def __init__(self, ctx=mx.cpu(), warmup=10, runs=50, inputs=None):
# Set the default Inputs
default_parameters = {
"lhs": (32, 1024, 1024),
"rhs": (32, 1024, 1000),
"initializer": nd.normal,
"transpose_a": False,
"transpose_b": False,
"forward_stype": None,
"run_backward": True,
"dtype": "float32"
}
super().__init__(ctx=ctx,
warmup=warmup,
runs=runs,
default_parameters=default_parameters,
custom_parameters=inputs)
self.lhs = get_mx_ndarray(ctx=self.ctx,
in_tensor=self.inputs["lhs"],
dtype=self.inputs["dtype"],
initializer=self.inputs["initializer"],
attach_grad=self.inputs["run_backward"])
self.rhs = get_mx_ndarray(ctx=self.ctx,
in_tensor=self.inputs["rhs"],
dtype=self.inputs["dtype"],
initializer=self.inputs["initializer"],
attach_grad=self.inputs["run_backward"])
def __init__(self, ctx=mx.cpu(), warmup=10, runs=50, inputs=None):
# Set the default Inputs.
# Default prediction is (32, 1000, 1) to mimic a prediction of batch_size=32 and 1000 class classification.
default_parameters = {
"pred": (32, 1000, 1),
"pred_initializer": nd.normal,
"label": (32, 1000, 1),
"label_initializer": nd.normal,
"weight": 1.0,
"run_backward": False,
"dtype": "float32"
}
super().__init__(ctx=ctx,
warmup=warmup,
runs=runs,
default_parameters=default_parameters,
custom_parameters=inputs)
# Create a random prediction and label tensor
self.pred = get_mx_ndarray(ctx=self.ctx,
in_tensor=self.inputs["pred"],
dtype=self.inputs["dtype"],
initializer=self.inputs["pred_initializer"],
attach_grad=self.inputs["run_backward"])
self.label = get_mx_ndarray(
ctx=self.ctx,
in_tensor=self.inputs["label"],
dtype=self.inputs["dtype"],
initializer=self.inputs["label_initializer"],
attach_grad=self.inputs["run_backward"])
self.block = loss.L2Loss(weight=self.inputs["weight"], batch_axis=0)
self.block.initialize(ctx=self.ctx)
def __init__(self, ctx=mx.cpu(), warmup=10, runs=50, inputs=None):
# Set the default Inputs.
# Default data is (32, 3, 256, 256)
default_parameters = {
"data": (32, 3, 256, 256),
"data_initializer": nd.normal,
"run_backward": True,
"dtype": "float32"
}
super().__init__(ctx=ctx,
warmup=warmup,
runs=runs,
default_parameters=default_parameters,
custom_parameters=inputs)
# Create a random prediction and label tensor
self.data = get_mx_ndarray(ctx=self.ctx,
in_tensor=self.inputs["data"],
dtype=self.inputs["dtype"],
initializer=self.inputs["data_initializer"],
attach_grad=self.inputs["run_backward"])
self.block = nn.BatchNorm()
self.block.initialize(ctx=self.ctx)
def __init__(self, ctx=mx.cpu(), warmup=10, runs=50, inputs=None):
# Set the default Inputs.
# Default data is (128, 512, 512) to mimic an input of batch_size=128 and a sample image of size 512*512.
default_parameters = {
"data": (128, 512, 512),
"data_initializer": nd.normal,
"run_backward": True,
"dtype": "float32"
}
super().__init__(ctx=ctx,
warmup=warmup,
runs=runs,
default_parameters=default_parameters,
custom_parameters=inputs)
self.data = get_mx_ndarray(ctx=self.ctx,
in_tensor=self.inputs["data"],
dtype=self.inputs["dtype"],
initializer=self.inputs["data_initializer"],
attach_grad=self.inputs["run_backward"])
self.block = nn.Flatten()
self.block.initialize(ctx=self.ctx)
def __init__(self, ctx=mx.cpu(), warmup=10, runs=50, inputs=None):
