# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# 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.
from __future__ import print_function
import os
import numpy as np
from .. import core
from ..framework import Program
from ..executor import global_scope
class InferenceTranspiler(object):
'''
Convert the fluid program to optimized inference program.
There are several optimizations:
- fuse convolution and batch normalization
- fuse batch normalization and relu (MKLDNN only)
Examples:
.. code-block:: python
# As InferenceTranspiler will modify the original program,
# please clone before use it.
inference_transpiler_program = program.clone()
t = fluid.InferenceTranspiler()
t.transpile(inference_transpiler_program, place)
'''
def transpile(self, program, place, scope=None):
'''
Run the transpiler.
Args:
program (Program): program to transpile
place (Place): inference place
scope (Scope|None): inference Scope
'''
if not isinstance(program, Program):
raise TypeError("program should be as Program type")
if not isinstance(place, core.CPUPlace) and not isinstance(
place, core.CUDAPlace):
raise TypeError("place should be as CPUPlace/CUDAPlace type")
if scope is None:
scope = global_scope()
if not isinstance(scope, core.Scope):
raise TypeError("scope should be as Scope type or None")
use_mkldnn = bool(os.getenv("FLAGS_use_mkldnn", False))
if use_mkldnn:
self._depthwise_conv_mkldnn(program)
self._fuse_batch_norm(program, place, scope)
if use_mkldnn:
self._fuse_conv_bias_mkldnn(program)
self._fuse_conv_relu_mkldnn(program)
self._fuse_conv_eltwise_mkldnn(program)
self._fuse_conv_relu_mkldnn(
program) # ResNet residual block merging
self._fuse_bn_relu_mkldnn(program)
def _depthwise_conv_mkldnn(self, program):
'''
Transpile the program by replacing depthwise_conv2d to conv2d for MKLDNN program.
The result is:
- before:
- any_other_op->depthwise_conv->any_other_op
- after:
- any_other_op->conv->any_other_op
:param program: program to transpile
:type program: Program
'''
self.block = program.block(0)
i = 0
while i < len(self.block.ops):
current_op = self.block.ops[i]
if current_op.type == 'depthwise_conv2d':
current_op.desc.set_type("conv2d")
i = i + 1
# TODO(luotao): use clone() method to flush the program.desc in force,
# since some large program.desc will not be flushed immediately.
# And a better solution will be considered later.
program = program.clone()
def _fuse_conv_eltwise_mkldnn(self, program):
'''
Transpile the program fusing elementwise_add into conv for MKLDNN
program. Elementwise add following convolution OP can be fused by adding
'fuse_residual_connection' attribute to convolution OP and replacing its output
Tensor with second parameter of elementwise_add.
The result of fuse is:
- before:
- conv->elementwise_add->any_other_op
- after:
- conv->any_other_op
:param program: program to transpile
:type program: Program
'''
self.block = program.block(0)
i = 0
while i < len(self.block.ops):
current_op = self.block.ops[i]
if current_op.type in ['conv2d']:
next_op = self.block.ops[i + 1]
if next_op.type == 'elementwise_add':
self._fuse_conv_eltwise(i, current_op, next_op)
self.block._remove_op(i + 1) # Remove old conv
self.block._remove_op(i + 1) # Remove elementwise_add
i = i + 1
self._adjust_input()
self._remove_unused_var()
# TODO(luotao): use clone() method to flush the program.desc in force,
# since some large program.desc will not be flushed immediately.
# And a better solution will be considered later.
program = program.clone()
def _fuse_conv_relu_mkldnn(self, program):
'''
Transpile the program by fused relu activation for MKLDNN program.
Relu activation following convolution OP can be fused by adding
'fuse_relu' attribute to convolution OP.
