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Paddle
未验证 提交 bf6e7cba 编写于 作者: Z Zhou Wei 提交者： GitHub
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updata 2.0 API english doc (#28525) 

* make Numpy version is below 1.19.3

* fix 2.0 doc
上级 7b1619e6
隐藏空白更改
内联 并排
Showing with 77 addition and 72 deletion
paddle/fluid/operators/unique_op.cu
浏览文件 @ bf6e7cba
......@@ -177,7 +177,7 @@ static void UniqueFlattendCUDATensor(const framework::ExecutionContext& context,
thrust::sort_by_key(thrust::device, in_data_hat, in_data_hat + num_input,
sorted_indices_data);
// 1. Calculate op result: 'out'：
// 1. Calculate op result: 'out'
Tensor range;
range.Resize(framework::make_ddim({num_input + 1}));
auto range_data_ptr = range.mutable_data<IndexT>(context.GetPlace());
......
paddle/fluid/pybind/imperative.cc
浏览文件 @ bf6e7cba
......@@ -685,8 +685,6 @@ void BindImperative(py::module *m_ptr) {
.. code-block:: python
import paddle
paddle.disable_static()
linear = Linear(32, 64)
data = paddle.uniform(shape=[30, 10, 32], -1, 1)
x = linear(data)
......@@ -704,19 +702,13 @@ void BindImperative(py::module *m_ptr) {
.. code-block:: python
import paddle
paddle.disable_static()
inputs = []
for _ in range(10):
tmp = paddle.ones([2, 2])
tmp.stop_gradient=False
inputs.append(tmp)
ret = paddle.sums(inputs2)
loss = paddle.sum(ret)
loss.backward()
print("Before clear_gradient {}".format(loss.grad))
loss.clear_gradient()
print("After clear_gradient {}".format(loss.grad))
input = paddle.uniform([10, 2])
linear = paddle.nn.Linear(2, 3)
out = linear(input)
out.backward()
print("Before clear_gradient, linear.weight.grad: {}".format(linear.weight.grad))
linear.weight.clear_gradient()
print("After clear_gradient, linear.weight.grad: {}".format(linear.weight.grad))
)DOC")
.def("clone",
[](std::shared_ptr<imperative::VarBase> &self) {
......
python/paddle/framework/__init__.py
浏览文件 @ bf6e7cba
......@@ -18,10 +18,7 @@ __all__ = [
'get_default_dtype', 'set_default_dtype'
]
__all__ += [
'grad', 'LayerList', 'load', 'save', 'to_variable', 'no_grad',
'DataParallel'
]
__all__ += ['grad', 'LayerList', 'load', 'save', 'no_grad', 'DataParallel']
from . import random
from .random import seed
......@@ -39,7 +36,6 @@ from ..fluid.core import VarBase #DEFINE_ALIAS
from paddle.fluid import core #DEFINE_ALIAS
from ..fluid.dygraph.base import no_grad_ as no_grad #DEFINE_ALIAS
from ..fluid.dygraph.base import to_variable #DEFINE_ALIAS
from ..fluid.dygraph.base import grad #DEFINE_ALIAS
from .io import save
from .io import load
......
python/paddle/framework/io.py
浏览文件 @ bf6e7cba
......@@ -225,8 +225,6 @@ def save(obj, path):
import paddle
paddle.disable_static()
emb = paddle.nn.Embedding(10, 10)
layer_state_dict = emb.state_dict()
paddle.save(layer_state_dict, "emb.pdparams")
......@@ -318,8 +316,6 @@ def load(path, **configs):
.. code-block:: python
import paddle
paddle.disable_static()
emb = paddle.nn.Embedding(10, 10)
layer_state_dict = emb.state_dict()
......
