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llama + spec: MTP Support (#22673)

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* spec: support MTP

* fix batch size

* rename files

* cont : simplify (#7)

* MTP: clean-up (#9)

* MTP: clean-up

* review: use llama_context_type instead of llama_graph_type

* review: remove llama_model_has_mtp

* review: fix convert issues

* convert: fix pycheck

* review: formatting

* use `mtp-` for identifying mtp models

* convert: fix mtp conversion

* mtp -> draft-mtp

* remove unused llama_arch

* add need_embd in speculative

* llama: allow partial seq_rm for GDN models for speculative decoding

Currently speculative checkpoint needs to restart from a checkpoint
after some draft tokens are not accepted, this leads to some wastage in
running the target again. This PR adds the ability to rollback upto
`draft_max` by storing the GDN intermediates.

* fix pending state

* vulkan: add GDN partial rollback

* meta: extend check to axis 1

* metal: add GDN partial rollback

Extend the gated delta net kernel to store intermediate states for
partial rollback support on the Metal backend.

- Add K (snapshot slot count) as a function constant
- Read input state from slot 0 of the 3D state tensor
- Write intermediate states to different slots during token loop
- For K=1, maintain backward-compatible single-slot behavior

Ref: https://github.com/ggml-org/llama.cpp/commit/8c05923630110223669f069af2000e9cf10c02bc

Assisted-by: llama.cpp:local pi

* delta_net_base: use ggml_pad instead of new_tensor

* review: add need_rs_seq

* review: rename part_bounded to n_rs

* review: deslop comments

* review: rename, add asserts

* server : adjust checkpoint logic (#11)

* server : adjust checkpoint logic

* cont : rm asserts

* server-context: fix early exit

* spec : fix compatibility with n-gram and add TODOs (#13)