# Set the default Inputs.
# Default data is (3, 512, 512) to mimic an input image of size 512*512 with 3 channels.
default_parameters = {
"data": (128, 512, 512),
"data_initializer": nd.normal,
"run_backward": True,
"dtype": "float32"
}
super().__init__(ctx=ctx,
warmup=warmup,
runs=runs,
default_parameters=default_parameters,
custom_parameters=inputs)
self.data = get_mx_ndarray(ctx=self.ctx,
in_tensor=self.inputs["data"],
dtype=self.inputs["dtype"],
initializer=self.inputs["data_initializer"],
attach_grad=self.inputs["run_backward"])
# Batchify the input data. (3, 1024, 1024) => (1, 3, 1024, 1024)
self.block = nn.Lambda(lambda x: nd.expand_dims(data=x, axis=0))
self.block.initialize(ctx=self.ctx)
def __init__(self, ctx=mx.cpu(), warmup=10, runs=50, inputs=None):
# Set the default Inputs
default_parameters = {
"data": (25, 32, 256),
"data_initializer": nd.normal,
"hidden_size": 100,
"num_layers": 1,
"layout": "TNC",
"dropout": 0,
"bidirectional": False,
"run_backward": True,
"dtype": "float32"
}
super().__init__(ctx=ctx,
warmup=warmup,
runs=runs,
default_parameters=default_parameters,
custom_parameters=inputs)
self.data = get_mx_ndarray(ctx=self.ctx,
in_tensor=self.inputs["data"],
dtype=self.inputs["dtype"],
initializer=self.inputs["data_initializer"],
attach_grad=self.inputs["run_backward"])
self.block = rnn.LSTM(hidden_size=self.inputs["hidden_size"],
num_layers=self.inputs["num_layers"],
layout=self.inputs["layout"],
dropout=self.inputs["dropout"],
bidirectional=self.inputs["bidirectional"],
dtype=self.inputs["dtype"])
self.block.initialize(ctx=self.ctx)
def __init__(self, ctx=mx.cpu(), warmup=5, runs=25, inputs=None):
# Set the default Inputs
default_parameters = {
"data": (32, 3, 256, 256),
"data_initializer": nd.normal,
"pool_size": (2, 2),
"strides": None,
"padding": (0, 0),
"layout": "NCHW",
"run_backward": True,
"dtype": "float32"
}
super().__init__(ctx=ctx,
warmup=warmup,
runs=runs,
default_parameters=default_parameters,
custom_parameters=inputs)
self.data = get_mx_ndarray(ctx=self.ctx,
in_tensor=self.inputs["data"],
dtype=self.inputs["dtype"],
initializer=self.inputs["data_initializer"],
attach_grad=self.inputs["run_backward"])
self.block = nn.MaxPool2D(pool_size=self.inputs["pool_size"],
strides=self.inputs["strides"],
padding=self.inputs["padding"],
layout=self.inputs["layout"])
self.block.initialize(ctx=self.ctx)
def __init__(self, ctx=mx.cpu(), warmup=10, runs=50, inputs=None):
# Set the default Inputs
default_parameters = {"this": (1024, 1024),
"other": (1024, 1024),
"initializer": nd.normal,
"run_backward": False,
"dtype": "float32"}
super().__init__(ctx=ctx, warmup=warmup, runs=runs, default_parameters=default_parameters,
custom_parameters=inputs)
self.this = get_mx_ndarray(ctx=self.ctx, in_tensor=self.inputs["this"],
dtype=self.inputs["dtype"],
initializer=self.inputs["initializer"],
attach_grad=self.inputs["run_backward"])
self.other = get_mx_ndarray(ctx=self.ctx, in_tensor=self.inputs["other"],
dtype=self.inputs["dtype"],
initializer=self.inputs["initializer"],
attach_grad=self.inputs["run_backward"])
def __init__(self, ctx=mx.cpu(), warmup=5, runs=25, inputs=None):
# Set the default Inputs
default_parameters = {
"data": (32, 3, 256, 256),
"data_initializer": nd.normal,
"channels": 64,
"kernel_size": (3, 3),