python/paddle/optimizer/lr.py
浏览文件 @ bf6e7cba
......@@ -226,14 +226,15 @@ class NoamDecay(LRScheduler):
scheduler = paddle.optimizer.lr.NoamDecay(d_model=0.01, warmup_steps=100, verbose=True)
sgd = paddle.optimizer.SGD(learning_rate=scheduler, parameters=linear.parameters())
for epoch in range(20):
for batch_id in range(2):
for batch_id in range(5):
x = paddle.uniform([10, 10])
out = linear(x)
loss = paddle.mean(out)
loss.backward()
sgd.step()
sgd.clear_gradients()
scheduler.step()
scheduler.step() # If you update learning rate each step
# scheduler.step() # If you update learning rate each epoch
# train on static graph mode
paddle.enable_static()
......@@ -251,7 +252,7 @@ class NoamDecay(LRScheduler):
exe = paddle.static.Executor()
exe.run(start_prog)
for epoch in range(20):
for batch_id in range(2):
for batch_id in range(5):
out = exe.run(
main_prog,
feed={
......@@ -259,7 +260,8 @@ class NoamDecay(LRScheduler):
'y': np.random.randn(3, 4, 5).astype('float32')
},
fetch_list=loss.name)
scheduler.step()
scheduler.step() # If you update learning rate each step
# scheduler.step() # If you update learning rate each epoch
"""
......@@ -322,14 +324,15 @@ class PiecewiseDecay(LRScheduler):
scheduler = paddle.optimizer.lr.PiecewiseDecay(boundaries=[3, 6, 9], values=[0.1, 0.2, 0.3, 0.4], verbose=True)
sgd = paddle.optimizer.SGD(learning_rate=scheduler, parameters=linear.parameters())
for epoch in range(20):
for batch_id in range(2):
for batch_id in range(5):
x = paddle.uniform([10, 10])
out = linear(x)
loss = paddle.mean(out)
loss.backward()
sgd.step()
sgd.clear_gradients()
scheduler.step()
scheduler.step() # If you update learning rate each step
# scheduler.step() # If you update learning rate each epoch
# train on static graph mode
paddle.enable_static()
......@@ -347,7 +350,7 @@ class PiecewiseDecay(LRScheduler):
exe = paddle.static.Executor()
exe.run(start_prog)
for epoch in range(20):
for batch_id in range(2):
for batch_id in range(5):
out = exe.run(
main_prog,
feed={
......@@ -355,7 +358,8 @@ class PiecewiseDecay(LRScheduler):
'y': np.random.randn(3, 4, 5).astype('float32')
},
fetch_list=loss.name)
scheduler.step()
scheduler.step() # If you update learning rate each step
# scheduler.step() # If you update learning rate each epoch
"""
def __init__(self, boundaries, values, last_epoch=-1, verbose=False):
......@@ -403,14 +407,15 @@ class NaturalExpDecay(LRScheduler):
scheduler = paddle.optimizer.lr.NaturalExpDecay(learning_rate=0.5, gamma=0.1, verbose=True)
sgd = paddle.optimizer.SGD(learning_rate=scheduler, parameters=linear.parameters())
for epoch in range(20):
for batch_id in range(2):
for batch_id in range(5):
x = paddle.uniform([10, 10])
out = linear(x)
loss = paddle.mean(out)
loss.backward()
sgd.step()
sgd.clear_gradients()
scheduler.step()
scheduler.step() # If you update learning rate each step
# scheduler.step() # If you update learning rate each epoch
# train on static graph mode
paddle.enable_static()
......@@ -428,7 +433,7 @@ class NaturalExpDecay(LRScheduler):
exe = paddle.static.Executor()
exe.run(start_prog)
for epoch in range(20):
for batch_id in range(2):
for batch_id in range(5):
out = exe.run(
main_prog,
feed={
......@@ -436,7 +441,8 @@ class NaturalExpDecay(LRScheduler):
'y': np.random.randn(3, 4, 5).astype('float32')
},
fetch_list=loss.name)
scheduler.step()
scheduler.step() # If you update learning rate each step
