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whisper.cpp/ggml/src/ggml-cuda/quantize.cu

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#include "quantize.cuh"
#include <cstdint>
__launch_bounds__(CUDA_QUANTIZE_BLOCK_SIZE, 1)
static __global__ void quantize_q8_1(
const float * x_ptr, void * vy_ptr,
const int64_t ne00, const int64_t s01, const int64_t s02, const int64_t s03,
const int64_t ne0, const uint32_t ne1, const uint3 ne2) {
ggml_cuda_pdl_lc();
const float * GGML_CUDA_RESTRICT x = x_ptr;
void * GGML_CUDA_RESTRICT vy = vy_ptr;
const int64_t i0 = (int64_t)blockDim.x*blockIdx.x + threadIdx.x;
if (i0 >= ne0) {
return;
}
const int64_t i3 = fastdiv(blockIdx.z, ne2);
const int64_t i2 = blockIdx.z - i3*ne2.z;
const int64_t i1 = blockIdx.y;
const int64_t & i00 = i0;
const int64_t & i01 = i1;
const int64_t & i02 = i2;
const int64_t & i03 = i3;
const int64_t i_cont = ((i3*ne2.z + i2) * ne1 + i1) * ne0 + i0;
block_q8_1 * y = (block_q8_1 *) vy;
const int64_t ib = i_cont / QK8_1; // block index
const int64_t iqs = i_cont % QK8_1; // quant index
ggml_cuda_pdl_sync();
const float xi = i0 < ne00 ? x[i03*s03 + i02*s02 + i01*s01 + i00] : 0.0f;
float amax = fabsf(xi);
float sum = xi;
amax = warp_reduce_max<QK8_1>(amax);
sum = warp_reduce_sum<QK8_1>(sum);
const float d = amax / 127.0f;
const int8_t q = amax == 0.0f ? 0 : roundf(xi / d);
y[ib].qs[iqs] = q;
if (iqs > 0) {
return;
}
y[ib].ds = make_half2(d, sum);
}
__device__ __forceinline__ uint8_t compute_e8m0_scale(float amax) {
if (!(amax > 0.0f)) {
return 0;
}
// FP4 E2M1: max exponent (unbiased) is 2.
constexpr int FP4_E2M1_EMAX = 2;
const float e = log2f(amax);
// "even" -> round-to-nearest integer, ties-to-even
const int e_int = __float2int_rn(e);
const int shared_exp = e_int - FP4_E2M1_EMAX;
int biased = shared_exp + 127;
biased = max(biased, 0);
biased = min(biased, 254);
return static_cast<uint8_t>(biased);
}
// scatter: grid over tokens, quantize once, write to all the token's compact rows
template <bool scatter>
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, const int n_expert_used) {
#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 k_block = i0_base / QK_FP4_MMQ;
const int64_t blocks_per_col = (ne0 + QK_FP4_MMQ - 1) / QK_FP4_MMQ;
if (k_block >= blocks_per_col) {
return;
}
const int sub = (i0_base % QK_FP4_MMQ) / QK_NVFP4_SUB;
int64_t base_idx;
if constexpr (scatter) {
base_idx = (int64_t) blockIdx.x * s02; // one physical row per token
} else {
const int64_t i2 = blockIdx.z % ne2;
const int64_t i3 = blockIdx.z / ne2;
const int64_t i01 = ids ? ids[blockIdx.x] : blockIdx.x;
base_idx = i3 * s03 + i2 * s02 + i01 * s01;
}
float vals_raw[QK_NVFP4_SUB];
float amax_raw = 0.0f;
#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);
}
block_fp4_mmq * y = (block_fp4_mmq *) vy;
if constexpr (scatter) {
#pragma unroll
for (int slot = 0; slot < n_expert_used; ++slot) {
const int64_t i = ids[(int64_t) blockIdx.x * n_expert_used + slot];
