Skip to content

P
Paddle

PaddlePaddle / Paddle
1 年多 前同步成功

通知 2305
Star 20932
Fork 5423
P
Paddle
未验证 提交 5d26d79f 编写于 作者: O ooooo-create 提交者： GitHub
浏览文件
操作

[xdoctest] reformat example code with google style in No.21-30 (#55849) 

* [Doctest]fix No.21, test=docs_preview

* Revert "[Doctest]fix No.21, test=docs_preview"

This reverts commit 76bcdb280e254d682be6fc6f85588f1940bb1ade.

* [Doctest]fix No.21, test=docs_preview

* fix bugs,test=docs_preview

* [Doctest]fix No.22-24,26,27, test=docs_preview

* update fix

* with pre-commit, test=docs_preview

* fix seed, test=docs_preview

* fix error, test=docs_preview

* fix seed, test=docs_preview

* fix seed, test=docs_preview
上级 5e96126d
隐藏空白更改
内联 并排
Showing with 554 addition and 523 deletion
python/paddle/autograd/py_layer.py
浏览文件 @ 5d26d79f
......@@ -33,23 +33,23 @@ class PyLayerContext:
Examples:
.. code-block:: python
import paddle
from paddle.autograd import PyLayer
class cus_tanh(PyLayer):
@staticmethod
def forward(ctx, x):
# ctx is a object of PyLayerContext.
y = paddle.tanh(x)
ctx.save_for_backward(y)
return y
@staticmethod
def backward(ctx, dy):
# ctx is a object of PyLayerContext.
y, = ctx.saved_tensor()
grad = dy * (1 - paddle.square(y))
return grad
>>> import paddle
>>> from paddle.autograd import PyLayer
>>> class cus_tanh(PyLayer):
... @staticmethod
... def forward(ctx, x):
... # ctx is a object of PyLayerContext.
... y = paddle.tanh(x)
... ctx.save_for_backward(y)
... return y
...
... @staticmethod
... def backward(ctx, dy):
... # ctx is a object of PyLayerContext.
... y, = ctx.saved_tensor()
... grad = dy * (1 - paddle.square(y))
... return grad
"""
def save_for_backward(self, *tensors):
......@@ -68,24 +68,24 @@ class PyLayerContext:
Examples:
.. code-block:: python
import paddle
from paddle.autograd import PyLayer
class cus_tanh(PyLayer):
@staticmethod
def forward(ctx, x):
# ctx is a context object that store some objects for backward.
y = paddle.tanh(x)
# Pass tensors to backward.
ctx.save_for_backward(y)
return y
@staticmethod
def backward(ctx, dy):
# Get the tensors passed by forward.
y, = ctx.saved_tensor()
grad = dy * (1 - paddle.square(y))
return grad
>>> import paddle
>>> from paddle.autograd import PyLayer
>>> class cus_tanh(PyLayer):
... @staticmethod
... def forward(ctx, x):
... # ctx is a context object that store some objects for backward.
... y = paddle.tanh(x)
... # Pass tensors to backward.
... ctx.save_for_backward(y)
... return y
...
... @staticmethod
... def backward(ctx, dy):
... # Get the tensors passed by forward.
... y, = ctx.saved_tensor()
... grad = dy * (1 - paddle.square(y))
... return grad
"""
self.container = tensors
......@@ -101,24 +101,24 @@ class PyLayerContext:
Examples:
.. code-block:: python
import paddle
from paddle.autograd import PyLayer
class cus_tanh(PyLayer):
@staticmethod
def forward(ctx, x):
# ctx is a context object that store some objects for backward.
y = paddle.tanh(x)
# Pass tensors to backward.
ctx.save_for_backward(y)
return y
@staticmethod
def backward(ctx, dy):
# Get the tensors passed by forward.
y, = ctx.saved_tensor()
grad = dy * (1 - paddle.square(y))
return grad
>>> import paddle
>>> from paddle.autograd import PyLayer
>>> class cus_tanh(PyLayer):
... @staticmethod
... def forward(ctx, x):
... # ctx is a context object that store some objects for backward.
... y = paddle.tanh(x)
... # Pass tensors to backward.
... ctx.save_for_backward(y)
... return y
...
... @staticmethod
... def backward(ctx, dy):
... # Get the tensors passed by forward.
... y, = ctx.saved_tensor()
... grad = dy * (1 - paddle.square(y))
... return grad
"""
return self.container
......@@ -135,30 +135,31 @@ class PyLayerContext:
Examples:
.. code-block:: python
import paddle
class Exp(paddle.autograd.PyLayer):
@staticmethod
def forward(ctx, x):
ctx.mark_not_inplace(x)
return x
@staticmethod
def backward(ctx, grad_output):
out = grad_output.exp()
return out
x = paddle.randn((1, 1))
x.stop_gradient = False
attn_layers = []
for idx in range(0, 2):
attn_layers.append(Exp())
for step in range(0, 2):
a = x
for j in range(0,2):
a = attn_layers[j].apply(x)
a.backward()
>>> import paddle
