benchmark/opperf/rules/default_params.py - mxnet - Git at Google

 # Licensed to the Apache Software Foundation (ASF) under one
 # or more contributor license agreements.  See the NOTICE file
 # distributed with this work for additional information
 # regarding copyright ownership.  The ASF licenses this file
 # to you under the Apache License, Version 2.0 (the
 # "License"); you may not use this file except in compliance
 # with the License.  You may obtain a copy of the License at
 #
 #   http://www.apache.org/licenses/LICENSE-2.0
 #
 # Unless required by applicable law or agreed to in writing,
 # software distributed under the License is distributed on an
 # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
 # KIND, either express or implied.  See the License for the
 # specific language governing permissions and limitations
 # under the License.
 import sys

 # We will use all operators inside NDArray Module
 # If you want to run benchmark for all operators in different namespace,
 # for example mxnet.numpy.op, update here. All operators for benchmarks
 # will be picked up from this module
 MX_OP_MODULE = sys.modules["mxnet.ndarray.op"]


 """Default Input Tensor shapes to use for benchmarking"""

 # For operators like concat, ElementWiseSum, squeeze, stack
 # argument data is passed as variable arg (*args)
 DEFAULT_ARGS = [(1024, 1024)]

 # For Unary operators like abs, arccos, arcsin etc..
 DEFAULT_DATA = [(1024, 1024), (10000, 1), (10000, 100)]

 # For Binary broadcast operators like - broadcast_add/sub/mod/logical_and etc..
 DEFAULT_LHS = [(1024, 1024), (10000, 10), (10000, 1)]
 DEFAULT_RHS = [(1024, 1024), (10000, 10), (10000, 1)]

 # For operators like - random_uniform, random_normal etc..
 DEFAULT_SHAPE = [(1024, 1024), (10000, 1), (10000, 100)]
 DEFAULT_SAMPLE = [(2,)]
 DEFAULT_LOW = [0]
 DEFAULT_HIGH = [5]
 DEFAULT_K = [1]
 DEFAULT_P = [1]

 # For operators like - sample_uniform, sample_normal etc..
 # NOTE: There are many overlapping operators in random_* and sample_*,
 # Ex: random_uniform, sample_uniform. Parameter names are same, but, for
 # random_* operators they are float/int and for sample_* operators they are NDArray.
 # Hence, below we append ND to mark the difference.
 DEFAULT_LOW_ND = [[0.0, 2.5]]
 DEFAULT_HIGH_ND = [[1.0, 3.7]]
 DEFAULT_MU_ND = [[2.0, 2.5]]
 DEFAULT_SIGMA = [[1.0, 3.7]]
 DEFAULT_ALPHA_ND = [[0.0, 2.5]]
 DEFAULT_BETA_ND = [[1.0, 0.7]]
 DEFAULT_LAM = [[1.0, 8.5]]
 DEFAULT_K_ND = [[20, 49]]
 DEFAULT_P_ND = [[0.4, 0.77]]

 # For reduction operators
 # NOTE: Data used is DEFAULT_DATA
 DEFAULT_AXIS_SHAPE = [(), 0, (0, 1)]

 # For sorting and searching operators
 # NOTE: Data used is DEFAULT_DATA
 DEFAULT_AXIS = [0]

 # For optimizer operators
 DEFAULT_WEIGHT = [(1024, 1024), (10000, 1), (10000, 100)]
 DEFAULT_GRAD = [(1024, 1024), (10000, 1), (10000, 100)]
 DEFAULT_MOM = [(1024, 1024), (10000, 1), (10000, 100)]
 DEFAULT_MEAN = [(1024, 1024), (10000, 1), (10000, 100)]
 DEFAULT_VAR = [(1024, 1024), (10000, 1), (10000, 100)]
 DEFAULT_N = [(1024, 1024), (10000, 1), (10000, 100)]
 DEFAULT_D = [(1024, 1024), (10000, 1), (10000, 100)]
 DEFAULT_V = [(1024, 1024), (10000, 1), (10000, 100)]
 DEFAULT_Z = [(1024, 1024), (10000, 1), (10000, 100)]
 DEFAULT_G = [(1024, 1024), (10000, 1), (10000, 100)]
 DEFAULT_DELTA = [(1024, 1024), (10000, 1), (10000, 100)]
 DEFAULT_LRS = [(0.1,0.1)]
 DEFAULT_LR = [0.1,0.5,0.9]
 DEFAULT_GAMMA_1 = [0.1,0.5,0.9]
 DEFAULT_GAMMA_2 = [0.1,0.5,0.9]
 DEFAULT_EPSILON = [1e-08]
 DEFAULT_BETA_1 = [0.1,0.5,0.9]
 DEFAULT_BETA_2 = [0.1,0.5,0.9]
 DEFAULT_T = [1,5]
 DEFAULT_RESCALE_GRAD = [0.4, 0.77]
 DEFAULT_CLIP_GRADIENT = [-1.0,0.8]
 DEFAULT_CLIP_WEIGHTS = [-1.0,0.8]
 DEFAULT_LAZY_UPDATE = [0,1]

