Skip to content

P
Paddle

Crayon鑫 / Paddle
与 Fork 源项目一致

Fork自 PaddlePaddle / Paddle

通知 1
Star 1
Fork 0
P
Paddle
提交 f063f2b7 编写于 作者: C chengduozh
浏览文件
操作

Fix clip.py 

test=release/1.2
上级 cc9028b9
隐藏空白更改
内联 并排
Showing with 772 addition and 122 deletion
paddle/fluid/API.spec
浏览文件 @ f063f2b7
......@@ -194,6 +194,8 @@ paddle.fluid.layers.grid_sampler ArgSpec(args=['x', 'grid', 'name'], varargs=Non
paddle.fluid.layers.log_loss ArgSpec(args=['input', 'label', 'epsilon', 'name'], varargs=None, keywords=None, defaults=(0.0001, None))
paddle.fluid.layers.add_position_encoding ArgSpec(args=['input', 'alpha', 'beta', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.bilinear_tensor_product ArgSpec(args=['x', 'y', 'size', 'act', 'name', 'param_attr', 'bias_attr'], varargs=None, keywords=None, defaults=(None, None, None, None))
paddle.fluid.layers.merge_selected_rows ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.get_tensor_from_selected_rows ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.data ArgSpec(args=['name', 'shape', 'append_batch_size', 'dtype', 'lod_level', 'type', 'stop_gradient'], varargs=None, keywords=None, defaults=(True, 'float32', 0, VarType.LOD_TENSOR, True))
paddle.fluid.layers.open_files ArgSpec(args=['filenames', 'shapes', 'lod_levels', 'dtypes', 'thread_num', 'buffer_size', 'pass_num', 'is_test'], varargs=None, keywords=None, defaults=(None, None, 1, None))
paddle.fluid.layers.read_file ArgSpec(args=['reader'], varargs=None, keywords=None, defaults=None)
......
paddle/fluid/operators/activation_op.cc
浏览文件 @ f063f2b7
......@@ -76,8 +76,8 @@ framework::OpKernelType GetKernelType(const framework::ExecutionContext& ctx,
}
#endif
return framework::OpKernelType(
framework::ToDataType(ctx.Input<framework::Tensor>(name)->type()),
ctx.GetPlace(), layout, library);
framework::GetDataTypeOfVar(ctx.InputVar(name)), ctx.GetPlace(), layout,
library);
}
class ActivationOp : public framework::OperatorWithKernel {
......
paddle/fluid/operators/activation_op.h
浏览文件 @ f063f2b7
......@@ -41,6 +41,12 @@ static std::unordered_set<std::string> InplaceOpSet = {
"floor", "reciprocal", "relu6", "soft_relu", "hard_sigmoid",
};
/* The following operator can be used to process SelectedRows, because the
* output of those operator for zero is zero too.
*/
static std::unordered_set<std::string> CanBeUsedBySelectedRows = {
"abs", "abs_grad", "square", "square_grad", "sqrt", "sqrt_grad"};
static bool IsInplace(std::string op) { return InplaceOpSet.count(op); }
template <typename DeviceContext, typename Functor>
......@@ -50,16 +56,38 @@ class ActivationKernel
using T = typename Functor::ELEMENT_TYPE;
void Compute(const framework::ExecutionContext& context) const override {
auto& X = detail::Ref(context.Input<framework::Tensor>("X"),
"Cannot get input tensor X, variable name = %s",
context.op().Input("X"));
auto& Out = detail::Ref(context.Output<framework::Tensor>("Out"),
"Cannot get output tensor Out, variable name = %s",
context.op().Output("Out"));
Out.mutable_data<T>(context.GetPlace());
auto x_var = context.InputVar("X");
auto out_var = context.OutputVar("Out");
PADDLE_ENFORCE(x_var != nullptr,
"Cannot get input Variable X, variable name = %s",
context.op().Input("X"));
PADDLE_ENFORCE(out_var != nullptr,
"Cannot get output Variable Out, variable name = %s",
context.op().Output("Out"));
framework::Tensor X, *Out;
if (CanBeUsedBySelectedRows.count(context.op().Type())) {
X = detail::Ref(
paddle::framework::GetLoDTensorOrSelectedRowsValueFromVar(*x_var),
"Cannot get input Tensor X, variable name = %s",
context.op().Input("X"));
Out = paddle::framework::GetMutableLoDTensorOrSelectedRowsValueFromVar(
out_var);
} else {
X = detail::Ref(context.Input<framework::Tensor>("X"),
"Cannot get input Tensor X, variable name = %s",
