inference.py

# Copyright (c) 2019, NVIDIA CORPORATION. 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.

import argparse
import math
import os
import random
import time
from heapq import nlargest
from itertools import chain, repeat
from pathlib import Path
from tqdm import tqdm

import dllogger
import torch
import numpy as np
import torch.distributed as distrib
import torch.nn.functional as F
from apex import amp
from apex.parallel import DistributedDataParallel
from dllogger import JSONStreamBackend, StdOutBackend, Verbosity

from jasper import config
from common import helpers
from common.dali.data_loader import DaliDataLoader
from common.dataset import (AudioDataset, FilelistDataset, get_data_loader,
                            SingleAudioDataset)
from common.features import BaseFeatures, FilterbankFeatures
from common.helpers import print_once, process_evaluation_epoch
from jasper.model import GreedyCTCDecoder, Jasper
from common.tb_dllogger import stdout_metric_format, unique_log_fpath


def get_parser():
    parser = argparse.ArgumentParser(description='Jasper')
    parser.add_argument('--batch_size', default=16, type=int,
                        help='Data batch size')
    parser.add_argument('--steps', default=0, type=int,
                        help='Eval this many steps for every worker')
    parser.add_argument('--warmup_steps', default=0, type=int,
                        help='Burn-in period before measuring latencies')
    parser.add_argument('--model_config', type=str, required=True,
                        help='Relative model config path given dataset folder')
    parser.add_argument('--dataset_dir', type=str,
                        help='Absolute path to dataset folder')
    parser.add_argument('--val_manifests', type=str, nargs='+',
                        help='Relative path to evaluation dataset manifest files')
    parser.add_argument('--ckpt', default=None, type=str,
                        help='Path to model checkpoint')
    parser.add_argument('--pad_leading', type=int, default=16,
                        help='Pads every batch with leading zeros '
                             'to counteract conv shifts of the field of view')
    parser.add_argument('--amp', '--fp16', action='store_true',
                        help='Use FP16 precision')
    parser.add_argument('--cudnn_benchmark', action='store_true',
                        help='Enable cudnn benchmark')
    parser.add_argument('--cpu', action='store_true',
                        help='Run inference on CPU')
    parser.add_argument("--seed", default=None, type=int, help='Random seed')
    parser.add_argument('--local_rank', default=os.getenv('LOCAL_RANK', 0),
                        type=int, help='GPU id used for distributed training')

    io = parser.add_argument_group('feature and checkpointing setup')
    io.add_argument('--dali_device', type=str, choices=['none', 'cpu', 'gpu'],
                    default='gpu', help='Use DALI pipeline for fast data processing')
    io.add_argument('--save_predictions', type=str, default=None,
                    help='Save predictions in text form at this location')
    io.add_argument('--save_logits', default=None, type=str,
                    help='Save output logits under specified path')
    io.add_argument('--transcribe_wav', type=str,
                    help='Path to a single .wav file (16KHz)')
    io.add_argument('--transcribe_filelist', type=str,
                    help='Path to a filelist with one .wav path per line')
    io.add_argument('-o', '--output_dir', default='results/',
                    help='Output folder to save audio (file per phrase)')
    io.add_argument('--log_file', type=str, default=None,
                    help='Path to a DLLogger log file')
    io.add_argument('--ema', action='store_true',
                    help='Load averaged model weights')
    io.add_argument('--torchscript', action='store_true',
                    help='Evaluate with a TorchScripted model')
    io.add_argument('--torchscript_export', action='store_true',
                    help='Export the model with torch.jit to the output_dir')
    io.add_argument('--override_config', type=str, action='append',
                    help='Overrides a value from a config .yaml.'
                         ' Syntax: `--override_config nested.config.key=val`.')
    return parser


def durs_to_percentiles(durations, ratios):
    durations = np.asarray(durations) * 1000  # in ms
    latency = durations

    latency = latency[5:]
    mean_latency = np.mean(latency)

    latency_worst = nlargest(math.ceil((1 - min(ratios)) * len(latency)), latency)
    latency_ranges = get_percentile(ratios, latency_worst, len(latency))
    latency_ranges[0.5] = mean_latency
    return latency_ranges


def get_percentile(ratios, arr, nsamples):
    res = {}
    for a in ratios:
        idx = max(int(nsamples * (1 - a)), 0)
        res[a] = arr[idx]
    return res


def torchscript_export(data_loader, audio_processor, model, greedy_decoder,
                       output_dir, use_amp, use_conv_masks, model_config, device,
                       save):

    audio_processor.to(device)

    for batch in data_loader:
        batch = [t.to(device, non_blocking=True) for t in batch]
        audio, audio_len, _, _ = batch
        feats, feat_lens = audio_processor(audio, audio_len)
        break

    print("\nExporting featurizer...")
    print("\nNOTE: Dithering causes warnings about non-determinism.\n")
    ts_feat = torch.jit.trace(audio_processor, (audio, audio_len))

    print("\nExporting acoustic model...")
    model(feats, feat_lens)
    ts_acoustic = torch.jit.trace(model, (feats, feat_lens))

    print("\nExporting decoder...")
    log_probs = model(feats, feat_lens)
    ts_decoder = torch.jit.script(greedy_decoder, log_probs)
    print("\nJIT export complete.")