"strides": (1, 1),
"padding": (0, 0),
"output_padding": (0, 0),
"dilation": (1, 1),
"layout": "NCHW",
"activation": None,
"run_backward": True,
"dtype": "float32"
}
super().__init__(ctx=ctx,
warmup=warmup,
runs=runs,
default_parameters=default_parameters,
custom_parameters=inputs)
self.data = get_mx_ndarray(ctx=self.ctx,
in_tensor=self.inputs["data"],
dtype=self.inputs["dtype"],
initializer=self.inputs["data_initializer"],
attach_grad=self.inputs["run_backward"])
self.block = nn.Conv2DTranspose(
channels=self.inputs["channels"],
kernel_size=self.inputs["kernel_size"],
strides=self.inputs["strides"],
padding=self.inputs["padding"],
output_padding=self.inputs["output_padding"],
dilation=self.inputs["dilation"],
layout=self.inputs["layout"],
activation=self.inputs["activation"])
self.block.initialize(ctx=self.ctx)
def __init__(self, ctx=mx.cpu(), warmup=10, runs=50, inputs=None):
# Set the default Inputs.
# Default data is (1, 1024) to mimic an input of batch_size=1 and a sample image of size 512*512.
# Default number of units 256 is referred from ResNet architecture as commonly used Dense Layer size.
# Default activation is None because we want to benchmark just dense layer operation.
default_parameters = {
"data": (512, 512),
"units": 256,
"activation": None,
"use_bias": True,
"flatten": True,
"data_initializer": nd.normal,
"weight_initializer": "Xavier",
"bias_initializer": "Zeros",
"run_backward": True,
"dtype": "float32"
}
super().__init__(ctx=ctx,
warmup=warmup,
runs=runs,
default_parameters=default_parameters,
custom_parameters=inputs)
self.data = get_mx_ndarray(ctx=self.ctx,
in_tensor=self.inputs["data"],
dtype=self.inputs["dtype"],
initializer=self.inputs["data_initializer"],
attach_grad=self.inputs["run_backward"])
self.block = nn.Dense(
units=self.inputs["units"],
activation=self.inputs["activation"],
use_bias=self.inputs["use_bias"],
flatten=self.inputs["flatten"],
dtype=self.inputs["dtype"],
weight_initializer=self.inputs["weight_initializer"],
bias_initializer=self.inputs["bias_initializer"])
self.block.initialize(ctx=self.ctx)
def __init__(self, ctx=mx.cpu(), warmup=10, runs=50, inputs=None):
# Set the default Inputs
default_parameters = {
"data": (1024, 1000),
"initializer": nd.normal,
"axis": -1,
"k": 10,
"ret_typ": "indices",
"is_ascend": True,
"run_backward": False,
"dtype": "float32"
}
super().__init__(ctx=ctx,
warmup=warmup,
runs=runs,
default_parameters=default_parameters,
custom_parameters=inputs)
self.data = get_mx_ndarray(ctx=self.ctx,
in_tensor=self.inputs["data"],
dtype=self.inputs["dtype"],
initializer=self.inputs["initializer"],
attach_grad=self.inputs["run_backward"])
def __init__(self, ctx=mx.cpu(), warmup=5, runs=25, inputs=None):
# Set the default Inputs
default_parameters = {
"data": (32, 3, 256),
"data_initializer": nd.normal,
"layout": "NCW",
"run_backward": True,
"dtype": "float32"
}
super().__init__(ctx=ctx,
warmup=warmup,
runs=runs,
default_parameters=default_parameters,
custom_parameters=inputs)
self.data = get_mx_ndarray(ctx=self.ctx,
in_tensor=self.inputs["data"],
dtype=self.inputs["dtype"],
initializer=self.inputs["data_initializer"],
attach_grad=self.inputs["run_backward"])
self.block = nn.GlobalAvgPool1D(layout=self.inputs["layout"])
self.block.initialize(ctx=self.ctx)