context.op().Input("X"));
Out = context.Output<framework::Tensor>("Out");
}
PADDLE_ENFORCE(Out != nullptr,
"Cannot get output tensor Out, variable name = %s",
context.op().Output("Out"));
Out->mutable_data<T>(context.GetPlace());
auto x = framework::EigenVector<T>::Flatten(X);
auto out = framework::EigenVector<T>::Flatten(Out);
auto out = framework::EigenVector<T>::Flatten(*Out);
auto* place =
context.template device_context<DeviceContext>().eigen_device();
Functor functor;
......@@ -78,14 +106,54 @@ class ActivationGradKernel
public:
using T = typename Functor::ELEMENT_TYPE;
void Compute(const framework::ExecutionContext& context) const override {
auto* Out = context.Input<framework::Tensor>("Out");
auto* dOut =
context.Input<framework::Tensor>(framework::GradVarName("Out"));
auto* dX = context.Output<framework::Tensor>(framework::GradVarName("X"));
auto out_var = context.InputVar("Out");
auto out_grad_var = context.InputVar(framework::GradVarName("Out"));
auto x_grad_var = context.OutputVar(framework::GradVarName("X"));
PADDLE_ENFORCE(out_var != nullptr,
"Cannot get input Variable Out, variable name = %s",
context.op().Input("Out"));
PADDLE_ENFORCE(out_grad_var != nullptr,
"Cannot get input Variable %s, variable name = %s",
framework::GradVarName("Out"),
context.op().Input(framework::GradVarName("Out")));
PADDLE_ENFORCE(x_grad_var != nullptr,
"Cannot get output Variable %s, variable name = %s",
framework::GradVarName("X"),
context.op().Output(framework::GradVarName("X")));
framework::Tensor Out, dOut, *dX;
if (CanBeUsedBySelectedRows.count(context.op().Type())) {
Out = detail::Ref(
paddle::framework::GetLoDTensorOrSelectedRowsValueFromVar(*out_var),
"Cannot get input Tensor Out, variable name = %s",
context.op().Input("Out"));
dOut =
detail::Ref(paddle::framework::GetLoDTensorOrSelectedRowsValueFromVar(
*out_grad_var),
"Cannot get input Tensor %s, variable name = %s",
framework::GradVarName("Out"),
context.op().Input(framework::GradVarName("Out")));
dX = paddle::framework::GetMutableLoDTensorOrSelectedRowsValueFromVar(
x_grad_var);
} else {
Out = detail::Ref(context.Input<framework::Tensor>("Out"),
"Cannot get input Tensor Out, variable name = %s",
context.op().Input("Out"));
dOut = detail::Ref(
context.Input<framework::Tensor>(framework::GradVarName("Out")),
"Cannot get input Tensor %s, variable name = %s",
framework::GradVarName("Out"),
context.op().Input(framework::GradVarName("Out")));
dX = context.Output<framework::Tensor>(framework::GradVarName("X"));
}
PADDLE_ENFORCE(dX != nullptr,
"Cannot get output tensor %s, variable name = %s",
framework::GradVarName("X"),
context.op().Output(framework::GradVarName("X")));
dX->mutable_data<T>(context.GetPlace());
auto dout = framework::EigenVector<T>::Flatten(*dOut);
auto out = framework::EigenVector<T>::Flatten(*Out);
auto dout = framework::EigenVector<T>::Flatten(dOut);
auto out = framework::EigenVector<T>::Flatten(Out);
auto dx = framework::EigenVector<T>::Flatten(*dX);
auto* place =
context.template device_context<DeviceContext>().eigen_device();
......@@ -96,8 +164,19 @@ class ActivationGradKernel
}
bool inplace = functor.Inplace();
if (!inplace) {
auto* X = context.Input<framework::Tensor>("X");
auto x = framework::EigenVector<T>::Flatten(*X);
auto x_var = context.InputVar("X");
PADDLE_ENFORCE(x_var != nullptr,
"Cannot get input tensor X, variable name = %s",
context.op().Input("X"));
framework::Tensor X;
if (CanBeUsedBySelectedRows.count(context.op().Type())) {
X = detail::Ref(
paddle::framework::GetLoDTensorOrSelectedRowsValueFromVar(*x_var));
} else {
X = detail::Ref(context.Input<framework::Tensor>("X"));
}
auto x = framework::EigenVector<T>::Flatten(X);
functor(*place, x, out, dout, dx);
} else {
VLOG(10) << " Inplace activation ";
......