 # For rearrange operators
 # NOTE: Data needs to be a 4D tensor for  operators like space_to_depth and depth_to_space
 # 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_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,
                    "sample": DEFAULT_SAMPLE,
                    "lhs": DEFAULT_LHS,
                    "rhs": DEFAULT_RHS,
                    "shape": DEFAULT_SHAPE,
                    "low": DEFAULT_LOW,
                    "high": DEFAULT_HIGH,
                    "low_nd": DEFAULT_LOW_ND,
                    "high_nd": DEFAULT_HIGH_ND,
                    "mu_nd": DEFAULT_MU_ND,
                    "sigma": DEFAULT_SIGMA,
                    "alpha_nd": DEFAULT_ALPHA_ND,
                    "beta_nd": DEFAULT_BETA_ND,
                    "lam_nd": DEFAULT_LAM,
                    "k": DEFAULT_K,
                    "p": DEFAULT_P,
                    "k_nd": DEFAULT_K_ND,
                    "p_nd": DEFAULT_P_ND,
                    "axis_shape": DEFAULT_AXIS_SHAPE,
                    "axis": DEFAULT_AXIS,
                    "weight" : DEFAULT_WEIGHT,
                    "weight32" : DEFAULT_WEIGHT,
                    "grad" : DEFAULT_GRAD,
                    "mean" : DEFAULT_MEAN,
                    "var" : DEFAULT_VAR,
                    "mom" : DEFAULT_MOM,
                    "n" : DEFAULT_N,
                    "d" : DEFAULT_D,
                    "v" : DEFAULT_V,
                    "z" : DEFAULT_Z,
                    "g" : DEFAULT_G,
                    "delta" : DEFAULT_DELTA,
                    "lr" : DEFAULT_LR,
                    "lrs" : DEFAULT_LRS,
                    "wds" : DEFAULT_LRS,
                    "gamma1" : DEFAULT_GAMMA_1,
                    "gamma2" : DEFAULT_GAMMA_2,
                    "epsilon" : DEFAULT_EPSILON,
                    "beta1" : DEFAULT_BETA_1,
                    "beta2" : DEFAULT_BETA_2,
                    "t" : DEFAULT_T,
                    "rescale_grad" : DEFAULT_RESCALE_GRAD,
                    "clip_grad" : DEFAULT_CLIP_GRADIENT,
                    "lazy_update" : DEFAULT_LAZY_UPDATE,
                    "data_4d": DEFAULT_DATA_4d,
                    "dim1": DEFAULT_DIM_1,
                    "dim2": DEFAULT_DIM_2,
                    "block_size": DEFAULT_BLOCK_SIZE,
                    "args": DEFAULT_ARGS}


