torch.Tensor]] = None,
padding_mask: Optional[torch.Tensor] = None,
mask: bool = True,
features_only: bool = False,
output_layer: Optional[int] = None,
) -> Dict[str, torch.Tensor]:
"""output layer is 1-based"""
features = self.forward_features(source)
if target_list is not None:
features, target_list = self.forward_targets(features, target_list)
features_pen = features.float().pow(2).mean()
features = features.transpose(1, 2)
features = self.layer_norm(features)
unmasked_features = features.clone()
if padding_mask is not None:
padding_mask = self.forward_padding_mask(features, padding_mask)
if self.post_extract_proj is not None:
features = self.post_extract_proj(features)
features = self.dropout_input(features)
unmasked_features = self.dropout_features(unmasked_features)
if mask:
x, mask_indices = self.apply_mask(
features, padding_mask, target_list
)
else:
x = features
mask_indices = None
# feature: (B, T, D), float
# target: (B, T), long
# x: (B, T, D), float
# padding_mask: (B, T), bool
# mask_indices: (B, T), bool
x, _ = self.encoder(
x,
padding_mask=padding_mask,
layer=None if output_layer is None else output_layer - 1
)
if features_only:
return {"x": x, "padding_mask": padding_mask, "features": features}
def compute_pred(proj_x, target, label_embs):
# compute logits for the i-th label set
y = torch.index_select(label_embs, 0, target.long())
negs = label_embs.unsqueeze(1).expand(-1, proj_x.size(0), -1)
if self.target_glu:
y = self.target_glu(y)
negs = self.target_glu(negs)
# proj_x: (S, D)
# y: (S, D)
# negs: (Neg, S, D)
return self.compute_nce(proj_x, y, negs)
label_embs_list = self.label_embs_concat.split(self.num_classes, 0)
if not self.skip_masked:
masked_indices = torch.logical_and(~padding_mask, mask_indices)
proj_x_m = self.final_proj(x[masked_indices])
if self.untie_final_proj:
proj_x_m_list = proj_x_m.chunk(len(target_list), dim=-1)
else:
proj_x_m_list = [proj_x_m for _ in range(len(target_list))]
logit_m_list = [
compute_pred(proj_x_m, t[masked_indices], label_embs_list[i])
for i, (proj_x_m, t) in enumerate(
zip(proj_x_m_list, target_list)
)
]
else:
logit_m_list = [None for _ in target_list]
if not self.skip_nomask:
nomask_indices = torch.logical_and(~padding_mask, ~mask_indices)
proj_x_u = self.final_proj(x[nomask_indices])
if self.untie_final_proj:
proj_x_u_list = proj_x_u.chunk(len(target_list), dim=-1)
else:
proj_x_u_list = [proj_x_u for _ in range(len(target_list))]
logit_u_list = [
compute_pred(proj_x_u, t[nomask_indices], label_embs_list[i])
for i, (proj_x_u, t) in enumerate(
zip(proj_x_u_list, target_list)
)
]
else:
logit_u_list = [None for _ in target_list]
result = {
"logit_m_list": logit_m_list,
"logit_u_list": logit_u_list,
"padding_mask": padding_mask,
"features_pen": features_pen,
}
return result
def extract_features(
self,
source: torch.Tensor,
padding_mask: Optional[torch.Tensor] = None,
mask: bool = False,
ret_conv: bool = False,
output_layer: Optional[int] = None,
) -> Tuple[torch.Tensor, torch.Tensor]:
res = self.forward(
source,
padding_mask=padding_mask,
mask=mask,
features_only=True,
output_layer=output_layer,
)
feature = res["features"] if ret_conv else res["x"]
return feature, res["padding_mask"]
def get_logits(self, net_output, is_masked=True):
if is_masked:
logits_list = net_output["logit_m_list"]
else:
logits_list = net_output["logit_u_list"]
logits_list = [x.float() for x in logits_list if x is not None]
return logits_list
def get_targets(self, net_output, is_masked=True):
logits_list = self.get_logits(net_output, is_masked)
targets_list = [
x.new_zeros(x.size(0), dtype=torch.long) for x in logits_list
]
return targets_list
def get_extra_losses(self, net_output):
extra_losses = []
names = []
if "features_pen" in net_output:
extra_losses.append(net_output["features_pen"])
names.append("features_pen")
return extra_losses, names
def remove_pretraining_modules(self):
self.target_glu = None
self.final_proj = None
|
bart_ls-main
|
fairseq-py/fairseq/models/hubert/hubert.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import re
from dataclasses import dataclass, field, fields
from typing import List, Optional
from fairseq import utils
from fairseq.dataclass import FairseqDataclass, ChoiceEnum
from omegaconf import II
DEFAULT_MAX_SOURCE_POSITIONS = 1024
DEFAULT_MAX_TARGET_POSITIONS = 1024
DEFAULT_MIN_PARAMS_TO_WRAP = int(1e8)
_NAME_PARSER = r"(decoder|encoder|quant_noise)_(.*)"
@dataclass
class EncDecBaseConfig(FairseqDataclass):
embed_path: Optional[str] = field(
default=None, metadata={"help": "path to pre-trained embedding"}
)
embed_dim: Optional[int] = field(
default=512, metadata={"help": "embedding dimension"}
)
ffn_embed_dim: int = field(
default=2048, metadata={"help": "embedding dimension for FFN"}
)
layers: int = field(default=6, metadata={"help": "number of layers"})
attention_heads: int = field(
default=8, metadata={"help": "number of attention heads"}
)
normalize_before: bool = field(
default=False, metadata={"help": "apply layernorm before each block"}
)
learned_pos: bool = field(
default=False, metadata={"help": "use learned positional embeddings"}
)
# args for "Reducing Transformer Depth on Demand with Structured Dropout" (Fan et al., 2019)
layerdrop: float = field(default=0, metadata={"help": "LayerDrop probability"})
layers_to_keep: Optional[List[int]] = field(
default=None, metadata={"help": "which layers to *keep* when pruning"}
)
@dataclass
class DecoderConfig(EncDecBaseConfig):
input_dim: int = II("model.decoder.embed_dim")
output_dim: int = field(
default=II("model.decoder.embed_dim"),
metadata={
"help": "decoder output dimension (extra linear layer if different from decoder embed dim)"
},
)
def __post_init__(self):
# II doesn't work if we are just creating the object outside of hydra so fix that
if self.input_dim == II("model.decoder.embed_dim"):
self.input_dim = self.embed_dim
if self.output_dim == II("model.decoder.embed_dim"):
self.output_dim = self.embed_dim
@dataclass
class QuantNoiseConfig(FairseqDataclass):
pq: float = field(
default=0.0,
metadata={"help": "iterative PQ quantization noise at training time"},
)
pq_block_size: int = field(
default=8,
metadata={"help": "block size of quantization noise at training time"},
)
scalar: float = field(
default=0.0,
metadata={
"help": "scalar quantization noise and scalar quantization at training time"
},
)
@dataclass
class TransformerConfig(FairseqDataclass):
activation_fn: ChoiceEnum(utils.get_available_activation_fns()) = field(
default="relu",
metadata={"help": "activation function to use"},
)
dropout: float = field(default=0.1, metadata={"help": "dropout probability"})
attention_dropout: float = field(
default=0.0, metadata={"help": "dropout probability for attention weights"}
)
activation_dropout: float = field(
default=0.0,
metadata={
"help": "dropout probability after activation in FFN.",
"alias": "--relu-dropout",
},
)
adaptive_input: bool = False
encoder: EncDecBaseConfig = EncDecBaseConfig()
# TODO should really be in the encoder config
max_source_positions: int = field(
default=DEFAULT_MAX_SOURCE_POSITIONS,
metadata={"help": "Maximum input length supported by the encoder"},
)
decoder: DecoderConfig = DecoderConfig()
# TODO should really be in the decoder config
max_target_positions: int = field(
default=DEFAULT_MAX_TARGET_POSITIONS,
metadata={"help": "Maximum output length supported by the decoder"},
)
share_decoder_input_output_embed: bool = field(
default=False, metadata={"help": "share decoder input and output embeddings"}
)
share_all_embeddings: bool = field(
default=False,
metadata={
"help": "share encoder, decoder and output embeddings (requires shared dictionary and embed dim)"
},
)
no_token_positional_embeddings: bool = field(
default=False,
metadata={
"help": "if True, disables positional embeddings (outside self attention)"
},
)
adaptive_softmax_cutoff: Optional[List[int]] = field(
default=None,
metadata={
"help": "list of adaptive softmax cutoff points. Must be used with adaptive_loss criterion"
},
)
adaptive_softmax_dropout: float = field(
default=0.0,
metadata={"help": "sets adaptive softmax dropout for the tail projections"},
)
adaptive_softmax_factor: float = field(
default=4, metadata={"help": "adaptive input factor"}
)
layernorm_embedding: bool = field(
default=False, metadata={"help": "add layernorm to embedding"}
)
tie_adaptive_weights: bool = field(
default=False,
metadata={
"help": "if set, ties the weights of adaptive softmax and adaptive input"
},
)
tie_adaptive_proj: bool = field(
default=False,
metadata={
"help": "if set, ties the projection weights of adaptive softmax and adaptive input"
},
)
no_scale_embedding: bool = field(
default=False, metadata={"help": "if True, dont scale embeddings"}
)
checkpoint_activations: bool = field(
default=False,
metadata={
"help": "checkpoint activations at each layer, which saves GPU memory usage at the cost of some additional compute"
},
)
offload_activations: bool = field(
default=False,
metadata={
"help": "checkpoint activations at each layer, then save to gpu. Sets --checkpoint-activations."
},
)
# args for "Cross+Self-Attention for Transformer Models" (Peitz et al., 2019)
no_cross_attention: bool = field(
default=False, metadata={"help": "do not perform cross-attention"}
)
cross_self_attention: bool = field(
default=False, metadata={"help": "perform cross+self-attention"}
)
# args for Training with Quantization Noise for Extreme Model Compression ({Fan*, Stock*} et al., 2020)
quant_noise: QuantNoiseConfig = field(default=QuantNoiseConfig())
min_params_to_wrap: int = field(
default=DEFAULT_MIN_PARAMS_TO_WRAP,
metadata={
"help": "minimum number of params for a layer to be wrapped with FSDP() when "
"training with --ddp-backend=fully_sharded. Smaller values will "
"improve memory efficiency, but may make torch.distributed "
"communication less efficient due to smaller input sizes. This option "
"is set to 0 (i.e., always wrap) when --checkpoint-activations or "
"--offload-activations are passed."
},
)
# DEPRECATED field, but some old checkpoints might have it
char_inputs: bool = field(
default=False, metadata={"help": "if set, model takes character ids as input"}
)
relu_dropout: float = 0.0
# config for "BASE Layers: Simplifying Training of Large, Sparse Models"
base_layers: Optional[int] = field(
default=0, metadata={"help": "number of BASE layers in total"}
)
base_sublayers: Optional[int] = field(
default=1, metadata={"help": "number of sublayers in each BASE layer"}
)
base_shuffle: Optional[int] = field(
default=1,
metadata={"help": "shuffle tokens between workers before computing assignment"},
)
export: bool = field(
default=False,
metadata={"help": "make the layernorm exportable with torchscript."},
)
# copied from transformer_lm but expected in transformer_decoder:
no_decoder_final_norm: bool = field(
default=False,
metadata={"help": "don't add an extra layernorm after the last decoder block"},
)
###
### @xwhan additional configs for long-context tasks ####
# positional encodings alternatives
alibi: bool = field(
default=False,
metadata={"help": "ALiBi position encodings"},
)
truncate_alibi: int = field(
default=None,
metadata={"help": "ALiBi position encodings"},
)
# xFormers integration
use_xformers: bool = field(
default=False,
metadata={"help": "whether to use attention mechanisms from xFormers"},
)
attention_name: str = field(
default="block_noglobal",
metadata={"help": "choose attention mechanisms"}
)
xformer_config: str = field(
default="{}",
metadata={"help": "additional hyperparameters of each attention mechanism"}
)
# pooling layers at the top of the encoder
pooling_layers: int = field(
default=0,
metadata={"help": "how many top layers in the transformer encoders to do pooling"}
)
# We need to make this hierarchical dataclass like the flat namespace
# __getattr__ and __setattr__ here allow backward compatibility
# for subclasses of Transformer(Legacy) that depend on read/write on
# the flat namespace.
def __getattr__(self, name):
match = re.match(_NAME_PARSER, name)
if match:
sub = getattr(self, match[1])
return getattr(sub, match[2])
raise AttributeError(f"invalid argument {name}.")
def __setattr__(self, name, value):
match = re.match(_NAME_PARSER, name)
if match:
sub = getattr(self, match[1])
setattr(sub, match[2], value)
else:
super().__setattr__(name, value)
@staticmethod
def _copy_keys(args, cls, prefix, seen):
"""
copy the prefixed keys (decoder_embed_dim) to the DC fields: decoder.embed_dim
"""
cfg = cls()
for fld in fields(cls):
# for all the fields in the DC, find the fields (e.g. embed_dim)
# in the namespace with the prefix (e.g. decoder)
# and set it on the dc.
args_key = f"{prefix}_{fld.name}"
if hasattr(args, args_key):
seen.add(args_key)
setattr(cfg, fld.name, getattr(args, args_key))
if hasattr(args, fld.name):
seen.add(fld.name)
setattr(cfg, fld.name, getattr(args, fld.name))
return cfg
@classmethod
def from_namespace(cls, args):
if args is None:
return None
if not isinstance(args, cls):
seen = set()
config = cls()
# currently, we can go generically from DC fields to args hierarchically
# but we can't easily deconstruct a flat namespace to a hierarchical
# DC. Mostly because we could have a sub-dc called `decoder-foo` that should not
# go to the sub struct called `decoder`. There are ways to go around this, but let's keep it simple
# for now.
for fld in fields(cls):
# concretelly, the transformer_config know what sub-dc it has, so we go through all the dc fields
# and if it's one that has a sub-dc, we build that sub-dc with `copy_keys()`
if fld.name == "decoder":
if hasattr(args, "decoder"):
# in some cases, the args we receive is already structured (as DictConfigs), so let's just build the correct DC
seen.add("decoder")
config.decoder = DecoderConfig(**args.decoder)
else:
config.decoder = cls._copy_keys(
args, DecoderConfig, "decoder", seen
)
elif fld.name == "encoder":
# same but for encoder
if hasattr(args, "encoder"):
seen.add("encoder")
config.encoder = EncDecBaseConfig(**args.encoder)
else:
config.encoder = cls._copy_keys(
args, EncDecBaseConfig, "encoder", seen
)
elif fld.name == "quant_noise":
# same but for quant_noise
if hasattr(args, "quant_noise"):
seen.add("quant_noise")
config.quant_noise = QuantNoiseConfig(**args.quant_noise)
else:
config.quant_noise = cls._copy_keys(
args, QuantNoiseConfig, "quant_noise", seen
)
elif hasattr(args, fld.name):
# if it's not a structure field, it's just a normal field, copy it over
seen.add(fld.name)
setattr(config, fld.name, getattr(args, fld.name))
# we got all the fields defined in the dataclass, but
# the argparse namespace might have extra args for two reasons:
# - we are in a legacy class so all the args are not declared in the dataclass. Ideally once everyone has defined a dataclass for their model, we won't need this
# - some places expect args to be there but never define them
args_dict = args._asdict() if hasattr(args, '_asdict') else vars(args) if hasattr(args, '__dict__') else {} # namedtupled doesn't have __dict__ :-/
for key, value in args_dict.items():
if key not in seen:
setattr(config, key, value)
return config
else:
return args
|
bart_ls-main
|
fairseq-py/fairseq/models/transformer/transformer_config.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from fairseq.dataclass.utils import gen_parser_from_dataclass
from fairseq.models import (
register_model,
register_model_architecture,
)
from fairseq.models.transformer.transformer_config import (
TransformerConfig,
DEFAULT_MAX_SOURCE_POSITIONS,
DEFAULT_MAX_TARGET_POSITIONS,
DEFAULT_MIN_PARAMS_TO_WRAP,
)
from fairseq.models.transformer.transformer_base import (
TransformerModelBase,
)
@register_model("transformer")
class TransformerModel(TransformerModelBase):
"""
This is the legacy implementation of the transformer model that
uses argparse for configuration.
"""
@classmethod
def hub_models(cls):
# fmt: off
def moses_subword(path):
return {
'path': path,
'tokenizer': 'moses',
'bpe': 'subword_nmt',
}
def moses_fastbpe(path):
return {
'path': path,
'tokenizer': 'moses',
'bpe': 'fastbpe',
}
def spm(path):
return {
'path': path,
'bpe': 'sentencepiece',
'tokenizer': 'space',
}
return {
'transformer.wmt14.en-fr': moses_subword('https://dl.fbaipublicfiles.com/fairseq/models/wmt14.en-fr.joined-dict.transformer.tar.bz2'),
'transformer.wmt16.en-de': 'https://dl.fbaipublicfiles.com/fairseq/models/wmt16.en-de.joined-dict.transformer.tar.bz2',
'transformer.wmt18.en-de': moses_subword('https://dl.fbaipublicfiles.com/fairseq/models/wmt18.en-de.ensemble.tar.gz'),
'transformer.wmt19.en-de': moses_fastbpe('https://dl.fbaipublicfiles.com/fairseq/models/wmt19.en-de.joined-dict.ensemble.tar.gz'),
'transformer.wmt19.en-ru': moses_fastbpe('https://dl.fbaipublicfiles.com/fairseq/models/wmt19.en-ru.ensemble.tar.gz'),
'transformer.wmt19.de-en': moses_fastbpe('https://dl.fbaipublicfiles.com/fairseq/models/wmt19.de-en.joined-dict.ensemble.tar.gz'),
'transformer.wmt19.ru-en': moses_fastbpe('https://dl.fbaipublicfiles.com/fairseq/models/wmt19.ru-en.ensemble.tar.gz'),
'transformer.wmt19.en-de.single_model': moses_fastbpe('https://dl.fbaipublicfiles.com/fairseq/models/wmt19.en-de.joined-dict.single_model.tar.gz'),
'transformer.wmt19.en-ru.single_model': moses_fastbpe('https://dl.fbaipublicfiles.com/fairseq/models/wmt19.en-ru.single_model.tar.gz'),
'transformer.wmt19.de-en.single_model': moses_fastbpe('https://dl.fbaipublicfiles.com/fairseq/models/wmt19.de-en.joined-dict.single_model.tar.gz'),
'transformer.wmt19.ru-en.single_model': moses_fastbpe('https://dl.fbaipublicfiles.com/fairseq/models/wmt19.ru-en.single_model.tar.gz'),
'transformer.wmt20.en-ta': spm('https://dl.fbaipublicfiles.com/fairseq/models/wmt20.en-ta.single.tar.gz'),
'transformer.wmt20.en-iu.news': spm('https://dl.fbaipublicfiles.com/fairseq/models/wmt20.en-iu.news.single.tar.gz'),
'transformer.wmt20.en-iu.nh': spm('https://dl.fbaipublicfiles.com/fairseq/models/wmt20.en-iu.nh.single.tar.gz'),
'transformer.wmt20.ta-en': spm('https://dl.fbaipublicfiles.com/fairseq/models/wmt20.ta-en.single.tar.gz'),
'transformer.wmt20.iu-en.news': spm('https://dl.fbaipublicfiles.com/fairseq/models/wmt20.iu-en.news.single.tar.gz'),
'transformer.wmt20.iu-en.nh': spm('https://dl.fbaipublicfiles.com/fairseq/models/wmt20.iu-en.nh.single.tar.gz'),
'transformer.flores101.mm100.615M': spm('https://dl.fbaipublicfiles.com/flores101/pretrained_models/flores101_mm100_615M.tar.gz'),
'transformer.flores101.mm100.175M': spm('https://dl.fbaipublicfiles.com/flores101/pretrained_models/flores101_mm100_175M.tar.gz'),
}
# fmt: on
def __init__(self, args, encoder, decoder):
cfg = TransformerConfig.from_namespace(args)
super().__init__(cfg, encoder, decoder)
self.args = args
@classmethod
def add_args(cls, parser):
"""Add model-specific arguments to the parser."""
# we want to build the args recursively in this case.
# do not set defaults so that settings defaults from various architectures still works
gen_parser_from_dataclass(
parser, TransformerConfig(), delete_default=True, with_prefix=""
)
@classmethod
def build_model(cls, args, task):
"""Build a new model instance."""
# make sure all arguments are present in older models
base_architecture(args)
if args.encoder_layers_to_keep:
args.encoder_layers = len(args.encoder_layers_to_keep.split(","))
if args.decoder_layers_to_keep:
args.decoder_layers = len(args.decoder_layers_to_keep.split(","))
if getattr(args, "max_source_positions", None) is None:
args.max_source_positions = DEFAULT_MAX_SOURCE_POSITIONS
if getattr(args, "max_target_positions", None) is None:
args.max_target_positions = DEFAULT_MAX_TARGET_POSITIONS
src_dict, tgt_dict = task.source_dictionary, task.target_dictionary
if args.share_all_embeddings:
if src_dict != tgt_dict:
raise ValueError("--share-all-embeddings requires a joined dictionary")
if args.encoder_embed_dim != args.decoder_embed_dim:
raise ValueError(
"--share-all-embeddings requires --encoder-embed-dim to match --decoder-embed-dim"
)
if args.decoder_embed_path and (
args.decoder_embed_path != args.encoder_embed_path
):
raise ValueError(
"--share-all-embeddings not compatible with --decoder-embed-path"
)
args.share_decoder_input_output_embed = True
if getattr(args, "offload_activations", False):
args.checkpoint_activations = True # offloading implies checkpointing
if not args.share_all_embeddings:
args.min_params_to_wrap = getattr(
args, "min_params_to_wrap", DEFAULT_MIN_PARAMS_TO_WRAP
)
cfg = TransformerConfig.from_namespace(args)
return super().build_model(cfg, task)
@classmethod
def build_embedding(cls, args, dictionary, embed_dim, path=None):
return super().build_embedding(
TransformerConfig.from_namespace(args), dictionary, embed_dim, path
)
@classmethod
def build_encoder(cls, args, src_dict, embed_tokens):
return super().build_encoder(
TransformerConfig.from_namespace(args), src_dict, embed_tokens
)
@classmethod
def build_decoder(cls, args, tgt_dict, embed_tokens):
return super().build_decoder(
TransformerConfig.from_namespace(args), tgt_dict, embed_tokens
)
# architectures
@register_model_architecture("transformer", "transformer_tiny")
def tiny_architecture(args):
args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 64)
args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 64)
args.encoder_layers = getattr(args, "encoder_layers", 2)
args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 2)
args.decoder_layers = getattr(args, "decoder_layers", 2)
args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 2)
return base_architecture(args)
@register_model_architecture("transformer", "transformer")
def base_architecture(args):
args.encoder_embed_path = getattr(args, "encoder_embed_path", None)
args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512)
args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 2048)
args.encoder_layers = getattr(args, "encoder_layers", 6)
args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 8)
args.encoder_normalize_before = getattr(args, "encoder_normalize_before", False)
args.encoder_learned_pos = getattr(args, "encoder_learned_pos", False)
args.decoder_embed_path = getattr(args, "decoder_embed_path", None)
args.decoder_embed_dim = getattr(args, "decoder_embed_dim", args.encoder_embed_dim)
args.decoder_ffn_embed_dim = getattr(
args, "decoder_ffn_embed_dim", args.encoder_ffn_embed_dim
)
args.decoder_layers = getattr(args, "decoder_layers", 6)
args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 8)
args.decoder_normalize_before = getattr(args, "decoder_normalize_before", False)
args.decoder_learned_pos = getattr(args, "decoder_learned_pos", False)
args.attention_dropout = getattr(args, "attention_dropout", 0.0)
args.activation_dropout = getattr(args, "activation_dropout", 0.0)
args.activation_fn = getattr(args, "activation_fn", "relu")
args.dropout = getattr(args, "dropout", 0.1)
args.adaptive_softmax_cutoff = getattr(args, "adaptive_softmax_cutoff", None)
args.adaptive_softmax_dropout = getattr(args, "adaptive_softmax_dropout", 0)
args.share_decoder_input_output_embed = getattr(
args, "share_decoder_input_output_embed", False
)
args.share_all_embeddings = getattr(args, "share_all_embeddings", False)
args.no_token_positional_embeddings = getattr(
args, "no_token_positional_embeddings", False
)
args.adaptive_input = getattr(args, "adaptive_input", False)
args.no_cross_attention = getattr(args, "no_cross_attention", False)
args.cross_self_attention = getattr(args, "cross_self_attention", False)
args.decoder_output_dim = getattr(
args, "decoder_output_dim", args.decoder_embed_dim
)
args.decoder_input_dim = getattr(args, "decoder_input_dim", args.decoder_embed_dim)
args.no_scale_embedding = getattr(args, "no_scale_embedding", False)
args.layernorm_embedding = getattr(args, "layernorm_embedding", False)
args.tie_adaptive_weights = getattr(args, "tie_adaptive_weights", False)
args.checkpoint_activations = getattr(args, "checkpoint_activations", False)
args.offload_activations = getattr(args, "offload_activations", False)
if args.offload_activations:
args.checkpoint_activations = True
args.encoder_layers_to_keep = getattr(args, "encoder_layers_to_keep", None)
args.decoder_layers_to_keep = getattr(args, "decoder_layers_to_keep", None)
args.encoder_layerdrop = getattr(args, "encoder_layerdrop", 0)
args.decoder_layerdrop = getattr(args, "decoder_layerdrop", 0)
args.quant_noise_pq = getattr(args, "quant_noise_pq", 0)
args.quant_noise_pq_block_size = getattr(args, "quant_noise_pq_block_size", 8)
args.quant_noise_scalar = getattr(args, "quant_noise_scalar", 0)
@register_model_architecture("transformer", "transformer_iwslt_de_en")
def transformer_iwslt_de_en(args):
args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512)
args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 1024)
args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 4)
args.encoder_layers = getattr(args, "encoder_layers", 6)
args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 512)
args.decoder_ffn_embed_dim = getattr(args, "decoder_ffn_embed_dim", 1024)
args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 4)
args.decoder_layers = getattr(args, "decoder_layers", 6)
base_architecture(args)
@register_model_architecture("transformer", "transformer_wmt_en_de")
def transformer_wmt_en_de(args):
base_architecture(args)
# parameters used in the "Attention Is All You Need" paper (Vaswani et al., 2017)
@register_model_architecture("transformer", "transformer_vaswani_wmt_en_de_big")
def transformer_vaswani_wmt_en_de_big(args):
args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 1024)
args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 4096)
args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 16)
args.encoder_normalize_before = getattr(args, "encoder_normalize_before", False)
args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 1024)
args.decoder_ffn_embed_dim = getattr(args, "decoder_ffn_embed_dim", 4096)
args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 16)
args.dropout = getattr(args, "dropout", 0.3)
base_architecture(args)
@register_model_architecture("transformer", "transformer_vaswani_wmt_en_fr_big")
def transformer_vaswani_wmt_en_fr_big(args):
args.dropout = getattr(args, "dropout", 0.1)
transformer_vaswani_wmt_en_de_big(args)
@register_model_architecture("transformer", "transformer_wmt_en_de_big")
def transformer_wmt_en_de_big(args):
args.attention_dropout = getattr(args, "attention_dropout", 0.1)
transformer_vaswani_wmt_en_de_big(args)
# default parameters used in tensor2tensor implementation
@register_model_architecture("transformer", "transformer_wmt_en_de_big_t2t")
def transformer_wmt_en_de_big_t2t(args):
args.encoder_normalize_before = getattr(args, "encoder_normalize_before", True)
args.decoder_normalize_before = getattr(args, "decoder_normalize_before", True)
args.attention_dropout = getattr(args, "attention_dropout", 0.1)
args.activation_dropout = getattr(args, "activation_dropout", 0.1)
transformer_vaswani_wmt_en_de_big(args)
|
bart_ls-main
|
fairseq-py/fairseq/models/transformer/transformer_legacy.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import math
from typing import Dict, List, Optional
import torch
import torch.nn as nn
from fairseq import utils
from fairseq.distributed import fsdp_wrap
from fairseq.models import FairseqEncoder
from fairseq.modules import (
FairseqDropout,
LayerDropModuleList,
LayerNorm,
PositionalEmbedding,
SinusoidalPositionalEmbedding,
)
from fairseq.modules import transformer_layer
from fairseq.modules.checkpoint_activations import checkpoint_wrapper
from fairseq.modules.quant_noise import quant_noise as apply_quant_noise_
from torch import Tensor
from fairseq.models.transformer import (
TransformerConfig,
)
# rewrite name for backward compatibility in `make_generation_fast_`
def module_name_fordropout(module_name: str) -> str:
if module_name == 'TransformerEncoderBase':
return 'TransformerEncoder'
else:
return module_name
class TransformerEncoderBase(FairseqEncoder):
"""
Transformer encoder consisting of *cfg.encoder.layers* layers. Each layer
is a :class:`TransformerEncoderLayer`.
Args:
args (argparse.Namespace): parsed command-line arguments
dictionary (~fairseq.data.Dictionary): encoding dictionary
embed_tokens (torch.nn.Embedding): input embedding
"""
def __init__(self, cfg, dictionary, embed_tokens):
self.cfg = cfg
super().__init__(dictionary)
self.register_buffer("version", torch.Tensor([3]))
self.dropout_module = FairseqDropout(
cfg.dropout, module_name=module_name_fordropout(self.__class__.__name__)
)
self.encoder_layerdrop = cfg.encoder.layerdrop
embed_dim = embed_tokens.embedding_dim
self.padding_idx = embed_tokens.padding_idx
self.max_source_positions = cfg.max_source_positions
self.embed_tokens = embed_tokens
self.embed_scale = 1.0 if cfg.no_scale_embedding else math.sqrt(embed_dim)
self.embed_positions = (
PositionalEmbedding(
cfg.max_source_positions,
embed_dim,
self.padding_idx,
learned=cfg.encoder.learned_pos,
block=False
)
if not cfg.no_token_positional_embeddings and not cfg.alibi
else None
)
if cfg.layernorm_embedding:
self.layernorm_embedding = LayerNorm(embed_dim, export=cfg.export)
else:
self.layernorm_embedding = None
if not cfg.adaptive_input and cfg.quant_noise.pq > 0:
self.quant_noise = apply_quant_noise_(
nn.Linear(embed_dim, embed_dim, bias=False),
cfg.quant_noise.pq,
cfg.quant_noise.pq_block_size,
)
else:
self.quant_noise = None
if self.encoder_layerdrop > 0.0:
self.layers = LayerDropModuleList(p=self.encoder_layerdrop)
else:
self.layers = nn.ModuleList([])
if cfg.pooling_layers:
self.layers.extend(
[self.build_encoder_layer(cfg) for i in range(cfg.encoder.layers - cfg.pooling_layers)]
)
self.layers.extend(
[self.build_encoder_layer(cfg, True) for i in range(cfg.pooling_layers)]
)
else:
self.layers.extend(
[self.build_encoder_layer(cfg) for i in range(cfg.encoder.layers)]
)
self.num_layers = len(self.layers)
if cfg.encoder.normalize_before:
self.layer_norm = LayerNorm(embed_dim, export=cfg.export)
else:
self.layer_norm = None
if cfg.alibi:
from fairseq.models.long_transformers import get_slopes
maxpos = self.max_source_positions
attn_heads = cfg.encoder_attention_heads
context_position = torch.arange(maxpos)[:, None]
memory_position = torch.arange(maxpos)[None, :]
relative_position = memory_position - context_position
relative_position = torch.abs(relative_position)
if cfg.truncate_alibi is not None:
relative_position[relative_position >= cfg.truncate_alibi] = cfg.truncate_alibi
relative_position = relative_position.unsqueeze(0).expand(attn_heads, -1,-1)
slopes = torch.Tensor(get_slopes(attn_heads)) * -1
self.alibi = slopes.unsqueeze(1).unsqueeze(1) * relative_position
self.alibi = self.alibi.view(1, attn_heads, maxpos, maxpos).cuda()
def build_encoder_layer(self, cfg, pooling=False):
if pooling:
from fairseq.models.long_transformers import pooling_layer
# layer = pooling_layer.TwoLevelEncoderLayer(cfg)
layer = pooling_layer.PoolEncoderLayer(cfg)
else:
layer = transformer_layer.TransformerEncoderLayerBase(cfg)
checkpoint = cfg.checkpoint_activations
if checkpoint:
offload_to_cpu = cfg.offload_activations
layer = checkpoint_wrapper(layer, offload_to_cpu=offload_to_cpu)
# if we are checkpointing, enforce that FSDP always wraps the
# checkpointed layer, regardless of layer size
min_params_to_wrap = cfg.min_params_to_wrap if not checkpoint else 0
layer = fsdp_wrap(layer, min_num_params=min_params_to_wrap)
return layer
def forward_embedding(
self, src_tokens, token_embedding: Optional[torch.Tensor] = None
):
# embed tokens and positions
if token_embedding is None:
token_embedding = self.embed_tokens(src_tokens)
x = embed = self.embed_scale * token_embedding
if self.embed_positions is not None:
x = embed + self.embed_positions(src_tokens)
if self.layernorm_embedding is not None:
x = self.layernorm_embedding(x)
x = self.dropout_module(x)
if self.quant_noise is not None:
x = self.quant_noise(x)
return x, embed
def forward(
self,
src_tokens,
src_lengths: Optional[torch.Tensor] = None,
return_all_hiddens: bool = False,
token_embeddings: Optional[torch.Tensor] = None,
key_padding_mask: Optional[torch.Tensor] = None, # @xwhan in order to add global mask
):
"""
Args:
src_tokens (LongTensor): tokens in the source language of shape
`(batch, src_len)`
src_lengths (torch.LongTensor): lengths of each source sentence of
shape `(batch)`
return_all_hiddens (bool, optional): also return all of the
intermediate hidden states (default: False).
token_embeddings (torch.Tensor, optional): precomputed embeddings
default `None` will recompute embeddings
Returns:
dict:
- **encoder_out** (Tensor): the last encoder layer's output of
shape `(src_len, batch, embed_dim)`
- **encoder_padding_mask** (ByteTensor): the positions of
padding elements of shape `(batch, src_len)`
- **encoder_embedding** (Tensor): the (scaled) embedding lookup
of shape `(batch, src_len, embed_dim)`
- **encoder_states** (List[Tensor]): all intermediate
hidden states of shape `(src_len, batch, embed_dim)`.
Only populated if *return_all_hiddens* is True.
"""
return self.forward_scriptable(
src_tokens, src_lengths, return_all_hiddens, token_embeddings, key_padding_mask
)
# TorchScript doesn't support super() method so that the scriptable Subclass
# can't access the base class model in Torchscript.
# Current workaround is to add a helper function with different name and
# call the helper function from scriptable Subclass.
def forward_scriptable(
self,
src_tokens,
src_lengths: Optional[torch.Tensor] = None,
return_all_hiddens: bool = False,
token_embeddings: Optional[torch.Tensor] = None,
key_padding_mask: Optional[torch.Tensor] = None, # @xwhan in order to add global mask
):
"""
Args:
src_tokens (LongTensor): tokens in the source language of shape
`(batch, src_len)`
src_lengths (torch.LongTensor): lengths of each source sentence of
shape `(batch)`
return_all_hiddens (bool, optional): also return all of the
intermediate hidden states (default: False).
token_embeddings (torch.Tensor, optional): precomputed embeddings
default `None` will recompute embeddings
Returns:
dict:
- **encoder_out** (Tensor): the last encoder layer's output of
shape `(src_len, batch, embed_dim)`
- **encoder_padding_mask** (ByteTensor): the positions of
padding elements of shape `(batch, src_len)`
- **encoder_embedding** (Tensor): the (scaled) embedding lookup
of shape `(batch, src_len, embed_dim)`
- **encoder_states** (List[Tensor]): all intermediate
hidden states of shape `(src_len, batch, embed_dim)`.
Only populated if *return_all_hiddens* is True.
"""
# compute padding mask
if key_padding_mask is None:
encoder_padding_mask = src_tokens.eq(self.padding_idx)
key_padding_mask = encoder_padding_mask
else:
encoder_padding_mask = key_padding_mask.eq(1) # key_padding_mask might -1 elements
has_pads = src_tokens.device.type == "xla" or encoder_padding_mask.any()
x, encoder_embedding = self.forward_embedding(src_tokens, token_embeddings)
# account for padding while computing the representation
if has_pads:
x = x * (1 - encoder_padding_mask.unsqueeze(-1).type_as(x))
# B x T x C -> T x B x C
x = x.transpose(0, 1)
encoder_states = []
if return_all_hiddens:
encoder_states.append(x)
# encoder layers
for layer in self.layers:
x = layer(
# x, encoder_padding_mask=encoder_padding_mask if has_pads else None
x, encoder_padding_mask=key_padding_mask, attn_bias=self.alibi if self.cfg.alibi else None
# always pass key_padding_mask
)
if return_all_hiddens:
assert encoder_states is not None
encoder_states.append(x)
if self.layer_norm is not None:
x = self.layer_norm(x)
# The Pytorch Mobile lite interpreter does not supports returning NamedTuple in
# `forward` so we use a dictionary instead.
# TorchScript does not support mixed values so the values are all lists.
# The empty list is equivalent to None.
src_lengths = src_tokens.ne(self.padding_idx).sum(dim=1, dtype=torch.int32).reshape(-1, 1).contiguous()
return {
"encoder_out": [x], # T x B x C
"encoder_padding_mask": [encoder_padding_mask], # B x T
"encoder_embedding": [encoder_embedding], # B x T x C
"encoder_states": encoder_states, # List[T x B x C]
"src_tokens": [],
"src_lengths": [src_lengths],
}
@torch.jit.export
def reorder_encoder_out(self, encoder_out: Dict[str, List[Tensor]], new_order):
"""
Reorder encoder output according to *new_order*.
Args:
encoder_out: output from the ``forward()`` method
new_order (LongTensor): desired order
Returns:
*encoder_out* rearranged according to *new_order*
"""
if len(encoder_out["encoder_out"]) == 0:
new_encoder_out = []
else:
new_encoder_out = [encoder_out["encoder_out"][0].index_select(1, new_order)]
if len(encoder_out["encoder_padding_mask"]) == 0:
new_encoder_padding_mask = []
else:
new_encoder_padding_mask = [
encoder_out["encoder_padding_mask"][0].index_select(0, new_order)
]
if len(encoder_out["encoder_embedding"]) == 0:
new_encoder_embedding = []
else:
new_encoder_embedding = [
encoder_out["encoder_embedding"][0].index_select(0, new_order)
]
if len(encoder_out["src_tokens"]) == 0:
src_tokens = []
else:
src_tokens = [(encoder_out["src_tokens"][0]).index_select(0, new_order)]
if len(encoder_out["src_lengths"]) == 0:
src_lengths = []
else:
src_lengths = [(encoder_out["src_lengths"][0]).index_select(0, new_order)]
encoder_states = encoder_out["encoder_states"]
if len(encoder_states) > 0:
for idx, state in enumerate(encoder_states):
encoder_states[idx] = state.index_select(1, new_order)
return {
"encoder_out": new_encoder_out, # T x B x C
"encoder_padding_mask": new_encoder_padding_mask, # B x T
"encoder_embedding": new_encoder_embedding, # B x T x C
"encoder_states": encoder_states, # List[T x B x C]
"src_tokens": src_tokens, # B x T
"src_lengths": src_lengths, # B x 1
}
def max_positions(self):
"""Maximum input length supported by the encoder."""
if self.embed_positions is None:
return self.max_source_positions
return min(self.max_source_positions, self.embed_positions.max_positions)
def upgrade_state_dict_named(self, state_dict, name):
"""Upgrade a (possibly old) state dict for new versions of fairseq."""
if isinstance(self.embed_positions, SinusoidalPositionalEmbedding):
weights_key = "{}.embed_positions.weights".format(name)
if weights_key in state_dict:
print("deleting {0}".format(weights_key))
del state_dict[weights_key]
state_dict[
"{}.embed_positions._float_tensor".format(name)
] = torch.FloatTensor(1)
for i in range(self.num_layers):
# update layer norms
self.layers[i].upgrade_state_dict_named(
state_dict, "{}.layers.{}".format(name, i)
)
version_key = "{}.version".format(name)
if utils.item(state_dict.get(version_key, torch.Tensor([1]))[0]) < 2:
# earlier checkpoints did not normalize after the stack of layers
self.layer_norm = None
self.normalize = False
state_dict[version_key] = torch.Tensor([1])
return state_dict
class TransformerEncoder(TransformerEncoderBase):
def __init__(self, args, dictionary, embed_tokens):
self.args = args
super().__init__(
TransformerConfig.from_namespace(args),
dictionary,
embed_tokens,
)
def build_encoder_layer(self, args):
return super().build_encoder_layer(
TransformerConfig.from_namespace(args),
)
|
bart_ls-main
|
fairseq-py/fairseq/models/transformer/transformer_encoder.py
|
# Copyright (c) Facebook Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""isort:skip_file"""
from .transformer_config import (
TransformerConfig,
DEFAULT_MAX_SOURCE_POSITIONS,
DEFAULT_MAX_TARGET_POSITIONS,
DEFAULT_MIN_PARAMS_TO_WRAP,
)
from .transformer_decoder import TransformerDecoder, TransformerDecoderBase, Linear
from .transformer_encoder import TransformerEncoder, TransformerEncoderBase
from .transformer_legacy import (
TransformerModel,
base_architecture,
tiny_architecture,
transformer_iwslt_de_en,
transformer_wmt_en_de,
transformer_vaswani_wmt_en_de_big,
transformer_vaswani_wmt_en_fr_big,
transformer_wmt_en_de_big,
transformer_wmt_en_de_big_t2t,
)
from .transformer_base import TransformerModelBase, Embedding
__all__ = [
"TransformerModelBase",
"TransformerConfig",
"TransformerDecoder",
"TransformerDecoderBase",
"TransformerEncoder",
"TransformerEncoderBase",
"TransformerModel",
"Embedding",
"Linear",
"base_architecture",
"tiny_architecture",
"transformer_iwslt_de_en",
"transformer_wmt_en_de",
"transformer_vaswani_wmt_en_de_big",
"transformer_vaswani_wmt_en_fr_big",
"transformer_wmt_en_de_big",
"transformer_wmt_en_de_big_t2t",
"DEFAULT_MAX_SOURCE_POSITIONS",
"DEFAULT_MAX_TARGET_POSITIONS",
"DEFAULT_MIN_PARAMS_TO_WRAP",
]
|
bart_ls-main
|
fairseq-py/fairseq/models/transformer/__init__.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import math
from typing import Any, Dict, List, Optional
import torch
import torch.nn as nn
from fairseq import utils
from fairseq.distributed import fsdp_wrap
from fairseq.models import FairseqIncrementalDecoder
from fairseq.models.transformer import TransformerConfig
from fairseq.modules import (
AdaptiveSoftmax,
BaseLayer,
FairseqDropout,
LayerDropModuleList,
LayerNorm,
PositionalEmbedding,
SinusoidalPositionalEmbedding,
)
from fairseq.modules import transformer_layer
from fairseq.modules.checkpoint_activations import checkpoint_wrapper
from fairseq.modules.quant_noise import quant_noise as apply_quant_noise_
from torch import Tensor
# rewrite name for backward compatibility in `make_generation_fast_`
def module_name_fordropout(module_name: str) -> str:
if module_name == 'TransformerDecoderBase':
return 'TransformerDecoder'
else:
return module_name
class TransformerDecoderBase(FairseqIncrementalDecoder):
"""
Transformer decoder consisting of *cfg.decoder.layers* layers. Each layer
is a :class:`TransformerDecoderLayer`.
