wehub-resource-sync
498 lines
18 KiB
Python
498 lines
18 KiB
Python
# Copyright (c) 2022 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 annotations
|
|
|
|
import copy
|
|
from typing import TYPE_CHECKING
|
|
|
|
import paddle
|
|
from paddle import nn
|
|
|
|
from .wrapper import ObserveWrapper
|
|
|
|
if TYPE_CHECKING:
|
|
from paddle.nn import Layer
|
|
|
|
from .factory import QuanterFactory
|
|
|
|
|
|
# TODO: Implement quanted layer and fill the mapping dict
|
|
DEFAULT_QAT_LAYER_MAPPINGS: dict[Layer, Layer] = {
|
|
nn.quant.Stub: nn.quant.stub.QuanterStub,
|
|
nn.Linear: nn.quant.qat.QuantedLinear,
|
|
nn.Conv2D: nn.quant.qat.QuantedConv2D,
|
|
}
|
|
|
|
DEFAULT_LEAVES = [nn.ReLU, nn.AvgPool2D]
|
|
|
|
|
|
class SingleLayerConfig:
|
|
r"""
|
|
Configure how to quantize the activations and weights of a single layer.
|
|
|
|
Args:
|
|
activation(QuanterFactory): The factory to create instance of quanter used to quantize activations.
|
|
weight(QuanterFactory): The factory to create instance of quanter used to quantize weights.
|
|
"""
|
|
|
|
def __init__(
|
|
self, activation: QuanterFactory, weight: QuanterFactory
|
|
) -> None:
|
|
self._activation = activation
|
|
self._weight = weight
|
|
|
|
@property
|
|
def activation(self) -> QuanterFactory:
|
|
return self._activation
|
|
|
|
@property
|
|
def weight(self) -> QuanterFactory:
|
|
return self._weight
|
|
|
|
def __str__(self):
|
|
return f"activation: {self._activation}\nweight: {self._weight}"
|
|
|
|
|
|
class QuantConfig:
|
|
r"""
|
|
Configure how to quantize a model or a part of the model. It will map each layer to
|
|
an instance of SingleLayerConfig by the settings. It provides diverse methods to set
|
|
the strategies of quantization.
|
|
|
|
Args:
|
|
activation(QuanterFactory | None): The global quantizer used to quantize the activations.
|
|
weight(QuanterFactory | None): The global quantizer used to quantize the weights.
|
|
|
|
Examples:
|
|
.. code-block:: pycon
|
|
|
|
>>> from paddle.quantization import QuantConfig
|
|
>>> from paddle.quantization.quanters import FakeQuanterWithAbsMaxObserver
|
|
|
|
>>> quanter = FakeQuanterWithAbsMaxObserver(moving_rate=0.9)
|
|
>>> q_config = QuantConfig(activation=quanter, weight=quanter)
|
|
>>> print(q_config)
|
|
Global config:
|
|
activation: FakeQuanterWithAbsMaxObserver(name=None,moving_rate=0.9,bit_length=8,dtype=float32)
|
|
weight: FakeQuanterWithAbsMaxObserver(name=None,moving_rate=0.9,bit_length=8,dtype=float32)
|
|
|
|
"""
|
|
|
|
def __init__(
|
|
self, activation: QuanterFactory | None, weight: QuanterFactory | None
|
|
) -> None:
|
|
if activation is None and weight is None:
|
|
self._global_config = None
|
|
else:
|
|
self._global_config = SingleLayerConfig(activation, weight)
|
|
self._layer2config = {}
|
|
self._prefix2config = {}
|
|
self._type2config = {}
|
|
self._model = None
|
|
self._qat_layer_mapping = copy.deepcopy(DEFAULT_QAT_LAYER_MAPPINGS)
|
|
self._customized_qat_layer_mapping = {}
|
|
|
|
self._customized_leaves = []
|
|
|
|
def add_layer_config(
|
|
self,
|
|
layer: Layer | list[Layer],
|
|
activation: QuanterFactory | None = None,
|
|
weight: QuanterFactory | None = None,
|
|
) -> None:
|
|
r"""
|
|
Set the quantization config by layer. It has the highest priority among
|
|
all the setting methods.
