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PaddleDetection
未验证 提交 12e1719f 编写于 作者: J Jiabin Yang 提交者： GitHub
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Merge pull request #14352 from JiabinYang/enhance_hierachical_sigmod_op 

Enhance hierarchical sigmoid op
上级 36e26a53 eda06906
隐藏空白更改
内联 并排
Showing with 765 addition and 131 deletion
paddle/fluid/API.spec
浏览文件 @ 12e1719f
......@@ -98,7 +98,7 @@ paddle.fluid.layers.sequence_reshape ArgSpec(args=['input', 'new_dim'], varargs=
paddle.fluid.layers.transpose ArgSpec(args=['x', 'perm', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.im2sequence ArgSpec(args=['input', 'filter_size', 'stride', 'padding', 'input_image_size', 'out_stride', 'name'], varargs=None, keywords=None, defaults=(1, 1, 0, None, 1, None))
paddle.fluid.layers.nce ArgSpec(args=['input', 'label', 'num_total_classes', 'sample_weight', 'param_attr', 'bias_attr', 'num_neg_samples', 'name', 'sampler', 'custom_dist', 'seed', 'is_sparse'], varargs=None, keywords=None, defaults=(None, None, None, None, None, 'uniform', None, 0, False))
paddle.fluid.layers.hsigmoid ArgSpec(args=['input', 'label', 'num_classes', 'param_attr', 'bias_attr', 'name'], varargs=None, keywords=None, defaults=(None, None, None))
paddle.fluid.layers.hsigmoid ArgSpec(args=['input', 'label', 'num_classes', 'param_attr', 'bias_attr', 'name', 'path_table', 'path_code', 'is_custom', 'is_sparse'], varargs=None, keywords=None, defaults=(None, None, None, None, None, False, False))
paddle.fluid.layers.beam_search ArgSpec(args=['pre_ids', 'pre_scores', 'ids', 'scores', 'beam_size', 'end_id', 'level', 'name'], varargs=None, keywords=None, defaults=(0, None))
paddle.fluid.layers.row_conv ArgSpec(args=['input', 'future_context_size', 'param_attr', 'act'], varargs=None, keywords=None, defaults=(None, None))
paddle.fluid.layers.multiplex ArgSpec(args=['inputs', 'index'], varargs=None, keywords=None, defaults=None)
......
paddle/fluid/framework/selected_rows.h
浏览文件 @ 12e1719f
......@@ -120,8 +120,22 @@ class SelectedRows {
*/
int64_t AutoGrownIndex(int64_t key, bool auto_grown, bool is_test = false);
void SyncIndex();
/*
* @brief Get the index of the key from id_to_index_ map.
*/
inline int64_t GetIndexFromId(int64_t key) {
auto iter = id_to_index_.find(key);
if (iter == id_to_index_.end()) {
return -1;
} else {
return iter->second;
}
}
void SyncIndex();
/*
* @brief Get complete Dims before
*/
DDim GetCompleteDims() const {
std::vector<int64_t> dims = vectorize(value_->dims());
dims[0] = height_;
......@@ -133,9 +147,10 @@ class SelectedRows {
// SelectedRows are simply concated when adding together. Until a
// SelectedRows add a Tensor, will the duplicate rows be handled.
Vector<int64_t> rows_;
std::unordered_map<int64_t, int64_t> id_to_index_;
std::unordered_map<int64_t, int64_t>
id_to_index_; // should not be used when rows_ has duplicate member
std::unique_ptr<Tensor> value_{nullptr};
int64_t height_;
int64_t height_; // height indicates the underline tensor's height
std::unique_ptr<RWLock> rwlock_{nullptr};
};
......
paddle/fluid/operators/hierarchical_sigmoid_op.cc
浏览文件 @ 12e1719f
......@@ -13,8 +13,8 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/hierarchical_sigmoid_op.h"
#include <string>
#include <vector>
namespace paddle {
namespace operators {
......@@ -70,13 +70,14 @@ class HierarchicalSigmoidOp : public framework::OperatorWithKernel {
const int64_t batch_size = ctx->GetInputDim("X")[0];
std::vector<int64_t> output_shape({batch_size, 1});
ctx->SetOutputDim("Out", framework::make_ddim(output_shape));
ctx->ShareLoD("X", /*->*/ "Out");
}
protected:
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext& ctx) const override {
return framework::OpKernelType(
framework::ToDataType(ctx.Input<framework::Tensor>("X")->type()),
framework::ToDataType(ctx.Input<framework::LoDTensor>("X")->type()),
ctx.GetPlace());
}
};
......@@ -86,27 +87,40 @@ class HierarchicalSigmoidOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("X",
"(Tensor, required) The input tensor with shape [N, D], "
"(LoDTensor, required) The input tensor with shape [N, D], "
"where N is the size of mini-batch, and D is the feature size.");
AddInput("W",
"(Tensor, required), The parameters of hierarchical "
"(LoDTensor, required), The parameters of hierarchical "
"sigmoid operator, each of them is a 2-D tensor, the shape is"
"[num_classes - 1, D].");
"[K, D]. Which K is the num of non-leaf node in Path Tree");
AddInput("Label",
"(Tensor, required), The labels of training data. It's a"
"(LoDTensor, required), The labels of training data. It's a"
"tensor with shape [N, 1].");
AddInput("PTable",
"(LoDTensor, optional), The Path Table from root to current word"
"it should have shape like [N, L], L is the length of the Path")
.AsDispensable();
AddInput(
"PathCode",
"(LoDTensor, optional), The Code on each Node of the Path from root "
"to current word"
"it should have shape like [N, L], L is the length of the Path")
.AsDispensable();
AddInput("Bias",
"(Tensor, optional), The bias is a tensor with shape"
"[1, num_classes - 1].");
AddOutput("Out",
"(Tensor, required) The output of hierarchical sigmoid operator."
"The shape is [N, 1].");
"(LoDTensor, optional), The bias is a tensor with shape or "
"[num_classes, 1]"
"[num_classes - 1, 1].")
.AsDispensable();
AddOutput(
"Out",
"(LoDTensor, required) The output of hierarchical sigmoid operator."
"The shape is [N, 1].");
AddOutput("PreOut",
"(Tensor, required) A intermedia 2-D tensor with shape "
"(LoDTensor, required) A intermedia 2-D tensor with shape "
"[batch_size, code_length], where code_length represents the "
"maximum path length from root to leaf nodes.")
.AsIntermediate();
AddAttr<AttrType>("num_classes", "(int, required), The number of classes")
AddAttr<AttrType>("num_classes", "(int, optional), The number of classes")
.SetDefault(2);
AddComment(R"DOC(
The hierarchical sigmoid operator organize the classes into a binary tree.
......@@ -115,6 +129,10 @@ belonging to the right branch. This idea is from
"F. Morin, Y. Bengio (AISTATS 05):
Hierarchical Probabilistic Neural Network Language Model."
)DOC");
AddAttr<bool>("is_sparse",
"(boolean, default false) "
"Sparse update.")
