cont [no ci]

ggml-ci

.github/workflows/build.yml

@@ -317,7 +317,7 @@ jobs:
           wget -qO - https://packages.lunarg.com/lunarg-signing-key-pub.asc | sudo apt-key add -
           sudo wget -qO /etc/apt/sources.list.d/lunarg-vulkan-jammy.list https://packages.lunarg.com/vulkan/lunarg-vulkan-jammy.list
           sudo apt-get update -y
-          sudo apt-get install -y build-essential mesa-vulkan-drivers vulkan-sdk
+          sudo apt-get install -y build-essential vulkan-sdk
 
       - name: Build
         id: cmake_build
@@ -327,12 +327,6 @@ jobs:
           cmake -DGGML_VULKAN=ON ..
           cmake --build . --config Release -j $(nproc)
 
-      - name: Test
-        id: cmake_test
-        run: |
-          cd build
-          ctest -L main --verbose --timeout 900
-
   ubuntu-22-cmake-hip:
     runs-on: ubuntu-22.04
     container: rocm/dev-ubuntu-22.04:6.0.2

README.md

@@ -98,7 +98,6 @@ Instructions for adding support for new models: [HOWTO-add-model.md](docs/develo
 - [x] [Jais](https://huggingface.co/inceptionai/jais-13b-chat)
 - [x] [Bielik-11B-v2.3](https://huggingface.co/collections/speakleash/bielik-11b-v23-66ee813238d9b526a072408a)
 - [x] [RWKV-6](https://github.com/BlinkDL/RWKV-LM)
-- [x] [GigaChat-20B-A3B](https://huggingface.co/ai-sage/GigaChat-20B-A3B-instruct)
 
 #### Multimodal
 
@@ -414,7 +413,7 @@ To learn more about model quantization, [read this documentation](examples/quant
 [^1]: [examples/perplexity/README.md](examples/perplexity/README.md)
 [^2]: [https://huggingface.co/docs/transformers/perplexity](https://huggingface.co/docs/transformers/perplexity)
 
-## [`llama-bench`](examples/llama-bench)
+## [`llama-bench`](example/bench)
 
 #### Benchmark the performance of the inference for various parameters.
 

common/arg.cpp

@@ -855,6 +855,13 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             params.sampling.ignore_eos = true;
         }
     ).set_sparam());
+    add_opt(common_arg(
+        {"--penalize-nl"},
+        string_format("penalize newline tokens (default: %s)", params.sampling.penalize_nl ? "true" : "false"),
+        [](common_params & params) {
+            params.sampling.penalize_nl = true;
+        }
+    ).set_sparam());
     add_opt(common_arg(
         {"--temp"}, "N",
         string_format("temperature (default: %.1f)", (double)params.sampling.temp),
@@ -909,9 +916,6 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         {"--repeat-last-n"}, "N",
         string_format("last n tokens to consider for penalize (default: %d, 0 = disabled, -1 = ctx_size)", params.sampling.penalty_last_n),
         [](common_params & params, int value) {
-            if (value < -1) {
-                throw std::runtime_error(string_format("error: invalid repeat-last-n = %d\n", value));
-            }
             params.sampling.penalty_last_n = value;
             params.sampling.n_prev = std::max(params.sampling.n_prev, params.sampling.penalty_last_n);
         }
@@ -966,9 +970,6 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         {"--dry-penalty-last-n"}, "N",
         string_format("set DRY penalty for the last n tokens (default: %d, 0 = disable, -1 = context size)", params.sampling.dry_penalty_last_n),
         [](common_params & params, int value) {
-            if (value < -1) {
-                throw std::runtime_error(string_format("error: invalid dry-penalty-last-n = %d\n", value));
-            }
             params.sampling.dry_penalty_last_n = value;
         }
     ).set_sparam());

common/common.cpp

@@ -940,25 +940,6 @@ struct common_init_result common_init_from_params(common_params & params) {
         params.sampling.ignore_eos = false;
     }
 
-    if (params.sampling.ignore_eos) {
-        for (llama_token i = 0; i < llama_n_vocab(model); i++) {
-            if (llama_token_is_eog(model, i)) {
-                LOG_INF("%s: added %s logit bias = %f\n", __func__, common_token_to_piece(lctx, i).c_str(), -INFINITY);
-                params.sampling.logit_bias.push_back({i, -INFINITY});
-            }
-        }
-    }
-
-    if (params.sampling.penalty_last_n == -1) {
-        LOG_INF("%s: setting penalty_last_n to ctx_size = %d\n", __func__, llama_n_ctx(lctx));
-        params.sampling.penalty_last_n = llama_n_ctx(lctx);
-    }
-
-    if (params.sampling.dry_penalty_last_n == -1) {
-        LOG_INF("%s: setting dry_penalty_last_n to ctx_size = %d\n", __func__, llama_n_ctx(lctx));
-        params.sampling.dry_penalty_last_n = llama_n_ctx(lctx);
-    }
-
     if (params.warmup) {
         LOG_WRN("%s: warming up the model with an empty run - please wait ... (--no-warmup to disable)\n", __func__);
 

common/common.h

@@ -95,7 +95,6 @@ enum common_sampler_type {
     COMMON_SAMPLER_TYPE_TEMPERATURE = 7,
     COMMON_SAMPLER_TYPE_XTC         = 8,
     COMMON_SAMPLER_TYPE_INFILL      = 9,
-    COMMON_SAMPLER_TYPE_PENALTIES   = 10,
 };
 
 // dimensionality reduction methods, used by cvector-generator
@@ -131,6 +130,7 @@ struct common_params_sampling {
     int32_t mirostat           = 0;     // 0 = disabled, 1 = mirostat, 2 = mirostat 2.0
     float   mirostat_tau       = 5.00f; // target entropy
     float   mirostat_eta       = 0.10f; // learning rate
+    bool    penalize_nl        = false; // consider newlines as a repeatable token
     bool    ignore_eos         = false;
     bool    no_perf            = false; // disable performance metrics
     bool    timing_per_token   = false;
@@ -139,7 +139,6 @@ struct common_params_sampling {
 
 
     std::vector<enum common_sampler_type> samplers = {
-        COMMON_SAMPLER_TYPE_PENALTIES,
         COMMON_SAMPLER_TYPE_DRY,
         COMMON_SAMPLER_TYPE_TOP_K,
         COMMON_SAMPLER_TYPE_TYPICAL_P,
@@ -194,13 +193,11 @@ struct common_params {
     float   defrag_thold          =  0.1f; // KV cache defragmentation threshold
 
     // offload params
-    std::vector<ggml_backend_dev_t> devices; // devices to use for offloading
-
-    int32_t n_gpu_layers      = -1;  // number of layers to store in VRAM (-1 - use default)
-    int32_t main_gpu          = 0;   // the GPU that is used for scratch and small tensors
-    float   tensor_split[128] = {0}; // how split tensors should be distributed across GPUs
-
-    enum llama_split_mode split_mode = LLAMA_SPLIT_MODE_LAYER; // how to split the model across GPUs
+    std::vector<ggml_backend_dev_t> devices;         // devices to use for offloading
+    int32_t n_gpu_layers                    =    -1; // number of layers to store in VRAM (-1 - use default)
+    int32_t main_gpu                        =     0; // the GPU that is used for scratch and small tensors
+    float   tensor_split[128]               =   {0}; // how split tensors should be distributed across GPUs
+    enum llama_split_mode        split_mode = LLAMA_SPLIT_MODE_LAYER; // how to split the model across GPUs
 
     struct cpu_params cpuparams;
     struct cpu_params cpuparams_batch;

common/sampling.cpp

@@ -161,20 +161,32 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, co
                 params.logit_bias.size(),
                 params.logit_bias.data()));
 
+    llama_sampler_chain_add(result->chain,
+            llama_sampler_init_penalties(
+                llama_n_vocab  (model),
+                llama_token_eos(model),
+                llama_token_nl (model),
+                params.penalty_last_n,
+                params.penalty_repeat,
+                params.penalty_freq,
+                params.penalty_present,
+                params.penalize_nl,
+                params.ignore_eos));
+
     if (params.mirostat == 0) {
         for (const auto & cnstr : params.samplers) {
             switch (cnstr) {
-                case COMMON_SAMPLER_TYPE_DRY:
+                    case COMMON_SAMPLER_TYPE_DRY:
                     {
-                        std::vector<const char *> c_breakers;
+                        std::vector<const char*> c_breakers;
                         c_breakers.reserve(params.dry_sequence_breakers.size());
-                        for (const auto & str : params.dry_sequence_breakers) {
+                        for (const auto& str : params.dry_sequence_breakers) {
                             c_breakers.push_back(str.c_str());
                         }
 
                         llama_sampler_chain_add(result->chain, llama_sampler_init_dry      (model, params.dry_multiplier, params.dry_base, params.dry_allowed_length, params.dry_penalty_last_n, c_breakers.data(), c_breakers.size()));
                     }
-                    break;
+                        break;
                 case COMMON_SAMPLER_TYPE_TOP_K:
                     llama_sampler_chain_add(result->chain, llama_sampler_init_top_k    (params.top_k));
                     break;
@@ -196,9 +208,6 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, co
                 case COMMON_SAMPLER_TYPE_INFILL:
                     llama_sampler_chain_add(result->chain, llama_sampler_init_infill   (model));
                     break;
-                case COMMON_SAMPLER_TYPE_PENALTIES:
-                    llama_sampler_chain_add(result->chain, llama_sampler_init_penalties(params.penalty_last_n, params.penalty_repeat, params.penalty_freq, params.penalty_present));
-                    break;
                 default:
                     GGML_ASSERT(false && "unknown sampler type");
             }
@@ -406,7 +415,6 @@ char common_sampler_type_to_chr(enum common_sampler_type cnstr) {
         case COMMON_SAMPLER_TYPE_TEMPERATURE: return 't';
         case COMMON_SAMPLER_TYPE_XTC:         return 'x';
         case COMMON_SAMPLER_TYPE_INFILL:      return 'i';
-        case COMMON_SAMPLER_TYPE_PENALTIES:   return 'e';
         default : return '?';
     }
 }
@@ -421,7 +429,6 @@ std::string common_sampler_type_to_str(enum common_sampler_type cnstr) {
         case COMMON_SAMPLER_TYPE_TEMPERATURE: return "temperature";
         case COMMON_SAMPLER_TYPE_XTC:         return "xtc";
         case COMMON_SAMPLER_TYPE_INFILL:      return "infill";
-        case COMMON_SAMPLER_TYPE_PENALTIES:   return "penalties";
         default : return "";
     }
 }
@@ -436,7 +443,6 @@ std::vector<common_sampler_type> common_sampler_types_from_names(const std::vect
         { "temperature", COMMON_SAMPLER_TYPE_TEMPERATURE },
         { "xtc",         COMMON_SAMPLER_TYPE_XTC },
         { "infill",      COMMON_SAMPLER_TYPE_INFILL },
-        { "penalties",   COMMON_SAMPLER_TYPE_PENALTIES },
     };
 
     // since samplers names are written multiple ways
@@ -483,7 +489,6 @@ std::vector<common_sampler_type> common_sampler_types_from_chars(const std::stri
         { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_TEMPERATURE), COMMON_SAMPLER_TYPE_TEMPERATURE },
         { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_XTC),         COMMON_SAMPLER_TYPE_XTC },
         { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_INFILL),      COMMON_SAMPLER_TYPE_INFILL },
-        { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_PENALTIES),   COMMON_SAMPLER_TYPE_PENALTIES },
     };
 
     std::vector<common_sampler_type> samplers;

convert_hf_to_gguf.py

@@ -525,9 +525,6 @@ class Model:
             else:
                 token: str = reverse_vocab[i]
                 if token in added_vocab:
-                    # We need to manually encode and decode the added tokens in case special characters
-                    # used for `\n` / `\t` have been manually added in the added tokens
-                    token = tokenizer.decode(tokenizer.encode(token))
                     if tokenizer.added_tokens_decoder[i].special or self.does_token_look_special(token):
                         toktypes.append(gguf.TokenType.CONTROL)
                     else:
@@ -574,9 +571,6 @@ class Model:
         if chkhsh == "8aeee3860c56296a157a1fe2fad249ec40aa59b1bb5709f4ade11c4e6fe652ed":
             # ref: https://huggingface.co/tiiuae/falcon-7b
             res = "falcon"
-        if chkhsh == "9d032fcbd5501f4a38150912590928bfb36091efb5df11b8e2124b0390e3fb1e":
-            # ref: https://huggingface.co/tiiuae/Falcon3-7B-Base
-            res = "falcon3"
         if chkhsh == "0876d13b50744004aa9aeae05e7b0647eac9d801b5ba4668afc01e709c15e19f":
             # ref: https://huggingface.co/BAAI/bge-small-en-v1.5
             res = "bert-bge"
@@ -670,9 +664,6 @@ class Model:
         if chkhsh == "8b5a93ed704057481f240da0be7e7dca721d7f8f4755263b6807227a2cbeae65":
             # ref: https://huggingface.co/sentence-transformers/stsb-roberta-base
             res = "roberta-bpe"
-        if chkhsh == "ad851be1dba641f2e3711822f816db2c265f788b37c63b4e1aeacb9ee92de8eb":
-            # ref: https://huggingface.co/ai-sage/GigaChat-20B-A3B-instruct
-            res = "gigachat"
 
         if res is None:
             logger.warning("\n")
@@ -3436,97 +3427,6 @@ class ArcticModel(Model):
                 raise ValueError(f"Unprocessed experts: {experts}")
 
 
-@Model.register("DeepseekForCausalLM")
-class DeepseekModel(Model):
-    model_arch = gguf.MODEL_ARCH.DEEPSEEK
-
-    def set_vocab(self):
-        try:
-            self._set_vocab_sentencepiece()
-        except FileNotFoundError:
-            self._set_vocab_gpt2()
-
-    def set_gguf_parameters(self):
-        super().set_gguf_parameters()
-        hparams = self.hparams
-        if "head_dim" in hparams:
-            rope_dim = hparams["head_dim"]
-        else:
-            rope_dim = hparams["hidden_size"] // hparams["num_attention_heads"]
-
-        self.gguf_writer.add_rope_dimension_count(rope_dim)
-        self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.NONE)
-        self.gguf_writer.add_leading_dense_block_count(hparams["first_k_dense_replace"])
-        self.gguf_writer.add_vocab_size(hparams["vocab_size"])
-        self.gguf_writer.add_expert_feed_forward_length(hparams["moe_intermediate_size"])
-        self.gguf_writer.add_expert_weights_scale(1.0)
-        self.gguf_writer.add_expert_count(hparams["n_routed_experts"])
-        self.gguf_writer.add_expert_shared_count(hparams["n_shared_experts"])
-
-    _experts: list[dict[str, Tensor]] | None = None
-
-    @staticmethod
-    def permute(weights: Tensor, n_head: int, n_head_kv: int | None):
-        if n_head_kv is not None and n_head != n_head_kv:
-            n_head = n_head_kv
-        return (weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:])
-                .swapaxes(1, 2)
-                .reshape(weights.shape))
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        n_head = self.hparams["num_attention_heads"]
-        n_kv_head = self.hparams.get("num_key_value_heads")
-
-        if name.endswith(("q_proj.weight", "q_proj.bias")):
-            data_torch = DeepseekModel.permute(data_torch, n_head, n_head)
-        if name.endswith(("k_proj.weight", "k_proj.bias")):
-            data_torch = DeepseekModel.permute(data_torch, n_head, n_kv_head)
-
-        # process the experts separately
-        if name.find("mlp.experts") != -1:
-            n_experts = self.hparams["n_routed_experts"]
-            assert bid is not None
-
-            if self._experts is None:
-                self._experts = [{} for _ in range(self.block_count)]
-
-            self._experts[bid][name] = data_torch
-
-            if len(self._experts[bid]) >= n_experts * 3:
-                tensors: list[tuple[str, Tensor]] = []
-
-                # merge the experts into a single 3d tensor
-                for w_name in ["down_proj", "gate_proj", "up_proj"]:
-                    datas: list[Tensor] = []
-
-                    for xid in range(n_experts):
-                        ename = f"model.layers.{bid}.mlp.experts.{xid}.{w_name}.weight"
-                        datas.append(self._experts[bid][ename])
-                        del self._experts[bid][ename]
-
-                    data_torch = torch.stack(datas, dim=0)
-
-                    merged_name = f"model.layers.{bid}.mlp.experts.{w_name}.weight"
-
-                    new_name = self.map_tensor_name(merged_name)
-
-                    tensors.append((new_name, data_torch))
-                return tensors
-            else:
-                return []
-
-        return [(self.map_tensor_name(name), data_torch)]
-
-    def prepare_tensors(self):
-        super().prepare_tensors()
-
-        if self._experts is not None:
-            # flatten `list[dict[str, Tensor]]` into `list[str]`
-            experts = [k for d in self._experts for k in d.keys()]
-            if len(experts) > 0:
-                raise ValueError(f"Unprocessed experts: {experts}")
-
-
 @Model.register("DeepseekV2ForCausalLM")
 class DeepseekV2Model(Model):
     model_arch = gguf.MODEL_ARCH.DEEPSEEK2

convert_hf_to_gguf_update.py

@@ -72,7 +72,6 @@ models = [
     {"name": "deepseek-coder", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/deepseek-ai/deepseek-coder-6.7b-base", },
     {"name": "falcon",         "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/tiiuae/falcon-7b", },
     {"name": "bert-bge",       "tokt": TOKENIZER_TYPE.WPM, "repo": "https://huggingface.co/BAAI/bge-small-en-v1.5", },
-    {"name": "falcon3",        "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/tiiuae/Falcon3-7B-Base", },
     {"name": "bert-bge-large", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/BAAI/bge-large-zh-v1.5", },
     {"name": "mpt",            "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/mosaicml/mpt-7b", },
     {"name": "starcoder",      "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/bigcode/starcoder2-3b", },
@@ -105,7 +104,6 @@ models = [
     {"name": "chameleon",      "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/facebook/chameleon-7b", },
     {"name": "minerva-7b",     "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/sapienzanlp/Minerva-7B-base-v1.0", },
     {"name": "roberta-bpe",    "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/sentence-transformers/stsb-roberta-base"},
-    {"name": "gigachat",       "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/ai-sage/GigaChat-20B-A3B-instruct"},
 ]
 
 

examples/batched/batched.cpp

@@ -65,7 +65,6 @@ int main(int argc, char ** argv) {
     llama_context * ctx = llama_new_context_with_model(model, ctx_params);
 
     auto sparams = llama_sampler_chain_default_params();
-    sparams.no_perf = false;
 
     llama_sampler * smpl = llama_sampler_chain_init(sparams);
 

examples/llava/qwen2_vl_surgery.py

@@ -88,8 +88,6 @@ def main(args):
     else:
         raise ValueError()
 
-    local_model = False
-    model_path = ""
     model_name = args.model_name
     print("model_name: ", model_name)
     qwen2vl = Qwen2VLForConditionalGeneration.from_pretrained(
@@ -99,10 +97,8 @@ def main(args):
     vcfg = cfg.vision_config
 
     if os.path.isdir(model_name):
-        local_model = True
         if model_name.endswith(os.sep):
             model_name = model_name[:-1]
-        model_path = model_name
         model_name = os.path.basename(model_name)
     fname_out = f"{model_name.replace('/', '-').lower()}-vision.gguf"
 
@@ -143,10 +139,7 @@ def main(args):
             it will be hardcoded in the `clip_image_build_graph` from `clip.cpp`.
     """
 
-    if local_model:
-        processor: Qwen2VLProcessor = AutoProcessor.from_pretrained(model_path)
-    else:
-        processor: Qwen2VLProcessor = AutoProcessor.from_pretrained(model_name)
+    processor: Qwen2VLProcessor = AutoProcessor.from_pretrained(model_name)
     fout.add_array("clip.vision.image_mean", processor.image_processor.image_mean) # type: ignore[reportAttributeAccessIssue]
     fout.add_array("clip.vision.image_std", processor.image_processor.image_std) # type: ignore[reportAttributeAccessIssue]
 

examples/main/README.md

@@ -177,11 +177,16 @@ Example usage: `--temp 0`
 
 -   `--repeat-penalty N`: Control the repetition of token sequences in the generated text default: 1.0, 1.0 = disabled).
 -   `--repeat-last-n N`: Last n tokens to consider for penalizing repetition (default: 64, 0 = disabled, -1 = ctx-size).
+-   `--no-penalize-nl`: Disable penalization for newline tokens when applying the repeat penalty.
 
