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// Copyright 2004-present Facebook. All Rights Reserved.
#ifndef CAFFE2_OPERATORS_BATCH_SPARSE_TO_DENSE_OP_H_
#define CAFFE2_OPERATORS_BATCH_SPARSE_TO_DENSE_OP_H_
#include "caffe2/core/context.h"
#include "caffe2/core/operator.h"
#include "caffe2/utils/math.h"
namespace caffe2 {
template <typename T, class Context>
class BatchSparseToDenseOp : public Operator<Context> {
public:
USE_OPERATOR_CONTEXT_FUNCTIONS;
USE_DISPATCH_HELPER;
template <class... Args>
explicit BatchSparseToDenseOp(Args&&... args)
: Operator<Context>(std::forward<Args>(args)...),
OP_SINGLE_ARG(int64_t, "dense_last_dim", dense_last_dim_, -1),
OP_SINGLE_ARG(T, "default_value", default_value_, static_cast<T>(0)) {}
bool RunOnDevice() override {
return DispatchHelper<TensorTypes<int32_t, int64_t>>::call(
this, Input(LENGTHS));
}
private:
template <typename TLen, typename TInd>
void FillInDenseValues(
const int64_t batch_size,
const int64_t indice_lengths,
const TLen* lengths_data,
const TInd* indices_data,
const T* values_data,
T* output_data,
Context* context);
template <typename TLen>
bool DoRunWithType() {
return DispatchHelper<
TensorTypes2<
int32_t,
int64_t,
GenericTensorImplementation>,
TLen>::call(this, Input(INDICES));
}
template <typename TLen, typename TInd>
bool DoRunWithType2() {
auto& lengths = Input(LENGTHS);
auto& indices = Input(INDICES);
auto& values = Input(VALUES);
CAFFE_ENFORCE_EQ(indices.numel(), values.numel());
CAFFE_ENFORCE_EQ(lengths.dim(), 1);
CAFFE_ENFORCE_EQ(indices.dim(), 1);
const TLen* lengths_data = lengths.template data<TLen>();
const TInd* indices_data = indices.template data<TInd>();
const T* values_data = values.template data<T>();
int64_t batch_size = lengths.numel();
vector<int64_t> output_shape = {batch_size};
if (InputSize() == 4) {
auto& shaper = Input(3);
CAFFE_ENFORCE_EQ(shaper.dim(), 2);
if (dense_last_dim_ == -1) {
dense_last_dim_ = shaper.size(1);
} else {
CAFFE_ENFORCE(
dense_last_dim_ == shaper.size(1),
"The last dim argument is not aligned with the shape input last dim");
}
} else {
CAFFE_ENFORCE(dense_last_dim_ >= 1, "The last dim of dense must be >= 1");
}
output_shape.push_back(dense_last_dim_);
auto* output = Output(0, output_shape, at::dtype<T>());
T* output_data = output->template mutable_data<T>();
math::Set(
output->numel(),
static_cast<T>(default_value_),
output_data,
&context_);
FillInDenseValues(
batch_size,
indices.numel(),
lengths_data,
indices_data,
values_data,
output_data,
&context_);
return true;
}
template <typename TLen>
bool DoRunWithOtherType2() {
CAFFE_THROW(
"BatchSparseToDense is not implemented on values of type ",
Input(VALUES).dtype().name(),
" with lengths of type ",
Input(LENGTHS).dtype().name(),
" and indices of type ",
Input(INDICES).dtype().name());
}
int64_t dense_last_dim_;
T default_value_;
INPUT_TAGS(LENGTHS, INDICES, VALUES);
// len_prefix_sum_ and len_prefix_tmp_ are buffers on the GPU. It is not used
// in the CPUContext implementation.
Tensor len_prefix_sum_{Context::GetDeviceType()};
Tensor len_prefix_tmp_{Context::GetDeviceType()};
};
template <typename T, class Context>
class BatchDenseToSparseOp : public Operator<Context> {
public:
USE_OPERATOR_CONTEXT_FUNCTIONS;
USE_DISPATCH_HELPER;
template <class... Args>
explicit BatchDenseToSparseOp(Args&&... args)
: Operator<Context>(std::forward<Args>(args)...) {}
bool RunOnDevice() {
return DispatchHelper<TensorTypes<int32_t, int64_t>>::call(
this, Input(LENGTHS));
}
private:
template <typename TLen, typename TInd>
void FillInSparseValues(
const int64_t batch_size,
const int64_t indice_lengths,
const TLen* lengths_data,
const TInd* indices_data,
const T* dense_data,
T* output_data,
Context* context);
template <typename TLen>
bool DoRunWithType() {
return DispatchHelper<
TensorTypes2<
int32_t,
int64_t,
GenericTensorImplementation>,
TLen>::call(this, Input(INDICES));
}
template <typename TLen, typename TInd>
bool DoRunWithType2() {
auto& lengths = Input(LENGTHS);
auto& indices = Input(INDICES);
auto& dense = Input(DENSE);
CAFFE_ENFORCE_EQ(lengths.dim(), 1);
CAFFE_ENFORCE_EQ(indices.dim(), 1);
CAFFE_ENFORCE_EQ(dense.dim(), 2);
const TLen* lengths_data = lengths.template data<TLen>();
const TInd* indices_data = indices.template data<TInd>();
const T* dense_data = dense.template data<T>();
int64_t batch_size = lengths.numel();
CAFFE_ENFORCE_EQ(batch_size, dense.size(0));
dense_last_dim_ = dense.size(1);
vector<int64_t> output_shape = indices.sizes().vec();
auto* output = Output(0, output_shape, at::dtype<T>());
T* output_data = output->template mutable_data<T>();
FillInSparseValues(
batch_size,
indices.numel(),
lengths_data,
indices_data,
dense_data,
output_data,
&context_);
return true;
}
template <typename TLen>
bool DoRunWithOtherType2() {
CAFFE_THROW(
"BatchDenseToSparse is not implemented on values of type ",
Input(DENSE).dtype().name(),
" with lengths of type ",
Input(LENGTHS).dtype().name(),
" and indices of type ",
Input(INDICES).dtype().name());
}
int64_t dense_last_dim_{};
INPUT_TAGS(LENGTHS, INDICES, DENSE);
// len_prefix_sum_ and len_prefix_tmp_ are buffers on the GPU. It is not used
// in the CPUContext implementation.
Tensor len_prefix_sum_{Context::GetDeviceType()};
Tensor len_prefix_tmp_{Context::GetDeviceType()};
};
} // namespace caffe2
#endif // CAFFE2_OPERATORS_BATCH_SPARSE_TO_DENSE_OP_H_
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