spec: add DSpark speculative decoding

common/arg.cpp

@@ -3866,6 +3866,14 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             params.speculative.draft.p_min = std::stof(value);
         }
     ).set_spec().set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}).set_env("LLAMA_ARG_SPEC_DRAFT_P_MIN"));
+    add_opt(common_arg(
+        {"--spec-draft-conf-min"}, "P",
+        string_format("DSpark: minimum predicted acceptance from the draft confidence head to keep a "
+                      "drafted token; truncates the block at the first position below it (0.0 = disabled) (default: %.2f)", (double)params.speculative.draft.conf_min),
+        [](common_params & params, const std::string & value) {
+            params.speculative.draft.conf_min = std::stof(value);
+        }
+    ).set_spec().set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}).set_env("LLAMA_ARG_SPEC_DRAFT_CONF_MIN"));
     add_opt(common_arg(
         {"--spec-draft-backend-sampling"},
         {"--no-spec-draft-backend-sampling"},

common/common.h

@@ -172,6 +172,7 @@ enum common_speculative_type {
     COMMON_SPECULATIVE_TYPE_DRAFT_EAGLE3,  // Eagle3 speculative decoding
     COMMON_SPECULATIVE_TYPE_DRAFT_MTP,     // Multi-token prediction
     COMMON_SPECULATIVE_TYPE_DRAFT_DFLASH,  // DFlash speculative decoding
+    COMMON_SPECULATIVE_TYPE_DRAFT_DSPARK,  // DSpark speculative decoding (DFlash + Markov head)
     COMMON_SPECULATIVE_TYPE_NGRAM_SIMPLE,  // simple self-speculative decoding based on n-grams
     COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K,   // self-speculative decoding with n-gram keys only
     COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K4V, // self-speculative decoding with n-gram keys and 4 m-gram values
@@ -330,6 +331,8 @@ struct common_params_speculative_draft {
     float p_split = 0.1f; // speculative decoding split probability
     float p_min   = 0.0f; // minimum speculative decoding probability (greedy)
 
+    float conf_min = 0.0f; // DSpark: min predicted acceptance from the confidence head (0 = disabled)
+
     bool backend_sampling = true; // offload draft sampling to the backend (default: on)
 
     common_params_model mparams;
@@ -387,7 +390,7 @@ struct common_params_speculative {
 
     uint32_t need_n_rs_seq() const {
         bool needs_rs_seq = std::any_of(types.begin(), types.end(), [&](auto t) {
-            return t == COMMON_SPECULATIVE_TYPE_DRAFT_MTP || t == COMMON_SPECULATIVE_TYPE_DRAFT_EAGLE3 || t == COMMON_SPECULATIVE_TYPE_DRAFT_DFLASH;
+            return t == COMMON_SPECULATIVE_TYPE_DRAFT_MTP || t == COMMON_SPECULATIVE_TYPE_DRAFT_EAGLE3 || t == COMMON_SPECULATIVE_TYPE_DRAFT_DFLASH || t == COMMON_SPECULATIVE_TYPE_DRAFT_DSPARK;
         });
 
         return needs_rs_seq ? draft.n_max : 0u;

common/speculative.cpp

@@ -34,6 +34,7 @@ const std::map<std::string, common_speculative_type> common_speculative_type_fro
     {"draft-eagle3",  COMMON_SPECULATIVE_TYPE_DRAFT_EAGLE3},
     {"draft-mtp",     COMMON_SPECULATIVE_TYPE_DRAFT_MTP},
     {"draft-dflash",  COMMON_SPECULATIVE_TYPE_DRAFT_DFLASH},
+    {"draft-dspark",  COMMON_SPECULATIVE_TYPE_DRAFT_DSPARK},
     {"ngram-simple",  COMMON_SPECULATIVE_TYPE_NGRAM_SIMPLE},
     {"ngram-map-k",   COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K},
     {"ngram-map-k4v", COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K4V},
@@ -918,15 +919,20 @@ struct common_speculative_impl_draft_dflash : public common_speculative_impl {
     int32_t     block_size    = 0;
     llama_token mask_token_id = 0;
 
+    // draft-dspark: the draft carries a Markov head and uses an anchor-first block layout
+    const bool is_dspark;
+
     const int32_t * target_layer_ids   = nullptr; // model_dft's extract layer indices
     uint32_t        target_layer_ids_n = 0;
 
     // scratch buffer for concatenated target features [n_tokens, n_embd_enc]
     std::vector<float> features_buf;
 
