def run_all_mxnet_operator_benchmarks(ctx=mx.cpu(), dtype='float32'):
"""Run all the MXNet operators (NDArray) benchmarks.
Returns
-------
Dictionary of benchmark results.
"""
mxnet_operator_benchmark_results = []
# *************************MXNET TENSOR OPERATOR BENCHMARKS*****************************
# Run all Unary operations benchmarks with default input values
mxnet_operator_benchmark_results.append(run_mx_unary_operators_benchmarks(ctx=ctx, dtype=dtype))
# Run all Binary Broadcast, element_wise operations benchmarks with default input values
mxnet_operator_benchmark_results.append(run_mx_binary_broadcast_operators_benchmarks(ctx=ctx,
dtype=dtype))
mxnet_operator_benchmark_results.append(run_mx_binary_element_wise_operators_benchmarks(ctx=ctx,
dtype=dtype))
# Run all GEMM operations benchmarks with default input values
mxnet_operator_benchmark_results.append(run_gemm_operators_benchmarks(ctx=ctx,
dtype=dtype))
# Run all Random sampling operations benchmarks with default input values
mxnet_operator_benchmark_results.append(run_mx_random_sampling_operators_benchmarks(ctx=ctx, dtype=dtype))
# Run all Reduction operations benchmarks with default input values
mxnet_operator_benchmark_results.append(run_mx_reduction_operators_benchmarks(ctx=ctx, dtype=dtype))
# Run all Sorting and Searching operations benchmarks with default input values
mxnet_operator_benchmark_results.append(run_sorting_searching_operators_benchmarks(ctx=ctx, dtype=dtype))
# Run all Array Rearrange operations benchmarks with default input values
mxnet_operator_benchmark_results.append(run_rearrange_operators_benchmarks(ctx=ctx, dtype=dtype))
# ************************ MXNET NN OPERATOR BENCHMARKS ****************************
# Run all basic NN operations benchmarks with default input values
mxnet_operator_benchmark_results.append(run_nn_basic_operators_benchmarks(ctx=ctx, dtype=dtype))
# Run all Activation operations benchmarks with default input values
mxnet_operator_benchmark_results.append(run_activation_operators_benchmarks(ctx=ctx, dtype=dtype))
# Run all Pooling operations benchmarks with default input values
mxnet_operator_benchmark_results.append(run_pooling_operators_benchmarks(ctx=ctx, dtype=dtype))
# Run all Convolution operations benchmarks with default input values
mxnet_operator_benchmark_results.append(run_convolution_operators_benchmarks(ctx=ctx, dtype=dtype))
# Run all Optimizer operations benchmarks with default input values
mxnet_operator_benchmark_results.append(run_optimizer_operators_benchmarks(ctx=ctx, dtype=dtype))
# Run all Transpose Convolution operations benchmarks with default input values
mxnet_operator_benchmark_results.append(run_transpose_convolution_operators_benchmarks(ctx=ctx, dtype=dtype))
# ****************************** PREPARE FINAL RESULTS ********************************
final_benchmark_result_map = merge_map_list(mxnet_operator_benchmark_results)
return final_benchmark_result_map
def run_all_mxnet_operator_benchmarks(ctx=mx.cpu(),
dtype='float32',
profiler='native',
warmup=25,
runs=100):
"""Run all the MXNet operators (NDArray) benchmarks.
Returns
-------
Dictionary of benchmark results.