 The `repeat-penalty` option helps prevent the model from generating repetitive or monotonous text. A higher value (e.g., 1.5) will penalize repetitions more strongly, while a lower value (e.g., 0.9) will be more lenient. The default value is 1.
 
 The `repeat-last-n` option controls the number of tokens in the history to consider for penalizing repetition. A larger value will look further back in the generated text to prevent repetitions, while a smaller value will only consider recent tokens. A value of 0 disables the penalty, and a value of -1 sets the number of tokens considered equal to the context size (`ctx-size`).
 
+Use the `--no-penalize-nl` option to disable newline penalization when applying the repeat penalty. This option is particularly useful for generating chat conversations, dialogues, code, poetry, or any text where newline tokens play a significant role in structure and formatting. Disabling newline penalization helps maintain the natural flow and intended formatting in these specific use cases.
+
+Example usage: `--repeat-penalty 1.15 --repeat-last-n 128 --no-penalize-nl`
+
 ### DRY Repetition Penalty
 
 DRY (Don't Repeat Yourself) sampling is an effective technique for reducing repetition in generated text even across long contexts by penalizing tokens based on their recent usage patterns (original [PR link](https://github.com/oobabooga/text-generation-webui/pull/5677)).

examples/server/CMakeLists.txt

@@ -15,7 +15,7 @@ set(TARGET_SRCS
     httplib.h
 )
 set(PUBLIC_ASSETS
-    index.html.gz
+    index.html
     loading.html
 )
 

examples/server/README.md

@@ -104,6 +104,7 @@ The project is under active development, and we are [looking for feedback and co
 | `-s, --seed SEED` | RNG seed (default: -1, use random seed for -1) |
 | `--sampling-seq SEQUENCE` | simplified sequence for samplers that will be used (default: dkypmxt) |
 | `--ignore-eos` | ignore end of stream token and continue generating (implies --logit-bias EOS-inf) |
+| `--penalize-nl` | penalize newline tokens (default: false) |
 | `--temp N` | temperature (default: 0.8) |
 | `--top-k N` | top-k sampling (default: 40, 0 = disabled) |
 | `--top-p N` | top-p sampling (default: 0.9, 1.0 = disabled) |
@@ -392,6 +393,8 @@ These words will not be included in the completion, so make sure to add them to
 
 `repeat_last_n`: Last n tokens to consider for penalizing repetition. Default: `64`, where `0` is disabled and `-1` is ctx-size.
 
+`penalize_nl`: Penalize newline tokens when applying the repeat penalty. Default: `true`
+
 `presence_penalty`: Repeat alpha presence penalty. Default: `0.0`, which is disabled.
 
 `frequency_penalty`: Repeat alpha frequency penalty. Default: `0.0`, which is disabled.
@@ -652,6 +655,7 @@ This endpoint is public (no API key check). By default, it is read-only. To make
       "mirostat": 0,
       "mirostat_tau": 5.0,
       "mirostat_eta": 0.10000000149011612,
+      "penalize_nl": false,
       "stop": [],
       "max_tokens": -1,
       "n_keep": 0,
@@ -841,6 +845,7 @@ Example:
       "mirostat": 0,
       "mirostat_tau": 5.0,
       "mirostat_eta": 0.10000000149011612,
+      "penalize_nl": false,
       "stop": [],
       "max_tokens": -1,
       "n_keep": 0,

examples/server/public_legacy/index-new.html

@@ -39,6 +39,7 @@
       temperature: 0.8, // adapt all following parameters to optimized min-p requierements. If for non-english, set to 0.6 or lower
       repeat_last_n: 0, // 0 = disable penalty, -1 = context size
       repeat_penalty: 1.0, // 1.0 = disabled
+      penalize_nl: false, // true only useful for infinite completion
       dry_multiplier: 0.0, // 0.0 = disabled, 0.8 works well
       dry_base: 1.75,     // 0.0 = disabled
       dry_allowed_length: 2, // tokens extending repetitions beyond this receive penalty, 2 works well

examples/server/public_legacy/index.html

@@ -303,6 +303,7 @@
       temperature: 0.7,
       repeat_last_n: 256, // 0 = disable penalty, -1 = context size
       repeat_penalty: 1.18, // 1.0 = disabled
+      penalize_nl: false,
       dry_multiplier: 0.0, // 0.0 = disabled, 0.8 works well
       dry_base: 1.75,     // 0.0 = disabled
       dry_allowed_length: 2, // tokens extending repetitions beyond this receive penalty, 2 works well
@@ -1005,6 +1006,7 @@
             ${FloatField({ label: "Temperature", max: 2.0, min: 0.0, name: "temperature", step: 0.01, value: params.value.temperature })}
             ${FloatField({ label: "Penalize repeat sequence", max: 2.0, min: 0.0, name: "repeat_penalty", step: 0.01, value: params.value.repeat_penalty })}
             ${IntField({ label: "Consider N tokens for penalize", max: 2048, min: 0, name: "repeat_last_n", value: params.value.repeat_last_n })}
+            ${BoolField({ label: "Penalize repetition of newlines", name: "penalize_nl", value: params.value.penalize_nl })}
             ${IntField({ label: "Top-K sampling", max: 100, min: -1, name: "top_k", value: params.value.top_k })}
             ${FloatField({ label: "Top-P sampling", max: 1.0, min: 0.0, name: "top_p", step: 0.01, value: params.value.top_p })}
             ${FloatField({ label: "Min-P sampling", max: 1.0, min: 0.0, name: "min_p", step: 0.01, value: params.value.min_p })}

examples/server/server.cpp

@@ -15,7 +15,7 @@
 #define MIMETYPE_JSON "application/json; charset=utf-8"
 
 // auto generated files (update with ./deps.sh)
-#include "index.html.gz.hpp"
+#include "index.html.hpp"
 #include "loading.html.hpp"
 
 #include <atomic>
@@ -135,6 +135,7 @@ struct slot_params {
             {"mirostat",                  sampling.mirostat},
             {"mirostat_tau",              sampling.mirostat_tau},
             {"mirostat_eta",              sampling.mirostat_eta},
+            {"penalize_nl",               sampling.penalize_nl},
             {"stop",                      antiprompt},
             {"max_tokens",                n_predict}, // User configured n_predict
             {"n_keep",                    n_keep},
@@ -183,7 +184,6 @@ struct server_task {
 
     static slot_params params_from_json_cmpl(
             const llama_model * model,
-            const llama_context * ctx,
             const common_params & params_base,
             const json & data) {
         slot_params params;
@@ -226,6 +226,7 @@ struct server_task {
         params.sampling.mirostat           = json_value(data, "mirostat",           defaults.sampling.mirostat);
         params.sampling.mirostat_tau       = json_value(data, "mirostat_tau",       defaults.sampling.mirostat_tau);
         params.sampling.mirostat_eta       = json_value(data, "mirostat_eta",       defaults.sampling.mirostat_eta);
+        params.sampling.penalize_nl        = json_value(data, "penalize_nl",        defaults.sampling.penalize_nl);
         params.sampling.seed               = json_value(data, "seed",               defaults.sampling.seed);
         params.sampling.n_probs            = json_value(data, "n_probs",            defaults.sampling.n_probs);
         params.sampling.min_keep           = json_value(data, "min_keep",           defaults.sampling.min_keep);
@@ -238,27 +239,8 @@ struct server_task {
         params.speculative.n_min = std::max(params.speculative.n_min, 2);
         params.speculative.n_max = std::max(params.speculative.n_max, 0);
 
-        // TODO: add more sanity checks for the input parameters
-
-        if (params.sampling.penalty_last_n < -1) {
-            throw std::runtime_error("Error: repeat_last_n must be >= -1");
-        }
-
-        if (params.sampling.dry_penalty_last_n < -1) {
-            throw std::runtime_error("Error: dry_penalty_last_n must be >= -1");
-        }
-
-        if (params.sampling.penalty_last_n == -1) {
-            // note: should be the slot's context and not the full context, but it's ok
-            params.sampling.penalty_last_n = llama_n_ctx(ctx);
-        }
-
-        if (params.sampling.dry_penalty_last_n == -1) {
-            params.sampling.dry_penalty_last_n = llama_n_ctx(ctx);
-        }
-
         if (params.sampling.dry_base < 1.0f) {
-            params.sampling.dry_base = defaults.sampling.dry_base;
+           params.sampling.dry_base = defaults.sampling.dry_base;
         }
 
         // sequence breakers for DRY
@@ -719,17 +701,14 @@ struct server_task_result_embd : server_task_result {
     int index = 0;
     std::vector<float> embedding;
 
-    int32_t n_tokens;
-
     virtual int get_index() override {
         return index;
     }
 
     virtual json to_json() override {
         return json {
-            {"index",            index},
-            {"embedding",        embedding},
-            {"tokens_evaluated", n_tokens},
+            {"index",     index},
+            {"embedding", embedding},
         };
     }
 };
@@ -738,17 +717,14 @@ struct server_task_result_rerank : server_task_result {
     int index = 0;
     float score = -1e6;
 
-    int32_t n_tokens;
-
     virtual int get_index() override {
         return index;
     }
 
     virtual json to_json() override {
         return json {
-            {"index",            index},
-            {"score",            score},
-            {"tokens_evaluated", n_tokens},
+            {"index", index},
+            {"score", score},
         };
     }
 };
@@ -1493,7 +1469,7 @@ struct server_context {
         n_ctx = llama_n_ctx(ctx);
 
         add_bos_token = llama_add_bos_token(model);
-        has_eos_token = llama_token_eos(model) != LLAMA_TOKEN_NULL;
+        has_eos_token = !llama_add_eos_token(model);
 
         if (!params_base.speculative.model.empty()) {
             SRV_INF("loading draft model '%s'\n", params_base.speculative.model.c_str());
@@ -2001,7 +1977,6 @@ struct server_context {
         auto res = std::make_unique<server_task_result_embd>();
         res->id    = slot.id_task;
         res->index = slot.index;
-        res->n_tokens = slot.n_prompt_tokens;
 
         const int n_embd = llama_n_embd(model);
 
@@ -2037,7 +2012,6 @@ struct server_context {
         auto res = std::make_unique<server_task_result_rerank>();
         res->id    = slot.id_task;
         res->index = slot.index;
-        res->n_tokens = slot.n_prompt_tokens;
 
         for (int i = 0; i < batch.n_tokens; ++i) {
             if (!batch.logits[i] || batch.seq_id[i][0] != slot.id) {
@@ -3407,7 +3381,7 @@ int main(int argc, char ** argv) {
                 task.index = i;
 
                 task.prompt_tokens    = std::move(tokenized_prompts[i]);
-                task.params           = server_task::params_from_json_cmpl(ctx_server.model, ctx_server.ctx, ctx_server.params_base, data);
+                task.params           = server_task::params_from_json_cmpl(ctx_server.model, ctx_server.params_base, data);
                 task.id_selected_slot = json_value(data, "id_slot", -1);
 
                 // OAI-compat
@@ -3854,13 +3828,8 @@ int main(int argc, char ** argv) {
             }
         } else {
             // using embedded static index.html
-            svr->Get("/", [](const httplib::Request & req, httplib::Response & res) {
-                if (req.get_header_value("Accept-Encoding").find("gzip") == std::string::npos) {
-                    res.set_content("Error: gzip is not supported by this browser", "text/plain");
-                } else {
-                    res.set_header("Content-Encoding", "gzip");
-                    res.set_content(reinterpret_cast<const char*>(index_html_gz), index_html_gz_len, "text/html; charset=utf-8");
-                }
+            svr->Get("/", [](const httplib::Request &, httplib::Response & res) {
+                res.set_content(reinterpret_cast<const char*>(index_html), index_html_len, "text/html; charset=utf-8");
                 return false;
             });
         }

examples/server/tests/unit/test_embedding.py

@@ -97,33 +97,3 @@ def test_same_prompt_give_same_result():
         vi = res.body['data'][i]['embedding']
         for x, y in zip(v0, vi):
             assert abs(x - y) < EPSILON
-
-
-@pytest.mark.parametrize(
-    "content,n_tokens",
-    [
-        ("I believe the meaning of life is", 7),
-        ("This is a test", 4),
-    ]
-)
-def test_embedding_usage_single(content, n_tokens):
-    global server
-    server.start()
-    res = server.make_request("POST", "/embeddings", data={"input": content})
-    assert res.status_code == 200
-    assert res.body['usage']['prompt_tokens'] == res.body['usage']['total_tokens']
-    assert res.body['usage']['prompt_tokens'] == n_tokens
-
-
-def test_embedding_usage_multiple():
-    global server
-    server.start()
-    res = server.make_request("POST", "/embeddings", data={
-        "input": [
-            "I believe the meaning of life is",
-            "I believe the meaning of life is",
-        ],
-    })
-    assert res.status_code == 200
-    assert res.body['usage']['prompt_tokens'] == res.body['usage']['total_tokens']
-    assert res.body['usage']['prompt_tokens'] == 2 * 7

examples/server/tests/unit/test_rerank.py

@@ -53,26 +53,3 @@ def test_invalid_rerank_req(documents):
     })
     assert res.status_code == 400
     assert "error" in res.body
-
-
-@pytest.mark.parametrize(
-    "query,doc1,doc2,n_tokens",
-    [
-        ("Machine learning is", "A machine", "Learning is", 19),
-        ("Which city?", "Machine learning is ", "Paris, capitale de la", 26),
-    ]
-)
-def test_rerank_usage(query, doc1, doc2, n_tokens):
-    global server
-    server.start()
-
-    res = server.make_request("POST", "/rerank", data={
-        "query": query,
-        "documents": [
-            doc1,
-            doc2,
-        ]
-    })
-    assert res.status_code == 200
-    assert res.body['usage']['prompt_tokens'] == res.body['usage']['total_tokens']
-    assert res.body['usage']['prompt_tokens'] == n_tokens

examples/server/themes/buttons-top/index.html

@@ -222,6 +222,7 @@
       temperature: 0.7,
       repeat_last_n: 256, // 0 = disable penalty, -1 = context size
       repeat_penalty: 1.18, // 1.0 = disabled
+      penalize_nl: false,
       top_k: 40, // <= 0 to use vocab size
       top_p: 0.95, // 1.0 = disabled
       min_p: 0.05, // 0 = disabled
@@ -778,6 +779,7 @@
             ${FloatField({ label: "Temperature", max: 2.0, min: 0.0, name: "temperature", step: 0.01, value: params.value.temperature })}
             ${FloatField({ label: "Penalize repeat sequence", max: 2.0, min: 0.0, name: "repeat_penalty", step: 0.01, value: params.value.repeat_penalty })}
             ${IntField({ label: "Consider N tokens for penalize", max: 2048, min: 0, name: "repeat_last_n", value: params.value.repeat_last_n })}
+            ${BoolField({ label: "Penalize repetition of newlines", name: "penalize_nl", value: params.value.penalize_nl })}
             ${IntField({ label: "Top-K sampling", max: 100, min: -1, name: "top_k", value: params.value.top_k })}
             ${FloatField({ label: "Top-P sampling", max: 1.0, min: 0.0, name: "top_p", step: 0.01, value: params.value.top_p })}
             ${FloatField({ label: "Min-P sampling", max: 1.0, min: 0.0, name: "min_p", step: 0.01, value: params.value.min_p })}

examples/server/themes/wild/index.html

@@ -225,6 +225,7 @@
       temperature: 0.7,
       repeat_last_n: 256, // 0 = disable penalty, -1 = context size
       repeat_penalty: 1.18, // 1.0 = disabled
+      penalize_nl: false,
       top_k: 40, // <= 0 to use vocab size
       top_p: 0.95, // 1.0 = disabled
       min_p: 0.05, // 0 = disabled
@@ -781,6 +782,7 @@
             ${FloatField({ label: "Temperature", max: 2.0, min: 0.0, name: "temperature", step: 0.01, value: params.value.temperature })}
             ${FloatField({ label: "Penalize repeat sequence", max: 2.0, min: 0.0, name: "repeat_penalty", step: 0.01, value: params.value.repeat_penalty })}
             ${IntField({ label: "Consider N tokens for penalize", max: 2048, min: 0, name: "repeat_last_n", value: params.value.repeat_last_n })}
+            ${BoolField({ label: "Penalize repetition of newlines", name: "penalize_nl", value: params.value.penalize_nl })}
             ${IntField({ label: "Top-K sampling", max: 100, min: -1, name: "top_k", value: params.value.top_k })}
             ${FloatField({ label: "Top-P sampling", max: 1.0, min: 0.0, name: "top_p", step: 0.01, value: params.value.top_p })}
             ${FloatField({ label: "Min-P sampling", max: 1.0, min: 0.0, name: "min_p", step: 0.01, value: params.value.min_p })}

examples/server/utils.hpp

@@ -560,7 +560,6 @@ static json oaicompat_completion_params_parse(
 
 static json format_embeddings_response_oaicompat(const json & request, const json & embeddings) {
     json data = json::array();
-    int32_t n_tokens = 0;
     int i = 0;
     for (const auto & elem : embeddings) {
         data.push_back(json{
@@ -568,16 +567,14 @@ static json format_embeddings_response_oaicompat(const json & request, const jso
             {"index",     i++},
             {"object",    "embedding"}
         });
-
-        n_tokens += json_value(elem, "tokens_evaluated", 0);
     }
 
     json res = json {
         {"model", json_value(request, "model", std::string(DEFAULT_OAICOMPAT_MODEL))},
         {"object", "list"},
-        {"usage", json {
-            {"prompt_tokens", n_tokens},
-            {"total_tokens", n_tokens}
+        {"usage", json { // TODO: fill
+            {"prompt_tokens", 0},
+            {"total_tokens", 0}
         }},
         {"data", data}
     };
@@ -587,23 +584,20 @@ static json format_embeddings_response_oaicompat(const json & request, const jso
 
 static json format_response_rerank(const json & request, const json & ranks) {
     json data = json::array();
-    int32_t n_tokens = 0;
     int i = 0;
     for (const auto & rank : ranks) {
         data.push_back(json{
             {"index",    i++},
             {"relevance_score", json_value(rank, "score", 0.0)},
         });
-
-        n_tokens += json_value(rank, "tokens_evaluated", 0);
     }
 
     json res = json {
         {"model", json_value(request, "model", std::string(DEFAULT_OAICOMPAT_MODEL))},
         {"object", "list"},
-        {"usage", json {
-            {"prompt_tokens", n_tokens},
-            {"total_tokens", n_tokens}
+        {"usage", json { // TODO: fill
+            {"prompt_tokens", 0},
+            {"total_tokens", 0}
         }},
         {"results", data}
     };

examples/server/webui/index.html

@@ -201,10 +201,6 @@
           <details class="collapse collapse-arrow bg-base-200 mb-2 overflow-visible">
             <summary class="collapse-title font-bold">Advanced config</summary>
             <div class="collapse-content">
-              <div class="flex flex-row items-center mb-2" v-if="isDev">
-                <!-- this button only shows in dev mode, used to import a demo conversation to test message rendering -->
-                <button class="btn" @click="debugImportDemoConv()">(debug) Import demo conversation</button>
-              </div>
               <div class="flex flex-row items-center mb-2">
                 <input type="checkbox" class="checkbox" v-model="config.showTokensPerSecond" />
                 <span class="ml-4">Show tokens per second</span>

