未验证 提交 10a13f9c 编写于 作者: X Xin Pan 提交者: GitHub

Merge pull request #13319 from qingqing01/quantize_transpiler_update

Fixed-point quantize transpiler.
......@@ -299,6 +299,10 @@ paddle.fluid.contrib.BeamSearchDecoder.read_array ArgSpec(args=['self', 'init',
paddle.fluid.contrib.BeamSearchDecoder.update_array ArgSpec(args=['self', 'array', 'value'], varargs=None, keywords=None, defaults=None)
paddle.fluid.contrib.memory_usage ArgSpec(args=['program', 'batch_size'], varargs=None, keywords=None, defaults=None)
paddle.fluid.contrib.op_freq_statistic ArgSpec(args=['program'], varargs=None, keywords=None, defaults=None)
paddle.fluid.contrib.QuantizeTranspiler.__init__ ArgSpec(args=['self', 'weight_bits', 'activation_bits', 'activation_quantize_type', 'weight_quantize_type', 'window_size'], varargs=None, keywords=None, defaults=(8, 8, 'abs_max', 'abs_max', 10000))
paddle.fluid.contrib.QuantizeTranspiler.convert_to_int8 ArgSpec(args=['self', 'program', 'place', 'scope'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.contrib.QuantizeTranspiler.freeze_program ArgSpec(args=['self', 'program', 'place', 'fuse_bn', 'scope'], varargs=None, keywords=None, defaults=(False, None))
paddle.fluid.contrib.QuantizeTranspiler.training_transpile ArgSpec(args=['self', 'program', 'startup_program'], varargs=None, keywords=None, defaults=(None, None))
paddle.fluid.transpiler.DistributeTranspiler.__init__ ArgSpec(args=['self', 'config'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.transpiler.DistributeTranspiler.get_pserver_program ArgSpec(args=['self', 'endpoint'], varargs=None, keywords=None, defaults=None)
paddle.fluid.transpiler.DistributeTranspiler.get_pserver_programs ArgSpec(args=['self', 'endpoint'], varargs=None, keywords=None, defaults=None)
......
......@@ -87,6 +87,7 @@ if (WITH_TESTING)
endif()
endif()
add_subdirectory(paddle/fluid/tests)
add_subdirectory(paddle/fluid/contrib/tests)
endif()
install(DIRECTORY ${PADDLE_PYTHON_PACKAGE_DIR}
DESTINATION opt/paddle/share/wheels
......
......@@ -20,8 +20,11 @@ from . import memory_usage_calc
from .memory_usage_calc import *
from . import op_frequence
from .op_frequence import *
from . import quantize
from .quantize import *
__all__ = []
__all__ += decoder.__all__
__all__ += memory_usage_calc.__all__
__all__ += op_frequence.__all__
__all__ += quantize.__all__
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
#
# 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
from . import quantize_transpiler
from .quantize_transpiler import *
__all__ = quantize_transpiler.__all__
# 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.
import collections
import numpy as np
from paddle.fluid.framework import default_main_program, default_startup_program, program_guard
from paddle.fluid.layer_helper import LayerHelper
from paddle.fluid import unique_name
from paddle.fluid import core
from paddle.fluid.initializer import Constant
from paddle.fluid.param_attr import ParamAttr
from paddle.fluid.layer_helper import LayerHelper
from paddle.fluid.layers.nn import autoincreased_step_counter
from paddle.fluid.framework import Variable
from paddle.fluid.executor import global_scope
from paddle.fluid.transpiler.inference_transpiler import InferenceTranspiler
__all__ = ['QuantizeTranspiler']
_QUANTIZABLE_OP_TYPES = ['conv2d', 'depthwise_conv2d', 'mul']
def _quantized_var_name(var_name):
"""
Return quantized variable name for the input `var_name`.
"""
return "%s.quantized" % (var_name)
def _dequantized_var_name(var_name):
"""
Return dequantized variable name for the input `var_name`.
"""
return "%s.dequantized" % (var_name)
def _quantized_scale_name(var_name):
"""
Return quantized variable name for the input `var_name`.
"""
return "%s.scale" % (var_name)
def _original_var_name(var_name):
"""
Return the original variable name.
