wehub-resource-sync
136 lines
4.2 KiB
Python
136 lines
4.2 KiB
Python
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from paddle.incubate.passes import ir
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def set_resnet_unit_attrs(resnet_unit, has_shortcut):
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resnet_unit.SetAttr("fuse_add", False)
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resnet_unit.SetAttr("act_type", "relu")
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resnet_unit.SetAttr("has_shortcut", has_shortcut)
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resnet_unit.SetAttr("data_format", 'NHWC')
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resnet_unit.SetAttr("dilation", 1)
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resnet_unit.Attr("stride").MappedPattern(
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op="conv2d", name="strides", element_index=0
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)
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resnet_unit.Attr("padding").MappedPattern(
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op="conv2d", name="paddings", element_index=0
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)
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resnet_unit.Attr("group").MappedPattern(op="conv2d", name="groups")
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resnet_unit.Attr("op_device").MappedPattern(op="conv2d", name="op_device")
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resnet_unit.Attr("op_namescope").MappedPattern(
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op="conv2d", name="op_namescope"
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)
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resnet_unit.Attr("momentum").MappedPattern(op="batch_norm", name="momentum")
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resnet_unit.Attr("epsilon").MappedPattern(op="batch_norm", name="epsilon")
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resnet_unit.Attr("use_global_stats").MappedPattern(
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op="batch_norm", name="use_global_stats"
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)
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def set_resnet_unit_outputs(resnet_unit, meanX, varX, meanZ=None, varZ=None):
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resnet_unit.SetOutputs(
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RunningMeanX=meanX,
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RunningVarX=varX,
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RunningMeanZ=meanZ,
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RunningVarZ=varZ,
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)
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@ir.RegisterPass
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def fuse_resnet_unit():
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def pattern_conv_bn(x, filter, scale, bias, mean, var):
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filter.Attr("shape")[0].Mod(32).EQ(0)
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filter.Attr("shape")[1].Mod(8).EQ(0)
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filter.Attr("shape")[2].EQ(1)
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filter.Attr("shape")[3].EQ(1)
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conv2d = ir.PassDesc.OP.conv2d(Input=x, Filter=filter)
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conv2d.SetAttr("data_format", 'NHWC')
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bn = ir.PassDesc.OP.batch_norm(
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X=conv2d, Bias=bias, Mean=mean, Scale=scale, Variance=var
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)
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return bn
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def pattern_one_input(x, filter, scale, bias, mean, var):
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bn = pattern_conv_bn(x, filter, scale, bias, mean, var)
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relu = ir.PassDesc.OP.relu(X=bn.Output("Y"))
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return relu
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def replace_one_input(x, filter, scale, bias, mean, var):
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resnet_unit = ir.PassDesc.OP.resnet_unit(
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X=x, FilterX=filter, ScaleX=scale, BiasX=bias, MeanX=mean, VarX=var
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)
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set_resnet_unit_attrs(resnet_unit, False)
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set_resnet_unit_outputs(resnet_unit, mean, var)
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return resnet_unit.Output("Y")
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def pattern_two_input(
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x,
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filterX,
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scaleX,
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biasX,
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meanX,
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varX,
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z,
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filterZ,
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scaleZ,
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biasZ,
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meanZ,
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varZ,
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):
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bnX = pattern_conv_bn(x, filterX, scaleX, biasX, meanX, varX)
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bnZ = pattern_conv_bn(z, filterZ, scaleZ, biasZ, meanZ, varZ)
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ewadd = ir.PassDesc.OP.elementwise_add(
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X=bnX.Output("Y"), Y=bnZ.Output("Y")
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)
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relu = ir.PassDesc.OP.relu(X=ewadd)
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return relu
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def replace_two_input(
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x,
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filterX,
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scaleX,
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biasX,
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meanX,
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varX,
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z,
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filterZ,
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scaleZ,
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biasZ,
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meanZ,
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varZ,
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):
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resnet_unit = ir.PassDesc.OP.resnet_unit(
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X=x,
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FilterX=filterX,
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ScaleX=scaleX,
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BiasX=biasX,
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MeanX=meanX,
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VarX=varX,
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Z=z,
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FilterZ=filterZ,
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ScaleZ=scaleZ,
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BiasZ=biasZ,
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MeanZ=meanZ,
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VarZ=varZ,
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)
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set_resnet_unit_attrs(resnet_unit, True)
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set_resnet_unit_outputs(resnet_unit, meanX, varX, meanZ, varZ)
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return resnet_unit.Output("Y")
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return (pattern_one_input, replace_one_input), (
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pattern_two_input,
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replace_two_input,
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)
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