sleepy/llama.cpp
1
0
Fork 0
Code Issues 9 Pull Requests Actions 103 Packages Projects Releases Wiki Activity

ggml-cuda: Repost of 21896: Blackwell native NVFP4 support (#22196)

Browse Source
This commit is contained in:
Michael Wand
2026-04-28 15:47:42 -07:00
committed by GitHub
parent 7b8443ac78
commit fc2b0053ff
8 changed files with 318 additions and 130 deletions
Download Patch File Download Diff File Expand all files Collapse all files
ggml/src/ggml-cuda/common.cuh
+12
View File
@@ -830,6 +830,18 @@ static __device__ __forceinline__ float ggml_cuda_ue4m3_to_fp32(uint8_t x) {
#endif // defined(GGML_USE_HIP) && defined(CDNA3) && defined(FP8_AVAILABLE) && HIP_VERSION >= 60200000
}
static __device__ __forceinline__ uint8_t ggml_cuda_fp32_to_ue4m3(float x) {
#if defined(BLACKWELL_MMA_AVAILABLE) // This is used for NVFP4 subblock scale quantizations only
if (!(x > 0.0f)) {
return 0;
}
const __nv_fp8_e4m3 xf(x);
return xf.__x;
#else
NO_DEVICE_CODE; // Used only for NVFP4 Scales for Activations, only for Blackwell
#endif // defined(BLACKWELL_MMA_AVAILABLE)
}
__device__ __forceinline__ uint8_t ggml_cuda_float_to_fp4_e2m1(float x, float e) {
const uint8_t sign_bit = (x < 0.0f) << 3;
float ax = fabsf(x) * e;
ggml/src/ggml-cuda/mma.cuh
+11 -1
View File
@@ -1015,7 +1015,8 @@ namespace ggml_cuda_mma {
#endif // AMD_MFMA_AVAILABLE
}
static __device__ __forceinline__ void mma_block_scaled(tile<16, 8, float> & D,
template <ggml_type type>
static __device__ __forceinline__ void mma_block_scaled_fp4(tile<16, 8, float> & D,
const tile<16, 8, int> & A,
const tile<8, 8, int> & B,
uint32_t a_scale,
@@ -1025,12 +1026,21 @@ namespace ggml_cuda_mma {
const int * Bxi = (const int *) B.x;
float * Dxi = (float *) D.x;
if constexpr (type == GGML_TYPE_MXFP4) {
asm volatile(
"mma.sync.aligned.kind::mxf4.block_scale.scale_vec::2X.m16n8k64.row.col.f32.e2m1.e2m1.f32.ue8m0 "
"{%0, %1, %2, %3}, {%4, %5, %6, %7}, {%8, %9}, {%0, %1, %2, %3}, "
"%10, {0, 0}, %11, {0, 0};"
: "+f"(Dxi[0]), "+f"(Dxi[1]), "+f"(Dxi[2]), "+f"(Dxi[3])
: "r"(Axi[0]), "r"(Axi[1]), "r"(Axi[2]), "r"(Axi[3]), "r"(Bxi[0]), "r"(Bxi[1]), "r"(a_scale), "r"(b_scale));
} else {
asm volatile(
"mma.sync.aligned.kind::mxf4nvf4.block_scale.scale_vec::4X.m16n8k64.row.col.f32.e2m1.e2m1.f32.ue4m3 "
"{%0, %1, %2, %3}, {%4, %5, %6, %7}, {%8, %9}, {%0, %1, %2, %3}, "
"%10, {0, 0}, %11, {0, 0};"
: "+f"(Dxi[0]), "+f"(Dxi[1]), "+f"(Dxi[2]), "+f"(Dxi[3])
: "r"(Axi[0]), "r"(Axi[1]), "r"(Axi[2]), "r"(Axi[3]), "r"(Bxi[0]), "r"(Bxi[1]), "r"(a_scale), "r"(b_scale));
}
#else
GGML_UNUSED_VARS(D, A, B, a_scale, b_scale);
#endif // BLACKWELL_MMA_AVAILABLE
ggml/src/ggml-cuda/mmq.cu
+9 -10
View File
@@ -122,7 +122,7 @@ void ggml_cuda_mul_mat_q(
|| GGML_CUDA_CC_IS_CDNA(cc);
// TODO: tighter pool buffer size vs q8 path
const bool use_native_mxfp4 = blackwell_mma_available(cc) && src0->type == GGML_TYPE_MXFP4;
const bool use_native_fp4 = blackwell_mma_available(cc) && (src0->type == GGML_TYPE_MXFP4 || src0->type == GGML_TYPE_NVFP4);
if (!ids) {
const size_t nbytes_src1_q8_1 = ne13*ne12 * ne11*ne10_padded * sizeof(block_q8_1)/QK8_1 +
@@ -133,9 +133,9 @@ void ggml_cuda_mul_mat_q(
const int64_t s11 = src1->nb[1] / ts_src1;
const int64_t s12 = src1->nb[2] / ts_src1;
const int64_t s13 = src1->nb[3] / ts_src1;
if (use_native_mxfp4) {
if (use_native_fp4) {
static_assert(sizeof(block_fp4_mmq) == 4 * sizeof(block_q8_1));
quantize_mmq_mxfp4_cuda(src1_d, nullptr, src1_q8_1.get(), src0->type, ne10, s11, s12, s13, ne10_padded,
quantize_mmq_fp4_cuda(src1_d, nullptr, src1_q8_1.get(), src0->type, ne10, s11, s12, s13, ne10_padded,
ne11, ne12, ne13, stream);
} else {
@@ -146,10 +146,8 @@ void ggml_cuda_mul_mat_q(
}
// Stride depends on quantization format
const int64_t s12 = use_native_mxfp4 ?
