You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
376 lines
14 KiB
376 lines
14 KiB
# Copyright (c) 2020 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 paddle.fluid.dygraph import layers
|
|
from paddle.fluid import core
|
|
from paddle.fluid import dygraph_utils
|
|
from paddle.fluid import unique_name
|
|
from paddle.fluid.param_attr import ParamAttr
|
|
from paddle.fluid.framework import _varbase_creator
|
|
from paddle.fluid.framework import in_dygraph_mode
|
|
from paddle.fluid.initializer import Constant
|
|
from paddle.fluid.data_feeder import check_variable_and_dtype
|
|
|
|
__all__ = [
|
|
'FakeQuantMovingAverage', 'FakeQuantAbsMax', 'QuantizedConv2D',
|
|
'QuantizedLinear'
|
|
]
|
|
|
|
|
|
class FakeQuantMovingAverage(layers.Layer):
|
|
"""
|
|
FakeQuantMovingAverage layer does the moving_average_abs_max quant and then dequant.
|
|
Its computational formula is described as below:
|
|
|
|
:math:`scale = (moving\_rate*accum+max(abs(x)))/(moving\_rate*state+1)`
|
|
:math:`range = 2^{bit\_length - 1} - 1`
|
|
:math:`Out = round(X / scale * range) * scale / range`
|
|
"""
|
|
|
|
def __init__(self,
|
|
name=None,
|
|
moving_rate=0.9,
|
|
quant_bits=8,
|
|
dtype='float32'):
|
|
super(FakeQuantMovingAverage, self).__init__()
|
|
self._moving_rate = moving_rate
|
|
self._quant_bits = quant_bits
|
|
|
|
scale_prefix = "{}.scale".format(
|
|
name) if name else 'quant_dequant.scale'
|
|
scale_attr = ParamAttr(
|
|
name=unique_name.generate(scale_prefix),
|
|
initializer=Constant(0.001),
|
|
trainable=False)
|
|
self._scale = self.create_parameter(
|
|
shape=[1], attr=scale_attr, dtype=dtype)
|
|
self._scale.stop_gradient = True
|
|
|
|
state_prefix = "{}.state".format(
|
|
name) if name else 'quant_dequant.state'
|
|
state_attr = ParamAttr(
|
|
name=unique_name.generate(state_prefix),
|
|
initializer=Constant(1),
|
|
trainable=False)
|
|
self._state = self.create_parameter(
|
|
shape=[1], attr=state_attr, dtype=dtype)
|
|
self._state.stop_gradient = True
|
|
|
|
accum_prefix = "{}.accum".format(
|
|
name) if name else 'quant_dequant.accum'
|
|
accum_attr = ParamAttr(
|
|
name=unique_name.generate(accum_prefix),
|
|
initializer=Constant(1),
|
|
trainable=False)
|
|
self._accum = self.create_parameter(
|
|
shape=[1], attr=accum_attr, dtype=dtype)
|
|
self._accum.stop_gradient = True
|
|
|
|
def forward(self, input):
|
|
if in_dygraph_mode():
|
|
attrs = ('moving_rate', self._moving_rate, 'bit_length',
|
|
self._quant_bits, 'is_test', not self.training)
|
|
quant_out = _varbase_creator(
|
|
type=input.type,
|
|
name="{}.quantized.dequantized".format(input.name),
|
|
shape=input.shape,
|
|
dtype=input.dtype,
|
|
persistable=False)
|
|
state = self._state if self.training else None
|
|
accum = self._accum if self.training else None
|
|
|
|
out, _, _, _ = core.ops.fake_quantize_dequantize_moving_average_abs_max(
|
|
input, self._scale, accum, state, quant_out, self._scale, state,
|
|
accum, *attrs)
|
|
return out
|
|
|
|
check_variable_and_dtype(input, 'input', ['float32'],
|
|
"FakeQuantMovingAverage")
|
|
attrs = {
|
|
'moving_rate': self._moving_rate,
|
|
'bit_length': self._quant_bits,
|
|
'is_test': not self.training
|
|
}
|
|