block_fp4_mmq * yb = y + (k_block * ne1 + i);
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 {
block_fp4_mmq * yb = y + (blockIdx.z * ((int64_t) blocks_per_col * ne1) + k_block * ne1 + blockIdx.x);
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;
}
GGML_UNUSED(n_expert_used);
#else
GGML_UNUSED(n_expert_used);
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
// scatter: grid over tokens, quantize once, write to all the token's compact rows
template <bool scatter>
static __global__ void quantize_mmq_mxfp4(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 int ne1,
const int ne2,
const int n_expert_used) {
constexpr int vals_per_scale = 32;
constexpr int vals_per_warp = 2 * vals_per_scale; // Each warp processes 2 blocks of 32 = 64 values
const int warp_id = threadIdx.y;
const int lane_id_32 = threadIdx.x;
const int nwarps = blockDim.y;
const int64_t warp_start_offset = (blockIdx.y * nwarps + warp_id) * vals_per_warp;
if (warp_start_offset >= ne0) {
return;
}
const int64_t block_fp4_mmq_size = QK_FP4_MMQ;
const int64_t k_block = warp_start_offset / block_fp4_mmq_size;
const int64_t quad_idx_in_block = (warp_start_offset % block_fp4_mmq_size) / vals_per_warp;
const int group_id = lane_id_32 / 4;
const int lane_in_group = lane_id_32 % 4;
const int base = group_id * 2;
ggml_cuda_pdl_sync();
int64_t base_pos;
if constexpr (scatter) {
base_pos = (int64_t) blockIdx.x * s02; // one physical row per token
} else {
const int64_t i2 = blockIdx.z % ne2;
const int64_t i3 = blockIdx.z / ne2;
const int64_t i01 = ids ? ids[blockIdx.x] : blockIdx.x;
base_pos = i3 * s03 + i2 * s02 + i01 * s01;
}
uint8_t scales[2];
char2 packed[2];
#pragma unroll
for (int b = 0; b < 2; ++b) {
const int64_t i0 = warp_start_offset + b * vals_per_scale + lane_id_32;
const float xi = (i0 < ne00) ? x[base_pos + i0] : 0.0f;
float amax = fabsf(xi);
#pragma unroll
for (int mask = 16; mask > 0; mask >>= 1) {
amax = fmaxf(amax, __shfl_xor_sync(0xFFFFFFFF, amax, mask, WARP_SIZE));
}
const uint8_t e = compute_e8m0_scale(amax);
scales[b] = e;
const float inv_s = (amax == 0.0f) ? 0.0f : __frcp_rn(ggml_cuda_e8m0_to_fp32(e));
#if CUDART_VERSION >= 12080
const float scaled_val = xi * inv_s;
const float val0 = __shfl_sync(0xFFFFFFFF, scaled_val, base, WARP_SIZE);
const float val1 = __shfl_sync(0xFFFFFFFF, scaled_val, base + 16, WARP_SIZE);
const float val2 = __shfl_sync(0xFFFFFFFF, scaled_val, base + 1, WARP_SIZE);
const float val3 = __shfl_sync(0xFFFFFFFF, scaled_val, base + 17, WARP_SIZE);
__nv_fp4x4_e2m1 fp4_packed(make_float4(val0, val1, val2, val3));
packed[b] = *(char2 *) &fp4_packed;
#else
// Fallback: manual FP4 conversion using LUT
const uint8_t q_val = ggml_cuda_float_to_fp4_e2m1(xi, inv_s);
const uint8_t q_lo_0 = __shfl_sync(0xFFFFFFFF, q_val, base, WARP_SIZE);
const uint8_t q_lo_1 = __shfl_sync(0xFFFFFFFF, q_val, base + 1, WARP_SIZE);
