mirror of https://github.com/hpcaitech/ColossalAI
140 lines
3.7 KiB
Python
140 lines
3.7 KiB
Python
# Copyright 2021 AlQuraishi Laboratory
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# Copyright 2021 DeepMind Technologies Limited
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from functools import partialmethod, partial
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import math
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from typing import Optional, List
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import torch
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import torch.nn as nn
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from .primitives import Linear, LayerNorm, Attention
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from .tensor_utils import (
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chunk_layer,
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permute_final_dims,
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flatten_final_dims,
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)
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class TriangleAttention(nn.Module):
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def __init__(
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self, c_in, c_hidden, no_heads, starting, inf=1e9
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):
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"""
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Args:
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c_in:
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Input channel dimension
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c_hidden:
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Overall hidden channel dimension (not per-head)
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no_heads:
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Number of attention heads
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"""
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super(TriangleAttention, self).__init__()
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self.c_in = c_in
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self.c_hidden = c_hidden
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self.no_heads = no_heads
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self.starting = starting
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self.inf = inf
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self.layer_norm = LayerNorm(self.c_in)
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self.linear = Linear(c_in, self.no_heads, bias=False, init="normal")
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self.mha = Attention(
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self.c_in, self.c_in, self.c_in, self.c_hidden, self.no_heads
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)
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@torch.jit.ignore
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def _chunk(self,
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x: torch.Tensor,
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biases: List[torch.Tensor],
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chunk_size: int,
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) -> torch.Tensor:
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mha_inputs = {
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"q_x": x,
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"kv_x": x,
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"biases": biases,
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}
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return chunk_layer(
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partial(self.mha),
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mha_inputs,
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chunk_size=chunk_size,
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no_batch_dims=len(x.shape[:-2]),
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)
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def forward(self,
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x: torch.Tensor,
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mask: Optional[torch.Tensor] = None,
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chunk_size: Optional[int] = None
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) -> torch.Tensor:
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"""
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Args:
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x:
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[*, I, J, C_in] input tensor (e.g. the pair representation)
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Returns:
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[*, I, J, C_in] output tensor
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"""
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if mask is None:
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# [*, I, J]
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mask = x.new_ones(
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x.shape[:-1],
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)
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# Shape annotations assume self.starting. Else, I and J are flipped
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if not self.starting:
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x = x.transpose(-2, -3)
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mask = mask.transpose(-1, -2)
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# [*, I, J, C_in]
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x = self.layer_norm(x)
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# [*, I, 1, 1, J]
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mask_bias = (self.inf * (mask - 1))[..., :, None, None, :]
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# [*, H, I, J]
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triangle_bias = permute_final_dims(self.linear(x), (2, 0, 1))
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# [*, 1, H, I, J]
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triangle_bias = triangle_bias.unsqueeze(-4)
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biases = [mask_bias, triangle_bias]
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if chunk_size is not None:
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x = self._chunk(x, biases, chunk_size)
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else:
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x = self.mha(q_x=x, kv_x=x, biases=biases)
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if not self.starting:
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x = x.transpose(-2, -3)
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return x
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class TriangleAttentionStartingNode(TriangleAttention):
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"""
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Implements Algorithm 13.
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"""
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__init__ = partialmethod(TriangleAttention.__init__, starting=True)
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class TriangleAttentionEndingNode(TriangleAttention):
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"""
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Implements Algorithm 14.
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"""
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__init__ = partialmethod(TriangleAttention.__init__, starting=False)
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