-    common_speculative_impl_draft_dflash(const common_params_speculative & params, uint32_t n_seq)
-        : common_speculative_impl(COMMON_SPECULATIVE_TYPE_DRAFT_DFLASH, n_seq)
+    common_speculative_impl_draft_dflash(const common_params_speculative & params, uint32_t n_seq,
+            common_speculative_type type = COMMON_SPECULATIVE_TYPE_DRAFT_DFLASH)
+        : common_speculative_impl(type, n_seq)
         , params(params.draft)
+        , is_dspark(type == COMMON_SPECULATIVE_TYPE_DRAFT_DSPARK)
     {
         auto * ctx_tgt = this->params.ctx_tgt;
         auto * ctx_dft = this->params.ctx_dft;
@@ -953,16 +959,18 @@ struct common_speculative_impl_draft_dflash : public common_speculative_impl {
         }
         mask_token_id = llama_vocab_mask(llama_model_get_vocab(model_dft));
 
-        LOG_INF("%s: adding speculative implementation 'draft-dflash'\n", __func__);
-        LOG_INF("%s: - n_max=%d, n_min=%d, p_min=%.2f\n", __func__, this->params.n_max, this->params.n_min, this->params.p_min);
+        LOG_INF("%s: adding speculative implementation '%s'\n", __func__, common_speculative_type_to_str(type).c_str());
+        LOG_INF("%s: - n_max=%d, n_min=%d, p_min=%.2f, conf_min=%.2f\n", __func__, this->params.n_max, this->params.n_min, this->params.p_min, this->params.conf_min);
         LOG_INF("%s: - block_size=%d, mask_token_id=%d, n_extract=%u\n", __func__, block_size, mask_token_id, target_layer_ids_n);
 
-        // DFlash input is [id_last, <mask> * (block_size-1)], so it can draft at most block_size-1 tokens per step
-        if (this->params.n_max > block_size - 1 || this->params.n_min > block_size - 1) {
-            LOG_WRN("%s: requested draft size (n_max=%d, n_min=%d) exceeds the trained DFlash block size %d -- clamping to %d\n",
-                    __func__, this->params.n_max, this->params.n_min, block_size, block_size - 1);
-            this->params.n_max = std::min(this->params.n_max, block_size - 1);
-            this->params.n_min = std::min(this->params.n_min, block_size - 1);
+        // DFlash input is [id_last, <mask> * (block_size-1)]: in-place denoising yields at most
+        // block_size-1 draft tokens, DSpark yield a full block_size draft tokens
+        const int32_t n_draft_max = is_dspark ? block_size : block_size - 1;
+        if (this->params.n_max > n_draft_max || this->params.n_min > n_draft_max) {
+            LOG_WRN("%s: requested draft size (n_max=%d, n_min=%d) exceeds the trained block size %d -- clamping to %d\n",
+                    __func__, this->params.n_max, this->params.n_min, block_size, n_draft_max);
+            this->params.n_max = std::min(this->params.n_max, n_draft_max);
+            this->params.n_min = std::min(this->params.n_min, n_draft_max);
         }
 
         batch        = llama_batch_init(llama_n_batch(ctx_dft), 0,          n_seq);
@@ -1131,7 +1139,7 @@ struct common_speculative_impl_draft_dflash : public common_speculative_impl {
                 n_draft = std::min(n_draft, dp.n_max);
             }
 
-            const int32_t n_block_tokens = n_draft + 1; // id_last + n_draft * <mask>
+            const int32_t n_block_tokens = n_draft + (is_dspark ? 0 : 1);
             i_block_beg[seq_id] = batch.n_tokens;
             n_block    [seq_id] = n_block_tokens;
             for (int32_t i = 0; i < n_block_tokens; ++i) {
@@ -1163,27 +1171,57 @@ struct common_speculative_impl_draft_dflash : public common_speculative_impl {
 
             auto & result = *dp.result;
 
-            // greedily read the predicted block at this sequence's noise positions 1..n_block_tokens-1
-            for (int32_t i = 1; i < n_block_tokens; ++i) {
-                common_sampler_sample(smpl, ctx_dft, beg + i, true);
+            if (is_dspark) {
+                // DSpark predicts the next token from position 0 and optionally truncates
+                // at the first position below the confidence threshold.
+                const float * conf = params.conf_min > 0.0f ? llama_get_embeddings_nextn(ctx_dft) : nullptr;
 
-                const auto * cur_p = common_sampler_get_candidates(smpl, true);
+                for (int32_t i = 0; i < n_block_tokens; ++i) {
+                    const int32_t idx = beg + i;
 