    if save:
        precision = "fp16" if use_amp else "fp32"
        module_name = f'{os.path.basename(model_config)}_{precision}'
        ts_feat.save(os.path.join(output_dir, module_name + "_feat.pt"))
        ts_acoustic.save(os.path.join(output_dir, module_name + "_acoustic.pt"))
        ts_decoder.save(os.path.join(output_dir, module_name + "_decoder.pt"))

    return ts_feat, ts_acoustic, ts_decoder


def main():

    parser = get_parser()
    args = parser.parse_args()

    log_fpath = args.log_file or str(Path(args.output_dir, 'nvlog_infer.json'))
    dllogger.init(backends=[
        JSONStreamBackend(Verbosity.DEFAULT, log_fpath, append=True),
        JSONStreamBackend(Verbosity.DEFAULT, unique_log_fpath(log_fpath)),
        StdOutBackend(Verbosity.VERBOSE, metric_format=stdout_metric_format)
    ])
    [dllogger.log("PARAMETER", {k: v}) for k, v in vars(args).items()]

    for step in ['DNN', 'data+DNN', 'data']:
        for c in [0.99, 0.95, 0.9, 0.5]:
            cs = 'avg' if c == 0.5 else f'{int(100*c)}%'
            dllogger.metadata(f'{step.lower()}_latency_{c}',
                              {'name': f'{step} latency {cs}',
                               'format': ':>7.2f', 'unit': 'ms'})
    dllogger.metadata(
        'eval_wer', {'name': 'WER', 'format': ':>3.2f', 'unit': '%'})

    if args.cpu:
        device = torch.device('cpu')
    else:
        assert torch.cuda.is_available()
        device = torch.device('cuda')
        torch.backends.cudnn.benchmark = args.cudnn_benchmark

    if args.seed is not None:
        torch.manual_seed(args.seed + args.local_rank)
        np.random.seed(args.seed + args.local_rank)
        random.seed(args.seed + args.local_rank)

    # set up distributed training
    multi_gpu = not args.cpu and int(os.environ.get('WORLD_SIZE', 1)) > 1
    if multi_gpu:
        torch.cuda.set_device(args.local_rank)
        distrib.init_process_group(backend='nccl', init_method='env://')
        print_once(f'Inference with {distrib.get_world_size()} GPUs')

    cfg = config.load(args.model_config)
    config.apply_config_overrides(cfg, args)

    symbols = helpers.add_ctc_blank(cfg['labels'])

    use_dali = args.dali_device in ('cpu', 'gpu')
    dataset_kw, features_kw = config.input(cfg, 'val')

    measure_perf = args.steps > 0

    # dataset
    if args.transcribe_wav or args.transcribe_filelist:

        if use_dali:
            print("DALI supported only with input .json files; disabling")
            use_dali = False

        assert not (args.transcribe_wav and args.transcribe_filelist)

        if args.transcribe_wav:
            dataset = SingleAudioDataset(args.transcribe_wav)
        else:
            dataset = FilelistDataset(args.transcribe_filelist)

        data_loader = get_data_loader(dataset,
                                      batch_size=1,
                                      multi_gpu=multi_gpu,
                                      shuffle=False,
                                      num_workers=0,
                                      drop_last=(True if measure_perf else False))

        _, features_kw = config.input(cfg, 'val')
        assert not features_kw['pad_to_max_duration']
        feat_proc = FilterbankFeatures(**features_kw)

    elif use_dali:
        # pad_to_max_duration is not supported by DALI - have simple padders
        if features_kw['pad_to_max_duration']:
            feat_proc = BaseFeatures(
                pad_align=features_kw['pad_align'],
                pad_to_max_duration=True,
                max_duration=features_kw['max_duration'],
                sample_rate=features_kw['sample_rate'],
                window_size=features_kw['window_size'],
                window_stride=features_kw['window_stride'])
            features_kw['pad_to_max_duration'] = False
        else:
            feat_proc = None

        data_loader = DaliDataLoader(
            gpu_id=args.local_rank or 0,
            dataset_path=args.dataset_dir,
            config_data=dataset_kw,
            config_features=features_kw,
            json_names=args.val_manifests,
            batch_size=args.batch_size,
            pipeline_type=("train" if measure_perf else "val"),  # no drop_last
            device_type=args.dali_device,
            symbols=symbols)

