logit_bias: apply configurable escalating EOG bias at low n_remain

common/arg.cpp

@@ -1205,6 +1205,15 @@ bool common_params_parse(int argc, char ** argv, common_params & params, llama_e
         exit(1); // for other exceptions, we exit with status code 1
     }
 
+    float &pafter = params.sampling.start_eog_after;
+    float &premain = params.sampling.start_eog_at_remain;
+    float const premain0 = premain;
+    float remain = params.n_predict - pafter;
+    if (premain < remain)
+      premain = remain;
+    if (params.sampling.eog_bias_per_tok)
+        LOG_INF("%s: n_predict=%d (first of start_eog_at_remain=%0.3g start_eog_after=%0.3g) => (remain=%0.3g) eog-bias-per-tok=%0.3g\n", __func__, (int) params.n_predict,
+                (double) premain0, (double) pafter, (double)premain, (double) params.sampling.eog_bias_per_tok);
     return true;
 }
 
@@ -1937,6 +1946,27 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             }
         }
     ).set_sparam());
+    add_opt(common_arg(
+        {"-eog", "--eog-bias-per-tok"}, "N",
+        string_format("when fewer than -start-eog-at-remain tokens are left to generate after -n, add this bias eog for each subsequent token (default: %.1f)", (double)params.sampling.eog_bias_per_tok),
+        [](common_params & params, const std::string & value) {
+            params.sampling.eog_bias_per_tok = std::stof(value);
+        }
+    ).set_sparam());
+    add_opt(common_arg(
+        {"-remain", "--start-eog-at-remain"}, "N",
+        string_format("start applying -eog bias when this many tokens remain of the -n max (default: %.1f)", (double)params.sampling.start_eog_at_remain),
+        [](common_params & params, const std::string & value) {
+            params.sampling.start_eog_at_remain = std::stof(value);
+        }
+    ).set_sparam());
+    add_opt(common_arg(
+        {"-after", "--start-eog-after"}, "N",
+        string_format("start applying -eog bias after this many tokens generated (default: %.1f); whichever happens first between -remain and -after applies", (double)params.sampling.start_eog_after),
+        [](common_params & params, const std::string & value) {
+            params.sampling.start_eog_after = std::stof(value);
+        }
+    ).set_sparam());
     add_opt(common_arg(
         {"--grammar"}, "GRAMMAR",
         string_format("BNF-like grammar to constrain generations (see samples in grammars/ dir) (default: '%s')", params.sampling.grammar.c_str()),

common/common.h

@@ -178,6 +178,13 @@ struct common_params_sampling {
 
     std::vector<llama_logit_bias> logit_bias; // logit biases to apply
 
+    float eog_bias_per_tok = 0; // escalating bias added to eog per token after:
+    /// this many remaining tokens (before applying eog_bias_per_tok) ...
+    float start_eog_at_remain = 0;
+    // or (whichever is first) after start_eog_after many generated:
+    /// (i.e. EOG logit bias = max(0,start_eog_after = max(start_eog_after, n_remain - start_eog_at_remain)) * eog_bias_per_tok)
+    float start_eog_after = 1e9;
+
     // print the parameters into a string
     std::string print() const;
 };

common/sampling.cpp

@@ -226,7 +226,10 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, co
             llama_sampler_init_logit_bias(
                 llama_vocab_n_tokens(vocab),
                 params.logit_bias.size(),
-                params.logit_bias.data()));
+                params.logit_bias.data(),
+                params.eog_bias_per_tok,
+                params.start_eog_at_remain,
+                vocab));
 
     if (params.mirostat == 0) {
         for (const auto & cnstr : params.samplers) {
@@ -335,18 +338,18 @@ void common_perf_print(const struct llama_context * ctx, const struct common_sam
     }
 }
 
-llama_token common_sampler_sample(struct common_sampler * gsmpl, struct llama_context * ctx, int idx, bool grammar_first) {
+llama_token common_sampler_sample(struct common_sampler * gsmpl, struct llama_context * ctx, int idx, bool grammar_first, float n_remain) {
     gsmpl->set_logits(ctx, idx);
 
     auto & grmr  = gsmpl->grmr;
     auto & chain = gsmpl->chain;
     auto & cur_p = gsmpl->cur_p; // initialized by set_logits
 
     if (grammar_first) {
-        llama_sampler_apply(grmr, &cur_p);
+        llama_sampler_apply(grmr, &cur_p, n_remain);
     }
 