const uint8_t q_hi_0 = __shfl_sync(0xFFFFFFFF, q_val, base + 16, WARP_SIZE);
const uint8_t q_hi_1 = __shfl_sync(0xFFFFFFFF, q_val, base + 17, WARP_SIZE);
char2 q;
q.x = (q_hi_0 << 4) | q_lo_0;
q.y = (q_hi_1 << 4) | q_lo_1;
packed[b] = q;
#endif // CUDART_VERSION >= 12080
}
block_fp4_mmq * y = (block_fp4_mmq *) vy;
if constexpr (scatter) {
#pragma unroll
for (int slot = 0; slot < n_expert_used; ++slot) {
const int64_t i = ids[(int64_t) blockIdx.x * n_expert_used + slot];
block_fp4_mmq * yb = y + (k_block * ne1 + i);
char2 * yqs2 = (char2 *) yb->qs;
if (lane_in_group == 0) {
yqs2[quad_idx_in_block * 16 + 0 * 8 + group_id] = packed[0];
yqs2[quad_idx_in_block * 16 + 1 * 8 + group_id] = packed[1];
}
if (lane_id_32 == 0) {
yb->d4[quad_idx_in_block] = (scales[1] << 8) | scales[0];
}
}
} else {
const int64_t ib0 = blockIdx.z * ((int64_t) ne1 * (ne0 / block_fp4_mmq_size));
block_fp4_mmq * yb = y + (ib0 + k_block * ne1 + blockIdx.x);
char2 * yqs2 = (char2 *) yb->qs;
if (lane_in_group == 0) {
yqs2[quad_idx_in_block * 16 + 0 * 8 + group_id] = packed[0];
yqs2[quad_idx_in_block * 16 + 1 * 8 + group_id] = packed[1];
}
if (lane_id_32 == 0) {
yb->d4[quad_idx_in_block] = (scales[1] << 8) | scales[0];
}
}
GGML_UNUSED(n_expert_used);
}
// scatter: grid over tokens, quantize once, write to all the token's compact rows
template <mmq_q8_1_ds_layout ds_layout, bool scatter>
static __global__ void quantize_mmq_q8_1(
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 int ne1, const int ne2, const int n_expert_used) {
constexpr int vals_per_scale = ds_layout == MMQ_Q8_1_DS_LAYOUT_D2S6 ? 64 : 32;
constexpr int vals_per_sum = ds_layout == MMQ_Q8_1_DS_LAYOUT_D2S6 ? 16 : 32;
const int64_t i0 = ((int64_t)blockDim.x*blockIdx.y + threadIdx.x)*4;
if (i0 >= ne0) {
return;
}
const int64_t i00 = i0;
ggml_cuda_pdl_sync();
int64_t base_idx;
if constexpr (scatter) {
base_idx = (int64_t) blockIdx.x * s02; // one physical row per token
} else {
const int64_t i2 = blockIdx.z % ne2;
const int64_t i3 = blockIdx.z / ne2;
const int64_t i01 = ids ? ids[blockIdx.x] : blockIdx.x;
base_idx = i3*s03 + i2*s02 + i01*s01;
}
const float4 * x4 = (const float4 *) x;
block_q8_1_mmq * y = (block_q8_1_mmq *) vy;
const int64_t k_block = i0 / QK8_1_MMQ; // column block in the channel
const int64_t iqs = i0 % QK8_1_MMQ; // quant index in block
// Load 4 floats per thread and calculate max. abs. value between them:
const float4 xi = i0 < ne00 ? x4[(base_idx + i00)/4] : make_float4(0.0f, 0.0f, 0.0f, 0.0f);
float amax = fabsf(xi.x);
amax = fmaxf(amax, fabsf(xi.y));
amax = fmaxf(amax, fabsf(xi.z));
amax = fmaxf(amax, fabsf(xi.w));
// Exchange max. abs. value between vals_per_scale/4 threads.
#pragma unroll
for (int offset = vals_per_scale/8; offset > 0; offset >>= 1) {
amax = fmaxf(amax, __shfl_xor_sync(0xFFFFFFFF, amax, offset, WARP_SIZE));
}
float sum;
if (ds_layout != MMQ_Q8_1_DS_LAYOUT_D4) {
sum = xi.x + xi.y + xi.z + xi.w;
// Calculate sums across vals_per_sum/4 threads.
