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Oct 31, 2025
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CUDA: Volta tensor core support for MMF #16843

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5d14386
CUDA: Volta tensor core support for MMF
JohannesGaessler Oct 28, 2025
7efb6ac
more generic checks for hardware support
JohannesGaessler Oct 30, 2025
53badac
Update ggml/src/ggml-cuda/mmf.cuh
JohannesGaessler Oct 31, 2025
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10 changes: 9 additions & 1 deletion ggml/src/ggml-cuda/common.cuh
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Original file line number Diff line number Diff line change
Expand Up @@ -224,6 +224,11 @@ static const char * cu_get_error_str(CUresult err) {
#define AMD_MFMA_AVAILABLE
#endif // defined(GGML_USE_HIP) && defined(CDNA) && !defined(GGML_HIP_NO_MMQ_MFMA)

// The Volta instructions are in principle available on Turing or newer but they are effectively unusable:
#if !defined(GGML_USE_HIP) && __CUDA_ARCH__ == GGML_CUDA_CC_VOLTA
#define VOLTA_MMA_AVAILABLE
#endif // !defined(GGML_USE_HIP) && __CUDA_ARCH__ == GGML_CUDA_CC_VOLTA

#if !defined(GGML_USE_HIP) && __CUDA_ARCH__ >= GGML_CUDA_CC_TURING
#define TURING_MMA_AVAILABLE
#endif // !defined(GGML_USE_HIP) && __CUDA_ARCH__ >= GGML_CUDA_CC_TURING
Expand Down Expand Up @@ -278,7 +283,10 @@ static bool amd_mfma_available(const int cc) {
#endif //!defined(GGML_HIP_NO_MMQ_MFMA)
}

// Volta technically had FP16 tensor cores but they work very differently compared to Turing and later.
static bool volta_mma_available(const int cc) {
return GGML_CUDA_CC_IS_NVIDIA(cc) && ggml_cuda_highest_compiled_arch(cc) == GGML_CUDA_CC_VOLTA;
}

static bool turing_mma_available(const int cc) {
return GGML_CUDA_CC_IS_NVIDIA(cc) && ggml_cuda_highest_compiled_arch(cc) >= GGML_CUDA_CC_TURING;
}
Expand Down
172 changes: 155 additions & 17 deletions ggml/src/ggml-cuda/mma.cuh
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Expand Up @@ -18,6 +18,10 @@

#include "common.cuh"

// On Volta each warp is doing 4 8x8 mma operations in parallel.
// The basic memory layout for a 32x8 output tile is to stack 4 input tiles in I direction and to mirror the B tile.
// However, the i indices in this file are by default permuted to simplify the index calculations.
// #define GGML_CUDA_MMA_NO_VOLTA_PERM

#if CUDART_VERSION >= 11080

Expand Down Expand Up @@ -86,6 +90,7 @@ namespace ggml_cuda_mma {
return 4 * (threadIdx.x / 32) + 8 * (l / 4) + (l % 4);
} else {
static_assert(I == -1 && J == -1, "template specialization not implemented");
return -1;
}
}

Expand All @@ -102,6 +107,32 @@ namespace ggml_cuda_mma {
return threadIdx.x % 32;
} else {
static_assert(I == -1 && J == -1, "template specialization not implemented");
return -1;
}
}
#elif __CUDA_ARCH__ == GGML_CUDA_CC_VOLTA
static constexpr int ne = I * J / 32;
T x[ne] = {0};

static __device__ __forceinline__ int get_i(const int l) {
if constexpr (I == 32 && J == 8) {
#ifdef GGML_CUDA_MMA_NO_VOLTA_PERM
return (((threadIdx.x % 16) / 4) * 8) | ((threadIdx.x / 16) * 4) | (l & 2) | (threadIdx.x % 2);
#else
return (l & 2) | (threadIdx.x & ~2);
#endif // GGML_CUDA_MMA_NO_VOLTA_PERM
} else {
static_assert(I == -1 && J == -1, "template specialization not implemented");
return -1;
}
}

