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未验证 提交 0c5781e5 编写于 作者: S Sonder 提交者： GitHub
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[xdoctest] reformat example code with google style No.116-119 (#56118)

上级 786c6e99
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Showing with 453 addition and 403 deletion
python/paddle/optimizer/optimizer.py
浏览文件 @ 0c5781e5
......@@ -125,40 +125,40 @@ class Optimizer:
Examples:
.. code-block:: python
#Take the subclass adam as an example
import paddle
linear = paddle.nn.Linear(10, 10)
inp = paddle.uniform(shape=[10, 10], min=-0.1, max=0.1)
out = linear(inp)
loss = paddle.mean(out)
adam = paddle.optimizer.Adam(learning_rate=0.1,
parameters=linear.parameters())
loss.backward()
adam.step()
adam.clear_grad()
#Take the subclass sgd as an example
#optimize parameters in linear_1 and linear2 in different options.
#Note that the learning_rate of linear_2 is 0.01.
linear_1 = paddle.nn.Linear(10, 10)
linear_2 = paddle.nn.Linear(10, 10)
inp = paddle.uniform(shape=[10, 10], min=-0.1, max=0.1)
out = linear_1(inp)
out = linear_2(out)
loss = paddle.mean(out)
sgd = paddle.optimizer.SGD(
learning_rate=0.1,
parameters=[{
'params': linear_1.parameters()
}, {
'params': linear_2.parameters(),
'weight_decay': 0.001,
'learning_rate': 0.1
}],
weight_decay=0.01)
loss.backward()
sgd.step()
sgd.clear_grad()
>>> # Take the subclass adam as an example
>>> import paddle
>>> linear = paddle.nn.Linear(10, 10)
>>> inp = paddle.uniform(shape=[10, 10], min=-0.1, max=0.1)
>>> out = linear(inp)
>>> loss = paddle.mean(out)
>>> adam = paddle.optimizer.Adam(learning_rate=0.1,
... parameters=linear.parameters())
>>> loss.backward()
>>> adam.step()
>>> adam.clear_grad()
>>> #Take the subclass sgd as an example
>>> #optimize parameters in linear_1 and linear2 in different options.
>>> #Note that the learning_rate of linear_2 is 0.01.
>>> linear_1 = paddle.nn.Linear(10, 10)
>>> linear_2 = paddle.nn.Linear(10, 10)
>>> inp = paddle.uniform(shape=[10, 10], min=-0.1, max=0.1)
>>> out = linear_1(inp)
>>> out = linear_2(out)
>>> loss = paddle.mean(out)
>>> sgd = paddle.optimizer.SGD(
... learning_rate=0.1,
... parameters=[{
... 'params': linear_1.parameters()
... }, {
... 'params': linear_2.parameters(),
... 'weight_decay': 0.001,
... 'learning_rate': 0.1
... }],
... weight_decay=0.01)
>>> loss.backward()
>>> sgd.step()
>>> sgd.clear_grad()
"""
......@@ -343,23 +343,23 @@ class Optimizer:
Examples:
.. code-block:: python
import paddle
>>> import paddle
emb = paddle.nn.Embedding(10, 10)
>>> emb = paddle.nn.Embedding(10, 10)
layer_state_dict = emb.state_dict()
paddle.save(layer_state_dict, "emb.pdparams")
>>> layer_state_dict = emb.state_dict()
>>> paddle.save(layer_state_dict, "emb.pdparams")
scheduler = paddle.optimizer.lr.NoamDecay(
d_model=0.01, warmup_steps=100, verbose=True)
adam = paddle.optimizer.Adam(
learning_rate=scheduler,
parameters=emb.parameters())
opt_state_dict = adam.state_dict()
paddle.save(opt_state_dict, "adam.pdopt")
>>> scheduler = paddle.optimizer.lr.NoamDecay(
... d_model=0.01, warmup_steps=100, verbose=True)
>>> adam = paddle.optimizer.Adam(
... learning_rate=scheduler,
... parameters=emb.parameters())
>>> opt_state_dict = adam.state_dict()
>>> paddle.save(opt_state_dict, "adam.pdopt")
opti_state_dict = paddle.load("adam.pdopt")
adam.set_state_dict(opti_state_dict)
>>> opti_state_dict = paddle.load("adam.pdopt")
