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# Copyright 2024-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import annotations
import warnings
from typing import Optional
import bitsandbytes as bnb
import torch
from peft.import_utils import is_bnb_4bit_available, is_bnb_available
from peft.tuners.tuners_utils import check_adapters_to_merge
from peft.utils.integrations import dequantize_bnb_weight
from peft.utils.other import transpose
from .layer import VeraLayer
if is_bnb_available():
class Linear8bitLt(torch.nn.Module, VeraLayer):
def __init__(
self,
base_layer: torch.nn.Module,
adapter_name: str,
vera_A,
vera_B,
r: int = 0,
vera_dropout: float = 0.0,
fan_in_fan_out: bool = False,
init_weights: bool = True,
d_initial: float = 0.1,
**kwargs,
) -> None:
super().__init__()
VeraLayer.__init__(self, base_layer)
self.fan_in_fan_out = fan_in_fan_out
self._active_adapter = adapter_name
self.update_layer(
adapter_name,
vera_A,
vera_B,
r,
vera_dropout=vera_dropout,
init_weights=init_weights,
d_initial=d_initial,
)
def merge(self, safe_merge: bool = False, adapter_names: Optional[list[str]] = None) -> None:
if self.merged:
warnings.warn(
f"Already following adapters were merged {','.join(self.merged_adapters)}. "
f"You are now additionally merging {','.join(self.active_adapters)}."
)
adapter_names = check_adapters_to_merge(self, adapter_names)
if not adapter_names:
return
for active_adapter in adapter_names:
if active_adapter not in self.vera_lambda_d.keys():
continue
warnings.warn(
"Merge vera module to 8-bit linear may get different generations due to rounding errors."
)
vera_data = self.get_delta_weight(active_adapter)
weight = self.get_base_layer().weight
state = self.get_base_layer().state
if state.SCB is None:
state.SCB = weight.SCB
output = dequantize_bnb_weight(weight, state)
w_data = output.to(vera_data.dtype).to(vera_data.device) + vera_data
if safe_merge and not torch.isfinite(w_data).all():
raise ValueError(
f"NaNs detected in the merged weights. The adapter {active_adapter} seems to be broken"
)
self.get_base_layer().weight = bnb.nn.Int8Params(
w_data.to("cpu"), requires_grad=False, has_fp16_weights=weight.has_fp16_weights
).to(weight.device)
state.reset_grads()
self.merged_adapters.append(active_adapter)
def unmerge(self) -> None:
if not self.merged:
warnings.warn("Already unmerged. Nothing to do")
return
while len(self.merged_adapters) > 0:
active_adapter = self.merged_adapters.pop()
if active_adapter not in self.vera_lambda_d.keys():
continue
warnings.warn(
"Unmerge vera module to 8-bit linear may get different generations due to rounding errors."
)
vera_data = self.get_delta_weight(active_adapter)
weight = self.get_base_layer().weight
state = self.get_base_layer().state
if state.SCB is None:
state.SCB = weight.SCB
output = dequantize_bnb_weight(weight, state=state)
w_data = output.to(vera_data.dtype).to(vera_data.device) - vera_data
self.get_base_layer().weight = bnb.nn.Int8Params(
w_data.to("cpu"), requires_grad=False, has_fp16_weights=weight.has_fp16_weights
).to(weight.device)
state.reset_grads()
def get_delta_weight(self, adapter) -> torch.Tensor:
"""
Compute the delta weight for the given adapter.
Args:
adapter (str): The name of the adapter for which the delta weight should be computed.
Returns:
torch.Tensor: The computed delta weight for the VeRA adapter.
Note:
This method implements the VeRA-specific weight update. Unlike LoRA, VeRA uses shared projection
matrices (vera_A and vera_B) across all layers, along with per-layer trainable parameters (lambda_d and
lambda_b).