* metal : cleanup

* llama : fix faulty bitwise check in recurrent memory

* server : disable RS-based MTP in combination with other spec types

* spec : add TODOs

* cont : fix comment

* cont : update comment

* common : fix logic for ngram + mtp compat

* llama-memory: enable checkpointing with partial rollback

* cont: add test-case for loading into a dirty ctx

* llama-memory-recurrent: clear rs_idx in clear

* download: fix mtp path

* llama-arch: fix enorm op

* docs: update docs

* conversion: fix type annotations

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
This commit is contained in:
Aman Gupta
2026-05-16 20:06:23 +08:00
committed by GitHub
parent b81c2cdd74
commit 255582687b
54 changed files with 2227 additions and 413 deletions
Download Patch File Download Diff File Expand all files Collapse all files
tools/server/README.md
+11 -8
View File
@@ -72,7 +72,6 @@ For the full list of features, please refer to [server's changelog](https://gith
| `-ctk, --cache-type-k TYPE` | KV cache data type for K<br/>allowed values: f32, f16, bf16, q8_0, q4_0, q4_1, iq4_nl, q5_0, q5_1<br/>(default: f16)<br/>(env: LLAMA_ARG_CACHE_TYPE_K) |
| `-ctv, --cache-type-v TYPE` | KV cache data type for V<br/>allowed values: f32, f16, bf16, q8_0, q4_0, q4_1, iq4_nl, q5_0, q5_1<br/>(default: f16)<br/>(env: LLAMA_ARG_CACHE_TYPE_V) |
| `-dt, --defrag-thold N` | KV cache defragmentation threshold (DEPRECATED)<br/>(env: LLAMA_ARG_DEFRAG_THOLD) |
| `--rpc SERVERS` | comma-separated list of RPC servers (host:port)<br/>(env: LLAMA_ARG_RPC) |
| `--mlock` | force system to keep model in RAM rather than swapping or compressing<br/>(env: LLAMA_ARG_MLOCK) |
| `--mmap, --no-mmap` | whether to memory-map model. (if mmap disabled, slower load but may reduce pageouts if not using mlock) (default: enabled)<br/>(env: LLAMA_ARG_MMAP) |
| `-dio, --direct-io, -ndio, --no-direct-io` | use DirectIO if available. (default: disabled)<br/>(env: LLAMA_ARG_DIO) |
@@ -111,8 +110,8 @@ For the full list of features, please refer to [server's changelog](https://gith
| `-v, --verbose, --log-verbose` | Set verbosity level to infinity (i.e. log all messages, useful for debugging) |
| `--offline` | Offline mode: forces use of cache, prevents network access<br/>(env: LLAMA_OFFLINE) |
| `-lv, --verbosity, --log-verbosity N` | Set the verbosity threshold. Messages with a higher verbosity will be ignored. Values:<br/> - 0: generic output<br/> - 1: error<br/> - 2: warning<br/> - 3: info<br/> - 4: debug<br/>(default: 3)<br/><br/>(env: LLAMA_LOG_VERBOSITY) |
| `--log-prefix` | Enable prefix in log messages<br/>(env: LLAMA_LOG_PREFIX) |
| `--log-timestamps` | Enable timestamps in log messages<br/>(env: LLAMA_LOG_TIMESTAMPS) |
| `--log-prefix, --no-log-prefix` | Enable prefix in log messages<br/>(env: LLAMA_ARG_LOG_PREFIX) |
| `--log-timestamps, --no-log-timestamps` | Enable timestamps in log messages<br/>(env: LLAMA_ARG_LOG_TIMESTAMPS) |
| `--spec-draft-type-k, -ctkd, --cache-type-k-draft TYPE` | KV cache data type for K for the draft model<br/>allowed values: f32, f16, bf16, q8_0, q4_0, q4_1, iq4_nl, q5_0, q5_1<br/>(default: f16)<br/>(env: LLAMA_ARG_SPEC_DRAFT_CACHE_TYPE_K) |