ne11 * ne10_padded * sizeof(block_fp4_mmq) /
(8 * QK_MXFP4 * sizeof(int)) // block_fp4_mmq holds 256 values (8 blocks of 32)
:
const int64_t s12 = use_native_fp4 ?
ne11 * ne10_padded * sizeof(block_fp4_mmq) / (QK_K * sizeof(int)) : // block_fp4_mmq holds 256 values
ne11 * ne10_padded * sizeof(block_q8_1) / (QK8_1 * sizeof(int));
const int64_t s13 = ne12*s12;
@@ -198,8 +196,8 @@ void ggml_cuda_mul_mat_q(
const int64_t s12 = src1->nb[2] / ts_src1;
const int64_t s13 = src1->nb[3] / ts_src1;
if (use_native_mxfp4) {
quantize_mmq_mxfp4_cuda(src1_d, ids_src1.get(), src1_q8_1.get(), src0->type, ne10, s11, s12, s13,
if (use_native_fp4) {
quantize_mmq_fp4_cuda(src1_d, ids_src1.get(), src1_q8_1.get(), src0->type, ne10, s11, s12, s13,
ne10_padded, ne11_flat, ne12_flat, ne13_flat, stream);
} else {
quantize_mmq_q8_1_cuda(src1_d, ids_src1.get(), src1_q8_1.get(), src0->type, ne10, s11, s12, s13,
@@ -208,7 +206,8 @@ void ggml_cuda_mul_mat_q(
CUDA_CHECK(cudaGetLastError());
}
const int64_t s12 = use_native_mxfp4 ? ne11 * ne10_padded * sizeof(block_fp4_mmq) / (8 * QK_MXFP4 * sizeof(int)) :
static_assert(QK_K == 8 * QK_MXFP4, "QK_K needs to be 8 * QK_MXFP4");
const int64_t s12 = use_native_fp4 ? ne11 * ne10_padded * sizeof(block_fp4_mmq) / (QK_K * sizeof(int)) :
ne11 * ne10_padded * sizeof(block_q8_1) / (QK8_1 * sizeof(int));
const int64_t s13 = ne12*s12;
ggml/src/ggml-cuda/mmq.cuh
+145 -81
View File
@@ -11,7 +11,7 @@ using namespace ggml_cuda_mma;
#define MMQ_DP4A_MAX_BATCH_SIZE 64 // Max. batch size to use for dp4a MMQ kernels when FP16 tensor cores are available.
#define MMQ_ITER_K 256
#define MMQ_ITER_K_MXFP4_FP4 512
#define MMQ_ITER_K_FP4 512
#define MMQ_NWARPS 8
typedef void (*load_tiles_mmq_t)(const char * __restrict__ x, int * x_tile, const int kbx0, const int i_max, const int stride);
@@ -46,9 +46,12 @@ struct block_q8_1_mmq {
int8_t qs[4*QK8_1]; // 128 values quantized to 8 bit each
};
// this struct is used for fp4 data types (currently only used for Blackwell)
// mxfp4 has block size 32, each int32 of d4 contains 2 e8m0 scales in the lower 16 bits
// nvfp4 has block size 16, each int32 of d4 contains 4 ue4m3 scales
struct block_fp4_mmq {
uint32_t d4[4]; // 8 E8M0 scales (1 per 32 values), 2 packed per uint32: d4[0]={s0,s1}, d4[1]={s2,s3}, etc.
