maxjhandsome
/
llvm-project
forked from OSchip/llvm-project
1
0
Fork 0
Code Issues Pull Requests Packages Releases Wiki Activity
llvm-project/clang/lib/Headers/__clang_cuda_texture_intrin...

741 lines
32 KiB
C++
Raw Blame History

/*===--- __clang_cuda_texture_intrinsics.h - Device-side texture support ---===
*
* Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
* See https://llvm.org/LICENSE.txt for license information.
* SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
*
*===-----------------------------------------------------------------------===
*
* This header provides in-header implmentations for NVCC's built-in
* __nv_tex_surf_handler() which is used by CUDA's texture-related headers. The
* built-in is unusual as it's actually a set of function overloads that use the
* first string literal argument as one of the overload parameters.
*/
#ifndef __CLANG_CUDA_TEXTURE_INTRINSICS_H__
#define __CLANG_CUDA_TEXTURE_INTRINSICS_H__
#ifndef __CUDA__
#error "This file is for CUDA compilation only."
#endif
// __nv_tex_surf_handler() provided by this header as a macro.
#define __nv_tex_surf_handler(__op, __ptr, ...) \
::__cuda_tex::__tex_fetch< \
::__cuda_tex::__Tag<::__cuda_tex::__tex_op_hash(__op)>>(__ptr, \
__VA_ARGS__)
#pragma push_macro("__ASM_OUT")
#pragma push_macro("__ASM_OUTP")
#pragma push_macro("__Args")
#pragma push_macro("__ID")
#pragma push_macro("__IDV")
#pragma push_macro("__IMPL_2DGATHER")
#pragma push_macro("__IMPL_ALIAS")
#pragma push_macro("__IMPL_ALIASI")
#pragma push_macro("__IMPL_F1")
#pragma push_macro("__IMPL_F3")
#pragma push_macro("__IMPL_F3N")
#pragma push_macro("__IMPL_F3S")
#pragma push_macro("__IMPL_S")
#pragma push_macro("__IMPL_S3")
#pragma push_macro("__IMPL_S3I")
#pragma push_macro("__IMPL_S3N")
#pragma push_macro("__IMPL_S3NI")
#pragma push_macro("__IMPL_S3S")
#pragma push_macro("__IMPL_S3SI")
#pragma push_macro("__IMPL_SI")
#pragma push_macro("__L")
#pragma push_macro("__STRIP_PARENS")
// Put all functions into anonymous namespace so they have internal linkage.
// The device-only function here must be internal in order to avoid ODR
// violations in case they are used from the files compiled with
// -fgpu-rdc. E.g. a library and an app using it may be built with a different
// version of this header file.
namespace {
// Put the implmentation into its own namespace so we don't pollute the TU.
namespace __cuda_tex {
// First, we need a perfect hash function and a few constexpr helper functions
// for converting a string literal into a numeric value which can be used to
// parametrize a template. We can not use string literals for that as that would
// require C++20.
//
// The hash function was generated with 'gperf' and then manually converted into
// its constexpr equivalent.
//
// NOTE: the perfect hashing scheme comes with inherent self-test. If the hash
// function has a collision for any of the texture operations, the compilation
// will fail due to an attempt to redefine a tag with the same value. If the
// header compiles, then the hash function is good enough for the job.
constexpr int __tex_len(const char *s) {
return (s[0] == 0) ? 0
: (s[1] == 0) ? 1
: (s[2] == 0) ? 2
: (s[3] == 0) ? 3
: (s[4] == 0) ? 4
: (s[5] == 0) ? 5
: (s[6] == 0) ? 6
: (s[7] == 0) ? 7
: (s[8] == 0) ? 8
: (s[9] == 0) ? 9
: (s[10] == 0) ? 10
: (s[11] == 0) ? 11
: (s[12] == 0) ? 12
: (s[13] == 0) ? 13
: (s[14] == 0) ? 14
: (s[15] == 0) ? 15
: (s[16] == 0) ? 16
: (s[17] == 0) ? 17
: (s[18] == 0) ? 18
: (s[19] == 0) ? 19
: (s[20] == 0) ? 20
: (s[21] == 0) ? 21
: (s[22] == 0) ? 22
: (s[23] == 0) ? 23
: (s[24] == 0) ? 24
: (s[25] == 0) ? 25
: (s[26] == 0) ? 26
: (s[27] == 0) ? 27
: (s[28] == 0) ? 28
: (s[29] == 0) ? 29
: (s[30] == 0) ? 30
: (s[31] == 0) ? 31
: 32;
}
constexpr int __tex_hash_map(int c) {
return (c == 49) ? 10
: (c == 50) ? 0
: (c == 51) ? 100
: (c == 52) ? 30
: (c == 67) ? 10
: (c == 68) ? 0
: (c == 69) ? 25
: (c == 72) ? 70
: (c == 77) ? 0
: (c == 96) ? 44
: (c == 99) ? 10
: (c == 100) ? 5
: (c == 101) ? 60
: (c == 102) ? 40
: (c == 103) ? 70
: (c == 104) ? 25
: (c == 112) ? 0
: (c == 114) ? 45
: (c == 117) ? 5
: (c == 118) ? 85
: (c == 120) ? 20
: 225;
}
constexpr int __tex_op_hash(const char *str) {
return __tex_len(str) + __tex_hash_map(str[7] + 1) + __tex_hash_map(str[6]) +
__tex_hash_map(str[5]) + __tex_hash_map(str[__tex_len(str) - 1]);
}
// Tag type to identify particular texture operation.