| `--spec-draft-type-v, -ctvd, --cache-type-v-draft TYPE` | KV cache data type for V for the draft model<br/>allowed values: f32, f16, bf16, q8_0, q4_0, q4_1, iq4_nl, q5_0, q5_1<br/>(default: f16)<br/>(env: LLAMA_ARG_SPEC_DRAFT_CACHE_TYPE_V) |
@@ -189,11 +188,15 @@ For the full list of features, please refer to [server's changelog](https://gith
| `--reuse-port` | allow multiple sockets to bind to the same port (default: disabled)<br/>(env: LLAMA_ARG_REUSE_PORT) |
| `--path PATH` | path to serve static files from (default: )<br/>(env: LLAMA_ARG_STATIC_PATH) |
| `--api-prefix PREFIX` | prefix path the server serves from, without the trailing slash (default: )<br/>(env: LLAMA_ARG_API_PREFIX) |
| `--ui-config JSON` / `--webui-config JSON` (deprecated) | JSON that provides default UI settings (overrides UI defaults)<br/>(env: LLAMA_ARG_UI_CONFIG / LLAMA_ARG_WEBUI_CONFIG) |
| `--ui-config-file PATH` / `--webui-config-file PATH` (deprecated) | JSON file that provides default UI settings (overrides UI defaults)<br/>(env: LLAMA_ARG_UI_CONFIG_FILE / LLAMA_ARG_WEBUI_CONFIG_FILE) |
| `--ui-mcp-proxy, --no-ui-mcp-proxy` / `--webui-mcp-proxy, --no-webui-mcp-proxy` (deprecated) | experimental: whether to enable MCP CORS proxy - do not enable in untrusted environments (default: disabled)<br/>(env: LLAMA_ARG_UI_MCP_PROXY / LLAMA_ARG_WEBUI_MCP_PROXY) |
| `--webui-config JSON` | [DEPRECATED: use --ui-config] JSON that provides default WebUI settings (overrides WebUI defaults)<br/>(env: LLAMA_ARG_WEBUI_CONFIG) |
| `--ui-config JSON` | JSON that provides default UI settings (overrides UI defaults)<br/>(env: LLAMA_ARG_UI_CONFIG) |
| `--webui-config-file PATH` | [DEPRECATED: use --ui-config-file] JSON file that provides default WebUI settings (overrides WebUI defaults)<br/>(env: LLAMA_ARG_WEBUI_CONFIG_FILE) |
| `--ui-config-file PATH` | JSON file that provides default UI settings (overrides UI defaults)<br/>(env: LLAMA_ARG_UI_CONFIG_FILE) |
| `--webui-mcp-proxy, --no-webui-mcp-proxy` | [DEPRECATED: use --ui-mcp-proxy/--no-ui-mcp-proxy] experimental: whether to enable MCP CORS proxy<br/>(env: LLAMA_ARG_WEBUI_MCP_PROXY) |
| `--ui-mcp-proxy, --no-ui-mcp-proxy` | experimental: whether to enable MCP CORS proxy - do not enable in untrusted environments (default: disabled)<br/>(env: LLAMA_ARG_UI_MCP_PROXY) |
| `--tools TOOL1,TOOL2,...` | experimental: whether to enable built-in tools for AI agents - do not enable in untrusted environments (default: no tools)<br/>specify "all" to enable all tools<br/>available tools: read_file, file_glob_search, grep_search, exec_shell_command, write_file, edit_file, apply_diff, get_datetime<br/>(env: LLAMA_ARG_TOOLS) |
| `--ui, --no-ui` / `--webui, --no-webui` (deprecated) | whether to enable the Web UI (default: enabled)<br/>(env: LLAMA_ARG_UI / LLAMA_ARG_WEBUI) |
| `--webui, --no-webui` | [DEPRECATED: use --ui/--no-ui] whether to enable the Web UI<br/>(env: LLAMA_ARG_WEBUI) |
| `--ui, --no-ui` | whether to enable the Web UI (default: enabled)<br/>(env: LLAMA_ARG_UI) |
| `--embedding, --embeddings` | restrict to only support embedding use case; use only with dedicated embedding models (default: disabled)<br/>(env: LLAMA_ARG_EMBEDDINGS) |