>>> adam.set_state_dict(opti_state_dict)
'''
if isinstance(self._learning_rate, LRScheduler):
......@@ -500,23 +500,22 @@ class Optimizer:
Examples:
.. code-block:: python
import paddle
linear = paddle.nn.Linear(10, 10)
adam = paddle.optimizer.Adam(0.1, parameters=linear.parameters())
# set learning rate manually by python float value
lr_list = [0.2, 0.3, 0.4, 0.5, 0.6]
for i in range(5):
adam.set_lr(lr_list[i])
lr = adam.get_lr()
print("current lr is {}".format(lr))
# Print:
# current lr is 0.2
# current lr is 0.3
# current lr is 0.4
# current lr is 0.5
# current lr is 0.6
>>> import paddle
>>> linear = paddle.nn.Linear(10, 10)
>>> adam = paddle.optimizer.Adam(0.1, parameters=linear.parameters())
>>> # set learning rate manually by python float value
>>> lr_list = [0.2, 0.3, 0.4, 0.5, 0.6]
>>> for i in range(5):
... adam.set_lr(lr_list[i])
... lr = adam.get_lr()
... print("current lr is {}".format(lr))
current lr is 0.2
current lr is 0.3
current lr is 0.4
current lr is 0.5
current lr is 0.6
"""
if not isinstance(value, (int, float)):
......@@ -570,24 +569,24 @@ class Optimizer:
Examples:
.. code-block:: python
import paddle
linear = paddle.nn.Linear(10, 10)
>>> import paddle
>>> linear = paddle.nn.Linear(10, 10)
adam = paddle.optimizer.Adam(0.1, parameters=linear.parameters())
>>> adam = paddle.optimizer.Adam(0.1, parameters=linear.parameters())
# set learning rate manually by class LRScheduler
scheduler = paddle.optimizer.lr.MultiStepDecay(learning_rate=0.5, milestones=[2,4,6], gamma=0.8)
adam.set_lr_scheduler(scheduler)
lr = adam.get_lr()
print("current lr is {}".format(lr))
# current lr is 0.5
>>> # set learning rate manually by class LRScheduler
>>> scheduler = paddle.optimizer.lr.MultiStepDecay(learning_rate=0.5, milestones=[2,4,6], gamma=0.8)
>>> adam.set_lr_scheduler(scheduler)
>>> lr = adam.get_lr()
>>> print("current lr is {}".format(lr))
current lr is 0.5
# set learning rate manually by another LRScheduler
scheduler = paddle.optimizer.lr.StepDecay(learning_rate=0.1, step_size=5, gamma=0.6)
adam.set_lr_scheduler(scheduler)
lr = adam.get_lr()
print("current lr is {}".format(lr))
# current lr is 0.1
>>> # set learning rate manually by another LRScheduler
>>> scheduler = paddle.optimizer.lr.StepDecay(learning_rate=0.1, step_size=5, gamma=0.6)
>>> adam.set_lr_scheduler(scheduler)
>>> lr = adam.get_lr()
>>> print("current lr is {}".format(lr))
current lr is 0.1
"""
from paddle.optimizer.lr import LRScheduler
......@@ -611,50 +610,79 @@ class Optimizer:
Examples:
.. code-block:: python
# train on default dynamic graph mode
import paddle
import numpy as np
emb = paddle.nn.Embedding(10, 3)
## example1: LRScheduler is not used, return the same value is all the same
adam = paddle.optimizer.Adam(0.01, parameters = emb.parameters())
for batch in range(10):
input = paddle.randint(low=0, high=5, shape=[5])
out = emb(input)
out.backward()
print("Learning rate of step{}: {}".format(batch, adam.get_lr())) # 0.01
adam.step()
## example2: StepDecay is used, return the scheduled learning rate
scheduler = paddle.optimizer.lr.StepDecay(learning_rate=0.5, step_size=2, gamma=0.1)
adam = paddle.optimizer.Adam(scheduler, parameters = emb.parameters())
for batch in range(10):
input = paddle.randint(low=0, high=5, shape=[5])
out = emb(input)
out.backward()
print("Learning rate of step{}: {}".format(batch, adam.get_lr())) # 0.5->0.05...
