Merge c591189213 into 338085c69e

common/arg.cpp

@@ -3447,6 +3447,16 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             params.speculative.ngram_min_hits = value;
         }
     ).set_examples({LLAMA_EXAMPLE_SERVER}));
+    add_opt(common_arg(
+        {"--spec-ckpt-num-tries"}, "N",
+        string_format("number of tries for speculative decoding with recurrent memory (default: %d)", params.speculative.ckpt_num_tries),
+        [](common_params & params, int value) {
+            if (value < 0 || value > 10) {
+                throw std::invalid_argument("number of tries must be between 0 and 10 inclusive");
+            }
+            params.speculative.ckpt_num_tries = value;
+        }
+    ).set_examples({LLAMA_EXAMPLE_SERVER}));
     add_opt(common_arg(
         {"-ctkd", "--cache-type-k-draft"}, "TYPE",
         string_format(

common/common.h

@@ -270,6 +270,7 @@ struct common_params_speculative {
     uint16_t ngram_size_n     = 12; // ngram size for lookup
     uint16_t ngram_size_m     = 48; // mgram size for speculative tokens
     uint16_t ngram_min_hits   =  1; // minimum hits at ngram/mgram lookup for mgram to be proposed
+    uint16_t ckpt_num_tries   =  0; // number of tries in case of recurrent memory
 
     std::shared_ptr<common_ngram_mod> ngram_mod;
 

common/ngram-map.cpp

@@ -231,7 +231,7 @@ void common_ngram_map_draft(common_ngram_map & map,
         GGML_ABORT("%s: cur_len exceeds UINT32_MAX: %zu", __func__, cur_len);
     }
 
-    if (map.idx_last_check  > cur_len) {
+    if (map.idx_last_check > cur_len) {
         // Should not happen because of common_ngram_map_begin().
         GGML_ABORT("%s: map.idx_last_check > cur_len: %zu > %zu", __func__, map.idx_last_check, cur_len);
     }
@@ -386,7 +386,7 @@ void common_ngram_map_draft(common_ngram_map & map,
         LOG_DBG("%s: key_idx = %zu, key_offset = %zu, key_num = %d, draft.size = %zu\n", __func__,
                 curr_key.key_idx, key_offset, curr_key.key_num, draft.size());
 
-        map.last_draft_created   = false;
+        map.last_draft_created   = true;
         map.last_draft_key_idx   = key_offset;
         map.last_draft_value_idx = 0; // value 0 is used for simple mode
         return;

tools/server/server-context.cpp

@@ -146,6 +146,13 @@ struct server_slot {
     llama_token  sampled; // in speculative mode, this is the last accepted token
     llama_tokens drafted;
 
+    // use of checkpoints in speculative mode
+    bool        spec_has_ckpt        = false; // true if a checkpoint for rollback after partial speculation has been created
+    uint16_t    spec_ckpt_n_denials  = 0;     // number of drafts not accepted at the current position
+    int         spec_ckpt_n_accepted = 0;     // number of accepted tokens at current position
+    size_t      spec_ckpt_size_part  = 0;     // size of partial checkpoint
+
+
     // stats
     size_t n_sent_text = 0; // number of sent text character
 
@@ -184,6 +191,11 @@ struct server_slot {
         n_draft_total = 0;
         n_draft_accepted = 0;
 
+        spec_ckpt_n_denials  = 0;
+        spec_ckpt_n_accepted = 0;
+        spec_has_ckpt        = false;
+        spec_ckpt_size_part  = 0;
+
         task_prev = std::move(task);
         task.reset();
 
@@ -742,7 +754,7 @@ private:
 
         const bool can_spec = common_speculative_is_compat(ctx);
         if (!can_spec) {
-            SRV_WRN("%s", "speculative decoding not supported by this context\n");
+            SRV_WRN("%s", "speculative decoding not supported by this context without checkpoints\n");
         }
 
         // initialize slots
@@ -757,7 +769,7 @@ private:
             slot.prompt.tokens.has_mtmd = mctx != nullptr;
 
             // try speculative decoding
-            if (can_spec) {
+            if (can_spec || params_base.speculative.ckpt_num_tries > 0) {
                 slot.spec = common_speculative_init(params_base.speculative, slot.ctx);
                 if (slot.spec) {
                     if (mctx) {
@@ -2041,8 +2053,9 @@ private:
             // generate draft tokens in speculative decoding mode
             // TODO: rework to have a single draft llama_context shared across all slots [TAG_SERVER_SPEC_REWORK]
             //       perform the speculative drafting for all sequences at the same time in a single batch
-            const int n_draft_max = slot.get_n_draft_max();
-            if (n_draft_max > 0) {
+            const int n_draft_max = (slot.spec_ckpt_n_accepted > 0) ? slot.spec_ckpt_n_accepted : slot.get_n_draft_max();
+            if (n_draft_max > 0 && (params_base.speculative.ckpt_num_tries == 0
+                                    || slot.spec_ckpt_n_denials < params_base.speculative.ckpt_num_tries)) {
                 if (mctx) {
                     // we should never reach this, as speculative is automatically disabled if mmproj is loaded
                     GGML_ABORT("not supported by multimodal");
@@ -2059,8 +2072,52 @@ private:
                     draft.resize(n_draft_max);
                 }
 