#pragma unroll
for (int offset = vals_per_sum/8; offset > 0; offset >>= 1) {
sum += __shfl_xor_sync(0xFFFFFFFF, sum, offset, WARP_SIZE);
}
}
const float d_inv = 127.0f / amax;
char4 q;
q.x = roundf(xi.x*d_inv);
q.y = roundf(xi.y*d_inv);
q.z = roundf(xi.z*d_inv);
q.w = roundf(xi.w*d_inv);
const float d = 1.0f / d_inv;
// write the block once (normal) or to each of the token's compact rows (scatter)
const int nwrite = scatter ? n_expert_used : 1;
#pragma unroll
for (int slot = 0; slot < nwrite; ++slot) {
int64_t ib;
if constexpr (scatter) {
const int64_t i = ids[(int64_t) blockIdx.x * n_expert_used + slot];
ib = k_block*ne1 + i;
} else {
const int64_t ib0 = blockIdx.z*((int64_t)gridDim.x*gridDim.y*blockDim.x/QK8_1); // first block of channel
ib = ib0 + k_block*ne1 + blockIdx.x;
}
// Write back 4 int8 values as a single 32 bit value for better memory bandwidth:
char4 * yqs4 = (char4 *) y[ib].qs;
yqs4[iqs/4] = q;
if (ds_layout == MMQ_Q8_1_DS_LAYOUT_D2S6) {
if (iqs % 16 == 0 && iqs < 96) {
y[ib].d2s6[2 + iqs/16] = sum;
if (iqs % 64 == 0) {
y[ib].d2s6[iqs/64] = d;
}
}
} else if (iqs % 32 == 0) {
if (ds_layout == MMQ_Q8_1_DS_LAYOUT_DS4) {
y[ib].ds4[iqs/32] = make_half2(d, sum);
} else {
y[ib].d4[iqs/32] = d;
}
}
}
GGML_UNUSED(n_expert_used);
}
void quantize_row_q8_1_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(!ids);
GGML_ASSERT(ne0 % QK8_1 == 0);
const uint3 ne2_fastdiv = init_fastdiv_values(ne2);
const int64_t block_num_x = (ne0 + CUDA_QUANTIZE_BLOCK_SIZE - 1) / CUDA_QUANTIZE_BLOCK_SIZE;
const dim3 num_blocks(block_num_x, ne1, ne2*ne3);
const dim3 block_size(CUDA_QUANTIZE_BLOCK_SIZE, 1, 1);
const ggml_cuda_kernel_launch_params launch_params = ggml_cuda_kernel_launch_params(num_blocks, block_size, 0, stream);
ggml_cuda_kernel_launch(quantize_q8_1, launch_params, x, vy, ne00, s01, s02, s03, ne0, ne1, ne2_fastdiv);
GGML_UNUSED(type_src0);
}
void quantize_mmq_q8_1_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(ne00 % 4 == 0);
GGML_ASSERT(ne0 % QK8_1_MMQ == 0);
// ne1 tends to assume the highest values, therefore use it as the "x" dimension of the CUDA grid:
const int64_t block_num_y = (ne0 + 4*CUDA_QUANTIZE_BLOCK_SIZE_MMQ - 1) / (4*CUDA_QUANTIZE_BLOCK_SIZE_MMQ);
const dim3 num_blocks(ne1, block_num_y, ne2*ne3);
const dim3 block_size(CUDA_QUANTIZE_BLOCK_SIZE_MMQ, 1, 1);
switch (mmq_get_q8_1_ds_layout(type_src0)) {
case MMQ_Q8_1_DS_LAYOUT_D4:
quantize_mmq_q8_1<MMQ_Q8_1_DS_LAYOUT_D4, false>
<<<num_blocks, block_size, 0, stream>>>(x, ids, vy, ne00, s01, s02, s03, ne0, ne1, ne2, /*n_expert_used=*/0);
break;
case MMQ_Q8_1_DS_LAYOUT_DS4:
quantize_mmq_q8_1<MMQ_Q8_1_DS_LAYOUT_DS4, false>
<<<num_blocks, block_size, 0, stream>>>(x, ids, vy, ne00, s01, s02, s03, ne0, ne1, ne2, /*n_expert_used=*/0);
break;
case MMQ_Q8_1_DS_LAYOUT_D2S6:
quantize_mmq_q8_1<MMQ_Q8_1_DS_LAYOUT_D2S6, false>
<<<num_blocks, block_size, 0, stream>>>(x, ids, vy, ne00, s01, s02, s03, ne0, ne1, ne2, /*n_expert_used=*/0);
break;
default:
GGML_ABORT("fatal error");
break;
}
}
// scatter=true reuses the quant kernel: grid over tokens, ids = inverse map (token slot -> compact row)
void quantize_scatter_mmq_q8_1_cuda(
const float * x, const int32_t * ids_src1_inv, void * vy, const ggml_type type_src0,