examples/server/webui/package-lock.json

@@ -8,12 +8,8 @@
       "name": "webui",
       "version": "0.0.0",
       "dependencies": {
-        "@sec-ant/readable-stream": "^0.6.0",
-        "@vscode/markdown-it-katex": "^1.1.1",
         "autoprefixer": "^10.4.20",
         "daisyui": "^4.12.14",
-        "highlight.js": "^11.10.0",
-        "katex": "^0.16.15",
         "markdown-it": "^14.1.0",
         "postcss": "^8.4.49",
         "tailwindcss": "^3.4.15",
@@ -22,7 +18,6 @@
         "vue": "^3.5.13"
       },
       "devDependencies": {
-        "sass-embedded": "^1.83.0",
         "vite": "^5.4.10"
       }
     },
@@ -38,13 +33,6 @@
         "url": "https://github.com/sponsors/sindresorhus"
       }
     },
-    "node_modules/@bufbuild/protobuf": {
-      "version": "2.2.3",
-      "resolved": "https://registry.npmjs.org/@bufbuild/protobuf/-/protobuf-2.2.3.tgz",
-      "integrity": "sha512-tFQoXHJdkEOSwj5tRIZSPNUuXK3RaR7T1nUrPgbYX1pUbvqqaaZAsfo+NXBPsz5rZMSKVFrgK1WL8Q/MSLvprg==",
-      "devOptional": true,
-      "license": "(Apache-2.0 AND BSD-3-Clause)"
-    },
     "node_modules/@esbuild/aix-ppc64": {
       "version": "0.21.5",
       "resolved": "https://registry.npmjs.org/@esbuild/aix-ppc64/-/aix-ppc64-0.21.5.tgz",
@@ -618,21 +606,6 @@
         "win32"
       ]
     },
-    "node_modules/@sec-ant/readable-stream": {
-      "version": "0.6.0",
-      "resolved": "https://registry.npmjs.org/@sec-ant/readable-stream/-/readable-stream-0.6.0.tgz",
-      "integrity": "sha512-uiBh8DrB5FN35gP6/o8JEhEQ7/ci1jUsOZO/VMUjyvTpjtV54VstOXVj1TvTj/wsT23pfX6butxxh3qufsW3+g==",
-      "license": "MIT"
-    },
-    "node_modules/@vscode/markdown-it-katex": {
-      "version": "1.1.1",
-      "resolved": "https://registry.npmjs.org/@vscode/markdown-it-katex/-/markdown-it-katex-1.1.1.tgz",
-      "integrity": "sha512-3KTlbsRBPJQLE2YmLL7K6nunTlU+W9T5+FjfNdWuIUKgxSS6HWLQHaO3L4MkJi7z7MpIPpY+g4N+cWNBPE/MSA==",
-      "license": "MIT",
-      "dependencies": {
-        "katex": "^0.16.4"
-      }
-    },
     "node_modules/@vue/compiler-dom": {
       "version": "3.5.13",
       "resolved": "https://registry.npmjs.org/@vue/compiler-dom/-/compiler-dom-3.5.13.tgz",
@@ -1031,13 +1004,6 @@
         "browserslist": ">= 4.21.0"
       }
     },
-    "node_modules/buffer-builder": {
-      "version": "0.2.0",
-      "resolved": "https://registry.npmjs.org/buffer-builder/-/buffer-builder-0.2.0.tgz",
-      "integrity": "sha512-7VPMEPuYznPSoR21NE1zvd2Xna6c/CloiZCfcMXR1Jny6PjX0N4Nsa38zcBFo/FMK+BlA+FLKbJCQ0i2yxp+Xg==",
-      "devOptional": true,
-      "license": "MIT/X11"
-    },
     "node_modules/caniuse-lite": {
       "version": "1.0.30001684",
       "resolved": "https://registry.npmjs.org/caniuse-lite/-/caniuse-lite-1.0.30001684.tgz",
@@ -1200,22 +1166,6 @@
         "node": ">=8.0"
       }
     },
-    "node_modules/colorjs.io": {
-      "version": "0.5.2",
-      "resolved": "https://registry.npmjs.org/colorjs.io/-/colorjs.io-0.5.2.tgz",
-      "integrity": "sha512-twmVoizEW7ylZSN32OgKdXRmo1qg+wT5/6C3xu5b9QsWzSFAhHLn2xd8ro0diCsKfCj1RdaTP/nrcW+vAoQPIw==",
-      "devOptional": true,
-      "license": "MIT"
-    },
-    "node_modules/commander": {
-      "version": "8.3.0",
-      "resolved": "https://registry.npmjs.org/commander/-/commander-8.3.0.tgz",
-      "integrity": "sha512-OkTL9umf+He2DZkUq8f8J9of7yL6RJKI24dVITBmNfZBmri9zYZQrKkuXiKhyfPSu8tUhnVBB1iKXevvnlR4Ww==",
-      "license": "MIT",
-      "engines": {
-        "node": ">= 12"
-      }
-    },
     "node_modules/css-selector-tokenizer": {
       "version": "0.8.0",
       "resolved": "https://registry.npmjs.org/css-selector-tokenizer/-/css-selector-tokenizer-0.8.0.tgz",
@@ -1523,31 +1473,6 @@
         "node": ">=10.13.0"
       }
     },
-    "node_modules/has-flag": {
-      "version": "4.0.0",
-      "resolved": "https://registry.npmjs.org/has-flag/-/has-flag-4.0.0.tgz",
-      "integrity": "sha512-EykJT/Q1KjTWctppgIAgfSO0tKVuZUjhgMr17kqTumMl6Afv3EISleU7qZUzoXDFTAHTDC4NOoG/ZxU3EvlMPQ==",
-      "devOptional": true,
-      "license": "MIT",
-      "engines": {
-        "node": ">=8"
-      }
-    },
-    "node_modules/highlight.js": {
-      "version": "11.10.0",
-      "resolved": "https://registry.npmjs.org/highlight.js/-/highlight.js-11.10.0.tgz",
-      "integrity": "sha512-SYVnVFswQER+zu1laSya563s+F8VDGt7o35d4utbamowvUNLLMovFqwCLSocpZTz3MgaSRA1IbqRWZv97dtErQ==",
-      "engines": {
-        "node": ">=12.0.0"
-      }
-    },
-    "node_modules/immutable": {
-      "version": "5.0.3",
-      "resolved": "https://registry.npmjs.org/immutable/-/immutable-5.0.3.tgz",
-      "integrity": "sha512-P8IdPQHq3lA1xVeBRi5VPqUm5HDgKnx0Ru51wZz5mjxHr5n3RWhjIpOFU7ybkUxfB+5IToy+OLaHYDBIWsv+uw==",
-      "devOptional": true,
-      "license": "MIT"
-    },
     "node_modules/is-glob": {
       "version": "4.0.3",
       "resolved": "https://registry.npmjs.org/is-glob/-/is-glob-4.0.3.tgz",
@@ -1578,22 +1503,6 @@
         "jiti": "bin/jiti.js"
       }
     },
-    "node_modules/katex": {
-      "version": "0.16.15",
-      "resolved": "https://registry.npmjs.org/katex/-/katex-0.16.15.tgz",
-      "integrity": "sha512-yE9YJIEAk2aZ+FL/G8r+UGw0CTUzEA8ZFy6E+8tc3spHUKq3qBnzCkI1CQwGoI9atJhVyFPEypQsTY7mJ1Pi9w==",
-      "funding": [
-        "https://opencollective.com/katex",
-        "https://github.com/sponsors/katex"
-      ],
-      "license": "MIT",
-      "dependencies": {
-        "commander": "^8.3.0"
-      },
-      "bin": {
-        "katex": "cli.js"
-      }
-    },
     "node_modules/lilconfig": {
       "version": "2.1.0",
       "resolved": "https://registry.npmjs.org/lilconfig/-/lilconfig-2.1.0.tgz",
@@ -2113,381 +2022,6 @@
       "integrity": "sha512-AYnb1nQyY49te+VRAVgmzfcgjYS91mY5P0TKUDCLEM+gNnA+3T6rWITXRLYCpahpqSQbN5cE+gHpnPyXjHWxcw==",
       "license": "MIT"
     },
-    "node_modules/rxjs": {
-      "version": "7.8.1",
-      "resolved": "https://registry.npmjs.org/rxjs/-/rxjs-7.8.1.tgz",
-      "integrity": "sha512-AA3TVj+0A2iuIoQkWEK/tqFjBq2j+6PO6Y0zJcvzLAFhEFIO3HL0vls9hWLncZbAAbK0mar7oZ4V079I/qPMxg==",
-      "devOptional": true,
-      "license": "Apache-2.0",
-      "dependencies": {
-        "tslib": "^2.1.0"
-      }
-    },
-    "node_modules/sass-embedded": {
-      "version": "1.83.0",
-      "resolved": "https://registry.npmjs.org/sass-embedded/-/sass-embedded-1.83.0.tgz",
-      "integrity": "sha512-/8cYZeL39evUqe0o//193na51Q1VWZ61qhxioQvLJwOtWIrX+PgNhCyD8RSuTtmzc4+6+waFZf899bfp/MCUwA==",
-      "devOptional": true,
-      "license": "MIT",
-      "dependencies": {
-        "@bufbuild/protobuf": "^2.0.0",
-        "buffer-builder": "^0.2.0",
-        "colorjs.io": "^0.5.0",
-        "immutable": "^5.0.2",
-        "rxjs": "^7.4.0",
-        "supports-color": "^8.1.1",
-        "sync-child-process": "^1.0.2",
-        "varint": "^6.0.0"
-      },
-      "bin": {
-        "sass": "dist/bin/sass.js"
-      },
-      "engines": {
-        "node": ">=16.0.0"
-      },
-      "optionalDependencies": {
-        "sass-embedded-android-arm": "1.83.0",
-        "sass-embedded-android-arm64": "1.83.0",
-        "sass-embedded-android-ia32": "1.83.0",
-        "sass-embedded-android-riscv64": "1.83.0",
-        "sass-embedded-android-x64": "1.83.0",
-        "sass-embedded-darwin-arm64": "1.83.0",
-        "sass-embedded-darwin-x64": "1.83.0",
-        "sass-embedded-linux-arm": "1.83.0",
-        "sass-embedded-linux-arm64": "1.83.0",
-        "sass-embedded-linux-ia32": "1.83.0",
-        "sass-embedded-linux-musl-arm": "1.83.0",
-        "sass-embedded-linux-musl-arm64": "1.83.0",
-        "sass-embedded-linux-musl-ia32": "1.83.0",
-        "sass-embedded-linux-musl-riscv64": "1.83.0",
-        "sass-embedded-linux-musl-x64": "1.83.0",
-        "sass-embedded-linux-riscv64": "1.83.0",
-        "sass-embedded-linux-x64": "1.83.0",
-        "sass-embedded-win32-arm64": "1.83.0",
-        "sass-embedded-win32-ia32": "1.83.0",
-        "sass-embedded-win32-x64": "1.83.0"
-      }
-    },
-    "node_modules/sass-embedded-android-arm": {
-      "version": "1.83.0",
-      "resolved": "https://registry.npmjs.org/sass-embedded-android-arm/-/sass-embedded-android-arm-1.83.0.tgz",
-      "integrity": "sha512-uwFSXzJlfbd4Px189xE5l+cxN8+TQpXdQgJec7TIrb4HEY7imabtpYufpVdqUVwT1/uiis5V4+qIEC4Vl5XObQ==",
-      "cpu": [
-        "arm"
-      ],
-      "license": "MIT",
-      "optional": true,
-      "os": [
-        "android"
-      ],
-      "engines": {
-        "node": ">=14.0.0"
-      }
-    },
-    "node_modules/sass-embedded-android-arm64": {
-      "version": "1.83.0",
-      "resolved": "https://registry.npmjs.org/sass-embedded-android-arm64/-/sass-embedded-android-arm64-1.83.0.tgz",
-      "integrity": "sha512-GBiCvM4a2rkWBLdYDxI6XYnprfk5U5c81g69RC2X6kqPuzxzx8qTArQ9M6keFK4+iDQ5N9QTwFCr0KbZTn+ZNQ==",
-      "cpu": [
-        "arm64"
-      ],
-      "license": "MIT",
-      "optional": true,
-      "os": [
-        "android"
-      ],
-      "engines": {
-        "node": ">=14.0.0"
-      }
-    },
-    "node_modules/sass-embedded-android-ia32": {
-      "version": "1.83.0",
-      "resolved": "https://registry.npmjs.org/sass-embedded-android-ia32/-/sass-embedded-android-ia32-1.83.0.tgz",
-      "integrity": "sha512-5ATPdGo2SICqAhiJl/Z8KQ23zH4sGgobGgux0TnrNtt83uHZ+r+To/ubVJ7xTkZxed+KJZnIpolGD8dQyQqoTg==",
-      "cpu": [
-        "ia32"
-      ],
-      "license": "MIT",
-      "optional": true,
-      "os": [
-        "android"
-      ],
-      "engines": {
-        "node": ">=14.0.0"
-      }
-    },
-    "node_modules/sass-embedded-android-riscv64": {
-      "version": "1.83.0",
-      "resolved": "https://registry.npmjs.org/sass-embedded-android-riscv64/-/sass-embedded-android-riscv64-1.83.0.tgz",
-      "integrity": "sha512-aveknUOB8GZewOzVn2Uwk+DKcncTR50Q6vtzslNMGbYnxtgQNHzy8A1qVEviNUruex+pHofppeMK4iMPFAbiEQ==",
-      "cpu": [
-        "riscv64"
-      ],
-      "license": "MIT",
-      "optional": true,
-      "os": [
-        "android"
-      ],
-      "engines": {
-        "node": ">=14.0.0"
-      }
-    },
-    "node_modules/sass-embedded-android-x64": {
-      "version": "1.83.0",
-      "resolved": "https://registry.npmjs.org/sass-embedded-android-x64/-/sass-embedded-android-x64-1.83.0.tgz",
-      "integrity": "sha512-WqIay/72ncyf9Ph4vS742J3a73wZihWmzFUwpn1OD6lme1Aj4eWzWIve5IVnlTEJgcZcDHu6ECID9IZgehJKoA==",
-      "cpu": [
-        "x64"
-      ],
-      "license": "MIT",
-      "optional": true,
-      "os": [
-        "android"
-      ],
-      "engines": {
-        "node": ">=14.0.0"
-      }
-    },
-    "node_modules/sass-embedded-darwin-arm64": {
-      "version": "1.83.0",
-      "resolved": "https://registry.npmjs.org/sass-embedded-darwin-arm64/-/sass-embedded-darwin-arm64-1.83.0.tgz",
-      "integrity": "sha512-XQl9QqgxFFIPm/CzHhmppse5o9ocxrbaAdC2/DAnlAqvYWBBtgFqPjGoYlej13h9SzfvNoogx+y9r+Ap+e+hYg==",
-      "cpu": [
-        "arm64"
-      ],
-      "license": "MIT",
-      "optional": true,
-      "os": [
-        "darwin"
-      ],
-      "engines": {
-        "node": ">=14.0.0"
-      }
-    },
-    "node_modules/sass-embedded-darwin-x64": {
-      "version": "1.83.0",
-      "resolved": "https://registry.npmjs.org/sass-embedded-darwin-x64/-/sass-embedded-darwin-x64-1.83.0.tgz",
-      "integrity": "sha512-ERQ7Tvp1kFOW3ux4VDFIxb7tkYXHYc+zJpcrbs0hzcIO5ilIRU2tIOK1OrNwrFO6Qxyf7AUuBwYKLAtIU/Nz7g==",
-      "cpu": [
-        "x64"
-      ],
-      "license": "MIT",
-      "optional": true,
-      "os": [
-        "darwin"
-      ],
-      "engines": {
-        "node": ">=14.0.0"
-      }
-    },
-    "node_modules/sass-embedded-linux-arm": {
-      "version": "1.83.0",
-      "resolved": "https://registry.npmjs.org/sass-embedded-linux-arm/-/sass-embedded-linux-arm-1.83.0.tgz",
-      "integrity": "sha512-baG9RYBJxUFmqwDNC9h9ZFElgJoyO3jgHGjzEZ1wHhIS9anpG+zZQvO8bHx3dBpKEImX+DBeLX+CxsFR9n81gQ==",
-      "cpu": [
-        "arm"
-      ],
-      "license": "MIT",
-      "optional": true,
-      "os": [
-        "linux"
-      ],
-      "engines": {
-        "node": ">=14.0.0"
-      }
-    },
-    "node_modules/sass-embedded-linux-arm64": {
-      "version": "1.83.0",
-      "resolved": "https://registry.npmjs.org/sass-embedded-linux-arm64/-/sass-embedded-linux-arm64-1.83.0.tgz",
-      "integrity": "sha512-syEAVTJt4qhaMLxrSwOWa46zdqHJdnqJkLUK+t9aCr8xqBZLPxSUeIGji76uOehQZ1C+KGFj6n9xstHN6wzOJw==",
-      "cpu": [
-        "arm64"
-      ],
-      "license": "MIT",
-      "optional": true,
-      "os": [
-        "linux"
-      ],
-      "engines": {
-        "node": ">=14.0.0"
-      }
-    },
-    "node_modules/sass-embedded-linux-ia32": {
-      "version": "1.83.0",
-      "resolved": "https://registry.npmjs.org/sass-embedded-linux-ia32/-/sass-embedded-linux-ia32-1.83.0.tgz",
-      "integrity": "sha512-RRBxQxMpoxu5+XcSSc6QR/o9asEwUzR8AbCS83RaXcdTIHTa/CccQsiAoDDoPlRsMTLqnzs0LKL4CfOsf7zBbA==",
-      "cpu": [
-        "ia32"
-      ],
-      "license": "MIT",
-      "optional": true,
-      "os": [
-        "linux"
-      ],
-      "engines": {
-        "node": ">=14.0.0"
-      }
-    },
-    "node_modules/sass-embedded-linux-musl-arm": {
-      "version": "1.83.0",
-      "resolved": "https://registry.npmjs.org/sass-embedded-linux-musl-arm/-/sass-embedded-linux-musl-arm-1.83.0.tgz",
-      "integrity": "sha512-Yc7u2TelCfBab+PRob9/MNJFh3EooMiz4urvhejXkihTiKSHGCv5YqDdtWzvyb9tY2Jb7YtYREVuHwfdVn3dTQ==",
-      "cpu": [
-        "arm"
-      ],
-      "license": "MIT",
-      "optional": true,
-      "os": [
-        "linux"
-      ],
-      "engines": {
-        "node": ">=14.0.0"
-      }
-    },
-    "node_modules/sass-embedded-linux-musl-arm64": {
-      "version": "1.83.0",
-      "resolved": "https://registry.npmjs.org/sass-embedded-linux-musl-arm64/-/sass-embedded-linux-musl-arm64-1.83.0.tgz",
-      "integrity": "sha512-Y7juhPHClUO2H5O+u+StRy6SEAcwZ+hTEk5WJdEmo1Bb1gDtfHvJaWB/iFZJ2tW0W1e865AZeUrC4OcOFjyAQA==",
-      "cpu": [
-        "arm64"
-      ],
-      "license": "MIT",
-      "optional": true,
-      "os": [
-        "linux"
-      ],
-      "engines": {
-        "node": ">=14.0.0"
-      }
-    },
-    "node_modules/sass-embedded-linux-musl-ia32": {
-      "version": "1.83.0",
-      "resolved": "https://registry.npmjs.org/sass-embedded-linux-musl-ia32/-/sass-embedded-linux-musl-ia32-1.83.0.tgz",
-      "integrity": "sha512-arQeYwGmwXV8byx5G1PtSzZWW1jbkfR5qrIHMEbTFSAvAxpqjgSvCvrHMOFd73FcMxVaYh4BX9LQNbKinkbEdg==",
-      "cpu": [
-        "ia32"
-      ],
-      "license": "MIT",
-      "optional": true,
-      "os": [
-        "linux"
-      ],
-      "engines": {
-        "node": ">=14.0.0"
-      }
-    },
-    "node_modules/sass-embedded-linux-musl-riscv64": {
-      "version": "1.83.0",
-      "resolved": "https://registry.npmjs.org/sass-embedded-linux-musl-riscv64/-/sass-embedded-linux-musl-riscv64-1.83.0.tgz",
-      "integrity": "sha512-E6uzlIWz59rut+Z3XR6mLG915zNzv07ISvj3GUNZENdHM7dF8GQ//ANoIpl5PljMQKp89GnYdvo6kj2gnaBf/g==",
-      "cpu": [
-        "riscv64"
-      ],
-      "license": "MIT",
-      "optional": true,
-      "os": [
-        "linux"
-      ],
-      "engines": {
-        "node": ">=14.0.0"
-      }
-    },
-    "node_modules/sass-embedded-linux-musl-x64": {
-      "version": "1.83.0",
-      "resolved": "https://registry.npmjs.org/sass-embedded-linux-musl-x64/-/sass-embedded-linux-musl-x64-1.83.0.tgz",
-      "integrity": "sha512-eAMK6tyGqvqr21r9g8BnR3fQc1rYFj85RGduSQ3xkITZ6jOAnOhuU94N5fwRS852Hpws0lXhET+7JHXgg3U18w==",
-      "cpu": [
-        "x64"
-      ],
-      "license": "MIT",
-      "optional": true,
-      "os": [
-        "linux"
-      ],
-      "engines": {
-        "node": ">=14.0.0"
-      }
-    },
-    "node_modules/sass-embedded-linux-riscv64": {
-      "version": "1.83.0",
-      "resolved": "https://registry.npmjs.org/sass-embedded-linux-riscv64/-/sass-embedded-linux-riscv64-1.83.0.tgz",
-      "integrity": "sha512-Ojpi78pTv02sy2fUYirRGXHLY3fPnV/bvwuC2i5LwPQw2LpCcFyFTtN0c5h4LJDk9P6wr+/ZB/JXU8tHIOlK+Q==",
-      "cpu": [
-        "riscv64"
-      ],
-      "license": "MIT",
-      "optional": true,
-      "os": [
-        "linux"
-      ],
-      "engines": {
-        "node": ">=14.0.0"
-      }
-    },
-    "node_modules/sass-embedded-linux-x64": {
-      "version": "1.83.0",
-      "resolved": "https://registry.npmjs.org/sass-embedded-linux-x64/-/sass-embedded-linux-x64-1.83.0.tgz",
-      "integrity": "sha512-3iLjlXdoPfgZRtX4odhRvka1BQs5mAXqfCtDIQBgh/o0JnGPzJIWWl9bYLpHxK8qb+uyVBxXYgXpI0sCzArBOw==",
-      "cpu": [
-        "x64"
-      ],
-      "license": "MIT",
-      "optional": true,
-      "os": [
-        "linux"
-      ],
-      "engines": {
-        "node": ">=14.0.0"
-      }
-    },
-    "node_modules/sass-embedded-win32-arm64": {
-      "version": "1.83.0",
-      "resolved": "https://registry.npmjs.org/sass-embedded-win32-arm64/-/sass-embedded-win32-arm64-1.83.0.tgz",
-      "integrity": "sha512-iOHw/8/t2dlTW3lOFwG5eUbiwhEyGWawivlKWJ8lkXH7fjMpVx2VO9zCFAm8RvY9xOHJ9sf1L7g5bx3EnNP9BQ==",
-      "cpu": [
-        "arm64"
-      ],
-      "license": "MIT",
-      "optional": true,
-      "os": [
-        "win32"
-      ],
-      "engines": {
-        "node": ">=14.0.0"
-      }
-    },
-    "node_modules/sass-embedded-win32-ia32": {
-      "version": "1.83.0",
-      "resolved": "https://registry.npmjs.org/sass-embedded-win32-ia32/-/sass-embedded-win32-ia32-1.83.0.tgz",
-      "integrity": "sha512-2PxNXJ8Pad4geVcTXY4rkyTr5AwbF8nfrCTDv0ulbTvPhzX2mMKEGcBZUXWn5BeHZTBc6whNMfS7d5fQXR9dDQ==",
-      "cpu": [
-        "ia32"
-      ],
-      "license": "MIT",
-      "optional": true,
-      "os": [
-        "win32"
-      ],
-      "engines": {
-        "node": ">=14.0.0"
-      }
-    },
-    "node_modules/sass-embedded-win32-x64": {
-      "version": "1.83.0",
-      "resolved": "https://registry.npmjs.org/sass-embedded-win32-x64/-/sass-embedded-win32-x64-1.83.0.tgz",
-      "integrity": "sha512-muBXkFngM6eLTNqOV0FQi7Dv9s+YRQ42Yem26mosdan/GmJQc81deto6uDTgrYn+bzFNmiXcOdfm+0MkTWK3OQ==",
-      "cpu": [
-        "x64"
-      ],
-      "license": "MIT",
-      "optional": true,
-      "os": [
-        "win32"
-      ],
-      "engines": {
-        "node": ">=14.0.0"
-      }
-    },
     "node_modules/sucrase": {
       "version": "3.35.0",
       "resolved": "https://registry.npmjs.org/sucrase/-/sucrase-3.35.0.tgz",
@@ -3107,45 +2641,6 @@
         "node": ">=8"
       }
     },
-    "node_modules/supports-color": {
-      "version": "8.1.1",
-      "resolved": "https://registry.npmjs.org/supports-color/-/supports-color-8.1.1.tgz",
-      "integrity": "sha512-MpUEN2OodtUzxvKQl72cUF7RQ5EiHsGvSsVG0ia9c5RbWGL2CI4C7EpPS8UTBIplnlzZiNuV56w+FuNxy3ty2Q==",
-      "devOptional": true,
-      "license": "MIT",
-      "dependencies": {
-        "has-flag": "^4.0.0"
-      },
-      "engines": {
-        "node": ">=10"
-      },
-      "funding": {
-        "url": "https://github.com/chalk/supports-color?sponsor=1"
-      }
-    },
-    "node_modules/sync-child-process": {
-      "version": "1.0.2",
-      "resolved": "https://registry.npmjs.org/sync-child-process/-/sync-child-process-1.0.2.tgz",
-      "integrity": "sha512-8lD+t2KrrScJ/7KXCSyfhT3/hRq78rC0wBFqNJXv3mZyn6hW2ypM05JmlSvtqRbeq6jqA94oHbxAr2vYsJ8vDA==",
-      "devOptional": true,
-      "license": "MIT",
-      "dependencies": {
-        "sync-message-port": "^1.0.0"
-      },
-      "engines": {
-        "node": ">=16.0.0"
-      }
-    },
-    "node_modules/sync-message-port": {
-      "version": "1.1.3",
-      "resolved": "https://registry.npmjs.org/sync-message-port/-/sync-message-port-1.1.3.tgz",
-      "integrity": "sha512-GTt8rSKje5FilG+wEdfCkOcLL7LWqpMlr2c3LRuKt/YXxcJ52aGSbGBAdI4L3aaqfrBt6y711El53ItyH1NWzg==",
-      "devOptional": true,
-      "license": "MIT",
-      "engines": {
-        "node": ">=16.0.0"
-      }
-    },
     "node_modules/tailwindcss": {
       "version": "3.4.15",
       "resolved": "https://registry.npmjs.org/tailwindcss/-/tailwindcss-3.4.15.tgz",
@@ -3189,26 +2684,12 @@
       "integrity": "sha512-iBHbi7BQxrFmwZUQJsT0SjNzlLLsXhvW/kg7EyOMVMBIrlnj/qYofwo1LVLZi+3GbUEo96Iu2eqToI2+lZoAEQ==",
       "license": "MIT"
     },
-    "node_modules/tslib": {
-      "version": "2.8.1",
-      "resolved": "https://registry.npmjs.org/tslib/-/tslib-2.8.1.tgz",
-      "integrity": "sha512-oJFu94HQb+KVduSUQL7wnpmqnfmLsOA/nAh6b6EH0wCEoK0/mPeXU6c3wKDV83MkOuHPRHtSXKKU99IBazS/2w==",
-      "devOptional": true,
-      "license": "0BSD"
-    },
     "node_modules/uc.micro": {
       "version": "2.1.0",
       "resolved": "https://registry.npmjs.org/uc.micro/-/uc.micro-2.1.0.tgz",
       "integrity": "sha512-ARDJmphmdvUk6Glw7y9DQ2bFkKBHwQHLi2lsaH6PPmz/Ka9sFOBsBluozhDltWmnv9u/cF6Rt87znRTPV+yp/A==",
       "license": "MIT"
     },
-    "node_modules/varint": {
-      "version": "6.0.0",
-      "resolved": "https://registry.npmjs.org/varint/-/varint-6.0.0.tgz",
-      "integrity": "sha512-cXEIW6cfr15lFv563k4GuVuW/fiwjknytD37jIOLSdSWuOI6WnO/oKwmP2FQTU2l01LP8/M5TSAJpzUaGe3uWg==",
-      "devOptional": true,
-      "license": "MIT"
-    },
     "node_modules/vite": {
       "version": "5.4.11",
       "resolved": "https://registry.npmjs.org/vite/-/vite-5.4.11.tgz",

