From e83dcdc3b44f09d702ffb22001ee1a90bcdf5fcf Mon Sep 17 00:00:00 2001 From: Fangjun Kuang Date: Thu, 21 Apr 2022 10:37:24 +0800 Subject: [PATCH 1/3] Copy files for editing. --- .../ASR/transducer_stateless3/__init__.py | 0 .../transducer_stateless3/asr_datamodule.py | 1 + .../ASR/transducer_stateless3/conformer.py | 1 + .../ASR/transducer_stateless3/decoder.py | 1 + .../encoder_interface.py | 1 + .../ASR/transducer_stateless3/joiner.py | 67 ++ .../ASR/transducer_stateless3/model.py | 193 ++++ .../ASR/transducer_stateless3/optim.py | 1 + .../ASR/transducer_stateless3/scaling.py | 1 + .../ASR/transducer_stateless3/train.py | 997 ++++++++++++++++++ 10 files changed, 1263 insertions(+) create mode 100644 egs/librispeech/ASR/transducer_stateless3/__init__.py create mode 120000 egs/librispeech/ASR/transducer_stateless3/asr_datamodule.py create mode 120000 egs/librispeech/ASR/transducer_stateless3/conformer.py create mode 120000 egs/librispeech/ASR/transducer_stateless3/decoder.py create mode 120000 egs/librispeech/ASR/transducer_stateless3/encoder_interface.py create mode 100644 egs/librispeech/ASR/transducer_stateless3/joiner.py create mode 100644 egs/librispeech/ASR/transducer_stateless3/model.py create mode 120000 egs/librispeech/ASR/transducer_stateless3/optim.py create mode 120000 egs/librispeech/ASR/transducer_stateless3/scaling.py create mode 100755 egs/librispeech/ASR/transducer_stateless3/train.py diff --git a/egs/librispeech/ASR/transducer_stateless3/__init__.py b/egs/librispeech/ASR/transducer_stateless3/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/egs/librispeech/ASR/transducer_stateless3/asr_datamodule.py b/egs/librispeech/ASR/transducer_stateless3/asr_datamodule.py new file mode 120000 index 000000000..a074d6085 --- /dev/null +++ b/egs/librispeech/ASR/transducer_stateless3/asr_datamodule.py @@ -0,0 +1 @@ +../pruned_transducer_stateless2/asr_datamodule.py \ No newline at end of file diff --git a/egs/librispeech/ASR/transducer_stateless3/conformer.py b/egs/librispeech/ASR/transducer_stateless3/conformer.py new file mode 120000 index 000000000..3b84b9573 --- /dev/null +++ b/egs/librispeech/ASR/transducer_stateless3/conformer.py @@ -0,0 +1 @@ +../pruned_transducer_stateless2/conformer.py \ No newline at end of file diff --git a/egs/librispeech/ASR/transducer_stateless3/decoder.py b/egs/librispeech/ASR/transducer_stateless3/decoder.py new file mode 120000 index 000000000..0793c5709 --- /dev/null +++ b/egs/librispeech/ASR/transducer_stateless3/decoder.py @@ -0,0 +1 @@ +../pruned_transducer_stateless2/decoder.py \ No newline at end of file diff --git a/egs/librispeech/ASR/transducer_stateless3/encoder_interface.py b/egs/librispeech/ASR/transducer_stateless3/encoder_interface.py new file mode 120000 index 000000000..b9aa0ae08 --- /dev/null +++ b/egs/librispeech/ASR/transducer_stateless3/encoder_interface.py @@ -0,0 +1 @@ +../pruned_transducer_stateless2/encoder_interface.py \ No newline at end of file diff --git a/egs/librispeech/ASR/transducer_stateless3/joiner.py b/egs/librispeech/ASR/transducer_stateless3/joiner.py new file mode 100644 index 000000000..35f75ed2a --- /dev/null +++ b/egs/librispeech/ASR/transducer_stateless3/joiner.py @@ -0,0 +1,67 @@ +# Copyright 2021 Xiaomi Corp. (authors: Fangjun Kuang) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import torch +import torch.nn as nn +from scaling import ScaledLinear + + +class Joiner(nn.Module): + def __init__( + self, + encoder_dim: int, + decoder_dim: int, + joiner_dim: int, + vocab_size: int, + ): + super().__init__() + + self.encoder_proj = ScaledLinear(encoder_dim, joiner_dim) + self.decoder_proj = ScaledLinear(decoder_dim, joiner_dim) + self.output_linear = ScaledLinear(joiner_dim, vocab_size) + + def forward( + self, + encoder_out: torch.Tensor, + decoder_out: torch.Tensor, + project_input: bool = True, + ) -> torch.Tensor: + """ + Args: + encoder_out: + Output from the encoder. Its shape is (N, T, s_range, C). + decoder_out: + Output from the decoder. Its shape is (N, T, s_range, C). + project_input: + If true, apply input projections encoder_proj and decoder_proj. + If this is false, it is the user's responsibility to do this + manually. + Returns: + Return a tensor of shape (N, T, s_range, C). + """ + assert encoder_out.ndim == decoder_out.ndim == 4 + assert encoder_out.shape[:-1] == decoder_out.shape[:-1] + + if project_input: + logit = self.encoder_proj(encoder_out) + self.decoder_proj( + decoder_out + ) + else: + logit = encoder_out + decoder_out + + logit = self.output_linear(torch.tanh(logit)) + + return logit diff --git a/egs/librispeech/ASR/transducer_stateless3/model.py b/egs/librispeech/ASR/transducer_stateless3/model.py new file mode 100644 index 000000000..599bf2506 --- /dev/null +++ b/egs/librispeech/ASR/transducer_stateless3/model.py @@ -0,0 +1,193 @@ +# Copyright 2021 Xiaomi Corp. (authors: Fangjun Kuang, Wei Kang) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +import k2 +import torch +import torch.nn as nn +from encoder_interface import EncoderInterface +from scaling import ScaledLinear + +from icefall.utils import add_sos + + +class Transducer(nn.Module): + """It implements https://arxiv.org/pdf/1211.3711.pdf + "Sequence Transduction with Recurrent Neural Networks" + """ + + def __init__( + self, + encoder: EncoderInterface, + decoder: nn.Module, + joiner: nn.Module, + encoder_dim: int, + decoder_dim: int, + joiner_dim: int, + vocab_size: int, + ): + """ + Args: + encoder: + It is the transcription network in the paper. Its accepts + two inputs: `x` of (N, T, encoder_dim) and `x_lens` of shape (N,). + It returns two tensors: `logits` of shape (N, T, encoder_dm) and + `logit_lens` of shape (N,). + decoder: + It is the prediction network in the paper. Its input shape + is (N, U) and its output shape is (N, U, decoder_dim). + It should contain one attribute: `blank_id`. + joiner: + It has two inputs with shapes: (N, T, encoder_dim) and (N, U, decoder_dim). + Its output shape is (N, T, U, vocab_size). Note that its output contains + unnormalized probs, i.e., not processed by log-softmax. + """ + super().