Apply some renaming suggestions.

Fix comments that were out of sync with the pull.

common/sampling.cpp

@@ -1,7 +1,7 @@
 #include "sampling.h"
 
-llama_sampling_state::~llama_sampling_state() {
+llama_sampling_context::~llama_sampling_context() {
-    for (auto & it : sequence_states) {
+    for (auto & it : sequence_contexts) {
         if (it.second.grammar != NULL) {
             llama_grammar_free(it.second.grammar);
             it.second.grammar = NULL;
@@ -9,48 +9,48 @@ llama_sampling_state::~llama_sampling_state() {
     }
 }
 
-llama_sampling_state llama_sampling_state_init(
+llama_sampling_context llama_sampling_context_init(
         const struct gpt_params & params,
                   llama_grammar * grammar) {
-  llama_sampling_state result;
+  llama_sampling_context result;
 
   result.params = params.sampling_params;
   result.grammar = grammar;
   return result;
 }
 
-// Note: Creates the state if it doesn't exist, so this always return something.
+// Note: Creates the context if it doesn't exist, so this always return something.
-llama_sampler_sequence_state & llama_sampling_get_sequence_state(
+llama_sampler_sequence_context & llama_sampling_get_sequence_context(
-              llama_sampling_state & state,
+              llama_sampling_context & sampling_ctx,
         const llama_seq_id             seq) {
-    const auto it = state.sequence_states.find(seq);
+    const auto it = sampling_ctx.sequence_contexts.find(seq);
-    if (it != state.sequence_states.end()) {
+    if (it != sampling_ctx.sequence_contexts.end()) {
         return it->second;
     }
-    llama_sampler_sequence_state new_state = {
+    llama_sampler_sequence_context new_ctx = {
-        2.0f * state.params.mirostat_tau,
+        2.0f * sampling_ctx.params.mirostat_tau,
-        state.grammar != NULL ? llama_grammar_copy(state.grammar) : NULL,
+        sampling_ctx.grammar != NULL ? llama_grammar_copy(sampling_ctx.grammar) : NULL,
     };
-    return state.sequence_states.insert({seq, new_state}).first->second;
+    return sampling_ctx.sequence_contexts.insert({seq, new_ctx}).first->second;
 }
 
-bool llama_sampling_state_reset(
+bool llama_sampling_context_reset(
-              llama_sampling_state & state,
+              llama_sampling_context & sampling_ctx,
         const llama_seq_id             seq) {
-    const auto it = state.sequence_states.find(seq);
+    const auto it = sampling_ctx.sequence_contexts.find(seq);
-    if (it == state.sequence_states.end()) return false;
+    if (it == sampling_ctx.sequence_contexts.end()) return false;
     if (it->second.grammar != NULL) {
         llama_grammar_free(it->second.grammar);
         it->second.grammar = NULL;
     }
-    state.sequence_states.erase(it);
+    sampling_ctx.sequence_contexts.erase(it);
     return true;
 }
 
-llama_token llama_sample_token(
+llama_token llama_sampling_sample(
                   struct llama_context * ctx,
                   struct llama_context * ctx_guidance,
-                  struct llama_sampling_state & state,
+                  struct llama_sampling_context & sampling_ctx,
         const std::vector<llama_token> & last_tokens,
          std::vector<llama_token_data> & candidates,
         const                      int   idx,
@@ -58,7 +58,7 @@ llama_token llama_sample_token(
     const int n_ctx   = llama_n_ctx(ctx);
     const int n_vocab = llama_n_vocab(llama_get_model(ctx));
 
-    const llama_sampling_params & params = state.params;
+    const llama_sampling_params & params = sampling_ctx.params;
     const float   temp            = params.temp;
     const int32_t top_k           = params.top_k <= 0 ? n_vocab : params.top_k;
     const float   top_p           = params.top_p;
@@ -115,10 +115,10 @@ llama_token llama_sample_token(
         }
     }
 
-    llama_sampler_sequence_state & seq_state = llama_sampling_get_sequence_state(state, seq);
+    llama_sampler_sequence_context & seq_ctx = llama_sampling_get_sequence_context(sampling_ctx, seq);
 
