[OpPerf] Take care of 4d param (#15736)

* take care of 4d * Trigger notification * Trigger notification * Trigger notification * Trigger notification * Trigger notification * Trigger notification * Trigger notification * Trigger notification * Trigger notification bcoz validation/edge passed but shows pending
apache · Aug 19, 2019 · 92d2319 · 92d2319
1 parent c50bfe2
commit 92d2319
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Showing 3 changed files with 10 additions and 6 deletions.
diff --git a/benchmark/opperf/rules/default_params.py b/benchmark/opperf/rules/default_params.py
@@ -97,10 +97,11 @@
 # Hence below we append 4d to mark the difference.
 # For depth_to_space, dimension 3 needs to be a multiple of 'block' and 1 should be a multiple of `block^2`
 DEFAULT_DATA_4d = [(1, 4, 2, 4), (10,25,10,100)]
-DEFAULT_DIM_1 = [0, 1, 2, 3]
-DEFAULT_DIM_2 = [1, 2, 3, 0]
 DEFAULT_BLOCK_SIZE = [2, 5]
 
+# For swapaxis operator
+DEFAULT_DIM_1 = [0]
+DEFAULT_DIM_2 = [1]
 
 # Default Inputs. MXNet Op Param Name to Default Input mapping
 DEFAULTS_INPUTS = {"data": DEFAULT_DATA,

diff --git a/benchmark/opperf/utils/benchmark_utils.py b/benchmark/opperf/utils/benchmark_utils.py
@@ -144,9 +144,9 @@ def run_performance_test(ops, inputs, run_backward=True,
 def run_op_benchmarks(ops, dtype, ctx, profiler, warmup, runs):
     # For each operator, run benchmarks
     mx_op_benchmark_results = []
-    for _, op_params in ops.items():
+    for op, op_params in ops.items():
         # Prepare inputs for the operator
-        inputs = prepare_op_inputs(op_params)
+        inputs = prepare_op_inputs(op, op_params)
         # Run benchmarks
         cur_op_res = run_performance_test(op_params["nd_op_handle"],
                                           run_backward=op_params["has_backward"],

diff --git a/benchmark/opperf/utils/op_registry_utils.py b/benchmark/opperf/utils/op_registry_utils.py
@@ -113,16 +113,19 @@ def prepare_op_inputs(arg_params, arg_values):
     return inputs
 
 
-def prepare_op_inputs(arg_params):
+def prepare_op_inputs(op, arg_params):
     inputs = []
 
+    # 4d tensor is needed only by following two ops
+    ops_4d = ['depth_to_space','space_to_depth']
+
     # 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"]):
         if "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 arg_name + "_4d" in DEFAULTS_INPUTS:
+        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 arg_name in DEFAULTS_INPUTS:
             arg_values[arg_name] = DEFAULTS_INPUTS[arg_name]