Added warmup/runs to gemm ops, debugging process failure

apache · Feb 12, 2020 · 87c18fc · 87c18fc
1 parent c064f47
commit 87c18fc
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Showing 3 changed files with 24 additions and 19 deletions.
diff --git a/benchmark/opperf/nd_operations/indexing_routines.py b/benchmark/opperf/nd_operations/indexing_routines.py
@@ -63,5 +63,5 @@ def run_indexing_routines_benchmarks(ctx=mx.cpu(), dtype='float32', profiler='na
     mx_indexing_ops = get_all_indexing_routines()
 
     # Run benchmarks
-    mx_indexing_op_results = run_op_benchmarks(mx_indexing_ops, dtype, ctx, profiler, warmup, runs)
+    mx_indexing_op_results = run_op_benchmarks(mx_indexing_ops, dtype, ctx, profiler, int64_tensor, warmup, runs)
     return mx_indexing_op_results
diff --git a/benchmark/opperf/opperf.py b/benchmark/opperf/opperf.py
@@ -75,7 +75,7 @@ def run_all_mxnet_operator_benchmarks(ctx=mx.cpu(), dtype='float32', profiler='n
 
     # 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))
+                                                                          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))

diff --git a/benchmark/opperf/utils/op_registry_utils.py b/benchmark/opperf/utils/op_registry_utils.py
@@ -122,32 +122,37 @@ def prepare_op_inputs(op, arg_params, int64_tensor):
     custom_data = ['Activation', 'LeakyReLU', 'Softmax', 'BilinearSampler', 'GridGenerator', 'sample_multinomial', 'linalg_maketrian']
 
     if int64_tensor == 'on':
-        DEFAULTS_INPUTS = DEFAULTS_INPUTS_LARGE_TENSOR
+        default_inputs = DEFAULTS_INPUTS_LARGE_TENSOR
+    else:
+        default_inputs = DEFAULTS_INPUTS
+
+    print(op)
 
     # Prepare op to default input mapping
     arg_values = {}
     for arg_name, arg_type in zip(arg_params["params"]["arg_names"],
                                   arg_params["params"]["arg_types"]):
+        print(arg_name)
         if "NDArray" in arg_type and op == "ravel_multi_index":
-            arg_values[arg_name] = DEFAULTS_INPUTS["ravel_data"]
-        elif op in custom_data and arg_name + "_" + op.lower() in DEFAULTS_INPUTS:
-            arg_values[arg_name] = DEFAULTS_INPUTS[arg_name + "_" + op.lower()]
-        elif "NDArray" in arg_type and arg_name + "_nd" in DEFAULTS_INPUTS:
-            arg_values[arg_name] = DEFAULTS_INPUTS[arg_name + "_nd"]
-        elif "NDArray" in arg_type and op in ops_4d and arg_name + "_4d" in DEFAULTS_INPUTS:
-            arg_values[arg_name] = DEFAULTS_INPUTS[arg_name + "_4d"]
-        elif "NDArray" in arg_type and op in ops_3d and arg_name + "_3d" in DEFAULTS_INPUTS:
-            arg_values[arg_name] = DEFAULTS_INPUTS[arg_name + "_3d"]
+            arg_values[arg_name] = default_inputs["ravel_data"]
+        elif op in custom_data and arg_name + "_" + op.lower() in default_inputs:
+            arg_values[arg_name] = default_inputs[arg_name + "_" + op.lower()]
+        elif "NDArray" in arg_type and arg_name + "_nd" in default_inputs:
+            arg_values[arg_name] = default_inputs[arg_name + "_nd"]
+        elif "NDArray" in arg_type and op in ops_4d and arg_name + "_4d" in default_inputs:
+            arg_values[arg_name] = default_inputs[arg_name + "_4d"]
+        elif "NDArray" in arg_type and op in ops_3d and arg_name + "_3d" in default_inputs:
+            arg_values[arg_name] = default_inputs[arg_name + "_3d"]
         elif "NDArray" in arg_type and op == 'softmax_cross_entropy':
-            arg_values[arg_name] = DEFAULTS_INPUTS[arg_name + "_smce"]
-        elif arg_name in DEFAULTS_INPUTS:
-            arg_values[arg_name] = DEFAULTS_INPUTS[arg_name]
-        elif "float" in arg_type and arg_name + "_float" in DEFAULTS_INPUTS:
-            arg_values[arg_name] = DEFAULTS_INPUTS[arg_name + "_float"]
-        elif "Shape" in arg_type and arg_name + "_shape" in DEFAULTS_INPUTS:
+            arg_values[arg_name] = default_inputs[arg_name + "_smce"]
+        elif arg_name in default_inputs:
+            arg_values[arg_name] = default_inputs[arg_name]
+        elif "float" in arg_type and arg_name + "_float" in default_inputs:
+            arg_values[arg_name] = default_inputs[arg_name + "_float"]
+        elif "Shape" in arg_type and arg_name + "_shape" in default_inputs:
             # This is for cases where in some ops 'axis' is Int in some ops a shape tuple.
             # Ex: axis in sum is shape, axis in sort is int.
-            arg_values[arg_name] = DEFAULTS_INPUTS[arg_name + "_shape"]
+            arg_values[arg_name] = default_inputs[arg_name + "_shape"]
 
     # Number of different inputs we want to use to test
     # the operator