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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>
#include <torch/csrc/jit/codegen/cuda/type_promotion.h>
class Val;
/*
* The operations defined in this header is intended as user facing functions.
* Generally users should not directly instantiate temporary TensorViews they
* should instead use the functions below which will automatically create IR
* nodes, and return a resulting TensorView of correctly tracked shapes.
*/
namespace torch {
namespace jit {
namespace fuser {
namespace cuda {
// Insertion of casting op to dtype, returns new resulting val
TORCH_CUDA_CU_API Val* castOp(DataType dtype, Val* v1);
TORCH_CUDA_CU_API TensorView* castOp(DataType dtype, TensorView* v1);
TORCH_CUDA_CU_API Val* bitCastOp(DataType dtype, Val* v1);
TORCH_CUDA_CU_API TensorView* bitCastOp(DataType dtype, TensorView* v1);
// Perform unary op type and return the output
TORCH_CUDA_CU_API Val* unaryOp(UnaryOpType type, Val* v1);
TORCH_CUDA_CU_API TensorView* unaryOp(UnaryOpType type, TensorView* v1);
TORCH_CUDA_CU_API Val* unaryIsOp(UnaryOpType type, Val* v1);
TORCH_CUDA_CU_API TensorView* unaryIsOp(UnaryOpType type, TensorView* v1);
TORCH_CUDA_CU_API Val* unaryOp(
UnaryOpType type,
Val* v1,
const TypePromotionConfig& config);
TORCH_CUDA_CU_API TensorView* unaryOp(
UnaryOpType type,
TensorView* v1,
const TypePromotionConfig& config);
// Perform binary op type on v1 and v2 and return a type promoted output.
// Mod, CeilDiv, and LT are considered Int only output operations for now.
TORCH_CUDA_CU_API Val* binaryOp(
BinaryOpType type,
Val* v1,
Val* v2,
DataType out_dtype = DataType::Null);
TORCH_CUDA_CU_API TensorView* binaryOp(
BinaryOpType type,
TensorView* v1,
Val* v2,
DataType out_dtype = DataType::Null);
TORCH_CUDA_CU_API TensorView* binaryOp(
BinaryOpType type,
Val* v1,
TensorView* v2,
DataType out_dtype = DataType::Null);
TORCH_CUDA_CU_API TensorView* binaryOp(
BinaryOpType type,
TensorView* v1,
TensorView* v2,
DataType out_dtype = DataType::Null);
TORCH_CUDA_CU_API Val* binaryOp(
BinaryOpType type,
Val* v1,
Val* v2,
const TypePromotionConfig& config);
TORCH_CUDA_CU_API TensorView* binaryOp(
BinaryOpType type,
TensorView* v1,
Val* v2,
const TypePromotionConfig& config);
TORCH_CUDA_CU_API TensorView* binaryOp(
BinaryOpType type,
Val* v1,
TensorView* v2,
const TypePromotionConfig& config);
TORCH_CUDA_CU_API TensorView* binaryOp(
BinaryOpType type,
TensorView* v1,
TensorView* v2,
const TypePromotionConfig& config);
// Perform a reduction operation on v1, initial value for reduction is init,
// reduces across axes, and reduction operation defined by BinaryOp.
TORCH_CUDA_CU_API TensorView* reductionOp(
BinaryOpType reduction_op_type,
const std::vector<int>& axes,
Val* init,
TensorView* v1,
bool keep_dim = false,
DataType dtype = DataType::Null);
//! Auxiliary Struct holding result of
//! a single welford op in ternsorview
class TORCH_CUDA_CU_API WelfordResult {
public:
TensorView* avg;
TensorView* var_sum;
TensorView* n;
explicit WelfordResult(
TensorView* in_avg,
TensorView* in_var_sum,
TensorView* in_n);
};
//! Welford operator on specified axes. This is currently the only scan op with
//! multiple outputs that is supported. May consider generalization if more scan
//! ops are added.