.SetDefault(false);
}
};
......@@ -124,16 +142,21 @@ class HierarchicalSigmoidGradOp : public framework::OperatorWithKernel {
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("W"), "Input(W) should not be null.");
PADDLE_ENFORCE(ctx->HasInput("Label"), "Input(Label) should not be null.");
PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
"Input(Out@Grad) should not be null");
PADDLE_ENFORCE(ctx->HasInput("PreOut"),
"Input(Preout) should not be null.");
PADDLE_ENFORCE(ctx->HasOutput(framework::GradVarName("W")),
"Output(W@Grad should not be null.)");
PADDLE_ENFORCE(ctx->HasOutput(framework::GradVarName("X")));
if (ctx->HasOutput(framework::GradVarName("Bias"))) {
ctx->SetOutputDim(framework::GradVarName("Bias"),
ctx->GetInputDim("Bias"));
"Output(W@Grad should not be null.");
PADDLE_ENFORCE(ctx->HasOutput(framework::GradVarName("X")),
"Output(X@Grad should not be null.");
if (!ctx->Attrs().Get<bool>("is_sparse")) {
if (ctx->HasOutput(framework::GradVarName("Bias"))) {
ctx->SetOutputDim(framework::GradVarName("Bias"),
ctx->GetInputDim("Bias"));
}
ctx->SetOutputDim(framework::GradVarName("W"), ctx->GetInputDim("W"));
}
ctx->SetOutputDim(framework::GradVarName("W"), ctx->GetInputDim("W"));
ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("X"));
}
......@@ -141,11 +164,55 @@ class HierarchicalSigmoidGradOp : public framework::OperatorWithKernel {
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext& ctx) const override {
return framework::OpKernelType(
framework::ToDataType(ctx.Input<framework::Tensor>("X")->type()),
framework::ToDataType(ctx.Input<framework::LoDTensor>("X")->type()),
ctx.GetPlace());
}
};
class HierarchicalSigmoidGradOpGradVarTypeInference
: public framework::VarTypeInference {
public:
void operator()(const framework::OpDesc& op_desc,
framework::BlockDesc* block) const override {
auto w_grad_var_name = op_desc.Output(framework::GradVarName("W")).front();
auto bias_grad_var_name_vec =
op_desc.Output(framework::GradVarName("Bias"));
std::string bias_grad_var_name;
bool hasBias = false;
if (bias_grad_var_name_vec.size()) {
hasBias = true;
bias_grad_var_name =
op_desc.Output(framework::GradVarName("Bias")).front();
}
auto attr = op_desc.GetAttr("is_sparse");
bool is_sparse = boost::get<bool>(attr);
if (is_sparse) {
VLOG(30) << "hierarchical_sigmoid_grad op " << framework::GradVarName("W")
<< " is set to SelectedRows";
block->Var(w_grad_var_name)
->SetType(framework::proto::VarType::SELECTED_ROWS);
if (hasBias) {
VLOG(30) << "hierarchical_sigmoid_grad op "
<< framework::GradVarName("Bias") << " is set to SelectedRows";
block->Var(bias_grad_var_name)
->SetType(framework::proto::VarType::SELECTED_ROWS);
}
} else {
VLOG(30) << "hierarchical_sigmoid_grad op " << framework::GradVarName("W")
<< " is set to LoDTensor";
block->Var(w_grad_var_name)
->SetType(framework::proto::VarType::LOD_TENSOR);
if (hasBias) {
VLOG(30) << "hierarchical_sigmoid_grad op "
<< framework::GradVarName("Bias") << " is set to LoDTensor";
block->Var(bias_grad_var_name)
->SetType(framework::proto::VarType::LOD_TENSOR);
}
}
block->Var(w_grad_var_name)->SetDataType(block->Var("W")->GetDataType());
}
};
} // namespace operators
} // namespace paddle
......@@ -153,7 +220,8 @@ namespace ops = paddle::operators;
REGISTER_OPERATOR(hierarchical_sigmoid, ops::HierarchicalSigmoidOp,
ops::HierarchicalSigmoidOpMaker<int>,
paddle::framework::DefaultGradOpDescMaker<true>);
REGISTER_OPERATOR(hierarchical_sigmoid_grad, ops::HierarchicalSigmoidGradOp);
REGISTER_OPERATOR(hierarchical_sigmoid_grad, ops::HierarchicalSigmoidGradOp,
ops::HierarchicalSigmoidGradOpGradVarTypeInference);
REGISTER_OP_CPU_KERNEL(
hierarchical_sigmoid,
ops::HierarchicalSigmoidOpKernel<paddle::platform::CPUDeviceContext, float>,
......
paddle/fluid/operators/hierarchical_sigmoid_op.h
浏览文件 @ 12e1719f
......@@ -14,12 +14,16 @@ limitations under the License. */
#pragma once
#include <iostream>
#include <set>
#include <vector>
#include "paddle/fluid/framework/mixed_vector.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/clip_op.h"
#include "paddle/fluid/operators/detail/safe_ref.h"
#include "paddle/fluid/operators/math/math_function.h"
#include "paddle/fluid/operators/math/matrix_bit_code.h"
#include "paddle/fluid/platform/transform.h"
namespace paddle {
namespace operators {
......@@ -28,20 +32,38 @@ template <typename T, int MajorType = Eigen::RowMajor,
using EigenMatrix = framework::EigenMatrix<T, MajorType, IndexType>;
using platform::Transform;
static std::vector<int64_t> PathToRows(const framework::LoDTensor& path) {
std::set<int64_t> rows;
for (int64_t i = 0; i < path.numel(); ++i) {
int64_t row = path.data<int64_t>()[i];
if (row < 0) {
continue;
}
rows.emplace(row);
}
return std::vector<int64_t>(rows.begin(), rows.end());
}
template <typename DeviceContext, typename T>
class HierarchicalSigmoidOpKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto* in = ctx.Input<framework::Tensor>("X");
auto* w = ctx.Input<framework::Tensor>("W");
auto* label = ctx.Input<framework::Tensor>("Label");
auto* bias = ctx.Input<framework::Tensor>("Bias");
auto* out = ctx.Output<framework::Tensor>("Out");
auto* pre_out = ctx.Output<framework::Tensor>("PreOut");
auto& in = detail::Ref(ctx.Input<framework::LoDTensor>("X"));
auto& w = detail::Ref(ctx.Input<framework::LoDTensor>("W"));
auto* path = ctx.Input<framework::LoDTensor>("PTable");
auto* code = ctx.Input<framework::LoDTensor>("PathCode");