Args:
args (argparse.Namespace): parsed command-line arguments
dictionary (~fairseq.data.Dictionary): decoding dictionary
embed_tokens (torch.nn.Embedding): output embedding
no_encoder_attn (bool, optional): whether to attend to encoder outputs
(default: False).
"""
def __init__(
self,
cfg,
dictionary,
embed_tokens,
no_encoder_attn=False,
output_projection=None,
):
self.cfg = cfg
super().__init__(dictionary)
self.register_buffer("version", torch.Tensor([3]))
self._future_mask = torch.empty(0)
self.dropout_module = FairseqDropout(
cfg.dropout, module_name=module_name_fordropout(self.__class__.__name__)
)
self.decoder_layerdrop = cfg.decoder.layerdrop
self.share_input_output_embed = cfg.share_decoder_input_output_embed
input_embed_dim = embed_tokens.embedding_dim
embed_dim = cfg.decoder.embed_dim
self.embed_dim = embed_dim
self.output_embed_dim = cfg.decoder.output_dim
self.padding_idx = embed_tokens.padding_idx
self.max_target_positions = cfg.max_target_positions
self.embed_tokens = embed_tokens
self.embed_scale = 1.0 if cfg.no_scale_embedding else math.sqrt(embed_dim)
if not cfg.adaptive_input and cfg.quant_noise.pq > 0:
self.quant_noise = apply_quant_noise_(
nn.Linear(embed_dim, embed_dim, bias=False),
cfg.quant_noise.pq,
cfg.quant_noise.pq_block_size,
)
else:
self.quant_noise = None
self.project_in_dim = (
Linear(input_embed_dim, embed_dim, bias=False)
if embed_dim != input_embed_dim
else None
)
self.embed_positions = (
PositionalEmbedding(
self.max_target_positions,
embed_dim,
self.padding_idx,
learned=cfg.decoder.learned_pos,
)
if not cfg.no_token_positional_embeddings and not cfg.alibi
else None
)
if cfg.layernorm_embedding:
self.layernorm_embedding = LayerNorm(embed_dim, export=cfg.export)
else:
self.layernorm_embedding = None
self.cross_self_attention = cfg.cross_self_attention
if self.decoder_layerdrop > 0.0:
self.layers = LayerDropModuleList(p=self.decoder_layerdrop)
else:
self.layers = nn.ModuleList([])
self.layers.extend(
[
self.build_decoder_layer(cfg, no_encoder_attn)
for _ in range(cfg.decoder.layers)
]
)
self.num_layers = len(self.layers)
if cfg.decoder.normalize_before and not cfg.no_decoder_final_norm:
self.layer_norm = LayerNorm(embed_dim, export=cfg.export)
else:
self.layer_norm = None
self.project_out_dim = (
Linear(embed_dim, self.output_embed_dim, bias=False)
if embed_dim != self.output_embed_dim and not cfg.tie_adaptive_weights
else None
)
self.adaptive_softmax = None
self.output_projection = output_projection
if self.output_projection is None:
self.build_output_projection(cfg, dictionary, embed_tokens)
# ALiBi position encodings
if cfg.alibi:
from fairseq.models.long_transformers import get_slopes
maxpos = self.max_target_positions
attn_heads = cfg.decoder_attention_heads
slopes = torch.Tensor(get_slopes(attn_heads))
#In the next line, the part after the * is what constructs the diagonal matrix (right matrix in Figure 3 in the paper).
#If you run it you'll see that it doesn't exactly print out the same matrix as we have in Figure 3, but one where all rows are identical.
#This works because the softmax operation is invariant to translation, and our bias functions are always linear.
self.alibi = slopes.unsqueeze(1).unsqueeze(1) * torch.arange(maxpos).unsqueeze(0).unsqueeze(0).expand(attn_heads, -1, -1)
self.alibi = self.alibi.view(attn_heads, 1, maxpos)
# breakpoint()
# self.alibi = self.alibi.repeat(args.max_tokens//maxpos, 1, 1) # batch_size, 1, 1
def build_output_projection(self, cfg, dictionary, embed_tokens):
if cfg.adaptive_softmax_cutoff is not None:
self.adaptive_softmax = AdaptiveSoftmax(
len(dictionary),
self.output_embed_dim,
utils.eval_str_list(cfg.adaptive_softmax_cutoff, type=int),
dropout=cfg.adaptive_softmax_dropout,
adaptive_inputs=embed_tokens if cfg.tie_adaptive_weights else None,
factor=cfg.adaptive_softmax_factor,
tie_proj=cfg.tie_adaptive_proj,
)
elif self.share_input_output_embed:
self.output_projection = nn.Linear(
self.embed_tokens.weight.shape[1],
self.embed_tokens.weight.shape[0],
bias=False,
)
self.output_projection.weight = self.embed_tokens.weight
else:
self.output_projection = nn.Linear(
self.output_embed_dim, len(dictionary), bias=False
)
nn.init.normal_(
self.output_projection.weight, mean=0, std=self.output_embed_dim ** -0.5
)
num_base_layers = cfg.base_layers
for i in range(num_base_layers):
self.layers.insert(
((i + 1) * cfg.decoder.layers) // (num_base_layers + 1),
BaseLayer(cfg),
)
def build_decoder_layer(self, cfg, no_encoder_attn=False):
layer = transformer_layer.TransformerDecoderLayerBase(cfg, no_encoder_attn)
checkpoint = cfg.checkpoint_activations
if checkpoint:
offload_to_cpu = cfg.offload_activations
layer = checkpoint_wrapper(layer, offload_to_cpu=offload_to_cpu)
# if we are checkpointing, enforce that FSDP always wraps the
# checkpointed layer, regardless of layer size
min_params_to_wrap = cfg.min_params_to_wrap if not checkpoint else 0
layer = fsdp_wrap(layer, min_num_params=min_params_to_wrap)
return layer
def forward(
self,
prev_output_tokens,
encoder_out: Optional[Dict[str, List[Tensor]]] = None,
incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None,
features_only: bool = False,
full_context_alignment: bool = False,
alignment_layer: Optional[int] = None,
alignment_heads: Optional[int] = None,
src_lengths: Optional[Any] = None,
return_all_hiddens: bool = False,
):
"""
Args:
prev_output_tokens (LongTensor): previous decoder outputs of shape
`(batch, tgt_len)`, for teacher forcing
encoder_out (optional): output from the encoder, used for
encoder-side attention, should be of size T x B x C
incremental_state (dict): dictionary used for storing state during
:ref:`Incremental decoding`
features_only (bool, optional): only return features without
applying output layer (default: False).
full_context_alignment (bool, optional): don't apply
auto-regressive mask to self-attention (default: False).
Returns:
tuple:
- the decoder's output of shape `(batch, tgt_len, vocab)`
- a dictionary with any model-specific outputs
"""
x, extra = self.extract_features(
prev_output_tokens,
encoder_out=encoder_out,
incremental_state=incremental_state,
full_context_alignment=full_context_alignment,
alignment_layer=alignment_layer,
alignment_heads=alignment_heads,
)
if not features_only:
x = self.output_layer(x)
return x, extra
def extract_features(
self,
prev_output_tokens,
encoder_out: Optional[Dict[str, List[Tensor]]],
incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None,
full_context_alignment: bool = False,
alignment_layer: Optional[int] = None,
alignment_heads: Optional[int] = None,
):
return self.extract_features_scriptable(
prev_output_tokens,
encoder_out,
incremental_state,
full_context_alignment,
alignment_layer,
alignment_heads,
)
"""
A scriptable subclass of this class has an extract_features method and calls
super().extract_features, but super() is not supported in torchscript. A copy of
this function is made to be used in the subclass instead.
"""
def extract_features_scriptable(
self,
prev_output_tokens,
encoder_out: Optional[Dict[str, List[Tensor]]],
incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None,
full_context_alignment: bool = False,
alignment_layer: Optional[int] = None,
alignment_heads: Optional[int] = None,
):
"""
Similar to *forward* but only return features.
Includes several features from "Jointly Learning to Align and
Translate with Transformer Models" (Garg et al., EMNLP 2019).
Args:
full_context_alignment (bool, optional): don't apply
auto-regressive mask to self-attention (default: False).
alignment_layer (int, optional): return mean alignment over
heads at this layer (default: last layer).
alignment_heads (int, optional): only average alignment over
this many heads (default: all heads).
Returns:
tuple:
- the decoder's features of shape `(batch, tgt_len, embed_dim)`
- a dictionary with any model-specific outputs
"""
bs, slen = prev_output_tokens.size()
if alignment_layer is None:
alignment_layer = self.num_layers - 1
enc: Optional[Tensor] = None
padding_mask: Optional[Tensor] = None
if encoder_out is not None and len(encoder_out["encoder_out"]) > 0:
enc = encoder_out["encoder_out"][0]
assert (
enc.size()[1] == bs
), f"Expected enc.shape == (t, {bs}, c) got {enc.shape}"
if encoder_out is not None and len(encoder_out["encoder_padding_mask"]) > 0:
padding_mask = encoder_out["encoder_padding_mask"][0]
# embed positions
positions = None
if self.embed_positions is not None:
positions = self.embed_positions(
prev_output_tokens, incremental_state=incremental_state
)
if incremental_state is not None:
prev_output_tokens = prev_output_tokens[:, -1:]
if positions is not None:
positions = positions[:, -1:]
# embed tokens and positions
x = self.embed_scale * self.embed_tokens(prev_output_tokens)
if self.quant_noise is not None:
x = self.quant_noise(x)
if self.project_in_dim is not None:
x = self.project_in_dim(x)
if positions is not None: # should be None when passed ALiBi
x += positions
if self.layernorm_embedding is not None:
x = self.layernorm_embedding(x)
x = self.dropout_module(x)
# B x T x C -> T x B x C
x = x.transpose(0, 1)
self_attn_padding_mask: Optional[Tensor] = None
if self.cross_self_attention or prev_output_tokens.eq(self.padding_idx).any():
self_attn_padding_mask = prev_output_tokens.eq(self.padding_idx)
# decoder layers
attn: Optional[Tensor] = None
inner_states: List[Optional[Tensor]] = [x]
for idx, layer in enumerate(self.layers):
if incremental_state is None and not full_context_alignment:
self_attn_mask = self.buffered_future_mask(x)
else:
self_attn_mask = None
x, layer_attn, _ = layer(
x,
enc,
padding_mask,
incremental_state,
self_attn_mask=self_attn_mask,
self_attn_padding_mask=self_attn_padding_mask,
need_attn=bool((idx == alignment_layer)),
need_head_weights=bool((idx == alignment_layer)),
)
inner_states.append(x)
if layer_attn is not None and idx == alignment_layer:
attn = layer_attn.float().to(x)
if attn is not None:
if alignment_heads is not None:
attn = attn[:alignment_heads]
# average probabilities over heads
attn = attn.mean(dim=0)
if self.layer_norm is not None:
x = self.layer_norm(x)
# T x B x C -> B x T x C
x = x.transpose(0, 1)
if self.project_out_dim is not None:
x = self.project_out_dim(x)
return x, {"attn": [attn], "inner_states": inner_states}
def output_layer(self, features):
"""Project features to the vocabulary size."""
if self.adaptive_softmax is None:
# project back to size of vocabulary
return self.output_projection(features)
else:
return features
def max_positions(self):
"""Maximum output length supported by the decoder."""
if self.embed_positions is None:
return self.max_target_positions
return min(self.max_target_positions, self.embed_positions.max_positions)
def buffered_future_mask(self, tensor):
dim = tensor.size(0)
# self._future_mask.device != tensor.device is not working in TorchScript. This is a workaround.
bsz = tensor.size(1)
if (
self._future_mask.size(0) == 0
or (not self._future_mask.device == tensor.device)
or (not self.cfg.alibi and self._future_mask.size(0) < dim)
or (self.cfg.alibi and self._future_mask.size(1) < dim) # sequence length gets longer
or (self.cfg.alibi and self._future_mask.size(0) < bsz*self.cfg.decoder_attention_heads) # batch size change during train/valid
):
self._future_mask = torch.triu(
utils.fill_with_neg_inf(torch.zeros([dim, dim])), 1
)
if self.cfg.alibi:
self._future_mask = self._future_mask.unsqueeze(0) + self.alibi.repeat(bsz, 1, 1)[:, :dim, :dim]
self._future_mask = self._future_mask.to(tensor)
if self.cfg.alibi:
self._future_mask = self._future_mask[:bsz*self.cfg.decoder_attention_heads, :dim, :dim]
return self._future_mask
return self._future_mask[:dim, :dim]
def upgrade_state_dict_named(self, state_dict, name):
"""Upgrade a (possibly old) state dict for new versions of fairseq."""
if isinstance(self.embed_positions, SinusoidalPositionalEmbedding):
weights_key = "{}.embed_positions.weights".format(name)
if weights_key in state_dict:
del state_dict[weights_key]
state_dict[
"{}.embed_positions._float_tensor".format(name)
] = torch.FloatTensor(1)
if f"{name}.output_projection.weight" not in state_dict:
if self.share_input_output_embed:
embed_out_key = f"{name}.embed_tokens.weight"
else:
embed_out_key = f"{name}.embed_out"
if embed_out_key in state_dict:
state_dict[f"{name}.output_projection.weight"] = state_dict[
embed_out_key
]
if not self.share_input_output_embed:
del state_dict[embed_out_key]
for i in range(self.num_layers):
# update layer norms
layer_norm_map = {
"0": "self_attn_layer_norm",
"1": "encoder_attn_layer_norm",
"2": "final_layer_norm",
}
for old, new in layer_norm_map.items():
for m in ("weight", "bias"):
k = "{}.layers.{}.layer_norms.{}.{}".format(name, i, old, m)
if k in state_dict:
state_dict[
"{}.layers.{}.{}.{}".format(name, i, new, m)
] = state_dict[k]
del state_dict[k]
version_key = "{}.version".format(name)
if utils.item(state_dict.get(version_key, torch.Tensor([1]))[0]) <= 2:
# earlier checkpoints did not normalize after the stack of layers
self.layer_norm = None
self.normalize = False
state_dict[version_key] = torch.Tensor([1])
return state_dict
def Linear(in_features, out_features, bias=True):
m = nn.Linear(in_features, out_features, bias)
nn.init.xavier_uniform_(m.weight)
if bias:
nn.init.constant_(m.bias, 0.0)
return m
class TransformerDecoder(TransformerDecoderBase):
def __init__(
self,
args,
dictionary,
embed_tokens,
no_encoder_attn=False,
output_projection=None,
):
self.args = args
super().__init__(
TransformerConfig.from_namespace(args),
dictionary,
embed_tokens,
no_encoder_attn=no_encoder_attn,
output_projection=output_projection,
)
def build_output_projection(self, args, dictionary, embed_tokens):
super().build_output_projection(
TransformerConfig.from_namespace(args), dictionary, embed_tokens
)
def build_decoder_layer(self, args, no_encoder_attn=False):
return super().build_decoder_layer(
TransformerConfig.from_namespace(args), no_encoder_attn=no_encoder_attn
)
|
bart_ls-main
|
fairseq-py/fairseq/models/transformer/transformer_decoder.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from typing import Dict, List, Optional, Tuple
import torch
import torch.nn as nn
from fairseq import utils
from fairseq.dataclass.utils import gen_parser_from_dataclass
from fairseq.distributed import fsdp_wrap
from fairseq.models import FairseqEncoderDecoderModel
from fairseq.models.transformer import (
TransformerEncoderBase,
TransformerDecoderBase,
TransformerConfig,
)
from torch import Tensor
class TransformerModelBase(FairseqEncoderDecoderModel):
"""
Transformer model from `"Attention Is All You Need" (Vaswani, et al, 2017)
<https://arxiv.org/abs/1706.03762>`_.
Args:
encoder (TransformerEncoder): the encoder
decoder (TransformerDecoder): the decoder
The Transformer model provides the following named architectures and
command-line arguments:
.. argparse::
:ref: fairseq.models.transformer_parser
:prog:
"""
def __init__(self, cfg, encoder, decoder):
super().__init__(encoder, decoder)
self.cfg = cfg
self.supports_align_args = True
@classmethod
def add_args(cls, parser):
"""Add model-specific arguments to the parser."""
# we want to build the args recursively in this case.
gen_parser_from_dataclass(
parser, TransformerConfig(), delete_default=False, with_prefix=""
)
@classmethod
def build_model(cls, cfg, task):
"""Build a new model instance."""
# -- TODO T96535332
# bug caused by interaction between OmegaConf II and argparsing
cfg.decoder.input_dim = int(cfg.decoder.input_dim)
cfg.decoder.output_dim = int(cfg.decoder.output_dim)
# --
if cfg.encoder.layers_to_keep:
cfg.encoder.layers = len(cfg.encoder.layers_to_keep.split(","))
if cfg.decoder.layers_to_keep:
cfg.decoder.layers = len(cfg.decoder.layers_to_keep.split(","))
src_dict, tgt_dict = task.source_dictionary, task.target_dictionary
if cfg.share_all_embeddings:
if src_dict != tgt_dict:
raise ValueError("--share-all-embeddings requires a joined dictionary")
if cfg.encoder.embed_dim != cfg.decoder.embed_dim:
raise ValueError(
"--share-all-embeddings requires --encoder-embed-dim to match --decoder-embed-dim"
)
if cfg.decoder.embed_path and (
cfg.decoder.embed_path != cfg.encoder.embed_path
):
raise ValueError(
"--share-all-embeddings not compatible with --decoder-embed-path"
)
encoder_embed_tokens = cls.build_embedding(
cfg, src_dict, cfg.encoder.embed_dim, cfg.encoder.embed_path
)
decoder_embed_tokens = encoder_embed_tokens
cfg.share_decoder_input_output_embed = True
else:
encoder_embed_tokens = cls.build_embedding(
cfg, src_dict, cfg.encoder.embed_dim, cfg.encoder.embed_path
)
decoder_embed_tokens = cls.build_embedding(
cfg, tgt_dict, cfg.decoder.embed_dim, cfg.decoder.embed_path
)
if cfg.offload_activations:
cfg.checkpoint_activations = True # offloading implies checkpointing
encoder = cls.build_encoder(cfg, src_dict, encoder_embed_tokens)
decoder = cls.build_decoder(cfg, tgt_dict, decoder_embed_tokens)
if not cfg.share_all_embeddings:
# fsdp_wrap is a no-op when --ddp-backend != fully_sharded
encoder = fsdp_wrap(encoder, min_num_params=cfg.min_params_to_wrap)
decoder = fsdp_wrap(decoder, min_num_params=cfg.min_params_to_wrap)
return cls(cfg, encoder, decoder)
@classmethod
def build_embedding(cls, cfg, dictionary, embed_dim, path=None):
num_embeddings = len(dictionary)
padding_idx = dictionary.pad()
emb = Embedding(num_embeddings, embed_dim, padding_idx)
# if provided, load from preloaded dictionaries
if path:
embed_dict = utils.parse_embedding(path)
utils.load_embedding(embed_dict, dictionary, emb)
return emb
@classmethod
def build_encoder(cls, cfg, src_dict, embed_tokens):
return TransformerEncoderBase(cfg, src_dict, embed_tokens)
@classmethod
def build_decoder(cls, cfg, tgt_dict, embed_tokens):
return TransformerDecoderBase(
cfg,
tgt_dict,
embed_tokens,
no_encoder_attn=cfg.no_cross_attention,
)
# TorchScript doesn't support optional arguments with variable length (**kwargs).
# Current workaround is to add union of all arguments in child classes.
def forward(
self,
src_tokens,
src_lengths,
prev_output_tokens,
return_all_hiddens: bool = True,
features_only: bool = False,
alignment_layer: Optional[int] = None,
alignment_heads: Optional[int] = None,
):
"""
Run the forward pass for an encoder-decoder model.
Copied from the base class, but without ``**kwargs``,
which are not supported by TorchScript.
"""
encoder_out = self.encoder(
src_tokens, src_lengths=src_lengths, return_all_hiddens=return_all_hiddens
)
decoder_out = self.decoder(
prev_output_tokens,
encoder_out=encoder_out,
features_only=features_only,
alignment_layer=alignment_layer,
alignment_heads=alignment_heads,
src_lengths=src_lengths,
return_all_hiddens=return_all_hiddens,
)
return decoder_out
# Since get_normalized_probs is in the Fairseq Model which is not scriptable,
# I rewrite the get_normalized_probs from Base Class to call the
# helper function in the Base Class.
@torch.jit.export
def get_normalized_probs(
self,
net_output: Tuple[Tensor, Optional[Dict[str, List[Optional[Tensor]]]]],
log_probs: bool,
sample: Optional[Dict[str, Tensor]] = None,
):
"""Get normalized probabilities (or log probs) from a net's output."""
return self.get_normalized_probs_scriptable(net_output, log_probs, sample)
def Embedding(num_embeddings, embedding_dim, padding_idx):
m = nn.Embedding(num_embeddings, embedding_dim, padding_idx=padding_idx)
nn.init.normal_(m.weight, mean=0, std=embedding_dim ** -0.5)
nn.init.constant_(m.weight[padding_idx], 0)
return m
|
bart_ls-main
|
fairseq-py/fairseq/models/transformer/transformer_base.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
from fairseq.utils import new_arange
# -------------- Helper Functions --------------------------------------------------- #
def load_libnat():
try:
from fairseq import libnat_cuda
return libnat_cuda, True
except ImportError as e:
print(str(e) + "... fall back to CPU version")
try:
from fairseq import libnat
return libnat, False
except ImportError as e:
import sys
sys.stderr.write(
"ERROR: missing libnat_cuda. run `python setup.py build_ext --inplace`\n"
)
raise e
def _get_ins_targets(in_tokens, out_tokens, padding_idx, unk_idx):
libnat, use_cuda = load_libnat()
def _get_ins_targets_cuda(in_tokens, out_tokens, padding_idx, unk_idx):
in_masks = in_tokens.ne(padding_idx)
out_masks = out_tokens.ne(padding_idx)
mask_ins_targets, masked_tgt_masks = libnat.generate_insertion_labels(
out_tokens.int(),
libnat.levenshtein_distance(
in_tokens.int(),
out_tokens.int(),
in_masks.sum(1).int(),
out_masks.sum(1).int(),
),
)
masked_tgt_masks = masked_tgt_masks.bool() & out_masks
mask_ins_targets = mask_ins_targets.type_as(in_tokens)[
:, 1 : in_masks.size(1)
].masked_fill_(~in_masks[:, 1:], 0)
masked_tgt_tokens = out_tokens.masked_fill(masked_tgt_masks, unk_idx)
return masked_tgt_masks, masked_tgt_tokens, mask_ins_targets
def _get_ins_targets_cpu(in_tokens, out_tokens, padding_idx, unk_idx):
in_seq_len, out_seq_len = in_tokens.size(1), out_tokens.size(1)
in_tokens_list = [
[t for t in s if t != padding_idx] for i, s in enumerate(in_tokens.tolist())
]
out_tokens_list = [
[t for t in s if t != padding_idx]
for i, s in enumerate(out_tokens.tolist())
]
full_labels = libnat.suggested_ed2_path(
in_tokens_list, out_tokens_list, padding_idx
)
mask_inputs = [
[len(c) if c[0] != padding_idx else 0 for c in a[:-1]] for a in full_labels
]
# generate labels
masked_tgt_masks = []
for mask_input in mask_inputs:
mask_label = []
for beam_size in mask_input[1:-1]: # HACK 1:-1
mask_label += [0] + [1 for _ in range(beam_size)]
masked_tgt_masks.append(
mask_label + [0 for _ in range(out_seq_len - len(mask_label))]
)
mask_ins_targets = [
mask_input[1:-1]
+ [0 for _ in range(in_seq_len - 1 - len(mask_input[1:-1]))]
for mask_input in mask_inputs
]
# transform to tensor
masked_tgt_masks = torch.tensor(
masked_tgt_masks, device=out_tokens.device
).bool()
mask_ins_targets = torch.tensor(mask_ins_targets, device=in_tokens.device)
masked_tgt_tokens = out_tokens.masked_fill(masked_tgt_masks, unk_idx)
return masked_tgt_masks, masked_tgt_tokens, mask_ins_targets
if use_cuda:
return _get_ins_targets_cuda(in_tokens, out_tokens, padding_idx, unk_idx)
return _get_ins_targets_cpu(in_tokens, out_tokens, padding_idx, unk_idx)
def _get_del_targets(in_tokens, out_tokens, padding_idx):
libnat, use_cuda = load_libnat()
def _get_del_targets_cuda(in_tokens, out_tokens, padding_idx):
in_masks = in_tokens.ne(padding_idx)
out_masks = out_tokens.ne(padding_idx)
word_del_targets = libnat.generate_deletion_labels(
in_tokens.int(),
libnat.levenshtein_distance(
in_tokens.int(),
out_tokens.int(),
in_masks.sum(1).int(),
out_masks.sum(1).int(),
),
)
word_del_targets = word_del_targets.type_as(in_tokens).masked_fill_(
~in_masks, 0
)
return word_del_targets
def _get_del_targets_cpu(in_tokens, out_tokens, padding_idx):
out_seq_len = out_tokens.size(1)
with torch.cuda.device_of(in_tokens):
in_tokens_list = [
[t for t in s if t != padding_idx]
for i, s in enumerate(in_tokens.tolist())
]
out_tokens_list = [
[t for t in s if t != padding_idx]
for i, s in enumerate(out_tokens.tolist())
]
full_labels = libnat.suggested_ed2_path(
in_tokens_list, out_tokens_list, padding_idx
)
word_del_targets = [b[-1] for b in full_labels]
word_del_targets = [
labels + [0 for _ in range(out_seq_len - len(labels))]
for labels in word_del_targets
]
# transform to tensor
word_del_targets = torch.tensor(word_del_targets, device=out_tokens.device)
return word_del_targets
if use_cuda:
return _get_del_targets_cuda(in_tokens, out_tokens, padding_idx)
return _get_del_targets_cpu(in_tokens, out_tokens, padding_idx)
def _apply_ins_masks(
in_tokens, in_scores, mask_ins_pred, padding_idx, unk_idx, eos_idx
):
in_masks = in_tokens.ne(padding_idx)
in_lengths = in_masks.sum(1)
# HACK: hacky way to shift all the paddings to eos first.
in_tokens.masked_fill_(~in_masks, eos_idx)
mask_ins_pred.masked_fill_(~in_masks[:, 1:], 0)
out_lengths = in_lengths + mask_ins_pred.sum(1)
out_max_len = out_lengths.max()
out_masks = new_arange(out_lengths, out_max_len)[None, :] < out_lengths[:, None]
reordering = (mask_ins_pred + in_masks[:, 1:].long()).cumsum(1)
out_tokens = (
in_tokens.new_zeros(in_tokens.size(0), out_max_len)
.fill_(padding_idx)
.masked_fill_(out_masks, unk_idx)
)
out_tokens[:, 0] = in_tokens[:, 0]
out_tokens.scatter_(1, reordering, in_tokens[:, 1:])
out_scores = None
if in_scores is not None:
in_scores.masked_fill_(~in_masks, 0)
out_scores = in_scores.new_zeros(*out_tokens.size())
out_scores[:, 0] = in_scores[:, 0]
out_scores.scatter_(1, reordering, in_scores[:, 1:])
return out_tokens, out_scores
def _apply_ins_words(in_tokens, in_scores, word_ins_pred, word_ins_scores, unk_idx):
word_ins_masks = in_tokens.eq(unk_idx)
out_tokens = in_tokens.masked_scatter(word_ins_masks, word_ins_pred[word_ins_masks])
if in_scores is not None:
out_scores = in_scores.masked_scatter(
word_ins_masks, word_ins_scores[word_ins_masks]
)
else:
out_scores = None
return out_tokens, out_scores
def _apply_del_words(
in_tokens, in_scores, in_attn, word_del_pred, padding_idx, bos_idx, eos_idx
):
# apply deletion to a tensor
in_masks = in_tokens.ne(padding_idx)
bos_eos_masks = in_tokens.eq(bos_idx) | in_tokens.eq(eos_idx)
max_len = in_tokens.size(1)
word_del_pred.masked_fill_(~in_masks, 1)
word_del_pred.masked_fill_(bos_eos_masks, 0)
reordering = new_arange(in_tokens).masked_fill_(word_del_pred, max_len).sort(1)[1]
out_tokens = in_tokens.masked_fill(word_del_pred, padding_idx).gather(1, reordering)
out_scores = None
if in_scores is not None:
out_scores = in_scores.masked_fill(word_del_pred, 0).gather(1, reordering)
out_attn = None
if in_attn is not None:
_mask = word_del_pred[:, :, None].expand_as(in_attn)
_reordering = reordering[:, :, None].expand_as(in_attn)
out_attn = in_attn.masked_fill(_mask, 0.0).gather(1, _reordering)
return out_tokens, out_scores, out_attn
def _skip(x, mask):
"""
Getting sliced (dim=0) tensor by mask. Supporting tensor and list/dict of tensors.
"""
if isinstance(x, int):
return x
if x is None:
return None
if isinstance(x, torch.Tensor):
if x.size(0) == mask.size(0):
return x[mask]
elif x.size(1) == mask.size(0):
return x[:, mask]
if isinstance(x, list):
return [_skip(x_i, mask) for x_i in x]
if isinstance(x, dict):
return {k: _skip(v, mask) for k, v in x.items()}
raise NotImplementedError
def _skip_encoder_out(encoder, encoder_out, mask):
if not mask.any():
return encoder_out
else:
return encoder.reorder_encoder_out(
encoder_out, mask.nonzero(as_tuple=False).squeeze()
)
def _fill(x, mask, y, padding_idx):
"""
Filling tensor x with y at masked positions (dim=0).
"""
if x is None:
return y
assert x.dim() == y.dim() and mask.size(0) == x.size(0)
assert x.dim() == 2 or (x.dim() == 3 and x.size(2) == y.size(2))
n_selected = mask.sum()
assert n_selected == y.size(0)
if n_selected == x.size(0):
return y
if x.size(1) < y.size(1):
dims = [x.size(0), y.size(1) - x.size(1)]
if x.dim() == 3:
dims.append(x.size(2))
x = torch.cat([x, x.new_zeros(*dims).fill_(padding_idx)], 1)
x[mask] = y
elif x.size(1) > y.size(1):
x[mask] = padding_idx
if x.dim() == 2:
x[mask, : y.size(1)] = y
else:
x[mask, : y.size(1), :] = y
else:
x[mask] = y
return x
|
bart_ls-main
|
fairseq-py/fairseq/models/nat/levenshtein_utils.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
from fairseq.models import register_model, register_model_architecture
from fairseq.models.nat import NATransformerModel
def _sequential_poisoning(s, V, beta=0.33, bos=2, eos=3, pad=1):
# s: input batch
# V: vocabulary size
rand_words = torch.randint(low=4, high=V, size=s.size(), device=s.device)
choices = torch.rand(size=s.size(), device=s.device)
choices.masked_fill_((s == pad) | (s == bos) | (s == eos), 1)
replace = choices < beta / 3
repeat = (choices >= beta / 3) & (choices < beta * 2 / 3)
swap = (choices >= beta * 2 / 3) & (choices < beta)
safe = choices >= beta
for i in range(s.size(1) - 1):
rand_word = rand_words[:, i]
next_word = s[:, i + 1]
self_word = s[:, i]
replace_i = replace[:, i]
swap_i = swap[:, i] & (next_word != 3)
repeat_i = repeat[:, i] & (next_word != 3)
safe_i = safe[:, i] | ((next_word == 3) & (~replace_i))
s[:, i] = (
self_word * (safe_i | repeat_i).long()
+ next_word * swap_i.long()
+ rand_word * replace_i.long()
)
s[:, i + 1] = (
next_word * (safe_i | replace_i).long()
+ self_word * (swap_i | repeat_i).long()
)
return s
def gumbel_noise(input, TINY=1e-8):
return (
input.new_zeros(*input.size())
.uniform_()
.add_(TINY)
.log_()
.neg_()
.add_(TINY)
.log_()
.neg_()
)
@register_model("iterative_nonautoregressive_transformer")
class IterNATransformerModel(NATransformerModel):
@staticmethod
def add_args(parser):
NATransformerModel.add_args(parser)
parser.add_argument(
"--train-step",
type=int,
help="number of refinement iterations during training",
)
parser.add_argument(
"--dae-ratio",
type=float,
help="the probability of switching to the denoising auto-encoder loss",
)
parser.add_argument(
"--stochastic-approx",
action="store_true",
help="sampling from the decoder as the inputs for next iteration",
)
@classmethod
def build_model(cls, args, task):
model = super().build_model(args, task)
model.train_step = getattr(args, "train_step", 4)
model.dae_ratio = getattr(args, "dae_ratio", 0.5)
model.stochastic_approx = getattr(args, "stochastic_approx", False)
return model
def forward(
self, src_tokens, src_lengths, prev_output_tokens, tgt_tokens, **kwargs
):
B, T = prev_output_tokens.size()
# encoding
encoder_out = self.encoder(src_tokens, src_lengths=src_lengths, **kwargs)
# length prediction
length_out = self.decoder.forward_length(
normalize=False, encoder_out=encoder_out
)
length_tgt = self.decoder.forward_length_prediction(
length_out, encoder_out, tgt_tokens
)
# decoding
word_ins_outs, word_ins_tgts, word_ins_masks = [], [], []
for t in range(self.train_step):
word_ins_out = self.decoder(
normalize=False,
prev_output_tokens=prev_output_tokens,
encoder_out=encoder_out,
step=t,
)
word_ins_tgt = tgt_tokens
word_ins_mask = word_ins_tgt.ne(self.pad)
word_ins_outs.append(word_ins_out)
word_ins_tgts.append(word_ins_tgt)
word_ins_masks.append(word_ins_mask)
if t < (self.train_step - 1):
# prediction for next iteration
if self.stochastic_approx:
word_ins_prediction = (
word_ins_out + gumbel_noise(word_ins_out)
).max(-1)[1]
else:
word_ins_prediction = word_ins_out.max(-1)[1]
prev_output_tokens = prev_output_tokens.masked_scatter(
word_ins_mask, word_ins_prediction[word_ins_mask]
)
if self.dae_ratio > 0:
# we do not perform denoising for the first iteration
corrputed = (
torch.rand(size=(B,), device=prev_output_tokens.device)
< self.dae_ratio
)
corrputed_tokens = _sequential_poisoning(
tgt_tokens[corrputed],
len(self.tgt_dict),
0.33,
self.bos,
self.eos,
self.pad,
)
prev_output_tokens[corrputed] = corrputed_tokens
# concat everything
word_ins_out = torch.cat(word_ins_outs, 0)
word_ins_tgt = torch.cat(word_ins_tgts, 0)
word_ins_mask = torch.cat(word_ins_masks, 0)
return {
"word_ins": {
"out": word_ins_out,
"tgt": word_ins_tgt,
"mask": word_ins_mask,
"ls": self.args.label_smoothing,
"nll_loss": True,
},
"length": {
"out": length_out,
"tgt": length_tgt,
"factor": self.decoder.length_loss_factor,
},
}
@register_model_architecture(
"iterative_nonautoregressive_transformer", "iterative_nonautoregressive_transformer"
)
def inat_base_architecture(args):
args.encoder_embed_path = getattr(args, "encoder_embed_path", None)
args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512)
args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 2048)
args.encoder_layers = getattr(args, "encoder_layers", 6)
args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 8)
args.encoder_normalize_before = getattr(args, "encoder_normalize_before", False)
args.encoder_learned_pos = getattr(args, "encoder_learned_pos", False)
args.decoder_embed_path = getattr(args, "decoder_embed_path", None)
args.decoder_embed_dim = getattr(args, "decoder_embed_dim", args.encoder_embed_dim)
args.decoder_ffn_embed_dim = getattr(
args, "decoder_ffn_embed_dim", args.encoder_ffn_embed_dim
)
args.decoder_layers = getattr(args, "decoder_layers", 6)
args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 8)
args.decoder_normalize_before = getattr(args, "decoder_normalize_before", False)
args.decoder_learned_pos = getattr(args, "decoder_learned_pos", False)
args.attention_dropout = getattr(args, "attention_dropout", 0.0)
args.activation_dropout = getattr(args, "activation_dropout", 0.0)
args.activation_fn = getattr(args, "activation_fn", "relu")
args.dropout = getattr(args, "dropout", 0.1)
args.adaptive_softmax_cutoff = getattr(args, "adaptive_softmax_cutoff", None)
args.adaptive_softmax_dropout = getattr(args, "adaptive_softmax_dropout", 0)
args.share_decoder_input_output_embed = getattr(
args, "share_decoder_input_output_embed", False
)
args.share_all_embeddings = getattr(args, "share_all_embeddings", False)
args.no_token_positional_embeddings = getattr(
args, "no_token_positional_embeddings", False
)
args.adaptive_input = getattr(args, "adaptive_input", False)
args.apply_bert_init = getattr(args, "apply_bert_init", False)
args.decoder_output_dim = getattr(
args, "decoder_output_dim", args.decoder_embed_dim
)
args.decoder_input_dim = getattr(args, "decoder_input_dim", args.decoder_embed_dim)
# --- special arguments ---
args.sg_length_pred = getattr(args, "sg_length_pred", False)
args.pred_length_offset = getattr(args, "pred_length_offset", False)
args.length_loss_factor = getattr(args, "length_loss_factor", 0.1)
args.ngram_predictor = getattr(args, "ngram_predictor", 1)
args.src_embedding_copy = getattr(args, "src_embedding_copy", False)
args.train_step = getattr(args, "train_step", 4)
args.dae_ratio = getattr(args, "dae_ratio", 0.5)
args.stochastic_approx = getattr(args, "stochastic_approx", False)
@register_model_architecture(
"iterative_nonautoregressive_transformer",
"iterative_nonautoregressive_transformer_wmt_en_de",
)
def iter_nat_wmt_en_de(args):
inat_base_architecture(args)
|
bart_ls-main
|
fairseq-py/fairseq/models/nat/iterative_nonautoregressive_transformer.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import math
import torch
from fairseq.models.transformer import (
TransformerDecoder,
TransformerEncoder,
TransformerModel,
)
from fairseq.modules.transformer_sentence_encoder import init_bert_params
def ensemble_encoder(func):
def wrapper(self, *args, **kwargs):
if self.ensemble_models is None or len(self.ensemble_models) == 1:
return func(self, *args, **kwargs)
encoder_outs = [func(model, *args, **kwargs, return_all_hiddens=True) for model in self.ensemble_models]
_encoder_out = encoder_outs[0].copy()
def stack(key):
outs = [e[key][0] for e in encoder_outs]
return [torch.stack(outs, -1) if outs[0] is not None else None]
_encoder_out["encoder_out"] = stack("encoder_out")
_encoder_out["encoder_embedding"] = stack("encoder_embedding")
num_layers = len(_encoder_out["encoder_states"])
if num_layers > 0:
_encoder_out["encoder_states"] = [
torch.stack([e["encoder_states"][i] for e in encoder_outs], -1)
for i in range(num_layers)
]
return _encoder_out
return wrapper
def ensemble_decoder(func):
def wrapper(self, normalize=False, encoder_out=None, *args, **kwargs):
if self.ensemble_models is None or len(self.ensemble_models) == 1:
return func(
self, normalize=normalize, encoder_out=encoder_out, *args, **kwargs
)
def _replace(encoder_out, new_val):
new_encoder_out = encoder_out.copy()
new_encoder_out["encoder_out"] = [new_val]
return new_encoder_out
action_outs = [
func(
model,
normalize=normalize,
encoder_out=_replace(
encoder_out,
encoder_out["encoder_out"][0][:, :, :, i]
),
*args,
**kwargs
)
for i, model in enumerate(self.ensemble_models)
]
if not isinstance(action_outs[0], tuple): # return multiple values
action_outs = [[a] for a in action_outs]
else:
action_outs = [list(a) for a in action_outs]
ensembled_outs = []
for i in range(len(action_outs[0])):
if i == 0 and normalize:
ensembled_outs += [
torch.logsumexp(
torch.stack([a[i] for a in action_outs], -1), dim=-1
)
- math.log(len(self.ensemble_models))
]
elif action_outs[0][i] is not None:
ensembled_outs += [torch.stack([a[i] for a in action_outs], -1)]
else:
ensembled_outs += [None]
if len(ensembled_outs) == 1:
return ensembled_outs[0]
return tuple(ensembled_outs)
return wrapper
class FairseqNATModel(TransformerModel):
"""
Abstract class for all nonautoregressive-based models
"""
def __init__(self, args, encoder, decoder):
super().__init__(args, encoder, decoder)
self.tgt_dict = decoder.dictionary
self.bos = decoder.dictionary.bos()
self.eos = decoder.dictionary.eos()
self.pad = decoder.dictionary.pad()
self.unk = decoder.dictionary.unk()
self.ensemble_models = None
@property
def allow_length_beam(self):
return False
@property
def allow_ensemble(self):
return True
def enable_ensemble(self, models):
self.encoder.ensemble_models = [m.encoder for m in models]
self.decoder.ensemble_models = [m.decoder for m in models]
@staticmethod
def add_args(parser):
TransformerModel.add_args(parser)
parser.add_argument(
"--apply-bert-init",
action="store_true",
help="use custom param initialization for BERT",
)
@classmethod
def build_decoder(cls, args, tgt_dict, embed_tokens):
decoder = FairseqNATDecoder(args, tgt_dict, embed_tokens)
if getattr(args, "apply_bert_init", False):
decoder.apply(init_bert_params)
return decoder
@classmethod
def build_encoder(cls, args, src_dict, embed_tokens):
encoder = FairseqNATEncoder(args, src_dict, embed_tokens)
if getattr(args, "apply_bert_init", False):
encoder.apply(init_bert_params)
return encoder
def forward_encoder(self, encoder_inputs):
return self.encoder(*encoder_inputs)
def forward_decoder(self, *args, **kwargs):
return NotImplementedError
def initialize_output_tokens(self, *args, **kwargs):
return NotImplementedError
def forward(self, *args, **kwargs):
return NotImplementedError
class FairseqNATEncoder(TransformerEncoder):
def __init__(self, args, dictionary, embed_tokens):
super().__init__(args, dictionary, embed_tokens)
self.ensemble_models = None
@ensemble_encoder
def forward(self, *args, **kwargs):
return super().forward(*args, **kwargs)
class FairseqNATDecoder(TransformerDecoder):
def __init__(self, args, dictionary, embed_tokens, no_encoder_attn=False):
super().__init__(args, dictionary, embed_tokens, no_encoder_attn)
self.ensemble_models = None
|
bart_ls-main
|
fairseq-py/fairseq/models/nat/fairseq_nat_model.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""isort:skip_file"""
from .fairseq_nat_model import *
from .nonautoregressive_transformer import *
from .nat_crf_transformer import *
from .iterative_nonautoregressive_transformer import *
from .cmlm_transformer import *
from .levenshtein_transformer import *
from .insertion_transformer import *
|
bart_ls-main
|
fairseq-py/fairseq/models/nat/__init__.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq.iterative_refinement_generator import DecoderOut
from fairseq.models import register_model, register_model_architecture
from fairseq.models.nat import FairseqNATDecoder, FairseqNATModel, ensemble_decoder
from fairseq.models.transformer import Embedding
from fairseq.modules import TransformerDecoderLayer
from fairseq.modules.transformer_sentence_encoder import init_bert_params
from .levenshtein_utils import (
_apply_del_words,
_apply_ins_masks,
_apply_ins_words,
_fill,
_get_del_targets,
_get_ins_targets,
_skip,
_skip_encoder_out,
)
@register_model("levenshtein_transformer")
class LevenshteinTransformerModel(FairseqNATModel):
@property
def allow_length_beam(self):
return False
@staticmethod
def add_args(parser):
FairseqNATModel.add_args(parser)
parser.add_argument(
"--early-exit",
default="6,6,6",
type=str,
help="number of decoder layers before word_del, mask_ins, word_ins",
)
parser.add_argument(
"--no-share-discriminator",
action="store_true",
help="separate parameters for discriminator",
)
parser.add_argument(
"--no-share-maskpredictor",
action="store_true",
help="separate parameters for mask-predictor",
)
parser.add_argument(
"--share-discriminator-maskpredictor",
action="store_true",
help="share the parameters for both mask-predictor and discriminator",
)
parser.add_argument(
"--sampling-for-deletion",
action="store_true",
help="instead of argmax, use sampling to predict the tokens",
)
@classmethod
def build_decoder(cls, args, tgt_dict, embed_tokens):
decoder = LevenshteinTransformerDecoder(args, tgt_dict, embed_tokens)
if getattr(args, "apply_bert_init", False):
decoder.apply(init_bert_params)
return decoder
def forward(
self, src_tokens, src_lengths, prev_output_tokens, tgt_tokens, **kwargs
):
assert tgt_tokens is not None, "forward function only supports training."