template <int N> struct __Tag;
#define __ID(__op) __Tag<__tex_op_hash(__op)>
// Tags for variants of particular operation. E.g. tex2Dgather can translate
// into 4 different instructions.
#define __IDV(__op, __variant) \
__Tag<10000 + __tex_op_hash(__op) * 100 + __variant>
// Helper classes for figuring out key data types for derived types.
// E.g. char2 has __base_t = char, __fetch_t = char4
template <class> struct __TypeInfoT;
// Type info for the fundamental types.
template <> struct __TypeInfoT<float> {
using __base_t = float;
using __fetch_t = float4;
};
template <> struct __TypeInfoT<char> {
using __base_t = char;
using __fetch_t = int4;
};
template <> struct __TypeInfoT<signed char> {
using __base_t = signed char;
using __fetch_t = int4;
};
template <> struct __TypeInfoT<unsigned char> {
using __base_t = unsigned char;
using __fetch_t = uint4;
};
template <> struct __TypeInfoT<short> {
using __base_t = short;
using __fetch_t = int4;
};
template <> struct __TypeInfoT<unsigned short> {
using __base_t = unsigned short;
using __fetch_t = uint4;
};
template <> struct __TypeInfoT<int> {
using __base_t = int;
using __fetch_t = int4;
};
template <> struct __TypeInfoT<unsigned int> {
using __base_t = unsigned int;
using __fetch_t = uint4;
};
// Derived base/fetch types for N-element vectors.
template <class __T> struct __TypeInfoT {
using __base_t = decltype(__T::x);
using __fetch_t = typename __TypeInfoT<__base_t>::__fetch_t;
};
// Classes that implement specific texture ops.
template <class __op> struct __tex_fetch_v4;
// Helper macros to strip parens from a macro argument.
#define __Args(...) __VA_ARGS__
#define __STRIP_PARENS(__X) __X
#define __L(__X) __STRIP_PARENS(__Args __X)
// Construct inline assembly output args.
// Results are stored in a temp var __r.
// isResident bool is pointed to by __ir
// Asm args for return values. It's a 4-element vector
#define __ASM_OUT(__t) \
("=" __t(__r.x), "=" __t(__r.y), "=" __t(__r.z), "=" __t(__r.w))
// .. possibly combined with a predicate.
#define __ASM_OUTP(__t) (__L(__ASM_OUT(__t)), "=h"(*__ir))
// Implements a single variant of texture fetch instruction.
#define __IMPL_F1(__rt, __dt, __args, __asm_op, __asm_outs, __asm_args) \
template <> \
__device__ __rt __run<__dt>(cudaTextureObject_t __obj, __L(__args)) { \
__rt __r; \
asm(__asm_op : __L(__asm_outs) : "l"(__obj), __L(__asm_args)); \
return __r; \
}
// Implements texture fetch instructions for int4/uint4/float4 data types.
#define __IMPL_F3(__args, __asm_op, __ctype, __asm_op_args, __asm_args) \
__IMPL_F1(int4, int4, __args, __asm_op ".s32." __ctype "\t" __asm_op_args, \
__ASM_OUT("r"), __asm_args) \
__IMPL_F1(uint4, uint4, __args, __asm_op ".u32." __ctype "\t" __asm_op_args, \
__ASM_OUT("r"), __asm_args) \
__IMPL_F1(float4, float4, __args, \
__asm_op ".f32." __ctype "\t" __asm_op_args, __ASM_OUT("f"), \
__asm_args)
// Implements 'sparse' texture fetch instructions for int4/uint4/float4 data
// types. Similar to above, but returns a boolean 'isPresent' value in addition
// to texture data,
#define __IMPL_F3S(__args, __asm_op, __ctype, __asm_op_args, __asm_args) \
__IMPL_F1(int4, int4, __args, __asm_op ".s32." __ctype "\t" __asm_op_args, \
__ASM_OUTP("r"), __asm_args) \
__IMPL_F1(uint4, uint4, __args, __asm_op ".u32." __ctype "\t" __asm_op_args, \
__ASM_OUTP("r"), __asm_args) \
__IMPL_F1(float4, float4, __args, \
__asm_op ".f32." __ctype "\t" __asm_op_args, __ASM_OUTP("f"), \
__asm_args)
// Similar to F3, but for integer data which is returned as normalized floats.