examples/server/webui/package.json

@@ -6,20 +6,14 @@
   "scripts": {
     "dev": "vite",
     "build": "vite build",
-    "preview": "vite preview",
-    "analyze": "ANALYZE=1 npx vite-bundle-visualizer"
+    "preview": "vite preview"
   },
   "devDependencies": {
-    "sass-embedded": "^1.83.0",
     "vite": "^5.4.10"
   },
   "dependencies": {
-    "@sec-ant/readable-stream": "^0.6.0",
-    "@vscode/markdown-it-katex": "^1.1.1",
     "autoprefixer": "^10.4.20",
     "daisyui": "^4.12.14",
-    "highlight.js": "^11.10.0",
-    "katex": "^0.16.15",
     "markdown-it": "^14.1.0",
     "postcss": "^8.4.49",
     "tailwindcss": "^3.4.15",

examples/server/webui/public/demo-conversation.json

@@ -1,33 +0,0 @@
-{
-  "demo": true,
-  "id": "conv-1734086746930",
-  "lastModified": 1734087548943,
-  "messages": [
-    {
-      "id": 1734086764521,
-      "role": "user",
-      "content": "this is a demo conversation, used in dev mode"
-    },
-    {
-      "id": 1734087548327,
-      "role": "assistant",
-      "content": "This is the formula:\n\n$\\frac{e^{x_i}}{\\sum_{j=1}^{n}e^{x_j}}$\n\nGiven an input vector \\(\\mathbf{x} = [x_1, x_2, \\ldots, x_n]\\)\n\n\\[\ny_i = \\frac{e^{x_i}}{\\sum_{j=1}^n e^{x_j}}\n\\]\n\nCode block latex:\n```latex\n\\frac{e^{x_i}}{\\sum_{j=1}^{n}e^{x_j}}\n```\n\nTest dollar sign: $1234 $4567\n\nInvalid latex syntax: $E = mc^$ and $$E = mc^$$",
-      "timings": {
-        "prompt_n": 1,
-        "prompt_ms": 28.923,
-        "predicted_n": 25,
-        "predicted_ms": 573.016
-      }
-    },
-    {
-      "id": 1734087548328,
-      "role": "user",
-      "content": "this is a demo conversation, used in dev mode"
-    },
-    {
-      "id": 1734087548329,
-      "role": "assistant",
-      "content": "Code block:\n```js\nconsole.log('hello world')\n```\n```sh\nls -la /dev\n```"
-    }
-  ]
-}

examples/server/webui/src/highlight-config.js

@@ -1,60 +0,0 @@
-import hljs from 'highlight.js/lib/core';
-
-// only import commonly used languages to reduce bundle size
-
-import python from 'highlight.js/lib/languages/python';
-import javascript from 'highlight.js/lib/languages/javascript';
-import json from 'highlight.js/lib/languages/json';
-import bash from 'highlight.js/lib/languages/bash';
-import yaml from 'highlight.js/lib/languages/yaml';
-import markdown from 'highlight.js/lib/languages/markdown';
-import scss from 'highlight.js/lib/languages/scss';
-import xml from 'highlight.js/lib/languages/xml';
-import ruby from 'highlight.js/lib/languages/ruby';
-import go from 'highlight.js/lib/languages/go';
-import java from 'highlight.js/lib/languages/java';
-import rust from 'highlight.js/lib/languages/rust';
-import scala from 'highlight.js/lib/languages/scala';
-import cpp from 'highlight.js/lib/languages/cpp';
-import csharp from 'highlight.js/lib/languages/csharp';
-import swift from 'highlight.js/lib/languages/swift';
-import dart from 'highlight.js/lib/languages/dart';
-import elixir from 'highlight.js/lib/languages/elixir';
-import kotlin from 'highlight.js/lib/languages/kotlin';
-import lua from 'highlight.js/lib/languages/lua';
-import php from 'highlight.js/lib/languages/php';
-import latex from 'highlight.js/lib/languages/latex';
-
-hljs.registerLanguage('python', python);
-hljs.registerLanguage('javascript', javascript);
-hljs.registerLanguage('json', json);
-hljs.registerLanguage('yaml', yaml);
-hljs.registerLanguage('markdown', markdown);
-hljs.registerLanguage('xml', xml);
-hljs.registerLanguage('ruby', ruby);
-hljs.registerLanguage('go', go);
-hljs.registerLanguage('java', java);
-hljs.registerLanguage('rust', rust);
-hljs.registerLanguage('scala', scala);
-hljs.registerLanguage('csharp', csharp);
-hljs.registerLanguage('swift', swift);
-hljs.registerLanguage('dart', dart);
-hljs.registerLanguage('elixir', elixir);
-hljs.registerLanguage('kotlin', kotlin);
-hljs.registerLanguage('lua', lua);
-hljs.registerLanguage('php', php);
-hljs.registerLanguage('latex', latex);
-
-// reuse some languages to further reduce bundle size
-
-hljs.registerLanguage('shell', bash);
-hljs.registerLanguage('bash', bash);
-hljs.registerLanguage('sh', bash);
-
-hljs.registerLanguage('css', scss);
-hljs.registerLanguage('scss', scss);
-
-hljs.registerLanguage('c', cpp);
-hljs.registerLanguage('cpp', cpp);
-
-export default hljs;

examples/server/webui/src/katex-gpt.js

@@ -1,66 +0,0 @@
-import katex from 'katex';
-
-// Adapted from https://github.com/SchneeHertz/markdown-it-katex-gpt
-// MIT license
-
-const defaultOptions = {
-  delimiters: [
-    { left: '\\[', right: '\\]', display: true },
-    { left: '\\(', right: '\\)', display: false },
-  ],
-};
-
-export function renderLatexHTML(content, display = false) {
-  return katex.renderToString(content, {
-    throwOnError: false,
-    output: 'mathml',
-    displayMode: display,
-  });
-}
-
-function escapedBracketRule(options) {
-  return (state, silent) => {
-    const max = state.posMax;
-    const start = state.pos;
-
-    for (const { left, right, display } of options.delimiters) {
-
-      // Check if it starts with the left delimiter
-      if (!state.src.slice(start).startsWith(left)) continue;
-
-      // Skip the length of the left delimiter
-      let pos = start + left.length;
-
-      // Find the matching right delimiter
-      while (pos < max) {
-        if (state.src.slice(pos).startsWith(right)) {
-          break;
-        }
-        pos++;
-      }
-
-      // No matching right delimiter found, skip to the next match
-      if (pos >= max) continue;
-
-      // If not in silent mode, convert LaTeX formula to MathML
-      if (!silent) {
-        const content = state.src.slice(start + left.length, pos);
-        try {
-          const renderedContent = renderLatexHTML(content, display);
-          const token = state.push('html_inline', '', 0);
-          token.content = renderedContent;
-        } catch (e) {
-          console.error(e);
-        }
-      }
-
-      // Update position, skip the length of the right delimiter
-      state.pos = pos + right.length;
-      return true;
-    }
-  }
-}
-
-export default function (md, options = defaultOptions) {
-  md.inline.ruler.after('text', 'escaped_bracket', escapedBracketRule(options));
-}

examples/server/webui/src/main.js

@@ -1,20 +1,8 @@
-import './styles.scss';
+import './styles.css';
 import { createApp, defineComponent, shallowRef, computed, h } from 'vue/dist/vue.esm-bundler.js';
 import MarkdownIt from 'markdown-it';
 import TextLineStream from 'textlinestream';
 
-// math formula rendering
-import 'katex/dist/katex.min.css';
-import markdownItKatexGpt from './katex-gpt';
-import markdownItKatexNormal from '@vscode/markdown-it-katex';
-
-// code highlighting
-import hljs from './highlight-config';
-import daisyuiThemes from 'daisyui/src/theming/themes';
-
-// ponyfill for missing ReadableStream asyncIterator on Safari
-import { asyncIterator } from "@sec-ant/readable-stream/ponyfill/asyncIterator";
-
 const isDev = import.meta.env.MODE === 'development';
 
 // utility functions
@@ -25,18 +13,15 @@ const escapeAttr = (str) => str.replace(/>/g, '>').replace(/"/g, '"');
 const copyStr = (str) => navigator.clipboard.writeText(str);
 
 // constants
-const BASE_URL = isDev
-  ? (localStorage.getItem('base') || 'https://localhost:8080') // for debugging
-  : (new URL('.', document.baseURI).href).toString().replace(/\/$/, ''); // for production
-console.log({ BASE_URL });
-
+const BASE_URL = localStorage.getItem('base') // for debugging
+  || (new URL('.', document.baseURI).href).toString().replace(/\/$/, ''); // for production
 const CONFIG_DEFAULT = {
   // Note: in order not to introduce breaking changes, please keep the same data type (number, string, etc) if you want to change the default value. Do not use null or undefined for default value.
   apiKey: '',
   systemMessage: 'You are a helpful assistant.',
   showTokensPerSecond: false,
   // make sure these default values are in sync with `common.h`
-  samplers: 'edkypmxt',
+  samplers: 'dkypmxt',
   temperature: 0.8,
   dynatemp_range: 0.0,
   dynatemp_exponent: 1.0,
@@ -84,39 +69,12 @@ const CONFIG_INFO = {
 // config keys having numeric value (i.e. temperature, top_k, top_p, etc)
 const CONFIG_NUMERIC_KEYS = Object.entries(CONFIG_DEFAULT).filter(e => isNumeric(e[1])).map(e => e[0]);
 // list of themes supported by daisyui
-const THEMES = ['light', 'dark']
-  // make sure light & dark are always at the beginning
-  .concat(Object.keys(daisyuiThemes).filter(t => t !== 'light' && t !== 'dark'));
+const THEMES = ['light', 'dark', 'cupcake', 'bumblebee', 'emerald', 'corporate', 'synthwave', 'retro', 'cyberpunk', 'valentine', 'halloween', 'garden', 'forest', 'aqua', 'lofi', 'pastel', 'fantasy', 'wireframe', 'black', 'luxury', 'dracula', 'cmyk', 'autumn', 'business', 'acid', 'lemonade', 'night', 'coffee', 'winter', 'dim', 'nord', 'sunset'];
 