"""
mxnet_operator_benchmark_results = []
# *************************MXNET TENSOR OPERATOR BENCHMARKS*****************************
# Run all Unary operations benchmarks with default input values
mxnet_operator_benchmark_results.append(
run_mx_unary_operators_benchmarks(ctx=ctx,
dtype=dtype,
profiler=profiler,
warmup=warmup,
runs=runs))
# Run all Binary Broadcast, element_wise, and miscellaneous operations benchmarks with default input values
mxnet_operator_benchmark_results.append(
run_mx_binary_broadcast_operators_benchmarks(ctx=ctx,
dtype=dtype,
profiler=profiler,
warmup=warmup,
runs=runs))
mxnet_operator_benchmark_results.append(
run_mx_binary_element_wise_operators_benchmarks(ctx=ctx,
dtype=dtype,
profiler=profiler,
warmup=warmup,
runs=runs))
mxnet_operator_benchmark_results.append(
run_mx_binary_misc_operators_benchmarks(ctx=ctx,
dtype=dtype,
profiler=profiler,
warmup=warmup,
runs=runs))
# Run all GEMM operations benchmarks with default input values
mxnet_operator_benchmark_results.append(
run_gemm_operators_benchmarks(ctx=ctx,
dtype=dtype,
profiler=profiler,
warmup=warmup,
runs=runs))
# Run all Random sampling operations benchmarks with default input values
mxnet_operator_benchmark_results.append(
run_mx_random_sampling_operators_benchmarks(ctx=ctx,
dtype=dtype,
profiler=profiler,
warmup=warmup,
runs=runs))
# Run all Reduction operations benchmarks with default input values
mxnet_operator_benchmark_results.append(
run_mx_reduction_operators_benchmarks(ctx=ctx,
dtype=dtype,
profiler=profiler,
warmup=warmup,
runs=runs))
# Run all Sorting and Searching operations benchmarks with default input values
mxnet_operator_benchmark_results.append(
run_sorting_searching_operators_benchmarks(ctx=ctx,
dtype=dtype,
profiler=profiler,
warmup=warmup,
runs=runs))
# Run all Array Rearrange operations benchmarks with default input values
mxnet_operator_benchmark_results.append(
run_rearrange_operators_benchmarks(ctx=ctx,
dtype=dtype,
profiler=profiler,
warmup=warmup,
runs=runs))
# Run all Indexing routines benchmarks with default input values
mxnet_operator_benchmark_results.append(
run_indexing_routines_benchmarks(ctx=ctx,
dtype=dtype,
profiler=profiler,
warmup=warmup,
runs=runs))
# ************************ MXNET NN OPERATOR BENCHMARKS ****************************
# Run all basic NN operations benchmarks with default input values
mxnet_operator_benchmark_results.append(
run_nn_basic_operators_benchmarks(ctx=ctx,
dtype=dtype,
profiler=profiler,
warmup=warmup,
runs=runs))
# Run all Activation operations benchmarks with default input values
mxnet_operator_benchmark_results.append(
run_activation_operators_benchmarks(ctx=ctx,
dtype=dtype,
profiler=profiler,
warmup=warmup,
runs=runs))
# Run all Pooling operations benchmarks with default input values
mxnet_operator_benchmark_results.append(
run_pooling_operators_benchmarks(ctx=ctx,
dtype=dtype,
profiler=profiler,
warmup=warmup,
runs=runs))
# Run all Convolution operations benchmarks with default input values
mxnet_operator_benchmark_results.append(
run_convolution_operators_benchmarks(ctx=ctx,
dtype=dtype,
profiler=profiler,
warmup=warmup,
runs=runs))
# Run all Optimizer operations benchmarks with default input values
mxnet_operator_benchmark_results.append(
run_optimizer_operators_benchmarks(ctx=ctx,
dtype=dtype,
profiler=profiler,
warmup=warmup,
runs=runs))
# Run all Transpose Convolution operations benchmarks with default input values
mxnet_operator_benchmark_results.append(
run_transpose_convolution_operators_benchmarks(ctx=ctx,
dtype=dtype,
profiler=profiler,
warmup=warmup,
runs=runs))
# Run all NN loss operations benchmarks with default input values
mxnet_operator_benchmark_results.append(
run_loss_operators_benchmarks(ctx=ctx,
dtype=dtype,
profiler=profiler,
warmup=warmup,
runs=runs))
# Run all Miscellaneous operations benchmarks with default input values
mxnet_operator_benchmark_results.append(
run_mx_misc_operators_benchmarks(ctx=ctx,
dtype=dtype,
profiler=profiler,
warmup=warmup,
runs=runs))
# Run all Linear Algebra operations benchmarks with default input values
mxnet_operator_benchmark_results.append(
run_linalg_operators_benchmarks(ctx=ctx,
dtype=dtype,
profiler=profiler,
warmup=warmup,
runs=runs))
# ****************************** PREPARE FINAL RESULTS ********************************
final_benchmark_result_map = merge_map_list(
mxnet_operator_benchmark_results)
return final_benchmark_result_map
def run_all_mxnet_operator_benchmarks(ctx=mx.cpu(),
dtype='float32',
profiler='native',
int64_tensor='off',
warmup=25,
runs=100):
"""Run all the MXNet operators (NDArray) benchmarks.
