Merge branch 'master' into finetune-lora

Makefile

@@ -498,7 +498,7 @@ beam-search: examples/beam-search/beam-search.cpp build-info.h ggml.o llama.o co
 finetune: examples/finetune/finetune.cpp build-info.h ggml.o llama.o common.o $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
 
-speculative: examples/speculative/speculative.cpp build-info.h ggml.o llama.o common.o $(OBJS)
+speculative: examples/speculative/speculative.cpp build-info.h ggml.o llama.o common.o grammar-parser.o $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
 
 ifdef LLAMA_METAL

common/common.cpp

@@ -790,7 +790,7 @@ std::tuple<struct llama_model *, struct llama_context *> llama_init_from_gpt_par
     {
         LOG("warming up the model with an empty run\n");
 
-        const std::vector<llama_token> tmp = { llama_token_bos(lctx), };
+        const std::vector<llama_token> tmp = { llama_token_bos(lctx), llama_token_eos(lctx), };
         llama_eval(lctx, tmp.data(), tmp.size(), 0, params.n_threads);
         llama_reset_timings(lctx);
     }

convert-falcon-hf-to-gguf.py

@@ -55,10 +55,10 @@ def count_model_parts(dir_model: Path) -> int:
 
 def parse_args() -> argparse.Namespace:
     parser = argparse.ArgumentParser(description="Convert a Falcon model to a GGML compatible file")
-    parser.add_argument("--vocab-only",  action="store_true",    help="extract only the vocab")
+    parser.add_argument("--vocab-only", action="store_true", help="extract only the vocab")
-    parser.add_argument("--outfile",     type=Path,              help="path to write to; default: based on input")
+    parser.add_argument("--outfile",    type=Path,           help="path to write to; default: based on input")
-    parser.add_argument("model",         type=Path,              help="directory containing model file, or model file itself (*.bin)")
+    parser.add_argument("model",        type=Path,           help="directory containing model file, or model file itself (*.bin)")
-    parser.add_argument("ftype",     type=int, choices=[0, 1],   help="output format - use 0 for float32, 1 for float16", default = 1)
+    parser.add_argument("ftype",        type=int,            help="output format - use 0 for float32, 1 for float16", choices=[0, 1], default = 1)
     return parser.parse_args()
 
 args = parse_args()

examples/speculative/speculative.cpp

@@ -6,6 +6,7 @@
 
 #include "common.h"
 #include "llama.h"
+#include "grammar-parser.h"
 
 #include <cmath>
 #include <cstdio>
@@ -109,16 +110,35 @@ int main(int argc, char ** argv) {
     // used to determine end of generation
     bool has_eos = false;
 
+    // grammar stuff
+    struct llama_grammar * grammar_dft = NULL;
+    struct llama_grammar * grammar_tgt = NULL;
+
+    grammar_parser::parse_state parsed_grammar;
+
+    // if requested - load the grammar, error checking is omitted for brevity
+    if (!params.grammar.empty()) {
+        parsed_grammar = grammar_parser::parse(params.grammar.c_str());
+        // will be empty (default) if there are parse errors
+        if (parsed_grammar.rules.empty()) {
+            return 1;
+        }
+
+        std::vector<const llama_grammar_element *> grammar_rules(parsed_grammar.c_rules());
+        grammar_tgt = llama_grammar_init(grammar_rules.data(), grammar_rules.size(), parsed_grammar.symbol_ids.at("root"));
+    }
+
     const auto t_dec_start = ggml_time_us();
 
     while (true) {
         LOG("drafted: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx_dft, drafted));
 
-        // sample from the drafted tokens if any
         int i_dft = 0;
         while (true) {
-            const llama_token id = llama_sample_token(ctx_tgt, NULL, NULL, params, last_tokens, candidates, i_dft);
+            // sample from the target model
+            const llama_token id = llama_sample_token(ctx_tgt, NULL, grammar_tgt, params, last_tokens, candidates, i_dft);
 
+            // remember which tokens were sampled - used for repetition penalties during sampling
             last_tokens.erase(last_tokens.begin());
             last_tokens.push_back(id);
 
@@ -134,8 +154,9 @@ int main(int argc, char ** argv) {
 
             ++n_predict;
 