# scheduler.step() # If you update learning rate each epoch
"""
def __init__(self, learning_rate, gamma, last_epoch=-1, verbose=False):
......@@ -481,14 +487,15 @@ class InverseTimeDecay(LRScheduler):
scheduler = paddle.optimizer.lr.InverseTimeDecay(learning_rate=0.5, gamma=0.1, verbose=True)
sgd = paddle.optimizer.SGD(learning_rate=scheduler, parameters=linear.parameters())
for epoch in range(20):
for batch_id in range(2):
for batch_id in range(5):
x = paddle.uniform([10, 10])
out = linear(x)
loss = paddle.mean(out)
loss.backward()
sgd.step()
sgd.clear_gradients()
scheduler.step()
scheduler.step() # If you update learning rate each step
# scheduler.step() # If you update learning rate each epoch
# train on static graph mode
paddle.enable_static()
......@@ -506,7 +513,7 @@ class InverseTimeDecay(LRScheduler):
exe = paddle.static.Executor()
exe.run(start_prog)
for epoch in range(20):
for batch_id in range(2):
for batch_id in range(5):
out = exe.run(
main_prog,
feed={
......@@ -514,7 +521,8 @@ class InverseTimeDecay(LRScheduler):
'y': np.random.randn(3, 4, 5).astype('float32')
},
fetch_list=loss.name)
scheduler.step()
scheduler.step() # If you update learning rate each step
# scheduler.step() # If you update learning rate each epoch
"""
......@@ -576,14 +584,15 @@ class PolynomialDecay(LRScheduler):
scheduler = paddle.optimizer.lr.PolynomialDecay(learning_rate=0.5, decay_steps=20, verbose=True)
sgd = paddle.optimizer.SGD(learning_rate=scheduler, parameters=linear.parameters())
for epoch in range(20):
for batch_id in range(2):
for batch_id in range(5):
x = paddle.uniform([10, 10])
out = linear(x)
loss = paddle.mean(out)
loss.backward()
sgd.step()
sgd.clear_gradients()
scheduler.step()
scheduler.step() # If you update learning rate each step
# scheduler.step() # If you update learning rate each epoch
# train on static graph mode
paddle.enable_static()
......@@ -601,7 +610,7 @@ class PolynomialDecay(LRScheduler):
exe = paddle.static.Executor()
exe.run(start_prog)
for epoch in range(20):
for batch_id in range(2):
for batch_id in range(5):
out = exe.run(
main_prog,
feed={
......@@ -609,7 +618,8 @@ class PolynomialDecay(LRScheduler):
'y': np.random.randn(3, 4, 5).astype('float32')
},
fetch_list=loss.name)
scheduler.step()
scheduler.step() # If you update learning rate each step
# scheduler.step() # If you update learning rate each epoch
"""
def __init__(self,
......@@ -691,14 +701,15 @@ class LinearWarmup(LRScheduler):
learning_rate=0.5, warmup_steps=20, start_lr=0, end_lr=0.5, verbose=True)
sgd = paddle.optimizer.SGD(learning_rate=scheduler, parameters=linear.parameters())
for epoch in range(20):
for batch_id in range(2):
for batch_id in range(5):
x = paddle.uniform([10, 10])
out = linear(x)
loss = paddle.mean(out)
loss.backward()
sgd.step()
sgd.clear_gradients()
scheduler.step()
scheduler.step() # If you update learning rate each step
# scheduler.step() # If you update learning rate each epoch
# train on static graph mode
paddle.enable_static()
......@@ -717,7 +728,7 @@ class LinearWarmup(LRScheduler):
exe = paddle.static.Executor()
exe.run(start_prog)
for epoch in range(20):
for batch_id in range(2):
for batch_id in range(5):
out = exe.run(
main_prog,
feed={
......@@ -725,7 +736,8 @@ class LinearWarmup(LRScheduler):
'y': np.random.randn(3, 4, 5).astype('float32')
},
fetch_list=loss.name)
scheduler.step()
scheduler.step() # If you update learning rate each step