+                const auto pos_min = llama_memory_seq_pos_min(llama_get_memory(ctx), slot.id);
+                const auto pos_max = llama_memory_seq_pos_max(llama_get_memory(ctx), slot.id);
+                bool do_checkpoint = !draft.empty() && params_base.speculative.ckpt_num_tries > 0
+                    && slot.prompt.checkpoints.size() < (size_t) params_base.n_ctx_checkpoints;
+                if (do_checkpoint && cached_text_tokens.size() > 5) {
+                    SLT_DBG(slot, "draft.size = %zu, n_spec_denials = %d, #ckpts=%zu, do_checkpoint = %s, pos_min = %d, pos_max = %d, tokens=[..., %d, %d, %d]\n",
+                        draft.size(), slot.spec_ckpt_n_denials,
+                        slot.prompt.checkpoints.size(),
+                        do_checkpoint ? "yes" : "no", pos_min, pos_max,
+                        cached_text_tokens[cached_text_tokens.size() - 3],
+                        cached_text_tokens[cached_text_tokens.size() - 2],
+                        cached_text_tokens[cached_text_tokens.size() - 1]);
+                }
+
+                if (do_checkpoint) {
+                    while (slot.prompt.checkpoints.size() >= (size_t) params_base.n_ctx_checkpoints) {
+                        // make room for the new checkpoint, if needed
+                        const auto & cur = slot.prompt.checkpoints.front();
+
+                        SLT_WRN(slot, "erasing old context checkpoint (pos_min = %d, pos_max = %d, size = %.3f MiB)\n",
+                                cur.pos_min, cur.pos_max, (float) cur.data.size() / 1024 / 1024);
+
+                        slot.prompt.checkpoints.erase(slot.prompt.checkpoints.begin());
+                    }
+
+                    const size_t checkpoint_size = llama_state_seq_get_size_ext(ctx, slot.id, 0);
+
+                    auto & cur = slot.prompt.checkpoints.emplace_back(server_prompt_checkpoint{
+                        /*.pos_min = */ pos_min,
+                        /*.pos_max = */ pos_max,
+                        /*.data    = */ std::vector<uint8_t>(checkpoint_size),
+                    });
+
+                    const size_t n = llama_state_seq_get_data_ext(ctx, cur.data.data(), checkpoint_size, slot.id, LLAMA_STATE_SEQ_FLAGS_PARTIAL_ONLY);
+
+                    SLT_INF(slot, "created context checkpoint %d of %d (pos_min = %d, pos_max = %d, size = %.3f MiB)\n",
+                            (int) slot.prompt.checkpoints.size(), params_base.n_ctx_checkpoints, cur.pos_min, cur.pos_max, (float) cur.data.size() / 1024 / 1024);
+
+                    slot.spec_ckpt_size_part = n;
+                    slot.spec_has_ckpt = true;
+                }
+
                 // add the sampled token to the batch
                 slot.i_batch_dft.push_back(batch.n_tokens);
+                SLT_DBG(slot, "before common_batch_add: sampled=%d, pos_next=%d, tokens.size=%zu, tokens.last=%d\n",
+                        slot.sampled, slot.prompt.tokens.pos_next(), slot.prompt.tokens.size(), slot.prompt.tokens[slot.prompt.tokens.size() -1]);
                 common_batch_add(batch, slot.sampled, slot.prompt.tokens.pos_next(), { slot.id }, true);
                 slot.prompt.tokens.push_back(slot.sampled);
 
@@ -2070,6 +2127,15 @@ private:
                     slot.i_batch = slot.i_batch_dft[0];
                     slot.drafted.clear();
                     slot.i_batch_dft.clear();
+
+                    if (slot.spec_has_ckpt) {
+                        slot.spec_ckpt_n_accepted = 0;
+                        slot.spec_ckpt_n_denials  = 0;
+
+                        // Delete Checkpoint
+                        slot.prompt.checkpoints.pop_back();
+                        slot.spec_has_ckpt = false;
+                    }
                 } else {
                     // keep track of total number of drafted tokens tested
                     slot.n_draft_total += draft.size();
@@ -2086,6 +2152,9 @@ private:
                 // no speculative decoding
                 slot.i_batch = batch.n_tokens;
 
