diff --git a/egs/librispeech/ASR/conv_emformer_transducer/asr_datamodule.py b/egs/librispeech/ASR/conv_emformer_transducer/asr_datamodule.py deleted file mode 120000 index b4e5427e0..000000000 --- a/egs/librispeech/ASR/conv_emformer_transducer/asr_datamodule.py +++ /dev/null @@ -1 +0,0 @@ -../pruned_transducer_stateless/asr_datamodule.py \ No newline at end of file diff --git a/egs/librispeech/ASR/conv_emformer_transducer/beam_search.py b/egs/librispeech/ASR/conv_emformer_transducer/beam_search.py deleted file mode 120000 index 227d2247c..000000000 --- a/egs/librispeech/ASR/conv_emformer_transducer/beam_search.py +++ /dev/null @@ -1 +0,0 @@ -../pruned_transducer_stateless/beam_search.py \ No newline at end of file diff --git a/egs/librispeech/ASR/conv_emformer_transducer/decode.py b/egs/librispeech/ASR/conv_emformer_transducer/decode.py deleted file mode 100755 index 47b4f9fd0..000000000 --- a/egs/librispeech/ASR/conv_emformer_transducer/decode.py +++ /dev/null @@ -1,550 +0,0 @@ -#!/usr/bin/env python3 -# -# Copyright 2021 Xiaomi Corporation (Author: 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. -""" -Usage: -(1) greedy search -./transducer_emformer/decode.py \ - --epoch 28 \ - --avg 15 \ - --exp-dir ./transducer_emformer/exp \ - --max-duration 100 \ - --decoding-method greedy_search - -(2) beam search -./transducer_emformer/decode.py \ - --epoch 28 \ - --avg 15 \ - --exp-dir ./transducer_emformer/exp \ - --max-duration 100 \ - --decoding-method beam_search \ - --beam-size 4 - -(3) modified beam search -./transducer_emformer/decode.py \ - --epoch 28 \ - --avg 15 \ - --exp-dir ./transducer_emformer/exp \ - --max-duration 100 \ - --decoding-method modified_beam_search \ - --beam-size 4 - -(4) fast beam search -./transducer_emformer/decode.py \ - --epoch 28 \ - --avg 15 \ - --exp-dir ./transducer_emformer/exp \ - --max-duration 1500 \ - --decoding-method fast_beam_search \ - --beam 4 \ - --max-contexts 4 \ - --max-states 8 -""" - - -import argparse -import logging -from collections import defaultdict -from pathlib import Path -from typing import Dict, List, Optional, Tuple - -import k2 -import sentencepiece as spm -import torch -import torch.nn as nn -from asr_datamodule import LibriSpeechAsrDataModule -from beam_search import ( - beam_search, - fast_beam_search, - greedy_search, - greedy_search_batch, - modified_beam_search, -) -from train import add_model_arguments, get_params, get_transducer_model - -from icefall.checkpoint import ( - average_checkpoints, - find_checkpoints, - load_checkpoint, -) -from icefall.utils import ( - AttributeDict, - setup_logger, - store_transcripts, - write_error_stats, -) - - -def get_parser(): - parser = argparse.ArgumentParser( - formatter_class=argparse.ArgumentDefaultsHelpFormatter - ) - - parser.add_argument( - "--epoch", - type=int, - default=28, - help="It specifies the checkpoint to use for decoding." - "Note: Epoch counts from 0.", - ) - parser.add_argument( - "--avg", - type=int, - default=15, - help="Number of checkpoints to average. Automatically select " - "consecutive checkpoints before the checkpoint specified by " - "'--epoch'. ", - ) - - parser.add_argument( - "--avg-last-n", - type=int, - default=0, - help="""If positive, --epoch and --avg are ignored and it - will use the last n checkpoints exp_dir/checkpoint-xxx.pt - where xxx is the number of processed batches while - saving that checkpoint. - """, - ) - - parser.add_argument( - "--exp-dir", - type=str, - default="transducer_emformer/exp", - help="The experiment dir", - ) - - parser.add_argument( - "--bpe-model", - type=str, - default="data/lang_bpe_500/bpe.model", - help="Path to the BPE model", - ) - - parser.add_argument( - "--decoding-method", - type=str, - default="greedy_search", - help="""Possible values are: - - greedy_search - - beam_search - - modified_beam_search - - fast_beam_search - """, - ) - - parser.add_argument( - "--beam-size", - type=int, - default=4, - help="""An interger indicating how many candidates we will keep for each - frame. Used only when --decoding-method is beam_search or - modified_beam_search.""", - ) - - parser.add_argument( - "--beam", - type=float, - default=4, - help="""A floating point value to calculate the cutoff score during beam - search (i.e., `cutoff = max-score - beam`), which is the same as the - `beam` in Kaldi. - Used only when --decoding-method is fast_beam_search""", - ) - - parser.add_argument( - "--max-contexts", - type=int, - default=4, - help="""Used only when --decoding-method is - fast_beam_search""", - ) - - parser.add_argument( - "--max-states", - type=int, - default=8, - help="""Used only when --decoding-method is - fast_beam_search""", - ) - - 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( - "--max-sym-per-frame", - type=int, - default=1, - help="""Maximum number of symbols per frame. - Used only when --decoding_method is greedy_search""", - ) - - add_model_arguments(parser) - - return parser - - -def decode_one_batch( - params: AttributeDict, - model: nn.Module, - sp: spm.SentencePieceProcessor, - batch: dict, - decoding_graph: Optional[k2.Fsa] = None, -) -> Dict[str, List[List[str]]]: - """Decode one batch and return the result in a dict. The dict has the - following format: - - - key: It indicates the setting used for decoding. For example, - if greedy_search is used, it would be "greedy_search" - If beam search with a beam size of 7 is used, it would be - "beam_7" - - value: It contains the decoding result. `len(value)` equals to - batch size. `value[i]` is the decoding result for the i-th - utterance in the given batch. - Args: - params: - It's the return value of :func:`get_params`. - model: - The neural model. - sp: - The BPE model. - batch: - It is the return value from iterating - `lhotse.dataset.K2SpeechRecognitionDataset`. See its documentation - for the format of the `batch`. - decoding_graph: - The decoding graph. Can be either a `k2.trivial_graph` or HLG, Used - only when --decoding_method is fast_beam_search. - Returns: - Return the decoding result. See above description for the format of - the returned dict. - """ - device = model.device - feature = batch["inputs"] - assert feature.ndim == 3 - - feature = feature.to(device) - # at entry, feature is (N, T, C) - - supervisions = batch["supervisions"] - feature_lens = supervisions["num_frames"].to(device) - - encoder_out, encoder_out_lens = model.encoder( - x=feature, x_lens=feature_lens - ) - hyps = [] - - if params.decoding_method == "fast_beam_search": - hyp_tokens = fast_beam_search( - model=model, - decoding_graph=decoding_graph, - encoder_out=encoder_out, - encoder_out_lens=encoder_out_lens, - beam=params.beam, - max_contexts=params.max_contexts, - max_states=params.max_states, - ) - for hyp in sp.decode(hyp_tokens): - hyps.append(hyp.split()) - elif ( - params.decoding_method == "greedy_search" - and params.max_sym_per_frame == 1 - ): - hyp_tokens = greedy_search_batch( - model=model, - encoder_out=encoder_out, - ) - for hyp in sp.decode(hyp_tokens): - hyps.append(hyp.split()) - elif params.decoding_method == "modified_beam_search": - hyp_tokens = modified_beam_search( - model=model, - encoder_out=encoder_out, - beam=params.beam_size, - ) - for hyp in sp.decode(hyp_tokens): - hyps.append(hyp.split()) - else: - batch_size = encoder_out.size(0) - - for i in range(batch_size): - # fmt: off - encoder_out_i = encoder_out[i:i+1, :encoder_out_lens[i]] - # fmt: on - if params.decoding_method == "greedy_search": - hyp = greedy_search( - model=model, - encoder_out=encoder_out_i, - max_sym_per_frame=params.max_sym_per_frame, - ) - elif params.decoding_method == "beam_search": - hyp = beam_search( - model=model, - encoder_out=encoder_out_i, - beam=params.beam_size, - ) - else: - raise ValueError( - f"Unsupported decoding method: {params.decoding_method}" - ) - hyps.append(sp.decode(hyp).split()) - - if params.decoding_method == "greedy_search": - return {"greedy_search": hyps} - elif params.decoding_method == "fast_beam_search": - return { - ( - f"beam_{params.beam}_" - f"max_contexts_{params.max_contexts}_" - f"max_states_{params.max_states}" - ): hyps - } - else: - return {f"beam_size_{params.beam_size}": hyps} - - -def decode_dataset( - dl: torch.utils.data.DataLoader, - params: AttributeDict, - model: nn.Module, - sp: spm.SentencePieceProcessor, - decoding_graph: Optional[k2.Fsa] = None, -) -> Dict[str, List[Tuple[List[str], List[str]]]]: - """Decode dataset. - - Args: - dl: - PyTorch's dataloader containing the dataset to decode. - params: - It is returned by :func:`get_params`. - model: - The neural model. - sp: - The BPE model. - decoding_graph: - The decoding graph. Can be either a `k2.trivial_graph` or HLG, Used - only when --decoding_method is fast_beam_search. - Returns: - Return a dict, whose key may be "greedy_search" if greedy search - is used, or it may be "beam_7" if beam size of 7 is used. - Its value is a list of tuples. Each tuple contains two elements: - The first is the reference transcript, and the second is the - predicted result. - """ - num_cuts = 0 - - try: - num_batches = len(dl) - except TypeError: - num_batches = "?" - - if params.decoding_method == "greedy_search": - log_interval = 100 - else: - log_interval = 2 - - results = defaultdict(list) - for batch_idx, batch in enumerate(dl): - texts = batch["supervisions"]["text"] - - hyps_dict = decode_one_batch( - params=params, - model=model, - sp=sp, - decoding_graph=decoding_graph, - batch=batch, - ) - - for name, hyps in hyps_dict.items(): - this_batch = [] - assert len(hyps) == len(texts) - for hyp_words, ref_text in zip(hyps, texts): - ref_words = ref_text.split() - this_batch.append((ref_words, hyp_words)) - - results[name].extend(this_batch) - - num_cuts += len(texts) - - if batch_idx % log_interval == 0: - batch_str = f"{batch_idx}/{num_batches}" - - logging.info( - f"batch {batch_str}, cuts processed until now is {num_cuts}" - ) - return results - - -def save_results( - params: AttributeDict, - test_set_name: str, - results_dict: Dict[str, List[Tuple[List[int], List[int]]]], -): - test_set_wers = dict() - for key, results in results_dict.items(): - recog_path = ( - params.res_dir / f"recogs-{test_set_name}-{key}-{params.suffix}.txt" - ) - store_transcripts(filename=recog_path, texts=results) - logging.info(f"The transcripts are stored in {recog_path}") - - # The following prints out WERs, per-word error statistics and aligned - # ref/hyp pairs. - errs_filename = ( - params.res_dir / f"errs-{test_set_name}-{key}-{params.suffix}.txt" - ) - with open(errs_filename, "w") as f: - wer = write_error_stats( - f, f"{test_set_name}-{key}", results, enable_log=True - ) - test_set_wers[key] = wer - - logging.info("Wrote detailed error stats to {}".format(errs_filename)) - - test_set_wers = sorted(test_set_wers.items(), key=lambda x: x[1]) - errs_info = ( - params.res_dir - / f"wer-summary-{test_set_name}-{key}-{params.suffix}.txt" - ) - with open(errs_info, "w") as f: - print("settings\tWER", file=f) - for key, val in test_set_wers: - print("{}\t{}".format(key, val), file=f) - - s = "\nFor {}, WER of different settings are:\n".format(test_set_name) - note = "\tbest for {}".format(test_set_name) - for key, val in test_set_wers: - s += "{}\t{}{}\n".format(key, val, note) - note = "" - logging.info(s) - - -@torch.no_grad() -def main(): - parser = get_parser() - LibriSpeechAsrDataModule.add_arguments(parser) - args = parser.parse_args() - args.exp_dir = Path(args.exp_dir) - - params = get_params() - params.update(vars(args)) - - assert params.decoding_method in ( - "greedy_search", - "beam_search", - "fast_beam_search", - "modified_beam_search", - ) - params.res_dir = params.exp_dir / params.decoding_method - - params.suffix = f"epoch-{params.epoch}-avg-{params.avg}" - if "fast_beam_search" in params.decoding_method: - params.suffix += f"-beam-{params.beam}" - params.suffix += f"-max-contexts-{params.max_contexts}" - params.suffix += f"-max-states-{params.max_states}" - elif "beam_search" in params.decoding_method: - params.suffix += f"-beam-{params.beam_size}" - else: - params.suffix += f"-context-{params.context_size}" - params.suffix += f"-max-sym-per-frame-{params.max_sym_per_frame}" - - setup_logger(f"{params.res_dir}/log-decode-{params.suffix}") - logging.info("Decoding started") - - device = torch.device("cpu") - if torch.cuda.is_available(): - device = torch.device("cuda", 0) - - 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.unk_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) - - if params.avg_last_n > 0: - filenames = find_checkpoints(params.exp_dir)[: params.avg_last_n] - logging.info(f"averaging {filenames}") - model.to(device) - model.load_state_dict(average_checkpoints(filenames, device=device)) - elif params.avg == 1: - load_checkpoint(f"{params.exp_dir}/epoch-{params.epoch}.pt", model) - else: - start = params.epoch - params.avg + 1 - filenames = [] - for i in range(start, params.epoch + 1): - if start >= 0: - filenames.append(f"{params.exp_dir}/epoch-{i}.pt") - logging.info(f"averaging {filenames}") - model.to(device) - model.load_state_dict(average_checkpoints(filenames, device=device)) - - model.to(device) - model.eval() - model.device = device - - if params.decoding_method == "fast_beam_search": - decoding_graph = k2.trivial_graph(params.vocab_size - 1, device=device) - else: - decoding_graph = None - - num_param = sum([p.numel() for p in model.parameters()]) - logging.info(f"Number of model parameters: {num_param}") - - librispeech = LibriSpeechAsrDataModule(args) - - test_clean_cuts = librispeech.test_clean_cuts() - test_other_cuts = librispeech.test_other_cuts() - - test_clean_dl = librispeech.test_dataloaders(test_clean_cuts) - test_other_dl = librispeech.test_dataloaders(test_other_cuts) - - test_sets = ["test-clean", "test-other"] - test_dl = [test_clean_dl, test_other_dl] - - for test_set, test_dl in zip(test_sets, test_dl): - results_dict = decode_dataset( - dl=test_dl, - params=params, - model=model, - sp=sp, - decoding_graph=decoding_graph, - ) - - save_results( - params=params, - test_set_name=test_set, - results_dict=results_dict, - ) - - logging.info("Done!") - - -if __name__ == "__main__": - main() diff --git a/egs/librispeech/ASR/conv_emformer_transducer/decoder.py b/egs/librispeech/ASR/conv_emformer_transducer/decoder.py deleted file mode 120000 index 0d5f10dc0..000000000 --- a/egs/librispeech/ASR/conv_emformer_transducer/decoder.py +++ /dev/null @@ -1 +0,0 @@ -../pruned_transducer_stateless/decoder.py \ No newline at end of file diff --git a/egs/librispeech/ASR/conv_emformer_transducer/emformer.py b/egs/librispeech/ASR/conv_emformer_transducer/emformer.py deleted file mode 100644 index 3c520f5c3..000000000 --- a/egs/librispeech/ASR/conv_emformer_transducer/emformer.py +++ /dev/null @@ -1,1770 +0,0 @@ -# Copyright 2022 Xiaomi Corporation (Author: Zengwei Yao) -# -# 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. -# -# It is modified based on https://github.com/pytorch/audio/blob/main/torchaudio/models/emformer.py. # noqa - -import math -import warnings -from typing import List, Optional, Tuple - -import torch -import torch.nn as nn -from encoder_interface import EncoderInterface -from subsampling import Conv2dSubsampling, VggSubsampling - -from icefall.utils import make_pad_mask - - -def _gen_attention_mask_block( - col_widths: List[int], - col_mask: List[bool], - num_rows: int, - device: torch.device, -) -> torch.Tensor: - assert len(col_widths) == len( - col_mask - ), "Length of col_widths must match that of col_mask" - - mask_block = [ - torch.ones(num_rows, col_width, device=device) - if is_ones_col - else torch.zeros(num_rows, col_width, device=device) - for col_width, is_ones_col in zip(col_widths, col_mask) - ] - return torch.cat(mask_block, dim=1) - - -class EmformerAttention(nn.Module): - r"""Emformer layer attention module. - - Args: - embed_dim (int): - Embedding dimension. - nhead (int): - Number of attention heads in each Emformer layer. - tanh_on_mem (bool, optional): - If ``True``, applies tanh to memory elements. (Default: ``False``) - negative_inf (float, optional): - Value to use for negative infinity in attention weights. (Default: -1e8) - """ - - def __init__( - self, - embed_dim: int, - nhead: int, - tanh_on_mem: bool = False, - negative_inf: float = -1e8, - ): - super().__init__() - - if embed_dim % nhead != 0: - raise ValueError( - f"embed_dim ({embed_dim}) is not a multiple of" - f"nhead ({nhead})." - ) - - self.embed_dim = embed_dim - self.nhead = nhead - self.tanh_on_mem = tanh_on_mem - self.negative_inf = negative_inf - self.head_dim = embed_dim // nhead - - self.scaling = self.head_dim ** -0.5 - - self.emb_to_key_value = nn.Linear(embed_dim, 2 * embed_dim, bias=True) - self.emb_to_query = nn.Linear(embed_dim, embed_dim, bias=True) - self.out_proj = nn.Linear(embed_dim, embed_dim, bias=True) - - # linear transformation for positional encoding. - self.linear_pos = nn.Linear(embed_dim, embed_dim, bias=False) - - # these two learnable bias are used in matrix c and matrix d - # as described in "Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context" Section 3.3 # noqa - self.pos_bias_u = nn.Parameter(torch.Tensor(nhead, self.head_dim)) - self.pos_bias_v = nn.Parameter(torch.Tensor(nhead, self.head_dim)) - - self._reset_parameters() - - def _reset_parameters(self) -> None: - nn.init.xavier_uniform_(self.emb_to_key_value.weight) - nn.init.constant_(self.emb_to_key_value.bias, 0.0) - - nn.init.xavier_uniform_(self.emb_to_query.weight) - nn.init.constant_(self.emb_to_query.bias, 0.0) - - nn.init.xavier_uniform_(self.out_proj.weight) - nn.init.constant_(self.out_proj.bias, 0.0) - - nn.init.xavier_uniform_(self.linear_pos.weight) - - nn.init.xavier_uniform_(self.pos_bias_u) - nn.init.xavier_uniform_(self.pos_bias_v) - - def _gen_attention_probs( - self, - attention_weights: torch.Tensor, - attention_mask: torch.Tensor, - padding_mask: Optional[torch.Tensor], - ) -> torch.Tensor: - """Given the entire attention weights, mask out unecessary connections - and optionally with padding positions, to obtain underlying chunk-wise - attention probabilities. - - B: batch size; - Q: length of query; - KV: length of key and value. - - Args: - attention_weights (torch.Tensor): - Attention weights computed on the entire concatenated tensor - with shape (B * nhead, Q, KV). - attention_mask (torch.Tensor): - Mask tensor where chunk-wise connections are filled with `False`, - and other unnecessary connections are filled with `True`, - with shape (Q, KV). - padding_mask (torch.Tensor, optional): - Mask tensor where the padding positions are fill with `True`, - and other positions are filled with `False`, with shapa `(B, KV)`. - - Returns: - A tensor of shape (B * nhead, Q, KV). - """ - attention_weights_float = attention_weights.float() - attention_weights_float = attention_weights_float.masked_fill( - attention_mask.unsqueeze(0), self.negative_inf - ) - if padding_mask is not None: - Q = attention_weights.size(1) - B = attention_weights.size(0) // self.nhead - attention_weights_float = attention_weights_float.view( - B, self.nhead, Q, -1 - ) - attention_weights_float = attention_weights_float.masked_fill( - padding_mask.unsqueeze(1).unsqueeze(2).to(torch.bool), - self.negative_inf, - ) - attention_weights_float = attention_weights_float.view( - B * self.nhead, Q, -1 - ) - - attention_probs = nn.functional.softmax( - attention_weights_float, dim=-1 - ).type_as(attention_weights) - - return attention_probs - - def _rel_shift(self, x: torch.Tensor) -> torch.Tensor: - """Compute relative positional encoding. - - Args: - x: Input tensor, of shape (B, nhead, U, PE). - U is the length of query vector. - For non-infer mode, PE = 2 * U - 1; - for infer mode, PE = L + 2 * U - 1. - - Returns: - A tensor of shape (B, nhead, U, out_len). - For non-infer mode, out_len = U; - for infer mode, out_len = L + U. - """ - B, nhead, U, PE = x.size() - B_stride = x.stride(0) - nhead_stride = x.stride(1) - U_stride = x.stride(2) - PE_stride = x.stride(3) - out_len = PE - (U - 1) - return x.as_strided( - size=(B, nhead, U, out_len), - stride=(B_stride, nhead_stride, U_stride - PE_stride, PE_stride), - storage_offset=PE_stride * (U - 1), - ) - - def _forward_impl( - self, - utterance: torch.Tensor, - lengths: torch.Tensor, - right_context: torch.Tensor, - summary: torch.Tensor, - memory: torch.Tensor, - attention_mask: torch.Tensor, - pos_emb: torch.Tensor, - left_context_key: Optional[torch.Tensor] = None, - left_context_val: Optional[torch.Tensor] = None, - ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: - """Underlying chunk-wise attention implementation. - - L: length of left_context; - S: length of summary; - M: length of memory; - Q: length of attention query; - KV: length of attention key and value. - - 1) Concat right_context, utterance, summary, - and compute query tensor with length Q = R + U + S. - 2) Concat memory, right_context, utterance, - and compute key, value tensors with length KV = M + R + U; - optionally with left_context_key and left_context_val (inference mode), - then KV = M + R + L + U. - 3) Compute entire attention scores with query, key, and value, - then apply attention_mask to get underlying chunk-wise attention scores. - - Args: - utterance (torch.Tensor): - Utterance frames, with shape (U, B, D). - lengths (torch.Tensor): - With shape (B,) and i-th element representing - number of valid frames for i-th batch element in utterance. - right_context (torch.Tensor): - Right context frames, with shape (R, B, D). - summary (torch.Tensor): - Summary elements, with shape (S, B, D). - memory (torch.Tensor): - Memory elements, with shape (M, B, D). - attention_mask (torch.Tensor): - Attention mask for underlying attention, with shape (Q, KV). - pos_emb (torch.Tensor): - Position encoding embedding, with shape (PE, D). - For training mode, PE = 2*U-1; - For infer mode, PE = L+2*U-1. - left_context_key (torch,Tensor, optional): - Cached attention key of left context from preceding computation, - with shape (L, B, D). - left_context_val (torch.Tensor, optional): - Cached attention value of left context from preceding computation, - with shape (L, B, D). - - Returns: - A tuple containing 4 tensors: - - output of right context and utterance, with shape (R + U, B, D). - - memory output, with shape (S, B, D). - - attention key, with shape (KV, B, D). - - attention value, with shape (KV, B, D). - """ - U, B, _ = utterance.size() - R = right_context.size(0) - M = memory.size(0) - - # Compute query with [right context, utterance, summary]. - query = self.emb_to_query( - torch.cat([right_context, utterance, summary]) - ) - # Compute key and value with [mems, right context, utterance]. - key, value = self.emb_to_key_value( - torch.cat([memory, right_context, utterance]) - ).chunk(chunks=2, dim=2) - - if left_context_key is not None and left_context_val is not None: - # This is for inference mode. Now compute key and value with - # [mems, right context, left context, uttrance] - key = torch.cat( - [key[: M + R], left_context_key, key[M + R :]] # noqa - ) - value = torch.cat( - [value[: M + R], left_context_val, value[M + R :]] # noqa - ) - Q = query.size(0) - KV = key.size(0) - - reshaped_key, reshaped_value = [ - tensor.contiguous() - .view(KV, B * self.nhead, self.head_dim) - .transpose(0, 1) - for tensor in [key, value] - ] # (B * nhead, KV, head_dim) - reshaped_query = query.contiguous().view( - Q, B, self.nhead, self.head_dim - ) - - # compute attention matrix ac - query_with_bais_u = ( - (reshaped_query + self.pos_bias_u) - .view(Q, B * self.nhead, self.head_dim) - .transpose(0, 1) - ) - matrix_ac = torch.bmm( - query_with_bais_u, reshaped_key.transpose(1, 2) - ) # (B * nhead, Q, KV) - - # compute attention matrix bd - utterance_with_bais_v = ( - reshaped_query[R : R + U] + self.pos_bias_v - ).permute(1, 2, 0, 3) - # (B, nhead, U, head_dim) - PE = pos_emb.size(0) - if left_context_key is not None and left_context_val is not None: - L = left_context_key.size(0) - assert PE == L + 2 * U - 1 - else: - assert PE == 2 * U - 1 - pos_emb = ( - self.linear_pos(pos_emb) - .view(PE, self.nhead, self.head_dim) - .transpose(0, 1) - .unsqueeze(0) - ) # (1, nhead, PE, head_dim) - matrix_bd_utterance = torch.matmul( - utterance_with_bais_v, pos_emb.transpose(-2, -1) - ) # (B, nhead, U, PE) - # rel-shift - matrix_bd_utterance = self._rel_shift( - matrix_bd_utterance - ) # (B, nhead, U, U or L + U) - matrix_bd_utterance = matrix_bd_utterance.contiguous().view( - B * self.nhead, U, -1 - ) - matrix_bd = torch.zeros_like(matrix_ac) - matrix_bd[:, R : R + U, M + R :] = matrix_bd_utterance - - attention_weights = (matrix_ac + matrix_bd) * self.scaling - - # Compute padding mask - if B == 1: - padding_mask = None - else: - padding_mask = make_pad_mask(KV - U + lengths) - - # Compute attention probabilities. - attention_probs = self._gen_attention_probs( - attention_weights, attention_mask, padding_mask - ) - - # Compute attention. - attention = torch.bmm(attention_probs, reshaped_value) - assert attention.shape == (B * self.nhead, Q, self.head_dim) - attention = ( - attention.transpose(0, 1).contiguous().view(Q, B, self.embed_dim) - ) - - # Apply output projection. - outputs = self.out_proj(attention) - - output_right_context_utterance = outputs[: R + U] - output_memory = outputs[R + U :] - if self.tanh_on_mem: - output_memory = torch.tanh(output_memory) - else: - output_memory = torch.clamp(output_memory, min=-10, max=10) - - return output_right_context_utterance, output_memory, key, value - - def forward( - self, - utterance: torch.Tensor, - lengths: torch.Tensor, - right_context: torch.Tensor, - summary: torch.Tensor, - memory: torch.Tensor, - attention_mask: torch.Tensor, - pos_emb: torch.Tensor, - ) -> Tuple[torch.Tensor, torch.Tensor]: - # TODO: Modify docs. - """Forward pass for training. - - B: batch size; - D: embedding dimension; - R: length of right_context; - U: length of utterance; - S: length of summary; - M: length of memory. - - Args: - utterance (torch.Tensor): - Utterance frames, with shape (U, B, D). - lengths (torch.Tensor): - With shape (B,) and i-th element representing - number of valid frames for i-th batch element in utterance. - right_context (torch.Tensor): - Right context frames, with shape (R, B, D). - summary (torch.Tensor): - Summary elements, with shape (S, B, D). - memory (torch.Tensor): - Memory elements, with shape (M, B, D). - attention_mask (torch.Tensor): - Attention mask for underlying chunk-wise attention, - with shape (Q, KV), where Q = R + U + S, KV = M + R + U. - pos_emb (torch.Tensor): - Position encoding embedding, with shape (PE, D). - For training mode, P = 2*U-1. - - Returns: - A tuple containing 2 tensors: - - output of right context and utterance, with shape (R + U, B, D). - - memory output, with shape (M, B, D), where M = S - 1 or M = 0. - """ - ( - output_right_context_utterance, - output_memory, - _, - _, - ) = self._forward_impl( - utterance, - lengths, - right_context, - summary, - memory, - attention_mask, - pos_emb, - ) - return output_right_context_utterance, output_memory[:-1] - - @torch.jit.export - def infer( - self, - utterance: torch.Tensor, - lengths: torch.Tensor, - right_context: torch.Tensor, - summary: torch.Tensor, - memory: torch.Tensor, - left_context_key: torch.Tensor, - left_context_val: torch.Tensor, - pos_emb: torch.Tensor, - ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: - """Forward pass for inference. - - B: batch size; - D: embedding dimension; - R: length of right_context; - U: length of utterance; - L: length of left_context; - S: length of summary; - M: length of memory; - - Args: - utterance (torch.Tensor): - Utterance frames, with shape (U, B, D). - lengths (torch.Tensor): - With shape (B,) and i-th element representing - number of valid frames for i-th batch element in utterance. - right_context (torch.Tensor): - Right context frames, with shape (R, B, D). - summary (torch.Tensor): - Summary element, with shape (1, B, D), or empty. - memory (torch.Tensor): - Memory elements, with shape (M, B, D). - left_context_key (torch,Tensor): - Cached attention key of left context from preceding computation, - with shape (L, B, D). - left_context_val (torch.Tensor): - Cached attention value of left context from preceding computation, - with shape (L, B, D). - pos_emb (torch.Tensor): - Position encoding embedding, with shape (PE, D). - For infer mode, PE = L+2*U-1. - - Returns: - A tuple containing 4 tensors: - - output of right context and utterance, with shape (R + U, B, D). - - memory output, with shape (1, B, D) or (0, B, D). - - attention key of left context and utterance, which would be cached - for next computation, with shape (L + U, B, D). - - attention value of left context and utterance, which would be - cached for next computation, with shape (L + U, B, D). - """ - # query: [right context, utterance, summary] - Q = right_context.size(0) + utterance.size(0) + summary.size(0) - # key, value: [memory, right context, left context, uttrance] - KV = ( - memory.size(0) - + right_context.size(0) # noqa - + left_context_key.size(0) # noqa - + utterance.size(0) # noqa - ) - attention_mask = torch.zeros(Q, KV).to( - dtype=torch.bool, device=utterance.device - ) - # Disallow attention bettween the summary vector with the memory bank - attention_mask[-1, : memory.size(0)] = True - ( - output_right_context_utterance, - output_memory, - key, - value, - ) = self._forward_impl( - utterance, - lengths, - right_context, - summary, - memory, - attention_mask, - pos_emb, - left_context_key=left_context_key, - left_context_val=left_context_val, - ) - right_context_end_idx = memory.size(0) + right_context.size(0) - return ( - output_right_context_utterance, - output_memory, - key[right_context_end_idx:], - value[right_context_end_idx:], - ) - - -class EmformerLayer(nn.Module): - """Emformer layer that constitutes Emformer. - - Args: - d_model (int): - Input dimension. - nhead (int): - Number of attention heads. - dim_feedforward (int): - Hidden layer dimension of feedforward network. - chunk_length (int): - Length of each input segment. - dropout (float, optional): - Dropout probability. (Default: 0.0) - cnn_module_kernel (int): - Kernel size of convolution module. - left_context_length (int, optional): - Length of left context. (Default: 0) - max_memory_size (int, optional): - Maximum number of memory elements to use. (Default: 0) - tanh_on_mem (bool, optional): - If ``True``, applies tanh to memory elements. (Default: ``False``) - negative_inf (float, optional): - Value to use for negative infinity in attention weights. (Default: -1e8) - causal (bool): - Whether use causal convolution (default=False). - """ - - def __init__( - self, - d_model: int, - nhead: int, - dim_feedforward: int, - chunk_length: int, - dropout: float = 0.0, - cnn_module_kernel: int = 3, - left_context_length: int = 0, - max_memory_size: int = 0, - tanh_on_mem: bool = False, - negative_inf: float = -1e8, - causal: bool = True, - ): - super().__init__() - - self.attention = EmformerAttention( - embed_dim=d_model, - nhead=nhead, - tanh_on_mem=tanh_on_mem, - negative_inf=negative_inf, - ) - self.summary_op = nn.AvgPool1d( - kernel_size=chunk_length, stride=chunk_length, ceil_mode=True - ) - - self.feed_forward_macaron = nn.Sequential( - nn.Linear(d_model, dim_feedforward), - Swish(), - nn.Dropout(dropout), - nn.Linear(dim_feedforward, d_model), - ) - - self.feed_forward = nn.Sequential( - nn.Linear(d_model, dim_feedforward), - Swish(), - nn.Dropout(dropout), - nn.Linear(dim_feedforward, d_model), - ) - - self.conv_module = ConvolutionModule( - d_model, - cnn_module_kernel, - causal=causal, - ) - - self.norm_ff_macaron = nn.LayerNorm(d_model) - self.norm_ff = nn.LayerNorm(d_model) - self.norm_mha = nn.LayerNorm(d_model) - self.norm_conv = nn.LayerNorm(d_model) - self.norm_final = nn.LayerNorm(d_model) - - self.dropout = nn.Dropout(dropout) - - self.ff_scale = 0.5 - self.left_context_length = left_context_length - self.chunk_length = chunk_length - self.max_memory_size = max_memory_size - self.d_model = d_model - self.use_memory = max_memory_size > 0 - - def _init_state( - self, batch_size: int, device: Optional[torch.device] - ) -> List[torch.Tensor]: - """Initialize states with zeros.""" - empty_memory = torch.zeros( - self.max_memory_size, batch_size, self.d_model, device=device - ) - left_context_key = torch.zeros( - self.left_context_length, batch_size, self.d_model, device=device - ) - left_context_val = torch.zeros( - self.left_context_length, batch_size, self.d_model, device=device - ) - past_length = torch.zeros( - 1, batch_size, dtype=torch.int32, device=device - ) - return [empty_memory, left_context_key, left_context_val, past_length] - - def _unpack_state( - self, state: List[torch.Tensor] - ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: - """Unpack cached states including: - 1) output memory from previous chunks in the lower layer; - 2) attention key and value of left context from proceeding chunk's - computation. - """ - past_length = state[3][0][0].item() - past_left_context_length = min(self.left_context_length, past_length) - past_memory_length = min( - self.max_memory_size, math.ceil(past_length / self.chunk_length) - ) - memory_start_idx = self.max_memory_size - past_memory_length - pre_memory = state[0][memory_start_idx:] - left_context_start_idx = ( - self.left_context_length - past_left_context_length - ) - left_context_key = state[1][left_context_start_idx:] - left_context_val = state[2][left_context_start_idx:] - return pre_memory, left_context_key, left_context_val - - def _pack_state( - self, - next_key: torch.Tensor, - next_val: torch.Tensor, - update_length: int, - memory: torch.Tensor, - state: List[torch.Tensor], - ) -> List[torch.Tensor]: - """Pack updated states including: - 1) output memory of current chunk in the lower layer; - 2) attention key and value in current chunk's computation, which would - be resued in next chunk's computation. - 3) length of current chunk. - """ - new_memory = torch.cat([state[0], memory]) - new_key = torch.cat([state[1], next_key]) - new_val = torch.cat([state[2], next_val]) - memory_start_idx = new_memory.size(0) - self.max_memory_size - state[0] = new_memory[memory_start_idx:] - key_start_idx = new_key.size(0) - self.left_context_length - state[1] = new_key[key_start_idx:] - val_start_idx = new_val.size(0) - self.left_context_length - state[2] = new_val[val_start_idx:] - state[3] = state[3] + update_length - return state - - def _apply_macaron_feed_foward_module( - self, right_context_utterance: torch.Tensor - ) -> Tuple[torch.Tensor, torch.Tensor]: - """Apply macaron style feed forward module.""" - residual = right_context_utterance - right_context_utterance = self.norm_ff_macaron(right_context_utterance) - right_context_utterance = residual + self.ff_scale * self.dropout( - self.feed_forward_macaron(right_context_utterance) - ) - return right_context_utterance - - def _apply_feed_forward_module( - self, right_context_utterance: torch.Tensor - ) -> torch.Tensor: - """Apply feed forward module.""" - residual = right_context_utterance - right_context_utterance = self.norm_ff(right_context_utterance) - right_context_utterance = residual + self.ff_scale * self.dropout( - self.feed_forward(right_context_utterance) - ) - return right_context_utterance - - def _apply_conv_module_forward( - self, - right_context_utterance: torch.Tensor, - right_context_end_idx: int, - ) -> torch.Tensor: - """Apply convolution module on utterance in non-infer mode.""" - utterance = right_context_utterance[right_context_end_idx:] - right_context = right_context_utterance[:right_context_end_idx] - - residual = utterance - utterance = self.norm_conv(utterance) - utterance, _ = self.conv_module(utterance) - utterance = residual + self.dropout(utterance) - right_context_utterance = torch.cat([right_context, utterance]) - return right_context_utterance - - def _apply_conv_module_infer( - self, - right_context_utterance: torch.Tensor, - right_context_end_idx: int, - conv_cache: Optional[torch.Tensor] = None, - ) -> torch.Tensor: - """Apply convolution module on utterance in infer mode.""" - utterance = right_context_utterance[right_context_end_idx:] - right_context = right_context_utterance[:right_context_end_idx] - - residual = utterance - utterance = self.norm_conv(utterance) - utterance, conv_cache = self.conv_module(utterance, conv_cache) - utterance = residual + self.dropout(utterance) - right_context_utterance = torch.cat([right_context, utterance]) - return right_context_utterance, conv_cache - - def _apply_attention_module_forward( - self, - right_context_utterance: torch.Tensor, - right_context_end_idx: int, - lengths: torch.Tensor, - memory: torch.Tensor, - pos_emb: torch.Tensor, - attention_mask: Optional[torch.Tensor] = None, - ) -> Tuple[torch.Tensor, torch.Tensor]: - """Apply attention module in non-infer mode.""" - if attention_mask is None: - raise ValueError( - "attention_mask must be not None in non-infer mode. " - ) - - residual = right_context_utterance - right_context_utterance = self.norm_mha(right_context_utterance) - utterance = right_context_utterance[right_context_end_idx:] - right_context = right_context_utterance[:right_context_end_idx] - - if self.use_memory: - summary = self.summary_op(utterance.permute(1, 2, 0)).permute( - 2, 0, 1 - ) - else: - summary = torch.empty(0).to( - dtype=utterance.dtype, device=utterance.device - ) - output_right_context_utterance, output_memory = self.attention( - utterance=utterance, - lengths=lengths, - right_context=right_context, - summary=summary, - memory=memory, - attention_mask=attention_mask, - pos_emb=pos_emb, - ) - right_context_utterance = residual + self.dropout( - output_right_context_utterance - ) - - return right_context_utterance, output_memory - - def _apply_attention_module_infer( - self, - right_context_utterance: torch.Tensor, - right_context_end_idx: int, - lengths: torch.Tensor, - memory: torch.Tensor, - pos_emb: torch.Tensor, - state: Optional[List[torch.Tensor]] = None, - ) -> Tuple[torch.Tensor, torch.Tensor, List[torch.Tensor]]: - """Apply attention in infer mode. - 1) Unpack cached states including: - - memory from previous chunks in the lower layer; - - attention key and value of left context from proceeding - chunk's compuation; - 2) Apply attention computation; - 3) Pack updated states including: - - output memory of current chunk in the lower layer; - - attention key and value in current chunk's computation, which would - be resued in next chunk's computation. - - length of current chunk. - """ - residual = right_context_utterance - right_context_utterance = self.norm_mha(right_context_utterance) - utterance = right_context_utterance[right_context_end_idx:] - right_context = right_context_utterance[:right_context_end_idx] - - if state is None: - state = self._init_state(utterance.size(1), device=utterance.device) - pre_memory, left_context_key, left_context_val = self._unpack_state( - state - ) - if self.use_memory: - summary = self.summary_op(utterance.permute(1, 2, 0)).permute( - 2, 0, 1 - ) - summary = summary[:1] - else: - summary = torch.empty(0).to( - dtype=utterance.dtype, device=utterance.device - ) - # pos_emb is of shape [PE, D], PE = L + 2 * U - 1, - # the relative distance j - i of key(j) and query(i) is in range of [-(L + U - 1), (U - 1)] # noqa - L = left_context_key.size(0) # L <= left_context_length - U = utterance.size(0) - PE = L + 2 * U - 1 - tot_PE = self.left_context_length + 2 * U - 1 - assert pos_emb.size(0) == tot_PE - pos_emb = pos_emb[tot_PE - PE :] - ( - output_right_context_utterance, - output_memory, - next_key, - next_val, - ) = self.attention.infer( - utterance=utterance, - lengths=lengths, - right_context=right_context, - summary=summary, - memory=pre_memory, - left_context_key=left_context_key, - left_context_val=left_context_val, - pos_emb=pos_emb, - ) - right_context_utterance = residual + self.dropout( - output_right_context_utterance - ) - state = self._pack_state( - next_key, next_val, utterance.size(0), memory, state - ) - return right_context_utterance, output_memory, state - - def forward( - self, - utterance: torch.Tensor, - lengths: torch.Tensor, - right_context: torch.Tensor, - memory: torch.Tensor, - attention_mask: torch.Tensor, - pos_emb: torch.Tensor, - ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: - r"""Forward pass for training. - 1) Apply layer normalization on input utterance and right context - before attention; - 2) Apply attention module, compute updated utterance, right context, - and memory; - 3) Apply feed forward module and layer normalization on output utterance - and right context. - - B: batch size; - D: embedding dimension; - R: length of right_context; - U: length of utterance; - M: length of memory. - - Args: - utterance (torch.Tensor): - Utterance frames, with shape (U, B, D). - lengths (torch.Tensor): - With shape (B,) and i-th element representing - number of valid frames for i-th batch element in utterance. - right_context (torch.Tensor): - Right context frames, with shape (R, B, D). - memory (torch.Tensor): - Memory elements, with shape (M, B, D). - attention_mask (torch.Tensor): - Attention mask for underlying attention module, - with shape (Q, KV), where Q = R + U + S, KV = M + R + U. - pos_emb (torch.Tensor): - Position encoding embedding, with shape (PE, D). - For training mode, P = 2*U-1. - - Returns: - A tuple containing 3 tensors: - - output utterance, with shape (U, B, D). - - output right context, with shape (R, B, D). - - output memory, with shape (M, B, D). - """ - right_context_utterance = torch.cat([right_context, utterance]) - right_context_end_idx = right_context.size(0) - - right_context_utterance = self._apply_macaron_feed_foward_module( - right_context_utterance - ) - - ( - right_context_utterance, - output_memory, - ) = self._apply_attention_module_forward( - right_context_utterance, - right_context_end_idx, - lengths, - memory, - pos_emb, - attention_mask, - ) - - right_context_utterance = self._apply_conv_module_forward( - right_context_utterance, right_context_end_idx - ) - - right_context_utterance = self._apply_feed_forward_module( - right_context_utterance - ) - - right_context_utterance = self.norm_final(right_context_utterance) - - output_utterance = right_context_utterance[right_context_end_idx:] - output_right_context = right_context_utterance[:right_context_end_idx] - return output_utterance, output_right_context, output_memory - - @torch.jit.export - def infer( - self, - utterance: torch.Tensor, - lengths: torch.Tensor, - right_context: torch.Tensor, - memory: torch.Tensor, - pos_emb: torch.Tensor, - state: Optional[List[torch.Tensor]] = None, - conv_cache: Optional[torch.Tensor] = None, - ) -> Tuple[torch.Tensor, torch.Tensor, List[torch.Tensor], torch.Tensor]: - """Forward pass for inference. - - 1) Apply layer normalization on input utterance and right context - before attention; - 2) Apply attention module with cached state, compute updated utterance, - right context, and memory, and update state; - 3) Apply feed forward module and layer normalization on output - utterance and right context. - - B: batch size; - D: embedding dimension; - R: length of right_context; - U: length of utterance; - M: length of memory. - - Args: - utterance (torch.Tensor): - Utterance frames, with shape (U, B, D). - lengths (torch.Tensor): - With shape (B,) and i-th element representing - number of valid frames for i-th batch element in utterance. - right_context (torch.Tensor): - Right context frames, with shape (R, B, D). - memory (torch.Tensor): - Memory elements, with shape (M, B, D). - state (List[torch.Tensor], optional): - List of tensors representing layer internal state generated in - preceding computation. (default=None) - pos_emb (torch.Tensor): - Position encoding embedding, with shape (PE, D). - For infer mode, PE = L+2*U-1. - conv_cache (torch.Tensor, optional): - Cache tensor of left context for causal convolution. - - Returns: - (Tensor, Tensor, List[torch.Tensor], Tensor): - - output utterance, with shape (U, B, D); - - output right_context, with shape (R, B, D); - - output memory, with shape (1, B, D) or (0, B, D). - - output state. - - updated conv_cache. - """ - right_context_utterance = torch.cat([right_context, utterance]) - right_context_end_idx = right_context.size(0) - - right_context_utterance = self._apply_macaron_feed_foward_module( - right_context_utterance - ) - - ( - right_context_utterance, - output_memory, - output_state, - ) = self._apply_attention_module_infer( - right_context_utterance, - right_context_end_idx, - lengths, - memory, - pos_emb, - state, - ) - - right_context_utterance, conv_cache = self._apply_conv_module_infer( - right_context_utterance, - right_context_end_idx, - conv_cache, - ) - - right_context_utterance = self._apply_feed_forward_module( - right_context_utterance - ) - - right_context_utterance = self.norm_final(right_context_utterance) - - output_utterance = right_context_utterance[right_context_end_idx:] - output_right_context = right_context_utterance[:right_context_end_idx] - return ( - output_utterance, - output_right_context, - output_memory, - output_state, - conv_cache, - ) - - -class EmformerEncoder(nn.Module): - """Implements the Emformer architecture introduced in - *Emformer: Efficient Memory Transformer Based Acoustic Model for Low Latency - Streaming Speech Recognition* - [:footcite:`shi2021emformer`]. - - Args: - d_model (int): - Input dimension. - nhead (int): - Number of attention heads in each emformer layer. - dim_feedforward (int): - Hidden layer dimension of each emformer layer's feedforward network. - num_encoder_layers (int): - Number of emformer layers to instantiate. - chunk_length (int): - Length of each input segment. - dropout (float, optional): - Dropout probability. (default: 0.0) - left_context_length (int, optional): - Length of left context. (default: 0) - right_context_length (int, optional): - Length of right context. (default: 0) - max_memory_size (int, optional): - Maximum number of memory elements to use. (default: 0) - tanh_on_mem (bool, optional): - If ``true``, applies tanh to memory elements. (default: ``false``) - negative_inf (float, optional): - Value to use for negative infinity in attention weights. (default: -1e8) - causal (bool): - Whether use causal convolution (default=False). - """ - - def __init__( - self, - chunk_length: int, - d_model: int = 256, - nhead: int = 4, - dim_feedforward: int = 2048, - num_encoder_layers: int = 12, - dropout: float = 0.1, - cnn_module_kernel: int = 3, - left_context_length: int = 0, - right_context_length: int = 0, - max_memory_size: int = 0, - tanh_on_mem: bool = False, - negative_inf: float = -1e8, - causal: bool = True, - ): - super().