Returns
-------
Dictionary of benchmark results.
"""
mxnet_operator_benchmark_results = []
# *************************MXNET TENSOR OPERATOR BENCHMARKS*****************************
# Run all Unary operations benchmarks with default input values
mxnet_operator_benchmark_results.append(
run_mx_unary_operators_benchmarks(ctx=ctx,
dtype=dtype,
profiler=profiler,
int64_tensor=int64_tensor,
warmup=warmup,
runs=runs))
# Run all Binary Broadcast, element_wise, and miscellaneous operations benchmarks with default input values
mxnet_operator_benchmark_results.append(
run_mx_binary_broadcast_operators_benchmarks(ctx=ctx,
dtype=dtype,
profiler=profiler,
int64_tensor=int64_tensor,
warmup=warmup,
runs=runs))
mxnet_operator_benchmark_results.append(
run_mx_binary_element_wise_operators_benchmarks(
ctx=ctx,
dtype=dtype,
profiler=profiler,
int64_tensor=int64_tensor,
warmup=warmup,
runs=runs))
mxnet_operator_benchmark_results.append(
run_mx_binary_misc_operators_benchmarks(ctx=ctx,
dtype=dtype,
profiler=profiler,
int64_tensor=int64_tensor,
warmup=warmup,
runs=runs))
# Run all GEMM operations benchmarks with default input values
mxnet_operator_benchmark_results.append(
run_gemm_operators_benchmarks(ctx=ctx,
dtype=dtype,
profiler=profiler,
int64_tensor=int64_tensor,
warmup=warmup,
runs=runs))
# Run all Random sampling operations benchmarks with default input values
mxnet_operator_benchmark_results.append(
run_mx_random_sampling_operators_benchmarks(ctx=ctx,
dtype=dtype,
profiler=profiler,
int64_tensor=int64_tensor,
warmup=warmup,
runs=runs))
# Run all Reduction operations benchmarks with default input values
mxnet_operator_benchmark_results.append(
run_mx_reduction_operators_benchmarks(ctx=ctx,
dtype=dtype,
profiler=profiler,
int64_tensor=int64_tensor,
warmup=warmup,
runs=runs))
# Run all Sorting and Searching operations benchmarks with default input values
mxnet_operator_benchmark_results.append(
run_sorting_searching_operators_benchmarks(ctx=ctx,
dtype=dtype,
profiler=profiler,
int64_tensor=int64_tensor,
warmup=warmup,
runs=runs))
# Run all Indexing routines benchmarks with default input values
mxnet_operator_benchmark_results.append(
run_indexing_routines_benchmarks(ctx=ctx,
dtype=dtype,
profiler=profiler,
int64_tensor=int64_tensor,
warmup=warmup,
runs=runs))
# Run all Array Rearrange operations benchmarks with default input values
mxnet_operator_benchmark_results.append(
run_rearrange_operators_benchmarks(ctx=ctx,
dtype=dtype,
profiler=profiler,
int64_tensor=int64_tensor,
warmup=warmup,
runs=runs))
# Run all Array Shape Manipulation operations benchmarks with default input values
mxnet_operator_benchmark_results.append(
run_shape_operators_benchmarks(ctx=ctx,
dtype=dtype,
profiler=profiler,
int64_tensor=int64_tensor,
warmup=warmup,
runs=runs))
# Run all Array Expansion operations benchmarks with default input values
mxnet_operator_benchmark_results.append(
run_expanding_operators_benchmarks(ctx=ctx,
dtype=dtype,
profiler=profiler,
int64_tensor=int64_tensor,
warmup=warmup,
runs=runs))