# scheduler.step() # If you update learning rate each epoch
"""
def __init__(self,
......@@ -814,14 +826,15 @@ class ExponentialDecay(LRScheduler):
scheduler = paddle.optimizer.lr.ExponentialDecay(learning_rate=0.5, gamma=0.9, verbose=True)
sgd = paddle.optimizer.SGD(learning_rate=scheduler, parameters=linear.parameters())
for epoch in range(20):
for batch_id in range(2):
for batch_id in range(5):
x = paddle.uniform([10, 10])
out = linear(x)
loss = paddle.mean(out)
loss.backward()
sgd.step()
sgd.clear_gradients()
scheduler.step()
scheduler.step() # If you update learning rate each step
# scheduler.step() # If you update learning rate each epoch
# train on static graph mode
paddle.enable_static()
......@@ -839,7 +852,7 @@ class ExponentialDecay(LRScheduler):
exe = paddle.static.Executor()
exe.run(start_prog)
for epoch in range(20):
for batch_id in range(2):
for batch_id in range(5):
out = exe.run(
main_prog,
feed={
......@@ -847,7 +860,8 @@ class ExponentialDecay(LRScheduler):
'y': np.random.randn(3, 4, 5).astype('float32')
},
fetch_list=loss.name)
scheduler.step()
scheduler.step() # If you update learning rate each step
# scheduler.step() # If you update learning rate each epoch
"""
def __init__(self, learning_rate, gamma, last_epoch=-1, verbose=False):
......@@ -901,14 +915,15 @@ class MultiStepDecay(LRScheduler):
scheduler = paddle.optimizer.lr.MultiStepDecay(learning_rate=0.5, milestones=[2, 4, 6], gamma=0.8, verbose=True)
sgd = paddle.optimizer.SGD(learning_rate=scheduler, parameters=linear.parameters())
for epoch in range(20):
for batch_id in range(2):
for batch_id in range(5):
x = paddle.uniform([10, 10])
out = linear(x)
loss = paddle.mean(out)
loss.backward()
sgd.step()
sgd.clear_gradients()
scheduler.step()
scheduler.step() # If you update learning rate each step
# scheduler.step() # If you update learning rate each epoch
# train on static graph mode
paddle.enable_static()
......@@ -926,7 +941,7 @@ class MultiStepDecay(LRScheduler):
exe = paddle.static.Executor()
exe.run(start_prog)
for epoch in range(20):
for batch_id in range(2):
for batch_id in range(5):
out = exe.run(
main_prog,
feed={
......@@ -934,7 +949,8 @@ class MultiStepDecay(LRScheduler):
'y': np.random.randn(3, 4, 5).astype('float32')
},
fetch_list=loss.name)
scheduler.step()
scheduler.step() # If you update learning rate each step
# scheduler.step() # If you update learning rate each epoch
"""
def __init__(self,
......@@ -1008,14 +1024,15 @@ class StepDecay(LRScheduler):
scheduler = paddle.optimizer.lr.StepDecay(learning_rate=0.5, step_size=5, gamma=0.8, verbose=True)
sgd = paddle.optimizer.SGD(learning_rate=scheduler, parameters=linear.parameters())
for epoch in range(20):
for batch_id in range(2):
for batch_id in range(5):
x = paddle.uniform([10, 10])
out = linear(x)
loss = paddle.mean(out)
loss.backward()
sgd.step()
sgd.clear_gradients()
scheduler.step()
scheduler.step() # If you update learning rate each step
# scheduler.step() # If you update learning rate each epoch
# train on static graph mode
paddle.enable_static()
......@@ -1033,7 +1050,7 @@ class StepDecay(LRScheduler):
exe = paddle.static.Executor()
exe.run(start_prog)
for epoch in range(20):
for batch_id in range(2):
for batch_id in range(5):
out = exe.run(
main_prog,
feed={
......@@ -1041,7 +1058,8 @@ class StepDecay(LRScheduler):
'y': np.random.randn(3, 4, 5).astype('float32')
},
fetch_list=loss.name)