__init__() + assert isinstance(encoder, EncoderInterface), type(encoder) + assert hasattr(decoder, "blank_id") + + self.encoder = encoder + self.decoder = decoder + self.joiner = joiner + + self.simple_am_proj = ScaledLinear( + encoder_dim, vocab_size, initial_speed=0.5 + ) + self.simple_lm_proj = ScaledLinear(decoder_dim, vocab_size) + + def forward( + self, + x: torch.Tensor, + x_lens: torch.Tensor, + y: k2.RaggedTensor, + prune_range: int = 5, + am_scale: float = 0.0, + lm_scale: float = 0.0, + warmup: float = 1.0, + ) -> torch.Tensor: + """ + Args: + x: + A 3-D tensor of shape (N, T, C). + x_lens: + A 1-D tensor of shape (N,). It contains the number of frames in `x` + before padding. + y: + A ragged tensor with 2 axes [utt][label]. It contains labels of each + utterance. + prune_range: + The prune range for rnnt loss, it means how many symbols(context) + we are considering for each frame to compute the loss. + am_scale: + The scale to smooth the loss with am (output of encoder network) + part + lm_scale: + The scale to smooth the loss with lm (output of predictor network) + part + warmup: + A value warmup >= 0 that determines which modules are active, values + warmup > 1 "are fully warmed up" and all modules will be active. + Returns: + Return the transducer loss. + + Note: + Regarding am_scale & lm_scale, it will make the loss-function one of + the form: + lm_scale * lm_probs + am_scale * am_probs + + (1-lm_scale-am_scale) * combined_probs + """ + assert x.ndim == 3, x.shape + assert x_lens.ndim == 1, x_lens.shape + assert y.num_axes == 2, y.num_axes + + assert x.size(0) == x_lens.size(0) == y.dim0 + + encoder_out, x_lens = self.encoder(x, x_lens, warmup=warmup) + assert torch.all(x_lens > 0) + + # Now for the decoder, i.e., the prediction network + row_splits = y.shape.row_splits(1) + y_lens = row_splits[1:] - row_splits[:-1] + + blank_id = self.decoder.blank_id + sos_y = add_sos(y, sos_id=blank_id) + + # sos_y_padded: [B, S + 1], start with SOS. + sos_y_padded = sos_y.pad(mode="constant", padding_value=blank_id) + + # decoder_out: [B, S + 1, decoder_dim] + decoder_out = self.decoder(sos_y_padded) + + # Note: y does not start with SOS + # y_padded : [B, S] + y_padded = y.pad(mode="constant", padding_value=0) + + y_padded = y_padded.to(torch.int64) + boundary = torch.zeros( + (x.size(0), 4), dtype=torch.int64, device=x.device + ) + boundary[:, 2] = y_lens + boundary[:, 3] = x_lens + + lm = self.simple_lm_proj(decoder_out) + am = self.simple_am_proj(encoder_out) + + with torch.cuda.amp.autocast(enabled=False): + simple_loss, (px_grad, py_grad) = k2.rnnt_loss_smoothed( + lm=lm.float(), + am=am.float(), + symbols=y_padded, + termination_symbol=blank_id, + lm_only_scale=lm_scale, + am_only_scale=am_scale, + boundary=boundary, + reduction="sum", + return_grad=True, + ) + + # ranges : [B, T, prune_range] + ranges = k2.get_rnnt_prune_ranges( + px_grad=px_grad, + py_grad=py_grad, + boundary=boundary, + s_range=prune_range, + ) + + # am_pruned : [B, T, prune_range, encoder_dim] + # lm_pruned : [B, T, prune_range, decoder_dim] + am_pruned, lm_pruned = k2.do_rnnt_pruning( + am=self.joiner.encoder_proj(encoder_out), + lm=self.joiner.decoder_proj(decoder_out), + ranges=ranges, + ) + + # logits : [B, T, prune_range, vocab_size] + + # project_input=False since we applied the decoder's input projections + # prior to do_rnnt_pruning (this is an optimization for speed). + logits = self.joiner(am_pruned, lm_pruned, project_input=False) + + with torch.cuda.amp.autocast(enabled=False): + pruned_loss = k2.rnnt_loss_pruned( + logits=logits.float(), + symbols=y_padded, + ranges=ranges, + termination_symbol=blank_id, + boundary=boundary, + reduction="sum", + ) + + return (simple_loss, pruned_loss) diff --git a/egs/librispeech/ASR/transducer_stateless3/optim.py b/egs/librispeech/ASR/transducer_stateless3/optim.py new file mode 120000 index 000000000..e2deb4492 --- /dev/null +++ b/egs/librispeech/ASR/transducer_stateless3/optim.py @@ -0,0 +1 @@ +../pruned_transducer_stateless2/optim.py \ No newline at end of file diff --git a/egs/librispeech/ASR/transducer_stateless3/scaling.py b/egs/librispeech/ASR/transducer_stateless3/scaling.py new file mode 120000 index 000000000..09d802cc4 --- /dev/null +++ b/egs/librispeech/ASR/transducer_stateless3/scaling.py @@ -0,0 +1 @@ +../pruned_transducer_stateless2/scaling.py \ No newline at end of file diff --git a/egs/librispeech/ASR/transducer_stateless3/train.py b/egs/librispeech/ASR/transducer_stateless3/train.py new file mode 100755 index 000000000..80617847a --- /dev/null +++ b/egs/librispeech/ASR/transducer_stateless3/train.py @@ -0,0 +1,997 @@ +#!/usr/bin/env python3 +# Copyright 2021 Xiaomi Corp. (authors: Fangjun Kuang, +# Wei Kang +# Mingshuang Luo) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +Usage: + +export CUDA_VISIBLE_DEVICES="0,1,2,3" + +./pruned_transducer_stateless2/train.py \ + --world-size 4 \ + --num-epochs 30 \ + --start-epoch 0 \ + --exp-dir pruned_transducer_stateless2/exp \ + --full-libri 1 \ + --max-duration 300 + +# For mix precision training: + +./pruned_transducer_stateless2/train.py \ + --world-size 4 \ + --num-epochs 30 \ + --start-epoch 0 \ + --use_fp16 1 \ + --exp-dir pruned_transducer_stateless2/exp \ + --full-libri 1 \ + --max-duration 550 + +""" + + +import argparse +import logging +import warnings +from pathlib import Path +from shutil import copyfile +from typing import Any, Dict, Optional, Tuple, Union + +import k2 +import optim +import sentencepiece as spm +import torch +import torch.multiprocessing as mp +import torch.nn as nn +from asr_datamodule import LibriSpeechAsrDataModule +from conformer import Conformer +from decoder import Decoder +from joiner import Joiner +from lhotse.cut import Cut +from lhotse.dataset.sampling.base import CutSampler +from lhotse.utils import fix_random_seed +from model import Transducer +from optim import Eden, Eve +from torch import Tensor +from torch.cuda.amp import GradScaler +from torch.nn.parallel import DistributedDataParallel as DDP +from torch.utils.tensorboard import SummaryWriter + +from icefall import diagnostics +from icefall.checkpoint import load_checkpoint, remove_checkpoints +from icefall.checkpoint import save_checkpoint as save_checkpoint_impl +from icefall.checkpoint import save_checkpoint_with_global_batch_idx +from icefall.dist import cleanup_dist, setup_dist +from icefall.env import get_env_info +from icefall.utils import AttributeDict, MetricsTracker, setup_logger, str2bool + +LRSchedulerType = Union[ + torch.optim.lr_scheduler._LRScheduler, optim.LRScheduler +] + + +def get_parser(): + parser = argparse.ArgumentParser( + formatter_class=argparse.ArgumentDefaultsHelpFormatter + ) + + parser.add_argument( + "--world-size", + type=int, + default=1, + help="Number of GPUs for DDP training.", + ) + + parser.add_argument( + "--master-port", + type=int, + default=12354, + help="Master port to use for DDP training.", + ) + + parser.add_argument( + "--tensorboard", + type=str2bool, + default=True, + help="Should various information be logged in tensorboard.", + ) + + parser.add_argument( + "--num-epochs", + type=int, + default=30, + help="Number of epochs to train.", + ) + + parser.add_argument( + "--start-epoch", + type=int, + default=0, + help="""Resume training from from