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expand bias_add and bias_add_grad op

This commit is contained in:
zengzitao 2020-10-30 11:46:56 +08:00
parent b3b553245f
commit febdb1850c
7 changed files with 151 additions and 3 deletions
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2
mindspore/_extends/graph_kernel/expanders/__init__.py
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@ -18,3 +18,5 @@ from .gelu import expand_gelu
from .layernorm import expand_layernorm
from .softmax import expand_softmax
from .square import expand_square
from .bias_add import expand_biasadd
from .bias_add_grad import expand_biasaddgrad

62
mindspore/_extends/graph_kernel/expanders/bias_add.py Normal file
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@ -0,0 +1,62 @@
# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed 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.
# ===========================================================================
"""generate json desc for bias_add"""
from mindspore._extends.graph_kernel.model import model_builder as builder
def expand_biasadd(expand_info):
"""BiasAdd expander"""
# get op info.
input_desc_0 = expand_info['input_desc'][0]
input_desc_1 = expand_info['input_desc'][1]
graph_builder = builder.GraphBuilder()
# generate a graph.
with graph_builder.graph_scope('main') as graph_scope:
# create tensor input.
input_x = graph_builder.tensor(
input_desc_0['shape'], input_desc_0['data_type'], input_desc_0['format'])
input_y = graph_builder.tensor(
input_desc_1['shape'], input_desc_1['data_type'], input_desc_1['format'])
graph_scope.set_input(input_x, input_y)
if input_x.data_format == "NCHW":
input_y_expand = graph_builder.emit(
'ExpandDims', [input_y], attrs={'axis': 1})
input_y_expand = graph_builder.emit(
'ExpandDims', [input_y_expand], attrs={'axis': 2})
result = graph_builder.emit('TensorAdd', [input_x, input_y_expand])
elif input_x.data_format == "DefaultFormat":
if len(input_x.shape) == 2:
result = graph_builder.emit('TensorAdd', [input_x, input_y])
elif len(input_x.shape) == 3:
input_y_expand = graph_builder.emit(
'ExpandDims', [input_y], attrs={'axis': 1})
result = graph_builder.emit(
'TensorAdd', [input_x, input_y_expand])
else:
input_y_expand = graph_builder.emit(
'ExpandDims', [input_y], attrs={'axis': 1})
input_y_expand = graph_builder.emit(
'ExpandDims', [input_y_expand], attrs={'axis': 2})
result = graph_builder.emit(
'TensorAdd', [input_x, input_y_expand])
else:
result = graph_builder.emit('TensorAdd', [input_x, input_y])
# set graph output.
graph_scope.set_output(result)
graph = graph_builder.get()[0]
return graph

48
mindspore/_extends/graph_kernel/expanders/bias_add_grad.py Normal file
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@ -0,0 +1,48 @@
# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed 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.
# ===========================================================================
"""generate json desc for bias_add"""
from mindspore._extends.graph_kernel.model import model_builder as builder
def expand_biasaddgrad(expand_info):
"""BiasAddGrad expander"""
# get op info.
input_desc_0 = expand_info['input_desc'][0]
graph_builder = builder.GraphBuilder()
# generate a graph.
with graph_builder.graph_scope('main') as graph_scope:
# create tensor input.
input_x = graph_builder.tensor(
input_desc_0['shape'], input_desc_0['data_type'], input_desc_0['format'])
graph_scope.set_input(input_x)
reduce_axis = ()
if input_x.data_format == 'NHWC':
reduce_axis = (0, 1, 2)
elif input_x.data_format == 'NCHW':
reduce_axis = (0, 2, 3)
# Default format shape's length maybe equal 2 to 4, so different shape's length reduce axis are differnet
else:
if len(input_x.shape) == 2:
reduce_axis = (0,)
elif len(input_x.shape) == 3:
reduce_axis = (0, 1)
else:
reduce_axis = (0, 2, 3)
result = graph_builder.emit('ReduceSum', [input_x], attrs={'reduce_axis': reduce_axis, 'keep_dims': False})
# set graph output.
graph_scope.set_output(result)
graph = graph_builder.get()[0]
return graph

4
mindspore/_extends/graph_kernel/model/model.py
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@ -296,6 +296,7 @@ class Graph:
def __init__(self, name, ops):
self.name = name
self.ops = ops # in topo order, can not use set
self.inputs = []
self.outputs = []
def set_processor(self, processor):
@ -341,6 +342,9 @@ class Graph:
if d not in self.ops:
outputs.append(op.output)
break
if self.inputs:
inputs = self.inputs
if self.outputs:
outputs = self.outputs
return inputs, outputs

35
mindspore/_extends/graph_kernel/model/model_builder.py
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@ -22,14 +22,36 @@ class OpInfer:
"""Op infer"""
@staticmethod
def default_reduce_infer(inputs, attrs):
"""Default reduce infer"""
shape = copy.deepcopy(inputs[0].shape)
for i in attrs['reduce_axis']:
shape[i] = 1
if attrs['keep_dims']:
for i in attrs['reduce_axis']:
shape[i] = 1
return shape
real_shape = []
for i, _ in enumerate(shape):
if i not in attrs['reduce_axis']:
real_shape.append(shape[i])
return real_shape
@staticmethod
def default_elementwise_infer(inputs, attrs):
"""Default elementwise infer"""
shape = (1,)
max_flatten_shape = 1
for t in inputs:
flatten_shape = 1
for s in t.shape:
flatten_shape *= s
if flatten_shape >= max_flatten_shape:
max_flatten_shape = flatten_shape
shape = t.shape
return shape
default_infer_shape_func = [
None,
lambda inputs, attrs: max([t.shape for t in inputs]),
default_elementwise_infer.__func__,
lambda inputs, attrs: max([t.shape for t in inputs]),
default_reduce_infer.__func__,
None,
@ -72,9 +94,16 @@ class OpInfer:
class GraphBuilder:
"""Graph builder"""
class GraphWrapper:
"""Graph wrapper"""
def __init__(self, name):
self.graph = Graph(name, [])
def set_input(self, *para):
for t in para:
t.para_type = Tensor.PARA_INPUT
self.graph.inputs.append(t)
def set_output(self, *para):
for t in para:
t.para_type = Tensor.PARA_OUTPUT

2
mindspore/ccsrc/backend/optimizer/graph_kernel/graph_kernel_helper.cc
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@ -702,6 +702,8 @@ FuncGraphPtr JsonDescToAnf(const std::string &json_desc, const std::vector<AnfNo
std::unordered_set<PrimitivePtr> GetExpandOps() {
std::unordered_set<PrimitivePtr> expand_ops = {
prim::kPrimSquare,
prim::kPrimBiasAdd,
prim::kPrimBiasAddGrad,
};
return expand_ops;
}

1
mindspore/core/base/core_ops.h
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@ -143,6 +143,7 @@ inline const PrimitivePtr kPrimDepthwiseConv2dNativeBackpropFilter =
std::make_shared<Primitive>("DepthwiseConv2dNativeBackpropFilter");
inline const PrimitivePtr kPrimDepthwiseConv2dNativeBackpropInput =
std::make_shared<Primitive>("DepthwiseConv2dNativeBackpropInput");
inline const PrimitivePtr kPrimBiasAdd = std::make_shared<Primitive>("BiasAdd");
inline const PrimitivePtr kPrimBiasAddGrad = std::make_shared<Primitive>("BiasAddGrad");
inline const PrimitivePtr kPrimSoftmaxCrossEntropyWithLogits =
std::make_shared<Primitive>("SoftmaxCrossEntropyWithLogits");