-    if (seq_state.grammar != NULL) {
+    if (seq_ctx.grammar != NULL) {
-        llama_sample_grammar(ctx, &cur_p, seq_state.grammar);
+        llama_sample_grammar(ctx, &cur_p, seq_ctx.grammar);
     }
 
     if (temp <= 0) {
@@ -128,10 +128,10 @@ llama_token llama_sample_token(
         if (mirostat == 1) {
             const int mirostat_m = 100;
             llama_sample_temp(ctx, &cur_p, temp);
-            id = llama_sample_token_mirostat(ctx, &cur_p, mirostat_tau, mirostat_eta, mirostat_m, &seq_state.mirostat_mu);
+            id = llama_sample_token_mirostat(ctx, &cur_p, mirostat_tau, mirostat_eta, mirostat_m, &seq_ctx.mirostat_mu);
         } else if (mirostat == 2) {
             llama_sample_temp(ctx, &cur_p, temp);
-            id = llama_sample_token_mirostat_v2(ctx, &cur_p, mirostat_tau, mirostat_eta, &seq_state.mirostat_mu);
+            id = llama_sample_token_mirostat_v2(ctx, &cur_p, mirostat_tau, mirostat_eta, &seq_ctx.mirostat_mu);
         } else {
             // Temperature sampling
             size_t min_keep = std::max(1, params.n_probs);
@@ -158,8 +158,8 @@ llama_token llama_sample_token(
         }
     }
 
-    if (seq_state.grammar != NULL) {
+    if (seq_ctx.grammar != NULL) {
-        llama_grammar_accept_token(ctx, seq_state.grammar, id);
+        llama_grammar_accept_token(ctx, seq_ctx.grammar, id);
     }
 
     return id;

common/sampling.h

@@ -34,64 +34,62 @@ typedef struct llama_sampling_params {
 
 } llama_sampling_params;
 
-// per-sequence sampler state
+// per-sequence sampler context
-typedef struct llama_sampler_sequence_state {
+typedef struct llama_sampler_sequence_context {
     float mirostat_mu; // mirostat sampler state
     llama_grammar * grammar;
-} llama_sampler_sequence_state;
+} llama_sampler_sequence_context;
 
-// general sampler state
+// general sampler context
-typedef struct llama_sampling_state {
+typedef struct llama_sampling_context {
-    ~llama_sampling_state();
+    ~llama_sampling_context();
 
     // parameters that will be used for sampling and when creating
-    // new llama_sampler_sequence_state instances
+    // new llama_sampler_sequence_context instances
     llama_sampling_params params;
 
-    // map of sequence ids to sampler states
+    // map of sequence ids to sampler contexts
-    std::unordered_map<llama_seq_id, llama_sampler_sequence_state> sequence_states;
+    std::unordered_map<llama_seq_id, llama_sampler_sequence_context> sequence_contexts;
 
-    // when non-NULL, new instances of llama_sampler_sequence_state
+    // when non-NULL, new instances of llama_sampler_sequence_context
     // will get a copy of the grammar here
     // note: only the pointer is stored here, it is not a copy of
     //       the grammar and shouldn't be freed
     llama_grammar * grammar;
-} llama_sampling_state;
+} llama_sampling_context;
 
 #include "common.h"
 
-// Create a new sampling state instance.
+// Create a new sampling context instance.
-llama_sampling_state llama_sampling_state_init(
+llama_sampling_context llama_sampling_context_init(
         const struct gpt_params & params,
                   llama_grammar * grammar = NULL);
 
-// Fetches the sampler state for the specified sequence id (defaults to 0).
+// Fetches the sampler context for the specified sequence id (defaults to 0).
-// If the state for that sequence id doesn't already exist, it will be created with
+// If the context for that sequence id doesn't already exist, it will be created with
-// default values based on the parameters in the state argument.
+// default values based on the parameters in the sampling_ctx argument.
-llama_sampler_sequence_state & llama_sampling_get_sequence_state(
+llama_sampler_sequence_context & llama_sampling_get_sequence_context(
-              llama_sampling_state & state,
+              llama_sampling_context & sampling_ctx,
         const llama_seq_id             seq = 0);
 
-// Reset the sampler states for the supplied sequence id (defaults to 0).
+// Reset the sampler context for the supplied sequence id (defaults to 0).
 // This is necessary to reuse a sequence id or free memory used by sequences
 // that are no longer required.
-bool llama_sampling_state_reset(
+bool llama_sampling_context_reset(
-              llama_sampling_state & state,
+              llama_sampling_context & sampling_ctx,
         const llama_seq_id             seq = 0);
 