>>> class Exp(paddle.autograd.PyLayer):
... @staticmethod
... def forward(ctx, x):
... ctx.mark_not_inplace(x)
... return x
...
... @staticmethod
... def backward(ctx, grad_output):
... out = grad_output.exp()
... return out
>>> paddle.seed(2023)
>>> x = paddle.randn((1, 1))
>>> x.stop_gradient = False
>>> attn_layers = []
>>> for idx in range(0, 2):
... attn_layers.append(Exp())
>>> for step in range(0, 2):
... a = x
... for j in range(0,2):
... a = attn_layers[j].apply(x)
... a.backward()
"""
self.not_inplace_tensors = args
......@@ -177,28 +178,28 @@ class PyLayerContext:
Examples:
.. code-block:: python
import paddle
from paddle.autograd import PyLayer
import numpy as np
class Tanh(PyLayer):
@staticmethod
def forward(ctx, x):
a = x + x
b = x + x + x
ctx.mark_non_differentiable(a)
return a, b
@staticmethod
def backward(ctx, grad_a, grad_b):
assert np.equal(grad_a.numpy(), paddle.zeros([1]).numpy())
assert np.equal(grad_b.numpy(), paddle.ones([1], dtype="float64").numpy())
return grad_b
x = paddle.ones([1], dtype="float64")
x.stop_gradient = False
a, b = Tanh.apply(x)
b.sum().backward()
>>> import paddle
>>> from paddle.autograd import PyLayer
>>> import numpy as np
>>> class Tanh(PyLayer):
... @staticmethod
... def forward(ctx, x):
... a = x + x
... b = x + x + x
... ctx.mark_non_differentiable(a)
... return a, b
...
... @staticmethod
... def backward(ctx, grad_a, grad_b):
... assert np.equal(grad_a.numpy(), paddle.zeros([1]).numpy())
... assert np.equal(grad_b.numpy(), paddle.ones([1], dtype="float64").numpy())
... return grad_b
>>> x = paddle.ones([1], dtype="float64")
>>> x.stop_gradient = False
>>> a, b = Tanh.apply(x)
>>> b.sum().backward()
"""
self.non_differentiable = args
......@@ -216,38 +217,38 @@ class PyLayerContext:
Examples:
.. code-block:: python
import paddle
from paddle.autograd import PyLayer
import numpy as np
class Tanh(PyLayer):
@staticmethod
def forward(ctx, x):
return x+x+x, x+x
@staticmethod
def backward(ctx, grad, grad2):
assert np.equal(grad2.numpy(), paddle.zeros([1]).numpy())
return grad
class Tanh2(PyLayer):
@staticmethod
def forward(ctx, x):
ctx.set_materialize_grads(False)
return x+x+x, x+x
@staticmethod
def backward(ctx, grad, grad2):
assert grad2==None
return grad
x = paddle.ones([1], dtype="float64")
x.stop_gradient = False
Tanh.apply(x)[0].backward()
x2 = paddle.ones([1], dtype="float64")
x2.stop_gradient = False
Tanh2.apply(x2)[0].backward()
>>> import paddle
>>> from paddle.autograd import PyLayer
>>> import numpy as np
>>> class Tanh(PyLayer):
... @staticmethod
... def forward(ctx, x):
... return x+x+x, x+x
...
... @staticmethod
... def backward(ctx, grad, grad2):
... assert np.equal(grad2.numpy(), paddle.zeros([1]).numpy())
... return grad
>>> class Tanh2(PyLayer):
... @staticmethod
... def forward(ctx, x):
... ctx.set_materialize_grads(False)
... return x+x+x, x+x
...
... @staticmethod
... def backward(ctx, grad, grad2):
... assert grad2==None
... return grad
>>> x = paddle.ones([1], dtype="float64")
>>> x.stop_gradient = False
>>> Tanh.apply(x)[0].backward()
>>> x2 = paddle.ones([1], dtype="float64")
>>> x2.stop_gradient = False
>>> Tanh2.apply(x2)[0].backward()
"""
self.materialize_grads = value
......@@ -290,30 +291,34 @@ class PyLayer(with_mateclass(PyLayerMeta, core.eager.PyLayer, PyLayerContext)):
Examples:
.. code-block:: python
import paddle
from paddle.autograd import PyLayer
class cus_tanh(PyLayer):
@staticmethod
def forward(ctx, x):
y = paddle.tanh(x)
# Pass tensors to backward.
ctx.save_for_backward(y)
return y
@staticmethod
def backward(ctx, dy):
# Get the tensors passed by forward.
y, = ctx.saved_tensor()
grad = dy * (1 - paddle.square(y))
return grad
data = paddle.randn([2, 3], dtype="float64")
data.stop_gradient = False
z = cus_tanh.apply(data)
z.mean().backward()
print(data.grad)
>>> import paddle
>>> from paddle.autograd import PyLayer
>>> class cus_tanh(PyLayer):
... @staticmethod
... def forward(ctx, x):
... y = paddle.tanh(x)
... # Pass tensors to backward.
... ctx.save_for_backward(y)
... return y
...
... @staticmethod
... def backward(ctx, dy):
... # Get the tensors passed by forward.
... y, = ctx.saved_tensor()