int8_t qs[4 * 32]; // 256 FP4 values packed as 4-bit pairs (2 per byte), 8 blocks of 32 values
uint32_t d4[4];
int8_t qs[4 * 32]; // 256 FP4 values packed as 4-bit pairs (2 per byte)
};
static_assert(sizeof(block_q8_1_mmq) == 4*QK8_1 + 4*sizeof(half2), "Unexpected block_q8_1_mmq size");
@@ -143,10 +146,11 @@ static int get_mmq_y_host(const int cc) {
static constexpr __device__ int get_iter_k([[maybe_unused]] const ggml_type type) {
#if defined(BLACKWELL_MMA_AVAILABLE)
return type == GGML_TYPE_MXFP4 ? MMQ_ITER_K_MXFP4_FP4 : MMQ_ITER_K;
#else
return MMQ_ITER_K;
if (type == GGML_TYPE_NVFP4 || type == GGML_TYPE_MXFP4) {
return MMQ_ITER_K_FP4;
}
#endif // defined(BLACKWELL_MMA_AVAILABLE)
return MMQ_ITER_K;
}
static constexpr __device__ int get_mmq_y_device() {
@@ -213,8 +217,8 @@ static constexpr __host__ __device__ tile_x_sizes mmq_get_dp4a_tile_x_sizes(ggml
}
#define MMQ_MMA_TILE_X_K_Q8_0 (2*MMQ_TILE_NE_K + 2*MMQ_TILE_NE_K/QI8_0 + 4)
#define MMQ_MMA_TILE_X_K_FP4 (2*MMQ_TILE_NE_K + 8 + 4) // MXFP4
#define MMQ_MMA_TILE_X_K_NVFP4 (2*MMQ_TILE_NE_K + MMQ_TILE_NE_K/2 + 4) // NVFP4
#define MMQ_MMA_TILE_X_K_FP4 (2*MMQ_TILE_NE_K + 8 + 4) // MXFP4 and NVFP4 Blackwell
#define MMQ_MMA_TILE_X_K_NVFP4 (2*MMQ_TILE_NE_K + MMQ_TILE_NE_K/2 + 4) // NVFP4 Generic
#define MMQ_MMA_TILE_X_K_Q8_1 (2*MMQ_TILE_NE_K + 2*MMQ_TILE_NE_K/QI8_0 + 4)
#define MMQ_MMA_TILE_X_K_Q2_K (2*MMQ_TILE_NE_K + MMQ_TILE_NE_K + 4)
#define MMQ_MMA_TILE_X_K_Q3_K (2*MMQ_TILE_NE_K + MMQ_TILE_NE_K/2 + 4)
@@ -240,7 +244,11 @@ static constexpr __host__ __device__ int mmq_get_mma_tile_x_k(ggml_type type) {
case GGML_TYPE_Q8_0: return MMQ_MMA_TILE_X_K_Q8_0;
// tile sizes are the same for Q8_1 and FP4 for blackwell
case GGML_TYPE_MXFP4: return MMQ_MMA_TILE_X_K_Q8_1;
#if defined(BLACKWELL_MMA_AVAILABLE)
case GGML_TYPE_NVFP4: return MMQ_MMA_TILE_X_K_FP4;
#else
case GGML_TYPE_NVFP4: return MMQ_MMA_TILE_X_K_NVFP4;
#endif // defined(BLACKWELL_MMA_AVAILABLE)
case GGML_TYPE_Q2_K: return MMQ_MMA_TILE_X_K_Q2_K;
case GGML_TYPE_Q3_K: return MMQ_MMA_TILE_X_K_Q3_K;
case GGML_TYPE_Q4_K: return MMQ_MMA_TILE_X_K_Q8_1;
@@ -934,6 +942,128 @@ static __device__ __forceinline__ void load_tiles_mxfp4_fp4(const char * __restr
}
}
#ifdef BLACKWELL_MMA_AVAILABLE
template <int mmq_y, bool need_check>
static __device__ __forceinline__ void load_tiles_nvfp4_nvfp4(const char * __restrict__ x,
int * __restrict__ x_tile,
const int kbx0,
const int i_max,
const int stride) {
constexpr int nwarps = mmq_get_nwarps_device();
constexpr int warp_size = ggml_cuda_get_physical_warp_size();
constexpr int iter_k = get_iter_k(GGML_TYPE_NVFP4);
constexpr int threads_per_row = iter_k / QK_NVFP4; // each thread processes 1 block
constexpr int rows_per_warp = warp_size / threads_per_row;
uint32_t * x_u32 = (uint32_t *) x_tile;
const int txi = threadIdx.x;
const int kbx = txi % threads_per_row;
const int row_in_warp = txi / threads_per_row;
const block_nvfp4 * bxi_base = (const block_nvfp4 *) x + kbx0 + kbx;
uint32_t * x_u32_scale = x_u32 + 64 + kbx;