TORCH_CUDA_CU_API WelfordResult Welford(
TensorView* tv,
const std::vector<int>& axes,
TensorView* init_avg = nullptr,
TensorView* init_var = nullptr,
// Initializes to 0 in function definition, doing this so we don't have to
// import IrBuilder just for this one interface.
Int* init_N = nullptr);
// TENSOR FACTORIES
TORCH_CUDA_CU_API TensorView* rand(
const std::vector<Val*>& shape,
DataType dtype);
TORCH_CUDA_CU_API TensorView* arange(Val* end, DataType dtype = DataType::Int);
TORCH_CUDA_CU_API TensorView* arange(
Val* start,
Val* end,
DataType dtype = DataType::Int);
TORCH_CUDA_CU_API TensorView* arange(
Val* start,
Val* end,
Val* step,
DataType dtype = DataType::Int);
// UNARY OPERATIONS
// abs
TORCH_CUDA_CU_API Val* abs(Val*);
TORCH_CUDA_CU_API TensorView* abs(TensorView*);
// acos
TORCH_CUDA_CU_API Val* acos(Val*);
TORCH_CUDA_CU_API TensorView* acos(TensorView*);
// asin
TORCH_CUDA_CU_API Val* asin(Val*);
TORCH_CUDA_CU_API TensorView* asin(TensorView*);
// atan
TORCH_CUDA_CU_API Val* atan(Val*);
TORCH_CUDA_CU_API TensorView* atan(TensorView*);
// atanh
TORCH_CUDA_CU_API Val* atanh(Val*);
TORCH_CUDA_CU_API TensorView* atanh(TensorView*);
// ceil
TORCH_CUDA_CU_API Val* ceil(Val*);
TORCH_CUDA_CU_API TensorView* ceil(TensorView*);
// cos
TORCH_CUDA_CU_API Val* cos(Val*);
TORCH_CUDA_CU_API TensorView* cos(TensorView*);
// cosh
TORCH_CUDA_CU_API Val* cosh(Val*);
TORCH_CUDA_CU_API TensorView* cosh(TensorView*);
// exp
TORCH_CUDA_CU_API Val* exp(Val*);
TORCH_CUDA_CU_API TensorView* exp(TensorView*);
// expm1
TORCH_CUDA_CU_API Val* expm1(Val*);
TORCH_CUDA_CU_API TensorView* expm1(TensorView*);
// erf
TORCH_CUDA_CU_API Val* erf(Val*);
TORCH_CUDA_CU_API TensorView* erf(TensorView*);
// erfc
TORCH_CUDA_CU_API Val* erfc(Val*);
TORCH_CUDA_CU_API TensorView* erfc(TensorView*);
// floor
TORCH_CUDA_CU_API Val* floor(Val*);
TORCH_CUDA_CU_API TensorView* floor(TensorView*);
// frac
TORCH_CUDA_CU_API Val* frac(Val*);
TORCH_CUDA_CU_API TensorView* frac(TensorView*);
// silu
TORCH_CUDA_CU_API Val* silu(Val*);
TORCH_CUDA_CU_API TensorView* silu(TensorView*);
// lgamma
TORCH_CUDA_CU_API Val* lgamma(Val*);
TORCH_CUDA_CU_API TensorView* lgamma(TensorView*);
// log
TORCH_CUDA_CU_API Val* log(Val*);
TORCH_CUDA_CU_API TensorView* log(TensorView*);
// log10
TORCH_CUDA_CU_API Val* log10(Val*);
TORCH_CUDA_CU_API TensorView* log10(TensorView*);
// log1p
TORCH_CUDA_CU_API Val* log1p(Val*);
TORCH_CUDA_CU_API TensorView* log1p(TensorView*);
// log2
TORCH_CUDA_CU_API Val* log2(Val*);
TORCH_CUDA_CU_API TensorView* log2(TensorView*);
// neg
TORCH_CUDA_CU_API Val* neg(Val*);
