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45d5b6be48
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/19433 For operator benchmark project, we need to cover a lot of operators, so the interface for adding operators needs to be very clean and simple. This diff is implementing a new interface to add op. Here is the logic to add new operator to the benchmark: ``` long_config = {} short_config = {} map_func add_test( [long_config, short_config], map_func, [caffe2 op] [pt op] ) ``` Reviewed By: zheng-xq Differential Revision: D14791191 fbshipit-source-id: ac6738507cf1b9d6013dc8e546a2022a9b177f05
53 lines
1.4 KiB
Python
53 lines
1.4 KiB
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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"""
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Return a list of cartesian product of input iterables.
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For example, cross_product(A, B) returns ((x,y) for x in A for y in B).
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"""
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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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def generate_configs(**configs):
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"""
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Given configs from users, we want to generate different combinations of
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those configs
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For example, given M = ((1, 2), N = (4, 5)) and sample_func being cross_product,
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we will generate ((1, 4), (1, 5), (2, 4), (2, 5))
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"""
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assert 'sample_func' in configs, "Missing sample_func to generat configs"
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results = configs['sample_func'](
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*[value for key, value in configs.items() if key != 'sample_func'])
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return results
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