auto& label = detail::Ref(ctx.Input<framework::LoDTensor>("Label"));
auto* bias = ctx.Input<framework::LoDTensor>("Bias");
auto* out = ctx.Output<framework::LoDTensor>("Out");
auto* pre_out = ctx.Output<framework::LoDTensor>("PreOut");
size_t num_classes = static_cast<size_t>(ctx.Attr<int>("num_classes"));
int64_t code_length = math::FindLastSet(num_classes - 1);
int64_t batch_size = in->dims()[0];
framework::Tensor sum;
bool is_custom = false;
if (path) {
is_custom = true;
}
int64_t code_length =
path ? path->dims()[1] : math::FindLastSet(num_classes - 1);
int64_t batch_size = in.dims()[0];
framework::LoDTensor sum;
auto& dev_ctx = ctx.template device_context<DeviceContext>();
auto* pre_out_data = pre_out->mutable_data<T>(
framework::make_ddim({batch_size, code_length}), ctx.GetPlace());
......@@ -52,7 +74,15 @@ class HierarchicalSigmoidOpKernel : public framework::OpKernel<T> {
zero(dev_ctx, pre_out, static_cast<T>(0.0));
auto& place = *ctx.template device_context<DeviceContext>().eigen_device();
math::RowwiseSum<DeviceContext, T> row_sum;
math::MatrixBitCodeFunctor<T> bit_code(num_classes, label->data<int64_t>());
std::unique_ptr<math::MatrixBitCodeFunctor<T>> bit_code;
if (!is_custom) {
bit_code.reset(new math::MatrixBitCodeFunctor<T>(num_classes,
label.data<int64_t>()));
} else {
bit_code.reset(new math::MatrixBitCodeFunctor<T>(*path, *code,
label.data<int64_t>()));
}
std::vector<int64_t> sum_dims({batch_size, 1UL});
sum.mutable_data<T>(framework::make_ddim(sum_dims), ctx.GetPlace());
......@@ -60,15 +90,15 @@ class HierarchicalSigmoidOpKernel : public framework::OpKernel<T> {
out->mutable_data<T>(ctx.GetPlace());
auto out_mat = framework::EigenVector<T>::Flatten(*out);
if (bias) {
bit_code.Add(pre_out, *bias);
bit_code->Add(*bias, pre_out);
}
bit_code.Mul(pre_out, *w, *in);
bit_code->Mul(pre_out, w, in);
// clip to [-40, 40]
Transform<DeviceContext> trans;
trans(ctx.template device_context<DeviceContext>(), pre_out_data,
pre_out_data + pre_out->numel(), pre_out_data,
ClipFunctor<T>(static_cast<T>(-40.0), static_cast<T>(40.0)));
bit_code.Sum(*pre_out, out, static_cast<T>(-1));
bit_code->Sum(*pre_out, out, static_cast<T>(-1));
// use softrelu to calculate cross entropy
pre_out_mat.device(place) = (static_cast<T>(1.0) + pre_out_mat.exp()).log();
row_sum(dev_ctx, *pre_out, &sum);
......@@ -84,50 +114,103 @@ template <typename DeviceContext, typename T>
class HierarchicalSigmoidGradOpKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto* in = ctx.Input<framework::Tensor>("X");
auto* w = ctx.Input<framework::Tensor>("W");
auto* in_grad = ctx.Output<framework::Tensor>(framework::GradVarName("X"));
auto* w_grad = ctx.Output<framework::Tensor>(framework::GradVarName("W"));
auto* bias_grad =
ctx.Output<framework::Tensor>(framework::GradVarName("Bias"));
auto* label = ctx.Input<framework::Tensor>("Label");
auto* pre_out = ctx.Input<framework::Tensor>("PreOut");
auto* out_grad =
ctx.Input<framework::Tensor>(framework::GradVarName("Out"));
framework::Tensor pre_out_grad;
pre_out_grad.mutable_data<T>(pre_out->dims(), ctx.GetPlace());
in_grad->mutable_data<T>(ctx.GetPlace());
w_grad->mutable_data<T>(ctx.GetPlace());
auto& in = detail::Ref(ctx.Input<framework::LoDTensor>("X"));
auto& w = detail::Ref(ctx.Input<framework::LoDTensor>("W"));
auto* path = ctx.Input<framework::LoDTensor>("PTable");
auto* code = ctx.Input<framework::LoDTensor>("PathCode");
auto* bias = ctx.Input<framework::LoDTensor>("Bias");
auto* in_grad =
ctx.Output<framework::LoDTensor>(framework::GradVarName("X"));
bool is_sparse = ctx.Attr<bool>("is_sparse");
auto& dev_ctx = ctx.template device_context<DeviceContext>();
math::SetConstant<DeviceContext, T> zero;
auto& label = detail::Ref(ctx.Input<framework::LoDTensor>("Label"));
auto& pre_out = detail::Ref(ctx.Input<framework::LoDTensor>("PreOut"));
auto& out_grad = detail::Ref(
ctx.Input<framework::LoDTensor>(framework::GradVarName("Out")));
framework::LoDTensor pre_out_grad;
pre_out_grad.mutable_data<T>(pre_out.dims(), ctx.GetPlace());
in_grad->mutable_data<T>(ctx.GetPlace());
zero(dev_ctx, in_grad, static_cast<T>(0.0));
zero(dev_ctx, w_grad, static_cast<T>(0.0));
size_t num_classes = static_cast<size_t>(ctx.Attr<int>("num_classes"));
math::MatrixBitCodeFunctor<T> bit_code(num_classes, label->data<int64_t>());
bool is_custom = false;
if (path) {
is_custom = true;
}
std::unique_ptr<math::MatrixBitCodeFunctor<T>> bit_code;
if (!is_custom) {
bit_code.reset(new math::MatrixBitCodeFunctor<T>(num_classes,
label.data<int64_t>()));
} else {
bit_code.reset(new math::MatrixBitCodeFunctor<T>(*path, *code,
label.data<int64_t>()));
}
auto& place = *ctx.template device_context<DeviceContext>().eigen_device();
auto pre_out_mat = EigenMatrix<T>::From(*pre_out);
auto pre_out_mat = EigenMatrix<T>::From(pre_out);
auto pre_out_grad_mat = EigenMatrix<T>::From(pre_out_grad);
auto out_grad_mat = EigenMatrix<T>::From(*out_grad);
auto out_grad_mat = EigenMatrix<T>::From(out_grad);
Eigen::array<int, 2> bcast{1, static_cast<int>(pre_out_grad.dims()[1])};
// softrelu derivative
pre_out_grad_mat.device(place) =
static_cast<T>(1.0) - static_cast<T>(1.0) / pre_out_mat.exp();
bit_code.Sub(&pre_out_grad); // the gradient of clip(w * x + b)
bit_code->Sub(&pre_out_grad); // the gradient of clip(w * x + b)
pre_out_grad_mat.device(place) =
pre_out_grad_mat * out_grad_mat.broadcast(bcast);
// TODO(guosheng): multiply pre_out_grad with subgradient of clipping to
// be consistent with the clipping in forward.