| `--rerank, --reranking` | enable reranking endpoint on server (default: disabled)<br/>(env: LLAMA_ARG_RERANKING) |
| `--api-key KEY` | API key to use for authentication, multiple keys can be provided as a comma-separated list (default: none)<br/>(env: LLAMA_API_KEY) |
@@ -248,7 +251,7 @@ For the full list of features, please refer to [server's changelog](https://gith
| `--spec-draft-device, -devd, --device-draft <dev1,dev2,..>` | comma-separated list of devices to use for offloading the draft model (none = don't offload)<br/>use --list-devices to see a list of available devices |
| `--spec-draft-ngl, -ngld, --gpu-layers-draft, --n-gpu-layers-draft N` | max. number of draft model layers to store in VRAM, either an exact number, 'auto', or 'all' (default: auto)<br/>(env: LLAMA_ARG_N_GPU_LAYERS_DRAFT) |
| `--spec-draft-model, -md, --model-draft FNAME` | draft model for speculative decoding (default: unused)<br/>(env: LLAMA_ARG_SPEC_DRAFT_MODEL) |
| `--spec-type none,draft-simple,draft-eagle3,ngram-simple,ngram-map-k,ngram-map-k4v,ngram-mod,ngram-cache` | comma-separated list of types of speculative decoding to use (default: none)<br/><br/>(env: LLAMA_ARG_SPEC_TYPE) |
| `--spec-type none,draft-simple,draft-eagle3,draft-mtp,ngram-simple,ngram-map-k,ngram-map-k4v,ngram-mod,ngram-cache` | comma-separated list of types of speculative decoding to use (default: none)<br/><br/>(env: LLAMA_ARG_SPEC_TYPE) |
| `--spec-ngram-mod-n-min N` | minimum number of ngram tokens to use for ngram-based speculative decoding (default: 48) |
| `--spec-ngram-mod-n-max N` | maximum number of ngram tokens to use for ngram-based speculative decoding (default: 64) |
| `--spec-ngram-mod-n-match N` | ngram-mod lookup length (default: 24) |
tools/server/server-context.cpp
+95 -48
View File
@@ -145,9 +145,9 @@ struct server_slot {
SLT_INF(*this, "clearing prompt with %zu tokens\n", prompt.tokens.size());
llama_memory_seq_rm(llama_get_memory(ctx_tgt), id, -1, -1);
common_context_seq_rm(ctx_tgt, id, -1, -1);
if (ctx_dft) {
llama_memory_seq_rm(llama_get_memory(ctx_dft), id, -1, -1);
common_context_seq_rm(ctx_dft, id, -1, -1);
}
prompt.tokens.clear();
@@ -238,8 +238,14 @@ struct server_slot {
(ggml_time_us() - t_start) / 1000.0, n_text, (int) prompt.tokens.size());
}
bool need_embd() const {
GGML_ASSERT(task);
return task->need_embd() || (spec && common_speculative_need_embd(spec));
}
// if the context does not have a memory module then all embeddings have to be computed within a single ubatch
// also we cannot split if the pooling would require any past tokens
// (MTP supports splitting — uses task->need_embd() not need_embd())
bool can_split() const {
GGML_ASSERT(task);
@@ -511,12 +517,12 @@ struct server_slot {
void copy_state_to(server_slot & other) const {
GGML_ASSERT(state == SLOT_STATE_DONE_PROMPT);
llama_memory_seq_rm(llama_get_memory(ctx_tgt), other.id, -1, -1);
llama_memory_seq_cp(llama_get_memory(ctx_tgt), id, other.id, -1, -1);
common_context_seq_rm(ctx_tgt, other.id, -1, -1);
common_context_seq_cp(ctx_tgt, id, other.id, -1, -1);
if (ctx_dft) {
llama_memory_seq_rm(llama_get_memory(ctx_dft), other.id, -1, -1);
llama_memory_seq_cp(llama_get_memory(ctx_dft), id, other.id, -1, -1);
common_context_seq_rm(ctx_dft, other.id, -1, -1);
