mirror of
https://github.com/k2-fsa/icefall.git
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fba5e67d5e
- Introduce unified AMP helpers (create_grad_scaler, torch_autocast) to handle deprecations in PyTorch ≥2.3.0 - Replace direct uses of torch.cuda.amp.GradScaler and torch.cuda.amp.autocast with the new utilities across all training and inference scripts - Update all torch.load calls to include weights_only=False for compatibility with newer PyTorch versions
246 lines
8.7 KiB
Python
246 lines
8.7 KiB
Python
# Copyright 2021 Xiaomi Corp. (authors: Fangjun Kuang, Wei Kang)
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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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from typing import Optional, Tuple
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import k2
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import torch
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import torch.nn as nn
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from encoder_interface import EncoderInterface
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from scaling import ScaledLinear
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from icefall.utils import add_sos, torch_autocast
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class Transducer(nn.Module):
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"""It implements https://arxiv.org/pdf/1211.3711.pdf
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"Sequence Transduction with Recurrent Neural Networks"
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"""
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def __init__(
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self,
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encoder: EncoderInterface,
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decoder: nn.Module,
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joiner: nn.Module,
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encoder_dim: int,
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decoder_dim: int,
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joiner_dim: int,
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vocab_size: int,
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decoder_giga: Optional[nn.Module] = None,
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joiner_giga: Optional[nn.Module] = None,
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):
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"""
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Args:
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encoder:
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It is the transcription network in the paper. Its accepts
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two inputs: `x` of (N, T, encoder_dim) and `x_lens` of shape (N,).
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It returns two tensors: `logits` of shape (N, T, encoder_dm) and
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`logit_lens` of shape (N,).
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decoder:
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It is the prediction network in the paper. Its input shape
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is (N, U) and its output shape is (N, U, decoder_dim).
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It should contain one attribute: `blank_id`.
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joiner:
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It has two inputs with shapes: (N, T, encoder_dim) and
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(N, U, decoder_dim). Its output shape is (N, T, U, vocab_size).
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Note that its output contains
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unnormalized probs, i.e., not processed by log-softmax.
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encoder_dim:
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Output dimension of the encoder network.
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decoder_dim:
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Output dimension of the decoder network.
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joiner_dim:
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Input dimension of the joiner network.
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vocab_size:
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Output dimension of the joiner network.
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decoder_giga:
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Optional. The decoder network for the GigaSpeech dataset.
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joiner_giga:
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Optional. The joiner network for the GigaSpeech dataset.
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"""
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super().__init__()
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assert isinstance(encoder, EncoderInterface), type(encoder)
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assert hasattr(decoder, "blank_id")
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self.encoder = encoder
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self.decoder = decoder
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self.joiner = joiner
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self.decoder_giga = decoder_giga
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self.joiner_giga = joiner_giga
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self.simple_am_proj = ScaledLinear(encoder_dim, vocab_size, initial_speed=0.5)
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self.simple_lm_proj = ScaledLinear(decoder_dim, vocab_size)
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if decoder_giga is not None:
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self.simple_am_proj_giga = ScaledLinear(
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encoder_dim, vocab_size, initial_speed=0.5
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)
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self.simple_lm_proj_giga = ScaledLinear(decoder_dim, vocab_size)
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def forward(
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self,
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x: torch.Tensor,
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x_lens: torch.Tensor,
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y: k2.RaggedTensor,
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libri: bool = True,
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prune_range: int = 5,
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am_scale: float = 0.0,
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lm_scale: float = 0.0,
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warmup: float = 1.0,
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reduction: str = "sum",
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delay_penalty: float = 0.0,
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) -> Tuple[torch.Tensor, torch.Tensor]:
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"""
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Args:
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x:
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A 3-D tensor of shape (N, T, C).
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x_lens:
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A 1-D tensor of shape (N,). It contains the number of frames in `x`
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before padding.
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y:
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A ragged tensor with 2 axes [utt][label]. It contains labels of each
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utterance.
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libri:
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True to use the decoder and joiner for the LibriSpeech dataset.
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False to use the decoder and joiner for the GigaSpeech dataset.
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prune_range:
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The prune range for rnnt loss, it means how many symbols(context)
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we are considering for each frame to compute the loss.