if (bias_grad) {
bias_grad->mutable_data<T>(ctx.GetPlace());
zero(dev_ctx, bias_grad, static_cast<T>(0.0));
bit_code.AddGrad(pre_out_grad, bias_grad);
if (!is_sparse) {
auto* bias_grad =
ctx.Output<framework::LoDTensor>(framework::GradVarName("Bias"));
if (bias_grad) {
bias_grad->mutable_data<T>(ctx.GetPlace());
zero(dev_ctx, bias_grad, static_cast<T>(0.0));
bit_code->AddGrad(pre_out_grad, bias_grad);
}
auto* w_grad =
ctx.Output<framework::LoDTensor>(framework::GradVarName("W"));
w_grad->mutable_data<T>(ctx.GetPlace());
zero(dev_ctx, w_grad, static_cast<T>(0.0));
bit_code->MulGradWeight(pre_out_grad, w_grad, in);
} else {
framework::Vector<int64_t> real_rows = PathToRows(*path);
auto* w_grad =
ctx.Output<framework::SelectedRows>(framework::GradVarName("W"));
w_grad->set_rows(real_rows);
// Build a map of id -> row_index to speed up finding the index of one id
w_grad->SyncIndex();
w_grad->set_height(w.dims()[0]);
auto* w_grad_value = w_grad->mutable_value();
framework::DDim temp_dim(w.dims());
set(temp_dim, 0, real_rows.size());
w_grad_value->mutable_data<T>(temp_dim, ctx.GetPlace());
zero(dev_ctx, w_grad_value, static_cast<T>(0.0));
auto* bias_grad =
ctx.Output<framework::SelectedRows>(framework::GradVarName("Bias"));
if (bias_grad) {
bias_grad->set_rows(real_rows);
// build ids -> rows index map
bias_grad->SyncIndex();
bias_grad->set_height(bias->dims()[0]);
auto* bias_grad_value = bias_grad->mutable_value();
std::vector<int64_t> dims = {static_cast<int64_t>(real_rows.size()),
bias->dims()[1]};
bias_grad_value->mutable_data<T>(framework::make_ddim(dims),
ctx.GetPlace());
zero(dev_ctx, bias_grad_value, static_cast<T>(0.0));
bit_code->AddGrad(pre_out_grad, bias_grad);
}
bit_code->MulGradWeight(pre_out_grad, w_grad, in);
}
bit_code.MulGradWeight(pre_out_grad, w_grad, *in);
bit_code.MulGradError(pre_out_grad, *w, in_grad);
bit_code->MulGradError(pre_out_grad, w, in_grad);
}
};
......
paddle/fluid/operators/math/matrix_bit_code.cc
浏览文件 @ 12e1719f
......@@ -19,16 +19,15 @@ namespace operators {
namespace math {
template <typename T>
void MatrixBitCodeFunctor<T>::Add(framework::Tensor* tmat,
const framework::Tensor& vec) {
SimpleCodeTable code_table(num_classes_);
void MatrixBitCodeFunctor<T>::Add(const framework::Tensor& vec,
framework::Tensor* tmat) {
size_t batch_size = tmat->dims()[0];
size_t width = tmat->dims()[1];
for (size_t i = 0; i < batch_size; ++i) {
auto code = code_table(static_cast<size_t>(ids_[i]));
int code_length = code.get_length();
auto code = code_table_->get_code(i);
int code_length = code->get_length();
for (int j = 0; j < code_length; ++j) {
size_t index = code.calc_index(j);
size_t index = code->calc_index(j);
tmat->data<T>()[i * width + j] += vec.data<T>()[index];
}
}
......@@ -37,31 +36,46 @@ void MatrixBitCodeFunctor<T>::Add(framework::Tensor* tmat,
template <typename T>
void MatrixBitCodeFunctor<T>::AddGrad(const framework::Tensor& tmat,
framework::Tensor* vec) {
SimpleCodeTable code_table(num_classes_);
size_t batch_size = tmat.dims()[0];
size_t width = tmat.dims()[1];
for (size_t i = 0; i < batch_size; ++i) {
auto code = code_table(static_cast<size_t>(ids_[i]));
int code_length = code.get_length();
auto code = code_table_->get_code(i);
int code_length = code->get_length();
for (int j = 0; j < code_length; ++j) {
size_t index = code.calc_index(j);
size_t index = code->calc_index(j);
vec->data<T>()[index] += tmat.data<T>()[i * width + j];
}
}
}
template <typename T>
void MatrixBitCodeFunctor<T>::AddGrad(const framework::Tensor& tmat,
framework::SelectedRows* vec) {
size_t batch_size = tmat.dims()[0];
size_t width = tmat.dims()[1];
for (size_t i = 0; i < batch_size; ++i) {
auto code = code_table_->get_code(i);
int code_length = code->get_length();
for (int j = 0; j < code_length; ++j) {
size_t index = code->calc_index(j);
int64_t row_index = vec->GetIndexFromId(static_cast<int64_t>(index));
vec->mutable_value()->data<T>()[row_index] +=
tmat.data<T>()[i * width + j];
}
}
}
template <typename T>
void MatrixBitCodeFunctor<T>::Sum(const framework::Tensor& tmat,
framework::Tensor* sum, T scale_sum) {
SimpleCodeTable code_table(num_classes_);
size_t num_samples = tmat.dims()[0];
size_t o_width = tmat.dims()[1];
for (size_t i = 0; i < num_samples; ++i) {
T sm = static_cast<T>(0.0);
auto code = code_table(static_cast<size_t>(ids_[i]));
int code_length = code.get_length();
auto code = code_table_->get_code(i);
int code_length = code->get_length();
for (int j = 0; j < code_length; ++j) {
if (code.calc_bit(j)) {
if (code->calc_bit(j)) {
// calc_bit starts from right most bit, while data in tmat[i] is in the
// reverse order.