paddle/fluid/operators/elementwise/elementwise_mul_op.h
浏览文件 @ f063f2b7
......@@ -60,15 +60,37 @@ template <typename DeviceContext, typename T>
class ElementwiseMulKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto* x = ctx.Input<framework::LoDTensor>("X");
auto x_var = ctx.InputVar("X");
PADDLE_ENFORCE(x_var != nullptr,
"Cannot get input Variable X, variable name = %s",
ctx.op().Input("X"));
auto* y = ctx.Input<framework::LoDTensor>("Y");
auto* z = ctx.Output<framework::LoDTensor>("Out");
framework::Tensor x, *z;
if (x_var->IsType<framework::SelectedRows>()) {
PADDLE_ENFORCE(y->dims().size() == 1 && y->dims()[0] == 1,
"For elementwise_op, if X is Sparse, Y must be scalar.");
auto& x_sele = x_var->Get<framework::SelectedRows>();
auto out_sele = ctx.Output<framework::SelectedRows>("Out");
x = x_sele.value();
out_sele->set_rows(x_sele.rows());
out_sele->set_height(x_sele.height());
out_sele->mutable_value()->Resize(x_sele.value().dims());
out_sele->mutable_value()->mutable_data(ctx.GetPlace(), x.type());
z = ctx.Output<framework::SelectedRows>("Out")->mutable_value();
} else if (x_var->IsType<framework::LoDTensor>()) {
x = x_var->Get<framework::LoDTensor>();
z = ctx.Output<framework::LoDTensor>("Out");
} else {
PADDLE_THROW("X's type[%s] is not supported by elementwise_op.",
x_var->Type().name());
}
z->mutable_data<T>(ctx.GetPlace());
if (x->numel() == y->numel()) {
elementwise_mul<DeviceContext, T>(ctx, x, y, z);
if (x.numel() == y->numel()) {
elementwise_mul<DeviceContext, T>(ctx, &x, y, z);
} else {
default_elementwise_mul<DeviceContext, T>(ctx, x, y, z);
default_elementwise_mul<DeviceContext, T>(ctx, &x, y, z);
}
}
};
......
paddle/fluid/operators/elementwise/elementwise_op.h
浏览文件 @ f063f2b7
......@@ -40,21 +40,28 @@ class ElementwiseOp : public framework::OperatorWithKernel {
PADDLE_ENFORCE(ctx->HasOutput("Out"),
"Output(Out) of elementwise op should not be null.");
PADDLE_ENFORCE(
ctx->GetInputsVarType("X").front() ==
framework::proto::VarType::LOD_TENSOR,
"The input var's type should be LoDTensor, but the received is %s",
ctx->Inputs("X").front(), ctx->GetInputsVarType("X").front());
PADDLE_ENFORCE(
ctx->GetInputsVarType("Y").front() ==
framework::proto::VarType::LOD_TENSOR,
"The input var's type should be LoDTensor, but the received is %s",
ctx->Inputs("Y").front(), ctx->GetInputsVarType("Y").front());
auto x_dim = ctx->GetInputDim("X");
auto y_dim = ctx->GetInputDim("Y");
PADDLE_ENFORCE_GE(x_dim.size(), y_dim.size(),
"Rank of first input must >= rank of second input.");
"The input var's type should be LoDTensor, but the received is %s [%s]",
ctx->GetInputsVarType("Y").front(), ctx->Inputs("Y").front());
if (ctx->GetInputsVarType("X").front() ==
framework::proto::VarType::LOD_TENSOR) {
auto x_dim = ctx->GetInputDim("X");
auto y_dim = ctx->GetInputDim("Y");
PADDLE_ENFORCE_GE(x_dim.size(), y_dim.size(),
"Rank of first input must >= rank of second input.");
} else if (ctx->GetInputsVarType("X").front() ==
framework::proto::VarType::SELECTED_ROWS) {
PADDLE_ENFORCE((ctx->GetInputDim("Y").size() == 1u) &&
(ctx->GetInputDim("Y")[0] == 1),
"For elementwise_op, if X is Sparse, "
"Y must be scalar.");
} else {
PADDLE_THROW("X's type[%s] is not supported by elementwise_op.",
ctx->GetInputsVarType("X").front());
}
ctx->ShareDim("X", /*->*/ "Out");
ctx->ShareLoD("X", /*->*/ "Out");
......