__init__() - - self.use_memory = max_memory_size > 0 - self.init_memory_op = nn.AvgPool1d( - kernel_size=chunk_length, - stride=chunk_length, - ceil_mode=True, - ) - - self.emformer_layers = nn.ModuleList( - [ - EmformerLayer( - d_model, - nhead, - dim_feedforward, - chunk_length, - dropout=dropout, - cnn_module_kernel=cnn_module_kernel, - left_context_length=left_context_length, - max_memory_size=max_memory_size, - tanh_on_mem=tanh_on_mem, - negative_inf=negative_inf, - causal=causal, - ) - for layer_idx in range(num_encoder_layers) - ] - ) - - self.encoder_pos = RelPositionalEncoding(d_model, dropout) - - self.left_context_length = left_context_length - self.right_context_length = right_context_length - self.chunk_length = chunk_length - self.max_memory_size = max_memory_size - - def _gen_right_context(self, x: torch.Tensor) -> torch.Tensor: - """Hard copy each chunk's right context and concat them.""" - T = x.shape[0] - num_segs = math.ceil( - (T - self.right_context_length) / self.chunk_length - ) - right_context_blocks = [] - for seg_idx in range(num_segs - 1): - start = (seg_idx + 1) * self.chunk_length - end = start + self.right_context_length - right_context_blocks.append(x[start:end]) - last_right_context_start_idx = T - self.right_context_length - right_context_blocks.append(x[last_right_context_start_idx:]) - return torch.cat(right_context_blocks) - - def _gen_attention_mask_col_widths( - self, chunk_idx: int, U: int - ) -> List[int]: - """Calculate column widths (key, value) in attention mask for the - chunk_idx chunk.""" - num_chunks = math.ceil(U / self.chunk_length) - rc = self.right_context_length - lc = self.left_context_length - rc_start = chunk_idx * rc - rc_end = rc_start + rc - chunk_start = max(chunk_idx * self.chunk_length - lc, 0) - chunk_end = min((chunk_idx + 1) * self.chunk_length, U) - R = rc * num_chunks - - if self.use_memory: - m_start = max(chunk_idx - self.max_memory_size, 0) - M = num_chunks - 1 - col_widths = [ - m_start, # before memory - chunk_idx - m_start, # memory - M - chunk_idx, # after memory - rc_start, # before right context - rc, # right context - R - rc_end, # after right context - chunk_start, # before chunk - chunk_end - chunk_start, # chunk - U - chunk_end, # after chunk - ] - else: - col_widths = [ - rc_start, # before right context - rc, # right context - R - rc_end, # after right context - chunk_start, # before chunk - chunk_end - chunk_start, # chunk - U - chunk_end, # after chunk - ] - - return col_widths - - def _gen_attention_mask(self, utterance: torch.Tensor) -> torch.Tensor: - """Generate attention mask for underlying chunk-wise attention - computation, where chunk-wise connections are filled with `False`, - and other unnecessary connections beyond chunk are filled with `True`. - - R: length of right_context; - U: length of utterance; - S: length of summary; - M: length of memory; - Q: length of attention query; - KV: length of attention key and value. - - The shape of attention mask is (Q, KV). - If self.use_memory is `True`: - query = [right_context, utterance, summary]; - key, value = [memory, right_context, utterance]; - Q = R + U + S, KV = M + R + U. - Otherwise: - query = [right_context, utterance] - key, value = [right_context, utterance] - Q = R + U, KV = R + U. - """ - U = utterance.size(0) - num_chunks = math.ceil(U / self.chunk_length) - - right_context_mask = [] - utterance_mask = [] - summary_mask = [] - - if self.use_memory: - num_cols = 9 - # right context and utterance both attend to memory, right context, - # utterance - right_context_utterance_cols_mask = [ - idx in [1, 4, 7] for idx in range(num_cols) - ] - # summary attends to right context, utterance - summary_cols_mask = [idx in [4, 7] for idx in range(num_cols)] - masks_to_concat = [right_context_mask, utterance_mask, summary_mask] - else: - num_cols = 6 - # right context and utterance both attend to right context and - # utterance - right_context_utterance_cols_mask = [ - idx in [1, 4] for idx in range(num_cols) - ] - summary_cols_mask = None - masks_to_concat = [right_context_mask, utterance_mask] - - for chunk_idx in range(num_chunks): - col_widths = self._gen_attention_mask_col_widths(chunk_idx, U) - - right_context_mask_block = _gen_attention_mask_block( - col_widths, - right_context_utterance_cols_mask, - self.right_context_length, - utterance.device, - ) - right_context_mask.append(right_context_mask_block) - - utterance_mask_block = _gen_attention_mask_block( - col_widths, - right_context_utterance_cols_mask, - min( - self.chunk_length, - U - chunk_idx * self.chunk_length, - ), - utterance.device, - ) - utterance_mask.append(utterance_mask_block) - - if summary_cols_mask is not None: - summary_mask_block = _gen_attention_mask_block( - col_widths, summary_cols_mask, 1, utterance.device - ) - summary_mask.append(summary_mask_block) - - attention_mask = ( - 1 - torch.cat([torch.cat(mask) for mask in masks_to_concat]) - ).to(torch.bool) - return attention_mask - - def forward( - self, x: torch.Tensor, lengths: torch.Tensor - ) -> Tuple[torch.Tensor, torch.Tensor]: - """Forward pass for training and non-streaming inference. - - B: batch size; - D: input dimension; - U: length of utterance. - - Args: - x (torch.Tensor): - Utterance frames right-padded with right context frames, - with shape (U + right_context_length, B, D). - lengths (torch.Tensor): - With shape (B,) and i-th element representing number of valid - utterance frames for i-th batch element in x, which contains the - right_context at the end. - - Returns: - A tuple of 2 tensors: - - output utterance frames, with shape (U, B, D). - - output_lengths, with shape (B,), without containing the - right_context at the end. - """ - U = x.size(0) - self.right_context_length - x, pos_emb = self.encoder_pos(x, pos_len=U, neg_len=U) - - right_context = self._gen_right_context(x) - utterance = x[:U] - output_lengths = torch.clamp(lengths - self.right_context_length, min=0) - attention_mask = self._gen_attention_mask(utterance) - memory = ( - self.init_memory_op(utterance.permute(1, 2, 0)).permute(2, 0, 1)[ - :-1 - ] - if self.use_memory - else torch.empty(0).to(dtype=x.dtype, device=x.device) - ) - - output = utterance - for layer in self.emformer_layers: - output, right_context, memory = layer( - output, - output_lengths, - right_context, - memory, - attention_mask, - pos_emb, - ) - - return output, output_lengths - - @torch.jit.export - def infer( - self, - x: torch.Tensor, - lengths: torch.Tensor, - states: Optional[List[List[torch.Tensor]]] = None, - conv_caches: Optional[List[torch.Tensor]] = None, - ) -> Tuple[ - torch.Tensor, torch.Tensor, List[List[torch.Tensor]], List[torch.Tensor] - ]: - """Forward pass for streaming inference. - - B: batch size; - D: input dimension; - U: length of utterance. - - Args: - x (torch.Tensor): - Utterance frames right-padded with right context frames, - with shape (U + right_context_length, B, D). - lengths (torch.Tensor): - With shape (B,) and i-th element representing number of valid - utterance frames for i-th batch element in x, which contains the - right_context at the end. - states (List[List[torch.Tensor]], optional): - Cached states from proceeding chunk's computation, where each - element (List[torch.Tensor]) corresponds to each emformer layer. - (default: None) - conv_caches (List[torch.Tensor], optional): - Cached tensors of left context for causal convolution, where each - element (Tensor) corresponds to each convolutional layer. - Returns: - (Tensor, Tensor, List[List[torch.Tensor]], List[torch.Tensor]): - - output utterance frames, with shape (U, B, D). - - output lengths, with shape (B,), without containing the - right_context at the end. - - updated states from current chunk's computation. - - updated convolution caches from current chunk. - """ - assert x.size(0) == self.chunk_length + self.right_context_length, ( - "Per configured chunk_length and right_context_length, " - f"expected size of {self.chunk_length + self.right_context_length} " - f"for dimension 1 of x, but got {x.size(1)}." - ) - - pos_len = self.chunk_length + self.left_context_length - neg_len = self.chunk_length - x, pos_emb = self.encoder_pos(x, pos_len=pos_len, neg_len=neg_len) - - right_context_start_idx = x.size(0) - self.right_context_length - right_context = x[right_context_start_idx:] - utterance = x[:right_context_start_idx] - output_lengths = torch.clamp(lengths - self.right_context_length, min=0) - memory = ( - self.init_memory_op(utterance.permute(1, 2, 0)).permute(2, 0, 1) - if self.use_memory - else torch.empty(0).to(dtype=x.dtype, device=x.device) - ) - output = utterance - output_states: List[List[torch.Tensor]] = [] - output_conv_caches: List[torch.Tensor] = [] - for layer_idx, layer in enumerate(self.emformer_layers): - ( - output, - right_context, - memory, - output_state, - output_conv_cache, - ) = layer.infer( - output, - output_lengths, - right_context, - memory, - pos_emb, - None if states is None else states[layer_idx], - None if conv_caches is None else conv_caches[layer_idx], - ) - output_states.append(output_state) - output_conv_caches.append(output_conv_cache) - - return output, output_lengths, output_states, output_conv_caches - - -class Emformer(EncoderInterface): - def __init__( - self, - num_features: int, - output_dim: int, - chunk_length: int, - subsampling_factor: int = 4, - d_model: int = 256, - nhead: int = 4, - dim_feedforward: int = 2048, - num_encoder_layers: int = 12, - dropout: float = 0.1, - cnn_module_kernel: int = 3, - vgg_frontend: bool = False, - left_context_length: int = 0, - right_context_length: int = 0, - max_memory_size: int = 0, - tanh_on_mem: bool = False, - negative_inf: float = -1e8, - causal: bool = True, - ): - super().__init__() - - self.subsampling_factor = subsampling_factor - self.right_context_length = right_context_length - if subsampling_factor != 4: - raise NotImplementedError("Support only 'subsampling_factor=4'.") - if chunk_length % 4 != 0: - raise NotImplementedError("chunk_length must be a mutiple of 4.") - if left_context_length != 0 and left_context_length % 4 != 0: - raise NotImplementedError( - "left_context_length must be 0 or a mutiple of 4." - ) - if right_context_length != 0 and right_context_length % 4 != 0: - raise NotImplementedError( - "right_context_length must be 0 or a mutiple of 4." - ) - - # self.encoder_embed converts the input of shape (N, T, num_features) - # to the shape (N, T//subsampling_factor, d_model). - # That is, it does two things simultaneously: - # (1) subsampling: T -> T//subsampling_factor - # (2) embedding: num_features -> d_model - if vgg_frontend: - self.encoder_embed = VggSubsampling(num_features, d_model) - else: - self.encoder_embed = Conv2dSubsampling(num_features, d_model) - - self.encoder = EmformerEncoder( - chunk_length // 4, - d_model, - nhead, - dim_feedforward, - num_encoder_layers, - dropout, - cnn_module_kernel, - left_context_length=left_context_length // 4, - right_context_length=right_context_length // 4, - max_memory_size=max_memory_size, - tanh_on_mem=tanh_on_mem, - negative_inf=negative_inf, - causal=causal, - ) - - # TODO(fangjun): remove dropout - self.encoder_output_layer = nn.Sequential( - nn.Dropout(p=dropout), nn.Linear(d_model, output_dim) - ) - - def forward( - self, x: torch.Tensor, x_lens: torch.Tensor - ) -> Tuple[torch.Tensor, torch.Tensor]: - """Forward pass for training and non-streaming inference. - - B: batch size; - D: feature dimension; - T: length of utterance. - - Args: - x (torch.Tensor): - Utterance frames right-padded with right context frames, - with shape (B, T, D). - x_lens (torch.Tensor): - With shape (B,) and i-th element representing number of valid - utterance frames for i-th batch element in x, containing the - right_context at the end. - - Returns: - (Tensor, Tensor): - - output logits, with shape (B, T', D), where - T' = ((T - 1) // 2 - 1) // 2 - self.right_context_length // 4. - - logits lengths, with shape (B,), without containing the - right_context at the end. - """ - x = self.encoder_embed(x) - x = x.permute(1, 0, 2) # (N, T, C) -> (T, N, C) - - # Caution: We assume the subsampling factor is 4! - with warnings.catch_warnings(): - warnings.simplefilter("ignore") - x_lens = ((x_lens - 1) // 2 - 1) // 2 - assert x.size(0) == x_lens.max().item() - - output, output_lengths = self.encoder(x, x_lens) # (T, N, C) - - logits = self.encoder_output_layer(output) - logits = logits.permute(1, 0, 2) # (T, N, C) ->(N, T, C) - - return logits, output_lengths - - @torch.jit.export - def infer( - self, - x: torch.Tensor, - x_lens: torch.Tensor, - states: Optional[List[List[torch.Tensor]]] = None, - conv_caches: Optional[List[torch.Tensor]] = None, - ) -> Tuple[torch.Tensor, torch.Tensor, List[List[torch.Tensor]]]: - """Forward pass for streaming inference. - - B: batch size; - D: feature dimension; - T: length of utterance. - - Args: - x (torch.Tensor): - Utterance frames right-padded with right context frames, - with shape (B, T, D). - lengths (torch.Tensor): - With shape (B,) and i-th element representing number of valid - utterance frames for i-th batch element in x, containing the - right_context at the end. - states (List[List[torch.Tensor]], optional): - Cached states from proceeding chunk's computation, where each - element (List[torch.Tensor]) corresponds to each emformer layer. - (default: None) - conv_caches (List[torch.Tensor], optional): - Cached tensors of left context for causal convolution, where each - element (Tensor) corresponds to each convolutional layer. - Returns: - (Tensor, Tensor): - - output logits, with shape (B, T', D), where - T' = ((T - 1) // 2 - 1) // 2 - self.right_context_length // 4. - - logits lengths, with shape (B,), without containing the - right_context at the end. - - updated states from current chunk's computation. - - updated convolution caches from current chunk. - """ - x = self.encoder_embed(x) - x = x.permute(1, 0, 2) # (N, T, C) -> (T, N, C) - - # Caution: We assume the subsampling factor is 4! - with warnings.catch_warnings(): - warnings.simplefilter("ignore") - x_lens = ((x_lens - 1) // 2 - 1) // 2 - assert x.size(0) == x_lens.max().item() - - ( - output, - output_lengths, - output_states, - output_conv_caches, - ) = self.encoder.infer(x, x_lens, states, conv_caches) - - logits = self.encoder_output_layer(output) - logits = logits.permute(1, 0, 2) # (T, N, C) ->(N, T, C) - - return logits, output_lengths, output_states, output_conv_caches - - -class ConvolutionModule(nn.Module): - """ConvolutionModule in Conformer model. - Modified from https://github.com/espnet/espnet/blob/master/espnet/nets/pytorch_backend/conformer/convolution.py # noqa - - Args: - channels (int): - The number of channels of conv layers. - kernel_size (int): - Kernerl size of conv layers. - bias (bool): - Whether to use bias in conv layers (default=True). - causal (bool): - Whether use causal convolution (default=False). - """ - - def __init__( - self, - channels: int, - kernel_size: int, - bias: bool = True, - causal: bool = True, - ) -> None: - """Construct an ConvolutionModule object.""" - super(ConvolutionModule, self).__init__() - # kernerl_size should be a odd number for 'SAME' padding - assert (kernel_size - 1) % 2 == 0 - - self.pointwise_conv1 = nn.Conv1d( - channels, - 2 * channels, - kernel_size=1, - stride=1, - padding=0, - bias=bias, - ) - - # from https://github.com/wenet-e2e/wenet/blob/main/wenet/transformer/convolution.py # noqa - if causal: - self.left_padding = kernel_size - 1 - padding = 0 - else: - self.left_padding = 0 - padding = (kernel_size - 1) // 2 - self.depthwise_conv = nn.Conv1d( - channels, - channels, - kernel_size, - stride=1, - padding=padding, - groups=channels, - bias=bias, - ) - self.norm = nn.LayerNorm(channels) - self.pointwise_conv2 = nn.Conv1d( - channels, - channels, - kernel_size=1, - stride=1, - padding=0, - bias=bias, - ) - self.activation = Swish() - - def forward( - self, - x: torch.Tensor, - cache: Optional[torch.Tensor] = None, - ) -> torch.Tensor: - """Compute convolution module. - - Args: - x (torch.Tensor): - Input tensor (#time, batch, channels). - cache (torch.Tensor, optional): - Cached tensor for left padding (#batch, channels, cache_time). - Returns: - A tuple of 2 tensors: - - output tensor (#time, batch, channels). - - updated cache tensor (#batch, channels, cache_time). - """ - # exchange the temporal dimension and the feature dimension - x = x.permute(1, 2, 0) # (#batch, channels, time). - - # 1D Depthwise Conv - if self.left_padding > 0: - # manualy padding self.lorder zeros to the left - # make depthwise_conv causal - if cache is None: - x = nn.functional.pad( - x, (self.left_padding, 0), "constant", 0.0 - ) - else: - assert cache.size(0) == x.size(0) # equal batch - assert cache.size(1) == x.size(1) # equal channel - assert cache.size(2) == self.left_padding - x = torch.cat([cache, x], dim=2) - new_cache = x[:, :, x.size(2) - self.left_padding :] # noqa - else: - # It's better we just return None if no cache is requried, - # However, for JIT export, here we just fake one tensor instead of - # None. - new_cache = None - - # GLU mechanism - x = self.pointwise_conv1(x) # (batch, 2*channels, time) - x = nn.functional.glu(x, dim=1) # (batch, channels, time) - - x = self.depthwise_conv(x) - # x is (batch, channels, time) - x = x.permute(0, 2, 1) - x = self.norm(x) - x = x.permute(0, 2, 1) - - x = self.activation(x) - - x = self.pointwise_conv2(x) # (batch, channel, time) - - return x.permute(2, 0, 1), new_cache - - -class RelPositionalEncoding(torch.nn.Module): - """Relative positional encoding module. - - See : Appendix B in "Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context" # noqa - Modified from https://github.com/espnet/espnet/blob/master/espnet/nets/pytorch_backend/transformer/embedding.py # noqa - - Args: - d_model: Embedding dimension. - dropout_rate: Dropout rate. - max_len: Maximum input length. - - """ - - def __init__( - self, d_model: int, dropout_rate: float, max_len: int = 5000 - ) -> None: - """Construct an PositionalEncoding object.""" - super(RelPositionalEncoding, self).__init__() - self.d_model = d_model - self.xscale = math.sqrt(self.d_model) - self.dropout = torch.nn.Dropout(p=dropout_rate) - self.pe = None - self.pos_len = max_len - self.neg_len = max_len - self.gen_pe() - - def gen_pe(self) -> None: - """Generate the positional encodings.""" - # Suppose `i` means to the position of query vecotr and `j` means the - # position of key vector. We use position relative positions when keys - # are to the left (i>j) and negative relative positions otherwise (i torch.Tensor: - """Get positional encoding given positive length and negative length.""" - if self.pe_positive.dtype != dtype or str( - self.pe_positive.device - ) != str(device): - self.pe_positive = self.pe_positive.to(dtype=dtype, device=device) - if self.pe_negative.dtype != dtype or str( - self.pe_negative.device - ) != str(device): - self.pe_negative = self.pe_negative.to(dtype=dtype, device=device) - pe = torch.cat( - [ - self.pe_positive[self.pos_len - pos_len :], - self.pe_negative[1:neg_len], - ], - dim=0, - ) - return pe - - def forward( - self, - x: torch.Tensor, - pos_len: int, - neg_len: int, - ) -> Tuple[torch.Tensor, torch.Tensor]: - """Add positional encoding. - - Args: - x (torch.Tensor): Input tensor (batch, time, `*`). - - Returns: - torch.Tensor: Encoded tensor (batch, time, `*`). - torch.Tensor: Encoded tensor (batch, 2*time-1, `*`). - - """ - x = x * self.xscale - if pos_len > self.pos_len or neg_len > self.neg_len: - self.pos_len = pos_len - self.neg_len = neg_len - self.gen_pe() - pos_emb = self.get_pe(pos_len, neg_len, x.device, x.dtype) - return self.dropout(x), self.dropout(pos_emb) - - -class Swish(torch.nn.Module): - """Construct an Swish object.""" - - def forward(self, x: torch.Tensor) -> torch.Tensor: - """Return Swich activation function.""" - return x * torch.sigmoid(x) diff --git a/egs/librispeech/ASR/conv_emformer_transducer/encoder_interface.py b/egs/librispeech/ASR/conv_emformer_transducer/encoder_interface.py deleted file mode 120000 index aa5d0217a..000000000 --- a/egs/librispeech/ASR/conv_emformer_transducer/encoder_interface.py +++ /dev/null @@ -1 +0,0 @@ -../transducer_stateless/encoder_interface.py \ No newline at end of file diff --git a/egs/librispeech/ASR/conv_emformer_transducer/joiner.py b/egs/librispeech/ASR/conv_emformer_transducer/joiner.py deleted file mode 120000 index 81ad47c55..000000000 --- a/egs/librispeech/ASR/conv_emformer_transducer/joiner.py +++ /dev/null @@ -1 +0,0 @@ -../pruned_transducer_stateless/joiner.py \ No newline at end of file diff --git a/egs/librispeech/ASR/conv_emformer_transducer/model.py b/egs/librispeech/ASR/conv_emformer_transducer/model.py deleted file mode 120000 index a61a0a23f..000000000 --- a/egs/librispeech/ASR/conv_emformer_transducer/model.py +++ /dev/null @@ -1 +0,0 @@ -../pruned_transducer_stateless/model.py \ No newline at end of file diff --git a/egs/librispeech/ASR/conv_emformer_transducer/noam.py b/egs/librispeech/ASR/conv_emformer_transducer/noam.py deleted file mode 100644 index e46bf35fb..000000000 --- a/egs/librispeech/ASR/conv_emformer_transducer/noam.py +++ /dev/null @@ -1,104 +0,0 @@ -# Copyright 2021 University of Chinese Academy of Sciences (author: Han Zhu) -# -# 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 - - -class Noam(object): - """ - Implements Noam optimizer. - - Proposed in - "Attention Is All You Need", https://arxiv.org/pdf/1706.03762.pdf - - Modified from - https://github.com/espnet/espnet/blob/master/espnet/nets/pytorch_backend/transformer/optimizer.py # noqa - - Args: - params: - iterable of parameters to optimize or dicts defining parameter groups - model_size: - attention dimension of the transformer model - factor: - learning rate factor - warm_step: - warmup steps - """ - - def __init__( - self, - params, - model_size: int = 256, - factor: float = 10.0, - warm_step: int = 25000, - weight_decay=0, - ) -> None: - """Construct an Noam object.""" - self.optimizer = torch.optim.Adam( - params, lr=0, betas=(0.9, 0.98), eps=1e-9, weight_decay=weight_decay - ) - self._step = 0 - self.warmup = warm_step - self.factor = factor - self.model_size = model_size - self._rate = 0 - - @property - def param_groups(self): - """Return param_groups.""" - return self.optimizer.param_groups - - def step(self): - """Update parameters and rate.""" - self._step += 1 - rate = self.rate() - for p in self.optimizer.param_groups: - p["lr"] = rate - self._rate = rate - self.optimizer.step() - - def rate(self, step=None): - """Implement `lrate` above.""" - if step is None: - step = self._step - return ( - self.factor - * self.model_size ** (-0.5) - * min(step ** (-0.5), step * self.warmup ** (-1.5)) - ) - - def zero_grad(self): - """Reset gradient.""" - self.optimizer.zero_grad() - - def state_dict(self): - """Return state_dict.""" - return { - "_step": self._step, - "warmup": self.warmup, - "factor": self.factor, - "model_size": self.model_size, - "_rate": self._rate, - "optimizer": self.optimizer.state_dict(), - } - - def load_state_dict(self, state_dict): - """Load state_dict.""" - for key, value in state_dict.items(): - if key == "optimizer": - self.optimizer.load_state_dict(state_dict["optimizer"]) - else: - setattr(self, key, value) diff --git a/egs/librispeech/ASR/conv_emformer_transducer/subsampling.py b/egs/librispeech/ASR/conv_emformer_transducer/subsampling.py deleted file mode 120000 index 6fee09e58..000000000 --- a/egs/librispeech/ASR/conv_emformer_transducer/subsampling.py +++ /dev/null @@ -1 +0,0 @@ -../conformer_ctc/subsampling.py \ No newline at end of file diff --git a/egs/librispeech/ASR/conv_emformer_transducer/test_emformer.py b/egs/librispeech/ASR/conv_emformer_transducer/test_emformer.py deleted file mode 100644 index 971abca97..000000000 --- a/egs/librispeech/ASR/conv_emformer_transducer/test_emformer.py +++ /dev/null @@ -1,590 +0,0 @@ -import torch - - -def test_emformer_attention_forward(): - from emformer import EmformerAttention - - B, D = 2, 256 - U, R = 12, 2 - chunk_length = 2 - attention = EmformerAttention(embed_dim=D, nhead=8) - - for use_memory in [True, False]: - if use_memory: - S = U // chunk_length - M = S - 1 - else: - S, M = 0, 0 - - Q, KV = R + U + S, M + R + U - utterance = torch.randn(U, B, D) - lengths = torch.randint(1, U + 1, (B,)) - lengths[0] = U - right_context = torch.randn(R, B, D) - summary = torch.randn(S, B, D) - memory = torch.randn(M, B, D) - attention_mask = torch.rand(Q, KV) >= 0.5 - - output_right_context_utterance, output_memory = attention( - utterance, - lengths, - right_context, - summary, - memory, - attention_mask, - ) - assert output_right_context_utterance.shape == (R + U, B, D) - assert output_memory.shape == (M, B, D) - - -def test_emformer_attention_infer(): - from emformer