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Merged
JohannesGaessler merged 8 commits into from
Jul 16, 2026
Merged

CUDA: dedup MoE gate/up activation quantization #25441

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6f286cf
CUDA: dedup MoE gate/up activation quantization (fp4)
praneshgo Jul 2, 2026
a155c70
CUDA: bounds-check token ids in build_tok2c_kernel
praneshgo Jul 7, 2026
1327f42
Refactor the code based on review comments
praneshgo Jul 9, 2026
1c687ce
Adding q8_1 support for dedup and addressing review comments
praneshgo Jul 11, 2026
5fda553
Add pragma unrolls
praneshgo Jul 14, 2026
afd0239
Remove redundant cudaMemsetAsync call
praneshgo Jul 14, 2026
b93a436
Removing follow up redundancies
praneshgo Jul 14, 2026
bc67b43
Merge branch 'master' of https://github.com/ggml-org/llama.cpp into p…
praneshgo Jul 15, 2026
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2 changes: 1 addition & 1 deletion ggml/src/ggml-cuda/mmf.cu
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Original file line number Diff line number Diff line change
Expand Up @@ -85,7 +85,7 @@ void ggml_cuda_mul_mat_f(ggml_backend_cuda_context & ctx, const ggml_tensor * sr
GGML_ASSERT(sis1 > 0);

ggml_cuda_launch_mm_ids_helper(ids_d, ids_src_compact_dev.get(), ids_dst_compact_dev.get(), expert_bounds_dev.get(),
static_cast<int>(n_experts), static_cast<int>(n_tokens), static_cast<int>(n_expert_used), static_cast<int>(ne11), si1, sis1, ctx.stream());
static_cast<int>(n_experts), static_cast<int>(n_tokens), static_cast<int>(n_expert_used), static_cast<int>(ne11), si1, sis1, /*write_inverse =*/ false, ctx.stream());
CUDA_CHECK(cudaGetLastError());

ids_info.ids_src_compact = ids_src_compact_dev.get();
Expand Down
31 changes: 18 additions & 13 deletions ggml/src/ggml-cuda/mmid.cu
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Expand Up @@ -27,7 +27,7 @@ template <int n_expert_used_template>
__launch_bounds__(ggml_cuda_get_physical_warp_size(), 1)
static __global__ void mm_ids_helper(
const int32_t * __restrict__ ids, int32_t * __restrict__ ids_src1, int32_t * __restrict__ ids_dst, int32_t * __restrict__ expert_bounds,
const int n_tokens, const int n_expert_used_var, const int nchannels_y, const int si1, const int sis1) {
const int n_tokens, const int n_expert_used_var, const int nchannels_y, const int si1, const int sis1, const bool write_inverse) {
constexpr int warp_size = ggml_cuda_get_physical_warp_size();
const int n_expert_used = n_expert_used_template == 0 ? n_expert_used_var : n_expert_used_template;
const int expert = blockIdx.x;
Expand Down Expand Up @@ -98,8 +98,13 @@ static __global__ void mm_ids_helper(
const mm_ids_helper_store store_it = store[itc];
const int it = store_it.it();
const int iex_used = store_it.iex_used();
ids_src1[nex_prev + itc] = it*sis1 + iex_used % nchannels_y;
ids_dst [nex_prev + itc] = it*n_expert_used + iex_used;
ids_dst[nex_prev + itc] = it*n_expert_used + iex_used;
// ids_src1 holds the forward map, or the inverse map (token slot -> compact row) for quant dedup
if (write_inverse) {
ids_src1[it*n_expert_used + iex_used] = nex_prev + itc;
} else {
ids_src1[nex_prev + itc] = it*sis1 + iex_used % nchannels_y;
}
}

if (threadIdx.x != 0) {
Expand All @@ -118,7 +123,7 @@ static __global__ void mm_ids_helper(
template <int n_expert_used_template>
static void launch_mm_ids_helper(
const int32_t * __restrict__ ids, int32_t * __restrict__ ids_src1, int32_t * __restrict__ ids_dst, int32_t * __restrict__ expert_bounds,
const int n_experts, const int n_tokens, const int n_expert_used_var, const int nchannels_y, const int si1, const int sis1, cudaStream_t stream) {
const int n_experts, const int n_tokens, const int n_expert_used_var, const int nchannels_y, const int si1, const int sis1, const bool write_inverse, cudaStream_t stream) {
GGML_ASSERT(n_tokens < (1 << 22) && "too few bits in mm_ids_helper_store");
GGML_ASSERT(n_expert_used_var < (1 << 10) && "too few bits in mm_ids_helper_store");

Expand All @@ -132,33 +137,33 @@ static void launch_mm_ids_helper(
const size_t nbytes_shared = n_tokens*sizeof(mm_ids_helper_store);
GGML_ASSERT(nbytes_shared <= smpbo);
mm_ids_helper<n_expert_used_template><<<num_blocks, block_size, nbytes_shared, stream>>>
(ids, ids_src1, ids_dst, expert_bounds, n_tokens, n_expert_used_var, nchannels_y, si1, sis1);
(ids, ids_src1, ids_dst, expert_bounds, n_tokens, n_expert_used_var, nchannels_y, si1, sis1, write_inverse);
}