paddle/fluid/operators/get_tensor_from_selected_rows_op.cc 0 → 100644
浏览文件 @ f063f2b7
/* Copyright (c) 2018 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. */
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/tensor_util.h"
namespace paddle {
namespace operators {
class GetTensorFromSelectedRowsOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext *ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"),
"GetTensorFromSelectedRowsOp must has input X.");
PADDLE_ENFORCE(ctx->HasOutput("Out"),
"GetTensorFromSelectedRowsOp must has output Out.");
PADDLE_ENFORCE(
ctx->GetInputsVarType("X").front() ==
framework::proto::VarType::SELECTED_ROWS,
"The input X's type should be SelectedRows, but the received is %s",
ctx->Inputs("X").front(), ctx->GetInputsVarType("X").front());
PADDLE_ENFORCE(
ctx->GetOutputsVarType("Out").front() ==
framework::proto::VarType::LOD_TENSOR,
"The output Out's type should be LoDTensor, but the received is %s",
ctx->Outputs("Out").front(), ctx->GetOutputsVarType("Out").front());
ctx->SetOutputDim("Out", ctx->GetInputDim("X"));
}
protected:
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext &ctx) const override {
return framework::OpKernelType(
framework::GetDataTypeOfVar(ctx.InputVar("X")), ctx.device_context());
}
};
class GetTensorFromSelectedRowsKernel {
public:
void operator()(const framework::ExecutionContext &ctx) const {
auto *x = ctx.Input<framework::SelectedRows>("X");
auto *out = ctx.Output<framework::LoDTensor>("Out");
out->Resize(x->value().dims());
out->mutable_data(ctx.GetPlace(), x->value().type());
framework::TensorCopy(x->value(), ctx.GetPlace(), ctx.device_context(),
out);
}
};
class GetTensorFromSelectedRowsOpProtoMaker
: public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("X", "The input type is SelectedRows.");
AddOutput("Out", "The output type is LoDTensor.");
AddComment(
R"DOC(
GetTensorFromSelectedRows Operator
GetTensorFromSelectedRows is used to get the tensor from SelectedRows.
)DOC");
}
};
class GetTensorFromSelectedRowsOpVarTypeInference
: public framework::VarTypeInference {
public:
void operator()(const framework::OpDesc &op_desc,
framework::BlockDesc *block) const final {
auto out_var_name = op_desc.Output("Out").front();
auto in_var_name = op_desc.Input("X").front();
auto out_var = block->FindRecursiveOrCreateVar(out_var_name);
auto in_var = block->FindRecursiveOrCreateVar(in_var_name);
out_var.SetType(framework::proto::VarType::LOD_TENSOR);
out_var.SetDataType(in_var.GetDataType());
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OPERATOR(get_tensor_from_selected_rows,
ops::GetTensorFromSelectedRowsOp,
ops::GetTensorFromSelectedRowsOpProtoMaker,
ops::GetTensorFromSelectedRowsOpVarTypeInference);
REGISTER_OP_CPU_KERNEL_FUNCTOR(get_tensor_from_selected_rows, float,
ops::GetTensorFromSelectedRowsKernel, double,
ops::GetTensorFromSelectedRowsKernel, int,
ops::GetTensorFromSelectedRowsKernel, int64_t,
ops::GetTensorFromSelectedRowsKernel);
#ifdef PADDLE_WITH_CUDA
REGISTER_OP_CUDA_KERNEL_FUNCTOR(get_tensor_from_selected_rows, float,
ops::GetTensorFromSelectedRowsKernel, double,
ops::GetTensorFromSelectedRowsKernel, int,
ops::GetTensorFromSelectedRowsKernel, int64_t,
ops::GetTensorFromSelectedRowsKernel);
#endif
paddle/fluid/operators/merge_selected_rows_op.cc 0 → 100644
浏览文件 @ f063f2b7
/* Copyright (c) 2018 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. */
#include "paddle/fluid/operators/merge_selected_rows_op.h"
namespace paddle {
namespace operators {
class MergeSelectedRowsOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"),
"Input(X) of MergeSelectedRowsOp should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Out"),
"Output(Out) of MergeSelectedRowsOp should not be null.");
ctx->ShareDim("X", /*->*/ "Out");
}
};
class MergeSelectedRowsOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("X",
"The input type is SelectedRows, and the selected rows may be "
"duplicated.");
AddOutput("Out",
"The output type is SelectedRows, and the selected rows are not "
"duplicated.");
AddComment(
R"DOC(
MergeSelectedRows Operator.