"""
if var_name.endswith('.quantized.dequantized'):
return var_name[:-len('.quantized.dequantized')]
if var_name.endswith('.quantized'):
return var_name[:-len('.quantized')]
if var_name.endswith('.dequantized'):
return var_name[:-len('.dequantized')]
if var_name.endswith('.scale'):
return var_name[:-len('.scale')]
else:
return var_name
def _is_float(v):
return isinstance(v, float) or isinstance(v, np.float32)
def quant(x, scale, num_bits):
y = np.round(x / scale * ((1 << (num_bits - 1)) - 1))
return y
class QuantizeTranspiler(object):
def __init__(self,
weight_bits=8,
activation_bits=8,
activation_quantize_type='abs_max',
weight_quantize_type='abs_max',
window_size=10000):
"""
Convert and rewrite the fluid Program according to weight and
activation quantization type.
Args:
weight_bits (int): quantization bit number for weights,
the bias is not quantized.
activation_bits (int): quantization bit number for activation.
activation_quantize_type (str): quantization type for activation,
now support 'abs_max', 'range_abs_max'. If use 'abs_max' mode,
the quantization scale will be calculated dynamically each step
in both training and testing period. If use 'range_abs_max',
a static quantization scale will be calculated during training
and used in inference.
weight_quantize_type (str): quantization type for weights,
support 'abs_max'. The 'range_abs_max' usually is not used for
weight, since weights are fixed once the model is well trained.
window_size (int): the window size for 'range_abs_max' quantization.
Examples:
.. code-block:: python
# the original program will be rewrite, if you don't want to
# change it, please clone at first.
# quantize_program = program.clone()
t = fluid.QuantizeTranspiler()
t.transpile(quantize_program)
"""
self.weight_bits = weight_bits
self.activation_bits = activation_bits
quant_type = ['abs_max', 'range_abs_max']
if weight_quantize_type not in quant_type:
raise ValueError(
"Unknown weight_quantize_type: '%s'. It can only be ",
"'abs_max' or 'range_abs_max'.", str(weight_quantize_type))
if activation_quantize_type not in quant_type:
raise ValueError(
"Unknown activation_quantize_type : '%s'. It can only be ",
"'abs_max' or 'range_abs_max'.", str(activation_quantize_type))
self.weight_quantize_type = weight_quantize_type
self.activation_quantize_type = activation_quantize_type
self.window_size = window_size
self.helper = LayerHelper(self.__class__.__name__)
self.fake_quant_op_types = [
'fake_quantize_abs_max', 'fake_quantize_range_abs_max'
]
self.fake_dequant_op_types = ['fake_dequantize_max_abs']
self.is_test = None
self.global_step = None
def training_transpile(self, program=None, startup_program=None):
"""Rewrites a training input program in place for simulated
quantization. Insert fake quantization and de-quantization ops into
program to simulate the error introduced by quantization. And change
the graident ops' input by using the faked quantization weights and
activation. Since the program is transformed in place, the graph
connection will change.
Args:
program (Program): the input program to be transpile.
"""