TORCH_CUDA_CU_API TensorView* neg(TensorView*);
// randlike
TORCH_CUDA_CU_API Val* randlike(Val*);
TORCH_CUDA_CU_API TensorView* randlike(TensorView*);
// real
TORCH_CUDA_CU_API Val* real(Val*);
TORCH_CUDA_CU_API TensorView* real(TensorView*);
// reciprocal
TORCH_CUDA_CU_API Val* reciprocal(Val*);
TORCH_CUDA_CU_API TensorView* reciprocal(TensorView*);
// relu
TORCH_CUDA_CU_API Val* relu(Val*);
TORCH_CUDA_CU_API TensorView* relu(TensorView*);
// rsqrt
TORCH_CUDA_CU_API Val* rsqrt(Val*);
TORCH_CUDA_CU_API TensorView* rsqrt(TensorView*);
// round
TORCH_CUDA_CU_API Val* round(Val*);
TORCH_CUDA_CU_API TensorView* round(TensorView*);
// set
TORCH_CUDA_CU_API Val* set(Val*);
TORCH_CUDA_CU_API TensorView* set(TensorView*);
// sigmoid
TORCH_CUDA_CU_API Val* sigmoid(Val*);
TORCH_CUDA_CU_API TensorView* sigmoid(TensorView*);
// sin
TORCH_CUDA_CU_API Val* sin(Val*);
TORCH_CUDA_CU_API TensorView* sin(TensorView*);
// sinh
TORCH_CUDA_CU_API Val* sinh(Val*);
TORCH_CUDA_CU_API TensorView* sinh(TensorView*);
// sqrt
TORCH_CUDA_CU_API Val* sqrt(Val*);
TORCH_CUDA_CU_API TensorView* sqrt(TensorView*);
// tan
TORCH_CUDA_CU_API Val* tan(Val*);
TORCH_CUDA_CU_API TensorView* tan(TensorView*);
// tanh
TORCH_CUDA_CU_API Val* tanh(Val*);
TORCH_CUDA_CU_API TensorView* tanh(TensorView*);
// trunc
TORCH_CUDA_CU_API Val* trunc(Val*);
TORCH_CUDA_CU_API TensorView* trunc(TensorView*);
// bitwise_not
TORCH_CUDA_CU_API Val* bitwise_not(Val*);
TORCH_CUDA_CU_API TensorView* bitwise_not(TensorView*);
// imag
TORCH_CUDA_CU_API Val* imag(Val*);
TORCH_CUDA_CU_API TensorView* imag(TensorView*);
// isfinite
TORCH_CUDA_CU_API Val* isfinite(Val*);
TORCH_CUDA_CU_API TensorView* isfinite(TensorView*);
// isinf
TORCH_CUDA_CU_API Val* isinf(Val*);
TORCH_CUDA_CU_API TensorView* isinf(TensorView*);
// isnan
TORCH_CUDA_CU_API Val* isnan(Val*);
TORCH_CUDA_CU_API TensorView* isnan(TensorView*);
// isneginf
TORCH_CUDA_CU_API Val* isneginf(Val*);
TORCH_CUDA_CU_API TensorView* isneginf(TensorView*);
// isposinf
TORCH_CUDA_CU_API Val* isposinf(Val*);
TORCH_CUDA_CU_API TensorView* isposinf(TensorView*);
// isreal
TORCH_CUDA_CU_API Val* isreal(Val*);
TORCH_CUDA_CU_API TensorView* isreal(TensorView*);
// print
TORCH_CUDA_CU_API Val* print(Val*);
TORCH_CUDA_CU_API TensorView* print(TensorView*);
// Broadcasts inp based on bool vector. Size of broadcast bool vector should be
// the number of dims desired in the broadcasted tensor. This vector should be
// true if output dim should be a broadcasted dim, and false if it is not a
// broadcasted dim. Number of false entires must match the number of input dims.