MergeSelectedRows is used to merge the duplicated rows of the input. The
output's row has no duplicated, and it is incremental in order.
Example:
Input:
X.rows is [0, 5, 5, 4, 19]
X.height is 20
X.value is:
[[1, 1]
[2, 2]
[3, 3]
[4, 4]
[6, 6]]
Output:
Out.row is [0, 4, 5, 19]
Out.height is 20
Out.value is:
[[1, 1]
[4, 4]
[5, 5]
[6, 6]]
)DOC");
}
};
class MergeSelectedRowsOpInferVarType
: public framework::PassInDtypeAndVarTypeToOutput {
protected:
std::unordered_map<std::string, std::string> GetInputOutputWithSameType()
const override {
return std::unordered_map<std::string, std::string>{{"X", /*->*/ "Out"}};
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
namespace plat = paddle::platform;
REGISTER_OPERATOR(merge_selected_rows, ops::MergeSelectedRowsOp,
ops::MergeSelectedRowsOpMaker,
ops::MergeSelectedRowsOpInferVarType);
REGISTER_OP_CPU_KERNEL(
merge_selected_rows,
ops::MergeSelectedRowsKernel<plat::CPUDeviceContext, float>,
ops::MergeSelectedRowsKernel<plat::CPUDeviceContext, double>);
paddle/fluid/operators/merge_selected_rows_op.cu.cc 0 → 100644
浏览文件 @ f063f2b7
/* Copyright (c) 2018 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. */
#include "paddle/fluid/operators/merge_selected_rows_op.h"
namespace ops = paddle::operators;
namespace plat = paddle::platform;
REGISTER_OP_CUDA_KERNEL(
merge_selected_rows,
ops::MergeSelectedRowsKernel<plat::CUDADeviceContext, float>,
ops::MergeSelectedRowsKernel<plat::CUDADeviceContext, double>);
paddle/fluid/operators/merge_selected_rows_op.h 0 → 100644
浏览文件 @ f063f2b7
/* Copyright (c) 2018 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. */
#pragma once
#include <string>
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/math/selected_rows_functor.h"
namespace paddle {
namespace operators {
template <typename DeviceContext, typename T>
class MergeSelectedRowsKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
auto* x = context.Input<framework::SelectedRows>("X");
auto* out = context.Output<framework::SelectedRows>("Out");
math::scatter::MergeAdd<DeviceContext, T> merge_func;
merge_func(context.template device_context<DeviceContext>(), *x, out);
}
};
} // namespace operators
} // namespace paddle
python/paddle/fluid/clip.py
浏览文件 @ f063f2b7
......@@ -271,7 +271,12 @@ class GradientClipByGlobalNorm(BaseGradientClipAttr):
"All parameters' 'clip_norm' of a same group should be the same"
)
square = grad * grad
merge_grad = grad
if grad.type == core.VarDesc.VarType.SELECTED_ROWS:
merge_grad = layers.merge_selected_rows(grad)
merge_grad = layers.get_tensor_from_selected_rows(merge_grad)
square = layers.square(merge_grad)
local_norm_var = layers.reduce_sum(input=square)
context[self.group_name].append(local_norm_var)
......@@ -292,6 +297,7 @@ class GradientClipByGlobalNorm(BaseGradientClipAttr):
new_grad = layers.elementwise_mul(
x=grad, y=self.context[group_scale_name])
return param, new_grad
......
python/paddle/fluid/layers/nn.py
浏览文件 @ f063f2b7
......@@ -169,6 +169,8 @@ __all__ = [
'log_loss',
'add_position_encoding',
'bilinear_tensor_product',
'merge_selected_rows',
'get_tensor_from_selected_rows',
]
......@@ -8128,6 +8130,29 @@ def mean(x, name=None):
return out
@templatedoc()
def merge_selected_rows(x, name=None):
"""
${comment}
Args:
x(${x_type}): ${x_comment}
name(basestring|None): Name of the output.