#pragma unroll
for (int i0 = 0; i0 < mmq_y; i0 += rows_per_warp * nwarps) {
int i = i0 + threadIdx.y * rows_per_warp + row_in_warp;
if constexpr (need_check) {
i = min(i, i_max);
}
const block_nvfp4 * bxi = bxi_base + i * stride;
const int row_base = i * MMQ_MMA_TILE_X_K_FP4;
const int q_base = row_base + 8 * kbx;
const uint32_t * src_qs = reinterpret_cast<const uint32_t *>(bxi->qs);
#pragma unroll
for (int sub = 0; sub < QK_NVFP4 / QK_NVFP4_SUB; ++sub) {
x_u32[q_base + 2 * sub + 0] = src_qs[2 * sub + 0];
x_u32[q_base + 2 * sub + 1] = src_qs[2 * sub + 1];
}
x_u32_scale[row_base] = get_int_b4(bxi->d, 0);
}
}
// Shared MMA kernel for MXFP4 and NVFP4 on Blackwell.
// Both quantizations encode values as e2m1 (FP4) and produce one uint32 scale per
// m16n8k64 MMA call; only the PTX kind (scale_vec::2X ue8m0 vs scale_vec::4X ue4m3)
// and the per-type stride constant differ.
template <int mmq_x, int mmq_y, ggml_type type>
static __device__ __forceinline__ void vec_dot_fp4_fp4_mma(const int * __restrict__ x,
const int * __restrict__ y,
float * __restrict__ sum,
const int k00) {
static_assert(type == GGML_TYPE_MXFP4 || type == GGML_TYPE_NVFP4,
"vec_dot_fp4_fp4_mma: type must be MXFP4 or NVFP4");
typedef tile<16, 8, int> tile_A;
typedef tile<8, 8, int> tile_B;
typedef tile<16, 8, float> tile_C;
constexpr int stride = MMQ_MMA_TILE_X_K_FP4;
constexpr int granularity = mmq_get_granularity_device(mmq_x);
constexpr int rows_per_warp = 2 * granularity;
constexpr int ntx = rows_per_warp / tile_C::I;
constexpr int nfrags = MMQ_TILE_NE_K / tile_A::J;
y += (threadIdx.y % ntx) * (tile_C::J * MMQ_TILE_Y_K);
const int * x_qs = (const int *) x;
const uint32_t * x_sc = (const uint32_t *) (x_qs + 2 * MMQ_TILE_NE_K);
const int * y_qs = (const int *) y + 4;
const uint32_t * y_sc = (const uint32_t *) y;
// 2 threads per quad supply the packed scale register to the block_scale MMA,
// see https://docs.nvidia.com/cuda/parallel-thread-execution/#warp-level-block-scaling
const int tidx_A = threadIdx.x / 4 + (threadIdx.x % 2) * 8;
const int tidx_B = threadIdx.x / 4;
const int i0 = (threadIdx.y / ntx) * rows_per_warp;
tile_A A[ntx][nfrags];
uint32_t scaleA[ntx][nfrags];
#pragma unroll
for (int n = 0; n < ntx; ++n) {
#pragma unroll
for (int frag = 0; frag < nfrags; ++frag) {
const int k0 = k00 + frag * tile_A::J;
load_ldmatrix(A[n][frag], x_qs + (i0 + n * tile_A::I) * stride + k0, stride);
scaleA[n][frag] = x_sc[(i0 + n * tile_A::I + tidx_A) * stride + k0 / tile_A::J];
}
}
#pragma unroll
for (int j0 = 0; j0 < mmq_x; j0 += ntx * tile_C::J) {
tile_B B[nfrags];
uint32_t scaleB[nfrags];
#pragma unroll
for (int frag = 0; frag < nfrags; ++frag) {
const int k0 = frag * tile_B::J;
load_generic(B[frag], y_qs + j0 * MMQ_TILE_Y_K + k0, MMQ_TILE_Y_K);
scaleB[frag] = y_sc[(j0 + tidx_B) * MMQ_TILE_Y_K + frag];
}
#pragma unroll
for (int n = 0; n < ntx; ++n) {
#pragma unroll
for (int frag = 0; frag < nfrags; ++frag) {
tile_C C = {};
mma_block_scaled_fp4<type>(C, A[n][frag], B[frag], scaleA[n][frag], scaleB[frag]);