... grad = dy * (1 - paddle.square(y))
... return grad
>>> paddle.seed(2023)
>>> data = paddle.randn([2, 3], dtype="float64")
>>> data.stop_gradient = False
>>> z = cus_tanh.apply(data)
>>> z.mean().backward()
>>> print(data.grad)
Tensor(shape=[2, 3], dtype=float64, place=Place(cpu), stop_gradient=True,
[[0.16604150, 0.05858341, 0.14051214],
[0.15677770, 0.01564609, 0.02991660]])
"""
@staticmethod
......@@ -333,23 +338,23 @@ class PyLayer(with_mateclass(PyLayerMeta, core.eager.PyLayer, PyLayerContext)):
Examples:
.. code-block:: python
import paddle
from paddle.autograd import PyLayer
class cus_tanh(PyLayer):
@staticmethod
def forward(ctx, x):
y = paddle.tanh(x)
# Pass tensors to backward.
ctx.save_for_backward(y)
return y
@staticmethod
def backward(ctx, dy):
# Get the tensors passed by forward.
y, = ctx.saved_tensor()
grad = dy * (1 - paddle.square(y))
return grad
>>> import paddle
>>> from paddle.autograd import PyLayer
>>> class cus_tanh(PyLayer):
... @staticmethod
... def forward(ctx, x):
... y = paddle.tanh(x)
... # Pass tensors to backward.
... ctx.save_for_backward(y)
... return y
...
... @staticmethod
... def backward(ctx, dy):
... # Get the tensors passed by forward.
... y, = ctx.saved_tensor()
... grad = dy * (1 - paddle.square(y))
... return grad
"""
raise NotImplementedError(
"You must implement the forward function for PyLayer."
......@@ -373,23 +378,23 @@ class PyLayer(with_mateclass(PyLayerMeta, core.eager.PyLayer, PyLayerContext)):
Examples:
.. code-block:: python
import paddle
from paddle.autograd import PyLayer
class cus_tanh(PyLayer):
@staticmethod
def forward(ctx, x):
y = paddle.tanh(x)
# Pass tensors to backward.
ctx.save_for_backward(y)
return y
@staticmethod
def backward(ctx, dy):
# Get the tensors passed by forward.
y, = ctx.saved_tensor()
grad = dy * (1 - paddle.square(y))
return grad
>>> import paddle
>>> from paddle.autograd import PyLayer
>>> class cus_tanh(PyLayer):
... @staticmethod
... def forward(ctx, x):
... y = paddle.tanh(x)
... # Pass tensors to backward.
... ctx.save_for_backward(y)
... return y
...
... @staticmethod
... def backward(ctx, dy):
... # Get the tensors passed by forward.
... y, = ctx.saved_tensor()
... grad = dy * (1 - paddle.square(y))
... return grad
"""
raise NotImplementedError(
......
python/paddle/autograd/saved_tensors_hooks.py
浏览文件 @ 5d26d79f
......@@ -45,58 +45,58 @@ class saved_tensors_hooks:
Examples:
.. code-block:: python
# Example1
import paddle
def pack_hook(x):
print("Packing", x)
return x.numpy()
def unpack_hook(x):
print("UnPacking", x)
return paddle.to_tensor(x)
a = paddle.ones([3,3])
b = paddle.ones([3,3]) * 2
a.stop_gradient = False
b.stop_gradient = False
with paddle.autograd.saved_tensors_hooks(pack_hook, unpack_hook):
y = paddle.multiply(a, b)
y.sum().backward()
# Example2
import paddle
from paddle.autograd import PyLayer
class cus_multiply(PyLayer):
@staticmethod
def forward(ctx, a, b):
y = paddle.multiply(a, b)
ctx.save_for_backward(a, b)
return y
@staticmethod
def backward(ctx, dy):
a,b = ctx.saved_tensor()
grad_a = dy * a
grad_b = dy * b
return grad_a, grad_b
def pack_hook(x):
print("Packing", x)
return x.numpy()
def unpack_hook(x):
print("UnPacking", x)
return paddle.to_tensor(x)
a = paddle.ones([3,3])
b = paddle.ones([3,3]) * 2
a.stop_gradient = False
b.stop_gradient = False
with paddle.autograd.saved_tensors_hooks(pack_hook, unpack_hook):
y = cus_multiply.apply(a, b)
y.sum().backward()
>>> # Example1
>>> import paddle
>>> def pack_hook(x):
... print("Packing", x)
... return x.numpy()
>>> def unpack_hook(x):
... print("UnPacking", x)
... return paddle.to_tensor(x)
>>> a = paddle.ones([3,3])
>>> b = paddle.ones([3,3]) * 2
>>> a.stop_gradient = False
>>> b.stop_gradient = False
>>> with paddle.autograd.saved_tensors_hooks(pack_hook, unpack_hook):
... y = paddle.multiply(a, b)
>>> y.sum().backward()
>>> # Example2
>>> import paddle
>>> from paddle.autograd import PyLayer
>>> class cus_multiply(PyLayer):
... @staticmethod
... def forward(ctx, a, b):
... y = paddle.multiply(a, b)
... ctx.save_for_backward(a, b)
... return y
...
... @staticmethod
... def backward(ctx, dy):
... a,b = ctx.saved_tensor()
... grad_a = dy * a
