mirror of https://github.com/hpcaitech/ColossalAI
100 lines
2.7 KiB
Python
100 lines
2.7 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 typing import Optional
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import torch
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import torch.nn as nn
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from .primitives import Linear, LayerNorm
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from .tensor_utils import chunk_layer
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class PairTransition(nn.Module):
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"""
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Implements Algorithm 15.
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"""
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def __init__(self, c_z, n):
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"""
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Args:
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c_z:
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Pair transition channel dimension
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n:
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Factor by which c_z is multiplied to obtain hidden channel
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dimension
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"""
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super(PairTransition, self).__init__()
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self.c_z = c_z
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self.n = n
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self.layer_norm = LayerNorm(self.c_z)
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self.linear_1 = Linear(self.c_z, self.n * self.c_z, init="relu")
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self.relu = nn.ReLU()
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self.linear_2 = Linear(self.n * self.c_z, c_z, init="final")
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def _transition(self, z, mask):
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# [*, N_res, N_res, C_hidden]
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z = self.linear_1(z)
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z = self.relu(z)
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# [*, N_res, N_res, C_z]
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z = self.linear_2(z) * mask
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return z
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@torch.jit.ignore
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def _chunk(self,
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z: torch.Tensor,
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mask: torch.Tensor,
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chunk_size: int,
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) -> torch.Tensor:
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return chunk_layer(
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self._transition,
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{"z": z, "mask": mask},
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chunk_size=chunk_size,
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no_batch_dims=len(z.shape[:-2]),
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)
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def forward(self,
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z: 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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z:
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[*, N_res, N_res, C_z] pair embedding
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Returns:
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[*, N_res, N_res, C_z] pair embedding update
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"""
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# DISCREPANCY: DeepMind forgets to apply the mask in this module.
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if mask is None:
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mask = z.new_ones(z.shape[:-1])
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# [*, N_res, N_res, 1]
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mask = mask.unsqueeze(-1)
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# [*, N_res, N_res, C_z]
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z = self.layer_norm(z)
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if chunk_size is not None:
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z = self._chunk(z, mask, chunk_size)
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else:
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z = self._transition(z=z, mask=mask)
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return z
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