adam.step()
scheduler.step()
# train on static graph mode
paddle.enable_static()
main_prog = paddle.static.Program()
start_prog = paddle.static.Program()
with paddle.static.program_guard(main_prog, start_prog):
x = paddle.static.data(name='x', shape=[None, 10])
z = paddle.static.nn.fc(x, 100)
loss = paddle.mean(z)
scheduler = paddle.optimizer.lr.StepDecay(learning_rate=0.5, step_size=2, gamma=0.1)
adam = paddle.optimizer.Adam(learning_rate=scheduler)
adam.minimize(loss)
exe = paddle.static.Executor()
exe.run(start_prog)
for batch in range(10):
print("Learning rate of step{}: {}", adam.get_lr()) # 0.5->0.05->0.005...
out = exe.run(main_prog, feed={'x': np.random.randn(3, 10).astype('float32')})
scheduler.step()
>>> # train on default dynamic graph mode
>>> import paddle
>>> import numpy as np
>>> emb = paddle.nn.Embedding(10, 3)
>>> ## example1: LRScheduler is not used, return the same value is all the same
>>> adam = paddle.optimizer.Adam(0.01, parameters = emb.parameters())
>>> for batch in range(10):
... input = paddle.randint(low=0, high=5, shape=[5])
... out = emb(input)
... out.backward()
... print("Learning rate of step{}: {}".format(batch, adam.get_lr())) # 0.01
... adam.step()
Learning rate of step0: 0.01
Learning rate of step1: 0.01
Learning rate of step2: 0.01
Learning rate of step3: 0.01
Learning rate of step4: 0.01
Learning rate of step5: 0.01
Learning rate of step6: 0.01
Learning rate of step7: 0.01
Learning rate of step8: 0.01
Learning rate of step9: 0.01
>>> ## example2: StepDecay is used, return the scheduled learning rate
>>> scheduler = paddle.optimizer.lr.StepDecay(learning_rate=0.5, step_size=2, gamma=0.1)
>>> adam = paddle.optimizer.Adam(scheduler, parameters = emb.parameters())
>>> for batch in range(10):
... input = paddle.randint(low=0, high=5, shape=[5])
... out = emb(input)
... out.backward()
... print("Learning rate of step{}: {}".format(batch, adam.get_lr())) # 0.5->0.05...
... adam.step()
... scheduler.step()
Learning rate of step0: 0.5
Learning rate of step1: 0.5
Learning rate of step2: 0.05
Learning rate of step3: 0.05
Learning rate of step4: 0.005000000000000001
Learning rate of step5: 0.005000000000000001
Learning rate of step6: 0.0005000000000000001
Learning rate of step7: 0.0005000000000000001
Learning rate of step8: 5.000000000000001e-05
Learning rate of step9: 5.000000000000001e-05
>>> # train on static graph mode
>>> paddle.enable_static()
>>> main_prog = paddle.static.Program()
>>> start_prog = paddle.static.Program()
>>> with paddle.static.program_guard(main_prog, start_prog):
... x = paddle.static.data(name='x', shape=[None, 10])
... z = paddle.static.nn.fc(x, 100)
... loss = paddle.mean(z)
... scheduler = paddle.optimizer.lr.StepDecay(learning_rate=0.5, step_size=2, gamma=0.1)
... adam = paddle.optimizer.Adam(learning_rate=scheduler)
... adam.minimize(loss)
>>> exe = paddle.static.Executor()
>>> exe.run(start_prog)
>>> for batch in range(10):
... print("Learning rate of step{}: {}".format(batch, adam.get_lr())) # 0.5->0.05->0.005...
