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Gunasekar 2023-09-11 21:30:53 +00:00 committed by huggingface-web
parent 07a048efa7
commit d655135ca1
1 changed files with 41 additions and 5 deletions
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modeling_mixformer_sequential.py
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@ -1,6 +1,36 @@
# Copyright (c) Microsoft Corporation. # Copyright (c) Microsoft Corporation.
# Licensed under the MIT license. # Licensed under the MIT license.
# BSD 3-Clause License
#
# Copyright (c) 2022, Tri Dao, trid@cs.stanford.edu.
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# * Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# * 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.
#
# * Neither the name of the copyright holder 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 HOLDER 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.
from __future__ import annotations from __future__ import annotations
import math import math
@ -21,7 +51,8 @@ from .configuration_mixformer_sequential import MixFormerSequentialConfig
@dataclass @dataclass
class InferenceParams: class InferenceParams:
"""Inference parameters that are passed to the main model in order """Inference parameters that are passed to the main model in order
to efficienly calculate and store the context during inference.""" to efficienly calculate and store the context during inference.
Adapted from https://github.com/Dao-AILab/flash-attention."""
max_sequence_len: int max_sequence_len: int
max_batch_size: int max_batch_size: int
sequence_len_offset: int = 0 sequence_len_offset: int = 0
@ -50,7 +81,8 @@ class Embedding(nn.Module):
return hidden_states return hidden_states
class RotaryEmbedding(nn.Module): class RotaryEmbedding(nn.Module):
"""PyTorch implementation of `flash-attn` RotaryEmbedding layer.""" """PyTorch implementation of `flash-attn` RotaryEmbedding layer.
Adapted from https://github.com/Dao-AILab/flash-attention."""
def __init__( def __init__(
self, self,
@ -187,7 +219,7 @@ class RotaryEmbedding(nn.Module):
def _update_kv_cache(kv, inference_params, layer_idx): def _update_kv_cache(kv, inference_params, layer_idx):
"""kv: (batch_size, seqlen, 2, nheads, head_dim) or (batch_size, 1, 2, nheads, head_dim) """kv: (batch_size, seqlen, 2, nheads, head_dim) or (batch_size, 1, 2, nheads, head_dim)
""" Adapted from https://github.com/Dao-AILab/flash-attention."""
# Pre-allocate memory for key-values for inference. # Pre-allocate memory for key-values for inference.
num_heads, head_dim = kv.shape[-2:] num_heads, head_dim = kv.shape[-2:]
if layer_idx not in inference_params.key_value_memory_dict: if layer_idx not in inference_params.key_value_memory_dict:
@ -281,6 +313,7 @@ class FusedMLP(nn.Module):
class SelfAttention(nn.Module): class SelfAttention(nn.Module):
"""Implement the scaled dot product attention with softmax. """Implement the scaled dot product attention with softmax.
Adapted from https://github.com/Dao-AILab/flash-attention.
Arguments Arguments
--------- ---------
softmax_scale: The temperature to use for the softmax attention. softmax_scale: The temperature to use for the softmax attention.
@ -329,6 +362,7 @@ class SelfAttention(nn.Module):
class CrossAttention(nn.Module): class CrossAttention(nn.Module):
"""Implement the scaled dot product attention with softmax. """Implement the scaled dot product attention with softmax.
Adapted from https://github.com/Dao-AILab/flash-attention.
Arguments Arguments
--------- ---------
softmax_scale: The temperature to use for the softmax attention. softmax_scale: The temperature to use for the softmax attention.
@ -412,7 +446,8 @@ def find_mha_dims(
class MHA(nn.Module): class MHA(nn.Module):
"""Multi-head attention layer.""" """Multi-head attention layer.
Adapted from https://github.com/Dao-AILab/flash-attention."""
def __init__( def __init__(
self, self,
@ -472,7 +507,8 @@ class MHA(nn.Module):
self.inner_cross_attn = CrossAttention(causal=causal, softmax_scale=softmax_scale, attention_dropout=dropout) self.inner_cross_attn = CrossAttention(causal=causal, softmax_scale=softmax_scale, attention_dropout=dropout)
def _update_kv_cache(self, kv: torch.FloatTensor, inference_params: InferenceParams) -> None: def _update_kv_cache(self, kv: torch.FloatTensor, inference_params: InferenceParams) -> None:
"""kv: (batch_size, seqlen, 2, nheads, head_dim) or (batch_size, 1, 2, nheads, head_dim)""" """kv: (batch_size, seqlen, 2, nheads, head_dim) or (batch_size, 1, 2, nheads, head_dim)
Adapted from https://github.com/Dao-AILab/flash-attention."""
assert self.layer_idx is not None, "Generation requires layer_idx in the constructor" assert self.layer_idx is not None, "Generation requires layer_idx in the constructor"