+            // check if the draft matches the target
             if (i_dft < (int) drafted.size() && id == drafted[i_dft]) {
-                LOG("drafted token %d accepted\n", id);
+                LOG("the sampled target token matches the %dth drafted token (%d, '%s') - accepted\n", i_dft, id, token_str.c_str());
                 ++n_accept;
                 ++n_past_tgt;
                 ++n_past_dft;
@@ -145,6 +166,14 @@ int main(int argc, char ** argv) {
             }
 
             // the drafted token was rejected or we are out of drafted tokens
+
+            if (i_dft < (int) drafted.size()) {
+                LOG("the %dth drafted token (%d, '%s') does not match the sampled target token (%d, '%s') - rejected\n",
+                        i_dft, drafted[i_dft], llama_token_to_piece(ctx_dft, drafted[i_dft]).c_str(), id, token_str.c_str());
+            } else {
+                LOG("out of drafted tokens\n");
+            }
+
             llama_eval(ctx_dft, &id, 1, n_past_dft, params.n_threads);
             ++n_past_dft;
 
@@ -158,7 +187,16 @@ int main(int argc, char ** argv) {
             break;
         }
 
-        // sample n_draft tokens from the draft model picking the best token
+        if (grammar_tgt) {
+            if (grammar_dft) {
+                llama_grammar_free(grammar_dft);
+            }
+            grammar_dft = llama_grammar_copy(grammar_tgt);
+
+            LOG("copied target grammar to draft grammar\n");
+        }
+
+        // sample n_draft tokens from the draft model using greedy decoding
         int n_past_cur = n_past_dft;
         for (int i = 0; i < n_draft; ++i) {
             float * logits = llama_get_logits(ctx_dft);
@@ -170,25 +208,40 @@ int main(int argc, char ** argv) {
 
             llama_token_data_array cur_p = { candidates.data(), candidates.size(), false };
 
+            if (grammar_dft != NULL) {
+                llama_sample_grammar(ctx_dft, &cur_p, grammar_dft);
+            }
+
             // computes softmax and sorts the candidates
             llama_sample_softmax(ctx_dft, &cur_p);
 
             for (int i = 0; i < 3; ++i) {
-                LOG(" - draft candidate %d: %d (%.3f)\n", i, cur_p.data[i].id, cur_p.data[i].p);
+                LOG(" - draft candidate %3d: %6d (%8.3f) '%s'\n", i, cur_p.data[i].id, cur_p.data[i].p, llama_token_to_piece(ctx_dft, cur_p.data[i].id).c_str());
             }
 
-            // too low probability, stop drafting
+            // TODO: better logic?
             if (cur_p.data[0].p < 2*cur_p.data[1].p) {
+                LOG("stopping drafting, probability too low: %.3f < 2*%.3f\n", cur_p.data[0].p, cur_p.data[1].p);
                 break;
             }
 
-            drafted.push_back(cur_p.data[0].id);
+            // drafted token
+            const llama_token id = cur_p.data[0].id;
+
+            drafted.push_back(id);
             ++n_drafted;
 
-            if (i < n_draft - 1) {
+            // no need to evaluate the last drafted token, since we won't use the result
-                // evaluate the drafted token on the draft model
+            if (i == n_draft - 1) {
-                llama_eval(ctx_dft, &drafted.back(), 1, n_past_cur, params.n_threads);
+                break;
-                ++n_past_cur;
+            }
+
+            // evaluate the drafted token on the draft model
+            llama_eval(ctx_dft, &drafted.back(), 1, n_past_cur, params.n_threads);
+            ++n_past_cur;
+
+            if (grammar_dft != NULL) {
+                llama_grammar_accept_token(ctx_dft, grammar_dft, id);
             }
         }
 
@@ -196,6 +249,7 @@ int main(int argc, char ** argv) {
         llama_eval(ctx_tgt, drafted.data(), drafted.size(), n_past_tgt, params.n_threads);
         ++n_past_tgt;
 
+        // the first token is always proposed by the traget model before the speculation loop
         drafted.erase(drafted.begin());
     }
 
@@ -226,6 +280,10 @@ int main(int argc, char ** argv) {
     llama_free(ctx_dft);
     llama_free_model(model_dft);
 