... out = exe.run(main_prog, feed={'x': np.random.randn(3, 10).astype('float32')})
... scheduler.step()
Learning rate of step0: 0.5
Learning rate of step1: 0.5
Learning rate of step2: 0.05
Learning rate of step3: 0.05
Learning rate of step4: 0.005000000000000001
Learning rate of step5: 0.005000000000000001
Learning rate of step6: 0.0005000000000000001
Learning rate of step7: 0.0005000000000000001
Learning rate of step8: 5.000000000000001e-05
Learning rate of step9: 5.000000000000001e-05
"""
if isinstance(self._learning_rate, float):
return self._learning_rate
......@@ -1146,17 +1174,17 @@ class Optimizer:
Examples:
.. code-block:: python
import paddle
x = paddle.arange(26, dtype="float32").reshape([2, 13])
linear = paddle.nn.Linear(13, 5)
# This can be any optimizer supported by dygraph.
adam = paddle.optimizer.Adam(learning_rate = 0.01,
parameters = linear.parameters())
out = linear(x)
out.backward()
adam.step()
adam.clear_grad()
>>> import paddle
>>> x = paddle.arange(26, dtype="float32").reshape([2, 13])
>>> linear = paddle.nn.Linear(13, 5)
>>> # This can be any optimizer supported by dygraph.
>>> adam = paddle.optimizer.Adam(learning_rate = 0.01,
... parameters = linear.parameters())
>>> out = linear(x)
>>> out.backward()
>>> adam.step()
>>> adam.clear_grad()
"""
act_no_grad_set = None
if framework.in_dygraph_mode():
......@@ -1218,16 +1246,16 @@ class Optimizer:
Examples:
.. code-block:: python
import paddle
>>> import paddle
inp = paddle.uniform([10, 10], dtype="float32", min=-0.1, max=0.1)
linear = paddle.nn.Linear(10, 10)
out = linear(inp)
loss = paddle.mean(out)
optimizer = paddle.optimizer.Adam(learning_rate=0.1,
parameters=linear.parameters())
params_grads = optimizer.backward(loss)
optimizer.apply_gradients(params_grads)
>>> inp = paddle.uniform([10, 10], dtype="float32", min=-0.1, max=0.1)
>>> linear = paddle.nn.Linear(10, 10)
>>> out = linear(inp)
>>> loss = paddle.mean(out)
>>> optimizer = paddle.optimizer.Adam(learning_rate=0.1,
... parameters=linear.parameters())
>>> params_grads = optimizer.backward(loss)
>>> optimizer.apply_gradients(params_grads)
"""
......@@ -1436,17 +1464,17 @@ class Optimizer:
Examples:
.. code-block:: python
import paddle
>>> import paddle
a = paddle.arange(26, dtype="float32").reshape([2, 13])
linear = paddle.nn.Linear(13, 5)
# This can be any optimizer supported by dygraph.
adam = paddle.optimizer.Adam(learning_rate = 0.01,
parameters = linear.parameters())
out = linear(a)
out.backward()
adam.step()
adam.clear_grad()
>>> a = paddle.arange(26, dtype="float32").reshape([2, 13])
>>> linear = paddle.nn.Linear(13, 5)
>>> # This can be any optimizer supported by dygraph.
>>> adam = paddle.optimizer.Adam(learning_rate = 0.01,
... parameters = linear.parameters())
>>> out = linear(a)
>>> out.backward()
>>> adam.step()
>>> adam.clear_grad()
"""
param_list = []
......@@ -1494,21 +1522,21 @@ class Optimizer:
Examples:
.. code-block:: python
import paddle
linear = paddle.nn.Linear(10, 10)
input = paddle.uniform(shape=[10, 10], min=-0.1, max=0.1)
out = linear(input)
loss = paddle.mean(out)
>>> import paddle
>>> linear = paddle.nn.Linear(10, 10)
>>> input = paddle.uniform(shape=[10, 10], min=-0.1, max=0.1)
>>> out = linear(input)
>>> loss = paddle.mean(out)
beta1 = paddle.to_tensor([0.9], dtype="float32")
beta2 = paddle.to_tensor([0.99], dtype="float32")
>>> beta1 = paddle.to_tensor([0.9], dtype="float32")
>>> beta2 = paddle.to_tensor([0.99], dtype="float32")
adam = paddle.optimizer.Adam(learning_rate=0.1,
parameters=linear.parameters(),
weight_decay=0.01)
loss.backward()
adam.minimize(loss)
adam.clear_grad()
>>> adam = paddle.optimizer.Adam(learning_rate=0.1,
... parameters=linear.parameters(),
... weight_decay=0.01)
>>> loss.backward()
>>> adam.minimize(loss)
>>> adam.clear_grad()
"""
assert isinstance(loss, Variable), "The loss should be an Tensor."
