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glm5-next: fix indexer scatter data race in the sparse mask #29745

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Merged
pwilkin merged 1 commit into from
Sep 30, 2026
Merged

glm5-next: fix indexer scatter data race in the sparse mask #29745

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26 changes: 17 additions & 9 deletions src/models/glm5-next.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -273,7 +273,7 @@ class llama_model_glm5_next::llm_graph_input_kpool : public llm_graph_input_i {
ggml_tensor * pool_idxs = nullptr; // I32 [kpool, n_pool] member cells per pool, n_kv sentinel for the padded pools
ggml_tensor * pool_mask = nullptr; // F32/F16 [n_pool, n_tokens]
ggml_tensor * tail_idxs = nullptr; // I32 [kpool - 1, n_tokens]
ggml_tensor * gather_mask = nullptr; // F32 [n_sel, 1, 1, n_tokens]
ggml_tensor * gather_mask = nullptr; // F32 [n_sel, 1, 1, n_tokens] 0 for live selection slots, -inf for dead ones
// n_new is never below 1, see build_inp_kpool
ggml_tensor * new_pool_idxs = nullptr; // I32 [kpool, n_new] members of the pools completed this ubatch
ggml_tensor * new_pool_rep = nullptr; // I64 [n_new] cell to write each new pooled key into
Expand Down Expand Up @@ -330,12 +330,11 @@ llama_model_glm5_next::llm_graph_input_kpool * llama_model_glm5_next::graph::bui
inp->n_sel = (uint32_t) n_sel;
inp->gather = (int64_t) n_tokens <= max_ub && (int64_t) n_kv > n_sel;

if (inp->gather) {
inp->gather_mask = ggml_new_tensor_4d(ctx0, GGML_TYPE_F32, n_sel, 1, 1, n_tokens);
ggml_set_input(inp->gather_mask);
// Keep the mask allocated even when no op reads it, because set_input_kpool always fills it.
ggml_build_forward_expand(gf, inp->gather_mask);
}
// Both paths read the slot mask: gather adds it to the scores, scatter maps its dead slots to dump rows.
inp->gather_mask = ggml_new_tensor_4d(ctx0, GGML_TYPE_F32, n_sel, 1, 1, n_tokens);
ggml_set_input(inp->gather_mask);
// Keep the mask allocated even when no op reads it, because set_input_kpool always fills it.
ggml_build_forward_expand(gf, inp->gather_mask);
}

inp->n_new = n_new;
Expand Down Expand Up @@ -892,13 +891,22 @@ ggml_tensor * llama_model_glm5_next::graph::build_kpool_select(

ggml_tensor * mask_seed = kq_mask->type == GGML_TYPE_F32 ? seed : ggml_cast(ctx0, seed, kq_mask->type);
mask_seed = ggml_fill(ctx0, mask_seed, -INFINITY);
ggml_tensor * mask_all = ggml_repeat_4d(ctx0, mask_seed, 1, n_kv + 1, n_tokens, 1);
mask_all = ggml_reshape_3d(ctx0, mask_all, 1, n_kv + 1, n_tokens);
ggml_tensor * mask_all = ggml_repeat_4d(ctx0, mask_seed, 1, n_kv + n_sel, n_tokens, 1);
mask_all = ggml_reshape_3d(ctx0, mask_all, 1, n_kv + n_sel, n_tokens);

ggml_tensor * zero_seed = ggml_fill(ctx0, seed, 0.0f);
ggml_tensor * zeros = ggml_repeat_4d(ctx0, zero_seed, 1, n_sel, n_tokens, 1);
zeros = ggml_reshape_3d(ctx0, zeros, 1, n_sel, n_tokens);

// Live slots (visible pools, real tail cells) address disjoint cells. Each dead slot writes its own dump row
// n_kv + slot, so the scatter indices of a token are unique: idx = dump + live*(idx - dump), live = exp(mask).
GGML_ASSERT(inp_kpool->gather_mask->ne[0] == n_sel && inp_kpool->gather_mask->ne[3] == n_tokens);
ggml_tensor * live = ggml_exp(ctx0, ggml_reshape_2d(ctx0, inp_kpool->gather_mask, n_sel, n_tokens));
ggml_tensor * dump = ggml_arange(ctx0, (float) n_kv, (float) (n_kv + n_sel), 1.0f);
ggml_tensor * idx_f = ggml_cast(ctx0, sel_idx, GGML_TYPE_F32);
idx_f = ggml_add(ctx0, ggml_mul(ctx0, ggml_sub(ctx0, idx_f, dump), live), dump);
sel_idx = ggml_cast(ctx0, idx_f, GGML_TYPE_I32);

ggml_tensor * sel = ggml_set_rows(ctx0, mask_all, zeros, ggml_reshape_3d(ctx0, sel_idx, n_sel, n_tokens, 1));
sel = ggml_view_2d(ctx0, sel, n_kv, n_tokens, sel->nb[2], 0);

Expand Down