# encoding
encoder_out = self.encoder(src_tokens, src_lengths=src_lengths, **kwargs)
# generate training labels for insertion
masked_tgt_masks, masked_tgt_tokens, mask_ins_targets = _get_ins_targets(
prev_output_tokens, tgt_tokens, self.pad, self.unk
)
mask_ins_targets = mask_ins_targets.clamp(min=0, max=255) # for safe prediction
mask_ins_masks = prev_output_tokens[:, 1:].ne(self.pad)
mask_ins_out, _ = self.decoder.forward_mask_ins(
normalize=False,
prev_output_tokens=prev_output_tokens,
encoder_out=encoder_out,
)
word_ins_out, _ = self.decoder.forward_word_ins(
normalize=False,
prev_output_tokens=masked_tgt_tokens,
encoder_out=encoder_out,
)
# make online prediction
if self.decoder.sampling_for_deletion:
word_predictions = torch.multinomial(
F.softmax(word_ins_out, -1).view(-1, word_ins_out.size(-1)), 1
).view(word_ins_out.size(0), -1)
else:
word_predictions = F.log_softmax(word_ins_out, dim=-1).max(2)[1]
word_predictions.masked_scatter_(
~masked_tgt_masks, tgt_tokens[~masked_tgt_masks]
)
# generate training labels for deletion
word_del_targets = _get_del_targets(word_predictions, tgt_tokens, self.pad)
word_del_out, _ = self.decoder.forward_word_del(
normalize=False,
prev_output_tokens=word_predictions,
encoder_out=encoder_out,
)
word_del_masks = word_predictions.ne(self.pad)
return {
"mask_ins": {
"out": mask_ins_out,
"tgt": mask_ins_targets,
"mask": mask_ins_masks,
"ls": 0.01,
},
"word_ins": {
"out": word_ins_out,
"tgt": tgt_tokens,
"mask": masked_tgt_masks,
"ls": self.args.label_smoothing,
"nll_loss": True,
},
"word_del": {
"out": word_del_out,
"tgt": word_del_targets,
"mask": word_del_masks,
},
}
def forward_decoder(
self, decoder_out, encoder_out, eos_penalty=0.0, max_ratio=None, **kwargs
):
output_tokens = decoder_out.output_tokens
output_scores = decoder_out.output_scores
attn = decoder_out.attn
history = decoder_out.history
bsz = output_tokens.size(0)
if max_ratio is None:
max_lens = torch.zeros_like(output_tokens).fill_(255)
else:
if not encoder_out["encoder_padding_mask"]:
max_src_len = encoder_out["encoder_out"].size(0)
src_lens = encoder_out["encoder_out"].new(bsz).fill_(max_src_len)
else:
src_lens = (~encoder_out["encoder_padding_mask"][0]).sum(1)
max_lens = (src_lens * max_ratio).clamp(min=10).long()
# delete words
# do not delete tokens if it is <s> </s>
can_del_word = output_tokens.ne(self.pad).sum(1) > 2
if can_del_word.sum() != 0: # we cannot delete, skip
word_del_score, word_del_attn = self.decoder.forward_word_del(
normalize=True,
prev_output_tokens=_skip(output_tokens, can_del_word),
encoder_out=_skip_encoder_out(self.encoder, encoder_out, can_del_word),
)
word_del_pred = word_del_score.max(-1)[1].bool()
_tokens, _scores, _attn = _apply_del_words(
output_tokens[can_del_word],
output_scores[can_del_word],
word_del_attn,
word_del_pred,
self.pad,
self.bos,
self.eos,
)
output_tokens = _fill(output_tokens, can_del_word, _tokens, self.pad)
output_scores = _fill(output_scores, can_del_word, _scores, 0)
attn = _fill(attn, can_del_word, _attn, 0.0)
if history is not None:
history.append(output_tokens.clone())
# insert placeholders
can_ins_mask = output_tokens.ne(self.pad).sum(1) < max_lens
if can_ins_mask.sum() != 0:
mask_ins_score, _ = self.decoder.forward_mask_ins(
normalize=True,
prev_output_tokens=_skip(output_tokens, can_ins_mask),
encoder_out=_skip_encoder_out(self.encoder, encoder_out, can_ins_mask),
)
if eos_penalty > 0.0:
mask_ins_score[:, :, 0] = mask_ins_score[:, :, 0] - eos_penalty
mask_ins_pred = mask_ins_score.max(-1)[1]
mask_ins_pred = torch.min(
mask_ins_pred, max_lens[can_ins_mask, None].expand_as(mask_ins_pred)
)
_tokens, _scores = _apply_ins_masks(
output_tokens[can_ins_mask],
output_scores[can_ins_mask],
mask_ins_pred,
self.pad,
self.unk,
self.eos,
)
output_tokens = _fill(output_tokens, can_ins_mask, _tokens, self.pad)
output_scores = _fill(output_scores, can_ins_mask, _scores, 0)
if history is not None:
history.append(output_tokens.clone())
# insert words
can_ins_word = output_tokens.eq(self.unk).sum(1) > 0
if can_ins_word.sum() != 0:
word_ins_score, word_ins_attn = self.decoder.forward_word_ins(
normalize=True,
prev_output_tokens=_skip(output_tokens, can_ins_word),
encoder_out=_skip_encoder_out(self.encoder, encoder_out, can_ins_word),
)
word_ins_score, word_ins_pred = word_ins_score.max(-1)
_tokens, _scores = _apply_ins_words(
output_tokens[can_ins_word],
output_scores[can_ins_word],
word_ins_pred,
word_ins_score,
self.unk,
)
output_tokens = _fill(output_tokens, can_ins_word, _tokens, self.pad)
output_scores = _fill(output_scores, can_ins_word, _scores, 0)
attn = _fill(attn, can_ins_word, word_ins_attn, 0.0)
if history is not None:
history.append(output_tokens.clone())
# delete some unnecessary paddings
cut_off = output_tokens.ne(self.pad).sum(1).max()
output_tokens = output_tokens[:, :cut_off]
output_scores = output_scores[:, :cut_off]
attn = None if attn is None else attn[:, :cut_off, :]
return decoder_out._replace(
output_tokens=output_tokens,
output_scores=output_scores,
attn=attn,
history=history,
)
def initialize_output_tokens(self, encoder_out, src_tokens):
initial_output_tokens = src_tokens.new_zeros(src_tokens.size(0), 2)
initial_output_tokens[:, 0] = self.bos
initial_output_tokens[:, 1] = self.eos
initial_output_scores = initial_output_tokens.new_zeros(
*initial_output_tokens.size()
).type_as(encoder_out["encoder_out"][0])
return DecoderOut(
output_tokens=initial_output_tokens,
output_scores=initial_output_scores,
attn=None,
step=0,
max_step=0,
history=None,
)
class LevenshteinTransformerDecoder(FairseqNATDecoder):
def __init__(self, args, dictionary, embed_tokens, no_encoder_attn=False):
super().__init__(
args, dictionary, embed_tokens, no_encoder_attn=no_encoder_attn
)
self.dictionary = dictionary
self.bos = dictionary.bos()
self.unk = dictionary.unk()
self.eos = dictionary.eos()
self.sampling_for_deletion = getattr(args, "sampling_for_deletion", False)
self.embed_mask_ins = Embedding(256, self.output_embed_dim * 2, None)
self.embed_word_del = Embedding(2, self.output_embed_dim, None)
# del_word, ins_mask, ins_word
self.early_exit = [int(i) for i in args.early_exit.split(",")]
assert len(self.early_exit) == 3
# copy layers for mask-predict/deletion
self.layers_msk = None
if getattr(args, "no_share_maskpredictor", False):
self.layers_msk = nn.ModuleList(
[
TransformerDecoderLayer(args, no_encoder_attn)
for _ in range(self.early_exit[1])
]
)
self.layers_del = None
if getattr(args, "no_share_discriminator", False):
self.layers_del = nn.ModuleList(
[
TransformerDecoderLayer(args, no_encoder_attn)
for _ in range(self.early_exit[0])
]
)
if getattr(args, "share_discriminator_maskpredictor", False):
assert getattr(
args, "no_share_discriminator", False
), "must set saperate discriminator"
self.layers_msk = self.layers_del
def extract_features(
self,
prev_output_tokens,
encoder_out=None,
early_exit=None,
layers=None,
**unused
):
"""
Similar to *forward* but only return features.
Inputs:
prev_output_tokens: Tensor(B, T)
encoder_out: a dictionary of hidden states and masks
Returns:
tuple:
- the decoder's features of shape `(batch, tgt_len, embed_dim)`
- a dictionary with any model-specific outputs
the LevenshteinTransformer decoder has full-attention to all generated tokens
"""
# embed positions
positions = (
self.embed_positions(prev_output_tokens)
if self.embed_positions is not None
else None
)
# embed tokens and positions
x = self.embed_scale * self.embed_tokens(prev_output_tokens)
if self.project_in_dim is not None:
x = self.project_in_dim(x)
if positions is not None:
x += positions
x = self.dropout_module(x)
# B x T x C -> T x B x C
x = x.transpose(0, 1)
attn = None
inner_states = [x]
# decoder layers
decoder_padding_mask = prev_output_tokens.eq(self.padding_idx)
layers = self.layers if layers is None else layers
early_exit = len(layers) if early_exit is None else early_exit
for _, layer in enumerate(layers[:early_exit]):
x, attn, _ = layer(
x,
encoder_out["encoder_out"][0]
if (encoder_out is not None and len(encoder_out["encoder_out"]) > 0)
else None,
encoder_out["encoder_padding_mask"][0]
if (
encoder_out is not None
and len(encoder_out["encoder_padding_mask"]) > 0
)
else None,
self_attn_mask=None,
self_attn_padding_mask=decoder_padding_mask,
)
inner_states.append(x)
if self.layer_norm:
x = self.layer_norm(x)
# T x B x C -> B x T x C
x = x.transpose(0, 1)
if self.project_out_dim is not None:
x = self.project_out_dim(x)
return x, {"attn": attn, "inner_states": inner_states}
@ensemble_decoder
def forward_mask_ins(self, normalize, encoder_out, prev_output_tokens, **unused):
features, extra = self.extract_features(
prev_output_tokens,
encoder_out=encoder_out,
early_exit=self.early_exit[1],
layers=self.layers_msk,
**unused
)
features_cat = torch.cat([features[:, :-1, :], features[:, 1:, :]], 2)
decoder_out = F.linear(features_cat, self.embed_mask_ins.weight)
if normalize:
return F.log_softmax(decoder_out, -1), extra["attn"]
return decoder_out, extra["attn"]
@ensemble_decoder
def forward_word_ins(self, normalize, encoder_out, prev_output_tokens, **unused):
features, extra = self.extract_features(
prev_output_tokens,
encoder_out=encoder_out,
early_exit=self.early_exit[2],
layers=self.layers,
**unused
)
decoder_out = self.output_layer(features)
if normalize:
return F.log_softmax(decoder_out, -1), extra["attn"]
return decoder_out, extra["attn"]
@ensemble_decoder
def forward_word_del(self, normalize, encoder_out, prev_output_tokens, **unused):
features, extra = self.extract_features(
prev_output_tokens,
encoder_out=encoder_out,
early_exit=self.early_exit[0],
layers=self.layers_del,
**unused
)
decoder_out = F.linear(features, self.embed_word_del.weight)
if normalize:
return F.log_softmax(decoder_out, -1), extra["attn"]
return decoder_out, extra["attn"]
@register_model_architecture("levenshtein_transformer", "levenshtein_transformer")
def levenshtein_base_architecture(args):
args.encoder_embed_path = getattr(args, "encoder_embed_path", None)
args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512)
args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 2048)
args.encoder_layers = getattr(args, "encoder_layers", 6)
args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 8)
args.encoder_normalize_before = getattr(args, "encoder_normalize_before", False)
args.encoder_learned_pos = getattr(args, "encoder_learned_pos", False)
args.decoder_embed_path = getattr(args, "decoder_embed_path", None)
args.decoder_embed_dim = getattr(args, "decoder_embed_dim", args.encoder_embed_dim)
args.decoder_ffn_embed_dim = getattr(
args, "decoder_ffn_embed_dim", args.encoder_ffn_embed_dim
)
args.decoder_layers = getattr(args, "decoder_layers", 6)
args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 8)
args.decoder_normalize_before = getattr(args, "decoder_normalize_before", False)
args.decoder_learned_pos = getattr(args, "decoder_learned_pos", False)
args.attention_dropout = getattr(args, "attention_dropout", 0.0)
args.activation_dropout = getattr(args, "activation_dropout", 0.0)
args.activation_fn = getattr(args, "activation_fn", "relu")
args.dropout = getattr(args, "dropout", 0.1)
args.adaptive_softmax_cutoff = getattr(args, "adaptive_softmax_cutoff", None)
args.adaptive_softmax_dropout = getattr(args, "adaptive_softmax_dropout", 0)
args.share_decoder_input_output_embed = getattr(
args, "share_decoder_input_output_embed", False
)
args.share_all_embeddings = getattr(args, "share_all_embeddings", False)
args.no_token_positional_embeddings = getattr(
args, "no_token_positional_embeddings", False
)
args.adaptive_input = getattr(args, "adaptive_input", False)
args.apply_bert_init = getattr(args, "apply_bert_init", False)
args.decoder_output_dim = getattr(
args, "decoder_output_dim", args.decoder_embed_dim
)
args.sampling_for_deletion = getattr(args, "sampling_for_deletion", False)
args.decoder_input_dim = getattr(args, "decoder_input_dim", args.decoder_embed_dim)
args.early_exit = getattr(args, "early_exit", "6,6,6")
args.no_share_discriminator = getattr(args, "no_share_discriminator", False)
args.no_share_maskpredictor = getattr(args, "no_share_maskpredictor", False)
args.share_discriminator_maskpredictor = getattr(
args, "share_discriminator_maskpredictor", False
)
args.no_share_last_layer = getattr(args, "no_share_last_layer", False)
@register_model_architecture(
"levenshtein_transformer", "levenshtein_transformer_wmt_en_de"
)
def levenshtein_transformer_wmt_en_de(args):
levenshtein_base_architecture(args)
# similar parameters used in the "Attention Is All You Need" paper (Vaswani et al., 2017)
@register_model_architecture(
"levenshtein_transformer", "levenshtein_transformer_vaswani_wmt_en_de_big"
)
def levenshtein_transformer_vaswani_wmt_en_de_big(args):
args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 1024)
args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 4096)
args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 16)
args.encoder_normalize_before = getattr(args, "encoder_normalize_before", False)
args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 1024)
args.decoder_ffn_embed_dim = getattr(args, "decoder_ffn_embed_dim", 4096)
args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 16)
args.dropout = getattr(args, "dropout", 0.3)
levenshtein_base_architecture(args)
# default parameters used in tensor2tensor implementation
@register_model_architecture(
"levenshtein_transformer", "levenshtein_transformer_wmt_en_de_big"
)
def levenshtein_transformer_wmt_en_de_big_t2t(args):
args.encoder_normalize_before = getattr(args, "encoder_normalize_before", True)
args.decoder_normalize_before = getattr(args, "decoder_normalize_before", True)
args.attention_dropout = getattr(args, "attention_dropout", 0.1)
args.activation_dropout = getattr(args, "activation_dropout", 0.1)
levenshtein_transformer_vaswani_wmt_en_de_big(args)
|
bart_ls-main
|
fairseq-py/fairseq/models/nat/levenshtein_transformer.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import math
import torch
import torch.nn.functional as F
from fairseq.models.nat import (
_apply_del_words,
_apply_ins_masks,
_apply_ins_words,
_fill,
_skip,
_skip_encoder_out,
)
class _EnsembleModelEncoder(object):
def __init__(self, models):
self.models = models
def reorder_encoder_out(self, encoder_outs, new_order):
encoder_outs = [
model.encoder.reorder_encoder_out(encoder_out, new_order)
for model, encoder_out in zip(self.models, encoder_outs)
]
return encoder_outs
class BasicEnsembleModel(torch.nn.Module):
"""A wrapper around an ensemble of models."""
def __init__(self, models):
super().__init__()
self.models = torch.nn.ModuleList(models)
self.bos = self.models[0].decoder.dictionary.bos()
self.eos = self.models[0].decoder.dictionary.eos()
self.pad = self.models[0].decoder.dictionary.pad()
self.unk = self.models[0].decoder.dictionary.unk()
self.encoder = _EnsembleModelEncoder(self.models)
def has_encoder(self):
return hasattr(self.models[0], "encoder")
def max_decoder_positions(self):
return min(m.max_decoder_positions() for m in self.models)
@torch.no_grad()
def forward_encoder(self, encoder_input):
if not self.has_encoder():
return None
return [model.forward_encoder(encoder_input) for model in self.models]
@torch.no_grad()
def forward_decoder(self, *inputs):
raise NotImplementedError
def initialize_output_tokens(self, *inputs):
raise NotImplementedError
class EnsembleLevT(BasicEnsembleModel):
"""A wrapper around an ensemble of models."""
def __init__(self, models):
super().__init__(models)
@torch.no_grad()
def forward_decoder(
self, decoder_out, encoder_outs, eos_penalty=0.0, max_ratio=None, **kwargs
):
# LevT ensembling
# A pipeline of three steps: deletion, placeholder, and word insertion.
# We need to average scores in each step in a pipeline way because of dependence.
# deletion
output_tokens = decoder_out.output_tokens
output_scores = decoder_out.output_scores
attn = decoder_out.attn
bsz = output_tokens.size(0)
if max_ratio is None:
max_lens = output_tokens.new().fill_(255)
else:
if not encoder_outs[0]["encoder_padding_mask"]:
src_lens = (
encoder_outs[0]["encoder_out"][0].new(bsz)
.fill_(encoder_outs[0]["encoder_out"][0].size(1))
)
else:
src_lens = (~encoder_outs[0]["encoder_padding_mask"][0]).sum(1)
max_lens = (src_lens * max_ratio).clamp(min=10).long()
# delete words
# do not delete tokens if it is <s> </s>
can_del_word = output_tokens.ne(self.pad).sum(1) > 2
if can_del_word.sum() != 0: # we cannot delete, skip
output_tokens, output_scores, attn = self.forward_word_del(
encoder_outs,
output_tokens,
output_scores,
attn,
can_del_word,
)
# insert placeholders
can_ins_mask = output_tokens.ne(self.pad).sum(1) < max_lens
if can_ins_mask.sum() != 0:
output_tokens, output_scores = self.forward_mask_ins(
encoder_outs,
output_tokens,
output_scores,
can_ins_mask,
eos_penalty,
max_lens,
)
# insert words
can_ins_word = output_tokens.eq(self.unk).sum(1) > 0
if can_ins_word.sum() != 0:
output_tokens, output_scores, attn = self.forward_word_ins(
encoder_outs,
output_tokens,
output_scores,
attn,
can_ins_word,
)
# delete some unnecessary paddings
cut_off = output_tokens.ne(self.pad).sum(1).max()
output_tokens = output_tokens[:, :cut_off]
output_scores = output_scores[:, :cut_off]
attn = None if attn is None else attn[:, :cut_off, :]
return decoder_out._replace(
output_tokens=output_tokens,
output_scores=output_scores,
attn=attn,
history=None,
)
def forward_word_del(
self, encoder_outs, output_tokens, output_scores, attn, can_del_word
):
word_del_score_avg = []
word_del_attn_avg = []
for model, encoder_out in zip(self.models, encoder_outs):
word_del_out, word_del_attn = model.decoder.forward_word_del(
_skip(output_tokens, can_del_word),
_skip_encoder_out(model.encoder, encoder_out, can_del_word),
)
word_del_score = F.log_softmax(word_del_out, 2)
word_del_score_avg.append(word_del_score)
word_del_attn_avg.append(word_del_attn)
word_del_score_avg = torch.logsumexp(
torch.stack(word_del_score_avg, dim=0), dim=0
) - math.log(len(self.models))
word_del_pred = word_del_score_avg.max(-1)[1].bool()
if word_del_attn_avg[0] is not None:
word_del_attn_avg = torch.stack(word_del_attn_avg, dim=0) / len(self.models)
else:
word_del_attn_avg = None
_tokens, _scores, _attn = _apply_del_words(
output_tokens[can_del_word],
output_scores[can_del_word],
word_del_attn_avg,
word_del_pred,
self.pad,
self.bos,
self.eos,
)
output_tokens = _fill(output_tokens, can_del_word, _tokens, self.pad)
output_scores = _fill(output_scores, can_del_word, _scores, 0)
attn = _fill(attn, can_del_word, _attn, 0.0)
return output_tokens, output_scores, attn
def forward_mask_ins(
self,
encoder_outs,
output_tokens,
output_scores,
can_ins_mask,
eos_penalty,
max_lens,
):
mask_ins_score_avg = []
for model, encoder_out in zip(self.models, encoder_outs):
mask_ins_out, _ = model.decoder.forward_mask_ins(
_skip(output_tokens, can_ins_mask),
_skip_encoder_out(model.encoder, encoder_out, can_ins_mask),
)
mask_ins_score = F.log_softmax(mask_ins_out, 2)
if eos_penalty > 0.0:
mask_ins_score[:, :, 0] -= eos_penalty
mask_ins_score_avg.append(mask_ins_score)
mask_ins_score_avg = torch.logsumexp(
torch.stack(mask_ins_score_avg, dim=0), dim=0
) - math.log(len(self.models))
mask_ins_pred = mask_ins_score_avg.max(-1)[1]
mask_ins_pred = torch.min(
mask_ins_pred, max_lens[can_ins_mask, None].expand_as(mask_ins_pred)
)
_tokens, _scores = _apply_ins_masks(
output_tokens[can_ins_mask],
output_scores[can_ins_mask],
mask_ins_pred,
self.pad,
self.unk,
self.eos,
)
output_tokens = _fill(output_tokens, can_ins_mask, _tokens, self.pad)
output_scores = _fill(output_scores, can_ins_mask, _scores, 0)
return output_tokens, output_scores
def forward_word_ins(
self, encoder_outs, output_tokens, output_scores, attn, can_ins_word
):
word_ins_score_avg = []
word_ins_attn_avg = []
for model, encoder_out in zip(self.models, encoder_outs):
word_ins_out, word_ins_attn = model.decoder.forward_word_ins(
_skip(output_tokens, can_ins_word),
_skip_encoder_out(model.encoder, encoder_out, can_ins_word),
)
word_ins_score = F.log_softmax(word_ins_out, 2)
word_ins_score_avg.append(word_ins_score)
word_ins_attn_avg.append(word_ins_attn)
word_ins_score_avg = torch.logsumexp(
torch.stack(word_ins_score_avg, dim=0), dim=0
) - math.log(len(self.models))
if word_ins_attn_avg[0] is not None:
word_ins_attn_avg = torch.stack(word_ins_attn_avg, dim=0) / len(self.models)
else:
word_ins_attn_avg = None
word_ins_score_max, word_ins_pred = word_ins_score_avg.max(-1)
_tokens, _scores = _apply_ins_words(
output_tokens[can_ins_word],
output_scores[can_ins_word],
word_ins_pred,
word_ins_score_max,
self.unk,
)
output_tokens = _fill(output_tokens, can_ins_word, _tokens, self.pad)
output_scores = _fill(output_scores, can_ins_word, _scores, 0)
attn = _fill(attn, can_ins_word, word_ins_attn, 0.0)
return output_tokens, output_scores, attn
def initialize_output_tokens(self, encoder_outs, src_tokens):
# LevT doesn't do length prediction.
return self.models[0].initialize_output_tokens(encoder_outs[0], src_tokens)
|
bart_ls-main
|
fairseq-py/fairseq/models/nat/nonautoregressive_ensembles.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
import torch.nn.functional as F
from fairseq import utils
from fairseq.iterative_refinement_generator import DecoderOut
from fairseq.models import register_model, register_model_architecture
from fairseq.models.nat import FairseqNATDecoder, FairseqNATModel, ensemble_decoder
from fairseq.models.transformer import Embedding
from fairseq.modules.transformer_sentence_encoder import init_bert_params
def _mean_pooling(enc_feats, src_masks):
# enc_feats: T x B x C
# src_masks: B x T or None
if src_masks is None:
enc_feats = enc_feats.mean(0)
else:
src_masks = (~src_masks).transpose(0, 1).type_as(enc_feats)
enc_feats = (
(enc_feats / src_masks.sum(0)[None, :, None]) * src_masks[:, :, None]
).sum(0)
return enc_feats
def _argmax(x, dim):
return (x == x.max(dim, keepdim=True)[0]).type_as(x)
def _uniform_assignment(src_lens, trg_lens):
max_trg_len = trg_lens.max()
steps = (src_lens.float() - 1) / (trg_lens.float() - 1) # step-size
# max_trg_len
index_t = utils.new_arange(trg_lens, max_trg_len).float()
index_t = steps[:, None] * index_t[None, :] # batch_size X max_trg_len
index_t = torch.round(index_t).long().detach()
return index_t
@register_model("nonautoregressive_transformer")
class NATransformerModel(FairseqNATModel):
@property
def allow_length_beam(self):
return True
@staticmethod
def add_args(parser):
FairseqNATModel.add_args(parser)
# length prediction
parser.add_argument(
"--src-embedding-copy",
action="store_true",
help="copy encoder word embeddings as the initial input of the decoder",
)
parser.add_argument(
"--pred-length-offset",
action="store_true",
help="predicting the length difference between the target and source sentences",
)
parser.add_argument(
"--sg-length-pred",
action="store_true",
help="stop the gradients back-propagated from the length predictor",
)
parser.add_argument(
"--length-loss-factor",
type=float,
help="weights on the length prediction loss",
)
@classmethod
def build_decoder(cls, args, tgt_dict, embed_tokens):
decoder = NATransformerDecoder(args, tgt_dict, embed_tokens)
if getattr(args, "apply_bert_init", False):
decoder.apply(init_bert_params)
return decoder
def forward(
self, src_tokens, src_lengths, prev_output_tokens, tgt_tokens, **kwargs
):
# encoding
encoder_out = self.encoder(src_tokens, src_lengths=src_lengths, **kwargs)
# length prediction
length_out = self.decoder.forward_length(
normalize=False, encoder_out=encoder_out
)
length_tgt = self.decoder.forward_length_prediction(
length_out, encoder_out, tgt_tokens
)
# decoding
word_ins_out = self.decoder(
normalize=False,
prev_output_tokens=prev_output_tokens,
encoder_out=encoder_out,
)
return {
"word_ins": {
"out": word_ins_out,
"tgt": tgt_tokens,
"mask": tgt_tokens.ne(self.pad),
"ls": self.args.label_smoothing,
"nll_loss": True,
},
"length": {
"out": length_out,
"tgt": length_tgt,
"factor": self.decoder.length_loss_factor,
},
}
def forward_decoder(self, decoder_out, encoder_out, decoding_format=None, **kwargs):
step = decoder_out.step
output_tokens = decoder_out.output_tokens
output_scores = decoder_out.output_scores
history = decoder_out.history
# execute the decoder
output_masks = output_tokens.ne(self.pad)
_scores, _tokens = self.decoder(
normalize=True,
prev_output_tokens=output_tokens,
encoder_out=encoder_out,
step=step,
).max(-1)
output_tokens.masked_scatter_(output_masks, _tokens[output_masks])
output_scores.masked_scatter_(output_masks, _scores[output_masks])
if history is not None:
history.append(output_tokens.clone())
return decoder_out._replace(
output_tokens=output_tokens,
output_scores=output_scores,
attn=None,
history=history,
)
def initialize_output_tokens(self, encoder_out, src_tokens):
# length prediction
length_tgt = self.decoder.forward_length_prediction(
self.decoder.forward_length(normalize=True, encoder_out=encoder_out),
encoder_out=encoder_out,
)
max_length = length_tgt.clamp_(min=2).max()
idx_length = utils.new_arange(src_tokens, max_length)
initial_output_tokens = src_tokens.new_zeros(
src_tokens.size(0), max_length
).fill_(self.pad)
initial_output_tokens.masked_fill_(
idx_length[None, :] < length_tgt[:, None], self.unk
)
initial_output_tokens[:, 0] = self.bos
initial_output_tokens.scatter_(1, length_tgt[:, None] - 1, self.eos)
initial_output_scores = initial_output_tokens.new_zeros(
*initial_output_tokens.size()
).type_as(encoder_out["encoder_out"][0])
return DecoderOut(
output_tokens=initial_output_tokens,
output_scores=initial_output_scores,
attn=None,
step=0,
max_step=0,
history=None,
)
def regenerate_length_beam(self, decoder_out, beam_size):
output_tokens = decoder_out.output_tokens
length_tgt = output_tokens.ne(self.pad).sum(1)
length_tgt = (
length_tgt[:, None]
+ utils.new_arange(length_tgt, 1, beam_size)
- beam_size // 2
)
length_tgt = length_tgt.view(-1).clamp_(min=2)
max_length = length_tgt.max()
idx_length = utils.new_arange(length_tgt, max_length)
initial_output_tokens = output_tokens.new_zeros(
length_tgt.size(0), max_length
).fill_(self.pad)
initial_output_tokens.masked_fill_(
idx_length[None, :] < length_tgt[:, None], self.unk
)
initial_output_tokens[:, 0] = self.bos
initial_output_tokens.scatter_(1, length_tgt[:, None] - 1, self.eos)
initial_output_scores = initial_output_tokens.new_zeros(
*initial_output_tokens.size()
).type_as(decoder_out.output_scores)
return decoder_out._replace(
output_tokens=initial_output_tokens, output_scores=initial_output_scores
)
class NATransformerDecoder(FairseqNATDecoder):
def __init__(self, args, dictionary, embed_tokens, no_encoder_attn=False):
super().__init__(
args, dictionary, embed_tokens, no_encoder_attn=no_encoder_attn
)
self.dictionary = dictionary
self.bos = dictionary.bos()
self.unk = dictionary.unk()
self.eos = dictionary.eos()
self.encoder_embed_dim = args.encoder_embed_dim
self.sg_length_pred = getattr(args, "sg_length_pred", False)
self.pred_length_offset = getattr(args, "pred_length_offset", False)
self.length_loss_factor = getattr(args, "length_loss_factor", 0.1)
self.src_embedding_copy = getattr(args, "src_embedding_copy", False)
self.embed_length = Embedding(256, self.encoder_embed_dim, None)
@ensemble_decoder
def forward(self, normalize, encoder_out, prev_output_tokens, step=0, **unused):
features, _ = self.extract_features(
prev_output_tokens,
encoder_out=encoder_out,
embedding_copy=(step == 0) & self.src_embedding_copy,
)
decoder_out = self.output_layer(features)
return F.log_softmax(decoder_out, -1) if normalize else decoder_out
@ensemble_decoder
def forward_length(self, normalize, encoder_out):
enc_feats = encoder_out["encoder_out"][0] # T x B x C
if len(encoder_out["encoder_padding_mask"]) > 0:
src_masks = encoder_out["encoder_padding_mask"][0] # B x T
else:
src_masks = None
enc_feats = _mean_pooling(enc_feats, src_masks)
if self.sg_length_pred:
enc_feats = enc_feats.detach()
length_out = F.linear(enc_feats, self.embed_length.weight)
return F.log_softmax(length_out, -1) if normalize else length_out
def extract_features(
self,
prev_output_tokens,
encoder_out=None,
early_exit=None,
embedding_copy=False,
**unused
):
"""
Similar to *forward* but only return features.
Inputs:
prev_output_tokens: Tensor(B, T)
encoder_out: a dictionary of hidden states and masks
Returns:
tuple:
- the decoder's features of shape `(batch, tgt_len, embed_dim)`
- a dictionary with any model-specific outputs
the LevenshteinTransformer decoder has full-attention to all generated tokens
"""
# embedding
if embedding_copy:
src_embd = encoder_out["encoder_embedding"][0]
if len(encoder_out["encoder_padding_mask"]) > 0:
src_mask = encoder_out["encoder_padding_mask"][0]
else:
src_mask = None
src_mask = (
~src_mask
if src_mask is not None
else prev_output_tokens.new_ones(*src_embd.size()[:2]).bool()
)
x, decoder_padding_mask = self.forward_embedding(
prev_output_tokens,
self.forward_copying_source(
src_embd, src_mask, prev_output_tokens.ne(self.padding_idx)
),
)
else:
x, decoder_padding_mask = self.forward_embedding(prev_output_tokens)
# B x T x C -> T x B x C
x = x.transpose(0, 1)
attn = None
inner_states = [x]
# decoder layers
for i, layer in enumerate(self.layers):
# early exit from the decoder.
if (early_exit is not None) and (i >= early_exit):
break
x, attn, _ = layer(
x,
encoder_out["encoder_out"][0]
if (encoder_out is not None and len(encoder_out["encoder_out"]) > 0)
else None,
encoder_out["encoder_padding_mask"][0]
if (
encoder_out is not None
and len(encoder_out["encoder_padding_mask"]) > 0
)
else None,
self_attn_mask=None,
self_attn_padding_mask=decoder_padding_mask,
)
inner_states.append(x)
if self.layer_norm:
x = self.layer_norm(x)
# T x B x C -> B x T x C
x = x.transpose(0, 1)
if self.project_out_dim is not None:
x = self.project_out_dim(x)
return x, {"attn": attn, "inner_states": inner_states}
def forward_embedding(self, prev_output_tokens, states=None):
# embed positions
positions = (
self.embed_positions(prev_output_tokens)
if self.embed_positions is not None
else None
)
# embed tokens and positions
if states is None:
x = self.embed_scale * self.embed_tokens(prev_output_tokens)
if self.project_in_dim is not None:
x = self.project_in_dim(x)
else:
x = states
if positions is not None:
x += positions
x = self.dropout_module(x)
decoder_padding_mask = prev_output_tokens.eq(self.padding_idx)
return x, decoder_padding_mask
def forward_copying_source(self, src_embeds, src_masks, tgt_masks):
length_sources = src_masks.sum(1)
length_targets = tgt_masks.sum(1)
mapped_inputs = _uniform_assignment(length_sources, length_targets).masked_fill(
~tgt_masks, 0
)
copied_embedding = torch.gather(
src_embeds,
1,
mapped_inputs.unsqueeze(-1).expand(
*mapped_inputs.size(), src_embeds.size(-1)
),
)
return copied_embedding
def forward_length_prediction(self, length_out, encoder_out, tgt_tokens=None):
enc_feats = encoder_out["encoder_out"][0] # T x B x C
if len(encoder_out["encoder_padding_mask"]) > 0:
src_masks = encoder_out["encoder_padding_mask"][0] # B x T
else:
src_masks = None
if self.pred_length_offset:
if src_masks is None:
src_lengs = enc_feats.new_ones(enc_feats.size(1)).fill_(
enc_feats.size(0)
)
else:
src_lengs = (~src_masks).transpose(0, 1).type_as(enc_feats).sum(0)
src_lengs = src_lengs.long()
if tgt_tokens is not None:
# obtain the length target
tgt_lengs = tgt_tokens.ne(self.padding_idx).sum(1).long()
if self.pred_length_offset:
length_tgt = tgt_lengs - src_lengs + 128
else:
length_tgt = tgt_lengs
length_tgt = length_tgt.clamp(min=0, max=255)
else:
# predict the length target (greedy for now)
# TODO: implementing length-beam
pred_lengs = length_out.max(-1)[1]
if self.pred_length_offset:
length_tgt = pred_lengs - 128 + src_lengs
else:
length_tgt = pred_lengs
return length_tgt
@register_model_architecture(
"nonautoregressive_transformer", "nonautoregressive_transformer"
)
def base_architecture(args):
args.encoder_embed_path = getattr(args, "encoder_embed_path", None)
args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512)
args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 2048)
args.encoder_layers = getattr(args, "encoder_layers", 6)
args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 8)
args.encoder_normalize_before = getattr(args, "encoder_normalize_before", False)
args.encoder_learned_pos = getattr(args, "encoder_learned_pos", False)
args.decoder_embed_path = getattr(args, "decoder_embed_path", None)
args.decoder_embed_dim = getattr(args, "decoder_embed_dim", args.encoder_embed_dim)
args.decoder_ffn_embed_dim = getattr(
args, "decoder_ffn_embed_dim", args.encoder_ffn_embed_dim
)
args.decoder_layers = getattr(args, "decoder_layers", 6)
args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 8)
args.decoder_normalize_before = getattr(args, "decoder_normalize_before", False)
args.decoder_learned_pos = getattr(args, "decoder_learned_pos", False)
args.attention_dropout = getattr(args, "attention_dropout", 0.0)
args.activation_dropout = getattr(args, "activation_dropout", 0.0)
args.activation_fn = getattr(args, "activation_fn", "relu")
args.dropout = getattr(args, "dropout", 0.1)
args.adaptive_softmax_cutoff = getattr(args, "adaptive_softmax_cutoff", None)
args.adaptive_softmax_dropout = getattr(args, "adaptive_softmax_dropout", 0)
args.share_decoder_input_output_embed = getattr(
args, "share_decoder_input_output_embed", False
)
args.share_all_embeddings = getattr(args, "share_all_embeddings", False)
args.no_token_positional_embeddings = getattr(
args, "no_token_positional_embeddings", False
)
args.adaptive_input = getattr(args, "adaptive_input", False)
args.apply_bert_init = getattr(args, "apply_bert_init", False)
args.decoder_output_dim = getattr(
args, "decoder_output_dim", args.decoder_embed_dim
)
args.decoder_input_dim = getattr(args, "decoder_input_dim", args.decoder_embed_dim)
# --- special arguments ---
args.sg_length_pred = getattr(args, "sg_length_pred", False)
args.pred_length_offset = getattr(args, "pred_length_offset", False)
args.length_loss_factor = getattr(args, "length_loss_factor", 0.1)
args.src_embedding_copy = getattr(args, "src_embedding_copy", False)
@register_model_architecture(
"nonautoregressive_transformer", "nonautoregressive_transformer_wmt_en_de"
)
def nonautoregressive_transformer_wmt_en_de(args):
base_architecture(args)
|
bart_ls-main
|
fairseq-py/fairseq/models/nat/nonautoregressive_transformer.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""
This file implements:
Ghazvininejad, Marjan, et al.
"Constant-time machine translation with conditional masked language models."
arXiv preprint arXiv:1904.09324 (2019).
"""
from fairseq.models import register_model, register_model_architecture
from fairseq.models.nat import NATransformerModel
from fairseq.utils import new_arange
def _skeptical_unmasking(output_scores, output_masks, p):
sorted_index = output_scores.sort(-1)[1]
boundary_len = (
(output_masks.sum(1, keepdim=True).type_as(output_scores) - 2) * p
).long()
skeptical_mask = new_arange(output_masks) < boundary_len
return skeptical_mask.scatter(1, sorted_index, skeptical_mask)
@register_model("cmlm_transformer")
class CMLMNATransformerModel(NATransformerModel):
@staticmethod
def add_args(parser):
NATransformerModel.add_args(parser)
def forward(
self, src_tokens, src_lengths, prev_output_tokens, tgt_tokens, **kwargs
):
assert not self.decoder.src_embedding_copy, "do not support embedding copy."