sm += tmat.data<T>()[i * o_width + j];
......@@ -75,7 +89,6 @@ template <typename T>
void MatrixBitCodeFunctor<T>::Mul(framework::Tensor* tmat,
const framework::Tensor& weight,
const framework::Tensor& input) {
SimpleCodeTable code_table(num_classes_);
size_t num_samples = tmat->dims()[0];
size_t tmat_width = tmat->dims()[1];
size_t input_width = input.dims()[1];
......@@ -84,10 +97,10 @@ void MatrixBitCodeFunctor<T>::Mul(framework::Tensor* tmat,
auto weight_value = weight.data<T>();
auto input_value = input.data<T>();
for (size_t i = 0; i < num_samples; ++i) {
auto code = code_table(static_cast<size_t>(ids_[i]));
int code_length = code.get_length();
auto code = code_table_->get_code(i);
int code_length = code->get_length();
for (int j = 0; j < code_length; ++j) {
size_t index = code.calc_index(j);
size_t index = code->calc_index(j);
T sum = static_cast<T>(0.0);
for (size_t k = 0; k < input_width; ++k) {
sum += weight_value[weight_width * index + k] *
......@@ -102,7 +115,6 @@ template <typename T>
void MatrixBitCodeFunctor<T>::MulGradWeight(const framework::Tensor& tmat,
framework::Tensor* weight,
const framework::Tensor& input) {
SimpleCodeTable code_table(num_classes_);
size_t num_samples = tmat.dims()[0];
size_t input_width = input.dims()[1];
size_t tmat_width = tmat.dims()[1];
......@@ -111,10 +123,10 @@ void MatrixBitCodeFunctor<T>::MulGradWeight(const framework::Tensor& tmat,
auto weight_value = weight->data<T>();
auto input_value = input.data<T>();
for (size_t i = 0; i < num_samples; ++i) {
auto code = code_table(static_cast<size_t>(ids_[i]));
int code_length = code.get_length();
auto code = code_table_->get_code(i);
int code_length = code->get_length();
for (int j = 0; j < code_length; ++j) {
size_t index = code.calc_index(j);
size_t index = code->calc_index(j);
for (size_t k = 0; k < input_width; ++k) {
weight_value[weight_width * index + k] +=
......@@ -124,11 +136,35 @@ void MatrixBitCodeFunctor<T>::MulGradWeight(const framework::Tensor& tmat,
}
}
template <typename T>
void MatrixBitCodeFunctor<T>::MulGradWeight(const framework::Tensor& tmat,
framework::SelectedRows* weight,
const framework::Tensor& input) {
size_t num_samples = tmat.dims()[0];
size_t input_width = input.dims()[1];
size_t tmat_width = tmat.dims()[1];
size_t weight_width = weight->value().dims()[1];
auto tmat_value = tmat.data<T>();
auto weight_value = weight->mutable_value()->data<T>();
auto input_value = input.data<T>();
for (size_t i = 0; i < num_samples; ++i) {
auto code = code_table_->get_code(i);
int code_length = code->get_length();
for (int j = 0; j < code_length; ++j) {
size_t index = code->calc_index(j);
for (size_t k = 0; k < input_width; ++k) {
int64_t row_index = weight->GetIndexFromId(static_cast<int64_t>(index));
weight_value[row_index * weight_width + k] +=
tmat_value[i * tmat_width + j] * input_value[input_width * i + k];
}
}
}
}
template <typename T>
void MatrixBitCodeFunctor<T>::MulGradError(const framework::Tensor& tmat,
const framework::Tensor& weight,
framework::Tensor* input) {
SimpleCodeTable code_table(num_classes_);
size_t num_samples = tmat.dims()[0];
size_t tmat_width = tmat.dims()[1];
size_t input_width = input->dims()[1];
......@@ -138,10 +174,10 @@ void MatrixBitCodeFunctor<T>::MulGradError(const framework::Tensor& tmat,
auto input_value = input->data<T>();
for (size_t i = 0; i < num_samples; ++i) {
auto code = code_table(static_cast<size_t>(ids_[i]));
int code_length = code.get_length();
auto code = code_table_->get_code(i);
int code_length = code->get_length();
for (int j = 0; j < code_length; ++j) {
size_t index = code.calc_index(j);
size_t index = code->calc_index(j);
for (size_t k = 0; k < input_width; ++k) {
input_value[input_width * i + k] +=
......@@ -154,14 +190,13 @@ void MatrixBitCodeFunctor<T>::MulGradError(const framework::Tensor& tmat,
template <typename T>
void MatrixBitCodeFunctor<T>::Sub(framework::Tensor* tmat) {
SimpleCodeTable code_table(num_classes_);
size_t num_samples = tmat->dims()[0];
size_t o_width = tmat->dims()[1];
for (size_t i = 0; i < num_samples; ++i) {
auto code = code_table(static_cast<size_t>(ids_[i]));
int code_length = code.get_length();
auto code = code_table_->get_code(i);
int code_length = code->get_length();
for (int j = 0; j < code_length; ++j) {
if (code.calc_bit(j)) {
if (code->calc_bit(j)) {
tmat->data<T>()[i * o_width + j] -= 1;
}
}
......
paddle/fluid/operators/math/matrix_bit_code.h
浏览文件 @ 12e1719f
......@@ -14,6 +14,8 @@ limitations under the License. */
#pragma once
#include "paddle/fluid/framework/eigen.h"
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/framework/selected_rows.h"
#include "paddle/fluid/framework/tensor.h"
#include "paddle/fluid/platform/device_context.h"
......@@ -92,9 +94,27 @@ inline int clz(const T& value) {
inline size_t FindLastSet(size_t x) { return sizeof(size_t) * 8 - clz(x); }
#endif // !_WIN32
// set a code interface to create multiple code
class Code {
public:
virtual ~Code() {}
virtual size_t calc_index(int bit) const = 0;
virtual bool calc_bit(int bit) const = 0;
virtual int get_length() const = 0;
};
// set a CodeTable interface to create multiple code table
class CodeTable {
public:
virtual std::unique_ptr<Code> get_code(int64_t code) const = 0;
virtual size_t size() const = 0;
virtual int get_max_code_length() const = 0;
virtual ~CodeTable() {}
};
struct SimpleCode {
SimpleCode(size_t code, size_t num_classes) : c_(code + num_classes) {}
class SimpleCode : public Code {
public:
SimpleCode(size_t code, size_t num_classes, const int64_t* ids)
: c_(static_cast<size_t>(ids[code]) + num_classes) {}
/**
* Here the id of root shoud be 1 rather than 0, thus the encoding of class c
* is `c + num_classes` and all siblings can get the same weight indice using
......@@ -104,41 +124,121 @@ struct SimpleCode {
* Binary classification path is the suffixes of encoding, thus leave out the
* left most bit in calc_bit.
*/
inline size_t calc_index(int bit) const { return (c_ >> (bit + 1)) - 1; }
inline bool calc_bit(int bit) const { return c_ & (1 << bit); }
inline int get_length() const { return FindLastSet(c_) - 1; }
size_t calc_index(int bit) const { return (c_ >> (bit + 1)) - 1; }
bool calc_bit(int bit) const { return c_ & (1 << bit); }
int get_length() const { return FindLastSet(c_) - 1; }
private:
size_t c_;
};
struct SimpleCodeTable {
explicit SimpleCodeTable(size_t num_classes) : num_classes_(num_classes) {}
SimpleCode operator()(size_t code) const {
return SimpleCode(code, num_classes_);
template <typename T>
class CustomCode : public Code {
public:
CustomCode(const framework::Tensor& ptable, const framework::Tensor& pcode,
const int64_t* ids, int index)
: ids_(ids), index_(index) {
ptable_ = ptable.Slice(index, index + 1);
pcode_ = pcode.Slice(index, index + 1);
}
/**
* Here the id of root shoud be 1 rather than 0, thus the encoding of class c
* is `c + num_classes` and all siblings can get the same weight indice using
* prefixes.
* Weight index is the prefixes of encoding, thus leave out the right most
* bit in calc_index.
* Binary classification path is the suffixes of encoding, thus leave out the
* left most bit in calc_bit.