 # These are names of MXNet operator parameters that is of type NDArray.
 # We maintain this list to automatically recognize these parameters are to be
 # given as NDArray and translate users inputs such as a shape tuple, Numpy Array or
 # a list to MXNet NDArray. This is just a convenience added so benchmark utility users
 # can just say shape of the tensor, and we automatically create Tensors.
 PARAMS_OF_TYPE_NDARRAY = ["lhs", "rhs", "data", "base", "exp", "sample",
                           "mu", "sigma", "lam", "alpha", "beta", "gamma", "k", "p",
                           "low", "high", "weight", "bias", "moving_mean", "moving_var",
                           "weight", "weight32", "grad", "mean", "var", "mom", "n", "d",
                           "v", "z", "g", "delta", "args"]
	# Licensed to the Apache Software Foundation (ASF) under one
	# or more contributor license agreements. See the NOTICE file
	# distributed with this work for additional information
	# regarding copyright ownership. The ASF licenses this file
	# to you under the Apache License, Version 2.0 (the
	# "License"); you may not use this file except in compliance
	# with the License. You may obtain a copy of the License at
	#
	# http://www.apache.org/licenses/LICENSE-2.0
	#
	# Unless required by applicable law or agreed to in writing,
	# software distributed under the License is distributed on an
	# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
	# KIND, either express or implied. See the License for the
	# specific language governing permissions and limitations
	# under the License.
	import sys

	# We will use all operators inside NDArray Module
	# If you want to run benchmark for all operators in different namespace,
	# for example mxnet.numpy.op, update here. All operators for benchmarks
	# will be picked up from this module
	MX_OP_MODULE = sys.modules["mxnet.ndarray.op"]


	"""Default Input Tensor shapes to use for benchmarking"""

	# For operators like concat, ElementWiseSum, squeeze, stack
	# argument data is passed as variable arg (*args)
	DEFAULT_ARGS = [(1024, 1024)]

	# For Unary operators like abs, arccos, arcsin etc..
	DEFAULT_DATA = [(1024, 1024), (10000, 1), (10000, 100)]

	# For Binary broadcast operators like - broadcast_add/sub/mod/logical_and etc..
	DEFAULT_LHS = [(1024, 1024), (10000, 10), (10000, 1)]
	DEFAULT_RHS = [(1024, 1024), (10000, 10), (10000, 1)]

	# For operators like - random_uniform, random_normal etc..
	DEFAULT_SHAPE = [(1024, 1024), (10000, 1), (10000, 100)]
	DEFAULT_SAMPLE = [(2,)]
	DEFAULT_LOW = [0]
	DEFAULT_HIGH = [5]
	DEFAULT_K = [1]
	DEFAULT_P = [1]

	# For operators like - sample_uniform, sample_normal etc..
	# NOTE: There are many overlapping operators in random_* and sample_*,
	# Ex: random_uniform, sample_uniform. Parameter names are same, but, for
	# random_* operators they are float/int and for sample_* operators they are NDArray.
	# Hence, below we append ND to mark the difference.
	DEFAULT_LOW_ND = [[0.0, 2.5]]
	DEFAULT_HIGH_ND = [[1.0, 3.7]]
	DEFAULT_MU_ND = [[2.0, 2.5]]
	DEFAULT_SIGMA = [[1.0, 3.7]]
	DEFAULT_ALPHA_ND = [[0.0, 2.5]]
	DEFAULT_BETA_ND = [[1.0, 0.7]]
	DEFAULT_LAM = [[1.0, 8.5]]
	DEFAULT_K_ND = [[20, 49]]
	DEFAULT_P_ND = [[0.4, 0.77]]

	# For reduction operators
	# NOTE: Data used is DEFAULT_DATA
	DEFAULT_AXIS_SHAPE = [(), 0, (0, 1)]

	# For sorting and searching operators
	# NOTE: Data used is DEFAULT_DATA
	DEFAULT_AXIS = [0]

	# For optimizer operators
	DEFAULT_WEIGHT = [(1024, 1024), (10000, 1), (10000, 100)]
	DEFAULT_GRAD = [(1024, 1024), (10000, 1), (10000, 100)]
	DEFAULT_MOM = [(1024, 1024), (10000, 1), (10000, 100)]
	DEFAULT_MEAN = [(1024, 1024), (10000, 1), (10000, 100)]
	DEFAULT_VAR = [(1024, 1024), (10000, 1), (10000, 100)]
	DEFAULT_N = [(1024, 1024), (10000, 1), (10000, 100)]
	DEFAULT_D = [(1024, 1024), (10000, 1), (10000, 100)]
	DEFAULT_V = [(1024, 1024), (10000, 1), (10000, 100)]
	DEFAULT_Z = [(1024, 1024), (10000, 1), (10000, 100)]
	DEFAULT_G = [(1024, 1024), (10000, 1), (10000, 100)]
	DEFAULT_DELTA = [(1024, 1024), (10000, 1), (10000, 100)]
	DEFAULT_LRS = [(0.1,0.1)]
	DEFAULT_LR = [0.1,0.5,0.9]
	DEFAULT_GAMMA_1 = [0.1,0.5,0.9]
	DEFAULT_GAMMA_2 = [0.1,0.5,0.9]
	DEFAULT_EPSILON = [1e-08]
	DEFAULT_BETA_1 = [0.1,0.5,0.9]
	DEFAULT_BETA_2 = [0.1,0.5,0.9]
	DEFAULT_T = [1,5]
	DEFAULT_RESCALE_GRAD = [0.4, 0.77]
	DEFAULT_CLIP_GRADIENT = [-1.0,0.8]
	DEFAULT_CLIP_WEIGHTS = [-1.0,0.8]
	DEFAULT_LAZY_UPDATE = [0,1]

