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llama.cpp
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remove i_major_dual (#18157) 

Co-authored-by: zhang hui <you@example.com>
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yulo 2025-12-18 19:50:56 +08:00 committed by GitHub
parent 9ce64aed7d
commit 54189c0d39
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1 changed files with 84 additions and 45 deletions
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129
ggml/src/ggml-cuda/mma.cuh
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@ -78,27 +78,25 @@ namespace ggml_cuda_mma {
// MIRRORED == Each data value is held exactly once per thread subgroup.
DATA_LAYOUT_I_MAJOR = 0, // Always used for Turing, Ampere, Ada Lovelace, consumer Blackwell, matrix A&B for RDNA4 and CDNA.
DATA_LAYOUT_J_MAJOR = 10, // Matrix C for CDNA and RDNA4, int and float matrix C for RDNA3.
DATA_LAYOUT_I_MAJOR_MIRRORED = 20,
DATA_LAYOUT_I_MAJOR_MIRRORED = 20, // Volta, matrix A&B for RDNA3.
DATA_LAYOUT_J_MAJOR_MIRRORED = 30,
DATA_LAYOUT_I_MAJOR_DUAL = 40, // Matrix A&B for RDNA3.
};
// Implemented mma combinations are:
// - (I_MAJOR, I_MAJOR) -> I_MAJOR
// - (I_MAJOR, I_MAJOR_MIRRORED) -> I_MAJOR
// - (I_MAJOR, J_MAJOR_MIRRORED) -> I_MAJOR
constexpr bool is_i_major(const data_layout dl) {
static constexpr bool is_i_major(const data_layout dl) {
return dl == DATA_LAYOUT_I_MAJOR ||
dl == DATA_LAYOUT_I_MAJOR_MIRRORED ||
dl == DATA_LAYOUT_I_MAJOR_DUAL;
dl == DATA_LAYOUT_I_MAJOR_MIRRORED;
}
constexpr data_layout get_input_data_layout() {
#if defined(RDNA3)
return DATA_LAYOUT_I_MAJOR_DUAL;
static constexpr __device__ data_layout get_input_data_layout() {
#if defined(RDNA3) || __CUDA_ARCH__ == GGML_CUDA_CC_VOLTA
return DATA_LAYOUT_I_MAJOR_MIRRORED;
#else
return DATA_LAYOUT_I_MAJOR;
#endif // defined(RDNA3)
#endif // defined(RDNA3) || __CUDA_ARCH__ == GGML_CUDA_CC_VOLTA
}
template <int I_, int J_, typename T, data_layout ds_=DATA_LAYOUT_I_MAJOR>
@ -462,11 +460,65 @@ namespace ggml_cuda_mma {
}
};
template <int I_, int J_, typename T>
struct tile<I_, J_, T, DATA_LAYOUT_I_MAJOR_MIRRORED> {
static constexpr int I = I_;
static constexpr int J = J_;
static constexpr data_layout dl = DATA_LAYOUT_I_MAJOR_MIRRORED;
// RDNA3
static constexpr int ne = I * J / 32 * 2;
T x[ne] = {0};
static constexpr __device__ bool supported() {
if (I == 16 && J == 16) return true;
if (I == 16 && J == 8) return true;
if (I == 16 && J == 4) return true;
return false;
}
static __device__ __forceinline__ int get_i(const int /*l*/) {
if constexpr (supported()) {
return threadIdx.x % 16;
} else {
NO_DEVICE_CODE;
return -1;
}
}
static __device__ __forceinline__ int get_j(const int l) {
if constexpr (supported()) {
return l;
} else {
NO_DEVICE_CODE;
return -1;
}
}
};
template <int I_, int J_>
struct tile<I_, J_, half2, DATA_LAYOUT_I_MAJOR_MIRRORED> {
static constexpr int I = I_;
static constexpr int J = J_;
static constexpr data_layout dl = DATA_LAYOUT_I_MAJOR_MIRRORED;
#if defined(RDNA3)
static constexpr int ne = tile<I_, J_, float, DATA_LAYOUT_I_MAJOR_MIRRORED>::ne;
half2 x[ne] = {{0.0f, 0.0f}};
static constexpr __device__ bool supported() {
return tile<I_, J_, float, DATA_LAYOUT_I_MAJOR_MIRRORED>::supported();
}
static __device__ __forceinline__ int get_i(const int l) {
return tile<I_, J_, float, DATA_LAYOUT_I_MAJOR_MIRRORED>::get_i(l);
}
static __device__ __forceinline__ int get_j(const int l) {
return tile<I_, J_, float, DATA_LAYOUT_I_MAJOR_MIRRORED>::get_j(l);
}
#else // Volta
static constexpr int ne = I * J / (WARP_SIZE/4);
half2 x[ne] = {{0.0f, 0.0f}};
@ -493,6 +545,29 @@ namespace ggml_cuda_mma {
return -1;
}
}
#endif // defined(RDNA3)
};
template <int I_, int J_>
struct tile<I_, J_, nv_bfloat162, DATA_LAYOUT_I_MAJOR_MIRRORED> {
static constexpr int I = I_;
static constexpr int J = J_;
static constexpr data_layout dl = DATA_LAYOUT_I_MAJOR_MIRRORED;
static constexpr int ne = tile<I_, J_, float, DATA_LAYOUT_I_MAJOR_MIRRORED>::ne;
nv_bfloat162 x[ne] = {{0.0f, 0.0f}};
static constexpr __device__ bool supported() {
return tile<I_, J_, float, DATA_LAYOUT_I_MAJOR_MIRRORED>::supported();
}
static __device__ __forceinline__ int get_i(const int l) {
return tile<I_, J_, float, DATA_LAYOUT_I_MAJOR_MIRRORED>::get_i(l);
}
static __device__ __forceinline__ int get_j(const int l) {
return tile<I_, J_, float, DATA_LAYOUT_I_MAJOR_MIRRORED>::get_j(l);
}
};
template <int I_, int J_>
@ -528,42 +603,6 @@ namespace ggml_cuda_mma {
}
};
template <int I_, int J_, typename T>
struct tile<I_, J_, T, DATA_LAYOUT_I_MAJOR_DUAL> {
static constexpr int I = I_;
static constexpr int J = J_;
static constexpr data_layout dl = DATA_LAYOUT_I_MAJOR_DUAL;
static constexpr int ne = I * J / 32 * 2;
T x[ne] = {0};
static constexpr __device__ bool supported() {
if (I == 16 && J == 16) return true;
if (I == 16 && J == 8) return true;
if (I == 16 && J == 4) return true;
return false;
}
static __device__ __forceinline__ int get_i(const int l) {
if constexpr (supported()) {
return threadIdx.x % 16;
} else {
NO_DEVICE_CODE;
return -1;
}
}
static __device__ __forceinline__ int get_j(const int l) {
if constexpr (supported()) {
return l;
} else {
NO_DEVICE_CODE;
return -1;
}
}
};
#if defined(TURING_MMA_AVAILABLE)
template <int I, int J>
static __device__ __forceinline__ tile<I, J/2, half2> get_half2(const tile<I, J, float> & tile_float) {