mirror of https://github.com/hpcaitech/ColossalAI
332 lines
9.0 KiB
Python
332 lines
9.0 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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import math
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import torch
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import torch.nn as nn
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from typing import Optional, List, Tuple
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from .primitives import (
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Linear,
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LayerNorm,
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Attention,
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GlobalAttention,
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_attention_chunked_trainable,
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)
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from .checkpointing import get_checkpoint_fn
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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 MSAAttention(nn.Module):
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def __init__(
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self,
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c_in,
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c_hidden,
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no_heads,
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pair_bias=False,
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c_z=None,
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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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Per-head hidden channel dimension
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no_heads:
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Number of attention heads
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pair_bias:
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Whether to use pair embedding bias
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c_z:
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Pair embedding channel dimension. Ignored unless pair_bias
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is true
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inf:
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A large number to be used in computing the attention mask
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"""
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super(MSAAttention, 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.pair_bias = pair_bias
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self.c_z = c_z
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self.inf = inf
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self.layer_norm_m = LayerNorm(self.c_in)
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self.layer_norm_z = None
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self.linear_z = None
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if self.pair_bias:
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self.layer_norm_z = LayerNorm(self.c_z)
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self.linear_z = Linear(
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self.c_z, self.no_heads, bias=False, init="normal"
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)
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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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m: 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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return chunk_layer(
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self.mha,
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{"q_x": m, "kv_x": m, "biases": biases},
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chunk_size=chunk_size,
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no_batch_dims=len(m.shape[:-2]),
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)
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def _prep_inputs(self,
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m: torch.Tensor,
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z: Optional[torch.Tensor],
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mask: Optional[torch.Tensor]
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) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
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# [*, N_seq, N_res, C_m]
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m = self.layer_norm_m(m)
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n_seq, n_res = m.shape[-3:-1]
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if mask is None:
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# [*, N_seq, N_res]
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mask = m.new_ones(
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m.shape[:-3] + (n_seq, n_res),
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)
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# [*, N_seq, 1, 1, N_res]
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mask_bias = (self.inf * (mask - 1))[..., :, None, None, :]
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# This step simply returns a larger view of the bias, and does not
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# consume additional memory.
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# [*, N_seq, no_heads, N_res, N_res]
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#bias = bias.expand(
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# ((-1,) * len(bias.shape[:-4])) + (-1, self.no_heads, n_res, -1)
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#)
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if (self.pair_bias and
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z is not None and # For the
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self.layer_norm_z is not None and # benefit of
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self.linear_z is not None # TorchScript
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):
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# [*, N_res, N_res, C_z]
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z = self.layer_norm_z(z)
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# [*, N_res, N_res, no_heads]
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z = self.linear_z(z)
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# [*, 1, no_heads, N_res, N_res]
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z = permute_final_dims(z, (2, 0, 1)).unsqueeze(-4)
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return m, mask_bias, z
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def forward(self,
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m: torch.Tensor,
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z: Optional[torch.Tensor] = None,
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mask: Optional[torch.Tensor] = None,
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chunk_size: Optional[int] = None,
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_chunk_logits: Optional[int] = None,
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_checkpoint_chunks: Optional[bool] = None,
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) -> torch.Tensor:
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"""
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Args:
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m:
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[*, N_seq, N_res, C_m] MSA embedding
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z:
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[*, N_res, N_res, C_z] pair embedding. Required only if
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pair_bias is True
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mask:
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[*, N_seq, N_res] MSA mask
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chunk_size:
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Size of chunks into which the inputs are split along their
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batch dimensions. A low value decreases memory overhead at the
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cost of slower execution. Chunking is not performed by default.
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"""
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m, mask_bias, z = self._prep_inputs(m, z, mask)
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biases = [mask_bias]
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if(z is not None):
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biases.append(z)
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if chunk_size is not None:
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m = self._chunk(m, biases, chunk_size)
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else:
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m = self.mha(
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q_x=m,
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kv_x=m,
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biases=biases
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)
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return m
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class MSARowAttentionWithPairBias(MSAAttention):
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"""
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Implements Algorithm 7.
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"""
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def __init__(self, c_m, c_z, c_hidden, no_heads, inf=1e9):
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"""
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Args:
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c_m:
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Input channel dimension
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c_z:
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Pair embedding channel dimension
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c_hidden:
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Per-head hidden channel dimension
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no_heads:
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Number of attention heads
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inf:
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Large number used to construct attention masks
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"""
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super(MSARowAttentionWithPairBias, self).__init__(
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c_m,
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c_hidden,
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no_heads,
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pair_bias=True,
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c_z=c_z,
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inf=inf,
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)
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class MSAColumnAttention(nn.Module):
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"""
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Implements Algorithm 8.
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By rights, this should also be a subclass of MSAAttention. Alas,
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most inheritance isn't supported by TorchScript.
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"""
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def __init__(self, c_m, c_hidden, no_heads, inf=1e9):
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"""
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Args:
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c_m:
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MSA channel dimension
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c_hidden:
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Per-head hidden channel dimension
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no_heads:
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Number of attention heads
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inf:
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Large number used to construct attention masks
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"""
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super(MSAColumnAttention, self).__init__()
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self.c_m = c_m
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self.c_hidden = c_hidden
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self.no_heads = no_heads
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self.inf = inf
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self._msa_att = MSAAttention(
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c_in=c_m,
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c_hidden=c_hidden,
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no_heads=no_heads,
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pair_bias=False,
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c_z=None,
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inf=inf,
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)
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def forward(self,
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m: 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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m:
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[*, N_seq, N_res, C_m] MSA embedding
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mask:
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[*, N_seq, N_res] MSA mask
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chunk_size:
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Size of chunks into which the inputs are split along their
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batch dimensions. A low value decreases memory overhead at the
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cost of slower execution. Chunking is not performed by default.
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"""
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# [*, N_res, N_seq, C_in]
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m = m.transpose(-2, -3)
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m = self._msa_att(m, chunk_size=chunk_size)
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# [*, N_seq, N_res, C_in]
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m = m.transpose(-2, -3)
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return m
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class MSAColumnGlobalAttention(nn.Module):
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def __init__(
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self, c_in, c_hidden, no_heads, inf=1e9, eps=1e-10,
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):
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super(MSAColumnGlobalAttention, 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.inf = inf
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self.eps = eps
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self.layer_norm_m = nn.LayerNorm(c_in)
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self.global_attention = GlobalAttention(
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c_in=c_in,
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c_hidden=c_hidden,
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no_heads=no_heads,
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inf=inf,
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eps=eps,
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)
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@torch.jit.ignore
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def _chunk(self,
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m: torch.Tensor,
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chunk_size: int,
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) -> torch.Tensor:
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mha_input = {
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"m": m,
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}
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return chunk_layer(
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self.global_attention,
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mha_input,
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chunk_size=chunk_size,
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no_batch_dims=len(m.shape[:-2]),
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)
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def forward(
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self,
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m: torch.Tensor,
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chunk_size: Optional[int] = None,
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) -> torch.Tensor:
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n_seq, n_res, c_in = m.shape[-3:]
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# [*, N_res, N_seq, C_in]
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m = m.transpose(-2, -3)
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# [*, N_res, N_seq, C_in]
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m = self.layer_norm_m(m)
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if chunk_size is not None:
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m = self._chunk(m, chunk_size)
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else:
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m = self.global_attention(m=m)
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# [*, N_seq, N_res, C_in]
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m = m.transpose(-2, -3)
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return m
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