speculative : simplify (cont)

ggml-ci

Author: ggerganov

common/sampling.cpp

@@ -320,7 +320,7 @@ llama_token common_sampler_sample(struct common_sampler * gsmpl, struct llama_co
     return cur_p.data[cur_p.selected].id;
 }
 
-std::vector<llama_token> common_sampler_sample_n(struct common_sampler * gsmpl, struct llama_context * ctx, const std::vector<int> & idxs, const std::vector<llama_token> & draft, bool grammar_first) {
+std::vector<llama_token> common_sampler_sample_n(struct common_sampler * gsmpl, struct llama_context * ctx, const std::vector<int> & idxs, const llama_tokens & draft, bool grammar_first) {
     GGML_ASSERT(idxs.size() == draft.size() + 1 && "idxs.size() must be draft.size() + 1");
 
     std::vector<llama_token> result;
@@ -342,23 +342,10 @@ std::vector<llama_token> common_sampler_sample_n(struct common_sampler * gsmpl,
     return result;
 }
 
-std::vector<llama_token> common_sampler_sample_n(struct common_sampler * gsmpl, struct llama_context * ctx, const struct llama_batch & batch, bool grammar_first) {
-    std::vector<int> idxs;
-    idxs.reserve(batch.n_tokens);
-
-    std::vector<llama_token> draft;
-    draft.reserve(batch.n_tokens);
-
-    for (int i = 0; i < batch.n_tokens; i++) {
-        if (batch.logits[i] == 0) {
-            continue;
-        }
-
-        if (idxs.size() > 0) {
-            GGML_ASSERT(batch.pos[idxs.back()] + 1 == batch.pos[i]);
-            draft.push_back(batch.token[i]);
-        }
-        idxs.push_back(i);
+std::vector<llama_token> common_sampler_sample_n(struct common_sampler * gsmpl, struct llama_context * ctx, const llama_tokens & draft, bool grammar_first) {
+    std::vector<int> idxs(draft.size() + 1);
+    for (size_t i = 0; i < idxs.size(); ++i) {
+        idxs[i] = i;
     }
 
     return common_sampler_sample_n(gsmpl, ctx, idxs, draft, grammar_first);

common/sampling.h

@@ -71,9 +71,10 @@ llama_token common_sampler_sample(struct common_sampler * gsmpl, struct llama_co
 //
 // returns at least 1 token, up to idxs.size()
 //
-std::vector<llama_token> common_sampler_sample_n(struct common_sampler * gsmpl, struct llama_context * ctx, const std::vector<int> & idxs, const std::vector<llama_token> & draft, bool grammar_first = false);
+std::vector<llama_token> common_sampler_sample_n(struct common_sampler * gsmpl, struct llama_context * ctx, const std::vector<int> & idxs, const llama_tokens & draft, bool grammar_first = false);
 
-std::vector<llama_token> common_sampler_sample_n(struct common_sampler * gsmpl, struct llama_context * ctx, const struct llama_batch & batch, bool grammar_first = false);
+// assume idxs == [ 0, 1, 2, ..., draft.size() ]
+std::vector<llama_token> common_sampler_sample_n(struct common_sampler * gsmpl, struct llama_context * ctx, const llama_tokens & draft, bool grammar_first = false);
 
 uint32_t common_sampler_get_seed(const struct common_sampler * gsmpl);
 

common/speculative.cpp

@@ -10,24 +10,19 @@
 #define SPEC_VOCAB_CHECK_START_TOKEN_ID 5
 
 struct common_speculative {
-    struct common_speculative_params params;
-
-    llama_batch batch;
-
     struct llama_context * ctx;
     struct common_sampler * smpl;
 
+    llama_batch batch;
     llama_tokens prompt;
 };
 
 struct common_speculative * common_speculative_init(
-        struct common_speculative_params params,
         struct llama_context * ctx_dft) {
     auto * result = new common_speculative {
-        /* .params = */ params,
-        /* .batch  = */ llama_batch_init(llama_n_batch(ctx_dft), 0, 1),
         /* .ctx    = */ ctx_dft,
         /* .smpl   = */ nullptr,
+        /* .batch  = */ llama_batch_init(llama_n_batch(ctx_dft), 0, 1),
         /* .prompt = */ {},
     };
 
@@ -130,12 +125,11 @@ bool common_speculative_are_compatible(
     return true;
 }
 