TORCH_CUDA_CU_API TensorView* broadcast(
TensorView* inp,
const std::vector<bool>& is_broadcast_dim);
// Expands input based on provided sizes. expand_sizes should be larger than
// the input's root domain (really rfactor) and will broadcast on inner
// dimensions. expand_sizes should be -1 for any dimension that should remain a
// symbolic size. For dimensions that remain broadcast after the expand should
// be set to 1, any dimension being expanded must be marked as a broadcast in
// the input and will be expanded to the provided constant size. Any dimension
// that's symbolic in the input but specified as a non -1 value will be set to
// that constant value.
TORCH_CUDA_CU_API TensorView* expand(
TensorView* inp,
const std::vector<Val*>& expanded_sizes);
// Expands input based on other. For dimensions in inp that are broadcast with a
// matching entry in other that's either a broadcast with expanded extent or a
// non broadcasted iter domain, inp will be expanded to other's size.
TORCH_CUDA_CU_API TensorView* expand_as(TensorView* inp, TensorView* other);
// BINARY OPERATIONS
// add
TORCH_CUDA_CU_API Val* add(Val* v1, Val* v2);
TORCH_CUDA_CU_API TensorView* add(TensorView* v1, Val* v2);
TORCH_CUDA_CU_API TensorView* add(Val* v1, TensorView* v2);
TORCH_CUDA_CU_API TensorView* add(TensorView* v1, TensorView* v2);
// atan2
TORCH_CUDA_CU_API Val* atan2(Val* v1, Val* v2);
TORCH_CUDA_CU_API TensorView* atan2(TensorView* v1, Val* v2);
TORCH_CUDA_CU_API TensorView* atan2(Val* v1, TensorView* v2);
TORCH_CUDA_CU_API TensorView* atan2(TensorView* v1, TensorView* v2);
// div
TORCH_CUDA_CU_API Val* div(Val* v1, Val* v2);
TORCH_CUDA_CU_API TensorView* div(TensorView* v1, Val* v2);
TORCH_CUDA_CU_API TensorView* div(Val* v1, TensorView* v2);
TORCH_CUDA_CU_API TensorView* div(TensorView* v1, TensorView* v2);
// fmod
TORCH_CUDA_CU_API Val* fmod(Val* v1, Val* v2);
TORCH_CUDA_CU_API TensorView* fmod(TensorView* v1, Val* v2);
TORCH_CUDA_CU_API TensorView* fmod(Val* v1, TensorView* v2);
TORCH_CUDA_CU_API TensorView* fmod(TensorView* v1, TensorView* v2);
// mul
TORCH_CUDA_CU_API Val* mul(Val* v1, Val* v2);
TORCH_CUDA_CU_API TensorView* mul(TensorView* v1, Val* v2);
TORCH_CUDA_CU_API TensorView* mul(Val* v1, TensorView* v2);
TORCH_CUDA_CU_API TensorView* mul(TensorView* v1, TensorView* v2);
// pow
TORCH_CUDA_CU_API Val* pow(Val* v1, Val* v2);
TORCH_CUDA_CU_API TensorView* pow(TensorView* v1, Val* v2);
TORCH_CUDA_CU_API TensorView* pow(Val* v1, TensorView* v2);
TORCH_CUDA_CU_API TensorView* pow(TensorView* v1, TensorView* v2);
// remainder
TORCH_CUDA_CU_API Val* remainder(Val* v1, Val* v2);
TORCH_CUDA_CU_API TensorView* remainder(TensorView* v1, Val* v2);
