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#ifndef CAFFE2_OPERATORS_REDUCE_FRONT_BACK_SUM_MEAN_OPS_H_
#define CAFFE2_OPERATORS_REDUCE_FRONT_BACK_SUM_MEAN_OPS_H_
#include "caffe2/core/context.h"
#include "caffe2/core/logging.h"
#include "caffe2/core/operator.h"
#include "caffe2/utils/math.h"
#include "c10/util/irange.h"
namespace caffe2 {
template <class Context, bool FIRSTDIMS, bool NORMALIZE>
class SumReduceDimsOp final : public Operator<Context> {
public:
template <class... Args>
explicit SumReduceDimsOp(Args&&... args)
: Operator<Context>(std::forward<Args>(args)...),
num_reduce_dims_(
this->template GetSingleArgument<int32_t>("num_reduce_dim", 1)) {}
USE_OPERATOR_CONTEXT_FUNCTIONS;
bool RunOnDevice() override {
return DispatchHelper<TensorTypes<int, int64_t, float, double>>::call(
this, Input(0));
}
template <typename T>
bool DoRunWithType() {
auto& X = Input(0);
CAFFE_ENFORCE(
num_reduce_dims_ >= 0 && num_reduce_dims_ <= X.dim(),
"For N-dim input tensor, support num_reduce_dims in range [0, N].");
vector<int64_t> output_shape;
int start_index = FIRSTDIMS ? num_reduce_dims_ : 0;
int end_index = FIRSTDIMS ? X.dim() : X.dim() - num_reduce_dims_;
for (const auto i : c10::irange(start_index, end_index)) {
output_shape.push_back(X.sizes()[i]);
}
auto* Y = Output(0, output_shape, at::dtype<T>());
const int rows = FIRSTDIMS ? X.size_to_dim(num_reduce_dims_)
: X.size_to_dim(X.dim() - num_reduce_dims_);
const int cols = FIRSTDIMS ? X.size_from_dim(num_reduce_dims_)
: X.size_from_dim(X.dim() - num_reduce_dims_);
const T* in_data = X.template data<T>();
T* out_data = Y->template mutable_data<T>();
if (cols == 0 || rows == 0) {
math::Set(Y->numel(), static_cast<T>(0), out_data, &context_);
return true;
}
const int32_t* lengths_data = nullptr;
if (InputSize() > 1) {
const auto& lengths = Input(1);
lengths_data = lengths.template data<int32_t>();
CAFFE_ENFORCE(
num_reduce_dims_ == 1,
"Given lengths input, the number of reduce dimensions should be one.");
const int batch_size = FIRSTDIMS ? cols : rows;
CAFFE_ENFORCE(
lengths.numel() == batch_size,
"The size of lengths vector doesn't match the batch size.");
}
Compute(rows, cols, in_data, lengths_data, out_data);
return true;
}
private:
template <typename T>
void Compute(
int rows,
int cols,
const T* in_data,
const int32_t* lengths_data,
T* out_data);
int num_reduce_dims_;
};
template <class Context, bool FIRSTDIMS, bool NORMALIZE>
class SumReduceDimsGradientOp final : public Operator<Context> {
public:
template <class... Args>
explicit SumReduceDimsGradientOp(Args&&... args)
: Operator<Context>(std::forward<Args>(args)...),
num_reduce_dims_(
this->template GetSingleArgument<int32_t>("num_reduce_dim", 1)) {}
USE_OPERATOR_CONTEXT_FUNCTIONS;
bool RunOnDevice() override {
return DispatchHelper<TensorTypes<int, long, float, double>>::call(
this, Input(0));
}
template <typename T>
bool DoRunWithType() {
auto& dY = Input(0);
auto& input_1 = Input(1);
vector<int64_t> dX_sizes;
// In previous diff we changed the semantic: Input(1) was changed from
// the shape of the input to the data tensor. This made the backward
// computation incompatible with old models. To fix this, we check
// the dimension and type of Input(1).
if (input_1.dim() == 1 && input_1.template IsType<int64_t>()) {
// Input(1) is the shape of the input
shape_.CopyFrom(input_1);
// Copy first dims
dX_sizes = vector<int64_t>(
shape_.template data<int64_t>(),
shape_.template data<int64_t>() + shape_.numel());
} else {
// Input(1) is data tensor X
dX_sizes = input_1.sizes().vec();
}
auto* dX = Output(0, dX_sizes, at::dtype<T>());
const int rows = FIRSTDIMS ? dX->size_to_dim(num_reduce_dims_)
: dX->size_to_dim(dX->dim() - num_reduce_dims_);
const int cols = FIRSTDIMS
? dX->size_from_dim(num_reduce_dims_)
: dX->size_from_dim(dX->dim() - num_reduce_dims_);
const int32_t* lengths_data = nullptr;
if (InputSize() > 2) {
const auto& lengths = Input(2);
lengths_data = lengths.template data<int32_t>();
CAFFE_ENFORCE(
num_reduce_dims_ == 1,
"Given lengths input, the number of reduce dimensions should be one.");
const int batch_size = FIRSTDIMS ? cols : rows;
CAFFE_ENFORCE(
lengths.numel() == batch_size,
"The size of lengths vector doesn't match the batch size.");
}
const T* dYdata = dY.template data<T>();
T* dXdata = dX->template mutable_data<T>();
Compute<T>(rows, cols, dYdata, lengths_data, dXdata);
return true;
}
private:
template <typename T>
void Compute(
int rows,
int cols,
const T* dYdata,
const int32_t* lengths_data,
T* dXdata);
int num_reduce_dims_;
// scratch space used for former version of this reducer
Tensor shape_{Context::GetDeviceType()};
};
} // namespace caffe2
#endif // CAFFE2_OPERATORS_REDUCE_FRONT_BACK_SUM_MEAN_OPS_H_
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