Merge branch 'ggml-org:master' into master

Author: Thireus

CONTRIBUTING.md

@@ -25,7 +25,7 @@ The project differentiates between 3 levels of contributors:
 - Squash-merge PRs
 - Use the following format for the squashed commit title: `<module> : <commit title> (#<issue_number>)`. For example: `utils : fix typo in utils.py (#1234)`
 - Optionally pick a `<module>` from here: https://github.com/ggml-org/llama.cpp/wiki/Modules
-- Let other maintainers, merge their own PRs
+- Let other maintainers merge their own PRs
 - When merging a PR, make sure you have a good understanding of the changes
 - Be mindful of maintenance: most of the work going into a feature happens after the PR is merged. If the PR author is not committed to contribute long-term, someone else needs to take responsibility (you)
 

common/common.cpp

@@ -961,15 +961,13 @@ struct common_init_result common_init_from_params(common_params & params) {
 
         bool has_eos = llama_vocab_eos(vocab) != LLAMA_TOKEN_NULL;
         bool has_sep = llama_vocab_sep(vocab) != LLAMA_TOKEN_NULL;
+        bool has_rerank_prompt = llama_model_chat_template(model, "rerank") != NULL;
 
-        if (!has_eos && !has_sep) {
-            LOG_WRN("%s: warning: vocab does not have an EOS token or SEP token, reranking will not work\n", __func__);
+        if (!has_eos && !has_sep && !has_rerank_prompt) {
+            LOG_WRN("%s: warning: vocab does not have an EOS token, SEP token, or rerank prompt. Reranking will not work\n", __func__);
             ok = false;
         } else if (!has_eos) {
             LOG_WRN("%s: warning: vocab does not have an EOS token, using SEP token as fallback\n", __func__);
-        } else if (!has_sep) {
-            LOG_WRN("%s: warning: vocab does not have a SEP token, reranking will not work\n", __func__);
-            ok = false;
         }
 
         if (!ok) {

convert_hf_to_gguf.py

@@ -3717,11 +3717,29 @@ def prepare_tensors(self):
 class Qwen3Model(Qwen2Model):
     model_arch = gguf.MODEL_ARCH.QWEN3
 
+    # extra logic for rerank models
+    is_rerank: bool = False
+    is_tied_embeddings: bool = False
+    token_false_id: int | None = None
+    token_true_id: int | None = None
+
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
+
+        # track for intern-s1-mini
         hparams = ModelBase.load_hparams(self.dir_model, is_mistral_format=False)
         self.origin_hf_arch = hparams.get('architectures', [None])[0]
 
+        # a bit hacky, but currently the only way to detect if this is a rerank model
+        # ref: https://huggingface.co/Qwen/Qwen3-Reranker-0.6B
+        readme_path = self.dir_model / "README.md"
+        readme_text = ""
+        if readme_path.exists():
+            with readme_path.open("r", encoding="utf-8") as f:
+                readme_text = f.read()
+        if "# Qwen3-Reranker" in readme_text:
+            self._find_rerank_config()
+
     def set_vocab(self):
         # deal with intern-s1-mini
         if self.origin_hf_arch == 'InternS1ForConditionalGeneration':
@@ -3730,6 +3748,53 @@ def set_vocab(self):
 
         super().set_vocab()
 