self.is_test = False
program = default_main_program() if program is None else program
startup_program = default_startup_program() if startup_program is \
None else startup_program
# marked the variable which has been quantized and dequantized.
dequanted_vars = [
collections.OrderedDict() for _ in range(len(program.blocks))
]
grad_op_types = ['%s_grad' % (type) for type in _QUANTIZABLE_OP_TYPES]
params = [p.name for p in program.global_block().iter_parameters()]
def _transpile_forward(block, op):
idx = block.ops.index(op)
block_id = block.idx
# insert quant op and dequant op
for name in op.input_arg_names:
if name in dequanted_vars[block_id]:
dequant_var = dequanted_vars[block_id][name]
else:
var = block.var(name)
quant_bits = self.weight_bits if var.name in params \
else self.activation_bits
quant_type = self.weight_quantize_type if var.name \
in params else self.activation_quantize_type
quant_var, scale_var = self._insert_quant_op(
block, idx, var, quant_bits, quant_type)
dequant_var = self._insert_dequant_op(
block, idx + 1, quant_var, scale_var, quant_bits)
dequanted_vars[block_id][name] = dequant_var
# rename the forward op inputs
op._rename_input(name, dequant_var.name)
def _transpile_backward(block, op):
block_id = block.idx
no_dequanted_input_vars = True
for name in op.input_arg_names:
if name in dequanted_vars[block_id]:
dequant_var = dequanted_vars[block_id][name]
op._rename_input(name, dequant_var.name)
no_dequanted_input_vars = False
if no_dequanted_input_vars:
raise ValueError("There is no dequanted inputs for op %s." %
(op.type))
with program_guard(program, startup_program):
self._create_global_step()
for block in program.blocks:
ops = list(block.ops)
block_id = block.idx
for op in ops:
# rewrite the forward ProgramDes
if op.type in _QUANTIZABLE_OP_TYPES:
_transpile_forward(block, op)
# rename the backward op inputs
if op.type in grad_op_types:
_transpile_backward(block, op)
def _create_global_step(self):
if self.weight_quantize_type == 'range_abs_max' or \
self.activation_quantize_type == 'range_abs_max':
self.global_step = autoincreased_step_counter()
def freeze_program(self, program, place, fuse_bn=False, scope=None):
"""Freeze input training program for inference.
Args:
program (Program): the input program to be transpile.
"""
self.is_test = True
scope = global_scope() if scope is None else scope
program = default_main_program() if program is None else program
if fuse_bn:
bn_fuse_transpiler = BNFuseTranspiler()
bn_fuse_transpiler.transpile(program, place)
persistable_vars = [
v.name
for v in filter(lambda var: var.persistable, program.list_vars())
]
op_in_rename_map = [
collections.OrderedDict() for _ in range(len(program.blocks))
]
op_out_rename_map = [
collections.OrderedDict() for _ in range(len(program.blocks))
]
var_scale_map = [
collections.OrderedDict() for _ in range(len(program.blocks))
]
def _remove_fake_quant_and_dequant_op(block, op):
idx = block.ops.index(op)
block_id = block.idx
k = op.output('Out')[0]
v = op.input('X')[0]
if v not in op_in_rename_map[block_id]:
op_in_rename_map[block_id][k] = v
else:
op_in_rename_map[block_id][k] = op_in_rename_map[block_id][v]
block._remove_op(idx)
def _insert_post_dequant_op(block, op):
idx = block.ops.index(op)
block_id = block.idx
max_range = None
scale_var = None
for name in op.input_arg_names:
if name in op_in_rename_map[block_id]:
op._rename_input(name, op_in_rename_map[block_id][name])
scale_v = var_scale_map[block_id][_original_var_name(name)]
if _original_var_name(name) in persistable_vars:
param_range = (1 << (self.weight_bits - 1)) - 1
act_range = (1 << (self.activation_bits - 1)) - 1
assert _is_float(scale_v)
max_range = param_range * act_range / scale_v
else:
assert isinstance(scale_v, Variable)
scale_var = var_scale_map[block_id][_original_var_name(
name)]
if len(op.output_arg_names) != 1:
raise ValueError("Only support one output, but op %s has"