void ggml_cuda_launch_mm_ids_helper(
const int32_t * __restrict__ ids, int32_t * __restrict__ ids_src1, int32_t * __restrict__ ids_dst, int32_t * __restrict__ expert_bounds,
const int n_experts, const int n_tokens, const int n_expert_used, const int nchannels_y, const int si1, const int sis1, cudaStream_t stream) {
const int n_experts, const int n_tokens, const int n_expert_used, const int nchannels_y, const int si1, const int sis1, const bool write_inverse, cudaStream_t stream) {
switch (n_expert_used) {
case 2:
launch_mm_ids_helper< 2>(ids, ids_src1, ids_dst, expert_bounds, n_experts, n_tokens, n_expert_used, nchannels_y, si1, sis1, stream);
launch_mm_ids_helper< 2>(ids, ids_src1, ids_dst, expert_bounds, n_experts, n_tokens, n_expert_used, nchannels_y, si1, sis1, write_inverse, stream);
break;
case 4:
launch_mm_ids_helper< 4>(ids, ids_src1, ids_dst, expert_bounds, n_experts, n_tokens, n_expert_used, nchannels_y, si1, sis1, stream);
launch_mm_ids_helper< 4>(ids, ids_src1, ids_dst, expert_bounds, n_experts, n_tokens, n_expert_used, nchannels_y, si1, sis1, write_inverse, stream);
break;
case 6:
launch_mm_ids_helper< 6>(ids, ids_src1, ids_dst, expert_bounds, n_experts, n_tokens, n_expert_used, nchannels_y, si1, sis1, stream);
launch_mm_ids_helper< 6>(ids, ids_src1, ids_dst, expert_bounds, n_experts, n_tokens, n_expert_used, nchannels_y, si1, sis1, write_inverse, stream);
break;
case 8:
launch_mm_ids_helper< 8>(ids, ids_src1, ids_dst, expert_bounds, n_experts, n_tokens, n_expert_used, nchannels_y, si1, sis1, stream);
launch_mm_ids_helper< 8>(ids, ids_src1, ids_dst, expert_bounds, n_experts, n_tokens, n_expert_used, nchannels_y, si1, sis1, write_inverse, stream);
break;
case 16:
launch_mm_ids_helper<16>(ids, ids_src1, ids_dst, expert_bounds, n_experts, n_tokens, n_expert_used, nchannels_y, si1, sis1, stream);
launch_mm_ids_helper<16>(ids, ids_src1, ids_dst, expert_bounds, n_experts, n_tokens, n_expert_used, nchannels_y, si1, sis1, write_inverse, stream);
break;
case 32:
launch_mm_ids_helper<32>(ids, ids_src1, ids_dst, expert_bounds, n_experts, n_tokens, n_expert_used, nchannels_y, si1, sis1, stream);
launch_mm_ids_helper<32>(ids, ids_src1, ids_dst, expert_bounds, n_experts, n_tokens, n_expert_used, nchannels_y, si1, sis1, write_inverse, stream);
break;
default:
launch_mm_ids_helper< 0>(ids, ids_src1, ids_dst, expert_bounds, n_experts, n_tokens, n_expert_used, nchannels_y, si1, sis1, stream);
launch_mm_ids_helper< 0>(ids, ids_src1, ids_dst, expert_bounds, n_experts, n_tokens, n_expert_used, nchannels_y, si1, sis1, write_inverse, stream);
break;
}
}
2 changes: 1 addition & 1 deletion ggml/src/ggml-cuda/mmid.cuh
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Original file line number Diff line number Diff line change
Expand Up @@ -2,4 +2,4 @@

void ggml_cuda_launch_mm_ids_helper(
const int32_t * ids, int32_t * ids_src1, int32_t * ids_dst, int32_t * expert_bounds,
int n_experts, int n_tokens, int n_expert_used, int nchannels_y, int si1, int sis1, cudaStream_t stream);
int n_experts, int n_tokens, int n_expert_used, int nchannels_y, int si1, int sis1, bool write_inverse, cudaStream_t stream);
17 changes: 15 additions & 2 deletions ggml/src/ggml-cuda/mmq.cu
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Original file line number Diff line number Diff line change
Expand Up @@ -175,13 +175,17 @@ void ggml_cuda_mul_mat_q(
ggml_cuda_pool_alloc<int32_t> ids_dst(ctx.pool(), ne_get_rows);
ggml_cuda_pool_alloc<int32_t> expert_bounds(ctx.pool(), ne02 + 1);

// gate/up activations are broadcast across experts (ne11 == 1): quantize each token once and
// scatter to its slots. ids_src1 then holds the inverse map (token slot -> compact row).
const bool dedup_bcast = ne11 == 1 && n_expert_used > 1;

{
GGML_ASSERT(ids->nb[0] == ggml_element_size(ids));
const int si1 = ids->nb[1] / ggml_element_size(ids);
const int sis1 = nb12 / nb11;

ggml_cuda_launch_mm_ids_helper((const int32_t *) ids->data, ids_src1.get(), ids_dst.get(), expert_bounds.get(),
ne02, ne12, n_expert_used, ne11, si1, sis1, stream);
ne02, ne12, n_expert_used, ne11, si1, sis1, /*write_inverse =*/ dedup_bcast, stream);
CUDA_CHECK(cudaGetLastError());
}

Expand All @@ -198,7 +202,16 @@ 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_fp4) {
if (dedup_bcast) {
// quantize each token once, scatter its block to all n_expert_used slots
if (use_native_fp4) {
quantize_scatter_mmq_fp4_cuda(src1_d, ids_src1.get(), src1_q8_1.get(), src0->type, ne10,
/*stride_token=*/s12, ne10_padded, ne12, ne11_flat, n_expert_used, stream);
} else {
quantize_scatter_mmq_q8_1_cuda(src1_d, ids_src1.get(), src1_q8_1.get(), src0->type, ne10,
/*stride_token=*/s12, ne10_padded, ne12, ne11_flat, n_expert_used, stream);
}
} else 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 {
Expand Down
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