apeforest commented on a change in pull request #17449: Implemented large
tensor flag for opperf testing
URL: https://github.com/apache/incubator-mxnet/pull/17449#discussion_r373707350
##########
File path: benchmark/opperf/nd_operations/nn_activation_operators.py
##########
@@ -55,55 +57,106 @@ def run_activation_operators_benchmarks(ctx=mx.cpu(),
dtype='float32', profiler=
Dictionary of results. Key -> Name of the operator, Value -> Benchmark
results.
"""
- # Relu and its variation
- relu_benchmark_res = run_performance_test([getattr(MX_OP_MODULE,
"LeakyReLU")],
- run_backward=True,
- dtype=dtype,
- ctx=ctx,
- profiler=profiler,
- inputs=[{"data": (1024, 1024),
"act_type": "leaky", "slope": 0.1},
- {"data": (10000, 1),
"act_type": "leaky", "slope": 0.1},
- {"data": (10000, 100),
"act_type": "leaky", "slope": 0.1},
- {"data": (1024, 1024),
"act_type": "elu", "slope": 0.1},
- {"data": (10000, 1),
"act_type": "elu", "slope": 0.1},
- {"data": (10000, 100),
"act_type": "elu", "slope": 0.1},
- {"data": (1024, 1024),
"act_type": "selu"},
- {"data": (10000, 1),
"act_type": "selu"},
- {"data": (10000, 100),
"act_type": "selu"},
- {"data": (1024, 1024),
"act_type": "prelu", "gamma": (1, 1024)},
- {"data": (10000, 1),
"act_type": "prelu", "gamma": (1, 1)},
- {"data": (10000, 100),
"act_type": "prelu", "gamma": (1, 100)}
- ],
- warmup=warmup,
- runs=runs)
+ if large_tensor == 'on':
+ # Relu and its variation
+ relu_benchmark_res = run_performance_test([getattr(MX_OP_MODULE,
"LeakyReLU")],
+ run_backward=True,
+ dtype=dtype,
+ ctx=ctx,
+ profiler=profiler,
+ inputs=[{"data": (2**16,
2**16), "act_type": "leaky", "slope": 0.1},
+ {"data": (2**4,
2**28), "act_type": "leaky", "slope": 0.1},
+ {"data": (4, 2**30),
"act_type": "leaky", "slope": 0.1},
+ {"data": (2**16,
2**16), "act_type": "elu", "slope": 0.1},
+ {"data": (2**4,
2**28), "act_type": "elu", "slope": 0.1},
+ {"data": (4, 2**30),
"act_type": "elu", "slope": 0.1},
+ {"data": (2**16,
2**16), "act_type": "selu"},
+ {"data": (2**4,
2**28), "act_type": "selu"},
+ {"data": (4, 2**30),
"act_type": "selu"},
+ {"data": (2**16,
2**16), "act_type": "prelu", "gamma": (1, 2**16)},
+ {"data": (2**4,
2**28), "act_type": "prelu", "gamma": (1, 2**28)},
+ {"data": (4, 2**30),
"act_type": "prelu", "gamma": (1, 2**30)}
+ ],
+ warmup=warmup,
+ runs=runs)
- # Sigmoid => Covered as part of Unary ops
- # Hard_Sigmoid
- hard_sigmoid_benchmark_res = run_performance_test([getattr(MX_OP_MODULE,
"hard_sigmoid")],
- run_backward=True,
- dtype=dtype,
- ctx=ctx,
- profiler=profiler,
- inputs=[{"data": (1024,
1024), "alpha": 0.25, "beta": 0.5},
- {"data": (10000,
1), "alpha": 0.25, "beta": 0.5},
- {"data": (10000,
100), "alpha": 0.25, "beta": 0.5}
- ],
- warmup=warmup,
- runs=runs)
+ # Sigmoid => Covered as part of Unary ops
+ # Hard_Sigmoid
+ hard_sigmoid_benchmark_res =
run_performance_test([getattr(MX_OP_MODULE, "hard_sigmoid")],
+ run_backward=True,
+ dtype=dtype,
+ ctx=ctx,
+ profiler=profiler,
+ inputs=[{"data":
(2**16, 2**16), "alpha": 0.25, "beta": 0.5},
+ {"data":
(2**4, 2**28), "alpha": 0.25, "beta": 0.5},
+ {"data": (4,
2**30), "alpha": 0.25, "beta": 0.5}
+ ],
+ warmup=warmup,
+ runs=runs)
- # Softmax, LogSoftmax
- softmax_benchmark_res = run_performance_test([getattr(MX_OP_MODULE,
"softmax"),
- getattr(MX_OP_MODULE,
"log_softmax")],
- run_backward=True,
- dtype=dtype,
- ctx=ctx,
- profiler=profiler,
- inputs=[{"data": (1024,
1024), "axis": -1, "temperature": 0.5},
- {"data": (10000, 1),
"axis": -1, "temperature": 0.5},
- {"data": (10000,
100), "axis": -1, "temperature": 0.5}
+ # Softmax, LogSoftmax
+ softmax_benchmark_res = run_performance_test([getattr(MX_OP_MODULE,
"softmax"),
+ getattr(MX_OP_MODULE,
"log_softmax")],
+ run_backward=True,
+ dtype=dtype,
+ ctx=ctx,
+ profiler=profiler,
+ inputs=[{"data": (2**16,
2**16), "axis": -1, "temperature": 0.5},
+ {"data": (2**4,
2**28), "axis": -1, "temperature": 0.5},
+ {"data": (4,
2**30), "axis": -1, "temperature": 0.5}
+ ],
+ warmup=warmup,
+ runs=runs)
+ else:
Review comment:
It seems the only difference between the if and else branch is the `inputs`
argument. Can we only generate different inputs in the if/else branch and pass
them to the same operator function?
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