-                for (int k = 0; k < std::min(3, (int) cur_p->size); ++k) {
-                    LOG_DBG(" - seq_id %d, draft candidate %3d, pos %3d: %6d (%8.3f) '%s'\n",
-                            seq_id, k, i - 1, cur_p->data[k].id, cur_p->data[k].p,
-                            common_token_to_piece(ctx_dft, cur_p->data[k].id).c_str());
-                }
+                    if (conf && conf[(size_t) idx * n_embd_dec] < params.conf_min) {
+                        break;
+                    }
 
-                const llama_token id = cur_p->data[0].id;
+                    common_sampler_sample(smpl, ctx_dft, idx, true);
 
-                if (cur_p->data[0].p < params.p_min) {
-                    break;
+                    const auto * cur_p = common_sampler_get_candidates(smpl, true);
+
+                    for (int k = 0; k < std::min(3, (int) cur_p->size); ++k) {
+                        LOG_DBG(" - seq_id %d, draft candidate %3d, pos %3d: %6d (%8.3f) '%s'\n",
+                                seq_id, k, i, cur_p->data[k].id, cur_p->data[k].p,
+                                common_token_to_piece(ctx_dft, cur_p->data[k].id).c_str());
+                    }
+
+                    const llama_token id = cur_p->data[0].id;
+
+                    common_sampler_accept(smpl, id, true);
+
+                    result.push_back(id);
                 }
+            } else {
+                // greedily read the predicted block at this sequence's noise positions 1..n_block_tokens-1
+                for (int32_t i = 1; i < n_block_tokens; ++i) {
+                    common_sampler_sample(smpl, ctx_dft, beg + i, true);
 
-                common_sampler_accept(smpl, id, true);
+                    const auto * cur_p = common_sampler_get_candidates(smpl, true);
 
-                result.push_back(id);
+                    for (int k = 0; k < std::min(3, (int) cur_p->size); ++k) {
+                        LOG_DBG(" - seq_id %d, draft candidate %3d, pos %3d: %6d (%8.3f) '%s'\n",
+                                seq_id, k, i - 1, cur_p->data[k].id, cur_p->data[k].p,
+                                common_token_to_piece(ctx_dft, cur_p->data[k].id).c_str());
+                    }
+
+                    const llama_token id = cur_p->data[0].id;
+
+                    if (cur_p->data[0].p < params.p_min) {
+                        break;
+                    }
+
+                    common_sampler_accept(smpl, id, true);
+
+                    result.push_back(id);
+                }
             }
 
             if (result.size() < (size_t) params.n_min) {
@@ -2145,6 +2183,7 @@ std::string common_speculative_type_to_str(common_speculative_type type) {
         case COMMON_SPECULATIVE_TYPE_DRAFT_EAGLE3:  return "draft-eagle3";
         case COMMON_SPECULATIVE_TYPE_DRAFT_MTP:     return "draft-mtp";
         case COMMON_SPECULATIVE_TYPE_DRAFT_DFLASH:  return "draft-dflash";
+        case COMMON_SPECULATIVE_TYPE_DRAFT_DSPARK:  return "draft-dspark";
         case COMMON_SPECULATIVE_TYPE_NGRAM_SIMPLE:  return "ngram-simple";
         case COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K:   return "ngram-map-k";
         case COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K4V: return "ngram-map-k4v";
@@ -2198,6 +2237,7 @@ int32_t common_speculative_n_max(const common_params_speculative * spec) {
             case COMMON_SPECULATIVE_TYPE_DRAFT_EAGLE3:
             case COMMON_SPECULATIVE_TYPE_DRAFT_MTP:
             case COMMON_SPECULATIVE_TYPE_DRAFT_DFLASH:
+            case COMMON_SPECULATIVE_TYPE_DRAFT_DSPARK:
                 n_max = std::max(n_max, std::max(0, spec->draft.n_max));
                 break;
             case COMMON_SPECULATIVE_TYPE_NGRAM_SIMPLE:
@@ -2342,6 +2382,7 @@ common_speculative * common_speculative_init(common_params_speculative & params,
         bool has_draft_eagle3 = (enabled_configs & (1u << COMMON_SPECULATIVE_TYPE_DRAFT_EAGLE3)) && params.draft.ctx_dft != nullptr;
         bool has_draft_mtp    = (enabled_configs & (1u << COMMON_SPECULATIVE_TYPE_DRAFT_MTP))    && params.draft.ctx_dft != nullptr;
         bool has_draft_dflash = (enabled_configs & (1u << COMMON_SPECULATIVE_TYPE_DRAFT_DFLASH)) && params.draft.ctx_dft != nullptr;
+        bool has_draft_dspark = (enabled_configs & (1u << COMMON_SPECULATIVE_TYPE_DRAFT_DSPARK)) && params.draft.ctx_dft != nullptr;
 