... grad_b = dy * b
... return grad_a, grad_b
>>> def pack_hook(x):
... print("Packing", x)
... return x.numpy()
>>> def unpack_hook(x):
... print("UnPacking", x)
... return paddle.to_tensor(x)
>>> a = paddle.ones([3,3])
>>> b = paddle.ones([3,3]) * 2
>>> a.stop_gradient = False
>>> b.stop_gradient = False
>>> with paddle.autograd.saved_tensors_hooks(pack_hook, unpack_hook):
... y = cus_multiply.apply(a, b)
>>> y.sum().backward()
"""
def __init__(self, pack_hook, unpack_hook):
......
python/paddle/framework/dtype.py
浏览文件 @ 5d26d79f
......@@ -58,15 +58,19 @@ def iinfo(dtype):
Examples:
.. code-block:: python
import paddle
iinfo_uint8 = paddle.iinfo(paddle.uint8)
print(iinfo_uint8)
# paddle.iinfo(min=0, max=255, bits=8, dtype=uint8)
print(iinfo_uint8.min) # 0
print(iinfo_uint8.max) # 255
print(iinfo_uint8.bits) # 8
print(iinfo_uint8.dtype) # uint8
>>> import paddle
>>> iinfo_uint8 = paddle.iinfo(paddle.uint8)
>>> print(iinfo_uint8)
paddle.iinfo(min=0, max=255, bits=8, dtype=uint8)
>>> print(iinfo_uint8.min)
0
>>> print(iinfo_uint8.max)
255
>>> print(iinfo_uint8.bits)
8
>>> print(iinfo_uint8.dtype)
uint8
"""
return core_iinfo(dtype)
......@@ -98,17 +102,25 @@ def finfo(dtype):
Examples:
.. code-block:: python
import paddle
finfo_float32 = paddle.finfo(paddle.float32)
print(finfo_float32.min) # -3.40282e+38
print(finfo_float32.max) # 3.40282e+38
print(finfo_float32.eps) # 1.19209e-07
print(finfo_float32.resolution) # 1e-06
print(finfo_float32.smallest_normal) # 1.17549e-38
print(finfo_float32.tiny) # 1.17549e-38
print(finfo_float32.bits) # 32
print(finfo_float32.dtype) # float32
>>> import paddle
>>> finfo_float32 = paddle.finfo(paddle.float32)
>>> print(finfo_float32.min)
-3.4028234663852886e+38
>>> print(finfo_float32.max)
3.4028234663852886e+38
>>> print(finfo_float32.eps)
1.1920928955078125e-07
>>> print(finfo_float32.resolution)
1e-06
>>> print(finfo_float32.smallest_normal)
1.1754943508222875e-38
>>> print(finfo_float32.tiny)
1.1754943508222875e-38
>>> print(finfo_float32.bits)
32
>>> print(finfo_float32.dtype)
float32
"""
return core_finfo(dtype)
python/paddle/framework/framework.py
浏览文件 @ 5d26d79f
......@@ -35,8 +35,8 @@ def set_default_dtype(d):
Examples:
.. code-block:: python
import paddle
paddle.set_default_dtype("float32")
>>> import paddle
>>> paddle.set_default_dtype("float32")
"""
if isinstance(d, type):
......@@ -76,7 +76,7 @@ def get_default_dtype():
Examples:
.. code-block:: python
import paddle
paddle.get_default_dtype()
>>> import paddle
>>> paddle.get_default_dtype()
"""
return LayerHelperBase.get_default_dtype()
python/paddle/framework/io.py
浏览文件 @ 5d26d79f
......@@ -677,100 +677,101 @@ def save(obj, path, protocol=4, **configs):
.. code-block:: python
:name: code-example-1
# example 1: dynamic graph
import paddle
emb = paddle.nn.Embedding(10, 10)
layer_state_dict = emb.state_dict()
# save state_dict of emb
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()
# save state_dict of optimizer
paddle.save(opt_state_dict, "adam.pdopt")
# save weight of emb
paddle.save(emb.weight, "emb.weight.pdtensor")
>>> # example 1: dynamic graph
>>> import paddle
>>> emb = paddle.nn.Embedding(10, 10)
>>> layer_state_dict = emb.state_dict()
>>> # save state_dict of emb
>>> 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()
>>> # save state_dict of optimizer
>>> paddle.save(opt_state_dict, "adam.pdopt")
>>> # save weight of emb
>>> paddle.save(emb.weight, "emb.weight.pdtensor")
.. code-block:: python
:name: code-example-2
# example 2: Save multiple state_dict at the same time
import paddle
from paddle import nn
from paddle.optimizer import Adam
>>> # example 2: Save multiple state_dict at the same time
>>> import paddle
>>> from paddle import nn
>>> from paddle.optimizer import Adam
layer = paddle.nn.Linear(3, 4)
adam = Adam(learning_rate=0.001, parameters=layer.parameters())
obj = {'model': layer.state_dict(), 'opt': adam.state_dict(), 'epoch': 100}
path = 'example/model.pdparams'
paddle.save(obj, path)
>>> layer = paddle.nn.Linear(3, 4)
>>> adam = Adam(learning_rate=0.001, parameters=layer.parameters())
>>> obj = {'model': layer.state_dict(), 'opt': adam.state_dict(), 'epoch': 100}