+                slot.spec_ckpt_n_denials  = 0;
+                slot.spec_ckpt_n_accepted = 0;
+
                 common_batch_add(batch, slot.sampled, slot.prompt.tokens.pos_next(), { slot.id }, true);
 
                 slot.prompt.tokens.push_back(slot.sampled);
@@ -2538,6 +2607,7 @@ private:
 
                         // no need for empty or small checkpoints
                         do_checkpoint = do_checkpoint && (pos_min >= 0 && pos_max >= 64);
+                        SLT_DBG(slot, "main/do_checkpoint = %s, pos_min = %d, pos_max = %d\n", do_checkpoint ? "yes" : "no", pos_min, pos_max);
 
                         // no need to create checkpoints that are too close together
                         do_checkpoint = do_checkpoint && (slot.prompt.checkpoints.empty() || pos_max > slot.prompt.checkpoints.back().pos_max + 64);
@@ -2797,12 +2867,49 @@ private:
 
                 const int64_t t_current = ggml_time_us();
 
-                slot.n_decoded += ids.size();
 
+                if (slot.spec_has_ckpt && ids.size() < n_draft + 1) {
+                    // the main model rejected some tokens, so we need to rollback to the state before sampling the draft tokens
+                    auto & ckpt = slot.prompt.checkpoints.back();
+                    SLT_INF(slot, "partial acceptance: %zu < %zu, restoring checkpoint (pos_min = %d, pos_max = %d)\n",
+                            ids.size() - 1, n_draft,
+                            ckpt.pos_min, ckpt.pos_max);
+                    const size_t n = llama_state_seq_set_data_ext(ctx,
+                            ckpt.data.data(), ckpt.size(), slot.id, LLAMA_STATE_SEQ_FLAGS_PARTIAL_ONLY);
+                    if (n != slot.spec_ckpt_size_part) {
+                        GGML_ABORT("%s: failed to restore context checkpoint (pos_min=%d, pos_max=%d, size=%zu, get_data_ext->%zu, set_data_ext->%zu",
+                                    __func__, ckpt.pos_min, ckpt.pos_max, ckpt.size(), slot.spec_ckpt_size_part, n);
+                    }
+                    SRV_INF("partial acceptance: %zu < %zu, restored checkpoint: got %zu bytes\n",
+                            ids.size() -1 , n_draft, n);
+
+                    // rollback to the state before sampling the draft tokens
+                    SLT_INF(slot, "partial acceptance: n_tokens=%d, n_draft=%zu, pos_max=%d\n",
+                            slot.prompt.n_tokens(), n_draft, ckpt.pos_max);
+
+                    slot.prompt.tokens.keep_first(ckpt.pos_max + 1);
+
+                    // Delete Checkpoint
+                    slot.prompt.checkpoints.pop_back();
+                    slot.spec_has_ckpt = false;
+
+                    // Inform the speculative implementation of the number of valid tokens.
+                    // common_speculative_accept(slot.spec, ids.size() - 1);
+
+                    slot.spec_ckpt_n_denials++;
+                    slot.spec_ckpt_n_accepted = (slot.spec_ckpt_n_denials < params_base.speculative.ckpt_num_tries) ? (int) (ids.size() - 1) : 0;
+
+                    common_batch_clear(batch);
+
+                    continue;
+                }
+
+                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
                 slot.n_draft_accepted += ids.size() - 1;
+                slot.spec_ckpt_n_accepted = 0;
 
                 // inform the speculative decoding about the number of accepted tokens
                 common_speculative_accept(slot.spec, ids.size() - 1);
@@ -2814,7 +2921,17 @@ private:
                 slot.prompt.tokens.insert({ids.begin(), ids.end() - 1});
                 slot.sampled = ids.back(); // last accepted token
 
-                llama_memory_seq_rm(llama_get_memory(ctx), slot.id, slot.prompt.n_tokens(), -1);
+                slot.spec_ckpt_n_denials = 0;
+                if (slot.spec_has_ckpt) {
+                    // Delete Checkpoint
+                    if (slot.prompt.checkpoints.empty()) {
+                        GGML_ABORT("missing checkpoint to delete");
+                    }
+                    slot.prompt.checkpoints.pop_back();
+                    slot.spec_has_ckpt = false;
+                } else {
+                    llama_memory_seq_rm(llama_get_memory(ctx), slot.id, slot.prompt.n_tokens(), -1);
+                }
 
                 for (size_t i = 0; i < ids.size(); ++i) {
                     completion_token_output result;