+    def _find_rerank_config(self):
+        from transformers import AutoTokenizer
+        tokenizer = AutoTokenizer.from_pretrained(self.dir_model)
+
+        self.is_rerank = True
+        self.is_tied_embeddings = self.hparams.get("tie_word_embeddings", False)
+        self.token_false_id = tokenizer.convert_tokens_to_ids("no")
+        self.token_true_id = tokenizer.convert_tokens_to_ids("yes")
+        self.sep_token_id = tokenizer.convert_tokens_to_ids("|")
+
+        assert self.token_false_id is not None and self.token_true_id is not None
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        if self.is_rerank:
+            self.gguf_writer.add_pooling_type(gguf.PoolingType.RANK)
+            self.gguf_writer.add_classifier_output_labels(["yes", "no"])
+            self.gguf_writer.add_chat_template([{
+                "name": "rerank",
+                "template": "<|im_start|>system\nJudge whether the Document meets the requirements based on the Query and the Instruct provided. Note that the answer can only be \"yes\" or \"no\".<|im_end|>\n"
+                            "<|im_start|>user\n<Instruct>: Given a web search query, retrieve relevant passages that answer the query\n<Query>: {query}\n<Document>: {document}<|im_end|>\n"
+                            "<|im_start|>assistant\n<think>\n\n</think>\n\n"
+            }])
+
+    def _get_cls_out_tensor(self, data_torch: Tensor) -> Tensor:
+        # extract "yes" and "no" tokens from the output lm_head tensor
+        false_row = data_torch[self.token_false_id]
+        true_row = data_torch[self.token_true_id]
+        return torch.stack([true_row, false_row], dim=0)
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        if self.is_rerank:
+            is_tied_head = self.is_tied_embeddings and "embed_tokens" in name
+            is_real_head = not self.is_tied_embeddings and "lm_head" in name
+            if is_tied_head or is_real_head:
+                cls_out_head = (
+                    gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.CLS_OUT] + ".weight",
+                    self._get_cls_out_tensor(data_torch),
+                )
+                if is_tied_head:
+                    embed = (self.map_tensor_name(name), data_torch)
+                    return [cls_out_head, embed]
+                if is_real_head:
+                    return [cls_out_head]
+
+        return super().modify_tensors(data_torch, name, bid)
+
 
 @ModelBase.register("Qwen3MoeForCausalLM")
 class Qwen3MoeModel(Qwen2MoeModel):

examples/embedding/embedding.cpp

@@ -95,8 +95,13 @@ int main(int argc, char ** argv) {
         params.n_batch = params.n_ctx;
     }
 
-    // For non-causal models, batch size must be equal to ubatch size
-    params.n_ubatch = params.n_batch;
+    // for non-causal models, batch size must be equal to ubatch size
+    if (params.attention_type != LLAMA_ATTENTION_TYPE_CAUSAL) {
+        params.n_ubatch = params.n_batch;
+    }
+
+    // get max number of sequences per batch
+    const int n_seq_max = llama_max_parallel_sequences();
 
     llama_backend_init();
     llama_numa_init(params.numa);
@@ -144,6 +149,7 @@ int main(int argc, char ** argv) {
     // get added sep and eos token, if any
     const std::string added_sep_token = llama_vocab_get_add_sep(vocab) ? llama_vocab_get_text(vocab, llama_vocab_sep(vocab)) : "";
     const std::string added_eos_token = llama_vocab_get_add_eos(vocab) ? llama_vocab_get_text(vocab, llama_vocab_eos(vocab)) : "";
+    const char * rerank_prompt = llama_model_chat_template(model, "rerank");
 
     // tokenize the prompts and trim
     std::vector<std::vector<int32_t>> inputs;
@@ -153,21 +159,28 @@ int main(int argc, char ** argv) {
         // split classification pairs and insert expected separator tokens
         if (pooling_type == LLAMA_POOLING_TYPE_RANK && prompt.find(params.cls_sep) != std::string::npos) {
             std::vector<std::string> pairs = split_lines(prompt, params.cls_sep);
-            std::string final_prompt;
-
-            for (size_t i = 0; i < pairs.size(); i++) {
-                final_prompt += pairs[i];
-                if (i != pairs.size() - 1) {
-                    if (!added_eos_token.empty()) {
-                        final_prompt += added_eos_token;
-                    }
-                    if (!added_sep_token.empty()) {
-                        final_prompt += added_sep_token;
+            if (rerank_prompt != nullptr) {
+                const std::string query = pairs[0];
+                const std::string doc = pairs[1];
+                std::string final_prompt = rerank_prompt;
+                string_replace_all(final_prompt, "{query}"   , query);
+                string_replace_all(final_prompt, "{document}", doc  );
+                inp = common_tokenize(vocab, final_prompt, true, true);
+            } else {
+                std::string final_prompt;
+                for (size_t i = 0; i < pairs.size(); i++) {
+                    final_prompt += pairs[i];
+                    if (i != pairs.size() - 1) {
+                        if (!added_eos_token.empty()) {
+                            final_prompt += added_eos_token;
+                        }
+                        if (!added_sep_token.empty()) {
+                            final_prompt += added_sep_token;
+                        }
                     }
                 }
+                inp = common_tokenize(ctx, final_prompt, true, true);
             }
-
-            inp = common_tokenize(ctx, final_prompt, true, true);
         } else {
             inp = common_tokenize(ctx, prompt, true, true);
         }
@@ -229,7 +242,7 @@ int main(int argc, char ** argv) {
         const uint64_t n_toks = inp.size();
 
