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提交 794117bb 编写于 作者: W wanghaox
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fix some comments

上级 36035467 c6ec26df
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Showing with 683 addition and 44 deletion
CMakeLists.txt
浏览文件 @ 794117bb
......@@ -109,7 +109,7 @@ else()
endif()
set(WITH_MKLML ${WITH_MKL})
if (WITH_MKL AND ${AVX2_FOUND})
if (WITH_MKL AND AVX2_FOUND)
set(WITH_MKLDNN ON)
else()
message(STATUS "Do not have AVX2 intrinsics and disabled MKL-DNN")
......
doc/api/v2/config/layer.rst
浏览文件 @ 794117bb
......@@ -382,6 +382,11 @@ cos_sim
.. autoclass:: paddle.v2.layer.cos_sim
:noindex:
l2_distance
-----------
.. autoclass:: paddle.v2.layer.l2_distance
:noindex:
trans
-----
.. autoclass:: paddle.v2.layer.trans
......
paddle/framework/backward.cc
浏览文件 @ 794117bb
......@@ -513,19 +513,14 @@ ParamGradInfoMap AppendBackward(
const int root_block_idx = 0;
auto root_block = program_desc.MutableBlock(root_block_idx);
// insert fill one op for target
// TODO(qiao) add some check to the target.
std::string fill_one_op_out = GradVarName(target.Name());
std::vector<int64_t> target_shape_desc = target.Shape();
std::vector<int> target_shape;
std::transform(target_shape_desc.begin(), target_shape_desc.end(),
std::back_inserter(target_shape),
[](int64_t dim) { return static_cast<int>(dim); });
bool is_scalar = target.Shape() == std::vector<int64_t>{1};
PADDLE_ENFORCE(is_scalar, "target should be scalar");
VLOG(3) << "backward from loss=" << target.Name()
<< " data_type=" << target.GetDataType();
std::unique_ptr<OpDescBind> fill_one_op(
new OpDescBind("fill_constant", {}, {{"Out", {fill_one_op_out}}},
{{"shape", target_shape},
{{"shape", std::vector<int>{1}},
{"value", static_cast<float>(1.0)},
{"data_type", target.GetDataType()}}));
// infer var type of fill_one_op
......
paddle/framework/backward_test.cc
浏览文件 @ 794117bb
......@@ -508,6 +508,7 @@ TEST(Backward, simple_single_op) {
op->SetOutput("Out", {"out"});
auto target = f::VarDescBind("out");
target.SetShape({1});
auto var_to_grad = AppendBackward(program, target, {});
ASSERT_EQ(block->AllOps().size(), 3UL);
......@@ -544,6 +545,7 @@ TEST(Backward, default_attribute) {
op->CheckAttrs();
auto target = f::VarDescBind("out");
target.SetShape({1});
AppendBackward(program, target, {});
ASSERT_EQ(block->AllOps().size(), 3UL);
......@@ -581,6 +583,7 @@ TEST(Backward, simple_mult_op) {
op3->SetOutput("Out", {"out3"});
auto target = f::VarDescBind("out3");
target.SetShape({1});
size_t forward_len = block->AllOps().size();
auto var_to_grad = AppendBackward(program, target, {});
......@@ -670,6 +673,7 @@ TEST(Backward, intermedia_var_no_grad) {
op4->SetOutput("Out", {"out4"});
auto target = f::VarDescBind("out4");
target.SetShape({1});
size_t forward_len = block->AllOps().size();
auto var_to_grad = AppendBackward(program, target, {"out3"});
......@@ -730,6 +734,7 @@ TEST(Backward, var_no_grad) {
op2->SetOutput("Z", {"z2"});
auto target = f::VarDescBind("z2");
target.SetShape({1});
size_t forward_len = block->AllOps().size();
auto var_to_grad = AppendBackward(program, target, {"z1"});
......@@ -810,6 +815,7 @@ TEST(Backward, shared_var) {
op3->SetOutput("Out", {"out3"});
auto target = f::VarDescBind("out3");
target.SetShape({1});
size_t forward_len = block->AllOps().size();
auto var_to_grad = AppendBackward(program, target, {});
......@@ -888,6 +894,7 @@ TEST(Backward, half_backward) {
op1->SetOutput("Out", {"out"});
auto target = f::VarDescBind("out");
target.SetShape({1});
size_t forward_len = block->AllOps().size();
auto var_to_grad = AppendBackward(program, target, {"b"});
f::OpDescBind *fill_op = block->AllOps()[forward_len];
......
paddle/framework/data_type.h
浏览文件 @ 794117bb
......@@ -46,6 +46,8 @@ inline std::type_index ToTypeIndex(DataType type) {
return typeid(int);
case DataType::INT64:
return typeid(int64_t);
case DataType::BOOL:
return typeid(bool);
default:
PADDLE_THROW("Not support type %d", type);
}
......@@ -66,6 +68,9 @@ inline void VisitDataType(DataType type, Visitor visitor) {
case DataType::INT64:
visitor.template operator()<int64_t>();
break;
case DataType::BOOL:
visitor.template operator()<bool>();
break;
default:
PADDLE_THROW("Not supported");
}
......
