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#ifndef CAFFE2_OPERATORS_SEQUENCE_OPS_H_
#define CAFFE2_OPERATORS_SEQUENCE_OPS_H_
#include "caffe2/core/operator.h"
#include "caffe2/core/tensor.h"
#include "caffe2/utils/math.h"
namespace caffe2 {
template <class Context>
class GatherPaddingOp final : public Operator<Context> {
public:
USE_OPERATOR_CONTEXT_FUNCTIONS;
template <class... Args>
explicit GatherPaddingOp(Args&&... args)
: Operator<Context>(std::forward<Args>(args)...),
startPaddingWidth_(
this->template GetSingleArgument<int>("padding_width", 1)),
endPaddingWidth_(
this->template GetSingleArgument<int>("end_padding_width", -1)) {
CAFFE_ENFORCE_GE(startPaddingWidth_, 0);
if (endPaddingWidth_ < 0) {
endPaddingWidth_ = startPaddingWidth_;
}
}
bool RunOnDevice() override {
if (startPaddingWidth_ == 0 && endPaddingWidth_ == 0) {
Output(0)->Resize(std::vector<int64_t>(0));
auto output_0_data = Output(0)->template mutable_data<int64_t>();
// TODO(zhengxq): as suggested by salex@, change this to a loop.
math::Set<int64_t, Context>(
Output(0)->numel(), 0, output_0_data, &context_);
if (OutputSize() == 2) {
Output(1)->Resize(std::vector<int64_t>(0));
auto output_1_data = Output(1)->template mutable_data<int64_t>();
math::Set<int64_t, Context>(
Output(1)->numel(), 0, output_1_data, &context_);
}
return true;
}
return DispatchHelper<TensorTypes<float, double, int, int64_t, bool>>::call(
this, Input(0));
}
template <typename T>
bool DoRunWithType() {
const auto& in = Input(0);
CAFFE_ENFORCE_GE(in.dim(), 1);
const int32_t outer_size = in.sizes()[0];
const auto block_size = in.size_from_dim(1);
const auto pad_width = startPaddingWidth_ + endPaddingWidth_;
// if no lengths is provided, assume it is a single full-span entry
const int32_t* lengths_ptr = &outer_size;
int64_t lengths_size = 1;
if (InputSize() > 1) {
const auto& lengths = Input(1);
lengths_ptr = lengths.template data<int32_t>();
lengths_size = lengths.numel();
}
std::vector<int64_t> padShape(in.sizes().begin() + 1, in.sizes().end());
// output will contain accumulator over paddings
Output(0)->Resize(padShape);
T* padding_start_ptr = Output(0)->template mutable_data<T>();
math::Set<T, Context>(block_size, 0.0, padding_start_ptr, &context_);
// if no end_padding is provided, assume it's the same as start_padding
T* padding_end_ptr = padding_start_ptr;
if (OutputSize() == 2) {
Output(1)->Resize(padShape);
padding_end_ptr = Output(1)->template mutable_data<T>();
math::Set<T, Context>(block_size, 0.0, padding_end_ptr, &context_);
}
GatherPadding<T>(
outer_size,
lengths_size,
block_size,
pad_width,
in.template data<T>(),
lengths_ptr,
padding_start_ptr,
padding_end_ptr);
return true;
}
private:
template <typename T>
void GatherPadding(
const int outer_size,
const int lengths_size,
const int block_size,
const int pad_width,
const T* in_ptr,
const int* lengths_ptr,
T* padding_start_ptr,
T* padding_end_ptr);
int startPaddingWidth_;
int endPaddingWidth_;
// Scratch space required by the CUDA version
Tensor lengths_prefix_sum_buffer_{Context::GetDeviceType()};
Tensor lengths_prefix_sum_{Context::GetDeviceType()};
};
template <class Context>
class RemovePaddingOp final : public Operator<Context> {
public:
USE_OPERATOR_CONTEXT_FUNCTIONS;
template <class... Args>
explicit RemovePaddingOp(Args&&... args)
: Operator<Context>(std::forward<Args>(args)...),
startPaddingWidth_(
this->template GetSingleArgument<int>("padding_width", 1)),
endPaddingWidth_(
this->template GetSingleArgument<int>("end_padding_width", -1)) {
CAFFE_ENFORCE_GE(startPaddingWidth_, 0);
if (endPaddingWidth_ < 0) {
endPaddingWidth_ = startPaddingWidth_;
}
}
bool RunOnDevice() override {
