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| # Licensed to the Apache Software Foundation (ASF) under one | ||
| # or more contributor license agreements. See the NOTICE file | ||
| # distributed with this work for additional information | ||
| # regarding copyright ownership. The ASF licenses this file | ||
| # to you 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. | ||
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| import time | ||
| import mxnet as mx | ||
| from mxnet.ndarray.sparse import adam_update | ||
| import numpy as np | ||
| import argparse | ||
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| mx.random.seed(0) | ||
| np.random.seed(0) | ||
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| parser = argparse.ArgumentParser(description='Benchmark adam updater') | ||
| parser.add_argument('--dim-in', type=int, default=240000, help='weight.shape[0]') | ||
| parser.add_argument('--dim-out', type=int, default=512, help='weight.shape[1]') | ||
| parser.add_argument('--nnr', type=int, default=5000, help='grad.indices.shape[0]') | ||
| parser.add_argument('--repeat', type=int, default=1000, help='num repeat') | ||
| parser.add_argument('--dense-grad', action='store_true', | ||
| help='if set to true, both gradient and weight are dense.') | ||
| parser.add_argument('--dense-state', action='store_true', | ||
| help='if set to true, states are dense, indicating standard update') | ||
| parser.add_argument('--cpu', action='store_true') | ||
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| args = parser.parse_args() | ||
| dim_in = args.dim_in | ||
| dim_out = args.dim_out | ||
| nnr = args.nnr | ||
| ctx = mx.cpu() if args.cpu else mx.gpu() | ||
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| ones = mx.nd.ones((dim_in, dim_out), ctx=ctx) | ||
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| if not args.dense_grad: | ||
| weight = ones.tostype('row_sparse') | ||
| indices = np.arange(dim_in) | ||
| np.random.shuffle(indices) | ||
| indices = np.unique(indices[:nnr]) | ||
| indices = mx.nd.array(indices, ctx=ctx) | ||
| grad = mx.nd.sparse.retain(weight, indices) | ||
| else: | ||
| weight = ones.copy() | ||
| grad = ones.copy() | ||
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| if args.dense_state: | ||
| mean = ones.copy() | ||
| else: | ||
| mean = ones.tostype('row_sparse') | ||
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| var = mean.copy() | ||
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| # warmup | ||
| for i in range(10): | ||
| adam_update(weight, grad, mean, var, out=weight, lr=1, wd=0, beta1=0.9, | ||
| beta2=0.99, rescale_grad=0.5, epsilon=1e-8) | ||
| weight.wait_to_read() | ||
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| # measure speed | ||
| a = time.time() | ||
| for i in range(args.repeat): | ||
| adam_update(weight, grad, mean, var, out=weight, lr=1, wd=0, beta1=0.9, | ||
| beta2=0.99, rescale_grad=0.5, epsilon=1e-8) | ||
| weight.wait_to_read() | ||
| b = time.time() | ||
| print(b - a) |
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