this epoch. + If it is positive, it will load checkpoint from + transducer_stateless2/exp/epoch-{start_epoch-1}.pt + """, + ) + + parser.add_argument( + "--start-batch", + type=int, + default=0, + help="""If positive, --start-epoch is ignored and + it loads the checkpoint from exp-dir/checkpoint-{start_batch}.pt + """, + ) + + parser.add_argument( + "--exp-dir", + type=str, + default="pruned_transducer_stateless2/exp", + help="""The experiment dir. + It specifies the directory where all training related + files, e.g., checkpoints, log, etc, are saved + """, + ) + + parser.add_argument( + "--bpe-model", + type=str, + default="data/lang_bpe_500/bpe.model", + help="Path to the BPE model", + ) + + parser.add_argument( + "--initial-lr", + type=float, + default=0.003, + help="The initial learning rate. This value should not need to be changed.", + ) + + parser.add_argument( + "--lr-batches", + type=float, + default=5000, + help="""Number of steps that affects how rapidly the learning rate decreases. + We suggest not to change this.""", + ) + + parser.add_argument( + "--lr-epochs", + type=float, + default=6, + help="""Number of epochs that affects how rapidly the learning rate decreases. + """, + ) + + parser.add_argument( + "--context-size", + type=int, + default=2, + help="The context size in the decoder. 1 means bigram; " + "2 means tri-gram", + ) + + parser.add_argument( + "--prune-range", + type=int, + default=5, + help="The prune range for rnnt loss, it means how many symbols(context)" + "we are using to compute the loss", + ) + + parser.add_argument( + "--lm-scale", + type=float, + default=0.25, + help="The scale to smooth the loss with lm " + "(output of prediction network) part.", + ) + + parser.add_argument( + "--am-scale", + type=float, + default=0.0, + help="The scale to smooth the loss with am (output of encoder network)" + "part.", + ) + + parser.add_argument( + "--simple-loss-scale", + type=float, + default=0.5, + help="To get pruning ranges, we will calculate a simple version" + "loss(joiner is just addition), this simple loss also uses for" + "training (as a regularization item). We will scale the simple loss" + "with this parameter before adding to the final loss.", + ) + + parser.add_argument( + "--seed", + type=int, + default=42, + help="The seed for random generators intended for reproducibility", + ) + + parser.add_argument( + "--print-diagnostics", + type=str2bool, + default=False, + help="Accumulate stats on activations, print them and exit.", + ) + + parser.add_argument( + "--save-every-n", + type=int, + default=8000, + help="""Save checkpoint after processing this number of batches" + periodically. We save checkpoint to exp-dir/ whenever + params.batch_idx_train % save_every_n == 0. The checkpoint filename + has the form: f'exp-dir/checkpoint-{params.batch_idx_train}.pt' + Note: It also saves checkpoint to `exp-dir/epoch-xxx.pt` at the + end of each epoch where `xxx` is the epoch number counting from 0. + """, + ) + + parser.add_argument( + "--keep-last-k", + type=int, + default=20, + help="""Only keep this number of checkpoints on disk. + For instance, if it is 3, there are only 3 checkpoints + in the exp-dir with filenames `checkpoint-xxx.pt`. + It does not affect checkpoints with name `epoch-xxx.pt`. + """, + ) + + parser.add_argument( + "--use-fp16", + type=str2bool, + default=False, + help="Whether to use half precision training.", + ) + + return parser + + +def get_params() -> AttributeDict: + """Return a dict containing training parameters. + + All training related parameters that are not passed from the commandline + are saved in the variable `params`. + + Commandline options are merged into `params` after they are parsed, so + you can also access them via `params`. + + Explanation of options saved in `params`: + + - best_train_loss: Best training loss so far. It is used to select + the model that has the lowest training loss. It is + updated during the training. + + - best_valid_loss: Best validation loss so far. It is used to select + the model that has the lowest validation loss. It is + updated during the training. + + - best_train_epoch: It is the epoch that has the best training loss. + + - best_valid_epoch: It is the epoch that has the best validation loss. + + - batch_idx_train: Used to writing statistics to tensorboard. It + contains number of batches trained so far across + epochs. + + - log_interval: Print training loss if batch_idx % log_interval` is 0 + + - reset_interval: Reset statistics if batch_idx % reset_interval is 0 + + - valid_interval: Run validation if batch_idx % valid_interval is 0 + + - feature_dim: The model input dim. It has to match the one used + in computing features. + + - subsampling_factor: The subsampling factor for the model. + + - encoder_dim: Hidden dim for multi-head attention model. + + - num_decoder_layers: Number of decoder layer of transformer decoder. + + - warm_step: The warm_step for Noam optimizer. + """ + params = AttributeDict( + { + "best_train_loss": float("inf"), + "best_valid_loss": float("inf"), + "best_train_epoch": -1, + "best_valid_epoch": -1, + "batch_idx_train": 0, + "log_interval": 50, + "reset_interval": 200, + "valid_interval": 3000, # For the 100h subset, use 800 + # parameters for conformer + "feature_dim": 80, + "subsampling_factor": 4, + "encoder_dim": 512, + "nhead": 8, + "dim_feedforward": 2048, + "num_encoder_layers": 12, + # parameters for decoder + "decoder_dim": 512, + # parameters for joiner + "joiner_dim": 512, + # parameters for Noam + "model_warm_step": 3000, # arg given to model, not for lrate + "env_info": get_env_info(), + } + ) + + return params + + +def get_encoder_model(params: AttributeDict) -> nn.Module: + # TODO: We can add an option to switch between Conformer and Transformer + encoder = Conformer( + num_features=params.feature_dim, + subsampling_factor=params.subsampling_factor, + d_model=params.encoder_dim, + nhead=params.nhead, + dim_feedforward=params.dim_feedforward, + num_encoder_layers=params.num_encoder_layers, + ) + return encoder + + +def get_decoder_model(params: AttributeDict) -> nn.Module: + decoder = Decoder( + vocab_size=params.vocab_size, + decoder_dim=params.decoder_dim, + blank_id=params.blank_id, + context_size=params.context_size, + ) + return decoder + + +def get_joiner_model(params: AttributeDict) -> nn.Module: + joiner = Joiner( + encoder_dim=params.encoder_dim, + decoder_dim=params.decoder_dim, + joiner_dim=params.joiner_dim, + vocab_size=params.vocab_size, + ) + return joiner + + +def get_transducer_model(params: AttributeDict) -> nn.Module: + encoder = get_encoder_model(params) + decoder = get_decoder_model(params) + joiner = get_joiner_model(params) + + model = Transducer( + encoder=encoder, + decoder=decoder, + joiner=joiner, + encoder_dim=params.encoder_dim, + decoder_dim=params.decoder_dim, + joiner_dim=params.joiner_dim, + vocab_size=params.vocab_size, + ) + return model + + +def load_checkpoint_if_available( + params: AttributeDict, + model: nn.Module, + optimizer: Optional[torch.optim.Optimizer] = None, + scheduler: Optional[LRSchedulerType] = None, +) -> Optional[Dict[str, Any]]: + """Load checkpoint from file. + + If params.start_batch is positive, it will load the checkpoint from + `params.exp_dir/checkpoint-{params.start_batch}.pt`. Otherwise, if + params.start_epoch is positive, it will load the checkpoint from + `params.start_epoch - 1`. + + Apart from loading state dict for `model` and `optimizer` it also updates + `best_train_epoch`, `best_train_loss`, `best_valid_epoch`, + and `best_valid_loss` in `params`. + + Args: + params: + The return value of :func:`get_params`. + model: + The training model. + optimizer: + The optimizer that we are using. + scheduler: + The scheduler that we are using. + Returns: + Return a dict containing previously saved training info. + """ + if params.start_batch > 0: + filename = params.exp_dir / f"checkpoint-{params.start_batch}.pt" + elif params.start_epoch > 0: + filename = params.exp_dir / f"epoch-{params.start_epoch-1}.pt" + else: + return None + + assert filename.is_file(), f"{filename} does not exist!" + + saved_params = load_checkpoint( + filename, + model=model, + optimizer=optimizer, + scheduler=scheduler, + ) + + keys = [ + "best_train_epoch", + "best_valid_epoch", + "batch_idx_train", + "best_train_loss", + "best_valid_loss", + ] + for k in keys: + params[k] = saved_params[k] + + if params.start_batch > 0: + if "cur_epoch" in saved_params: + params["start_epoch"] = saved_params["cur_epoch"] + + if "cur_batch_idx" in saved_params: + params["cur_batch_idx"] = saved_params["cur_batch_idx"] + + return saved_params + + +def save_checkpoint( + params: AttributeDict, + model: nn.Module, + optimizer: Optional[torch.optim.Optimizer] = None, + scheduler: Optional[LRSchedulerType] = None, + sampler: Optional[CutSampler] = None, + scaler: Optional[GradScaler] = None, + rank: int = 0, +) -> None: + """Save model, optimizer, scheduler and training stats to file. + + Args: + params: + It is returned by :func:`get_params`. + model: + The training model. + optimizer: + The optimizer used in the training. + sampler: + The sampler for the training dataset. + scaler: + The scaler used for mix precision training. + """ + if rank != 0: + return + filename = params.exp_dir / f"epoch-{params.cur_epoch}.pt" + save_checkpoint_impl( + filename=filename, + model=model, + params=params, + optimizer=optimizer, + scheduler=scheduler, + sampler=sampler, + scaler=scaler, + rank=rank, + ) + + if params.best_train_epoch == params.cur_epoch: + best_train_filename = params.exp_dir / "best-train-loss.pt" + copyfile(src=filename, dst=best_train_filename) + + if params.best_valid_epoch == params.cur_epoch: + best_valid_filename = params.exp_dir / "best-valid-loss.pt" + copyfile(src=filename, dst=best_valid_filename) + + +def compute_loss( + params: AttributeDict, + model: nn.Module, + sp: spm.SentencePieceProcessor, + batch: dict, + is_training: bool, + warmup: float = 1.0, +) -> Tuple[Tensor, MetricsTracker]: + """ + Compute CTC loss given the model and its inputs. + + Args: + params: + Parameters for training. See :func:`get_params`. + model: + The model for training. It is an instance of Conformer in our case. + batch: + A batch of data. See `lhotse.dataset.K2SpeechRecognitionDataset()` + for the content in it. + is_training: + True for training. False for validation. When it is True, this + function enables autograd during computation; when it is False, it + disables autograd. + warmup: a floating point value which increases throughout training; + values >= 1.0 are fully warmed up and have all modules present. + """ + device = model.device + feature = batch["inputs"] + # at entry, feature is (N, T, C) + assert feature.ndim == 3 + feature = feature.to(device) + + supervisions = batch["supervisions"] + feature_lens = supervisions["num_frames"].to(device) + + texts = batch["supervisions"]["text"] + y = sp.encode(texts, out_type=int) + y = k2.RaggedTensor(y).to(device) + + with torch.set_grad_enabled(is_training): + simple_loss, pruned_loss = model( + x=feature, + x_lens=feature_lens, + y=y, + prune_range=params.prune_range, + am_scale=params.am_scale, + lm_scale=params.lm_scale, + warmup=warmup, + ) + # after the main warmup step, we keep pruned_loss_scale small + # for the same amount of time (model_warm_step), to avoid + # overwhelming the simple_loss and causing it to diverge, + # in case it had not fully learned the alignment yet. + pruned_loss_scale = ( + 0.0 + if warmup < 1.0 + else (0.1 if warmup > 1.0 and warmup < 2.0 else 1.0) + ) + loss = ( + params.simple_loss_scale * simple_loss + + pruned_loss_scale * pruned_loss + ) + + assert loss.requires_grad == is_training + + info = MetricsTracker() + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + info["frames"] = ( + (feature_lens // params.subsampling_factor).sum().item() + ) + + # Note: We use reduction=sum while computing the loss. + info["loss"] = loss.detach().cpu().item() + info["simple_loss"] = simple_loss.detach().cpu().item() + info["pruned_loss"] = pruned_loss.detach().cpu().item() + + return loss, info + + +def compute_validation_loss( + params: AttributeDict, + model: nn.Module, + sp: spm.SentencePieceProcessor, + valid_dl: torch.utils.data.DataLoader, + world_size: int = 1, +) -> MetricsTracker: + """Run the validation process.""" + model.eval() + + tot_loss = MetricsTracker() + + for batch_idx, batch in enumerate(valid_dl): + loss, loss_info = compute_loss( + params=params, + model=model, + sp=sp, + batch=batch, + is_training=False, + ) + assert loss.requires_grad is False + tot_loss = tot_loss + loss_info + + if world_size > 1: + tot_loss.reduce(loss.device) + + loss_value = tot_loss["loss"] / tot_loss["frames"] + if loss_value < params.best_valid_loss: + params.best_valid_epoch = params.cur_epoch + params.best_valid_loss = loss_value + + return tot_loss + + +def train_one_epoch( + params: AttributeDict, + model: nn.Module, + optimizer: torch.optim.Optimizer, + scheduler: LRSchedulerType, + sp: spm.SentencePieceProcessor, + train_dl: torch.utils.data.DataLoader, + valid_dl: torch.utils.data.DataLoader, + scaler: GradScaler, + tb_writer: Optional[SummaryWriter] = None, + world_size: int = 1, + rank: int = 0, +) -> None: + """Train the model for one epoch. + + The training loss from the mean of all frames is saved in + `params.train_loss`. It runs