" more than one output." % (op.type))
out_var = block.var(op.output_arg_names[0])
dequant_var = block.create_var(
name=_dequantized_var_name(out_var.name),
type=out_var.type,
shape=out_var.shape,
dtype=out_var.dtype)
# insert fake_dequantize_op
dequant_op = block._insert_op(
idx + 1,
type="fake_dequantize_max_abs",
attrs={'max_range': float(max_range)},
inputs={"X": out_var,
'Scale': scale_var},
outputs={"Out": dequant_var})
op_out_rename_map[block_id][out_var.name] = dequant_var.name
return dequant_var
def _load_var(name):
return np.array(scope.find_var(name).get_tensor())
def _restore_var(name, arr):
t = scope.find_var(name).get_tensor()
t.set(arr, place)
for block in program.blocks:
ops = list(block.ops)
block_id = block.idx
for op in ops:
op_type = op.type
# insert dequant_op after fc/conv, need to rename
# input of the followed ops
for name in op.input_arg_names:
if name in op_out_rename_map[block_id]:
op._rename_input(name,
op_out_rename_map[block_id][name])
if op_type in self.fake_quant_op_types:
in_arg_name = op.input('X')[0]
if in_arg_name in persistable_vars:
if self.weight_quantize_type == 'abs_max':
param = _load_var(in_arg_name)
scale_v = np.max(np.abs(param))
else:
scale_v = _load_var(op.output('OutScale')[0])
var_scale_map[block_id][in_arg_name] = scale_v
else:
scale_v = block.var(op.output('OutScale')[0])
var_scale_map[block_id][in_arg_name] = scale_v
if in_arg_name in persistable_vars:
_remove_fake_quant_and_dequant_op(block, op)
# quantize weight and restore
param_t = _load_var(in_arg_name)
param_q_t = quant(param_t, scale_v, self.weight_bits)
_restore_var(in_arg_name, param_q_t)
if op_type in self.fake_dequant_op_types:
_remove_fake_quant_and_dequant_op(block, op)
if op_type in _QUANTIZABLE_OP_TYPES:
dequant_var = _insert_post_dequant_op(block, op)
# remove the unused var in ProgramDesc
self._remove_unused_var(program)
#program = program.clone()
def convert_to_int8(self, program, place, scope=None):
scope = global_scope() if scope is None else scope
program = default_main_program() if program is None else program
def _load_var(name):
return np.array(scope.find_var(name).get_tensor())
global_block = program.global_block()
def convert_to_int8(var):
int8_var_name = var.name + ".int8"
int8_var = global_block.create_parameter(
name=int8_var_name.encode('ascii'),
type=var.type,
dtype=core.VarDesc.VarType.INT8,
shape=var.shape)
tensor = _load_var(var.name)
scope.var(int8_var_name)
int8_tensor = scope.find_var(int8_var_name).get_tensor()
int8_tensor.set(tensor.astype(np.int8), place)
return int8_var
input_map = {}
for block in program.blocks:
for op in list(block.ops):
if op.type in _QUANTIZABLE_OP_TYPES:
for name in op.input_arg_names:
var = block.var(name)
if var.persistable:
if name not in input_map:
int8_var = convert_to_int8(var)
input_map[name] = int8_var.name
op._rename_input(name, input_map[name])
self._remove_unused_var(program)
def _remove_unused_var(self, program):
all_remove_vars = []
for block in program.blocks:
args = []
for op in block.ops:
args += op.input_arg_names
args += op.output_arg_names
args = list(set(args))
var_names = block.vars.keys()
sub_block_remove_vars = []
for var in var_names:
if var not in args:
sub_block_remove_vars.append(var)
all_remove_vars.append(sub_block_remove_vars)
remove_vars = [list(set(v)) for v in all_remove_vars]
for i, block in enumerate(program.blocks):
for v in remove_vars[i]:
block._remove_var(v)
def _insert_quant_abs_max_op(self, block, idx, var, quant_bits):
"""Insert fake_quantize_abs_max op.
"""
quant_var = block.create_var(
name=_quantized_var_name(var.name),
type=var.type,
shape=var.shape,
dtype=var.dtype)
scale = block.create_var(
name=_quantized_scale_name(var.name),
type=var.type,
shape=var.shape,
dtype=var.dtype)
quant_op = block._insert_op(
idx,
type='fake_quantize_abs_max',
attrs={'bit_length': quant_bits},
inputs={'X': var},
outputs={'Out': quant_var,
'OutScale': scale})
