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CUDA: int8 tensor cores for MMQ (q4_K, q5_K, q6_K) (#7860)

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Johannes Gäßler 2024-06-11 08:26:07 +02:00 • committed by GitHub
parent c2ce6c47e4
commit bdcb8f4222
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GPG key ID: B5690EEEBB952194
2 changed files with 360 additions and 6 deletions
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66
ggml-cuda/mma.cuh
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@ -1,5 +1,27 @@
#include "common.cuh"
struct mma_int_A_I16K4 {
static constexpr int I = 16;
static constexpr int K = 4;
static constexpr int ne = 2;
int x[ne] = {0};
static __device__ __forceinline__ int get_i(const int l) {
const int ret = (l%2) * (I/2) + threadIdx.x / K;
GGML_CUDA_ASSUME(ret >= 0);
GGML_CUDA_ASSUME(ret < I);
return ret;
}
static __device__ __forceinline__ int get_k(const int /* l */) {
const int ret = threadIdx.x % K;
GGML_CUDA_ASSUME(ret >= 0);
GGML_CUDA_ASSUME(ret < K);
return ret;
}
};
struct mma_int_A_I16K8 {
static constexpr int I = 16;
static constexpr int K = 8;
@ -22,6 +44,28 @@ struct mma_int_A_I16K8 {
}
};
struct mma_int_B_J8K4 {
static constexpr int J = 8;
static constexpr int K = 4;
static constexpr int ne = 1;
int x[ne] = {0};
static __device__ __forceinline__ int get_j(const int /* l */) {
const int ret = threadIdx.x / K;
GGML_CUDA_ASSUME(ret >= 0);
GGML_CUDA_ASSUME(ret < J);
return ret;
}
static __device__ __forceinline__ int get_k(const int /* l */) {
const int ret = threadIdx.x % K;
GGML_CUDA_ASSUME(ret >= 0);
GGML_CUDA_ASSUME(ret < K);
return ret;
}
};
struct mma_int_B_J8K8 {
static constexpr int J = 8;
static constexpr int K = 8;
@ -65,6 +109,28 @@ struct mma_int_C_I16J8 {
return ret;
}
__device__ __forceinline__ void mma_K4(const mma_int_A_I16K4 & mma_A, const mma_int_B_J8K4 & mma_B) {
#ifdef INT8_MMA_AVAILABLE
#if __CUDA_ARCH__ >= CC_AMPERE
asm("mma.sync.aligned.m16n8k16.row.col.s32.s8.s8.s32 {%0, %1, %2, %3}, {%4, %5}, {%6}, {%0, %1, %2, %3};"
: "+r"(x[0]), "+r"(x[1]), "+r"(x[2]), "+r"(x[3])
: "r"(mma_A.x[0]), "r"(mma_A.x[1]), "r"(mma_B.x[0]));
#else
// On Turing m16n8k16 mma is not available, use 2x m8n8k16 mma instead:
asm("mma.sync.aligned.m8n8k16.row.col.s32.s8.s8.s32 {%0, %1}, {%2}, {%3}, {%0, %1};"
: "+r"(x[0]), "+r"(x[1])
: "r"(mma_A.x[0]), "r"(mma_B.x[0]));
asm("mma.sync.aligned.m8n8k16.row.col.s32.s8.s8.s32 {%0, %1}, {%2}, {%3}, {%0, %1};"
: "+r"(x[2]), "+r"(x[3])
: "r"(mma_A.x[1]), "r"(mma_B.x[0]));
#endif // __CUDA_ARCH__ >= CC_AMPERE
#else
GGML_UNUSED(mma_A);
GGML_UNUSED(mma_B);
NO_DEVICE_CODE;
#endif // INT8_MMA_AVAILABLE
}
__device__ __forceinline__ void mma_K8(const mma_int_A_I16K8 & mma_A, const mma_int_B_J8K8 & mma_B) {
#ifdef INT8_MMA_AVAILABLE
#if __CUDA_ARCH__ >= CC_AMPERE