 // markdown support
 const VueMarkdown = defineComponent(
   (props) => {
-    const md = shallowRef(new MarkdownIt({
-      breaks: true,
-      highlight: function (str, lang) { // Add highlight.js
-        if (lang && hljs.getLanguage(lang)) {
-          try {
-            return '<pre><code class="hljs">' +
-                   hljs.highlight(str, { language: lang, ignoreIllegals: true }).value +
-                   '</code></pre>';
-          } catch (__) {}
-        }
-        return '<pre><code class="hljs">' + md.value.utils.escapeHtml(str) + '</code></pre>';
-      }
-    }));
-    // support latex with double dollar sign and square brackets
-    md.value.use(markdownItKatexGpt, {
-      delimiters: [
-        { left: '\\[', right: '\\]', display: true },
-        { left: '\\(', right: '\\)', display: false },
-        { left: '$$', right: '$$', display: false },
-        // do not add single dollar sign here, other wise it will confused with dollar used for money symbol
-      ],
-      throwOnError: false,
-    });
-    // support latex with single dollar sign
-    md.value.use(markdownItKatexNormal, { throwOnError: false });
-    // add copy button to code blocks
+    const md = shallowRef(new MarkdownIt({ breaks: true }));
     const origFenchRenderer = md.value.renderer.rules.fence;
     md.value.renderer.rules.fence = (tokens, idx, ...args) => {
       const content = tokens[idx].content;
@@ -286,7 +244,7 @@ async function* sendSSEPostRequest(url, fetchOptions) {
   const lines = res.body
     .pipeThrough(new TextDecoderStream())
     .pipeThrough(new TextLineStream());
-  for await (const line of asyncIterator(lines)) {
+  for await (const line of lines) {
     if (isDev) console.log({line});
     if (line.startsWith('data:') && !line.endsWith('[DONE]')) {
       const data = JSON.parse(line.slice(5));
@@ -320,7 +278,6 @@ const mainApp = createApp({
       themes: THEMES,
       configDefault: {...CONFIG_DEFAULT},
       configInfo: {...CONFIG_INFO},
-      isDev,
     }
   },
   computed: {},
@@ -332,7 +289,6 @@ const mainApp = createApp({
       if (this.isGenerating) chatScrollToBottom(true);
     });
     resizeObserver.observe(pendingMsgElem);
-    this.setSelectedTheme(this.selectedTheme);
   },
   watch: {
     viewingConvId: function(val, oldVal) {
@@ -349,8 +305,6 @@ const mainApp = createApp({
     },
     setSelectedTheme(theme) {
       this.selectedTheme = theme;
-      document.body.setAttribute('data-theme', theme);
-      document.body.setAttribute('data-color-scheme', daisyuiThemes[theme]?.['color-scheme'] ?? 'auto');
       StorageUtils.setTheme(theme);
     },
     newConversation() {
@@ -559,17 +513,6 @@ const mainApp = createApp({
     fetchMessages() {
       this.messages = StorageUtils.getOneConversation(this.viewingConvId)?.messages ?? [];
     },
-
-    // debug functions
-    async debugImportDemoConv() {
-      const res = await fetch('/demo-conversation.json');
-      const demoConv = await res.json();
-      StorageUtils.remove(demoConv.id);
-      for (const msg of demoConv.messages) {
-        StorageUtils.appendMsg(demoConv.id, msg);
-      }
-      this.fetchConversation();
-    }
   },
 });
 mainApp.config.errorHandler = alert;

examples/server/webui/src/styles.css

@@ -0,0 +1,26 @@
+@tailwind base;
+@tailwind components;
+@tailwind utilities;
+
+.markdown {
+  h1, h2, h3, h4, h5, h6, ul, ol, li { all: revert; }
+  pre {
+    @apply whitespace-pre-wrap rounded-lg p-2;
+    border: 1px solid currentColor;
+  }
+  /* TODO: fix markdown table */
+}
+
+.show-on-hover {
+  @apply md:opacity-0 md:group-hover:opacity-100;
+}
+.btn-mini {
+  @apply cursor-pointer hover:shadow-md;
+}
+.chat-screen { max-width: 900px; }
+
+.chat-bubble-base-300 {
+  --tw-bg-opacity: 1;
+  --tw-text-opacity: 1;
+  @apply bg-base-300 text-base-content;
+}

examples/server/webui/src/styles.scss

@@ -1,48 +0,0 @@
-@use "sass:meta";
-
-@tailwind base;
-@tailwind components;
-@tailwind utilities;
-
-.markdown {
-  h1, h2, h3, h4, h5, h6, ul, ol, li { all: revert; }
-  pre {
-    @apply whitespace-pre-wrap rounded-lg p-2;
-    border: 1px solid currentColor;
-  }
-  /* TODO: fix markdown table */
-}
-
-.show-on-hover {
-  @apply md:opacity-0 md:group-hover:opacity-100;
-}
-.btn-mini {
-  @apply cursor-pointer hover:shadow-md;
-}
-.chat-screen { max-width: 900px; }
-
-.chat-bubble-base-300 {
-  --tw-bg-opacity: 1;
-  --tw-text-opacity: 1;
-  @apply bg-base-300 text-base-content;
-}
-
-/* Highlight.js */
-[data-color-scheme='light'] {
-  @include meta.load-css('highlight.js/styles/stackoverflow-light');
-}
-[data-color-scheme='dark'] {
-  @include meta.load-css('highlight.js/styles/stackoverflow-dark');
-}
-[data-color-scheme='auto'] {
-  @media (prefers-color-scheme: light) {
-    @include meta.load-css('highlight.js/styles/stackoverflow-light');
-  }
-  @media (prefers-color-scheme: dark) {
-    @include meta.load-css('highlight.js/styles/stackoverflow-dark');
-  }
-}
-.hljs {
-  background: transparent !important;
-  padding: 0.5em !important;
-}

examples/server/webui/vite.config.js

@@ -2,9 +2,6 @@
 import { viteSingleFile } from 'vite-plugin-singlefile';
 import path from 'path';
 import fs from 'fs';
-import zlib from 'zlib';
-
-const MAX_BUNDLE_SIZE = 1.5 * 1024 * 1024; // only increase when absolutely necessary
 
 const GUIDE_FOR_FRONTEND = `
 <!--
@@ -15,45 +12,25 @@ const GUIDE_FOR_FRONTEND = `
 -->
 `.trim();
 
-const BUILD_PLUGINS = [
-  viteSingleFile(),
-  (function llamaCppPlugin() {
-    let config;
-    return {
-      name: 'llamacpp:build',
-      apply: 'build',
-      async configResolved(_config) {
-        config = _config;
-      },
-      writeBundle() {
-        const outputIndexHtml = path.join(config.build.outDir, 'index.html');
-        const content = GUIDE_FOR_FRONTEND + '\n' + fs.readFileSync(outputIndexHtml, 'utf-8');
-        const compressed = zlib.gzipSync(Buffer.from(content, 'utf-8'), { level: 9 });
-
-        // because gzip header contains machine-specific info, we must remove these data from the header
-        // timestamp
-        compressed[0x4] = 0;
-        compressed[0x5] = 0;
-        compressed[0x6] = 0;
-        compressed[0x7] = 0;
-        // OS
-        compressed[0x9] = 0;
-
-        if (compressed.byteLength > MAX_BUNDLE_SIZE) {
-          throw new Error(
-            `Bundle size is too large (${Math.ceil(compressed.byteLength / 1024)} KB).\n` +
-            `Please reduce the size of the frontend or increase MAX_BUNDLE_SIZE in vite.config.js.\n`,
-          );
-        }
-
-        const targetOutputFile = path.join(config.build.outDir, '../../public/index.html.gz');
-        fs.writeFileSync(targetOutputFile, compressed);
-      }
-    }
-  })(),
-];
-
-/** @type {import('vite').UserConfig} */
 export default {
-  plugins: process.env.ANALYZE ? [] : BUILD_PLUGINS,
+  plugins: [
+    viteSingleFile(),
+    (function llamaCppPlugin() {
+      let config;
+      return {
+        name: 'llamacpp:build',
+        apply: 'build',
+        async configResolved(_config) {
+          config = _config;
+        },
+        writeBundle() {
+          const outputIndexHtml = path.join(config.build.outDir, 'index.html');
+          const content = fs.readFileSync(outputIndexHtml, 'utf-8');
+
+          const targetOutputFile = path.join(config.build.outDir, '../../public/index.html');
+          fs.writeFileSync(targetOutputFile, GUIDE_FOR_FRONTEND + '\n' + content);
+        }
+      }
+    })(),
+  ],
 };

ggml/src/ggml-alloc.c

@@ -534,6 +534,7 @@ static void ggml_gallocr_allocate_node(ggml_gallocr_t galloc, struct ggml_tensor
         size_t offset = ggml_dyn_tallocr_alloc(alloc, size, node);
         hn->buffer_id = buffer_id;
         hn->offset = offset;
+        return;
     }
 }
 

ggml/src/ggml-cpu/CMakeLists.txt

@@ -179,7 +179,7 @@ function(ggml_add_cpu_backend_variant_impl tag_name)
         endif()
     elseif (CMAKE_OSX_ARCHITECTURES STREQUAL "x86_64" OR CMAKE_GENERATOR_PLATFORM_LWR MATCHES "^(x86_64|i686|amd64|x64|win32)$" OR
             (NOT CMAKE_OSX_ARCHITECTURES AND NOT CMAKE_GENERATOR_PLATFORM_LWR AND
-            CMAKE_SYSTEM_PROCESSOR MATCHES "^(x86_64|i686|AMD64|amd64)$"))
+            CMAKE_SYSTEM_PROCESSOR MATCHES "^(x86_64|i686|AMD64)$"))
         if (MSVC)
             # instruction set detection for MSVC only
             if (GGML_NATIVE)

ggml/src/ggml-cpu/ggml-cpu.cpp

@@ -394,11 +394,8 @@ static bool ggml_backend_cpu_device_supports_op(ggml_backend_dev_t dev, const st
     switch (op->op) {
         case GGML_OP_CPY:
             return
-                op->type != GGML_TYPE_IQ3_XXS &&
-                op->type != GGML_TYPE_IQ3_S   &&
                 op->type != GGML_TYPE_IQ2_XXS &&
                 op->type != GGML_TYPE_IQ2_XS  &&
-                op->type != GGML_TYPE_IQ2_S   &&
                 op->type != GGML_TYPE_IQ1_S   &&
                 op->type != GGML_TYPE_IQ1_M; // missing type_traits.from_float
         case GGML_OP_MUL_MAT:

ggml/src/ggml-impl.h

@@ -551,22 +551,6 @@ static inline ggml_bf16_t ggml_compute_fp32_to_bf16(float s) {
 #define GGML_FP32_TO_BF16(x) ggml_compute_fp32_to_bf16(x)
 #define GGML_BF16_TO_FP32(x) ggml_compute_bf16_to_fp32(x)
 
-// expose GGUF internals for test code
-
-GGML_API size_t gguf_type_size(enum gguf_type type);
-
-GGML_API struct gguf_context * gguf_init_from_file_impl(FILE * file, struct gguf_init_params params);
-
-struct gguf_buf {
-    void * data;
-    size_t size;
-    size_t offset;
-};
-GGML_API struct gguf_buf gguf_buf_init(size_t size);
-GGML_API void gguf_buf_free(struct gguf_buf buf);
-
-GGML_API void gguf_write_to_buf(const struct gguf_context * ctx, struct gguf_buf * buf, bool only_meta);
-
 #ifdef __cplusplus
 }
 #endif

ggml/src/ggml-vulkan/ggml-vulkan.cpp

@@ -245,7 +245,6 @@ struct vk_device_struct {
     vk_pipeline pipeline_im2col_f32, pipeline_im2col_f32_f16;
     vk_pipeline pipeline_timestep_embedding_f32;
     vk_pipeline pipeline_pool2d_f32;
-    vk_pipeline pipeline_rwkv_wkv6_f32;
 
     // [2][2][2] is for {f16acc,f32acc}x{large,small_rows}x{unaligned, aligned}
     vk_pipeline pipeline_flash_attn_f32_f16_D64[GGML_TYPE_COUNT][2][2][2];
@@ -529,13 +528,6 @@ struct vk_op_pool2d_push_constants {
     int32_t p0; int32_t p1;
 };
 
-struct vk_op_rwkv_wkv6_push_constants {
-    uint32_t B;
-    uint32_t T;
-    uint32_t C;
-    uint32_t H;
-};
-
 // Allow pre-recording command buffers
 struct vk_staging_memcpy {
     vk_staging_memcpy(void * _dst, const void * _src, size_t _n) : dst(_dst), src(_src), n(_n) {}
@@ -1371,7 +1363,7 @@ static bool ggml_vk_matmul_shmem_support(const vk_device& device, const std::vec
     // Needs to be kept up to date on shader changes
     const uint32_t bank_conflict_offset = device->coopmat_support ? 8 : 1;
     const uint32_t type_size = device->fp16 ? sizeof(ggml_fp16_t) : sizeof(float);
-    const uint32_t warps = warptile[0] / warptile[10];
+    const uint32_t warps = warptile[0] / device->subgroup_size;
 
     const uint32_t load_bufs = (warptile[1] + warptile[2]) * (warptile[3] + bank_conflict_offset) * type_size;
     const uint32_t mmid_row_ids = mul_mat_id ? 3072 * sizeof(uint32_t) : 0;
@@ -1385,9 +1377,8 @@ static void ggml_vk_load_shaders(vk_device& device) {
 
     std::cerr << "ggml_vulkan: Compiling shaders";
 
-    // some shaders have a minimum subgroup size
+    // some shaders require the subgroup size to be 16 or larger
     const uint32_t subgroup_size_16 = std::max(device->subgroup_size, 16u);
-    const uint32_t subgroup_size_32 = std::max(device->subgroup_size, 32u);
 
     // mulmat
     std::vector<uint32_t> l_warptile, m_warptile, s_warptile,
@@ -1454,7 +1445,7 @@ static void ggml_vk_load_shaders(vk_device& device) {
 
         l_warptile_mmq = { 128, 128, 128, 32, device->subgroup_size * 2, 64, 2, tm_l, tn_l, tk_l, device->subgroup_size };
         m_warptile_mmq = { 128,  64,  64, 32, device->subgroup_size, 32, 2, tm_m, tn_m, tk_m, device->subgroup_size };
-        s_warptile_mmq = { subgroup_size_32, 32, 32, 32, 32, 32, 2, tm_s, tn_s, tk_s, device->subgroup_size };
+        s_warptile_mmq = { subgroup_size_16, 32, 32, 32, 32, 32, 2, tm_s, tn_s, tk_s, device->subgroup_size };
 
         l_mmq_wg_denoms = l_wg_denoms = {128, 128, 1 };
         m_mmq_wg_denoms = m_wg_denoms = { 64,  64, 1 };
@@ -1873,7 +1864,7 @@ static void ggml_vk_load_shaders(vk_device& device) {
     ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q4_K], "mul_mat_vec_q4_k_f32_f32", mul_mat_vec_q4_k_f32_f32_len, mul_mat_vec_q4_k_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, {subgroup_size_16}, 1, true);
     ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q5_K], "mul_mat_vec_q5_k_f32_f32", mul_mat_vec_q5_k_f32_f32_len, mul_mat_vec_q5_k_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, {subgroup_size_16}, 1, true);
     ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q6_K], "mul_mat_vec_q6_k_f32_f32", mul_mat_vec_q6_k_f32_f32_len, mul_mat_vec_q6_k_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, {subgroup_size_16}, 1, true);
-    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_IQ4_NL], "mul_mat_vec_iq4_nl_f32_f32", mul_mat_vec_iq4_nl_f32_f32_len, mul_mat_vec_iq4_nl_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm, 1, 1}, {subgroup_size_16, 2*rm}, 1, true);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_IQ4_NL], "mul_mat_vec_iq4_nl_f32_f32", mul_mat_vec_iq4_nl_f32_f32_len, mul_mat_vec_iq4_nl_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm, 1, 1}, {device->subgroup_size, 2*rm}, 1, true);
 
     ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_F32 ], "mul_mat_vec_f32_f16_f32",  mul_mat_vec_f32_f16_f32_len,  mul_mat_vec_f32_f16_f32_data,  "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1);
     ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_F16 ], "mul_mat_vec_f16_f16_f32",  mul_mat_vec_f16_f16_f32_len,  mul_mat_vec_f16_f16_f32_data,  "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1);
@@ -1887,7 +1878,7 @@ static void ggml_vk_load_shaders(vk_device& device) {
     ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q4_K], "mul_mat_vec_q4_k_f16_f32", mul_mat_vec_q4_k_f16_f32_len, mul_mat_vec_q4_k_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, {subgroup_size_16}, 1, true);
     ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q5_K], "mul_mat_vec_q5_k_f16_f32", mul_mat_vec_q5_k_f16_f32_len, mul_mat_vec_q5_k_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, {subgroup_size_16}, 1, true);
     ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q6_K], "mul_mat_vec_q6_k_f16_f32", mul_mat_vec_q6_k_f16_f32_len, mul_mat_vec_q6_k_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, {subgroup_size_16}, 1, true);
-    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_IQ4_NL], "mul_mat_vec_iq4_nl_f16_f32", mul_mat_vec_iq4_nl_f16_f32_len, mul_mat_vec_iq4_nl_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm, 1, 1}, {subgroup_size_16, 2*rm}, 1, true);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_IQ4_NL], "mul_mat_vec_iq4_nl_f16_f32", mul_mat_vec_iq4_nl_f16_f32_len, mul_mat_vec_iq4_nl_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm, 1, 1}, {device->subgroup_size, 2*rm}, 1, true);
 
     ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_F32 ], "mul_mat_vec_id_f32_f32",  mul_mat_vec_id_f32_f32_len,  mul_mat_vec_id_f32_f32_data,  "main", 4, sizeof(vk_mat_vec_id_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1);
     ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_F16 ], "mul_mat_vec_id_f16_f32",  mul_mat_vec_id_f16_f32_len,  mul_mat_vec_id_f16_f32_data,  "main", 4, sizeof(vk_mat_vec_id_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1);
@@ -1901,7 +1892,7 @@ static void ggml_vk_load_shaders(vk_device& device) {
     ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q4_K], "mul_mat_vec_id_q4_k_f32", mul_mat_vec_id_q4_k_f32_len, mul_mat_vec_id_q4_k_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {1, 1, 1}, {subgroup_size_16}, 1, true);
     ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q5_K], "mul_mat_vec_id_q5_k_f32", mul_mat_vec_id_q5_k_f32_len, mul_mat_vec_id_q5_k_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {1, 1, 1}, {subgroup_size_16}, 1, true);
     ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q6_K], "mul_mat_vec_id_q6_k_f32", mul_mat_vec_id_q6_k_f32_len, mul_mat_vec_id_q6_k_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {1, 1, 1}, {subgroup_size_16}, 1, true);
-    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_IQ4_NL], "mul_mat_vec_id_iq4_nl_f32", mul_mat_vec_id_iq4_nl_f32_len, mul_mat_vec_id_iq4_nl_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {2*rm, 1, 1}, {subgroup_size_16, 2*rm}, 1, true);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_IQ4_NL], "mul_mat_vec_id_iq4_nl_f32", mul_mat_vec_id_iq4_nl_f32_len, mul_mat_vec_id_iq4_nl_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {2*rm, 1, 1}, {device->subgroup_size, 2*rm}, 1, true);
 
     // dequant shaders
     ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_F32 ], "f32_to_f16",   dequant_f32_len,  dequant_f32_data,  "main", 2, 5 * sizeof(uint32_t), {256 * 16, 1, 1}, {}, 1);
@@ -2023,8 +2014,6 @@ static void ggml_vk_load_shaders(vk_device& device) {
 
     ggml_vk_create_pipeline(device, device->pipeline_pool2d_f32, "pool2d_f32", pool2d_f32_len, pool2d_f32_data, "main", 2, sizeof(vk_op_pool2d_push_constants), {512, 1, 1}, {}, 1);
 
-    ggml_vk_create_pipeline(device, device->pipeline_rwkv_wkv6_f32, "rwkv_wkv6_f32", rwkv_wkv6_f32_len, rwkv_wkv6_f32_data, "main", 7, sizeof(vk_op_rwkv_wkv6_push_constants), {1, 1, 1}, {device->subgroup_size}, 1);
-
     for (auto &c : compiles) {
         c.wait();
     }
@@ -5033,11 +5022,6 @@ static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const
             return ctx->device->pipeline_pool2d_f32;
         }
         return nullptr;
-    case GGML_OP_RWKV_WKV6:
-        if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
-            return ctx->device->pipeline_rwkv_wkv6_f32;
-        }
-        return nullptr;
     case GGML_OP_LEAKY_RELU:
         if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
             return ctx->device->pipeline_leaky_relu_f32;
@@ -5440,134 +5424,6 @@ static void ggml_vk_div(ggml_backend_vk_context * ctx, vk_context& subctx, const
     }, dryrun);
 }
 