// Only instantiates fetch functions for int4/uint4.
#define __IMPL_F3N(__args, __asm_op, __ctype, __asm_op_args, __asm_args) \
__IMPL_F1(float4, int4, __args, __asm_op ".s32." __ctype "\t" __asm_op_args, \
__ASM_OUT("r"), __asm_args) \
__IMPL_F1(float4, uint4, __args, \
__asm_op ".u32." __ctype "\t" __asm_op_args, __ASM_OUT("r"), \
__asm_args)
// Instantiates __tex_fetch_v4 with regular fetch functions.
#define __IMPL_S3I(__op, __args, __asm_op, __ctype, __asm_op_args, __asm_args) \
template <> struct __tex_fetch_v4<__op> { \
template <class T> \
__device__ static T __run(cudaTextureObject_t __obj, __L(__args)); \
__IMPL_F3(__args, __asm_op, __ctype, __asm_op_args, __asm_args) \
}
// Same, but for sparse ops. Only available on sm_60+
#if !defined(__CUDA_ARCH__) || (__CUDA_ARCH__ >= 600)
#define __IMPL_S3SI(__op, __args, __asm_op, __ctype, __asm_op_args, \
__asm_args) \
template <> struct __tex_fetch_v4<__op> { \
template <class T> \
__device__ static T __run(cudaTextureObject_t __obj, __L(__args)); \
__IMPL_F3S(__args, __asm_op, __ctype, __asm_op_args, __asm_args) \
}
#else
#define __IMPL_S3SI(__op, __args, __asm_op, __ctype, __asm_op_args, __asm_args)
#endif
// Same, but for normalized float ops.
#define __IMPL_S3NI(__op, __args, __asm_op, __ctype, __asm_op_args, \
__asm_args) \
template <> struct __tex_fetch_v4<__op> { \
template <class T> \
__device__ static float4 __run(cudaTextureObject_t __obj, __L(__args)); \
__IMPL_F3N(__args, __asm_op, __ctype, __asm_op_args, __asm_args) \
}
// Regular and normalized float ops share a lot of similarities. This macro
// instantiates both variants -- normal for __op and normalized for __opn.
#define __IMPL_SI(__op, __opn, __args, __asm_op, __ctype, __asm_op_args, \
__asm_args) \
__IMPL_S3I(__op, __args, __asm_op, __ctype, __asm_op_args, __asm_args); \
__IMPL_S3NI(__opn, __args, __asm_op, __ctype, __asm_op_args, __asm_args)
// Convenience macros which converts string literal __op into a __Tag,
#define __IMPL_S3(__op, __args, __asm_op, __ctype, __asm_op_args, __asm_args) \
__IMPL_S3I(__ID(__op), __args, __asm_op, __ctype, __asm_op_args, __asm_args)
#define __IMPL_S3S(__op, __args, __asm_op, __ctype, __asm_op_args, __asm_args) \
__IMPL_S3SI(__ID(__op), __args, __asm_op, __ctype, __asm_op_args, __asm_args)
#define __IMPL_S3N(__op, __args, __asm_op, __ctype, __asm_op_args, __asm_args) \
__IMPL_S3NI(__ID(__op), __args, __asm_op, __ctype, __asm_op_args, __asm_args)
#define __IMPL_S(__op, __opn, __args, __asm_op, __ctype, __asm_op_args, \
__asm_args) \
__IMPL_SI(__ID(__op), __ID(__opn), __args, __asm_op, __ctype, __asm_op_args, \
__asm_args)
// CUDA headers have some 'legacy' texture oprerations that duplicate
// functionality. So, we just inherit it, instead of refining a copy.
#define __IMPL_ALIASI(__op, __opn) \
template <> struct __tex_fetch_v4<__op> : __tex_fetch_v4<__opn> {}
#define __IMPL_ALIAS(__op, __opn) __IMPL_ALIASI(__ID(__op), __ID(__opn))
// Now we can instantiate everything we need for each specific texture fetch
// variant.