Returns:
out(${out_type}): ${out_comment}
"""
helper = LayerHelper("merge_selected_rows", **locals())
out = helper.create_variable_for_type_inference(dtype=x.dtype)
helper.append_op(
type="merge_selected_rows",
inputs={"X": x},
attrs={},
outputs={"Out": out})
return out
@templatedoc()
def mul(x, y, x_num_col_dims=1, y_num_col_dims=1, name=None):
"""
......@@ -8776,3 +8801,26 @@ def bilinear_tensor_product(x,
# add activation
return helper.append_activation(out)
@templatedoc()
def get_tensor_from_selected_rows(x, name=None):
"""
${comment}
Args:
x(${x_type}): ${x_comment}
name(basestring|None): Name of the output.
Returns:
out(${out_type}): ${out_comment}
"""
helper = LayerHelper('get_tensor_from_selected_rows', **locals())
out = helper.create_variable_for_type_inference(dtype=x.dtype)
helper.append_op(
type='get_tensor_from_selected_rows',
inputs={'X': x},
outputs={'Out': out},
attrs={})
return out
python/paddle/fluid/tests/test_gradient_clip.py 已删除 100644 → 0
浏览文件 @ cc9028b9
# Copyright (c) 2018 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 print_function
import numpy as np
import paddle
import paddle.fluid as fluid
BATCH_SIZE = 128
CLIP = 1
prog = fluid.framework.Program()
with fluid.program_guard(main_program=prog):
image = fluid.layers.data(name='x', shape=[784], dtype='float32')
hidden1 = fluid.layers.fc(input=image, size=128, act='relu')
hidden2 = fluid.layers.fc(input=hidden1, size=64, act='relu')
predict = fluid.layers.fc(input=hidden2, size=10, act='softmax')
label = fluid.layers.data(name='y', shape=[1], dtype='int64')
cost = fluid.layers.cross_entropy(input=predict, label=label)
avg_cost = fluid.layers.mean(cost)
prog_clip = prog.clone()
avg_cost_clip = prog_clip.block(0).var(avg_cost.name)
p_g = fluid.backward.append_backward(loss=avg_cost)
p_g_clip = fluid.backward.append_backward(loss=avg_cost_clip)
with fluid.program_guard(main_program=prog_clip):
fluid.clip.set_gradient_clip(
fluid.clip.GradientClipByGlobalNorm(clip_norm=CLIP))
p_g_clip = fluid.clip.append_gradient_clip_ops(p_g_clip)
grad_list = [elem[1] for elem in p_g]
grad_clip_list = [elem[1] for elem in p_g_clip]
train_reader = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.mnist.train(), buf_size=8192),
batch_size=BATCH_SIZE)
place = fluid.CPUPlace()
exe = fluid.Executor(place)
feeder = fluid.DataFeeder(feed_list=[image, label], place=place)
exe.run(fluid.default_startup_program())
count = 0
for data in train_reader():
count += 1
if count > 5:
break
out = exe.run(prog, feed=feeder.feed(data), fetch_list=grad_list)
out_clip = exe.run(prog_clip,
feed=feeder.feed(data),
fetch_list=grad_clip_list)
global_norm = 0
for v in out[1:]:
global_norm += np.sum(np.power(v, 2))
global_norm = np.sqrt(global_norm)
global_norm_clip = 0
for v in out_clip[1:]:
global_norm_clip += np.sum(np.power(v, 2))
global_norm_clip = np.sqrt(global_norm_clip)
if not np.isclose(
a=global_norm_clip, b=np.minimum(global_norm, CLIP), rtol=5e-3):
exit(1)
exit(0)
python/paddle/fluid/tests/unittests/CMakeLists.txt
浏览文件 @ f063f2b7
......@@ -43,13 +43,14 @@ if(APPLE)
list(REMOVE_ITEM TEST_OPS test_desc_clone)
list(REMOVE_ITEM TEST_OPS test_program_code)
endif(NOT WITH_DISTRIBUTE)
message(WARNING "These tests has been disabled in OSX before being fixed: \n test_fuse_elewise_add_act_pass \n test_detection_map_op \n test_dist_se_resnext")
message(WARNING "These tests has been disabled in OSX before being fixed: \n test_gradient_clip \n test_fuse_elewise_add_act_pass \n test_detection_map_op \n test_dist_se_resnext")
# this op is not support on mac
list(REMOVE_ITEM TEST_OPS test_fusion_seqexpand_concat_fc_op)
# TODO: add the unitest back when it fixed
list(REMOVE_ITEM TEST_OPS test_detection_map_op)
list(REMOVE_ITEM TEST_OPS test_dist_se_resnext)
list(REMOVE_ITEM TEST_OPS test_fuse_elewise_add_act_pass)
list(REMOVE_ITEM TEST_OPS test_gradient_clip)
endif()
if(NOT WITH_MKLML)
# this op is not support on openblas
......