#pragma unroll
for (int l = 0; l < tile_C::ne; ++l) {
sum[(j0 / tile_C::J + n) * tile_C::ne + l] += C.x[l];
}
}
}
}
}
#endif // BLACKWELL_MMA_AVAILABLE
template <int mmq_y, bool need_check>
static __device__ __forceinline__ void load_tiles_nvfp4(const char * __restrict__ x,
@@ -1163,77 +1293,6 @@ static __device__ __forceinline__ void vec_dot_q8_0_q8_1_mma(
#endif // defined(AMD_MFMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE)
}
template <int mmq_x, int mmq_y>
static __device__ __forceinline__ void vec_dot_mxfp4_mxfp4_mma(const int * __restrict__ x,
const int * __restrict__ y,
float * __restrict__ sum,
const int k00) {
typedef tile<16, 8, int> tile_A;
typedef tile<8, 8, int> tile_B;
typedef tile<16, 8, float> tile_C; // Output is float for native scaled MMA
constexpr int granularity = mmq_get_granularity_device(mmq_x);
constexpr int rows_per_warp = 2 * granularity;
constexpr int ntx = rows_per_warp / tile_C::I; // Number of x minitiles per warp.
y += (threadIdx.y % ntx) * (tile_C::J * MMQ_TILE_Y_FP4_K);
// Match layout from load_tiles_mxfp4_fp4
const int * x_qs = (const int *) x;
const uint32_t * x_sc = (const uint32_t *) (x_qs + 2 * MMQ_TILE_NE_K);
const int * y_qs = (const int *) y + 4;
const uint32_t * y_sc = (const uint32_t *) y;
// tile_A has a length of 64 logical values vs. 32 values in block_mxfp4
tile_A A[ntx][MMQ_TILE_NE_K / (2 * QI_MXFP4)];
uint32_t scaleA[ntx][MMQ_TILE_NE_K / (2 * QI_MXFP4)];
// Block scale
// Each thread has to point to a 4 byte scale value
// https://docs.nvidia.com/cuda/parallel-thread-execution/#warp-level-block-scaling
const int i0 = (threadIdx.y / ntx) * rows_per_warp;
#pragma unroll
for (int n = 0; n < ntx; ++n) {
#pragma unroll
for (int k01 = 0; k01 < MMQ_TILE_NE_K; k01 += 2 * QI_MXFP4) {
const int k0 = k00 + k01;
load_ldmatrix(A[n][k01 / (2 * QI_MXFP4)], x_qs + (i0 + n * tile_A::I) * MMQ_MMA_TILE_X_K_FP4 + k0,
MMQ_MMA_TILE_X_K_FP4);
// based on block-scaling document, 2 threads in each quad need to supply to the scale value
const int tidx = threadIdx.x / 4 + (threadIdx.x % 2) * 8;
scaleA[n][k01 / (2 * QI_MXFP4)] =
*(x_sc + (i0 + n * tile_A::I + tidx) * MMQ_MMA_TILE_X_K_FP4 + k0 / (2 * QI_MXFP4));
}
}
#pragma unroll
for (int j0 = 0; j0 < mmq_x; j0 += ntx * tile_C::J) {
#pragma unroll
for (int k01 = 0; k01 < MMQ_TILE_NE_K; k01 += 2 * QI_MXFP4) {
tile_B B;
uint32_t scaleB; // 2xN scales
load_generic(B, y_qs + j0 * MMQ_TILE_Y_FP4_K + k01, MMQ_TILE_Y_FP4_K);
scaleB = y_sc[(j0 + threadIdx.x / 4) * MMQ_TILE_Y_FP4_K + k01 / (2 * QI_MXFP4)];
#pragma unroll
for (int n = 0; n < ntx; ++n) {
tile_C C;
mma_block_scaled(C, A[n][k01 / (2 * QI_MXFP4)], B, scaleA[n][k01 / (2 * QI_MXFP4)], scaleB);
#pragma unroll
for (int l = 0; l < tile_C::ne; ++l) {
sum[(j0 / tile_C::J + n) * tile_C::ne + l] += C.x[l];
}
}
}
}
}
template <int mmq_x, int mmq_y>
static __device__ __forceinline__ void vec_dot_q8_1_q8_1_dp4a(
@@ -3259,7 +3318,7 @@ struct mmq_type_traits<mmq_x, mmq_y, need_check, GGML_TYPE_MXFP4> {