__IMPL_S("__tex1D_v2", "__tex1D_rmnf_v2", (float __x), "tex.1d.v4", "f32",
"{%0, %1, %2, %3}, [%4, {%5}];", ("f"(__x)));
__IMPL_S("__tex1Dfetch_v2", "__tex1Dfetch_rmnf_v2", (int __x), "tex.1d.v4",
"s32", "{%0, %1, %2, %3}, [%4, {%5}];", ("r"(__x)));
__IMPL_ALIAS("__itex1D", "__tex1D_v2");
__IMPL_ALIAS("__itex1Dfetch", "__tex1Dfetch_v2");
__IMPL_S("__tex1DGrad_v2", "__tex1DGrad_rmnf_v2",
(float __x, float __dPdx, float __dPdy), "tex.grad.1d.v4", "f32",
"{%0, %1, %2, %3}, [%4, {%5}], {%6}, {%7};",
("f"(__x), "f"(__dPdx), "f"(__dPdy)));
__IMPL_ALIAS("__itex1DGrad", "__tex1DGrad_v2");
__IMPL_S("__tex1DLayered_v2", "__tex1DLayered_rmnf_v2",
(float __x, int __layer), "tex.a1d.v4", "f32",
"{%0, %1, %2, %3}, [%4, {%5, %6}];", ("r"(__layer), "f"(__x)));
__IMPL_ALIAS("__itex1DLayered", "__tex1DLayered_v2");
__IMPL_S("__tex1DLayeredGrad_v2", "__tex1DLayeredGrad_rmnf_v2",
(float __x, int __layer, float __dPdx, float __dPdy),
"tex.grad.a1d.v4", "f32",
"{%0, %1, %2, %3}, [%4, {%5, %6}], {%7}, {%8};",
("r"(__layer), "f"(__x), "f"(__dPdx), "f"(__dPdy)));
__IMPL_ALIAS("__itex1DLayeredGrad", "__tex1DLayeredGrad_v2");
__IMPL_S("__tex1DLayeredLod_v2", "__tex1DLayeredLod_rmnf_v2",
(float __x, int __layer, float __level), "tex.level.a1d.v4", "f32",
"{%0, %1, %2, %3}, [%4, {%5, %6}], %7;",
("r"(__layer), "f"(__x), "f"(__level)));
__IMPL_ALIAS("__itex1DLayeredLod", "__tex1DLayeredLod_v2");
__IMPL_S("__tex1DLod_v2", "__tex1DLod_rmnf_v2", (float __x, float __level),
"tex.level.1d.v4", "f32", "{%0, %1, %2, %3}, [%4, {%5}], %6;",
("f"(__x), "f"(__level)));
__IMPL_ALIAS("__itex1DLod", "__tex1DLod_v2");
// 2D
__IMPL_S("__tex2D_v2", "__tex2D_rmnf_v2", (float __x, float __y), "tex.2d.v4",
"f32", "{%0, %1, %2, %3}, [%4, {%5, %6}];", ("f"(__x), "f"(__y)));
__IMPL_ALIAS("__itex2D", "__tex2D_v2");
__IMPL_S3S("__itex2D_sparse", (float __x, float __y, unsigned char *__ir),
"{.reg .pred %%p0;\n\t"
"tex.2d.v4",
"f32",
"{%0, %1, %2, %3}|%%p0, [%5, {%6, %7}];\n\t"
" selp.u16 %4, 1, 0, %%p0; }",
("f"(__x), "f"(__y)));
__IMPL_S("__tex2DGrad_v2", "__tex2DGrad_rmnf_v2",
(float __x, float __y, const float2 *__dPdx, const float2 *__dPdy),
"tex.grad.2d.v4", "f32",
"{%0, %1, %2, %3}, [%4, {%5, %6}], {%7, %8}, {%9, %10};",
("f"(__x), "f"(__y), "f"(__dPdx->x), "f"(__dPdx->y), "f"(__dPdy->x),
"f"(__dPdy->y)));
__IMPL_ALIAS("__itex2DGrad_v2", "__tex2DGrad_v2");
__IMPL_S3S("__itex2DGrad_sparse",
(float __x, float __y, const float2 *__dPdx, const float2 *__dPdy,
unsigned char *__ir),
"{.reg .pred %%p0;\n\t"
"tex.grad.2d.v4",
"f32",
"{%0, %1, %2, %3}|%%p0, [%5, {%6, %7}], {%8, %9}, {%10, %11};\n\t"
"selp.u16 %4, 1, 0, %%p0; }",
("f"(__x), "f"(__y), "f"(__dPdx->x), "f"(__dPdx->y), "f"(__dPdy->x),
"f"(__dPdy->y)));
__IMPL_S("__tex2DLayered_v2", "__tex2DLayered_rmnf_v2",
(float __x, float __y, int __layer), "tex.a2d.v4", "f32",
"{%0, %1, %2, %3}, [%4, {%5, %6, %7, %7}];",
("r"(__layer), "f"(__x), "f"(__y)));
__IMPL_ALIAS("__itex2DLayered", "__tex2DLayered_v2");
__IMPL_S3S("__itex2DLayered_sparse",
(float __x, float __y, int __layer, unsigned char *__ir),
"{.reg .pred %%p0;\n\t"
"tex.a2d.v4",
"f32",