>>> path = 'example/model.pdparams'
>>> paddle.save(obj, path)
.. code-block:: python
:name: code-example-3
# example 3: static graph
import paddle
import paddle.static as static
>>> # example 3: static graph
>>> import paddle
>>> import paddle.static as static
paddle.enable_static()
>>> paddle.enable_static()
# create network
x = paddle.static.data(name="x", shape=[None, 224], dtype='float32')
z = paddle.static.nn.fc(x, 10)
>>> # create network
>>> x = paddle.static.data(name="x", shape=[None, 224], dtype='float32')
>>> z = paddle.static.nn.fc(x, 10)
place = paddle.CPUPlace()
exe = paddle.static.Executor(place)
exe.run(paddle.static.default_startup_program())
prog = paddle.static.default_main_program()
for var in prog.list_vars():
if list(var.shape) == [224, 10]:
tensor = var.get_value()
break
>>> place = paddle.CPUPlace()
>>> exe = paddle.static.Executor(place)
>>> exe.run(paddle.static.default_startup_program())
>>> prog = paddle.static.default_main_program()
>>> for var in prog.list_vars():
... if list(var.shape) == [224, 10]:
... tensor = var.get_value()
... break
# save/load tensor
path_tensor = 'temp/tensor.pdtensor'
paddle.save(tensor, path_tensor)
>>> # save/load tensor
>>> path_tensor = 'temp/tensor.pdtensor'
>>> paddle.save(tensor, path_tensor)
# save/load state_dict
path_state_dict = 'temp/model.pdparams'
paddle.save(prog.state_dict("param"), path_tensor)
>>> # save/load state_dict
>>> path_state_dict = 'temp/model.pdparams'
>>> paddle.save(prog.state_dict("param"), path_tensor)
.. code-block:: python
:name: code-example-4
# example 4: save program
import paddle
>>> # example 4: save program
>>> import paddle
paddle.enable_static()
>>> paddle.enable_static()
data = paddle.static.data(
name='x_static_save', shape=(None, 224), dtype='float32')
y_static = z = paddle.static.nn.fc(data, 10)
main_program = paddle.static.default_main_program()
path = "example/main_program.pdmodel"
paddle.save(main_program, path)
>>> data = paddle.static.data(
... name='x_static_save', shape=(None, 224), dtype='float32')
>>> y_static = z = paddle.static.nn.fc(data, 10)
>>> main_program = paddle.static.default_main_program()
>>> path = "example/main_program.pdmodel"
>>> paddle.save(main_program, path)
.. code-block:: python
:name: code-example-5
# example 5: save object to memory
from io import BytesIO
import paddle
from paddle.nn import Linear
paddle.disable_static()
>>> # example 5: save object to memory
>>> from io import BytesIO
>>> import paddle
>>> from paddle.nn import Linear
>>> paddle.disable_static()
linear = Linear(5, 10)
state_dict = linear.state_dict()
byio = BytesIO()
paddle.save(state_dict, byio)
tensor = paddle.randn([2, 3], dtype='float32')
paddle.save(tensor, byio)
>>> linear = Linear(5, 10)
>>> state_dict = linear.state_dict()
>>> byio = BytesIO()
>>> paddle.save(state_dict, byio)
>>> paddle.seed(2023)
>>> tensor = paddle.randn([2, 3], dtype='float32')
>>> paddle.save(tensor, byio)
'''
if _is_file_path(path):
......@@ -938,115 +939,115 @@ def load(path, **configs):
.. code-block:: python
:name: code-example-1
# example 1: dynamic graph
import paddle
emb = paddle.nn.Embedding(10, 10)
layer_state_dict = emb.state_dict()
# save state_dict of emb
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()
# save state_dict of optimizer
paddle.save(opt_state_dict, "adam.pdopt")
# save weight of emb
paddle.save(emb.weight, "emb.weight.pdtensor")
# load state_dict of emb
load_layer_state_dict = paddle.load("emb.pdparams")
# load state_dict of optimizer
load_opt_state_dict = paddle.load("adam.pdopt")
# load weight of emb
load_weight = paddle.load("emb.weight.pdtensor")
>>> # example 1: dynamic graph
>>> import paddle
>>> emb = paddle.nn.Embedding(10, 10)
>>> layer_state_dict = emb.state_dict()
>>> # save state_dict of emb
>>> 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()
>>> # save state_dict of optimizer
>>> paddle.save(opt_state_dict, "adam.pdopt")
>>> # save weight of emb
>>> paddle.save(emb.weight, "emb.weight.pdtensor")
>>> # load state_dict of emb
>>> load_layer_state_dict = paddle.load("emb.pdparams")
>>> # load state_dict of optimizer
>>> load_opt_state_dict = paddle.load("adam.pdopt")