......@@ -1562,17 +1590,17 @@ class Optimizer:
Examples:
.. code-block:: python
import paddle
a = paddle.arange(26, dtype="float32").reshape([2, 13])
linear = paddle.nn.Linear(13, 5)
# This can be any optimizer supported by dygraph.
adam = paddle.optimizer.Adam(learning_rate = 0.01,
parameters = linear.parameters())
out = linear(a)
out.backward()
adam.step()
adam.clear_grad()
>>> import paddle
>>> a = paddle.arange(26, dtype="float32").reshape([2, 13])
>>> linear = paddle.nn.Linear(13, 5)
>>> # This can be any optimizer supported by dygraph.
>>> adam = paddle.optimizer.Adam(learning_rate = 0.01,
... parameters = linear.parameters())
>>> out = linear(a)
>>> out.backward()
>>> adam.step()
>>> adam.clear_grad()
"""
if paddle.fluid.dygraph.base.in_declarative_mode():
self._declarative_step()
......
python/paddle/quantization/config.py
浏览文件 @ 0c5781e5
......@@ -70,12 +70,15 @@ class QuantConfig:
Examples:
.. code-block:: python
from paddle.quantization import QuantConfig
from paddle.quantization.quanters import FakeQuanterWithAbsMaxObserver
>>> 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)
>>> 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)
"""
......@@ -100,31 +103,36 @@ class QuantConfig:
weight: QuanterFactory = None,
):
r"""
Set the quantization config by layer. It has the highest priority among
all the setting methods.
Args:
layer(Union[Layer, list]): One or a list of layers.
activation(QuanterFactory): Quanter used for activations.
weight(QuanterFactory): Quanter used for weights.
Examples:
.. code-block:: python
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)
print(q_config)
Set the quantization config by layer. It has the highest priority among
all the setting methods.
Args:
layer(Union[Layer, list]): One or a list of layers.
activation(QuanterFactory): Quanter used for activations.
weight(QuanterFactory): Quanter used for weights.
Examples:
.. code-block:: python
>>> 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
>>> print(q_config)
Global config:
None
Layer prefix config:
{'linear_0': <paddle.quantization.config.SingleLayerConfig object at 0x7fe41a680ee0>}
"""
if isinstance(layer, list):
......@@ -144,31 +152,36 @@ class QuantConfig:
weight: QuanterFactory = None,
):
r"""
Set the quantization config by full name of layer. Its priority is
lower than `add_layer_config`.
Args:
layer_name(Union[str, list]): One or a list of layers' full name.
activation(QuanterFactory): Quanter used for activations.
weight(QuanterFactory): Quanter used for weights.
Examples:
.. code-block:: python
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)
print(q_config)
Set the quantization config by full name of layer. Its priority is
lower than `add_layer_config`.
Args:
layer_name(Union[str, list]): One or a list of layers' full name.
activation(QuanterFactory): Quanter used for activations.
weight(QuanterFactory): Quanter used for weights.
Examples:
.. code-block:: python
>>> 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
>>> print(q_config)
Global config:
None
Layer prefix config:
{'linear_0': <paddle.quantization.config.SingleLayerConfig object at 0x7fe41a680fd0>}
"""
if isinstance(layer_name, str):
......@@ -198,22 +211,27 @@ class QuantConfig:
weight(QuanterFactory): Quanter used for weights.
Examples:
.. code-block:: python
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)
print(q_config)
.. code-block:: python
>>> 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
>>> 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(
......@@ -240,18 +258,18 @@ class QuantConfig:
target(type): The type of layers that will be converted to.
Examples:
.. code-block:: python
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:
def forward(self, x):
pass
# add some code for quantization simulation
q_config.add_qat_layer_mapping(Conv2D, CustomizedQuantedConv2D)
.. code-block:: python
>>> 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:
... 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
......@@ -272,13 +290,13 @@ class QuantConfig:
layer_type(type): The type of layer to be declared as leaf.