+    if (grammar_dft != NULL) {
+        llama_grammar_free(grammar_dft);
+        llama_grammar_free(grammar_tgt);
+    }
     llama_backend_free();
 
     fprintf(stderr, "\n\n");

grammars/json_arr.gbnf

@@ -0,0 +1,34 @@
+# This is the same as json.gbnf but we restrict whitespaces at the end of the root array
+# Useful for generating JSON arrays
+
+root   ::= arr
+value  ::= object | array | string | number | ("true" | "false" | "null") ws
+
+arr  ::=
+  "[\n" ws (
+            value
+    (",\n" ws value)*
+  )? "]"
+
+object ::=
+  "{" ws (
+            string ":" ws value
+    ("," ws string ":" ws value)*
+  )? "}" ws
+
+array  ::=
+  "[" ws (
+            value
+    ("," ws value)*
+  )? "]" ws
+
+string ::=
+  "\"" (
+    [^"\\] |
+    "\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F]) # escapes
+  )* "\"" ws
+
+number ::= ("-"? ([0-9] | [1-9] [0-9]*)) ("." [0-9]+)? ([eE] [-+]? [0-9]+)? ws
+
+# Optional space: by convention, applied in this grammar after literal chars when allowed
+ws ::= ([ \t\n] ws)?

k_quants.c

@@ -83,7 +83,7 @@ static float make_qx_quants(int n, int nmax, const float * restrict x, int8_t *
         float ax = fabsf(x[i]);
         if (ax > amax) { amax = ax; max = x[i]; }
     }
-    if (!amax) { // all zero
+    if (amax < 1e-30f) { // all zero
         for (int i = 0; i < n; ++i) {
             L[i] = 0;
         }
@@ -1086,6 +1086,12 @@ void quantize_row_q6_K_reference(const float * restrict x, block_q6_K * restrict
 
         }
 
+        if (!max_abs_scale) {
+            memset(&y[i], 0, sizeof(block_q6_K));
+            y[i].d = ggml_fp32_to_fp16(0.f);
+            continue;
+        }
+
         float iscale = -128.f/max_scale;
         y[i].d = ggml_fp32_to_fp16(1/iscale);
         for (int ib = 0; ib < QK_K/16; ++ib) {

llama.cpp

@@ -2942,7 +2942,12 @@ static bool llama_eval_internal(
 
     // for big prompts, if BLAS is enabled, it is better to use only one thread
     // otherwise, the threads are spin-lock waiting for the BLAS calls and are degrading the performance
-    n_threads = N >= 32 && ggml_cpu_has_blas() && !ggml_cpu_has_gpublas() ? 1 : n_threads;
+    // TODO: this is mostly important for Apple Silicon where CBLAS is still performing very well
+    //       we still need some threads to process all non-mul_mat ops, but not too much to avoid interfering
+    //       with the BLAS calls. need a better solution
+    if (N >= 32 && ggml_cpu_has_blas() && !ggml_cpu_has_gpublas()) {
+        n_threads = std::min(4, n_threads);
+    }
 
     struct ggml_tensor * res        = gf->nodes[gf->n_nodes - 1];
     struct ggml_tensor * embeddings = gf->nodes[gf->n_nodes - 2];
@@ -3850,6 +3855,25 @@ void llama_grammar_free(struct llama_grammar * grammar) {
     delete grammar;
 }
 
+struct llama_grammar * llama_grammar_copy(const struct llama_grammar * grammar) {
+    llama_grammar * result = new llama_grammar{ grammar->rules, grammar->stacks, grammar->partial_utf8 };
+
+    // redirect elements in stacks to point to new rules
+    for (size_t is = 0; is < result->stacks.size(); is++) {
+        for (size_t ie = 0; ie < result->stacks[is].size(); ie++) {
+            for (size_t ir0 = 0; ir0 < grammar->rules.size(); ir0++) {
+                for (size_t ir1 = 0; ir1 < grammar->rules[ir0].size(); ir1++) {
+                    if (grammar->stacks[is][ie] == &grammar->rules[ir0][ir1]) {
+                         result->stacks[is][ie]  =  &result->rules[ir0][ir1];
+                    }
+                }
+            }
+        }
+    }
+
+    return result;
+}
+
 //
 // sampling
 //

llama.h

@@ -423,6 +423,8 @@ extern "C" {
 
     LLAMA_API void llama_grammar_free(struct llama_grammar * grammar);
 
+    LLAMA_API struct llama_grammar * llama_grammar_copy(const struct llama_grammar * grammar);
+
     //
     // Sampling functions
     //