import EmformerAttention - - B, D = 2, 256 - R, L = 4, 2 - chunk_length = 2 - U = chunk_length - attention = EmformerAttention(embed_dim=D, nhead=8) - - for use_memory in [True, False]: - if use_memory: - S, M = 1, 3 - else: - S, M = 0, 0 - - utterance = torch.randn(U, B, D) - lengths = torch.randint(1, U + 1, (B,)) - lengths[0] = U - right_context = torch.randn(R, B, D) - summary = torch.randn(S, B, D) - memory = torch.randn(M, B, D) - left_context_key = torch.randn(L, B, D) - left_context_val = torch.randn(L, B, D) - - ( - output_right_context_utterance, - output_memory, - next_key, - next_val, - ) = attention.infer( - utterance, - lengths, - right_context, - summary, - memory, - left_context_key, - left_context_val, - ) - assert output_right_context_utterance.shape == (R + U, B, D) - assert output_memory.shape == (S, B, D) - assert next_key.shape == (L + U, B, D) - assert next_val.shape == (L + U, B, D) - - -def test_emformer_layer_forward(): - from emformer import EmformerLayer - - B, D = 2, 256 - U, R, L = 12, 2, 5 - chunk_length = 2 - - for use_memory in [True, False]: - if use_memory: - S = U // chunk_length - M = S - 1 - else: - S, M = 0, 0 - - layer = EmformerLayer( - d_model=D, - nhead=8, - dim_feedforward=1024, - chunk_length=chunk_length, - cnn_module_kernel=3, - left_context_length=L, - max_memory_size=M, - causal=True, - ) - - Q, KV = R + U + S, M + R + U - utterance = torch.randn(U, B, D) - lengths = torch.randint(1, U + 1, (B,)) - lengths[0] = U - right_context = torch.randn(R, B, D) - memory = torch.randn(M, B, D) - attention_mask = torch.rand(Q, KV) >= 0.5 - - output_utterance, output_right_context, output_memory = layer( - utterance, - lengths, - right_context, - memory, - attention_mask, - ) - assert output_utterance.shape == (U, B, D) - assert output_right_context.shape == (R, B, D) - assert output_memory.shape == (M, B, D) - - -def test_emformer_layer_infer(): - from emformer import EmformerLayer - - B, D = 2, 256 - R, L = 2, 5 - chunk_length = 2 - U = chunk_length - K = 3 - - for use_memory in [True, False]: - if use_memory: - M = 3 - else: - M = 0 - - layer = EmformerLayer( - d_model=D, - nhead=8, - dim_feedforward=1024, - chunk_length=chunk_length, - cnn_module_kernel=K, - left_context_length=L, - max_memory_size=M, - causal=True, - ) - - utterance = torch.randn(U, B, D) - lengths = torch.randint(1, U + 1, (B,)) - lengths[0] = U - right_context = torch.randn(R, B, D) - memory = torch.randn(M, B, D) - state = None - conv_cache = None - ( - output_utterance, - output_right_context, - output_memory, - output_state, - output_conv_cache, - ) = layer.infer( - utterance, lengths, right_context, memory, state, conv_cache - ) - assert output_utterance.shape == (U, B, D) - assert output_right_context.shape == (R, B, D) - if use_memory: - assert output_memory.shape == (1, B, D) - else: - assert output_memory.shape == (0, B, D) - assert len(output_state) == 4 - assert output_state[0].shape == (M, B, D) - assert output_state[1].shape == (L, B, D) - assert output_state[2].shape == (L, B, D) - assert output_state[3].shape == (1, B) - assert output_conv_cache.shape == (B, D, K - 1) - - -def test_emformer_encoder_forward(): - from emformer import EmformerEncoder - - B, D = 2, 256 - U, R, L = 12, 2, 5 - chunk_length = 2 - - for use_memory in [True, False]: - if use_memory: - S = U // chunk_length - M = S - 1 - else: - S, M = 0, 0 - - encoder = EmformerEncoder( - chunk_length=chunk_length, - d_model=D, - dim_feedforward=1024, - num_encoder_layers=2, - cnn_module_kernel=3, - left_context_length=L, - right_context_length=R, - max_memory_size=M, - causal=True, - ) - - x = torch.randn(U + R, B, D) - lengths = torch.randint(1, U + R + 1, (B,)) - lengths[0] = U + R - - output, output_lengths = encoder(x, lengths) - assert output.shape == (U, B, D) - assert torch.equal(output_lengths, torch.clamp(lengths - R, min=0)) - - -def test_emformer_encoder_infer(): - from emformer import EmformerEncoder - - B, D = 2, 256 - R, L = 2, 5 - chunk_length = 2 - U = chunk_length - num_chunks = 3 - num_encoder_layers = 2 - K = 3 - - for use_memory in [True, False]: - if use_memory: - M = 3 - else: - M = 0 - - encoder = EmformerEncoder( - chunk_length=chunk_length, - d_model=D, - dim_feedforward=1024, - num_encoder_layers=num_encoder_layers, - cnn_module_kernel=K, - left_context_length=L, - right_context_length=R, - max_memory_size=M, - causal=True, - ) - - states = None - conv_caches = None - for chunk_idx in range(num_chunks): - x = torch.randn(U + R, B, D) - lengths = torch.randint(1, U + R + 1, (B,)) - lengths[0] = U + R - output, output_lengths, states, conv_caches = encoder.infer( - x, lengths, states, conv_caches - ) - assert output.shape == (U, B, D) - assert torch.equal(output_lengths, torch.clamp(lengths - R, min=0)) - assert len(states) == num_encoder_layers - for state in states: - assert len(state) == 4 - assert state[0].shape == (M, B, D) - assert state[1].shape == (L, B, D) - assert state[2].shape == (L, B, D) - assert torch.equal( - state[3], (chunk_idx + 1) * U * torch.ones_like(state[3]) - ) - for conv_cache in conv_caches: - assert conv_cache.shape == (B, D, K - 1) - - -def test_emformer_forward(): - from emformer import Emformer - - num_features = 80 - output_dim = 1000 - chunk_length = 8 - L, R = 128, 4 - B, D, U = 2, 256, 80 - for use_memory in [True, False]: - if use_memory: - M = 3 - else: - M = 0 - model = Emformer( - num_features=num_features, - output_dim=output_dim, - chunk_length=chunk_length, - subsampling_factor=4, - d_model=D, - cnn_module_kernel=3, - left_context_length=L, - right_context_length=R, - max_memory_size=M, - vgg_frontend=False, - causal=True, - ) - x = torch.randn(B, U + R + 3, num_features) - x_lens = torch.randint(1, U + R + 3 + 1, (B,)) - x_lens[0] = U + R + 3 - logits, output_lengths = model(x, x_lens) - assert logits.shape == (B, U // 4, output_dim) - assert torch.equal( - output_lengths, - torch.clamp(((x_lens - 1) // 2 - 1) // 2 - R // 4, min=0), - ) - - -def test_emformer_infer(): - from emformer import Emformer - - num_features = 80 - output_dim = 1000 - chunk_length = 8 - U = chunk_length - L, R = 128, 4 - B, D = 2, 256 - num_chunks = 3 - num_encoder_layers = 2 - K = 3 - for use_memory in [True, False]: - if use_memory: - M = 3 - else: - M = 0 - model = Emformer( - num_features=num_features, - output_dim=output_dim, - chunk_length=chunk_length, - subsampling_factor=4, - d_model=D, - num_encoder_layers=num_encoder_layers, - cnn_module_kernel=K, - left_context_length=L, - right_context_length=R, - max_memory_size=M, - vgg_frontend=False, - causal=True, - ) - states = None - conv_caches = None - for chunk_idx in range(num_chunks): - x = torch.randn(B, U + R + 3, num_features) - x_lens = torch.randint(1, U + R + 3 + 1, (B,)) - x_lens[0] = U + R + 3 - logits, output_lengths, states, conv_caches = model.infer( - x, x_lens, states, conv_caches - ) - assert logits.shape == (B, U // 4, output_dim) - assert torch.equal( - output_lengths, - torch.clamp(((x_lens - 1) // 2 - 1) // 2 - R // 4, min=0), - ) - assert len(states) == num_encoder_layers - for state in states: - assert len(state) == 4 - assert state[0].shape == (M, B, D) - assert state[1].shape == (L // 4, B, D) - assert state[2].shape == (L // 4, B, D) - assert torch.equal( - state[3], - U // 4 * (chunk_idx + 1) * torch.ones_like(state[3]), - ) - for conv_cache in conv_caches: - assert conv_cache.shape == (B, D, K - 1) - - -def test_emformer_encoder_layer_forward_infer_consistency(): - from emformer import EmformerEncoder - - chunk_length = 4 - num_chunks = 3 - U = chunk_length * num_chunks - L, R = 1, 2 - D = 256 - num_encoder_layers = 1 - memory_sizes = [0, 3] - K = 3 - - for M in memory_sizes: - encoder = EmformerEncoder( - chunk_length=chunk_length, - d_model=D, - dim_feedforward=1024, - num_encoder_layers=num_encoder_layers, - left_context_length=L, - right_context_length=R, - max_memory_size=M, - dropout=0.1, - cnn_module_kernel=K, - causal=True, - ) - encoder.eval() - encoder_layer = encoder.emformer_layers[0] - - x = torch.randn(U + R, 1, D) - lengths = torch.tensor([U]) - right_context = encoder._gen_right_context(x) - utterance = x[: x.size(0) - R] - attention_mask = encoder._gen_attention_mask(utterance) - memory = ( - encoder.init_memory_op(utterance.permute(1, 2, 0)).permute(2, 0, 1)[ - :-1 - ] - if encoder.use_memory - else torch.empty(0).to(dtype=x.dtype, device=x.device) - ) - ( - forward_output_utterance, - forward_output_right_context, - forward_output_memory, - ) = encoder_layer( - utterance, - lengths, - right_context, - memory, - attention_mask, - ) - - state = None - conv_cache = None - for chunk_idx in range(num_chunks): - start_idx = chunk_idx * chunk_length - end_idx = start_idx + chunk_length - chunk = x[start_idx:end_idx] - chunk_right_context = x[end_idx : end_idx + R] # noqa - chunk_length = torch.tensor([chunk_length]) - chunk_memory = ( - encoder.init_memory_op(chunk.permute(1, 2, 0)).permute(2, 0, 1) - if encoder.use_memory - else torch.empty(0).to(dtype=x.dtype, device=x.device) - ) - ( - infer_output_chunk, - infer_right_context, - infer_output_memory, - state, - conv_cache, - ) = encoder_layer.infer( - chunk, - chunk_length, - chunk_right_context, - chunk_memory, - state, - conv_cache, - ) - forward_output_chunk = forward_output_utterance[start_idx:end_idx] - assert torch.allclose( - infer_output_chunk, - forward_output_chunk, - atol=1e-5, - rtol=0.0, - ) - - -def test_emformer_encoder_forward_infer_consistency(): - from emformer import EmformerEncoder - - chunk_length = 4 - num_chunks = 3 - U = chunk_length * num_chunks - L, R = 1, 2 - D = 256 - num_encoder_layers = 3 - K = 3 - memory_sizes = [0, 3] - - for M in memory_sizes: - encoder = EmformerEncoder( - chunk_length=chunk_length, - d_model=D, - dim_feedforward=1024, - num_encoder_layers=num_encoder_layers, - left_context_length=L, - right_context_length=R, - max_memory_size=M, - dropout=0.1, - cnn_module_kernel=K, - causal=True, - ) - encoder.eval() - - x = torch.randn(U + R, 1, D) - lengths = torch.tensor([U + R]) - - forward_output, forward_output_lengths = encoder(x, lengths) - - states = None - conv_caches = None - for chunk_idx in range(num_chunks): - start_idx = chunk_idx * chunk_length - end_idx = start_idx + chunk_length - chunk = x[start_idx : end_idx + R] # noqa - chunk_right_context = x[end_idx : end_idx + R] # noqa - chunk_length = torch.tensor([chunk_length]) - ( - infer_output_chunk, - infer_output_lengths, - states, - conv_caches, - ) = encoder.infer( - chunk, - chunk_length, - states, - conv_caches, - ) - forward_output_chunk = forward_output[start_idx:end_idx] - assert torch.allclose( - infer_output_chunk, - forward_output_chunk, - atol=1e-5, - rtol=0.0, - ) - - -def test_emformer_forward_infer_consistency(): - from emformer import Emformer - - num_features = 80 - output_dim = 1000 - chunk_length = 8 - num_chunks = 3 - U = chunk_length * num_chunks - L, R = 128, 4 - D = 256 - num_encoder_layers = 2 - K = 3 - memory_sizes = [0, 3] - - for M in memory_sizes: - model = Emformer( - num_features=num_features, - output_dim=output_dim, - chunk_length=chunk_length, - subsampling_factor=4, - d_model=D, - num_encoder_layers=num_encoder_layers, - cnn_module_kernel=K, - left_context_length=L, - right_context_length=R, - max_memory_size=M, - dropout=0.1, - vgg_frontend=False, - causal=True, - ) - model.eval() - - x = torch.randn(1, U + R + 3, num_features) - x_lens = torch.tensor([x.size(1)]) - - # forward mode - forward_logits, _ = model(x, x_lens) - - states = None - conv_caches = None - for chunk_idx in range(num_chunks): - start_idx = chunk_idx * chunk_length - end_idx = start_idx + chunk_length - chunk = x[:, start_idx : end_idx + R + 3] # noqa - lengths = torch.tensor([chunk.size(1)]) - ( - infer_chunk_logits, - output_lengths, - states, - conv_caches, - ) = model.infer(chunk, lengths, states, conv_caches) - forward_chunk_logits = forward_logits[ - :, start_idx // 4 : end_idx // 4 # noqa - ] - assert torch.allclose( - infer_chunk_logits, - forward_chunk_logits, - atol=1e-5, - rtol=0.0, - ) - - -if __name__ == "__main__": - test_emformer_attention_forward() - test_emformer_attention_infer() - test_emformer_layer_forward() - test_emformer_layer_infer() - test_emformer_encoder_forward() - test_emformer_encoder_infer() - test_emformer_forward() - test_emformer_infer() - test_emformer_encoder_layer_forward_infer_consistency() - test_emformer_encoder_forward_infer_consistency() - test_emformer_forward_infer_consistency() diff --git a/egs/librispeech/ASR/conv_emformer_transducer/train.py b/egs/librispeech/ASR/conv_emformer_transducer/train.py deleted file mode 100755 index 5152be1a1..000000000 --- a/egs/librispeech/ASR/conv_emformer_transducer/train.py +++ /dev/null @@ -1,1016 +0,0 @@ -#!/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" - -./transducer_emformer/train.py \ - --world-size 4 \ - --num-epochs 30 \ - --start-epoch 0 \ - --exp-dir transducer_emformer/exp \ - --full-libri 1 \ - --max-duration 300 -""" - - -import argparse -import logging -import warnings -from pathlib import Path -from shutil import copyfile -from typing import Any, Dict, Optional, Tuple - -import k2 -import sentencepiece as spm -import torch -import torch.multiprocessing as mp -import torch.nn as nn -from asr_datamodule import LibriSpeechAsrDataModule -from decoder import Decoder -from emformer import Emformer -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 noam import Noam -from torch import Tensor -from torch.nn.parallel import DistributedDataParallel as DDP -from torch.nn.utils import clip_grad_norm_ -from torch.utils.tensorboard import SummaryWriter - -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, - measure_gradient_norms, - measure_weight_norms, - optim_step_and_measure_param_change, - setup_logger, - str2bool, -) - - -def add_model_arguments(parser: argparse.ArgumentParser): - parser.add_argument( - "--attention-dim", - type=int, - default=512, - help="Attention dim for the Emformer", - ) - - parser.add_argument( - "--nhead", - type=int, - default=8, - help="Number of attention heads for the Emformer", - ) - - parser.add_argument( - "--dim-feedforward", - type=int, - default=2048, - help="Feed-forward dimension for the Emformer", - ) - - parser.add_argument( - "--num-encoder-layers", - type=int, - default=12, - help="Number of encoder layers for the Emformer", - ) - - parser.add_argument( - "--cnn-module-kernel", - type=int, - default=3, - help="Kernel size for the convolution module.", - ) - - parser.add_argument( - "--left-context-length", - type=int, - default=120, - help="Number of frames for the left context in the Emformer", - ) - - parser.add_argument( - "--chunk-length", - type=int, - default=16, - help="Number of frames for each segment in the Emformer", - ) - - parser.add_argument( - "--right-context-length", - type=int, - default=4, - help="Number of frames for right context in the Emformer", - ) - - parser.add_argument( - "--memory-size", - type=int, - default=0, - help="Number of entries in the memory for the Emformer", - ) - - parser.add_argument( - "--causal-conv", - type=str2bool, - default=True, - help="Whether use causal convolution.", - ) - - -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_emformer/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="transducer_emformer/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( - "--lr-factor", - type=float, - default=5.0, - help="The lr_factor for Noam optimizer", - ) - - 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( - "--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`. - """, - ) - - add_model_arguments(parser) - - 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. - - - attention_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 - "log_diagnostics": False, - # parameters for Emformer - "feature_dim": 80, - "subsampling_factor": 4, - "vgg_frontend": False, - # parameters for decoder - "embedding_dim": 512, - # parameters for Noam - "warm_step": 80000, # For the 100h subset, use 20000 - "env_info": get_env_info(), - } - ) - - return params - - -def get_encoder_model(params: AttributeDict) -> nn.Module: - encoder = Emformer( - num_features=params.feature_dim, - output_dim=params.vocab_size, - subsampling_factor=params.subsampling_factor, - d_model=params.attention_dim, - nhead=params.nhead, - dim_feedforward=params.dim_feedforward, - num_encoder_layers=params.num_encoder_layers, - cnn_module_kernel=params.cnn_module_kernel, - vgg_frontend=params.vgg_frontend, - left_context_length=params.left_context_length, - chunk_length=params.chunk_length, - right_context_length=params.right_context_length, - max_memory_size=params.memory_size, - causal=params.causal_conv, - ) - return encoder - - -def get_decoder_model(params: AttributeDict) -> nn.Module: - decoder = Decoder( - vocab_size=params.vocab_size, - embedding_dim=params.embedding_dim, - blank_id=params.blank_id, - unk_id=params.unk_id, - context_size=params.context_size, - ) - return decoder - - -def get_joiner_model(params: AttributeDict) -> nn.Module: - joiner = Joiner( - input_dim=params.vocab_size, - inner_dim=params.embedding_dim, - output_dim=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, - ) - return model - - -def load_checkpoint_if_available( - params: AttributeDict, - model: nn.Module, - optimizer: Optional[torch.optim.Optimizer] = 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. - 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, - ) - - 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, - sampler: Optional[CutSampler] = 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. - """ - 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, - sampler=sampler, - 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, -) -> 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 Emformer 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. - """ - 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, - ) - loss = params.simple_loss_scale * simple_loss + 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, - sp: spm.SentencePieceProcessor, - train_dl: torch.utils.data.DataLoader, - valid_dl: torch.utils.data.DataLoader, - 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. - train_dl: - Dataloader for the training dataset. - valid_dl: - Dataloader for the validation dataset. - 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() - - def maybe_log_gradients(tag: str): - if ( - params.log_diagnostics - and tb_writer is not None - and params.batch_idx_train % (params.log_interval * 5) == 0 - ): - tb_writer.add_scalars( - tag, - measure_gradient_norms(model, norm="l2"), - global_step=params.batch_idx_train, - ) - - def maybe_log_weights(tag: str): - if ( - params.log_diagnostics - and tb_writer is not None - and params.batch_idx_train % (params.log_interval * 5) == 0 - ): - tb_writer.add_scalars( - tag, - measure_weight_norms(model, norm="l2"), - global_step=params.batch_idx_train, - ) - - def maybe_log_param_relative_changes(): - if ( - params.log_diagnostics - and tb_writer is not None - and params.batch_idx_train % (params.log_interval * 5) == 0 - ): - deltas = optim_step_and_measure_param_change(model, optimizer) - tb_writer.add_scalars( - "train/relative_param_change_per_minibatch", - deltas, - global_step=params.batch_idx_train, - ) - else: - optimizer.step() - - 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"]) - - 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. - - loss.backward() - - maybe_log_weights("train/param_norms") - maybe_log_gradients("train/grad_norms") - maybe_log_param_relative_changes() - - optimizer.zero_grad() - - 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, - sampler=train_dl.sampler, - 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: - logging.info( - f"Epoch {params.cur_epoch}, " - f"batch {batch_idx}, loss[{loss_info}], " - f"tot_loss[{tot_loss}], batch size: {batch_size}" - ) - - if tb_writer is not None: - 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 = 800 - params.warm_step = 20000 - - 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.unk_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 = Noam( - model.parameters(), - model_size=params.attention_dim, - factor=params.lr_factor, - warm_step=params.warm_step, - ) - - if checkpoints and "optimizer" in checkpoints: - logging.info("Loading optimizer state dict") - optimizer.load_state_dict(checkpoints["optimizer"]) - - 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 - - num_in_total = len(train_cuts) - - train_cuts = train_cuts.filter(remove_short_and_long_utt) - - num_left = len(train_cuts) - num_removed = num_in_total - num_left - removed_percent = num_removed / num_in_total * 100 - - logging.info(f"Before removing short and long utterances: {num_in_total}") - logging.info(f"After removing short and long utterances: {num_left}") - logging.info(f"Removed {num_removed} utterances ({removed_percent:.5f}%)") - - 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) - - scan_pessimistic_batches_for_oom( - model=model, - train_dl=train_dl, - optimizer=optimizer, - sp=sp, - params=params, - ) - - for epoch in range(params.start_epoch, params.num_epochs): - fix_random_seed(params.seed + epoch) - train_dl.sampler.set_epoch(epoch) - - cur_lr = optimizer._rate - if tb_writer is not None: - tb_writer.add_scalar( - "train/learning_rate", cur_lr, params.batch_idx_train - ) - tb_writer.add_scalar("train/epoch", epoch, params.batch_idx_train) - - if rank == 0: - logging.info("epoch {}, learning rate {}".format(epoch, cur_lr)) - - params.cur_epoch = epoch - - train_one_epoch( - params=params, - model=model, - optimizer=optimizer, - sp=sp, - train_dl=train_dl, - valid_dl=valid_dl, - tb_writer=tb_writer, - world_size=world_size, - rank=rank, - ) - - save_checkpoint( - params=params, - model=model, - optimizer=optimizer, - sampler=train_dl.sampler, - 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: - optimizer.zero_grad() - loss, _ = compute_loss( - params=params, - model=model, - sp=sp, - batch=batch, - is_training=True, - ) - loss.backward() - clip_grad_norm_(model.parameters(), 5.0, 2.0) - optimizer.step() - 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() diff --git a/egs/librispeech/ASR/emformer_pruned_transducer_stateless/asr_datamodule.py b/egs/librispeech/ASR/emformer_pruned_transducer_stateless/asr_datamodule.py deleted file mode 120000 index b4e5427e0..000000000 --- a/egs/librispeech/ASR/emformer_pruned_transducer_stateless/asr_datamodule.py +++ /dev/null @@ -1 +0,0 @@ -../pruned_transducer_stateless/asr_datamodule.py \ No newline at end of file diff --git a/egs/librispeech/ASR/emformer_pruned_transducer_stateless/beam_search.py b/egs/librispeech/ASR/emformer_pruned_transducer_stateless/beam_search.py deleted file mode 120000 index 227d2247c..000000000 --- a/egs/librispeech/ASR/emformer_pruned_transducer_stateless/beam_search.py +++ /dev/null @@ -1 +0,0 @@ -../pruned_transducer_stateless/beam_search.py \ No newline at end of file diff --git a/egs/librispeech/ASR/emformer_pruned_transducer_stateless/decode.py b/egs/librispeech/ASR/emformer_pruned_transducer_stateless/decode.py deleted file mode 100755 index 47b4f9fd0..000000000 --- a/egs/librispeech/ASR/emformer_pruned_transducer_stateless/decode.py +++ /dev/null @@ -1,550 +0,0 @@ -#!