-static void ggml_vk_op_f32_rwkv6(ggml_backend_vk_context * ctx, vk_context& subctx, ggml_tensor * dst, const vk_op_rwkv_wkv6_push_constants&& pc, bool dryrun = false) {
-    const ggml_tensor * k = dst->src[0];
-    const ggml_tensor * v = dst->src[1];
-    const ggml_tensor * r = dst->src[2];
-    const ggml_tensor * tf = dst->src[3];
-    const ggml_tensor * td = dst->src[4];
-    const ggml_tensor * state = dst->src[5];
-
-    GGML_ASSERT(!ggml_is_quantized(k->type));
-    GGML_ASSERT(!ggml_is_quantized(v->type));
-    GGML_ASSERT(!ggml_is_quantized(r->type));
-    GGML_ASSERT(!ggml_is_quantized(tf->type));
-    GGML_ASSERT(!ggml_is_quantized(td->type));
-    GGML_ASSERT(!ggml_is_quantized(state->type));
-    GGML_ASSERT(dst->buffer != nullptr);
-
-    vk_pipeline pipeline = ggml_vk_op_get_pipeline(ctx, k, v, r, dst, GGML_OP_RWKV_WKV6);
-    GGML_ASSERT(pipeline != nullptr);
-
-    if (dryrun) {
-        ggml_pipeline_request_descriptor_sets(ctx->device, pipeline, 1);
-        return;
-    }
-
-    ggml_backend_vk_buffer_context * dst_buf_ctx = (ggml_backend_vk_buffer_context *)dst->buffer->context;
-    ggml_backend_vk_buffer_context * k_buf_ctx = (ggml_backend_vk_buffer_context *)k->buffer->context;
-    ggml_backend_vk_buffer_context * v_buf_ctx = (ggml_backend_vk_buffer_context *)v->buffer->context;
-    ggml_backend_vk_buffer_context * r_buf_ctx = (ggml_backend_vk_buffer_context *)r->buffer->context;
-    ggml_backend_vk_buffer_context * tf_buf_ctx = (ggml_backend_vk_buffer_context *)tf->buffer->context;
-    ggml_backend_vk_buffer_context * td_buf_ctx = (ggml_backend_vk_buffer_context *)td->buffer->context;
-    ggml_backend_vk_buffer_context * state_buf_ctx = (ggml_backend_vk_buffer_context *)state->buffer->context;
-
-    ggml_vk_sync_buffers(subctx);
-
-    vk_buffer d_D, d_K, d_V, d_R, d_TF, d_TD, d_State;
-    uint64_t k_offset, v_offset, r_offset, tf_offset, td_offset, state_offset, dst_offset;
-    bool K_uma = false, V_uma = false, R_uma = false, TF_uma = false, TD_uma = false, STATE_uma = false, DST_uma = false;
-
-    if (ctx->device->uma) {
-        ggml_vk_host_get(ctx->device, k->data, d_K, k_offset);
-        ggml_vk_host_get(ctx->device, v->data, d_V, v_offset);
-        ggml_vk_host_get(ctx->device, r->data, d_R, r_offset);
-        ggml_vk_host_get(ctx->device, tf->data, d_TF, tf_offset);
-        ggml_vk_host_get(ctx->device, td->data, d_TD, td_offset);
-        ggml_vk_host_get(ctx->device, state->data, d_State, state_offset);
-        ggml_vk_host_get(ctx->device, dst->data, d_D, dst_offset);
-
-        K_uma = d_K != nullptr;
-        V_uma = d_V != nullptr;
-        R_uma = d_R != nullptr;
-        TF_uma = d_TF != nullptr;
-        TD_uma = d_TD != nullptr;
-        STATE_uma = d_State != nullptr;
-        DST_uma = d_D != nullptr;
-    }
-
-    if (!K_uma) {
-        d_K = k_buf_ctx->dev_buffer;
-        k_offset = vk_tensor_offset(k) + k->view_offs;
-    }
-    if (!V_uma) {
-        d_V = v_buf_ctx->dev_buffer;
-        v_offset = vk_tensor_offset(v) + v->view_offs;
-    }
-    if (!R_uma) {
-        d_R = r_buf_ctx->dev_buffer;
-        r_offset = vk_tensor_offset(r) + r->view_offs;
-    }
-    if (!TF_uma) {
-        d_TF = tf_buf_ctx->dev_buffer;
-        tf_offset = vk_tensor_offset(tf) + tf->view_offs;
-    }
-    if (!TD_uma) {
-        d_TD = td_buf_ctx->dev_buffer;
-        td_offset = vk_tensor_offset(td) + td->view_offs;
-    }
-    if (!STATE_uma) {
-        d_State = state_buf_ctx->dev_buffer;
-        state_offset = vk_tensor_offset(state) + state->view_offs;
-    }
-    if (!DST_uma) {
-        d_D = dst_buf_ctx->dev_buffer;
-        dst_offset = vk_tensor_offset(dst) + dst->view_offs;
-    }
-
-    const uint64_t k_size = ggml_nbytes(k);
-    const uint64_t v_size = ggml_nbytes(v);
-    const uint64_t r_size = ggml_nbytes(r);
-    const uint64_t tf_size = ggml_nbytes(tf);
-    const uint64_t td_size = ggml_nbytes(td);
-    const uint64_t state_size = ggml_nbytes(state);
-    const uint64_t dst_size = ggml_nbytes(dst);
-
-    std::array<uint32_t, 3> elements = {
-        (uint32_t)(pc.B * pc.H),
-        1,
-        1
-    };
-
-    ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, {
-        vk_subbuffer{ d_K, k_offset, k_size },
-        vk_subbuffer{ d_V, v_offset, v_size },
-        vk_subbuffer{ d_R, r_offset, r_size },
-        vk_subbuffer{ d_TF, tf_offset, tf_size },
-        vk_subbuffer{ d_TD, td_offset, td_size },
-        vk_subbuffer{ d_State, state_offset, state_size },
-        vk_subbuffer{ d_D, dst_offset, dst_size }
-    }, sizeof(vk_op_rwkv_wkv6_push_constants), &pc, elements);
-}
-
-static void ggml_vk_rwkv_wkv6(ggml_backend_vk_context * ctx, vk_context& subctx, ggml_tensor * dst, bool dryrun = false) {
-    const size_t seq_length = dst->src[0]->ne[3];
-    const size_t n_embed = dst->ne[0];
-    const size_t n_heads = dst->src[0]->ne[2];
-    const size_t n_seqs = dst->src[5]->ne[1];
-
-    ggml_vk_op_f32_rwkv6(
-        ctx, subctx, dst,
-        {
-            (uint32_t)n_seqs,
-            (uint32_t)seq_length,
-            (uint32_t)n_embed,
-            (uint32_t)n_heads,
-        },
-        dryrun
-    );
-}
-
 static void ggml_vk_concat(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, bool dryrun = false) {
     int * op_params = (int *)dst->op_params;
 
@@ -6713,7 +6569,6 @@ static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * nod
     case GGML_OP_IM2COL:
     case GGML_OP_TIMESTEP_EMBEDDING:
     case GGML_OP_POOL_2D:
-    case GGML_OP_RWKV_WKV6:
     case GGML_OP_LEAKY_RELU:
     case GGML_OP_FLASH_ATTN_EXT:
         break;
@@ -6913,11 +6768,6 @@ static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * nod
     case GGML_OP_FLASH_ATTN_EXT:
         ggml_vk_flash_attn(ctx, compute_ctx, src0, src1, src2, src3, node, dryrun);
 
-        break;
-
-    case GGML_OP_RWKV_WKV6:
-        ggml_vk_rwkv_wkv6(ctx, compute_ctx, node, dryrun);
-
         break;
     default:
         return false;
@@ -6998,7 +6848,6 @@ static bool ggml_vk_compute_forward(ggml_backend_vk_context * ctx, ggml_tensor *
     case GGML_OP_IM2COL:
     case GGML_OP_TIMESTEP_EMBEDDING:
     case GGML_OP_POOL_2D:
-    case GGML_OP_RWKV_WKV6:
     case GGML_OP_LEAKY_RELU:
     case GGML_OP_REPEAT:
         buf = tensor->buffer;
@@ -7875,7 +7724,6 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm
         case GGML_OP_IM2COL:
         case GGML_OP_TIMESTEP_EMBEDDING:
         case GGML_OP_POOL_2D:
-        case GGML_OP_RWKV_WKV6:
         case GGML_OP_LEAKY_RELU:
             return true;
         default:
@@ -8452,11 +8300,7 @@ static void ggml_vk_check_results_0(ggml_tensor * tensor) {
     } else if (tensor->op == GGML_OP_LEAKY_RELU) {
         const float * op_params = (const float *)tensor->op_params;
         tensor_clone = ggml_leaky_relu(ggml_ctx, src0_clone, op_params[0], false);
-    } else if (tensor->op == GGML_OP_RWKV_WKV6) {
-        tensor_clone = ggml_rwkv_wkv6(ggml_ctx, tensor->src[0], tensor->src[1], tensor->src[2], tensor->src[3],
-        tensor->src[4], tensor->src[5]);
-    }
-    else {
+    } else {
         std::cerr << "Missing vk_check_results OP: " << ggml_op_name(tensor->op) << std::endl;
         GGML_ABORT("fatal error");
     }

ggml/src/ggml-vulkan/vulkan-shaders/soft_max.comp

@@ -32,7 +32,7 @@ shared FLOAT_TYPE vals[BLOCK_SIZE];
 void soft_max(uint num_iters) {
     const uint tid = gl_LocalInvocationID.x;
     const uint rowx = gl_WorkGroupID.z * 262144 + gl_WorkGroupID.y * 512 + gl_WorkGroupID.x;
-    const uint rowy = (p.KY > 0) ? (rowx % p.KY) : 0;
+    const uint rowy = rowx % p.KY;
 
     if (rowx >= p.nrows_x) {
         return;

ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp

@@ -479,8 +479,6 @@ void process_shaders() {
 
     string_to_spv("pool2d_f32", "pool2d.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"D_TYPE", "float"}}));
 
-    string_to_spv("rwkv_wkv6_f32", "wkv6.comp", merge_maps(base_dict, {{"A_TYPE", "float"}}));
-
     for (auto &c : compiles) {
         c.wait();
     }

ggml/src/ggml-vulkan/vulkan-shaders/wkv6.comp

@@ -1,87 +0,0 @@
-#version 450
-
-#extension GL_EXT_control_flow_attributes : require
-
-#define BLOCK_SIZE 64
-layout(local_size_x = BLOCK_SIZE, local_size_y = 1, local_size_z = 1) in;
-
-layout(push_constant) uniform Parameters {
-    uint B;
-    uint T;
-    uint C;
-    uint H;
-};
-
-layout(binding = 0) readonly buffer KBuf { A_TYPE k[]; };
-layout(binding = 1) readonly buffer VBuf { A_TYPE v[]; };
-layout(binding = 2) readonly buffer RBuf { A_TYPE r[]; };
-layout(binding = 3) readonly buffer TimeFBuf { A_TYPE tf[]; };
-layout(binding = 4) readonly buffer TimeDBuf { A_TYPE td[]; };
-layout(binding = 5) readonly buffer StateBuf { A_TYPE state_in[]; };
-layout(binding = 6) buffer DstBuf { A_TYPE dst[]; };
-
-shared A_TYPE _k[BLOCK_SIZE], _r[BLOCK_SIZE], _tf[BLOCK_SIZE], _td[BLOCK_SIZE];
-
-void main() {
-    const uint head_size = BLOCK_SIZE;
-    const uint batch_id = gl_WorkGroupID.x / H;
-    const uint head_id = gl_WorkGroupID.x % H;
-    const uint tid = gl_LocalInvocationID.x;
-
-    const uint state_size = C * head_size;
-    const uint n_seq_tokens = T / B;
-
-    if (batch_id >= B || head_id >= H) {
-        return;
-    }
-
-    A_TYPE state[BLOCK_SIZE];
-    [[unroll]] for (uint i = 0; i < head_size; i++) {
-        state[i] = state_in[batch_id * state_size + head_id * head_size * head_size
-                          + i * head_size + tid];
-    }
-
-    barrier();
-    _tf[tid] = tf[head_id * head_size + tid];
-    barrier();
-
-    const uint start_t = batch_id * n_seq_tokens * C + head_id * head_size + tid;
-    const uint end_t = (batch_id + 1) * n_seq_tokens * C + head_id * head_size + tid;
-
-    for (uint t = start_t; t < end_t; t += C) {
-        barrier();
-        _k[tid] = k[t];
-        _r[tid] = r[t];
-        _td[tid] = td[t];
-        barrier();
-
-        const A_TYPE v_val = v[t];
-        A_TYPE y = 0.0;
-
-        [[unroll]] for (uint j = 0; j < head_size; j += 4) {
-            vec4 k_vec = vec4(_k[j], _k[j+1], _k[j+2], _k[j+3]);
-            vec4 r_vec = vec4(_r[j], _r[j+1], _r[j+2], _r[j+3]);
-            vec4 tf_vec = vec4(_tf[j], _tf[j+1], _tf[j+2], _tf[j+3]);
-            vec4 td_vec = vec4(_td[j], _td[j+1], _td[j+2], _td[j+3]);
-            vec4 s_vec = vec4(state[j], state[j+1], state[j+2], state[j+3]);
-
-            vec4 kv = k_vec * v_val;
-
-            vec4 temp = tf_vec * kv + s_vec;
-            y += dot(r_vec, temp);
-
-            s_vec = s_vec * td_vec + kv;
-            state[j] = s_vec.x;
-            state[j+1] = s_vec.y;
-            state[j+2] = s_vec.z;
-            state[j+3] = s_vec.w;
-        }
-
-        dst[t] = y;
-    }
-
-    [[unroll]] for (uint i = 0; i < head_size; i++) {
-        dst[T * C + batch_id * state_size + head_id * head_size * head_size
-            + i * head_size + tid] = state[i];
-    }
-}

ggml/src/ggml.c

@@ -6037,12 +6037,12 @@ struct ggml_tensor * ggml_graph_get_tensor(const struct ggml_cgraph * cgraph, co
 
 struct ggml_tensor * ggml_graph_get_grad(const struct ggml_cgraph * cgraph, const struct ggml_tensor * node) {
     const size_t igrad = ggml_hash_find(&cgraph->visited_hash_set, node);
-    return igrad != GGML_HASHSET_FULL && ggml_bitset_get(cgraph->visited_hash_set.used, igrad) && cgraph->grads ? cgraph->grads[igrad] : NULL;
+    return igrad != GGML_HASHSET_FULL && ggml_bitset_get(cgraph->visited_hash_set.used, igrad) ? cgraph->grads[igrad] : NULL;
 }
 
 struct ggml_tensor * ggml_graph_get_grad_acc(const struct ggml_cgraph * cgraph, const struct ggml_tensor * node) {
     const size_t igrad = ggml_hash_find(&cgraph->visited_hash_set, node);
-    return igrad != GGML_HASHSET_FULL && ggml_bitset_get(cgraph->visited_hash_set.used, igrad) && cgraph->grad_accs ? cgraph->grad_accs[igrad] : NULL;
+    return igrad != GGML_HASHSET_FULL && ggml_bitset_get(cgraph->visited_hash_set.used, igrad) ? cgraph->grad_accs[igrad] : NULL;
 }
 
 void ggml_graph_print(const struct ggml_cgraph * cgraph) {
@@ -6489,7 +6489,7 @@ struct gguf_context {
     void * data;
 };
 
-size_t gguf_type_size(enum gguf_type type) {
+static size_t gguf_type_size(enum gguf_type type) {
     GGML_ASSERT(0 <= type && type < GGUF_TYPE_COUNT);
     return GGUF_TYPE_SIZE[type];
 }
@@ -6617,7 +6617,13 @@ struct gguf_context * gguf_init_empty(void) {
     return ctx;
 }
 
-struct gguf_context * gguf_init_from_file_impl(FILE * file, struct gguf_init_params params) {
+struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_params params) {
+    FILE * file = ggml_fopen(fname, "rb");
+    if (!file) {
+        fprintf(stderr, "%s: failed to open '%s': '%s'\n", __func__, fname, strerror(errno));
+        return NULL;
+    }
+
     // offset from start of file
     size_t offset = 0;
 
@@ -6630,6 +6636,7 @@ struct gguf_context * gguf_init_from_file_impl(FILE * file, struct gguf_init_par
         for (uint32_t i = 0; i < sizeof(magic); i++) {
             if (magic[i] != GGUF_MAGIC[i]) {
                 fprintf(stderr, "%s: invalid magic characters '%c%c%c%c'\n", __func__, magic[0], magic[1], magic[2], magic[3]);
+                fclose(file);
                 return NULL;
             }
         }
@@ -6640,6 +6647,7 @@ struct gguf_context * gguf_init_from_file_impl(FILE * file, struct gguf_init_par
     struct gguf_context * ctx = calloc(1, sizeof(struct gguf_context));
     if (!ctx) {
         fprintf(stderr, "%s: failed to allocate memory for context\n", __func__);
+        fclose(file);
         return NULL;
     }
 
@@ -6657,6 +6665,7 @@ struct gguf_context * gguf_init_from_file_impl(FILE * file, struct gguf_init_par
 
         if (ctx->header.version == 1) {
             fprintf(stderr, "%s: GGUFv1 is no longer supported. please use a more up-to-date version\n", __func__);
+            fclose(file);
             gguf_free(ctx);
             return NULL;
         }
@@ -6669,6 +6678,7 @@ struct gguf_context * gguf_init_from_file_impl(FILE * file, struct gguf_init_par
 
         if (!ok) {
             fprintf(stderr, "%s: failed to read header\n", __func__);
+            fclose(file);
             gguf_free(ctx);
             return NULL;
         }
@@ -6678,13 +6688,12 @@ struct gguf_context * gguf_init_from_file_impl(FILE * file, struct gguf_init_par
     {
         const uint64_t n_kv = ctx->header.n_kv;
 
-        if (n_kv > 0) {
-            ctx->kv = calloc(n_kv, sizeof(struct gguf_kv));
-            if (!ctx->kv) {
-                fprintf(stderr, "%s: failed to allocate memory for kv pairs\n", __func__);
-                gguf_free(ctx);
-                return NULL;
-            }
+        ctx->kv = calloc(n_kv, sizeof(struct gguf_kv));
+        if (!ctx->kv) {
+            fprintf(stderr, "%s: failed to allocate memory for kv pairs\n", __func__);
+            fclose(file);
+            gguf_free(ctx);
+            return NULL;
         }
 
         for (uint64_t i = 0; i < n_kv; ++i) {
@@ -6731,6 +6740,7 @@ struct gguf_context * gguf_init_from_file_impl(FILE * file, struct gguf_init_par
                                     // prevent from integer overflow in the malloc below
                                     if (kv->value.arr.n >= SIZE_MAX/gguf_type_size(kv->value.arr.type)) {
                                         fprintf(stderr, "%s: array size is too large (%" PRIu64 ")\n", __func__, kv->value.arr.n);
+                                        fclose(file);
                                         gguf_free(ctx);
                                         return NULL;
                                     }
@@ -6738,6 +6748,7 @@ struct gguf_context * gguf_init_from_file_impl(FILE * file, struct gguf_init_par
                                     kv->value.arr.data = calloc(kv->value.arr.n, gguf_type_size(kv->value.arr.type));
                                     if (!kv->value.arr.data) {
                                         fprintf(stderr, "%s: failed to allocate memory for array\n", __func__);
+                                        fclose(file);
                                         gguf_free(ctx);
                                         return NULL;
                                     }
@@ -6749,6 +6760,7 @@ struct gguf_context * gguf_init_from_file_impl(FILE * file, struct gguf_init_par
                                     // prevent from integer overflow in the malloc below
                                     if (kv->value.arr.n >= SIZE_MAX/sizeof(struct gguf_str)) {
                                         fprintf(stderr, "%s: array size is too large (%" PRIu64 ")\n", __func__, kv->value.arr.n);
+                                        fclose(file);
                                         gguf_free(ctx);
                                         return NULL;
                                     }
@@ -6756,6 +6768,7 @@ struct gguf_context * gguf_init_from_file_impl(FILE * file, struct gguf_init_par
                                     kv->value.arr.data = calloc(kv->value.arr.n, sizeof(struct gguf_str));
                                     if (!kv->value.arr.data) {
                                         fprintf(stderr, "%s: failed to allocate memory for array\n", __func__);
+                                        fclose(file);
                                         gguf_free(ctx);
                                         return NULL;
                                     }
@@ -6786,6 +6799,7 @@ struct gguf_context * gguf_init_from_file_impl(FILE * file, struct gguf_init_par
 
         if (!ok) {
             fprintf(stderr, "%s: failed to read key-value pairs\n", __func__);
+            fclose(file);
             gguf_free(ctx);
             return NULL;
         }
@@ -6796,6 +6810,7 @@ struct gguf_context * gguf_init_from_file_impl(FILE * file, struct gguf_init_par
         ctx->infos = calloc(ctx->header.n_tensors, sizeof(struct gguf_tensor_info));
         if (!ctx->infos) {
             fprintf(stderr, "%s: failed to allocate memory for tensor infos\n", __func__);
+            fclose(file);
             gguf_free(ctx);
             return NULL;
         }
@@ -6831,6 +6846,7 @@ struct gguf_context * gguf_init_from_file_impl(FILE * file, struct gguf_init_par
 
             if (!ok) {
                 fprintf(stderr, "%s: failed to read tensor info\n", __func__);
+                fclose(file);
                 gguf_free(ctx);
                 return NULL;
             }
@@ -6873,6 +6889,7 @@ struct gguf_context * gguf_init_from_file_impl(FILE * file, struct gguf_init_par
                 // this tensor type support have been removed:
                 fprintf(stderr, "%s: tensor '%s' of type %d: %s\n",
                         __func__, info->name.data, (int) info->type, ggml_type_name(info->type));
+                fclose(file);
                 gguf_free(ctx);
                 return NULL;
             }
@@ -6880,6 +6897,7 @@ struct gguf_context * gguf_init_from_file_impl(FILE * file, struct gguf_init_par
             if (ne % ggml_blck_size(info->type) != 0) {
                 fprintf(stderr, "%s: tensor '%s' of type %d (%s) number of elements (%" PRId64 ") is not a multiple of block size (%" PRId64 ")\n",
                         __func__, info->name.data, (int) info->type, ggml_type_name(info->type), ne, ggml_blck_size(info->type));
+                fclose(file);
                 gguf_free(ctx);
                 return NULL;
             }
@@ -6911,6 +6929,7 @@ struct gguf_context * gguf_init_from_file_impl(FILE * file, struct gguf_init_par
         *params.ctx = ggml_init(pdata);
         if (*params.ctx == NULL) {
             fprintf(stderr, "%s: failed to initialize context\n", __func__);
+            fclose(file);
             gguf_free(ctx);
             return NULL;
         }
@@ -6929,6 +6948,7 @@ struct gguf_context * gguf_init_from_file_impl(FILE * file, struct gguf_init_par
 
             if (!ok) {
                 fprintf(stderr, "%s: failed to read tensor data\n", __func__);
+                fclose(file);
                 ggml_free(ctx_data);
                 gguf_free(ctx);
                 return NULL;
@@ -6967,6 +6987,7 @@ struct gguf_context * gguf_init_from_file_impl(FILE * file, struct gguf_init_par
 
         if (!ok) {
             fprintf(stderr, "%s: failed to read the tensor data\n", __func__);
+            fclose(file);
             ggml_free(ctx_data);
             gguf_free(ctx);
             return NULL;
@@ -6975,19 +6996,9 @@ struct gguf_context * gguf_init_from_file_impl(FILE * file, struct gguf_init_par
         ggml_set_no_alloc(ctx_data, params.no_alloc);
     }
 
