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#pragma once
#include <ATen/core/ivalue.h>
#include <c10/util/Exception.h>
#include <torch/csrc/jit/ir/ir.h>
namespace torch {
namespace jit {
namespace fuser {
namespace cuda {
// This should match the tensor used in the code generation (almost exactly)
template <typename T, int N>
struct TensorArgCodegen {
T& operator[](int64_t ind) {
return data[ind];
};
T* data;
int64_t size[N];
int64_t stride[N];
constexpr int nDims() {
return N;
}
void setSize(int i, int64_t s) {
size[i] = s;
}
void setStride(int i, int64_t s) {
stride[i] = s;
}
};
template <typename T>
struct TensorArgCodegen<T, 0> {
T& operator[](int64_t ind) {
return data[ind];
};
T* data;
constexpr int nDims() {
return 0;
}
void setSize(int, int64_t) {
TORCH_INTERNAL_ASSERT(false, "Tried to set size of a 0-dim tensor");
}
void setStride(int, int64_t) {
TORCH_INTERNAL_ASSERT(false, "Tried to set stride of a 0-dim tensor");
}
};
struct ArgAbstract {
virtual ~ArgAbstract() {}
virtual void* arg() = 0;
};
struct ULongArg : public ArgAbstract {
uint64_t val_;
ULongArg(uint64_t _val) : val_(_val){};
void* arg() {
return &val_;
}
};
struct LongArg : public ArgAbstract {
int64_t val_;
LongArg(int64_t _val) : val_(_val){};
void* arg() {
return &val_;
}
};
struct IntArg : public ArgAbstract {
int val_;
IntArg(int _val) : val_(_val){};
void* arg() {
return &val_;
}
};
struct FloatArg : public ArgAbstract {
float val_;
FloatArg(float _val) : val_(_val){};
void* arg() {
return &val_;
}
};
struct TensorArgAbstract : ArgAbstract {
virtual ~TensorArgAbstract(){};
virtual void setSize(int i, int64_t size) = 0;
virtual void setStride(int i, int64_t stride) = 0;
virtual void setPointer(void* ptr) = 0;
};
// This should match the tensor used in the code generation (almost exactly)
template <typename TENSOR_TYPE>
struct TensorArg : public TensorArgAbstract {
TENSOR_TYPE instance_;
void setSize(int i, int64_t size) override {
instance_.setSize(i, size);
}
void setStride(int i, int64_t stride) override {
instance_.setStride(i, stride);
}
void setPointer(void* ptr) override {
instance_.data = static_cast<decltype(TENSOR_TYPE::data)>(ptr);
}
void* arg() override {
return &instance_;
}
};
template <typename T>
std::unique_ptr<TensorArgAbstract> getTensorArg(int nDims) {
switch (nDims) {
case (0):
return std::make_unique<TensorArg<TensorArgCodegen<T, 0>>>();
case (1):
return std::make_unique<TensorArg<TensorArgCodegen<T, 1>>>();
case (2):
return std::make_unique<TensorArg<TensorArgCodegen<T, 2>>>();
case (3):
return std::make_unique<TensorArg<TensorArgCodegen<T, 3>>>();
case (4):
return std::make_unique<TensorArg<TensorArgCodegen<T, 4>>>();
case (5):
return std::make_unique<TensorArg<TensorArgCodegen<T, 5>>>();
case (6):
return std::make_unique<TensorArg<TensorArgCodegen<T, 6>>>();
case (7):
return std::make_unique<TensorArg<TensorArgCodegen<T, 7>>>();
case (8):
return std::make_unique<TensorArg<TensorArgCodegen<T, 8>>>();
default:
TORCH_INTERNAL_ASSERT(
false,
"Tried to gerneate a tensor to run a generated kernel with ",
nDims,
" dimensions, however it must be a 1-8 dimensional tensor.");
}
}
std::unique_ptr<TensorArgAbstract> getTensorArg(
c10::ScalarType dtype,
int nDims);
class KernelArgumentHolder {
public:
// Push a tensor to the arguments
void push(const at::Tensor& tensor);
// Push a scalar or integer to the arguments
void push(const IValue& val);
void push(const uint64_t& val);
// Create buffer, flatten arguments into it, align by 8 Bytes, return pointers
// in the buffer
void** getBuffer();
void push(const c10::ArrayRef<c10::IValue>& args);
void push(const std::vector<at::Tensor>& tensors);
void appendPhiloxRNGSeed(uint64_t rand_offset);
private:
std::vector<std::unique_ptr<ArgAbstract>> arguments_;
std::vector<void*> void_ptrs_;
bool changed_ = true;
};
} // namespace cuda
} // namespace fuser
} // namespace jit
} // namespace torch
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