 // this is a common sampling function used across the examples for convenience
 // it can serve as a starting point for implementing your own sampling function
-// Note: When using multiple sequences, it is the caller's responsibility to delete
+// Note: When using multiple sequences, it is the caller's responsibility to call
-//       the item in params.sampler_state when a sequence ends and samplers that rely on
+//       llama_sampling_context_reset when a sequence ends
-//       state are being used.
 //
 // required:
 //  - ctx:          context to use for sampling
-//  - params: sampling parameters
+//  - sampling_ctx: sampling-specific context
 //
 // optional:
 //  - ctx_guidance:  context to use for classifier-free guidance, ignore if NULL
-//  - grammar:       grammar to use for sampling, ignore if NULL
 //  - last_tokens:   needed for repetition penalty, ignore if empty
 //  - idx:           sample from llama_get_logits_ith(ctx, idx)
 //  - seq:           sequence id to associate sampler state with
@@ -100,10 +98,10 @@ bool llama_sampling_state_reset(
 //  - token:      sampled token
 //  - candidates: vector of candidate tokens
 //
-llama_token llama_sample_token(
+llama_token llama_sampling_sample(
                   struct llama_context * ctx,
                   struct llama_context * ctx_guidance,
-                  struct llama_sampling_state & state,
+                  struct llama_sampling_context & sampling_ctx,
         const std::vector<llama_token> & last_tokens,
          std::vector<llama_token_data> & candidates,
         const                      int   idx = 0,

examples/infill/infill.cpp

@@ -423,7 +423,7 @@ int main(int argc, char ** argv) {
 
     const int n_vocab = llama_n_vocab(model);
 
-    llama_sampling_state sampling_state = llama_sampling_state_init(params, grammar);
+    llama_sampling_context sampling_context = llama_sampling_context_init(params, grammar);
     std::vector<llama_token_data> candidates;
     candidates.reserve(n_vocab);
 
@@ -542,7 +542,7 @@ int main(int argc, char ** argv) {
 
         if ((int) embd_inp.size() <= n_consumed && !is_interacting) {
 
-            const llama_token id = llama_sample_token(ctx, ctx_guidance, sampling_state, last_tokens, candidates);
+            const llama_token id = llama_sampling_sample(ctx, ctx_guidance, sampling_context, last_tokens, candidates);
 
             last_tokens.erase(last_tokens.begin());
             last_tokens.push_back(id);

examples/main/main.cpp

@@ -470,7 +470,7 @@ int main(int argc, char ** argv) {
 
     const int n_vocab = llama_n_vocab(model);
 
-    llama_sampling_state sampling_state = llama_sampling_state_init(params, grammar);
+    llama_sampling_context sampling_context = llama_sampling_context_init(params, grammar);
     std::vector<llama_token_data> candidates;
     candidates.reserve(n_vocab);
 
@@ -627,7 +627,7 @@ int main(int argc, char ** argv) {
                 LOG("saved session to %s\n", path_session.c_str());
             }
 
-            const llama_token id = llama_sample_token(ctx, ctx_guidance, sampling_state, last_tokens, candidates);
+            const llama_token id = llama_sampling_sample(ctx, ctx_guidance, sampling_context, last_tokens, candidates);
 
             last_tokens.erase(last_tokens.begin());
             last_tokens.push_back(id);

examples/parallel/parallel.cpp

@@ -125,7 +125,7 @@ int main(int argc, char ** argv) {
     params.logits_all = true;
     std::tie(model, ctx) = llama_init_from_gpt_params(params);
 
-    llama_sampling_state sampling_state = llama_sampling_state_init(params, NULL);
+    llama_sampling_context sampling_context = llama_sampling_context_init(params, NULL);
 
     // load the prompts from an external file if there are any
     if (params.prompt.empty()) {
@@ -341,7 +341,7 @@ int main(int argc, char ** argv) {
                 //printf("client %d, seq %d, token %d, pos %d, batch %d\n",
                 //        client.id, client.seq_id, client.sampled, client.n_decoded, client.i_batch);
 