paddle/gserver/layers/L2DistanceLayer.cpp 0 → 100644
浏览文件 @ 794117bb
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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 "L2DistanceLayer.h"
#include "paddle/utils/Logging.h"
#include "paddle/utils/Stat.h"
namespace paddle {
REGISTER_LAYER(l2_distance, L2DistanceLayer);
bool L2DistanceLayer::init(const LayerMap& layerMap,
const ParameterMap& parameterMap) {
/* Initialize the basic parent class */
Layer::init(layerMap, parameterMap);
CHECK_EQ(inputLayers_.size(), 2UL) << "The L2DistanceLayer accepts two and "
<< "only two inputs.";
CHECK_EQ(getSize(), 1UL) << "The output dimensionality of L2DistanceLayer "
<< "is fixed to be 1.";
return true;
}
void L2DistanceLayer::forward(PassType passType) {
Layer::forward(passType);
const auto inV1 = getInputValue(0);
const auto inV2 = getInputValue(1);
CHECK(inV1 && inV2);
CHECK_EQ(inV1->getHeight(), inV2->getHeight())
<< "The height of two inputs of this layer must be the same.";
CHECK_EQ(inV1->getWidth(), inV2->getWidth())
<< "The width of two inputs of this layer must be the same.";
int batchSize = inV1->getHeight();
int output_dim = getSize();
{
REGISTER_TIMER_INFO("L2DistanceBpAtvTimer", getName().c_str());
reserveOutput(batchSize, output_dim);
auto outV = getOutputValue();
CHECK(outV) << "The output matrix should not be null.";
Matrix::resizeOrCreate(
inputSub_, inV1->getHeight(), inV1->getWidth(), false, useGpu_);
inputSub_->assign(*inV1);
inputSub_->sub(*inV2);
outV->sumOfProducts(*inputSub_, *inputSub_, 1, 0);
outV->sqrt2(*outV);
}
}
void L2DistanceLayer::backward(const UpdateCallback& callback) {
const auto outG = getOutputGrad();
const auto outV = getOutputValue();
CHECK(outG && outV);
auto inGrad1 = getInputGrad(0);
auto inGrad2 = getInputGrad(1);
{
REGISTER_TIMER_INFO("L2DistanceBpAtvTimer", getName().c_str());
if (inGrad1 || inGrad2) {
outV->scalarDiv(*outV, 1.);
outV->dotMul(*outG, *outV);
}
if (inGrad1) inGrad1->addRowScale(0, *inputSub_, *outV);
if (inGrad2) {
inputSub_->mulScalar(-1.);
inGrad2->addRowScale(0, *inputSub_, *outV);
}
}
}
} // namespace paddle
paddle/gserver/layers/L2DistanceLayer.h 0 → 100644
浏览文件 @ 794117bb
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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 "Layer.h"
#include "paddle/math/Matrix.h"
namespace paddle {
/**
* @brief The layer calculates the l2 distance between two input vectors.
* \f[
* f(\bf{x}, \bf{y}) = \sqrt{\sum_{i=1}^D(x_i - y_i)}
* \f]
*
* - Input1: A vector (batchSize * dataDim)
* - Input2: A vector (batchSize * dataDim)
* - Output: A vector (batchSize * 1)
*
* The configuration api is: l2_distance_layer.
*/
class L2DistanceLayer : public Layer {
public:
explicit L2DistanceLayer(const LayerConfig& config) : Layer(config) {}
~L2DistanceLayer() {}
bool init(const LayerMap& layerMap,
const ParameterMap& parameterMap) override;
void forward(PassType passType) override;
void backward(const UpdateCallback& callback = nullptr) override;
private:
// Store the result of subtracting Input2 from Input1 in forward computation,
// which will be reused in backward computation.
MatrixPtr inputSub_;
};
} // namespace paddle
paddle/gserver/tests/test_LayerGrad.cpp
浏览文件 @ 794117bb
......@@ -583,6 +583,7 @@ TEST(Layer, maxoutLayer) {
testLayerGrad(config, "maxout", 10, false, useGpu);
}
}
void testFcLayer(string format, size_t nnz) {
TestConfig config;
config.biasSize = 1024;
......@@ -2444,6 +2445,25 @@ TEST(Layer, ScaleSubRegionLayer) {
}
}
TEST(Layer, L2DistanceLayer) {
TestConfig config;
config.layerConfig.set_type("l2_distance");
config.layerConfig.set_size(1);
config.biasSize = 0;
const size_t input_dim = 27;
const size_t batch_size = 11;
config.inputDefs.push_back({INPUT_DATA, "layer_0", input_dim, 0});
config.inputDefs.push_back({INPUT_DATA, "layer_1", input_dim, 0});
config.layerConfig.add_inputs();
config.layerConfig.add_inputs();
for (auto useGpu : {false, true}) {
testLayerGrad(config, "l2_distance", batch_size, false, useGpu);
}
}
int main(int argc, char** argv) {
testing::InitGoogleTest(&argc, argv);
initMain(argc, argv);
......
