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.
128 lines
4.6 KiB
Python
128 lines
4.6 KiB
Python
#
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# 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 time
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import os
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import json
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import argparse
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import numpy as np
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import tensorflow as tf
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from neumf import ncf_model_ops
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def parse_args():
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parser = argparse.ArgumentParser(description="Benchmark inference performance of the NCF model")
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parser.add_argument('--load_checkpoint_path', default=None, type=str,
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help='Path to the checkpoint file to be loaded. If None will use random weights')
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parser.add_argument('--n_users', default=138493, type=int,
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help='Number of users. Defaults to the number of users in the ml-20m dataset after preprocessing')
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parser.add_argument('--n_items', default=26744, type=int,
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help='Number of items. Defaults to the number of users in the ml-20m dataset after preprocessing')
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parser.add_argument('-f', '--factors', type=int, default=64,
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help='Number of predictive factors')
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parser.add_argument('--layers', nargs='+', type=int,
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default=[256, 256, 128, 64],
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help='Sizes of hidden layers for MLP')
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parser.add_argument('--batch_size', default=1, type=int, help='Batch size for inference')
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parser.add_argument('--num_batches', default=20, type=int,
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help='Number of batches for which to measure latency and throughput')
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parser.add_argument('--no_amp', dest='amp', action='store_false', default=True,
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help='Disable mixed precision')
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parser.add_argument('--xla', dest='xla', action='store_true', default=False,
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help='Enable XLA')
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parser.add_argument('--log_path', default='nvlog.json', type=str,
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help='Path to the path to store benchmark results')
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return parser.parse_args()
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def main():
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args = parse_args()
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if args.amp:
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os.environ["TF_ENABLE_AUTO_MIXED_PRECISION"] = "1"
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# Input tensors
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users = tf.placeholder(tf.int32, shape=(None,))
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items = tf.placeholder(tf.int32, shape=(None,))
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dropout = tf.placeholder_with_default(0.0, shape=())
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# Model ops and saver
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logits_op = ncf_model_ops(
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users=users,
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items=items,
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labels=None,
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dup_mask=None,
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params={
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'fp16': False,
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'val_batch_size': args.batch_size,
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'num_users': args.n_users,
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'num_items': args.n_items,
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'num_factors': args.factors,
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'mf_reg': 0,
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'layer_sizes': args.layers,
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'layer_regs': [0. for i in args.layers],
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'dropout': 0.0,
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'sigmoid': True,
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'top_k': None,
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'learning_rate': None,
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'beta_1': None,
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'beta_2': None,
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'epsilon': None,
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'loss_scale': None,
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},
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mode='INFERENCE'
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)
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config = tf.ConfigProto()
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config.gpu_options.allow_growth = True
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if args.xla:
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config.graph_options.optimizer_options.global_jit_level = tf.OptimizerOptions.ON_1
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sess = tf.Session(config=config)
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saver = tf.train.Saver()
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if args.load_checkpoint_path:
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saver.restore(sess, args.load_checkpoint_path)
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else:
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# Manual initialize weights
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sess.run(tf.global_variables_initializer())
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sess.run(tf.local_variables_initializer())
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users_batch = np.random.randint(size=args.batch_size, low=0, high=args.n_users)
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items_batch = np.random.randint(size=args.batch_size, low=0, high=args.n_items)
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latencies = []
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for _ in range(args.num_batches):
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start = time.time()
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logits = sess.run(logits_op, feed_dict={users: users_batch, items: items_batch, dropout: 0.0 })
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latencies.append(time.time() - start)
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results = {
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'args' : vars(args),
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'best_inference_throughput' : args.batch_size / min(latencies),
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'best_inference_latency' : min(latencies),
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'inference_latencies' : latencies
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}
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print('RESULTS: ', json.dumps(results, indent=4))
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if args.log_path is not None:
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json.dump(results, open(args.log_path, 'w'), indent=4)
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if __name__ == '__main__':
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main()
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