# Run all Array Rounding operations benchmarks with default input values
mxnet_operator_benchmark_results.append(
run_rounding_operators_benchmarks(ctx=ctx,
dtype=dtype,
profiler=profiler,
int64_tensor=int64_tensor,
warmup=warmup,
runs=runs))
# Run all Array Join & Split operations benchmarks with default input values
mxnet_operator_benchmark_results.append(
run_join_split_operators_benchmarks(ctx=ctx,
dtype=dtype,
profiler=profiler,
int64_tensor=int64_tensor,
warmup=warmup,
runs=runs))
# ************************ MXNET NN OPERATOR BENCHMARKS ****************************
# Run all basic NN operations benchmarks with default input values
mxnet_operator_benchmark_results.append(
run_nn_basic_operators_benchmarks(ctx=ctx,
dtype=dtype,
profiler=profiler,
int64_tensor=int64_tensor,
warmup=warmup,
runs=runs))
# Run all Activation operations benchmarks with default input values
mxnet_operator_benchmark_results.append(
run_activation_operators_benchmarks(ctx=ctx,
dtype=dtype,
profiler=profiler,
int64_tensor=int64_tensor,
warmup=warmup,
runs=runs))
# Run all Pooling operations benchmarks with default input values
mxnet_operator_benchmark_results.append(
run_pooling_operators_benchmarks(ctx=ctx,
dtype=dtype,
profiler=profiler,
int64_tensor=int64_tensor,
warmup=warmup,
runs=runs))
# Run all Convolution operations benchmarks with default input values
mxnet_operator_benchmark_results.append(
run_convolution_operators_benchmarks(ctx=ctx,
dtype=dtype,
profiler=profiler,
int64_tensor=int64_tensor,
warmup=warmup,
runs=runs))
# Run all Optimizer operations benchmarks with default input values
mxnet_operator_benchmark_results.append(
run_optimizer_operators_benchmarks(ctx=ctx,
dtype=dtype,
profiler=profiler,
int64_tensor=int64_tensor,
warmup=warmup,
runs=runs))
# Run all Transpose Convolution operations benchmarks with default input values
mxnet_operator_benchmark_results.append(
run_transpose_convolution_operators_benchmarks(
ctx=ctx,
dtype=dtype,
profiler=profiler,
int64_tensor=int64_tensor,
warmup=warmup,
runs=runs))
# Run all NN loss operations benchmarks with default input values
mxnet_operator_benchmark_results.append(
run_loss_operators_benchmarks(ctx=ctx,
dtype=dtype,
profiler=profiler,
int64_tensor=int64_tensor,
warmup=warmup,
runs=runs))
# Run all Miscellaneous operations benchmarks with default input values
mxnet_operator_benchmark_results.append(
run_mx_misc_operators_benchmarks(ctx=ctx,
dtype=dtype,
profiler=profiler,
int64_tensor=int64_tensor,
warmup=warmup,
runs=runs))
# Linear Algebra operators do not work with int64 tensor data. Issue tracked here: https://github.com/apache/incubator-mxnet/issues/17716
if int64_tensor == 'off':
# Run all Linear Algebra operations benchmarks with default input values
mxnet_operator_benchmark_results.append(
run_linalg_operators_benchmarks(ctx=ctx,
dtype=dtype,
profiler=profiler,
int64_tensor=int64_tensor,
warmup=warmup,
runs=runs))
# ****************************** PREPARE FINAL RESULTS ********************************
final_benchmark_result_map = merge_map_list(
mxnet_operator_benchmark_results)
return final_benchmark_result_map
