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Merged
ggerganov merged 16 commits into from
Jun 21, 2026
+168 −74
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Support Step3.5/3.7 flash mtp3 #24340

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e98e0b3
add mtp_layer_offset + include nextn flags in graph reuse
forforever73 Jun 7, 2026
1208d99
add llama_set_mtp_layer_offset + llama_model_n_nextn_layer API
forforever73 Jun 7, 2026
0b8aa51
offset head select + require all MTP blocks
forforever73 Jun 7, 2026
e0fb9ff
speculative multi-head process()
forforever73 Jun 7, 2026
34f68f5
speculative multi-head draft()
forforever73 Jun 7, 2026
ae013e3
gather outputs via inp_out_ids
forforever73 Jun 8, 2026
1885d8f
cleanup
forforever73 Jun 9, 2026
f4a2c12
fix core
forforever73 Jun 9, 2026
9a0ff26
minor cleanup
forforever73 Jun 9, 2026
2952d83
merged draft_multi_head into draft()
forforever73 Jun 14, 2026
48a7484
mtp rename nextn
forforever73 Jun 14, 2026
2ce24fb
Apply suggestions from code review
forforever73 Jun 14, 2026
9b8f3b6
clean-up comments
forforever73 Jun 14, 2026
7a0a247
fix for multi seq
forforever73 Jun 15, 2026
9858fd2
apply suggestions && chain-heads comment
forforever73 Jun 21, 2026
60b04d3
add a reference for chain_heads discussion
forforever73 Jun 21, 2026
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108 changes: 78 additions & 30 deletions common/speculative.cpp
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Expand Up @@ -825,7 +825,13 @@ struct common_speculative_impl_draft_mtp : public common_speculative_impl {

int32_t n_embd = 0;

bool is_mem_shared = false;
// One MTP draft driver, three modes (set once in the ctor):
// is_mem_shared (gemma4): shares the target KV, runs all heads in one graph.
// chain_heads (step35): n_mtp_layers trained heads, one per draft step.
// neither (qwen35 / qwen35moe): a single trained MTP head.
int32_t n_mtp_layers = 1;
bool is_mem_shared = false; // gemma4
bool chain_heads = false; // derived in the ctor: n_mtp_layers > 1 && !is_mem_shared

// Per-sequence cross-batch carryover: pair (h_p, x_{p+1}) at MTP pos p+1.
// The last h-row of one process() call needs the first token of the NEXT
Expand All @@ -840,11 +846,6 @@ struct common_speculative_impl_draft_mtp : public common_speculative_impl {
std::vector<std::vector<float>> verify_h;
std::vector<int32_t> verify_h_rows;

// Per-seq draft length from the last draft() call, used in accept() to
// roll back ctx_dft's recurrent state past the AR draft's redundant
// pre-advancement before process() mirrored the verify batch.
std::vector<uint16_t> last_n_drafted;

common_speculative_impl_draft_mtp(const common_params_speculative & params, uint32_t n_seq)
: common_speculative_impl(COMMON_SPECULATIVE_TYPE_DRAFT_MTP, n_seq)
, params(params.draft)
Expand All @@ -856,6 +857,7 @@ struct common_speculative_impl_draft_mtp : public common_speculative_impl {
n_embd = llama_model_n_embd_out(llama_get_model(ctx_dft));
GGML_ASSERT(n_embd == llama_model_n_embd(llama_get_model(ctx_tgt)) &&
"MTP input row width must match the target h_nextn width");
n_mtp_layers = std::max(1, (int) llama_model_n_layer_nextn(llama_get_model(ctx_dft)));

LOG_INF("%s: adding speculative implementation 'draft-mtp'\n", __func__);
LOG_INF("%s: - n_max=%d, n_min=%d, p_min=%.2f, n_embd=%d, backend_sampling=%d\n", __func__, this->params.n_max, this->params.n_min, this->params.p_min, n_embd, (int) this->params.backend_sampling);
Expand Down Expand Up @@ -902,6 +904,11 @@ struct common_speculative_impl_draft_mtp : public common_speculative_impl {
llama_set_embeddings_nextn(ctx_dft, true, /*masked*/ true);

is_mem_shared = llama_get_ctx_other(ctx_dft) == ctx_tgt;
chain_heads = n_mtp_layers > 1 && !is_mem_shared;

if (chain_heads) {
this->params.n_max = std::min(this->params.n_max, n_mtp_layers);
}

pending_h.assign(n_seq, std::vector<float>(n_embd, 0.0f));