TORCH_CUDA_CU_API TensorView* remainder(Val* v1, TensorView* v2);
TORCH_CUDA_CU_API TensorView* remainder(TensorView* v1, TensorView* v2);
// sub
TORCH_CUDA_CU_API Val* sub(Val* v1, Val* v2);
TORCH_CUDA_CU_API TensorView* sub(TensorView* v1, Val* v2);
TORCH_CUDA_CU_API TensorView* sub(Val* v1, TensorView* v2);
TORCH_CUDA_CU_API TensorView* sub(TensorView* v1, TensorView* v2);
// Integer binary ops
// mod
TORCH_CUDA_CU_API Val* mod(Val* v1, Val* v2);
TORCH_CUDA_CU_API TensorView* mod(TensorView* v1, Val* v2);
TORCH_CUDA_CU_API TensorView* mod(Val* v1, TensorView* v2);
TORCH_CUDA_CU_API TensorView* mod(TensorView* v1, TensorView* v2);
// ceilDiv
TORCH_CUDA_CU_API Val* ceilDiv(Val* v1, Val* v2);
TORCH_CUDA_CU_API TensorView* ceilDiv(TensorView* v1, Val* v2);
TORCH_CUDA_CU_API TensorView* ceilDiv(Val* v1, TensorView* v2);
TORCH_CUDA_CU_API TensorView* ceilDiv(TensorView* v1, TensorView* v2);
// Bitwise binary ops
// bitwise_and
TORCH_CUDA_CU_API Val* bitwise_and(Val* v1, Val* v2);
TORCH_CUDA_CU_API TensorView* bitwise_and(TensorView* v1, Val* v2);
TORCH_CUDA_CU_API TensorView* bitwise_and(Val* v1, TensorView* v2);
TORCH_CUDA_CU_API TensorView* bitwise_and(TensorView* v1, TensorView* v2);
// bitwise_left_shift
TORCH_CUDA_CU_API Val* bitwise_left_shift(Val* v1, Val* v2);
TORCH_CUDA_CU_API TensorView* bitwise_left_shift(TensorView* v1, Val* v2);
TORCH_CUDA_CU_API TensorView* bitwise_left_shift(Val* v1, TensorView* v2);
TORCH_CUDA_CU_API TensorView* bitwise_left_shift(
TensorView* v1,
TensorView* v2);
// bitwise_right_shift
TORCH_CUDA_CU_API Val* bitwise_right_shift(Val* v1, Val* v2);
TORCH_CUDA_CU_API TensorView* bitwise_right_shift(TensorView* v1, Val* v2);
TORCH_CUDA_CU_API TensorView* bitwise_right_shift(Val* v1, TensorView* v2);
TORCH_CUDA_CU_API TensorView* bitwise_right_shift(
TensorView* v1,
TensorView* v2);
// bitwise_or
TORCH_CUDA_CU_API Val* bitwise_or(Val* v1, Val* v2);
TORCH_CUDA_CU_API TensorView* bitwise_or(TensorView* v1, Val* v2);
TORCH_CUDA_CU_API TensorView* bitwise_or(Val* v1, TensorView* v2);
TORCH_CUDA_CU_API TensorView* bitwise_or(TensorView* v1, TensorView* v2);
// bitwise_xor
TORCH_CUDA_CU_API Val* bitwise_xor(Val* v1, Val* v2);
TORCH_CUDA_CU_API TensorView* bitwise_xor(TensorView* v1, Val* v2);
TORCH_CUDA_CU_API TensorView* bitwise_xor(Val* v1, TensorView* v2);
TORCH_CUDA_CU_API TensorView* bitwise_xor(TensorView* v1, TensorView* v2);
// Logical binary ops
// eq
TORCH_CUDA_CU_API Val* eq(Val* v1, Val* v2);
TORCH_CUDA_CU_API TensorView* eq(TensorView* v1, Val* v2);
TORCH_CUDA_CU_API TensorView* eq(Val* v1, TensorView* v2);
TORCH_CUDA_CU_API TensorView* eq(TensorView* v1, TensorView* v2);
// ge