-    return ctx;
-}
-
-struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_params params) {
-    FILE * file = ggml_fopen(fname, "rb");
-    if (!file) {
-        fprintf(stderr, "%s: failed to open '%s': '%s'\n", __func__, fname, strerror(errno));
-        return NULL;
-    }
-
-    struct gguf_context * result = gguf_init_from_file_impl(file, params);
     fclose(file);
-    return result;
+
+    return ctx;
 }
 
 void gguf_free(struct gguf_context * ctx) {
@@ -7449,7 +7460,13 @@ void gguf_set_tensor_data(struct gguf_context * ctx, const char * name, const vo
 //    fwrite(val, sizeof(char), size, file);
 //}
 
-struct gguf_buf gguf_buf_init(size_t size) {
+struct gguf_buf {
+    void * data;
+    size_t size;
+    size_t offset;
+};
+
+static struct gguf_buf gguf_buf_init(size_t size) {
     struct gguf_buf buf = {
         /*buf.data   =*/ size == 0 ? NULL : GGML_CALLOC(1, size),
         /*buf.size   =*/ size,
@@ -7459,7 +7476,7 @@ struct gguf_buf gguf_buf_init(size_t size) {
     return buf;
 }
 
-void gguf_buf_free(struct gguf_buf buf) {
+static void gguf_buf_free(struct gguf_buf buf) {
     if (buf.data) {
         GGML_FREE(buf.data);
     }
@@ -7497,7 +7514,7 @@ static void gguf_bwrite_el(struct gguf_buf * buf, const void * val, size_t el_si
     buf->offset += el_size;
 }
 
-void gguf_write_to_buf(const struct gguf_context * ctx, struct gguf_buf * buf, bool only_meta) {
+static void gguf_write_to_buf(const struct gguf_context * ctx, struct gguf_buf * buf, bool only_meta) {
     // write header
     gguf_bwrite_el(buf, &ctx->header.magic,     sizeof(ctx->header.magic));
     gguf_bwrite_el(buf, &ctx->header.version,   sizeof(ctx->header.version));

gguf-py/gguf/constants.py

@@ -249,7 +249,6 @@ class MODEL_ARCH(IntEnum):
     OLMOE        = auto()
     OPENELM      = auto()
     ARCTIC       = auto()
-    DEEPSEEK     = auto()
     DEEPSEEK2    = auto()
     CHATGLM      = auto()
     BITNET       = auto()
@@ -413,7 +412,6 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
     MODEL_ARCH.OLMOE:          "olmoe",
     MODEL_ARCH.OPENELM:        "openelm",
     MODEL_ARCH.ARCTIC:         "arctic",
-    MODEL_ARCH.DEEPSEEK:       "deepseek",
     MODEL_ARCH.DEEPSEEK2:      "deepseek2",
     MODEL_ARCH.CHATGLM:        "chatglm",
     MODEL_ARCH.BITNET:         "bitnet",
@@ -1160,29 +1158,6 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
         MODEL_TENSOR.FFN_DOWN_EXP,
         MODEL_TENSOR.FFN_UP_EXP,
     ],
-    MODEL_ARCH.DEEPSEEK: [
-        MODEL_TENSOR.TOKEN_EMBD,
-        MODEL_TENSOR.OUTPUT_NORM,
-        MODEL_TENSOR.OUTPUT,
-        MODEL_TENSOR.ROPE_FREQS,
-        MODEL_TENSOR.ATTN_NORM,
-        MODEL_TENSOR.ATTN_Q,
-        MODEL_TENSOR.ATTN_K,
-        MODEL_TENSOR.ATTN_V,
-        MODEL_TENSOR.ATTN_OUT,
-        MODEL_TENSOR.ATTN_ROT_EMBD,
-        MODEL_TENSOR.FFN_GATE_INP,
-        MODEL_TENSOR.FFN_NORM,
-        MODEL_TENSOR.FFN_GATE,
-        MODEL_TENSOR.FFN_DOWN,
-        MODEL_TENSOR.FFN_UP,
-        MODEL_TENSOR.FFN_GATE_EXP,
-        MODEL_TENSOR.FFN_DOWN_EXP,
-        MODEL_TENSOR.FFN_UP_EXP,
-        MODEL_TENSOR.FFN_GATE_SHEXP,
-        MODEL_TENSOR.FFN_DOWN_SHEXP,
-        MODEL_TENSOR.FFN_UP_SHEXP,
-    ],
     MODEL_ARCH.DEEPSEEK2: [
         MODEL_TENSOR.TOKEN_EMBD,
         MODEL_TENSOR.OUTPUT_NORM,
@@ -1405,10 +1380,6 @@ MODEL_TENSOR_SKIP: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
         MODEL_TENSOR.ROPE_FREQS,
         MODEL_TENSOR.ATTN_ROT_EMBD,
     ],
-    MODEL_ARCH.DEEPSEEK: [
-        MODEL_TENSOR.ROPE_FREQS,
-        MODEL_TENSOR.ATTN_ROT_EMBD,
-    ],
     MODEL_ARCH.DEEPSEEK2: [
         MODEL_TENSOR.ROPE_FREQS,
         MODEL_TENSOR.ATTN_ROT_EMBD,

gguf-py/gguf/tensor_mapping.py

@@ -306,7 +306,7 @@ class TensorNameMap:
 
         MODEL_TENSOR.FFN_UP_SHEXP: (
             "model.layers.{bid}.mlp.shared_expert.up_proj",  # qwen2moe
-            "model.layers.{bid}.mlp.shared_experts.up_proj", # deepseek deepseek2
+            "model.layers.{bid}.mlp.shared_experts.up_proj", # deepseek2
         ),
 
         # AWQ-activation gate
@@ -338,7 +338,7 @@ class TensorNameMap:
 
         MODEL_TENSOR.FFN_GATE_SHEXP: (
             "model.layers.{bid}.mlp.shared_expert.gate_proj",  # qwen2moe
-            "model.layers.{bid}.mlp.shared_experts.gate_proj", # deepseek deepseek2
+            "model.layers.{bid}.mlp.shared_experts.gate_proj", # deepseek2
         ),
 
         # Feed-forward down
@@ -379,7 +379,7 @@ class TensorNameMap:
 
         MODEL_TENSOR.FFN_DOWN_SHEXP: (
             "model.layers.{bid}.mlp.shared_expert.down_proj",  # qwen2moe
-            "model.layers.{bid}.mlp.shared_experts.down_proj", # deepseek deepseek2
+            "model.layers.{bid}.mlp.shared_experts.down_proj", # deepseek2
         ),
 
         MODEL_TENSOR.ATTN_Q_NORM: (

gguf-py/pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "gguf"
-version = "0.13.0"
+version = "0.11.0"
 description = "Read and write ML models in GGUF for GGML"
 authors = ["GGML <ggml@ggml.ai>"]
 packages = [

include/llama.h

@@ -1139,12 +1139,16 @@ extern "C" {
                           const char * grammar_str,
                           const char * grammar_root);
 
-    /// NOTE: Avoid using on the full vocabulary as searching for repeated tokens can become slow. For example, apply top-k or top-p sampling first.
     LLAMA_API struct llama_sampler * llama_sampler_init_penalties(
-                             int32_t   penalty_last_n,   // last n tokens to penalize (0 = disable penalty, -1 = context size)
-                               float   penalty_repeat,   // 1.0 = disabled
-                               float   penalty_freq,     // 0.0 = disabled
-                               float   penalty_present); // 0.0 = disabled
+                             int32_t   n_vocab,         // llama_n_vocab()
+                         llama_token   special_eos_id,  // llama_token_eos()
+                         llama_token   linefeed_id,     // llama_token_nl()
+                             int32_t   penalty_last_n,  // last n tokens to penalize (0 = disable penalty, -1 = context size)
+                               float   penalty_repeat,  // 1.0 = disabled
+                               float   penalty_freq,    // 0.0 = disabled
+                               float   penalty_present, // 0.0 = disabled
+                                bool   penalize_nl,     // consider newlines as a repeatable token
+                                bool   ignore_eos);     // ignore the end-of-sequence token
 
     ///  @details DRY sampler, designed by p-e-w, as described in: https://github.com/oobabooga/text-generation-webui/pull/5677, porting Koboldcpp implementation authored by pi6am: https://github.com/LostRuins/koboldcpp/pull/982
     LLAMA_API struct llama_sampler *    llama_sampler_init_dry(

scripts/compare-commits.sh

@@ -20,13 +20,11 @@ if [ -n "$GGML_CUDA" ]; then
     cmake_opts="-DGGML_CUDA=ON"
 fi
 
-dir="build-bench"
-
 function run {
-    rm -fr ${dir} > /dev/null
-    cmake -B ${dir} -S . $cmake_opts > /dev/null
-    cmake --build ${dir} -t llama-bench > /dev/null
-    ${dir}/bin/llama-bench -o sql -oe md $bench_args | sqlite3 llama-bench.sqlite
+    rm -fr build > /dev/null
+    cmake -B build -S . $cmake_opts > /dev/null
+    cmake --build build -t llama-bench > /dev/null
+    build/bin/llama-bench -o sql -oe md $bench_args | sqlite3 llama-bench.sqlite
 }
 
 git checkout $1 > /dev/null

scripts/sync-ggml.last

@@ -1 +1 @@
-e6d93f40dffe8733d5d72f1d8fa6b3ca27ae899f
+74d66b63eaf207a24f3e93bb922aba131cbf2906

src/llama-sampling.cpp

@@ -1396,15 +1396,19 @@ struct llama_sampler * llama_sampler_init_grammar_impl(const struct llama_vocab
 // penalties
 
 struct llama_sampler_penalties {
+    const int32_t     n_vocab;
+    const llama_token special_eos_id;
+    const llama_token linefeed_id;
+
     const int32_t penalty_last_n;
     const float   penalty_repeat;
     const float   penalty_freq;
     const float   penalty_present;
 
-    ring_buffer<llama_token> prev;
+    const bool    penalize_nl;
+    const bool    ignore_eos;
 
-    // a frequency map to count token occurrences
-    std::unordered_map<llama_token, int> token_count;
+    ring_buffer<llama_token> prev;
 };
 
 static const char * llama_sampler_penalties_name(const struct llama_sampler * /*smpl*/) {
@@ -1417,50 +1421,76 @@ static void llama_sampler_penalties_accept(struct llama_sampler * smpl, llama_to
         return;
     }
 
-    ctx->token_count[token]++;
-
-    // if the ring buffer is full, remove the oldest token
-    if (ctx->prev.size() >= (size_t) ctx->penalty_last_n) {
-        const auto old = ctx->prev.front();
-
-        ctx->token_count[old]--;
-        if (ctx->token_count[old] == 0) {
-            ctx->token_count.erase(old);
-        }
-    }
-
     ctx->prev.push_back(token);
-
-#if 0
-    // sanity check
-    std::unordered_map<llama_token, int> tmp;
-    for (int i = 0; i < std::min<int>(ctx->penalty_last_n, ctx->prev.size()); ++i) {
-        tmp[ctx->prev.rat(i)]++;
-    }
-
-    assert(ctx->token_count == tmp);
-#endif
 }
 
 static void llama_sampler_penalties_apply(struct llama_sampler * smpl, llama_token_data_array * cur_p) {
     auto * ctx = (llama_sampler_penalties *) smpl->ctx;
 
+    if (ctx->ignore_eos) {
+        assert(ctx->special_eos_id >= 0);
+
+        // optimistically check if the candidates are not yet sorted/shuffled/truncated
+        if (cur_p->size > (size_t) ctx->special_eos_id && cur_p->data[ctx->special_eos_id].id == ctx->special_eos_id) {
+            cur_p->data[ctx->special_eos_id].logit = -INFINITY;
+        } else {
+            // else, search for the special EOS token
+            for (size_t i = 0; i < cur_p->size; ++i) {
+                if (cur_p->data[i].id == ctx->special_eos_id) {
+                    cur_p->data[i].logit = -INFINITY;
+                    break;
+                }
+            }
+        }
+    }
+
     if ((ctx->penalty_last_n == 0) ||
         (ctx->penalty_repeat == 1.0f && ctx->penalty_freq == 0.0f && ctx->penalty_present == 0.0f)) {
         return;
     }
 
+    bool nl_found = false;
+    size_t nl_idx = 0;
+    float nl_logit = -INFINITY;
+    if (!ctx->penalize_nl) {
+        assert(ctx->linefeed_id >= 0);
+
+        // optimistically check if the candidates are not yet sorted/shuffled/truncated
+        if (cur_p->size > (size_t) ctx->linefeed_id && cur_p->data[ctx->linefeed_id].id == ctx->linefeed_id) {
+            nl_found = true;
+            nl_idx = ctx->linefeed_id;
+            nl_logit = cur_p->data[ctx->linefeed_id].logit;
+        } else {
+            // else, search for the linefeed token
+            for (size_t i = 0; i < cur_p->size; ++i) {
+                if (cur_p->data[i].id == ctx->linefeed_id) {
+                    nl_found = true;
+                    nl_idx = i;
+                    nl_logit = cur_p->data[i].logit;
+                    break;
+                }
+            }
+        }
+    }
+
+    // Create a frequency map to count occurrences of each token in last_tokens
+    // TODO: optimize this by maintaining the token count in the sampler context
+    using llama_token_cnt = std::unordered_map<llama_token, int>;
+    llama_token_cnt token_count;
+
+    for (int i = 0; i < std::min<int>(ctx->penalty_last_n, ctx->prev.size()); ++i) {
+        token_count[ctx->prev.rat(i)]++;
+    }
+
     // Apply frequency and presence penalties to the cur_p
     for (size_t i = 0; i < cur_p->size; ++i) {
-        const auto token_iter = ctx->token_count.find(cur_p->data[i].id);
-        if (token_iter == ctx->token_count.end()) {
+        const auto token_iter = token_count.find(cur_p->data[i].id);
+        if (token_iter == token_count.end()) {
             continue;
         }
 
         const int count = token_iter->second;
 
-        assert(count > 0 && count <= ctx->penalty_last_n);
-
         // The academic publication that described this technique actually just only divided, but that would cause tokens with negative logits to become more likely, which is obviously wrong.
         // This is common fix for this problem, which is to multiply by the penalty instead of dividing.
         if (cur_p->data[i].logit <= 0) {
@@ -1473,21 +1503,30 @@ static void llama_sampler_penalties_apply(struct llama_sampler * smpl, llama_tok
     }
 
     cur_p->sorted = false;
+
+    if (!ctx->penalize_nl && nl_found) {
+        // restore the logit of the newline token if it was penalized
+        cur_p->data[nl_idx].logit = nl_logit;
+    }
 }
 
 static void llama_sampler_penalties_reset(struct llama_sampler * smpl) {
     auto * ctx = (llama_sampler_penalties *) smpl->ctx;
     ctx->prev.clear();
-    ctx->token_count.clear();
 }
 
 static struct llama_sampler * llama_sampler_penalties_clone(const struct llama_sampler * smpl) {
     const auto * ctx = (const llama_sampler_penalties *) smpl->ctx;
     auto * result = llama_sampler_init_penalties(
+            ctx->n_vocab,
+            ctx->special_eos_id,
+            ctx->linefeed_id,
             ctx->penalty_last_n,
             ctx->penalty_repeat,
             ctx->penalty_freq,
-            ctx->penalty_present);
+            ctx->penalty_present,
+            ctx->penalize_nl,
+            ctx->ignore_eos);
 