"{%0, %1, %2, %3}|%%p0, [%5, {%6, %7, %8, %8}];\n\t"
"selp.u16 %4, 1, 0, %%p0; }",
("r"(__layer), "f"(__x), "f"(__y)));
__IMPL_S("__tex2DLayeredGrad_v2", "__tex2DLayeredGrad_rmnf_v2",
(float __x, float __y, int __layer, const float2 *__dPdx,
const float2 *__dPdy),
"tex.grad.a2d.v4", "f32",
"{%0, %1, %2, %3}, [%4, {%5, %6, %7, %7}], {%8, %9}, {%10, %11};",
("r"(__layer), "f"(__x), "f"(__y), "f"(__dPdx->x), "f"(__dPdx->y),
"f"(__dPdy->x), "f"(__dPdy->y)));
__IMPL_ALIAS("__itex2DLayeredGrad_v2", "__tex2DLayeredGrad_v2");
__IMPL_S3S(
"__itex2DLayeredGrad_sparse",
(float __x, float __y, int __layer, const float2 *__dPdx,
const float2 *__dPdy, unsigned char *__ir),
"{.reg .pred %%p0;\n\t"
"tex.grad.a2d.v4",
"f32",
"{%0, %1, %2, %3}|%%p0, [%5, {%6, %7, %8, %8}], {%9, %10}, {%11, %12};\n\t"
"selp.u16 %4, 1, 0, %%p0; }",
("r"(__layer), "f"(__x), "f"(__y), "f"(__dPdx->x), "f"(__dPdx->y),
"f"(__dPdy->x), "f"(__dPdy->y)));
__IMPL_S("__tex2DLayeredLod_v2", "__tex2DLayeredLod_rmnf_v2",
(float __x, float __y, int __layer, float __level), "tex.level.a2d.v4",
"f32", "{%0, %1, %2, %3}, [%4, {%5, %6, %7, %7}], %8;",
("r"(__layer), "f"(__x), "f"(__y), "f"(__level)));
__IMPL_ALIAS("__itex2DLayeredLod", "__tex2DLayeredLod_v2");
__IMPL_S3S("__itex2DLayeredLod_sparse",
(float __x, float __y, int __layer, float __level,
unsigned char *__ir),
"{.reg .pred %%p0;\n\t"
"tex.level.a2d.v4",
"f32",
"{%0, %1, %2, %3}|%%p0, [%5, {%6, %7, %8, %8}], %9;\n\t"
"selp.u16 %4, 1, 0, %%p0; }",
("r"(__layer), "f"(__x), "f"(__y), "f"(__level)));
__IMPL_S("__tex2DLod_v2", "__tex2DLod_rmnf_v2",
(float __x, float __y, float __level), "tex.level.2d.v4", "f32",
"{%0, %1, %2, %3}, [%4, {%5, %6}], %7;",
("f"(__x), "f"(__y), "f"(__level)));
__IMPL_ALIAS("__itex2DLod", "__tex2DLod_v2");
__IMPL_S3S("__itex2DLod_sparse",
(float __x, float __y, float __level, unsigned char *__ir),
"{.reg .pred %%p0;\n\t"
"tex.level.2d.v4",
"f32",
"{%0, %1, %2, %3}|%%p0, [%5, {%6, %7}], %8;\n\t"
"selp.u16 %4, 1, 0, %%p0; }",
("f"(__x), "f"(__y), "f"(__level)));
// 2D gather is special. Unlike other variants that translate into exactly one
// asm instruction, it uses one of the four different instructions selected by
// __comp. We implement each instruction variant separately, and dispatch the
// right one from the manually implemented 'umbrella' fetch.
#define __IMPL_2DGATHER(variant, instr) \
__IMPL_SI(__IDV("__tex2Dgather_v2", variant), \
__IDV("__tex2Dgather_rmnf_v2", variant), \
(float __x, float __y, int __comp), instr, "f32", \
"{%0, %1, %2, %3}, [%4, {%5, %6}];", ("f"(__x), "f"(__y))); \
__IMPL_ALIASI(__IDV("__itex2Dgather", variant), \
__IDV("__tex2Dgather_v2", variant)); \
__IMPL_S3SI(__IDV("__itex2Dgather_sparse", variant), \
(float __x, float __y, unsigned char *__ir, int __comp), \
"{.reg .pred %%p0;\n\t" instr, "f32", \
"{%0, %1, %2, %3}|%%p0, [%5, {%6, %7}];\n\t" \
"selp.u16 %4, 1, 0, %%p0; }", \
("f"(__x), "f"(__y)));
__IMPL_2DGATHER(0, "tld4.r.2d.v4");
__IMPL_2DGATHER(1, "tld4.g.2d.v4");
__IMPL_2DGATHER(2, "tld4.b.2d.v4");
__IMPL_2DGATHER(3, "tld4.a.2d.v4");
// Umbrella dispatcher -- calls into specific 2Dgather variant.