*/
size_t calc_index(int bit) const { return ptable_.data<T>()[bit]; }
bool calc_bit(int bit) const { return pcode_.data<T>()[bit]; }
int get_length() const {
int length = 0;
for (int i = 0; i < static_cast<int>(ptable_.dims()[1]); i++) {
if (ptable_.data<T>()[i] >= 0) {
length++;
} else {
return length;
}
}
return length;
}
private:
framework::Tensor ptable_;
framework::Tensor pcode_;
const int64_t* ids_;
const int index_;
};
class SimpleCodeTable : public CodeTable {
public:
SimpleCodeTable(size_t num_classes, const int64_t* ids)
: num_classes_(num_classes), ids_(ids) {}
std::unique_ptr<Code> get_code(int64_t code) const {
std::unique_ptr<Code> coder(new SimpleCode(code, num_classes_, ids_));
return coder;
}
size_t size() const { return num_classes_; }
int get_max_code_length() const { return FindLastSet(num_classes_ - 1); }
private:
size_t num_classes_;
const int64_t* ids_;
};
template <typename T>
class CustomCodeTable : public CodeTable {
public:
CustomCodeTable(const framework::Tensor& ptable,
const framework::Tensor& pcode, const int64_t* ids)
: ptable_(ptable), pcode_(pcode), ids_(ids) {}
std::unique_ptr<Code> get_code(int64_t code) const {
std::unique_ptr<Code> coder(new CustomCode<T>(ptable_, pcode_, ids_, code));
return coder;
}
size_t size() const { return static_cast<size_t>(ptable_.dims()[1]); }
int get_max_code_length() const {
return static_cast<size_t>(ptable_.dims()[1]);
}
private:
const framework::Tensor& ptable_;
const framework::Tensor& pcode_;
const int64_t* ids_;
};
template <typename T>
class MatrixBitCodeFunctor {
public:
explicit MatrixBitCodeFunctor(size_t num_classes, const int64_t* ids)
: num_classes_(num_classes), ids_(ids) {}
MatrixBitCodeFunctor(size_t num_classes, const int64_t* ids)
: num_classes_(num_classes),
ids_(ids),
code_table_(new SimpleCodeTable(num_classes, ids)) {}
MatrixBitCodeFunctor(const framework::Tensor& ptable,
const framework::Tensor& pcode, const int64_t* ids)
: num_classes_(static_cast<size_t>(ptable.dims()[1])),
ids_(ids),
code_table_(new CustomCodeTable<int64_t>(ptable, pcode, ids)) {}
/* For j < code_length
tmat(i, j) += vec(0, index(i, j))
*/
void Add(framework::Tensor* tmat, const framework::Tensor& vec);
void Add(const framework::Tensor& vec, framework::Tensor* tmat);
/* For j < code_length
vec(0, index(i, j)) += tmat(i, j)
*/
void AddGrad(const framework::Tensor& tmat, framework::Tensor* vec);
/* For selected rows For j < code_length
vec(0, index(i, j)) += tmat(i, j)
*/
void AddGrad(const framework::Tensor& tmat, framework::SelectedRows* vec);
/* For j < code_length
sum(i, 0) = \sum_j bit(i, j) * tmat(i, j)
*/
......@@ -159,6 +259,12 @@ class MatrixBitCodeFunctor {
*/
void MulGradWeight(const framework::Tensor& tmat, framework::Tensor* weight,
const framework::Tensor& input);
/* For SelectedRows Weight, For index(i, j) >= 0:
weight.row(index(i, j)) += tmat(i, j) * input.row(i)
*/
void MulGradWeight(const framework::Tensor& tmat,
framework::SelectedRows* weight,
const framework::Tensor& input);
/* For j < code_length
input.row(i) += tmat(i, j) * weight.row(index(i, j))
*/
......@@ -167,6 +273,7 @@ class MatrixBitCodeFunctor {
size_t num_classes_;
const int64_t* ids_;
std::unique_ptr<CodeTable> code_table_;
};
} // namespace math
} // namespace operators
......
python/paddle/fluid/layers/nn.py
浏览文件 @ 12e1719f
......@@ -4587,27 +4587,43 @@ def hsigmoid(input,
num_classes,
param_attr=None,
bias_attr=None,
name=None):
name=None,
path_table=None,
path_code=None,
is_custom=False,
is_sparse=False):
"""
The hierarchical sigmoid operator is used to accelerate the training
process of language model. This operator organizes the classes into a
complete binary tree, each leaf node represents a class(a word) and each
complete binary tree, or you can use is_custom to pass your own tree to
implement hierarchical. Each leaf node represents a class(a word) and each
internal node acts as a binary classifier. For each word there's a unique
path from root to it's leaf node, hsigmoid calculate the cost for each
internal node on the path, and sum them to get a total cost. hsigmoid can
achive a acceleration from :math:`O(N)` to :math:`O(logN)`, where :math:`N`
represents the size of word dict.
Refer to `Hierarchical Probabilistic Neural Network Language Model
Using default tree you can Refer to `Hierarchical Probabilistic Neural Network Language Model
<http://www.iro.umontreal.ca/~lisa/pointeurs/hierarchical-nnlm-aistats05.pdf>`_
And if you want to use the costumed tree by set 'is_custom' as true you may need to do following things first:
1. using your word dict to build a binary tree, each leaf node should be an word of your word dict
2. build a dict to store word_id -> word's leaf to root path, we call it path_table.
3. build a dict to store word_id -> code of word's leaf to root path, we call it path_code. Code
means label of each binary classification, using 1 indicate true, 0 indicate false.
4. now, each word should has its path and code along the path, you can pass a batch of path and code
related to the same batch of inputs.
Args:
input (Variable): The input tensor variable with shape
:math:`[N \\times D]`, where :math:`N` is the size of mini-batch,
and :math:`D` is the feature size.
label (Variable): The tensor variable contains labels of training data.
It's a tensor with shape is :math:`[N \\times 1]`.
num_classes: (int), The number of classes, must not be less than 2.
num_classes: (int), The number of classes, must not be less than 2. with default tree this has to be set,
it should never be None under is_custom=False, but while is_custom is true, it should be non leaf num
which indicates the num of classes using by binary classify.
param_attr (ParamAttr|None): The parameter attribute for learnable parameters/weights
of hsigmoid. If it is set to None or one attribute of ParamAttr, hsigmoid
will create ParamAttr as param_attr. If the Initializer of the param_attr
......@@ -4619,9 +4635,19 @@ def hsigmoid(input,
is not set, the bias is initialized zero. Default: None.
name (str|None): A name for this layer(optional). If set None, the layer
will be named automatically. Default: None.
path_table: (Variable|None) this variable can store each batch of samples' path to root,
it should be in leaf -> root order
path_table should have the same shape with path_code, and for each sample i path_table[i] indicates a np.array like
structure and each element in this array is indexes in parent nodes' Weight Matrix.
path_code: (Variable|None) this variable can store each batch of samples' code,
each code consist with every code of parent nodes. it should be in leaf -> root order
is_custom: (bool|False)using user defined binary tree instead of default complete binary tree, if costum is
set you need to set path_table/path_code/num_classes, otherwise num_classes should be set
is_sparse: (bool|False)using sparse update instead of dense update, if set, the gradient
of W and input will be sparse.