Examples:
.. code-block:: python
.. code-block:: python
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)
>>> 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)
......@@ -379,22 +397,22 @@ class QuantConfig:
model(Layer): The model to be specified by the config.
Examples:
.. code-block:: python
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)
.. code-block:: python
>>> 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)
......
python/paddle/quantization/factory.py
浏览文件 @ 0c5781e5
......@@ -83,21 +83,22 @@ def quanter(class_name):
Examples:
.. code-block:: python
# Given codes in ./customized_quanter.py
from paddle.quantization import quanter
from paddle.quantization import BaseQuanter
@quanter("CustomizedQuanter")
class CustomizedQuanterLayer(BaseQuanter):
def __init__(self, arg1, kwarg1=None):
pass
# Used in ./test.py
# from .customized_quanter import CustomizedQuanter
from paddle.quantization import QuantConfig
arg1_value = "test"
kwarg1_value = 20
quanter = CustomizedQuanter(arg1_value, kwarg1=kwarg1_value)
q_config = QuantConfig(activation=quanter, weight=quanter)
>>> # doctest: +SKIP
>>> # Given codes in ./customized_quanter.py
>>> from paddle.quantization import quanter
>>> from paddle.quantization import BaseQuanter
>>> @quanter("CustomizedQuanter")
>>> class CustomizedQuanterLayer(BaseQuanter):
... def __init__(self, arg1, kwarg1=None):
... pass
>>> # Used in ./test.py
>>> # from .customized_quanter import CustomizedQuanter
>>> from paddle.quantization import QuantConfig
>>> arg1_value = "test"
>>> kwarg1_value = 20
>>> quanter = CustomizedQuanter(arg1_value, kwarg1=kwarg1_value)
>>> q_config = QuantConfig(activation=quanter, weight=quanter)
"""
......
python/paddle/quantization/imperative/qat.py
浏览文件 @ 0c5781e5
......@@ -135,79 +135,81 @@ class ImperativeQuantAware:
during training. If this attribute is not sets or the attribute is
false, the Layer would be qunatized in training.
Examples 1:
.. code-block:: python
import paddle
from paddle.static.quantization \
import ImperativeQuantAware
from paddle.vision.models \
import resnet
model = resnet.resnet50(pretrained=True)
imperative_qat = ImperativeQuantAware(
weight_quantize_type='abs_max',
activation_quantize_type='moving_average_abs_max')
# Add the fake quant logical.
# The original model will be rewrite.
# The outscale of outputs in supportted layers would be calculated.
imperative_qat.quantize(model)
# Fine-tune the quantized model
# ...
# Save quant model for the inference.
imperative_qat.save_quantized_model(
layer=model,
model_path="./resnet50_qat",
input_spec=[
paddle.static.InputSpec(
shape=[None, 3, 224, 224], dtype='float32')])
Examples 2:
.. code-block:: python
import paddle
from paddle.static.quantization \
import ImperativeQuantAware
class ImperativeModel(paddle.nn.Layer):
def __init__(self):
super().__init__()
# self.linear_0 would skip the quantization.
self.linear_0 = paddle.nn.Linear(784, 400)
self.linear_0.skip_quant = True
# self.linear_1 would not skip the quantization.
self.linear_1 = paddle.nn.Linear(400, 10)
self.linear_1.skip_quant = False
def forward(self, inputs):
x = self.linear_0(inputs)
x = self.linear_1(inputs)
return x
model = ImperativeModel()
imperative_qat = ImperativeQuantAware(
weight_quantize_type='abs_max',
activation_quantize_type='moving_average_abs_max')
# Add the fake quant logical.
# The original model will be rewrite.
#
# There is only one Layer(self.linear1) would be added the
# fake quant logical.
imperative_qat.quantize(model)
# Fine-tune the quantized model
# ...
# Save quant model for the inference.
imperative_qat.save_quantized_model(
layer=model,
model_path="./imperative_model_qat")
Examples:
.. code-block:: python
>>> import paddle
>>> from paddle.static.quantization import (
... ImperativeQuantAware,
... )
>>> from paddle.vision.models import (
... resnet,
... )
>>> model = resnet.resnet50(pretrained=True)
>>> imperative_qat = ImperativeQuantAware(
... weight_quantize_type='abs_max',
... activation_quantize_type='moving_average_abs_max')
>>> # Add the fake quant logical.