# encoding
encoder_out = self.encoder(src_tokens, src_lengths=src_lengths, **kwargs)
# length prediction
length_out = self.decoder.forward_length(
normalize=False, encoder_out=encoder_out
)
length_tgt = self.decoder.forward_length_prediction(
length_out, encoder_out, tgt_tokens
)
# decoding
word_ins_out = self.decoder(
normalize=False,
prev_output_tokens=prev_output_tokens,
encoder_out=encoder_out,
)
word_ins_mask = prev_output_tokens.eq(self.unk)
return {
"word_ins": {
"out": word_ins_out,
"tgt": tgt_tokens,
"mask": word_ins_mask,
"ls": self.args.label_smoothing,
"nll_loss": True,
},
"length": {
"out": length_out,
"tgt": length_tgt,
"factor": self.decoder.length_loss_factor,
},
}
def forward_decoder(self, decoder_out, encoder_out, decoding_format=None, **kwargs):
step = decoder_out.step
max_step = decoder_out.max_step
output_tokens = decoder_out.output_tokens
output_scores = decoder_out.output_scores
history = decoder_out.history
# execute the decoder
output_masks = output_tokens.eq(self.unk)
_scores, _tokens = self.decoder(
normalize=True,
prev_output_tokens=output_tokens,
encoder_out=encoder_out,
).max(-1)
output_tokens.masked_scatter_(output_masks, _tokens[output_masks])
output_scores.masked_scatter_(output_masks, _scores[output_masks])
if history is not None:
history.append(output_tokens.clone())
# skeptical decoding (depend on the maximum decoding steps.)
if (step + 1) < max_step:
skeptical_mask = _skeptical_unmasking(
output_scores, output_tokens.ne(self.pad), 1 - (step + 1) / max_step
)
output_tokens.masked_fill_(skeptical_mask, self.unk)
output_scores.masked_fill_(skeptical_mask, 0.0)
if history is not None:
history.append(output_tokens.clone())
return decoder_out._replace(
output_tokens=output_tokens,
output_scores=output_scores,
attn=None,
history=history,
)
@register_model_architecture("cmlm_transformer", "cmlm_transformer")
def cmlm_base_architecture(args):
args.encoder_embed_path = getattr(args, "encoder_embed_path", None)
args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512)
args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 2048)
args.encoder_layers = getattr(args, "encoder_layers", 6)
args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 8)
args.encoder_normalize_before = getattr(args, "encoder_normalize_before", False)
args.encoder_learned_pos = getattr(args, "encoder_learned_pos", False)
args.decoder_embed_path = getattr(args, "decoder_embed_path", None)
args.decoder_embed_dim = getattr(args, "decoder_embed_dim", args.encoder_embed_dim)
args.decoder_ffn_embed_dim = getattr(
args, "decoder_ffn_embed_dim", args.encoder_ffn_embed_dim
)
args.decoder_layers = getattr(args, "decoder_layers", 6)
args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 8)
args.decoder_normalize_before = getattr(args, "decoder_normalize_before", False)
args.decoder_learned_pos = getattr(args, "decoder_learned_pos", False)
args.attention_dropout = getattr(args, "attention_dropout", 0.0)
args.activation_dropout = getattr(args, "activation_dropout", 0.0)
args.activation_fn = getattr(args, "activation_fn", "relu")
args.dropout = getattr(args, "dropout", 0.1)
args.adaptive_softmax_cutoff = getattr(args, "adaptive_softmax_cutoff", None)
args.adaptive_softmax_dropout = getattr(args, "adaptive_softmax_dropout", 0)
args.share_decoder_input_output_embed = getattr(
args, "share_decoder_input_output_embed", False
)
args.share_all_embeddings = getattr(args, "share_all_embeddings", True)
args.no_token_positional_embeddings = getattr(
args, "no_token_positional_embeddings", False
)
args.adaptive_input = getattr(args, "adaptive_input", False)
args.apply_bert_init = getattr(args, "apply_bert_init", False)
args.decoder_output_dim = getattr(
args, "decoder_output_dim", args.decoder_embed_dim
)
args.decoder_input_dim = getattr(args, "decoder_input_dim", args.decoder_embed_dim)
# --- special arguments ---
args.sg_length_pred = getattr(args, "sg_length_pred", False)
args.pred_length_offset = getattr(args, "pred_length_offset", False)
args.length_loss_factor = getattr(args, "length_loss_factor", 0.1)
args.ngram_predictor = getattr(args, "ngram_predictor", 1)
args.src_embedding_copy = getattr(args, "src_embedding_copy", False)
@register_model_architecture("cmlm_transformer", "cmlm_transformer_wmt_en_de")
def cmlm_wmt_en_de(args):
cmlm_base_architecture(args)
|
bart_ls-main
|
fairseq-py/fairseq/models/nat/cmlm_transformer.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from fairseq.models import register_model, register_model_architecture
from fairseq.models.nat import NATransformerModel, base_architecture
from fairseq.modules import DynamicCRF
@register_model("nacrf_transformer")
class NACRFTransformerModel(NATransformerModel):
def __init__(self, args, encoder, decoder):
super().__init__(args, encoder, decoder)
self.crf_layer = DynamicCRF(
num_embedding=len(self.tgt_dict),
low_rank=args.crf_lowrank_approx,
beam_size=args.crf_beam_approx,
)
@property
def allow_ensemble(self):
return False
@staticmethod
def add_args(parser):
NATransformerModel.add_args(parser)
parser.add_argument(
"--crf-lowrank-approx",
type=int,
help="the dimension of low-rank approximation of transition",
)
parser.add_argument(
"--crf-beam-approx",
type=int,
help="the beam size for apporixmating the normalizing factor",
)
parser.add_argument(
"--word-ins-loss-factor",
type=float,
help="weights on NAT loss used to co-training with CRF loss.",
)
def forward(
self, src_tokens, src_lengths, prev_output_tokens, tgt_tokens, **kwargs
):
# encoding
encoder_out = self.encoder(src_tokens, src_lengths=src_lengths, **kwargs)
# length prediction
length_out = self.decoder.forward_length(
normalize=False, encoder_out=encoder_out
)
length_tgt = self.decoder.forward_length_prediction(
length_out, encoder_out, tgt_tokens
)
# decoding
word_ins_out = self.decoder(
normalize=False,
prev_output_tokens=prev_output_tokens,
encoder_out=encoder_out,
)
word_ins_tgt, word_ins_mask = tgt_tokens, tgt_tokens.ne(self.pad)
# compute the log-likelihood of CRF
crf_nll = -self.crf_layer(word_ins_out, word_ins_tgt, word_ins_mask)
crf_nll = (crf_nll / word_ins_mask.type_as(crf_nll).sum(-1)).mean()
return {
"word_ins": {
"out": word_ins_out,
"tgt": word_ins_tgt,
"mask": word_ins_mask,
"ls": self.args.label_smoothing,
"nll_loss": True,
"factor": self.args.word_ins_loss_factor,
},
"word_crf": {"loss": crf_nll},
"length": {
"out": length_out,
"tgt": length_tgt,
"factor": self.decoder.length_loss_factor,
},
}
def forward_decoder(self, decoder_out, encoder_out, decoding_format=None, **kwargs):
output_tokens = decoder_out.output_tokens
output_scores = decoder_out.output_scores
history = decoder_out.history
# execute the decoder and get emission scores
output_masks = output_tokens.ne(self.pad)
word_ins_out = self.decoder(
normalize=False, prev_output_tokens=output_tokens, encoder_out=encoder_out
)
# run viterbi decoding through CRF
_scores, _tokens = self.crf_layer.forward_decoder(word_ins_out, output_masks)
output_tokens.masked_scatter_(output_masks, _tokens[output_masks])
output_scores.masked_scatter_(output_masks, _scores[output_masks])
if history is not None:
history.append(output_tokens.clone())
return decoder_out._replace(
output_tokens=output_tokens,
output_scores=output_scores,
attn=None,
history=history,
)
@register_model_architecture("nacrf_transformer", "nacrf_transformer")
def nacrf_base_architecture(args):
args.crf_lowrank_approx = getattr(args, "crf_lowrank_approx", 32)
args.crf_beam_approx = getattr(args, "crf_beam_approx", 64)
args.word_ins_loss_factor = getattr(args, "word_ins_loss_factor", 0.5)
args.encoder_normalize_before = getattr(args, "encoder_normalize_before", True)
args.decoder_normalize_before = getattr(args, "decoder_normalize_before", True)
base_architecture(args)
|
bart_ls-main
|
fairseq-py/fairseq/models/nat/nat_crf_transformer.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import numpy as np
import torch
import torch.nn.functional as F
from fairseq.models import register_model, register_model_architecture
from fairseq.models.nat import (
FairseqNATModel,
LevenshteinTransformerDecoder,
LevenshteinTransformerModel,
ensemble_decoder,
)
from fairseq.models.transformer import Linear
from fairseq.modules.transformer_sentence_encoder import init_bert_params
from fairseq.utils import new_arange
class NegativeDistanceScore(object):
def __init__(self):
# pre-compute some values
self.scores = {}
self.scores[0.5] = self.compute_score_full(50, 0.5)
self.scores[1.0] = self.compute_score_full(50, 1.0)
self.scores[2.0] = self.compute_score_full(50, 2.0)
def __call__(self, i, L, tau):
if (tau is None) or (tau > 1000):
return 1 / L
if tau in self.scores:
if L < self.scores[tau].shape[0]:
return self.scores[tau][L - 1, i]
return self.compute_score(L, tau)[i]
def compute_score(self, L, tau):
s = np.array([-abs(L / 2 - i) / tau for i in range(L)])
s = np.exp(s - s.max())
return s / s.sum()
def compute_score_full(self, L, tau):
s = -abs(np.arange(0, L - 1)[:, None] / 2 - np.arange(L)[None, :]) / tau
s = np.tril(s, 0) + np.triu(s - float("inf"), 1)
s = np.exp(s - s.max(1, keepdims=True))
return s / s.sum(1, keepdims=True)
neg_scorer = NegativeDistanceScore()
def _get_ins_targets(in_tokens, out_tokens, padding_idx, unk_idx, vocab_size, tau=None):
try:
from fairseq import libnat
except ImportError as e:
import sys
sys.stderr.write("ERROR: missing libnat. run `pip install --editable .`\n")
raise e
B = in_tokens.size(0)
T = in_tokens.size(1)
V = vocab_size
with torch.cuda.device_of(in_tokens):
in_tokens_list = [
[t for t in s if t != padding_idx] for i, s in enumerate(in_tokens.tolist())
]
out_tokens_list = [
[t for t in s if t != padding_idx]
for i, s in enumerate(out_tokens.tolist())
]
full_labels = libnat.suggested_ed2_path(
in_tokens_list, out_tokens_list, padding_idx
)
insert_labels = [a[:-1] for a in full_labels]
# numericalize1
insert_label_tensors = in_tokens.new_zeros(B * (T - 1) * V).float()
insert_index, insert_labels = zip(
*[
(w + (j + i * (T - 1)) * V, neg_scorer(k, len(label), tau))
for i, labels in enumerate(insert_labels)
for j, label in enumerate(labels[1:-1])
for k, w in enumerate(label)
]
) # HACK 1:-1
insert_index, insert_labels = [
torch.tensor(list(a), device=in_tokens.device)
for a in [insert_index, insert_labels]
]
insert_label_tensors.scatter_(0, insert_index.long(), insert_labels)
insert_label_tensors = insert_label_tensors.view(B, T - 1, V)
return insert_label_tensors
def _apply_ins_words(in_tokens, in_scores, word_ins_pred, word_ins_scores, padding_idx):
padding_masks = in_tokens[:, 1:].eq(padding_idx)
word_ins_scores.masked_fill_(padding_masks, 0.0)
word_ins_pred.masked_fill_(padding_masks, padding_idx)
in_coords = new_arange(in_tokens).type_as(in_scores)
# shift all padding predictions to infinite
out_coords = (in_coords[:, 1:] - 0.5).masked_fill(
word_ins_pred.eq(padding_idx), float("inf")
)
out_coords = torch.cat([in_coords, out_coords], 1).sort(-1)[1]
out_tokens = torch.cat([in_tokens, word_ins_pred], 1).gather(1, out_coords)
out_scores = torch.cat([in_scores, word_ins_scores], 1).gather(1, out_coords)
return out_tokens, out_scores
@register_model("insertion_transformer")
class InsertionTransformerModel(LevenshteinTransformerModel):
def __init__(self, args, encoder, decoder):
super().__init__(args, encoder, decoder)
@staticmethod
def add_args(parser):
FairseqNATModel.add_args(parser)
parser.add_argument("--label-tau", default=None, type=float)
@classmethod
def build_decoder(cls, args, tgt_dict, embed_tokens):
decoder = InsertionTransformerDecoder(args, tgt_dict, embed_tokens)
if getattr(args, "apply_bert_init", False):
decoder.apply(init_bert_params)
return decoder
def forward(
self, src_tokens, src_lengths, prev_output_tokens, tgt_tokens, **kwargs
):
assert tgt_tokens is not None, "forward function only supports training."
# encoding
encoder_out = self.encoder(src_tokens, src_lengths=src_lengths, **kwargs)
# generate training labels for insertion
word_ins_out = self.decoder.forward_word_ins(
normalize=False,
prev_output_tokens=prev_output_tokens,
encoder_out=encoder_out,
)
word_ins_tgt = _get_ins_targets(
prev_output_tokens,
tgt_tokens,
self.pad,
self.unk,
len(self.tgt_dict),
tau=self.decoder.label_tau,
).type_as(word_ins_out)
word_ins_masks = prev_output_tokens[:, 1:].ne(self.pad)
return {
"word_ins": {
"out": word_ins_out,
"tgt": word_ins_tgt,
"mask": word_ins_masks,
"ls": self.args.label_smoothing,
"nll_loss": True,
}
}
def forward_decoder(
self, decoder_out, encoder_out, eos_penalty=0.0, max_ratio=None, **kwargs
):
output_tokens = decoder_out.output_tokens
output_scores = decoder_out.output_scores
history = decoder_out.history
# TODO: decoding for InsertionTransformer
word_ins_score = self.decoder.forward_word_ins(
normalize=True, prev_output_tokens=output_tokens, encoder_out=encoder_out
)
if eos_penalty > 0.0:
word_ins_score[:, :, self.pad] -= eos_penalty
word_ins_score, word_ins_pred = word_ins_score.max(-1)
output_tokens, output_scores = _apply_ins_words(
output_tokens, output_scores, word_ins_pred, word_ins_score, self.pad
)
# delete some unnecessary paddings
cut_off = output_tokens.ne(self.pad).sum(1).max()
output_tokens = output_tokens[:, :cut_off]
output_scores = output_scores[:, :cut_off]
if history is not None:
history.append(output_tokens.clone())
return decoder_out._replace(
output_tokens=output_tokens,
output_scores=output_scores,
attn=None,
history=history,
)
class InsertionTransformerDecoder(LevenshteinTransformerDecoder):
def __init__(self, args, dictionary, embed_tokens, no_encoder_attn=False):
# use the TransformerDecoder's __init__
super(LevenshteinTransformerDecoder, self).__init__(
args, dictionary, embed_tokens, no_encoder_attn=no_encoder_attn
)
self.dictionary = dictionary
self.bos = dictionary.bos()
self.unk = dictionary.unk()
self.eos = dictionary.eos()
self.pool_out = Linear(self.output_embed_dim * 2, self.output_embed_dim)
self.label_tau = getattr(args, "label_tau", None)
@ensemble_decoder
def forward_word_ins(self, normalize, encoder_out, prev_output_tokens):
features = self.extract_features(prev_output_tokens, encoder_out=encoder_out)[0]
features = self.pool_out(
torch.cat([features[:, :-1, :], features[:, 1:, :]], 2)
)
decoder_out = self.output_layer(features)
return F.log_softmax(decoder_out, -1) if normalize else decoder_out
def forward_mask_ins(self, *args, **kwargs):
raise NotImplementedError
def forward_word_del(self, *args, **kwargs):
raise NotImplementedError
@register_model_architecture("insertion_transformer", "insertion_transformer")
def insertion_base_architecture(args):
args.encoder_embed_path = getattr(args, "encoder_embed_path", None)
args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512)
args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 2048)
args.encoder_layers = getattr(args, "encoder_layers", 6)
args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 8)
args.encoder_normalize_before = getattr(args, "encoder_normalize_before", False)
args.encoder_learned_pos = getattr(args, "encoder_learned_pos", False)
args.decoder_embed_path = getattr(args, "decoder_embed_path", None)
args.decoder_embed_dim = getattr(args, "decoder_embed_dim", args.encoder_embed_dim)
args.decoder_ffn_embed_dim = getattr(
args, "decoder_ffn_embed_dim", args.encoder_ffn_embed_dim
)
args.decoder_layers = getattr(args, "decoder_layers", 6)
args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 8)
args.decoder_normalize_before = getattr(args, "decoder_normalize_before", False)
args.decoder_learned_pos = getattr(args, "decoder_learned_pos", False)
args.attention_dropout = getattr(args, "attention_dropout", 0.0)
args.activation_dropout = getattr(args, "activation_dropout", 0.0)
args.activation_fn = getattr(args, "activation_fn", "relu")
args.dropout = getattr(args, "dropout", 0.1)
args.adaptive_softmax_cutoff = getattr(args, "adaptive_softmax_cutoff", None)
args.adaptive_softmax_dropout = getattr(args, "adaptive_softmax_dropout", 0)
args.share_decoder_input_output_embed = getattr(
args, "share_decoder_input_output_embed", False
)
args.share_all_embeddings = getattr(args, "share_all_embeddings", False)
args.no_token_positional_embeddings = getattr(
args, "no_token_positional_embeddings", False
)
args.adaptive_input = getattr(args, "adaptive_input", False)
args.apply_bert_init = getattr(args, "apply_bert_init", False)
args.decoder_output_dim = getattr(
args, "decoder_output_dim", args.decoder_embed_dim
)
args.decoder_input_dim = getattr(args, "decoder_input_dim", args.decoder_embed_dim)
# special for insertion transformer
args.label_tau = getattr(args, "label_tau", None)
|
bart_ls-main
|
fairseq-py/fairseq/models/nat/insertion_transformer.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import logging
import torch
from fairseq.models import register_model, register_model_architecture
from fairseq.models.roberta import (
init_bert_params,
roberta_base_architecture,
RobertaEncoder,
RobertaModel,
)
import math
from typing import Dict, Optional
from torch import Tensor, nn
from fairseq.modules import (
LayerDropModuleList,
TransformerEncoderLayer,
MultiheadAttention,
PositionalEmbedding,
)
from fairseq.modules.quant_noise import quant_noise
import torch.nn.functional as F
from fairseq.models.transformer import TransformerEncoder
from .utils import sliding_chunks_matmul_pv, sliding_chunks_matmul_qk
logger = logging.getLogger(__name__)
@register_model("sliding_window_roberta")
class SlidingWindownModel(RobertaModel):
@staticmethod
def add_args(parser):
RobertaModel.add_args(parser)
parser.add_argument(
"--attention-window", type=int,
)
parser.add_argument(
"--train-global",
action="store_true",
help="Whether to set CLS as global token during pre-training"
)
@classmethod
def build_model(cls, args, task):
"""Build a new model instance."""
from omegaconf import OmegaConf
if OmegaConf.is_config(args):
OmegaConf.set_struct(args, False)
# make sure all arguments are present
base_architecture(args)
if not hasattr(args, "max_positions"):
args.max_positions = args.tokens_per_sample
encoder = SlidingWindowEncoder(args, task.source_dictionary)
if OmegaConf.is_config(args):
OmegaConf.set_struct(args, True)
return cls(args, encoder)
def safe_getattr(obj, k, default=None):
from omegaconf import OmegaConf
if OmegaConf.is_config(obj):
return obj[k] if k in obj and obj[k] is not None else default
return getattr(obj, k, default)
@register_model_architecture("sliding_window_roberta", "sliding_window_base")
def base_architecture(args):
args.attention_window = safe_getattr(args, "attention_window", 1024) # equavalent to 512 in longformer
args.train_global = safe_getattr(args, "train_global", False)
roberta_base_architecture(args)
@register_model_architecture("sliding_window_roberta", "sliding_window_large")
def large_architecture(args):
args.encoder_layers = safe_getattr(args, "encoder_layers", 24)
args.encoder_embed_dim = safe_getattr(args, "encoder_embed_dim", 1024)
args.encoder_ffn_embed_dim = safe_getattr(args, "encoder_ffn_embed_dim", 4096)
args.encoder_attention_heads = safe_getattr(args, "encoder_attention_heads", 16)
base_architecture(args)
class SlidingWindowEncoder(RobertaEncoder):
def build_encoder(self, args, dictionary, embed_tokens):
encoder = SWTransformerEncoder(args, dictionary, embed_tokens)
encoder.apply(init_bert_params)
return encoder
def forward(self, src_tokens, features_only=False, return_all_hiddens=False, masked_tokens=None, **unused):
# pad src_tokens to multiplier of attention window size
_, seqlen = src_tokens.size()
w = max(self.sentence_encoder.window_per_layer) * 2
padding_len = (w - seqlen % w) % w
x, extra = self.extract_features(
src_tokens, return_all_hiddens=return_all_hiddens, key_padding_mask=unused.get("key_padding_mask", None)
)
if masked_tokens is not None:
masked_tokens = F.pad(masked_tokens, (0, padding_len), value=False)
if not features_only:
x = self.output_layer(x, masked_tokens=masked_tokens)
return x, extra
def extract_features(self, src_tokens, return_all_hiddens=False, **kwargs):
encoder_out = self.sentence_encoder(
src_tokens,
return_all_hiddens=return_all_hiddens,
token_embeddings=kwargs.get("token_embeddings", None), key_padding_mask=kwargs.get("key_padding_mask", None),
)
# T x B x C -> B x T x C
features = encoder_out["encoder_out"][0].transpose(0, 1)
inner_states = encoder_out["encoder_states"] if return_all_hiddens else None
return features, {"inner_states": inner_states}
class SWTransformerEncoder(TransformerEncoder):
def __init__(self, args, dictionary, embed_tokens):
super().__init__(args, dictionary, embed_tokens)
if self.encoder_layerdrop > 0.0:
self.layers = LayerDropModuleList(p=self.encoder_layerdrop)
else:
self.layers = torch.nn.ModuleList([])
self.window_per_layer = [int(args.attention_window[i // (self.num_layers // len(args.attention_window))]) for i in range(self.num_layers)]
self.layers.extend(
[self.build_sw_encoder_layer(args, self.window_per_layer[i], self.padding_idx) for i in range(args.encoder_layers)])
self.num_layers = len(self.layers)
def build_sw_encoder_layer(self, args, window_size, padding_idx):
return SWTransformerEncoderLayer(args, window_size, padding_idx)
def forward(
self,
src_tokens,
src_lengths: Optional[torch.Tensor] = None,
return_all_hiddens: bool = False,
token_embeddings: Optional[torch.Tensor] = None,
key_padding_mask: Optional[torch.Tensor] = None, # @xwhan in order to add global mask
):
# sliding-window attention sequence length requirements
_, seqlen = src_tokens.size()
w = max(self.window_per_layer) * 2
padding_len = (w - seqlen % w) % w
src_tokens = F.pad(src_tokens, (0, padding_len), value=self.padding_idx)
if key_padding_mask is not None:
key_padding_mask = F.pad(key_padding_mask, (0, padding_len), value=1)
return self.forward_scriptable(
src_tokens, src_lengths, return_all_hiddens, token_embeddings, key_padding_mask
)
class SWTransformerEncoderLayer(TransformerEncoderLayer):
def __init__(self, args, window_size, padding_idx):
super().__init__(args)
# replace self-attn
self.window_size = window_size
self.padding_idx = padding_idx
self.self_attn = self.build_sw_self_attention(self.embed_dim, window_size, padding_idx, args)
def forward(
self,
x,
encoder_padding_mask: Optional[Tensor],
attn_mask: Optional[Tensor] = None
):
if attn_mask is not None:
attn_mask = (attn_mask * -1e8).type_as(attn_mask) # -1 in attn_mask means global attention
return super().forward(x, encoder_padding_mask, attn_mask=attn_mask)
def build_sw_self_attention(self, embed_dim, window_size, padding_idx, args):
return SWSelfAttention(
embed_dim,
args.encoder_attention_heads,
dropout=args.attention_dropout,
self_attention=True,
q_noise=self.quant_noise,
qn_block_size=self.quant_noise_block_size,
window_size=window_size,
padding_idx=padding_idx,
max_source_positions=args.max_source_positions,
train_global=args.train_global,
)
class SWSelfAttention(MultiheadAttention):
def __init__(self,
embed_dim,
num_heads,
kdim=None,
vdim=None,
dropout=0.0,
bias=True,
add_bias_kv=False,
add_zero_attn=False,
self_attention=False,
encoder_decoder_attention=False,
q_noise=0.0,
qn_block_size=8,
window_size=256,
padding_idx=1,
max_source_positions=1024,
train_global=False
):
super().__init__(embed_dim, num_heads, kdim, vdim, dropout,
bias, add_bias_kv, add_zero_attn, self_attention,
encoder_decoder_attention, q_noise, qn_block_size)
self.attention_window = window_size
self.train_global = train_global
self.padding_idx = padding_idx
self.k_proj_global = quant_noise(
nn.Linear(self.kdim, embed_dim, bias=bias), q_noise, qn_block_size
)
self.v_proj_global = quant_noise(
nn.Linear(self.vdim, embed_dim, bias=bias), q_noise, qn_block_size
)
self.q_proj_global = quant_noise(
nn.Linear(embed_dim, embed_dim, bias=bias), q_noise, qn_block_size
)
def forward(self,
query,
key: Optional[Tensor],
value: Optional[Tensor],
key_padding_mask: Optional[Tensor] = None,
incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None,
need_weights: bool = True,
static_kv: bool = False,
attn_mask: Optional[Tensor] = None,
before_softmax: bool = False,
need_head_weights: bool = False
):
if need_head_weights:
need_weights = True
# attn_mark None
# key padding mask 0,1 bool tensor 1 means masked position (bsz x seqlen), -1 means global attention
if key_padding_mask is None:
seqlen, bsz, _= query.size()
key_padding_mask = query.new_zeros(bsz, seqlen).bool()
if attn_mask is None:
# bos token as global attention
attn_mask = key_padding_mask.type_as(query)
num_global_masks = attn_mask.eq(-1).sum()
#TODO whether to use first token as global token at pretraining time
if self.train_global:
if num_global_masks == 0:
attn_mask[:,0] = -1
attn_mask = (attn_mask * -1e8).type_as(query)
if len(attn_mask.size()) > 2:
attention_mask = attn_mask.squeeze(dim=2).squeeze(dim=1)
else:
attention_mask = attn_mask
key_padding_mask = attention_mask < 0
extra_attention_mask = attention_mask > 0
remove_from_windowed_attention_mask = attention_mask != 0
# num of global tokens
num_extra_indices_per_batch = extra_attention_mask.long().sum(dim=1)
max_num_extra_indices_per_batch = num_extra_indices_per_batch.max()
if max_num_extra_indices_per_batch <= 0:
extra_attention_mask = None
else:
extra_attention_mask_nonzeros = extra_attention_mask.nonzero(as_tuple=True)
zero_to_max_range = torch.arange(0, max_num_extra_indices_per_batch, device=extra_attention_mask.device)
# mask indicating which values are actually going to be padding
num_extra_indices_per_batch = extra_attention_mask.long().sum(dim=1)
selection_padding_mask = zero_to_max_range < num_extra_indices_per_batch.unsqueeze(dim=-1)
# 2) location of the non-padding values in the selected global attention
selection_padding_mask_nonzeros = selection_padding_mask.nonzero(as_tuple=True)
# 3) location of the padding values in the selected global attention
selection_padding_mask_zeros = (selection_padding_mask == 0).nonzero(as_tuple=True)
seq_len, bsz, embed_dim = query.size()
assert self.self_attention
q = self.q_proj(query)
k = self.k_proj(query)
v = self.v_proj(query)
q *= self.scaling
q = q.view(seq_len, bsz, self.num_heads, self.head_dim).transpose(0, 1)
k = k.view(seq_len, bsz, self.num_heads, self.head_dim).transpose(0, 1)
attn_weights = sliding_chunks_matmul_qk(q, k, self.attention_window, padding_value=0) # bsz, seq_len, num_heads, 2 * w + 1
if remove_from_windowed_attention_mask is not None:
# This implementation is fast and takes very little memory because num_heads x hidden_size = 1
# from (bsz x seq_len) to (bsz x seq_len x num_heads x hidden_size)
remove_from_windowed_attention_mask = remove_from_windowed_attention_mask.unsqueeze(dim=-1).unsqueeze(dim=-1)
# cast to float/half then replace 1's with -inf
float_mask = remove_from_windowed_attention_mask.type_as(q).masked_fill(remove_from_windowed_attention_mask, -10000.0)
# repeat_size = 1 if isinstance(self.attention_dilation, int) else len(self.attention_dilation)
repeat_size = 1
float_mask = float_mask.repeat(1, 1, repeat_size, 1)
ones = float_mask.new_ones(size=float_mask.size()) # tensor of ones
# diagonal mask with zeros everywhere and -inf inplace of padding
d_mask = sliding_chunks_matmul_qk(ones, float_mask, self.attention_window, padding_value=0)
attn_weights += d_mask
assert list(attn_weights.size()) == [bsz, seq_len, self.num_heads, self.attention_window * 2 + 1]
# the extra attention
if extra_attention_mask is not None:
selected_k = k.new_zeros(bsz, max_num_extra_indices_per_batch, self.num_heads, self.head_dim)
selected_k[selection_padding_mask_nonzeros] = k[extra_attention_mask_nonzeros]
# (bsz, seq_len, num_heads, max_num_extra_indices_per_batch)
selected_attn_weights = torch.einsum('blhd,bshd->blhs', (q, selected_k))
selected_attn_weights[selection_padding_mask_zeros[0], :, :, selection_padding_mask_zeros[1]] = -10000
# concat to attn_weights
# (bsz, seq_len, num_heads, extra attention count + 2*window+1)
attn_weights = torch.cat((selected_attn_weights, attn_weights), dim=-1)
attn_weights_float = F.softmax(attn_weights, dim=-1, dtype=torch.float32) # use fp32 for numerical stability
if key_padding_mask is not None:
# softmax sometimes inserts NaN if all positions are masked, replace them with 0
attn_weights_float = torch.masked_fill(attn_weights_float, key_padding_mask.unsqueeze(-1).unsqueeze(-1),
0.0)
attn_weights = attn_weights_float.type_as(attn_weights)
#attn_probs = F.dropout(attn_weights_float.type_as(attn_weights), p=self.dropout, training=self.training)
attn_probs = self.dropout_module(attn_weights)
v = v.view(seq_len, bsz, self.num_heads, self.head_dim).transpose(0, 1)
attn = 0
if extra_attention_mask is not None:
selected_attn_probs = attn_probs.narrow(-1, 0, max_num_extra_indices_per_batch)
selected_v = v.new_zeros(bsz, max_num_extra_indices_per_batch, self.num_heads, self.head_dim)
selected_v[selection_padding_mask_nonzeros] = v[extra_attention_mask_nonzeros]
# use `matmul` because `einsum` crashes sometimes with fp16
# attn = torch.einsum('blhs,bshd->blhd', (selected_attn_probs, selected_v))
attn += torch.matmul(selected_attn_probs.transpose(1, 2), selected_v.transpose(1, 2).type_as(selected_attn_probs)).transpose(1, 2)
attn_probs = attn_probs.narrow(-1, max_num_extra_indices_per_batch, attn_probs.size(-1) - max_num_extra_indices_per_batch).contiguous()
attn += sliding_chunks_matmul_pv(attn_probs, v, self.attention_window)
attn = attn.type_as(query)
assert list(attn.size()) == [bsz, seq_len, self.num_heads, self.head_dim]
attn = attn.transpose(0, 1).reshape(seq_len, bsz, embed_dim).contiguous()
if extra_attention_mask is not None:
selected_hidden_states = query.new_zeros(max_num_extra_indices_per_batch, bsz, embed_dim)
selected_hidden_states[selection_padding_mask_nonzeros[::-1]] = query[extra_attention_mask_nonzeros[::-1]]
q = self.q_proj_global(selected_hidden_states)
k = self.k_proj_global(query)
v = self.v_proj_global(query)
q /= math.sqrt(self.head_dim)
q = q.contiguous().view(max_num_extra_indices_per_batch, bsz * self.num_heads, self.head_dim).transpose(0, 1) # (bsz*self.num_heads, max_num_extra_indices_per_batch, head_dim)
k = k.contiguous().view(-1, bsz * self.num_heads, self.head_dim).transpose(0, 1) # bsz * self.num_heads, seq_len, head_dim)
v = v.contiguous().view(-1, bsz * self.num_heads, self.head_dim).transpose(0, 1) # bsz * self.num_heads, seq_len, head_dim)
attn_weights = torch.bmm(q, k.transpose(1, 2))
assert list(attn_weights.size()) == [bsz * self.num_heads, max_num_extra_indices_per_batch, seq_len]
attn_weights = attn_weights.view(bsz, self.num_heads, max_num_extra_indices_per_batch, seq_len)
attn_weights[selection_padding_mask_zeros[0], :, selection_padding_mask_zeros[1], :] = -10000.0
if key_padding_mask is not None:
attn_weights = attn_weights.masked_fill(
key_padding_mask.unsqueeze(1).unsqueeze(2),
-10000.0,
)
attn_weights = attn_weights.view(bsz * self.num_heads, max_num_extra_indices_per_batch, seq_len)
attn_weights_float = F.softmax(attn_weights, dim=-1, dtype=torch.float32)
attn_weights = attn_weights_float.type_as(attn_weights) # use fp32 for numerical stability
# attn_probs = F.dropout(attn_weights_float.type_as(attn_weights), p=self.dropout, training=self.training)
attn_probs = self.dropout_module(attn_weights)
selected_attn = torch.bmm(attn_probs, v)
assert list(selected_attn.size()) == [bsz * self.num_heads, max_num_extra_indices_per_batch, self.head_dim]
selected_attn_4d = selected_attn.view(bsz, self.num_heads, max_num_extra_indices_per_batch, self.head_dim)
nonzero_selected_attn = selected_attn_4d[selection_padding_mask_nonzeros[0], :, selection_padding_mask_nonzeros[1]]
attn[extra_attention_mask_nonzeros[::-1]] = nonzero_selected_attn.view(len(selection_padding_mask_nonzeros[0]), -1).type_as(query)
# context_layer = attn # seqlen x bsz x embed_dim
attn = self.out_proj(attn)
attn_weights: Optional[Tensor] = None
if need_weights:
if extra_attention_mask is not None:
attn_weights = attn_weights.view(bsz, self.num_heads, max_num_extra_indices_per_batch, seq_len)
else:
attn_weights = attn_weights.permute(0, 2, 1, 3) #bsz x head x seqlen x head_dim
return attn, attn_weights
|
bart_ls-main
|
fairseq-py/fairseq/models/long_transformers/sliding_window.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from audioop import cross
import logging
from typing import Dict, Optional, Tuple
import torch
from typing import Dict, Optional
from torch import Tensor, nn
from fairseq.modules import (
LayerNorm,
TransformerEncoderLayer,
MultiheadAttention,
)
import torch.nn.functional as F
from fairseq.models.transformer import TransformerEncoder
from functools import partial, reduce
from fairseq.distributed import fsdp_wrap
from inspect import isfunction
from operator import mul
from fairseq.modules.checkpoint_activations import checkpoint_wrapper
from .block import BlockTransformerEncoderLayer, BlockSelfAttention
"""
A hacky implementation of simple block attention transformer
"""
class TopDownTransformerEncoder(TransformerEncoder):
def __init__(self, args, dictionary, embed_tokens):
super().__init__(args, dictionary, embed_tokens)
self.args = args
del self.layers
layers = [self.build_sw_encoder_layer(args, args.window_size, self.padding_idx) for i in range(args.encoder_n1)]
layers += [self.build_td_encoder_layer(args, args.window_size, self.padding_idx) for i in range(args.encoder_n3)]
self.layers = nn.ModuleList(layers)
self.top_pool = nn.AvgPool1d(32, stride=24)
self.top_layers = nn.ModuleList([self.build_encoder_layer(args) for i in range(args.encoder_n2)])
self.n1 = args.encoder_n1
self.n2 = args.encoder_n2
self.n3 = args.encoder_n3
self.num_layers = len(self.layers)
def build_sw_encoder_layer(self, args, window_size, padding_idx):
layer = BlockTransformerEncoderLayer(args, window_size, padding_idx)
checkpoint = args.checkpoint_activations
if checkpoint:
offload_to_cpu = self.cfg.offload_activations
layer = checkpoint_wrapper(layer, offload_to_cpu=offload_to_cpu)
min_params_to_wrap = self.cfg.min_params_to_wrap if not checkpoint else 0
layer = fsdp_wrap(layer, min_num_params=min_params_to_wrap)
return layer
def build_td_encoder_layer(self, args, window_size, padding_idx):
layer = TopDownEncoderLayer(args, window_size, padding_idx)
checkpoint = args.checkpoint_activations
if checkpoint:
offload_to_cpu = self.cfg.offload_activations
layer = checkpoint_wrapper(layer, offload_to_cpu=offload_to_cpu)
min_params_to_wrap = self.cfg.min_params_to_wrap if not checkpoint else 0
layer = fsdp_wrap(layer, min_num_params=min_params_to_wrap)
return layer
def forward(
self,
src_tokens,
src_lengths: Optional[torch.Tensor] = None,
return_all_hiddens: bool = False,
token_embeddings: Optional[torch.Tensor] = None,
key_padding_mask: Optional[torch.Tensor] = None, # @xwhan in order to add global mask
):
return self.forward_scriptable(
src_tokens, src_lengths, return_all_hiddens, token_embeddings, key_padding_mask
)
def forward_scriptable(
self,
src_tokens,
src_lengths: Optional[torch.Tensor] = None,
return_all_hiddens: bool = False,
token_embeddings: Optional[torch.Tensor] = None,
key_padding_mask: Optional[torch.Tensor] = None, # @xwhan in order to add global mask
):
"""
Args:
src_tokens (LongTensor): tokens in the source language of shape
`(batch, src_len)`
src_lengths (torch.LongTensor): lengths of each source sentence of
shape `(batch)`
return_all_hiddens (bool, optional): also return all of the
intermediate hidden states (default: False).
token_embeddings (torch.Tensor, optional): precomputed embeddings
default `None` will recompute embeddings
Returns:
dict:
- **encoder_out** (Tensor): the last encoder layer's output of
shape `(src_len, batch, embed_dim)`
- **encoder_padding_mask** (ByteTensor): the positions of
padding elements of shape `(batch, src_len)`
- **encoder_embedding** (Tensor): the (scaled) embedding lookup
of shape `(batch, src_len, embed_dim)`
- **encoder_states** (List[Tensor]): all intermediate
hidden states of shape `(src_len, batch, embed_dim)`.
Only populated if *return_all_hiddens* is True.
"""
# compute padding mask
if key_padding_mask is None:
encoder_padding_mask = src_tokens.eq(self.padding_idx)
key_padding_mask = encoder_padding_mask
else:
encoder_padding_mask = key_padding_mask.eq(1) # key_padding_mask might -1 elements
has_pads = src_tokens.device.type == "xla" or encoder_padding_mask.any()
x, encoder_embedding = self.forward_embedding(src_tokens, token_embeddings)
# account for padding while computing the representation
if has_pads:
x = x * (1 - encoder_padding_mask.unsqueeze(-1).type_as(x))
# B x T x C -> T x B x C
x = x.transpose(0, 1)
encoder_states = []
if return_all_hiddens:
encoder_states.append(x)
# bottom up
for layer in self.layers[:self.n1]:
x = layer(
# x, encoder_padding_mask=encoder_padding_mask if has_pads else None
x, encoder_padding_mask=key_padding_mask # always pass key_padding_mask
)
if return_all_hiddens:
assert encoder_states is not None
encoder_states.append(x)
# Pool and compute top level
top_x = x.transpose(0, 2) # T x B x C -> C x B x T
# Can multiple with weights here later for weighted pooling
top_x = self.top_pool(top_x)
top_x = top_x.transpose(0, 2) # T_pool x B x C
for layer in self.top_layers:
top_x = layer(top_x,encoder_padding_mask=None)
# Top Down layers with cross attention
for layer in self.layers[self.n1:]:
x = layer(
# x, encoder_padding_mask=encoder_padding_mask if has_pads else None
x, top_x, encoder_padding_mask=key_padding_mask # always pass key_padding_mask
)
if return_all_hiddens:
assert encoder_states is not None
encoder_states.append(x)
if self.layer_norm is not None:
x = self.layer_norm(x)