return quant_var, scale
def _insert_quant_range_abs_max_op(self, block, idx, var, quant_bits):
"""Insert fake_quantize_range_abs_max
"""
quant_var = block.create_var(
name=_quantized_var_name(var.name),
type=var.type,
shape=var.shape,
dtype=var.dtype)
scale = self.helper.create_parameter(
attr=ParamAttr(
name=_quantized_scale_name(var.name),
initializer=Constant(0.001),
trainable=False),
shape=[1],
dtype=var.dtype)
scale.stop_gradient = True
ins = {'X': var, 'InScale': scale}
outs = {'Out': quant_var, 'OutScale': scale}
if not self.is_test:
# A global step counter variable with type int64
scales = self.helper.create_global_variable(
name=unique_name.generate('scales'),
persistable=True,
dtype=var.dtype,
shape=[self.window_size])
self.helper.set_variable_initializer(
scales, initializer=Constant(value=0))
ins['Iter'] = self.global_step
outs['OutScales'] = scales
attrs = {
'window_size': self.window_size,
'bit_length': quant_bits,
'is_test': self.is_test
}
quant_op = block._insert_op(
idx,
type='fake_quantize_range_abs_max',
attrs=attrs,
inputs=ins,
outputs=outs)
return quant_var, scale
def _insert_quant_op(self, block, idx, var, quant_bits, quant_type):
"""
Insert fake_quantize_op
"""
if quant_type == 'abs_max':
return self._insert_quant_abs_max_op(block, idx, var, quant_bits)
elif quant_type == 'range_abs_max':
return self._insert_quant_range_abs_max_op(block, idx, var,
quant_bits)
def _insert_dequant_op(self, block, idx, var, scale, quant_bits):
"""
Insert fake_quantize_op
"""
dequant_var = block.create_var(
name=_dequantized_var_name(var.name),
type=var.type,
shape=var.shape,
dtype=var.dtype)
# insert fake_dequantize_op
max_range = (1 << (quant_bits - 1)) - 1
dequant_op = block._insert_op(
idx,
type="fake_dequantize_max_abs",
attrs={'max_range': float(max_range)},
inputs={"X": var,
'Scale': scale},
outputs={"Out": dequant_var})
return dequant_var
class BNFuseTranspiler(InferenceTranspiler):
def _fuse_param(self, current_op, bn_op, bias_op, with_bias):
def _update_param(op, param_name, new_param):
var = self.block.vars[param_name]
tensor = self.scope.find_var(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).get_tensor())
bias_bn = _load_param(bn_op.input("Bias")[0]) #Bias
scale_bn = _load_param(bn_op.input("Scale")[0]) #Scale
mean_bn = _load_param(bn_op.input("Mean")[0]) #Mean
var_bn = _load_param(bn_op.input("Variance")[0]) #Variance
if current_op.type in ['conv2d', 'depthwise_conv2d']:
current_param = _load_param(
_original_var_name(current_op.input("Filter")[0]))
elif current_op.type == 'mul':
current_param = _load_param(
_original_var_name(current_op.input("Y")[0]))
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/fc
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
if current_op.type in ['conv2d', 'depthwise_conv2d']:
_update_param(current_op,
_original_var_name(current_op.input("Filter")[0]),
dst_param)
elif current_op.type == 'mul':
_update_param(current_op,
_original_var_name(current_op.input("Y")[0]),
dst_param)
_update_param(bias_op, bias_op.input("Y")[0], bias)
# collect the renamed input
self.input_map[bn_op.output("Y")[0]] = bias_op.output("Out")[0]
file(GLOB TEST_OPS RELATIVE "${CMAKE_CURRENT_SOURCE_DIR}" "test_*.py")
string(REPLACE ".py" "" TEST_OPS "${TEST_OPS}")
foreach(src ${TEST_OPS})
py_test(${src} SRCS ${src}.py)
endforeach()
# 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.
import numpy as np
import six
import unittest
import paddle
import paddle.fluid as fluid
from paddle.fluid.contrib.quantize.quantize_transpiler import _original_var_name
from paddle.fluid.contrib.quantize.quantize_transpiler import QuantizeTranspiler
def linear_fc(num):
data = fluid.layers.data(name='image', shape=[1, 32, 32], dtype='float32')
label = fluid.layers.data(name='label', shape=[1], dtype='int64')
hidden = data
for _ in six.moves.xrange(num):
hidden = fluid.layers.fc(hidden, size=128, act='relu')