inputs = {"X": [input], "InScale": [self._scale]}
|
|
quant_out = self._helper.create_variable(
|
|
name="{}.quantized.dequantized".format(input.name),
|
|
dtype=input.dtype,
|
|
type=core.VarDesc.VarType.LOD_TENSOR,
|
|
persistable=False,
|
|
stop_gradient=False)
|
|
outputs = {"Out": [quant_out], "OutScale": [self._scale]}
|
|
|
|
if self.training:
|
|
inputs['InState'] = [self._state]
|
|
inputs['InAccum'] = [self._accum]
|
|
outputs['OutState'] = [self._state]
|
|
outputs['OutAccum'] = [self._accum]
|
|
|
|
self._helper.append_op(
|
|
type="fake_quantize_dequantize_moving_average_abs_max",
|
|
inputs=inputs,
|
|
outputs=outputs,
|
|
attrs=attrs)
|
|
|
|
return quant_out
|
|
|
|
|
|
class FakeQuantAbsMax(layers.Layer):
|
|
"""
|
|
FakeQuantAbsMax layer does the abs_max quant and then dequant.
|
|
Its computational formula is described as below:
|
|
|
|
:math:`scale = max(abs(X))`
|
|
:math:`range = 2^{bit\_length - 1} - 1`
|
|
:math:`Out = round(X / scale * range) * scale / range`
|
|
"""
|
|
|
|
def __init__(self,
|
|
name=None,
|
|
quant_bits=8,
|
|
dtype='float32',
|
|
quant_on_weight=False):
|
|
super(FakeQuantAbsMax, self).__init__()
|
|
self._quant_bits = quant_bits
|
|
self._dtype = dtype
|
|
self._name = name
|
|
scale_prefix = "{}.scale".format(
|
|
name) if name else 'quant_dequant.scale'
|
|
self._scale_name = unique_name.generate(scale_prefix)
|
|
if quant_on_weight:
|
|
scale_attr = ParamAttr(
|
|
name=self._scale_name,
|
|
initializer=Constant(0.0),
|
|
trainable=False)
|
|
self._scale = self.create_parameter(
|
|
shape=[1], attr=scale_attr, dtype=self._dtype)
|
|
self._scale.stop_gradient = True
|
|
else:
|
|
self._scale = None
|
|
|
|
def forward(self, input):
|
|
if in_dygraph_mode():
|
|
attrs = ('bit_length', self._quant_bits)
|
|
quant_out = _varbase_creator(
|
|
type=input.type,
|
|
name="{}.quantized.dequantized".format(input.name),
|
|
shape=input.shape,
|
|
dtype=input.dtype,
|
|
persistable=False)
|
|
out_scale = self._scale
|
|
if not out_scale:
|
|
out_scale = _varbase_creator(
|
|
type=core.VarDesc.VarType.LOD_TENSOR,
|
|
name=self._scale_name,
|
|
shape=[1],
|
|
dtype=self._dtype,
|
|
persistable=False)
|
|
out_scale.stop_gradient = True
|
|
out, _, = core.ops.fake_quantize_dequantize_abs_max(
|
|
input, quant_out, out_scale, *attrs)
|
|
return out
|
|
|
|
check_variable_and_dtype(input, 'input', ['float32'], "FakeQuantAbsMax")
|
|
attrs = {'bit_length': self._quant_bits}
|
|
inputs = {"X": [input]}
|
|
quant_out = self._helper.create_variable(
|
|
name="{}.quantized.dequantized".format(input.name),
|
|
dtype=input.dtype,
|
|
type=core.VarDesc.VarType.LOD_TENSOR,
|
|
persistable=False,
|
|
stop_gradient=False)
|
|
out_scale = self._scale
|
|
if not out_scale:
|
|
out_scale = self._helper.create_variable(
|
|
name=self._scale_name,
|
|
dtype=self._dtype,
|
|
type=core.VarDesc.VarType.LOD_TENSOR,
|
|
persistable=False,
|
|
stop_gradient=True)
|
|
outputs = {"Out": [quant_out], "OutScale": [out_scale]}
|
|
|
|
self._helper.append_op(
|
|
type="fake_quantize_dequantize_abs_max",
|
|
inputs=inputs,
|
|
outputs=outputs,
|
|
attrs=attrs)
|
|
|
|
return quant_out
|
|
|
|
|
|
def _get_fake_quant_type(quant_type, name, moving_rate, quant_bits, dtype,