    else:
        dataset = AudioDataset(args.dataset_dir,
                               args.val_manifests,
                               symbols,
                               **dataset_kw)

        data_loader = get_data_loader(dataset,
                                      args.batch_size,
                                      multi_gpu=multi_gpu,
                                      shuffle=False,
                                      num_workers=4,
                                      drop_last=False)

        feat_proc = FilterbankFeatures(**features_kw)

    model = Jasper(encoder_kw=config.encoder(cfg),
                   decoder_kw=config.decoder(cfg, n_classes=len(symbols)))

    if args.ckpt is not None:
        print(f'Loading the model from {args.ckpt} ...')
        checkpoint = torch.load(args.ckpt, map_location="cpu")
        key = 'ema_state_dict' if args.ema else 'state_dict'
        state_dict = helpers.convert_v1_state_dict(checkpoint[key])
        model.load_state_dict(state_dict, strict=True)

    model.to(device)
    model.eval()

    if feat_proc is not None:
        feat_proc.to(device)
        feat_proc.eval()

    if args.amp:
        model = model.half()

    if args.torchscript:
        greedy_decoder = GreedyCTCDecoder()

        feat_proc, model, greedy_decoder = torchscript_export(
            data_loader, feat_proc, model, greedy_decoder, args.output_dir,
            use_amp=args.amp, use_conv_masks=True, model_toml=args.model_toml,
            device=device, save=args.torchscript_export)

    if multi_gpu:
        model = DistributedDataParallel(model)

    agg = {'txts': [], 'preds': [], 'logits': []}
    dur = {'data': [], 'dnn': [], 'data+dnn': []}

    looped_loader = chain.from_iterable(repeat(data_loader))
    greedy_decoder = GreedyCTCDecoder()

    sync = lambda: torch.cuda.synchronize() if device.type == 'cuda' else None

    steps = args.steps + args.warmup_steps or len(data_loader)
    with torch.no_grad():

        for it, batch in enumerate(tqdm(looped_loader, initial=1, total=steps)):

            if use_dali:
                feats, feat_lens, txt, txt_lens = batch
                if feat_proc is not None:
                    feats, feat_lens = feat_proc(feats, feat_lens)
            else:
                batch = [t.to(device, non_blocking=True) for t in batch]
                audio, audio_lens, txt, txt_lens = batch
                feats, feat_lens = feat_proc(audio, audio_lens)

            sync()
            t1 = time.time()

            if args.amp:
                feats = feats.half()

            feats = F.pad(feats, (args.pad_leading, 0))
            feat_lens += args.pad_leading

            if model.encoder.use_conv_masks:
                log_probs, log_prob_lens = model(feats, feat_lens)
            else:
                log_probs = model(feats, feat_lens)

            preds = greedy_decoder(log_probs)

            sync()
            t2 = time.time()

            # burn-in period; wait for a new loader due to num_workers
            if it >= 1 and (args.steps == 0 or it >= args.warmup_steps):
                dur['data'].append(t1 - t0)
                dur['dnn'].append(t2 - t1)
                dur['data+dnn'].append(t2 - t0)

            if txt is not None:
                agg['txts'] += helpers.gather_transcripts([txt], [txt_lens],
                                                          symbols)
            agg['preds'] += helpers.gather_predictions([preds], symbols)
            agg['logits'].append(log_probs)

            if it + 1 == steps:
                break

            sync()
            t0 = time.time()

        # communicate the results
        if args.transcribe_wav:
            for idx, p in enumerate(agg['preds']):
                print_once(f'Prediction {idx+1: >3}: {p}')

        elif args.transcribe_filelist:
            pass

        elif not multi_gpu or distrib.get_rank() == 0:
            wer, _ = process_evaluation_epoch(agg)

            dllogger.log(step=(), data={'eval_wer': 100 * wer})

        if args.save_predictions:
            with open(args.save_predictions, 'w') as f:
                f.write('\n'.join(agg['preds']))

        if args.save_logits:
            logits = torch.cat(agg['logits'], dim=0).cpu()
            torch.save(logits, args.save_logits)

    # report timings
    if len(dur['data']) >= 20:
        ratios = [0.9, 0.95, 0.99]
        for stage in dur:
            lat = durs_to_percentiles(dur[stage], ratios)
            for k in [0.99, 0.95, 0.9, 0.5]:
                kk = str(k).replace('.', '_')
                dllogger.log(step=(), data={f'{stage.lower()}_latency_{kk}': lat[k]})

    else:
        print_once('Not enough samples to measure latencies.')


if __name__ == "__main__":
    main()