def run_mx_binary_element_wise_operators_benchmarks(ctx=mx.cpu(),
dtype='float32',
warmup=25,
runs=100):
"""Runs benchmarks with the given context and precision (dtype)for all the binary
element_wise operators in MXNet.
Parameters
----------
ctx: mx.ctx
Context to run benchmarks
dtype: str, default 'float32'
Precision to use for benchmarks
warmup: int, default 10
Number of times to run for warmup
runs: int, default 50
Number of runs to capture benchmark results
Returns
-------
Dictionary of results. Key -> Name of the operator, Value -> Benchmark results.
"""
# Fetch all Binary Element_wise Operators
mx_binary_element_wise_ops = get_all_elemen_wise_binary_operators()
# Run benchmarks
mx_binary_op_results = run_op_benchmarks(mx_binary_element_wise_ops, dtype,
ctx, warmup, runs)
return mx_binary_op_results
def run_mx_binary_element_wise_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 binary
element_wise 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 10
Number of times to run for warmup
runs: int, default 50
Number of runs to capture benchmark results
Returns
-------
Dictionary of results. Key -> Name of the operator, Value -> Benchmark results.
"""
# Fetch all Binary Element_wise Operators
mx_binary_element_wise_ops = get_all_elemen_wise_binary_operators()
# Run benchmarks
mx_binary_op_results = run_op_benchmarks(mx_binary_element_wise_ops, dtype, ctx, profiler, int64_tensor, warmup, runs)
return mx_binary_op_results