if (startPaddingWidth_ == 0 && endPaddingWidth_ == 0) {
Output(0)->CopyFrom(Input(0), true /*async*/);
if (OutputSize() == 2) {
Output(1)->CopyFrom(Input(1), true /*async*/);
}
return true;
}
return DispatchHelper<TensorTypes<float, double, int, int64_t, bool>>::call(
this, Input(0));
}
template <typename T>
bool DoRunWithType();
private:
int startPaddingWidth_;
int endPaddingWidth_;
// Scratch space required by the CUDA version
Tensor lengths_prefix_sum_buffer_{Context::GetDeviceType()};
Tensor lengths_prefix_sum_{Context::GetDeviceType()};
};
template <class Context>
class AddPaddingOp final : public Operator<Context> {
public:
USE_OPERATOR_CONTEXT_FUNCTIONS;
template <class... Args>
explicit AddPaddingOp(Args&&... args)
: Operator<Context>(std::forward<Args>(args)...),
startPaddingWidth_(
this->template GetSingleArgument<int>("padding_width", 1)),
endPaddingWidth_(
this->template GetSingleArgument<int>("end_padding_width", -1)) {
CAFFE_ENFORCE_GE(startPaddingWidth_, 0);
if (endPaddingWidth_ < 0) {
endPaddingWidth_ = startPaddingWidth_;
}
}
bool RunOnDevice() override {
if (startPaddingWidth_ == 0 && endPaddingWidth_ == 0) {
Output(0)->CopyFrom(Input(0), true /*async*/);
if (OutputSize() == 2) {
Output(1)->CopyFrom(Input(1), true /*async*/);
}
return true;
}
return DispatchHelper<TensorTypes<float, double, int, int64_t, bool>>::call(
this, Input(0));
}
template <typename T>
bool DoRunWithType() {
const auto& in = Input(0);
CAFFE_ENFORCE_GE(in.dim(), 1);
const int32_t outer_size = in.sizes()[0];
const auto block_size = in.size_from_dim(1);
// if no lengths is provided, assume it is a single full-span entry
const int32_t* lengths_ptr = nullptr;
int32_t lengths_size = 1;
if (InputSize() > 1) {
const auto& lengths = Input(1);
lengths_ptr = lengths.template data<int32_t>();
lengths_size = lengths.numel();
}
// fetch paddings
// input_size == 2 : pad with zeros
// input_size == 3 : start and end paddings are the same
// input_size == 4 : different start and end paddings
const T* padding_start_ptr = nullptr;
const T* padding_end_ptr = nullptr;
if (InputSize() >= 3) {
auto& padding_start = Input(2);
CAFFE_ENFORCE_EQ(block_size, padding_start.numel());
padding_start_ptr = padding_start.template data<T>();
}
if (InputSize() == 4) {
auto& padding_end = Input(3);
CAFFE_ENFORCE_EQ(block_size, padding_end.numel());
padding_end_ptr = padding_end.template data<T>();
} else {
padding_end_ptr = padding_start_ptr;
}
auto out_dims = in.sizes().vec();
out_dims[0] += (startPaddingWidth_ + endPaddingWidth_) * lengths_size;
auto* out = Output(0, std::move(out_dims), at::dtype<T>());
const auto* in_ptr = in.template data<T>();
auto* out_ptr = out->template mutable_data<T>();
return MakePadding<T>(
in_ptr,
out_ptr,
lengths_ptr,
lengths_size,
outer_size,
padding_start_ptr,
padding_end_ptr,
block_size);
}
private:
template <typename T>
bool MakePadding(
const T* in_ptr,
T* out_ptr,
const int32_t* lengths_ptr,
int32_t lengths_size,
int32_t outer_size,
const T* padding_start_ptr,
const T* padding_end_ptr,
int64_t block_size);
int startPaddingWidth_;
int endPaddingWidth_;
// Scratch space required by the CUDA version
Tensor lengths_prefix_sum_buffer_{Context::GetDeviceType()};
Tensor lengths_prefix_sum_{Context::GetDeviceType()};
};
template <class Context>
class PadEmptySamplesOp : public Operator<Context> {
public:
USE_OPERATOR_CONTEXT_FUNCTIONS;
template <class... Args>
explicit PadEmptySamplesOp(Args&&... args)
: Operator<Context>(std::forward<Args>(args)...) {}
bool RunOnDevice() override;
};
} // namespace caffe2
#endif // CAFFE2_OPERATORS_SEQUENCE_OPS_H_
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