|
|
|
|
Args:
|
|
layer(Layer|list[Layer]]): One or a list of layers.
|
|
activation(QuanterFactory | None): Quanter used for activations. Default is None.
|
|
weight(QuanterFactory | None): Quanter used for weights. Default is None.
|
|
|
|
Examples:
|
|
.. code-block:: pycon
|
|
|
|
>>> import paddle
|
|
>>> from paddle.nn import Linear
|
|
>>> from paddle.quantization import QuantConfig
|
|
>>> from paddle.quantization.quanters import FakeQuanterWithAbsMaxObserver
|
|
|
|
>>> class Model(paddle.nn.Layer):
|
|
... def __init__(self):
|
|
... super().__init__()
|
|
... self.fc = Linear(576, 120)
|
|
>>> model = Model()
|
|
>>> quanter = FakeQuanterWithAbsMaxObserver(moving_rate=0.9)
|
|
>>> q_config = QuantConfig(activation=None, weight=None)
|
|
>>> q_config.add_layer_config([model.fc], activation=quanter, weight=quanter)
|
|
>>> # doctest: +SKIP('random memory address')
|
|
>>> print(q_config)
|
|
Global config:
|
|
None
|
|
Layer prefix config:
|
|
{'linear_0': <paddle.quantization.config.SingleLayerConfig object at 0x7fe41a680ee0>}
|
|
|
|
"""
|
|
if isinstance(layer, list):
|
|
for _element in layer:
|
|
self.add_layer_config(
|
|
_element, activation=activation, weight=weight
|
|
)
|
|
else:
|
|
self.add_name_config(
|
|
layer.full_name(), activation=activation, weight=weight
|
|
)
|
|
|
|
def add_name_config(
|
|
self,
|
|
layer_name: str | list[str],
|
|
activation: QuanterFactory | None = None,
|
|
weight: QuanterFactory | None = None,
|
|
) -> None:
|
|
r"""
|
|
Set the quantization config by full name of layer. Its priority is
|
|
lower than `add_layer_config`.
|
|
|
|
Args:
|
|
layer_name(str|list[str]): One or a list of layers' full name.
|
|
activation(QuanterFactory | None): Quanter used for activations. Default is None.
|
|
weight(QuanterFactory | None): Quanter used for weights. Default is None.
|
|
|
|
Examples:
|
|
.. code-block:: pycon
|
|
|
|
>>> import paddle
|
|
>>> from paddle.nn import Linear
|
|
>>> from paddle.quantization import QuantConfig
|
|
>>> from paddle.quantization.quanters import FakeQuanterWithAbsMaxObserver
|
|
|
|
>>> class Model(paddle.nn.Layer):
|
|
... def __init__(self):
|
|
... super().__init__()
|
|
... self.fc = Linear(576, 120)
|
|
>>> model = Model()
|
|
>>> quanter = FakeQuanterWithAbsMaxObserver(moving_rate=0.9)
|
|
>>> q_config = QuantConfig(activation=None, weight=None)
|
|
>>> q_config.add_name_config([model.fc.full_name()], activation=quanter, weight=quanter)
|
|
>>> # doctest: +SKIP('random memory address')
|
|
>>> print(q_config)
|
|
Global config:
|
|
None
|
|
Layer prefix config:
|
|
{'linear_0': <paddle.quantization.config.SingleLayerConfig object at 0x7fe41a680fd0>}
|
|
|
|
"""
|
|
if isinstance(layer_name, str):
|
|
config = SingleLayerConfig(activation, weight)
|
|
self._prefix2config[layer_name] = config
|
|
if isinstance(layer_name, list):
|
|
for _element in layer_name:
|
|
self.add_name_config(
|
|
_element, activation=activation, weight=weight
|
|
)
|
|
|
|
def add_type_config(
|
|
self,
|
|
layer_type: type[Layer] | list[type[Layer]],
|
|
activation: QuanterFactory | None = None,
|
|
weight: QuanterFactory | None = None,
|
|
) -> None:
|
|
r"""
|
|
Set the quantization config by the type of layer. The `layer_type` should be
|
|
subclass of `paddle.nn.Layer`. Its priority is lower than `add_layer_config`
|
|
and `add_name_config`.
