a644350589
Tacotron2+Waveglow/PyT * AMP support * Data preprocessing for Tacotron 2 training * Fixed dropouts on LSTMCells SSD/PyT * script and notebook for inference * AMP support * README update * updates to examples/* BERT/PyT * initial release GNMT/PyT * Default container updated to NGC PyTorch 19.05-py3 * Mixed precision training implemented using APEX AMP * Added inference throughput and latency results on NVIDIA Tesla V100 16G * Added option to run inference on user-provided raw input text from command line NCF/PyT * Updated performance tables. * Default container changed to PyTorch 19.06-py3. * Caching validation negatives between runs Transformer/PyT * new README * jit support added UNet Medical/TF * inference example scripts added * inference benchmark measuring latency added * TRT/TF-TRT support added * README updated GNMT/TF * Performance improvements Small updates (mostly README) for other models.
71 lines
2.8 KiB
Python
71 lines
2.8 KiB
Python
# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import argparse
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import re
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import sys
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from subprocess import Popen, PIPE
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parser = argparse.ArgumentParser(description='Translate')
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parser.add_argument('--executable', default='nmt.py', help='path to nmt.py')
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parser.add_argument('--infer_batch_size', metavar='B1,[B2,...]',
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default='64', help='batch sizes separated by comma')
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parser.add_argument('--beam_width', metavar='W1,[W2,...]',
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default='5', help='beam widths separated by comma')
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args, other_args = parser.parse_known_args()
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batch_sizes = list(map(int, args.infer_batch_size.split(',')))
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beam_widths = list(map(int, args.beam_width.split(',')))
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def pr(*args, column_len=14):
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for arg in args:
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if type(arg) is float:
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arg = '{:.2f}'.format(arg)
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arg = str(arg)
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print('', arg.ljust(column_len), end=' |')
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print()
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pr('batch size', 'beam width', 'bleu', 'sentences/sec', 'tokens/sec',
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'latency_avg', 'latency_50', 'latency_90', 'latency_95', 'latency_99', 'latency_100')
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for batch_size in batch_sizes:
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for beam_width in beam_widths:
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cmd = ['python', args.executable, '--beam_width', str(beam_width),
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'--infer_batch_size', str(batch_size), '--mode', 'infer'] + other_args
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proc = Popen(cmd, stdout=PIPE, stderr=PIPE)
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out, err = proc.communicate()
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bleu_search_res = re.search(rb'\nbleu is ((\d|.)+)', out)
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speed_search_res = re.search(
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rb'\neval time for ckpt: ((\d|.)+) mins \(((\d|.)+) sent/sec, ((\d|.)+) tokens/sec\)', out)
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latencies = []
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for lat in ['avg', '50', '90', '95', '99', '100']:
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latencies.append(re.search(r'\neval latency_{} for ckpt: ((\d|.)+) ms'.format(lat).encode(), out))
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if bleu_search_res is None or speed_search_res is None or any(filter(lambda x: x is None, latencies)):
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print('AN ERROR OCCURRED WHILE RUNNING:', cmd, file=sys.stderr)
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print('-' * 20, 'STDOUT', '-' * 20, file=sys.stderr)
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print(out.decode())
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print('-' * 20, 'STDERR', '-' * 20, file=sys.stderr)
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print(err.decode())
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exit(1)
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bleu = float(bleu_search_res.group(1))
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sentences_per_sec, tokens_per_sec = map(float, speed_search_res.group(3, 5))
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latencies = list(map(lambda x: float(x.group(1)), latencies))
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pr(batch_size, beam_width, bleu, sentences_per_sec, tokens_per_sec, *latencies)
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