paddle/operators/CMakeLists.txt
浏览文件 @ 794117bb
......@@ -87,6 +87,11 @@ function(op_library TARGET)
file(APPEND ${pybind_file} "USE_OP(pool2d_cudnn);\n")
endif()
if ("${TARGET}" STREQUAL "logical_op")
set(pybind_flag 1)
file(APPEND ${pybind_file} "USE_OP(logical_and);\n")
endif()
# pool_with_index_op contains several operators
if ("${TARGET}" STREQUAL "pool_with_index_op")
set(pybind_flag 1)
......
paddle/operators/logical_op.cc 0 → 100644
浏览文件 @ 794117bb
/* Copyright (c) 2016 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/operators/logical_op.h"
#include "paddle/framework/op_registry.h"
namespace paddle {
namespace operators {
template <typename OpComment>
class BinaryLogicalOpProtoMaker : public framework::OpProtoAndCheckerMaker {
public:
BinaryLogicalOpProtoMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
OpComment comment;
AddInput("X",
string::Sprintf("(LoDTensor) Left hand operand of %s operator",
comment.type));
AddInput("Y",
string::Sprintf("(LoDTensor) Right hand operand of %s operator",
comment.type));
AddOutput("Out", string::Sprintf(
"(LoDTensor) n-dim bool tensor. Each element is %s",
comment.equation));
AddComment(string::Sprintf(R"DOC(%s Operator
It operates element-wise on X and Y, and returns the Out. X, Y and Out are N-dim boolean tensors.
Each element of Out is calculated by %s
)DOC",
comment.type, comment.equation));
}
};
template <typename OpComment>
class UnaryLogicalOpProtoMaker : public framework::OpProtoAndCheckerMaker {
public:
UnaryLogicalOpProtoMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
OpComment comment;
AddInput("X", string::Sprintf("(LoDTensor) Operand of %s operator",
comment.type));
AddOutput("Out", string::Sprintf(
"(LoDTensor) n-dim bool tensor. Each element is %s",
comment.equation));
AddComment(string::Sprintf(R"DOC(%s Operator
It operates element-wise on X, and returns the Out. X and Out are N-dim boolean tensors.
Each element of Out is calculated by %s
)DOC",
comment.type, comment.equation));
}
};
template <typename OpComment>
class BinaryLogicalOpInferShape : public framework::InferShapeBase {
public:
void operator()(framework::InferShapeContext *context) const override {
OpComment comment;
PADDLE_ENFORCE(context->HasInput("X"),
"Input(X) of %s operator must not be null", comment.type);
PADDLE_ENFORCE(context->HasInput("Y"),
"Input(Y) of %s operator must not be null", comment.type);
auto dim_x = context->GetInputDim("X");
auto dim_y = context->GetInputDim("Y");
PADDLE_ENFORCE_EQ(framework::product(dim_x), framework::product(dim_y),
"The number of elements in X and Y should be same");
context->SetOutputDim("Out", context->GetInputDim("X"));
context->ShareLoD("X", "Out");
}
};
template <typename OpComment>
class UnaryLogicalOpInferShape : public framework::InferShapeBase {
public:
void operator()(framework::InferShapeContext *context) const override {
OpComment comment;
PADDLE_ENFORCE(context->HasInput("X"),
"Input(X) of %s operator must not be null", comment.type);
auto dim_x = context->GetInputDim("X");
context->SetOutputDim("Out", context->GetInputDim("X"));
context->ShareLoD("X", "Out");
}
};
class LogicalOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
framework::OpKernelType GetKernelType(
const framework::ExecutionContext &ctx) const override {
framework::OpKernelType kt = OperatorWithKernel::GetKernelType(ctx);
// LogicalOp kernel's device type is decided by input tensor place
kt.place_ = ctx.Input<framework::LoDTensor>("X")->place();
return kt;
}
};
} // namespace operators
} // namespace paddle
#define REGISTER_BINARY_LOGICAL_OP(op_type, _equation) \
struct _##op_type##Comment { \
static char type[]; \
static char equation[]; \
}; \
char _##op_type##Comment::type[]{#op_type}; \
char _##op_type##Comment::equation[]{_equation}; \
REGISTER_OPERATOR( \
op_type, ::paddle::operators::LogicalOp, \
::paddle::operators::BinaryLogicalOpProtoMaker<_##op_type##Comment>, \
::paddle::operators::BinaryLogicalOpInferShape<_##op_type##Comment>, \
::paddle::framework::EmptyGradOpMaker);
#define REGISTER_UNARY_LOGICAL_OP(op_type, _equation) \
struct _##op_type##Comment { \
static char type[]; \
static char equation[]; \
}; \
char _##op_type##Comment::type[]{#op_type}; \
char _##op_type##Comment::equation[]{_equation}; \
REGISTER_OPERATOR( \
op_type, ::paddle::operators::LogicalOp, \