>>> # The original model will be rewrite.
>>> # The outscale of outputs in supportted layers would be calculated.
>>> imperative_qat.quantize(model)
>>> # Fine-tune the quantized model
>>> # ...
>>> # Save quant model for the inference.
>>> imperative_qat.save_quantized_model(
... layer=model,
... model_path="./resnet50_qat",
... input_spec=[
... paddle.static.InputSpec(
... shape=[None, 3, 224, 224], dtype='float32')])
.. code-block:: python
>>> import paddle
>>> from paddle.static.quantization import (
... ImperativeQuantAware,
... )
>>> class ImperativeModel(paddle.nn.Layer):
... def __init__(self):
... super().__init__()
... # self.linear_0 would skip the quantization.
... self.linear_0 = paddle.nn.Linear(784, 400)
... self.linear_0.skip_quant = True
... # self.linear_1 would not skip the quantization.
... self.linear_1 = paddle.nn.Linear(400, 10)
... self.linear_1.skip_quant = False
... def forward(self, inputs):
... x = self.linear_0(inputs)
... x = self.linear_1(inputs)
... return x
>>> model = ImperativeModel()
>>> imperative_qat = ImperativeQuantAware(
... weight_quantize_type='abs_max',
... activation_quantize_type='moving_average_abs_max')
>>> # Add the fake quant logical.
>>> # The original model will be rewrite.
>>> #
>>> # There is only one Layer(self.linear1) would be added the
>>> # fake quant logical.
>>> imperative_qat.quantize(model)
>>> # Fine-tune the quantized model
>>> # ...
>>> # Save quant model for the inference.
>>> imperative_qat.save_quantized_model(
... layer=model,
... model_path="./imperative_model_qat")
"""
super().__init__()
self.fuse_conv_bn = fuse_conv_bn
......@@ -245,39 +247,40 @@ class ImperativeQuantAware:
None
Examples:
.. code-block:: python
import paddle
from paddle.static.quantization \
import ImperativeQuantAware
class ImperativeModel(paddle.nn.Layer):
def __init__(self):
super().__init__()
# self.linear_0 would skip the quantization.
self.linear_0 = paddle.nn.Linear(784, 400)
self.linear_0.skip_quant = True
# self.linear_1 would not skip the quantization.
self.linear_1 = paddle.nn.Linear(400, 10)
self.linear_1.skip_quant = False
def forward(self, inputs):
x = self.linear_0(inputs)
x = self.linear_1(inputs)
return x
model = ImperativeModel()
imperative_qat = ImperativeQuantAware(
weight_quantize_type='abs_max',
activation_quantize_type='moving_average_abs_max')
# Add the fake quant logical.
# The original model will be rewrite.
#
# There is only one Layer(self.linear1) would be added the
# fake quant logical.
imperative_qat.quantize(model)
.. code-block:: python
>>> import paddle
>>> from paddle.static.quantization import (
... ImperativeQuantAware,
... )
>>> class ImperativeModel(paddle.nn.Layer):
... def __init__(self):
... super().__init__()
... # self.linear_0 would skip the quantization.
... self.linear_0 = paddle.nn.Linear(784, 400)
... self.linear_0.skip_quant = True
... # self.linear_1 would not skip the quantization.
... self.linear_1 = paddle.nn.Linear(400, 10)
... self.linear_1.skip_quant = False
... def forward(self, inputs):
... x = self.linear_0(inputs)
... x = self.linear_1(inputs)
... return x
>>> model = ImperativeModel()
>>> imperative_qat = ImperativeQuantAware(
... weight_quantize_type='abs_max',
... activation_quantize_type='moving_average_abs_max')
>>> # Add the fake quant logical.
>>> # The original model will be rewrite.
>>> #
>>> # There is only one Layer(self.linear1) would be added the
>>> # fake quant logical.
>>> imperative_qat.quantize(model)
"""
assert isinstance(
model, paddle.nn.Layer
......
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