# The Pytorch Mobile lite interpreter does not supports returning NamedTuple in
# `forward` so we use a dictionary instead.
# TorchScript does not support mixed values so the values are all lists.
# The empty list is equivalent to None.
src_lengths = src_tokens.ne(self.padding_idx).sum(dim=1, dtype=torch.int32).reshape(-1, 1).contiguous()
return {
"encoder_out": [x], # T x B x C
"encoder_padding_mask": [encoder_padding_mask], # B x T
"encoder_embedding": [encoder_embedding], # B x T x C
"encoder_states": encoder_states, # List[T x B x C]
"src_tokens": [],
"src_lengths": [src_lengths],
}
class TopDownEncoderLayer(TransformerEncoderLayer):
def __init__(self, args, window_size, padding_idx):
super().__init__(args)
# replace self-attn
self.window_size = window_size
self.padding_idx = padding_idx
self.self_attn = self.build_sw_self_attention(self.embed_dim, window_size, padding_idx, args)
# init cross attention
self.cross_attn = self.build_self_attention(self.embed_dim, args)
# init cross layernorm
self.cross_attn_layer_norm = LayerNorm(self.embed_dim, export=args.export)
def forward(
self,
x,
top_x,
encoder_padding_mask: Optional[Tensor],
attn_mask: Optional[Tensor] = None
):
if attn_mask is not None:
attn_mask = attn_mask.masked_fill(
attn_mask.to(torch.bool),
-1e8 if x.dtype == torch.float32 else -1e4
)
residual = x
if self.normalize_before:
x = self.self_attn_layer_norm(x)
x, _ = self.self_attn(
query=x,
key=x,
value=x,
key_padding_mask=encoder_padding_mask,
need_weights=False,
attn_mask=attn_mask,
)
cross_x, _ = self.cross_attn(
query=x,
key=top_x,
value=top_x,
need_weights=False,
)
x = (cross_x + x)/2 # divide by 2 to maintain similar scale for subsequent pre-trained layers
x = self.dropout_module(x)
x = self.residual_connection(x, residual)
if not self.normalize_before:
x = self.self_attn_layer_norm(x)
residual = x
if self.normalize_before:
x = self.final_layer_norm(x)
x = self.activation_fn(self.fc1(x))
x = self.activation_dropout_module(x)
x = self.fc2(x)
x = self.dropout_module(x)
x = self.residual_connection(x, residual)
if not self.normalize_before:
x = self.final_layer_norm(x)
return x
def build_sw_self_attention(self, embed_dim, window_size, padding_idx, args):
return BlockSelfAttention(
embed_dim,
args.encoder_attention_heads,
dropout=args.attention_dropout,
self_attention=True,
q_noise=self.quant_noise,
qn_block_size=self.quant_noise_block_size,
window_size=window_size,
padding_idx=padding_idx,
)
def build_cross_attention(self, embed_dim, cfg):
return MultiheadAttention(
embed_dim,
cfg.encoder.attention_heads,
dropout=cfg.attention_dropout,
self_attention=False,
q_noise=self.quant_noise,
qn_block_size=self.quant_noise_block_size,
use_xformers=cfg.use_xformers,
attention_name=cfg.attention_name,
xformer_config=None if not cfg.use_xformers else cfg.xformer_config,
)
|
bart_ls-main
|
fairseq-py/fairseq/models/long_transformers/top_down.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""
Long-context model pretraining with fast blocksparse and extrapolation attentions
"""
from typing import Optional
import logging
import math
import torch
import torch.nn as nn
from fairseq import utils, modules
from fairseq.utils import safe_getattr
from fairseq.models import register_model, register_model_architecture
from fairseq.models.transformer import TransformerModel, TransformerConfig
from fairseq.modules.transformer_sentence_encoder import init_bert_params
from fairseq.models.transformer.transformer_config import TransformerConfig
import copy
from fairseq.models.roberta import RobertaEncoder
logger = logging.getLogger(__name__)
@register_model("loco_variant")
class LOCOVariantModel(TransformerModel):
__jit_unused_properties__ = ["supported_targets"]
def __init__(self, args, encoder, decoder):
super().__init__(args, encoder, decoder)
generator_architecture(args)
self.generator = RobertaEncoder(args, encoder.dictionary)
if not self.args.train_generator:
for p in self.generator.parameters():
p.requires_grad = False
self.pad_idx = self.encoder.dictionary.pad()
self.bos = self.encoder.dictionary.bos()
self.sentinel_start_idx = self.encoder.dictionary.index("<sentinel_0>")
self.sentinel_end_idx = len(self.encoder.dictionary) - 1
# We follow BERT's random weight initialization
self.apply(init_bert_params)
self.classification_heads = nn.ModuleDict()
if hasattr(self.encoder, "dictionary"):
self.eos: int = self.encoder.dictionary.eos()
@staticmethod
def add_args(parser):
super(LOCOVariantModel, LOCOVariantModel).add_args(parser)
parser.add_argument(
"--pooler-dropout",
type=float,
metavar="D",
help="dropout probability in the masked_lm pooler layers",
)
parser.add_argument(
"--pooler-activation-fn",
choices=utils.get_available_activation_fns(),
help="activation function to use for pooler layer",
)
parser.add_argument(
"--finetune",
action="store_true",
help="different forwards used for pretraining and finetuning"
)
parser.add_argument(
"--train-generator",
action="store_true",
)
parser.add_argument(
"--generator-xformer-config",
type=str,
metavar="D",
)
parser.add_argument(
"--easy-span-ops",
default='sample',
type=str,
metavar="D",
help="do we want to unmask the easy spans or replace those with <mask> or sampled tokens from the MLM"
)
@classmethod
def build_decoder(cls, args, tgt_dict, embed_tokens):
# use vanilla attention for now
args.use_xformers = False # HACK, disable efficient attentions for cross-attention & decoder-side attention
return super().build_decoder(
TransformerConfig.from_namespace(args), tgt_dict, embed_tokens
)
@classmethod
def build_generator(cls, args, src_dict, embed_tokens):
return super().build_encoder(
TransformerConfig.from_namespace(args), src_dict, embed_tokens
)
@property
def supported_targets(self):
return {"self"}
def _sum_spans(self, input, starts, ends):
"""
sum the span values to the start of each span;
zero out all other positions
"""
starts_before = torch.roll(starts, -1, -1)
input_cumsum = torch.cumsum(input, dim=-1)
input_cumsum[starts.bool()] = input_cumsum[ends.bool()] - input_cumsum[starts_before.bool()]
sumed = input_cumsum * starts
return sumed
def _avg_spans(self, span_sum, span_lens, starts):
span_sum[starts.bool()] = span_sum[starts.bool()] / span_lens[starts.bool()]
return span_sum
"""
utils from T5's objective
"""
def _create_sentinels(self, mask_indices):
"""
mask_indices: binary mask
start spans as sentinel ids and other masked positions as -1
"""
start_indices = mask_indices - torch.roll(mask_indices, 1, -1) * mask_indices
start_indices[:,0] = mask_indices[:,0]
sentinel_ids = torch.where(start_indices != 0, torch.cumsum(start_indices, dim=-1), start_indices)
assert sentinel_ids.max() + self.sentinel_start_idx - 1 <= self.sentinel_end_idx, (sentinel_ids.max() + self.sentinel_start_idx - 1, self.sentinel_end_idx, sentinel_ids)
sentinel_ids = torch.where(sentinel_ids != 0, (sentinel_ids + self.sentinel_start_idx - 1), 0)
sentinel_ids -= mask_indices - start_indices
return sentinel_ids
def _build_inputs(self, masked_input, span_mask):
sentinel_ids = self._create_sentinels(span_mask)
masked_input = torch.where(sentinel_ids != 0, sentinel_ids, masked_input)
src_lens = (masked_input >= 0).sum(-1)
# src_tokens padded to max_source_positions, useful for blocksparse attention
src_tokens = masked_input.new_full((masked_input.size(0), self.cfg.max_source_positions), self.pad_idx)
fill_indices = torch.arange(masked_input.size(-1)).to(masked_input)
fill_indices = fill_indices < src_lens.unsqueeze(-1)
assert fill_indices.sum() == (masked_input >= 0).sum() # = 0 for sequence starts
src_tokens[:,:masked_input.size(-1)][fill_indices] = masked_input[masked_input >= 0]
return src_tokens, src_lens
def _build_targets(self, masked_target, span_mask, pad_mask, eos_mask):
"""
masked_targets: masked positions as their original token ids and
other positions as pad index
span_mask: indicating hard spans
eos_mask: end of sequence as 0
pad_mask: padding positions as 0
"""
unmasked_positions = ~span_mask.bool()
unmasked_positions[:,0] = 0
sentinel_ids = self._create_sentinels(unmasked_positions.to(masked_target))
sentinel_ids *= pad_mask
sentinel_ids *= eos_mask
# target: masked positions with sentinel ids or -1;
# bos, eos and padding positions with value 1
target = torch.where(sentinel_ids != 0, sentinel_ids, masked_target)
target[~eos_mask] = self.eos
target[:,0] = self.bos
tgt_lens = (target.abs() != 1).sum(-1)
tgt_tokens = target.new_full(target.size(), self.pad_idx)
fill_indices = torch.arange(tgt_tokens.size(-1)).to(tgt_tokens)
fill_indices = fill_indices < tgt_lens.unsqueeze(-1)
tgt_tokens[fill_indices] = target[target.abs() != 1]
tgt_tokens = tgt_tokens[:,:tgt_lens.max()]
# truncating if needed
if tgt_tokens.size(-1) > self.args.max_target_positions:
end_positions = (tgt_tokens == self.eos).nonzero(as_tuple=True)[1]
sample_exceeds = end_positions >= (self.args.max_target_positions - 1)
tgt_tokens = torch.cat(
[tgt_tokens[:,:self.args.max_target_positions-1], tgt_tokens[:,-1:]], dim=-1
)
tgt_tokens[:,-1] = torch.where(sample_exceeds, self.eos, tgt_tokens[:,-1])
decoder_input = tgt_tokens.clone()
decoder_input[:,0] = self.eos
decoder_input[:,1:] = tgt_tokens[:,:-1]
return tgt_tokens, decoder_input
def forward(
self,
src_tokens,
src_lengths,
prev_output_tokens: Optional[torch.Tensor] = None,
features_only: bool = False,
classification_head_name: Optional[str] = None,
token_embeddings: Optional[torch.Tensor] = None,
return_all_hiddens: bool = True,
alignment_layer: Optional[int] = None,
alignment_heads: Optional[int] = None,
masked_unfiltered: Optional[torch.Tensor] = None,
):
if classification_head_name is not None:
features_only = True
if not self.cfg.finetune:
"""
use an encoder-only model to build long-range objectives
"""
masked_tokens_unfiltered = masked_unfiltered.ne(self.pad_idx).to(src_tokens)
src_tokens_for_mlm = copy.deepcopy(src_tokens)
if self.cfg.train_generator:
masked_logits = self.generator(
src_tokens_for_mlm,
src_lengths=src_lengths,
return_all_hiddens=return_all_hiddens,
)[0]
token_loss = modules.cross_entropy(
masked_logits.view(-1, masked_logits.size(-1)),
masked_unfiltered.view(-1),
reduction='none',
ignore_index=self.pad_idx,
).view(masked_unfiltered.size())
masked_cnt = masked_tokens_unfiltered.sum()
mlm_loss = token_loss.sum() / masked_cnt
else:
with torch.no_grad():
masked_logits = self.generator(
src_tokens,
src_lengths=src_lengths,
return_all_hiddens=return_all_hiddens,
)[0]
token_loss = modules.cross_entropy(
masked_logits.view(-1, masked_logits.size(-1)),
masked_unfiltered.view(-1),
reduction='none',
ignore_index=self.pad_idx).view(masked_unfiltered.size())
# log likelihood averaged by span lengths
span_starts = masked_tokens_unfiltered - torch.roll(masked_tokens_unfiltered, 1, -1) * masked_tokens_unfiltered
span_starts[:,0] = masked_tokens_unfiltered[:,0]
span_ends = masked_tokens_unfiltered - torch.roll(masked_tokens_unfiltered, -1, -1) * masked_tokens_unfiltered
span_ends[:,-1] = masked_tokens_unfiltered[:,-1]
# span_starts: binary mask marking the start of each span
# span_ends: binary mask marking the end of each span
# span_lens: span lens calculated put at the starts of each span
span_loss = self._sum_spans(token_loss, span_starts, span_ends)
span_lens = self._sum_spans(masked_tokens_unfiltered, span_starts, span_ends)
span_loss_avg = self._avg_spans(span_loss, span_lens, span_starts)
# find the hard spans, i.e, topk largest loss
span_counts = span_starts.sum(-1).min()
hard_span_starts = span_loss_avg.topk(k=math.floor(span_counts*self.cfg.sample_ratio), dim=-1)[1] # bsz * num_hard_spans
hard_span_ends = span_lens.gather(1, index=hard_span_starts) + hard_span_starts
# masking source with only the hard spans
row_idx = torch.arange(hard_span_starts.size(0)).unsqueeze(1).repeat(1, hard_span_starts.size(1)).to(hard_span_starts)
hard_mask = span_starts.new_zeros(span_starts.size())
hard_mask[row_idx.view(-1), hard_span_starts.view(-1)] = 1
hard_mask[row_idx.view(-1), hard_span_ends.view(-1)] = 1
hard_mask = (hard_mask.cumsum(dim=-1) % 2) == 1
hard_mask = hard_mask.type_as(masked_tokens_unfiltered)
# filter our easy span masks
mask_off = torch.logical_xor(hard_mask, masked_tokens_unfiltered)
if self.cfg.easy_span_ops == 'recover':
src_tokens[mask_off] = masked_unfiltered[mask_off]
elif self.cfg.easy_span_ops == 'sample':
# do not sample special tokens
logits = masked_logits.detach().clone()
logits[:,:,self.pad_idx] = float('-inf')
logits[:,:,self.eos] = float('-inf')
logits[:,:,self.bos] = float('-inf')
logits[:,:,self.sentinel_start_idx:] = float('-inf')
sampled = torch.multinomial(logits.softmax(-1).view(-1, logits.size(-1)), 1)
sampled = sampled.view(src_tokens.size())
src_tokens[mask_off] = sampled[mask_off]
else:
assert self.cfg.easy_span_ops == 'masked'
# build the denoising targets
src_pad_mask = src_tokens.ne(self.pad_idx)
src_eos_mask = src_tokens.ne(self.eos)
src_tokens, src_lengths = self._build_inputs(src_tokens, hard_mask)
target, prev_output_tokens = self._build_targets(masked_unfiltered, hard_mask, src_pad_mask, src_eos_mask)
encoder_out = self.encoder(
src_tokens,
src_lengths=src_lengths,
token_embeddings=token_embeddings,
return_all_hiddens=return_all_hiddens
)
x, extra = self.decoder(
prev_output_tokens,
encoder_out=encoder_out,
features_only=features_only,
alignment_layer=alignment_layer,
alignment_heads=alignment_heads,
src_lengths=src_lengths,
return_all_hiddens=return_all_hiddens,
)
eos: int = self.eos
if classification_head_name is not None:
sentence_representation = x[
src_tokens.eq(eos), :
].view(x.size(0), -1, x.size(-1))[:, -1, :]
for k, head in self.classification_heads.items():
# for torch script only supports iteration
if k == classification_head_name:
x = head(sentence_representation)
break
if not self.cfg.finetune:
if self.cfg.train_generator:
return x, target, mlm_loss, extra
return x, target, extra
return x, extra
def upgrade_state_dict_named(self, state_dict, name):
super().upgrade_state_dict_named(state_dict, name)
prefix = name + "." if name != "" else ""
current_head_names = (
[]
if not hasattr(self, "classification_heads")
else self.classification_heads.keys()
)
# Handle new classification heads present in the state dict.
keys_to_delete = []
for k in state_dict.keys():
if not k.startswith(prefix + "classification_heads."):
continue
head_name = k[len(prefix + "classification_heads.") :].split(".")[0]
num_classes = state_dict[
prefix + "classification_heads." + head_name + ".out_proj.weight"
].size(0)
inner_dim = state_dict[
prefix + "classification_heads." + head_name + ".dense.weight"
].size(0)
if getattr(self.args, "load_checkpoint_heads", False):
if head_name not in current_head_names:
self.register_classification_head(head_name, num_classes, inner_dim)
else:
if head_name not in current_head_names:
logger.warning(
"deleting classification head ({}) from checkpoint "
"not present in current model: {}".format(head_name, k)
)
keys_to_delete.append(k)
elif (
num_classes
!= self.classification_heads[head_name].out_proj.out_features
or inner_dim
!= self.classification_heads[head_name].dense.out_features
):
logger.warning(
"deleting classification head ({}) from checkpoint "
"with different dimensions than current model: {}".format(
head_name, k
)
)
keys_to_delete.append(k)
for k in keys_to_delete:
del state_dict[k]
def truncate_emb(key):
if key in state_dict:
state_dict[key] = state_dict[key][:-1, :]
# When finetuning on translation task, remove last row of
# embedding matrix that corresponds to mask_idx token.
loaded_dict_size = state_dict["encoder.embed_tokens.weight"].size(0)
if (
loaded_dict_size == len(self.encoder.dictionary) + 1
and "<mask>" not in self.encoder.dictionary
):
truncate_emb("encoder.embed_tokens.weight")
truncate_emb("decoder.embed_tokens.weight")
truncate_emb("encoder.output_projection.weight")
truncate_emb("decoder.output_projection.weight")
# When continued pretraining on new set of languages for mbart,
# add extra lang embeddings at the end of embed_tokens.
# Note: newly added languages are assumed to have been added at the end.
if self.args.task == "multilingual_denoising" and loaded_dict_size < len(
self.encoder.dictionary
):
logger.info(
"Adding extra language embeddings not found in pretrained model for "
"continued pretraining of MBART on new set of languages."
)
loaded_mask_token_embedding = state_dict["encoder.embed_tokens.weight"][
-1, :
]
num_langids_to_add = len(self.encoder.dictionary) - loaded_dict_size
embed_dim = state_dict["encoder.embed_tokens.weight"].size(1)
new_lang_embed_to_add = torch.zeros(num_langids_to_add, embed_dim)
nn.init.normal_(new_lang_embed_to_add, mean=0, std=embed_dim ** -0.5)
new_lang_embed_to_add = new_lang_embed_to_add.to(
dtype=state_dict["encoder.embed_tokens.weight"].dtype,
)
state_dict["encoder.embed_tokens.weight"] = torch.cat(
[
state_dict["encoder.embed_tokens.weight"][
: loaded_dict_size - 1, :
],
new_lang_embed_to_add,
loaded_mask_token_embedding.unsqueeze(0),
]
)
state_dict["decoder.embed_tokens.weight"] = torch.cat(
[
state_dict["decoder.embed_tokens.weight"][
: loaded_dict_size - 1, :
],
new_lang_embed_to_add,
loaded_mask_token_embedding.unsqueeze(0),
]
)
# Copy any newly-added classification heads into the state dict
# with their current weights.
if hasattr(self, "classification_heads"):
cur_state = self.classification_heads.state_dict()
for k, v in cur_state.items():
if prefix + "classification_heads." + k not in state_dict:
logger.info("Overwriting " + prefix + "classification_heads." + k)
state_dict[prefix + "classification_heads." + k] = v
def generator_architecture(args):
# options to use different sizes of generator models
args.encoder_layers = safe_getattr(args, "generator_layers", 12)
args.encoder_embed_dim = safe_getattr(args, "generator_embed_dim", 768)
args.encoder_ffn_embed_dim = safe_getattr(args, "generator_ffn_embed_dim", 3072)
args.encoder_attention_heads = safe_getattr(args, "generator_attention_heads", 12)
args.max_positions = safe_getattr(args, "max_source_positions", 8192)
args.encoder_learned_pos = safe_getattr(args, "generator_learned_pos", True)
args.encoder_normalize_before = safe_getattr(args, "generator_normalize_before", False)
args.untie_weights_roberta = safe_getattr(args, "untie_weights_roberta", False)
# xformers config
args.use_xformers = safe_getattr(args, "generator_use_xformers", True)
args.attention_name = safe_getattr(args, "generator_attention_name", 'bs_local')
args.xformer_config = safe_getattr(args, 'generator_xformer_config', '{"block_size": 128, "max_seq_len": 8192}')
@register_model_architecture("loco_variant", "loco_variant_large")
def loco_large_architecture(args):
args.finetune = safe_getattr(args, "finetune", False)
args.train_generator = safe_getattr(args, "train_generator", False)
# @xwhan is it necessary to put it here? had issues
def getattr(args, key, value):
return value
args.encoder_embed_path = getattr(args, "encoder_embed_path", None)
args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 1024)
args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 4 * 1024)
args.encoder_layers = getattr(args, "encoder_layers", 12)
args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 16)
args.encoder_normalize_before = getattr(args, "encoder_normalize_before", False)
args.encoder_learned_pos = getattr(args, "encoder_learned_pos", True)
args.decoder_embed_path = getattr(args, "decoder_embed_path", None)
args.decoder_embed_dim = getattr(args, "decoder_embed_dim", args.encoder_embed_dim)
args.decoder_ffn_embed_dim = getattr(
args, "decoder_ffn_embed_dim", args.encoder_ffn_embed_dim
)
args.decoder_layers = getattr(args, "decoder_layers", 12)
args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 16)
args.decoder_normalize_before = getattr(args, "decoder_normalize_before", False)
args.decoder_learned_pos = getattr(args, "decoder_learned_pos", True)
args.attention_dropout = getattr(args, "attention_dropout", 0.0)
args.relu_dropout = getattr(args, "relu_dropout", 0.0)
args.dropout = getattr(args, "dropout", 0.1)
args.max_target_positions = safe_getattr(args, "max_target_positions", 1024) #hack
args.max_source_positions = safe_getattr(args, "max_source_positions", 1024)
args.adaptive_softmax_cutoff = getattr(args, "adaptive_softmax_cutoff", None)
args.adaptive_softmax_dropout = getattr(args, "adaptive_softmax_dropout", 0)
args.share_decoder_input_output_embed = getattr(
args, "share_decoder_input_output_embed", True
)
args.share_all_embeddings = getattr(args, "share_all_embeddings", True)
args.decoder_output_dim = getattr(
args, "decoder_output_dim", args.decoder_embed_dim
)
args.decoder_input_dim = getattr(args, "decoder_input_dim", args.decoder_embed_dim)
args.no_scale_embedding = getattr(args, "no_scale_embedding", True)
args.layernorm_embedding = getattr(args, "layernorm_embedding", True)
args.activation_fn = getattr(args, "activation_fn", "gelu")
args.pooler_activation_fn = getattr(args, "pooler_activation_fn", "tanh")
args.pooler_dropout = getattr(args, "pooler_dropout", 0.0)
|
bart_ls-main
|
fairseq-py/fairseq/models/long_transformers/model_debug.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from .sliding_window import *
from .loco_model import *
from .utils import *
from .model_debug import *
|
bart_ls-main
|
fairseq-py/fairseq/models/long_transformers/__init__.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from typing import Dict, Optional, Tuple
from fairseq.modules.multihead_attention import MultiheadAttention
import torch
from typing import Dict, Optional
from torch import Tensor, nn
from fairseq.modules import (
TransformerEncoderLayer,
)
import torch.nn.functional as F
class PoolEncoderLayer(TransformerEncoderLayer):
def __init__(self, cfg):
super().__init__(cfg)
self.top_pool = nn.AvgPool1d(18, stride=12, padding=9)
self.top_pool_mask = nn.AvgPool1d(18, stride=12, padding=9,count_include_pad=False)
# init top level attention
self.pool_attn = self.build_cross_attention(self.embed_dim, cfg)
def forward(
self,
x,
encoder_padding_mask: Optional[Tensor],
attn_mask: Optional[Tensor] = None,
attn_bias: Optional[Tensor] = None # relative position encoding
):
if attn_mask is not None:
attn_mask = attn_mask.masked_fill(
attn_mask.to(torch.bool),
-1e8 if x.dtype == torch.float32 else -1e4
)
residual = x
if self.normalize_before:
x = self.self_attn_layer_norm(x)
x, _ = self.self_attn(
query=x,
key=x,
value=x,
key_padding_mask=encoder_padding_mask,
need_weights=False,
attn_mask=attn_mask,
)
# Project x to get K, Q
k = self.pool_attn.k_proj(x)
v = self.pool_attn.v_proj(x)
# account for padding before pooling
if (encoder_padding_mask is not None) and encoder_padding_mask.any():
k = k * (1 - encoder_padding_mask.unsqueeze(-1).permute(1, 0 ,2).type_as(k))
v = v * (1 - encoder_padding_mask.unsqueeze(-1).permute(1, 0 ,2).type_as(v))
# Pool K, Q
pool_k = self.apply_pool(k)
pool_v = self.apply_pool(v)
# breakpoint()
# Do not attend to pooled padding tokens
if encoder_padding_mask is not None:
# pool_mask = self.top_pool(encoder_padding_mask.to(k)).type_as(encoder_padding_mask)
pool_mask = self.top_pool_mask(encoder_padding_mask.to(k)).floor().type_as(encoder_padding_mask)
else:
pool_mask = None
cross_x, _ = self.pool_attn(
query=x,
key=pool_k,
value=pool_v,
key_padding_mask=pool_mask,
need_weights=False,
)
# cross_x = torch.nan_to_num(cross_x)
x = cross_x + x
x = self.dropout_module(x)
x = self.residual_connection(x, residual)
if not self.normalize_before:
x = self.self_attn_layer_norm(x)
residual = x
if self.normalize_before:
x = self.final_layer_norm(x)
x = self.activation_fn(self.fc1(x))
x = self.activation_dropout_module(x)
x = self.fc2(x)
x = self.dropout_module(x)
x = self.residual_connection(x, residual)
if not self.normalize_before:
x = self.final_layer_norm(x)
return x
def apply_pool(self, input):
out = input.permute(1, 2 ,0) # T x B x C -> B x C x T
out = self.top_pool(out)
return out.permute(2, 0 ,1) # T_pool x B x C
def build_cross_attention(self, embed_dim, cfg):
return MultiheadAttentionNoProj(
embed_dim,
cfg.encoder.attention_heads,
dropout=cfg.attention_dropout,
self_attention=False,
q_noise=self.quant_noise,
qn_block_size=self.quant_noise_block_size,
)
class TwoLevelEncoderLayer(TransformerEncoderLayer):
def __init__(self, cfg):
super().__init__(cfg)
self.top_pool = nn.AvgPool1d(18, stride=12, padding=9)
# init top level attention
self.pool_attn = self.build_cross_attention(self.embed_dim, cfg)
def forward(
self,
x,
encoder_padding_mask: Optional[Tensor],
attn_mask: Optional[Tensor] = None,
attn_bias: Optional[Tensor] = None # relative position encoding
):
if attn_mask is not None:
attn_mask = attn_mask.masked_fill(
attn_mask.to(torch.bool),
-1e8 if x.dtype == torch.float32 else -1e4
)
residual = x
if self.normalize_before:
x = self.self_attn_layer_norm(x)
x, _ = self.self_attn(
query=x,
key=x,
value=x,
key_padding_mask=encoder_padding_mask,
need_weights=False,
attn_mask=attn_mask,
)
# Project x to get K, Q
k = self.pool_attn.k_proj(x)
v = self.pool_attn.v_proj(x)
# account for padding before pooling
# breakpoint()
if (encoder_padding_mask is not None) and encoder_padding_mask.any():
k = k * (1 - encoder_padding_mask.unsqueeze(-1).permute(1, 0 ,2).type_as(k))
v = v * (1 - encoder_padding_mask.unsqueeze(-1).permute(1, 0 ,2).type_as(v))
# Pool K, Q
pool_k = self.apply_pool(k)
pool_v = self.apply_pool(v)
# Do not attend to pooled padding tokens
if encoder_padding_mask is not None:
pool_mask = self.top_pool(encoder_padding_mask.float()).type_as(encoder_padding_mask)
else:
pool_mask = None
cross_x, _ = self.pool_attn(
query=x,
key=pool_k,
value=pool_v,
key_padding_mask=pool_mask,
need_weights=False,
)
x = cross_x + x
x = self.dropout_module(x)
x = self.residual_connection(x, residual)
if not self.normalize_before:
x = self.self_attn_layer_norm(x)
residual = x
if self.normalize_before:
x = self.final_layer_norm(x)
x = self.activation_fn(self.fc1(x))
x = self.activation_dropout_module(x)
x = self.fc2(x)
x = self.dropout_module(x)
x = self.residual_connection(x, residual)
if not self.normalize_before:
x = self.final_layer_norm(x)
return x
def apply_pool(self, input):
out = input.permute(1, 2 ,0) # T x B x C -> B x C x T
out = self.top_pool(out)
return out.permute(2, 0 ,1) # T_pool x B x C
def build_cross_attention(self, embed_dim, cfg):
return MultiheadAttentionNoProj(
embed_dim,
cfg.encoder.attention_heads,
dropout=cfg.attention_dropout,
self_attention=False,
q_noise=self.quant_noise,
qn_block_size=self.quant_noise_block_size,
)
class MultiheadAttentionNoProj(MultiheadAttention):
"""Multi-headed attention where the key, value are assumed to be pre-projected
this is done to support pooling between projection and attention calculation
"""
def forward(
self,
query,
key: Optional[Tensor],
value: Optional[Tensor],
key_padding_mask: Optional[Tensor] = None,
incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None,
need_weights: bool = True,
static_kv: bool = False,
attn_mask: Optional[Tensor] = None,
before_softmax: bool = False,
need_head_weights: bool = False,
) -> Tuple[Tensor, Optional[Tensor]]:
"""Input shape: Time x Batch x Channel
Args:
key_padding_mask (ByteTensor, optional): mask to exclude
keys that are pads, of shape `(batch, src_len)`, where
padding elements are indicated by 1s.
need_weights (bool, optional): return the attention weights,
averaged over heads (default: False).
attn_mask (ByteTensor, optional): typically used to
implement causal attention, where the mask prevents the
attention from looking forward in time (default: None).
before_softmax (bool, optional): return the raw attention
weights and values before the attention softmax.
need_head_weights (bool, optional): return the attention
weights for each head. Implies *need_weights*. Default:
return the average attention weights over all heads.
"""
if need_head_weights:
need_weights = True
is_tpu = query.device.type == "xla"
tgt_len, bsz, embed_dim = query.size()
src_len = tgt_len
assert embed_dim == self.embed_dim, f"query dim {embed_dim} != {self.embed_dim}"
assert list(query.size()) == [tgt_len, bsz, embed_dim]
if key is not None:
src_len, key_bsz, _ = key.size()
if not torch.jit.is_scripting():
assert key_bsz == bsz
assert value is not None
assert src_len, bsz == value.shape[:2]
return F.multi_head_attention_forward(
query,
key,
value,
self.embed_dim,
self.num_heads,
torch.empty([0]),
torch.cat((self.q_proj.bias, torch.zeros_like(self.k_proj.bias), torch.zeros_like(self.v_proj.bias))),
self.bias_k,
self.bias_v,
self.add_zero_attn,
self.dropout_module.p,
self.out_proj.weight,
self.out_proj.bias,
self.training or self.dropout_module.apply_during_inference,
key_padding_mask,
need_weights,
attn_mask,
use_separate_proj_weight=True,
q_proj_weight=self.q_proj.weight,
k_proj_weight=torch.eye(*self.k_proj.weight.size(), out=torch.empty_like(self.k_proj.weight)),
v_proj_weight=torch.eye(*self.v_proj.weight.size(), out=torch.empty_like(self.v_proj.weight)),
)
|
bart_ls-main
|
fairseq-py/fairseq/models/long_transformers/pooling_layer.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""
Local attention functions from https://github.com/allenai/longformer/
"""
import torch
import torch.nn.functional as F
from typing import Union
from functools import lru_cache
import math
def get_slopes(n):
def get_slopes_power_of_2(n):
start = (2**(-2**-(math.log2(n)-3)))
ratio = start
return [start*ratio**i for i in range(n)]
if math.log2(n).is_integer():
return get_slopes_power_of_2(n) #In the paper, we only train models that have 2^a heads for some a. This function has
else: #some good properties that only occur when the input is a power of 2. To maintain that even
closest_power_of_2 = 2**math.floor(math.log2(n)) #when the number of heads is not a power of 2, we use this workaround.
return get_slopes_power_of_2(closest_power_of_2) + get_slopes(2*closest_power_of_2)[0::2][:n-closest_power_of_2]
def _get_invalid_locations_mask_fixed_dilation(seq_len: int, w: int, d: int):
diagonals_list = []
for j in range(-d * w, d, d):
diagonal_mask = torch.zeros(seq_len, device='cpu', dtype=torch.uint8)
diagonal_mask[:-j] = 1
diagonals_list.append(diagonal_mask)
return torch.stack(diagonals_list, dim=-1)
@lru_cache()
def _get_invalid_locations_mask(w: int, d: Union[torch.Tensor,int], autoregressive: bool, device: str):
if isinstance(d, int):
affected_seq_len = w * d
mask = _get_invalid_locations_mask_fixed_dilation(affected_seq_len, w, d)
mask = mask[None, :, None, :]
else:
affected_seq_len = w * d.max()
head_masks = []
d_list = d.cpu().numpy().tolist()
for d in d_list:
one_head_mask = _get_invalid_locations_mask_fixed_dilation(affected_seq_len, w, d)
head_masks.append(one_head_mask)
mask = torch.stack(head_masks, dim=-2)
mask = mask[None, :, :, :]
ending_mask = None if autoregressive else mask.flip(dims=(1, 3)).bool().to(device)
return affected_seq_len, mask.bool().to(device), ending_mask
def mask_invalid_locations(input_tensor: torch.Tensor, w: int, d: Union[torch.Tensor, int], autoregressive: bool) -> torch.Tensor:
# d: dilation?
affected_seq_len, beginning_mask, ending_mask = _get_invalid_locations_mask(w, d, autoregressive, input_tensor.device)
seq_len = input_tensor.size(1)
beginning_input = input_tensor[:, :affected_seq_len, :, :w+1]
beginning_mask = beginning_mask[:, :seq_len].expand(beginning_input.size())
beginning_input.masked_fill_(beginning_mask, -float('inf'))
if not autoregressive:
ending_input = input_tensor[:, -affected_seq_len:, :, -(w+1):]
ending_mask = ending_mask[:, -seq_len:].expand(ending_input.size())
ending_input.masked_fill_(ending_mask, -float('inf'))
def _skew(x, direction, padding_value):
'''Convert diagonals into columns (or columns into diagonals depending on `direction`'''
x_padded = F.pad(x, direction, value=padding_value) # bsz*num_heads x chunks x (2w+1) x 2w
x_padded = x_padded.view(*x_padded.size()[:-2], x_padded.size(-1), x_padded.size(-2))
return x_padded
def _skew2(x, padding_value):
'''shift every row 1 step to right converting columns into diagonals'''
# X = B x C x M x L
B, C, M, L = x.size()
x = F.pad(x, (0, M + 1), value=padding_value) # B x C x M x (L+M+1)
x = x.view(B, C, -1) # B x C x ML+MM+M
x = x[:, :, :-M] # B x C x ML+MM
x = x.view(B, C, M, M + L) # B x C, M x L+M
x = x[:, :, :, :-1]
return x
def _chunk(x, w):
'''convert into overlapping chunkings. Chunk size = 2w, overlap size = w'''
# non-overlapping chunks of size = 2w
x = x.view(x.size(0), x.size(1) // (w * 2), w * 2, x.size(2))
# use `as_strided` to make the chunks overlap with an overlap size = w
chunk_size = list(x.size())
chunk_size[1] = chunk_size[1] * 2 - 1
chunk_stride = list(x.stride())
# chunk_stride[1]: 512 x dim
chunk_stride[1] = chunk_stride[1] // 2
return x.as_strided(size=chunk_size, stride=chunk_stride)
def sliding_chunks_matmul_qk(q: torch.Tensor, k: torch.Tensor, w: int, padding_value: float):
'''Matrix multiplicatio of query x key tensors using with a sliding window attention pattern.
This implementation splits the input into overlapping chunks of size 2w (e.g. 512 for pretrained Longformer)
with an overlap of size w'''
bsz, seqlen, num_heads, head_dim = q.size()
assert seqlen % (w * 2) == 0
assert q.size() == k.size()
chunks_count = seqlen // w - 1
# group bsz and num_heads dimensions into one, then chunk seqlen into chunks of size w * 2
q = q.transpose(1, 2).reshape(bsz * num_heads, seqlen, head_dim)
k = k.transpose(1, 2).reshape(bsz * num_heads, seqlen, head_dim)
chunk_q = _chunk(q, w) # (B*H, num_of_overlapping_windows, head_dim)
chunk_k = _chunk(k, w)
# matrix multipication
# bcxd: bsz*num_heads x chunks x 2w x head_dim
# bcyd: bsz*num_heads x chunks x 2w x head_dim
# bcxy: bsz*num_heads x chunks x 2w x 2w
chunk_attn = torch.einsum('bcxd,bcyd->bcxy', (chunk_q, chunk_k)) # multiply
# convert diagonals into columns
diagonal_chunk_attn = _skew(chunk_attn, direction=(0, 0, 0, 1), padding_value=padding_value)
# allocate space for the overall attention matrix where the chunks are compined. The last dimension
# has (w * 2 + 1) columns. The first (w) columns are the w lower triangles (attention from a word to
# w previous words). The following column is attention score from each word to itself, then
# followed by w columns for the upper triangle.
diagonal_attn = diagonal_chunk_attn.new_empty((bsz * num_heads, chunks_count + 1, w, w * 2 + 1))
# copy parts from diagonal_chunk_attn into the compined matrix of attentions
# - copying the main diagonal and the upper triangle
diagonal_attn[:, :-1, :, w:] = diagonal_chunk_attn[:, :, :w, :w + 1]
diagonal_attn[:, -1, :, w:] = diagonal_chunk_attn[:, -1, w:, :w + 1] # Potential BUG: invalid attn weights
# - copying the lower triangle
diagonal_attn[:, 1:, :, :w] = diagonal_chunk_attn[:, :, - (w + 1):-1, w + 1:]
diagonal_attn[:, 0, 1:w, 1:w] = diagonal_chunk_attn[:, 0, :w - 1, 1 - w:]
# separate bsz and num_heads dimensions again
diagonal_attn = diagonal_attn.view(bsz, num_heads, seqlen, 2 * w + 1).transpose(2, 1)
mask_invalid_locations(diagonal_attn, w, 1, False)
return diagonal_attn
def sliding_chunks_matmul_pv(prob: torch.Tensor, v: torch.Tensor, w: int):
'''Same as sliding_chunks_matmul_qk but for prob and value tensors. It is expecting the same output
format from sliding_chunks_matmul_qk'''
bsz, seqlen, num_heads, head_dim = v.size()
assert seqlen % (w * 2) == 0
assert prob.size()[:3] == v.size()[:3]
assert prob.size(3) == 2 * w + 1
chunks_count = seqlen // w - 1
# group bsz and num_heads dimensions into one, then chunk seqlen into chunks of size 2w
chunk_prob = prob.transpose(1, 2).reshape(bsz * num_heads, seqlen // w, w, 2 * w + 1)
# group bsz and num_heads dimensions into one
v = v.transpose(1, 2).reshape(bsz * num_heads, seqlen, head_dim)
# pad seqlen with w at the beginning of the sequence and another w at the end
padded_v = F.pad(v, (0, 0, w, w), value=-1)
# chunk padded_v into chunks of size 3w and an overlap of size w
chunk_v_size = (bsz * num_heads, chunks_count + 1, 3 * w, head_dim)
chunk_v_stride = padded_v.stride()
chunk_v_stride = chunk_v_stride[0], w * chunk_v_stride[1], chunk_v_stride[1], chunk_v_stride[2]
chunk_v = padded_v.as_strided(size=chunk_v_size, stride=chunk_v_stride)
skewed_prob = _skew2(chunk_prob, padding_value=0)
context = torch.einsum('bcwd,bcdh->bcwh', (skewed_prob, chunk_v))
return context.view(bsz, num_heads, seqlen, head_dim).transpose(1, 2)
def pad_to_window_size(input_ids: torch.Tensor, attention_mask: torch.Tensor,
one_sided_window_size: int, pad_token_id: int):
'''A helper function to pad tokens and mask to work with the sliding_chunks implementation of Longformer selfattention.
Input:
input_ids = torch.Tensor(bsz x seqlen): ids of wordpieces
attention_mask = torch.Tensor(bsz x seqlen): attention mask
one_sided_window_size = int: window size on one side of each token
pad_token_id = int: tokenizer.pad_token_id
Returns
(input_ids, attention_mask) padded to length divisible by 2 * one_sided_window_size
'''
w = 2 * one_sided_window_size
seqlen = input_ids.size(1)
padding_len = (w - seqlen % w) % w
input_ids = F.pad(input_ids, (0, padding_len), value=pad_token_id)
attention_mask = F.pad(attention_mask, (0, padding_len), value=False) # no attention on the padding tokens
return input_ids, attention_mask
|
bart_ls-main
|
fairseq-py/fairseq/models/long_transformers/utils.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""
Long-context model pretraining with fast blocksparse and extrapolation attentions
"""
from typing import Optional
import logging
import math
import torch
import torch.nn as nn
from fairseq import utils, modules
from fairseq.utils import safe_getattr
from fairseq.models import register_model, register_model_architecture
from fairseq.models.transformer import TransformerModel, TransformerConfig
from fairseq.modules.transformer_sentence_encoder import init_bert_params
from fairseq.models.transformer.transformer_config import TransformerConfig
import copy
from fairseq.models.roberta import RobertaEncoder
logger = logging.getLogger(__name__)
@register_model("loco")
class LOCOModel(TransformerModel):
__jit_unused_properties__ = ["supported_targets"]
def __init__(self, args, encoder, decoder):
super().__init__(args, encoder, decoder)
generator_architecture(args)
self.generator = RobertaEncoder(args, encoder.dictionary)
if not self.args.train_generator:
for p in self.generator.parameters():
p.requires_grad = False
self.pad_idx = self.encoder.dictionary.pad()
self.bos = self.encoder.dictionary.bos()
self.sentinel_start_idx = self.encoder.dictionary.index("<sentinel_0>")
self.sentinel_end_idx = len(self.encoder.dictionary) - 1
# We follow BERT's random weight initialization
self.apply(init_bert_params)
self.classification_heads = nn.ModuleDict()
if hasattr(self.encoder, "dictionary"):
self.eos: int = self.encoder.dictionary.eos()
@staticmethod
def add_args(parser):
super(LOCOModel, LOCOModel).add_args(parser)
parser.add_argument(
"--pooler-dropout",
type=float,
metavar="D",
help="dropout probability in the masked_lm pooler layers",
)
parser.add_argument(
"--pooler-activation-fn",
choices=utils.get_available_activation_fns(),
help="activation function to use for pooler layer",
)
parser.add_argument(
"--finetune",
action="store_true",
help="different forwards used for pretraining and finetuning"
)
parser.add_argument(
"--train-generator",
action="store_true",
)
parser.add_argument(
"--generator-xformer-config",
type=str,
metavar="D",
)
parser.add_argument(
"--generator-layers",
type=int,
)
@classmethod
def build_decoder(cls, args, tgt_dict, embed_tokens):
# use vanilla attention for now
args.use_xformers = False # HACK, disable efficient attentions for cross-attention & decoder-side attention
return super().build_decoder(
TransformerConfig.from_namespace(args), tgt_dict, embed_tokens
)
@classmethod
def build_generator(cls, args, src_dict, embed_tokens):
return super().build_encoder(
TransformerConfig.from_namespace(args), src_dict, embed_tokens
)
@property
def supported_targets(self):
return {"self"}
def _sum_spans(self, input, starts, ends):
"""
sum the span values to the start of each span;
zero out all other positions
"""
starts_before = torch.roll(starts, -1, -1)
input_cumsum = torch.cumsum(input, dim=-1)
input_cumsum[starts.bool()] = input_cumsum[ends.bool()] - input_cumsum[starts_before.bool()]
sumed = input_cumsum * starts
return sumed
def _avg_spans(self, span_sum, span_lens, starts):
span_sum[starts.bool()] = span_sum[starts.bool()] / span_lens[starts.bool()]
return span_sum
"""
utils from T5's objective
"""
def _create_sentinels(self, mask_indices):
"""
mask_indices: binary mask
start spans as sentinel ids and other masked positions as -1
"""
start_indices = mask_indices - torch.roll(mask_indices, 1, -1) * mask_indices
start_indices[:,0] = mask_indices[:,0]
sentinel_ids = torch.where(start_indices != 0, torch.cumsum(start_indices, dim=-1), start_indices)
assert sentinel_ids.max() + self.sentinel_start_idx - 1 <= self.sentinel_end_idx, (sentinel_ids.max() + self.sentinel_start_idx - 1, self.sentinel_end_idx, sentinel_ids)
sentinel_ids = torch.where(sentinel_ids != 0, (sentinel_ids + self.sentinel_start_idx - 1), 0)
sentinel_ids -= mask_indices - start_indices
return sentinel_ids
def _build_inputs(self, masked_input, span_mask):
sentinel_ids = self._create_sentinels(span_mask)
masked_input = torch.where(sentinel_ids != 0, sentinel_ids, masked_input)
src_lens = (masked_input >= 0).sum(-1)
# src_tokens padded to max_source_positions, useful for blocksparse attention
src_tokens = masked_input.new_full((masked_input.size(0), self.cfg.max_source_positions), self.pad_idx)
fill_indices = torch.arange(masked_input.size(-1)).to(masked_input)
fill_indices = fill_indices < src_lens.unsqueeze(-1)
assert fill_indices.sum() == (masked_input >= 0).sum() # = 0 for sequence starts
src_tokens[:,:masked_input.size(-1)][fill_indices] = masked_input[masked_input >= 0]
return src_tokens, src_lens
def _build_targets(self, masked_target, span_mask, pad_mask, eos_mask):
"""
masked_targets: masked positions as their original token ids and
other positions as pad index
eos_mask: end of sequence as 0
pad_mask: padding positions as 0
"""
unmasked_positions = ~span_mask.bool()
unmasked_positions[:,0] = 0
sentinel_ids = self._create_sentinels(unmasked_positions.to(masked_target))
sentinel_ids *= pad_mask
sentinel_ids *= eos_mask
# target: masked positions with sentinel ids or -1;
# bos, eos and padding positions with value 1
target = torch.where(sentinel_ids != 0, sentinel_ids, masked_target)
target[~eos_mask] = self.eos
target[:,0] = self.bos
tgt_lens = (target.abs() != 1).sum(-1)
tgt_tokens = target.new_full(target.size(), self.pad_idx)
fill_indices = torch.arange(tgt_tokens.size(-1)).to(tgt_tokens)
fill_indices = fill_indices < tgt_lens.unsqueeze(-1)
tgt_tokens[fill_indices] = target[target.abs() != 1]
tgt_tokens = tgt_tokens[:,:tgt_lens.max()]
# truncating if needed
if tgt_tokens.size(-1) > self.args.max_target_positions:
end_positions = (tgt_tokens == self.eos).nonzero(as_tuple=True)[1]
sample_exceeds = end_positions >= (self.args.max_target_positions - 1)
tgt_tokens = torch.cat(
[tgt_tokens[:,:self.args.max_target_positions-1], tgt_tokens[:,-1:]], dim=-1
)
tgt_tokens[:,-1] = torch.where(sample_exceeds, self.eos, tgt_tokens[:,-1])
decoder_input = tgt_tokens.clone()
decoder_input[:,0] = self.eos
decoder_input[:,1:] = tgt_tokens[:,:-1]
return tgt_tokens, decoder_input
def forward(
self,
src_tokens,
src_lengths,
prev_output_tokens: Optional[torch.Tensor] = None,
features_only: bool = False,
classification_head_name: Optional[str] = None,
token_embeddings: Optional[torch.Tensor] = None,
return_all_hiddens: bool = True,
alignment_layer: Optional[int] = None,
alignment_heads: Optional[int] = None,
masked_unfiltered: Optional[torch.Tensor] = None,
):
if classification_head_name is not None:
features_only = True
if not self.cfg.finetune:
"""
use an encoder-only model to build long-range objectives
"""
masked_tokens_unfiltered = masked_unfiltered.ne(self.pad_idx).to(src_tokens) # 1: the masked tokens (before hard sampling)
src_tokens_for_mlm = copy.deepcopy(src_tokens)
if self.cfg.train_generator:
masked_logits = self.generator(
src_tokens_for_mlm,
src_lengths=src_lengths,
return_all_hiddens=return_all_hiddens,
)[0]
token_loss = modules.cross_entropy(
masked_logits.view(-1, masked_logits.size(-1)),
masked_unfiltered.view(-1),
reduction='none',
ignore_index=self.pad_idx,
).view(masked_unfiltered.size())
masked_cnt = masked_tokens_unfiltered.sum()
mlm_loss = token_loss.sum() / masked_cnt
else:
with torch.no_grad():
masked_logits = self.generator(
src_tokens,
src_lengths=src_lengths,
return_all_hiddens=return_all_hiddens,
)[0]
token_loss = modules.cross_entropy(
masked_logits.view(-1, masked_logits.size(-1)),
masked_unfiltered.view(-1),
reduction='none',
ignore_index=self.pad_idx).view(masked_unfiltered.size())
# 1 marking the span starts
span_starts = masked_tokens_unfiltered - torch.roll(masked_tokens_unfiltered, 1, -1) * masked_tokens_unfiltered
span_starts[:,0] = masked_tokens_unfiltered[:,0]
span_ends = masked_tokens_unfiltered - torch.roll(masked_tokens_unfiltered, -1, -1) * masked_tokens_unfiltered
span_ends[:,-1] = masked_tokens_unfiltered[:,-1]
# span_starts: binary mask marking the start of each span
# span_ends: binary mask marking the end of each span
# span_lens: span lens calculated put at the starts of each span
span_loss = self._sum_spans(token_loss, span_starts, span_ends)
span_lens = self._sum_spans(masked_tokens_unfiltered, span_starts, span_ends)
span_loss_avg = self._avg_spans(span_loss, span_lens, span_starts)
# find the hard spans, i.e, topk largest loss
span_counts = span_starts.sum(-1).min()
hard_span_starts = span_loss_avg.topk(k=math.floor(span_counts*self.cfg.sample_ratio), dim=-1)[1] # bsz * num_hard_spans
hard_span_ends = span_lens.gather(1, index=hard_span_starts) + hard_span_starts
# masking source with only the hard spans
row_idx = torch.arange(hard_span_starts.size(0)).unsqueeze(1).repeat(1, hard_span_starts.size(1)).to(hard_span_starts)
hard_mask = span_starts.new_zeros(span_starts.size())
hard_mask[row_idx.view(-1), hard_span_starts.view(-1)] = 1
hard_mask[row_idx.view(-1), hard_span_ends.view(-1)] = 1
hard_mask = (hard_mask.cumsum(dim=-1) % 2) == 1
hard_mask = hard_mask.type_as(masked_tokens_unfiltered)
# filter our easy span masks
mask_off = torch.logical_xor(hard_mask, masked_tokens_unfiltered)
src_tokens[mask_off] = masked_unfiltered[mask_off]
# build the denoising targets
src_pad_mask = src_tokens.ne(self.pad_idx)
src_eos_mask = src_tokens.ne(self.eos)
src_tokens, src_lengths = self._build_inputs(src_tokens, hard_mask)
target, prev_output_tokens = self._build_targets(masked_unfiltered, hard_mask, src_pad_mask, src_eos_mask)
encoder_out = self.encoder(
src_tokens,
src_lengths=src_lengths,
token_embeddings=token_embeddings,
return_all_hiddens=return_all_hiddens
)
x, extra = self.decoder(
prev_output_tokens,
encoder_out=encoder_out,
features_only=features_only,
alignment_layer=alignment_layer,
alignment_heads=alignment_heads,
src_lengths=src_lengths,
return_all_hiddens=return_all_hiddens,
)
eos: int = self.eos
if classification_head_name is not None:
sentence_representation = x[
src_tokens.eq(eos), :
].view(x.size(0), -1, x.size(-1))[:, -1, :]
for k, head in self.classification_heads.items():
# for torch script only supports iteration
if k == classification_head_name:
x = head(sentence_representation)
break
if not self.cfg.finetune:
if self.cfg.train_generator:
return x, target, mlm_loss, extra
return x, target, extra
return x, extra
def upgrade_state_dict_named(self, state_dict, name):
super().upgrade_state_dict_named(state_dict, name)
prefix = name + "." if name != "" else ""
current_head_names = (
[]
if not hasattr(self, "classification_heads")
else self.classification_heads.keys()
)
# Handle new classification heads present in the state dict.
keys_to_delete = []
for k in state_dict.keys():
if not k.startswith(prefix + "classification_heads."):
continue
head_name = k[len(prefix + "classification_heads.") :].split(".")[0]
num_classes = state_dict[
prefix + "classification_heads." + head_name + ".out_proj.weight"
].size(0)
inner_dim = state_dict[
prefix + "classification_heads." + head_name + ".dense.weight"
].size(0)
if getattr(self.args, "load_checkpoint_heads", False):
if head_name not in current_head_names:
self.register_classification_head(head_name, num_classes, inner_dim)
else:
if head_name not in current_head_names:
logger.warning(
"deleting classification head ({}) from checkpoint "
"not present in current model: {}".format(head_name, k)
)
keys_to_delete.append(k)
elif (
num_classes
!= self.classification_heads[head_name].out_proj.out_features
or inner_dim
!= self.classification_heads[head_name].dense.out_features
):
logger.warning(
"deleting classification head ({}) from checkpoint "
"with different dimensions than current model: {}".format(
head_name, k
)
)
keys_to_delete.append(k)
for k in keys_to_delete:
del state_dict[k]
def truncate_emb(key):
if key in state_dict:
state_dict[key] = state_dict[key][:-1, :]
# When finetuning on translation task, remove last row of
# embedding matrix that corresponds to mask_idx token.
loaded_dict_size = state_dict["encoder.embed_tokens.weight"].size(0)
if (
loaded_dict_size == len(self.encoder.dictionary) + 1
and "<mask>" not in self.encoder.dictionary
):
truncate_emb("encoder.embed_tokens.weight")
truncate_emb("decoder.embed_tokens.weight")
truncate_emb("encoder.output_projection.weight")
truncate_emb("decoder.output_projection.weight")
# When continued pretraining on new set of languages for mbart,
# add extra lang embeddings at the end of embed_tokens.
# Note: newly added languages are assumed to have been added at the end.
if self.args.task == "multilingual_denoising" and loaded_dict_size < len(
self.encoder.dictionary
):
logger.info(
"Adding extra language embeddings not found in pretrained model for "
"continued pretraining of MBART on new set of languages."