-    llama_sampler_apply(chain, &cur_p);
+    llama_sampler_apply(chain, &cur_p, n_remain);
 
     GGML_ASSERT(cur_p.selected != -1 && "no selected token during sampling - check your sampling configuration");
 
@@ -361,7 +364,7 @@ llama_token common_sampler_sample(struct common_sampler * gsmpl, struct llama_co
         llama_token_data       single_token_data       = { id, 1.0f, 0.0f };
         llama_token_data_array single_token_data_array = { &single_token_data, 1, -1, false };
 
-        llama_sampler_apply(grmr, &single_token_data_array);
+        llama_sampler_apply(grmr, &single_token_data_array, n_remain);
 
         const bool is_valid = single_token_data_array.data[0].logit != -INFINITY;
         if (is_valid) {
@@ -373,23 +376,23 @@ llama_token common_sampler_sample(struct common_sampler * gsmpl, struct llama_co
     // if the token is not valid, sample again, but first apply the grammar sampler and then the sampling chain
     gsmpl->set_logits(ctx, idx);
 
-    llama_sampler_apply(grmr,  &cur_p);
-    llama_sampler_apply(chain, &cur_p);
+    llama_sampler_apply(grmr,  &cur_p, n_remain);
+    llama_sampler_apply(chain, &cur_p, n_remain);
 
     GGML_ASSERT(cur_p.selected != -1 && "no selected token during re-sampling - check your sampling configuration");
 
     return cur_p.data[cur_p.selected].id;
 }
 
-std::vector<llama_token> common_sampler_sample_and_accept_n(struct common_sampler * gsmpl, struct llama_context * ctx, const std::vector<int> & idxs, const llama_tokens & draft, bool grammar_first) {
+std::vector<llama_token> common_sampler_sample_and_accept_n(struct common_sampler * gsmpl, struct llama_context * ctx, const std::vector<int> & idxs, const llama_tokens & draft, bool grammar_first, float n_remain) {
     GGML_ASSERT(idxs.size() == draft.size() + 1 && "idxs.size() must be draft.size() + 1");
 
     std::vector<llama_token> result;
     result.reserve(idxs.size());
 
     size_t i = 0;
     for (; i < draft.size(); i++) {
-        const llama_token id = common_sampler_sample(gsmpl, ctx, idxs[i], grammar_first);
+        const llama_token id = common_sampler_sample(gsmpl, ctx, idxs[i], grammar_first, n_remain);
 
         common_sampler_accept(gsmpl, id, true);
 
@@ -401,7 +404,7 @@ std::vector<llama_token> common_sampler_sample_and_accept_n(struct common_sample
     }
 
     if (i == draft.size()) {
-        const llama_token id = common_sampler_sample(gsmpl, ctx, idxs[i], grammar_first);
+        const llama_token id = common_sampler_sample(gsmpl, ctx, idxs[i], grammar_first, n_remain);
 
         common_sampler_accept(gsmpl, id, true);
 
@@ -411,13 +414,13 @@ std::vector<llama_token> common_sampler_sample_and_accept_n(struct common_sample
     return result;
 }
 
-std::vector<llama_token> common_sampler_sample_and_accept_n(struct common_sampler * gsmpl, struct llama_context * ctx, const llama_tokens & draft, bool grammar_first) {
+std::vector<llama_token> common_sampler_sample_and_accept_n(struct common_sampler * gsmpl, struct llama_context * ctx, const llama_tokens & draft, bool grammar_first, float n_remain) {
     std::vector<int> idxs(draft.size() + 1);
     for (size_t i = 0; i < idxs.size(); ++i) {
         idxs[i] = i;
     }
 