::paddle::operators::UnaryLogicalOpProtoMaker<_##op_type##Comment>, \
::paddle::operators::UnaryLogicalOpInferShape<_##op_type##Comment>, \
::paddle::framework::EmptyGradOpMaker);
REGISTER_BINARY_LOGICAL_OP(logical_and, "Out = X && Y");
REGISTER_BINARY_LOGICAL_KERNEL(logical_and, CPU,
paddle::operators::LogicalAndFunctor);
REGISTER_BINARY_LOGICAL_OP(logical_or, "Out = X && Y");
REGISTER_BINARY_LOGICAL_KERNEL(logical_or, CPU,
paddle::operators::LogicalOrFunctor);
REGISTER_UNARY_LOGICAL_OP(logical_not, "Out = !X");
REGISTER_UNARY_LOGICAL_KERNEL(logical_not, CPU,
paddle::operators::LogicalNotFunctor);
REGISTER_BINARY_LOGICAL_OP(logical_xor, "Out = (X || Y) && !(X && Y)");
REGISTER_BINARY_LOGICAL_KERNEL(logical_xor, CPU,
paddle::operators::LogicalXorFunctor);
paddle/operators/logical_op.cu 0 → 100644
浏览文件 @ 794117bb
/* Copyright (c) 2016 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/operators/logical_op.h"
REGISTER_BINARY_LOGICAL_KERNEL(logical_and, GPU,
paddle::operators::LogicalAndFunctor);
REGISTER_BINARY_LOGICAL_KERNEL(logical_or, GPU,
paddle::operators::LogicalOrFunctor);
REGISTER_UNARY_LOGICAL_KERNEL(logical_not, GPU,
paddle::operators::LogicalNotFunctor);
REGISTER_BINARY_LOGICAL_KERNEL(logical_xor, GPU,
paddle::operators::LogicalXorFunctor);
paddle/operators/logical_op.h 0 → 100644
浏览文件 @ 794117bb
/* Copyright (c) 2016 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 <math.h>
#include <type_traits>
#include "paddle/framework/op_registry.h"
#include "paddle/platform/transform.h"
namespace paddle {
namespace operators {
template <typename T>
struct LogicalAndFunctor {
using ELEM_TYPE = T;
HOSTDEVICE bool operator()(const T& a, const T& b) const { return a && b; }
};
template <typename T>
struct LogicalOrFunctor {
using ELEM_TYPE = T;
HOSTDEVICE bool operator()(const T& a, const T& b) const { return a || b; }
};
template <typename T>
struct LogicalNotFunctor {
using ELEM_TYPE = T;
HOSTDEVICE bool operator()(const T& a) const { return !a; }
};
template <typename T>
struct LogicalXorFunctor {
using ELEM_TYPE = T;
HOSTDEVICE bool operator()(const T& a, const T& b) const {
return (a || b) && !(a && b);
}
};
template <typename Place, typename Functor>
class BinaryLogicalOpKernel
: public framework::OpKernel<typename Functor::ELEM_TYPE> {
public:
void Compute(const framework::ExecutionContext& context) const override {
using T = typename Functor::ELEM_TYPE;
auto* x = context.Input<framework::Tensor>("X");
auto* y = context.Input<framework::Tensor>("Y");
auto* out = context.Output<framework::Tensor>("Out");
Functor binary_func;
platform::Transform<Place> trans;
trans(context.device_context(), x->data<T>(), x->data<T>() + x->numel(),
y->data<T>(), out->mutable_data<bool>(context.GetPlace()),
binary_func);
}
};
template <typename Place, typename Functor>
class UnaryLogicalOpKernel
: public framework::OpKernel<typename Functor::ELEM_TYPE> {
public:
void Compute(const framework::ExecutionContext& context) const override {
using T = typename Functor::ELEM_TYPE;
auto* x = context.Input<framework::Tensor>("X");
auto* out = context.Output<framework::Tensor>("Out");
Functor unary_func;
platform::Transform<Place> trans;
trans(context.device_context(), x->data<T>(), x->data<T>() + x->numel(),
out->mutable_data<bool>(context.GetPlace()), unary_func);
}
};
} // namespace operators
} // namespace paddle
#define REGISTER_BINARY_LOGICAL_KERNEL(op_type, dev, functor) \
REGISTER_OP_##dev##_KERNEL( \
op_type, ::paddle::operators::BinaryLogicalOpKernel< \
::paddle::platform::dev##Place, functor<bool>>);
#define REGISTER_UNARY_LOGICAL_KERNEL(op_type, dev, functor) \
REGISTER_OP_##dev##_KERNEL( \
op_type, ::paddle::operators::UnaryLogicalOpKernel< \
::paddle::platform::dev##Place, functor<bool>>);
paddle/operators/sequence_slice_op.h
浏览文件 @ 794117bb
......@@ -77,13 +77,13 @@ class SequenceSliceOpKernel : public framework::OpKernel<T> {
for (size_t i = 0; i < n; ++i) {
PADDLE_ENFORCE_LT(0, offset_data[i],
"The offset must greater than zero")
"The offset[%d] must greater than zero.", i)
PADDLE_ENFORCE_LT(0, length_data[i],
"The length must greater than zero")
"The length[%d] must greater than zero.", i)
PADDLE_ENFORCE_LT(
lod[0][i] + offset_data[i] + length_data[i],
lod[0][i + 1],
"The target tensor's length overflow")
"The target tensor's length overflow.")