)
loaded_mask_token_embedding = state_dict["encoder.embed_tokens.weight"][
-1, :
]
num_langids_to_add = len(self.encoder.dictionary) - loaded_dict_size
embed_dim = state_dict["encoder.embed_tokens.weight"].size(1)
new_lang_embed_to_add = torch.zeros(num_langids_to_add, embed_dim)
nn.init.normal_(new_lang_embed_to_add, mean=0, std=embed_dim ** -0.5)
new_lang_embed_to_add = new_lang_embed_to_add.to(
dtype=state_dict["encoder.embed_tokens.weight"].dtype,
)
state_dict["encoder.embed_tokens.weight"] = torch.cat(
[
state_dict["encoder.embed_tokens.weight"][
: loaded_dict_size - 1, :
],
new_lang_embed_to_add,
loaded_mask_token_embedding.unsqueeze(0),
]
)
state_dict["decoder.embed_tokens.weight"] = torch.cat(
[
state_dict["decoder.embed_tokens.weight"][
: loaded_dict_size - 1, :
],
new_lang_embed_to_add,
loaded_mask_token_embedding.unsqueeze(0),
]
)
# Copy any newly-added classification heads into the state dict
# with their current weights.
if hasattr(self, "classification_heads"):
cur_state = self.classification_heads.state_dict()
for k, v in cur_state.items():
if prefix + "classification_heads." + k not in state_dict:
logger.info("Overwriting " + prefix + "classification_heads." + k)
state_dict[prefix + "classification_heads." + k] = v
def generator_architecture(args):
# options to use different sizes of generator models
args.encoder_layers = safe_getattr(args, "generator_layers", 6)
args.encoder_embed_dim = safe_getattr(args, "generator_embed_dim", 768)
args.encoder_ffn_embed_dim = safe_getattr(args, "generator_ffn_embed_dim", 3072)
args.encoder_attention_heads = safe_getattr(args, "generator_attention_heads", 12)
args.max_positions = safe_getattr(args, "max_source_positions", 8192)
args.encoder_learned_pos = safe_getattr(args, "generator_learned_pos", True)
args.encoder_normalize_before = safe_getattr(args, "generator_normalize_before", False)
args.untie_weights_roberta = safe_getattr(args, "untie_weights_roberta", False)
# xformers config
args.use_xformers = safe_getattr(args, "generator_use_xformers", True)
args.attention_name = safe_getattr(args, "generator_attention_name", 'block_noglobal')
args.xformer_config = safe_getattr(args, 'generator_xformer_config', '{"block_size": 512}')
args.pooling_layers = safe_getattr(args, "generator_pooling_layers", 0)
@register_model_architecture("loco", "loco_large")
def loco_large_architecture(args):
args.finetune = safe_getattr(args, "finetune", False)
args.train_generator = safe_getattr(args, "train_generator", False)
# # @xwhan is it necessary to put it here? had issues
# def getattr(args, key, value):
# return value
args.encoder_embed_path = getattr(args, "encoder_embed_path", None)
args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 1024)
args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 4 * 1024)
args.encoder_layers = getattr(args, "encoder_layers", 12)
args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 16)
args.encoder_normalize_before = getattr(args, "encoder_normalize_before", False)
args.encoder_learned_pos = getattr(args, "encoder_learned_pos", True)
args.decoder_embed_path = getattr(args, "decoder_embed_path", None)
args.decoder_embed_dim = getattr(args, "decoder_embed_dim", args.encoder_embed_dim)
args.decoder_ffn_embed_dim = getattr(
args, "decoder_ffn_embed_dim", args.encoder_ffn_embed_dim
)
args.decoder_layers = getattr(args, "decoder_layers", 12)
args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 16)
args.decoder_normalize_before = getattr(args, "decoder_normalize_before", False)
args.decoder_learned_pos = getattr(args, "decoder_learned_pos", True)
args.attention_dropout = getattr(args, "attention_dropout", 0.0)
args.relu_dropout = getattr(args, "relu_dropout", 0.0)
args.dropout = getattr(args, "dropout", 0.1)
args.max_target_positions = safe_getattr(args, "max_target_positions", 1024) #hack
args.max_source_positions = safe_getattr(args, "max_source_positions", 1024)
args.adaptive_softmax_cutoff = getattr(args, "adaptive_softmax_cutoff", None)
args.adaptive_softmax_dropout = getattr(args, "adaptive_softmax_dropout", 0)
args.share_decoder_input_output_embed = getattr(
args, "share_decoder_input_output_embed", True
)
args.share_all_embeddings = getattr(args, "share_all_embeddings", True)
args.decoder_output_dim = getattr(
args, "decoder_output_dim", args.decoder_embed_dim
)
args.decoder_input_dim = getattr(args, "decoder_input_dim", args.decoder_embed_dim)
args.no_scale_embedding = getattr(args, "no_scale_embedding", True)
args.layernorm_embedding = getattr(args, "layernorm_embedding", True)
args.activation_fn = getattr(args, "activation_fn", "gelu")
args.pooler_activation_fn = getattr(args, "pooler_activation_fn", "tanh")
args.pooler_dropout = getattr(args, "pooler_dropout", 0.0)
@register_model_architecture("loco", "loco_base")
def loco_base_architecture(args):
args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 768)
args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 4 * 768)
args.encoder_layers = getattr(args, "encoder_layers", 6)
args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 12)
args.decoder_layers = getattr(args, "decoder_layers", 6)
args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 12)
loco_large_architecture(args)
@register_model_architecture("loco", "loco_xlarge")
def loco_xlarge_architecture(args):
loco_large_architecture(args)
args.encoder_layers = 24
args.decoder_layers = 24
|
bart_ls-main
|
fairseq-py/fairseq/models/long_transformers/loco_model.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import logging
from typing import Dict, Optional, Tuple
import torch
from typing import Dict, Optional
from torch import Tensor, nn
from fairseq.modules import (
TransformerEncoderLayer,
MultiheadAttention,
)
import torch.nn.functional as F
from fairseq.models.transformer import TransformerEncoder
from functools import partial, reduce
from fairseq.distributed import fsdp_wrap
from inspect import isfunction
from operator import mul
from fairseq.modules.checkpoint_activations import checkpoint_wrapper
"""
A hacky implementation of simple block attention transformer
"""
class BlockTransformerEncoder(TransformerEncoder):
def __init__(self, args, dictionary, embed_tokens):
super().__init__(args, dictionary, embed_tokens)
self.args = args
del self.layers
self.layers = nn.ModuleList([self.build_sw_encoder_layer(args, args.window_size, self.padding_idx) for i in range(args.encoder_layers)])
self.num_layers = len(self.layers)
def build_sw_encoder_layer(self, args, window_size, padding_idx):
layer = BlockTransformerEncoderLayer(args, window_size, padding_idx)
checkpoint = args.checkpoint_activations
if checkpoint:
offload_to_cpu = self.cfg.offload_activations
layer = checkpoint_wrapper(layer, offload_to_cpu=offload_to_cpu)
min_params_to_wrap = self.cfg.min_params_to_wrap if not checkpoint else 0
layer = fsdp_wrap(layer, min_num_params=min_params_to_wrap)
return layer
def forward(
self,
src_tokens,
src_lengths: Optional[torch.Tensor] = None,
return_all_hiddens: bool = False,
token_embeddings: Optional[torch.Tensor] = None,
key_padding_mask: Optional[torch.Tensor] = None, # @xwhan in order to add global mask
):
return self.forward_scriptable(
src_tokens, src_lengths, return_all_hiddens, token_embeddings, key_padding_mask
)
class BlockTransformerEncoderLayer(TransformerEncoderLayer):
def __init__(self, args, window_size, padding_idx):
super().__init__(args)
# replace self-attn
self.window_size = window_size
self.padding_idx = padding_idx
self.self_attn = self.build_sw_self_attention(self.embed_dim, window_size, padding_idx, args)
def forward(
self,
x,
encoder_padding_mask: Optional[Tensor],
attn_mask: Optional[Tensor] = None,
attn_bias: Optional[Tensor] = None,
):
if attn_mask is not None:
attn_mask = (attn_mask * -1e8).type_as(attn_mask) # -1 in attn_mask means global attention
return super().forward(x, encoder_padding_mask, attn_mask=attn_mask)
def build_sw_self_attention(self, embed_dim, window_size, padding_idx, args):
return BlockSelfAttention(
embed_dim,
args.encoder_attention_heads,
dropout=args.attention_dropout,
self_attention=True,
q_noise=self.quant_noise,
qn_block_size=self.quant_noise_block_size,
window_size=window_size,
padding_idx=padding_idx,
)
class BlockSelfAttention(MultiheadAttention):
def __init__(self,
embed_dim,
num_heads,
kdim=None,
vdim=None,
dropout=0.0,
bias=True,
add_bias_kv=False,
add_zero_attn=False,
self_attention=False,
encoder_decoder_attention=False,
q_noise=0.0,
qn_block_size=8,
window_size=1024,
padding_idx=1,
):
super().__init__(embed_dim, num_heads, kdim, vdim, dropout,
bias, add_bias_kv, add_zero_attn, self_attention,
encoder_decoder_attention, q_noise, qn_block_size)
self.window_size = window_size
self.padding_idx = padding_idx
self.drop_attn = self.dropout_module
def forward(self,
query,
key: Optional[Tensor],
value: Optional[Tensor],
key_padding_mask: Optional[Tensor] = None,
incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None,
need_weights: bool = True,
static_kv: bool = False,
attn_mask: Optional[Tensor] = None,
before_softmax: bool = False,
need_head_weights: bool = False,
attn_bias: Optional[Tensor] = None,
):
seq_len, bsz, embed_dim = query.size()
if key_padding_mask is not None:
assert key_padding_mask.size(0) == bsz
assert key_padding_mask.size(1) == seq_len
assert self.self_attention
q = self.q_proj(query)
k = self.k_proj(query)
v = self.v_proj(query)
q = (
q.contiguous()
.view(seq_len, bsz * self.num_heads, self.head_dim)
.transpose(0, 1)
)
if k is not None:
k = (
k.contiguous()
.view(-1, bsz * self.num_heads, self.head_dim)
.transpose(0, 1)
)
if v is not None:
v = (
v.contiguous()
.view(-1, bsz * self.num_heads, self.head_dim)
.transpose(0, 1)
)
y = self.block_attn_forward(q, k, v, key_padding_mask=key_padding_mask)
assert list(y.size()) == [bsz * self.num_heads, seq_len, self.head_dim], (y.size(), query.size(), q.size())
y = y.transpose(0, 1).contiguous().view(seq_len, bsz, embed_dim)
y = self.out_proj(y)
return y, None
def block_attn_forward(
self,
q: torch.Tensor,
k: torch.Tensor,
v: torch.Tensor,
att_mask: Optional[torch.Tensor] = None,
key_padding_mask: Optional[Tensor] = None,
*args, **kwargs
):
bh = q.size(0)
orig_seq_len = q.size(1)
bsz = bh // self.num_heads
head_dim = q.size(-1)
assert key_padding_mask is not None
key_padding_mask = key_padding_mask.to(q)
key_padding_mask[:,0] = -1
# pad the input length to factors of bucket size
def _pad_to_window_size(x, window_size):
seq_len = x.size(-2)
pad_len = (window_size - seq_len % window_size) % window_size
return F.pad(x, (0,0,0,pad_len), value=0), pad_len
q, _ = _pad_to_window_size(q, self.window_size)
k, _ = _pad_to_window_size(k, self.window_size)
v, _ = _pad_to_window_size(v, self.window_size)
if key_padding_mask.shape[1] % self.window_size != 0:
pad_len = (self.window_size - key_padding_mask.shape[1] % self.window_size) % self.window_size
# key padding mask: 1 means padding tokens
key_padding_mask = torch.cat([key_padding_mask, key_padding_mask.new_ones(key_padding_mask.size(0), pad_len).to(key_padding_mask)], dim=1)
# global attention tokens
extra_attention_mask = key_padding_mask < 0
num_extra_indices_per_batch = extra_attention_mask.long().sum(dim=1)
max_num_extra_indices_per_batch = num_extra_indices_per_batch.max()
hard_mask = key_padding_mask == 1
if max_num_extra_indices_per_batch <= 0:
extra_attention_mask = None
else:
extra_attention_mask_nonzeros = extra_attention_mask.nonzero(as_tuple=True)
zero_to_max_range = torch.arange(0, max_num_extra_indices_per_batch, device=extra_attention_mask.device)
# mask indicating which values are actually going to be padding
num_extra_indices_per_batch = extra_attention_mask.long().sum(dim=1)
selection_padding_mask = zero_to_max_range < num_extra_indices_per_batch.unsqueeze(dim=-1)
# 2) location of the non-padding values in the selected global attention
selection_padding_mask_nonzeros = selection_padding_mask.nonzero(as_tuple=True)
# 3) location of the padding values in the selected global attention
selection_padding_mask_zeros = (selection_padding_mask == 0).nonzero(as_tuple=True)
# every token attend to global tokens
if extra_attention_mask is not None:
selected_k = k.new_zeros(bsz, max_num_extra_indices_per_batch, self.num_heads, head_dim)
k_splited = k.view(bsz, self.num_heads, -1, head_dim).transpose(1,2)
q_splited = q.view(bsz, self.num_heads, -1, head_dim).transpose(1,2)
v_splited = v.view(bsz, self.num_heads, -1, head_dim).transpose(1,2)
selected_k[selection_padding_mask_nonzeros] = k_splited[extra_attention_mask_nonzeros]
# (bsz, seq_len, num_heads, max_num_extra_indices_per_batch)
selected_attn_weights = torch.einsum('blhd,bshd->blhs', (q_splited, selected_k)) * (head_dim ** -0.5)
selected_attn_weights[selection_padding_mask_zeros[0], :, :, selection_padding_mask_zeros[1]] = -10000
attn_weights_over_g_tokens = selected_attn_weights.transpose(1,2)
selected_v = v.new_zeros(bsz, max_num_extra_indices_per_batch, self.num_heads, head_dim)
selected_v[selection_padding_mask_nonzeros] = v_splited[extra_attention_mask_nonzeros]
selected_v = selected_v.transpose(1,2).contiguous().view(bsz*self.num_heads, max_num_extra_indices_per_batch, head_dim)
tgt_len = k.size(1)
buckets = q.shape[1] // self.window_size
b_q = bucket(buckets, q)
b_k, b_v = map(partial(bucket, buckets), (k, v)) # BH * bct * n_b * D
dots = torch.einsum('buie,buje->buij', b_q, b_k) * (head_dim ** -0.5)
mask_value = -10000
# # mask
# if key_padding_mask is not None:
q_mask = default(key_padding_mask.eq(0), lambda: torch.ones((bsz, tgt_len), device=q.device).bool())
# 1 means not masking
kv_mask = q_mask
mq, mk = bucket(buckets, q_mask), bucket(buckets, kv_mask) # B * bkt * n_b
expand_head_and_merge_into_batch = lambda x: merge_dims(0, 1, expand_dim(x.unsqueeze(1), 1, self.num_heads))
mq, mk = map(expand_head_and_merge_into_batch, (mq, mk)) # BH * bkt * n_b
mask = mq[:, :, :, None] * mk[:, :, None, :]
dots.masked_fill_(~mask, mask_value)
del mask
block_attn_weights = dots.view(bsz*self.num_heads, -1, self.window_size)
if extra_attention_mask is not None:
attn_weights_over_g_tokens = attn_weights_over_g_tokens.view(bsz*self.num_heads, -1, max_num_extra_indices_per_batch)
all_attn = torch.cat([block_attn_weights, attn_weights_over_g_tokens], dim=-1)
else:
all_attn = block_attn_weights
all_attn_probs = all_attn.softmax(dim=-1)
all_attn_probs = self.drop_attn(all_attn_probs)
C = 0
# calculate block attention
block_attn_probs = all_attn_probs[:, :, :block_attn_weights.shape[-1]]
block_attn_probs = block_attn_probs.view(bsz*self.num_heads, -1, self.window_size, self.window_size)
C += block_attn_probs.matmul(b_v).view(bsz*self.num_heads, -1, head_dim)
if extra_attention_mask is not None:
attn_probs_over_g_tokens = all_attn_probs[:,:,-attn_weights_over_g_tokens.shape[-1]:]
C += attn_probs_over_g_tokens.matmul(selected_v)
# global tokens to attend all other tokens
selected_q = q_splited.new_zeros(bsz, max_num_extra_indices_per_batch, self.num_heads, head_dim)
selected_q[selection_padding_mask_nonzeros] = q_splited[extra_attention_mask_nonzeros]
g2all_attn_weights = selected_q.transpose(1,2).matmul(k_splited.permute(0,2,3,1)) * (head_dim ** -0.5)
g2all_attn_weights[selection_padding_mask_zeros[0], :, selection_padding_mask_zeros[1], :] = -10000.0
if hard_mask is not None:
g2all_attn_weights = g2all_attn_weights.masked_fill(
hard_mask.unsqueeze(1).unsqueeze(2),
-10000.0,
)
g2all_attn_probs_float = F.softmax(g2all_attn_weights, dim=-1, dtype=torch.float32)
g2all_attn_probs = self.drop_attn(g2all_attn_probs_float.type_as(g2all_attn_weights))
g2all_attn = g2all_attn_probs.matmul(v.view(bsz, self.num_heads, -1, head_dim)) # (batch_size, self.num_head, max_num_extra_indices_per_batch, head_dim)
nonzero_global_attn = g2all_attn[selection_padding_mask_nonzeros[0], :, selection_padding_mask_nonzeros[1]]
C = C.view(bsz, self.num_heads, -1, head_dim)
C[extra_attention_mask_nonzeros[0],:,extra_attention_mask_nonzeros[1]] = nonzero_global_attn
C = C.view(bsz*self.num_heads, -1, head_dim)
C = C[:,:orig_seq_len]
return C
import math
class BlockSWSelfAttention(BlockSelfAttention):
def forward(self,
query,
key: Optional[Tensor],
value: Optional[Tensor],
key_padding_mask: Optional[Tensor] = None,
incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None,
need_weights: bool = True,
static_kv: bool = False,
attn_mask: Optional[Tensor] = None,
before_softmax: bool = False,
need_head_weights: bool = False,
attn_bias: Optional[Tensor] = None,
):
seq_len, bsz, embed_dim = query.size()
if key_padding_mask is not None:
assert key_padding_mask.size(0) == bsz
assert key_padding_mask.size(1) == seq_len
assert self.self_attention
q = self.q_proj(query)
k = self.proj_pool(self.k_proj, key)
v = self.proj_pool(self.v_proj, value)
q = (
q.contiguous()
.view(seq_len, bsz * self.num_heads, self.head_dim)
.transpose(0, 1)
)
if k is not None:
k = (
k.contiguous()
.view(-1, bsz * self.num_heads, self.head_dim)
.transpose(0, 1)
)
if v is not None:
v = (
v.contiguous()
.view(-1, bsz * self.num_heads, self.head_dim)
.transpose(0, 1)
)
y = self.block_attn_forward(q, k, v, key_padding_mask=key_padding_mask)
assert list(y.size()) == [bsz * self.num_heads, seq_len, self.head_dim], (y.size(), query.size(), q.size())
y = y.transpose(0, 1).contiguous().view(seq_len, bsz, embed_dim)
y = self.out_proj(y)
return y, None
def proj_pool(self,proj,input):
input = proj(input)
# Pool
input = input.permute(1, 2 ,0) # T x B x C -> B x C x T
input = F.avg_pool1d(input, kernel_size=18, stride=12, padding=9)
input = input.permute(2, 0 ,1) # T_pool x B x C
return input
def block_attn_forward(
self,
q: torch.Tensor,
k: torch.Tensor,
v: torch.Tensor,
att_mask: Optional[torch.Tensor] = None,
key_padding_mask: Optional[Tensor] = None,
*args, **kwargs
):
batch_size = q.shape[0] // self.num_heads
sequence_length = q.shape[1]
key_padding_mask = key_padding_mask.to(q)
Q = q.view(batch_size, self.num_heads, -1, self.head_dim).mul(1./math.sqrt(self.head_dim))
K = k.view(batch_size, self.num_heads, -1, self.head_dim).transpose(1,2).reshape(batch_size, -1, self.embed_dim)
V = v.view(batch_size, self.num_heads, -1, self.head_dim).transpose(1,2).reshape(batch_size, -1, self.embed_dim)
# needs centain sequence length to make the block wise local attention work
def _pad_to_window_size(x, window_size):
seq_len = x.size(-2)
pad_len = (window_size - seq_len % window_size) % window_size
return F.pad(x, (0,0,0,pad_len), value=0), pad_len
Q, _ = _pad_to_window_size(Q, self.window_size)
K, _ = _pad_to_window_size(K, self.window_size)
V, _ = _pad_to_window_size(V, self.window_size)
if key_padding_mask.shape[1] % self.window_size != 0:
pad_len = (self.window_size - key_padding_mask.shape[1] % self.window_size) % self.window_size
# key padding mask: 1 means padding tokens
key_padding_mask = torch.cat([key_padding_mask, key_padding_mask.new_ones(key_padding_mask.size(0), pad_len).to(key_padding_mask)], dim=1)
K = self.split_heads(K) # (B, H, seq_len, head_dim)
V = self.split_heads(V)
padding_mask = key_padding_mask != 0 # True means masked position
win_attn_weights = self.sliding_chunks_matmul_qk_v2(Q, K, padding_mask) # bsz x num_heads x seqlen x 2winsize
all_attn = win_attn_weights.float().softmax(dim=-1).to(win_attn_weights)
hard_mask = key_padding_mask == 1
all_attn = all_attn.masked_fill(hard_mask[:,None,:,None], 0)
all_attn = all_attn.to(q)
all_attn = self.drop_attn(all_attn)
win_attn_probs = all_attn[:,:,:,:win_attn_weights.shape[-1]]
seq_len = win_attn_probs.shape[2]
win_attn_probs = win_attn_probs.view(batch_size, self.num_heads, seq_len // self.window_size, self.window_size,-1)
V_tiles = self.get_tiles_v2(V, transpose=False)
outputs = win_attn_probs.matmul(V_tiles).view(batch_size, self.num_heads, seq_len, self.head_dim)
# get rid of the padding positions
outputs = outputs[:,:,:sequence_length].view(-1, sequence_length, self.head_dim)
return outputs
def split_heads(self, X):
X = X.reshape(X.size(0), X.size(1), self.num_heads, self.head_dim)
X = X.transpose(1, 2)
return X
def sliding_chunks_matmul_qk_v2(self, Q, K, padding_mask):
bsz, num_heads, seqlen, d_h = Q.shape
if self.window_size > 0:
# Q, K: bsz x num_heads x seqlen x d_head
# padding_mask: bsz x seqlen
mask_tiles = self.get_tiled_mask_v2(padding_mask)
K_tiles = self.get_tiles_v2(K, transpose=True)
Q_tiles = Q.view(bsz, num_heads, seqlen // self.window_size, self.window_size, d_h)
# bsz x num_heads x seqlen//winsize x winsize x 2winsize
qk_scores = Q_tiles.matmul(K_tiles)
qk_scores = qk_scores.masked_fill(mask_tiles, -10000)
return qk_scores.view(bsz, num_heads, seqlen, -1)
else:
qk_scores = torch.sum(Q*K, dim=-1, keepdim=True)
return qk_scores
def get_tiled_mask_v2(self, mask):
# only mask along the key dimension
bsz, seqlen = mask.shape
ext_len = max(self.window_size//2, 1)
mask = F.pad(mask, (ext_len, ext_len), value=True) # (bsz, seq_len + 2*ext_len)
out_shape = (bsz, seqlen//self.window_size, 2*ext_len + self.window_size)
in_stride = mask.stride()
out_stride = (in_stride[0], in_stride[1]*self.window_size, in_stride[1])
return mask.as_strided(size=out_shape, stride=out_stride)[:, None, :, None, :]
def get_tiles_v2(self, x, transpose=False):
if self.window_size <= 0:
return x
bsz, n_heads, seqlen, d_h = x.shape
n_groups = seqlen // self.window_size
ext_len = max(self.window_size//2, 1)
x = F.pad(x, (0, 0, ext_len, ext_len), value=0)
strides = x.stride()
if transpose:
out_shape = (bsz, n_heads, n_groups, d_h, 2 * ext_len + self.window_size)
out_stride = (strides[0], strides[1], self.window_size * strides[2], strides[3], strides[2])
else:
out_shape = (bsz, n_heads, n_groups, 2 * ext_len + self.window_size, d_h)
out_stride = (strides[0], strides[1], self.window_size * strides[2], strides[2], strides[3])
return torch.as_strided(x, size=out_shape, stride=out_stride)
def expand_dim(t, dim, k):
expand_shape = [-1] * len(t.shape)
expand_shape[dim] = k
return t.expand(*expand_shape)
def merge_dims(ind_from, ind_to, tensor):
shape = list(tensor.shape)
arr_slice = slice(ind_from, ind_to + 1)
shape[arr_slice] = [reduce(mul, shape[arr_slice])]
return tensor.reshape(*shape)
def default(x, d):
if x is None:
return d if not isfunction(d) else d()
return x
def bucket(buckets, t, dim=1):
shape = list(t.shape)
shape[dim:dim+1] = [buckets, -1]
return t.reshape(*shape)
def unbucket(t, dim=1):
shape = list(t.shape)
shape[dim:dim+2] = [-1]
return t.reshape(*shape)
|
bart_ls-main
|
fairseq-py/fairseq/models/long_transformers/block.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from .hub_interface import * # noqa
from .model import * # noqa
|
bart_ls-main
|
fairseq-py/fairseq/models/bart/__init__.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""
BART: Denoising Sequence-to-Sequence Pre-training for
Natural Language Generation, Translation, and Comprehension
"""
from typing import Optional
import logging
import torch
import torch.nn as nn
from fairseq import utils
from fairseq.utils import safe_getattr
from fairseq.models import register_model, register_model_architecture
from fairseq.models.transformer import TransformerModel
from fairseq.modules.transformer_sentence_encoder import init_bert_params
from fairseq.models.transformer.transformer_config import TransformerConfig
from .hub_interface import BARTHubInterface
logger = logging.getLogger(__name__)
@register_model("bart")
class BARTModel(TransformerModel):
__jit_unused_properties__ = ["supported_targets"]
@classmethod
def hub_models(cls):
return {
"bart.base": "http://dl.fbaipublicfiles.com/fairseq/models/bart.base.tar.gz",
"bart.large": "http://dl.fbaipublicfiles.com/fairseq/models/bart.large.tar.gz",
"bart.large.mnli": "http://dl.fbaipublicfiles.com/fairseq/models/bart.large.mnli.tar.gz",
"bart.large.cnn": "http://dl.fbaipublicfiles.com/fairseq/models/bart.large.cnn.tar.gz",
"bart.large.xsum": "http://dl.fbaipublicfiles.com/fairseq/models/bart.large.xsum.tar.gz",
}
def __init__(self, args, encoder, decoder):
super().__init__(args, encoder, decoder)
# We follow BERT's random weight initialization
self.apply(init_bert_params)
self.classification_heads = nn.ModuleDict()
if hasattr(self.encoder, "dictionary"):
self.eos: int = self.encoder.dictionary.eos()
@staticmethod
def add_args(parser):
super(BARTModel, BARTModel).add_args(parser)
parser.add_argument(
"--pooler-dropout",
type=float,
metavar="D",
help="dropout probability in the masked_lm pooler layers",
)
parser.add_argument(
"--pooler-activation-fn",
choices=utils.get_available_activation_fns(),
help="activation function to use for pooler layer",
)
parser.add_argument(
"--spectral-norm-classification-head",
action="store_true",
help="Apply spectral normalization on the classification head",
)
parser.add_argument(
'--restrict-position-embed',
action='store_true',
default=False,
help="do no extend the position embeddings"
)
parser.add_argument(
'--sliding-window',
action='store_true',
default=False,
help="use sliding window attention as in longformer",
)
parser.add_argument(
'--block-attention',
action='store_true',
default=False,
help="use block attention",
)
parser.add_argument(
'--top-down',
action='store_true',
default=False,
help="use block attention",
)
@classmethod
def build_decoder(cls, args, tgt_dict, embed_tokens):
# use vanilla attention for now
args.use_xformers = False
return super().build_decoder(
TransformerConfig.from_namespace(args), tgt_dict, embed_tokens
)
@classmethod
def build_encoder(cls, args, src_dict, embed_tokens):
# if args.sliding_window:
# from fairseq.models.long_transformers.sliding_window import SWTransformerEncoder
# args.attention_window = [512]
# return SWTransformerEncoder(args, src_dict, embed_tokens)
if args.block_attention:
args.window_size = 1024
from fairseq.models.long_transformers.block import BlockTransformerEncoder
return BlockTransformerEncoder(args, src_dict, embed_tokens)
# if args.top_down:
# args.window_size = 1024
# args.encoder_n1 = 8
# args.encoder_n2 = 2
# args.encoder_n3 = 4
# from fairseq.models.long_transformers.top_down import TopDownTransformerEncoder
# return TopDownTransformerEncoder(args, src_dict, embed_tokens)
return super().build_encoder(
TransformerConfig.from_namespace(args), src_dict, embed_tokens
)
def load_state_dict(
self,
state_dict,
strict=True,
model_cfg = None,
args = None,
):
# if self.args.top_down:
# strict=False
return super().load_state_dict(state_dict, strict, model_cfg, args)
@property
def supported_targets(self):
return {"self"}
def forward(
self,
src_tokens,
src_lengths,
prev_output_tokens,
features_only: bool = False,
classification_head_name: Optional[str] = None,
token_embeddings: Optional[torch.Tensor] = None,
return_all_hiddens: bool = True,
alignment_layer: Optional[int] = None,
alignment_heads: Optional[int] = None,
masked_unfiltered: Optional[torch.Tensor] = None,
):
if classification_head_name is not None:
features_only = True
encoder_out = self.encoder(
src_tokens,
src_lengths=src_lengths,
token_embeddings=token_embeddings,
return_all_hiddens=return_all_hiddens
)
x, extra = self.decoder(
prev_output_tokens,
encoder_out=encoder_out,
features_only=features_only,
alignment_layer=alignment_layer,
alignment_heads=alignment_heads,
src_lengths=src_lengths,
return_all_hiddens=return_all_hiddens,
)
eos: int = self.eos
if classification_head_name is not None:
sentence_representation = x[
src_tokens.eq(eos), :
].view(x.size(0), -1, x.size(-1))[:, -1, :]
for k, head in self.classification_heads.items():
# for torch script only supports iteration
if k == classification_head_name:
x = head(sentence_representation)
break
return x, extra
@classmethod
def from_pretrained(
cls,
model_name_or_path,
checkpoint_file="model.pt",
data_name_or_path=".",
bpe="gpt2",
sample_break_mode="eos",
**kwargs,
):
from fairseq import hub_utils
x = hub_utils.from_pretrained(
model_name_or_path,
checkpoint_file,
data_name_or_path,
archive_map=cls.hub_models(),
bpe=bpe,
load_checkpoint_heads=True,
sample_break_mode=sample_break_mode,
**kwargs,
)
return BARTHubInterface(x["args"], x["task"], x["models"][0])
def register_classification_head(
self, name, num_classes=None, inner_dim=None, **kwargs
):
"""Register a classification head."""
logger.info("Registering classification head: {0}".format(name))
if name in self.classification_heads:
prev_num_classes = self.classification_heads[name].out_proj.out_features
prev_inner_dim = self.classification_heads[name].dense.out_features
if num_classes != prev_num_classes or inner_dim != prev_inner_dim:
logger.warning(
're-registering head "{}" with num_classes {} (prev: {}) '
"and inner_dim {} (prev: {})".format(
name, num_classes, prev_num_classes, inner_dim, prev_inner_dim
)
)
self.classification_heads[name] = BARTClassificationHead(
input_dim=self.args.encoder_embed_dim,
inner_dim=inner_dim or self.args.encoder_embed_dim,
num_classes=num_classes,
activation_fn=self.args.pooler_activation_fn,
pooler_dropout=self.args.pooler_dropout,
do_spectral_norm=getattr(
self.args, "spectral_norm_classification_head", False
),
)
def upgrade_state_dict_named(self, state_dict, name):
super().upgrade_state_dict_named(state_dict, name)
prefix = name + "." if name != "" else ""
current_head_names = (
[]
if not hasattr(self, "classification_heads")
else self.classification_heads.keys()
)
# Handle new classification heads present in the state dict.
keys_to_delete = []
for k in state_dict.keys():
if not k.startswith(prefix + "classification_heads."):
continue
head_name = k[len(prefix + "classification_heads.") :].split(".")[0]
num_classes = state_dict[
prefix + "classification_heads." + head_name + ".out_proj.weight"
].size(0)
inner_dim = state_dict[
prefix + "classification_heads." + head_name + ".dense.weight"
].size(0)
if getattr(self.args, "load_checkpoint_heads", False):
if head_name not in current_head_names:
self.register_classification_head(head_name, num_classes, inner_dim)
else:
if head_name not in current_head_names:
logger.warning(
"deleting classification head ({}) from checkpoint "
"not present in current model: {}".format(head_name, k)
)
keys_to_delete.append(k)
elif (
num_classes
!= self.classification_heads[head_name].out_proj.out_features
or inner_dim
!= self.classification_heads[head_name].dense.out_features
):
logger.warning(
"deleting classification head ({}) from checkpoint "
"with different dimensions than current model: {}".format(
head_name, k
)
)
keys_to_delete.append(k)
for k in keys_to_delete:
del state_dict[k]
def truncate_emb(key):
if key in state_dict:
state_dict[key] = state_dict[key][:-1, :]
# When finetuning on translation task, remove last row of
# embedding matrix that corresponds to mask_idx token.
loaded_dict_size = state_dict["encoder.embed_tokens.weight"].size(0)
if (
loaded_dict_size == len(self.encoder.dictionary) + 1
and "<mask>" not in self.encoder.dictionary
):
truncate_emb("encoder.embed_tokens.weight")
truncate_emb("decoder.embed_tokens.weight")
truncate_emb("encoder.output_projection.weight")
truncate_emb("decoder.output_projection.weight")
# When continued pretraining on new set of languages for mbart,
# add extra lang embeddings at the end of embed_tokens.
# Note: newly added languages are assumed to have been added at the end.
if self.args.task == "multilingual_denoising" and loaded_dict_size < len(
self.encoder.dictionary
):
logger.info(
"Adding extra language embeddings not found in pretrained model for "
"continued pretraining of MBART on new set of languages."
