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Feat (nn/sdpa): quantization of scaled dot-product attention (#1090)
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| """ | ||
| Copyright (C) 2024, Advanced Micro Devices, Inc. | ||
| Copyright (c) 2016- Facebook, Inc (Adam Paszke) | ||
| Copyright (c) 2014- Facebook, Inc (Soumith Chintala) | ||
| Copyright (c) 2011-2014 Idiap Research Institute (Ronan Collobert) | ||
| Copyright (c) 2012-2014 Deepmind Technologies (Koray Kavukcuoglu) | ||
| Copyright (c) 2011-2012 NEC Laboratories America (Koray Kavukcuoglu) | ||
| Copyright (c) 2011-2013 NYU (Clement Farabet) | ||
| Copyright (c) 2006-2010 NEC Laboratories America (Ronan Collobert, Leon Bottou, Iain Melvin, Jason Weston) | ||
| Copyright (c) 2006 Idiap Research Institute (Samy Bengio) | ||
| Copyright (c) 2001-2004 Idiap Research Institute (Ronan Collobert, Samy Bengio, Johnny Mariethoz) | ||
| All rights reserved. | ||
| Redistribution and use in source and binary forms, with or without | ||
| modification, are permitted provided that the following conditions are met: | ||
| 1. Redistributions of source code must retain the above copyright | ||
| notice, this list of conditions and the following disclaimer. | ||
| 2. Redistributions in binary form must reproduce the above copyright | ||
| notice, this list of conditions and the following disclaimer in the | ||
| documentation and/or other materials provided with the distribution. | ||
| 3. Neither the names of AMD, Facebook, Deepmind Technologies, NYU, | ||
| NEC Laboratories America and IDIAP Research Institute nor the names | ||
| of its contributors may be used to endorse or promote products derived | ||
| from this software without specific prior written permission. | ||
| THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" | ||
| AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE | ||
| IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE | ||
| ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE | ||
| LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR | ||
| CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF | ||
| SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS | ||
| INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN | ||
| CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) | ||
| ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE | ||
| POSSIBILITY OF SUCH DAMAGE. | ||
| """ | ||
|
|
||
| import math | ||
| from typing import Optional, Tuple, Union | ||
|
|
||
| import torch | ||
| from torch import Tensor | ||
| from torch.nn import Module | ||
| from torch.nn import Parameter | ||
| import torch.nn.functional as F | ||
|
|
||
| from brevitas.quant.scaled_int import Int8ActPerTensorFloat | ||
| from brevitas.quant.scaled_int import Uint8ActPerTensorFloat | ||
|
|
||
| from .quant_activation import QuantIdentity | ||
|
|
||
|
|
||
| class ScaledDotProductAttention(Module): | ||
|
|
||
| def forward( | ||
| self, | ||
| query: Tensor, | ||
| key: Tensor, | ||
| value: Tensor, | ||
| attn_mask: Optional[Tensor] = None, | ||
| dropout_p: float = 0.0, | ||
| is_causal: bool = False, | ||
| scale: Optional[float] = None, | ||
| enable_gqa: bool = False): | ||
| r""" | ||
| Args: | ||
| query (Tensor): Query tensor; shape :math:`(N, ..., Hq, L, E)`. | ||
| key (Tensor): Key tensor; shape :math:`(N, ..., H, S, E)`. | ||
| value (Tensor): Value tensor; shape :math:`(N, ..., H, S, Ev)`. | ||
| attn_mask (optional Tensor): Attention mask; shape must be broadcastable to the shape of attention weights, | ||
| which is :math:`(N,..., L, S)`. Two types of masks are supported. | ||
| A boolean mask where a value of True indicates that the element *should* take part in attention. | ||
| A float mask of the same type as query, key, value that is added to the attention score. | ||
| dropout_p (float): Dropout probability; if greater than 0.0, dropout is applied | ||
| is_causal (bool): If set to true, the attention masking is a lower triangular matrix when the mask is a | ||
| square matrix. The attention masking has the form of the upper left causal bias due to the alignment | ||
| (see :class:`torch.nn.attention.bias.CausalBias`) when the mask is a non-square matrix. | ||
| An error is thrown if both attn_mask and is_causal are set. | ||
| scale (optional float, keyword-only): Scaling factor applied prior to softmax. If None, the default value is set | ||
| to :math:`\frac{1}{\sqrt{E}}`. | ||
| enable_gqa (bool): Ignored to make calling interface compatible with PyTorch >v2.5. Always set to False. | ||
| Returns: | ||
| output (Tensor): Attention output; shape :math:`(N, ..., Hq, L, Ev)`. | ||
| Shape legend: | ||
| - :math:`N: \text{Batch size} ... : \text{Any number of other batch dimensions (optional)}` | ||
| - :math:`S: \text{Source sequence length}` | ||
| - :math:`L: \text{Target sequence length}` | ||
| - :math:`E: \text{Embedding dimension of the query and key}` | ||
| - :math:`Ev: \text{Embedding dimension of the value}` | ||
| - :math:`Hq: \text{Number of heads of query}` | ||
| - :math:`H: \text{Number of heads of key and value}` | ||
| """ | ||
| kwargs = {} | ||
| if scale is not None: | ||