common_context_seq_cp(ctx_dft, id, other.id, -1, -1);
}
other.n_decoded = n_decoded;
@@ -775,10 +781,40 @@ private:
}
auto cparams = common_context_params_to_llama(params_dft);
const bool spec_mtp = std::find(params_base.speculative.types.begin(),
params_base.speculative.types.end(),
COMMON_SPECULATIVE_TYPE_DRAFT_MTP) != params_base.speculative.types.end();
if (spec_mtp) {
cparams.ctx_type = LLAMA_CONTEXT_TYPE_MTP;
}
// note: for small models maybe we can set this to the maximum possible draft from all speculative types
// the extra memory for small models is likely negligible?
cparams.n_rs_seq = 0;
ctx_dft.reset(llama_init_from_model(model_dft.get(), cparams));
ctx_dft_seq_rm_type = common_context_can_seq_rm(ctx_dft.get());
params_base.speculative.draft.ctx_tgt = ctx_tgt;
params_base.speculative.draft.ctx_dft = ctx_dft.get();
} else if (std::find(params_base.speculative.types.begin(), params_base.speculative.types.end(),
COMMON_SPECULATIVE_TYPE_DRAFT_MTP) != params_base.speculative.types.end()) {
SRV_INF("creating MTP draft context against the target model '%s'\n",
params_base.model.path.c_str());
auto cparams_mtp = common_context_params_to_llama(params_base);
cparams_mtp.ctx_type = LLAMA_CONTEXT_TYPE_MTP;
cparams_mtp.n_rs_seq = 0;
ctx_dft.reset(llama_init_from_model(model_tgt, cparams_mtp));
if (ctx_dft == nullptr) {
SRV_ERR("%s", "failed to create MTP context\n");
return false;
}
ctx_dft_seq_rm_type = common_context_can_seq_rm(ctx_dft.get());
params_base.speculative.draft.ctx_tgt = ctx_tgt;
params_base.speculative.draft.ctx_dft = ctx_dft.get();
}
@@ -2194,12 +2230,12 @@ private:
SLT_WRN(slot, "slot context shift, n_keep = %d, n_left = %d, n_discard = %d\n", n_keep, n_left, n_discard);
llama_memory_seq_rm (llama_get_memory(ctx_tgt), slot.id, n_keep , n_keep + n_discard);
llama_memory_seq_add(llama_get_memory(ctx_tgt), slot.id, n_keep + n_discard, slot.prompt.n_tokens(), -n_discard);
common_context_seq_rm (ctx_tgt, slot.id, n_keep , n_keep + n_discard);
common_context_seq_add(ctx_tgt, slot.id, n_keep + n_discard, slot.prompt.n_tokens(), -n_discard);
if (ctx_dft) {
llama_memory_seq_rm (llama_get_memory(ctx_dft.get()), slot.id, n_keep , n_keep + n_discard);
llama_memory_seq_add(llama_get_memory(ctx_dft.get()), slot.id, n_keep + n_discard, slot.prompt.tokens.pos_next(), -n_discard);
common_context_seq_rm (ctx_dft.get(), slot.id, n_keep , n_keep + n_discard);
common_context_seq_add(ctx_dft.get(), slot.id, n_keep + n_discard, slot.prompt.tokens.pos_next(), -n_discard);
}
// add generated tokens to cache
@@ -2306,14 +2342,23 @@ private:
slot.n_draft_total += draft.size();
// TODO: avoid restoring the draft context and re-evaluating the drafted tokens when not needed [TAG_SPEC_AVOID_DRAFT_REEVAL]
if (ctx_dft) {
ckpt.load_dft(ctx_dft.get(), slot.id, LLAMA_STATE_SEQ_FLAGS_PARTIAL_ONLY | LLAMA_STATE_SEQ_FLAGS_ON_DEVICE);
const bool use_ckpt_dft = ctx_dft_seq_rm_type == COMMON_CONTEXT_SEQ_RM_TYPE_FULL;
llama_memory_seq_rm(llama_get_memory(ctx_dft.get()), slot.id, ckpt.pos_max + 1, -1);
if (ctx_dft) {
if (use_ckpt_dft) {
ckpt.load_dft(ctx_dft.get(), slot.id, LLAMA_STATE_SEQ_FLAGS_PARTIAL_ONLY | LLAMA_STATE_SEQ_FLAGS_ON_DEVICE);
}
