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207 lines
8.0 KiB
207 lines
8.0 KiB
# Copyright (c) 2018 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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import numpy as np
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import os
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import shutil
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from . import core
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class InferenceTranspiler:
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def transpile(self, program, scope, place):
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'''
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Transpile the program to a inference program by fused batch normalization.
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The batch normalization followed the convolution or fully connected layer
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can be integrated with them. Doing so will give us a forward acceleration,
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especially in environments like mobile or embedded.
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For input X:
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- Conv process: X = input * W + bias
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- Batch norm process: X' = (X - mean) / std
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- Scale Process: Y = a * X' + b
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After fuse into one operation:
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Y = (input * W + bias - mean) / std * a + b
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= input * a * W / std + ((bias - mean) / std * a + b)
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The operator transformation is:
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- before:
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- conv->batch_norm->any_other_op (bias == 0)
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- conv->elementwise_add->batch_norm->any_other_op (bias != 0)
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- after:
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- conv->elementwise_add->any_other_op
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The transpile stages are:
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1. insert elementwise_add op when bias == 0.
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2. fuse the batch_norm's parameters to conv and elementwise_add operators.
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3. remove batch_norm ops which are not used in any other ops.
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4. adjust the input of any_other_op to be the output of elementwise_add operator.
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5. remove unused variables.
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:param program: program to transpile
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:type program: Program
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:param scope: inference scope
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:type scope: Scope
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:param place: inference place
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:type place: Place
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:return: program by fused batch normalization
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:rtype: Program
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'''
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self.scope = scope
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self.place = place
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self.block = program.block(0)
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self.input_map = {} # store the input names should be adjusted
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i = 0
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while i < len(self.block.ops):
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current_op = self.block.ops[i]
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# TODO(luotao1): consider only conv2d now. fc would be delt later.
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if current_op.type in ['conv2d']:
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next_op = self.block.ops[i + 1]
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# conv2d without bias
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if (next_op.type == 'batch_norm'):
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# insert bias op
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bias_op = self._insert_bias_op(i + 1, current_op, next_op)
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# fuse batch_norm
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self._fuse_param(current_op, next_op, bias_op, 0)
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# remove batch_norm_op
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self.block.remove_op(i + 2)
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i = i + 1
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# conv2d with bias, the next_op.type is elementwise_add
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elif (next_op.type == 'elementwise_add'):
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next_next_op = self.block.ops[i + 2]
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if (next_next_op.type == 'batch_norm'):
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# fuse batch_norm
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self._fuse_param(current_op, next_next_op, next_op, 1)
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# remove batch_norm_op
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self.block.remove_op(i + 2)
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i = i + 1
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i = i + 1
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self._adjust_input()
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self._remove_unused_var()
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# TODO(luotao): use clone() method to flush the program.desc in force,
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# since some large program.desc will not be flushed immediately.
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# And a better solution will be considered later.
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return program.clone()
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# ====================== private transpiler functions =====================
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def _insert_bias_op(self, index, current_op, bn_op):
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'''
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Construct elementwise_add operator for adding bias
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and insert it into program.
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:param index: insert location of bias_op
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:type index: Int
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:param current_op: current operator (conv or fc)
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:type current_op: Operator
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:param bn_op: batch norm operator
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:type bn_op: Operator
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:return: bias_op
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:rtype: Operator
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'''
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# The input of bias_op is current_op's output and Bias of bn_op
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# The output of bias_op is bn_op's output
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x_var = self.block.var(current_op.output("Output")[0])
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y_var = self.block.var(bn_op.input("Bias")[0])
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out_var = self.block.var(bn_op.output("Y")[0])
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bias_op = self.block.insert_op(
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index,
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type="elementwise_add",
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inputs={"X": x_var,
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"Y": y_var},
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outputs={"Out": out_var},
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attrs={"axis": 1}) # dim_start=1
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return bias_op
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def _fuse_param(self, current_op, bn_op, bias_op, with_bias):
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'''
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fuse the batch_norm_op' parameters to current_op (conv or fc)
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:param current_op: current operator (conv or fc)
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:type current_op: Operator
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:param bn_op: batch norm operator
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:type bn_op: Operator
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:param bias_op: elementwise_add operator for adding bias
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:type bias_op: Operator
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:param with_bias: If current operator has bias, with_bias = 1; otherwise 0.
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:type with_bias: Int
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'''
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def _load_tensor(param_name):
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return self.scope.find_var(param_name[0]).get_tensor()
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def _load_param(param_name):
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return np.array(_load_tensor(param_name))
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bias_bn = _load_param(bn_op.input("Bias")) #Bias
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scale_bn = _load_param(bn_op.input("Scale")) #Scale
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mean_bn = _load_param(bn_op.input("Mean")) #Mean
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var_bn = _load_param(bn_op.input("Variance")) #Variance
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# TODO(luotao1): consider only conv2d now. fc would be delt later.
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current_param = _load_param(current_op.input("Filter"))
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current_tensor = _load_tensor(current_op.input("Filter"))
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std_bn = np.float32(np.sqrt(np.add(var_bn, 1e-5)))
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tmp = np.float32(np.divide(scale_bn, std_bn))
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# add bias of batch_norm_op to conv2d
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if with_bias:
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bias = _load_param(bias_op.input("Y"))
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else:
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bias = np.zeros(bias_bn.shape)
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bias = np.float32(
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np.add(np.multiply(np.subtract(bias, mean_bn), tmp), bias_bn))
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bias_tensor = _load_tensor(bias_op.input("Y"))
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bias_tensor.set(bias, self.place)
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# re-compute weight of conv2d
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tmp = tmp.reshape(tmp.shape[0], -1)
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dst_param = current_param.reshape((tmp.shape[0], -1))
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dst_param = np.float32(np.multiply(dst_param, tmp))
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dst_param = dst_param.reshape(current_param.shape)
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# set the updated parameters
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current_tensor.set(np.array(dst_param), self.place)
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# collect the renamed input
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self.input_map[bn_op.output("Y")[0]] = bias_op.output("Out")[0]
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def _adjust_input(self):
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for i in range(len(self.block.ops)):
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current_op = self.block.ops[i]
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for input_arg in current_op.input_arg_names:
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if input_arg in self.input_map:
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current_op.rename_input(input_arg,
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self.input_map[input_arg])
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def _remove_unused_var(self):
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'''
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remove unused varibles in program
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'''
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args = []
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for i in range(len(self.block.ops)):
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current_op = self.block.ops[i]
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args += current_op.input_arg_names
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args += current_op.output_arg_names
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args = list(set(args)) # unique the input and output arguments
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for var in self.block.vars.keys():
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if var not in args:
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self.block.remove_var(var)
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