const int64_t ne00, const int64_t stride_token, const int64_t ne0,
const int64_t n_tokens, const int64_t nrows_dst, const int n_expert_used, cudaStream_t stream) {
GGML_ASSERT(ne00 % 4 == 0);
GGML_ASSERT(ne0 % QK8_1_MMQ == 0);
const int64_t block_num_y = (ne0 + 4*CUDA_QUANTIZE_BLOCK_SIZE_MMQ - 1) / (4*CUDA_QUANTIZE_BLOCK_SIZE_MMQ);
const dim3 num_blocks(n_tokens, block_num_y, 1);
const dim3 block_size(CUDA_QUANTIZE_BLOCK_SIZE_MMQ, 1, 1);
switch (mmq_get_q8_1_ds_layout(type_src0)) {
case MMQ_Q8_1_DS_LAYOUT_D4:
quantize_mmq_q8_1<MMQ_Q8_1_DS_LAYOUT_D4, true><<<num_blocks, block_size, 0, stream>>>(
x, ids_src1_inv, vy, ne00, /*s01=*/0, /*s02=*/stride_token, /*s03=*/0, ne0, /*ne1=*/(int) nrows_dst, /*ne2=*/1, n_expert_used);
break;
case MMQ_Q8_1_DS_LAYOUT_DS4:
quantize_mmq_q8_1<MMQ_Q8_1_DS_LAYOUT_DS4, true><<<num_blocks, block_size, 0, stream>>>(
x, ids_src1_inv, vy, ne00, /*s01=*/0, /*s02=*/stride_token, /*s03=*/0, ne0, /*ne1=*/(int) nrows_dst, /*ne2=*/1, n_expert_used);
break;
case MMQ_Q8_1_DS_LAYOUT_D2S6:
quantize_mmq_q8_1<MMQ_Q8_1_DS_LAYOUT_D2S6, true><<<num_blocks, block_size, 0, stream>>>(
x, ids_src1_inv, vy, ne00, /*s01=*/0, /*s02=*/stride_token, /*s03=*/0, ne0, /*ne1=*/(int) nrows_dst, /*ne2=*/1, n_expert_used);
break;
default:
GGML_ABORT("fatal error");
break;
}
}
// scatter=true reuses the quant kernels: grid over tokens, ids = inverse map (token slot -> compact row)
void quantize_scatter_mmq_fp4_cuda(
const float * x, const int32_t * ids_src1_inv, void * vy, const ggml_type type_src0,
const int64_t ne00, const int64_t stride_token, const int64_t ne0,
const int64_t n_tokens, const int64_t nrows_dst, const int n_expert_used, cudaStream_t stream) {
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(n_tokens, block_num_y, 1);
quantize_mmq_nvfp4<true><<<num_blocks, block_size, 0, stream>>>(
x, ids_src1_inv, vy, ne00, /*s01=*/0, /*s02=*/stride_token, /*s03=*/0, ne0, /*ne1=*/nrows_dst, /*ne2=*/1, n_expert_used);
} else {
GGML_ASSERT(type_src0 == GGML_TYPE_MXFP4);
constexpr int nwarps = 8;
constexpr int vals_per_block = nwarps * 2 * QK_MXFP4;
const int64_t block_num_y = (ne0 + vals_per_block - 1) / vals_per_block;
const dim3 block_size(WARP_SIZE, nwarps, 1);
const dim3 num_blocks(n_tokens, block_num_y, 1);
quantize_mmq_mxfp4<true><<<num_blocks, block_size, 0, stream>>>(
x, ids_src1_inv, vy, ne00, /*s01=*/0, /*s02=*/stride_token, /*s03=*/0, ne0, /*ne1=*/(int) nrows_dst, /*ne2=*/1, n_expert_used);
}
}
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<false><<<num_blocks, block_size, 0, stream>>>(
x, ids, vy, ne00, s01, s02, s03, ne0, ne1, ne2, /*n_expert_used=*/0);
} else {
GGML_ASSERT(ne0 % (2 * QK_MXFP4) == 0);
constexpr int nwarps = 8;
constexpr int vals_per_warp = 2 * QK_MXFP4;
constexpr int vals_per_block = nwarps * vals_per_warp;
const int64_t block_num_y = (ne0 + vals_per_block - 1) / vals_per_block;
const dim3 num_blocks(ne1, block_num_y, ne2 * ne3);
const dim3 block_size(WARP_SIZE, nwarps, 1);
quantize_mmq_mxfp4<false><<<num_blocks, block_size, 0, stream>>>(x, ids, vy, ne00, s01, s02, s03, ne0, ne1, ne2, /*n_expert_used=*/0);
}
}
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