/usr/bin/env python3 -# -# Copyright 2021 Xiaomi Corporation (Author: 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. -""" -Usage: -(1) greedy search -./transducer_emformer/decode.py \ - --epoch 28 \ - --avg 15 \ - --exp-dir ./transducer_emformer/exp \ - --max-duration 100 \ - --decoding-method greedy_search - -(2) beam search -./transducer_emformer/decode.py \ - --epoch 28 \ - --avg 15 \ - --exp-dir ./transducer_emformer/exp \ - --max-duration 100 \ - --decoding-method beam_search \ - --beam-size 4 - -(3) modified beam search -./transducer_emformer/decode.py \ - --epoch 28 \ - --avg 15 \ - --exp-dir ./transducer_emformer/exp \ - --max-duration 100 \ - --decoding-method modified_beam_search \ - --beam-size 4 - -(4) fast beam search -./transducer_emformer/decode.py \ - --epoch 28 \ - --avg 15 \ - --exp-dir ./transducer_emformer/exp \ - --max-duration 1500 \ - --decoding-method fast_beam_search \ - --beam 4 \ - --max-contexts 4 \ - --max-states 8 -""" - - -import argparse -import logging -from collections import defaultdict -from pathlib import Path -from typing import Dict, List, Optional, Tuple - -import k2 -import sentencepiece as spm -import torch -import torch.nn as nn -from asr_datamodule import LibriSpeechAsrDataModule -from beam_search import ( - beam_search, - fast_beam_search, - greedy_search, - greedy_search_batch, - modified_beam_search, -) -from train import add_model_arguments, get_params, get_transducer_model - -from icefall.checkpoint import ( - average_checkpoints, - find_checkpoints, - load_checkpoint, -) -from icefall.utils import ( - AttributeDict, - setup_logger, - store_transcripts, - write_error_stats, -) - - -def get_parser(): - parser = argparse.ArgumentParser( - formatter_class=argparse.ArgumentDefaultsHelpFormatter - ) - - parser.add_argument( - "--epoch", - type=int, - default=28, - help="It specifies the checkpoint to use for decoding." - "Note: Epoch counts from 0.", - ) - parser.add_argument( - "--avg", - type=int, - default=15, - help="Number of checkpoints to average. Automatically select " - "consecutive checkpoints before the checkpoint specified by " - "'--epoch'. ", - ) - - parser.add_argument( - "--avg-last-n", - type=int, - default=0, - help="""If positive, --epoch and --avg are ignored and it - will use the last n checkpoints exp_dir/checkpoint-xxx.pt - where xxx is the number of processed batches while - saving that checkpoint. - """, - ) - - parser.add_argument( - "--exp-dir", - type=str, - default="transducer_emformer/exp", - help="The experiment dir", - ) - - parser.add_argument( - "--bpe-model", - type=str, - default="data/lang_bpe_500/bpe.model", - help="Path to the BPE model", - ) - - parser.add_argument( - "--decoding-method", - type=str, - default="greedy_search", - help="""Possible values are: - - greedy_search - - beam_search - - modified_beam_search - - fast_beam_search - """, - ) - - parser.add_argument( - "--beam-size", - type=int, - default=4, - help="""An interger indicating how many candidates we will keep for each - frame. Used only when --decoding-method is beam_search or - modified_beam_search.""", - ) - - parser.add_argument( - "--beam", - type=float, - default=4, - help="""A floating point value to calculate the cutoff score during beam - search (i.e., `cutoff = max-score - beam`), which is the same as the - `beam` in Kaldi. - Used only when --decoding-method is fast_beam_search""", - ) - - parser.add_argument( - "--max-contexts", - type=int, - default=4, - help="""Used only when --decoding-method is - fast_beam_search""", - ) - - parser.add_argument( - "--max-states", - type=int, - default=8, - help="""Used only when --decoding-method is - fast_beam_search""", - ) - - 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( - "--max-sym-per-frame", - type=int, - default=1, - help="""Maximum number of symbols per frame. - Used only when --decoding_method is greedy_search""", - ) - - add_model_arguments(parser) - - return parser - - -def decode_one_batch( - params: AttributeDict, - model: nn.Module, - sp: spm.SentencePieceProcessor, - batch: dict, - decoding_graph: Optional[k2.Fsa] = None, -) -> Dict[str, List[List[str]]]: - """Decode one batch and return the result in a dict. The dict has the - following format: - - - key: It indicates the setting used for decoding. For example, - if greedy_search is used, it would be "greedy_search" - If beam search with a beam size of 7 is used, it would be - "beam_7" - - value: It contains the decoding result. `len(value)` equals to - batch size. `value[i]` is the decoding result for the i-th - utterance in the given batch. - Args: - params: - It's the return value of :func:`get_params`. - model: - The neural model. - sp: - The BPE model. - batch: - It is the return value from iterating - `lhotse.dataset.K2SpeechRecognitionDataset`. See its documentation - for the format of the `batch`. - decoding_graph: - The decoding graph. Can be either a `k2.trivial_graph` or HLG, Used - only when --decoding_method is fast_beam_search. - Returns: - Return the decoding result. See above description for the format of - the returned dict. - """ - device = model.device - feature = batch["inputs"] - assert feature.ndim == 3 - - feature = feature.to(device) - # at entry, feature is (N, T, C) - - supervisions = batch["supervisions"] - feature_lens = supervisions["num_frames"].to(device) - - encoder_out, encoder_out_lens = model.encoder( - x=feature, x_lens=feature_lens - ) - hyps = [] - - if params.decoding_method == "fast_beam_search": - hyp_tokens = fast_beam_search( - model=model, - decoding_graph=decoding_graph, - encoder_out=encoder_out, - encoder_out_lens=encoder_out_lens, - beam=params.beam, - max_contexts=params.max_contexts, - max_states=params.max_states, - ) - for hyp in sp.decode(hyp_tokens): - hyps.append(hyp.split()) - elif ( - params.decoding_method == "greedy_search" - and params.max_sym_per_frame == 1 - ): - hyp_tokens = greedy_search_batch( - model=model, - encoder_out=encoder_out, - ) - for hyp in sp.decode(hyp_tokens): - hyps.append(hyp.split()) - elif params.decoding_method == "modified_beam_search": - hyp_tokens = modified_beam_search( - model=model, - encoder_out=encoder_out, - beam=params.beam_size, - ) - for hyp in sp.decode(hyp_tokens): - hyps.append(hyp.split()) - else: - batch_size = encoder_out.size(0) - - for i in range(batch_size): - # fmt: off - encoder_out_i = encoder_out[i:i+1, :encoder_out_lens[i]] - # fmt: on - if params.decoding_method == "greedy_search": - hyp = greedy_search( - model=model, - encoder_out=encoder_out_i, - max_sym_per_frame=params.max_sym_per_frame, - ) - elif params.decoding_method == "beam_search": - hyp = beam_search( - model=model, - encoder_out=encoder_out_i, - beam=params.beam_size, - ) - else: - raise ValueError( - f"Unsupported decoding method: {params.decoding_method}" - ) - hyps.append(sp.decode(hyp).split()) - - if params.decoding_method == "greedy_search": - return {"greedy_search": hyps} - elif params.decoding_method == "fast_beam_search": - return { - ( - f"beam_{params.beam}_" - f"max_contexts_{params.max_contexts}_" - f"max_states_{params.max_states}" - ): hyps - } - else: - return {f"beam_size_{params.beam_size}": hyps} - - -def decode_dataset( - dl: torch.utils.data.DataLoader, - params: AttributeDict, - model: nn.Module, - sp: spm.SentencePieceProcessor, - decoding_graph: Optional[k2.Fsa] = None, -) -> Dict[str, List[Tuple[List[str], List[str]]]]: - """Decode dataset. - - Args: - dl: - PyTorch's dataloader containing the dataset to decode. - params: - It is returned by :func:`get_params`. - model: - The neural model. - sp: - The BPE model. - decoding_graph: - The decoding graph. Can be either a `k2.trivial_graph` or HLG, Used - only when --decoding_method is fast_beam_search. - Returns: - Return a dict, whose key may be "greedy_search" if greedy search - is used, or it may be "beam_7" if beam size of 7 is used. - Its value is a list of tuples. Each tuple contains two elements: - The first is the reference transcript, and the second is the - predicted result. - """ - num_cuts = 0 - - try: - num_batches = len(dl) - except TypeError: - num_batches = "?" - - if params.decoding_method == "greedy_search": - log_interval = 100 - else: - log_interval = 2 - - results = defaultdict(list) - for batch_idx, batch in enumerate(dl): - texts = batch["supervisions"]["text"] - - hyps_dict = decode_one_batch( - params=params, - model=model, - sp=sp, - decoding_graph=decoding_graph, - batch=batch, - ) - - for name, hyps in hyps_dict.items(): - this_batch = [] - assert len(hyps) == len(texts) - for hyp_words, ref_text in zip(hyps, texts): - ref_words = ref_text.split() - this_batch.append((ref_words, hyp_words)) - - results[name].extend(this_batch) - - num_cuts += len(texts) - - if batch_idx % log_interval == 0: - batch_str = f"{batch_idx}/{num_batches}" - - logging.info( - f"batch {batch_str}, cuts processed until now is {num_cuts}" - ) - return results - - -def save_results( - params: AttributeDict, - test_set_name: str, - results_dict: Dict[str, List[Tuple[List[int], List[int]]]], -): - test_set_wers = dict() - for key, results in results_dict.items(): - recog_path = ( - params.res_dir / f"recogs-{test_set_name}-{key}-{params.suffix}.txt" - ) - store_transcripts(filename=recog_path, texts=results) - logging.info(f"The transcripts are stored in {recog_path}") - - # The following prints out WERs, per-word error statistics and aligned - # ref/hyp pairs. - errs_filename = ( - params.res_dir / f"errs-{test_set_name}-{key}-{params.suffix}.txt" - ) - with open(errs_filename, "w") as f: - wer = write_error_stats( - f, f"{test_set_name}-{key}", results, enable_log=True - ) - test_set_wers[key] = wer - - logging.info("Wrote detailed error stats to {}".format(errs_filename)) - - test_set_wers = sorted(test_set_wers.items(), key=lambda x: x[1]) - errs_info = ( - params.res_dir - / f"wer-summary-{test_set_name}-{key}-{params.suffix}.txt" - ) - with open(errs_info, "w") as f: - print("settings\tWER", file=f) - for key, val in test_set_wers: - print("{}\t{}".format(key, val), file=f) - - s = "\nFor {}, WER of different settings are:\n".format(test_set_name) - note = "\tbest for {}".format(test_set_name) - for key, val in test_set_wers: - s += "{}\t{}{}\n".format(key, val, note) - note = "" - logging.info(s) - - -@torch.no_grad() -def main(): - parser = get_parser() - LibriSpeechAsrDataModule.add_arguments(parser) - args = parser.parse_args() - args.exp_dir = Path(args.exp_dir) - - params = get_params() - params.update(vars(args)) - - assert params.decoding_method in ( - "greedy_search", - "beam_search", - "fast_beam_search", - "modified_beam_search", - ) - params.res_dir = params.exp_dir / params.decoding_method - - params.suffix = f"epoch-{params.epoch}-avg-{params.avg}" - if "fast_beam_search" in params.decoding_method: - params.suffix += f"-beam-{params.beam}" - params.suffix += f"-max-contexts-{params.max_contexts}" - params.suffix += f"-max-states-{params.max_states}" - elif "beam_search" in params.decoding_method: - params.suffix += f"-beam-{params.beam_size}" - else: - params.suffix += f"-context-{params.context_size}" - params.suffix += f"-max-sym-per-frame-{params.max_sym_per_frame}" - - setup_logger(f"{params.res_dir}/log-decode-{params.suffix}") - logging.info("Decoding started") - - device = torch.device("cpu") - if torch.cuda.is_available(): - device = torch.device("cuda", 0) - - 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.unk_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) - - if params.avg_last_n > 0: - filenames = find_checkpoints(params.exp_dir)[: params.avg_last_n] - logging.info(f"averaging {filenames}") - model.to(device) - model.load_state_dict(average_checkpoints(filenames, device=device)) - elif params.avg == 1: - load_checkpoint(f"{params.exp_dir}/epoch-{params.epoch}.pt", model) - else: - start = params.epoch - params.avg + 1 - filenames = [] - for i in range(start, params.epoch + 1): - if start >= 0: - filenames.append(f"{params.exp_dir}/epoch-{i}.pt") - logging.info(f"averaging {filenames}") - model.to(device) - model.load_state_dict(average_checkpoints(filenames, device=device)) - - model.to(device) - model.eval() - model.device = device - - if params.decoding_method == "fast_beam_search": - decoding_graph = k2.trivial_graph(params.vocab_size - 1, device=device) - else: - decoding_graph = None - - num_param = sum([p.numel() for p in model.parameters()]) - logging.info(f"Number of model parameters: {num_param}") - - librispeech = LibriSpeechAsrDataModule(args) - - test_clean_cuts = librispeech.test_clean_cuts() - test_other_cuts = librispeech.test_other_cuts() - - test_clean_dl = librispeech.test_dataloaders(test_clean_cuts) - test_other_dl = librispeech.test_dataloaders(test_other_cuts) - - test_sets = ["test-clean", "test-other"] - test_dl = [test_clean_dl, test_other_dl] - - for test_set, test_dl in zip(test_sets, test_dl): - results_dict = decode_dataset( - dl=test_dl, - params=params, - model=model, - sp=sp, - decoding_graph=decoding_graph, - ) - - save_results( - params=params, - test_set_name=test_set, - results_dict=results_dict, - ) - - logging.info("Done!") - - -if __name__ == "__main__": - main() diff --git a/egs/librispeech/ASR/emformer_pruned_transducer_stateless/decoder.py b/egs/librispeech/ASR/emformer_pruned_transducer_stateless/decoder.py deleted file mode 120000 index 0d5f10dc0..000000000 --- a/egs/librispeech/ASR/emformer_pruned_transducer_stateless/decoder.py +++ /dev/null @@ -1 +0,0 @@ -../pruned_transducer_stateless/decoder.py \ No newline at end of file diff --git a/egs/librispeech/ASR/emformer_pruned_transducer_stateless/emformer.py b/egs/librispeech/ASR/emformer_pruned_transducer_stateless/emformer.py deleted file mode 100644 index 0f4aad163..000000000 --- a/egs/librispeech/ASR/emformer_pruned_transducer_stateless/emformer.py +++ /dev/null @@ -1,1632 +0,0 @@ -# Copyright 2022 Xiaomi Corporation (Author: Zengwei Yao) -# -# 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. -# -# It is modified based on -# https://github.com/pytorch/audio/blob/main/torchaudio/models/emformer.py. - -import math -import warnings -from typing import List, Optional, Tuple - -import torch -import torch.nn as nn -from encoder_interface import EncoderInterface -from subsampling import Conv2dSubsampling, VggSubsampling - -from icefall.utils import make_pad_mask - - -def _get_activation_module(activation: str) -> nn.Module: - if activation == "relu": - return nn.ReLU() - elif activation == "gelu": - return nn.GELU() - elif activation == "silu": - return nn.SiLU() - else: - raise ValueError(f"Unsupported activation {activation}") - - -def _gen_attention_mask_block( - col_widths: List[int], - col_mask: List[bool], - num_rows: int, - device: torch.device, -) -> torch.Tensor: - assert len(col_widths) == len( - col_mask - ), "Length of col_widths must match that of col_mask" - - mask_block = [ - torch.ones(num_rows, col_width, device=device) - if is_ones_col - else torch.zeros(num_rows, col_width, device=device) - for col_width, is_ones_col in zip(col_widths, col_mask) - ] - return torch.cat(mask_block, dim=1) - - -def unstack_states( - states: List[List[torch.Tensor]], -) -> List[List[List[torch.Tensor]]]: - """Unstack the emformer state corresponding to a batch of utterances - into a list of states, were the i-th entry is the state from the i-th - utterance in the batch. - - Args: - states: - A list-of-list of tensors. ``len(states)`` equals to number of - layers in the emformer. ``states[i]]`` contains the states for - the i-th layer. ``states[i][k]`` is either a 3-D tensor of shape - ``(T, N, C)`` or a 2-D tensor of shape ``(C, N)`` - """ - batch_size = states[0][0].size(1) - num_layers = len(states) - - ans = [None] * batch_size - for i in range(batch_size): - ans[i] = [[] for _ in range(num_layers)] - - for li, layer in enumerate(states): - for s in layer: - s_list = s.unbind(dim=1) - # We will use stack(dim=1) later in stack_states() - for bi, b in enumerate(ans): - b[li].append(s_list[bi]) - return ans - - -def stack_states( - state_list: List[List[List[torch.Tensor]]], -) -> List[List[torch.Tensor]]: - """Stack list of emformer states that correspond to separate utterances - into a single emformer state so that it can be used as an input for - emformer when those utterances are formed into a batch. - - Note: - It is the inverse of :func:`unstack_states`. - - Args: - state_list: - Each element in state_list corresponding to the internal state - of the emformer model for a single utterance. - Returns: - Return a new state corresponding to a batch of utterances. - See the input argument of :func:`unstack_states` for the meaning - of the returned tensor. - """ - batch_size = len(state_list) - ans = [] - for layer in state_list[0]: - # layer is a list of tensors - if batch_size > 1: - ans.append([[s] for s in layer]) - # Note: We will stack ans[layer][s][] later to get ans[layer][s] - else: - ans.append([s.unsqueeze(1) for s in layer]) - - for b, states in enumerate(state_list[1:], 1): - for li, layer in enumerate(states): - for si, s in enumerate(layer): - ans[li][si].append(s) - if b == batch_size - 1: - ans[li][si] = torch.stack(ans[li][si], dim=1) - # We will use unbind(dim=1) later in unstack_states() - return ans - - -class EmformerAttention(nn.Module): - r"""Emformer layer attention module. - - Args: - embed_dim (int): - Embedding dimension. - nhead (int): - Number of attention heads in each Emformer layer. - dropout (float): - A Dropout layer on attn_output_weights. (Default: 0.0) - tanh_on_mem (bool, optional): - If ``True``, applies tanh to memory elements. (Default: ``False``) - negative_inf (float, optional): - Value to use for negative infinity in attention weights. (Default: -1e8) - """ - - def __init__( - self, - embed_dim: int, - nhead: int, - dropout: float = 0.0, - tanh_on_mem: bool = False, - negative_inf: float = -1e8, - ): - super().__init__() - - if embed_dim % nhead != 0: - raise ValueError( - f"embed_dim ({embed_dim}) is not a multiple of nhead ({nhead})." - ) - self.embed_dim = embed_dim - self.nhead = nhead - self.tanh_on_mem = tanh_on_mem - self.negative_inf = negative_inf - self.head_dim = embed_dim // nhead - - self.dropout = dropout - - self.scaling = self.head_dim ** -0.5 - - self.emb_to_key_value = nn.Linear(embed_dim, 2 * embed_dim, bias=True) - self.emb_to_query = nn.Linear(embed_dim, embed_dim, bias=True) - self.out_proj = nn.Linear(embed_dim, embed_dim, bias=True) - - # linear transformation for positional encoding. - self.linear_pos = nn.Linear(embed_dim, embed_dim, bias=False) - - # these two learnable bias are used in matrix c and matrix d - # as described in "Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context" Section 3.3 # noqa - self.pos_bias_u = nn.Parameter(torch.Tensor(nhead, self.head_dim)) - self.pos_bias_v = nn.Parameter(torch.Tensor(nhead, self.head_dim)) - - self._reset_parameters() - - def _reset_parameters(self) -> None: - nn.init.xavier_uniform_(self.emb_to_key_value.weight) - nn.init.constant_(self.emb_to_key_value.bias, 0.0) - - nn.init.xavier_uniform_(self.emb_to_query.weight) - nn.init.constant_(self.emb_to_query.bias, 0.0) - - nn.init.xavier_uniform_(self.out_proj.weight) - nn.init.constant_(self.out_proj.bias, 0.0) - - nn.init.xavier_uniform_(self.linear_pos.weight) - - nn.init.xavier_uniform_(self.pos_bias_u) - nn.init.xavier_uniform_(self.pos_bias_v) - - def _gen_attention_probs( - self, - attention_weights: torch.Tensor, - attention_mask: torch.Tensor, - padding_mask: Optional[torch.Tensor], - ) -> torch.Tensor: - """Given the entire attention weights, mask out unecessary connections - and optionally with padding positions, to obtain underlying chunk-wise - attention probabilities. - - B: batch size; - Q: length of query; - KV: length of key and value. - - Args: - attention_weights (torch.Tensor): - Attention weights computed on the entire concatenated tensor - with shape (B * nhead, Q, KV). - attention_mask (torch.Tensor): - Mask tensor where chunk-wise connections are filled with `False`, - and other unnecessary connections are filled with `True`, - with shape (Q, KV). - padding_mask (torch.Tensor, optional): - Mask tensor where the padding positions are fill with `True`, - and other positions are filled with `False`, with shapa `(B, KV)`. - - Returns: - A tensor of shape (B * nhead, Q, KV). - """ - attention_weights_float = attention_weights.float() - attention_weights_float = attention_weights_float.masked_fill( - attention_mask.unsqueeze(0), self.negative_inf - ) - if padding_mask is not None: - Q = attention_weights.size(1) - B = attention_weights.size(0) // self.nhead - attention_weights_float = attention_weights_float.view( - B, self.nhead, Q, -1 - ) - attention_weights_float = attention_weights_float.masked_fill( - padding_mask.unsqueeze(1).unsqueeze(2).to(torch.bool), - self.negative_inf, - ) - attention_weights_float = attention_weights_float.view( - B * self.nhead, Q, -1 - ) - - attention_probs = nn.functional.softmax( - attention_weights_float, dim=-1 - ).type_as(attention_weights) - - attention_probs = nn.functional.dropout( - attention_probs, p=self.dropout, training=self.training - ) - return attention_probs - - def _rel_shift(self, x: torch.Tensor) -> torch.Tensor: - """Compute relative positional encoding. - - Args: - x: Input tensor, of shape (B, nhead, U, PE). - U is the length of query vector. - For non-infer mode, PE = 2 * U - 1; - for infer mode, PE = L + 2 * U - 1. - - Returns: - A tensor of shape (B, nhead, U, out_len). - For non-infer mode, out_len = U; - for infer mode, out_len = L + U. - """ - B, nhead, U, PE = x.size() - B_stride = x.stride(0) - nhead_stride = x.stride(1) - U_stride = x.stride(2) - PE_stride = x.stride(3) - out_len = PE - (U - 1) - return x.as_strided( - size=(B, nhead, U, out_len), - stride=(B_stride, nhead_stride, U_stride - PE_stride, PE_stride), - storage_offset=PE_stride * (U - 1), - ) - - def _forward_impl( - self, - utterance: torch.Tensor, - lengths: torch.Tensor, - right_context: torch.Tensor, - summary: torch.Tensor, - memory: torch.Tensor, - attention_mask: torch.Tensor, - pos_emb: torch.Tensor, - left_context_key: Optional[torch.Tensor] = None, - left_context_val: Optional[torch.Tensor] = None, - ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: - """Underlying chunk-wise attention implementation. - - L: length of left_context; - S: length of summary; - M: length of memory; - Q: length of attention query; - KV: length of attention key and value. - - 1) Concat right_context, utterance, summary, - and compute query with length Q = R + U + S. - 2) Concat memory, right_context, utterance, - and compute key, value with length KV = M + R + U; - also with left_context_key and left_context_val for infererence mode, - then KV = M + R + L + U. - 3) Compute entire attention scores with above query, key, and value, - then apply attention_mask to get underlying chunk-wise attention scores. - - Args: - utterance (torch.Tensor): - Utterance frames, with shape (U, B, D). - lengths (torch.Tensor): - With shape (B,) and i-th element representing - number of valid frames for i-th batch element in utterance. - right_context (torch.Tensor): - Right context frames, with shape (R, B, D). - summary (torch.Tensor): - Summary elements, with shape (S, B, D). - memory (torch.Tensor): - Memory elements, with shape (M, B, D). - attention_mask (torch.Tensor): - Attention mask for underlying attention, with shape (Q, KV). - pos_emb (torch.Tensor): - Position encoding embedding, with shape (PE, D). - For training mode, PE = 2 * U - 1; - For inference mode, PE = L + 2 * U - 1. - left_context_key (torch,Tensor, optional): - Cached attention key of left context from preceding computation, - with shape (L, B, D). It is used for inference mode. - left_context_val (torch.Tensor, optional): - Cached attention value of left context from preceding computation, - with shape (L, B, D). It is used for inference mode. - - Returns: - A tuple containing 4 tensors: - - output of right context and utterance, with shape (R + U, B, D). - - memory output, with shape (S, B, D). - - attention key, with shape (KV, B, D). - - attention value, with shape (KV, B, D). - """ - U, B, _ = utterance.size() - R = right_context.size(0) - M = memory.size(0) - - # compute query with [right context, utterance, summary]. - query = self.emb_to_query( - torch.cat([right_context, utterance, summary]) - ) - # compute key and value with [mems, right context, utterance]. - key, value = self.emb_to_key_value( - torch.cat([memory, right_context, utterance]) - ).chunk(chunks=2, dim=2) - - if left_context_key is not None and left_context_val is not None: - # compute key and value with - # [mems, right context, left context, uttrance] - key = torch.cat([key[: M + R], left_context_key, key[M + R :]]) - value = torch.cat( - [value[: M + R], left_context_val, value[M + R :]] - ) - Q = query.size(0) - KV = key.size(0) - - reshaped_key, reshaped_value = [ - tensor.contiguous() - .view(KV, B * self.nhead, self.head_dim) - .transpose(0, 1) - for tensor in [key, value] - ] # both of shape (B * nhead, KV, head_dim) - reshaped_query = query.contiguous().view( - Q, B, self.nhead, self.head_dim - ) - - # compute attention score - # first compute attention matrix a and matrix c - # as described in "Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context" Section 3.3 # noqa - query_with_bais_u = ( - (reshaped_query + self.pos_bias_u) - .view(Q, B * self.nhead, self.head_dim) - .transpose(0, 1) - ) - matrix_ac = torch.bmm( - query_with_bais_u, reshaped_key.transpose(1, 2) - ) # (B * nhead, Q, KV) - - # second, compute attention matrix b and matrix d - # relative positional encoding is applied on the part of attention - # between query: [utterance] -> key, value: [left_context, utterance] - utterance_with_bais_v = ( - reshaped_query[R : R + U] + self.pos_bias_v - ).permute(1, 2, 0, 3) - # (B, nhead, U, head_dim) - PE = pos_emb.size(0) - if left_context_key is not None and left_context_val is not None: - L = left_context_key.size(0) - assert PE == L + 2 * U - 1 - else: - assert PE == 2 * U - 1 - pos_emb = ( - self.linear_pos(pos_emb) - .view(PE, self.nhead, self.head_dim) - .transpose(0, 1) - .unsqueeze(0) - ) # (1, nhead, PE, head_dim) - matrix_bd_utterance = torch.matmul( - utterance_with_bais_v, pos_emb.transpose(-2, -1) - ) # (B, nhead, U, PE) - # rel-shift operation - matrix_bd_utterance = self._rel_shift(matrix_bd_utterance) - # (B, nhead, U, U) for training mode; - # (B, nhead, U, L + U) for inference mode. - matrix_bd_utterance = matrix_bd_utterance.contiguous().view( - B * self.nhead, U, -1 - ) - matrix_bd = torch.zeros_like(matrix_ac) - matrix_bd[:, R : R + U, M + R :] = matrix_bd_utterance - - attention_weights = (matrix_ac + matrix_bd) * self.scaling - - # compute padding mask - if B == 1: - padding_mask = None - else: - padding_mask = make_pad_mask(KV - U + lengths) - - # compute attention probabilities - attention_probs = self._gen_attention_probs( - attention_weights, attention_mask, padding_mask - ) - - # compute attention outputs - attention = torch.bmm(attention_probs, reshaped_value) - assert attention.shape == (B * self.nhead, Q, self.head_dim) - attention = ( - attention.transpose(0, 1).contiguous().view(Q, B, self.embed_dim) - ) - - # apply output projection - outputs = self.out_proj(attention) - - output_right_context_utterance = outputs[: R + U] - output_memory = outputs[R + U :] - if self.tanh_on_mem: - output_memory = torch.tanh(output_memory) - else: - output_memory = torch.clamp(output_memory, min=-10, max=10) - - return output_right_context_utterance, output_memory, key, value - - def forward( - self, - utterance: torch.Tensor, - lengths: torch.Tensor, - right_context: torch.Tensor, - summary: torch.Tensor, - memory: torch.Tensor, - attention_mask: torch.Tensor, - pos_emb: torch.Tensor, - ) -> Tuple[torch.Tensor, torch.Tensor]: - # TODO: Modify docs. - """Forward pass for training. - - B: batch size; - D: embedding dimension; - R: length of right_context; - U: length of utterance; - S: length of summary; - M: length of memory. - - Args: - utterance (torch.Tensor): - Utterance frames, with shape (U, B, D). - lengths (torch.Tensor): - With shape (B,) and i-th element representing - number of valid frames for i-th batch element in utterance. - right_context (torch.Tensor): - Right context frames, with shape (R, B, D). - summary (torch.Tensor): - Summary elements with shape (S, B, D) or an empty tensor. - memory (torch.Tensor): - Memory elements, with shape (M, B, D). - attention_mask (torch.Tensor): - Attention mask for underlying chunk-wise attention, - with shape (Q, KV), where Q = R + U + S, KV = M + R + U. - pos_emb (torch.Tensor): - Position encoding embedding, with shape (PE, D). - where PE = 2 * U - 1. - - Returns: - A tuple containing 2 tensors: - - output of right context and utterance, with shape (R + U, B, D). - - memory output, with shape (M, B, D), where M = S - 1 or M = 0. - """ - ( - output_right_context_utterance, - output_memory, - _, - _, - ) = self._forward_impl( - utterance, - lengths, - right_context, - summary, - memory, - attention_mask, - pos_emb, - ) - return output_right_context_utterance, output_memory[:-1] - - @torch.jit.export - def infer( - self, - utterance: torch.Tensor, - lengths: torch.Tensor, - right_context: torch.Tensor, - summary: torch.Tensor, - memory: torch.Tensor, - left_context_key: torch.Tensor, - left_context_val: torch.Tensor, - pos_emb: torch.Tensor, - ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: - """Forward pass for inference. - - B: batch size; - D: embedding dimension; - R: length of right_context; - U: length of utterance; - L: length of left_context; - S: length of summary; - M: length of memory; - - Args: - utterance (torch.Tensor): - Utterance frames, with shape (U, B, D). - lengths (torch.Tensor): - With shape (B,) and i-th element representing - number of valid frames for i-th batch element in utterance. - right_context (torch.Tensor): - Right context frames, with shape (R, B, D). - summary (torch.Tensor): - Summary element with shape (1, B, D), or an empty tensor. - memory (torch.Tensor): - Memory elements, with shape (M, B, D). - left_context_key (torch,Tensor): - Cached attention key of left context from preceding computation, - with shape (L, B, D). - left_context_val (torch.Tensor): - Cached attention value of left context from preceding computation, - with shape (L, B, D). - pos_emb (torch.Tensor): - Position encoding embedding, with shape (PE, D), - where PE = L + 2 * U - 1. - - Returns: - A tuple containing 4 tensors: - - output of right context and utterance, with shape (R + U, B, D). - - memory output, with shape (1, B, D) or (0, B, D). - - attention key of left context and utterance, which would be cached - for next computation, with shape (L + U, B, D). - - attention value of left context and utterance, which would be - cached for next computation, with shape (L + U, B, D). - """ - U = utterance.size(0) - R = right_context.size(0) - L = left_context_key.size(0) - S = summary.size(0) - M = memory.size(0) - - # query: [right context, utterance, summary] - Q = R + U + S - # key, value: [memory, right context, left context, uttrance] - KV = M + R + L + U - attention_mask = torch.zeros(Q, KV).to( - dtype=torch.bool, device=utterance.device - ) - # disallow attention bettween the summary vector with the memory bank - attention_mask[-1, : memory.size(0)] = True - ( - output_right_context_utterance, - output_memory, - key, - value, - ) = self._forward_impl( - utterance, - lengths, - right_context, - summary, - memory, - attention_mask, - pos_emb, - left_context_key=left_context_key, - left_context_val=left_context_val, - ) - return ( - output_right_context_utterance, - output_memory, - key[M + R :], - value[M + R :], - ) - - -class EmformerLayer(nn.Module): - """Emformer layer that constitutes Emformer. - - Args: - d_model (int): - Input dimension. - nhead (int): - Number of attention heads. - dim_feedforward (int): - Hidden layer dimension of feedforward network. - chunk_length (int): - Length of each input segment. - dropout (float, optional): - Dropout probability. (Default: 0.0) - activation (str, optional): - Activation function to use in feedforward network. - Must be one of ("relu", "gelu", "silu"). (Default: "relu") - left_context_length (int, optional): - Length of left context. (Default: 0) - max_memory_size (int, optional): - Maximum number of memory elements to use. (Default: 0) - (Default: ``None``) - tanh_on_mem (bool, optional): - If ``True``, applies tanh to memory elements. (Default: ``False``) - negative_inf (float, optional): - Value to use for negative infinity in attention weights. (Default: -1e8) - """ - - def __init__( - self, - d_model: int, - nhead: int, - dim_feedforward: int, - chunk_length: int, - dropout: float = 0.0, - activation: str = "relu", - left_context_length: int = 0, - max_memory_size: int = 0, - tanh_on_mem: bool = False, - negative_inf: float = -1e8, - ): - super().__init__() - - self.attention = EmformerAttention( - embed_dim=d_model, - nhead=nhead, - dropout=dropout, - tanh_on_mem=tanh_on_mem, - negative_inf=negative_inf, - ) - self.dropout = nn.Dropout(dropout) - self.summary_op = nn.AvgPool1d( - kernel_size=chunk_length, stride=chunk_length, ceil_mode=True - ) - - activation_module = _get_activation_module(activation) - self.pos_ff = nn.Sequential( - nn.LayerNorm(d_model), - nn.Linear(d_model, dim_feedforward), - activation_module, - nn.Dropout(dropout), - nn.Linear(dim_feedforward, d_model), - nn.Dropout(dropout), - ) - self.layer_norm_input = nn.LayerNorm(d_model) - self.layer_norm_output = nn.LayerNorm(d_model) - - self.left_context_length = left_context_length - self.chunk_length = chunk_length - self.max_memory_size = max_memory_size - self.d_model = d_model - - self.use_memory = max_memory_size > 0 - - def _init_state( - self, batch_size: int, device: Optional[torch.device] - ) -> List[torch.Tensor]: - """Initialize states with zeros.""" - empty_memory = torch.zeros( - self.max_memory_size, batch_size, self.d_model, device=device - ) - left_context_key = torch.zeros( - self.left_context_length, batch_size, self.d_model, device=device - ) - left_context_val = torch.zeros( - self.left_context_length, batch_size, self.d_model, device=device - ) - past_length = torch.zeros( - 1, batch_size, dtype=torch.int32, device=device - ) - return [empty_memory, left_context_key, left_context_val, past_length] - - def _unpack_state( - self, state: List[torch.Tensor] - ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: - """Unpack cached states including: - 1) output memory from previous chunks in the lower layer; - 2) attention key and value of left context from proceeding chunk's - computation. - """ - past_length = state[3][0][0].item() - past_left_context_length = min(self.left_context_length, past_length) - past_memory_length = min( - self.max_memory_size, math.ceil(past_length / self.chunk_length) - ) - memory_start_idx = self.max_memory_size - past_memory_length - pre_memory = state[0][memory_start_idx:] - left_context_start_idx = ( - self.left_context_length - past_left_context_length - ) - left_context_key = state[1][left_context_start_idx:] - left_context_val = state[2][left_context_start_idx:] - return pre_memory, left_context_key, left_context_val - - def _pack_state( - self, - next_key: torch.Tensor, - next_val: torch.Tensor, - update_length: int, - memory: torch.Tensor, - state: List[torch.Tensor], - ) -> List[torch.Tensor]: - """Pack updated states including: - 1) output memory of current chunk in the lower layer; - 2) attention key and value in current chunk's computation, which would - be resued in next chunk's computation. - 3) length of current chunk. - """ - new_memory = torch.cat([state[0], memory]) - new_key = torch.cat([state[1], next_key]) - new_val = torch.cat([state[2], next_val]) - memory_start_idx = new_memory.size(0) - self.max_memory_size - state[0] = new_memory[memory_start_idx:] - key_start_idx = new_key.size(0) - self.left_context_length - state[1] = new_key[key_start_idx:] - val_start_idx = new_val.size(0) - self.left_context_length - state[2] = new_val[val_start_idx:] - state[3] = state[3] + update_length - return state - - def _apply_pre_attention_layer_norm( - self, utterance: torch.Tensor, right_context: torch.Tensor - ) -> Tuple[torch.Tensor, torch.Tensor]: - """Apply layer normalization before attention.""" - layer_norm_input = self.layer_norm_input( - torch.cat([right_context, utterance]) - ) - R = right_context.size(0) - layer_norm_utterance = layer_norm_input[R:] - layer_norm_right_context = layer_norm_input[:R] - return layer_norm_utterance, layer_norm_right_context - - def _apply_post_attention_ffn_layer_norm( - self, - output_right_context_utterance: torch.Tensor, - utterance: torch.Tensor, - right_context: torch.Tensor, - ) -> Tuple[torch.Tensor, torch.Tensor]: - """Apply feed forward and layer normalization after attention.""" - # apply residual connection between input and attention output. - result = self.dropout(output_right_context_utterance) + torch.cat( - [right_context, utterance] - ) - # apply feedforward module and residual connection. - result = self.pos_ff(result) + result - # apply layer normalization for output. - result = self.layer_norm_output(result) - - R = right_context.size(0) - output_utterance = result[R:] - output_right_context = result[:R] - return output_utterance, output_right_context - - def _apply_attention_forward( - self, - utterance: torch.Tensor, - lengths: torch.Tensor, - right_context: torch.Tensor, - memory: torch.Tensor, - attention_mask: Optional[torch.Tensor], - pos_emb: torch.Tensor, - ) -> Tuple[torch.Tensor, torch.Tensor]: - """Apply attention in non-infer mode.""" - if attention_mask is None: - raise ValueError( - "attention_mask must be not None in non-infer mode. " - ) - if self.use_memory: - summary = self.summary_op(utterance.permute(1, 2, 0)).permute( - 2, 0, 1 - ) - else: - summary = torch.empty(0).to( - dtype=utterance.dtype, device=utterance.device - ) - output_right_context_utterance, output_memory = self.attention( - utterance=utterance, - lengths=lengths, - right_context=right_context, - summary=summary, - memory=memory, - attention_mask=attention_mask, - pos_emb=pos_emb, - ) - return output_right_context_utterance, output_memory - - def _apply_attention_infer( - self, - utterance: torch.Tensor, - lengths: torch.Tensor, - right_context: torch.Tensor, - memory: torch.Tensor, - pos_emb: torch.Tensor, - state: Optional[List[torch.Tensor]] = None, - ) -> Tuple[torch.Tensor, torch.Tensor, List[torch.Tensor]]: - """Apply attention in infer mode. - 1) Unpack cached states including: - - memory from previous chunks in the lower layer; - - attention key and value of left context from proceeding - chunk's compuation; - 2) Apply attention computation; - 3) Pack updated states including: - - output memory of current chunk in the lower layer; - - attention key and value in current chunk's computation, which would - be resued in next chunk's computation. - - length of current chunk. - """ - if state is None: - state = self._init_state(utterance.size(1), device=utterance.device) - pre_memory, left_context_key, left_context_val = self._unpack_state( - state - ) - if self.use_memory: - summary = self.summary_op(utterance.permute(1, 2, 0)).permute( - 2, 0, 1 - ) - summary = summary[:1] - else: - summary = torch.empty(0).to( - dtype=utterance.dtype, device=utterance.device - ) - # pos_emb is of shape [PE, D], where PE = L + 2 * U - 1, - # for query of [utterance] (i), key-value [left_context, utterance] (j), - # the max relative distance i - j is L + U - 1 - # the min relative distance i - j is -(U - 1) - L = left_context_key.size(0) # L <= left_context_length - U = utterance.size(0) - PE = L + 2 * U - 1 - tot_PE = self.left_context_length + 2 * U - 1 - assert pos_emb.size(0) == tot_PE - pos_emb = pos_emb[tot_PE - PE :] - ( - output_right_context_utterance, - output_memory, - next_key, - next_val, - ) = self.attention.infer( - utterance=utterance, - lengths=lengths, - right_context=right_context, - summary=summary, - memory=pre_memory, - left_context_key=left_context_key, - left_context_val=left_context_val, - pos_emb=pos_emb, - ) - state = self._pack_state( - next_key, next_val, utterance.size(0), memory, state - ) - return output_right_context_utterance, output_memory, state - - def forward( - self, - utterance: torch.Tensor, - lengths: torch.Tensor, - right_context: torch.Tensor, - memory: torch.Tensor, - attention_mask: torch.Tensor, - pos_emb: torch.Tensor, - ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: - r"""Forward pass for training. - 1) Apply layer normalization on input utterance and right context - before attention; - 2) Apply attention module, compute updated utterance, right context, - and memory; - 3) Apply feed forward module and layer normalization on output utterance - and right context. - - B: batch size; - D: embedding dimension; - R: length of right_context; - U: length of utterance; - M: length of memory. - - Args: - utterance (torch.Tensor): - Utterance frames, with shape (U, B, D). - lengths (torch.Tensor): - With shape (B,) and i-th element representing - number of valid frames for i-th batch element in utterance. - right_context (torch.Tensor): - Right context frames, with shape (R, B, D). - memory (torch.Tensor): - Memory elements, with shape (M, B, D). - attention_mask (torch.Tensor): - Attention mask for underlying attention module, - with shape (Q, KV), where Q = R + U + S, KV = M + R + U. - pos_emb (torch.Tensor): - Position encoding embedding, with shape (PE, D), - where PE = 2 * U - 1. - - Returns: - A tuple containing 3 tensors: - - output utterance, with shape (U, B, D). - - output right context, with shape (R, B, D). - - output memory, with shape (M, B, D). - """ - ( - layer_norm_utterance, - layer_norm_right_context, - ) = self._apply_pre_attention_layer_norm(utterance, right_context) - ( - output_right_context_utterance, - output_memory, - ) = self._apply_attention_forward( - layer_norm_utterance, - lengths, - layer_norm_right_context, - memory, - attention_mask, - pos_emb, - ) - ( - output_utterance, - output_right_context, - ) = self._apply_post_attention_ffn_layer_norm( - output_right_context_utterance, utterance, right_context - ) - return output_utterance, output_right_context, output_memory - - @torch.jit.export - def infer( - self, - utterance: torch.Tensor, - lengths: torch.Tensor, - right_context: torch.Tensor, - memory: torch.Tensor, - pos_emb: torch.Tensor, - state: Optional[List[torch.Tensor]] = None, - ) -> Tuple[torch.Tensor, torch.Tensor, List[torch.Tensor], torch.Tensor]: - """Forward pass for inference. - - 1) Apply layer normalization on input utterance and right context - before attention; - 2) Apply attention module with cached state, compute updated utterance, - right context, and memory, and update state; - 3) Apply feed forward module and layer normalization on output - utterance and right context. - - B: batch size; - D: embedding dimension; - R: length of right_context; - U: length of utterance; - M: length of memory. - - Args: - utterance (torch.Tensor): - Utterance frames, with shape (U, B, D). - lengths (torch.Tensor): - With shape (B,) and i-th element representing - number of valid frames for i-th batch element in utterance. - right_context (torch.Tensor): - Right context frames, with shape (R, B, D). - memory (torch.Tensor): - Memory elements, with shape (M, B, D). - state (List[torch.Tensor], optional): - List of tensors representing layer internal state generated in - preceding computation. (default=None) - pos_emb (torch.Tensor): - Position encoding embedding, with shape (PE, D), - where PE = L + 2 * U - 1. - - Returns: - (Tensor, Tensor, List[torch.Tensor], Tensor): - - output utterance, with shape (U, B, D); - - output right_context, with shape (R, B, D); - - output memory, with shape (1, B, D) or (0, B, D). - - output state. - """ - ( - layer_norm_utterance, - layer_norm_right_context, - ) = self._apply_pre_attention_layer_norm(utterance, right_context) - ( - output_right_context_utterance, - output_memory, - output_state, - ) = self._apply_attention_infer( - layer_norm_utterance, - lengths, - layer_norm_right_context, - memory, - pos_emb, - state, - ) - ( - output_utterance, - output_right_context, - ) = self._apply_post_attention_ffn_layer_norm( - output_right_context_utterance, utterance, right_context - ) - return ( - output_utterance, - output_right_context, - output_memory, - output_state, - ) - - -class EmformerEncoder(nn.Module): - """Implements the Emformer architecture introduced in - *Emformer: Efficient Memory Transformer Based Acoustic Model for Low Latency - Streaming Speech Recognition* - [:footcite:`shi2021emformer`]. - - Args: - d_model (int): - Input dimension. - nhead (int): - Number of attention heads in each emformer layer. - dim_feedforward (int): - Hidden layer dimension of each emformer layer's feedforward network. - num_encoder_layers (int): - Number of emformer layers to instantiate. - chunk_length (int): - Length of each input segment. - dropout (float, optional): - Dropout probability. (default: 0.0) - activation (str, optional): - Activation function to use in each emformer layer's feedforward network. - Must be one of ("relu", "gelu", "silu"). (default: "relu") - left_context_length (int, optional): - Length of left context. (default: 0) - right_context_length (int, optional): - Length of right context. (default: 0) - max_memory_size (int, optional): - Maximum number of memory elements to use. (default: 0) - tanh_on_mem (bool, optional): - If ``true``, applies tanh to memory elements. (default: ``false``) - negative_inf (float, optional): - Value to use for negative infinity in attention weights. (default: -1e8) - """ - - def __init__( - self, - chunk_length: int, - d_model: int = 256, - nhead: int = 4, - dim_feedforward: int = 2048, - num_encoder_layers: int = 12, - dropout: float = 0.1, - activation: str = "relu", - left_context_length: int = 0, - right_context_length: int = 0, - max_memory_size: int = 0, - tanh_on_mem: bool = False, - negative_inf: float = -1e8, - ): - super().