python/paddle/fluid/tests/unittests/test_get_tensor_from_selected_rows_op.py 0 → 100644
浏览文件 @ f063f2b7
# Copyright (c) 2018 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 print_function
import unittest
import paddle.fluid.core as core
import numpy as np
from paddle.fluid.op import Operator
class TestGetTensorFromSelectedRows(unittest.TestCase):
def get_places(self):
places = [core.CPUPlace()]
if core.is_compiled_with_cuda():
places.append(core.CUDAPlace(0))
return places
def check_with_place(self, place):
scope = core.Scope()
x_rows = [0, 5, 5, 4, 20]
height = 20
row_numel = 2
np_array = np.ones((len(x_rows), row_numel)).astype("float32")
np_array[1, :] = 2.0
np_array[2, :] = 3.0
np_array[3, :] = 4.0
# initialize input variable X
x = scope.var('X').get_selected_rows()
x.set_rows(x_rows)
x.set_height(height)
x_tensor = x.get_tensor()
x_tensor.set(np_array, place)
# initialize input variable Out
out = scope.var("Out").get_tensor()
op = Operator("get_tensor_from_selected_rows", X="X", Out="Out")
op.run(scope, place)
out_array = np.array(out)
self.assertEqual((5, 2), out_array.shape)
assert (out_array == np_array).all()
def test_check_output(self):
for place in self.get_places():
self.check_with_place(place)
if __name__ == "__main__":
unittest.main()
python/paddle/fluid/tests/unittests/test_gradient_clip.py 0 → 100644
浏览文件 @ f063f2b7
# Copyright (c) 2018 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 print_function
import unittest
import numpy as np
import paddle
import paddle.fluid.core as core
import paddle.fluid as fluid
BATCH_SIZE = 128
CLIP = 1
def bow_net(data,
label,
dict_dim,
emb_dim=128,
hid_dim=128,
hid_dim2=96,
class_dim=2):
"""
BOW net
This model is from https://github.com/PaddlePaddle/models:
fluid/PaddleNLP/text_classification/nets.py
"""
emb = fluid.layers.embedding(
input=data, is_sparse=True, size=[dict_dim, emb_dim])
bow = fluid.layers.sequence_pool(input=emb, pool_type='sum')
bow_tanh = fluid.layers.tanh(bow)
fc_1 = fluid.layers.fc(input=bow_tanh, size=hid_dim, act="tanh")
fc_2 = fluid.layers.fc(input=fc_1, size=hid_dim2, act="tanh")
prediction = fluid.layers.fc(input=[fc_2], size=class_dim, act="softmax")
cost = fluid.layers.cross_entropy(input=prediction, label=label)
avg_cost = fluid.layers.mean(x=cost)
return avg_cost
class TestGradientClip(unittest.TestCase):
def setUp(self):
self.word_dict = paddle.dataset.imdb.word_dict()
self.BATCH_SIZE = 2
self.train_data = paddle.batch(
paddle.dataset.imdb.train(self.word_dict),
batch_size=self.BATCH_SIZE)
def get_places(self):
places = [core.CPUPlace()]
if core.is_compiled_with_cuda():
places.append(core.CUDAPlace(0))
return places
def check_operators(self, place):
prog = fluid.framework.Program()
startup_program = fluid.framework.Program()
with fluid.program_guard(
main_program=prog, startup_program=startup_program):
image = fluid.layers.data(name='x', shape=[784], dtype='float32')
label = fluid.layers.data(name='y', shape=[1], dtype='int64')
hidden1 = fluid.layers.fc(input=image, size=128, act='relu')
hidden2 = fluid.layers.fc(input=hidden1, size=64, act='relu')
predict = fluid.layers.fc(input=hidden2, size=10, act='softmax')
cost = fluid.layers.cross_entropy(input=predict, label=label)
avg_cost = fluid.layers.mean(cost)
prog_clip = prog.clone()
avg_cost_clip = prog_clip.block(0).var(avg_cost.name)
p_g = fluid.backward.append_backward(loss=avg_cost)
p_g_clip = fluid.backward.append_backward(loss=avg_cost_clip)
with fluid.program_guard(main_program=prog_clip):
fluid.clip.set_gradient_clip(
fluid.clip.GradientClipByGlobalNorm(clip_norm=CLIP))