>>> # load weight of emb
>>> load_weight = paddle.load("emb.weight.pdtensor")
.. code-block:: python
:name: code-example-2
# example 2: Load multiple state_dict at the same time
import paddle
from paddle import nn
from paddle.optimizer import Adam
>>> # example 2: Load multiple state_dict at the same time
>>> import paddle
>>> from paddle import nn
>>> from paddle.optimizer import Adam
layer = paddle.nn.Linear(3, 4)
adam = Adam(learning_rate=0.001, parameters=layer.parameters())
obj = {'model': layer.state_dict(), 'opt': adam.state_dict(), 'epoch': 100}
path = 'example/model.pdparams'
paddle.save(obj, path)
obj_load = paddle.load(path)
>>> layer = paddle.nn.Linear(3, 4)
>>> adam = Adam(learning_rate=0.001, parameters=layer.parameters())
>>> obj = {'model': layer.state_dict(), 'opt': adam.state_dict(), 'epoch': 100}
>>> path = 'example/model.pdparams'
>>> paddle.save(obj, path)
>>> obj_load = paddle.load(path)
.. code-block:: python
:name: code-example-3
# example 3: static graph
import paddle
import paddle.static as static
>>> # example 3: static graph
>>> import paddle
>>> import paddle.static as static
paddle.enable_static()
>>> paddle.enable_static()
# create network
x = paddle.static.data(name="x", shape=[None, 224], dtype='float32')
z = paddle.static.nn.fc(x, 10)
>>> # create network
>>> x = paddle.static.data(name="x", shape=[None, 224], dtype='float32')
>>> z = paddle.static.nn.fc(x, 10)
place = paddle.CPUPlace()
exe = paddle.static.Executor(place)
exe.run(paddle.static.default_startup_program())
prog = paddle.static.default_main_program()
for var in prog.list_vars():
if list(var.shape) == [224, 10]:
tensor = var.get_value()
break
>>> place = paddle.CPUPlace()
>>> exe = paddle.static.Executor(place)
>>> exe.run(paddle.static.default_startup_program())
>>> prog = paddle.static.default_main_program()
>>> for var in prog.list_vars():
... if list(var.shape) == [224, 10]:
... tensor = var.get_value()
... break
# save/load tensor
path_tensor = 'temp/tensor.pdtensor'
paddle.save(tensor, path_tensor)
load_tensor = paddle.load(path_tensor)
>>> # save/load tensor
>>> path_tensor = 'temp/tensor.pdtensor'
>>> paddle.save(tensor, path_tensor)
>>> load_tensor = paddle.load(path_tensor)
# save/load state_dict
path_state_dict = 'temp/model.pdparams'
paddle.save(prog.state_dict("param"), path_tensor)
load_state_dict = paddle.load(path_tensor)
>>> # save/load state_dict
>>> path_state_dict = 'temp/model.pdparams'
>>> paddle.save(prog.state_dict("param"), path_tensor)
>>> load_state_dict = paddle.load(path_tensor)
.. code-block:: python
:name: code-example-4
# example 4: load program
import paddle
>>> # example 4: load program
>>> import paddle
paddle.enable_static()
>>> paddle.enable_static()
data = paddle.static.data(
name='x_static_save', shape=(None, 224), dtype='float32')
y_static = z = paddle.static.nn.fc(data, 10)
main_program = paddle.static.default_main_program()
path = "example/main_program.pdmodel"
paddle.save(main_program, path)
load_main = paddle.load(path)
print(load_main)
>>> data = paddle.static.data(
... name='x_static_save', shape=(None, 224), dtype='float32')
>>> y_static = z = paddle.static.nn.fc(data, 10)
>>> main_program = paddle.static.default_main_program()
>>> path = "example/main_program.pdmodel"
>>> paddle.save(main_program, path)
>>> load_main = paddle.load(path)
.. code-block:: python
:name: code-example-5
# example 5: save object to memory
from io import BytesIO
import paddle
from paddle.nn import Linear
paddle.disable_static()
linear = Linear(5, 10)
state_dict = linear.state_dict()
byio = BytesIO()
paddle.save(state_dict, byio)
tensor = paddle.randn([2, 3], dtype='float32')
paddle.save(tensor, byio)
byio.seek(0)
# load state_dict
dict_load = paddle.load(byio)
>>> # example 5: save object to memory
>>> from io import BytesIO
>>> import paddle
>>> from paddle.nn import Linear
>>> paddle.disable_static()
>>> linear = Linear(5, 10)
>>> state_dict = linear.state_dict()
>>> byio = BytesIO()
>>> paddle.save(state_dict, byio)
>>> paddle.seed(2023)
>>> tensor = paddle.randn([2, 3], dtype='float32')
>>> paddle.save(tensor, byio)
>>> byio.seek(0)