template <> struct __tex_fetch_v4<__ID("__tex2Dgather_v2")> {
template <class __T>
__device__ static __T __run(cudaTextureObject_t __obj, float __x, float __y,
int __comp) {
switch (__comp) {
case 0:
return __tex_fetch_v4<__IDV("__tex2Dgather_v2", 0)>::__run<__T>(
__obj, __x, __y, __comp);
case 1:
return __tex_fetch_v4<__IDV("__tex2Dgather_v2", 1)>::__run<__T>(
__obj, __x, __y, __comp);
case 2:
return __tex_fetch_v4<__IDV("__tex2Dgather_v2", 2)>::__run<__T>(
__obj, __x, __y, __comp);
case 3:
return __tex_fetch_v4<__IDV("__tex2Dgather_v2", 3)>::__run<__T>(
__obj, __x, __y, __comp);
}
}
};
__IMPL_ALIAS("__itex2Dgather", "__tex2Dgather_v2");
template <> struct __tex_fetch_v4<__ID("__tex2Dgather_rmnf_v2")> {
template <class __T>
__device__ static float4 __run(cudaTextureObject_t __obj, float __x,
float __y, int __comp) {
switch (__comp) {
case 0:
return __tex_fetch_v4<__IDV("__tex2Dgather_rmnf_v2", 0)>::__run<__T>(
__obj, __x, __y, __comp);
case 1:
return __tex_fetch_v4<__IDV("__tex2Dgather_rmnf_v2", 1)>::__run<__T>(
__obj, __x, __y, __comp);
case 2:
return __tex_fetch_v4<__IDV("__tex2Dgather_rmnf_v2", 2)>::__run<__T>(
__obj, __x, __y, __comp);
case 3:
return __tex_fetch_v4<__IDV("__tex2Dgather_rmnf_v2", 3)>::__run<__T>(
__obj, __x, __y, __comp);
}
}
};
#if !defined(__CUDA_ARCH__) || (__CUDA_ARCH__ >= 600)
template <> struct __tex_fetch_v4<__ID("__itex2Dgather_sparse")> {
template <class __T>
__device__ static __T __run(cudaTextureObject_t __obj, float __x, float __y,
unsigned char *__ir, int __comp) {
switch (__comp) {
case 0:
return __tex_fetch_v4<__IDV("__itex2Dgather_sparse", 0)>::__run<__T>(
__obj, __x, __y, __ir, __comp);
case 1:
return __tex_fetch_v4<__IDV("__itex2Dgather_sparse", 1)>::__run<__T>(
__obj, __x, __y, __ir, __comp);
case 2:
return __tex_fetch_v4<__IDV("__itex2Dgather_sparse", 2)>::__run<__T>(
__obj, __x, __y, __ir, __comp);
case 3:
return __tex_fetch_v4<__IDV("__itex2Dgather_sparse", 3)>::__run<__T>(
__obj, __x, __y, __ir, __comp);
}
}
};
#endif
// 3D
__IMPL_S("__tex3D_v2", "__tex3D_rmnf_v2", (float __x, float __y, float __z),
"tex.3d.v4", "f32", "{%0, %1, %2, %3}, [%4, {%5, %6, %7, %7}];",
("f"(__x), "f"(__y), "f"(__z)));
__IMPL_ALIAS("__itex3D", "__tex3D_v2");
__IMPL_S3S("__itex3D_sparse",
(float __x, float __y, float __z, unsigned char *__ir),
"{.reg .pred %%p0;\n\t"
"tex.3d.v4",
"f32",
"{%0, %1, %2, %3}|%%p0, [%5, {%6, %7, %8, %8}];\n\t"
"selp.u16 %4, 1, 0, %%p0; }",
("f"(__x), "f"(__y), "f"(__z)));
__IMPL_S("__tex3DGrad_v2", "__tex3DGrad_rmnf_v2",
(float __x, float __y, float __z, const float4 *__dPdx,
const float4 *__dPdy),
"tex.grad.3d.v4", "f32",
"{%0, %1, %2, %3}, [%4, {%5, %6, %7, %7}], "
"{%8, %9, %10, %10}, {%11, %12, %13, %13};",
("f"(__x), "f"(__y), "f"(__z), "f"(__dPdx->x), "f"(__dPdx->y),
"f"(__dPdx->z), "f"(__dPdy->x), "f"(__dPdy->y), "f"(__dPdy->z)));
__IMPL_ALIAS("__itex3DGrad_v2", "__tex3DGrad_v2");
__IMPL_S3S("__itex3DGrad_sparse",
(float __x, float __y, float __z, const float4 *__dPdx,
const float4 *__dPdy, unsigned char *__ir),
"{.reg .pred %%p0;\n\t"
"tex.grad.3d.v4",
"f32",
"{%0, %1, %2, %3}|%%p0, [%5, {%6, %7, %8, %8}], "
"{%9, %10, %11, %11}, {%12, %13, %14, %14};\n\t"
"selp.u16 %4, 1, 0, %%p0; }",
("f"(__x), "f"(__y), "f"(__z), "f"(__dPdx->x), "f"(__dPdx->y),
"f"(__dPdx->z), "f"(__dPdy->x), "f"(__dPdy->y), "f"(__dPdy->z)));
__IMPL_S("__tex3DLod_v2", "__tex3DLod_rmnf_v2",
(float __x, float __y, float __z, float __level), "tex.level.3d.v4",
"f32", "{%0, %1, %2, %3}, [%4, {%5, %6, %7, %7}], %8;",
("f"(__x), "f"(__y), "f"(__z), "f"(__level)));
__IMPL_ALIAS("__itex3DLod", "__tex3DLod_v2");
__IMPL_S3S("__itex3DLod_sparse",
(float __x, float __y, float __z, float __level,
