llama : apply classifier-free guidance to logits directly

common/sampling.cpp

@@ -190,6 +190,11 @@ static llama_token llama_sampling_sample_impl(
         logits[it->first] += it->second;
     }
 
+    if (ctx_cfg) {
+        float * logits_guidance = llama_get_logits_ith(ctx_cfg, idx);
+        llama_sample_apply_guidance(ctx_main, logits, logits_guidance, params.cfg_scale);
+    }
+
     cur.clear();
 
     for (llama_token token_id = 0; token_id < n_vocab; token_id++) {
@@ -198,10 +203,6 @@ static llama_token llama_sampling_sample_impl(
 
     llama_token_data_array cur_p = { cur.data(), cur.size(), false };
 
-    if (ctx_cfg) {
-        llama_sample_classifier_free_guidance(ctx_main, &cur_p, ctx_cfg, params.cfg_scale);
-    }
-
     // apply penalties
     const auto& penalty_tokens = params.use_penalty_prompt_tokens ? params.penalty_prompt_tokens : prev;
     const int penalty_tokens_used_size = std::min((int)penalty_tokens.size(), penalty_last_n);

llama.cpp

@@ -7898,39 +7898,59 @@ static void llama_log_softmax(float * array, size_t size) {
     }
 }
 
+void llama_sample_apply_guidance(
+          struct llama_context * ctx,
+                         float * logits,
+                         float * logits_guidance,
+                         float   scale) {
+    GGML_ASSERT(ctx);
+
+    const auto t_start_sample_us = ggml_time_us();
+    const auto n_vocab = llama_n_vocab(llama_get_model(ctx));
+
+    llama_log_softmax(logits, n_vocab);
+    llama_log_softmax(logits_guidance, n_vocab);
+
+    for (int i = 0; i < n_vocab; ++i) {
+              auto & l = logits[i];
+        const auto & g = logits_guidance[i];
+
+        l = scale * (l - g) + g;
+    }
+
+    ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
+}
+
 void llama_sample_classifier_free_guidance(
           struct llama_context * ctx,
         llama_token_data_array * candidates,
           struct llama_context * guidance_ctx,
                          float   scale) {
-    int64_t t_start_sample_us = ggml_time_us();
-
     GGML_ASSERT(ctx);
+    int64_t t_start_sample_us;
 
-    auto n_vocab = llama_n_vocab(llama_get_model(ctx));
+    t_start_sample_us = ggml_time_us();
+    const size_t n_vocab = llama_n_vocab(llama_get_model(ctx));
 
-    GGML_ASSERT(n_vocab == (int)candidates->size);
+    GGML_ASSERT(n_vocab == candidates->size);
     GGML_ASSERT(!candidates->sorted);
 
-    std::vector<float> logits_base;
-    logits_base.reserve(candidates->size);
-    for (size_t i = 0; i < candidates->size; ++i) {
-        logits_base.push_back(candidates->data[i].logit);
+    std::vector<float> logits_base(n_vocab);
+    for (size_t i = 0; i < n_vocab; ++i) {
+        logits_base[i] = candidates->data[i].logit;
     }
-    llama_log_softmax(logits_base.data(), candidates->size);
 
-    float* logits_guidance = llama_get_logits(guidance_ctx);
-    llama_log_softmax(logits_guidance, n_vocab);
+    float * logits_guidance = llama_get_logits(guidance_ctx);
 
-    for (int i = 0; i < n_vocab; ++i) {
-        float logit_guidance = logits_guidance[i];
-        float logit_base = logits_base[i];
-        candidates->data[i].logit = scale * (logit_base - logit_guidance) + logit_guidance;
-    }
+    ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
+    llama_sample_apply_guidance(ctx, logits_base.data(), logits_guidance, scale);
+    t_start_sample_us = ggml_time_us();
 
-    if (ctx) {
-        ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
+    for (size_t i = 0; i < n_vocab; ++i) {
+        candidates->data[i].logit = logits_base[i];
     }
+
+    ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
 }
 
 llama_token llama_sample_token_mirostat(struct llama_context * ctx, llama_token_data_array * candidates, float tau, float eta, int32_t m, float * mu) {

llama.h

@@ -714,14 +714,21 @@ extern "C" {
                            float   penalty_present);
 
     /// @details Apply classifier-free guidance to the logits as described in academic paper "Stay on topic with Classifier-Free Guidance" https://arxiv.org/abs/2306.17806
-    /// @param candidates A vector of `llama_token_data` containing the candidate tokens, the logits must be directly extracted from the original generation context without being sorted.
-    /// @params guidance_ctx A separate context from the same model. Other than a negative prompt at the beginning, it should have all generated and user input tokens copied from the main context.
-    /// @params scale Guidance strength. 1.0f means no guidance. Higher values mean stronger guidance.
-    LLAMA_API void llama_sample_classifier_free_guidance(
+    /// @param logits Logits extracted from the original generation context.
+    /// @param logits_guidance Logits extracted from a separate context from the same model. Other than a negative prompt at the beginning, it should have all generated and user input tokens copied from the main context.
+    /// @param scale Guidance strength. 1.0f means no guidance. Higher values mean stronger guidance.
+    LLAMA_API void llama_sample_apply_guidance(
+              struct llama_context * ctx,
+                             float * logits,
+                             float * logits_guidance,
+                             float   scale);
+
+    LLAMA_API DEPRECATED(void llama_sample_classifier_free_guidance(
               struct llama_context * ctx,
             llama_token_data_array * candidates,
               struct llama_context * guidance_ctx,
-                             float   scale);
+                             float   scale),
+              "use llama_sample_apply_guidance() instead");
 
     /// @details Sorts candidate tokens by their logits in descending order and calculate probabilities based on logits.
     LLAMA_API void llama_sample_softmax(