the validation process every + `params.valid_interval` batches. + + Args: + params: + It is returned by :func:`get_params`. + model: + The model for training. + optimizer: + The optimizer we are using. + scheduler: + The learning rate scheduler, we call step() every step. + train_dl: + Dataloader for the training dataset. + valid_dl: + Dataloader for the validation dataset. + scaler: + The scaler used for mix precision training. + tb_writer: + Writer to write log messages to tensorboard. + world_size: + Number of nodes in DDP training. If it is 1, DDP is disabled. + rank: + The rank of the node in DDP training. If no DDP is used, it should + be set to 0. + """ + model.train() + + tot_loss = MetricsTracker() + + cur_batch_idx = params.get("cur_batch_idx", 0) + + for batch_idx, batch in enumerate(train_dl): + if batch_idx < cur_batch_idx: + continue + cur_batch_idx = batch_idx + + params.batch_idx_train += 1 + batch_size = len(batch["supervisions"]["text"]) + + with torch.cuda.amp.autocast(enabled=params.use_fp16): + loss, loss_info = compute_loss( + params=params, + model=model, + sp=sp, + batch=batch, + is_training=True, + warmup=(params.batch_idx_train / params.model_warm_step), + ) + # summary stats + tot_loss = (tot_loss * (1 - 1 / params.reset_interval)) + loss_info + + # NOTE: We use reduction==sum and loss is computed over utterances + # in the batch and there is no normalization to it so far. + scaler.scale(loss).backward() + scheduler.step_batch(params.batch_idx_train) + scaler.step(optimizer) + scaler.update() + optimizer.zero_grad() + + if params.print_diagnostics and batch_idx == 5: + return + + if ( + params.batch_idx_train > 0 + and params.batch_idx_train % params.save_every_n == 0 + ): + params.cur_batch_idx = batch_idx + save_checkpoint_with_global_batch_idx( + out_dir=params.exp_dir, + global_batch_idx=params.batch_idx_train, + model=model, + params=params, + optimizer=optimizer, + scheduler=scheduler, + sampler=train_dl.sampler, + scaler=scaler, + rank=rank, + ) + del params.cur_batch_idx + remove_checkpoints( + out_dir=params.exp_dir, + topk=params.keep_last_k, + rank=rank, + ) + + if batch_idx % params.log_interval == 0: + cur_lr = scheduler.get_last_lr()[0] + logging.info( + f"Epoch {params.cur_epoch}, " + f"batch {batch_idx}, loss[{loss_info}], " + f"tot_loss[{tot_loss}], batch size: {batch_size}, " + f"lr: {cur_lr:.2e}" + ) + + if tb_writer is not None: + tb_writer.add_scalar( + "train/learning_rate", cur_lr, params.batch_idx_train + ) + + loss_info.write_summary( + tb_writer, "train/current_", params.batch_idx_train + ) + tot_loss.write_summary( + tb_writer, "train/tot_", params.batch_idx_train + ) + + if batch_idx > 0 and batch_idx % params.valid_interval == 0: + logging.info("Computing validation loss") + valid_info = compute_validation_loss( + params=params, + model=model, + sp=sp, + valid_dl=valid_dl, + world_size=world_size, + ) + model.train() + logging.info(f"Epoch {params.cur_epoch}, validation: {valid_info}") + if tb_writer is not None: + valid_info.write_summary( + tb_writer, "train/valid_", params.batch_idx_train + ) + + loss_value = tot_loss["loss"] / tot_loss["frames"] + params.train_loss = loss_value + if params.train_loss < params.best_train_loss: + params.best_train_epoch = params.cur_epoch + params.best_train_loss = params.train_loss + + +def run(rank, world_size, args): + """ + Args: + rank: + It is a value between 0 and `world_size-1`, which is + passed automatically by `mp.spawn()` in :func:`main`. + The node with rank 0 is responsible for saving checkpoint. + world_size: + Number of GPUs for DDP training. + args: + The return value of get_parser().parse_args() + """ + params = get_params() + params.update(vars(args)) + if params.full_libri is False: + params.valid_interval = 1600 + + fix_random_seed(params.seed) + if world_size > 1: + setup_dist(rank, world_size, params.master_port) + + setup_logger(f"{params.exp_dir}/log/log-train") + logging.info("Training started") + + if args.tensorboard and rank == 0: + tb_writer = SummaryWriter(log_dir=f"{params.exp_dir}/tensorboard") + else: + tb_writer = None + + device = torch.device("cpu") + if torch.cuda.is_available(): + device = torch.device("cuda", rank) + logging.info(f"Device: {device}") + + sp = spm.SentencePieceProcessor() + sp.load(params.bpe_model) + + # is defined in local/train_bpe_model.py + params.blank_id = sp.piece_to_id("") + params.vocab_size = sp.get_piece_size() + + logging.info(params) + + logging.info("About to create model") + model = get_transducer_model(params) + + num_param = sum([p.numel() for p in model.parameters()]) + logging.info(f"Number of model parameters: {num_param}") + + checkpoints = load_checkpoint_if_available(params=params, model=model) + + model.to(device) + if world_size > 1: + logging.info("Using DDP") + model = DDP(model, device_ids=[rank]) + model.device = device + + optimizer = Eve(model.parameters(), lr=params.initial_lr) + + scheduler = Eden(optimizer, params.lr_batches, params.lr_epochs) + + if checkpoints and "optimizer" in checkpoints: + logging.info("Loading optimizer state dict") + optimizer.load_state_dict(checkpoints["optimizer"]) + + if ( + checkpoints + and "scheduler" in checkpoints + and checkpoints["scheduler"] is not None + ): + logging.info("Loading scheduler state dict") + scheduler.load_state_dict(checkpoints["scheduler"]) + + if params.print_diagnostics: + opts = diagnostics.TensorDiagnosticOptions( + 2 ** 22 + ) # allow 4 megabytes per sub-module + diagnostic = diagnostics.attach_diagnostics(model, opts) + + librispeech = LibriSpeechAsrDataModule(args) + + train_cuts = librispeech.train_clean_100_cuts() + if params.full_libri: + train_cuts += librispeech.train_clean_360_cuts() + train_cuts += librispeech.train_other_500_cuts() + + def remove_short_and_long_utt(c: Cut): + # Keep only utterances with duration between 1 second and 20 seconds + # + # Caution: There is a reason to select 20.0 here. Please see + # ../local/display_manifest_statistics.py + # + # You should use ../local/display_manifest_statistics.py to get + # an utterance duration distribution for your dataset to select + # the threshold + return 1.0 <= c.duration <= 20.0 + + train_cuts = train_cuts.filter(remove_short_and_long_utt) + + if params.start_batch > 0 and checkpoints and "sampler" in checkpoints: + # We only load the sampler's state dict when it loads a checkpoint + # saved in the middle of an epoch + sampler_state_dict = checkpoints["sampler"] + else: + sampler_state_dict = None + + train_dl = librispeech.train_dataloaders( + train_cuts, sampler_state_dict=sampler_state_dict + ) + + valid_cuts = librispeech.dev_clean_cuts() + valid_cuts += librispeech.dev_other_cuts() + valid_dl = librispeech.valid_dataloaders(valid_cuts) + + if not params.print_diagnostics: + scan_pessimistic_batches_for_oom( + model=model, + train_dl=train_dl, + optimizer=optimizer, + sp=sp, + params=params, + ) + + scaler = GradScaler(enabled=params.use_fp16) + if checkpoints and "grad_scaler" in checkpoints: + logging.info("Loading grad scaler state dict") + scaler.load_state_dict(checkpoints["grad_scaler"]) + + for epoch in range(params.start_epoch, params.num_epochs): + scheduler.step_epoch(epoch) + fix_random_seed(params.seed + epoch) + train_dl.sampler.set_epoch(epoch) + + if tb_writer is not None: + tb_writer.add_scalar("train/epoch", epoch, params.batch_idx_train) + + params.cur_epoch = epoch + + train_one_epoch( + params=params, + model=model, + optimizer=optimizer, + scheduler=scheduler, + sp=sp, + train_dl=train_dl, + valid_dl=valid_dl, + scaler=scaler, + tb_writer=tb_writer, + world_size=world_size, + rank=rank, + ) + + if params.print_diagnostics: + diagnostic.print_diagnostics() + break + + save_checkpoint( + params=params, + model=model, + optimizer=optimizer, + scheduler=scheduler, + sampler=train_dl.sampler, + scaler=scaler, + rank=rank, + ) + + logging.info("Done!") + + if world_size > 1: + torch.distributed.barrier() + cleanup_dist() + + +def scan_pessimistic_batches_for_oom( + model: nn.Module, + train_dl: torch.utils.data.DataLoader, + optimizer: torch.optim.Optimizer, + sp: spm.SentencePieceProcessor, + params: AttributeDict, +): + from lhotse.dataset import find_pessimistic_batches + + logging.info( + "Sanity check -- see if any of the batches in epoch 0 would cause OOM." + ) + batches, crit_values = find_pessimistic_batches(train_dl.sampler) + for criterion, cuts in batches.items(): + batch = train_dl.dataset[cuts] + try: + # warmup = 0.0 is so that the derivs for the pruned loss stay zero + # (i.e. are not remembered by the decaying-average in adam), because + # we want to avoid these params being subject to shrinkage in adam. + with torch.cuda.amp.autocast(enabled=params.use_fp16): + loss, _ = compute_loss( + params=params, + model=model, + sp=sp, + batch=batch, + is_training=True, + warmup=0.0, + ) + loss.backward() + optimizer.step() + optimizer.zero_grad() + except RuntimeError as e: + if "CUDA out of memory" in str(e): + logging.error( + "Your GPU ran out of memory with the current " + "max_duration setting. We recommend decreasing " + "max_duration and trying again.\n" + f"Failing criterion: {criterion} " + f"(={crit_values[criterion]}) ..." + ) + raise + + +def main(): + parser = get_parser() + LibriSpeechAsrDataModule.add_arguments(parser) + args = parser.parse_args() + args.exp_dir = Path(args.exp_dir) + + world_size = args.world_size + assert world_size >= 1 + if world_size > 1: + mp.spawn(run, args=(world_size, args), nprocs=world_size, join=True) + else: + run(rank=0, world_size=1, args=args) + + +torch.set_num_threads(1) +torch.set_num_interop_threads(1) + +if __name__ == "__main__": + main() From e4d45adf5a24c2f6b2a9735c0ad3bdac7b402bb8 Mon Sep 17 00:00:00 2001 From: Fangjun Kuang Date: Thu, 21 Apr 2022 11:01:08 +0800 Subject: [PATCH 2/3] Change model.py and joiner.py to use torchaudio's RNN-T loss. --- egs/librispeech/ASR/README.md | 1 + .../ASR/transducer_stateless3/joiner.py | 38 +++++-- .../ASR/transducer_stateless3/model.py | 102 ++++-------------- 3 files changed, 51 insertions(+), 90 deletions(-) diff --git a/egs/librispeech/ASR/README.md b/egs/librispeech/ASR/README.md index de9d6d50a..24b29b653 100644 --- a/egs/librispeech/ASR/README.md +++ b/egs/librispeech/ASR/README.md @@ -14,6 +14,7 @@ The following table lists the differences among them. | `transducer` | Conformer | LSTM | | | `transducer_stateless` | Conformer | Embedding + Conv1d | Using optimized_transducer from computing RNN-T loss | | `transducer_stateless2` | Conformer | Embedding + Conv1d | Using torchaudio for computing RNN-T loss | +| `transducer_stateless3` | Conformer(modified) | Embedding + Conv1d | Using torchaudio for computing RNN-T loss | | `transducer_lstm` | LSTM | LSTM | | | `transducer_stateless_multi_datasets` | Conformer | Embedding + Conv1d | Using data from GigaSpeech as extra training data | | `pruned_transducer_stateless` | Conformer | Embedding + Conv1d | Using k2 pruned RNN-T loss | diff --git a/egs/librispeech/ASR/transducer_stateless3/joiner.py b/egs/librispeech/ASR/transducer_stateless3/joiner.py index 35f75ed2a..18a6cd845 100644 --- a/egs/librispeech/ASR/transducer_stateless3/joiner.py +++ b/egs/librispeech/ASR/transducer_stateless3/joiner.py @@ -33,6 +33,10 @@ class Joiner(nn.Module): self.decoder_proj = ScaledLinear(decoder_dim, joiner_dim) self.output_linear = ScaledLinear(joiner_dim, vocab_size) + self.encoder_dim = encoder_dim + self.decoder_dim = decoder_dim + self.joiner_dim = joiner_dim + def forward( self, encoder_out: torch.Tensor, @@ -42,9 +46,9 @@ class Joiner(nn.Module): """ Args: encoder_out: - Output from the encoder. Its shape is (N, T, s_range, C). + Output from the encoder. Its shape is (N, T, joiner_dim). decoder_out: - Output from the decoder. Its shape is (N, T, s_range, C). + Output from the decoder. Its shape is (N, U, joiner_dim). project_input: If true, apply input projections encoder_proj and decoder_proj. If this is false, it is the user's responsibility to do this @@ -52,16 +56,30 @@ class Joiner(nn.Module): Returns: Return a tensor of shape (N, T, s_range, C). """ - assert encoder_out.ndim == decoder_out.ndim == 4 - assert encoder_out.shape[:-1] == decoder_out.shape[:-1] + assert encoder_out.ndim == decoder_out.ndim == 3 + assert encoder_out.size(0) == decoder_out.size(0) if project_input: - logit = self.encoder_proj(encoder_out) + self.decoder_proj( - decoder_out - ) + assert encoder_out.size(2) == self.encoder_dim + assert decoder_out.size(2) == self.decoder_dim + encoder_out = self.encoder_proj(encoder_out) + decoder_out = self.decoder_proj(decoder_out) else: - logit = encoder_out + decoder_out + assert encoder_out.size(2) == self.joiner_dim + assert decoder_out.size(2) == self.joiner_dim - logit = self.output_linear(torch.tanh(logit)) + encoder_out = encoder_out.unsqueeze(2) # (N, T, 1, C) + decoder_out = decoder_out.unsqueeze(1) # (N, 1, U, C) + x = encoder_out + decoder_out # (N, T, U, C) - return logit + activations = torch.tanh(x) + + logits = self.output_linear(activations) + + if not self.training: + # We reuse the beam_search.py from transducer_stateless, + # which expects that the joiner network outputs + # a 2-D tensor. + logits = logits.squeeze(2).squeeze(1) + + return logits diff --git a/egs/librispeech/ASR/transducer_stateless3/model.py b/egs/librispeech/ASR/transducer_stateless3/model.py index 599bf2506..7c4881c72 