loss = fluid.layers.cross_entropy(input=hidden, label=label)
loss = fluid.layers.mean(loss)
return loss
def residual_block(num):
def conv_bn_layer(input,
ch_out,
filter_size,
stride,
padding,
act='relu',
bias_attr=False):
tmp = fluid.layers.conv2d(
input=input,
filter_size=filter_size,
num_filters=ch_out,
stride=stride,
padding=padding,
act=None,
bias_attr=bias_attr)
return fluid.layers.batch_norm(input=tmp, act=act)
data = fluid.layers.data(name='image', shape=[1, 32, 32], dtype='float32')
label = fluid.layers.data(name='label', shape=[1], dtype='int64')
hidden = data
for _ in six.moves.xrange(num):
conv = conv_bn_layer(hidden, 16, 3, 1, 1, act=None, bias_attr=True)
short = conv_bn_layer(hidden, 16, 1, 1, 0, act=None)
hidden = fluid.layers.elementwise_add(x=conv, y=short, act='relu')
fc = fluid.layers.fc(input=hidden, size=10)
loss = fluid.layers.cross_entropy(input=fc, label=label)
loss = fluid.layers.mean(loss)
return loss
def conv_net(img, label):
conv_pool_1 = fluid.nets.simple_img_conv_pool(
input=img,
filter_size=5,
num_filters=20,
pool_size=2,
pool_stride=2,
act="relu")
conv_pool_1 = fluid.layers.batch_norm(conv_pool_1)
conv_pool_2 = fluid.nets.simple_img_conv_pool(
input=conv_pool_1,
filter_size=5,
num_filters=50,
pool_size=2,
pool_stride=2,
act="relu")
prediction = fluid.layers.fc(input=conv_pool_2, size=10, act='softmax')
loss = fluid.layers.cross_entropy(input=prediction, label=label)
avg_loss = fluid.layers.mean(loss)
return avg_loss
class TestQuantizeTranspiler(unittest.TestCase):
def setUp(self):
# since quant_op and dequant_op is not ready, use cos and sin for test
self.weight_quant_op_type = 'fake_quantize_abs_max'
self.dequant_op_type = 'fake_dequantize_max_abs'
self.quantizable_op_and_inputs = {
'conv2d': ['Input', 'Filter'],
'depthwise_conv2d': ['Input', 'Filter'],
'mul': ['X', 'Y']
}
self.quantizable_op_grad_and_inputs = {
'conv2d_grad': ['Input', 'Filter'],
'depthwise_conv2d_grad': ['Input', 'Filter'],
'mul_grad': ['X', 'Y']
}
def check_program(self, program):
quantized_ops = {}
persistable_vars = [
v.name
for v in filter(lambda var: var.persistable, program.list_vars())
]
for block in program.blocks:
for idx, op in enumerate(block.ops):
# check forward
if op.type in self.quantizable_op_and_inputs:
for i, arg_name in enumerate(op.input_arg_names):
quant_op_type = self.weight_quant_op_type if \
_original_var_name(arg_name) \
in persistable_vars else self.act_quant_op_type
self.assertTrue(
arg_name.endswith('.quantized.dequantized'))
if arg_name not in quantized_ops:
self.assertEqual(block.ops[idx - 2 * i - 1].type,
self.dequant_op_type)
self.assertEqual(block.ops[idx - 2 * i - 2].type,
quant_op_type)
quantized_ops[arg_name] = block.ops[idx - 2 * i - 2]
else:
op_idx = block.ops.index(quantized_ops[arg_name])
self.assertLess(op_idx, idx)
# check backward
if op.type in self.quantizable_op_grad_and_inputs:
for pname in self.quantizable_op_grad_and_inputs[op.type]:
arg_name = op.input(pname)[0]
self.assertTrue(
arg_name.endswith('.quantized.dequantized'))
self.assertTrue(arg_name in quantized_ops)
def linear_fc_quant(self, quant_type):
main = fluid.Program()
startup = fluid.Program()
with fluid.program_guard(main, startup):
loss = linear_fc(3)
opt = fluid.optimizer.Adam(learning_rate=0.001)
opt.minimize(loss)
t = QuantizeTranspiler(activation_quantize_type=quant_type)
t.training_transpile(main)
self.check_program(main)
def test_linear_fc_quant_abs_max(self):
self.act_quant_op_type = 'fake_quantize_abs_max'
self.linear_fc_quant('abs_max')
def test_linear_fc_quant_range_abs_max(self):
self.act_quant_op_type = 'fake_quantize_range_abs_max'
self.linear_fc_quant('range_abs_max')
def residual_block_quant(self, quant_type):
main = fluid.Program()
startup = fluid.Program()
with fluid.program_guard(main, startup):
loss = residual_block(2)
opt = fluid.optimizer.Adam(learning_rate=0.001)
opt.minimize(loss)