|
|
quant_on_weight):
|
|
fake_quant_map = {
|
|
'abs_max':
|
|
lambda: FakeQuantAbsMax(name, quant_bits, dtype, quant_on_weight),
|
|
'moving_average_abs_max':
|
|
lambda: FakeQuantMovingAverage(name, moving_rate, quant_bits, dtype)
|
|
}
|
|
return fake_quant_map[quant_type]()
|
|
|
|
|
|
class QuantizedConv2D(layers.Layer):
|
|
"""
|
|
The computational logic of QuantizedConv2D is the same with Conv2D.
|
|
The only difference is that its inputs are all fake quantized.
|
|
"""
|
|
|
|
def __init__(self,
|
|
layer,
|
|
weight_bits=8,
|
|
activation_bits=8,
|
|
moving_rate=0.9,
|
|
weight_quantize_type='abs_max',
|
|
activation_quantize_type='abs_max'):
|
|
super(QuantizedConv2D, self).__init__()
|
|
# For Conv2D
|
|
self._groups = getattr(layer, '_groups')
|
|
self._stride = getattr(layer, '_stride')
|
|
self._padding = getattr(layer, '_padding')
|
|
self._dilation = getattr(layer, '_dilation')
|
|
self._act = getattr(layer, '_act')
|
|
self._use_cudnn = getattr(layer, '_use_cudnn')
|
|
self._dtype = getattr(layer, '_dtype')
|
|
self._l_type = getattr(layer, '_l_type')
|
|
self.weight = getattr(layer, 'weight')
|
|
self.bias = getattr(layer, 'bias')
|
|
# For FakeQuant
|
|
self._fake_quant_weight = _get_fake_quant_type(
|
|
weight_quantize_type, self.weight.name, moving_rate, weight_bits,
|
|
self._dtype, True)
|
|
self._fake_quant_input = _get_fake_quant_type(
|
|
activation_quantize_type,
|
|
layer.full_name(), moving_rate, activation_bits, self._dtype, False)
|
|
|
|
def forward(self, input):
|
|
quant_input = self._fake_quant_input(input)
|
|
quant_weight = self._fake_quant_weight(self.weight)
|
|
|
|
if in_dygraph_mode() and self._l_type == 'conv2d':
|
|
attrs = ('strides', self._stride, 'paddings', self._padding,
|
|
'dilations', self._dilation, 'groups', self._groups
|
|
if self._groups else 1, 'use_cudnn', self._use_cudnn)
|
|
pre_bias = core.ops.conv2d(quant_input, quant_weight, *attrs)
|
|
|
|
pre_act = dygraph_utils._append_bias_in_dygraph(pre_bias, self.bias,
|
|
1)
|
|
return dygraph_utils._append_activation_in_dygraph(pre_act,
|
|
self._act)
|
|
check_variable_and_dtype(quant_input, 'input',
|
|
['float16', 'float32', 'float64'],
|
|
'QuantizedConv2D')
|
|
attrs = {
|
|
'strides': self._stride,
|
|
'paddings': self._padding,
|
|
'dilations': self._dilation,
|
|
'groups': self._groups if self._groups else 1,
|
|
'use_cudnn': self._use_cudnn,
|
|
'use_mkldnn': False,
|
|
}
|
|
pre_bias = self._helper.create_variable_for_type_inference(
|
|
dtype=self._dtype)
|
|
|
|
self._helper.append_op(
|
|
type=self._l_type,
|
|
inputs={
|
|
'Input': quant_input,
|
|
'Filter': quant_weight,
|
|
},
|
|
outputs={"Output": pre_bias},
|
|
attrs=attrs)
|
|
|
|
if self.bias is not None:
|
|
pre_act = self._helper.create_variable_for_type_inference(
|
|
dtype=self._dtype)
|
|
self._helper.append_op(
|
|
type='elementwise_add',
|
|
inputs={'X': [pre_bias],
|
|
'Y': [self.bias]},
|
|
outputs={'Out': [pre_act]},
|
|
attrs={'axis': 1})
|
|
else:
|
|
pre_act = pre_bias
|
|
|
|
return self._helper.append_activation(pre_act, act=self._act)
|
|
|
|
|
|
class QuantizedLinear(layers.Layer):
|
|
"""
|
|
The computational logic of QuantizedLinear is the same with Linear.