p_g_clip = fluid.clip.append_gradient_clip_ops(p_g_clip)
grad_list = [elem[1] for elem in p_g]
grad_clip_list = [elem[1] for elem in p_g_clip]
train_reader = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.mnist.train(), buf_size=8192),
batch_size=BATCH_SIZE)
exe = fluid.Executor(place)
feeder = fluid.DataFeeder(feed_list=[image, label], place=place)
exe.run(startup_program)
count = 0
for data in train_reader():
count += 1
if count > 5:
break
out = exe.run(prog, feed=feeder.feed(data), fetch_list=grad_list)
out_clip = exe.run(prog_clip,
feed=feeder.feed(data),
fetch_list=grad_clip_list)
global_norm = 0
for v in out[1:]:
global_norm += np.sum(np.power(v, 2))
global_norm = np.sqrt(global_norm)
global_norm_clip = 0
for v in out_clip[1:]:
global_norm_clip += np.sum(np.power(v, 2))
global_norm_clip = np.sqrt(global_norm_clip)
assert np.isclose(
a=global_norm_clip, b=np.minimum(global_norm, CLIP), rtol=5e-3)
def check_sparse_gradient_clip(self, place):
prog = fluid.framework.Program()
startup_program = fluid.framework.Program()
with fluid.program_guard(
main_program=prog, startup_program=startup_program):
data = fluid.layers.data(
name="words", shape=[1], dtype="int64", lod_level=1)
label = fluid.layers.data(name="label", shape=[1], dtype="int64")
cost = bow_net(data, label, len(self.word_dict))
fluid.clip.set_gradient_clip(
clip=fluid.clip.GradientClipByGlobalNorm(clip_norm=5.0))
sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.01)
sgd_optimizer.minimize(cost)
exe = fluid.Executor(place)
feeder = fluid.DataFeeder(feed_list=[data, label], place=place)
exe.run(startup_program)
data = next(self.train_data())
val = exe.run(prog, feed=feeder.feed(data), fetch_list=[cost])[0]
self.assertEqual((1, ), val.shape)
print(val)
self.assertFalse(np.isnan(val))
def test_operators(self):
self.check_operators(core.CPUPlace())
def test_sparse_gradient_clip(self):
for place in self.get_places():
self.check_sparse_gradient_clip(place)
if __name__ == '__main__':
unittest.main()
python/paddle/fluid/tests/unittests/test_merge_selectedrows_op.py 0 → 100644
浏览文件 @ f063f2b7
# Copyright (c) 2018 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 print_function
import unittest
import paddle.fluid.core as core
import numpy as np
from paddle.fluid.op import Operator
class TestMergeSelectedRows(unittest.TestCase):
def get_places(self):
places = [core.CPUPlace()]
if core.is_compiled_with_cuda():
places.append(core.CUDAPlace(0))
return places
def check_with_place(self, place):
scope = core.Scope()
x_rows = [0, 5, 5, 4, 19]
out_rows = [0, 4, 5, 19]
height = 20
row_numel = 2
np_array = np.ones((len(x_rows), row_numel)).astype("float32")
np_array[1, :] = 2.0
np_array[2, :] = 3.0
np_array[3, :] = 4.0
# initialize input variable X
x = scope.var('X').get_selected_rows()
x.set_rows(x_rows)
x.set_height(height)
x_tensor = x.get_tensor()
x_tensor.set(np_array, place)
# initialize input variable Out
out = scope.var("Out").get_selected_rows()
op = Operator("merge_selected_rows", X="X", Out="Out")
op.run(scope, place)
self.assertEqual(out.rows(), out_rows)
self.assertEqual(out.height(), height)
out_array = np.array(out.get_tensor())
self.assertEqual((4, 2), out_array.shape)
assert (out_array[0, :] == 1.0).all()
assert (out_array[1, :] == 4.0).all()
assert (out_array[2, :] == 5.0).all()
assert (out_array[3, :] == 1.0).all()
def test_check_output(self):
for place in self.get_places():
self.check_with_place(place)
if __name__ == "__main__":
unittest.main()
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