66 lines
1.7 KiB
Python
66 lines
1.7 KiB
Python
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
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#
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# This source code is licensed under the BSD license found in the
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# LICENSE file in the root directory of this source tree.
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from dataclasses import dataclass
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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 xformers.components.positional_embedding import (
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PositionEmbedding,
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PositionEmbeddingConfig,
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register_positional_embedding,
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)
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@dataclass
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class VocabEmbeddingConfig(PositionEmbeddingConfig):
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vocab_size: int
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dropout: float
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@register_positional_embedding("vocab", VocabEmbeddingConfig)
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class VocabEmbedding(PositionEmbedding):
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def __init__(
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self,
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dim_model: int,
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seq_len: int,
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vocab_size: int,
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dropout: float = 0.0,
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*args,
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**kwargs
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):
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super().__init__()
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self.vocab_size = vocab_size
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self.dim_model = dim_model
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self.dropout = torch.nn.Dropout(p=dropout)
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self.position_embeddings = nn.Embedding(seq_len, self.dim_model)
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self.word_embeddings = nn.Embedding(self.vocab_size, self.dim_model)
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self.position_ids: Optional[torch.Tensor] = None
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self.init_weights()
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def init_weights(self, gain: float = 1.0):
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torch.nn.init.normal_(self.position_embeddings.weight, std=0.02 * gain)
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torch.nn.init.normal_(self.word_embeddings.weight, std=0.02 * gain)
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def forward(self, x: torch.Tensor):
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position_ids = torch.arange(x.shape[1], dtype=torch.long, device=x.device)[
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None, :
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].repeat(x.shape[0], 1)
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X_token = self.word_embeddings(x)
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X_pos = self.position_embeddings(position_ids)
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X = X_token + X_pos
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X = self.dropout(X)
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return X
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