static __device__ __forceinline__ int get_j(const int l) {
if constexpr (I == 32 && J == 8) {
return (threadIdx.x & 2) | (l & (4 + 1));
} else {
static_assert(I == -1 && J == -1, "template specialization not implemented");
return -1;
}
}
#else
Expand All @@ -111,26 +142,28 @@ namespace ggml_cuda_mma {
static __device__ __forceinline__ int get_i(const int l) {
if constexpr (I == 8 && (J == 4 || J == 8)) {
return threadIdx.x / 4;
} else if constexpr (I == 16 && J == 8) {
return (l / 2) * 8 + threadIdx.x / 4;
} else if constexpr ((I == 16 || I == 32) && J == 8) {
return ((l / 2) * 8) | (threadIdx.x / 4);
} else if constexpr (I == 16 && J == 16) {
return ((l / 2) % 2) * 8 + threadIdx.x / 4;
return (((l / 2) % 2) * 8) | (threadIdx.x / 4);
} else {
static_assert(I == -1 && J == -1, "template specialization not implemented");
return -1;
}
}

static __device__ __forceinline__ int get_j(const int l) {
if constexpr (I == 8 && J == 4) {
return threadIdx.x % 4;
} else if constexpr (I == 8 && J == 8) {
return 4 * l + threadIdx.x % 4;
} else if constexpr (I == 16 && J == 8) {
return 2 * (threadIdx.x % 4) + l % 2;
return (l * 4) | (threadIdx.x % 4);
} else if constexpr ((I == 16 || I == 32) && J == 8) {
return ((threadIdx.x % 4) * 2) | (l % 2);
} else if constexpr (I == 16 && J == 16) {
return 8 * (l / 4) + 2 * (threadIdx.x % 4) + l % 2;
return ((l / 4) * 8) | ((threadIdx.x % 4) * 2) | (l % 2);
} else {
static_assert(I == -1 && J == -1, "template specialization not implemented");
return -1;
}
}
#endif // defined(GGML_USE_HIP)
Expand All @@ -140,32 +173,68 @@ namespace ggml_cuda_mma {
struct tile<I_, J_, half2> {
static constexpr int I = I_;
static constexpr int J = J_;

#if __CUDA_ARCH__ == GGML_CUDA_CC_VOLTA
static constexpr int ne = I == 8 && J == 8 ? I * J / (WARP_SIZE/4) : I * J / WARP_SIZE;
half2 x[ne] = {{0.0f, 0.0f}};

static __device__ __forceinline__ int get_i(const int l) {
if constexpr (I == 8 && J == 8) {
return ((threadIdx.x / 16) * 4) | (threadIdx.x % 4);
} else if constexpr (I == 32 && J == 8) {
#ifdef GGML_CUDA_MMA_NO_VOLTA_PERM
return (((threadIdx.x % 16) / 4) * 8) | ((threadIdx.x / 16) * 4) | (threadIdx.x % 4);
#else
return threadIdx.x;
#endif // GGML_CUDA_MMA_NO_VOLTA_PERM
} else {
static_assert(I == -1 && J == -1, "template specialization not implemented");
return -1;
}
}

static __device__ __forceinline__ int get_j(const int l) {
if constexpr ((I == 8 || I == 32) && J == 8) {
return l;
} else {
static_assert(I == -1 && J == -1, "template specialization not implemented");
return -1;
}
}
#else
static constexpr int ne = I * J / WARP_SIZE;
half2 x[ne] = {{0.0f, 0.0f}};

static __device__ __forceinline__ int get_i(const int l) {
if constexpr (I == 8 && J == 8) {
return threadIdx.x / 4;
} else if constexpr (I == 16 && J == 4) {
return l * 8 + threadIdx.x / 4;
return (l * 8) | (threadIdx.x / 4);
} else if constexpr (I == 16 && J == 8) {
return (l % 2) * 8 + threadIdx.x / 4;
return ((l % 2) * 8) | (threadIdx.x / 4);
} else if constexpr (I == 32 && J == 8) {
return ((l / 4) * 16) | ((l % 2) * 8) | (threadIdx.x / 4);
} else {
static_assert(I == -1 && J == -1, "template specialization not implemented");
return -1;
}
}

static __device__ __forceinline__ int get_j(const int l) {
if constexpr (I == 8 && J == 8) {
return l * 4 + threadIdx.x % 4;
return (l * 4) | (threadIdx.x % 4);
} else if constexpr (I == 16 && J == 4) {
return threadIdx.x % 4;
} else if constexpr (I == 16 && J == 8) {
return (l / 2) * 4 + threadIdx.x % 4;
return ((l / 2) * 4) | (threadIdx.x % 4);
} else if constexpr (I == 32 && J == 8) {
return ((l & 2) * 2) | (threadIdx.x % 4);
} else {
static_assert(I == -1 && J == -1, "template specialization not implemented");
return -1;
}
}
#endif // __CUDA_ARCH__ == GGML_CUDA_CC_VOLTA
};

template <int I_, int J_>
Expand All @@ -179,23 +248,25 @@ namespace ggml_cuda_mma {
if constexpr (I == 8 && J == 8) {
return threadIdx.x / 4;
} else if constexpr (I == 16 && J == 4) {
return l * 8 + threadIdx.x / 4;
return (l * 8) | (threadIdx.x / 4);
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is this more performant? I find the earlier version easier to read