>>> # load state_dict
>>> dict_load = paddle.load(byio)
'''
......
python/paddle/framework/io_utils.py
浏览文件 @ 5d26d79f
......@@ -94,12 +94,13 @@ def is_persistable(var):
Examples:
.. code-block:: python
import paddle
import paddle.fluid as fluid
>>> # doctest: +SKIP('ValueError: var fc.b not in this block')
>>> import paddle
>>> import paddle.fluid as fluid
paddle.enable_static()
param = fluid.default_main_program().global_block().var('fc.b')
res = fluid.io.is_persistable(param)
>>> paddle.enable_static()
>>> param = fluid.default_main_program().global_block().var('fc.b')
>>> res = fluid.io.is_persistable(param)
"""
if (
var.desc.type() == core.VarDesc.VarType.FEED_MINIBATCH
......@@ -124,12 +125,13 @@ def is_parameter(var):
Examples:
.. code-block:: python
import paddle
import paddle.fluid as fluid
>>> # doctest: +SKIP('ValueError: var fc.w not in this block')
>>> import paddle
>>> import paddle.fluid as fluid
paddle.enable_static()
param = fluid.default_main_program().global_block().var('fc.w')
res = fluid.io.is_parameter(param)
>>> paddle.enable_static()
>>> param = fluid.default_main_program().global_block().var('fc.w')
>>> res = fluid.io.is_parameter(param)
"""
return isinstance(var, Parameter)
......
python/paddle/framework/random.py
浏览文件 @ 5d26d79f
......@@ -34,8 +34,8 @@ def seed(seed):
Examples:
.. code-block:: python
import paddle
gen = paddle.seed(102)
>>> import paddle
>>> gen = paddle.seed(102)
"""
# TODO(zhiqiu): 1. remove program.random_seed when all random-related op upgrade
......@@ -75,8 +75,8 @@ def get_rng_state(device=None):
GeneratorState: object.
Examples:
.. code-block:: python
import paddle
sts = paddle.get_rng_state()
>>> import paddle
>>> sts = paddle.get_rng_state()
"""
state_list = []
if device is None:
......@@ -129,8 +129,8 @@ def get_cuda_rng_state():
Examples:
.. code-block:: python
import paddle
sts = paddle.get_cuda_rng_state()
>>> import paddle
>>> sts = paddle.get_cuda_rng_state()
"""
state_list = []
......@@ -158,9 +158,9 @@ def set_rng_state(state_list, device=None):
Examples:
.. code-block:: python
import paddle
sts = paddle.get_rng_state()
paddle.set_rng_state(sts)
>>> import paddle
>>> sts = paddle.get_rng_state()
>>> paddle.set_rng_state(sts)
"""
if device is None:
......@@ -223,9 +223,9 @@ def set_cuda_rng_state(state_list):
Examples:
.. code-block:: python
import paddle
sts = paddle.get_cuda_rng_state()
paddle.set_cuda_rng_state(sts)
>>> import paddle
>>> sts = paddle.get_cuda_rng_state()
>>> paddle.set_cuda_rng_state(sts)
"""
if core.is_compiled_with_cuda():
......
python/paddle/nn/initializer/Bilinear.py
浏览文件 @ 5d26d79f
......@@ -36,29 +36,29 @@ class Bilinear(Initializer):
.. code-block:: python
import math
import paddle
import paddle.nn as nn
from paddle.regularizer import L2Decay
factor = 2
C = 2
B = 8
H = W = 32
w_attr = paddle.ParamAttr(learning_rate=0.,
regularizer=L2Decay(0.),
initializer=nn.initializer.Bilinear())
data = paddle.rand([B, 3, H, W], dtype='float32')
conv_up = nn.Conv2DTranspose(3,
out_channels=C,
kernel_size=2 * factor - factor % 2,
padding=int(
math.ceil((factor - 1) / 2.)),
stride=factor,
weight_attr=w_attr,
bias_attr=False)
x = conv_up(data)
>>> import math
>>> import paddle
>>> import paddle.nn as nn
>>> from paddle.regularizer import L2Decay
>>> factor = 2
>>> C = 2
>>> B = 8
>>> H = W = 32
>>> w_attr = paddle.ParamAttr(learning_rate=0.,
... regularizer=L2Decay(0.),
... initializer=nn.initializer.Bilinear())
>>> data = paddle.rand([B, 3, H, W], dtype='float32')
>>> conv_up = nn.Conv2DTranspose(3,
... out_channels=C,
... kernel_size=2 * factor - factor % 2,
... padding=int(
... math.ceil((factor - 1) / 2.)),
... stride=factor,
... weight_attr=w_attr,
... bias_attr=False)
>>> x = conv_up(data)
Where, `out_channels=C` and `groups=C` means this is channel-wise transposed
convolution. The filter shape will be (C, 1, K, K) where K is `kernel_size`,
......
python/paddle/nn/initializer/assign.py
浏览文件 @ 5d26d79f
......@@ -153,53 +153,62 @@ class Assign(NumpyArrayInitializer):
Examples:
.. code-block:: python
import paddle
import numpy as np
# numpy array
data_1 = paddle.ones(shape=[1, 2], dtype='float32')