)
loaded_mask_token_embedding = state_dict["encoder.embed_tokens.weight"][
-1, :
]
num_langids_to_add = len(self.encoder.dictionary) - loaded_dict_size
embed_dim = state_dict["encoder.embed_tokens.weight"].size(1)
new_lang_embed_to_add = torch.zeros(num_langids_to_add, embed_dim)
nn.init.normal_(new_lang_embed_to_add, mean=0, std=embed_dim ** -0.5)
new_lang_embed_to_add = new_lang_embed_to_add.to(
dtype=state_dict["encoder.embed_tokens.weight"].dtype,
)
state_dict["encoder.embed_tokens.weight"] = torch.cat(
[
state_dict["encoder.embed_tokens.weight"][
: loaded_dict_size - 1, :
],
new_lang_embed_to_add,
loaded_mask_token_embedding.unsqueeze(0),
]
)
state_dict["decoder.embed_tokens.weight"] = torch.cat(
[
state_dict["decoder.embed_tokens.weight"][
: loaded_dict_size - 1, :
],
new_lang_embed_to_add,
loaded_mask_token_embedding.unsqueeze(0),
]
)
# Copy any newly-added classification heads into the state dict
# with their current weights.
if hasattr(self, "classification_heads"):
cur_state = self.classification_heads.state_dict()
for k, v in cur_state.items():
if prefix + "classification_heads." + k not in state_dict:
logger.info("Overwriting " + prefix + "classification_heads." + k)
state_dict[prefix + "classification_heads." + k] = v
class BARTClassificationHead(nn.Module):
"""Head for sentence-level classification tasks."""
def __init__(
self,
input_dim,
inner_dim,
num_classes,
activation_fn,
pooler_dropout,
do_spectral_norm=False,
):
super().__init__()
self.dense = nn.Linear(input_dim, inner_dim)
self.activation_fn = utils.get_activation_fn(activation_fn)
self.dropout = nn.Dropout(p=pooler_dropout)
self.out_proj = nn.Linear(inner_dim, num_classes)
if do_spectral_norm:
self.out_proj = torch.nn.utils.spectral_norm(self.out_proj)
def forward(self, features, **kwargs):
x = features
x = self.dropout(x)
x = self.dense(x)
x = self.activation_fn(x)
x = self.dropout(x)
x = self.out_proj(x)
return x
def safe_getattr(obj, k, default=None):
from omegaconf import OmegaConf
if OmegaConf.is_config(obj):
return obj[k] if k in obj and obj[k] is not None else default
return getattr(obj, k, default)
@register_model_architecture("bart", "bart_large")
def bart_large_architecture(args):
# # @xwhan has to put it here due to a bug
# def getattr(args, key, value):
# return value
args.encoder_embed_path = getattr(args, "encoder_embed_path", None)
args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 1024)
args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 4 * 1024)
args.encoder_layers = getattr(args, "encoder_layers", 12)
args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 16)
args.encoder_normalize_before = getattr(args, "encoder_normalize_before", False)
args.encoder_learned_pos = getattr(args, "encoder_learned_pos", True)
args.decoder_embed_path = getattr(args, "decoder_embed_path", None)
args.decoder_embed_dim = getattr(args, "decoder_embed_dim", args.encoder_embed_dim)
args.decoder_ffn_embed_dim = getattr(
args, "decoder_ffn_embed_dim", args.encoder_ffn_embed_dim
)
args.decoder_layers = getattr(args, "decoder_layers", 12)
args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 16)
args.decoder_normalize_before = getattr(args, "decoder_normalize_before", False)
args.decoder_learned_pos = getattr(args, "decoder_learned_pos", True)
args.attention_dropout = getattr(args, "attention_dropout", 0.0)
args.relu_dropout = getattr(args, "relu_dropout", 0.0)
args.dropout = getattr(args, "dropout", 0.1)
args.max_target_positions = safe_getattr(args, "max_target_positions", 1024) #hack
args.max_source_positions = safe_getattr(args, "max_source_positions", 1024)
args.adaptive_softmax_cutoff = getattr(args, "adaptive_softmax_cutoff", None)
args.adaptive_softmax_dropout = getattr(args, "adaptive_softmax_dropout", 0)
args.share_decoder_input_output_embed = getattr(
args, "share_decoder_input_output_embed", True
)
args.share_all_embeddings = getattr(args, "share_all_embeddings", True)
args.decoder_output_dim = getattr(
args, "decoder_output_dim", args.decoder_embed_dim
)
args.decoder_input_dim = getattr(args, "decoder_input_dim", args.decoder_embed_dim)
args.no_scale_embedding = getattr(args, "no_scale_embedding", True)
args.layernorm_embedding = getattr(args, "layernorm_embedding", True)
args.activation_fn = getattr(args, "activation_fn", "gelu")
args.pooler_activation_fn = getattr(args, "pooler_activation_fn", "tanh")
args.pooler_dropout = getattr(args, "pooler_dropout", 0.0)
@register_model_architecture("bart", "bart_prelayernorm")
def bart_prelayernorm_architecture(args):
def getattr(args, key, value):
return value
args.encoder_embed_path = getattr(args, "encoder_embed_path", None)
args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 1024)
args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 4 * 1024)
args.encoder_layers = getattr(args, "encoder_layers", 12)
args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 16)
args.encoder_normalize_before = getattr(args, "encoder_normalize_before", True)
args.encoder_learned_pos = getattr(args, "encoder_learned_pos", True)
args.decoder_embed_path = getattr(args, "decoder_embed_path", None)
args.decoder_embed_dim = getattr(args, "decoder_embed_dim", args.encoder_embed_dim)
args.decoder_ffn_embed_dim = getattr(
args, "decoder_ffn_embed_dim", args.encoder_ffn_embed_dim
)
args.decoder_layers = getattr(args, "decoder_layers", 12)
args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 16)
args.decoder_normalize_before = getattr(args, "decoder_normalize_before", True)
args.decoder_learned_pos = getattr(args, "decoder_learned_pos", True)
args.attention_dropout = getattr(args, "attention_dropout", 0.0)
args.relu_dropout = getattr(args, "relu_dropout", 0.0)
args.dropout = getattr(args, "dropout", 0.1)
args.max_target_positions = safe_getattr(args, "max_target_positions", 1024)
args.max_source_positions = safe_getattr(args, "max_source_positions", 1024)
args.adaptive_softmax_cutoff = getattr(args, "adaptive_softmax_cutoff", None)
args.adaptive_softmax_dropout = getattr(args, "adaptive_softmax_dropout", 0)
args.share_decoder_input_output_embed = getattr(
args, "share_decoder_input_output_embed", True
)
args.share_all_embeddings = getattr(args, "share_all_embeddings", True)
args.decoder_output_dim = getattr(
args, "decoder_output_dim", args.decoder_embed_dim
)
args.decoder_input_dim = getattr(args, "decoder_input_dim", args.decoder_embed_dim)
args.no_scale_embedding = getattr(args, "no_scale_embedding", False)
args.layernorm_embedding = getattr(args, "layernorm_embedding", False)
args.activation_fn = getattr(args, "activation_fn", "gelu")
args.pooler_activation_fn = getattr(args, "pooler_activation_fn", "tanh")
args.pooler_dropout = getattr(args, "pooler_dropout", 0.0)
@register_model_architecture("bart", "bart_base")
def bart_base_architecture(args):
args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 768)
args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 4 * 768)
args.encoder_layers = getattr(args, "encoder_layers", 6)
args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 12)
args.decoder_layers = getattr(args, "decoder_layers", 6)
args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 12)
bart_large_architecture(args)
@register_model_architecture("bart", "bart_xlarge")
def bart_base_architecture(args):
bart_large_architecture(args)
args.encoder_layers = 24
args.decoder_layers = 24
@register_model_architecture("bart", "bart_slarge")
def bart_base_architecture(args):
bart_large_architecture(args)
args.encoder_layers = 16
args.decoder_layers = 16
@register_model_architecture("bart", "mbart_large")
def mbart_large_architecture(args):
args.no_scale_embedding = getattr(args, "no_scale_embedding", False)
bart_large_architecture(args)
@register_model_architecture("bart", "mbart_base")
def mbart_base_architecture(args):
args.no_scale_embedding = getattr(args, "no_scale_embedding", False)
bart_base_architecture(args)
@register_model_architecture("bart", "mbart_base_wmt20")
def mbart_base_wmt20_architecture(args):
args.layernorm_embedding = getattr(args, "layernorm_embedding", False)
mbart_base_architecture(args)
|
bart_ls-main
|
fairseq-py/fairseq/models/bart/model.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import copy
import logging
from typing import Dict, List
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import utils
from fairseq.data import encoders
from fairseq.hub_utils import GeneratorHubInterface
from omegaconf import open_dict
logger = logging.getLogger(__name__)
class BARTHubInterface(GeneratorHubInterface):
"""A simple PyTorch Hub interface to BART.
Usage: https://github.com/pytorch/fairseq/tree/main/examples/bart
"""
def __init__(self, cfg, task, model):
super().__init__(cfg, task, [model])
self.model = self.models[0]
def encode(
self, sentence: str, *addl_sentences, no_separator=True
) -> torch.LongTensor:
"""
BPE-encode a sentence (or multiple sentences).
Every sequence begins with a beginning-of-sentence (`<s>`) symbol.
Every sentence ends with an end-of-sentence (`</s>`).
Example (single sentence): `<s> a b c </s>`
Example (sentence pair): `<s> d e f </s> 1 2 3 </s>`
The BPE encoding follows GPT-2. One subtle detail is that the GPT-2 BPE
requires leading spaces. For example::
>>> bart.encode('Hello world').tolist()
[0, 31414, 232, 2]
>>> bart.encode(' world').tolist()
[0, 232, 2]
>>> bart.encode('world').tolist()
[0, 8331, 2]
"""
tokens = self.bpe.encode(sentence)
# if len(tokens.split(" ")) > min(self.max_positions) - 2:
if len(tokens.split(" ")) > self.max_positions[0] - 2:
# tokens = " ".join(tokens.split(" ")[: min(self.max_positions) - 2])
tokens = " ".join(tokens.split(" ")[: self.max_positions[0] - 2])
bpe_sentence = "<s> " + tokens + " </s>"
for s in addl_sentences:
bpe_sentence += " </s>" if not no_separator else ""
bpe_sentence += " " + self.bpe.encode(s) + " </s>"
tokens = self.task.source_dictionary.encode_line(bpe_sentence, append_eos=False)
return tokens.long()
def decode(self, tokens: torch.LongTensor):
assert tokens.dim() == 1
tokens = tokens.cpu().numpy()
if tokens[0] == self.task.source_dictionary.bos():
tokens = tokens[1:] # remove <s>
eos_mask = tokens == self.task.source_dictionary.eos()
doc_mask = eos_mask[1:] & eos_mask[:-1]
sentences = np.split(tokens, doc_mask.nonzero()[0] + 1)
sentences = [
self.bpe.decode(self.task.source_dictionary.string(s)) for s in sentences
]
if len(sentences) == 1:
return sentences[0]
return sentences
def _build_sample(self, src_tokens: List[torch.LongTensor]):
# assert torch.is_tensor(src_tokens)
dataset = self.task.build_dataset_for_inference(
src_tokens,
[x.numel() for x in src_tokens],
)
sample = dataset.collater(dataset)
sample = utils.apply_to_sample(lambda tensor: tensor.to(self.device), sample)
return sample
def generate(
self,
tokenized_sentences: List[torch.LongTensor],
*args,
inference_step_args=None,
skip_invalid_size_inputs=False,
**kwargs
) -> List[List[Dict[str, torch.Tensor]]]:
inference_step_args = inference_step_args or {}
if "prefix_tokens" in inference_step_args:
raise NotImplementedError("prefix generation not implemented for BART")
res = []
# inference_step_args["prefix_tokens"] = src_tokens.new_full(
# (src_tokens.size(0), 1), fill_value=self.task.source_dictionary.bos()
# ).to(device=self.device)
results = super().generate(
tokenized_sentences,
*args,
inference_step_args=inference_step_args,
skip_invalid_size_inputs=skip_invalid_size_inputs,
**kwargs
)
# for id, hypos in zip(batch['id'].tolist(), results):
# res.append((id, hypos))
# res = [hypos for _, hypos in sorted(res, key=lambda x: x[0])]
# return res
return results
def extract_features(
self, tokens: torch.LongTensor, return_all_hiddens: bool = False
) -> torch.Tensor:
if tokens.dim() == 1:
tokens = tokens.unsqueeze(0)
if tokens.size(-1) > min(self.model.max_positions()):
raise ValueError(
"tokens exceeds maximum length: {} > {}".format(
tokens.size(-1), self.model.max_positions()
)
)
tokens.to(device=self.device),
prev_output_tokens = tokens.clone()
prev_output_tokens[:, 0] = tokens.gather(
1,
(tokens.ne(self.task.source_dictionary.pad()).sum(dim=1) - 1).unsqueeze(-1),
).squeeze()
prev_output_tokens[:, 1:] = tokens[:, :-1]
features, extra = self.model(
src_tokens=tokens,
src_lengths=None,
prev_output_tokens=prev_output_tokens,
features_only=True,
return_all_hiddens=return_all_hiddens,
)
if return_all_hiddens:
# convert from T x B x C -> B x T x C
inner_states = extra["inner_states"]
return [inner_state.transpose(0, 1) for inner_state in inner_states]
else:
return features # just the last layer's features
def register_classification_head(
self, name: str, num_classes: int = None, embedding_size: int = None, **kwargs
):
self.model.register_classification_head(
name, num_classes=num_classes, embedding_size=embedding_size, **kwargs
)
def predict(self, head: str, tokens: torch.LongTensor, return_logits: bool = False):
if tokens.dim() == 1:
tokens = tokens.unsqueeze(0)
features = self.extract_features(tokens.to(device=self.device))
sentence_representation = features[
tokens.eq(self.task.source_dictionary.eos()), :
].view(features.size(0), -1, features.size(-1))[:, -1, :]
logits = self.model.classification_heads[head](sentence_representation)
if return_logits:
return logits
return F.log_softmax(logits, dim=-1)
def fill_mask(
self,
masked_inputs: List[str],
topk: int = 5,
match_source_len: bool = True,
**generate_kwargs
):
masked_token = '<mask>'
batch_tokens = []
for masked_input in masked_inputs:
assert masked_token in masked_input, \
"please add one {} token for the input".format(masked_token)
text_spans = masked_input.split(masked_token)
text_spans_bpe = (' {0} '.format(masked_token)).join(
[self.bpe.encode(text_span.rstrip()) for text_span in text_spans]
).strip()
tokens = self.task.source_dictionary.encode_line(
'<s> ' + text_spans_bpe + ' </s>',
append_eos=False,
add_if_not_exist=False,
).long()
batch_tokens.append(tokens)
# ensure beam size is at least as big as topk
generate_kwargs['beam'] = max(
topk,
generate_kwargs.get('beam', -1),
)
generate_kwargs['match_source_len'] = match_source_len
batch_hypos = self.generate(batch_tokens, **generate_kwargs)
return [
[(self.decode(hypo['tokens']), hypo['score']) for hypo in hypos[:topk]]
for hypos in batch_hypos
]
|
bart_ls-main
|
fairseq-py/fairseq/models/bart/hub_interface.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from .wav2vec import * # noqa
from .wav2vec2 import * # noqa
from .wav2vec2_asr import * # noqa
|
bart_ls-main
|
fairseq-py/fairseq/models/wav2vec/__init__.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from argparse import Namespace
import contextlib
import copy
import math
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from dataclasses import dataclass, field
from omegaconf import MISSING, II, open_dict
from typing import Any, Optional
from fairseq import checkpoint_utils, tasks, utils
from fairseq.dataclass import FairseqDataclass
from fairseq.dataclass.utils import convert_namespace_to_omegaconf
from fairseq.tasks import FairseqTask
from fairseq.models import (
BaseFairseqModel,
FairseqEncoder,
FairseqEncoderDecoderModel,
FairseqIncrementalDecoder,
register_model,
)
from fairseq.models.wav2vec.wav2vec2 import MASKING_DISTRIBUTION_CHOICES
from fairseq.modules import (
LayerNorm,
PositionalEmbedding,
TransformerDecoderLayer,
)
@dataclass
class Wav2Vec2AsrConfig(FairseqDataclass):
w2v_path: str = field(
default=MISSING, metadata={"help": "path to wav2vec 2.0 model"}
)
no_pretrained_weights: bool = field(
default=False, metadata={"help": "if true, does not load pretrained weights"}
)
dropout_input: float = field(
default=0.0,
metadata={"help": "dropout to apply to the input (after feat extr)"},
)
final_dropout: float = field(
default=0.0,
metadata={"help": "dropout after transformer and before final projection"},
)
dropout: float = field(
default=0.0, metadata={"help": "dropout probability inside wav2vec 2.0 model"}
)
attention_dropout: float = field(
default=0.0,
metadata={
"help": "dropout probability for attention weights inside wav2vec 2.0 model"
},
)
activation_dropout: float = field(
default=0.0,
metadata={
"help": "dropout probability after activation in FFN inside wav2vec 2.0 model"
},
)
conv_feature_layers: Optional[str] = field(
default="[(512, 10, 5)] + [(512, 3, 2)] * 4 + [(512,2,2)] + [(512,2,2)]",
metadata={
"help": (
"string describing convolutional feature extraction "
"layers in form of a python list that contains "
"[(dim, kernel_size, stride), ...]"
),
},
)
encoder_embed_dim: Optional[int] = field(
default=768, metadata={"help": "encoder embedding dimension"}
)
# masking
apply_mask: bool = field(
default=False, metadata={"help": "apply masking during fine-tuning"}
)
mask_length: int = field(
default=10, metadata={"help": "repeat the mask indices multiple times"}
)
mask_prob: float = field(
default=0.5,
metadata={
"help": "probability of replacing a token with mask (normalized by length)"
},
)
mask_selection: MASKING_DISTRIBUTION_CHOICES = field(
default="static", metadata={"help": "how to choose masks"}
)
mask_other: float = field(
default=0,
metadata={
"help": "secondary mask argument (used for more complex distributions), "
"see help in compute_mask_indices"
},
)
no_mask_overlap: bool = field(
default=False, metadata={"help": "whether to allow masks to overlap"}
)
mask_min_space: Optional[int] = field(
default=1,
metadata={"help": "min space between spans (if no overlap is enabled)"},
)
# channel masking
mask_channel_length: int = field(
default=10, metadata={"help": "length of the mask for features (channels)"}
)
mask_channel_prob: float = field(
default=0.0, metadata={"help": "probability of replacing a feature with 0"}
)
mask_channel_selection: MASKING_DISTRIBUTION_CHOICES = field(
default="static",
metadata={"help": "how to choose mask length for channel masking"},
)
mask_channel_other: float = field(
default=0,
metadata={
"help": "secondary mask argument (used for more complex distributions), "
"see help in compute_mask_indicesh"
},
)
no_mask_channel_overlap: bool = field(
default=False, metadata={"help": "whether to allow channel masks to overlap"}
)
freeze_finetune_updates: int = field(
default=0, metadata={"help": "dont finetune wav2vec for this many updates"}
)
feature_grad_mult: float = field(
default=0.0, metadata={"help": "reset feature grad mult in wav2vec 2.0 to this"}
)
layerdrop: float = field(
default=0.0, metadata={"help": "probability of dropping a layer in wav2vec 2.0"}
)
mask_channel_min_space: Optional[int] = field(
default=1,
metadata={"help": "min space between spans (if no overlap is enabled)"},
)
mask_channel_before: bool = False
normalize: bool = II("task.normalize")
data: str = II("task.data")
# this holds the loaded wav2vec args
w2v_args: Any = None
@dataclass
class Wav2Vec2CtcConfig(Wav2Vec2AsrConfig):
blank_weight: float = 0
blank_mode: str = "add"
@register_model("wav2vec_ctc", dataclass=Wav2Vec2CtcConfig)
class Wav2VecCtc(BaseFairseqModel):
def __init__(self, cfg: Wav2Vec2CtcConfig, w2v_encoder: BaseFairseqModel):
super().__init__()
self.cfg = cfg
self.w2v_encoder = w2v_encoder
self.blank_weight = cfg.blank_weight
self.blank_mode = cfg.blank_mode
def upgrade_state_dict_named(self, state_dict, name):
super().upgrade_state_dict_named(state_dict, name)
return state_dict
@classmethod
def build_model(cls, cfg: Wav2Vec2CtcConfig, task: FairseqTask):
"""Build a new model instance."""
w2v_encoder = Wav2VecEncoder(cfg, len(task.target_dictionary))
return cls(cfg, w2v_encoder)
def get_logits(self, net_output, normalize=False):
logits = net_output["encoder_out"]
if self.blank_weight != 0:
if self.blank_mode == "add":
logits[..., 0] += self.blank_weight
elif self.blank_mode == "set":
logits[..., 0] = self.blank_weight
else:
raise Exception(f"invalid blank mode {self.blank_mode}")
if net_output["padding_mask"] is not None and net_output["padding_mask"].any():
logits[net_output["padding_mask"].T][..., 0] = float("inf")
logits[net_output["padding_mask"].T][..., 1:] = float("-inf")
if normalize:
logits = utils.log_softmax(logits.float(), dim=-1)
return logits
def get_normalized_probs(self, net_output, log_probs):
"""Get normalized probabilities (or log probs) from a net's output."""
logits = self.get_logits(net_output)
if log_probs:
return utils.log_softmax(logits.float(), dim=-1)
else:
return utils.softmax(logits.float(), dim=-1)
def forward(self, **kwargs):
x = self.w2v_encoder(**kwargs)
return x
@dataclass
class Wav2Vec2Seq2SeqConfig(Wav2Vec2AsrConfig):
decoder_embed_dim: int = field(
default=768, metadata={"help": "decoder embedding dimension"}
)
decoder_ffn_embed_dim: int = field(
default=3072, metadata={"help": "decoder embedding dimension for FFN"}
)
decoder_layers: int = field(default=6, metadata={"help": "num of decoder layers"})
decoder_layerdrop: float = field(
default=0.0, metadata={"help": "decoder layerdrop chance"}
)
decoder_attention_heads: int = field(
default=4, metadata={"help": "num decoder attention heads"}
)
decoder_learned_pos: bool = field(
default=False,
metadata={"help": "use learned positional embeddings in the decoder"},
)
decoder_normalize_before: bool = field(
default=False, metadata={"help": "apply layernorm before each decoder block"}
)
no_token_positional_embeddings: bool = field(
default=False,
metadata={
"help": "if set, disables positional embeddings (outside self attention)"
},
)
decoder_dropout: float = field(
default=0.0, metadata={"help": "dropout probability in the decoder"}
)
decoder_attention_dropout: float = field(
default=0.0,
metadata={
"help": "dropout probability for attention weights inside the decoder"
},
)
decoder_activation_dropout: float = field(
default=0.0,
metadata={
"help": "dropout probability after activation in FFN inside the decoder"
},
)
max_target_positions: int = field(
default=2048, metadata={"help": "max target positions"}
)
share_decoder_input_output_embed: bool = field(
default=False, metadata={"help": "share decoder input and output embeddings"}
)
autoregressive: bool = II("task.autoregressive")
@register_model("wav2vec_seq2seq", dataclass=Wav2Vec2Seq2SeqConfig)
class Wav2Vec2Seq2SeqModel(FairseqEncoderDecoderModel):
def __init__(self, encoder, decoder):
super().__init__(encoder, decoder)
@classmethod
def build_model(cls, cfg: Wav2Vec2Seq2SeqConfig, task: FairseqTask):
"""Build a new model instance."""
assert (
cfg.autoregressive
), "Please set task.autoregressive=true for seq2seq asr models"
src_dict, tgt_dict = task.source_dictionary, task.target_dictionary
def build_embedding(dictionary, embed_dim):
num_embeddings = len(dictionary)
padding_idx = dictionary.pad()
emb = Embedding(num_embeddings, embed_dim, padding_idx)
return emb
decoder_embed_tokens = build_embedding(tgt_dict, cfg.decoder_embed_dim)
encoder = cls.build_encoder(cfg)
decoder = cls.build_decoder(cfg, tgt_dict, decoder_embed_tokens)
return Wav2Vec2Seq2SeqModel(encoder, decoder)
@classmethod
def build_encoder(cls, cfg: Wav2Vec2AsrConfig):
return Wav2VecEncoder(cfg)
@classmethod
def build_decoder(cls, cfg: Wav2Vec2Seq2SeqConfig, tgt_dict, embed_tokens):
return TransformerDecoder(cfg, tgt_dict, embed_tokens)
def forward(self, **kwargs):
encoder_out = self.encoder(**kwargs)
decoder_out = self.decoder(encoder_out=encoder_out, **kwargs)
return decoder_out
def upgrade_state_dict_named(self, state_dict, name):
super().upgrade_state_dict_named(state_dict, name)
return state_dict
class Wav2VecEncoder(FairseqEncoder):
def __init__(self, cfg: Wav2Vec2AsrConfig, output_size=None):
self.apply_mask = cfg.apply_mask
arg_overrides = {
"dropout": cfg.dropout,
"activation_dropout": cfg.activation_dropout,
"dropout_input": cfg.dropout_input,
"attention_dropout": cfg.attention_dropout,
"mask_length": cfg.mask_length,
"mask_prob": cfg.mask_prob,
"mask_selection": cfg.mask_selection,
"mask_other": cfg.mask_other,
"no_mask_overlap": cfg.no_mask_overlap,
"mask_channel_length": cfg.mask_channel_length,
"mask_channel_prob": cfg.mask_channel_prob,
"mask_channel_before": cfg.mask_channel_before,
"mask_channel_selection": cfg.mask_channel_selection,
"mask_channel_other": cfg.mask_channel_other,
"no_mask_channel_overlap": cfg.no_mask_channel_overlap,
"encoder_layerdrop": cfg.layerdrop,
"feature_grad_mult": cfg.feature_grad_mult,
}
if cfg.w2v_args is None:
state = checkpoint_utils.load_checkpoint_to_cpu(cfg.w2v_path, arg_overrides)
w2v_args = state.get("cfg", None)
if w2v_args is None:
w2v_args = convert_namespace_to_omegaconf(state["args"])
w2v_args.criterion = None
w2v_args.lr_scheduler = None
cfg.w2v_args = w2v_args
else:
state = None
w2v_args = cfg.w2v_args
if isinstance(w2v_args, Namespace):
cfg.w2v_args = w2v_args = convert_namespace_to_omegaconf(w2v_args)
assert cfg.normalize == w2v_args.task.normalize, (
"Fine-tuning works best when data normalization is the same. "
"Please check that --normalize is set or unset for both pre-training and here"
)
w2v_args.task.data = cfg.data
task = tasks.setup_task(w2v_args.task)
model = task.build_model(w2v_args.model)
if state is not None and not cfg.no_pretrained_weights:
model.load_state_dict(state["model"], strict=True)
model.remove_pretraining_modules()
super().__init__(task.source_dictionary)
d = w2v_args.model.encoder_embed_dim
self.w2v_model = model
self.final_dropout = nn.Dropout(cfg.final_dropout)
self.freeze_finetune_updates = cfg.freeze_finetune_updates
self.num_updates = 0
targ_d = None
self.proj = None
if output_size is not None:
targ_d = output_size
elif getattr(cfg, "decoder_embed_dim", d) != d:
targ_d = cfg.decoder_embed_dim
if targ_d is not None:
self.proj = Linear(d, targ_d)
def set_num_updates(self, num_updates):
"""Set the number of parameters updates."""
super().set_num_updates(num_updates)
self.num_updates = num_updates
def forward(self, source, padding_mask, **kwargs):
w2v_args = {
"source": source,
"padding_mask": padding_mask,
"mask": self.apply_mask and self.training,
}
ft = self.freeze_finetune_updates <= self.num_updates
with torch.no_grad() if not ft else contextlib.ExitStack():
res = self.w2v_model.extract_features(**w2v_args)
x = res["x"]
padding_mask = res["padding_mask"]
# B x T x C -> T x B x C
x = x.transpose(0, 1)
x = self.final_dropout(x)
if self.proj:
x = self.proj(x)
return {
"encoder_out": x, # T x B x C
"padding_mask": padding_mask, # B x T,
"layer_results": res["layer_results"],
}
def forward_torchscript(self, net_input):
if torch.jit.is_scripting():
return self.forward(net_input["source"], net_input["padding_mask"])
else:
return self.forward_non_torchscript(net_input)
def reorder_encoder_out(self, encoder_out, new_order):
if encoder_out["encoder_out"] is not None:
encoder_out["encoder_out"] = encoder_out["encoder_out"].index_select(
1, new_order
)
if encoder_out["padding_mask"] is not None:
encoder_out["padding_mask"] = encoder_out[
"padding_mask"
].index_select(0, new_order)
return encoder_out
def max_positions(self):
"""Maximum input length supported by the encoder."""
return None
def upgrade_state_dict_named(self, state_dict, name):
return state_dict
class TransformerDecoder(FairseqIncrementalDecoder):
"""
Transformer decoder consisting of *args.decoder_layers* layers. Each layer
is a :class:`TransformerDecoderLayer`.
Args:
args (argparse.Namespace): parsed command-line arguments
dictionary (~fairseq.data.Dictionary): decoding dictionary
embed_tokens (torch.nn.Embedding): output embedding
no_encoder_attn (bool, optional): whether to attend to encoder outputs
(default: False).
"""
def __init__(
self,
cfg: Wav2Vec2Seq2SeqConfig,
dictionary,
embed_tokens,
no_encoder_attn=False,
):
super().__init__(dictionary)
self.dropout = cfg.decoder_dropout
self.share_input_output_embed = cfg.share_decoder_input_output_embed
input_embed_dim = embed_tokens.embedding_dim
embed_dim = cfg.decoder_embed_dim
self.output_embed_dim = cfg.decoder_embed_dim
self.layerdrop = cfg.decoder_layerdrop
self.padding_idx = embed_tokens.padding_idx
self.max_target_positions = cfg.max_target_positions
self.embed_tokens = embed_tokens
self.embed_scale = math.sqrt(embed_dim) # todo: try with input_embed_dim
self.project_in_dim = (
Linear(input_embed_dim, embed_dim, bias=False)
if embed_dim != input_embed_dim
else None
)
self.embed_positions = (
PositionalEmbedding(
cfg.max_target_positions,
embed_dim,
self.padding_idx,
learned=cfg.decoder_learned_pos,
)
if not cfg.no_token_positional_embeddings
else None
)
# TODO: update this when transformer gets converted to dataclass configs
transformer_cfg = copy.deepcopy(cfg)
with open_dict(transformer_cfg):
transformer_cfg.dropout = transformer_cfg.decoder_dropout
transformer_cfg.attention_dropout = (
transformer_cfg.decoder_attention_dropout
)
transformer_cfg.activation_dropout = (
transformer_cfg.decoder_activation_dropout
)
self.layers = nn.ModuleList([])
self.layers.extend(
[
TransformerDecoderLayer(transformer_cfg, no_encoder_attn)
for _ in range(transformer_cfg.decoder_layers)
]
)
if not self.share_input_output_embed:
self.embed_out = nn.Parameter(
torch.Tensor(len(dictionary), self.output_embed_dim)
)
nn.init.normal_(self.embed_out, mean=0, std=self.output_embed_dim ** -0.5)
if transformer_cfg.decoder_normalize_before:
self.layer_norm = LayerNorm(embed_dim)
else:
self.layer_norm = None
def forward(
self, prev_output_tokens, encoder_out=None, incremental_state=None, **unused
):
"""
Args:
prev_output_tokens (LongTensor): previous decoder outputs of shape
`(batch, tgt_len)`, for teacher forcing
encoder_out (Tensor, optional): output from the encoder, used for
encoder-side attention
incremental_state (dict): dictionary used for storing state during
:ref:`Incremental decoding`
Returns:
tuple:
- the decoder's output of shape `(batch, tgt_len, vocab)`
- a dictionary with any model-specific outputs
"""
prev_output_tokens = prev_output_tokens.long()
x, extra = self.extract_features(
prev_output_tokens, encoder_out, incremental_state
)
x = self.output_layer(x)
return x, extra
def extract_features(
self, prev_output_tokens, encoder_out=None, incremental_state=None, **unused
):
"""
Similar to *forward* but only return features.
Returns:
tuple:
- the decoder's features of shape `(batch, tgt_len, embed_dim)`
- a dictionary with any model-specific outputs
"""
# embed positions
positions = (
self.embed_positions(
prev_output_tokens, incremental_state=incremental_state
)
if self.embed_positions is not None
else None
)
if incremental_state is not None:
prev_output_tokens = prev_output_tokens[:, -1:]
if positions is not None:
positions = positions[:, -1:]
# embed tokens and positions
x = self.embed_scale * self.embed_tokens(prev_output_tokens)
if self.project_in_dim is not None:
x = self.project_in_dim(x)
if positions is not None:
x += positions
x = F.dropout(x, p=self.dropout, training=self.training)
# B x T x C -> T x B x C
x = x.transpose(0, 1)
attn = None
inner_states = [x]
# decoder layers
self_attn_padding_mask = None
if prev_output_tokens.eq(self.padding_idx).any():
self_attn_padding_mask = prev_output_tokens.eq(self.padding_idx)
for layer in self.layers:
dropout_probability = np.random.random()
if not self.training or (dropout_probability > self.layerdrop):
x, attn, _ = layer(
x,
encoder_out["encoder_out"] if encoder_out is not None else None,
encoder_out["padding_mask"] if encoder_out is not None else None,
incremental_state,
self_attn_mask=self.buffered_future_mask(x)
if incremental_state is None
else None,
self_attn_padding_mask=self_attn_padding_mask
)
inner_states.append(x)
if self.layer_norm:
x = self.layer_norm(x)
# T x B x C -> B x T x C
x = x.transpose(0, 1)
return x, {"attn": attn, "inner_states": inner_states}
def output_layer(self, features, **kwargs):
"""Project features to the vocabulary size."""
# project back to size of vocabulary
if self.share_input_output_embed:
return F.linear(features, self.embed_tokens.weight)
else:
return F.linear(features, self.embed_out)
def max_positions(self):
"""Maximum output length supported by the decoder."""
if self.embed_positions is None:
return self.max_target_positions
return min(self.max_target_positions, self.embed_positions.max_positions)
def buffered_future_mask(self, tensor):
dim = tensor.size(0)
if (
not hasattr(self, "_future_mask")
or self._future_mask is None
or self._future_mask.device != tensor.device
or self._future_mask.size(0) < dim
):
self._future_mask = torch.triu(
utils.fill_with_neg_inf(tensor.new(dim, dim)), 1
)
return self._future_mask[:dim, :dim]
def upgrade_state_dict_named(self, state_dict, name):
return state_dict
def Embedding(num_embeddings, embedding_dim, padding_idx):
m = nn.Embedding(num_embeddings, embedding_dim, padding_idx=padding_idx)
nn.init.normal_(m.weight, mean=0, std=embedding_dim ** -0.5)
nn.init.constant_(m.weight[padding_idx], 0)
return m
def Linear(in_features, out_features, bias=True):
m = nn.Linear(in_features, out_features, bias)
nn.init.xavier_uniform_(m.weight)
if bias:
nn.init.constant_(m.bias, 0.0)
return m
|
bart_ls-main
|
fairseq-py/fairseq/models/wav2vec/wav2vec2_asr.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import math
from dataclasses import dataclass, field
from typing import List, Tuple
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import utils
from fairseq.data.data_utils import compute_mask_indices
from fairseq.dataclass import ChoiceEnum, FairseqDataclass
from fairseq.models import BaseFairseqModel, register_model
from fairseq.modules import (
Fp32GroupNorm,
Fp32LayerNorm,
GradMultiply,
GumbelVectorQuantizer,
LayerNorm,
MultiheadAttention,
SamePad,
TransposeLast,
)
from fairseq.modules.transformer_sentence_encoder import init_bert_params
from fairseq.utils import buffered_arange, index_put, is_xla_tensor
EXTRACTOR_MODE_CHOICES = ChoiceEnum(["default", "layer_norm"])
MASKING_DISTRIBUTION_CHOICES = ChoiceEnum(["static", "uniform", "normal", "poisson"])
@dataclass
class Wav2Vec2Config(FairseqDataclass):
extractor_mode: EXTRACTOR_MODE_CHOICES = field(
default="default",
metadata={
"help": "mode for feature extractor. default has a single group norm with d "
"groups in the first conv block, whereas layer_norm has layer norms in "
"every block (meant to use with normalize=True)"
},
)
encoder_layers: int = field(
default=12, metadata={"help": "num encoder layers in the transformer"}
)
encoder_embed_dim: int = field(
default=768, metadata={"help": "encoder embedding dimension"}
)
encoder_ffn_embed_dim: int = field(
default=3072, metadata={"help": "encoder embedding dimension for FFN"}
)
encoder_attention_heads: int = field(
default=12, metadata={"help": "num encoder attention heads"}
)
activation_fn: ChoiceEnum(utils.get_available_activation_fns()) = field(
default="gelu", metadata={"help": "activation function to use"}
)
# dropouts
dropout: float = field(
default=0.1, metadata={"help": "dropout probability for the transformer"}
)
attention_dropout: float = field(
default=0.1, metadata={"help": "dropout probability for attention weights"}
)
activation_dropout: float = field(
default=0.0, metadata={"help": "dropout probability after activation in FFN"}
)
encoder_layerdrop: float = field(
default=0.0, metadata={"help": "probability of dropping a tarnsformer layer"}
)
dropout_input: float = field(
default=0.0,
metadata={"help": "dropout to apply to the input (after feat extr)"},
)
dropout_features: float = field(
default=0.0,
metadata={"help": "dropout to apply to the features (after feat extr)"},
)
final_dim: int = field(
default=0,
metadata={
"help": "project final representations and targets to this many dimensions."
"set to encoder_embed_dim is <= 0"
},
)
layer_norm_first: bool = field(
default=False, metadata={"help": "apply layernorm first in the transformer"}
)
conv_feature_layers: str = field(
default="[(512, 10, 5)] + [(512, 3, 2)] * 4 + [(512,2,2)] + [(512,2,2)]",
metadata={
"help": "string describing convolutional feature extraction layers in form of a python list that contains "
"[(dim, kernel_size, stride), ...]"
},
)
conv_bias: bool = field(
default=False, metadata={"help": "include bias in conv encoder"}
)
logit_temp: float = field(
default=0.1, metadata={"help": "temperature to divide logits by"}
)
quantize_targets: bool = field(
default=False, metadata={"help": "use quantized targets"}
)
quantize_input: bool = field(
default=False, metadata={"help": "use quantized inputs"}
)
same_quantizer: bool = field(
default=False, metadata={"help": "use same quantizer for inputs and targets"}
)
target_glu: bool = field(
default=False, metadata={"help": "adds projection + glu to targets"}
)
feature_grad_mult: float = field(
default=1.0, metadata={"help": "multiply feature extractor var grads by this"}
)
quantizer_depth: int = field(
default=1,
metadata={"help": "number of quantizer layers"},
)
quantizer_factor: int = field(
default=3,
metadata={
"help": "dimensionality increase for inner quantizer layers (if depth > 1)"
},
)
latent_vars: int = field(
default=320,
metadata={"help": "number of latent variables V in each group of the codebook"},
)
latent_groups: int = field(
default=2,
metadata={"help": "number of groups G of latent variables in the codebook"},
)
latent_dim: int = field(
default=0,
metadata={
"help": "if > 0, uses this dimensionality for latent variables. "
"otherwise uses final_dim / latent_groups"
},
)
# masking
mask_length: int = field(default=10, metadata={"help": "mask length"})
mask_prob: float = field(
default=0.65, metadata={"help": "probability of replacing a token with mask"}
)
mask_selection: MASKING_DISTRIBUTION_CHOICES = field(
default="static", metadata={"help": "how to choose mask length"}
)
mask_other: float = field(
default=0,
metadata={
"help": "secondary mask argument (used for more complex distributions), "
"see help in compute_mask_indices"
},
)
no_mask_overlap: bool = field(
default=False, metadata={"help": "whether to allow masks to overlap"}
)
mask_min_space: int = field(
default=1,
metadata={"help": "min space between spans (if no overlap is enabled)"},
)
# channel masking
mask_channel_length: int = field(
default=10, metadata={"help": "length of the mask for features (channels)"}
)
mask_channel_prob: float = field(
default=0.0, metadata={"help": "probability of replacing a feature with 0"}
)
mask_channel_before: bool = False
mask_channel_selection: MASKING_DISTRIBUTION_CHOICES = field(
default="static",
metadata={"help": "how to choose mask length for channel masking"},
)
mask_channel_other: float = field(
default=0,
metadata={
"help": "secondary mask argument (used for more complex distributions), "
"see help in compute_mask_indicesh"
},
)
no_mask_channel_overlap: bool = field(
default=False, metadata={"help": "whether to allow channel masks to overlap"}
)
mask_channel_min_space: int = field(
default=1,
metadata={"help": "min space between spans (if no overlap is enabled)"},
)
# negative selection
num_negatives: int = field(
default=100,
metadata={"help": "number of negative examples from the same sample"},
)
negatives_from_everywhere: bool = field(
default=False,
metadata={"help": "sample negatives from everywhere, not just masked states"},
)
cross_sample_negatives: int = field(
default=0, metadata={"help": "number of negative examples from the any sample"}
)
codebook_negatives: int = field(
default=0, metadata={"help": "number of negative examples codebook"}
)
# positional embeddings
conv_pos: int = field(
default=128,
metadata={"help": "number of filters for convolutional positional embeddings"},
)
conv_pos_groups: int = field(
default=16,
metadata={"help": "number of groups for convolutional positional embedding"},
)
latent_temp: Tuple[float, float, float] = field(
default=(2, 0.5, 0.999995),
metadata={
"help": "temperature for latent variable sampling. "
"can be tuple of 3 values (start, end, decay)"
},
)
@register_model("wav2vec2", dataclass=Wav2Vec2Config)
class Wav2Vec2Model(BaseFairseqModel):
def __init__(self, cfg: Wav2Vec2Config):
super().__init__()
self.cfg = cfg
feature_enc_layers = eval(cfg.conv_feature_layers)
self.embed = feature_enc_layers[-1][0]
self.feature_extractor = ConvFeatureExtractionModel(
conv_layers=feature_enc_layers,
dropout=0.0,
mode=cfg.extractor_mode,
conv_bias=cfg.conv_bias,
)
self.post_extract_proj = (
nn.Linear(self.embed, cfg.encoder_embed_dim)
if self.embed != cfg.encoder_embed_dim and not cfg.quantize_input
else None
)
self.mask_prob = cfg.mask_prob
self.mask_selection = cfg.mask_selection
self.mask_other = cfg.mask_other
self.mask_length = cfg.mask_length
self.no_mask_overlap = cfg.no_mask_overlap
self.mask_min_space = cfg.mask_min_space
self.mask_channel_prob = cfg.mask_channel_prob
self.mask_channel_before = cfg.mask_channel_before
self.mask_channel_selection = cfg.mask_channel_selection
self.mask_channel_other = cfg.mask_channel_other
self.mask_channel_length = cfg.mask_channel_length
self.no_mask_channel_overlap = cfg.no_mask_channel_overlap
self.mask_channel_min_space = cfg.mask_channel_min_space
self.dropout_input = nn.Dropout(cfg.dropout_input)
self.dropout_features = nn.Dropout(cfg.dropout_features)
self.feature_grad_mult = cfg.feature_grad_mult
self.quantizer = None
self.input_quantizer = None
self.n_negatives = cfg.num_negatives
self.cross_sample_negatives = cfg.cross_sample_negatives
self.codebook_negatives = cfg.codebook_negatives
self.negatives_from_everywhere = cfg.negatives_from_everywhere
self.logit_temp = cfg.logit_temp
final_dim = cfg.final_dim if cfg.final_dim > 0 else cfg.encoder_embed_dim
if cfg.quantize_targets:
vq_dim = cfg.latent_dim if cfg.latent_dim > 0 else final_dim
self.quantizer = GumbelVectorQuantizer(
dim=self.embed,
num_vars=cfg.latent_vars,
temp=cfg.latent_temp,
groups=cfg.latent_groups,
combine_groups=False,
vq_dim=vq_dim,
time_first=True,
weight_proj_depth=cfg.quantizer_depth,
weight_proj_factor=cfg.quantizer_factor,
)
self.project_q = nn.Linear(vq_dim, final_dim)
else:
self.project_q = nn.Linear(self.embed, final_dim)
if cfg.quantize_input:
if cfg.same_quantizer and self.quantizer is not None:
vq_dim = final_dim
self.input_quantizer = self.quantizer
else:
vq_dim = cfg.latent_dim if cfg.latent_dim > 0 else cfg.encoder_embed_dim
self.input_quantizer = GumbelVectorQuantizer(
dim=self.embed,
num_vars=cfg.latent_vars,
temp=cfg.latent_temp,
groups=cfg.latent_groups,
combine_groups=False,
vq_dim=vq_dim,
time_first=True,
weight_proj_depth=cfg.quantizer_depth,
weight_proj_factor=cfg.quantizer_factor,
)
self.project_inp = nn.Linear(vq_dim, cfg.encoder_embed_dim)
self.mask_emb = nn.Parameter(
torch.FloatTensor(cfg.encoder_embed_dim).uniform_()
)
self.encoder = TransformerEncoder(cfg)
self.layer_norm = LayerNorm(self.embed)
self.target_glu = None
if cfg.target_glu:
self.target_glu = nn.Sequential(
nn.Linear(final_dim, final_dim * 2), nn.GLU()
)
self.final_proj = nn.Linear(cfg.encoder_embed_dim, final_dim)
def upgrade_state_dict_named(self, state_dict, name):
super().upgrade_state_dict_named(state_dict, name)
"""Upgrade a (possibly old) state dict for new versions of fairseq."""
return state_dict
@classmethod
def build_model(cls, cfg: Wav2Vec2Config, task=None):
"""Build a new model instance."""