}
out->mutable_data<T>(ctx.GetPlace());
......
python/paddle/trainer/config_parser.py
浏览文件 @ 794117bb
......@@ -3342,6 +3342,20 @@ class RowL2NormLayer(LayerBase):
self.set_layer_size(input_layer.size)
@config_layer('cos')
class CosSimLayer(LayerBase):
def __init__(self, name, inputs, cos_scale=1, device=None):
super(CosSimLayer, self).__init__(
name, 'cos', 1, inputs=inputs, device=device)
config_assert(
len(self.inputs) == 2,
'The CosSimLayer expects two and only two inputs.')
config_assert(
self.get_input_layer(0).size == self.get_input_layer(1).size,
'The two inputs of CosSimLayer must have the same dimensionality.')
self.config.cos_scale = cos_scale
@config_layer('cos_vm')
class CosSimVecMatLayer(LayerBase):
def __init__(self, name, size, inputs, cos_scale=1.0, device=None):
......@@ -3349,10 +3363,24 @@ class CosSimVecMatLayer(LayerBase):
name, 'cos_vm', size, inputs=inputs, device=device)
self.config.cos_scale = cos_scale
config_assert(
len(self.inputs) == 2, 'CosSimVecMatLayer must have 2 inputs')
len(self.inputs) == 2, 'The CosSimVecMatLayer must have 2 inputs.')
config_assert(
size * self.get_input_layer(0).size == self.get_input_layer(1).size,
'Wrong input size for CosSimVecMatLayer')
'Wrong input size for CosSimVecMatLayer.')
@config_layer('l2_distance')
class L2DistanceLayer(LayerBase):
def __init__(self, name, inputs, device=None):
super(L2DistanceLayer, self).__init__(
name, 'l2_distance', 1, inputs=inputs, device=device)
config_assert(
len(self.inputs) == 2, ('The L2DistanceLayer must have '
'and only have 2 inputs.'))
config_assert(
self.get_input_layer(0).size == self.get_input_layer(1).size,
('Two inputs of the L2DistanceLayer must have '
'the same dimensionality.'))
@config_layer('sampling_id')
......@@ -3396,18 +3424,6 @@ class AverageLayer(LayerBase):
self.create_bias_parameter(bias, self.config.size)
@config_layer('cos')
class CosSimLayer(LayerBase):
def __init__(self, name, inputs, cos_scale=1, device=None):
super(CosSimLayer, self).__init__(
name, 'cos', 1, inputs=inputs, device=device)
config_assert(len(self.inputs) == 2, 'CosSimLayer must have 2 inputs')
config_assert(
self.get_input_layer(0).size == self.get_input_layer(1).size,
'inputs of CosSimLayer must have same dim')
self.config.cos_scale = cos_scale
@config_layer('tensor')
class TensorLayer(LayerBase):
def __init__(self, name, size, inputs, bias=True, **xargs):
......
python/paddle/trainer_config_helpers/layers.py
浏览文件 @ 794117bb
......@@ -51,6 +51,7 @@ __all__ = [
'last_seq',
'first_seq',
'cos_sim',
'l2_distance_layer',
'hsigmoid',
'conv_projection',
'square_error_cost',
......@@ -168,6 +169,7 @@ class LayerType(object):
COST = 'cost'
COSINE_SIM_VEC = 'cos_vm'
COSINE_SIM = 'cos'
L2_DISTANCE = 'l2_distance'
HSIGMOID = 'hsigmoid'
CONV_LAYER = 'conv'
CONVTRANS_LAYER = 'convt'
......@@ -2334,6 +2336,51 @@ def cos_sim(a, b, scale=1, size=1, name=None, layer_attr=None):
return LayerOutput(name, LayerType.COSINE_SIM, parents=[a, b], size=size)
@wrap_name_default()
@layer_support()
def l2_distance_layer(x, y, name=None, layer_attr=None):
"""
This layer calculates and returns the Euclidean distance between two input
vectors x and y. The equation is as follows:
.. math::
l2_distance(\\mathbf{x}, \\mathbf{y}) = \\sqrt{\\sum_{i=1}^D(x_i - y_i)}
The output size of this layer is fixed to be 1. Note that the above
computation is for one sample. Multiple samples are processed in one batch.