|
|
|
|
Args:
|
|
layer_type(type[Layer] | list[type[Layer]]): One or a list of layers' type. It should be subclass of
|
|
`paddle.nn.Layer`. Python built-in function `type()` can be used to get the type of a layer.
|
|
activation(QuanterFactory | None): Quanter used for activations. Default is None.
|
|
weight(QuanterFactory | None): Quanter used for weights. Default is None.
|
|
|
|
Examples:
|
|
.. code-block:: pycon
|
|
|
|
>>> import paddle
|
|
>>> from paddle.nn import Linear
|
|
>>> from paddle.quantization import QuantConfig
|
|
>>> from paddle.quantization.quanters import (
|
|
... FakeQuanterWithAbsMaxObserver,
|
|
... )
|
|
|
|
>>> class Model(paddle.nn.Layer):
|
|
... def __init__(self):
|
|
... super().__init__()
|
|
... self.fc = Linear(576, 120)
|
|
>>> model = Model()
|
|
>>> quanter = FakeQuanterWithAbsMaxObserver(moving_rate=0.9)
|
|
>>> q_config = QuantConfig(activation=None, weight=None)
|
|
>>> q_config.add_type_config([Linear], activation=quanter, weight=quanter)
|
|
>>> # doctest: +SKIP('random memory address')
|
|
>>> print(q_config)
|
|
Global config:
|
|
None
|
|
Layer type config:
|
|
{<class 'paddle.nn.layer.common.Linear'>: <paddle.quantization.config.SingleLayerConfig object at 0x7fe41a680a60>}
|
|
|
|
"""
|
|
if isinstance(layer_type, type) and issubclass(
|
|
layer_type, paddle.nn.Layer
|
|
):
|
|
config = SingleLayerConfig(activation, weight)
|
|
self._type2config[layer_type] = config
|
|
if isinstance(layer_type, list):
|
|
for _element in layer_type:
|
|
self.add_type_config(
|
|
_element, activation=activation, weight=weight
|
|
)
|
|
|
|
def add_qat_layer_mapping(
|
|
self, source: type[Layer], target: type[Layer]
|
|
) -> None:
|
|
r"""
|
|
Add rules converting layers to simulated quantization layers
|
|
before quantization-aware training. It will convert layers
|
|
with type `source` to layers with type `target`. `source` and
|
|
`target` should be subclass of `paddle.nn.Layer`. And a default
|
|
mapping is provided by property `default_qat_layer_mapping`.
|
|
|
|
Args:
|
|
source(type[Layer]): The type of layers that will be converted.
|
|
target(type[Layer]): The type of layers that will be converted to.
|
|
|
|
Examples:
|
|
.. code-block:: pycon
|
|
|
|
>>> import paddle
|
|
>>> from paddle.nn import Conv2D
|
|
>>> from paddle.quantization import QuantConfig
|
|
>>> from paddle.quantization.quanters import (
|
|
... FakeQuanterWithAbsMaxObserver,
|
|
... )
|
|
>>> quanter = FakeQuanterWithAbsMaxObserver(moving_rate=0.9)
|
|
>>> q_config = QuantConfig(activation=None, weight=None)
|
|
>>> class CustomizedQuantedConv2D(paddle.nn.Layer):
|
|
... def forward(self, x):
|
|
... pass
|
|
... # add some code for quantization simulation
|
|
>>> q_config.add_qat_layer_mapping(Conv2D, CustomizedQuantedConv2D)
|
|
"""
|
|
assert isinstance(source, type) and issubclass(
|
|
source, paddle.nn.Layer
|
|
), (
|
|
"The source layer to be placed should be a subclass of paddle.nn.Layer"
|
|
)
|
|
assert isinstance(target, type) and issubclass(
|
|
target, paddle.nn.Layer
|
|
), "The target layer should be a subclass of paddle.nn.qat.Layer"
|
|
self._qat_layer_mapping[source] = target
|
|
self._customized_qat_layer_mapping[source] = target
|
|
|
|
def add_customized_leaf(self, layer_type: type[Layer]) -> None:
|
|
r"""
|
|
Declare the customized layer as leaf of model for quantization.