100644 --- a/egs/librispeech/ASR/transducer_stateless3/model.py +++ b/egs/librispeech/ASR/transducer_stateless3/model.py @@ -63,19 +63,11 @@ class Transducer(nn.Module): self.decoder = decoder self.joiner = joiner - self.simple_am_proj = ScaledLinear( - encoder_dim, vocab_size, initial_speed=0.5 - ) - self.simple_lm_proj = ScaledLinear(decoder_dim, vocab_size) - def forward( self, x: torch.Tensor, x_lens: torch.Tensor, y: k2.RaggedTensor, - prune_range: int = 5, - am_scale: float = 0.0, - lm_scale: float = 0.0, warmup: float = 1.0, ) -> torch.Tensor: """ @@ -88,26 +80,11 @@ class Transducer(nn.Module): y: A ragged tensor with 2 axes [utt][label]. It contains labels of each utterance. - prune_range: - The prune range for rnnt loss, it means how many symbols(context) - we are considering for each frame to compute the loss. - am_scale: - The scale to smooth the loss with am (output of encoder network) - part - lm_scale: - The scale to smooth the loss with lm (output of predictor network) - part warmup: A value warmup >= 0 that determines which modules are active, values warmup > 1 "are fully warmed up" and all modules will be active. Returns: Return the transducer loss. - - Note: - Regarding am_scale & lm_scale, it will make the loss-function one of - the form: - lm_scale * lm_probs + am_scale * am_probs + - (1-lm_scale-am_scale) * combined_probs """ assert x.ndim == 3, x.shape assert x_lens.ndim == 1, x_lens.shape @@ -115,8 +92,8 @@ class Transducer(nn.Module): assert x.size(0) == x_lens.size(0) == y.dim0 - encoder_out, x_lens = self.encoder(x, x_lens, warmup=warmup) - assert torch.all(x_lens > 0) + encoder_out, encoder_out_lens = self.encoder(x, x_lens, warmup=warmup) + assert torch.all(encoder_out_lens > 0) # Now for the decoder, i.e., the prediction network row_splits = y.shape.row_splits(1) @@ -125,69 +102,34 @@ class Transducer(nn.Module): blank_id = self.decoder.blank_id sos_y = add_sos(y, sos_id=blank_id) - # sos_y_padded: [B, S + 1], start with SOS. + # sos_y_padded: [B, U + 1], start with SOS. sos_y_padded = sos_y.pad(mode="constant", padding_value=blank_id) - # decoder_out: [B, S + 1, decoder_dim] + # decoder_out: [B, U + 1, decoder_dim] decoder_out = self.decoder(sos_y_padded) + logits = self.joiner( + encoder_out=encoder_out, + decoder_out=decoder_out, + project_input=True, + ) + # Note: y does not start with SOS - # y_padded : [B, S] + # y_padded : [B, U] y_padded = y.pad(mode="constant", padding_value=0) - y_padded = y_padded.to(torch.int64) - boundary = torch.zeros( - (x.size(0), 4), dtype=torch.int64, device=x.device - ) - boundary[:, 2] = y_lens - boundary[:, 3] = x_lens - - lm = self.simple_lm_proj(decoder_out) - am = self.simple_am_proj(encoder_out) - - with torch.cuda.amp.autocast(enabled=False): - simple_loss, (px_grad, py_grad) = k2.rnnt_loss_smoothed( - lm=lm.float(), - am=am.float(), - symbols=y_padded, - termination_symbol=blank_id, - lm_only_scale=lm_scale, - am_only_scale=am_scale, - boundary=boundary, - reduction="sum", - return_grad=True, - ) - - # ranges : [B, T, prune_range] - ranges = k2.get_rnnt_prune_ranges( - px_grad=px_grad, - py_grad=py_grad, - boundary=boundary, - s_range=prune_range, + assert hasattr(torchaudio.functional, "rnnt_loss"), ( + f"Current torchaudio version: {torchaudio.__version__}\n" + "Please install a version >= 0.10.0" ) - # am_pruned : [B, T, prune_range, encoder_dim] - # lm_pruned : [B, T, prune_range, decoder_dim] - am_pruned, lm_pruned = k2.do_rnnt_pruning( - am=self.joiner.encoder_proj(encoder_out), - lm=self.joiner.decoder_proj(decoder_out), - ranges=ranges, + loss = torchaudio.functional.rnnt_loss( + logits=logits, + targets=y_padded, + logit_lengths=encoder_out_lens, + target_lengths=y_lens, + blank=blank_id, + reduction="sum", ) - # logits : [B, T, prune_range, vocab_size] - - # project_input=False since we applied the decoder's input projections - # prior to do_rnnt_pruning (this is an optimization for speed). - logits = self.joiner(am_pruned, lm_pruned, project_input=False) - - with torch.cuda.amp.autocast(enabled=False): - pruned_loss = k2.rnnt_loss_pruned( - logits=logits.float(), - symbols=y_padded, - ranges=ranges, - termination_symbol=blank_id, - boundary=boundary, - reduction="sum", - ) - - return (simple_loss, pruned_loss) + return loss From 3d0474c98639d06fe638d7dc98769e7fa7c70032 Mon Sep 17 00:00:00 2001 From: Fangjun Kuang Date: Thu, 21 Apr 2022 11:49:52 +0800 Subject: [PATCH 3/3] Fix style issues. --- .../ASR/transducer_stateless3/model.py | 9 +- .../ASR/transducer_stateless3/train.py | 136 ++++-------------- 2 files changed, 32 insertions(+), 113 deletions(-) diff --git a/egs/librispeech/ASR/transducer_stateless3/model.py b/egs/librispeech/ASR/transducer_stateless3/model.py index 7c4881c72..8ef3af5f4 100644 --- a/egs/librispeech/ASR/transducer_stateless3/model.py +++ b/egs/librispeech/ASR/transducer_stateless3/model.py @@ -18,8 +18,8 @@ import k2 import torch import torch.nn as nn +import torchaudio from encoder_interface import EncoderInterface -from scaling import ScaledLinear from icefall.utils import add_sos @@ -51,9 +51,10 @@ class Transducer(nn.Module): is (N, U) and its output shape is (N, U, decoder_dim). It should contain one attribute: `blank_id`. joiner: - It has two inputs with shapes: (N, T, encoder_dim) and (N, U, decoder_dim). - Its output shape is (N, T, U, vocab_size). Note that its output contains - unnormalized probs, i.e., not processed by log-softmax. + It has two inputs with shapes: (N, T, encoder_dim) and + (N, U, decoder_dim). + Its output shape is (N, T, U, vocab_size). Note that its output + contains unnormalized probs, i.e., not processed by log-softmax. """ super().__init__() assert isinstance(encoder, EncoderInterface), type(encoder) diff --git a/egs/librispeech/ASR/transducer_stateless3/train.py b/egs/librispeech/ASR/transducer_stateless3/train.py index 80617847a..ba67ba8d3 100755 --- a/egs/librispeech/ASR/transducer_stateless3/train.py +++ b/egs/librispeech/ASR/transducer_stateless3/train.py @@ -21,22 +21,21 @@ Usage: export CUDA_VISIBLE_DEVICES="0,1,2,3" -./pruned_transducer_stateless2/train.py \ +./transducer_stateless3/train.py \ --world-size 4 \ --num-epochs 30 \ --start-epoch 0 \ - --exp-dir pruned_transducer_stateless2/exp \ + --exp-dir transducer_stateless3/exp \ --full-libri 1 \ --max-duration 300 # For mix precision training: -./pruned_transducer_stateless2/train.py \ +./transducer_stateless3/train.py \ --world-size 4 \ --num-epochs 30 \ --start-epoch 0 \ - --use_fp16 1 \ - --exp-dir pruned_transducer_stateless2/exp \ + --exp-dir transducer_stateless3/exp \ --full-libri 1 \ --max-duration 550 @@ -138,7 +137,7 @@ def