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#pragma once
#include <torch/csrc/jit/tensorexpr/fwd_decls.h>
#include <torch/csrc/jit/tensorexpr/lowerings.h>
#include <torch/csrc/jit/tensorexpr/tensor.h>
namespace torch::jit::tensorexpr {
struct TensorInfo {
std::vector<int64_t> dims;
c10::ScalarType dtype;
};
std::optional<TensorInfo> getTensorInfo(const BufHandle& b);
int64_t normalizeAndCheckIndex(int64_t idx, int64_t list_size);
// Convert boolean to integer, if needed.
ExprHandle boolToInteger(const ExprHandle& x);
ExprHandle promoteToDtype(ExprHandle e, ScalarType dt);
void promoteInputs(
std::vector<ExprHandle>& inputs,
const int typeConstraints = kAllTypes);
ExprHandle promoteIntegerToDefaultType(const ExprHandle& e);
ExprHandle promoteHalfToFloat(const ExprHandle& e);
ExprHandle demoteOutput(
const ExprHandle& e,
const std::optional<ScalarType> type);
std::vector<ExprHandle> broadcastShapes(
std::vector<std::vector<ExprHandle>> shapes);
std::vector<ExprHandle> broadcastShapes(
const std::vector<ExprHandle>& a,
const std::vector<ExprHandle>& b);
std::vector<ExprHandle> valueShape(const ArgValue& v);
ExprHandle tensorOrConstant(
const ArgValue& v,
const std::vector<ExprHandle>& axes);
ExprHandle scalarOrConstant(const ArgValue& v);
ExprHandle broadcast(const BufHandle& b, const std::vector<ExprHandle>& axes);
ExprHandle constant(const ArgValue& v);
ExprHandle clamp(
const ExprHandle& cmin,
const ExprHandle& cmax,
const ExprHandle& input);
Tensor computeChunk(
const std::vector<ArgValue>& inputs,
const std::vector<ExprHandle>& outputShape,
const std::vector<ExprHandle>& outputStrides,
const std::optional<ScalarType>& outputType,
at::Device device);
Tensor computeTranspose(
const std::vector<ArgValue>& inputs,
const std::vector<ExprHandle>& outputShape,
const std::vector<ExprHandle>& outputStrides,
const std::optional<ScalarType>& outputType,
at::Device device);
Tensor computeExpand(
const std::vector<ArgValue>& inputs,
const std::vector<ExprHandle>& outputShape,
const std::vector<ExprHandle>& outputStrides,
const std::optional<ScalarType>& outputType,
at::Device device);
Tensor computeReshape(
const std::vector<ArgValue>& inputs,
const std::vector<ExprHandle>& outputShape,
const std::vector<ExprHandle>& outputStrides,
const std::optional<ScalarType>& outputType,
at::Device device);
Tensor computeFlatten(
const std::vector<ArgValue>& inputs,
const std::vector<ExprHandle>& outputShape,
const std::vector<ExprHandle>& outputStrides,
const std::optional<ScalarType>& outputType,
at::Device device);
Tensor computeCatWoConditionals(
const std::vector<ArgValue>& inputs,
const std::vector<ExprHandle>& outputShape);
Tensor computeCat(
const std::vector<ArgValue>& inputs,
const std::vector<ExprHandle>& outputShape,
const std::vector<ExprHandle>& outputStrides,
const std::optional<ScalarType>& outputType,
at::Device device);
Tensor computeEmbedding(
const std::vector<ArgValue>& inputs,
const std::vector<ExprHandle>& outputShape,
const std::vector<ExprHandle>& outputStrides,
const std::optional<ScalarType>& outputType,
at::Device device);
} // namespace torch::jit::tensorexpr
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