Expand All @@ -910,8 +917,6 @@ struct common_speculative_impl_draft_mtp : public common_speculative_impl {

verify_h.assign(n_seq, {});
verify_h_rows.assign(n_seq, 0);

last_n_drafted.assign(n_seq, 0);
}

~common_speculative_impl_draft_mtp() override {
Expand Down Expand Up @@ -1017,9 +1022,33 @@ struct common_speculative_impl_draft_mtp : public common_speculative_impl {
set_h(i_batch_beg[seq_id], pending_h[seq_id].data());
}

const int32_t rc = llama_decode(ctx_dft, batch);
if (rc != 0) {
LOG_ERR("%s: llama_decode(ctx_dft) failed rc=%d (pos=%d)\n", __func__, (int) rc, (int) batch_in.pos[0]);
auto * mem_dft = llama_get_memory(ctx_dft);

bool ok = true;
for (int head = 0; head < n_mtp_layers; ++head) {
if (chain_heads) {
for (llama_seq_id seq_id = 0; seq_id < (llama_seq_id) n_seq; ++seq_id) {
if (i_batch_beg[seq_id] < 0) {
continue;
}
llama_memory_seq_rm(mem_dft, seq_id, batch_in.pos[i_batch_beg[seq_id]], -1);
}
llama_set_nextn_layer_offset(ctx_dft, head);
}

const int32_t rc = llama_decode(ctx_dft, batch);
if (rc != 0) {
LOG_ERR("%s: llama_decode(ctx_dft) head=%d failed rc=%d (pos=%d)\n",
__func__, head, (int) rc, (int) batch_in.pos[0]);
ok = false;
break;
}
}

if (chain_heads) {
llama_set_nextn_layer_offset(ctx_dft, 0); // restore default for non-draft decodes
}
Comment on lines +1034 to +1060

@ggerganov ggerganov Jun 15, 2026

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I don't understand the logic here - seems incorrect. Every head iteration will basically erase the result of the previous iteration.

@forforever73 forforever73 Jun 15, 2026

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Each head runs a different layer, set_nextn_layer_offset(head) makes graph_mtp build layer n_layer()+head, i.e. 45/46/47. So each head writes its own k_l[il]/v_l[il].
seq_rm here doesn't drop any KV data — it just clears the cell metadata so find_slot hands back the same cells at the same positions for every head. Without it, head 46/47 would land on fresh cells and we'd get duplicate positions in v_cells .
So after the loop those cells hold valid KV for all three MTP layers at once. it's the teacher-forcing catch-up that seeds each head's layer so the next draft() round attends to a correct, target-aligned cache.

@ggerganov ggerganov Jun 15, 2026

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Ok, got it. That's interesting.

if (!ok) {
return false;
}
}
Expand Down Expand Up @@ -1057,6 +1086,8 @@ struct common_speculative_impl_draft_mtp : public common_speculative_impl {
const float * h_row = nullptr;
const size_t row_bytes = (size_t) n_embd * sizeof(float);

std::vector<int> i_last(n_seq, -1);

for (llama_seq_id seq_id = 0; seq_id < (llama_seq_id) n_seq; ++seq_id) {
auto & dp = dparams[seq_id];

Expand All @@ -1072,20 +1103,37 @@ struct common_speculative_impl_draft_mtp : public common_speculative_impl {

h_row = pending_h[seq_id].data();
std::memcpy(batch.embd + n_embd*(batch.n_tokens - 1), h_row, row_bytes);
}

int ret = llama_decode(ctx_dft, batch);
if (ret != 0) {
LOG_WRN("%s: llama_decode returned %d\n", __func__, ret);
return;
i_last[seq_id] = batch.n_tokens - 1;
}

int i = 0;

while (n_drafting > 0) {
int i_batch = 0;
// chained heads: every trained head keeps its own KV, so reset
// each sequence's draft region and select head `i` before re-decoding the
// accumulated prefix.
if (chain_heads) {
auto * mem_dft = llama_get_memory(ctx_dft);
for (llama_seq_id seq_id = 0; seq_id < (llama_seq_id) n_seq; ++seq_id) {
if (drafting[seq_id]) {
llama_memory_seq_rm(mem_dft, seq_id, dparams[seq_id].n_past, -1);
}
}
llama_set_nextn_layer_offset(ctx_dft, i);
}

common_batch_clear(batch);
int ret = llama_decode(ctx_dft, batch);
if (ret != 0) {
LOG_WRN("%s: llama_decode[%d] returned %d\n", __func__, i, ret);
break;
}

// the growing-KV paths rebuild the batch with only the new tokens (the KV
// already holds the prefix); chained heads keep accumulating into it
if (!chain_heads) {
common_batch_clear(batch);
}

for (llama_seq_id seq_id = 0; seq_id < (llama_seq_id) n_seq; ++seq_id) {
if (!drafting[seq_id]) {
Expand All @@ -1094,9 +1142,8 @@ struct common_speculative_impl_draft_mtp : public common_speculative_impl {

auto * smpl = smpls[seq_id].get();

common_sampler_sample(smpl, ctx_dft, i_batch, true);
h_row = llama_get_embeddings_nextn_ith(ctx_dft, i_batch);
++i_batch;
common_sampler_sample(smpl, ctx_dft, i_last[seq_id], true);
h_row = llama_get_embeddings_nextn_ith(ctx_dft, i_last[seq_id]);

const auto * cur_p = common_sampler_get_candidates(smpl, true);