	# For rearrange operators
	# NOTE: Data needs to be a 4D tensor for operators like space_to_depth and depth_to_space
	# 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_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,
	"sample": DEFAULT_SAMPLE,
	"lhs": DEFAULT_LHS,
	"rhs": DEFAULT_RHS,
	"shape": DEFAULT_SHAPE,
	"low": DEFAULT_LOW,
	"high": DEFAULT_HIGH,
	"low_nd": DEFAULT_LOW_ND,
	"high_nd": DEFAULT_HIGH_ND,
	"mu_nd": DEFAULT_MU_ND,
	"sigma": DEFAULT_SIGMA,
	"alpha_nd": DEFAULT_ALPHA_ND,
	"beta_nd": DEFAULT_BETA_ND,
	"lam_nd": DEFAULT_LAM,
	"k": DEFAULT_K,
	"p": DEFAULT_P,
	"k_nd": DEFAULT_K_ND,
	"p_nd": DEFAULT_P_ND,
	"axis_shape": DEFAULT_AXIS_SHAPE,
	"axis": DEFAULT_AXIS,
	"weight" : DEFAULT_WEIGHT,
	"weight32" : DEFAULT_WEIGHT,
	"grad" : DEFAULT_GRAD,
	"mean" : DEFAULT_MEAN,
	"var" : DEFAULT_VAR,
	"mom" : DEFAULT_MOM,
	"n" : DEFAULT_N,
	"d" : DEFAULT_D,
	"v" : DEFAULT_V,
	"z" : DEFAULT_Z,
	"g" : DEFAULT_G,
	"delta" : DEFAULT_DELTA,
	"lr" : DEFAULT_LR,
	"lrs" : DEFAULT_LRS,
	"wds" : DEFAULT_LRS,
	"gamma1" : DEFAULT_GAMMA_1,
	"gamma2" : DEFAULT_GAMMA_2,
	"epsilon" : DEFAULT_EPSILON,
	"beta1" : DEFAULT_BETA_1,
	"beta2" : DEFAULT_BETA_2,
	"t" : DEFAULT_T,
	"rescale_grad" : DEFAULT_RESCALE_GRAD,
	"clip_grad" : DEFAULT_CLIP_GRADIENT,
	"lazy_update" : DEFAULT_LAZY_UPDATE,
	"data_4d": DEFAULT_DATA_4d,
	"dim1": DEFAULT_DIM_1,
	"dim2": DEFAULT_DIM_2,
	"block_size": DEFAULT_BLOCK_SIZE,
	"args": DEFAULT_ARGS}


	# These are names of MXNet operator parameters that is of type NDArray.
	# We maintain this list to automatically recognize these parameters are to be
	# given as NDArray and translate users inputs such as a shape tuple, Numpy Array or
	# a list to MXNet NDArray. This is just a convenience added so benchmark utility users
	# can just say shape of the tensor, and we automatically create Tensors.
	PARAMS_OF_TYPE_NDARRAY = ["lhs", "rhs", "data", "base", "exp", "sample",
	"mu", "sigma", "lam", "alpha", "beta", "gamma", "k", "p",
	"low", "high", "weight", "bias", "moving_mean", "moving_var",
	"weight", "weight32", "grad", "mean", "var", "mom", "n", "d",
	"v", "z", "g", "delta", "args"]