PaddleSpeech/paddlespeech/s2t/exps/u2/model.py

# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# 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.
"""Contains U2 model."""
import json
import os
import time
from collections import defaultdict
from collections import OrderedDict
from contextlib import nullcontext
from typing import Optional

import jsonlines
import numpy as np
import paddle
from paddle import distributed as dist
from paddle.io import DataLoader
from yacs.config import CfgNode

from paddlespeech.s2t.io.collator import SpeechCollator
from paddlespeech.s2t.io.dataset import ManifestDataset
from paddlespeech.s2t.io.sampler import SortagradBatchSampler
from paddlespeech.s2t.io.sampler import SortagradDistributedBatchSampler
from paddlespeech.s2t.models.u2 import U2Model
from paddlespeech.s2t.training.optimizer import OptimizerFactory
from paddlespeech.s2t.training.reporter import ObsScope
from paddlespeech.s2t.training.reporter import report
from paddlespeech.s2t.training.scheduler import LRSchedulerFactory
from paddlespeech.s2t.training.timer import Timer
from paddlespeech.s2t.training.trainer import Trainer
from paddlespeech.s2t.utils import ctc_utils
from paddlespeech.s2t.utils import error_rate
from paddlespeech.s2t.utils import layer_tools
from paddlespeech.s2t.utils import mp_tools
from paddlespeech.s2t.utils.log import Log
from paddlespeech.s2t.utils.utility import UpdateConfig

logger = Log(__name__).getlog()


class U2Trainer(Trainer):
    @classmethod
    def params(cls, config: Optional[CfgNode]=None) -> CfgNode:
        # training config
        default = CfgNode(
            dict(
                n_epoch=50,  # train epochs
                log_interval=100,  # steps
                accum_grad=1,  # accum grad by # steps
                global_grad_clip=5.0,  # the global norm clip
            ))
        default.optim = 'adam'
        default.optim_conf = CfgNode(
            dict(
                lr=5e-4,  # learning rate
                weight_decay=1e-6,  # the coeff of weight decay
            ))
        default.scheduler = 'warmuplr'
        default.scheduler_conf = CfgNode(
            dict(
                warmup_steps=25000,
                lr_decay=1.0,  # learning rate decay
            ))

        if config is not None:
            config.merge_from_other_cfg(default)
        return default

    def __init__(self, config, args):
        super().__init__(config, args)

    def train_batch(self, batch_index, batch_data, msg):
        train_conf = self.config.training
        start = time.time()

        # forward
        utt, audio, audio_len, text, text_len = batch_data
        loss, attention_loss, ctc_loss = self.model(audio, audio_len, text,
                                                    text_len)

        # loss div by `batch_size * accum_grad`
        loss /= train_conf.accum_grad
        losses_np = {'loss': float(loss) * train_conf.accum_grad}
        if attention_loss:
            losses_np['att_loss'] = float(attention_loss)
        if ctc_loss:
            losses_np['ctc_loss'] = float(ctc_loss)

        # loss backward
        if (batch_index + 1) % train_conf.accum_grad != 0:
            # Disable gradient synchronizations across DDP processes.
            # Within this context, gradients will be accumulated on module
            # variables, which will later be synchronized.
            # When using cpu w/o DDP, model does not have `no_sync`
            context = self.model.no_sync if (hasattr(self.model, "no_sync") and
                                             self.parallel) else nullcontext
        else:
            # Used for single gpu training and DDP gradient synchronization
            # processes.
            context = nullcontext
        with context():
            loss.backward()
            layer_tools.print_grads(self.model, print_func=None)

        # optimizer step
        if (batch_index + 1) % train_conf.accum_grad == 0:
            self.optimizer.step()
            self.optimizer.clear_grad()
            self.lr_scheduler.step()
            self.iteration += 1

        iteration_time = time.time() - start

        for k, v in losses_np.items():
            report(k, v)
        report("batch_size", self.config.collator.batch_size)
        report("accum", train_conf.accum_grad)
        report("step_cost", iteration_time)

        if (batch_index + 1) % train_conf.accum_grad == 0:
            if dist.get_rank() == 0 and self.visualizer:
                losses_np_v = losses_np.copy()
                losses_np_v.update({"lr": self.lr_scheduler()})
                self.visualizer.add_scalars("step", losses_np_v,
                                            self.iteration - 1)