unsigned char *__ir),
"{.reg .pred %%p0;\n\t"
"tex.level.3d.v4",
"f32",
"{%0, %1, %2, %3}|%%p0, [%5, {%6, %7, %8, %8}], %9;\n\t"
"selp.u16 %4, 1, 0, %%p0; }",
("f"(__x), "f"(__y), "f"(__z), "f"(__level)));
// Cubemap
__IMPL_S("__texCubemap_v2", "__texCubemap_rmnf_v2",
(float __x, float __y, float __z), "tex.cube.v4", "f32",
"{%0, %1, %2, %3}, [%4, {%5, %6, %7, %7}];",
("f"(__x), "f"(__y), "f"(__z)));
__IMPL_ALIAS("__itexCubemap", "__texCubemap_v2");
__IMPL_S3S("__itexCubemap_sparse",
(float __x, float __y, float __z, unsigned char *__ir),
"{.reg .pred %%p0;\n\t"
"tex.cube.v4",
"f32",
"{%0, %1, %2, %3}|%%p0, [%5, {%6, %7, %8, %8}];\n\t"
"selp.u16 %4, 1, 0, %%p0; }",
("f"(__x), "f"(__y), "f"(__z)));
__IMPL_S("__texCubemapGrad_v2", "__texCubemapGrad_rmnf_v2",
(float __x, float __y, float __z, const float4 *__dPdx,
const float4 *__dPdy),
"tex.grad.cube.v4", "f32",
"{%0, %1, %2, %3}, [%4, {%5, %6, %7, %7}], "
"{%8, %9, %10, %10}, {%11, %12, %13, %13};",
("f"(__x), "f"(__y), "f"(__z), "f"(__dPdx->x), "f"(__dPdx->y),
"f"(__dPdx->z), "f"(__dPdy->x), "f"(__dPdy->y), "f"(__dPdy->z)));
__IMPL_ALIAS("__itexCubemapGrad_v2", "__texCubemapGrad_v2");
__IMPL_S("__texCubemapLayered_v2", "__texCubemapLayered_rmnf_v2",
(float __x, float __y, float __z, int __layer), "tex.acube.v4", "f32",
"{%0, %1, %2, %3}, [%4, {%5, %6, %7, %8}];",
("r"(__layer), "f"(__x), "f"(__y), "f"(__z)));
__IMPL_ALIAS("__itexCubemapLayered", "__texCubemapLayered_v2");
__IMPL_S("__texCubemapLayeredGrad_v2", "__texCubemapLayeredGrad_rmnf_v2",
(float __x, float __y, float __z, int __layer, const float4 *__dPdx,
const float4 *__dPdy),
"tex.grad.acube.v4", "f32",
"{%0, %1, %2, %3}, [%4, {%5, %6, %7, %8}], "
"{%9, %10, %11, %11}, {%12, %13, %14, %14};",
("r"(__layer), "f"(__x), "f"(__y), "f"(__z), "f"(__dPdx->x),
"f"(__dPdx->y), "f"(__dPdx->z), "f"(__dPdy->x), "f"(__dPdy->y),
"f"(__dPdy->z)));
__IMPL_ALIAS("__itexCubemapLayeredGrad_v2", "__texCubemapLayeredGrad_v2");
__IMPL_S("__texCubemapLayeredLod_v2", "__texCubemapLayeredLod_rmnf_v2",
(float __x, float __y, float __z, int __layer, float __level),
"tex.level.acube.v4", "f32",
"{%0, %1, %2, %3}, [%4, {%5, %6, %7, %8}], %9;",
("r"(__layer), "f"(__x), "f"(__y), "f"(__z), "f"(__level)));
__IMPL_ALIAS("__itexCubemapLayeredLod", "__texCubemapLayeredLod_v2");
__IMPL_S("__texCubemapLod_v2", "__texCubemapLod_rmnf_v2",
(float __x, float __y, float __z, float __level), "tex.level.cube.v4",
"f32", "{%0, %1, %2, %3}, [%4, {%5, %6, %7, %7}], %8;",
("f"(__x), "f"(__y), "f"(__z), "f"(__level)));
__IMPL_ALIAS("__itexCubemapLod", "__texCubemapLod_v2");
// Helper class for extracting slice of data from V4 fetch results.
template <class __DestT, class __SrcT> struct __convert {
template <int __NElements = sizeof(__DestT) /
sizeof(typename __TypeInfoT<__DestT>::__base_t)>
__device__ static __DestT __run(__SrcT __v);
template <> __device__ static __DestT __run<1>(__SrcT __v) { return {__v.x}; }
template <> __device__ static __DestT __run<2>(__SrcT __v) {
return {__v.x, __v.y};
}
template <> __device__ static __DestT __run<3>(__SrcT __v) {
return {__v.x, __v.y, __v.z};
}
template <> __device__ static __DestT __run<4>(__SrcT __v) {
return {__v.x, __v.y, __v.z, __v.w};
}
};
// These are the top-level function overloads the __nv_tex_surf_handler expands
// to. Each overload deals with one of the several ways __nv_tex_surf_handler
// is called by CUDA headers. In the end, each of the overloads does the same
// job -- it figures out which `__tex_fetch_v4::run` variant should be used to
// fetch texture data and which `__convert::run` is needed to convert it into
// appropriate return type.