static constexpr int vdr = VDR_MXFP4_Q8_1_MMQ;
#ifdef BLACKWELL_MMA_AVAILABLE
static constexpr load_tiles_mmq_t load_tiles = load_tiles_mxfp4_fp4<mmq_y, need_check>;
static constexpr vec_dot_mmq_t vec_dot_mma = vec_dot_mxfp4_mxfp4_mma<mmq_x, mmq_y>;
static constexpr vec_dot_mmq_t vec_dot_mma = vec_dot_fp4_fp4_mma<mmq_x, mmq_y, GGML_TYPE_MXFP4>;
#else
static constexpr load_tiles_mmq_t load_tiles = load_tiles_mxfp4<mmq_y, need_check>;
static constexpr vec_dot_mmq_t vec_dot_mma = vec_dot_q8_0_q8_1_mma<mmq_x, mmq_y, MMQ_Q8_1_DS_LAYOUT_D4>;
@@ -3270,8 +3329,13 @@ struct mmq_type_traits<mmq_x, mmq_y, need_check, GGML_TYPE_MXFP4> {
template <int mmq_x, int mmq_y, bool need_check>
struct mmq_type_traits<mmq_x, mmq_y, need_check, GGML_TYPE_NVFP4> {
static constexpr int vdr = VDR_NVFP4_Q8_1_MMQ;
#ifdef BLACKWELL_MMA_AVAILABLE
static constexpr load_tiles_mmq_t load_tiles = load_tiles_nvfp4_nvfp4<mmq_y, need_check>;
static constexpr vec_dot_mmq_t vec_dot_mma = vec_dot_fp4_fp4_mma<mmq_x, mmq_y, GGML_TYPE_NVFP4>;
#else
static constexpr load_tiles_mmq_t load_tiles = load_tiles_nvfp4<mmq_y, need_check>;
static constexpr vec_dot_mmq_t vec_dot_mma = vec_dot_q8_0_16_q8_1_mma<mmq_x, mmq_y>;
#endif // BLACKWELL_MMA_AVAILABLE
static constexpr vec_dot_mmq_t vec_dot_dp4a = vec_dot_q8_0_16_q8_1_dp4a<mmq_x, mmq_y>;
};
@@ -3406,7 +3470,7 @@ static __device__ __forceinline__ void mul_mat_q_process_tile(
#if defined(BLACKWELL_MMA_AVAILABLE)
// FP4 tile stores 8 blocks
constexpr int ne_block = (type == GGML_TYPE_MXFP4) ? 8 * QK_MXFP4 : 4 * QK8_1;
constexpr int ne_block = (type == GGML_TYPE_MXFP4 || type == GGML_TYPE_NVFP4) ? QK_K : 4 * QK8_1;
#else
constexpr int ne_block = 4 * QK8_1;
#endif // defined(BLACKWELL_MMA_AVAILABLE)
ggml/src/ggml-cuda/mmvq.cu
+3
View File
@@ -115,6 +115,7 @@ static constexpr __host__ __device__ int get_mmvq_mmid_max_batch_pascal_older(gg
case GGML_TYPE_IQ4_NL: return 6;
case GGML_TYPE_IQ4_XS: return 5;
case GGML_TYPE_MXFP4: return 4;
case GGML_TYPE_NVFP4: return 4;
case GGML_TYPE_Q2_K: return 4;
case GGML_TYPE_Q3_K: return 4;
case GGML_TYPE_Q4_0: return 6;
@@ -135,6 +136,7 @@ static constexpr __host__ __device__ int get_mmvq_mmid_max_batch_turing_plus(ggm
case GGML_TYPE_IQ3_S: return 6;
case GGML_TYPE_IQ3_XXS: return 7;
case GGML_TYPE_MXFP4: return 7;
case GGML_TYPE_NVFP4: return 8;
case GGML_TYPE_Q2_K: return 7;
case GGML_TYPE_Q3_K: return 5;
default: return MMVQ_MAX_BATCH_SIZE;
@@ -221,6 +223,7 @@ static constexpr __host__ __device__ int get_mmvq_mmid_max_batch_rdna4(ggml_type
case GGML_TYPE_IQ4_NL: return 7;
case GGML_TYPE_IQ4_XS: return 5;
case GGML_TYPE_MXFP4: return 5;
case GGML_TYPE_NVFP4: return 5;
case GGML_TYPE_Q3_K: return 4;
case GGML_TYPE_Q4_0: return 7;
case GGML_TYPE_Q4_1: return 7;
ggml/src/ggml-cuda/quantize.cu
+113 -13
View File
@@ -70,6 +70,102 @@ __device__ __forceinline__ uint8_t compute_e8m0_scale(float amax) {
return static_cast<uint8_t>(biased);
}
static __global__ void quantize_mmq_nvfp4(
const float * __restrict__ x, const int32_t * __restrict__ ids, void * __restrict__ vy,