    @paddle.no_grad()
    def valid(self):
        self.model.eval()
        logger.info(f"Valid Total Examples: {len(self.valid_loader.dataset)}")
        valid_losses = defaultdict(list)
        num_seen_utts = 1
        total_loss = 0.0
        for i, batch in enumerate(self.valid_loader):
            utt, audio, audio_len, text, text_len = batch
            loss, attention_loss, ctc_loss = self.model(audio, audio_len, text,
                                                        text_len)
            if paddle.isfinite(loss):
                num_utts = batch[1].shape[0]
                num_seen_utts += num_utts
                total_loss += float(loss) * num_utts
                valid_losses['val_loss'].append(float(loss))
                if attention_loss:
                    valid_losses['val_att_loss'].append(float(attention_loss))
                if ctc_loss:
                    valid_losses['val_ctc_loss'].append(float(ctc_loss))

            if (i + 1) % self.config.training.log_interval == 0:
                valid_dump = {k: np.mean(v) for k, v in valid_losses.items()}
                valid_dump['val_history_loss'] = total_loss / num_seen_utts

                # logging
                msg = f"Valid: Rank: {dist.get_rank()}, "
                msg += "epoch: {}, ".format(self.epoch)
                msg += "step: {}, ".format(self.iteration)
                msg += "batch: {}/{}, ".format(i + 1, len(self.valid_loader))
                msg += ', '.join('{}: {:>.6f}'.format(k, v)
                                 for k, v in valid_dump.items())
                logger.info(msg)

        logger.info('Rank {} Val info val_loss {}'.format(
            dist.get_rank(), total_loss / num_seen_utts))
        return total_loss, num_seen_utts

    def do_train(self):
        """The training process control by step."""
        # !!!IMPORTANT!!!
        # Try to export the model by script, if fails, we should refine
        # the code to satisfy the script export requirements
        # script_model = paddle.jit.to_static(self.model)
        # script_model_path = str(self.checkpoint_dir / 'init')
        # paddle.jit.save(script_model, script_model_path)

        self.before_train()

        logger.info(f"Train Total Examples: {len(self.train_loader.dataset)}")
        while self.epoch < self.config.training.n_epoch:
            with Timer("Epoch-Train Time Cost: {}"):
                self.model.train()
                try:
                    data_start_time = time.time()
                    for batch_index, batch in enumerate(self.train_loader):
                        dataload_time = time.time() - data_start_time
                        msg = "Train:"
                        observation = OrderedDict()
                        with ObsScope(observation):
                            report("Rank", dist.get_rank())
                            report("epoch", self.epoch)
                            report('step', self.iteration)
                            report("lr", self.lr_scheduler())
                            self.train_batch(batch_index, batch, msg)
                            self.after_train_batch()
                            report('iter', batch_index + 1)
                            report('total', len(self.train_loader))
                            report('reader_cost', dataload_time)
                        observation['batch_cost'] = observation[
                            'reader_cost'] + observation['step_cost']
                        observation['samples'] = observation['batch_size']
                        observation['ips[sent./sec]'] = observation[
                            'batch_size'] / observation['batch_cost']
                        for k, v in observation.items():
                            msg += f" {k}: "
                            msg += f"{v:>.8f}" if isinstance(v,
                                                             float) else f"{v}"
                            msg += ","
                        msg = msg[:-1]  # remove the last ","
                        if (batch_index + 1
                            ) % self.config.training.log_interval == 0:
                            logger.info(msg)
                        data_start_time = time.time()
                except Exception as e:
                    logger.error(e)
                    raise e

            with Timer("Eval Time Cost: {}"):
                total_loss, num_seen_utts = self.valid()
                if dist.get_world_size() > 1:
                    num_seen_utts = paddle.to_tensor(num_seen_utts)
                    # the default operator in all_reduce function is sum.
                    dist.all_reduce(num_seen_utts)
                    total_loss = paddle.to_tensor(total_loss)
                    dist.all_reduce(total_loss)
                    cv_loss = total_loss / num_seen_utts
                    cv_loss = float(cv_loss)
                else:
                    cv_loss = total_loss / num_seen_utts

            logger.info(
                'Epoch {} Val info val_loss {}'.format(self.epoch, cv_loss))
            if self.visualizer:
                self.visualizer.add_scalars(
                    'epoch', {'cv_loss': cv_loss,
                              'lr': self.lr_scheduler()}, self.epoch)

            self.save(tag=self.epoch, infos={'val_loss': cv_loss})
            self.new_epoch()

    def setup_dataloader(self):
        config = self.config.clone()
        config.defrost()
        config.collator.keep_transcription_text = False