TORCH_CUDA_CU_API Val* ge(Val* v1, Val* v2);
TORCH_CUDA_CU_API TensorView* ge(TensorView* v1, Val* v2);
TORCH_CUDA_CU_API TensorView* ge(Val* v1, TensorView* v2);
TORCH_CUDA_CU_API TensorView* ge(TensorView* v1, TensorView* v2);
// gt
TORCH_CUDA_CU_API Val* gt(Val* v1, Val* v2);
TORCH_CUDA_CU_API TensorView* gt(TensorView* v1, Val* v2);
TORCH_CUDA_CU_API TensorView* gt(Val* v1, TensorView* v2);
TORCH_CUDA_CU_API TensorView* gt(TensorView* v1, TensorView* v2);
// le
TORCH_CUDA_CU_API Val* le(Val* v1, Val* v2);
TORCH_CUDA_CU_API TensorView* le(TensorView* v1, Val* v2);
TORCH_CUDA_CU_API TensorView* le(Val* v1, TensorView* v2);
TORCH_CUDA_CU_API TensorView* le(TensorView* v1, TensorView* v2);
// lt
TORCH_CUDA_CU_API Val* lt(Val* v1, Val* v2);
TORCH_CUDA_CU_API TensorView* lt(TensorView* v1, Val* v2);
TORCH_CUDA_CU_API TensorView* lt(Val* v1, TensorView* v2);
TORCH_CUDA_CU_API TensorView* lt(TensorView* v1, TensorView* v2);
// ne
TORCH_CUDA_CU_API Val* ne(Val* v1, Val* v2);
TORCH_CUDA_CU_API TensorView* ne(TensorView* v1, Val* v2);
TORCH_CUDA_CU_API TensorView* ne(Val* v1, TensorView* v2);
TORCH_CUDA_CU_API TensorView* ne(TensorView* v1, TensorView* v2);
// REDUCTION OPERATIONS
TORCH_CUDA_CU_API TensorView* sum(
TensorView* v1,
const std::vector<int>& reduction_axes,
bool keep_dim = false,
DataType dtype = DataType::Null);
TORCH_CUDA_CU_API TensorView* max(
TensorView* v1,
const std::vector<int>& reduction_axes,
bool keep_dim = false,
DataType dtype = DataType::Null);
TORCH_CUDA_CU_API TensorView* min(
TensorView* v1,
const std::vector<int>& reduction_axes,
bool keep_dim = false,
DataType dtype = DataType::Null);
// COMPOUND OPERATIONS
// add_alpha
TORCH_CUDA_CU_API Val* add_alpha(Val* v1, Val* v2, Val* s);
TORCH_CUDA_CU_API TensorView* add_alpha(TensorView* v1, Val* v2, Val* s);
TORCH_CUDA_CU_API TensorView* add_alpha(Val* v1, TensorView* v2, Val* s);
TORCH_CUDA_CU_API TensorView* add_alpha(TensorView* v1, TensorView* v2, Val* s);
// sub_alpha
TORCH_CUDA_CU_API Val* sub_alpha(Val* v1, Val* v2, Val* s);
TORCH_CUDA_CU_API TensorView* sub_alpha(TensorView* v1, Val* v2, Val* s);
TORCH_CUDA_CU_API TensorView* sub_alpha(Val* v1, TensorView* v2, Val* s);
TORCH_CUDA_CU_API TensorView* sub_alpha(TensorView* v1, TensorView* v2, Val* s);
// lerp
TORCH_CUDA_CU_API Val* lerp(Val* start, Val* end, Val* weight);
TORCH_CUDA_CU_API TensorView* lerp(TensorView* start, Val* end, Val* weight);
TORCH_CUDA_CU_API TensorView* lerp(Val* start, TensorView* end, Val* weight);
TORCH_CUDA_CU_API TensorView* lerp(Val* start, Val* end, TensorView* weight);
TORCH_CUDA_CU_API TensorView* lerp(
TensorView* start,
TensorView* end,
Val* weight);
TORCH_CUDA_CU_API TensorView* lerp(