The example usage is:
.. code-block:: python
l2_sim = l2_distance(x=layer1, y=layer2)
:param name: The name of this layer. It is optional.
:type name: basestring
:param x: The first input x for this layer, whose output is a matrix with
dimensionality N x D. N is the sample number in a mini-batch.
D is the dimensionality of x's output.
:type x: LayerOutput
:param y: The second input y for this layer, whose output is a matrix with
dimensionality N x D. N is the sample number in a mini-batch.
D is the dimensionality of y's output.
:type y: LayerOutput
:param layer_attr: The extra layer attributes, for example, drop rate.
See ExtraLayerAttribute for more details.
:type layer_attr: ExtraLayerAttribute
:return: The returned LayerOutput object.
:rtype: LayerOutput
"""
assert isinstance(x, LayerOutput) and isinstance(y, LayerOutput)
Layer(
name=name,
type=LayerType.L2_DISTANCE,
inputs=[x.name, y.name],
**ExtraLayerAttribute.to_kwargs(layer_attr))
return LayerOutput(name, LayerType.L2_DISTANCE, parents=[x, y], size=1)
@wrap_name_default()
@wrap_bias_attr_default(has_bias=True)
@wrap_param_attr_default()
......@@ -3873,7 +3920,7 @@ def recurrent_layer(input,
:type input: LayerOutput
:param act: Activation type. TanhActivation is the default activation.
:type act: BaseActivation
:param bias_attr: The parameter attribute for bias. If this parameter is set to
:param bias_attr: The parameter attribute for bias. If this parameter is set to
False or an object whose type is not ParameterAttribute,
no bias is defined. If the parameter is set to True,
the bias is initialized to zero.
......
python/paddle/trainer_config_helpers/tests/configs/file_list.sh
浏览文件 @ 794117bb
......@@ -10,7 +10,8 @@ test_prelu_layer test_row_conv test_detection_output_layer test_multibox_loss_la
test_recursive_topology test_gated_unit_layer test_clip_layer test_row_l2_norm_layer
test_kmax_seq_socre_layer test_sub_nested_seq_select_layer test_scale_shift_layer
test_seq_slice_layer test_cross_entropy_over_beam test_roi_pool_layer test_pooling3D_layer
test_conv3d_layer test_deconv3d_layer test_BatchNorm3D test_resize_layer test_scale_sub_region_layer
test_dot_prod_layer)
test_conv3d_layer test_deconv3d_layer test_BatchNorm3D test_resize_layer
test_conv3d_layer test_deconv3d_layer test_BatchNorm3D test_resize_layer
test_scale_sub_region_layer test_dot_prod_layer test_l2_distance_layer)
export whole_configs=(test_split_datasource)
python/paddle/trainer_config_helpers/tests/configs/protostr/test_l2_distance_layer.protostr 0 → 100644
浏览文件 @ 794117bb
type: "nn"
layers {
name: "x"
type: "data"
size: 128
active_type: ""
}
layers {
name: "y"
type: "data"
size: 128
active_type: ""
}
layers {
name: "__l2_distance_layer_0__"
type: "l2_distance"
size: 1
active_type: ""
inputs {
input_layer_name: "x"
}
inputs {
input_layer_name: "y"
}
}
input_layer_names: "x"
input_layer_names: "y"
output_layer_names: "__l2_distance_layer_0__"
sub_models {
name: "root"
layer_names: "x"
layer_names: "y"
layer_names: "__l2_distance_layer_0__"
input_layer_names: "x"
input_layer_names: "y"
output_layer_names: "__l2_distance_layer_0__"
is_recurrent_layer_group: false
}
python/paddle/trainer_config_helpers/tests/configs/test_l2_distance_layer.py 0 → 100644
浏览文件 @ 794117bb
from paddle.trainer_config_helpers import *
outputs(
l2_distance_layer(
x=data_layer(
name='x', size=128), y=data_layer(
name='y', size=128)))
python/paddle/v2/fluid/tests/test_logical_op.py 0 → 100644
浏览文件 @ 794117bb
import op_test
import unittest
import numpy as np
def create_test_class(op_type, callback, binary_op=True):
class Cls(op_test.OpTest):
def setUp(self):
a = np.random.choice(a=[True, False], size=(10, 7)).astype(bool)
if binary_op:
b = np.random.choice(a=[True, False], size=(10, 7)).astype(bool)
c = callback(a, b)
else:
c = callback(a)
self.outputs = {'Out': c}
self.op_type = op_type
if binary_op:
self.inputs = {'X': a, 'Y': b}
else:
self.inputs = {'X': a}
def test_output(self):
self.check_output()
Cls.__name__ = op_type
globals()[op_type] = Cls
create_test_class('logical_and', lambda _a, _b: np.logical_and(_a, _b))