The result of fuse is:
- before:
- conv->relu->any_other_op
- after:
- conv->any_other_op
:param program: program to transpile
:type program: Program
'''
self.block = program.block(0)
i = 0
while i < len(self.block.ops):
current_op = self.block.ops[i]
if current_op.type in ['conv2d']:
next_op = self.block.ops[i + 1]
if next_op.type == 'relu':
# modify bnorm OP to include relu
current_op._set_attr("fuse_relu", True)
# remove relu OP
self.block._remove_op(i + 1)
i = i + 1
# TODO(luotao): use clone() method to flush the program.desc in force,
# since some large program.desc will not be flushed immediately.
# And a better solution will be considered later.
program = program.clone()
def _fuse_bn_relu_mkldnn(self, program):
'''
Transpile the program by fused relu activation for MKLDNN program.
Relu activation following batch norm OP can be fused by adding
:math:`fuse_with_relu` attribute to batch norm OP.
The result of fuse is:
- before:
- batch_norm->relu->any_other_op
- after:
- batch_norm->any_other_op
:param program: program to transpile
:type program: Program
'''
self.block = program.block(0)
i = 0
while i < len(self.block.ops) - 1:
current_op = self.block.ops[i]
if current_op.type in ['batch_norm']:
next_op = self.block.ops[i + 1]
if next_op.type == 'relu':
# modify bnorm OP to include relu
current_op._set_attr("fuse_with_relu", True)
# remove relu OP
self.block._remove_op(i + 1)
i = i + 1
self._remove_unused_var()
# TODO(luotao): use clone() method to flush the program.desc in force,
# since some large program.desc will not be flushed immediately.
# And a better solution will be considered later.
program = program.clone()
def _fuse_conv_bias_mkldnn(self, program):
'''
Transpile the program by fused convolution and elementwise_add.
Replace conv2d and elementwise_add ops with a new conv2d op
based on an old conv2d op and the :math:`Bias` taken from
elementwise_add.
For input :math:`X`:
- Conv process: :math:`X = input * W`
- Elementwise_add process: :math` X = X + bias`
After fuse into one operation:
.. math::
X = input * W + bias
The operator transformation is:
- before:
- conv->elementwise_add->any_other_op
- after:
- conv->any_other_op
The transpile stages are:
1. Extract bias and output variables from elementwise_add.
2. Extract Input, Weight and attributes from conv op.
3. Create a new convolution op based on extracted params.
4. Remove old conv op.
5. Remove elementwise_add.
5. Remove unused variables.
Args:
program (Program): program to transpile
'''
self.block = program.block(0)
i = 0
while i < len(self.block.ops) - 2:
current_op = self.block.ops[i]
next_op = self.block.ops[i + 1]
# conv2d with bias
if current_op.type in ['conv2d'] and \
next_op.type in ['elementwise_add']:
self._fuse_conv_bias(i, current_op, next_op)
self.block._remove_op(i + 1) # Remove old conv
self.block._remove_op(i + 1) # Remove elementwise_add
i = i + 1
self._remove_unused_var()
# TODO(luotao): use clone() method to flush the program.desc in force,
# since some large program.desc will not be flushed immediately.
# And a better solution will be considered later.
program = program.clone()
def _fuse_batch_norm(self, program, place, scope):
'''
Transpile the program by fused batch normalization.
The batch normalization followed the convolution or fully connected layer
can be integrated with them. Doing so will give us a forward acceleration,
especially in environments like mobile or embedded.
For input :math:`X`:
- Conv process: :math:`X = input * W + bias`
- Batch norm process: :math:`X' = (X - mean) / std`
- Scale Process: :math:`Y = a * X' + b`
After fuse into one operation:
.. math::
Y &= (input * W + bias - mean) / std * a + b \\\\
&= input * a * W / std + ((bias - mean) / std * a + b)
The operator transformation is:
- before:
- conv->batch_norm->any_other_op (bias == 0)
- conv->elementwise_add->batch_norm->any_other_op (bias != 0)
- after:
- conv->elementwise_add->any_other_op
The transpile stages are:
1. insert elementwise_add op when bias == 0.
