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122 lines
4.2 KiB
Python
122 lines
4.2 KiB
Python
#!/usr/bin/env python3
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# Copyright 2023 Xiaomi Corp. (authors: Daniel Povey)
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#
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# See ../../../../LICENSE for clarification regarding multiple authors
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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 torch
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from torch import nn, Tensor
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from subformer import Subformer
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from scaling import Balancer
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class TextEmbedder(nn.Module):
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def __init__(self,
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vocab_size: int,
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embedding_dim: int):
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super().__init__()
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self.embed = nn.Embedding(
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num_embeddings=vocab_size,
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embedding_dim=embedding_dim)
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self.conv1 = nn.Conv1d(embedding_dim,
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embedding_dim,
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groups=embedding_dim,
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kernel_size=2)
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self.balancer1 = Balancer(embedding_dim,
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channel_dim=1,
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min_positive=0.1,
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min_abs=1.0,
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max_abs=2.0)
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self.activation1 = nn.ReLU()
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self.conv2 = nn.Conv1d(embedding_dim,
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embedding_dim,
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kernel_size=2)
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self.balancer2 = Balancer(embedding_dim,
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channel_dim=1,
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min_positive=0.1,
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min_abs=1.0,
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max_abs=2.0)
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self.activation2 = nn.ReLU()
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self.out_proj = nn.Linear(embedding_dim,
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embedding_dim,
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bias=False)
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def forward(self,
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text: Tensor) -> Tensor:
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"""
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Args:
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text: Tensor of shape (seq_len, batch_size), containing integer indexes
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0 <= text < vocab_size.
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Returns:
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Tensor of shape (seq_len, batch_size, embedding_dim)
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"""
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x = self.embed(text) # (seq_len, batch_size, embedding_dim)
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x = x.permute(1, 2, 0) # N,C,H, i.e. (batch_size, embedding_dim, seq_len)
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x = torch.nn.functional.pad(x, (1, 0))
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x = self.conv1(x)
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x = self.balancer1(x) # make sure no channel has all zeros.
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x = self.activation1(x)
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x = torch.nn.functional.pad(x, (1, 0))
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x = self.conv2(x)
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x = self.balancer2(x)
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x = self.activation2(x)
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x = x.permute(2, 0, 1) # (seq_len, batch_size, embedding_dim)
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x = self.out_proj(x)
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return x
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class SubformerLM(nn.Module):
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def __init__(self,
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encoder_embed: nn.Module,
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encoder: Subformer,
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decoder: nn.Module):
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super().__init__()
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self.encoder_embed = encoder_embed
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self.encoder = encoder # does subsampling
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self.decoder = decoder
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def forward(self,
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labels: Tensor):
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"""
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Compute array of log-probs
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Args:
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labels: a Tensor containing the labels (in the range 0..num_symbols-1), of shape (batch_size, seq_len).
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Returns:
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a Tensor containing the log-probs for each label, of shape (batch_size, seq_len).
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"""
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(batch_size, seq_len) = labels.shape
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chunk_size = 1
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labels_shifted = labels.t() # (time, batch)
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labels_shifted = torch.cat((torch.zeros_like(labels_shifted[:1]),
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labels_shifted[:-1]),
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dim=0)
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x = self.encoder_embed(labels_shifted)
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x_lens = torch.full((batch_size,), seq_len,
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dtype=torch.long, device=labels.device)
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# x_lens is after subsampling. Actually we don't need it.
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(x, x_lens) = self.encoder(x, x_lens)
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logprobs = self.decoder(labels, x)
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return logprobs
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