return cls(cfg)
def apply_mask(
self,
x,
padding_mask,
mask_indices=None,
mask_channel_indices=None,
):
B, T, C = x.shape
if self.mask_channel_prob > 0 and self.mask_channel_before:
mask_channel_indices = compute_mask_indices(
(B, C),
None,
self.mask_channel_prob,
self.mask_channel_length,
self.mask_channel_selection,
self.mask_channel_other,
no_overlap=self.no_mask_channel_overlap,
min_space=self.mask_channel_min_space,
)
mask_channel_indices = (
torch.from_numpy(mask_channel_indices)
.to(x.device)
.unsqueeze(1)
.expand(-1, T, -1)
)
x[mask_channel_indices] = 0
if self.mask_prob > 0:
if mask_indices is None:
mask_indices = compute_mask_indices(
(B, T),
padding_mask,
self.mask_prob,
self.mask_length,
self.mask_selection,
self.mask_other,
min_masks=2,
no_overlap=self.no_mask_overlap,
min_space=self.mask_min_space,
)
mask_indices = torch.from_numpy(mask_indices).to(x.device)
x = index_put(x, mask_indices, self.mask_emb)
else:
mask_indices = None
if self.mask_channel_prob > 0 and not self.mask_channel_before:
if mask_channel_indices is None:
mask_channel_indices = compute_mask_indices(
(B, C),
None,
self.mask_channel_prob,
self.mask_channel_length,
self.mask_channel_selection,
self.mask_channel_other,
no_overlap=self.no_mask_channel_overlap,
min_space=self.mask_channel_min_space,
)
mask_channel_indices = (
torch.from_numpy(mask_channel_indices)
.to(x.device)
.unsqueeze(1)
.expand(-1, T, -1)
)
x = index_put(x, mask_channel_indices, 0)
return x, mask_indices
def sample_negatives(self, y, num, padding_count=None):
if self.n_negatives == 0 and self.cross_sample_negatives == 0:
return y.new(0)
bsz, tsz, fsz = y.shape
y = y.view(-1, fsz) # BTC => (BxT)C
# FIXME: what happens if padding_count is specified?
cross_high = tsz * bsz
high = tsz - (padding_count or 0)
with torch.no_grad():
assert high > 1, f"{bsz,tsz,fsz}"
if self.n_negatives > 0:
tszs = (
buffered_arange(num)
.unsqueeze(-1)
.expand(-1, self.n_negatives)
.flatten()
)
neg_idxs = torch.randint(
low=0, high=high - 1, size=(bsz, self.n_negatives * num)
)
neg_idxs[neg_idxs >= tszs] += 1
if self.cross_sample_negatives > 0:
tszs = (
buffered_arange(num)
.unsqueeze(-1)
.expand(-1, self.cross_sample_negatives)
.flatten()
)
cross_neg_idxs = torch.randint(
low=0,
high=cross_high - 1,
size=(bsz, self.cross_sample_negatives * num),
)
cross_neg_idxs[cross_neg_idxs >= tszs] += 1
if self.n_negatives > 0:
for i in range(1, bsz):
neg_idxs[i] += i * high
else:
neg_idxs = cross_neg_idxs
if self.cross_sample_negatives > 0 and self.n_negatives > 0:
neg_idxs = torch.cat([neg_idxs, cross_neg_idxs], dim=1)
negs = y[neg_idxs.view(-1)]
negs = negs.view(
bsz, num, self.n_negatives + self.cross_sample_negatives, fsz
).permute(
2, 0, 1, 3
) # to NxBxTxC
return negs, neg_idxs
def compute_preds(self, x, y, negatives):
neg_is_pos = (y == negatives).all(-1)
y = y.unsqueeze(0)
targets = torch.cat([y, negatives], dim=0)
logits = torch.cosine_similarity(x.float(), targets.float(), dim=-1).type_as(x)
logits = logits / self.logit_temp
if is_xla_tensor(logits) or neg_is_pos.any():
fillval = -float(2 ** 30)
if not hasattr(self, "_inftensor"):
self._inftensor = (
torch.tensor(fillval).to(x.device)
if is_xla_tensor(logits)
else float("-inf")
)
logits[1:] = index_put(logits[1:], neg_is_pos, self._inftensor)
return logits
def _get_feat_extract_output_lengths(self, input_lengths: torch.LongTensor):
"""
Computes the output length of the convolutional layers
"""
def _conv_out_length(input_length, kernel_size, stride):
return torch.floor((input_length - kernel_size) / stride + 1)
conv_cfg_list = eval(self.cfg.conv_feature_layers)
for i in range(len(conv_cfg_list)):
input_lengths = _conv_out_length(
input_lengths, conv_cfg_list[i][1], conv_cfg_list[i][2]
)
return input_lengths.to(torch.long)
def forward(
self,
source,
padding_mask=None,
mask=True,
features_only=False,
layer=None,
mask_indices=None,
mask_channel_indices=None,
padding_count=None,
):
if self.feature_grad_mult > 0:
features = self.feature_extractor(source)
if self.feature_grad_mult != 1.0:
features = GradMultiply.apply(features, self.feature_grad_mult)
else:
with torch.no_grad():
features = self.feature_extractor(source)
features_pen = features.float().pow(2).mean()
features = features.transpose(1, 2)
features = self.layer_norm(features)
unmasked_features = features.clone()
if padding_mask is not None and padding_mask.any():
input_lengths = (1 - padding_mask.long()).sum(-1)
# apply conv formula to get real output_lengths
output_lengths = self._get_feat_extract_output_lengths(input_lengths)
padding_mask = torch.zeros(
features.shape[:2], dtype=features.dtype, device=features.device
)
# these two operations makes sure that all values
# before the output lengths indices are attended to
padding_mask[
(
torch.arange(padding_mask.shape[0], device=padding_mask.device),
output_lengths - 1,
)
] = 1
padding_mask = (1 - padding_mask.flip([-1]).cumsum(-1).flip([-1])).bool()
else:
padding_mask = None
if self.post_extract_proj is not None:
features = self.post_extract_proj(features)
features = self.dropout_input(features)
unmasked_features = self.dropout_features(unmasked_features)
num_vars = None
code_ppl = None
prob_ppl = None
curr_temp = None
if self.input_quantizer:
q = self.input_quantizer(features, produce_targets=False)
features = q["x"]
num_vars = q["num_vars"]
code_ppl = q["code_perplexity"]
prob_ppl = q["prob_perplexity"]
curr_temp = q["temp"]
features = self.project_inp(features)
if mask:
x, mask_indices = self.apply_mask(
features,
padding_mask,
mask_indices=mask_indices,
mask_channel_indices=mask_channel_indices,
)
if not is_xla_tensor(x) and mask_indices is not None:
# tpu-comment: reducing the size in a dynamic way causes
# too many recompilations on xla.
y = unmasked_features[mask_indices].view(
unmasked_features.size(0), -1, unmasked_features.size(-1)
)
else:
y = unmasked_features
else:
x = features
y = unmasked_features
mask_indices = None
x, layer_results = self.encoder(x, padding_mask=padding_mask, layer=layer)
if features_only:
return {
"x": x,
"padding_mask": padding_mask,
"features": unmasked_features,
"layer_results": layer_results,
}
if self.quantizer:
q = self.quantizer(y, produce_targets=False)
y = q["x"]
num_vars = q["num_vars"]
code_ppl = q["code_perplexity"]
prob_ppl = q["prob_perplexity"]
curr_temp = q["temp"]
y = self.project_q(y)
if self.negatives_from_everywhere:
neg_cands = self.quantizer(unmasked_features, produce_targets=False)[
"x"
]
negs, _ = self.sample_negatives(
neg_cands,
y.size(1),
padding_count=padding_count,
)
negs = self.project_q(negs)
else:
negs, _ = self.sample_negatives(
y,
y.size(1),
padding_count=padding_count,
)
if self.codebook_negatives > 0:
cb_negs = self.quantizer.sample_from_codebook(
y.size(0) * y.size(1), self.codebook_negatives
)
cb_negs = cb_negs.view(
self.codebook_negatives, y.size(0), y.size(1), -1
) # order doesnt matter
cb_negs = self.project_q(cb_negs)
negs = torch.cat([negs, cb_negs], dim=0)
else:
y = self.project_q(y)
if self.negatives_from_everywhere:
negs, _ = self.sample_negatives(
unmasked_features,
y.size(1),
padding_count=padding_count,
)
negs = self.project_q(negs)
else:
negs, _ = self.sample_negatives(
y,
y.size(1),
padding_count=padding_count,
)
if not is_xla_tensor(x):
# tpu-comment: reducing the size in a dynamic way causes
# too many recompilations on xla.
x = x[mask_indices].view(x.size(0), -1, x.size(-1))
if self.target_glu:
y = self.target_glu(y)
negs = self.target_glu(negs)
x = self.final_proj(x)
x = self.compute_preds(x, y, negs)
result = {
"x": x,
"padding_mask": padding_mask,
"features_pen": features_pen,
}
if prob_ppl is not None:
result["prob_perplexity"] = prob_ppl
result["code_perplexity"] = code_ppl
result["num_vars"] = num_vars
result["temp"] = curr_temp
return result
def quantize(self, x):
assert self.quantizer is not None
x = self.feature_extractor(x)
x = x.transpose(1, 2)
x = self.layer_norm(x)
return self.quantizer.forward_idx(x)
def extract_features(self, source, padding_mask, mask=False, layer=None):
res = self.forward(
source, padding_mask, mask=mask, features_only=True, layer=layer
)
return res
def get_logits(self, net_output):
logits = net_output["x"]
logits = logits.transpose(0, 2)
logits = logits.reshape(-1, logits.size(-1))
return logits
def get_targets(self, sample, net_output, expand_steps=True):
x = net_output["x"]
return x.new_zeros(x.size(1) * x.size(2), dtype=torch.long)
def get_extra_losses(self, net_output):
pen = []
if "prob_perplexity" in net_output:
pen.append(
(net_output["num_vars"] - net_output["prob_perplexity"])
/ net_output["num_vars"]
)
if "features_pen" in net_output:
pen.append(net_output["features_pen"])
return pen
def remove_pretraining_modules(self):
self.quantizer = None
self.project_q = None
self.target_glu = None
self.final_proj = None
class ConvFeatureExtractionModel(nn.Module):
def __init__(
self,
conv_layers: List[Tuple[int, int, int]],
dropout: float = 0.0,
mode: str = "default",
conv_bias: bool = False,
):
super().__init__()
assert mode in {"default", "layer_norm"}
def block(
n_in,
n_out,
k,
stride,
is_layer_norm=False,
is_group_norm=False,
conv_bias=False,
):
def make_conv():
conv = nn.Conv1d(n_in, n_out, k, stride=stride, bias=conv_bias)
nn.init.kaiming_normal_(conv.weight)
return conv
assert (
is_layer_norm and is_group_norm
) == False, "layer norm and group norm are exclusive"
if is_layer_norm:
return nn.Sequential(
make_conv(),
nn.Dropout(p=dropout),
nn.Sequential(
TransposeLast(),
Fp32LayerNorm(dim, elementwise_affine=True),
TransposeLast(),
),
nn.GELU(),
)
elif is_group_norm:
return nn.Sequential(
make_conv(),
nn.Dropout(p=dropout),
Fp32GroupNorm(dim, dim, affine=True),
nn.GELU(),
)
else:
return nn.Sequential(make_conv(), nn.Dropout(p=dropout), nn.GELU())
in_d = 1
self.conv_layers = nn.ModuleList()
for i, cl in enumerate(conv_layers):
assert len(cl) == 3, "invalid conv definition: " + str(cl)
(dim, k, stride) = cl
self.conv_layers.append(
block(
in_d,
dim,
k,
stride,
is_layer_norm=mode == "layer_norm",
is_group_norm=mode == "default" and i == 0,
conv_bias=conv_bias,
)
)
in_d = dim
def forward(self, x):
# BxT -> BxCxT
x = x.unsqueeze(1)
for conv in self.conv_layers:
x = conv(x)
return x
class TransformerEncoder(nn.Module):
def __init__(self, args):
super().__init__()
self.dropout = args.dropout
self.embedding_dim = args.encoder_embed_dim
self.pos_conv = nn.Conv1d(
self.embedding_dim,
self.embedding_dim,
kernel_size=args.conv_pos,
padding=args.conv_pos // 2,
groups=args.conv_pos_groups,
)
dropout = 0
std = math.sqrt((4 * (1.0 - dropout)) / (args.conv_pos * self.embedding_dim))
nn.init.normal_(self.pos_conv.weight, mean=0, std=std)
nn.init.constant_(self.pos_conv.bias, 0)
self.pos_conv = nn.utils.weight_norm(self.pos_conv, name="weight", dim=2)
self.pos_conv = nn.Sequential(self.pos_conv, SamePad(args.conv_pos), nn.GELU())
self.layers = nn.ModuleList(
[
TransformerSentenceEncoderLayer(
embedding_dim=self.embedding_dim,
ffn_embedding_dim=args.encoder_ffn_embed_dim,
num_attention_heads=args.encoder_attention_heads,
dropout=self.dropout,
attention_dropout=args.attention_dropout,
activation_dropout=args.activation_dropout,
activation_fn=args.activation_fn,
layer_norm_first=args.layer_norm_first,
)
for _ in range(args.encoder_layers)
]
)
self.layer_norm_first = args.layer_norm_first
self.layer_norm = LayerNorm(self.embedding_dim)
self.layerdrop = args.encoder_layerdrop
self.apply(init_bert_params)
def forward(self, x, padding_mask=None, layer=None):
x, layer_results = self.extract_features(x, padding_mask, layer)
if self.layer_norm_first and layer is None:
x = self.layer_norm(x)
return x, layer_results
def extract_features(self, x, padding_mask=None, tgt_layer=None):
if padding_mask is not None:
x = index_put(x, padding_mask, 0)
x_conv = self.pos_conv(x.transpose(1, 2))
x_conv = x_conv.transpose(1, 2)
x = x + x_conv
if not self.layer_norm_first:
x = self.layer_norm(x)
x = F.dropout(x, p=self.dropout, training=self.training)
# B x T x C -> T x B x C
x = x.transpose(0, 1)
layer_results = []
r = None
for i, layer in enumerate(self.layers):
dropout_probability = np.random.random()
if not self.training or (dropout_probability > self.layerdrop):
x, z = layer(x, self_attn_padding_mask=padding_mask, need_weights=False)
if tgt_layer is not None:
layer_results.append((x, z))
if i == tgt_layer:
r = x
break
if r is not None:
x = r
# T x B x C -> B x T x C
x = x.transpose(0, 1)
return x, layer_results
def max_positions(self):
"""Maximum output length supported by the encoder."""
return self.args.max_positions
def upgrade_state_dict_named(self, state_dict, name):
"""Upgrade a (possibly old) state dict for new versions of fairseq."""
return state_dict
class TransformerSentenceEncoderLayer(nn.Module):
"""
Implements a Transformer Encoder Layer used in BERT/XLM style pre-trained
models.
"""
def __init__(
self,
embedding_dim: float = 768,
ffn_embedding_dim: float = 3072,
num_attention_heads: float = 8,
dropout: float = 0.1,
attention_dropout: float = 0.1,
activation_dropout: float = 0.1,
activation_fn: str = "relu",
layer_norm_first: bool = False,
) -> None:
super().__init__()
# Initialize parameters
self.embedding_dim = embedding_dim
self.dropout = dropout
self.activation_dropout = activation_dropout
# Initialize blocks
self.activation_fn = utils.get_activation_fn(activation_fn)
self.self_attn = MultiheadAttention(
self.embedding_dim,
num_attention_heads,
dropout=attention_dropout,
self_attention=True,
)
self.dropout1 = nn.Dropout(dropout)
self.dropout2 = nn.Dropout(self.activation_dropout)
self.dropout3 = nn.Dropout(dropout)
self.layer_norm_first = layer_norm_first
# layer norm associated with the self attention layer
self.self_attn_layer_norm = LayerNorm(self.embedding_dim)
self.fc1 = nn.Linear(self.embedding_dim, ffn_embedding_dim)
self.fc2 = nn.Linear(ffn_embedding_dim, self.embedding_dim)
# layer norm associated with the position wise feed-forward NN
self.final_layer_norm = LayerNorm(self.embedding_dim)
def forward(
self,
x: torch.Tensor,
self_attn_mask: torch.Tensor = None,
self_attn_padding_mask: torch.Tensor = None,
need_weights: bool = False,
att_args=None,
):
"""
LayerNorm is applied either before or after the self-attention/ffn
modules similar to the original Transformer imlementation.
"""
residual = x
if self.layer_norm_first:
x = self.self_attn_layer_norm(x)
x, attn = self.self_attn(
query=x,
key=x,
value=x,
key_padding_mask=self_attn_padding_mask,
attn_mask=self_attn_mask,
)
x = self.dropout1(x)
x = residual + x
residual = x
x = self.final_layer_norm(x)
x = self.activation_fn(self.fc1(x))
x = self.dropout2(x)
x = self.fc2(x)
x = self.dropout3(x)
x = residual + x
else:
x, attn = self.self_attn(
query=x,
key=x,
value=x,
key_padding_mask=self_attn_padding_mask,
)
x = self.dropout1(x)
x = residual + x
x = self.self_attn_layer_norm(x)
residual = x
x = self.activation_fn(self.fc1(x))
x = self.dropout2(x)
x = self.fc2(x)
x = self.dropout3(x)
x = residual + x
x = self.final_layer_norm(x)
return x, attn
|
bart_ls-main
|
fairseq-py/fairseq/models/wav2vec/wav2vec2.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from dataclasses import dataclass, field
import logging
import math
from typing import Optional, Tuple
from omegaconf import II
import sys
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq.dataclass import ChoiceEnum, FairseqDataclass
from fairseq.models import BaseFairseqModel, register_model
from fairseq.modules import (
Fp32GroupNorm,
Fp32LayerNorm,
GumbelVectorQuantizer,
KmeansVectorQuantizer,
TransposeLast,
)
from fairseq.tasks import FairseqTask
from fairseq.utils import buffered_arange
logger = logging.getLogger(__name__)
AGGREGATOR_CHOICES = ChoiceEnum(["cnn", "gru"])
PROJECT_FEATURES_CHOICES = ChoiceEnum(["none", "same", "new"])
ACTIVATION_CHOICES = ChoiceEnum(["relu", "gelu"])
VQ_TYPE_CHOICES = ChoiceEnum(["none", "gumbel", "kmeans"])
@dataclass
class Wav2VecConfig(FairseqDataclass):
prediction_steps: int = field(
default=12, metadata={"help": "number of steps ahead to predict"}
)
sample_distance: Optional[int] = field(
default=None,
metadata={
"help": "sample distance from target. does not work properly with cross-sampling"
},
)
cross_sample_negatives: int = field(
default=0, metadata={"help": "num of cross sampled negatives"}
)
num_negatives: int = field(
default=10, metadata={"help": "num of sampled negatives"}
)
conv_feature_layers: str = field(
default="[(512, 10, 5), (512, 8, 4), (512, 4, 2), (512, 4, 2), (512, 4, 2), (512, 1, 1), (512, 1, 1), (512, 1, 1)]",
metadata={
"help": "convolutional feature extraction layers [(dim, kernel_size, stride), ...]"
},
)
conv_aggregator_layers: str = field(
default="[(512, 2, 1), (512, 3, 1), (512, 4, 1), (512, 5, 1), (512, 6, 1), (512, 7, 1), (512, 8, 1), (512, 9, 1), (512, 10, 1), (512, 11, 1), (512, 12, 1), (512, 13, 1)]",
metadata={
"help": "convolutional aggregator layers [(dim, kernel_size, stride), ...]"
},
)
dropout: float = field(
default=0.0, metadata={"help": "dropout to apply within the model"}
)
dropout_features: float = field(
default=0.0, metadata={"help": "dropout to apply to the features"}
)
dropout_agg: float = field(
default=0.0, metadata={"help": "dropout to apply after aggregation step"}
)
aggregator: AGGREGATOR_CHOICES = field(
default="cnn", metadata={"help": "type of aggregator to use"}
)
gru_dim: int = field(default=512, metadata={"help": "GRU dimensionality"})
no_conv_bias: bool = field(
default=False, metadata={"help": "if set, does not learn bias for conv layers"}
)
agg_zero_pad: bool = field(
default=False,
metadata={"help": "if set, zero pads in aggregator instead of repl pad"},
)
skip_connections_feat: bool = field(
default=False,
metadata={"help": "if set, adds skip connections to the feature extractor"},
)
skip_connections_agg: bool = field(
default=True,
metadata={"help": "if set, adds skip connections to the aggregator"},
)
residual_scale: float = field(
default=0.5, metadata={"help": "scales residual by sqrt(value)"}
)
log_compression: bool = field(
default=True,
metadata={"help": "if set, adds a log compression to feature extractor"},
)
balanced_classes: bool = field(
default=False,
metadata={"help": "if set, loss is scaled to balance for number of negatives"},
)
project_features: PROJECT_FEATURES_CHOICES = field(
default="none",
metadata={
"help": "if not none, features are projected using the (same or new) aggregator"
},
)
non_affine_group_norm: bool = field(
default=False, metadata={"help": "if set, group norm is not affine"}
)
offset: str = field(
default="auto",
metadata={
"help": "if set to 'auto', it is computed automatically from the receptive field, else set to int value"
},
)
activation: ACTIVATION_CHOICES = field(
default="relu",
metadata={
"help": "if set to 'auto', it is computed automatically from the receptive field, else set to int value"
},
)
vq_type: VQ_TYPE_CHOICES = field(
default="none", metadata={"help": "which type of quantizer to use"}
)
vq_vars: int = field(
default=320,
metadata={"help": "project to this many vector quantized variables per group"},
)
vq_groups: int = field(
default=2, metadata={"help": "number of groups of latent variables"}
)
vq_dim: int = field(
default=0,
metadata={
"help": "uses this dimensionality for quantized vectors. 0 to use model dim // groups"
},
)
vq_depth: int = field(
default=1, metadata={"help": "number of layers for vq weight projection"}
)
combine_groups: bool = field(
default=False, metadata={"help": "if set, variables are shared among groups"}
)
vq_temp: Tuple[float, float, float] = field(
default=(2.0, 0.5, 0.999995),
metadata={
"help": "temperature for latent variable sampling with gumbel softmax. should be a tuple of 3 values (start, end, decay)"
},
)
vq_gamma: float = field(
default=0.25,
metadata={"help": "gamma parameter for kmeans style vector quantization"},
)
infonce: bool = II("criterion.infonce")
@register_model("wav2vec", dataclass=Wav2VecConfig)
class Wav2VecModel(BaseFairseqModel):
@classmethod
def build_model(cls, cfg: Wav2VecConfig, task: FairseqTask):
"""Build a new model instance."""
model = Wav2VecModel(cfg)
logger.info(model)
return model
def __init__(self, cfg: Wav2VecConfig):
super().__init__()
self.prediction_steps = cfg.prediction_steps
offset = cfg.offset
if cfg.activation == "relu":
activation = nn.ReLU()
elif cfg.activation == "gelu":
activation = nn.GELU()
else:
raise Exception("unknown activation " + cfg.activation)
feature_enc_layers = eval(cfg.conv_feature_layers)
self.feature_extractor = ConvFeatureExtractionModel(
conv_layers=feature_enc_layers,
dropout=0.0,
log_compression=cfg.log_compression,
skip_connections=cfg.skip_connections_feat,
residual_scale=cfg.residual_scale,
non_affine_group_norm=cfg.non_affine_group_norm,
activation=activation,
)
embed = feature_enc_layers[-1][0]
self.vector_quantizer = None
if cfg.vq_type == "gumbel":
self.vector_quantizer = GumbelVectorQuantizer(
dim=embed,
num_vars=cfg.vq_vars,
temp=cfg.vq_temp,
groups=cfg.vq_groups,
combine_groups=cfg.combine_groups,
vq_dim=cfg.vq_dim if cfg.vq_dim > 0 else embed,
time_first=False,
activation=activation,
weight_proj_depth=cfg.vq_depth,
weight_proj_factor=2,
)
elif cfg.vq_type == "kmeans":
self.vector_quantizer = KmeansVectorQuantizer(
dim=embed,
num_vars=cfg.vq_vars,
groups=cfg.vq_groups,
combine_groups=cfg.combine_groups,
vq_dim=cfg.vq_dim if cfg.vq_dim > 0 else embed,
time_first=False,
gamma=cfg.vq_gamma,
)
else:
assert (
cfg.vq_type == "none" or cfg.vq_type is None
), "Unknown quantizer type"
if cfg.offset == "auto":
jin = 0
rin = 0
for _, k, stride in feature_enc_layers:
if rin == 0:
rin = k
rin = rin + (k - 1) * jin
if jin == 0:
jin = stride
else:
jin *= stride
offset = math.ceil(rin / jin)
offset = int(offset)
def make_aggregator():
if cfg.aggregator == "cnn":
agg_layers = eval(cfg.conv_aggregator_layers)
agg_dim = agg_layers[-1][0]
feature_aggregator = ConvAggegator(
conv_layers=agg_layers,
embed=embed,
dropout=cfg.dropout,
skip_connections=cfg.skip_connections_agg,
residual_scale=cfg.residual_scale,
non_affine_group_norm=cfg.non_affine_group_norm,
conv_bias=not cfg.no_conv_bias,
zero_pad=cfg.agg_zero_pad,
activation=activation,
)
elif cfg.aggregator == "gru":
agg_dim = cfg.gru_dim
feature_aggregator = nn.Sequential(
TransposeLast(),
nn.GRU(
input_size=embed,
hidden_size=agg_dim,
num_layers=1,
dropout=cfg.dropout,
),
TransposeLast(deconstruct_idx=0),
)
else:
raise Exception("unknown aggregator type " + cfg.aggregator)
return feature_aggregator, agg_dim
self.feature_aggregator, agg_dim = make_aggregator()
self.wav2vec_predictions = Wav2VecPredictionsModel(
in_dim=agg_dim,
out_dim=embed,
prediction_steps=cfg.prediction_steps,
n_negatives=cfg.num_negatives,
cross_sample_negatives=cfg.cross_sample_negatives,
sample_distance=cfg.sample_distance,
dropout=cfg.dropout,
offset=offset,
balanced_classes=cfg.balanced_classes,
infonce=cfg.infonce,
)
self.dropout_feats = nn.Dropout(p=cfg.dropout_features)
self.dropout_agg = nn.Dropout(p=cfg.dropout_agg)
if cfg.project_features == "none":
self.project_features = None
elif cfg.project_features == "same":
self.project_features = self.feature_aggregator
elif cfg.project_features == "new":
self.project_features, _ = make_aggregator()
def forward(self, source):
result = {}
features = self.feature_extractor(source)
if self.vector_quantizer:
q_res = self.vector_quantizer(features)
features = q_res["x"]
for k in q_res.keys():
if k != "x":
result[k] = q_res[k]
x = self.dropout_feats(features)
x = self.feature_aggregator(x)
x = self.dropout_agg(x)
if self.project_features is not None:
features = self.project_features(features)
x, targets = self.wav2vec_predictions(x, features)
result["cpc_logits"] = x
result["cpc_targets"] = targets
return result
def upgrade_state_dict_named(self, state_dict, name):
super().upgrade_state_dict_named(state_dict, name)
def max_positions(self):
"""Maximum length supported by the model."""
return sys.maxsize
def get_logits(self, net_output):
logits = net_output["cpc_logits"]
return logits
def get_targets(self, sample, net_output):
t = net_output["cpc_targets"]
if isinstance(t, tuple):
t = t[0]
return t.contiguous()
def get_target_weights(self, targets, net_output):
targets = net_output["cpc_targets"]
if isinstance(targets, tuple) and targets[-1] is not None:
return targets[-1]
return None
def get_extra_losses(self, net_output):
loss = None
if "prob_perplexity" in net_output:
loss = net_output["num_vars"] - net_output["prob_perplexity"]
elif "kmeans_loss" in net_output:
loss = net_output["kmeans_loss"]
return loss
def norm_block(is_layer_norm, dim, affine=True):
if is_layer_norm:
mod = nn.Sequential(
TransposeLast(),
Fp32LayerNorm(dim, elementwise_affine=affine),
TransposeLast(),
)
else:
mod = Fp32GroupNorm(1, dim, affine=affine)
return mod
class ConvFeatureExtractionModel(nn.Module):
def __init__(
self,
conv_layers,
dropout,
log_compression,
skip_connections,
residual_scale,
non_affine_group_norm,
activation,
):
super().__init__()
def block(n_in, n_out, k, stride):
return nn.Sequential(
nn.Conv1d(n_in, n_out, k, stride=stride, bias=False),
nn.Dropout(p=dropout),
norm_block(
is_layer_norm=False, dim=n_out, affine=not non_affine_group_norm
),
activation,
)
in_d = 1
self.conv_layers = nn.ModuleList()
for dim, k, stride in conv_layers:
self.conv_layers.append(block(in_d, dim, k, stride))
in_d = dim
self.log_compression = log_compression
self.skip_connections = skip_connections
self.residual_scale = math.sqrt(residual_scale)
def forward(self, x):
# BxT -> BxCxT
x = x.unsqueeze(1)
for conv in self.conv_layers:
residual = x
x = conv(x)
if self.skip_connections and x.size(1) == residual.size(1):
tsz = x.size(2)
r_tsz = residual.size(2)
residual = residual[..., :: r_tsz // tsz][..., :tsz]
x = (x + residual) * self.residual_scale
if self.log_compression:
x = x.abs()
x = x + 1
x = x.log()
return x
class ZeroPad1d(nn.Module):
def __init__(self, pad_left, pad_right):
super().__init__()
self.pad_left = pad_left
self.pad_right = pad_right
def forward(self, x):
return F.pad(x, (self.pad_left, self.pad_right))
class ConvAggegator(nn.Module):
def __init__(
self,
conv_layers,
embed,
dropout,
skip_connections,
residual_scale,
non_affine_group_norm,
conv_bias,
zero_pad,
activation,
):
super().__init__()
def block(n_in, n_out, k, stride):
# padding dims only really make sense for stride = 1
ka = k // 2
kb = ka - 1 if k % 2 == 0 else ka
pad = (
ZeroPad1d(ka + kb, 0) if zero_pad else nn.ReplicationPad1d((ka + kb, 0))
)
return nn.Sequential(
pad,
nn.Conv1d(n_in, n_out, k, stride=stride, bias=conv_bias),
nn.Dropout(p=dropout),
norm_block(False, n_out, affine=not non_affine_group_norm),
activation,
)
in_d = embed
self.conv_layers = nn.ModuleList()
self.residual_proj = nn.ModuleList()
for dim, k, stride in conv_layers:
if in_d != dim and skip_connections:
self.residual_proj.append(nn.Conv1d(in_d, dim, 1, bias=False))
else:
self.residual_proj.append(None)
self.conv_layers.append(block(in_d, dim, k, stride))
in_d = dim
self.conv_layers = nn.Sequential(*self.conv_layers)
self.skip_connections = skip_connections
self.residual_scale = math.sqrt(residual_scale)
def forward(self, x):
for rproj, conv in zip(self.residual_proj, self.conv_layers):
residual = x
x = conv(x)
if self.skip_connections:
if rproj is not None:
residual = rproj(residual)
x = (x + residual) * self.residual_scale
return x
class Wav2VecPredictionsModel(nn.Module):
def __init__(
self,
in_dim,
out_dim,
prediction_steps,
n_negatives,
cross_sample_negatives,
sample_distance,
dropout,
offset,
balanced_classes,
infonce,
):
super().__init__()
self.n_negatives = n_negatives
self.cross_sample_negatives = cross_sample_negatives
self.sample_distance = sample_distance
self.project_to_steps = nn.ConvTranspose2d(
in_dim, out_dim, (1, prediction_steps)
)
self.dropout = nn.Dropout(p=dropout)
self.offset = offset
self.balanced_classes = balanced_classes
self.infonce = infonce
def sample_negatives(self, y):
bsz, fsz, tsz = y.shape
y = y.transpose(0, 1) # BCT -> CBT
y = y.contiguous().view(fsz, -1) # CBT => C(BxT)
cross_high = tsz * bsz
high = tsz if self.sample_distance is None else min(tsz, self.sample_distance)
assert high > 1
neg_idxs = torch.randint(low=0, high=high, size=(bsz, self.n_negatives * tsz))
with torch.no_grad():
if self.n_negatives > 0:
tszs = (
buffered_arange(tsz)
.unsqueeze(-1)
.expand(-1, self.n_negatives)
.flatten()
)
neg_idxs = torch.randint(
low=0, high=high - 1, size=(bsz, self.n_negatives * tsz)
)
neg_idxs[neg_idxs >= tszs] += 1
if self.cross_sample_negatives > 0:
tszs = (
buffered_arange(tsz)
.unsqueeze(-1)
.expand(-1, self.cross_sample_negatives)
.flatten()
)
cross_neg_idxs = torch.randint(
low=0,
high=cross_high - 1,
size=(bsz, self.cross_sample_negatives * tsz),
)
cross_neg_idxs[cross_neg_idxs >= tszs] += 1
if self.n_negatives > 0:
for i in range(1, bsz):
neg_idxs[i] += i * high
else:
neg_idxs = cross_neg_idxs
if self.cross_sample_negatives > 0 and self.n_negatives > 0:
neg_idxs = torch.cat([neg_idxs, cross_neg_idxs], dim=1)
negs = y[..., neg_idxs.view(-1)]
negs = negs.view(
fsz, bsz, self.n_negatives + self.cross_sample_negatives, tsz
).permute(
2, 1, 0, 3
) # to NxBxCxT
return negs
def forward(self, x, y):
x = x.unsqueeze(-1)
x = self.project_to_steps(x) # BxCxTxS
x = self.dropout(x)
negatives = self.sample_negatives(y)
y = y.unsqueeze(0)
targets = torch.cat([y, negatives], dim=0) # Copies x B x C x T
copies = targets.size(0)
bsz, dim, tsz, steps = x.shape
steps = min(steps, tsz - self.offset)
predictions = x.new(
bsz * copies * (tsz - self.offset + 1) * steps
- ((steps + 1) * steps // 2) * copies * bsz
)
if self.infonce:
labels = predictions.new_full(
(predictions.shape[0] // copies,), 0, dtype=torch.long
)
else:
labels = torch.zeros_like(predictions)
weights = (
torch.full_like(labels, 1 / self.n_negatives)
if self.balanced_classes and not self.infonce
else None
)
start = end = 0
for i in range(steps):
offset = i + self.offset
end = start + (tsz - offset) * bsz * copies
if self.infonce:
predictions[start:end] = torch.einsum(
"bct,nbct->tbn", x[..., :-offset, i], targets[..., offset:]
).flatten()
else:
pos_num = (end - start) // copies
predictions[start:end] = torch.einsum(
"bct,nbct->nbt", x[..., :-offset, i], targets[..., offset:]
).flatten()
labels[start : start + pos_num] = 1.0
if weights is not None:
weights[start : start + pos_num] = 1.0
start = end
assert end == predictions.numel(), "{} != {}".format(end, predictions.numel())
if self.infonce:
predictions = predictions.view(-1, copies)
else:
if weights is not None:
labels = (labels, weights)
return predictions, labels
|
bart_ls-main
|
fairseq-py/fairseq/models/wav2vec/wav2vec.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import argparse
import logging
import torch.nn as nn
import fairseq.checkpoint_utils
from fairseq.models import (
FairseqEncoderDecoderModel,
register_model,
register_model_architecture,
)
from fairseq.models.transformer import TransformerDecoder
from fairseq.models.roberta import model as roberta
logger = logging.getLogger(__name__)
@register_model("roberta_enc_dec")
class RobertaEncDecModel(FairseqEncoderDecoderModel):
@staticmethod
def add_args(parser):
parser.add_argument(
"--pretrained-mlm-checkpoint",
default=None,
type=str,
metavar="PRETRAINED",
help="path to pretrained mlm checkpoint",
)
parser.add_argument(
"--pretrained-decoder", action="store_true", help="reload decoder"
)
parser.add_argument(
"--hack-layernorm-embedding",
action="store_true",
help="hack to reload old models trained with encoder-normalize-before=False (no equivalent to encoder-normalize-before=False and layernorm_embedding=False",
)
parser.add_argument(
"--share-decoder-input-output-embed",
action="store_true",
help="share decoder input and output embeddings",
)
parser.add_argument(
"--share-all-embeddings",
action="store_true",
help="share encoder, decoder and output embeddings"
" (requires shared dictionary and embed dim)",
)
@classmethod
def build_model(cls, args, task):
"""Build a new model instance."""
# make sure all arguments are present
base_enc_dec_architecture(args)
if args.pretrained_mlm_checkpoint:
arg_overrides = None
if args.hack_layernorm_embedding:
arg_overrides = {"layernorm_embedding": False}
loaded = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[args.pretrained_mlm_checkpoint], arg_overrides=arg_overrides
)
([roberta_enc], _cfg, _task) = loaded
else:
# Do we need to edit untie_weights here ?
share_in_out = (
args.share_decoder_input_output_embed or args.share_all_embeddings
)
args.untie_weights_roberta = not share_in_out
if args.hack_layernorm_embedding:
args.layernorm_embedding = False
args.encoder_normalize_before = False
roberta_enc = roberta.RobertaModel.build_model(args, task)
return cls.from_roberta(roberta_enc, args, task.source_dictionary)
@staticmethod
def from_roberta(roberta_enc: roberta.RobertaModel, args, dictionary):
encoder = roberta_enc.encoder.sentence_encoder
vocab_size, embed_dim = encoder.embed_tokens.weight.shape
if args.share_all_embeddings:
lm_head = roberta_enc.encoder.lm_head
assert encoder.embed_tokens.weight is lm_head.weight, (
"Can't use --share-all-embeddings with a model "
"that was pretraiend with --untie-weights-roberta_enc"
)
else:
lm_head = roberta.RobertaLMHead(
embed_dim, vocab_size, roberta_enc.args.activation_fn
)
dec_embs = nn.Embedding(vocab_size, embed_dim, dictionary.pad())
if args.share_all_embeddings or args.share_decoder_input_output_embed:
# Note: I wasn't able to use Embedding _weight parameter to achive this sharing.
dec_embs.weight = lm_head.weight
decoder = TransformerDecoder(
RobertaEncDecModel.read_args_from_roberta(roberta_enc.args),
dictionary,
dec_embs,
no_encoder_attn=False,
output_projection=lm_head,
)
if getattr(args, "pretrained_decoder", False):
decoder_dict = encoder.state_dict()
# TODO: hide setting "encoder_attn" layers behind a flag.
for k, w in list(decoder_dict.items()):
if ".self_attn" in k:
k_enc_attn = k.replace(".self_attn", ".encoder_attn")
decoder_dict[k_enc_attn] = w.detach().clone()
for k, w in lm_head.state_dict().items():
decoder_dict["output_projection." + k] = w
missing_keys, unexpected_keys = decoder.load_state_dict(
decoder_dict, strict=False
)
# missing_keys = [m for m in missing_keys if ".encoder_attn" not in m]
assert not missing_keys and not unexpected_keys, (
"Failed to load state dict. "
f"Missing keys: {missing_keys}. "
f"Unexpected keys: {unexpected_keys}."
)
if args.share_all_embeddings:
assert decoder.output_projection.weight is decoder.embed_tokens.weight
assert encoder.embed_tokens.weight is decoder.embed_tokens.weight
elif args.share_decoder_input_output_embed:
assert decoder.output_projection.weight is decoder.embed_tokens.weight
assert encoder.embed_tokens.weight is not decoder.embed_tokens.weight
else:
assert decoder.output_projection.weight is not decoder.embed_tokens.weight
assert encoder.embed_tokens.weight is not decoder.embed_tokens.weight
return RobertaEncDecModel(encoder, decoder)
@staticmethod
def read_args_from_roberta(roberta_args: argparse.Namespace):
# TODO: this would become easier if encoder/decoder where using a similar
# TransformerConfig object
args = argparse.Namespace(**vars(roberta_args))
attr_map = [
("encoder_attention_heads", "decoder_attention_heads"),
("encoder_embed_dim", "decoder_embed_dim"),
("encoder_embed_dim", "decoder_output_dim"),
("encoder_normalize_before", "decoder_normalize_before"),
("encoder_layers_to_keep", "decoder_layers_to_keep"),
("encoder_ffn_embed_dim", "decoder_ffn_embed_dim"),
("encoder_layerdrop", "decoder_layerdrop"),
("encoder_layers", "decoder_layers"),
("encoder_learned_pos", "decoder_learned_pos"),
# should this be set from here ?
("max_positions", "max_target_positions"),
]
for k1, k2 in attr_map:
setattr(args, k2, getattr(roberta_args, k1))
args.adaptive_softmax_cutoff = getattr(args, "adaptive_softmax_cutoff", None)
args.adaptive_softmax_dropout = getattr(args, "adaptive_softmax_dropout", 0)
args.share_decoder_input_output_embed = not roberta_args.untie_weights_roberta
return args
def upgrade_state_dict_named(self, state_dict, name):
prefix = name + "." if name != "" else ""
super().upgrade_state_dict_named(state_dict, name)
old_keys = list(state_dict.keys())
# rename decoder -> encoder before upgrading children modules
for k in old_keys:
if k.startswith(prefix + "encoder.lm_head"):
state_dict.pop(k)
continue
new_k = k
new_k = new_k.replace(".sentence_encoder.", ".")
new_k = new_k.replace("decoder.lm_head.", "decoder.output_projection.")
if k == new_k:
continue
# print(k, "->", new_k)
state_dict[new_k] = state_dict.pop(k)
@register_model_architecture("roberta_enc_dec", "roberta_enc_dec")
def base_enc_dec_architecture(args):
args.hack_layernorm_embedding = getattr(args, "hack_layernorm_embedding", False)
args.pretrained_mlm_checkpoint = getattr(args, "pretrained_mlm_checkpoint", None)
args.pretrained_decoder = getattr(args, "pretrained_decoder", None)
args.share_all_embeddings = getattr(args, "share_all_embeddings", False)
args.share_decoder_input_output_embed = getattr(
args, "share_decoder_input_output_embed", False
)
roberta.base_architecture(args)
|
bart_ls-main
|
fairseq-py/fairseq/models/roberta/enc_dec.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""
GottBERT: a pure German Language Model
"""
from fairseq.models import register_model
from .hub_interface import RobertaHubInterface
from .model import RobertaModel
@register_model('gottbert')
class GottbertModel(RobertaModel):
@classmethod
def hub_models(cls):
return {
'gottbert-base': 'https://dl.gottbert.de/fairseq/models/gottbert-base.tar.gz',
}
@classmethod
def from_pretrained(cls,
model_name_or_path,
checkpoint_file='model.pt',
data_name_or_path='.',
bpe='hf_byte_bpe',
bpe_vocab='vocab.json',
bpe_merges='merges.txt',
bpe_add_prefix_space=False,
**kwargs
):
from fairseq import hub_utils
x = hub_utils.from_pretrained(
model_name_or_path,
checkpoint_file,
data_name_or_path,
archive_map=cls.hub_models(),
bpe=bpe,
load_checkpoint_heads=True,
bpe_vocab=bpe_vocab,
bpe_merges=bpe_merges,
bpe_add_prefix_space=bpe_add_prefix_space,
**kwargs,
)
return RobertaHubInterface(x['args'], x['task'], x['models'][0])
|
bart_ls-main
|
fairseq-py/fairseq/models/roberta/model_gottbert.py
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""
Unsupervised Cross-lingual Representation Learning at Scale
"""
from fairseq.models import register_model
from .hub_interface import RobertaHubInterface
from .model import RobertaModel
@register_model("xlmr")
class XLMRModel(RobertaModel):
@classmethod
def hub_models(cls):
return {
"xlmr.base": "http://dl.fbaipublicfiles.com/fairseq/models/xlmr.base.tar.gz",
"xlmr.large": "http://dl.fbaipublicfiles.com/fairseq/models/xlmr.large.tar.gz",
"xlmr.xl": "http://dl.fbaipublicfiles.com/fairseq/models/xlmr/xlmr.xl.tar.gz",
"xlmr.xxl": "http://dl.fbaipublicfiles.com/fairseq/models/xlmr/xlmr.xxl.tar.gz",
}
@classmethod
def from_pretrained(
cls,
model_name_or_path,
checkpoint_file="model.pt",
data_name_or_path=".",
bpe="sentencepiece",
**kwargs
):
from fairseq import hub_utils
x = hub_utils.from_pretrained(
model_name_or_path,
checkpoint_file,
data_name_or_path,
archive_map=cls.hub_models(),
bpe=bpe,
load_checkpoint_heads=True,
**kwargs,
)
return RobertaHubInterface(x["args"], x["task"], x["models"][0])
|
bart_ls-main
|
fairseq-py/fairseq/models/roberta/model_xlmr.py
|
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