-void common_speculative_add_draft(
+llama_tokens common_speculative_gen_draft(
         struct common_speculative * spec,
-        struct llama_batch & batch_tgt,
+        struct common_speculative_params params,
         const llama_tokens & prompt_tgt,
-        llama_token id_last,
-        llama_token n_past_tgt) {
+        llama_token id_last) {
     auto & batch  = spec->batch;
     auto & ctx    = spec->ctx;
     auto & smpl   = spec->smpl;
@@ -144,7 +138,7 @@ void common_speculative_add_draft(
     int reuse_i = 0;
     int reuse_n = 0;
 
-    const int n_ctx = llama_n_ctx(ctx) - spec->params.n_draft;
+    const int n_ctx = llama_n_ctx(ctx) - params.n_draft;
 
     const int i_start = std::max<int>(0, (int) prompt_tgt.size() - n_ctx);
 
@@ -156,7 +150,7 @@ void common_speculative_add_draft(
             cur++;
         }
 
-        if ((cur >= spec->params.n_reuse || prompt_tgt.size() <= n_ctx) && cur > reuse_n) {
+        if ((cur >= params.n_reuse || prompt_tgt.size() <= n_ctx) && cur > reuse_n) {
             reuse_i = i;
             reuse_n = cur;
         }
@@ -207,8 +201,11 @@ void common_speculative_add_draft(
 
     common_sampler_reset(smpl);
 
+    llama_tokens result;
+    result.reserve(params.n_draft);
+
     // sample n_draft tokens from the draft model
-    for (int i = 0; i < spec->params.n_draft; ++i) {
+    for (int i = 0; i < params.n_draft; ++i) {
         common_batch_clear(batch);
 
         common_sampler_sample(smpl, ctx, 0, true);
@@ -224,15 +221,15 @@ void common_speculative_add_draft(
         const llama_token id = cur_p->data[0].id;
 
         // only collect very high-confidence draft tokens
-        if (cur_p->data[0].p < spec->params.p_min) {
+        if (cur_p->data[0].p < params.p_min) {
             break;
         }
 
         common_sampler_accept(smpl, id, true);
 
-        common_batch_add(batch_tgt, id, n_past_tgt + i, { 0 }, true);
+        result.push_back(id);
 
-        if (batch_tgt.n_tokens > spec->params.n_draft) {
+        if (result.size() >= params.n_draft) {
             break;
         }
 
@@ -244,9 +241,5 @@ void common_speculative_add_draft(
         prompt.push_back(id);
     }
 
-    // don't waste time on small batches
-    // TODO: do not evaluate the draft model for that many rounds
-    if (batch_tgt.n_tokens < spec->params.n_min) {
-        batch_tgt.n_tokens = 1;
-    }
+    return result;
 }

common/speculative.h

@@ -7,15 +7,12 @@ struct common_speculative;
 
 struct common_speculative_params {
     int n_draft = 16;
-    int n_min   = 5;  // do not add drafts smaller than this, TODO: leave this to user?
     int n_reuse = 256;
 
     float p_min = 0.9f;
 };
 
-struct common_speculative * common_speculative_init(
-        struct common_speculative_params params,
-        struct llama_context * ctx_dft);
+struct common_speculative * common_speculative_init(struct llama_context * ctx_dft);
 
 void common_speculative_free(struct common_speculative * spec);
 
@@ -25,9 +22,8 @@ bool common_speculative_are_compatible(
 
 // sample up to n_draft tokens and add them to the batch using the draft model
 //
-void common_speculative_add_draft(
+llama_tokens common_speculative_gen_draft(
         struct common_speculative * spec,
-        struct llama_batch & batch_tgt,
+        struct common_speculative_params params,
         const llama_tokens & prompt,
-        llama_token id_last,
-        llama_token n_past_tgt);
+        llama_token id_last);

examples/speculative-simple/speculative-simple.cpp

@@ -13,6 +13,9 @@
 int main(int argc, char ** argv) {
     common_params params;
 
+    // minimum size of the draft to use
+    const int n_min = 5;
+
     if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_SPECULATIVE)) {
         return 1;
     }
@@ -92,31 +95,29 @@ int main(int argc, char ** argv) {
     // everything until here is standard initialization
     // the relevant stuff for speculative decoding starts here
 
-    const int n_input = inp.size();
-
     const auto t_enc_start = ggml_time_us();
 
     // target model sampling context
     struct common_sampler * smpl = common_sampler_init(model_tgt, params.sparams);
 
     // eval the prompt
-    llama_decode(ctx_tgt, llama_batch_get_one(inp.data(), n_input - 1));
+    llama_decode(ctx_tgt, llama_batch_get_one(inp.data(), inp.size() - 1));
 
     // note: keep the last token separate!
     llama_token id_last = inp.back();
 