create_test_class('logical_or', lambda _a, _b: np.logical_or(_a, _b))
create_test_class('logical_not', lambda _a: np.logical_not(_a), False)
create_test_class('logical_xor', lambda _a, _b: np.logical_xor(_a, _b))
if __name__ == '__main__':
unittest.main()
python/paddle/v2/fluid/tests/test_optimizer.py
浏览文件 @ 794117bb
......@@ -16,14 +16,18 @@ class TestOptimizer(unittest.TestCase):
dtype="float32", shape=[10, 8], lod_level=0, name="mul.y")
mul_out = block.create_var(
dtype="float32", shape=[5, 8], lod_level=0, name="mul.out")
mean_out = block.create_var(
dtype="float32", shape=[1], lod_level=0, name="mean.out")
block.append_op(
type="mul",
inputs={"X": mul_x,
"Y": mul_y},
outputs={"Out": mul_out},
attrs={"x_num_col_dims": 1})
block.append_op(
type="mean", inputs={"X": mul_out}, outputs={"Out": mean_out})
sgd_optimizer = optimizer.SGDOptimizer(learning_rate=0.01)
opts = sgd_optimizer.minimize(mul_out, init_program)
opts = sgd_optimizer.minimize(mean_out, init_program)
self.assertEqual(len(opts), 1)
sgd_op = opts[0]
self.assertEqual(sgd_op.type, "sgd")
......@@ -44,12 +48,16 @@ class TestOptimizer(unittest.TestCase):
"Y": mul_y},
outputs={"Out": mul_out},
attrs={"x_num_col_dims": 1})
mean_out = block.create_var(
dtype="float32", shape=[1], lod_level=0, name="mean.out")
block.append_op(
type="mean", inputs={"X": mul_out}, outputs={"Out": mean_out})
global_step = block.create_var(
dtype="float32", shape=[1], lod_level=0, name="step")
learning_rate = 0.01
sgd_optimizer = optimizer.SGDOptimizer(
learning_rate=learning_rate, global_step=global_step)
opts = sgd_optimizer.minimize(mul_out, init_program)
opts = sgd_optimizer.minimize(mean_out, init_program)
self.assertEqual(len(opts), 2)
sgd_op = opts[0]
self.assertEqual(sgd_op.type, "sgd")
......@@ -90,7 +98,11 @@ class TestMomentumOptimizer(unittest.TestCase):
learning_rate = 0.01
momentum_optimizer = self.MockMomentum(
learning_rate=learning_rate, momentum=0.2)
params_grads = append_backward_ops(mul_out)
mean_out = block.create_var(
dtype="float32", shape=[1], lod_level=0, name="mean.out")
block.append_op(
type="mean", inputs={"X": mul_out}, outputs={"Out": mean_out})
params_grads = append_backward_ops(mean_out)
self.assertEqual(len(params_grads), 1)
self.assertEqual(len(momentum_optimizer.get_accumulators()), 0)
opts = momentum_optimizer.create_optimization_pass(
......@@ -132,10 +144,14 @@ class TestMomentumOptimizer(unittest.TestCase):
"Y": mul_y},
outputs={"Out": mul_out},
attrs={"x_num_col_dims": 1})
mean_out = block.create_var(
dtype="float32", shape=[1], lod_level=0, name="mean.out")
block.append_op(
type="mean", inputs={"X": mul_out}, outputs={"Out": mean_out})
learning_rate = 0.01
momentum_optimizer = self.MockMomentum(
learning_rate=learning_rate, momentum=0.2, use_nesterov=True)
params_grads = append_backward_ops(mul_out)
params_grads = append_backward_ops(mean_out)
self.assertEqual(len(params_grads), 1)
self.assertEqual(len(momentum_optimizer.get_accumulators()), 0)
opts = momentum_optimizer.create_optimization_pass(
......@@ -186,10 +202,14 @@ class TestAdagradOptimizer(unittest.TestCase):
"Y": mul_y},
outputs={"Out": mul_out},
attrs={"x_num_col_dims": 1})
mean_out = block.create_var(
dtype="float32", shape=[1], lod_level=0, name="mean.out")
block.append_op(
type="mean", inputs={"X": mul_out}, outputs={"Out": mean_out})
learning_rate = 0.01
adagrad_optimizer = self.MockAdagrad(
learning_rate=learning_rate, epsilon=1.0e-6)
params_grads = append_backward_ops(mul_out)
params_grads = append_backward_ops(mean_out)
self.assertEqual(len(params_grads), 1)
self.assertEqual(len(adagrad_optimizer.get_accumulators()), 0)
opts = adagrad_optimizer.create_optimization_pass(params_grads, mul_out,
......@@ -242,10 +262,14 @@ class TestAdamOptimizer(unittest.TestCase):
"Y": mul_y},
outputs={"Out": mul_out},
attrs={"x_num_col_dims": 1})
mean_out = block.create_var(
dtype="float32", shape=[1], lod_level=0, name="mean.out")
block.append_op(
type="mean", inputs={"X": mul_out}, outputs={"Out": mean_out})
learning_rate = 0.01
adam_optimizer = self.MockAdam(
