diff --git a/benchmark/opperf/nd_operations/nn_activation_operators.py b/benchmark/opperf/nd_operations/nn_activation_operators.py index 83813fe13c2e..b77777cc04dd 100644 --- a/benchmark/opperf/nd_operations/nn_activation_operators.py +++ b/benchmark/opperf/nd_operations/nn_activation_operators.py @@ -16,28 +16,36 @@ # under the License. import mxnet as mx -from benchmark.opperf.utils.benchmark_utils import run_performance_test -from benchmark.opperf.utils.common_utils import merge_map_list -from benchmark.opperf.rules.default_params import MX_OP_MODULE + +from benchmark.opperf.utils.op_registry_utils import get_all_nn_activation_operators +from benchmark.opperf.utils.benchmark_utils import run_op_benchmarks """Performance benchmark tests for MXNet NDArray Activation Operators. -1. LeakyRelu - 1.1 Elu - 1.2 Selu - 1.3 Leaky - 1.4 PRelu - 1.5 RRelu -3. Hard_Sigmoid -4. Softmax -5. Log_Softmax +1. LeakyReLU + 1.1 elu + 1.2 selu + 1.3 leaky + 1.4 gelu +2. hard_sigmoid +3. Softmax +4. SoftmaxActivation +5. softmax +6. log_softmax +7. softmin +8. Activation + 8.1 relu + 8.2 sigmoid + 8.3 softrelu + 8.4 softsign + 8.5 tanh """ def run_activation_operators_benchmarks(ctx=mx.cpu(), dtype='float32', profiler='native', warmup=25, runs=100): """Runs benchmarks with the given context and precision (dtype)for all the activation - operators (relu, sigmoid, softmax) in MXNet. + operators in MXNet. Parameters ---------- @@ -45,6 +53,8 @@ def run_activation_operators_benchmarks(ctx=mx.cpu(), dtype='float32', profiler= Context to run benchmarks dtype: str, default 'float32' Precision to use for benchmarks + profiler: str, default 'native' + Module to use for tracking benchmark excecution time warmup: int, default 25 Number of times to run for warmup runs: int, default 100 @@ -55,56 +65,11 @@ 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) - - # 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) - # 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} - ], - warmup=warmup, - runs=runs) + # Fetch all NN Activation Operators + mx_activation_ops = get_all_nn_activation_operators() - # Prepare combined results - mx_activation_op_results = merge_map_list(relu_benchmark_res + hard_sigmoid_benchmark_res + softmax_benchmark_res) + # Run benchmarks + mx_activation_op_results = run_op_benchmarks(mx_activation_ops, dtype, ctx, profiler, warmup, runs) return mx_activation_op_results + \ No newline at end of file diff --git a/benchmark/opperf/rules/default_params.py b/benchmark/opperf/rules/default_params.py index 596dceb16480..022a1baf3cec 100644 --- a/benchmark/opperf/rules/default_params.py +++ b/benchmark/opperf/rules/default_params.py @@ -134,6 +134,10 @@ DEFAULT_LABEL = [(100,100)] DEFAULT_DATA_SMCE = [(1024, 1024)] DEFAULT_LABEL_SMCE = [(1024,)] +# For NN operators +DEFAULT_ACT_TYPE_LR = ['leaky', 'elu', 'selu', 'gelu'] +DEFAULT_ACT_TYPE_ACTIVATION = ['relu', 'sigmoid', 'softrelu', 'softsign', 'tanh'] +DEFAULT_LABEL_SOFTMAX = [(1024, 1024), (10000, 1), (10000, 100)] # For linalg operators DEFAULT_A = [(1024, 1024)] @@ -218,7 +222,10 @@ "B": DEFAULT_B, "C": DEFAULT_C, "A_linalg_maketrian": DEFAULT_A_MT, - "axes": DEFAULT_AXES} + "axes": DEFAULT_AXES, + "act_type_leakyrelu": DEFAULT_ACT_TYPE_LR, + "label_softmax": DEFAULT_LABEL_SOFTMAX, + "act_type_activation": DEFAULT_ACT_TYPE_ACTIVATION} # These are names of MXNet operator parameters that is of type NDArray. diff --git a/benchmark/opperf/utils/op_registry_utils.py b/benchmark/opperf/utils/op_registry_utils.py index eb2adba05d18..b9f1e45bbd37 100644 --- a/benchmark/opperf/utils/op_registry_utils.py +++ b/benchmark/opperf/utils/op_registry_utils.py @@ -117,8 +117,9 @@ def prepare_op_inputs(op, arg_params): # 3d tensor is needed by following ops ops_3d = ['CTCLoss', 'ctc_loss'] - - custom_data = ['BilinearSampler', 'GridGenerator', 'sample_multinomial', 'linalg_maketrian'] + + # For ops with args that need to change shape/value for different ops + custom_data = ['Activation', 'LeakyReLU', 'Softmax', 'BilinearSampler', 'GridGenerator', 'sample_multinomial', 'linalg_maketrian'] # Prepare op to default input mapping arg_values = {} @@ -310,6 +311,26 @@ def get_all_reduction_operators(): return reduction_mx_operators +def get_all_nn_activation_operators(): + """Gets all NN Activation operators registered with MXNet. + + Returns + ------- + {"operator_name": {"has_backward", "nd_op_handle", "params"}} + """ + nn_activation_ops = ['Softmax', 'SoftmaxActivation', 'softmin', 'Activation', 'LeakyReLU', 'hard_sigmoid', 'softmax', 'log_softmax'] + + # Get all mxnet operators + mx_operators = _get_all_mxnet_operators() + + # Filter for NN Activation operators + nn_activation_mx_operators = {} + for op_name, _ in mx_operators.items(): + if op_name in nn_activation_ops: + nn_activation_mx_operators[op_name] = mx_operators[op_name] + return nn_activation_mx_operators + + def get_all_optimizer_operators(): """Gets all Optimizer operators registered with MXNet.