-    return common_sampler_sample_and_accept_n(gsmpl, ctx, idxs, draft, grammar_first);
+    return common_sampler_sample_and_accept_n(gsmpl, ctx, idxs, draft, grammar_first, n_remain);
 }
 
 uint32_t common_sampler_get_seed(const struct common_sampler * gsmpl) {

common/sampling.h

@@ -58,7 +58,7 @@ void common_perf_print(const struct llama_context * ctx, const struct common_sam
 // if grammar_first is true, the grammar is applied before the samplers (slower)
 // useful in cases where all the resulting candidates (not just the sampled one) must fit the grammar
 //
-llama_token common_sampler_sample(struct common_sampler * gsmpl, struct llama_context * ctx, int idx, bool grammar_first = false);
+llama_token common_sampler_sample(struct common_sampler * gsmpl, struct llama_context * ctx, int idx, bool grammar_first = false, float n_remain = 0);
 
 // generalized version of common_sampler_sample
 //
@@ -76,10 +76,10 @@ 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_and_accept_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_and_accept_n(struct common_sampler * gsmpl, struct llama_context * ctx, const std::vector<int> & idxs, const llama_tokens & draft, bool grammar_first = false, float n_remain = 0);
 
 // assume idxs == [ 0, 1, 2, ..., draft.size() ]
-std::vector<llama_token> common_sampler_sample_and_accept_n(struct common_sampler * gsmpl, struct llama_context * ctx, const llama_tokens & draft, bool grammar_first = false);
+std::vector<llama_token> common_sampler_sample_and_accept_n(struct common_sampler * gsmpl, struct llama_context * ctx, const llama_tokens & draft, bool grammar_first = false, float n_remain = 0);
 
 uint32_t common_sampler_get_seed(const struct common_sampler * gsmpl);
 

common/speculative.cpp

@@ -238,12 +238,12 @@ llama_tokens common_speculative_gen_draft(
     llama_decode(ctx, batch);
 
     common_sampler_reset(smpl);
-
+    int n_remain = params.n_draft;
     // sample n_draft tokens from the draft model
     for (int i = 0; i < params.n_draft; ++i) {
         common_batch_clear(batch);
 
-        common_sampler_sample(smpl, ctx, 0, true);
+        common_sampler_sample(smpl, ctx, 0, true, --n_remain);
 
         const auto * cur_p = common_sampler_get_candidates(smpl);
 

examples/batched/batched.cpp

@@ -162,7 +162,9 @@ int main(int argc, char ** argv) {
 
     const auto t_main_start = ggml_time_us();
 
+    int n_remain = n_predict;
     while (n_cur <= n_predict) {
+        --n_remain;
         // prepare the next batch
         common_batch_clear(batch);
 
@@ -173,7 +175,7 @@ int main(int argc, char ** argv) {
                 continue;
             }
 
-            const llama_token new_token_id = llama_sampler_sample(smpl, ctx, i_batch[i]);
+            const llama_token new_token_id = llama_sampler_sample(smpl, ctx, i_batch[i], n_remain);
 
             // is it an end of generation? -> mark the stream as finished
             if (llama_vocab_is_eog(vocab, new_token_id) || n_cur == n_predict) {

examples/gritlm/gritlm.cpp

@@ -108,7 +108,7 @@ static std::string generate(llama_context * ctx, llama_sampler * smpl, const std
 
     std::vector<llama_token> inputs = common_tokenize(vocab, prompt, false, true);
     int32_t i_current_token = 0;
-
+    int n_remain = 32;
     while (true) {
         common_batch_clear(bat);
         {
@@ -122,7 +122,7 @@ static std::string generate(llama_context * ctx, llama_sampler * smpl, const std
 
         llama_decode(ctx, bat);
 
-        llama_token token = llama_sampler_sample(smpl, ctx, bat.n_tokens - 1);
+        llama_token token = llama_sampler_sample(smpl, ctx, bat.n_tokens - 1, --n_remain);
 
         if (token == eos_token) {
             break;

examples/lookahead/lookahead.cpp

@@ -253,6 +253,7 @@ int main(int argc, char ** argv) {
 
         int seq_id_best = 0;
 
+        int n_remain = N;
         for (int v = 0; v < N; ++v) {
             int i_batch = 0;
 
@@ -274,8 +275,9 @@ int main(int argc, char ** argv) {
                 }
             }
 
+            --n_remain;
             // sample the next token
-            id = common_sampler_sample(smpl, ctx, i_batch);
+            id = common_sampler_sample(smpl, ctx, i_batch, n_remain);
 
             common_sampler_accept(smpl, id, true);
 