__init__() - - self.use_memory = max_memory_size > 0 - self.init_memory_op = nn.AvgPool1d( - kernel_size=chunk_length, - stride=chunk_length, - ceil_mode=True, - ) - - self.emformer_layers = nn.ModuleList( - [ - EmformerLayer( - d_model, - nhead, - dim_feedforward, - chunk_length, - dropout=dropout, - activation=activation, - left_context_length=left_context_length, - max_memory_size=max_memory_size, - tanh_on_mem=tanh_on_mem, - negative_inf=negative_inf, - ) - for layer_idx in range(num_encoder_layers) - ] - ) - - self.encoder_pos = RelPositionalEncoding(d_model, dropout) - - self.left_context_length = left_context_length - self.right_context_length = right_context_length - self.chunk_length = chunk_length - self.max_memory_size = max_memory_size - - def _gen_right_context(self, x: torch.Tensor) -> torch.Tensor: - """Hard copy each chunk's right context and concat them.""" - T = x.shape[0] - num_chunks = math.ceil( - (T - self.right_context_length) / self.chunk_length - ) - right_context_blocks = [] - for seg_idx in range(num_chunks - 1): - start = (seg_idx + 1) * self.chunk_length - end = start + self.right_context_length - right_context_blocks.append(x[start:end]) - right_context_blocks.append(x[T - self.right_context_length :]) # noqa - return torch.cat(right_context_blocks) - - def _gen_attention_mask_col_widths( - self, chunk_idx: int, U: int - ) -> List[int]: - """Calculate column widths (key, value) in attention mask for the - chunk_idx chunk.""" - num_chunks = math.ceil(U / self.chunk_length) - rc = self.right_context_length - lc = self.left_context_length - rc_start = chunk_idx * rc - rc_end = rc_start + rc - chunk_start = max(chunk_idx * self.chunk_length - lc, 0) - chunk_end = min((chunk_idx + 1) * self.chunk_length, U) - R = rc * num_chunks - - if self.use_memory: - m_start = max(chunk_idx - self.max_memory_size, 0) - M = num_chunks - 1 - col_widths = [ - m_start, # before memory - chunk_idx - m_start, # memory - M - chunk_idx, # after memory - rc_start, # before right context - rc, # right context - R - rc_end, # after right context - chunk_start, # before chunk - chunk_end - chunk_start, # chunk - U - chunk_end, # after chunk - ] - else: - col_widths = [ - rc_start, # before right context - rc, # right context - R - rc_end, # after right context - chunk_start, # before chunk - chunk_end - chunk_start, # chunk - U - chunk_end, # after chunk - ] - - return col_widths - - def _gen_attention_mask(self, utterance: torch.Tensor) -> torch.Tensor: - """Generate attention mask for underlying chunk-wise attention - computation, where chunk-wise connections are filled with `False`, - and other unnecessary connections beyond chunk are filled with `True`. - - R: length of right_context; - U: length of utterance; - S: length of summary; - M: length of memory; - Q: length of attention query; - KV: length of attention key and value. - - The shape of attention mask is (Q, KV). - If self.use_memory is `True`: - query = [right_context, utterance, summary]; - key, value = [memory, right_context, utterance]; - Q = R + U + S, KV = M + R + U. - Otherwise: - query = [right_context, utterance] - key, value = [right_context, utterance] - Q = R + U, KV = R + U. - """ - U = utterance.size(0) - num_chunks = math.ceil(U / self.chunk_length) - - right_context_mask = [] - utterance_mask = [] - summary_mask = [] - - if self.use_memory: - num_cols = 9 - # right context and utterance both attend to memory, right context, - # utterance - right_context_utterance_cols_mask = [ - idx in [1, 4, 7] for idx in range(num_cols) - ] - # summary attends to right context, utterance - summary_cols_mask = [idx in [4, 7] for idx in range(num_cols)] - masks_to_concat = [right_context_mask, utterance_mask, summary_mask] - else: - num_cols = 6 - # right context and utterance both attend to right context and - # utterance - right_context_utterance_cols_mask = [ - idx in [1, 4] for idx in range(num_cols) - ] - summary_cols_mask = None - masks_to_concat = [right_context_mask, utterance_mask] - - for chunk_idx in range(num_chunks): - col_widths = self._gen_attention_mask_col_widths(chunk_idx, U) - - right_context_mask_block = _gen_attention_mask_block( - col_widths, - right_context_utterance_cols_mask, - self.right_context_length, - utterance.device, - ) - right_context_mask.append(right_context_mask_block) - - utterance_mask_block = _gen_attention_mask_block( - col_widths, - right_context_utterance_cols_mask, - min( - self.chunk_length, - U - chunk_idx * self.chunk_length, - ), - utterance.device, - ) - utterance_mask.append(utterance_mask_block) - - if summary_cols_mask is not None: - summary_mask_block = _gen_attention_mask_block( - col_widths, summary_cols_mask, 1, utterance.device - ) - summary_mask.append(summary_mask_block) - - attention_mask = ( - 1 - torch.cat([torch.cat(mask) for mask in masks_to_concat]) - ).to(torch.bool) - return attention_mask - - def forward( - self, x: torch.Tensor, lengths: torch.Tensor - ) -> Tuple[torch.Tensor, torch.Tensor]: - """Forward pass for training and non-streaming inference. - - B: batch size; - D: input dimension; - U: length of utterance. - - Args: - x (torch.Tensor): - Utterance frames right-padded with right context frames, - with shape (U + right_context_length, B, D). - lengths (torch.Tensor): - With shape (B,) and i-th element representing number of valid - utterance frames for i-th batch element in x, which contains the - right_context at the end. - - Returns: - A tuple of 2 tensors: - - output utterance frames, with shape (U, B, D). - - output_lengths, with shape (B,), without containing the - right_context at the end. - """ - U = x.size(0) - self.right_context_length - - # for query of [utterance] (i), key-value [utterance] (j), - # the max relative distance i - j is U - 1 - # the min relative distance i - j is -(U - 1) - x, pos_emb = self.encoder_pos(x, pos_len=U, neg_len=U) - - right_context = self._gen_right_context(x) - utterance = x[:U] - output_lengths = torch.clamp(lengths - self.right_context_length, min=0) - attention_mask = self._gen_attention_mask(utterance) - memory = ( - self.init_memory_op(utterance.permute(1, 2, 0)).permute(2, 0, 1)[ - :-1 - ] - if self.use_memory - else torch.empty(0).to(dtype=x.dtype, device=x.device) - ) - - output = utterance - for layer in self.emformer_layers: - output, right_context, memory = layer( - output, - output_lengths, - right_context, - memory, - attention_mask, - pos_emb, - ) - - return output, output_lengths - - @torch.jit.export - def infer( - self, - x: torch.Tensor, - lengths: torch.Tensor, - states: Optional[List[List[torch.Tensor]]] = None, - ) -> Tuple[torch.Tensor, torch.Tensor, List[List[torch.Tensor]]]: - """Forward pass for streaming inference. - - B: batch size; - D: input dimension; - U: length of utterance. - - Args: - x (torch.Tensor): - Utterance frames right-padded with right context frames, - with shape (U + right_context_length, B, D). - lengths (torch.Tensor): - With shape (B,) and i-th element representing number of valid - utterance frames for i-th batch element in x, which contains the - right_context at the end. - states (List[List[torch.Tensor]], optional): - Cached states from proceeding chunk's computation, where each - element (List[torch.Tensor]) corresponding to each emformer layer. - (default: None) - - Returns: - (Tensor, Tensor, List[List[torch.Tensor]]): - - output utterance frames, with shape (U, B, D). - - output lengths, with shape (B,), without containing the - right_context at the end. - - updated states from current chunk's computation. - """ - assert x.size(0) == self.chunk_length + self.right_context_length, ( - "Per configured chunk_length and right_context_length, " - f"expected size of {self.chunk_length + self.right_context_length} " - f"for dimension 1 of x, but got {x.size(1)}." - ) - - pos_len = self.chunk_length + self.left_context_length - neg_len = self.chunk_length - # for query of [utterance] (i), key-value [left_context, utterance] (j), - # the max relative distance i - j is L + U - 1 - # the min relative distance i - j is -(U - 1) - x, pos_emb = self.encoder_pos(x, pos_len=pos_len, neg_len=neg_len) - - right_context = x[self.chunk_length :] - utterance = x[: self.chunk_length] - output_lengths = torch.clamp(lengths - self.right_context_length, min=0) - memory = ( - self.init_memory_op(utterance.permute(1, 2, 0)).permute(2, 0, 1) - if self.use_memory - else torch.empty(0).to(dtype=x.dtype, device=x.device) - ) - output = utterance - output_states: List[List[torch.Tensor]] = [] - for layer_idx, layer in enumerate(self.emformer_layers): - output, right_context, memory, output_state = layer.infer( - output, - output_lengths, - right_context, - memory, - pos_emb, - None if states is None else states[layer_idx], - ) - output_states.append(output_state) - - return output, output_lengths, output_states - - -class Emformer(EncoderInterface): - def __init__( - self, - num_features: int, - output_dim: int, - chunk_length: int, - subsampling_factor: int = 4, - d_model: int = 256, - nhead: int = 4, - dim_feedforward: int = 2048, - num_encoder_layers: int = 12, - dropout: float = 0.1, - vgg_frontend: bool = False, - activation: str = "relu", - left_context_length: int = 0, - right_context_length: int = 0, - max_memory_size: int = 0, - tanh_on_mem: bool = False, - negative_inf: float = -1e8, - ): - super().__init__() - - self.subsampling_factor = subsampling_factor - self.right_context_length = right_context_length - self.chunk_length = chunk_length - self.left_context_length = left_context_length - if subsampling_factor != 4: - raise NotImplementedError("Support only 'subsampling_factor=4'.") - if chunk_length % 4 != 0: - raise NotImplementedError("chunk_length must be a mutiple of 4.") - if left_context_length != 0 and left_context_length % 4 != 0: - raise NotImplementedError( - "left_context_length must be 0 or a mutiple of 4." - ) - if right_context_length != 0 and right_context_length % 4 != 0: - raise NotImplementedError( - "right_context_length must be 0 or a mutiple of 4." - ) - - # self.encoder_embed converts the input of shape (N, T, num_features) - # to the shape (N, T//subsampling_factor, d_model). - # That is, it does two things simultaneously: - # (1) subsampling: T -> T//subsampling_factor - # (2) embedding: num_features -> d_model - if vgg_frontend: - self.encoder_embed = VggSubsampling(num_features, d_model) - else: - self.encoder_embed = Conv2dSubsampling(num_features, d_model) - - self.encoder = EmformerEncoder( - chunk_length // 4, - d_model, - nhead, - dim_feedforward, - num_encoder_layers, - dropout, - activation, - left_context_length=left_context_length // 4, - right_context_length=right_context_length // 4, - max_memory_size=max_memory_size, - tanh_on_mem=tanh_on_mem, - negative_inf=negative_inf, - ) - - # TODO(fangjun): remove dropout - self.encoder_output_layer = nn.Sequential( - nn.Dropout(p=dropout), nn.Linear(d_model, output_dim) - ) - - def forward( - self, x: torch.Tensor, x_lens: torch.Tensor - ) -> Tuple[torch.Tensor, torch.Tensor]: - """Forward pass for training and non-streaming inference. - - B: batch size; - D: feature dimension; - T: length of utterance. - - Args: - x (torch.Tensor): - Utterance frames right-padded with right context frames, - with shape (B, T, D). - x_lens (torch.Tensor): - With shape (B,) and i-th element representing number of valid - utterance frames for i-th batch element in x, containing the - right_context at the end. - - Returns: - (Tensor, Tensor): - - output logits, with shape (B, T', D), where - T' = ((T - 1) // 2 - 1) // 2 - self.right_context_length // 4. - - logits lengths, with shape (B,), without containing the - right_context at the end. - """ - x = self.encoder_embed(x) - x = x.permute(1, 0, 2) # (N, T, C) -> (T, N, C) - - # Caution: We assume the subsampling factor is 4! - with warnings.catch_warnings(): - warnings.simplefilter("ignore") - x_lens = ((x_lens - 1) // 2 - 1) // 2 - assert x.size(0) == x_lens.max().item() - - output, output_lengths = self.encoder(x, x_lens) # (T, N, C) - - logits = self.encoder_output_layer(output) - logits = logits.permute(1, 0, 2) # (T, N, C) ->(N, T, C) - - return logits, output_lengths - - @torch.jit.export - def infer( - self, - x: torch.Tensor, - x_lens: torch.Tensor, - states: Optional[List[List[torch.Tensor]]] = None, - ) -> Tuple[torch.Tensor, torch.Tensor, List[List[torch.Tensor]]]: - """Forward pass for streaming inference. - - B: batch size; - D: feature dimension; - T: length of utterance. - - Args: - x (torch.Tensor): - Utterance frames right-padded with right context frames, - with shape (B, T, D). - lengths (torch.Tensor): - With shape (B,) and i-th element representing number of valid - utterance frames for i-th batch element in x, containing the - right_context at the end. - states (List[List[torch.Tensor]], optional): - Cached states from proceeding chunk's computation, where each - element (List[torch.Tensor]) corresponding to each emformer layer. - (default: None) - Returns: - (Tensor, Tensor): - - output logits, with shape (B, T', D), where - T' = ((T - 1) // 2 - 1) // 2 - self.right_context_length // 4. - - logits lengths, with shape (B,), without containing the - right_context at the end. - - updated states from current chunk's computation. - """ - x = self.encoder_embed(x) - x = x.permute(1, 0, 2) # (N, T, C) -> (T, N, C) - - # Caution: We assume the subsampling factor is 4! - with warnings.catch_warnings(): - warnings.simplefilter("ignore") - x_lens = ((x_lens - 1) // 2 - 1) // 2 - assert x.size(0) == x_lens.max().item() - - output, output_lengths, output_states = self.encoder.infer( - x, x_lens, states - ) # (T, N, C) - - logits = self.encoder_output_layer(output) - logits = logits.permute(1, 0, 2) # (T, N, C) ->(N, T, C) - - return logits, output_lengths, output_states - - -class RelPositionalEncoding(torch.nn.Module): - """Relative positional encoding module. - - See : Appendix B in "Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context" # noqa - Modified from https://github.com/espnet/espnet/blob/master/espnet/nets/pytorch_backend/transformer/embedding.py # noqa - - Args: - d_model: Embedding dimension. - dropout_rate: Dropout rate. - max_len: Maximum input length. - - """ - - def __init__( - self, d_model: int, dropout_rate: float, max_len: int = 5000 - ) -> None: - """Construct an PositionalEncoding object.""" - super(RelPositionalEncoding, self).__init__() - self.d_model = d_model - self.xscale = math.sqrt(self.d_model) - self.dropout = torch.nn.Dropout(p=dropout_rate) - self.pe = None - self.pos_len = max_len - self.neg_len = max_len - self.gen_pe() - - def gen_pe(self) -> None: - """Generate the positional encodings.""" - # Suppose `i` means to the position of query vecotr and `j` means the - # position of key vector. We use position relative positions when keys - # are to the left (i>j) and negative relative positions otherwise (i torch.Tensor: - """Get positional encoding given positive length and negative length.""" - if self.pe_positive.dtype != dtype or str( - self.pe_positive.device - ) != str(device): - self.pe_positive = self.pe_positive.to(dtype=dtype, device=device) - if self.pe_negative.dtype != dtype or str( - self.pe_negative.device - ) != str(device): - self.pe_negative = self.pe_negative.to(dtype=dtype, device=device) - pe = torch.cat( - [ - self.pe_positive[self.pos_len - pos_len :], - self.pe_negative[1:neg_len], - ], - dim=0, - ) - return pe - - def forward( - self, - x: torch.Tensor, - pos_len: int, - neg_len: int, - ) -> Tuple[torch.Tensor, torch.Tensor]: - """Add positional encoding. - - Args: - x (torch.Tensor): Input tensor (batch, time, `*`). - - Returns: - torch.Tensor: Encoded tensor (batch, time, `*`). - torch.Tensor: Encoded tensor (batch, 2*time-1, `*`). - - """ - x = x * self.xscale - if pos_len > self.pos_len or neg_len > self.neg_len: - self.pos_len = pos_len - self.neg_len = neg_len - self.gen_pe() - pos_emb = self.get_pe(pos_len, neg_len, x.device, x.dtype) - return self.dropout(x), self.dropout(pos_emb) diff --git a/egs/librispeech/ASR/emformer_pruned_transducer_stateless/encoder_interface.py b/egs/librispeech/ASR/emformer_pruned_transducer_stateless/encoder_interface.py deleted file mode 120000 index aa5d0217a..000000000 --- a/egs/librispeech/ASR/emformer_pruned_transducer_stateless/encoder_interface.py +++ /dev/null @@ -1 +0,0 @@ -../transducer_stateless/encoder_interface.py \ No newline at end of file diff --git a/egs/librispeech/ASR/emformer_pruned_transducer_stateless/joiner.py b/egs/librispeech/ASR/emformer_pruned_transducer_stateless/joiner.py deleted file mode 120000 index 81ad47c55..000000000 --- a/egs/librispeech/ASR/emformer_pruned_transducer_stateless/joiner.py +++ /dev/null @@ -1 +0,0 @@ -../pruned_transducer_stateless/joiner.py \ No newline at end of file diff --git a/egs/librispeech/ASR/emformer_pruned_transducer_stateless/model.py b/egs/librispeech/ASR/emformer_pruned_transducer_stateless/model.py deleted file mode 120000 index a61a0a23f..000000000 --- a/egs/librispeech/ASR/emformer_pruned_transducer_stateless/model.py +++ /dev/null @@ -1 +0,0 @@ -../pruned_transducer_stateless/model.py \ No newline at end of file diff --git a/egs/librispeech/ASR/emformer_pruned_transducer_stateless/noam.py b/egs/librispeech/ASR/emformer_pruned_transducer_stateless/noam.py deleted file mode 100644 index e46bf35fb..000000000 --- a/egs/librispeech/ASR/emformer_pruned_transducer_stateless/noam.py +++ /dev/null @@ -1,104 +0,0 @@ -# Copyright 2021 University of Chinese Academy of Sciences (author: Han Zhu) -# -# 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 - - -class Noam(object): - """ - Implements Noam optimizer. - - Proposed in - "Attention Is All You Need", https://arxiv.org/pdf/1706.03762.pdf - - Modified from - https://github.com/espnet/espnet/blob/master/espnet/nets/pytorch_backend/transformer/optimizer.py # noqa - - Args: - params: - iterable of parameters to optimize or dicts defining parameter groups - model_size: - attention dimension of the transformer model - factor: - learning rate factor - warm_step: - warmup steps - """ - - def __init__( - self, - params, - model_size: int = 256, - factor: float = 10.0, - warm_step: int = 25000, - weight_decay=0, - ) -> None: - """Construct an Noam object.""" - self.optimizer = torch.optim.Adam( - params, lr=0, betas=(0.9, 0.98), eps=1e-9, weight_decay=weight_decay - ) - self._step = 0 - self.warmup = warm_step - self.factor = factor - self.model_size = model_size - self._rate = 0 - - @property - def param_groups(self): - """Return param_groups.""" - return self.optimizer.param_groups - - def step(self): - """Update parameters and rate.""" - self._step += 1 - rate = self.rate() - for p in self.optimizer.param_groups: - p["lr"] = rate - self._rate = rate - self.optimizer.step() - - def rate(self, step=None): - """Implement `lrate` above.""" - if step is None: - step = self._step - return ( - self.factor - * self.model_size ** (-0.5) - * min(step ** (-0.5), step * self.warmup ** (-1.5)) - ) - - def zero_grad(self): - """Reset gradient.""" - self.optimizer.zero_grad() - - def state_dict(self): - """Return state_dict.""" - return { - "_step": self._step, - "warmup": self.warmup, - "factor": self.factor, - "model_size": self.model_size, - "_rate": self._rate, - "optimizer": self.optimizer.state_dict(), - } - - def load_state_dict(self, state_dict): - """Load state_dict.""" - for key, value in state_dict.items(): - if key == "optimizer": - self.optimizer.load_state_dict(state_dict["optimizer"]) - else: - setattr(self, key, value) diff --git a/egs/librispeech/ASR/emformer_pruned_transducer_stateless/subsampling.py b/egs/librispeech/ASR/emformer_pruned_transducer_stateless/subsampling.py deleted file mode 120000 index 6fee09e58..000000000 --- a/egs/librispeech/ASR/emformer_pruned_transducer_stateless/subsampling.py +++ /dev/null @@ -1 +0,0 @@ -../conformer_ctc/subsampling.py \ No newline at end of file diff --git a/egs/librispeech/ASR/emformer_pruned_transducer_stateless/test_emformer.py b/egs/librispeech/ASR/emformer_pruned_transducer_stateless/test_emformer.py deleted file mode 100644 index b2b1000cc..000000000 --- a/egs/librispeech/ASR/emformer_pruned_transducer_stateless/test_emformer.py +++ /dev/null @@ -1,804 +0,0 @@ -import torch - - -def test_emformer_attention_forward(): - from emformer import EmformerAttention - - B, D = 2, 256 - chunk_length = 4 - right_context_length = 2 - num_chunks = 3 - U = num_chunks * chunk_length - R = num_chunks * right_context_length - attention = EmformerAttention(embed_dim=D, nhead=8) - - for use_memory in [True, False]: - if use_memory: - S = num_chunks - M = S - 1 - else: - S, M = 0, 0 - - Q, KV = R + U + S, M + R + U - utterance = torch.randn(U, B, D) - lengths = torch.randint(1, U + 1, (B,)) - lengths[0] = U - right_context = torch.randn(R, B, D) - summary = torch.randn(S, B, D) - memory = torch.randn(M, B, D) - attention_mask = torch.rand(Q, KV) >= 0.5 - PE = 2 * U - 1 - pos_emb = torch.randn(PE, D) - - output_right_context_utterance, output_memory = attention( - utterance, - lengths, - right_context, - summary, - memory, - attention_mask, - pos_emb, - ) - assert output_right_context_utterance.shape == (R + U, B, D) - assert output_memory.shape == (M, B, D) - - -def test_emformer_attention_infer(): - from emformer import EmformerAttention - - B, D = 2, 256 - U = 4 - R = 2 - L = 3 - attention = EmformerAttention(embed_dim=D, nhead=8) - - for use_memory in [True, False]: - if use_memory: - S, M = 1, 3 - else: - S, M = 0, 0 - - utterance = torch.randn(U, B, D) - lengths = torch.randint(1, U + 1, (B,)) - lengths[0] = U - right_context = torch.randn(R, B, D) - summary = torch.randn(S, B, D) - memory = torch.randn(M, B, D) - left_context_key = torch.randn(L, B, D) - left_context_val = torch.randn(L, B, D) - PE = L + 2 * U - 1 - pos_emb = torch.randn(PE, D) - - ( - output_right_context_utterance, - output_memory, - next_key, - next_val, - ) = attention.infer( - utterance, - lengths, - right_context, - summary, - memory, - left_context_key, - left_context_val, - pos_emb, - ) - assert output_right_context_utterance.shape == (R + U, B, D) - assert output_memory.shape == (S, B, D) - assert next_key.shape == (L + U, B, D) - assert next_val.shape == (L + U, B, D) - - -def test_emformer_layer_forward(): - from emformer import EmformerLayer - - B, D = 2, 256 - chunk_length = 4 - right_context_length = 2 - left_context_length = 2 - num_chunks = 3 - U = num_chunks * chunk_length - R = num_chunks * right_context_length - - for use_memory in [True, False]: - if use_memory: - S = num_chunks - M = S - 1 - else: - S, M = 0, 0 - - layer = EmformerLayer( - d_model=D, - nhead=8, - dim_feedforward=1024, - chunk_length=chunk_length, - left_context_length=left_context_length, - max_memory_size=M, - ) - - Q, KV = R + U + S, M + R + U - utterance = torch.randn(U, B, D) - lengths = torch.randint(1, U + 1, (B,)) - lengths[0] = U - right_context = torch.randn(R, B, D) - memory = torch.randn(M, B, D) - attention_mask = torch.rand(Q, KV) >= 0.5 - PE = 2 * U - 1 - pos_emb = torch.randn(PE, D) - - output_utterance, output_right_context, output_memory = layer( - utterance, lengths, right_context, memory, attention_mask, pos_emb - ) - assert output_utterance.shape == (U, B, D) - assert output_right_context.shape == (R, B, D) - assert output_memory.shape == (M, B, D) - - -def test_emformer_layer_infer(): - from emformer import EmformerLayer - - B, D = 2, 256 - U = 4 - R = 2 - L = 3 - - for use_memory in [True, False]: - if use_memory: - M = 3 - else: - M = 0 - - layer = EmformerLayer( - d_model=D, - nhead=8, - dim_feedforward=1024, - chunk_length=U, - left_context_length=L, - max_memory_size=M, - ) - - utterance = torch.randn(U, B, D) - lengths = torch.randint(1, U + 1, (B,)) - lengths[0] = U - right_context = torch.randn(R, B, D) - memory = torch.randn(M, B, D) - state = None - PE = L + 2 * U - 1 - pos_emb = torch.randn(PE, D) - ( - output_utterance, - output_right_context, - output_memory, - output_state, - ) = layer.infer( - utterance, - lengths, - right_context, - memory, - pos_emb, - state, - ) - assert output_utterance.shape == (U, B, D) - assert output_right_context.shape == (R, B, D) - if use_memory: - assert output_memory.shape == (1, B, D) - else: - assert output_memory.shape == (0, B, D) - assert len(output_state) == 4 - assert output_state[0].shape == (M, B, D) - assert output_state[1].shape == (L, B, D) - assert output_state[2].shape == (L, B, D) - assert output_state[3].shape == (1, B) - - -def test_emformer_encoder_forward(): - from emformer import EmformerEncoder - - B, D = 2, 256 - chunk_length = 4 - right_context_length = 2 - left_context_length = 2 - num_chunks = 3 - U = num_chunks * chunk_length - - for use_memory in [True, False]: - if use_memory: - S = num_chunks - M = S - 1 - else: - S, M = 0, 0 - - encoder = EmformerEncoder( - chunk_length=chunk_length, - d_model=D, - dim_feedforward=1024, - num_encoder_layers=2, - left_context_length=left_context_length, - right_context_length=right_context_length, - max_memory_size=M, - ) - - x = torch.randn(U + right_context_length, B, D) - lengths = torch.randint(1, U + right_context_length + 1, (B,)) - lengths[0] = U + right_context_length - - output, output_lengths = encoder(x, lengths) - assert output.shape == (U, B, D) - assert torch.equal( - output_lengths, torch.clamp(lengths - right_context_length, min=0) - ) - - -def test_emformer_encoder_infer(): - from emformer import EmformerEncoder - - B, D = 2, 256 - num_encoder_layers = 2 - chunk_length = 4 - right_context_length = 2 - left_context_length = 2 - num_chunks = 3 - - for use_memory in [True, False]: - if use_memory: - M = 3 - else: - M = 0 - - encoder = EmformerEncoder( - chunk_length=chunk_length, - d_model=D, - dim_feedforward=1024, - num_encoder_layers=num_encoder_layers, - left_context_length=left_context_length, - right_context_length=right_context_length, - max_memory_size=M, - ) - - states = None - for chunk_idx in range(num_chunks): - x = torch.randn(chunk_length + right_context_length, B, D) - lengths = torch.randint( - 1, chunk_length + right_context_length + 1, (B,) - ) - lengths[0] = chunk_length + right_context_length - output, output_lengths, states = encoder.infer(x, lengths, states) - assert output.shape == (chunk_length, B, D) - assert torch.equal( - output_lengths, - torch.clamp(lengths - right_context_length, min=0), - ) - assert len(states) == num_encoder_layers - for state in states: - assert len(state) == 4 - assert state[0].shape == (M, B, D) - assert state[1].shape == (left_context_length, B, D) - assert state[2].shape == (left_context_length, B, D) - assert torch.equal( - state[3], - (chunk_idx + 1) * chunk_length * torch.ones_like(state[3]), - ) - - -def test_emformer_forward(): - from emformer import Emformer - - num_features = 80 - chunk_length = 16 - right_context_length = 8 - left_context_length = 8 - num_chunks = 3 - U = num_chunks * chunk_length - output_dim = 1000 - B, D = 2, 256 - for use_memory in [True, False]: - if use_memory: - M = 3 - else: - M = 0 - model = Emformer( - num_features=num_features, - output_dim=output_dim, - chunk_length=chunk_length, - subsampling_factor=4, - d_model=D, - left_context_length=left_context_length, - right_context_length=right_context_length, - max_memory_size=M, - vgg_frontend=False, - ) - x = torch.randn(B, U + right_context_length + 3, num_features) - x_lens = torch.randint(1, U + right_context_length + 3 + 1, (B,)) - x_lens[0] = U + right_context_length + 3 - logits, output_lengths = model(x, x_lens) - assert logits.shape == (B, U // 4, output_dim) - assert torch.equal( - output_lengths, - torch.clamp( - ((x_lens - 1) // 2 - 1) // 2 - right_context_length // 4, min=0 - ), - ) - - -def test_emformer_infer(): - from emformer import Emformer - - num_features = 80 - output_dim = 1000 - chunk_length = 8 - U = chunk_length - left_context_length, right_context_length = 128, 4 - B, D = 2, 256 - num_chunks = 3 - num_encoder_layers = 2 - for use_memory in [True, False]: - if use_memory: - M = 3 - else: - M = 0 - model = Emformer( - num_features=num_features, - output_dim=output_dim, - chunk_length=chunk_length, - subsampling_factor=4, - d_model=D, - num_encoder_layers=num_encoder_layers, - left_context_length=left_context_length, - right_context_length=right_context_length, - max_memory_size=M, - vgg_frontend=False, - ) - states = None - for chunk_idx in range(num_chunks): - x = torch.randn(B, U + right_context_length + 3, num_features) - x_lens = torch.randint(1, U + right_context_length + 3 + 1, (B,)) - x_lens[0] = U + right_context_length + 3 - logits, output_lengths, states = model.infer(x, x_lens, states) - assert logits.shape == (B, U // 4, output_dim) - assert torch.equal( - output_lengths, - torch.clamp( - ((x_lens - 1) // 2 - 1) // 2 - right_context_length // 4, - min=0, - ), - ) - assert len(states) == num_encoder_layers - for state in states: - assert len(state) == 4 - assert state[0].shape == (M, B, D) - assert state[1].shape == (left_context_length // 4, B, D) - assert state[2].shape == (left_context_length // 4, B, D) - assert torch.equal( - state[3], - U // 4 * (chunk_idx + 1) * torch.ones_like(state[3]), - ) - - -def test_emformer_attention_forward_infer_consistency(): - # TODO: delete - from emformer import EmformerEncoder - - chunk_length = 4 - num_chunks = 3 - U = chunk_length * num_chunks - L, R = 1, 2 - D = 256 - num_encoder_layers = 1 - memory_sizes = [0, 3] - - for M in memory_sizes: - encoder = EmformerEncoder( - chunk_length=chunk_length, - d_model=D, - dim_feedforward=1024, - num_encoder_layers=num_encoder_layers, - left_context_length=L, - right_context_length=R, - max_memory_size=M, - dropout=0.1, - ) - encoder.eval() - encoder_layer = encoder.emformer_layers[0] - - x = torch.randn(U + R, 1, D) - lengths = torch.tensor([U]) - right_context = encoder._gen_right_context(x) - utterance = x[: x.size(0) - R] - attention_mask = encoder._gen_attention_mask(utterance) - memory = ( - encoder.init_memory_op(utterance.permute(1, 2, 0)).permute(2, 0, 1)[ - :-1 - ] - if encoder.use_memory - else torch.empty(0).to(dtype=x.dtype, device=x.device) - ) - ( - forward_output_right_context_utterance, - forward_output_memory, - ) = encoder_layer._apply_attention_forward( - utterance, - lengths, - right_context, - memory, - attention_mask, - ) - forward_output_utterance = forward_output_right_context_utterance[ - right_context.size(0) : # noqa - ] - - state = None - for chunk_idx in range(num_chunks): - start_idx = chunk_idx * chunk_length - end_idx = start_idx + chunk_length - chunk = x[start_idx:end_idx] - chunk_right_context = x[end_idx : end_idx + R] # noqa - chunk_length = torch.tensor([chunk_length]) - chunk_memory = ( - encoder.init_memory_op(chunk.permute(1, 2, 0)).permute(2, 0, 1) - if encoder.use_memory - else torch.empty(0).to(dtype=x.dtype, device=x.device) - ) - ( - infer_output_right_context_utterance, - infer_output_memory, - state, - ) = encoder_layer._apply_attention_infer( - chunk, - chunk_length, - chunk_right_context, - chunk_memory, - state, - ) - infer_output_chunk = infer_output_right_context_utterance[ - chunk_right_context.size(0) : # noqa - ] - forward_output_chunk = forward_output_utterance[start_idx:end_idx] - assert torch.allclose( - infer_output_chunk, - forward_output_chunk, - atol=1e-6, - rtol=0.0, - ) - - -def test_emformer_layer_forward_infer_consistency(): - from emformer import EmformerEncoder - - chunk_length = 4 - num_chunks = 3 - U = chunk_length * num_chunks - left_context_length, right_context_length = 1, 2 - D = 256 - num_encoder_layers = 1 - memory_sizes = [0, 3] - - for M in memory_sizes: - encoder = EmformerEncoder( - chunk_length=chunk_length, - d_model=D, - dim_feedforward=1024, - num_encoder_layers=num_encoder_layers, - left_context_length=left_context_length, - right_context_length=right_context_length, - max_memory_size=M, - dropout=0.1, - ) - encoder.eval() - encoder_layer = encoder.emformer_layers[0] - encoder_pos = encoder.encoder_pos - - x = torch.randn(U + right_context_length, 1, D) - - # training mode with full utterance - x_forward, pos_emb = encoder_pos(x, U, U) - lengths = torch.tensor([U]) - right_context = encoder._gen_right_context(x_forward) - utterance = x_forward[:U] - attention_mask = encoder._gen_attention_mask(utterance) - memory = ( - encoder.init_memory_op(utterance.permute(1, 2, 0)).permute(2, 0, 1)[ - :-1 - ] - if encoder.use_memory - else torch.empty(0).to(dtype=x.dtype, device=x.device) - ) - ( - forward_output_utterance, - forward_output_right_context, - forward_output_memory, - ) = encoder_layer( - utterance, - lengths, - right_context, - memory, - attention_mask, - pos_emb, - ) - - state = None - for chunk_idx in range(num_chunks): - start_idx = chunk_idx * chunk_length - end_idx = start_idx + chunk_length - cur_x, pos_emb = encoder_pos( - x[start_idx : end_idx + right_context_length], - pos_len=chunk_length + left_context_length, - neg_len=chunk_length, - ) - chunk = cur_x[:chunk_length] - chunk_right_context = cur_x[chunk_length:] - chunk_memory = ( - encoder.init_memory_op(chunk.permute(1, 2, 0)).permute(2, 0, 1) - if encoder.use_memory - else torch.empty(0).to(dtype=x.dtype, device=x.device) - ) - ( - infer_output_chunk, - infer_right_context, - infer_output_memory, - state, - ) = encoder_layer.infer( - chunk, - torch.tensor([chunk_length]), - chunk_right_context, - chunk_memory, - pos_emb, - state, - ) - forward_output_chunk = forward_output_utterance[start_idx:end_idx] - assert torch.allclose( - infer_output_chunk, - forward_output_chunk, - atol=1e-5, - rtol=0.0, - ) - - -def test_emformer_encoder_forward_infer_consistency(): - from emformer import EmformerEncoder - - chunk_length = 4 - num_chunks = 3 - U = chunk_length * num_chunks - left_context_length, right_context_length = 1, 2 - D = 256 - num_encoder_layers = 3 - memory_sizes = [0, 3] - - for M in memory_sizes: - encoder = EmformerEncoder( - chunk_length=chunk_length, - d_model=D, - dim_feedforward=1024, - num_encoder_layers=num_encoder_layers, - left_context_length=left_context_length, - right_context_length=right_context_length, - max_memory_size=M, - dropout=0.1, - ) - encoder.eval() - - x = torch.randn(U + right_context_length, 1, D) - lengths = torch.tensor([U + right_context_length]) - - # training mode with full utterance - forward_output, forward_output_lengths = encoder(x, lengths) - - # streaming inference mode with individual chunks - states = None - for chunk_idx in range(num_chunks): - start_idx = chunk_idx * chunk_length - end_idx = start_idx + chunk_length - chunk = x[start_idx : end_idx + right_context_length] # noqa - chunk_length = torch.tensor([chunk_length]) - infer_output_chunk, infer_output_lengths, states = encoder.infer( - chunk, chunk_length, states - ) - forward_output_chunk = forward_output[start_idx:end_idx] - assert torch.allclose( - infer_output_chunk, - forward_output_chunk, - atol=1e-5, - rtol=0.0, - ) - - -def test_emformer_infer_batch_single_consistency(): - """Test consistency of cached states and output logits between single - utterance inference and batch inference.""" - from emformer import Emformer - - num_features = 80 - output_dim = 1000 - chunk_length = 8 - num_chunks = 3 - U = num_chunks * chunk_length - left_context_length, right_context_length = 128, 4 - B, D = 2, 256 - num_encoder_layers = 2 - for use_memory in [True, False]: - if use_memory: - M = 3 - else: - M = 0 - model = Emformer( - num_features=num_features, - output_dim=output_dim, - chunk_length=chunk_length, - subsampling_factor=4, - d_model=D, - num_encoder_layers=num_encoder_layers, - left_context_length=left_context_length, - right_context_length=right_context_length, - max_memory_size=M, - vgg_frontend=False, - ) - model.eval() - - def save_states(states): - saved_states = [] - for layer_idx in range(len(states)): - layer_state = [] - layer_state.append(states[layer_idx][0].clone()) # memory - layer_state.append( - states[layer_idx][1].clone() - ) # left_context_key - layer_state.append( - states[layer_idx][2].clone() - ) # left_context_val - layer_state.append(states[layer_idx][3].clone()) # past_length - saved_states.append(layer_state) - return saved_states - - def assert_states_equal(saved_states, states, sample_idx): - for layer_idx in range(len(saved_states)): - # assert eqaul memory - assert torch.allclose( - states[layer_idx][0], - saved_states[layer_idx][0][ - :, sample_idx : sample_idx + 1 # noqa - ], - atol=1e-5, - rtol=0.0, - ) - # assert equal left_context_key - assert torch.allclose( - states[layer_idx][1], - saved_states[layer_idx][1][ - :, sample_idx : sample_idx + 1 # noqa - ], - atol=1e-5, - rtol=0.0, - ) - # assert equal left_context_val - assert torch.allclose( - states[layer_idx][2], - saved_states[layer_idx][2][ - :, sample_idx : sample_idx + 1 # noqa - ], - atol=1e-5, - rtol=0.0, - ) - # assert eqaul past_length - assert torch.equal( - states[layer_idx][3], - saved_states[layer_idx][3][ - :, sample_idx : sample_idx + 1 # noqa - ], - ) - - x = torch.randn(B, U + right_context_length + 3, num_features) - - # batch-wise inference - batch_logits = [] - batch_states = [] - states = None - for chunk_idx in range(num_chunks): - start_idx = chunk_idx * chunk_length - end_idx = start_idx + chunk_length - chunk = x[:, start_idx : end_idx + right_context_length + 3] # noqa - lengths = torch.tensor( - [chunk_length + right_context_length + 3] - ).expand(B) - logits, output_lengths, states = model.infer(chunk, lengths, states) - batch_logits.append(logits) - batch_states.append(save_states(states)) - batch_logits = torch.cat(batch_logits, dim=1) - - # single-wise inference - single_logits = [] - for sample_idx in range(B): - sample = x[sample_idx : sample_idx + 1] # noqa - chunk_logits = [] - states = None - for chunk_idx in range(num_chunks): - start_idx = chunk_idx * chunk_length - end_idx = start_idx + chunk_length - chunk = sample[ - :, start_idx : end_idx + right_context_length + 3 - ] - lengths = torch.tensor( - [chunk_length + right_context_length + 3] - ) - logits, output_lengths, states = model.infer( - chunk, lengths, states - ) - chunk_logits.append(logits) - assert_states_equal(batch_states[chunk_idx], states, sample_idx) - - chunk_logits = torch.cat(chunk_logits, dim=1) - single_logits.append(chunk_logits) - - single_logits = torch.cat(single_logits, dim=0) - - assert torch.allclose(batch_logits, single_logits, atol=1e-5, rtol=0.0) - - -def test_emformer_infer_states_stack(): - from emformer import Emformer, unstack_states, stack_states - - num_features = 80 - output_dim = 1000 - chunk_length = 8 - U = chunk_length - left_context_length, right_context_length = 128, 4 - B, D = 2, 256 - num_encoder_layers = 2 - for use_memory in [True, False]: - if use_memory: - M = 3 - else: - M = 0 - model = Emformer( - num_features=num_features, - output_dim=output_dim, - chunk_length=chunk_length, - subsampling_factor=4, - d_model=D, - num_encoder_layers=num_encoder_layers, - left_context_length=left_context_length, - right_context_length=right_context_length, - max_memory_size=M, - vgg_frontend=False, - ) - - x = torch.randn(B, U + right_context_length + 3, num_features) - x_lens = torch.full((B,), U + right_context_length + 3) - logits, output_lengths, states = model.infer( - x, - x_lens, - ) - states2 = stack_states(unstack_states(states)) - - for ss, ss2 in zip(states, states2): - for s, s2 in zip(ss, ss2): - assert torch.allclose(s, s2), f"{s.sum()}, {s2.sum()}" - - -def test_rel_positional_encoding(): - from emformer import RelPositionalEncoding - - D = 256 - pos_enc = RelPositionalEncoding(D, dropout_rate=0.1) - pos_len = 100 - neg_len = 100 - x = torch.randn(2, D) - x, pos_emb = pos_enc(x, pos_len, neg_len) - assert pos_emb.shape == (pos_len + neg_len - 1, D) - - -if __name__ == "__main__": - test_emformer_attention_forward() - test_emformer_attention_infer() - test_emformer_layer_forward() - test_emformer_layer_infer() - test_emformer_encoder_forward() - test_emformer_encoder_infer() - test_emformer_forward() - test_emformer_infer() - # test_emformer_attention_forward_infer_consistency() - test_emformer_layer_forward_infer_consistency() - test_emformer_encoder_forward_infer_consistency() - test_emformer_infer_batch_single_consistency() - test_emformer_infer_states_stack() - test_rel_positional_encoding() diff --git a/egs/librispeech/ASR/emformer_pruned_transducer_stateless/train.py b/egs/librispeech/ASR/emformer_pruned_transducer_stateless/train.py deleted file mode 100755 index 18a845a93..000000000 --- a/egs/librispeech/ASR/emformer_pruned_transducer_stateless/train.py +++ /dev/null @@ -1,1008 +0,0 @@ -#!/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" - -./transducer_emformer/train.py \ - --world-size 4 \ - --num-epochs 30 \ - --start-epoch 0 \ - --exp-dir transducer_emformer/exp \ - --full-libri 1 \ - --max-duration 300 -""" - - -import argparse -import logging -import warnings -from pathlib import Path -from shutil import copyfile -from typing import Any, Dict, Optional, Tuple - -import k2 -import sentencepiece as spm -import torch -import torch.multiprocessing as mp -import torch.nn as nn -from asr_datamodule import LibriSpeechAsrDataModule -from decoder import Decoder -from emformer import Emformer -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 noam import Noam -from torch import Tensor -from torch.nn.parallel import DistributedDataParallel as DDP -from torch.nn.utils import clip_grad_norm_ -from torch.utils.tensorboard import SummaryWriter - -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, - measure_gradient_norms, - measure_weight_norms, - optim_step_and_measure_param_change, - setup_logger, - str2bool, -) - - -def add_model_arguments(parser: argparse.ArgumentParser): - parser.add_argument( - "--attention-dim", - type=int, - default=512, - help="Attention dim for the Emformer", - ) - - parser.add_argument( - "--nhead", - type=int, - default=8, - help="Number of attention heads for the Emformer", - ) - - parser.add_argument( - "--dim-feedforward", - type=int, - default=2048, - help="Feed-forward dimension for the Emformer", - ) - - parser.add_argument( - "--num-encoder-layers", - type=int, - default=12, - help="Number of encoder layers for the Emformer", - ) - - parser.add_argument( - "--left-context-length", - type=int, - default=120, - help="Number of frames for the left context in the Emformer", - ) - - parser.add_argument( - "--chunk-length", - type=int, - default=16, - help="Number of frames for each segment in the Emformer", - ) - - parser.add_argument( - "--right-context-length", - type=int, - default=4, - help="Number of frames for right context in the Emformer", - ) - - parser.add_argument( - "--memory-size", - type=int, - default=0, - help="Number of entries in the memory for the Emformer", - ) - - parser.add_argument( - "--tanh-on-mem", - type=str2bool, - default=False, - help="Whether to apply tanh on memory", - ) - - -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_emformer/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="transducer_emformer/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( - "--lr-factor", - type=float, - default=5.0, - help="The lr_factor for Noam optimizer", - ) - - 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( - "--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`. - """, - ) - - add_model_arguments(parser) - - 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. - - - attention_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 - "log_diagnostics": False, - # parameters for Emformer - "feature_dim": 80, - "subsampling_factor": 4, - "vgg_frontend": False, - # parameters for decoder - "embedding_dim": 512, - # parameters for Noam - "warm_step": 80000, # For the 100h subset, use 20000 - "env_info": get_env_info(), - } - ) - - return params - - -def get_encoder_model(params: AttributeDict) -> nn.Module: - encoder = Emformer( - num_features=params.feature_dim, - output_dim=params.vocab_size, - subsampling_factor=params.subsampling_factor, - d_model=params.attention_dim, - nhead=params.nhead, - dim_feedforward=params.dim_feedforward, - num_encoder_layers=params.num_encoder_layers, - vgg_frontend=params.vgg_frontend, - left_context_length=params.left_context_length, - chunk_length=params.chunk_length, - right_context_length=params.right_context_length, - max_memory_size=params.memory_size, - tanh_on_mem=params.tanh_on_mem, - ) - return encoder - - -def get_decoder_model(params: AttributeDict) -> nn.Module: - decoder = Decoder( - vocab_size=params.vocab_size, - embedding_dim=params.embedding_dim, - blank_id=params.blank_id, - unk_id=params.unk_id, - context_size=params.context_size, - ) - return decoder - - -def get_joiner_model(params: AttributeDict) -> nn.Module: - joiner = Joiner( - input_dim=params.vocab_size, - inner_dim=params.embedding_dim, - output_dim=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, - ) - return model - - -def load_checkpoint_if_available( - params: AttributeDict, - model: nn.Module, - optimizer: Optional[torch.optim.Optimizer] = 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. - 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, - ) - - 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, - sampler: Optional[CutSampler] = 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. - """ - 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, - sampler=sampler, - 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, -) -> 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 Emformer 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. - """ - 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, - ) - loss = params.simple_loss_scale * simple_loss + 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, - sp: spm.SentencePieceProcessor, - train_dl: torch.utils.data.DataLoader, - valid_dl: torch.utils.data.DataLoader, - 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. - train_dl: - Dataloader for the training dataset. - valid_dl: - Dataloader for the validation dataset. - 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() - - def maybe_log_gradients(tag: str): - if ( - params.log_diagnostics - and tb_writer is not None - and params.batch_idx_train % (params.log_interval * 5) == 0 - ): - tb_writer.add_scalars( - tag, - measure_gradient_norms(model, norm="l2"), - global_step=params.batch_idx_train, - ) - - def maybe_log_weights(tag: str): - if ( - params.log_diagnostics - and tb_writer is not None - and params.batch_idx_train % (params.log_interval * 5) == 0 - ): - tb_writer.add_scalars( - tag, - measure_weight_norms(model, norm="l2"), - global_step=params.batch_idx_train, - ) - - def maybe_log_param_relative_changes(): - if ( - params.log_diagnostics - and tb_writer is not None - and params.batch_idx_train % (params.log_interval * 5) == 0 - ): - deltas = optim_step_and_measure_param_change(model, optimizer) - tb_writer.add_scalars( - "train/relative_param_change_per_minibatch", - deltas, - global_step=params.batch_idx_train, - ) - else: - optimizer.step() - - 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"]) - - 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. - - loss.backward() - - maybe_log_weights("train/param_norms") - maybe_log_gradients("train/grad_norms") - maybe_log_param_relative_changes() - - optimizer.zero_grad() - - 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, - sampler=train_dl.sampler, - 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: - logging.info( - f"Epoch {params.cur_epoch}, " - f"batch {batch_idx}, loss[{loss_info}], " - f"tot_loss[{tot_loss}], batch size: {batch_size}" - ) - - if tb_writer is not None: - 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 = 800 - params.warm_step = 20000 - - 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.unk_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 = Noam( - model.parameters(), - model_size=params.attention_dim, - factor=params.lr_factor, - warm_step=params.warm_step, - ) - - if checkpoints and "optimizer" in checkpoints: - logging.info("Loading optimizer state dict") - optimizer.load_state_dict(checkpoints["optimizer"]) - - 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 - - num_in_total = len(train_cuts) - - train_cuts = train_cuts.filter(remove_short_and_long_utt) - - num_left = len(train_cuts) - num_removed = num_in_total - num_left - removed_percent = num_removed / num_in_total * 100 - - logging.info(f"Before removing short and long utterances: {num_in_total}") - logging.info(f"After removing short and long utterances: {num_left}") - logging.info(f"Removed {num_removed} utterances ({removed_percent:.5f}%)") - - 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) - - scan_pessimistic_batches_for_oom( - model=model, - train_dl=train_dl, - optimizer=optimizer, - sp=sp, - params=params, - ) - - for epoch in range(params.start_epoch, params.num_epochs): - fix_random_seed(params.seed + epoch) - train_dl.sampler.set_epoch(epoch) - - cur_lr = optimizer._rate - if tb_writer is not None: - tb_writer.add_scalar( - "train/learning_rate", cur_lr, params.batch_idx_train - ) - tb_writer.add_scalar("train/epoch", epoch, params.batch_idx_train) - - if rank == 0: - logging.info("epoch {}, learning rate {}".format(epoch, cur_lr)) - - params.cur_epoch = epoch - - train_one_epoch( - params=params, - model=model, - optimizer=optimizer, - sp=sp, - train_dl=train_dl, - valid_dl=valid_dl, - tb_writer=tb_writer, - world_size=world_size, - rank=rank, - ) - - save_checkpoint( - params=params, - model=model, - optimizer=optimizer, - sampler=train_dl.sampler, - 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: - optimizer.zero_grad() - loss, _ = compute_loss( - params=params, - model=model, - sp=sp, - batch=batch, - is_training=True, - ) - loss.backward() - clip_grad_norm_(model.parameters(), 5.0, 2.0) - optimizer.step() - 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()