Returns:
Out: (Tensor) The cost of hierarchical sigmoid operator. the shape is [N, 1]
Out: (LodTensor) The cost of hierarchical sigmoid operator. the shape is [N, 1]
Examples:
......@@ -4637,27 +4663,62 @@ def hsigmoid(input,
out = helper.create_variable_for_type_inference(dtype)
pre_out = helper.create_variable_for_type_inference(dtype)
dim = input.shape[1]
if num_classes < 2:
raise ValueError("num_classes must not be less than 2.")
weights = helper.create_parameter(
attr=helper.param_attr,
shape=[num_classes - 1, dim],
is_bias=False,
dtype=input.dtype)
inputs = {"X": input, "W": weights, "Label": label}
if helper.bias_attr:
bias = helper.create_parameter(
attr=helper.bias_attr,
shape=[1, num_classes - 1],
is_bias=True,
if ((num_classes is None) or (num_classes < 2)) and (not is_custom):
raise ValueError(
"num_classes must not be less than 2 with default tree")
if (is_custom) and (path_code is None):
raise ValueError("path_code should not be None with costum tree")
elif (is_custom) and (path_table is None):
raise ValueError("path_table should not be None with costum tree")
elif (is_custom) and (num_classes is None):
raise ValueError("num_classes should not be None with costum tree")
else:
pass
weights = None
if not is_custom:
weights = helper.create_parameter(
attr=helper.param_attr,
shape=[num_classes - 1, dim],
is_bias=False,
dtype=input.dtype)
inputs['Bias'] = bias
else:
weights = helper.create_parameter(
attr=helper.param_attr,
shape=[num_classes, dim],
is_bias=False,
dtype=input.dtype)
inputs = {
"X": input,
"W": weights,
"PTable": path_table,
"PathCode": path_code,
"Label": label
}
if helper.bias_attr:
if not is_custom:
bias = helper.create_parameter(
attr=helper.bias_attr,
shape=[num_classes - 1, 1],
is_bias=True,
dtype=input.dtype)
inputs['Bias'] = bias
else:
bias = helper.create_parameter(
attr=helper.bias_attr,
shape=[num_classes, 1],
is_bias=True,
dtype=input.dtype)
inputs['Bias'] = bias
helper.append_op(
type="hierarchical_sigmoid",
inputs=inputs,
outputs={"Out": out,
"PreOut": pre_out},
attrs={"num_classes": num_classes})
attrs={"num_classes": num_classes,
"is_sparse": is_sparse})
return out
......
python/paddle/fluid/tests/unittests/test_hsigmoid_op.py
浏览文件 @ 12e1719f
......@@ -16,6 +16,8 @@ from __future__ import print_function
import unittest
import numpy as np
import paddle.fluid.core as core
import paddle.fluid as fluid
import math
from op_test import OpTest
......@@ -40,6 +42,29 @@ class CodeTable(object):
return self.c & (1 << bit)
class CodeTableWithCustomTree(object):
def __init__(self, path_table, path_code, index):
self.ptable_ = path_table
self.pcode_ = path_code
self.index_ = index
def cal_index(self, bit):
return self.ptable_[self.index_][bit]
def get_length(self):
length = 0
for ele in self.ptable_[self.index_]: # find the first -1 to stop trace
if ele >= 0:
length = length + 1
else:
return length
return length
def cal_bit(self, bit):
return self.pcode_[self.index_][bit]
def hsigmoid(x, w, label, bias, num_classes):
batch_size = x.shape[0]
code_length = find_latest_set(num_classes - 1)
......@@ -52,7 +77,7 @@ def hsigmoid(x, w, label, bias, num_classes):
length = code_table.get_length()
for j in range(length):
idx = code_table.cal_index(j)
pre_output[i][j] += bias[0][idx]
pre_output[i][j] += bias[idx][0]
for i in range(batch_size):
code_table = CodeTable(num_classes, label[i])
length = code_table.get_length()
......@@ -77,17 +102,58 @@ def hsigmoid(x, w, label, bias, num_classes):
return pre_output, out
def hsigmoidWithCustomTree(x, w, path_table, path_code, label, bias,
num_classes):
batch_size = x.shape[0]
code_length = len(path_table[0])
code_table = [0 for _ in range(code_length)]
# init pre_out with shape [N, code_length]
pre_output = np.zeros((batch_size, code_length))
pre_sum = np.zeros((batch_size, 1))
out = np.zeros((batch_size, 1)).astype("float32")
if isinstance(bias, np.ndarray):
for i in range(batch_size):
code_table = CodeTableWithCustomTree(path_table, path_code, i)
length = code_table.get_length()
for j in range(length):
idx = code_table.cal_index(j)
pre_output[i][j] += bias[idx][0]
for i in range(batch_size):
code_table = CodeTableWithCustomTree(path_table, path_code, i)
length = code_table.get_length()
for j in range(length):
idx = code_table.cal_index(j)
pre_output[i][j] += np.dot(w[idx], x[i])
# clip[-40.0, 40.0]
pre_output = np.clip(pre_output, -40.0, 40.0)
# out(i, 0) = \sum_j bit(i, j) * preout(i, j)
for i in range(batch_size):
code_table = CodeTableWithCustomTree(path_table, path_code, i)
length = code_table.get_length()
sum = 0.0
for j in range(length):
if code_table.cal_bit(j):
sum += pre_output[i][j]
out[i] = -1.0 * sum
# soft relu
pre_output = np.log(1 + np.exp(pre_output))
pre_sum = pre_output.sum(1).reshape((batch_size, 1))
out += pre_sum
return pre_output, out
class TestHSigmoidOp(OpTest):
def setUp(self):
self.op_type = "hierarchical_sigmoid"
num_classes = 6
feature_size = 8
batch_size = 4
x = np.random.random((batch_size, feature_size)).astype("float32")
w = np.random.random((num_classes - 1, feature_size)).astype("float32")
x = np.random.random((batch_size, feature_size)).astype("float32") * 2
w = np.random.random(
(num_classes - 1, feature_size)).astype("float32") * 2
label = np.random.randint(0, num_classes, (batch_size, 1))
bias = np.random.random((1, num_classes - 1)).astype("float32")
self.attrs = {'num_classes': num_classes}
bias = np.random.random((num_classes - 1, 1)).astype("float32")
self.attrs = {'num_classes': num_classes, 'is_sparse': False}
self.inputs = {'X': x, 'W': w, 'Label': label, 'Bias': bias}
pre_output, out = hsigmoid(x, w, label, bias, num_classes)
self.outputs = {'PreOut': pre_output, 'Out': out}
......@@ -99,5 +165,185 @@ class TestHSigmoidOp(OpTest):
self.check_grad(['Bias', 'X', 'W'], ['Out'], no_grad_set=set('Label'))
class TestHSigmoidOpSparse(OpTest):
def setUp(self):
self.op_type = "hierarchical_sigmoid"
num_classes = 6 #using 1,2,3,4,5,6 to build a huffman tree and select 1,2,5,6 as sample
feature_size = 8