        # train/valid dataset, return token ids
        config.data.manifest = config.data.train_manifest
        train_dataset = ManifestDataset.from_config(config)

        config.data.manifest = config.data.dev_manifest
        dev_dataset = ManifestDataset.from_config(config)

        collate_fn_train = SpeechCollator.from_config(config)

        config.collator.augmentation_config = ""
        collate_fn_dev = SpeechCollator.from_config(config)

        if self.parallel:
            batch_sampler = SortagradDistributedBatchSampler(
                train_dataset,
                batch_size=config.collator.batch_size,
                num_replicas=None,
                rank=None,
                shuffle=True,
                drop_last=True,
                sortagrad=config.collator.sortagrad,
                shuffle_method=config.collator.shuffle_method)
        else:
            batch_sampler = SortagradBatchSampler(
                train_dataset,
                shuffle=True,
                batch_size=config.collator.batch_size,
                drop_last=True,
                sortagrad=config.collator.sortagrad,
                shuffle_method=config.collator.shuffle_method)
        self.train_loader = DataLoader(
            train_dataset,
            batch_sampler=batch_sampler,
            collate_fn=collate_fn_train,
            num_workers=config.collator.num_workers, )
        self.valid_loader = DataLoader(
            dev_dataset,
            batch_size=config.collator.batch_size,
            shuffle=False,
            drop_last=False,
            collate_fn=collate_fn_dev,
            num_workers=config.collator.num_workers, )

        # test dataset, return raw text
        config.data.manifest = config.data.test_manifest
        # filter test examples, will cause less examples, but no mismatch with training
        # and can use large batch size , save training time, so filter test egs now.
        config.data.min_input_len = 0.0  # second
        config.data.max_input_len = float('inf')  # second
        config.data.min_output_len = 0.0  # tokens
        config.data.max_output_len = float('inf')  # tokens
        config.data.min_output_input_ratio = 0.00
        config.data.max_output_input_ratio = float('inf')

        test_dataset = ManifestDataset.from_config(config)
        # return text ord id
        config.collator.keep_transcription_text = True
        config.collator.augmentation_config = ""
        self.test_loader = DataLoader(
            test_dataset,
            batch_size=config.decoding.batch_size,
            shuffle=False,
            drop_last=False,
            collate_fn=SpeechCollator.from_config(config),
            num_workers=config.collator.num_workers, )
        # return text token id
        config.collator.keep_transcription_text = False
        self.align_loader = DataLoader(
            test_dataset,
            batch_size=config.decoding.batch_size,
            shuffle=False,
            drop_last=False,
            collate_fn=SpeechCollator.from_config(config),
            num_workers=config.collator.num_workers, )
        logger.info("Setup train/valid/test/align Dataloader!")

    def setup_model(self):
        config = self.config
        model_conf = config.model

        with UpdateConfig(model_conf):
            model_conf.input_dim = self.train_loader.collate_fn.feature_size
            model_conf.output_dim = self.train_loader.collate_fn.vocab_size

        model = U2Model.from_config(model_conf)

        if self.parallel:
            model = paddle.DataParallel(model)

        logger.info(f"{model}")
        layer_tools.print_params(model, logger.info)

        train_config = config.training
        optim_type = train_config.optim
        optim_conf = train_config.optim_conf
        scheduler_type = train_config.scheduler
        scheduler_conf = train_config.scheduler_conf

        scheduler_args = {
            "learning_rate": optim_conf.lr,
            "verbose": False,
            "warmup_steps": scheduler_conf.warmup_steps,
            "gamma": scheduler_conf.lr_decay,
            "d_model": model_conf.encoder_conf.output_size,
        }
        lr_scheduler = LRSchedulerFactory.from_args(scheduler_type,
                                                    scheduler_args)