-                const llama_token id = llama_sample_token(ctx, NULL, sampling_state, client.tokens_prev, candidates, client.i_batch - i, client.seq_id);
+                const llama_token id = llama_sampling_sample(ctx, NULL, sampling_context, client.tokens_prev, candidates, client.i_batch - i, client.seq_id);
 
                 if (client.n_decoded == 1) {
                     // start measuring generation time after the first token to make sure all concurrent clients
@@ -386,7 +386,7 @@ int main(int argc, char ** argv) {
 
                     n_total_prompt += client.n_prompt;
                     n_total_gen    += client.n_decoded;
-                    llama_sampling_state_reset(sampling_state, client.seq_id);
+                    llama_sampling_context_reset(sampling_context, client.seq_id);
                     client.seq_id = -1;
                 }
 

examples/server/server.cpp

@@ -200,7 +200,7 @@ struct llama_server_context
     llama_model *model = nullptr;
     llama_context *ctx = nullptr;
     gpt_params params;
-    llama_sampling_state sampling_state;
+    llama_sampling_context sampling_context;
     int n_ctx;
 
     grammar_parser::parse_state parsed_grammar;
@@ -255,7 +255,7 @@ struct llama_server_context
         if (grammar != nullptr) {
             llama_grammar_free(grammar);
             grammar = nullptr;
-            sampling_state = llama_sampling_state_init(params, NULL);
+            sampling_context = llama_sampling_context_init(params, NULL);
         }
     }
 
@@ -341,7 +341,7 @@ struct llama_server_context
             grammar = llama_grammar_init(
                 grammar_rules.data(), grammar_rules.size(), parsed_grammar.symbol_ids.at("root"));
         }
-        sampling_state = llama_sampling_state_init(params, grammar);
+        sampling_context = llama_sampling_context_init(params, grammar);
         return true;
     }
 
@@ -542,7 +542,7 @@ struct llama_server_context
             std::vector<llama_token_data> candidates;
             candidates.reserve(llama_n_vocab(model));
 
-            result.tok = llama_sample_token(ctx, NULL, sampling_state, last_n_tokens, candidates);
+            result.tok = llama_sampling_sample(ctx, NULL, sampling_context, last_n_tokens, candidates);
 
             llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false };
 
@@ -1210,7 +1210,7 @@ static void parse_options_completion(const json &body, llama_server_context &lla
         }
     }
 
-    llama.sampling_state = llama_sampling_state_init(llama.params, llama.grammar);
+    llama.sampling_context = llama_sampling_context_init(llama.params, llama.grammar);
 
     LOG_VERBOSE("completion parameters parsed", format_generation_settings(llama));
 }

examples/speculative/speculative.cpp

@@ -125,7 +125,7 @@ int main(int argc, char ** argv) {
         grammar_tgt = llama_grammar_init(grammar_rules.data(), grammar_rules.size(), parsed_grammar.symbol_ids.at("root"));
     }
 
-    llama_sampling_state sampling_state = llama_sampling_state_init(params, grammar_tgt);
+    llama_sampling_context sampling_context = llama_sampling_context_init(params, grammar_tgt);
 
     const auto t_dec_start = ggml_time_us();
 
@@ -136,7 +136,7 @@ int main(int argc, char ** argv) {
 
         while (true) {
             // sample from the target model
-            llama_token id = llama_sample_token(ctx_tgt, NULL, sampling_state, last_tokens, candidates, i_dft);
+            llama_token id = llama_sampling_sample(ctx_tgt, NULL, sampling_context, last_tokens, candidates, i_dft);
 
             // remember which tokens were sampled - used for repetition penalties during sampling
             last_tokens.erase(last_tokens.begin());
@@ -215,9 +215,9 @@ int main(int argc, char ** argv) {
             }
             // Note: Hardcoded to sequence id 0, if this ever supports parallel generation
             //       that will need to change.
-            auto it = sampling_state.sequence_states.find(0);
+            auto it = sampling_context.sequence_contexts.find(0);
-            GGML_ASSERT(it != sampling_state.sequence_states.end());
+            GGML_ASSERT(it != sampling_context.sequence_contexts.end());
-            // This is necessary because each sequence id in sequence_states
+            // This is necessary because each sequence id in sequence_contexts
             // uses a copy of the original grammar.
             grammar_dft = llama_grammar_copy(it->second.grammar);