Expand Down Expand Up @@ -1130,6 +1177,10 @@ struct common_speculative_impl_draft_mtp : public common_speculative_impl {
continue;
}

if (chain_heads) {
batch.logits[i_last[seq_id]] = false;
}

if (is_mem_shared) {
// note: with shared memory (e.g. Gemma4 assistants) we use the same position for all draft tokens
// ref: https://github.com/huggingface/transformers/blob/effde20942e3f82a1b97449f60b3a48c5ff96145/docs/source/en/model_doc/gemma4_assistant.md?plain=1#L36-L37
Expand All @@ -1138,22 +1189,21 @@ struct common_speculative_impl_draft_mtp : public common_speculative_impl {
common_batch_add(batch, id, dp.n_past + i + 1, { seq_id }, true);
}
std::memcpy(batch.embd + n_embd*(batch.n_tokens - 1), h_row, row_bytes);
}

if (batch.n_tokens == 0) {
break;
i_last[seq_id] = batch.n_tokens - 1;
}

// evaluate the drafted tokens on the draft model
ret = llama_decode(ctx_dft, batch);
if (ret != 0) {
LOG_WRN("%s: llama_decode[%d] returned %d\n", __func__, i, ret);
if (batch.n_tokens == 0) {
break;
}

++i;
}

if (chain_heads) {
llama_set_nextn_layer_offset(ctx_dft, 0); // restore default for non-draft decodes
}

for (llama_seq_id seq_id = 0; seq_id < (llama_seq_id) n_seq; ++seq_id) {
auto & dp = dparams[seq_id];
if (!dp.drafting) {
Expand All @@ -1163,8 +1213,6 @@ struct common_speculative_impl_draft_mtp : public common_speculative_impl {
if (dp.result->size() < (size_t) params.n_min) {
dp.result->clear();
}

last_n_drafted[seq_id] = (uint16_t) dp.result->size();
}
}

Expand Down
17 changes: 9 additions & 8 deletions include/llama.h
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Expand Up @@ -558,14 +558,15 @@ extern "C" {
LLAMA_API const struct llama_vocab * llama_model_get_vocab(const struct llama_model * model);
LLAMA_API enum llama_rope_type llama_model_rope_type(const struct llama_model * model);

LLAMA_API int32_t llama_model_n_ctx_train(const struct llama_model * model);
LLAMA_API int32_t llama_model_n_embd (const struct llama_model * model);
LLAMA_API int32_t llama_model_n_embd_inp (const struct llama_model * model);
LLAMA_API int32_t llama_model_n_embd_out (const struct llama_model * model);
LLAMA_API int32_t llama_model_n_layer (const struct llama_model * model);
LLAMA_API int32_t llama_model_n_head (const struct llama_model * model);
LLAMA_API int32_t llama_model_n_head_kv (const struct llama_model * model);
LLAMA_API int32_t llama_model_n_swa (const struct llama_model * model);
LLAMA_API int32_t llama_model_n_ctx_train (const struct llama_model * model);
LLAMA_API int32_t llama_model_n_embd (const struct llama_model * model);
LLAMA_API int32_t llama_model_n_embd_inp (const struct llama_model * model);
LLAMA_API int32_t llama_model_n_embd_out (const struct llama_model * model);
LLAMA_API int32_t llama_model_n_layer (const struct llama_model * model);
LLAMA_API int32_t llama_model_n_layer_nextn(const struct llama_model * model);
LLAMA_API int32_t llama_model_n_head (const struct llama_model * model);
LLAMA_API int32_t llama_model_n_head_kv (const struct llama_model * model);
LLAMA_API int32_t llama_model_n_swa (const struct llama_model * model);

// Get the model's RoPE frequency scaling factor
LLAMA_API float llama_model_rope_freq_scale_train(const struct llama_model * model);
Expand Down
8 changes: 8 additions & 0 deletions src/llama-context.cpp
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Expand Up @@ -1156,6 +1156,10 @@ void llama_context::set_embeddings_layer_inp(uint32_t lid, bool enable) {
sched_need_reserve = true;
}

void llama_context::set_nextn_layer_offset(int32_t offset) {
cparams.nextn_layer_offset = offset;
}

void llama_context::set_causal_attn(bool value) {
LLAMA_LOG_DEBUG("%s: value = %d\n", __func__, value);