        def optimizer_args(
                config,
                parameters,
                lr_scheduler=None, ):
            train_config = config.training
            optim_type = train_config.optim
            optim_conf = train_config.optim_conf
            scheduler_type = train_config.scheduler
            scheduler_conf = train_config.scheduler_conf
            return {
                "grad_clip": train_config.global_grad_clip,
                "weight_decay": optim_conf.weight_decay,
                "learning_rate": lr_scheduler
                if lr_scheduler else optim_conf.lr,
                "parameters": parameters,
                "epsilon": 1e-9 if optim_type == 'noam' else None,
                "beta1": 0.9 if optim_type == 'noam' else None,
                "beat2": 0.98 if optim_type == 'noam' else None,
            }

        optimzer_args = optimizer_args(config, model.parameters(), lr_scheduler)
        optimizer = OptimizerFactory.from_args(optim_type, optimzer_args)

        self.model = model
        self.optimizer = optimizer
        self.lr_scheduler = lr_scheduler
        logger.info("Setup model/optimizer/lr_scheduler!")


class U2Tester(U2Trainer):
    @classmethod
    def params(cls, config: Optional[CfgNode]=None) -> CfgNode:
        # decoding config
        default = CfgNode(
            dict(
                alpha=2.5,  # Coef of LM for beam search.
                beta=0.3,  # Coef of WC for beam search.
                cutoff_prob=1.0,  # Cutoff probability for pruning.
                cutoff_top_n=40,  # Cutoff number for pruning.
                lang_model_path='models/lm/common_crawl_00.prune01111.trie.klm',  # Filepath for language model.
                decoding_method='attention',  # Decoding method. Options: 'attention', 'ctc_greedy_search',
                # 'ctc_prefix_beam_search', 'attention_rescoring'
                error_rate_type='wer',  # Error rate type for evaluation. Options `wer`, 'cer'
                num_proc_bsearch=8,  # # of CPUs for beam search.
                beam_size=10,  # Beam search width.
                batch_size=16,  # decoding batch size
                ctc_weight=0.0,  # ctc weight for attention rescoring decode mode.
                decoding_chunk_size=-1,  # decoding chunk size. Defaults to -1.
                # <0: for decoding, use full chunk.
                # >0: for decoding, use fixed chunk size as set.
                # 0: used for training, it's prohibited here.
                num_decoding_left_chunks=-1,  # number of left chunks for decoding. Defaults to -1.
                simulate_streaming=False,  # simulate streaming inference. Defaults to False.
            ))

        if config is not None:
            config.merge_from_other_cfg(default)
        return default

    def __init__(self, config, args):
        super().__init__(config, args)

    def ordid2token(self, texts, texts_len):
        """ ord() id to chr() chr """
        trans = []
        for text, n in zip(texts, texts_len):
            n = n.numpy().item()
            ids = text[:n]
            trans.append(''.join([chr(i) for i in ids]))
        return trans

    def compute_metrics(self,
                        utts,
                        audio,
                        audio_len,
                        texts,
                        texts_len,
                        fout=None):
        cfg = self.config.decoding
        errors_sum, len_refs, num_ins = 0.0, 0, 0
        errors_func = error_rate.char_errors if cfg.error_rate_type == 'cer' else error_rate.word_errors
        error_rate_func = error_rate.cer if cfg.error_rate_type == 'cer' else error_rate.wer

        start_time = time.time()
        text_feature = self.test_loader.collate_fn.text_feature
        target_transcripts = self.ordid2token(texts, texts_len)
        result_transcripts, result_tokenids = self.model.decode(
            audio,
            audio_len,
            text_feature=text_feature,
            decoding_method=cfg.decoding_method,
            lang_model_path=cfg.lang_model_path,
            beam_alpha=cfg.alpha,
            beam_beta=cfg.beta,
            beam_size=cfg.beam_size,
            cutoff_prob=cfg.cutoff_prob,
            cutoff_top_n=cfg.cutoff_top_n,
            num_processes=cfg.num_proc_bsearch,
            ctc_weight=cfg.ctc_weight,
            decoding_chunk_size=cfg.decoding_chunk_size,
            num_decoding_left_chunks=cfg.num_decoding_left_chunks,
            simulate_streaming=cfg.simulate_streaming)
        decode_time = time.time() - start_time