|
|
The only difference is that its inputs are all fake quantized.
|
|
"""
|
|
|
|
def __init__(self,
|
|
layer,
|
|
weight_bits=8,
|
|
activation_bits=8,
|
|
moving_rate=0.9,
|
|
weight_quantize_type='abs_max',
|
|
activation_quantize_type='abs_max'):
|
|
super(QuantizedLinear, self).__init__()
|
|
# For Linear
|
|
self._act = getattr(layer, '_act')
|
|
self._dtype = getattr(layer, '_dtype')
|
|
self.weight = getattr(layer, 'weight')
|
|
self.bias = getattr(layer, 'bias')
|
|
# For FakeQuant
|
|
self._fake_quant_weight = _get_fake_quant_type(
|
|
weight_quantize_type, self.weight.name, moving_rate, weight_bits,
|
|
self._dtype, True)
|
|
self._fake_quant_input = _get_fake_quant_type(
|
|
activation_quantize_type,
|
|
layer.full_name(), moving_rate, activation_bits, self._dtype, False)
|
|
|
|
def forward(self, input):
|
|
quant_input = self._fake_quant_input(input)
|
|
quant_weight = self._fake_quant_weight(self.weight)
|
|
if in_dygraph_mode():
|
|
pre_bias = _varbase_creator(dtype=input.dtype)
|
|
core.ops.matmul(quant_input, quant_weight, pre_bias, 'transpose_X',
|
|
False, 'transpose_Y', False, "alpha", 1)
|
|
pre_act = dygraph_utils._append_bias_in_dygraph(
|
|
pre_bias, self.bias, axis=len(input.shape) - 1)
|
|
|
|
return dygraph_utils._append_activation_in_dygraph(pre_act,
|
|
self._act)
|
|
|
|
check_variable_and_dtype(input, 'input',
|
|
['float16', 'float32', 'float64'],
|
|
"QuantizedLinear")
|
|
attrs = {
|
|
"transpose_X": False,
|
|
"transpose_Y": False,
|
|
"alpha": 1,
|
|
}
|
|
inputs = {"X": [quant_input], "Y": [quant_weight]}
|
|
mul_out = self._helper.create_variable_for_type_inference(self._dtype)
|
|
|
|
self._helper.append_op(
|
|
type="matmul",
|
|
inputs=inputs,
|
|
outputs={"Out": [mul_out]},
|
|
attrs=attrs)
|
|
if self.bias is not None:
|
|
pre_activation = self._helper.create_variable_for_type_inference(
|
|
dtype=self._dtype)
|
|
self._helper.append_op(
|
|
type='elementwise_add',
|
|
inputs={'X': [mul_out],
|
|
'Y': [self.bias]},
|
|
outputs={'Out': [pre_activation]},
|
|
attrs={'axis': len(input.shape) - 1})
|
|
else:
|
|
pre_activation = mul_out
|
|
return self._helper.append_activation(pre_activation, act=self._act)
|