         // encode if at capacity
-        if (batch.n_tokens + n_toks > n_batch) {
+        if (batch.n_tokens + n_toks > n_batch || s >= n_seq_max) {
             float * out = emb + e * n_embd;
             batch_decode(ctx, batch, out, s, n_embd, params.embd_normalize);
             e += pooling_type == LLAMA_POOLING_TYPE_NONE ? batch.n_tokens : s;

examples/model-conversion/Makefile

@@ -118,13 +118,17 @@ embedding-convert-model:
 
 embedding-run-original-model:
 	$(call validate_embedding_model_path,embedding-run-original-model)
-	@EMBEDDING_MODEL_PATH="$(EMBEDDING_MODEL_PATH)" ./scripts/embedding/run-original-model.py
+	@EMBEDDING_MODEL_PATH="$(EMBEDDING_MODEL_PATH)" \
+	./scripts/embedding/run-original-model.py \
+	$(if $(PROMPTS_FILE),--prompts-file "$(PROMPTS_FILE)")
 
 embedding-run-converted-model:
-	@CONVERTED_EMBEDDING_MODEL="$(CONVERTED_EMBEDDING_MODEL)" ./scripts/embedding/run-converted-model.sh ${CONVERTED_EMBEDDING_MODEL}
+	@./scripts/embedding/run-converted-model.sh $(CONVERTED_EMBEDDING_MODEL) \
+	$(if $(PROMPTS_FILE),--prompts-file "$(PROMPTS_FILE)")
 
 embedding-verify-logits: embedding-run-original-model embedding-run-converted-model
-	@./scripts/embedding/compare-embeddings-logits.sh
+	@./scripts/embedding/compare-embeddings-logits.sh \
+	$(if $(PROMPTS_FILE),--prompts-file "$(PROMPTS_FILE)")
 
 embedding-inspect-original-model:
 	$(call validate_embedding_model_path,embedding-inspect-original-model)
@@ -156,7 +160,8 @@ embedding-quantize-model:
 	$(call quantize_model,$(CONVERTED_EMBEDDING_MODEL),QUANTIZED_EMBEDDING_MODEL)
 
 embedding-run-quantized-model:
-	@./scripts/embedding/run-converted-model.sh ${QUANTIZED_EMBEDDING_MODEL}
+	@./scripts/embedding/run-converted-model.sh $(QUANTIZED_EMBEDDING_MODEL) \
+	$(if $(PROMPTS_FILE),--prompts-file "$(PROMPTS_FILE)")
 
 ###
 ### Perplexity targets/recipes

examples/model-conversion/logits.cpp

@@ -151,6 +151,35 @@ int main(int argc, char ** argv) {
         logits = llama_get_embeddings(ctx);
         n_logits = llama_model_n_embd(model) * batch.n_tokens;
         type = "-embeddings";
+
+        const int n_embd = llama_model_n_embd(model);
+        const int n_embd_count = batch.n_tokens;
+
+        printf("Embedding dimension: %d\n", n_embd);
+        printf("\n");
+
+        // Print embeddings in the specified format
+        for (int j = 0; j < n_embd_count; j++) {
+            printf("embedding %d: ", j);
+
+            // Print first 3 values
+            for (int i = 0; i < 3 && i < n_embd; i++) {
+                printf("%9.6f ", logits[j * n_embd + i]);
+            }
+
+            printf(" ... ");
+
+            // Print last 3 values
+            for (int i = n_embd - 3; i < n_embd; i++) {
+                if (i >= 0) {
+                    printf("%9.6f ", logits[j * n_embd + i]);
+                }
+            }
+
+            printf("\n");
+        }
+        printf("\n");
+
         printf("Embeddings size: %d\n", n_logits);
     } else {
         logits = llama_get_logits_ith(ctx, batch.n_tokens - 1);
@@ -183,22 +212,23 @@ int main(int argc, char ** argv) {
         return 1;
     }
     for (int i = 0; i < n_logits; i++) {
-        fprintf(f, "%d: %.6f\n", i, logits[i]);  // Added index and changed format
+        fprintf(f, "%d: %.6f\n", i, logits[i]);
     }
     fclose(f);
 