        for utt, target, result, rec_tids in zip(
                utts, target_transcripts, result_transcripts, result_tokenids):
            errors, len_ref = errors_func(target, result)
            errors_sum += errors
            len_refs += len_ref
            num_ins += 1
            if fout:
                fout.write({
                    "utt": utt,
                    "refs": [target],
                    "hyps": [result],
                    "hyps_tokenid": [rec_tids],
                })
            logger.info(f"Utt: {utt}")
            logger.info(f"Ref: {target}")
            logger.info(f"Hyp: {result}")
            logger.info("One example error rate [%s] = %f" %
                        (cfg.error_rate_type, error_rate_func(target, result)))

        return dict(
            errors_sum=errors_sum,
            len_refs=len_refs,
            num_ins=num_ins,  # num examples
            error_rate=errors_sum / len_refs,
            error_rate_type=cfg.error_rate_type,
            num_frames=audio_len.sum().numpy().item(),
            decode_time=decode_time)

    @mp_tools.rank_zero_only
    @paddle.no_grad()
    def test(self):
        assert self.args.result_file
        self.model.eval()
        logger.info(f"Test Total Examples: {len(self.test_loader.dataset)}")

        stride_ms = self.test_loader.collate_fn.stride_ms
        error_rate_type = None
        errors_sum, len_refs, num_ins = 0.0, 0, 0
        num_frames = 0.0
        num_time = 0.0
        with jsonlines.open(self.args.result_file, 'w') as fout:
            for i, batch in enumerate(self.test_loader):
                metrics = self.compute_metrics(*batch, fout=fout)
                num_frames += metrics['num_frames']
                num_time += metrics["decode_time"]
                errors_sum += metrics['errors_sum']
                len_refs += metrics['len_refs']
                num_ins += metrics['num_ins']
                error_rate_type = metrics['error_rate_type']
                rtf = num_time / (num_frames * stride_ms)
                logger.info(
                    "RTF: %f, Error rate [%s] (%d/?) = %f" %
                    (rtf, error_rate_type, num_ins, errors_sum / len_refs))

        rtf = num_time / (num_frames * stride_ms)
        msg = "Test: "
        msg += "epoch: {}, ".format(self.epoch)
        msg += "step: {}, ".format(self.iteration)
        msg += "RTF: {}, ".format(rtf)
        msg += "Final error rate [%s] (%d/%d) = %f" % (
            error_rate_type, num_ins, num_ins, errors_sum / len_refs)
        logger.info(msg)

        # test meta results
        err_meta_path = os.path.splitext(self.args.result_file)[0] + '.err'
        err_type_str = "{}".format(error_rate_type)
        with open(err_meta_path, 'w') as f:
            data = json.dumps({
                "epoch":
                self.epoch,
                "step":
                self.iteration,
                "rtf":
                rtf,
                error_rate_type:
                errors_sum / len_refs,
                "dataset_hour": (num_frames * stride_ms) / 1000.0 / 3600.0,
                "process_hour":
                num_time / 1000.0 / 3600.0,
                "num_examples":
                num_ins,
                "err_sum":
                errors_sum,
                "ref_len":
                len_refs,
                "decode_method":
                self.config.decoding.decoding_method,
            })
            f.write(data + '\n')

    @paddle.no_grad()
    def align(self):
        ctc_utils.ctc_align(
            self.model, self.align_loader, self.config.decoding.batch_size,
            self.align_loader.collate_fn.stride_ms,
            self.align_loader.collate_fn.vocab_list, self.args.result_file)

    def load_inferspec(self):
        """infer model and input spec.

        Returns:
            nn.Layer: inference model
            List[paddle.static.InputSpec]: input spec.
        """
        from paddlespeech.s2t.models.u2 import U2InferModel
        infer_model = U2InferModel.from_pretrained(self.test_loader,
                                                   self.config.model.clone(),
                                                   self.args.checkpoint_path)
        feat_dim = self.test_loader.collate_fn.feature_size
        input_spec = [
            paddle.static.InputSpec(shape=[1, None, feat_dim],
                                    dtype='float32'),  # audio, [B,T,D]
            paddle.static.InputSpec(shape=[1],
                                    dtype='int64'),  # audio_length, [B]
        ]
        return infer_model, input_spec

    @paddle.no_grad()
    def export(self):
        infer_model, input_spec = self.load_inferspec()
        assert isinstance(input_spec, list), type(input_spec)
        infer_model.eval()
        static_model = paddle.jit.to_static(infer_model, input_spec=input_spec)
        logger.info(f"Export code: {static_model.forward.code}")
        paddle.jit.save(static_model, self.args.export_path)