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# MIT License
# Copyright (c) Microsoft
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
# Copyright (c) [2025] [Microsoft]
# SPDX-License-Identifier: MIT
from typing import *
import torch
import math
from . import DEBUG, BACKEND
if BACKEND == 'xformers':
import xformers.ops as xops
elif BACKEND == 'flash_attn':
import flash_attn
elif BACKEND == 'sdpa':
from torch.nn.functional import scaled_dot_product_attention as sdpa
elif BACKEND == 'naive':
pass
else:
raise ValueError(f"Unknown attention backend: {BACKEND}")
__all__ = [
'scaled_dot_product_attention',
]
def _naive_sdpa(q, k, v):
"""
Naive implementation of scaled dot product attention.
"""
q = q.permute(0, 2, 1, 3) # [N, H, L, C]
k = k.permute(0, 2, 1, 3) # [N, H, L, C]
v = v.permute(0, 2, 1, 3) # [N, H, L, C]
scale_factor = 1 / math.sqrt(q.size(-1))
attn_weight = q @ k.transpose(-2, -1) * scale_factor
attn_weight = torch.softmax(attn_weight, dim=-1)
out = attn_weight @ v
out = out.permute(0, 2, 1, 3) # [N, L, H, C]
return out
@overload
def scaled_dot_product_attention(qkv: torch.Tensor) -> torch.Tensor:
"""
Apply scaled dot product attention.
Args:
qkv (torch.Tensor): A [N, L, 3, H, C] tensor containing Qs, Ks, and Vs.
"""
...
@overload
def scaled_dot_product_attention(q: torch.Tensor, kv: torch.Tensor) -> torch.Tensor:
"""
Apply scaled dot product attention.
Args:
q (torch.Tensor): A [N, L, H, C] tensor containing Qs.
kv (torch.Tensor): A [N, L, 2, H, C] tensor containing Ks and Vs.
"""
...
@overload
def scaled_dot_product_attention(q: torch.Tensor, k: torch.Tensor, v: torch.Tensor) -> torch.Tensor:
"""
Apply scaled dot product attention.
Args:
q (torch.Tensor): A [N, L, H, Ci] tensor containing Qs.
k (torch.Tensor): A [N, L, H, Ci] tensor containing Ks.
v (torch.Tensor): A [N, L, H, Co] tensor containing Vs.
Note:
k and v are assumed to have the same coordinate map.
"""
...
def scaled_dot_product_attention(*args, **kwargs):
arg_names_dict = {
1: ['qkv'],
2: ['q', 'kv'],
3: ['q', 'k', 'v']
}
num_all_args = len(args) + len(kwargs)
assert num_all_args in arg_names_dict, f"Invalid number of arguments, got {num_all_args}, expected 1, 2, or 3"
for key in arg_names_dict[num_all_args][len(args):]:
assert key in kwargs, f"Missing argument {key}"
if num_all_args == 1:
qkv = args[0] if len(args) > 0 else kwargs['qkv']
assert len(qkv.shape) == 5 and qkv.shape[2] == 3, f"Invalid shape for qkv, got {qkv.shape}, expected [N, L, 3, H, C]"
device = qkv.device
elif num_all_args == 2:
q = args[0] if len(args) > 0 else kwargs['q']
kv = args[1] if len(args) > 1 else kwargs['kv']
assert q.shape[0] == kv.shape[0], f"Batch size mismatch, got {q.shape[0]} and {kv.shape[0]}"
assert len(q.shape) == 4, f"Invalid shape for q, got {q.shape}, expected [N, L, H, C]"
assert len(kv.shape) == 5, f"Invalid shape for kv, got {kv.shape}, expected [N, L, 2, H, C]"
device = q.device
elif num_all_args == 3:
q = args[0] if len(args) > 0 else kwargs['q']
k = args[1] if len(args) > 1 else kwargs['k']
v = args[2] if len(args) > 2 else kwargs['v']
assert q.shape[0] == k.shape[0] == v.shape[0], f"Batch size mismatch, got {q.shape[0]}, {k.shape[0]}, and {v.shape[0]}"
assert len(q.shape) == 4, f"Invalid shape for q, got {q.shape}, expected [N, L, H, Ci]"
assert len(k.shape) == 4, f"Invalid shape for k, got {k.shape}, expected [N, L, H, Ci]"
assert len(v.shape) == 4, f"Invalid shape for v, got {v.shape}, expected [N, L, H, Co]"
device = q.device
if BACKEND == 'xformers':
if num_all_args == 1:
q, k, v = qkv.unbind(dim=2)
elif num_all_args == 2:
k, v = kv.unbind(dim=2)
out = xops.memory_efficient_attention(q, k, v)
elif BACKEND == 'flash_attn':
if num_all_args == 1:
out = flash_attn.flash_attn_qkvpacked_func(qkv)
elif num_all_args == 2:
out = flash_attn.flash_attn_kvpacked_func(q, kv)
elif num_all_args == 3:
out = flash_attn.flash_attn_func(q, k, v)
elif BACKEND == 'sdpa':
if num_all_args == 1:
q, k, v = qkv.unbind(dim=2)
elif num_all_args == 2:
k, v = kv.unbind(dim=2)
q = q.permute(0, 2, 1, 3) # [N, H, L, C]
k = k.permute(0, 2, 1, 3) # [N, H, L, C]
v = v.permute(0, 2, 1, 3) # [N, H, L, C]
out = sdpa(q, k, v) # [N, H, L, C]
out = out.permute(0, 2, 1, 3) # [N, L, H, C]
elif BACKEND == 'naive':
if num_all_args == 1:
q, k, v = qkv.unbind(dim=2)
elif num_all_args == 2:
k, v = kv.unbind(dim=2)
out = _naive_sdpa(q, k, v)
else:
raise ValueError(f"Unknown attention module: {BACKEND}")
return out
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