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Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/18740 Test utilities for writing Caffe2/PyTorch performance microbenchmarks. Brief description of the file structure * benchmark_core.py : core utiltiites for running microbenchmark tests * benchmark_caffe2.py : Caffe2 specific benchmark utilitites * benchmark_pytorch.py: PyTorch specific benchmark utilities * benchmark_runner.py : Main function. Currently it can run the microbenchmark tests in a stand-alone mode. The next step is to have this integrate with AI-PEP. The utilities are located at https://github.com/pytorch/pytorch/tree/master/test to have access to both Caffe2/PyTorch Python's frontend. Include two operator microbenchmarks; support both Caffe2/PyTorch: * MatMul * Add Reference: PyTorch benchmarks : https://github.com/pytorch/benchmark/tree/master/timing/python. In this work, we start with two example binary operators MatMul and Add, but eventually we should to cover unary operators like in the PyTorch benchmark repo. Reviewed By: zheng-xq Differential Revision: D13887111 fbshipit-source-id: b7a56b95448c9ec3e674b0de0ffb96af4439bfce
36 lines
834 B
Python
36 lines
834 B
Python
from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from __future__ import unicode_literals
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import numpy as np
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import itertools
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import random
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"""Performance microbenchmarks's utils.
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This module contains utilities for writing microbenchmark tests.
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"""
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def shape_to_string(shape):
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return ', '.join([str(x) for x in shape])
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def numpy_random_fp32(*shape):
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"""Return a random numpy tensor of float32 type.
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"""
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# TODO: consider more complex/custom dynamic ranges for
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# comprehensive test coverage.
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return np.random.rand(*shape).astype(np.float32)
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def cross_product(*inputs):
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return (list(itertools.product(*inputs)))
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def get_n_rand_nums(min_val, max_val, n):
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random.seed((1 << 32) - 1)
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return random.sample(range(min_val, max_val), n)
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