"""
# Retrieve shared projection matrices
vera_A = self.vera_A[adapter]
vera_B = self.vera_B[adapter]
# Retrieve per-layer trainable parameters
device = vera_B.device
dtype = vera_B.dtype
# In case users wants to merge the adapter weights that are in
# (b)float16 while being on CPU, we need to cast the weights to float32, perform the merge and then cast back to
# (b)float16 because some CPUs have slow bf16/fp16 matmuls.
cast_to_fp32 = device.type == "cpu" and (dtype == torch.float16 or dtype == torch.bfloat16)
lambda_d = self.vera_lambda_d[adapter]
lambda_b = self.vera_lambda_b[adapter]
if cast_to_fp32:
vera_A = vera_A.float()
vera_B = vera_B.float()
lambda_d = lambda_d.float()
lambda_b = lambda_b.float()
sliced_A = vera_A[:, : self.in_features].to(lambda_d.device)
sliced_B = vera_B[: self.out_features, :].to(lambda_d.device)
lambda_b = lambda_b.unsqueeze(-1)
lambda_d = lambda_d.unsqueeze(-1)
# VeRA-specific computation:
# 1. Apply lambda_d to the input projection (vera_A)
# 2. Apply lambda_b to the output projection (vera_B)
# 3. Compute the outer product of the scaled projections
output_tensor = transpose((lambda_b * sliced_B) @ (lambda_d * sliced_A), self.fan_in_fan_out)
if cast_to_fp32:
output_tensor = output_tensor.to(dtype=dtype)
return output_tensor
def forward(self, x: torch.Tensor, *args, **kwargs) -> torch.Tensor:
"""
Perform the forward pass using the VeRA adapter.
Args:
x (torch.Tensor): Input tensor.
Returns:
torch.Tensor: Output tensor after applying the VeRA adaptation.
Note:
This method implements the VeRA-specific forward pass. It applies the shared projections (vera_A and
vera_B) along with the per-layer trainable parameters (lambda_d and lambda_b) to compute the adapter
output.
"""
if self.disable_adapters:
if self.merged:
self.unmerge()
result = self.base_layer(x, *args, **kwargs)
elif self.merged:
result = self.base_layer(x, *args, **kwargs)
else:
result = self.base_layer(x, *args, **kwargs)
for active_adapter in self.active_adapters:
if active_adapter not in self.vera_lambda_d.keys():
continue
lambda_d = self.vera_lambda_d[active_adapter]
lambda_b = self.vera_lambda_b[active_adapter]
vera_A = self.vera_A[active_adapter]
vera_B = self.vera_B[active_adapter]
dropout = self.vera_dropout[active_adapter]
requires_conversion = not torch.is_autocast_enabled()
if requires_conversion:
expected_dtype = result.dtype
compute_dtype = lambda_d.dtype
if x.dtype != compute_dtype:
x = x.to(compute_dtype)
sliced_A = vera_A[:, : self.in_features].to(x.device)
sliced_B = vera_B[: self.out_features, :].to(x.device)
x_temp = dropout(x.to(lambda_d.dtype))
adapter_output = lambda_b * torch.nn.functional.linear(
lambda_d * torch.nn.functional.linear(x_temp, sliced_A), sliced_B
)
if requires_conversion:
adapter_output = adapter_output.to(expected_dtype)
result = result + adapter_output
# Ensure the output tensor has the same dtype as the input tensor
return result.to(x.dtype)
def __repr__(self) -> str:
rep = super().__repr__()
return "vera." + rep
if is_bnb_4bit_available():
class Linear4bit(torch.nn.Module, VeraLayer):
def __init__(
self,
base_layer: torch.nn.Module,
adapter_name: str,
vera_A,
vera_B,
r: int = 0,
vera_dropout: float = 0.0,
fan_in_fan_out: bool = False,
init_weights: bool = True,
d_initial: float = 0.1,
**kwargs,
) -> None:
super().__init__()
VeraLayer.__init__(self, base_layer)
self.fan_in_fan_out = fan_in_fan_out
self._active_adapter = adapter_name
self.update_layer(
adapter_name,
vera_A,
vera_B,
r,
vera_dropout=vera_dropout,
init_weights=init_weights,
d_initial=d_initial,
)
def merge(self, safe_merge: bool = False, adapter_names: Optional[list[str]] = None) -> None:
if self.merged:
warnings.warn(
f"Already following adapters were merged {','.join(self.merged_adapters)}. "
f"You are now additionally merging {','.join(self.active_adapters)}."
