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#ifndef CAFFE2_OPERATORS_REDUCTION_OPS_H_
#define CAFFE2_OPERATORS_REDUCTION_OPS_H_
#include "caffe2/core/common_omp.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 <typename T, class Context>
class SumElementsOp : public Operator<Context> {
public:
USE_OPERATOR_CONTEXT_FUNCTIONS;
explicit SumElementsOp(const OperatorDef& operator_def, Workspace* ws)
: Operator<Context>(operator_def, ws),
average_(this->template GetSingleArgument<bool>("average", false)) {}
explicit SumElementsOp(const OperatorDef& operator_def, Workspace* ws, bool average)
: Operator<Context>(operator_def, ws), average_(average) {}
#if !defined(CAFFE2_IS_XPLAT_BUILD) && !defined(C10_MOBILE)
explicit SumElementsOp(const c10::FunctionSchema& schema, std::vector<c10::IValue> inputs, std::vector<c10::IValue*> outputs)
: Operator<Context>(schema, std::move(inputs), std::move(outputs)),
average_(this->template GetSingleArgument<bool>("average", false)) {}
explicit SumElementsOp(const c10::FunctionSchema& schema, std::vector<c10::IValue> inputs, std::vector<c10::IValue*> outputs, bool average)
: Operator<Context>(schema, std::move(inputs), std::move(outputs)), average_(average) {}
#endif
~SumElementsOp() {}
bool RunOnDevice() override {
auto& X = Input(0);
auto* sum = Output(0, vector<int64_t>(), at::dtype<T>());
T* data = sum->template mutable_data<T>();
math::Sum<T, Context>(
X.numel(), X.template data<T>(), data, &context_, &scratch_);
if (average_ && X.numel() > 0) {
math::Scale<float, T, Context>(
1,
static_cast<T>(1.) / X.numel(),
sum->template data<T>(),
data,
&context_);
}
return true;
}
private:
bool average_;
Tensor scratch_{Context::GetDeviceType()};
};
template <typename T, class Context>
class SumElementsIntOp : public Operator<Context> {
public:
USE_OPERATOR_CONTEXT_FUNCTIONS;
template <class... Args>
explicit SumElementsIntOp(Args&&... args)
: Operator<Context>(std::forward<Args>(args)...) {}
~SumElementsIntOp() {}
bool RunOnDevice() override {
auto& X = Input(0);
auto* sum = Output(0, vector<int64_t>(), at::dtype<T>());
T* data = sum->template mutable_data<T>();
math::Sum<T, Context>(
X.numel(), X.template data<T>(), data, &context_, &scratch_);
return true;
}
private:
Tensor scratch_{Context::GetDeviceType()};
};
template <typename T, class Context>
class SumElementsGradientOp : public Operator<Context> {
public:
USE_OPERATOR_CONTEXT_FUNCTIONS;
explicit SumElementsGradientOp(const OperatorDef& operator_def, Workspace* ws)
: Operator<Context>(operator_def, ws),
average_(this->template GetSingleArgument<bool>("average", false)) {}
explicit SumElementsGradientOp(const OperatorDef& operator_def, Workspace* ws, bool average)
: Operator<Context>(operator_def, ws), average_(average) {}
#if !defined(CAFFE2_IS_XPLAT_BUILD) && !defined(C10_MOBILE)
explicit SumElementsGradientOp(const c10::FunctionSchema& schema, std::vector<c10::IValue> inputs, std::vector<c10::IValue*> outputs)
: Operator<Context>(schema, std::move(inputs), std::move(outputs)),
average_(this->template GetSingleArgument<bool>("average", false)) {}
explicit SumElementsGradientOp(const c10::FunctionSchema& schema, std::vector<c10::IValue> inputs, std::vector<c10::IValue*> outputs, bool average)
: Operator<Context>(schema, std::move(inputs), std::move(outputs)), average_(average) {}
#endif
~SumElementsGradientOp() {}
bool RunOnDevice() override;
private:
bool average_;
};
template <class Context>
class SumSqrElementsOp : public Operator<Context> {
public:
USE_SIMPLE_CTOR_DTOR(SumSqrElementsOp)
USE_OPERATOR_CONTEXT_FUNCTIONS;
bool RunOnDevice() override {
return DispatchHelper<TensorTypes<float, double>>::call(this, Input(0));
}
template <typename T>
bool DoRunWithType() {
bool average = this->template GetSingleArgument<bool>("average", false);
auto& X = Input(0);
auto* sum = Output(0, vector<int64_t>(), at::dtype<T>());
math::SumSqr<T, Context>(
X.numel(),
X.template data<T>(),
sum->template mutable_data<T>(),
&context_,
&scratch_);
if (average && X.numel() > 0) {
math::Scale<float, T, Context>(
1,
float(1.) / X.numel(),
sum->template data<T>(),
sum->template mutable_data<T>(),
&context_);
}
return true;
}
private:
Tensor scratch_{Context::GetDeviceType()};
};
template <typename T, class Context, bool ROWWISE>
class MaxReductionOp : public Operator<Context> {
public:
USE_SIMPLE_CTOR_DTOR(MaxReductionOp)
USE_OPERATOR_CONTEXT_FUNCTIONS;
bool RunOnDevice() override {
auto& X = Input(0);
CAFFE_ENFORCE_EQ(X.dim(), 3);
const int batch_size = X.dim32(0);
const int M = X.dim32(1);
const int N = X.dim32(2);
auto* Y = Output(0, {batch_size, ROWWISE ? M : N}, at::dtype<T>());
if (ROWWISE) {
math::RowwiseMax<T, Context>(
batch_size * M,
N,
X.template data<T>(),
Y->template mutable_data<T>(),
&context_);
} else {
const int input_size = N * M;
for (const auto i : c10::irange(batch_size)) {
math::ColwiseMax<T, Context>(
M,
N,
X.template data<T>() + i * input_size,
Y->template mutable_data<T>() + i * N,
&context_);
}
}
return true;
}
};
template <typename T, class Context, bool ROWWISE>
class MaxReductionGradientOp : public Operator<Context> {
public:
USE_SIMPLE_CTOR_DTOR(MaxReductionGradientOp)
USE_OPERATOR_CONTEXT_FUNCTIONS;
bool RunOnDevice() override;
};
} // namespace caffe2
#endif
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