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It's a good thing you're asking that because I misremembered the table from the CUDA documentation showing instruction throughput. The way I remembered it integer additions and binary operations had the same throughput but on a silicon level you would have lower power draw. In actually though the throughput of additions is twice that of binary operations.

} else if constexpr (I == 16 && J == 8) {
return (l % 2) * 8 + threadIdx.x / 4;
return ((l % 2) * 8) | (threadIdx.x / 4);
} else {
static_assert(I == -1 && J == -1, "template specialization not implemented");
return -1;
}
}

static __device__ __forceinline__ int get_j(const int l) {
if constexpr (I == 8 && J == 8) {
return l * 4 + threadIdx.x % 4;
return (l * 4) | (threadIdx.x % 4);
} else if constexpr (I == 16 && J == 4) {
return threadIdx.x % 4;
} else if constexpr (I == 16 && J == 8) {
return (l / 2) * 4 + threadIdx.x % 4;
return ((l / 2) * 4) | (threadIdx.x % 4);
} else {
static_assert(I == -1 && J == -1, "template specialization not implemented");
return -1;
}
}
};
Expand Down Expand Up @@ -263,8 +334,12 @@ namespace ggml_cuda_mma {
: "=r"(xi[0]), "=r"(xi[1])
: "l"(xs));
#else
load_generic(xs0, stride);
GGML_UNUSED(t);
#if __CUDA_ARCH__ == GGML_CUDA_CC_VOLTA
GGML_UNUSED_VARS(t, xs0, stride);
NO_DEVICE_CODE;
#else
load_generic(t, xs0, stride);
#endif // __CUDA_ARCH__ == GGML_CUDA_CC_VOLTA
#endif // TURING_MMA_AVAILABLE
}

Expand All @@ -277,11 +352,35 @@ namespace ggml_cuda_mma {
asm volatile("ldmatrix.sync.aligned.m8n8.x4.b16 {%0, %1, %2, %3}, [%4];"
: "=r"(xi[0]), "=r"(xi[1]), "=r"(xi[2]), "=r"(xi[3])
: "l"(xs));
#else
#if __CUDA_ARCH__ == GGML_CUDA_CC_VOLTA
GGML_UNUSED_VARS(t, xs0, stride);
NO_DEVICE_CODE;
#else
load_generic(t, xs0, stride);
#endif // __CUDA_ARCH__ == GGML_CUDA_CC_VOLTA
#endif // TURING_MMA_AVAILABLE
}

template <typename T>
static __device__ __forceinline__ void load_ldmatrix(
tile<32, 8, T> & t, const T * __restrict__ xs0, const int stride) {
#if __CUDA_ARCH__ == GGML_CUDA_CC_VOLTA
#if 1
// TODO: more generic handling
static_assert(sizeof(T) == 4, "bad type size");
ggml_cuda_memcpy_1<4*sizeof(T)>(t.x + 0, xs0 + t.get_i(0)*stride + 0);
ggml_cuda_memcpy_1<4*sizeof(T)>(t.x + 4, xs0 + t.get_i(4)*stride + 4);
#else
load_generic(t, xs0, stride);
#endif // 1
#else
tile<16, 8, T> * t16 = (tile<16, 8, T> *) &t;
load_ldmatrix(t16[0], xs0 + 0*stride, stride);
load_ldmatrix(t16[1], xs0 + 16*stride, stride);
#endif // __CUDA_ARCH__ == GGML_CUDA_CC_VOLTA
}

template <typename T>
static __device__ __forceinline__ void load_ldmatrix_trans(
tile<16, 8, T> & t, const T * __restrict__ xs0, const int stride) {
Expand Down Expand Up @@ -546,4 +645,43 @@ namespace ggml_cuda_mma {
NO_DEVICE_CODE;
#endif // AMD_MFMA_AVAILABLE
}