|
|
The leaf layer is quantized as one layer. The sublayers of
|
|
leaf layer will not be quantized.
|
|
|
|
Args:
|
|
layer_type(type[Layer]): The type of layer to be declared as leaf.
|
|
|
|
Examples:
|
|
.. code-block:: pycon
|
|
|
|
>>> from paddle.nn import Sequential
|
|
>>> from paddle.quantization import QuantConfig
|
|
>>> from paddle.quantization.quanters import FakeQuanterWithAbsMaxObserver
|
|
>>> q_config = QuantConfig(activation=None, weight=None)
|
|
>>> q_config.add_customized_leaf(Sequential)
|
|
|
|
"""
|
|
self._customized_leaves.append(layer_type)
|
|
|
|
@property
|
|
def customized_leaves(self) -> list[type[Layer]]:
|
|
r"""
|
|
Get all the customized leaves.
|
|
"""
|
|
return self._customized_leaves
|
|
|
|
def _need_observe(self, layer: Layer):
|
|
r"""
|
|
Whether the layer should be observed by observer.
|
|
"""
|
|
return self._is_leaf(layer) and self._has_observer_config(layer)
|
|
|
|
def _get_qat_layer(self, layer: Layer):
|
|
q_config = self._get_config_by_layer(layer)
|
|
|
|
target_type = self._customized_qat_layer_mapping.get(
|
|
type(layer), self.qat_layer_mappings.get(type(layer))
|
|
)
|
|
return target_type(layer, q_config)
|
|
|
|
def _has_observer_config(self, layer: Layer):
|
|
r"""
|
|
Whether the layer has been configured for activation quantization.
|
|
"""
|
|
_config = self._get_config_by_layer(layer)
|
|
return _config is not None and _config.activation is not None
|
|
|
|
def _is_leaf(self, layer: Layer):
|
|
return (
|
|
self._is_default_leaf(layer)
|
|
or self._is_real_leaf(layer)
|
|
or self._is_customized_leaf(layer)
|
|
)
|
|
|
|
def _is_default_leaf(self, layer: Layer):
|
|
return type(layer) in DEFAULT_LEAVES
|
|
|
|
def _is_real_leaf(self, layer: Layer):
|
|
r"""
|
|
The leaf is real leaf when it has no sublayers.
|
|
"""
|
|
return layer._sub_layers is None or len(layer._sub_layers) == 0
|
|
|
|
def _is_customized_leaf(self, layer: Layer):
|
|
return type(layer) in self.customized_leaves
|
|
|
|
def _get_observer(self, layer: Layer):
|
|
r"""
|
|
Create an instance of observer or quanter according to the
|
|
given layer's quantization config.
|
|
"""
|
|
_config = self._get_config_by_layer(layer)
|
|
_observer = None if _config is None else _config.activation
|
|
return None if _observer is None else _observer._instance(layer)
|
|
|
|
def _get_observe_wrapper(self, layer: Layer):
|
|
_observer = self._get_observer(layer)
|
|
return ObserveWrapper(_observer, layer)
|
|
|
|
@property
|
|
def qat_layer_mappings(self) -> dict[Layer, Layer]:
|
|
return self._qat_layer_mapping
|
|
|
|
@property
|
|
def default_qat_layer_mapping(self) -> dict[Layer, Layer]:
|
|
return DEFAULT_QAT_LAYER_MAPPINGS
|
|
|
|
@property
|
|
def global_config(self) -> SingleLayerConfig:
|
|
return self._global_config
|
|
|
|
def _get_config_by_layer(self, layer) -> SingleLayerConfig:
|
|
return self._layer2config.get(layer, None)
|
|
|
|
def _is_quantifiable(self, layer: Layer):
|
|
r"""
|
|
The layer is quantifiable when it configured by activation quanter/observer
|
|
or weight quanter/observer.