-    auto prompt_dft = std::vector<llama_token>(inp.begin(), inp.end() - 1);
+    // all tokens currently in the target context
+    auto prompt_tgt = std::vector<llama_token>(inp.begin(), inp.end() - 1);
 
     int n_past = inp.size() - 1;
 
     // init the speculator
     struct common_speculative_params params_spec;
     params_spec.n_draft   = n_draft;
-    params_spec.n_min     = 5;
     params_spec.n_reuse   = 256;
     params_spec.p_min     = 0.9f;
 
-    struct common_speculative * spec = common_speculative_init(params_spec, ctx_dft);
+    struct common_speculative * spec = common_speculative_init(ctx_dft);
 
     llama_batch batch_tgt = llama_batch_init(llama_n_batch(ctx_tgt), 0, 1);
 
@@ -125,21 +126,30 @@ int main(int argc, char ** argv) {
     const auto t_dec_start = ggml_time_us();
 
     while (true) {
-        // always have a token to evaluate from before
-        common_batch_clear(batch_tgt);
-        common_batch_add  (batch_tgt, id_last, n_past, { 0 }, true);
-
-        // optionally, append draft tokens to the target batch
+        // optionally, generate draft tokens that can be appended to the target batch
         //
         // this is the most important part of the speculation. the more probable tokens that are provided here
         // the better the performance will be. in theory, this computation can be performed asynchronously and even
         // offloaded to a remote device. it doesn't even have to be based on an LLM. instead, it can provide tokens
         // from a cache or lookup tables.
         //
-        common_speculative_add_draft(spec, batch_tgt, prompt_dft, id_last, n_past + 1);
+        llama_tokens draft = common_speculative_gen_draft(spec, params_spec, prompt_tgt, id_last);
+
+        // always have a token to evaluate from before - id_last
+        common_batch_clear(batch_tgt);
+        common_batch_add  (batch_tgt, id_last, n_past++, { 0 }, true);
 
         // evaluate the target model on [id_last, draft0, draft1, ..., draftN-1]
         {
+            // do not waste time on small drafts
+            if (draft.size() < n_min) {
+                draft.clear();
+            }
+
+            for (size_t i = 0; i < draft.size(); ++i) {
+                common_batch_add(batch_tgt, draft[i], n_past + i, { 0 }, true);
+            }
+
             //LOG_DBG("target batch: %s\n", string_from(ctx_tgt, batch_tgt).c_str());
 
             llama_decode(ctx_tgt, batch_tgt);
@@ -152,11 +162,11 @@ int main(int argc, char ** argv) {
         // available logits from the batch and sample the next token until we run out of logits or the sampler
         // disagrees with the draft
         //
-        const auto ids = common_sampler_sample_n(smpl, ctx_tgt, batch_tgt);
+        const auto ids = common_sampler_sample_n(smpl, ctx_tgt, draft);
 
         GGML_ASSERT(ids.size() > 0); // there will always be at least one accepted token
 
-        n_past    += ids.size();
+        n_past    += ids.size() - 1;
         n_drafted += batch_tgt.n_tokens - 1;
         n_accept  += ids.size() - 1;
 
@@ -192,16 +202,16 @@ int main(int argc, char ** argv) {
                 break;
             }
 
-            LOG_DBG("accepted %d draft tokens, the last target token is: (%d, '%s')\n", (int) ids.size() - 1, id, token_str.c_str());
+            LOG_DBG("accepted %d/%d draft tokens, the last target token is: (%d, '%s')\n", (int) ids.size() - 1, (int) draft.size(), id, token_str.c_str());
 
             {
                 LOG_DBG("clear kv cache from any extra tokens, n_past = %d\n", n_past);
 
                 llama_kv_cache_seq_rm(ctx_tgt, 0, n_past, -1);
             }
 
-            prompt_dft.push_back(id_last);
-            prompt_dft.insert(prompt_dft.end(), ids.begin(), ids.end() - 1);
+            prompt_tgt.push_back(id_last);
+            prompt_tgt.insert(prompt_tgt.end(), ids.begin(), ids.end() - 1);
 
             // remember the last accepted token for the next iteration
             id_last = id;
@@ -210,6 +220,8 @@ int main(int argc, char ** argv) {
 
     auto t_dec_end = ggml_time_us();
 
+    const int n_input = inp.size();
+
     LOG("\n\n");
 
     LOG_INF("encoded %4d tokens in %8.3f seconds, speed: %8.3f t/s\n", n_input,   (t_enc_end - t_enc_start) / 1e6f, inp.size() / ((t_enc_end - t_enc_start) / 1e6f));