benchmark/opperf/nd_operations/reduction_operators.py

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"""Performance benchmark tests for MXNet NDArray Reduction 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 10 reduction Operators are covered:

['max', 'max_axis', 'mean', 'min', 'min_axis', 'nanprod', 'nansum', 'prod', 'sum', 'sum_axis']

"""

import mxnet as mx

from benchmark.opperf.utils.op_registry_utils import get_all_reduction_operators
from benchmark.opperf.utils.benchmark_utils import run_op_benchmarks


def run_mx_reduction_operators_benchmarks(ctx=mx.cpu(), dtype='float32', profiler='native', int64_tensor='off', warmup=25, runs=100):
    """Runs benchmarks with the given context, precision (dtype), and input data size (int64_tensor) for all the reduction
    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)
    int64_tensor: str, default 'off'
        Input tensor size to use for tests (if on, dimensions >= 2**32)
    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 Reduction Operators
    mx_reduction_broadcast_ops = get_all_reduction_operators()
    # Run benchmarks
    mx_reduction_op_results = run_op_benchmarks(mx_reduction_broadcast_ops, dtype, ctx, profiler, int64_tensor, warmup, runs)
    return mx_reduction_op_results