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#pragma once
#include <c10/macros/Export.h>
#include <torch/csrc/jit/codegen/cuda/ir_interface_nodes.h>
#include <torch/csrc/jit/codegen/cuda/type.h>
//
// The operations defined in this header is intended as user facing functions.
// The user will provide the necessary input TensorViews and the function will
// create the correct intermediate nodes and return the output TensorViews.
//
namespace torch {
namespace jit {
namespace fuser {
namespace cuda {
struct ForwardNormResult {
TensorView* output = nullptr;
TensorView* mean = nullptr;
TensorView* invstd = nullptr;
};
struct BackwardNormResult {
TensorView* grad_input = nullptr;
TensorView* grad_weight = nullptr;
TensorView* grad_bias = nullptr;
};
struct ForwardRMSNormResult {
TensorView* output = nullptr;
TensorView* invstd = nullptr;
};
struct BackwardRMSNormResult {
TensorView* grad_input = nullptr;
TensorView* grad_weight = nullptr;
};
struct VarMeanResult {
TensorView* var = nullptr;
TensorView* mean = nullptr;
};
TORCH_CUDA_CU_API TensorView* mean(
TensorView* x,
const std::vector<int>& dims,
bool keepdim);
TORCH_CUDA_CU_API TensorView* variance(
TensorView* x,
const std::vector<int>& dims,
bool unbiased,
bool keepdim);
TORCH_CUDA_CU_API TensorView* variance(
TensorView* x,
const std::vector<int>& dims,
int64_t correction,
bool keepdim);
TORCH_CUDA_CU_API VarMeanResult variance_mean(
TensorView* x,
const std::vector<int>& dims,
int64_t correction,
bool keepdim);
TORCH_CUDA_CU_API TensorView* standard_deviation(
TensorView* x,
const std::vector<int>& dims,
bool unbiased,
bool keepdim);
TORCH_CUDA_CU_API TensorView* softmax(TensorView* x, int dim);
TORCH_CUDA_CU_API TensorView* softmax_backward(
TensorView* dy,
TensorView* y,
const int dim);
TORCH_CUDA_CU_API TensorView* log_softmax(TensorView* x, int dim);
TORCH_CUDA_CU_API TensorView* log_softmax_backward(
TensorView* dy,
TensorView* y,
const int dim);
TORCH_CUDA_CU_API ForwardNormResult layer_norm(
TensorView* x,
const std::vector<int64_t>& norm_shape,
TensorView* weight,
TensorView* bias,
Val* eps);
TORCH_CUDA_CU_API ForwardNormResult layer_norm(
TensorView* x,
const size_t kNormShapeNumDims,
TensorView* weight,
TensorView* bias,
Val* eps);
TORCH_CUDA_CU_API ForwardRMSNormResult rms_norm(
TensorView* x,
const std::vector<int64_t>& norm_shape,
TensorView* weight,
Val* eps);
TORCH_CUDA_CU_API ForwardRMSNormResult rms_norm(
TensorView* x,
const size_t kNormShapeNumDims,
TensorView* weight,
Val* eps);
TORCH_CUDA_CU_API BackwardNormResult layer_norm_backward(
TensorView* dy,
TensorView* x,
const std::vector<int64_t>& norm_shape,
TensorView* mean,
TensorView* rstd,
TensorView* weight,
TensorView* bias,
const std::vector<bool>& output_mask);
TORCH_CUDA_CU_API BackwardRMSNormResult rms_norm_backward(
TensorView* dy,
TensorView* x,
const std::vector<int64_t>& norm_shape,
TensorView* rstd,
TensorView* weight,
const std::vector<bool>& output_mask);
TORCH_CUDA_CU_API ForwardNormResult batch_norm(
TensorView* x,
TensorView* weight,
TensorView* bias,
TensorView* running_mean,
TensorView* running_var,
const bool kTraining,
Val* momentum,
Val* eps,
bool channels_last = false);
TORCH_CUDA_CU_API BackwardNormResult batch_norm_backward(
TensorView* x,
TensorView* dy,
TensorView* weight,
TensorView* running_mean,
TensorView* running_var,
TensorView* save_mean,
TensorView* save_invstd,
const bool kTraining,
Val* eps,
const std::vector<bool>& output_mask,
bool channels_last = false);
TORCH_CUDA_CU_API ForwardNormResult instance_norm(
TensorView* x,
TensorView* weight,
TensorView* bias,
TensorView* running_mean,
TensorView* running_var,
const bool kUseInputStats, // kTraining?
Val* momentum,
Val* eps,
bool channels_last = false);
TORCH_CUDA_CU_API BackwardNormResult instance_norm_backward(
TensorView* x,
TensorView* dy,
TensorView* weight,
TensorView* running_mean,
TensorView* running_var,
TensorView* save_mean,
TensorView* save_invstd,
const bool kTraining,
Val* eps,
const std::vector<bool>& output_mask,
bool channels_last = false);
} // namespace cuda
} // namespace fuser
} // namespace jit
} // namespace torch
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