connorgoggins commented on a change in pull request #17542: [OpPerf] Add norm,
cast ops, remaining optimizer ops
URL: https://github.com/apache/incubator-mxnet/pull/17542#discussion_r377967894
##########
File path: benchmark/opperf/utils/benchmark_utils.py
##########
@@ -31,28 +31,72 @@
def _prepare_op_inputs(inputs, run_backward, dtype, ctx):
mx.random.seed(41)
kwargs_list = []
- args_list = []
for inp in inputs:
kwargs = {}
for key, value in inp.items():
- if key in PARAMS_OF_TYPE_NDARRAY and key=='args':
- args_list.append(get_mx_ndarray(ctx=ctx, in_tensor=value,
- dtype=dtype,
- initializer=nd.normal,
- attach_grad=run_backward))
- elif key in PARAMS_OF_TYPE_NDARRAY:
+ if key in PARAMS_OF_TYPE_NDARRAY:
kwargs[key] = get_mx_ndarray(ctx=ctx, in_tensor=value,
dtype=dtype,
initializer=nd.normal,
attach_grad=run_backward)
else:
kwargs[key] = value
kwargs_list.append(kwargs)
- return args_list, kwargs_list
+ return kwargs_list
-def _run_nd_operator_performance_test(op, inputs, run_backward, warmup, runs,
args_list, kwargs_list, profiler):
+def parse_input_ndarray(input_dict):
+ """Parse input for ndarray and extract array shape for better readability
+
+ Parameters
+ ----------
+ input_dict : dict
+ Dictionary of input
+
+ Input Dictionary
+
+ 'inputs': {'weight':
+ [[ 2.2122064 0.7740038 1.0434405 1.1839255 1.8917114 ]
+ [-1.2347414 -1.771029 -0.45138445 0.57938355 -1.856082 ]
+ [-1.9768796 -0.20801921 0.2444218 -0.03716067 -0.48774993]
+ [-0.02261727 0.57461417 1.4661262 0.6862904 0.35496104]
+ [ 1.0731696 0.12017461 -0.9711102 -0.77569664 -0.7882176 ]]
+ <NDArray 5x5 @cpu(0)>, 'grad':
+ [[ 0.7417728 -1.4734439 -1.0730928 -1.0424827 -1.3278849 ]
+ [-1.4749662 -0.52414197 1.2662556 0.8950642 -0.6015945 ]
+ [ 1.2040559 -0.9712193 -0.58256227 0.3717077 0.9300072 ]
+ [-1.4225755 -0.5176199 2.0088325 0.2863085 0.5604595 ]
+ [ 0.96975976 -0.52853745 -1.88909 0.65479124 -0.45481315]]
+ <NDArray 5x5 @cpu(0)>, 'mean':
+ [[ 0.32510808 -1.3002341 0.3679345 1.4534262 0.24154152]
+ [ 0.47898006 0.96885103 -1.0218245 -0.06812762 -0.31868345]
+ [-0.17634277 0.35655284 0.74419165 0.7787424 0.6087823 ]
+ [ 1.0741756 0.06642842 0.8486986 -0.8003802 -0.16882208]
+ [ 0.93632793 0.357444 0.77932847 -1.0103073 -0.39157307]]
+ <NDArray 5x5 @cpu(0)>, 'var':
+ [[ 1.3166187 -0.43292624 0.71535987 0.9254156 -0.90495086]
+ [-0.074684 0.82254 -1.8785107 0.8858836 1.9118724 ]
+ [ 0.33342266 0.11883813 -1.9198899 -0.67558455 1.007749 ]
+ [-0.35391203 1.6323917 -0.33354783 -1.7378405 0.7737382 ]
+ [ 0.89126545 3.2904532 -1.1976235 1.8938874 -0.5669272 ]]
+ <NDArray 5x5 @cpu(0)>, 't': 1, 'wd': 0.1}
+
+ Output
+ {'inputs': {'weight': '<NDArray 5x5 @cpu(0)>', 'grad': '<NDArray 5x5
@cpu(0)>', 'mean': '<NDArray 5x5 @cpu(0)>', 'var': '<NDArray 5x5 @cpu(0)>',
't': 1, 'wd': 0.1}
Review comment:
Nice! Very clean output format.
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