get_parser(): parser.add_argument( "--exp-dir", type=str, - default="pruned_transducer_stateless2/exp", + default="transducer_stateless3/exp", help="""The experiment dir. It specifies the directory where all training related files, e.g., checkpoints, log, etc, are saved @@ -156,7 +155,8 @@ def get_parser(): "--initial-lr", type=float, default=0.003, - help="The initial learning rate. This value should not need to be changed.", + help="The initial learning rate. This value should not need to be " + "changed.", ) parser.add_argument( @@ -183,40 +183,6 @@ def get_parser(): "2 means tri-gram", ) - parser.add_argument( - "--prune-range", - type=int, - default=5, - help="The prune range for rnnt loss, it means how many symbols(context)" - "we are using to compute the loss", - ) - - parser.add_argument( - "--lm-scale", - type=float, - default=0.25, - help="The scale to smooth the loss with lm " - "(output of prediction network) part.", - ) - - parser.add_argument( - "--am-scale", - type=float, - default=0.0, - help="The scale to smooth the loss with am (output of encoder network)" - "part.", - ) - - parser.add_argument( - "--simple-loss-scale", - type=float, - default=0.5, - help="To get pruning ranges, we will calculate a simple version" - "loss(joiner is just addition), this simple loss also uses for" - "training (as a regularization item). We will scale the simple loss" - "with this parameter before adding to the final loss.", - ) - parser.add_argument( "--seed", type=int, @@ -255,13 +221,6 @@ def get_parser(): """, ) - parser.add_argument( - "--use-fp16", - type=str2bool, - default=False, - help="Whether to use half precision training.", - ) - return parser @@ -318,7 +277,7 @@ def get_params() -> AttributeDict: "batch_idx_train": 0, "log_interval": 50, "reset_interval": 200, - "valid_interval": 3000, # For the 100h subset, use 800 + "valid_interval": 3000, # For the 100h subset, use 1600 # parameters for conformer "feature_dim": 80, "subsampling_factor": 4, @@ -506,7 +465,6 @@ def compute_loss( sp: spm.SentencePieceProcessor, batch: dict, is_training: bool, - warmup: float = 1.0, ) -> Tuple[Tensor, MetricsTracker]: """ Compute CTC loss given the model and its inputs. @@ -523,8 +481,6 @@ def compute_loss( True for training. False for validation. When it is True, this function enables autograd during computation; when it is False, it disables autograd. - warmup: a floating point value which increases throughout training; - values >= 1.0 are fully warmed up and have all modules present. """ device = model.device feature = batch["inputs"] @@ -540,27 +496,10 @@ def compute_loss( y = k2.RaggedTensor(y).to(device) with torch.set_grad_enabled(is_training): - simple_loss, pruned_loss = model( + loss = model( x=feature, x_lens=feature_lens, y=y, - prune_range=params.prune_range, - am_scale=params.am_scale, - lm_scale=params.lm_scale, - warmup=warmup, - ) - # after the main warmup step, we keep pruned_loss_scale small - # for the same amount of time (model_warm_step), to avoid - # overwhelming the simple_loss and causing it to diverge, - # in case it had not fully learned the alignment yet. - pruned_loss_scale = ( - 0.0 - if warmup < 1.0 - else (0.1 if warmup > 1.0 and warmup < 2.0 else 1.0) - ) - loss = ( - params.simple_loss_scale * simple_loss - + pruned_loss_scale * pruned_loss ) assert loss.requires_grad == is_training @@ -574,8 +513,6 @@ def compute_loss( # Note: We use reduction=sum while computing the loss. info["loss"] = loss.detach().cpu().item() - info["simple_loss"] = simple_loss.detach().cpu().item() - info["pruned_loss"] = pruned_loss.detach().cpu().item() return loss, info @@ -622,7 +559,6 @@ def train_one_epoch( sp: spm.SentencePieceProcessor, train_dl: torch.utils.data.DataLoader, valid_dl: torch.utils.data.DataLoader, - scaler: GradScaler, tb_writer: Optional[SummaryWriter] = None, world_size: int = 1, rank: int = 0, @@ -646,8 +582,6 @@ def train_one_epoch( Dataloader for the training dataset. valid_dl: Dataloader for the validation dataset. - scaler: - The scaler used for mix precision training. tb_writer: Writer to write log messages to tensorboard. world_size: @@ -670,25 +604,22 @@ def train_one_epoch( params.batch_idx_train += 1 batch_size = len(batch["supervisions"]["text"]) - with torch.cuda.amp.autocast(enabled=params.use_fp16): - loss, loss_info = compute_loss( - params=params, - model=model, - sp=sp, - batch=batch, - is_training=True, - warmup=(params.batch_idx_train / params.model_warm_step), - ) + loss, loss_info = compute_loss( + params=params, + model=model, + sp=sp, + batch=batch, + is_training=True, + ) # summary stats tot_loss = (tot_loss * (1 - 1 / params.reset_interval)) + loss_info # NOTE: We use reduction==sum and loss is computed over utterances # in the batch and there is no normalization to it so far. - scaler.scale(loss).backward() - scheduler.step_batch(params.batch_idx_train) - scaler.step(optimizer) - scaler.update() optimizer.zero_grad() + loss.backward() + scheduler.step_batch(params.batch_idx_train) + optimizer.step() if params.print_diagnostics and batch_idx == 5: return @@ -706,7 +637,6 @@ def train_one_epoch( optimizer=optimizer, scheduler=scheduler, sampler=train_dl.sampler, - scaler=scaler, rank=rank, ) del params.cur_batch_idx @@ -883,11 +813,6 @@ def run(rank, world_size, args): params=params, ) - scaler = GradScaler(enabled=params.use_fp16) - if checkpoints and "grad_scaler" in checkpoints: - logging.info("Loading grad scaler state dict") - scaler.load_state_dict(checkpoints["grad_scaler"]) - for epoch in range(params.start_epoch, params.num_epochs): scheduler.step_epoch(epoch) fix_random_seed(params.seed + epoch) @@ -906,7 +831,6 @@ def run(rank, world_size, args): sp=sp, train_dl=train_dl, valid_dl=valid_dl, - scaler=scaler, tb_writer=tb_writer, world_size=world_size, rank=rank, @@ -922,7 +846,6 @@ def run(rank, world_size, args): optimizer=optimizer, scheduler=scheduler, sampler=train_dl.sampler, - scaler=scaler, rank=rank, ) @@ -949,21 +872,16 @@ def scan_pessimistic_batches_for_oom( for criterion, cuts in batches.items(): batch = train_dl.dataset[cuts] try: - # warmup = 0.0 is so that the derivs for the pruned loss stay zero - # (i.e. are not remembered by the decaying-average in adam), because - # we want to avoid these params being subject to shrinkage in adam. - with torch.cuda.amp.autocast(enabled=params.use_fp16): - loss, _ = compute_loss( - params=params, - model=model, - sp=sp, - batch=batch, - is_training=True, - warmup=0.0, - ) + loss, _ = compute_loss( + params=params, + model=model, + sp=sp, + batch=batch, + is_training=True, + ) + optimizer.zero_grad() loss.backward() optimizer.step() - optimizer.zero_grad() except RuntimeError as e: if "CUDA out of memory" in str(e): logging.error(