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random_sampling_operators.py
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random_sampling_operators.py
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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
#
# https://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.
"""Performance benchmark tests for MXNet NDArray Random Sampling Operations.
1. Operators are automatically fetched from MXNet operator registry.
2. Default Inputs are generated. See rules/default_params.py. You can override the default values.
Below 18 random sampling Operators are covered:
['random_exponential', 'random_gamma', 'random_generalized_negative_binomial', 'random_negative_binomial',
'random_normal', 'random_poisson', 'random_randint', 'random_uniform', 'sample_exponential', 'sample_gamma',
'sample_generalized_negative_binomial', 'sample_multinomial', 'sample_negative_binomial', 'sample_normal',
'sample_poisson', 'sample_uniform', 'GridGenerator', 'BilinearSampler']
"""
import mxnet as mx
from benchmark.opperf.utils.benchmark_utils import run_op_benchmarks
from benchmark.opperf.utils.op_registry_utils import get_all_random_sampling_operators
def run_mx_random_sampling_operators_benchmarks(ctx=mx.cpu(), dtype='float32', profiler='native', warmup=25, runs=100):
"""Runs benchmarks with the given context and precision (dtype)for all the random sampling
operators in MXNet.
Parameters
----------
ctx: mx.ctx
Context to run benchmarks
dtype: str, default 'float32'
Precision to use for benchmarks
profiler: str, default 'native'
Type of Profiler to use (native/python)
warmup: int, default 25
Number of times to run for warmup
runs: int, default 100
Number of runs to capture benchmark results
Returns
-------
Dictionary of results. Key -> Name of the operator, Value -> Benchmark results.
"""
# Fetch all Random Sampling Operators
mx_random_sample_ops = get_all_random_sampling_operators()
# Run benchmarks
mx_random_sample_op_results = run_op_benchmarks(mx_random_sample_ops, dtype, ctx, profiler, warmup, runs)
return mx_random_sample_op_results