common_context_seq_rm(ctx_dft.get(), slot.id, ckpt.pos_max + 1, -1);
}
if (!draft.empty()) {
const bool use_ckpt_tgt = ctx_tgt_seq_rm_type == COMMON_CONTEXT_SEQ_RM_TYPE_FULL;
const bool use_ckpt_tgt =
ctx_tgt_seq_rm_type == COMMON_CONTEXT_SEQ_RM_TYPE_FULL ||
(ctx_tgt_seq_rm_type == COMMON_CONTEXT_SEQ_RM_TYPE_RS && draft.size() > llama_n_rs_seq(ctx_tgt));
const bool use_ckpt_dft =
(ctx_dft_seq_rm_type == COMMON_CONTEXT_SEQ_RM_TYPE_RS && draft.size() > llama_n_rs_seq(ctx_dft.get()));
if (use_ckpt_tgt) {
//const int64_t t_start = ggml_time_us();
@@ -2328,6 +2373,10 @@ private:
(float) ckpt.size() / 1024 / 1024,
(float) ckpt.data_dft.size() / 1024 / 1024);
}
if (use_ckpt_dft) {
ckpt.update_dft(ctx_dft.get(), slot.id, LLAMA_STATE_SEQ_FLAGS_PARTIAL_ONLY | LLAMA_STATE_SEQ_FLAGS_ON_DEVICE);
}
}
}
@@ -2499,12 +2548,12 @@ private:
const int64_t kv_shift = (int64_t) head_p - (int64_t) head_c;
llama_memory_seq_rm (llama_get_memory(ctx_tgt), slot.id, head_p, head_c);
llama_memory_seq_add(llama_get_memory(ctx_tgt), slot.id, head_c, head_c + n_match, kv_shift);
common_context_seq_rm (ctx_tgt, slot.id, head_p, head_c);
common_context_seq_add(ctx_tgt, slot.id, head_c, head_c + n_match, kv_shift);
if (ctx_dft) {
llama_memory_seq_rm (llama_get_memory(ctx_dft.get()), slot.id, head_p, head_c);
llama_memory_seq_add(llama_get_memory(ctx_dft.get()), slot.id, head_c, head_c + n_match, kv_shift);
common_context_seq_rm (ctx_dft.get(), slot.id, head_p, head_c);
common_context_seq_add(ctx_dft.get(), slot.id, head_c, head_c + n_match, kv_shift);
}
for (size_t i = 0; i < n_match; i++) {
@@ -2667,18 +2716,10 @@ private:
SLT_TRC(slot, "cached n_tokens = %d, memory_seq_rm [%d, end)\n", slot.prompt.n_tokens(), p0);
if (!llama_memory_seq_rm(llama_get_memory(ctx_tgt), slot.id, p0, -1)) {
SLT_WRN(slot, "failed to truncate tokens with position >= %d - clearing the memory\n", p0);
slot.prompt_clear(true);
// there is no common part left
slot.n_prompt_tokens_cache = 0;
} else {
if (ctx_dft && !llama_memory_seq_rm(llama_get_memory(ctx_dft.get()), slot.id, p0, -1)) {
GGML_ABORT("failed to truncate draft context\n");
}
}
common_context_seq_rm(ctx_tgt, slot.id, p0, -1);
if (ctx_dft) {
common_context_seq_rm(ctx_dft.get(), slot.id, p0, -1);
}
// If using an alora, there may be uncached tokens that come
// before the invocation sequence. When this happens, the
@@ -2703,9 +2744,11 @@ private:
// checkpoints are created only if:
// - the model does not support partial sequence removal
// - the model uses SWA (and we are not using `swa_full`)
// - the model supports partial sequence removal but only up to a fixed bound
do_checkpoint = do_checkpoint && (
(ctx_tgt_seq_rm_type == COMMON_CONTEXT_SEQ_RM_TYPE_FULL) ||
(n_swa > 0));
ctx_tgt_seq_rm_type == COMMON_CONTEXT_SEQ_RM_TYPE_FULL ||
ctx_tgt_seq_rm_type == COMMON_CONTEXT_SEQ_RM_TYPE_RS ||
n_swa > 0);
bool has_mtmd = false;
@@ -2758,12 +2801,14 @@ private:
break;
}
// embedding requires all tokens in the batch to be output
// embedding requires all tokens in the batch to be output;
// MTP also wants logits at every prompt position so the
// streaming hook can mirror t_h_pre_norm into ctx_dft.
common_batch_add(batch,
cur_tok,
slot.prompt.tokens.pos_next(),
{ slot.id },
slot.task->need_embd());
