DeepLearningExamples/PyTorch/SpeechSynthesis/FastPitch/inference_perf.py

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import argparse
import json
import time

import torch
import numpy as np

import dllogger as DLLogger
from apex import amp
from dllogger import StdOutBackend, JSONStreamBackend, Verbosity

import models
from inference import load_and_setup_model, MeasureTime


def parse_args(parser):
    """
    Parse commandline arguments.
    """
    parser.add_argument('--amp-run', action='store_true',
                        help='inference with AMP')
    parser.add_argument('-bs', '--batch-size', type=int, default=1)
    parser.add_argument('-o', '--output', type=str, required=True,
                        help='Directory to save results')
    parser.add_argument('--log-file', type=str, default='nvlog.json',
                        help='Filename for logging')
    return parser


def main():
    """
    Launches inference benchmark.
    Inference is executed on a single GPU.
    """
    parser = argparse.ArgumentParser(
        description='PyTorch FastPitch Inference Benchmark')
    parser = parse_args(parser)
    args, _ = parser.parse_known_args()

    log_file = args.log_file
    DLLogger.init(backends=[JSONStreamBackend(Verbosity.DEFAULT,
                                              args.log_file),
                            StdOutBackend(Verbosity.VERBOSE)])
    for k,v in vars(args).items():
        DLLogger.log(step="PARAMETER", data={k:v})
    DLLogger.log(step="PARAMETER", data={'model_name': 'FastPitch_PyT'})

    model = load_and_setup_model('FastPitch', parser, None, args.amp_run,
                                 'cuda', unk_args=[], forward_is_infer=True,
                                 ema=False, jitable=True)

    # FIXME Temporarily disabled due to nn.LayerNorm fp16 casting bug in pytorch:20.02-py3 and 20.03
    # model = torch.jit.script(model)

    warmup_iters = 3
    iters = 1
    gen_measures = MeasureTime()
    all_frames = 0
    for i in range(-warmup_iters, iters):
        text_padded = torch.randint(low=0, high=148, size=(args.batch_size, 128),
                                    dtype=torch.long).to('cuda')
        input_lengths = torch.IntTensor([text_padded.size(1)] * args.batch_size).to('cuda')
        durs = torch.ones_like(text_padded).mul_(4).to('cuda')

        with torch.no_grad(), gen_measures:
            mels, *_ = model(text_padded, input_lengths, dur_tgt=durs)
        num_frames = mels.size(0) * mels.size(2)

        if i >= 0:
            all_frames += num_frames
            DLLogger.log(step=(i,), data={"latency": gen_measures[-1]})
            DLLogger.log(step=(i,), data={"frames/s": num_frames / gen_measures[-1]})

    measures = gen_measures[warmup_iters:]
    DLLogger.log(step=(), data={'avg latency': np.mean(measures)})
    DLLogger.log(step=(), data={'avg frames/s': all_frames / np.sum(measures)})
    DLLogger.flush()

if __name__ == '__main__':
    main()