2. fuse the batch_norm's parameters to conv and elementwise_add operators.
3. remove batch_norm ops which are not used in any other ops.
4. adjust the input of any_other_op to be the output of elementwise_add operator.
5. remove unused variables.
Args:
program (Program): program to transpile
place (Place): inference place
scope (Scope): inference Scope
'''
self.scope = scope
self.place = place
self.block = program.block(0)
self.input_map = {} # store the input names should be adjusted
i = 0
while i < len(self.block.ops) - 2:
current_op = self.block.ops[i]
# TODO(luotao1): consider only conv2d now. fc would be delt later.
if current_op.type in ['conv2d']:
# TODO(luotao1): consider single chain network now.
# For branch network, we counldn't use block.ops[i + 1] as
# the judgment condition.
next_op = self.block.ops[i + 1]
# conv2d without bias
if (next_op.type == 'batch_norm'):
# insert bias op
bias_op = self._insert_bias_op(i + 1, current_op, next_op)
# fuse batch_norm
self._fuse_param(current_op, next_op, bias_op, 0)
# remove batch_norm_op
self.block._remove_op(i + 2)
i = i + 1
# conv2d with bias, the next_op.type is elementwise_add
elif (next_op.type == 'elementwise_add'):
next_next_op = self.block.ops[i + 2]
if (next_next_op.type == 'batch_norm'):
# fuse batch_norm
self._fuse_param(current_op, next_next_op, next_op, 1)
# remove batch_norm_op
self.block._remove_op(i + 2)
i = i + 1
i = i + 1
self._adjust_input()
self._remove_unused_var()
# TODO(luotao): use clone() method to flush the program.desc in force,
# since some large program.desc will not be flushed immediately.
# And a better solution will be considered later.
program = program.clone()
# ====================== private transpiler functions =====================
def _insert_bias_op(self, index, current_op, bn_op):
'''
Construct elementwise_add operator for adding bias
and insert it into program.
:param index: insert location of bias_op
:type index: Int
:param current_op: current operator (conv or fc)
:type current_op: Operator
:param bn_op: batch norm operator
:type bn_op: Operator
:return: bias_op
:rtype: Operator
'''
# The input of bias_op is current_op's output and Bias of bn_op
# The output of bias_op is bn_op's output
x_var = self.block.var(current_op.output("Output")[0])
y_var = self.block.var(bn_op.input("Bias")[0])
out_var = self.block.var(bn_op.output("Y")[0])
bias_op = self.block._insert_op(
index,
type="elementwise_add",
inputs={"X": x_var,
"Y": y_var},
outputs={"Out": out_var},
attrs={"axis": 1}) # dim_start=1
return bias_op
def _fuse_param(self, current_op, bn_op, bias_op, with_bias):
'''
fuse the batch_norm_op' parameters to current_op (conv or fc)
:param current_op: current operator (conv or fc)
:type current_op: Operator
:param bn_op: batch norm operator
:type bn_op: Operator
:param bias_op: elementwise_add operator for adding bias
:type bias_op: Operator
:param with_bias: If current operator has bias, with_bias = 1; otherwise 0.
:type with_bias: Int
'''
def _update_param(op, old_param_name, new_param):
# For the sake of remaining the original variables the same as before,
# create new variables in scope to store the new parameters.
old_param_name = old_param_name[0]
old_var = self.block.vars[old_param_name]
new_param_name = old_param_name + '_fuse_bn'
new_var = self.block.create_parameter(
name=new_param_name.encode('ascii'),
type=old_var.type,
dtype=old_var.dtype,
shape=old_var.shape)
op._rename_input(old_param_name, new_param_name)
self.scope.var(new_param_name)
tensor = self.scope.find_var(new_param_name).get_tensor()
tensor.set(np.array(new_param), self.place)
def _load_param(param_name):
return np.array(self.scope.find_var(param_name[0]).get_tensor())