learning_rate=learning_rate, beta1=0.9, beta2=0.999)
params_grads = append_backward_ops(mul_out)
params_grads = append_backward_ops(mean_out)
self.assertEqual(len(params_grads), 1)
self.assertEqual(len(adam_optimizer.get_accumulators()), 0)
opts = adam_optimizer.create_optimization_pass(params_grads, mul_out,
......@@ -300,10 +324,14 @@ class TestAdamaxOptimizer(unittest.TestCase):
"Y": mul_y},
outputs={"Out": mul_out},
attrs={"x_num_col_dims": 1})
mean_out = block.create_var(
dtype="float32", shape=[1], lod_level=0, name="mean.out")
block.append_op(
type="mean", inputs={"X": mul_out}, outputs={"Out": mean_out})
learning_rate = 0.01
adamax_optimizer = self.MockAdamax(
learning_rate=learning_rate, beta1=0.9, beta2=0.999)
params_grads = append_backward_ops(mul_out)
params_grads = append_backward_ops(mean_out)
self.assertEqual(len(params_grads), 1)
self.assertEqual(len(adamax_optimizer.get_accumulators()), 0)
opts = adamax_optimizer.create_optimization_pass(params_grads, mul_out,
......@@ -355,10 +383,14 @@ class TestDecayedAdagradOptimizer(unittest.TestCase):
"Y": mul_y},
outputs={"Out": mul_out},
attrs={"x_num_col_dims": 1})
mean_out = block.create_var(
dtype="float32", shape=[1], lod_level=0, name="mean.out")
block.append_op(
type="mean", inputs={"X": mul_out}, outputs={"Out": mean_out})
learning_rate = 0.01
decayed_adagrad_optimizer = self.MockDecayedAdagrad(
learning_rate=learning_rate, decay=0.95, epsilon=1.0e-6)
params_grads = append_backward_ops(mul_out)
params_grads = append_backward_ops(mean_out)
self.assertEqual(len(params_grads), 1)
self.assertEqual(len(decayed_adagrad_optimizer.get_accumulators()), 0)
opts = decayed_adagrad_optimizer.create_optimization_pass(
......
python/paddle/v2/fluid/tests/test_program.py
浏览文件 @ 794117bb
import unittest
import paddle.v2.fluid.core as core
from paddle.v2.fluid.framework import Program
from paddle.v2.fluid.framework import g_main_program
......@@ -98,21 +97,26 @@ class TestProgram(unittest.TestCase):
"Y": add_y},
outputs={"Out": add_out},
attrs={"x_num_col_dims": 1})
mean_out = block.create_var(
dtype="float32", shape=[1], lod_level=0, name="mean.out")
block.append_op(
type="mean", inputs={"X": add_out}, outputs={"Out": mean_out})
self.assertEqual(mul_op.idx, 0)
self.assertEqual(add_op.idx, 1)
param_to_grad = prog.append_backward(add_out, set())
param_to_grad = prog.append_backward(mean_out, set())
def grad_name(name):
return name + "@GRAD"
for var_name in ("mul.x", "mul.y", "mul.out", "add.y", "add.out"):
for var_name in ("mul.x", "mul.y", "mul.out", "add.y", "add.out",
"mean.out"):
self.assertEqual(param_to_grad[var_name][0], grad_name(var_name))
self.assertEqual(param_to_grad[var_name][1], 0)
expect_ops = [
"mul", "elementwise_add", "fill_constant", "elementwise_add_grad",
"mul_grad"
"mul", "elementwise_add", "mean", "fill_constant", "mean_grad",
"elementwise_add_grad", "mul_grad"
]
actual_ops = []
for op in block.ops:
......
python/paddle/v2/fluid/tests/test_regularizer.py
浏览文件 @ 794117bb
......@@ -29,7 +29,11 @@ class TestL2DecayRegularizer(unittest.TestCase):
"Y": mul_y},
outputs={"Out": mul_out},
attrs={"x_num_col_dims": 1})
params_grads = append_backward_ops(mul_out)
mean_out = block.create_var(
dtype="float32", shape=[1], lod_level=0, name="mean.out")
block.append_op(
type="mean", inputs={"X": mul_out}, outputs={"Out": mean_out})
params_grads = append_backward_ops(mean_out)
self.assertEqual(len(params_grads), 1)
count_ops = len(block.ops)
params_grads = optimizer.append_regularization_ops(params_grads)
......@@ -62,7 +66,11 @@ class TestL1DecayRegularizer(unittest.TestCase):
"Y": mul_y},
outputs={"Out": mul_out},
attrs={"x_num_col_dims": 1})
params_grads = append_backward_ops(mul_out)
mean_out = block.create_var(
dtype="float32", shape=[1], lod_level=0, name="mean.out")
block.append_op(
type="mean", inputs={"X": mul_out}, outputs={"Out": mean_out})
params_grads = append_backward_ops(mean_out)
self.assertEqual(len(params_grads), 1)
count_ops = len(block.ops)
params_grads = optimizer.append_regularization_ops(params_grads)
......
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