 
 
@@ -2352,7 +2393,7 @@ common_speculative * common_speculative_init(common_params_speculative & params,
         bool has_ngram_mod     = (enabled_configs & (1u << COMMON_SPECULATIVE_TYPE_NGRAM_MOD));
 
         // when adding a new type - update here the logic above
-        static_assert(COMMON_SPECULATIVE_TYPE_COUNT == 10);
+        static_assert(COMMON_SPECULATIVE_TYPE_COUNT == 11);
 
         // this list here defines the priority of the speculators
         // the one with highest priority are listed first
@@ -2385,6 +2426,9 @@ common_speculative * common_speculative_init(common_params_speculative & params,
         if (has_draft_dflash) {
             configs.push_back(common_speculative_config(COMMON_SPECULATIVE_TYPE_DRAFT_DFLASH, params));
         }
+        if (has_draft_dspark) {
+            configs.push_back(common_speculative_config(COMMON_SPECULATIVE_TYPE_DRAFT_DSPARK, params));
+        }
     }
 
     std::vector<std::unique_ptr<common_speculative_impl>> impls = {};
@@ -2409,6 +2453,11 @@ common_speculative * common_speculative_init(common_params_speculative & params,
                 impls.push_back(std::make_unique<common_speculative_impl_draft_dflash>(config.params, n_seq));
                 break;
             }
+            case COMMON_SPECULATIVE_TYPE_DRAFT_DSPARK: {
+                impls.push_back(std::make_unique<common_speculative_impl_draft_dflash>(
+                        config.params, n_seq, COMMON_SPECULATIVE_TYPE_DRAFT_DSPARK));
+                break;
+            }
             case COMMON_SPECULATIVE_TYPE_NGRAM_SIMPLE: {
                 common_ngram_map ngram_map = get_common_ngram_map(config.type, config.params.ngram_simple);
 

conversion/__init__.py

@@ -52,6 +52,7 @@
     "DeepseekV3ForCausalLM": "deepseek",
     "DeepseekV32ForCausalLM": "deepseek",
     "DFlashDraftModel": "qwen",
+    "Qwen3DSparkModel": "qwen",
     "DeepseekV4ForCausalLM": "deepseek",
     "DistilBertForMaskedLM": "bert",
     "DistilBertForSequenceClassification": "bert",

conversion/qwen.py

@@ -688,3 +688,23 @@ def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Ca
         if not name.startswith("model."):
             name = "model." + name
         return super().filter_tensors((name, gen))
+
+
+@ModelBase.register("Qwen3DSparkModel")
+class DSparkModel(DFlashModel):
+    # DSpark = DFlash + a semi-autoregressive Markov head
+    model_arch = gguf.MODEL_ARCH.DFLASH
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        # normalize the flat DeepSpec schema to DFlash's nested dflash_config
+        self.hparams.setdefault("dflash_config", {
+            k: self.hparams[k] for k in ("target_layer_ids", "mask_token_id") if k in self.hparams
+        })
+
+    @classmethod
+    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
+        name, gen = item
+        if name.endswith(("embed_tokens.weight", "lm_head.weight")):
+            return None
+        return super().filter_tensors((name, gen))

docs/speculative.md

@@ -78,6 +78,38 @@ See:
 
 - #22105
 
+### DSpark (`draft-dspark`)
+
+DSpark extends DFlash with a semi-autoregressive _Markov head_: the draft still emits a whole
+block per forward pass, but each block position's logits are biased by a low-rank term keyed on
+the previous token, chained in-graph across the block. This keeps drafting at one decode per
+block while recovering some of the left-to-right signal that pure block diffusion loses.
+
+The draft is a small DeepSpec checkpoint trained for a specific target (for example
+[`deepseek-ai/dspark_qwen3_4b_block7`](https://huggingface.co/deepseek-ai/dspark_qwen3_4b_block7)
+for `Qwen/Qwen3-4B`). Convert it with `--target-model-dir` so it inherits the target's tokenizer
+and token embeddings:
+
+```bash
+python convert_hf_to_gguf.py deepseek-ai/dspark_qwen3_4b_block7 \
+    --target-model-dir Qwen/Qwen3-4B --outtype bf16 --outfile Qwen3-4B-DSpark.gguf
+
+llama-server -m Qwen3-4B.gguf -md Qwen3-4B-DSpark.gguf \
+    --spec-type draft-dspark --spec-draft-n-max 7 -fa on --jinja
+```
+
+`--spec-draft-n-max` is clamped to the draft model's trained block size.
+
+`--spec-draft-conf-min P` truncates each drafted block at the first position whose predicted
+acceptance (from the draft's confidence head, if present) falls below `P` (default 0 = disabled).
+
+Currently only drafts with a Qwen3 backbone are supported; support for other backbones
+(e.g. Gemma4) is planned.
+
+See:
+
+- #25173
+
 ### n-gram Cache (`ngram-cache`)
 