-    // Print first and last 10 logits for quick verification
-    printf("First 10 logits: ");
-    for (int i = 0; i < 10 && i < n_logits; i++) {
-        printf("%.6f ", logits[i]);
-    }
-    printf("\n");
+    if (!embedding_mode) {
+        printf("First 10 logits: ");
+        for (int i = 0; i < 10 && i < n_logits; i++) {
+            printf("%.6f ", logits[i]);
+        }
+        printf("\n");
 
-    printf("Last 10 logits: ");
-    for (int i = n_logits - 10; i < n_logits; i++) {
-        if (i >= 0) printf("%.6f ", logits[i]);
+        printf("Last 10 logits: ");
+        for (int i = n_logits - 10; i < n_logits; i++) {
+            if (i >= 0) printf("%.6f ", logits[i]);
+        }
+        printf("\n\n");
     }
-    printf("\n\n");
 
     printf("Logits saved to %s\n", bin_filename);
     printf("Logits saved to %s\n", txt_filename);

examples/model-conversion/scripts/embedding/compare-embeddings-logits.sh

@@ -2,8 +2,37 @@
 
 set -e
 
-MODEL_PATH="${1:-"$EMBEDDING_MODEL_PATH"}"
-MODEL_NAME="${2:-$(basename "$MODEL_PATH")}"
+# Parse command line arguments
+MODEL_PATH=""
+MODEL_NAME=""
+PROMPTS_FILE=""
+
+# First argument is always model path
+if [ $# -gt 0 ] && [[ "$1" != --* ]]; then
+    MODEL_PATH="$1"
+    shift
+fi
+
+# Parse remaining arguments
+while [[ $# -gt 0 ]]; do
+    case $1 in
+        --prompts-file|-pf)
+            PROMPTS_FILE="$2"
+            shift 2
+            ;;
+        *)
+            # If MODEL_NAME not set and this isn't a flag, use as model name
+            if [ -z "$MODEL_NAME" ] && [[ "$1" != --* ]]; then
+                MODEL_NAME="$1"
+            fi
+            shift
+            ;;
+    esac
+done
+
+# Set defaults
+MODEL_PATH="${MODEL_PATH:-"$EMBEDDING_MODEL_PATH"}"
+MODEL_NAME="${MODEL_NAME:-$(basename "$MODEL_PATH")}"
 
 if [ -t 0 ]; then
     CPP_EMBEDDINGS="data/llamacpp-${MODEL_NAME}-embeddings.bin"
@@ -35,8 +64,18 @@ with open('$TEMP_FILE', 'wb') as f:
     trap "rm -f $TEMP_FILE" EXIT
 fi
 
-python scripts/utils/semantic_check.py --model-path $MODEL_PATH \
+# Build the semantic_check.py command
+SEMANTIC_CMD="python scripts/utils/semantic_check.py --model-path $MODEL_PATH \
     --python-embeddings data/pytorch-${MODEL_NAME}-embeddings.bin \
-    --cpp-embeddings $CPP_EMBEDDINGS \
-    --prompt "Hello world today"
+    --cpp-embeddings $CPP_EMBEDDINGS"
+
+# Add prompts file if specified, otherwise use default prompt
+if [ -n "$PROMPTS_FILE" ]; then
+    SEMANTIC_CMD="$SEMANTIC_CMD --prompts-file \"$PROMPTS_FILE\""
+else
+    SEMANTIC_CMD="$SEMANTIC_CMD --prompt \"Hello world today\""
+fi
+
+# Execute the command
+eval $SEMANTIC_CMD