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am_scale:
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The scale to smooth the loss with am (output of encoder network)
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part
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lm_scale:
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The scale to smooth the loss with lm (output of predictor network)
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part
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warmup:
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A value warmup >= 0 that determines which modules are active, values
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warmup > 1 "are fully warmed up" and all modules will be active.
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reduction:
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"sum" to sum the losses over all utterances in the batch.
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"none" to return the loss in a 1-D tensor for each utterance
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in the batch.
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delay_penalty:
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A constant value used to penalize symbol delay, to encourage
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streaming models to emit symbols earlier.
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See https://github.com/k2-fsa/k2/issues/955 and
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https://arxiv.org/pdf/2211.00490.pdf for more details.
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Returns:
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Return the transducer loss.
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Note:
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Regarding am_scale & lm_scale, it will make the loss-function one of
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the form:
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lm_scale * lm_probs + am_scale * am_probs +
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(1-lm_scale-am_scale) * combined_probs
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"""
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assert reduction in ("sum", "none"), reduction
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assert x.ndim == 3, x.shape
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assert x_lens.ndim == 1, x_lens.shape
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assert y.num_axes == 2, y.num_axes
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assert x.size(0) == x_lens.size(0) == y.dim0
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encoder_out, encoder_out_lens = self.encoder(x, x_lens, warmup=warmup)
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assert torch.all(encoder_out_lens > 0)
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if libri:
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decoder = self.decoder
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simple_lm_proj = self.simple_lm_proj
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simple_am_proj = self.simple_am_proj
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joiner = self.joiner
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else:
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decoder = self.decoder_giga
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simple_lm_proj = self.simple_lm_proj_giga
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simple_am_proj = self.simple_am_proj_giga
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joiner = self.joiner_giga
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# Now for the decoder, i.e., the prediction network
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row_splits = y.shape.row_splits(1)
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y_lens = row_splits[1:] - row_splits[:-1]
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blank_id = decoder.blank_id
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sos_y = add_sos(y, sos_id=blank_id)
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# sos_y_padded: [B, S + 1], start with SOS.
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sos_y_padded = sos_y.pad(mode="constant", padding_value=blank_id)
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# decoder_out: [B, S + 1, decoder_dim]
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decoder_out = decoder(sos_y_padded)
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# Note: y does not start with SOS
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# y_padded : [B, S]
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y_padded = y.pad(mode="constant", padding_value=0)
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y_padded = y_padded.to(torch.int64)
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boundary = torch.zeros((x.size(0), 4), dtype=torch.int64, device=x.device)
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boundary[:, 2] = y_lens
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boundary[:, 3] = encoder_out_lens
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lm = simple_lm_proj(decoder_out)
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am = simple_am_proj(encoder_out)
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with torch_autocast(enabled=False):
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simple_loss, (px_grad, py_grad) = k2.rnnt_loss_smoothed(
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lm=lm.float(),
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am=am.float(),
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symbols=y_padded,
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termination_symbol=blank_id,
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lm_only_scale=lm_scale,
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am_only_scale=am_scale,
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boundary=boundary,
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reduction=reduction,
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delay_penalty=delay_penalty,
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return_grad=True,
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)
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# ranges : [B, T, prune_range]
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ranges = k2.get_rnnt_prune_ranges(
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px_grad=px_grad,
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py_grad=py_grad,
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boundary=boundary,
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s_range=prune_range,
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)
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# am_pruned : [B, T, prune_range, encoder_dim]
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# lm_pruned : [B, T, prune_range, decoder_dim]
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am_pruned, lm_pruned = k2.do_rnnt_pruning(
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am=joiner.encoder_proj(encoder_out),
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lm=joiner.decoder_proj(decoder_out),
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ranges=ranges,
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)
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# logits : [B, T, prune_range, vocab_size]
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# project_input=False since we applied the decoder's input projections
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# prior to do_rnnt_pruning (this is an optimization for speed).
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logits = joiner(am_pruned, lm_pruned, project_input=False)
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with torch_autocast(enabled=False):
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pruned_loss = k2.rnnt_loss_pruned(
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logits=logits.float(),
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symbols=y_padded,
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ranges=ranges,
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termination_symbol=blank_id,
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boundary=boundary,
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delay_penalty=delay_penalty,
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reduction=reduction,
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)
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return (simple_loss, pruned_loss)
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