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https://github.com/pytorch/pytorch.git
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19e6886576
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/19997 ghimport-source-id: 420d4a68a1ef879beee2734adba8abb575e0b0ab Differential Revision: D15231375 Pulled By: ilia-cher fbshipit-source-id: ce7248ea2ebb54d25c9d831c6e3f23f3534557dd
76 lines
2.0 KiB
Python
76 lines
2.0 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 (({'M': 1}, {'N' : 4}),
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({'M': 1}, {'N' : 5}),
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({'M': 2}, {'N' : 4}),
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({'M': 2}, {'N' : 5}))
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"""
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assert 'sample_func' in configs, "Missing sample_func to generat configs"
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result = []
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for key, values in configs.items():
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if key == 'sample_func':
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continue
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tmp_result = []
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for value in values:
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tmp_result.append({key : value})
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result.append(tmp_result)
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results = configs['sample_func'](*result)
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return results
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def is_caffe2_enabled(framework_arg):
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return 'Caffe2' in framework_arg
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def is_pytorch_enabled(framework_arg):
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return 'PyTorch' in framework_arg
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def get_requested_frameworks(framework_arg):
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return [fr.strip() for fr in framework_arg.split(',') if len(fr.strip()) > 0]
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