parameters: (ctx=mx.cpu(), dtype='float32', profiler='native', int64_tensor='off', warmup=25, runs=100)
return_statement: return final_benchmark_result_map
docstring: Run all the MXNet operators (NDArray) benchmarks.
  
      Returns
      -------
      Dictionary of benchmark results.
docstring_summary: Run all the MXNet operators (NDArray) benchmarks.
docstring_tokens: Run all the MXNet operators ( NDArray ) benchmarks .
function: 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
function_tokens: def run_all_mxnet_operator_benchmarks ( ctx = mx . cpu ( ) , dtype = 'float32' , profiler = 'native' , int64_tensor = 'off' , warmup = 25 , runs = 100 ) : 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