)
adapter_names = check_adapters_to_merge(self, adapter_names)
if not adapter_names:
return
for active_adapter in adapter_names:
if active_adapter not in self.vera_lambda_d.keys():
continue
warnings.warn(
"Merge vera module to 4-bit linear may get different generations due to rounding errors."
)
vera_data = self.get_delta_weight(active_adapter)
weight = self.get_base_layer().weight
kwargs = weight.__dict__
w_data = bnb.functional.dequantize_4bit(weight.data, weight.quant_state) + vera_data
if safe_merge and not torch.isfinite(w_data).all():
raise ValueError(
f"NaNs detected in the merged weights. The adapter {active_adapter} seems to be broken"
)
self.get_base_layer().weight = bnb.nn.Params4bit(w_data.to("cpu"), requires_grad=False, **kwargs).to(
weight.device
)
self.merged_adapters.append(active_adapter)
def unmerge(self) -> None:
if not self.merged:
warnings.warn("Already unmerged. Nothing to do")
return
while len(self.merged_adapters) > 0:
active_adapter = self.merged_adapters.pop()
if active_adapter not in self.vera_lambda_d.keys():
continue
warnings.warn(
"Unmerge vera module to 4-bit linear may get different generations due to rounding errors."
)
vera_data = self.get_delta_weight(active_adapter)
weight = self.get_base_layer().weight
kwargs = weight.__dict__
w_data = bnb.functional.dequantize_4bit(weight.data, weight.quant_state) - vera_data
self.get_base_layer().weight = bnb.nn.Params4bit(w_data.to("cpu"), requires_grad=False, **kwargs).to(
weight.device
)
def get_delta_weight(self, adapter) -> torch.Tensor:
vera_A = self.vera_A[adapter]
vera_B = self.vera_B[adapter]
device = vera_B.device
dtype = vera_B.dtype
cast_to_fp32 = device.type == "cpu" and (dtype == torch.float16 or dtype == torch.bfloat16)
lambda_d = self.vera_lambda_d[adapter]
lambda_b = self.vera_lambda_b[adapter]
if cast_to_fp32:
vera_A = vera_A.float()
vera_B = vera_B.float()
lambda_d = lambda_d.float()
lambda_b = lambda_b.float()
sliced_A = vera_A[:, : self.in_features].to(lambda_d.device)
sliced_B = vera_B[: self.out_features, :].to(lambda_d.device)
lambda_b = lambda_b.unsqueeze(-1)
lambda_d = lambda_d.unsqueeze(-1)
output_tensor = transpose((lambda_b * sliced_B) @ (lambda_d * sliced_A), self.fan_in_fan_out)
if cast_to_fp32:
output_tensor = output_tensor.to(dtype=dtype)
return output_tensor
def forward(self, x: torch.Tensor, *args, **kwargs) -> torch.Tensor:
if self.disable_adapters:
if self.merged:
self.unmerge()
result = self.base_layer(x, *args, **kwargs)
elif self.merged:
result = self.base_layer(x, *args, **kwargs)
else:
result = self.base_layer(x, *args, **kwargs)
result = result.clone()
for active_adapter in self.active_adapters:
if active_adapter not in self.vera_lambda_d.keys():
continue
lambda_d = self.vera_lambda_d[active_adapter]
lambda_b = self.vera_lambda_b[active_adapter]
vera_A = self.vera_A[active_adapter]
vera_B = self.vera_B[active_adapter]
dropout = self.vera_dropout[active_adapter]
requires_conversion = not torch.is_autocast_enabled()
if requires_conversion:
expected_dtype = result.dtype
compute_dtype = lambda_d.dtype
if x.dtype != compute_dtype:
x = x.to(compute_dtype)
sliced_A = vera_A[:, : self.in_features].to(x.device)
sliced_B = vera_B[: self.out_features, :].to(x.device)
x_temp = dropout(x.to(lambda_d.dtype))
adapter_output = lambda_b * torch.nn.functional.linear(
lambda_d * torch.nn.functional.linear(x_temp, sliced_A), sliced_B
)
if requires_conversion:
adapter_output = adapter_output.to(expected_dtype)
result = result + adapter_output
# Ensure the output tensor has the same dtype as the input tensor
return result.to(x.dtype)
def __repr__(self) -> str:
rep = super().__repr__()
return "vera." + rep
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