const int64_t ne00, const int64_t s01, const int64_t s02, const int64_t s03,
const int64_t ne0, const int64_t ne1, const int64_t ne2) {
#if defined(BLACKWELL_MMA_AVAILABLE)
const int64_t i0_base = ((int64_t) blockDim.x * blockIdx.y + threadIdx.x) * QK_NVFP4_SUB;
if (i0_base >= ne0) {
return;
}
const int64_t i1 = blockIdx.x;
const int64_t i2 = blockIdx.z % ne2;
const int64_t i3 = blockIdx.z / ne2;
const int64_t i01 = ids ? ids[i1] : i1;
const int64_t k_block = i0_base / QK_K;
const int64_t blocks_per_col = (ne0 + QK_K - 1) / QK_K;
if (k_block >= blocks_per_col) {
return;
}
const int64_t ib = blockIdx.z * ((int64_t) blocks_per_col * ne1) + k_block * ne1 + blockIdx.x;
block_fp4_mmq * y = (block_fp4_mmq *) vy;
block_fp4_mmq * yb = y + ib;
const int sub = (i0_base % QK_K) / QK_NVFP4_SUB;
float vals_raw[QK_NVFP4_SUB];
float amax_raw = 0.0f;
const int64_t base_idx = i3 * s03 + i2 * s02 + i01 * s01;
#pragma unroll
for (int k = 0; k < QK_NVFP4_SUB; k++) {
const int64_t i00 = i0_base + k;
if (i00 < ne00) {
const float v = x[base_idx + i00];
vals_raw[k] = v;
amax_raw = fmaxf(amax_raw, fabsf(v));
} else {
vals_raw[k] = 0.0f;
}
}
static constexpr int test_offsets[5] = { 0, -1, 1, -2, 2};
const int first_fp8_code = (int) ggml_cuda_fp32_to_ue4m3(amax_raw / 6.0f);
float best_err = FLT_MAX;
uint8_t fp8_code = 0;
float subblock_scale = 0.0f;
#pragma unroll // Check +/- 2 to find best code to reduce NVFP4 activation loss. Negligible overhead on Blackwell.
for (int i = 0; i < 5; i++) {
const int test_code = first_fp8_code + test_offsets[i];
if (test_code < 0 || test_code > 0x7e) {
continue;
}
const uint8_t code = (uint8_t) test_code;
const float test_scale = ggml_cuda_ue4m3_to_fp32(code);
const float test_inv_scale = test_scale > 0.0f ? 0.5f / test_scale : 0.0f;
float cur_err = 0.0f;
#pragma unroll
for (int k = 0; k < QK_NVFP4_SUB; ++k) {
const float v = vals_raw[k];
const uint8_t q = ggml_cuda_float_to_fp4_e2m1(v, test_inv_scale);
const float err_diff = fabsf(v) - fabsf(kvalues_mxfp4[q & 0x7]) * test_scale;
cur_err = fmaf(err_diff, err_diff, cur_err);
}
if (cur_err < best_err) {
best_err = cur_err;
fp8_code = test_code;
subblock_scale = test_scale;
}
}
const float inv_scale = subblock_scale > 0.0f ? 0.5f / subblock_scale : 0.0f;
uint32_t q0 = 0;
uint32_t q1 = 0;
#pragma unroll // this is faster than the previous __nv_fp4x4_e2m1
for (int k = 0; k < QK_NVFP4_SUB / 4; ++k) {
q0 |= (uint32_t) ggml_cuda_float_to_fp4_e2m1(vals_raw[k + 0], inv_scale) << (8 * k);
q0 |= (uint32_t) ggml_cuda_float_to_fp4_e2m1(vals_raw[k + 8], inv_scale) << (8 * k + 4);
q1 |= (uint32_t) ggml_cuda_float_to_fp4_e2m1(vals_raw[k + 4], inv_scale) << (8 * k);
q1 |= (uint32_t) ggml_cuda_float_to_fp4_e2m1(vals_raw[k + 12], inv_scale) << (8 * k + 4);
}
uint32_t * yqs = reinterpret_cast<uint32_t *>(yb->qs);
yqs[2 * sub + 0] = q0;
yqs[2 * sub + 1] = q1;
reinterpret_cast<uint8_t *>(yb->d4)[sub] = fp8_code;
#else
NO_DEVICE_CODE; // This is for Blackwell NVFP4 activations only.