@@ -349,10 +351,11 @@ int main(int argc, char ** argv) {
                     tokens_j[j] = tokens_j[j + 1];
                 }
 
+                unsigned constexpr NA = (unsigned)-1;
                 if (v == 0) {
                     // sample from the last level
                     for (int i = 0; i < W; i++) {
-                        tokens_j[N - 2][i] = common_sampler_sample(smpl, ctx, ngrams_cur.size()*(N-1) + W*(N - 2) + i);
+                      tokens_j[N - 2][i] = common_sampler_sample(smpl, ctx, ngrams_cur.size()*(N-1) + W*(N - 2) + i, NA);
                     }
                 } else {
                     for (int i = 0; i < W; i++) {

examples/lookup/lookup.cpp

@@ -117,7 +117,8 @@ int main(int argc, char ** argv){
         int i_dft = 0;
         while (true) {
             // sample from the target model
-            llama_token id = common_sampler_sample(smpl, ctx, i_dft);
+            unsigned const n_remain = params.n_predict - n_predict;
+            llama_token id = common_sampler_sample(smpl, ctx, i_dft, n_remain);
 
             common_sampler_accept(smpl, id, true);
 

examples/passkey/passkey.cpp

@@ -217,10 +217,12 @@ int main(int argc, char ** argv) {
 
     const auto t_main_start = ggml_time_us();
 
+    int n_remain = n_len - n_cur;
     while (n_cur <= n_len) {
+        --n_remain;
         // sample the next token
         {
-            const llama_token new_token_id = llama_sampler_sample(smpl, ctx, batch.n_tokens - 1);
+          const llama_token new_token_id = llama_sampler_sample(smpl, ctx, batch.n_tokens - 1, n_remain);
 
             // is it an end of generation?
             if (llama_vocab_is_eog(vocab, new_token_id) || n_cur == n_len) {

examples/save-load-state/save-load-state.cpp

@@ -76,8 +76,10 @@ int main(int argc, char ** argv) {
     // first run
     printf("\nfirst run: %s", params.prompt.c_str());
 
+    int n_remain = params.n_predict;
     for (auto i = 0; i < params.n_predict; i++) {
-        auto next_token     = llama_sampler_sample(smpl, ctx, -1);
+        --n_remain;
+        auto next_token     = llama_sampler_sample(smpl, ctx, -1, n_remain);
         auto next_token_str = common_token_to_piece(ctx, next_token);
 
         printf("%s", next_token_str.c_str());
@@ -128,8 +130,10 @@ int main(int argc, char ** argv) {
     n_past = n_past_saved;
 
     // second run
+    n_remain = params.n_predict;
     for (auto i = 0; i < params.n_predict; i++) {
-        auto next_token     = llama_sampler_sample(smpl2, ctx2, -1);
+        --n_remain;
+        auto next_token     = llama_sampler_sample(smpl2, ctx2, -1, n_remain);
         auto next_token_str = common_token_to_piece(ctx2, next_token);
 
         printf("%s", next_token_str.c_str());
@@ -209,8 +213,10 @@ int main(int argc, char ** argv) {
     }
 
     // third run with seq 1 instead of 0
+    n_remain = params.n_predict;
     for (auto i = 0; i < params.n_predict; i++) {
-        auto next_token     = llama_sampler_sample(smpl3, ctx3, -1);
+        --n_remain;
+        auto next_token     = llama_sampler_sample(smpl3, ctx3, -1, n_remain);
         auto next_token_str = common_token_to_piece(ctx3, next_token);
 
         printf("%s", next_token_str.c_str());

examples/simple-chat/simple-chat.cpp

@@ -110,7 +110,9 @@ int main(int argc, char ** argv) {
         // prepare a batch for the prompt
         llama_batch batch = llama_batch_get_one(prompt_tokens.data(), prompt_tokens.size());
         llama_token new_token_id;
+        int n_remain = batch.n_tokens;
         while (true) {
+            --n_remain;
             // check if we have enough space in the context to evaluate this batch
             int n_ctx = llama_n_ctx(ctx);
             int n_ctx_used = llama_memory_seq_pos_max(llama_get_memory(ctx), 0);
@@ -125,7 +127,7 @@ int main(int argc, char ** argv) {
             }
 