Expand Down Expand Up @@ -3699,6 +3703,10 @@ void llama_set_embeddings_layer_inp(llama_context * ctx, uint32_t lid, bool valu
ctx->set_embeddings_layer_inp(lid, value);
}

void llama_set_nextn_layer_offset(llama_context * ctx, int32_t offset) {
ctx->set_nextn_layer_offset(offset);
}

llama_memory_t llama_get_memory(const struct llama_context * ctx) {
if (!ctx) {
return nullptr;
Expand Down
1 change: 1 addition & 0 deletions src/llama-context.h
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Expand Up @@ -115,6 +115,7 @@ struct llama_context {
void set_embeddings (bool value);
void set_embeddings_nextn(bool value, bool masked);
void set_embeddings_layer_inp(uint32_t lid, bool enable);
void set_nextn_layer_offset(int32_t offset);
void set_causal_attn(bool value);
void set_warmup(bool value);

Expand Down
2 changes: 2 additions & 0 deletions src/llama-cparams.h
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Expand Up @@ -18,6 +18,8 @@ struct llama_cparams {
int32_t n_threads; // number of threads to use for generation
int32_t n_threads_batch; // number of threads to use for batch processing

int32_t nextn_layer_offset = 0;

float rope_freq_base;
float rope_freq_scale;

Expand Down
5 changes: 5 additions & 0 deletions src/llama-ext.h
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Expand Up @@ -95,6 +95,11 @@ LLAMA_API llama_memory_breakdown llama_get_memory_breakdown(const struct llama_c
// If masked == false, output the embeddings for all tokens in the batch regardless of batch.logits
LLAMA_API void llama_set_embeddings_nextn(struct llama_context * ctx, bool value, bool masked);

// Select which appended NextN block the DECODER_MTP graph runs (offset past
// the trunk: il = n_layer() + offset). Used by the speculative NextN driver to
// chain multiple trained NextN heads. Default 0 (first head).
LLAMA_API void llama_set_nextn_layer_offset(struct llama_context * ctx, int32_t offset);

// mirrors:
// LLAMA_API float * llama_get_embeddings(struct llama_context * ctx);
LLAMA_API float * llama_get_embeddings_nextn(struct llama_context * ctx);
Expand Down
7 changes: 5 additions & 2 deletions src/llama-graph.h
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Expand Up @@ -683,8 +683,11 @@ struct llm_graph_params {
}

return
cparams.embeddings == other.cparams.embeddings &&
cparams.causal_attn == other.cparams.causal_attn &&
cparams.embeddings == other.cparams.embeddings &&
cparams.embeddings_nextn == other.cparams.embeddings_nextn &&
cparams.embeddings_nextn_masked == other.cparams.embeddings_nextn_masked &&
cparams.nextn_layer_offset == other.cparams.nextn_layer_offset &&

@ggerganov ggerganov Jun 21, 2026

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This is correct, but effectively it will disable graph reuse during drafting. However, there isn't a better way to do it for now as we don't have a mechanism to do layer selection at compute time. It's something to think about in the future.

@ggerganov ggerganov Jun 21, 2026

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Add TODO to not forget:

diff --git a/src/llama-graph.h b/src/llama-graph.h
index d2a1b39d4..ac00d6cc6 100644
--- a/src/llama-graph.h
+++ b/src/llama-graph.h
@@ -682,11 +682,15 @@ struct llm_graph_params {
             }
         }
 
+        // TODO: https://github.com/ggml-org/llama.cpp/pull/24340#discussion_r3448035248
+        if (cparams.nextn_layer_offset != other.cparams.nextn_layer_offset) {
+            return false;
+        }
+
         return
             cparams.embeddings              == other.cparams.embeddings              &&
             cparams.embeddings_nextn        == other.cparams.embeddings_nextn        &&
             cparams.embeddings_nextn_masked == other.cparams.embeddings_nextn_masked &&
-            cparams.nextn_layer_offset      == other.cparams.nextn_layer_offset      &&
             cparams.causal_attn             == other.cparams.causal_attn             &&
             arch  == other.arch  &&
             gtype == other.gtype &&

cparams.causal_attn == other.cparams.causal_attn &&
arch == other.arch &&
gtype == other.gtype &&
cvec == other.cvec &&
Expand Down
4 changes: 4 additions & 0 deletions src/llama-model.cpp
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Expand Up @@ -2312,6 +2312,10 @@ int32_t llama_model_n_layer(const llama_model * model) {
return model->hparams.n_layer();
}

int32_t llama_model_n_layer_nextn(const llama_model * model) {
return model->hparams.n_layer_nextn;
}

int32_t llama_model_n_head(const llama_model * model) {
return model->hparams.n_head();
}
Expand Down
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