// __nv_tex_surf_handler("__tex...", &ret, cudaTextureObject_t handle, args...);
// Data type and return type are based on ret.
template <class __op, class __T, class... __Args>
__device__ static void __tex_fetch(__T *__ptr, cudaTextureObject_t __handle,
__Args... __args) {
using __FetchT = typename __TypeInfoT<__T>::__fetch_t;
*__ptr = __convert<__T, __FetchT>::__run(
__tex_fetch_v4<__op>::template __run<__FetchT>(__handle, __args...));
}
// texture<> objects get magically converted into a texture reference. However,
// there's no way to convert them to cudaTextureObject_t on C++ level. So, we
// cheat a bit and use inline assembly to do it. It costs us an extra register
// and a move, but that is easy for ptxas to optimize away.
template <class __T>
__device__ cudaTextureObject_t __tex_handle_to_obj(__T __handle) {
cudaTextureObject_t __obj;
asm("mov.b64 %0, %1; " : "=l"(__obj) : "l"(__handle));
return __obj;
}
// __nv_tex_surf_handler ("__tex...", &ret, textureReference, args...);
// Data type and return type is based on ret.
template <class __op, class __T, class __HandleT, class... __Args>
__device__ static void __tex_fetch(__T *__ptr, __HandleT __handle,
__Args... __args) {
using __FetchT = typename __TypeInfoT<__T>::__fetch_t;
*__ptr = __convert<__T, __FetchT>::__run(
__tex_fetch_v4<__op>::template __run<__FetchT>(
__tex_handle_to_obj(__handle), __args...));
}
// __nv_tex_surf_handler ("__tex...", &type_dummy, &ret, texture<...>, args...);
// cudaReadModeNormalizedFloat fetches always return float4.
template <class __op, class __DataT, class __RetT, int __TexT, class... __Args>
__device__ static void
__tex_fetch(__DataT *, __RetT *__ptr,
texture<__DataT, __TexT, cudaReadModeNormalizedFloat> __handle,
__Args... __args) {
using __FetchT = typename __TypeInfoT<__DataT>::__fetch_t;
*__ptr = __convert<__RetT, float4>::__run(
__tex_fetch_v4<__op>::template __run<__FetchT>(
__tex_handle_to_obj(__handle), __args...));
}
// __nv_tex_surf_handler ("__tex...", &type_dummy, &ret, texture<...>, args...);
// For cudaReadModeElementType fetch return type is based on type_dummy.
template <class __op, class __DataT, class __RetT, int __TexT, class... __Args>
__device__ static void
__tex_fetch(__DataT *, __RetT *__ptr,
texture<__DataT, __TexT, cudaReadModeElementType> __handle,
__Args... __args) {
using __FetchT = typename __TypeInfoT<__DataT>::__fetch_t;
*__ptr = __convert<__RetT, __FetchT>::__run(
__tex_fetch_v4<__op>::template __run<__FetchT>(
__tex_handle_to_obj(__handle), __args...));
}
} // namespace __cuda_tex
} // namespace
#pragma pop_macro("__ASM_OUT")
#pragma pop_macro("__ASM_OUTP")
#pragma pop_macro("__Args")
#pragma pop_macro("__ID")
#pragma pop_macro("__IDV")
#pragma pop_macro("__IMPL_2DGATHER")
#pragma pop_macro("__IMPL_ALIAS")
#pragma pop_macro("__IMPL_ALIASI")
#pragma pop_macro("__IMPL_F1")
#pragma pop_macro("__IMPL_F3")
#pragma pop_macro("__IMPL_F3N")
#pragma pop_macro("__IMPL_F3S")
#pragma pop_macro("__IMPL_S")
#pragma pop_macro("__IMPL_S3")
#pragma pop_macro("__IMPL_S3I")
#pragma pop_macro("__IMPL_S3N")
#pragma pop_macro("__IMPL_S3NI")
#pragma pop_macro("__IMPL_S3S")
#pragma pop_macro("__IMPL_S3SI")
#pragma pop_macro("__IMPL_SI")
#pragma pop_macro("__L")
#pragma pop_macro("__STRIP_PARENS")
#endif // __CLANG_CUDA_TEXTURE_INTRINSICS_H__
Reference in New Issue View Git Blame Copy Permalink