             // sample the next token
-            new_token_id = llama_sampler_sample(smpl, ctx, -1);
+            new_token_id = llama_sampler_sample(smpl, ctx, -1, n_remain);
 
             // is it an end of generation?
             if (llama_vocab_is_eog(vocab, new_token_id)) {

examples/simple/simple.cpp

@@ -151,6 +151,8 @@ int main(int argc, char ** argv) {
     int n_decode = 0;
     llama_token new_token_id;
 
+    int n_remain = n_predict;
+
     for (int n_pos = 0; n_pos + batch.n_tokens < n_prompt + n_predict; ) {
         // evaluate the current batch with the transformer model
         if (llama_decode(ctx, batch)) {
@@ -162,7 +164,7 @@ int main(int argc, char ** argv) {
 
         // sample the next token
         {
-            new_token_id = llama_sampler_sample(smpl, ctx, -1);
+            new_token_id = llama_sampler_sample(smpl, ctx, -1, --n_remain);
 
             // is it an end of generation?
             if (llama_vocab_is_eog(vocab, new_token_id)) {

include/llama.h

@@ -1197,7 +1197,7 @@ extern "C" {
     struct llama_sampler_i {
         const char *           (*name)  (const struct llama_sampler * smpl);                                 // can be NULL
         void                   (*accept)(      struct llama_sampler * smpl, llama_token token);              // can be NULL
-        void                   (*apply) (      struct llama_sampler * smpl, llama_token_data_array * cur_p); // required
+        void                   (*apply) (      struct llama_sampler * smpl, llama_token_data_array * cur_p, float n_remain); // required
         void                   (*reset) (      struct llama_sampler * smpl);                                 // can be NULL
         struct llama_sampler * (*clone) (const struct llama_sampler * smpl);                                 // can be NULL if ctx is NULL
         void                   (*free)  (      struct llama_sampler * smpl);                                 // can be NULL if ctx is NULL
@@ -1215,7 +1215,7 @@ extern "C" {
     LLAMA_API struct llama_sampler * llama_sampler_init  (const struct llama_sampler_i * iface, llama_sampler_context_t ctx);
     LLAMA_API const char *           llama_sampler_name  (const struct llama_sampler * smpl);
     LLAMA_API void                   llama_sampler_accept(      struct llama_sampler * smpl, llama_token token);
-    LLAMA_API void                   llama_sampler_apply (      struct llama_sampler * smpl, llama_token_data_array * cur_p);
+    LLAMA_API void                   llama_sampler_apply (      struct llama_sampler * smpl, llama_token_data_array * cur_p, float n_remain);
     LLAMA_API void                   llama_sampler_reset (      struct llama_sampler * smpl);
     LLAMA_API struct llama_sampler * llama_sampler_clone (const struct llama_sampler * smpl);
     // important: do not free if the sampler has been added to a llama_sampler_chain (via llama_sampler_chain_add)
@@ -1346,7 +1346,10 @@ extern "C" {
     LLAMA_API struct llama_sampler * llama_sampler_init_logit_bias(
                              int32_t   n_vocab,
                              int32_t   n_logit_bias,
-              const llama_logit_bias * logit_bias);
+                             const llama_logit_bias * logit_bias,
+                             float eog_bias_per_tok,
+                             float start_eog_at_remain,
+                             const struct llama_vocab *vocab);
 
     // this sampler is meant to be used for fill-in-the-middle infilling
     // it's supposed to be used after top_k + top_p sampling
@@ -1384,7 +1387,7 @@ extern "C" {
     //    llama_sampler_accept(smpl, token);
     //    return token;
     // Returns the sampled token
-    LLAMA_API llama_token llama_sampler_sample(struct llama_sampler * smpl, struct llama_context * ctx, int32_t idx);
+    LLAMA_API llama_token llama_sampler_sample(struct llama_sampler * smpl, struct llama_context * ctx, int32_t idx, float n_remain);
 
     // TODO: extend in the future
     //LLAMA_API void llama_decode_with_sampler(struct llama_context * ctx, struct llama_sampler * smpl, struct llama_batch batch, ...);