#endif // defined(BLACKWELL_MMA_AVAILABLE)
}
// quantize values in the format mxfp4 is stored which is interleaved nibbles
// i.e. a block a0-a31 is represented as a0a16,a1a17 ...a15a31
static __global__ void quantize_mmq_mxfp4(const float * __restrict__ x,
@@ -316,19 +412,22 @@ void quantize_mmq_q8_1_cuda(
}
}
void quantize_mmq_mxfp4_cuda(const float * x,
const int32_t * ids,
void * vy,
[[maybe_unused]] const ggml_type type_src0,
const int64_t ne00,
const int64_t s01,
const int64_t s02,
const int64_t s03,
const int64_t ne0,
const int64_t ne1,
const int64_t ne2,
const int64_t ne3,
cudaStream_t stream) {
void quantize_mmq_fp4_cuda(
const float * x, const int32_t * ids, void * vy, const ggml_type type_src0,
const int64_t ne00, const int64_t s01, const int64_t s02, const int64_t s03,
const int64_t ne0, const int64_t ne1, const int64_t ne2, const int64_t ne3, cudaStream_t stream) {
GGML_ASSERT(type_src0 == GGML_TYPE_MXFP4 || type_src0 == GGML_TYPE_NVFP4);
GGML_ASSERT(ne0 > 0);
if (type_src0 == GGML_TYPE_NVFP4) {
GGML_ASSERT(ne00 % QK_NVFP4 == 0);
constexpr int nvfp4_block_size = 128;
const int64_t block_num_y = (ne0 + QK_NVFP4_SUB * nvfp4_block_size - 1) / (QK_NVFP4_SUB * nvfp4_block_size);
const dim3 block_size(nvfp4_block_size, 1, 1);
const dim3 num_blocks(ne1, block_num_y, ne2 * ne3);
quantize_mmq_nvfp4<<<num_blocks, block_size, 0, stream>>>(
x, ids, vy, ne00, s01, s02, s03, ne0, ne1, ne2);
} else {
GGML_ASSERT(ne0 % (2 * QK_MXFP4) == 0);
constexpr int nwarps = 8;
@@ -341,3 +440,4 @@ void quantize_mmq_mxfp4_cuda(const float * x,
quantize_mmq_mxfp4<<<num_blocks, block_size, 0, stream>>>(x, ids, vy, ne00, s01, s02, s03, ne0, ne1, ne2);
}
}
ggml/src/ggml-cuda/quantize.cuh
+1 -1
View File
@@ -26,7 +26,7 @@ void quantize_mmq_q8_1_cuda(
ggml_type type_src0, int64_t ne00, int64_t s01, int64_t s02, int64_t s03,
int64_t ne0, int64_t ne1, int64_t ne2, int64_t ne3, cudaStream_t stream);
void quantize_mmq_mxfp4_cuda(const float * x,
void quantize_mmq_fp4_cuda(const float * x,
const int32_t * ids,
void * vy,
ggml_type type_src0,
tests/test-backend-ops.cpp
+2 -2
View File
@@ -3815,7 +3815,7 @@ struct test_mul_mat : public test_case {
double max_nmse_err(ggml_backend_t backend) override {
// for blackwell we quantize activations to mxfp4 instead of q8_1 so we add higher tolerance
if (type_a == GGML_TYPE_MXFP4 && backend_has_feature(backend, "BLACKWELL_NATIVE_FP4")) {
if ((type_a == GGML_TYPE_MXFP4 || type_a == GGML_TYPE_NVFP4) && backend_has_feature(backend, "BLACKWELL_NATIVE_FP4")) {
return 2e-2;
}
return max_nmse_err();
@@ -3951,7 +3951,7 @@ struct test_mul_mat_id : public test_case {
double max_nmse_err(ggml_backend_t backend) override {
// for blackwell we quantize activations to mxfp4 instead of q8_1 so we add higher tolerance
if (type_a == GGML_TYPE_MXFP4 && backend_has_feature(backend, "BLACKWELL_NATIVE_FP4")) {
if ((type_a == GGML_TYPE_MXFP4 || type_a == GGML_TYPE_NVFP4) && backend_has_feature(backend, "BLACKWELL_NATIVE_FP4")) {
return 2e-2;
}
return max_nmse_err();