batch_size = 4
x = np.random.random((batch_size, feature_size)).astype("float32")
w = np.random.random((num_classes - 1, feature_size)).astype("float32")
label = np.array([0, 1, 4, 5])
path_table = np.array(
[(0, 2, -1, -1, -1), (0, 1, 3, -1, -1), (0, 1, 4, -1, -1),
(0, 2, -1, -1,
-1)]) #np.array to store 1,2,5,6s' non-leaf path(root -> leaf)
path_code = np.array([(0, 0, -1, -1, -1), (1, 1, 1, -1, -1), (
1, 0, 0, -1, -1), (0, 1, -1, -1, -1)]) #np.array to store
bias = np.random.random((num_classes - 1, 1)).astype("float32")
self.attrs = {'num_classes': num_classes, 'is_sparse': True}
self.inputs = {
'X': x,
'W': w,
'PTable': path_table,
'PathCode': path_code,
'Label': label,
'Bias': bias
}
pre_output, out = hsigmoidWithCustomTree(x, w, path_table, path_code,
label, bias, num_classes)
self.outputs = {'PreOut': pre_output, 'Out': out}
def test_check_output(self):
self.check_output()
class TestHSigmoidOpWithSparseGrad(unittest.TestCase):
def hs_net_conf(self, is_sparse):
input_word = fluid.layers.data(name="x", shape=[1], dtype='int64')
path_table = fluid.layers.data(
name='path_table', shape=[3], dtype='int64')
path_code = fluid.layers.data(
name='path_code', shape=[3], dtype='int64')
label = fluid.layers.data(name='label', shape=[1], dtype='int64')
data_list = [input_word, path_table, path_code, label]
emb = fluid.layers.embedding(
input=input_word,
is_sparse=is_sparse,
size=[3, 3],
param_attr=fluid.ParamAttr(initializer=fluid.initializer.Normal(
scale=1 / math.sqrt(3))))
cost = fluid.layers.hsigmoid(
input=emb,
label=label,
bias_attr=True,
num_classes=3,
path_table=path_table,
path_code=path_code,
is_custom=True,
is_sparse=is_sparse)
avg_cost = fluid.layers.reduce_mean(cost)
return avg_cost, data_list
def training_test(self, is_sparse):
with fluid.program_guard(fluid.Program(), fluid.Program()):
start_up = fluid.default_startup_program()
start_up.random_seed = 1 # Fix random seed
x = np.arange(6).reshape(6)
path_table = np.array([(1, 2, -1), (1, 2, -1)])
path_code = np.array([(1, 0, -1), (0, 0, -1)])
label = np.array([1, 4])
loss, data_list = self.hs_net_conf(is_sparse)
optimizer = fluid.optimizer.SGD(learning_rate=1e-3)
optimizer.minimize(loss)
main_program = fluid.default_main_program()
place = fluid.CPUPlace()
feeder = fluid.DataFeeder(feed_list=data_list, place=place)
exe = fluid.Executor(place)
exe.run(start_up)
result = list()
for i in range(10):
data = [([[x[i % 2]]], [list(path_table[i % 2])],
[list(path_code[i % 2])], [label[i % 2]])]
loss_val = exe.run(main_program,
feed=feeder.feed(data),
fetch_list=[loss])
result.append(loss_val)
return result
def test_hs_grad_with_sparse(self):
dense_result = self.training_test(is_sparse=False)
sparse_result = self.training_test(is_sparse=True)
assert (dense_result == sparse_result)
class TestHSigmoidOpWithCostumTree(OpTest):
def setUp(self):
self.op_type = "hierarchical_sigmoid"
num_classes = 6 #using 1,2,3,4,5,6 to build a huffman tree and select 1,2,5,6 as sample
feature_size = 8
batch_size = 4
x = np.random.random((batch_size, feature_size)).astype("float32") * 2
w = np.random.random(
(num_classes - 1, feature_size)).astype("float32") * 2
label = np.array([0, 1, 4, 5])
path_table = np.array(
[(0, 2, -1, -1, -1), (0, 1, 3, -1, -1), (0, 1, 4, -1, -1),
(0, 2, -1, -1,
-1)]) #np.array to store 1,2,5,6s' non-leaf path(root -> leaf)
path_code = np.array([(0, 0, -1, -1, -1), (1, 1, 1, -1, -1), (
1, 0, 0, -1, -1), (0, 1, -1, -1, -1)]) #np.array to store
bias = np.random.random((num_classes - 1, 1)).astype("float32")
self.attrs = {'num_classes': num_classes, 'is_sparse': False}
self.inputs = {
'X': x,
'W': w,
'PTable': path_table,
'PathCode': path_code,
'Label': label,
'Bias': bias
}
pre_output, out = hsigmoidWithCustomTree(x, w, path_table, path_code,
label, bias, num_classes)
self.outputs = {'PreOut': pre_output, 'Out': out}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(['Bias', 'X', 'W'], ['Out'], no_grad_set=set('Label'))
class TestHSigmoidOpWithCostumTreeWithoutBias(OpTest):
def setUp(self):
self.op_type = "hierarchical_sigmoid"
num_classes = 6 #using 1,2,3,4,5,6 to build a huffman tree and select 1,2,5,6 as sample
feature_size = 8
batch_size = 4
x = np.random.random((batch_size, feature_size)).astype("float32") * 2
w = np.random.random(
(num_classes - 1, feature_size)).astype("float32") * 2
label = np.array([0, 1, 4, 5])
path_table = np.array(
[(0, 2, -1, -1, -1), (0, 1, 3, -1, -1), (0, 1, 4, -1, -1),
(0, 2, -1, -1,
-1)]) #np.array to store 1,2,5,6s' non-leaf path(root -> leaf)
path_code = np.array([(0, 0, -1, -1, -1), (1, 1, 1, -1, -1), (
1, 0, 0, -1, -1), (0, 1, -1, -1, -1)]) #np.array to store
# bias = np.random.random((num_classes - 1, 1)).astype("float32")
self.attrs = {'num_classes': num_classes, 'is_sparse': False}
self.inputs = {
'X': x,
'W': w,
'PTable': path_table,
'PathCode': path_code,
'Label': label,
}
pre_output, out = hsigmoidWithCustomTree(
x=x,
w=w,
path_table=path_table,
path_code=path_code,
label=label,
bias=None,
num_classes=num_classes)
self.outputs = {'PreOut': pre_output, 'Out': out}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(['X', 'W'], ['Out'], no_grad_set=set('Label'))
if __name__ == '__main__':
unittest.main()
python/paddle/fluid/tests/unittests/test_layers.py
浏览文件 @ 12e1719f
......@@ -185,6 +185,25 @@ class TestBook(unittest.TestCase):
input=x, label=y, num_classes=2))
print(str(program))
# test hsigmod with custom tree structure
program2 = Program()
with program_guard(program2):
x2 = layers.data(name='x2', shape=[4, 8], dtype='float32')
y2 = layers.data(name='y2', shape=[4], dtype='int64')
path_table = layers.data(
name='path_table', shape=[4, 6], dtype='int64')
path_code = layers.data(
name='path_code', shape=[4, 6], dtype='int64')
self.assertIsNotNone(
layers.hsigmoid(
input=x2,
label=y2,
num_classes=6,
path_table=path_table,
path_code=path_code,
is_custom=True))
print(str(program2))
def test_sequence_expand(self):
program = Program()
with program_guard(program):
......
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