scheduler.step()
scheduler.step() # If you update learning rate each step
# scheduler.step() # If you update learning rate each epoch
"""
def __init__(self,
......@@ -1102,14 +1120,15 @@ class LambdaDecay(LRScheduler):
scheduler = paddle.optimizer.lr.LambdaDecay(learning_rate=0.5, lr_lambda=lambda x:0.95**x, verbose=True)
sgd = paddle.optimizer.SGD(learning_rate=scheduler, parameters=linear.parameters())
for epoch in range(20):
for batch_id in range(2):
for batch_id in range(5):
x = paddle.uniform([10, 10])
out = linear(x)
loss = paddle.mean(out)
loss.backward()
sgd.step()
sgd.clear_gradients()
scheduler.step()
scheduler.step() # If you update learning rate each step
# scheduler.step() # If you update learning rate each epoch
# train on static graph mode
paddle.enable_static()
......@@ -1127,7 +1146,7 @@ class LambdaDecay(LRScheduler):
exe = paddle.static.Executor()
exe.run(start_prog)
for epoch in range(20):
for batch_id in range(2):
for batch_id in range(5):
out = exe.run(
main_prog,
feed={
......@@ -1135,7 +1154,8 @@ class LambdaDecay(LRScheduler):
'y': np.random.randn(3, 4, 5).astype('float32')
},
fetch_list=loss.name)
scheduler.step()
scheduler.step() # If you update learning rate each step
# scheduler.step() # If you update learning rate each epoch
"""
......@@ -1200,14 +1220,15 @@ class ReduceOnPlateau(LRScheduler):
scheduler = paddle.optimizer.lr.ReduceOnPlateau(learning_rate=1.0, factor=0.5, patience=5, verbose=True)
sgd = paddle.optimizer.SGD(learning_rate=scheduler, parameters=linear.parameters())
for epoch in range(20):
for batch_id in range(2):
for batch_id in range(5):
x = paddle.uniform([10, 10])
out = linear(x)
loss = paddle.mean(out)
loss.backward()
sgd.step()
sgd.clear_gradients()
scheduler.step(loss)
scheduler.step(loss) # If you update learning rate each step
# scheduler.step(loss) # If you update learning rate each epoch
# train on static graph mode
paddle.enable_static()
......@@ -1225,7 +1246,7 @@ class ReduceOnPlateau(LRScheduler):
exe = paddle.static.Executor()
exe.run(start_prog)
for epoch in range(20):
for batch_id in range(2):
for batch_id in range(5):
out = exe.run(
main_prog,
feed={
......@@ -1233,7 +1254,8 @@ class ReduceOnPlateau(LRScheduler):
'y': np.random.randn(3, 4, 5).astype('float32')
},
fetch_list=loss.name)
scheduler.step(out[0])
scheduler.step(out[0]) # If you update learning rate each step
# scheduler.step(out[0]) # If you update learning rate each epoch
"""
......@@ -1268,7 +1290,6 @@ class ReduceOnPlateau(LRScheduler):
"The type of 'learning_rate' in 'ReduceOnPlateau' must be 'float', but received %s."
% type(learning_rate))
self.verbose = verbose
self.patience = patience
self.threshold = threshold
self.threshold_mode = threshold_mode
......@@ -1406,7 +1427,7 @@ class CosineAnnealingDecay(LRScheduler):
scheduler = paddle.optimizer.lr.CosineAnnealingDecay(learning_rate=0.5, T_max=10, verbose=True)
sgd = paddle.optimizer.SGD(learning_rate=scheduler, parameters=linear.parameters())
for epoch in range(20):
for batch_id in range(2):
for batch_id in range(5):
x = paddle.uniform([10, 10])
out = linear(x)
loss = paddle.mean(out)
......@@ -1431,7 +1452,7 @@ class CosineAnnealingDecay(LRScheduler):
exe = paddle.static.Executor()
exe.run(start_prog)
for epoch in range(20):
for batch_id in range(2):
for batch_id in range(5):
out = exe.run(
main_prog,
feed={
......
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