t = QuantizeTranspiler(activation_quantize_type=quant_type)
t.training_transpile(main)
self.check_program(main)
def test_residual_block_abs_max(self):
self.act_quant_op_type = 'fake_quantize_abs_max'
self.residual_block_quant('abs_max')
def test_residual_block_range_abs_max(self):
self.act_quant_op_type = 'fake_quantize_range_abs_max'
self.residual_block_quant('range_abs_max')
def freeze_program(self, use_cuda):
def build_program(main, startup, is_test):
with fluid.unique_name.guard():
with fluid.program_guard(main, startup):
img = fluid.layers.data(
name='image', shape=[1, 28, 28], dtype='float32')
label = fluid.layers.data(
name='label', shape=[1], dtype='int64')
loss = conv_net(img, label)
if not is_test:
opt = fluid.optimizer.Adam(learning_rate=0.001)
opt.minimize(loss)
return [img, label], loss
main = fluid.Program()
startup = fluid.Program()
test_program = fluid.Program()
feeds, loss = build_program(main, startup, False)
build_program(test_program, startup, True)
test_program = test_program.clone(for_test=True)
quant_transpiler = QuantizeTranspiler()
quant_transpiler.training_transpile(main)
quant_transpiler.training_transpile(test_program)
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
exe = fluid.Executor(place)
iter = 5
batch_size = 8
class_num = 10
exe.run(startup)
train_reader = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.mnist.train(), buf_size=500),
batch_size=batch_size)
test_reader = paddle.batch(
paddle.dataset.mnist.test(), batch_size=batch_size)
feeder = fluid.DataFeeder(feed_list=feeds, place=place)
with fluid.program_guard(main):
for _ in range(iter):
data = next(train_reader())
loss_v = exe.run(program=main,
feed=feeder.feed(data),
fetch_list=[loss])
with fluid.program_guard(test_program):
test_data = next(test_reader())
w_var = fluid.framework._get_var('conv2d_1.w_0.quantized',
test_program)
# Testing during training
test_loss1, w_quant = exe.run(program=test_program,
feed=feeder.feed(test_data),
fetch_list=[loss, w_var])
# Freeze program for inference, but the weight of fc/conv is still float type.
quant_transpiler.freeze_program(test_program, place)
test_loss2, = exe.run(program=test_program,
feed=feeder.feed(test_data),
fetch_list=[loss])
self.assertAlmostEqual(test_loss1, test_loss2, delta=1e-3)
w_freeze = np.array(fluid.global_scope().find_var('conv2d_1.w_0')
.get_tensor())
self.assertEqual(np.sum(w_freeze), np.sum(w_quant))
# Convert parameter to 8-bit.
quant_transpiler.convert_to_int8(test_program, place)
# Save the 8-bit parameter and model file.
fluid.io.save_inference_model('model_8bit', ['image', 'label'],
[loss], exe, test_program)
# Test whether the 8-bit parameter and model file can be loaded successfully.
[infer, feed, fetch] = fluid.io.load_inference_model('model_8bit',
exe)
# Check the loaded 8-bit weight.
w_8bit = np.array(fluid.global_scope().find_var('conv2d_1.w_0.int8')
.get_tensor())
self.assertEqual(w_8bit.dtype, np.int8)
self.assertEqual(np.sum(w_8bit), np.sum(w_freeze))
def test_freeze_program_cuda(self):
if fluid.core.is_compiled_with_cuda():
with fluid.unique_name.guard():
self.freeze_program(True)
def test_freeze_program_cpu(self):
with fluid.unique_name.guard():
self.freeze_program(False)
if __name__ == '__main__':
unittest.main()
......@@ -20,6 +20,10 @@ from .memory_optimization_transpiler import memory_optimize, release_memory
from .ps_dispatcher import HashName, RoundRobin
__all__ = [
"DistributeTranspiler", "memory_optimize", "release_memory", "HashName",
"RoundRobin", "DistributeTranspilerConfig"
"DistributeTranspiler",
"memory_optimize",
"release_memory",
"HashName",
"RoundRobin",
"DistributeTranspilerConfig",
]
......@@ -106,6 +106,7 @@ packages=['paddle',
'paddle.fluid.layers',
'paddle.fluid.contrib',
'paddle.fluid.contrib.decoder',
'paddle.fluid.contrib.quantize',
'paddle.fluid.transpiler',
'paddle.fluid.transpiler.details']
......
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