TensorView* start,
Val* end,
TensorView* weight);
TORCH_CUDA_CU_API TensorView* lerp(
Val* start,
TensorView* end,
TensorView* weight);
TORCH_CUDA_CU_API TensorView* lerp(
TensorView* start,
TensorView* end,
TensorView* weight);
// addcmul
TORCH_CUDA_CU_API Val* addcmul(Val* v1, Val* v2, Val* v3, Val* s);
TORCH_CUDA_CU_API TensorView* addcmul(TensorView* v1, Val* v2, Val* v3, Val* s);
TORCH_CUDA_CU_API TensorView* addcmul(Val* v1, TensorView* v2, Val* v3, Val* s);
TORCH_CUDA_CU_API TensorView* addcmul(Val* v1, Val* v2, TensorView* v3, Val* s);
TORCH_CUDA_CU_API TensorView* addcmul(
TensorView* v1,
TensorView* v2,
Val* v3,
Val* s);
TORCH_CUDA_CU_API TensorView* addcmul(
TensorView* v1,
Val* v2,
TensorView* v3,
Val* s);
TORCH_CUDA_CU_API TensorView* addcmul(
Val* v1,
TensorView* v2,
TensorView* v3,
Val* s);
TORCH_CUDA_CU_API TensorView* addcmul(
TensorView* v1,
TensorView* v2,
TensorView* v3,
Val* s);
// TERNARY OPERATIONS
// where
TORCH_CUDA_CU_API Val* where(Val* c, Val* v1, Val* v2);
TORCH_CUDA_CU_API TensorView* where(TensorView* c, Val* v1, Val* v2);
TORCH_CUDA_CU_API TensorView* where(Val* c, TensorView* v1, Val* v2);
TORCH_CUDA_CU_API TensorView* where(Val* c, Val* v1, TensorView* v2);
TORCH_CUDA_CU_API TensorView* where(TensorView* c, TensorView* v1, Val* v2);
TORCH_CUDA_CU_API TensorView* where(TensorView* c, Val* v1, TensorView* v2);
TORCH_CUDA_CU_API TensorView* where(Val* c, TensorView* v1, TensorView* v2);
TORCH_CUDA_CU_API TensorView* where(
TensorView* c,
TensorView* v1,
TensorView* v2);
// threshold
TORCH_CUDA_CU_API Val* threshold(Val* in, Val* thresh, Val* value);
TORCH_CUDA_CU_API TensorView* threshold(
TensorView* in,
Val* thresh,
Val* value);
// clamp
TORCH_CUDA_CU_API Val* clamp(Val* in, Val* min_val, Val* max_val);
TORCH_CUDA_CU_API TensorView* clamp(TensorView* in, Val* min_val, Val* max_val);
//! Internal operator for supporting backward graphs
//!
//! example:
//! v1 = T1 [I0(10),I1(20),I2(30),I3(40)]
//! v2 = sum_to(v1,{30,1}) ------> v2 = T2[I2,R3 (keep_dim)]
//!
//! This operator will return v1* directly if sizes of v1 root domain
//! is already the same as shape.
//!
//! Name of sum_to is different from NV fuser naming,
//! this is to align with the operator name of at::sum_to.
TORCH_CUDA_CU_API TensorView* sum_to(
TensorView* v1,
const std::vector<Int*>& sum_to_size);
TORCH_CUDA_CU_API TensorView* sum_to(
TensorView* v1,
const std::vector<int64_t>& sum_to_size);
//! Shift a tensor to a direction specified by offsets.
//!
//! Example:
//! t0: 2D tensor of size N by M
//! t1 = shift(t0, {1, -1});
//!
//! then:
//! t1[i, j] = t0[i-1, j+1] for 1 <= i < N and 0 <= j < M-1.
//! t1[i, j] = 0, otherwise
//!