| kwargs["scale"] = scale | ||
| if enable_gqa: | ||
| kwargs["enable_gqa"] = enable_gqa | ||
| return F.scaled_dot_product_attention( | ||
| query=query, | ||
| key=key, | ||
| value=value, | ||
| attn_mask=attn_mask, | ||
| dropout_p=dropout_p, | ||
| is_causal=is_causal, | ||
| **kwargs) | ||
|
|
||
|
|
||
| class QuantScaledDotProductAttention(Module): | ||
|
|
||
| def __init__( | ||
| self, | ||
| softmax_input_quant=None, | ||
| attn_output_weights_quant=Uint8ActPerTensorFloat, | ||
| q_scaled_quant=Int8ActPerTensorFloat, | ||
| k_transposed_quant=Int8ActPerTensorFloat, | ||
| v_quant=Int8ActPerTensorFloat, | ||
| sdpa_output_quant=None, | ||
| **kwargs) -> None: | ||
| super(QuantScaledDotProductAttention, self).__init__() | ||
|
|
||
| def filter_kwargs(prefix): | ||
| return {k[len(prefix):]: v for k, v in kwargs.items() if k.startswith(prefix)} | ||
|
|
||
| self.q_scaled_quant = QuantIdentity(act_quant=q_scaled_quant, **filter_kwargs('q_scaled_')) | ||
| self.k_transposed_quant = QuantIdentity( | ||
| act_quant=k_transposed_quant, **filter_kwargs('k_transposed_')) | ||
| self.v_quant = QuantIdentity(act_quant=v_quant, **filter_kwargs('v_')) | ||
| self.softmax_input_quant = QuantIdentity( | ||
| act_quant=softmax_input_quant, **filter_kwargs('softmax_input_')) | ||
| self.attn_output_weights_quant = QuantIdentity( | ||
| act_quant=attn_output_weights_quant, **filter_kwargs('attn_output_weights_')) | ||
| self.sdpa_output_quant = QuantIdentity( | ||
| act_quant=sdpa_output_quant, **filter_kwargs('sdpa_output_')) | ||
|
|
||
| def forward( | ||
| self, | ||
| query: Tensor, | ||
| key: Tensor, | ||
| value: Tensor, | ||
| attn_mask: Optional[Tensor] = None, | ||
| dropout_p: float = 0.0, | ||
| is_causal: bool = False, | ||
| scale: Optional[float] = None, | ||
| enable_gqa: bool = False): | ||
| r""" | ||
| Args: | ||
| query (Tensor): Query tensor; shape :math:`(N, ..., Hq, L, E)`. | ||
| key (Tensor): Key tensor; shape :math:`(N, ..., H, S, E)`. | ||
| value (Tensor): Value tensor; shape :math:`(N, ..., H, S, Ev)`. | ||
| attn_mask (optional Tensor): Attention mask; shape must be broadcastable to the shape of attention weights, | ||
| which is :math:`(N,..., L, S)`. Two types of masks are supported. | ||
| A boolean mask where a value of True indicates that the element *should* take part in attention. | ||
| A float mask of the same type as query, key, value that is added to the attention score. | ||
| dropout_p (float): Dropout probability; if greater than 0.0, dropout is applied | ||
| is_causal (bool): If set to true, the attention masking is a lower triangular matrix when the mask is a | ||
| square matrix. The attention masking has the form of the upper left causal bias due to the alignment | ||
| (see :class:`torch.nn.attention.bias.CausalBias`) when the mask is a non-square matrix. | ||
| An error is thrown if both attn_mask and is_causal are set. | ||
| scale (optional float, keyword-only): Scaling factor applied prior to softmax. If None, the default value is set | ||
| to :math:`\frac{1}{\sqrt{E}}`. | ||
| enable_gqa (bool): Ignored to make calling interface compatible with PyTorch >v2.5. Always set to False. | ||
| Returns: | ||
| output (Tensor): Attention output; shape :math:`(N, ..., Hq, L, Ev)`. | ||
| Shape legend: | ||
| - :math:`N: \text{Batch size} ... : \text{Any number of other batch dimensions (optional)}` | ||
| - :math:`S: \text{Source sequence length}` | ||
| - :math:`L: \text{Target sequence length}` | ||
| - :math:`E: \text{Embedding dimension of the query and key}` | ||
| - :math:`Ev: \text{Embedding dimension of the value}` | ||
| - :math:`Hq: \text{Number of heads of query}` | ||
| - :math:`H: \text{Number of heads of key and value}` | ||
| """ | ||
| L, S = query.size(-2), key.size(-2) | ||
| scale_factor = 1 / math.sqrt(query.size(-1)) if scale is None else scale | ||
| if attn_mask is None: | ||
| attn_bias = torch.zeros(L, S, dtype=query.dtype, device=query.device) | ||
| else: | ||
| attn_bias = torch.zeros(size=attn_mask.shape, dtype=query.dtype, device=query.device) | ||
| if is_causal: | ||
| assert attn_mask is None | ||
| temp_mask = torch.ones(L, S, dtype=torch.bool, device=query.device).tril(diagonal=0) | ||
| attn_bias.masked_fill_(temp_mask.logical_not(), float("-inf")) | ||
| attn_bias.to(dtype=query.dtype, device=query.device) | ||
|
|
||
| if attn_mask is not None: | ||
| if attn_mask.dtype == torch.bool: | ||
| attn_bias.masked_fill_(attn_mask.logical_not(), float("-inf")) | ||
| else: | ||
| attn_bias += attn_mask | ||
| q_scaled = self.q_scaled_quant(query * scale_factor) | ||
| k_transpose = self.k_transposed_quant(key.transpose(-2, -1)) | ||
| attn_weight = q_scaled @ k_transpose | ||
| attn_weight += attn_bias | ||
| attn_weight = self.softmax_input_quant(attn_weight) | ||
| attn_weight = torch.softmax(attn_weight, dim=-1) | ||
| attn_weight = torch.dropout(attn_weight, dropout_p, train=True) | ||
| attn_weight = self.attn_output_weights_quant(attn_weight) | ||
| attn_output = attn_weight @ self.v_quant(value) | ||
| attn_output = self.sdpa_output_quant(attn_output) | ||
| return attn_output |
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