slot.need_embd());
slot.prompt.tokens.push_back(cur_tok);
slot.n_prompt_tokens_processed++;
@@ -2877,7 +2922,7 @@ private:
slot_batched->lora[alora_disabled_id].scale = alora_scale;
}
llama_set_embeddings(ctx_tgt, slot_batched->task->need_embd());
llama_set_embeddings(ctx_tgt, slot_batched->need_embd());
}
if (batch.n_tokens == 0) {
@@ -3140,13 +3185,8 @@ private:
// verify and try to accept the draft
{
const bool use_ckpt_tgt = ctx_tgt_seq_rm_type == COMMON_CONTEXT_SEQ_RM_TYPE_FULL;
// only save the sampler sampler state if we use checkpoints
common_sampler_ptr smpl_save;
if (use_ckpt_tgt) {
smpl_save.reset(common_sampler_clone(slot.smpl.get()));
}
// save the sampler sampler state in case we need to restore it
common_sampler_ptr smpl_save(common_sampler_clone(slot.smpl.get()));
GGML_ASSERT(slot.spec_i_batch.size() == n_draft + 1);
auto accepted = common_sampler_sample_and_accept_n(slot.smpl.get(), slot.ctx_tgt, slot.spec_i_batch, slot.spec_draft);
@@ -3154,8 +3194,14 @@ private:
GGML_ASSERT(accepted.size() >= 1);
const uint32_t n_rollback = slot.spec_draft.size() + 1 - accepted.size();
const bool use_ckpt_tgt =
ctx_tgt_seq_rm_type == COMMON_CONTEXT_SEQ_RM_TYPE_FULL ||
(ctx_tgt_seq_rm_type == COMMON_CONTEXT_SEQ_RM_TYPE_RS && n_rollback > llama_n_rs_seq(ctx_tgt));
// check for partial draft acceptance
if (accepted.size() < slot.spec_draft.size() + 1) {
if (n_rollback > 0) {
if (use_ckpt_tgt) {
if (trace > 0) {
SLT_INF(slot, "accepted %2zu/%2zu draft tokens (restore checkpoint)\n", accepted.size() - 1, slot.spec_draft.size());
@@ -3171,13 +3217,13 @@ private:
{
ckpt.load_tgt(slot.ctx_tgt, slot.id, LLAMA_STATE_SEQ_FLAGS_PARTIAL_ONLY | LLAMA_STATE_SEQ_FLAGS_ON_DEVICE);
llama_memory_seq_rm(llama_get_memory(slot.ctx_tgt), slot.id, ckpt.pos_max + 1, -1);
common_context_seq_rm(slot.ctx_tgt, slot.id, ckpt.pos_max + 1, -1);
}
if (slot.ctx_dft) {
ckpt.load_dft(slot.ctx_dft, slot.id, LLAMA_STATE_SEQ_FLAGS_PARTIAL_ONLY | LLAMA_STATE_SEQ_FLAGS_ON_DEVICE);
llama_memory_seq_rm(llama_get_memory(slot.ctx_dft), slot.id, ckpt.pos_max + 1, -1);
common_context_seq_rm(slot.ctx_dft, slot.id, ckpt.pos_max + 1, -1);
}
slot.prompt.tokens.keep_first(ckpt.n_tokens);
@@ -3200,7 +3246,6 @@ private:
const auto ids = std::move(slot.spec_draft);
slot.n_decoded += ids.size();
slot.t_token_generation = std::max<int64_t>(1, t_current - slot.t_start_generation) / 1e3;
// update how many tokens out of those tested were accepted
@@ -3213,9 +3258,9 @@ private:
slot.sampled = ids.back(); // last accepted token
SLT_DBG(slot, "add accepted tokens: sampled=%d, ids.size=%zu, n_draft=%zu\n", slot.sampled, ids.size(), n_draft);
llama_memory_seq_rm(llama_get_memory(slot.ctx_tgt), slot.id, slot.prompt.tokens.pos_next(), -1);
common_context_seq_rm(slot.ctx_tgt, slot.id, slot.prompt.tokens.pos_next(), -1);
if (slot.ctx_dft) {
llama_memory_seq_rm(llama_get_memory(slot.ctx_dft), slot.id, slot.prompt.tokens.pos_next(), -1);
common_context_seq_rm(slot.ctx_dft, slot.id, slot.prompt.tokens.pos_next(), -1);
}
for (size_t i = 0; i < ids.size(); ++i) {
@@ -3227,6 +3272,8 @@ private:
// TODO: set result.probs
slot.n_decoded += 1;
if (!process_token(result, slot)) {
slot.print_timings();
send_final_response(slot);