//! The pad option controls how out-of-boundary accesses are
//! handled. It specifies how many zeros are logically padded. If no
//! pad option is given, it automatically pads the input tensor so
//! that the output tensor has the same extent for each axis.
//!
//! When a padding value is smaller than the absolute value of a shift
//! offset, the output axis still has the same extent but its start or
//! stop offset is moved inward to signify those outside of the offset
//! are invalid.
//!
//! It is not allowed to use padding values that are larger than shift
//! offsets, which would mean output extentes would be larger than
//! input extents
TORCH_CUDA_CU_API TensorView* shift(
TensorView* inp,
const std::vector<int>& offsets,
const std::vector<int>& pad_width = {});
TORCH_CUDA_CU_API TensorView* shift(
TensorView* inp,
const std::vector<int>& offsets,
bool pad);
//! Gather a window of nearby elements for each element.
//!
//! Each window of size window_shape is stored as a additional
//! innermost domain, meaning that the number of dimensions of the
//! output tensor doubles. The pad_width parameter specifies the
//! padding width of each side of each axis. The strides parameter
//! specifies striding of the operation. Non-unit striding is
//! implemented with strided split, whose outer output domain becomes
//! the root domain for subsequent consumers. The inner output domain
//! becomes a Stride domain, which is ignored by subsequent consumers.
//! Only valid input ranges are fed into strided splits.
//!
//! When trim_out_of_bounds is true, the values at the first and last
//! ends that are outside of the start and stop offsets are
//! effetively trimmed by partial split by 1.
//!
//! Example 1:
//! t0: 2D tensor of [N, M]
//! t1 = gather(t0, {1, 3}, {{0, 0}, {1, 1}});
//!
//! then:
//! t1: [N, M, 1, 3]
//! t1[i, j, k, l] = The value at the window position of [k, l]
//! for t0[i, j]
//!
//! Example 2.1 (without trimming):
//! t0: 2D tensor of [N, M]
//! t1 = gather(t0, {2, 2}, {{0, 0}, {0, 0}});
//!
//! then:
//! t1: [N (stop offset: 1), M (stop offset: 1, 2, 2)]
//!
//! Example 2.1 (with trimming)
//! t0: 2D tensor of [N, M]
//! t1 = gather(t0, {2, 2}, {{0, 0}, {0, 0}}, true);
//!
//! then:
//! t1: [ceilDiv(N - 1, 1), ceilDiv(M - 1, 1), 2, 2]
//!
//! Example 3:
//! t0: 2D tensor of [N, M]
//! t1 = gather(t0, {3, 3}, {{0, 0}, {0, 0}}, {3, 3});
//!
//! then:
//! t1: [ceilDiv(N - 2, 3), ceilDiv(M - 2, 3), 2, 2]
//!
TORCH_CUDA_CU_API TensorView* gather(
TensorView* inp,
const std::vector<int>& window_shape,
const std::vector<std::vector<int>>& pad_width,
const std::vector<int>& strides = {},
bool trim_out_of_bounds = false);
// Append a new IterDomain to the end of a TenorView to allow
// iterating on a vector type. The input tensor must have
// vector dtype.
TORCH_CUDA_CU_API TensorView* viewAsScalar(TensorView* inp);
//! A fused pointwise multiply and sum
//! operator that instantiates the following
//! fused pattern:
//! c = mul(tv_a, tv_b);
//! return sum(c, axes)
//!
//! \param tv_a first multiply operand
//! \param tv_b second multiply operand
//! \param axes axes to sum over
//! \param init sum initial value
//!
//! Note & TODO:
//! currently only support lowering to a mma op
//! through this interface and only support fp16 inputs.
//! will support converting back to multiply and reduce in
//! a follow up.
TORCH_CUDA_CU_API TensorView* fusedMultiplySum(
TensorView* tv_a,
TensorView* tv_b,
const std::vector<int>& axes,
Val* init = nullptr);
} // namespace cuda
} // namespace fuser
} // namespace jit
} // namespace torch
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