template <typename T1, typename T2, int J, int K>
static __device__ __forceinline__ void mma(
tile<32, J, T1> & D, const tile<32, K, T2> & A, const tile<J, K, T2> & B) {
tile<16, J, T1> * D16 = (tile<16, J, T1> *) &D;
tile<16, K, T2> * A16 = (tile<16, K, T2> *) &A;
mma(D16[0], A16[0], B);
mma(D16[1], A16[1], B);
}

static __device__ __forceinline__ void mma(
tile<32, 8, float> & D, const tile<32, 8, half2> & A, const tile<8, 8, half2> & B) {
#if __CUDA_ARCH__ == GGML_CUDA_CC_VOLTA
const int * Axi = (const int *) A.x;
const int * Bxi = (const int *) B.x;
int * Dxi = (int *) D.x;
asm("mma.sync.aligned.m8n8k4.row.col.f32.f16.f16.f32 "
"{%0, %1, %2, %3, %4, %5, %6, %7}, {%8, %9}, {%10, %11}, {%0, %1, %2, %3, %4, %5, %6, %7};"
: "+r"(Dxi[0]), "+r"(Dxi[1]), "+r"(Dxi[2]), "+r"(Dxi[3]), "+r"(Dxi[4]), "+r"(Dxi[5]), "+r"(Dxi[6]), "+r"(Dxi[7])
: "r"(Axi[0]), "r"(Axi[1]), "r"(Bxi[0]), "r"(Bxi[1]));
asm("mma.sync.aligned.m8n8k4.row.col.f32.f16.f16.f32 "
"{%0, %1, %2, %3, %4, %5, %6, %7}, {%8, %9}, {%10, %11}, {%0, %1, %2, %3, %4, %5, %6, %7};"
: "+r"(Dxi[0]), "+r"(Dxi[1]), "+r"(Dxi[2]), "+r"(Dxi[3]), "+r"(Dxi[4]), "+r"(Dxi[5]), "+r"(Dxi[6]), "+r"(Dxi[7])
: "r"(Axi[2]), "r"(Axi[3]), "r"(Bxi[2]), "r"(Bxi[3]));
asm("mma.sync.aligned.m8n8k4.row.col.f32.f16.f16.f32 "
"{%0, %1, %2, %3, %4, %5, %6, %7}, {%8, %9}, {%10, %11}, {%0, %1, %2, %3, %4, %5, %6, %7};"
: "+r"(Dxi[0]), "+r"(Dxi[1]), "+r"(Dxi[2]), "+r"(Dxi[3]), "+r"(Dxi[4]), "+r"(Dxi[5]), "+r"(Dxi[6]), "+r"(Dxi[7])
: "r"(Axi[4]), "r"(Axi[5]), "r"(Bxi[4]), "r"(Bxi[5]));
asm("mma.sync.aligned.m8n8k4.row.col.f32.f16.f16.f32 "
"{%0, %1, %2, %3, %4, %5, %6, %7}, {%8, %9}, {%10, %11}, {%0, %1, %2, %3, %4, %5, %6, %7};"
: "+r"(Dxi[0]), "+r"(Dxi[1]), "+r"(Dxi[2]), "+r"(Dxi[3]), "+r"(Dxi[4]), "+r"(Dxi[5]), "+r"(Dxi[6]), "+r"(Dxi[7])
: "r"(Axi[6]), "r"(Axi[7]), "r"(Bxi[6]), "r"(Bxi[7]));
#else
tile<16, 8, float> * D16 = (tile<16, 8, float> *) &D;
tile<16, 8, half2> * A16 = (tile<16, 8, half2> *) &A;
mma(D16[0], A16[0], B);
mma(D16[1], A16[1], B);
#endif // __CUDA_ARCH__ >= GGML_CUDA_CC_AMPERE
}
}
2 changes: 1 addition & 1 deletion ggml/src/ggml-cuda/mmf.cu
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Expand Up @@ -148,7 +148,7 @@ bool ggml_cuda_should_use_mmf(enum ggml_type type, int cc, int warp_size, const
case GGML_TYPE_F32:
return ampere_mma_available(cc);
case GGML_TYPE_F16:
return turing_mma_available(cc);
return volta_mma_available(cc) || turing_mma_available(cc);
case GGML_TYPE_BF16:
return ampere_mma_available(cc);
default:
Expand Down
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