|
|
"""
|
|
return layer in self._layer2config
|
|
|
|
def _specify(self, model: Layer):
|
|
r"""
|
|
Specify the quantization config of each sublayer in model.
|
|
For each layer in sublayers of mode,
|
|
1. Set the config by global config
|
|
2. Overwrite the config with parents' config
|
|
3. Overwrite the config with config set by layer's type
|
|
4. Overwrite the config with config set by layer's full name
|
|
5. Overwrite the config with config set by layer
|
|
|
|
Args:
|
|
model(Layer): The model to be specified by the config.
|
|
|
|
Examples:
|
|
.. code-block:: pycon
|
|
|
|
>>> import paddle
|
|
>>> from paddle.nn import Linear, Sequential
|
|
>>> from paddle.quantization import QuantConfig
|
|
>>> from paddle.quantization.quanters import FakeQuanterWithAbsMaxObserver
|
|
|
|
>>> class Model(paddle.nn.Layer):
|
|
... def __init__(self):
|
|
... super().__init__()
|
|
... self.fc = Sequential(Linear(576, 120), Linear(576, 120))
|
|
>>> model = Model()
|
|
>>> quanter = FakeQuanterWithAbsMaxObserver(moving_rate=0.9)
|
|
>>> q_config = QuantConfig(activation=None, weight=None)
|
|
>>> q_config.add_layer_config([model.fc], activation=quanter, weight=quanter)
|
|
>>> q_config._specify(model)
|
|
"""
|
|
self._model = model
|
|
self._specify_helper(self._model)
|
|
|
|
def _specify_helper(self, model: Layer):
|
|
for child in model.children():
|
|
layer_prefix = child.full_name()
|
|
config = self._layer2config.get(model, self.global_config)
|
|
|
|
config = self._type2config.get(type(child), config)
|
|
config = self._prefix2config.get(layer_prefix, config)
|
|
if config is not None:
|
|
self._layer2config[child] = config
|
|
self._specify_helper(child)
|
|
return self
|
|
|
|
def details(self) -> str:
|
|
r"""
|
|
Get the formatted details of current config.
|
|
"""
|
|
if self._model is None:
|
|
return self.__str__()
|
|
return self._details_helper(self._model)
|
|
|
|
def _details_helper(self, layer: Layer):
|
|
sublayer_lines = []
|
|
for name, sublayer in layer.named_children():
|
|
sublayer_str = self._details_helper(sublayer)
|
|
sublayer_str = self._addindent(sublayer_str, 2)
|
|
if sublayer in self._layer2config:
|
|
sublayer_lines.append(
|
|
'('
|
|
+ name
|
|
+ '): '
|
|
+ sublayer_str
|
|
+ ', '
|
|
+ str(self._layer2config[sublayer])
|
|
)
|
|
|
|
final_str = layer.__class__.__name__ + '('
|
|
if sublayer_lines:
|
|
final_str += '\n ' + '\n '.join(sublayer_lines) + '\n'
|
|
|
|
final_str += ')'
|
|
return final_str
|
|
|
|
def _addindent(self, string, indent):
|
|
s1 = string.split('\n')
|
|
if len(s1) == 1:
|
|
return string
|
|
s2 = []
|
|
for idx, line in enumerate(s1):
|
|
if idx > 0:
|
|
s2.append(str((indent * ' ') + line))
|
|
return s1[0] + '\n' + '\n'.join(s2)
|
|
|
|
def __str__(self):
|
|
result = ""
|
|
result += f"Global config:\n{self._global_config}\n"
|
|
if len(self._type2config) > 0:
|
|
result += f"Layer type config:\n{self._type2config}\n"
|
|
if len(self._prefix2config) > 0:
|
|
result += f"Layer prefix config: \n{self._prefix2config}\n"
|
|
return result
|