 An n-gram is a sequence of n tokens. The n-gram cache implementation maintains statistics about short n-gram sequences.
@@ -173,7 +205,7 @@ If a draft model is combined with a draftless decoding the draftless decoding ha
 ### General Speculative Parameters
 
 ```
---spec-type [none|draft-simple|draft-eagle3|draft-dflash|draft-mtp|ngram-cache|ngram-simple|ngram-map-k|ngram-map-k4v|ngram-mod]
+--spec-type [none|draft-simple|draft-eagle3|draft-dflash|draft-dspark|draft-mtp|ngram-cache|ngram-simple|ngram-map-k|ngram-map-k4v|ngram-mod]
                                         comma-separated list of types of speculative decoding to use
                                         (default: none)
                                         (env: LLAMA_ARG_SPEC_TYPE)
@@ -314,6 +346,7 @@ Specifies a comma-separated list of speculative decoding types to use.
 | `draft-simple` | Use a simple draft model for speculation |
 | `draft-eagle3` | Use an EAGLE-3 draft model that reads the target's hidden states |
 | `draft-dflash` | Use a DFlash block-diffusion draft model that emits a block per step |
+| `draft-dspark` | Use a DSpark draft model (DFlash backbone + semi-autoregressive Markov head) |
 | `draft-mtp` | Use Multi Token Prediction (MTP) heads from the main model |
 | `ngram-cache` | Use n-gram cache lookup |
 | `ngram-simple` | Use simple n-gram pattern matching |

gguf-py/gguf/constants.py

@@ -954,6 +954,10 @@ class MODEL_TENSOR(IntEnum):
     # eagle3
     FC                     = auto()  # feature fusion layer
     D2T                    = auto()  # draft to target vocabulary mapping
+    # dspark
+    DSPARK_MARKOV_W1       = auto()  # markov head: prev-token embed
+    DSPARK_MARKOV_W2       = auto()  # markov head: bias projection
+    DSPARK_CONF_PROJ       = auto()  # confidence head
     # lfm2 audio
     A_ENC_NORM_CONV        = auto()
     A_ENC_LINEAR_POS       = auto()
@@ -1561,6 +1565,9 @@ class MODEL_TENSOR(IntEnum):
     MODEL_TENSOR.NEXTN_SHARED_HEAD_HEAD:    "blk.{bid}.nextn.shared_head_head",
     MODEL_TENSOR.NEXTN_SHARED_HEAD_NORM:    "blk.{bid}.nextn.shared_head_norm",
     MODEL_TENSOR.FC:                        "fc",
+    MODEL_TENSOR.DSPARK_MARKOV_W1:          "markov_w1",
+    MODEL_TENSOR.DSPARK_MARKOV_W2:          "markov_w2",
+    MODEL_TENSOR.DSPARK_CONF_PROJ:          "conf_proj",
     MODEL_TENSOR.D2T:                       "d2t",
 }
 
@@ -4236,6 +4243,10 @@ class MODEL_TENSOR(IntEnum):
         MODEL_TENSOR.FFN_UP,
         MODEL_TENSOR.FC,
         MODEL_TENSOR.ENC_OUTPUT_NORM,
+        # optional DSpark heads
+        MODEL_TENSOR.DSPARK_MARKOV_W1,
+        MODEL_TENSOR.DSPARK_MARKOV_W2,
+        MODEL_TENSOR.DSPARK_CONF_PROJ,
     ],
     MODEL_ARCH.MISTRAL4: [
         MODEL_TENSOR.TOKEN_EMBD,

gguf-py/gguf/tensor_mapping.py

@@ -1290,6 +1290,18 @@ class TensorNameMap:
             "model.fc", # dflash
         ),
 
+        MODEL_TENSOR.DSPARK_MARKOV_W1: (
+            "model.markov_head.markov_w1", # dspark
+        ),
+
+        MODEL_TENSOR.DSPARK_MARKOV_W2: (
+            "model.markov_head.markov_w2", # dspark
+        ),
+
+        MODEL_TENSOR.DSPARK_CONF_PROJ: (
+            "model.confidence_head.proj", # dspark
+        ),
+
         MODEL_TENSOR.CLS: (
             "classifier",       # jina
             "classifier.dense", # roberta