bias_bn = _load_param(bn_op.input("Bias")) #Bias
scale_bn = _load_param(bn_op.input("Scale")) #Scale
mean_bn = _load_param(bn_op.input("Mean")) #Mean
var_bn = _load_param(bn_op.input("Variance")) #Variance
# TODO(luotao1): consider only conv2d now. fc would be delt later.
current_param = _load_param(current_op.input("Filter"))
std_bn = np.float32(np.sqrt(np.add(var_bn, 1e-5)))
tmp = np.float32(np.divide(scale_bn, std_bn))
# add bias of batch_norm_op to conv2d
if with_bias:
bias = _load_param(bias_op.input("Y"))
else:
bias = np.zeros(bias_bn.shape)
bias = np.float32(
np.add(np.multiply(np.subtract(bias, mean_bn), tmp), bias_bn))
# re-compute weight of conv2d
tmp = tmp.reshape(tmp.shape[0], -1)
dst_param = current_param.reshape((tmp.shape[0], -1))
dst_param = np.float32(np.multiply(dst_param, tmp))
dst_param = dst_param.reshape(current_param.shape)
# update parameters
_update_param(current_op, current_op.input("Filter"), dst_param)
_update_param(bias_op, bias_op.input("Y"), bias)
# collect the renamed input
self.input_map[bn_op.output("Y")[0]] = bias_op.output("Out")[0]
def _fuse_conv_bias(self, index, conv_op, elementwise_add_op):
'''
fuse the conv op with elementwise_add
:param index: index of the conv_op in ops list
:type index: Int
:param conv_op: convolution operator
:type conv_op: Operator
:param elementwise_add_op: convolution's bias operator
:type elementwise_add_op: Operator
'''
bias_var = self.block.var(elementwise_add_op.input("Y")[0])
out_var = self.block.var(elementwise_add_op.output("Out")[0])
filter_var = self.block.var(conv_op.input("Filter")[0])
in_var = self.block.var(conv_op.input("Input")[0])
attrs = {name: conv_op.attr(name) for name in conv_op.attr_names}
self.block._insert_op(
index,
type="conv2d",
inputs={"Input": in_var,
"Filter": filter_var,
"Bias": bias_var},
outputs={"Output": out_var},
attrs=attrs)
def _fuse_conv_eltwise(self, index, conv_op, eltwise_op):
'''
fuse the conv op with elementwise_add
:param conv_op: convolution operator
:type conv_op: Operator
:param eltwise_op: operator adding data from skip connection
:type eltwise_op: Operator
'''
eltwise_input = "X"
if eltwise_op.input("X")[0] == conv_op.output("Output")[0]:
eltwise_input = "Y"
residual_var = self.block.vars[eltwise_op.input(eltwise_input)[0]]
out_var = self.block.vars[eltwise_op.output("Out")[0]]
filter_var = self.block.vars[conv_op.input("Filter")[0]]
in_var = self.block.vars[conv_op.input("Input")[0]]
bias_var = self.block.vars[conv_op.input("Bias")[0]]
conv_op._set_attr("fuse_residual_connection", True)
attrs = {name: conv_op.attr(name) for name in conv_op.attr_names}
self.block._insert_op(
index,
type="conv2d",
inputs={
"Input": in_var,
"Filter": filter_var,
"Bias": bias_var,
"ResidualData": residual_var
},
outputs={"Output": out_var},
attrs=attrs)
def _adjust_input(self):
for i in range(len(self.block.ops)):
current_op = self.block.ops[i]
for input_arg in current_op.input_arg_names:
if input_arg in self.input_map:
current_op._rename_input(input_arg,
self.input_map[input_arg])
def _remove_unused_var(self):
'''
remove unused varibles in program
'''
args = []
for i in range(len(self.block.ops)):
current_op = self.block.ops[i]
args += current_op.input_arg_names
args += current_op.output_arg_names
args = list(set(args)) # unique the input and output arguments
for var in list(self.block.vars.keys()):
if var not in args:
self.block._remove_var(var)