weight_attr_1 = paddle.framework.ParamAttr(
name="linear_weight_1",
initializer=paddle.nn.initializer.Assign(np.array([2, 2])))
bias_attr_1 = paddle.framework.ParamAttr(
name="linear_bias_1",
initializer=paddle.nn.initializer.Assign(np.array([2])))
linear_1 = paddle.nn.Linear(2, 2, weight_attr=weight_attr_1, bias_attr=bias_attr_1)
# linear_1.weight: [2. 2.]
# linear_1.bias: [2.]
res_1 = linear_1(data_1)
# res_1: [6.]
# python list
data_2 = paddle.ones(shape=[1, 2], dtype='float32')
weight_attr_2 = paddle.framework.ParamAttr(
name="linear_weight_2",
initializer=paddle.nn.initializer.Assign([2, 2]))
bias_attr_2 = paddle.framework.ParamAttr(
name="linear_bias_2",
initializer=paddle.nn.initializer.Assign([2]))
linear_2 = paddle.nn.Linear(2, 2, weight_attr=weight_attr_2, bias_attr=bias_attr_2)
# linear_2.weight: [2. 2.]
# linear_2.bias: [2.]
res_2 = linear_2(data_2)
# res_2: [6.]
# tensor
data_3 = paddle.ones(shape=[1, 2], dtype='float32')
weight_attr_3 = paddle.framework.ParamAttr(
name="linear_weight_3",
initializer=paddle.nn.initializer.Assign(paddle.full([2], 2)))
bias_attr_3 = paddle.framework.ParamAttr(
name="linear_bias_3",
initializer=paddle.nn.initializer.Assign(paddle.full([1], 2)))
linear_3 = paddle.nn.Linear(2, 2, weight_attr=weight_attr_3, bias_attr=bias_attr_3)
# linear_3.weight: [2. 2.]
# linear_3.bias: [2.]
res_3 = linear_3(data_3)
# res_3: [6.]
>>> import paddle
>>> import numpy as np
>>> # numpy array
>>> data_1 = paddle.ones(shape=[1, 2], dtype='float32')
>>> weight_attr_1 = paddle.framework.ParamAttr(
... name="linear_weight_1",
... initializer=paddle.nn.initializer.Assign(np.array([2, 2])))
>>> bias_attr_1 = paddle.framework.ParamAttr(
... name="linear_bias_1",
... initializer=paddle.nn.initializer.Assign(np.array([2])))
>>> linear_1 = paddle.nn.Linear(2, 2, weight_attr=weight_attr_1, bias_attr=bias_attr_1)
>>> print(linear_1.weight.numpy())
[2. 2.]
>>> print(linear_1.bias.numpy())
[2.]
>>> res_1 = linear_1(data_1)
>>> print(res_1.numpy())
[6.]
>>> # python list
>>> data_2 = paddle.ones(shape=[1, 2], dtype='float32')
>>> weight_attr_2 = paddle.framework.ParamAttr(
... name="linear_weight_2",
... initializer=paddle.nn.initializer.Assign([2, 2]))
>>> bias_attr_2 = paddle.framework.ParamAttr(
... name="linear_bias_2",
... initializer=paddle.nn.initializer.Assign([2]))
>>> linear_2 = paddle.nn.Linear(2, 2, weight_attr=weight_attr_2, bias_attr=bias_attr_2)
>>> print(linear_2.weight.numpy())
[2. 2.]
>>> print(linear_2.bias.numpy())
[2.]
>>> res_2 = linear_2(data_2)
>>> print(res_2.numpy())
[6.]
>>> # tensor
>>> data_3 = paddle.ones(shape=[1, 2], dtype='float32')
>>> weight_attr_3 = paddle.framework.ParamAttr(
... name="linear_weight_3",
... initializer=paddle.nn.initializer.Assign(paddle.full([2], 2)))
>>> bias_attr_3 = paddle.framework.ParamAttr(
... name="linear_bias_3",
... initializer=paddle.nn.initializer.Assign(paddle.full([1], 2)))
>>> linear_3 = paddle.nn.Linear(2, 2, weight_attr=weight_attr_3, bias_attr=bias_attr_3)
>>> print(linear_3.weight.numpy())
[2. 2.]
>>> print(linear_3.bias.numpy())
[2.]
>>> res_3 = linear_3(data_3)
>>> print(res_3.numpy())
[6.]
"""
def __init__(self, value, name=None):
......
python/paddle/nn/initializer/constant.py
浏览文件 @ 5d26d79f
......@@ -88,18 +88,20 @@ class Constant(ConstantInitializer):
Examples:
.. code-block:: python
import paddle
import paddle.nn as nn
data = paddle.rand([30, 10, 2], dtype='float32')
linear = nn.Linear(2,
4,
weight_attr=nn.initializer.Constant(value=2.0))
res = linear(data)
print(linear.weight)
# Tensor(shape=[2, 4], dtype=float32, place=Place(gpu:0), stop_gradient=False,
# [[2., 2., 2., 2.],
# [2., 2., 2., 2.]])
>>> import paddle
>>> import paddle.nn as nn
>>> paddle.seed(2023)
>>> data = paddle.rand([30, 10, 2], dtype='float32')
>>> linear = nn.Linear(2,
... 4,
... weight_attr=nn.initializer.Constant(value=2.0))
>>> res = linear(data)
>>> print(linear.weight)
Parameter containing:
Tensor(shape=[2, 4], dtype=float32, place=Place(cpu), stop_gradient=False,
[[2., 2., 2., 2.],
[2., 2., 2., 2.]])
"""
......
Markdown is supported
0% 或 .
You are about to add 0 people to the discussion. Proceed with caution.
先完成此消息的编辑！
想要评论请 注册 或