     // copy the state
     {
@@ -1513,21 +1552,38 @@ static struct llama_sampler_i llama_sampler_penalties_i = {
 };
 
 struct llama_sampler * llama_sampler_init_penalties(
+        int32_t n_vocab,
+        llama_token special_eos_id,
+        llama_token linefeed_id,
         int32_t penalty_last_n,
         float penalty_repeat,
         float penalty_freq,
-        float penalty_present) {
+        float penalty_present,
+        bool penalize_nl,
+        bool ignore_eos) {
+    if (linefeed_id == LLAMA_TOKEN_NULL) {
+        penalize_nl = true;
+    }
+
+    if (special_eos_id == LLAMA_TOKEN_NULL) {
+        ignore_eos = false;
+    }
+
     penalty_last_n = std::max(penalty_last_n, 0);
 
     return new llama_sampler {
         /* .iface = */ &llama_sampler_penalties_i,
         /* .ctx   = */ new llama_sampler_penalties {
+            /* .n_vocab         = */ n_vocab,
+            /* .special_eos_id  = */ special_eos_id,
+            /* .linefeed_id     = */ linefeed_id,
             /* .penalty_last_n  = */ penalty_last_n,
             /* .penalty_repeat  = */ penalty_repeat,
             /* .penalty_freq    = */ penalty_freq,
             /* .penalty_present = */ penalty_present,
+            /* .penalize_nl     = */ penalize_nl,
+            /* .ignore_eos      = */ ignore_eos,
             /* .prev            = */ ring_buffer<llama_token>(penalty_last_n),
-            /* .token_count     = */ {},
         },
     };
 }
@@ -1555,8 +1611,7 @@ static void get_overlapping_token_sequences(const llama_vocab & vocab, const std
         if (word.find(str) != std::string::npos) {
             token_sequences.emplace(token_id, std::vector<llama_token>());
         } else {
-            size_t word_len = word.size();
-            size_t str_len = str.size();
+            size_t word_len = word.size(), str_len = str.size();
             size_t pos = -1;
             while ((pos = word.find(str[0], pos + 1)) != std::string::npos) {
                 bool match = true;

src/llama.cpp

@@ -184,7 +184,6 @@ enum llm_arch {
     LLM_ARCH_OLMOE,
     LLM_ARCH_OPENELM,
     LLM_ARCH_ARCTIC,
-    LLM_ARCH_DEEPSEEK,
     LLM_ARCH_DEEPSEEK2,
     LLM_ARCH_CHATGLM,
     LLM_ARCH_BITNET,
@@ -240,7 +239,6 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
     { LLM_ARCH_OLMOE,           "olmoe"        },
     { LLM_ARCH_OPENELM,         "openelm"      },
     { LLM_ARCH_ARCTIC,          "arctic"       },
-    { LLM_ARCH_DEEPSEEK,        "deepseek"     },
     { LLM_ARCH_DEEPSEEK2,       "deepseek2"    },
     { LLM_ARCH_CHATGLM,         "chatglm"      },
     { LLM_ARCH_BITNET,          "bitnet"       },
@@ -1311,33 +1309,6 @@ static const std::map<llm_arch, std::map<llm_tensor, const char *>> LLM_TENSOR_N
             { LLM_TENSOR_FFN_UP_EXPS,     "blk.%d.ffn_up_exps" },
         },
     },
-    {
-        LLM_ARCH_DEEPSEEK,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,         "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,        "output_norm" },
-            { LLM_TENSOR_OUTPUT,             "output" },
-            { LLM_TENSOR_ROPE_FREQS,         "rope_freqs" },
-            { LLM_TENSOR_ATTN_NORM,          "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_Q,             "blk.%d.attn_q" },
-            { LLM_TENSOR_ATTN_K,             "blk.%d.attn_k" },
-            { LLM_TENSOR_ATTN_V,             "blk.%d.attn_v" },
-            { LLM_TENSOR_ATTN_OUT,           "blk.%d.attn_output" },
-            { LLM_TENSOR_ATTN_ROT_EMBD,      "blk.%d.attn_rot_embd" },
-            { LLM_TENSOR_FFN_GATE_INP,       "blk.%d.ffn_gate_inp" },
-            { LLM_TENSOR_FFN_NORM,           "blk.%d.ffn_norm" },
-            { LLM_TENSOR_FFN_GATE,           "blk.%d.ffn_gate" },
-            { LLM_TENSOR_FFN_DOWN,           "blk.%d.ffn_down" },
-            { LLM_TENSOR_FFN_UP,             "blk.%d.ffn_up" },
-            { LLM_TENSOR_FFN_GATE_EXPS,      "blk.%d.ffn_gate_exps" },
-            { LLM_TENSOR_FFN_DOWN_EXPS,      "blk.%d.ffn_down_exps" },
-            { LLM_TENSOR_FFN_UP_EXPS,        "blk.%d.ffn_up_exps" },
-            { LLM_TENSOR_FFN_GATE_INP_SHEXP, "blk.%d.ffn_gate_inp_shexp" },
-            { LLM_TENSOR_FFN_GATE_SHEXP,     "blk.%d.ffn_gate_shexp" },
-            { LLM_TENSOR_FFN_DOWN_SHEXP,     "blk.%d.ffn_down_shexp" },
-            { LLM_TENSOR_FFN_UP_SHEXP,       "blk.%d.ffn_up_shexp" },
-        },
-    },
     {
         LLM_ARCH_DEEPSEEK2,
         {
@@ -1612,7 +1583,6 @@ enum llm_chat_template {
     LLM_CHAT_TEMPLATE_MISTRAL_V3_TEKKEN,
     LLM_CHAT_TEMPLATE_MISTRAL_V7,
     LLM_CHAT_TEMPLATE_PHI_3,
-    LLM_CHAT_TEMPLATE_FALCON_3,
     LLM_CHAT_TEMPLATE_ZEPHYR,
     LLM_CHAT_TEMPLATE_MONARCH,
     LLM_CHAT_TEMPLATE_GEMMA,
@@ -1630,7 +1600,6 @@ enum llm_chat_template {
     LLM_CHAT_TEMPLATE_EXAONE_3,
     LLM_CHAT_TEMPLATE_RWKV_WORLD,
     LLM_CHAT_TEMPLATE_GRANITE,
-    LLM_CHAT_TEMPLATE_GIGACHAT,
     LLM_CHAT_TEMPLATE_UNKNOWN,
 };
 
@@ -1645,7 +1614,6 @@ static const std::map<std::string, llm_chat_template> LLM_CHAT_TEMPLATES = {
     { "mistral-v3-tekken", LLM_CHAT_TEMPLATE_MISTRAL_V3_TEKKEN },
     { "mistral-v7",        LLM_CHAT_TEMPLATE_MISTRAL_V7        },
     { "phi3",              LLM_CHAT_TEMPLATE_PHI_3             },
-    { "falcon3",           LLM_CHAT_TEMPLATE_FALCON_3          },
     { "zephyr",            LLM_CHAT_TEMPLATE_ZEPHYR            },
     { "monarch",           LLM_CHAT_TEMPLATE_MONARCH           },
     { "gemma",             LLM_CHAT_TEMPLATE_GEMMA             },
@@ -1663,7 +1631,6 @@ static const std::map<std::string, llm_chat_template> LLM_CHAT_TEMPLATES = {
     { "exaone3",           LLM_CHAT_TEMPLATE_EXAONE_3          },
     { "rwkv-world",        LLM_CHAT_TEMPLATE_RWKV_WORLD        },
     { "granite",           LLM_CHAT_TEMPLATE_GRANITE           },
-    { "gigachat",          LLM_CHAT_TEMPLATE_GIGACHAT          },
 };
 
 static llm_arch llm_arch_from_string(const std::string & name) {
@@ -6127,19 +6094,6 @@ static void llm_load_hparams(
                     model.type = e_model::MODEL_UNKNOWN;
                 }
             } break;
-        case LLM_ARCH_DEEPSEEK:
-            {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
-                ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT, hparams.n_layer_dense_lead);
-                ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp);
-                ml.get_key(LLM_KV_EXPERT_SHARED_COUNT, hparams.n_expert_shared);
-                ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE, hparams.expert_weights_scale);
-
-                switch (hparams.n_layer) {
-                    case 28: model.type = e_model::MODEL_20B; break;
-                    default: model.type = e_model::MODEL_UNKNOWN;
-                }
-            } break;
         case LLM_ARCH_DEEPSEEK2:
             {
                 bool is_lite = (hparams.n_layer == 27);
@@ -6475,11 +6429,6 @@ static void llm_load_vocab(
             } else if (
                     tokenizer_pre == "falcon") {
                 vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_FALCON;
-            } else if (
-                    tokenizer_pre == "falcon3") {
-                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_LLAMA3;
-                vocab.tokenizer_ignore_merges = true;
-                vocab.tokenizer_add_bos = true;
             } else if (
                     tokenizer_pre == "mpt") {
                 vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_MPT;
@@ -6491,7 +6440,6 @@ static void llm_load_vocab(
                     tokenizer_pre == "phi-2"   ||
                     tokenizer_pre == "jina-es" ||
                     tokenizer_pre == "jina-de" ||
-                    tokenizer_pre == "gigachat"   ||
                     tokenizer_pre == "jina-v1-en" ||
                     tokenizer_pre == "jina-v2-es" ||
                     tokenizer_pre == "jina-v2-de" ||
@@ -7143,13 +7091,6 @@ static void llm_load_print_meta(llama_model_loader & ml, llama_model & model) {
 
     LLAMA_LOG_INFO("%s: max token length = %d\n", __func__, vocab.max_token_len);
 
-    if (model.arch == LLM_ARCH_DEEPSEEK) {
-        LLAMA_LOG_INFO("%s: n_layer_dense_lead   = %d\n",     __func__, hparams.n_layer_dense_lead);
-        LLAMA_LOG_INFO("%s: n_ff_exp             = %d\n",     __func__, hparams.n_ff_exp);
-        LLAMA_LOG_INFO("%s: n_expert_shared      = %d\n",     __func__, hparams.n_expert_shared);
-        LLAMA_LOG_INFO("%s: expert_weights_scale = %.1f\n",   __func__, hparams.expert_weights_scale);
-    }
-
     if (model.arch == LLM_ARCH_DEEPSEEK2) {
         LLAMA_LOG_INFO("%s: n_layer_dense_lead   = %d\n",     __func__, hparams.n_layer_dense_lead);
         LLAMA_LOG_INFO("%s: n_lora_q             = %d\n",     __func__, hparams.n_lora_q);
@@ -8924,55 +8865,6 @@ static bool llm_load_tensors(
                         layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {n_embd,   n_ff, n_expert}, 0);
                     }
                 } break;
-            case LLM_ARCH_DEEPSEEK:
-                {
-
-                    const int64_t n_ff_exp        = hparams.n_ff_exp;
-                    const int64_t n_expert_shared = hparams.n_expert_shared;
-
-                    model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-
-                    // output
-                    model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-                    model.output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        auto & layer = model.layers[i];
-
-                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
-
-                        layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd}, 0);
-                        layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_gqa}, 0);
-                        layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_gqa}, 0);
-                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
-                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
-
-                        if (i < (int) hparams.n_layer_dense_lead) {
-                            layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
-                            layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
-                            layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
-                        } else {
-                            layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0);
-
-                            if (n_expert == 0) {
-                                throw std::runtime_error("n_expert must be > 0");
-                            }
-                            if (n_expert_used == 0) {
-                                throw std::runtime_error("n_expert_used must be > 0");
-                            }
-
-                            // MoE branch
-                            layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {  n_embd, n_ff_exp, n_expert}, 0);
-                            layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp,   n_embd, n_expert}, 0);
-                            layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {  n_embd, n_ff_exp, n_expert}, 0);
-
-                            // Shared expert branch
-                            layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, n_ff_exp * n_expert_shared}, 0);
-                            layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {        n_ff_exp * n_expert_shared, n_embd}, 0);
-                            layer.ffn_up_shexp   = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP,   "weight", i), {n_embd, n_ff_exp * n_expert_shared}, 0);
-                        }
-                    }
-                } break;
             case LLM_ARCH_DEEPSEEK2:
                 {
                     const bool is_lite = (hparams.n_layer == 27);
@@ -15327,161 +15219,6 @@ struct llm_build_context {
         return gf;
     }
 
-    struct ggml_cgraph * build_deepseek() {
-        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);
-
-        // mutable variable, needed during the last layer of the computation to skip unused tokens
-        int32_t n_tokens = this->n_tokens;
-
-        const int64_t n_embd_head = hparams.n_embd_head_v;
-        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
-        GGML_ASSERT(n_embd_head == hparams.n_rot);
-
-        struct ggml_tensor * cur;
-        struct ggml_tensor * inpL;
-
-        inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb);
-
-        // inp_pos - contains the positions
-        struct ggml_tensor * inp_pos = build_inp_pos();
-
-        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
-        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
-        const float kq_scale = hparams.f_attention_scale == 0.0f ? 1.0f/sqrtf(float(n_embd_head)) : hparams.f_attention_scale;
-        for (int il = 0; il < n_layer; ++il) {
-            struct ggml_tensor * inpSA = inpL;
-
-            // norm
-            cur = llm_build_norm(ctx0, inpL, hparams,
-                    model.layers[il].attn_norm, NULL,
-                    LLM_NORM_RMS, cb, il);
-            cb(cur, "attn_norm", il);
-
-            // self-attention
-            {
-                // rope freq factors for llama3; may return nullptr for llama2 and other models
-                struct ggml_tensor * rope_factors = build_rope_factors(il);
-
-                // compute Q and K and RoPE them
-                struct ggml_tensor * Qcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, cur);
-                cb(Qcur, "Qcur", il);
-                if (model.layers[il].bq) {
-                    Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
-                    cb(Qcur, "Qcur", il);
-                }
-
-                struct ggml_tensor * Kcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, cur);
-                cb(Kcur, "Kcur", il);
-                if (model.layers[il].bk) {
-                    Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
-                    cb(Kcur, "Kcur", il);
-                }
-
-                struct ggml_tensor * Vcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, cur);
-                cb(Vcur, "Vcur", il);
-                if (model.layers[il].bv) {
-                    Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
-                    cb(Vcur, "Vcur", il);
-                }
-
-                Qcur = ggml_rope_ext(
-                    ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, rope_factors,
-                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
-                    ext_factor, attn_factor, beta_fast, beta_slow
-                );
-                cb(Qcur, "Qcur", il);
-
-                Kcur = ggml_rope_ext(
-                    ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, rope_factors,
-                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
-                    ext_factor, attn_factor, beta_fast, beta_slow
-                );
-                cb(Kcur, "Kcur", il);
-
-                cur = llm_build_kv(ctx0, lctx, kv_self, gf,
-                        model.layers[il].wo, model.layers[il].bo,
-                        Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, kq_scale, cb, il);
-            }
-
-            if (il == n_layer - 1) {
-                // skip computing output for unused tokens
-                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
-                n_tokens = n_outputs;
-                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
-                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
-            }
-
-
-            struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
-            cb(ffn_inp, "ffn_inp", il);
-
-            cur = llm_build_norm(ctx0, ffn_inp, hparams,
-                    model.layers[il].ffn_norm, NULL,
-                    LLM_NORM_RMS, cb, il);
-            cb(cur, "ffn_norm", il);
-
-            if ((uint32_t) il < hparams.n_layer_dense_lead) {
-                cur = llm_build_ffn(ctx0, lctx, cur,
-                        model.layers[il].ffn_up,   NULL, NULL,
-                        model.layers[il].ffn_gate, NULL, NULL,
-                        model.layers[il].ffn_down, NULL, NULL,
-                        NULL,
-                        LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
-                cb(cur, "ffn_out", il);
-            } else {
-                // MoE branch
-                ggml_tensor * moe_out =
-                        llm_build_moe_ffn(ctx0, lctx, cur,
-                            model.layers[il].ffn_gate_inp,
-                            model.layers[il].ffn_up_exps,
-                            model.layers[il].ffn_gate_exps,
-                            model.layers[il].ffn_down_exps,
-                            n_expert, n_expert_used,
-                            LLM_FFN_SILU, false,
-                            false, hparams.expert_weights_scale,
-                            cb, il);
-                cb(moe_out, "ffn_moe_out", il);
-
-                // FFN shared expert
-                {
-                    ggml_tensor * ffn_shexp = llm_build_ffn(ctx0, lctx, cur,
-                            model.layers[il].ffn_up_shexp,   NULL, NULL,
-                            model.layers[il].ffn_gate_shexp, NULL, NULL,
-                            model.layers[il].ffn_down_shexp, NULL, NULL,
-                            NULL,
-                            LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
-                    cb(ffn_shexp, "ffn_shexp", il);
-
-                    cur = ggml_add(ctx0, moe_out, ffn_shexp);
-                    cb(cur, "ffn_out", il);
-                }
-            }
-
-            cur = ggml_add(ctx0, cur, ffn_inp);
-            cur = lctx.cvec.apply_to(ctx0, cur, il);
-            cb(cur, "l_out", il);
-
-            // input for next layer
-            inpL = cur;
-        }
-
-        cur = inpL;
-
-        cur = llm_build_norm(ctx0, cur, hparams,
-                model.output_norm, NULL,
-                LLM_NORM_RMS, cb, -1);
-        cb(cur, "result_norm", -1);
-
-        // lm_head
-        cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
-
-        cb(cur, "result_output", -1);
-
-        ggml_build_forward_expand(gf, cur);
-
-        return gf;
-    }
-
     struct ggml_cgraph * build_deepseek2() {
         struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);
 
@@ -17169,10 +16906,6 @@ static struct ggml_cgraph * llama_build_graph(
             {
                 result = llm.build_arctic();
             } break;
-        case LLM_ARCH_DEEPSEEK:
-            {
-                result = llm.build_deepseek();
-            } break;
         case LLM_ARCH_DEEPSEEK2:
             {
                 result = llm.build_deepseek2();
@@ -20404,7 +20137,6 @@ enum llama_rope_type llama_rope_type(const struct llama_model * model) {
         case LLM_ARCH_COMMAND_R:
         case LLM_ARCH_OLMO:
         case LLM_ARCH_ARCTIC:
-        case LLM_ARCH_DEEPSEEK:
         case LLM_ARCH_DEEPSEEK2:
         case LLM_ARCH_CHATGLM:
         case LLM_ARCH_GRANITE:
@@ -22226,8 +21958,6 @@ static llm_chat_template llama_chat_detect_template(const std::string & tmpl) {
         }
     } else if (tmpl_contains("<|assistant|>") && tmpl_contains("<|end|>")) {
         return LLM_CHAT_TEMPLATE_PHI_3;
-    } else if (tmpl_contains("<|assistant|>") && tmpl_contains("<|user|>")) {
-        return LLM_CHAT_TEMPLATE_FALCON_3;
     } else if (tmpl_contains("<|user|>") && tmpl_contains("<|endoftext|>")) {
         return LLM_CHAT_TEMPLATE_ZEPHYR;
     } else if (tmpl_contains("bos_token + message['role']")) {
@@ -22272,8 +22002,6 @@ static llm_chat_template llama_chat_detect_template(const std::string & tmpl) {
         return LLM_CHAT_TEMPLATE_RWKV_WORLD;
     } else if (tmpl_contains("<|start_of_role|>")) {
         return LLM_CHAT_TEMPLATE_GRANITE;
-    } else if (tmpl_contains("message['role'] + additional_special_tokens[0] + message['content'] + additional_special_tokens[1]")) {
-        return LLM_CHAT_TEMPLATE_GIGACHAT;
     }
     return LLM_CHAT_TEMPLATE_UNKNOWN;
 }
@@ -22380,15 +22108,6 @@ static int32_t llama_chat_apply_template_internal(
         if (add_ass) {
             ss << "<|assistant|>\n";
         }
-    } else if (tmpl == LLM_CHAT_TEMPLATE_FALCON_3) {
-        // Falcon 3
-        for (auto message : chat) {
-            std::string role(message->role);
-            ss << "<|" << role << "|>\n" << message->content << "\n";
-        }
-        if (add_ass) {
-            ss << "<|assistant|>\n";
-        }
     } else if (tmpl == LLM_CHAT_TEMPLATE_ZEPHYR) {
         // zephyr template
         for (auto message : chat) {
@@ -22606,32 +22325,6 @@ static int32_t llama_chat_apply_template_internal(
         if (add_ass) {
             ss << "<|start_of_role|>assistant<|end_of_role|>\n";
         }
-    } else if (tmpl == LLM_CHAT_TEMPLATE_GIGACHAT) {
-        // GigaChat template
-        bool has_system = !chat.empty() && std::string(chat[0]->role) == "system";
-
-        // Handle system message if present
-        if (has_system) {
-            ss << "<s>" << chat[0]->content << "<|message_sep|>";
-        } else {
-            ss << "<s>";
-        }
-
-        // Process remaining messages
-        for (size_t i = has_system ? 1 : 0; i < chat.size(); i++) {
-            std::string role(chat[i]->role);
-            if (role == "user") {
-                ss << "user<|role_sep|>" << chat[i]->content << "<|message_sep|>"
-                << "available functions<|role_sep|>[]<|message_sep|>";
-            } else if (role == "assistant") {
-                ss << "assistant<|role_sep|>" << chat[i]->content << "<|message_sep|>";
-            }
-        }
-
-        // Add generation prompt if needed
-        if (add_ass) {
-            ss << "assistant<|role_sep|>";
-        }
     } else {
         // template not supported
         return -1;
@@ -22651,15 +22344,15 @@ int32_t llama_chat_apply_template(
     std::string curr_tmpl(tmpl == nullptr ? "" : tmpl);
     if (tmpl == nullptr) {
         GGML_ASSERT(model != nullptr);
-
-        // load template from model, if available
-        const auto & it = model->gguf_kv.find("tokenizer.chat_template");
-        if (it != model->gguf_kv.end() && it->second.size() > 0) {
-            curr_tmpl = it->second;
-        }
-        else {
+        // load template from model
+        std::vector<char> model_template(2048, 0); // longest known template is about 1200 bytes
+        std::string template_key = "tokenizer.chat_template";
+        int32_t res = llama_model_meta_val_str(model, template_key.c_str(), model_template.data(), model_template.size());
+        if (res < 0) {
             // worst case: there is no information about template, we will use chatml by default
-            curr_tmpl = "chatml";  // see llama_chat_apply_template_internal
+            curr_tmpl = "chatml"; // see llama_chat_apply_template_internal
+        } else {
+            curr_tmpl = std::string(model_template.data(), model_template.size());
         }
     }
 

tests/CMakeLists.txt

@@ -129,7 +129,6 @@ llama_target_and_test(test-arg-parser.cpp)
 llama_target_and_test(test-chat-template.cpp)
 
 # llama_target_and_test(test-opt.cpp) # SLOW
-llama_target_and_test(test-gguf.cpp)
 llama_target_and_test(test-backend-ops.cpp)
 
 llama_target_and_test(test-model-load-cancel.cpp  LABEL "model")

tests/test-backend-ops.cpp

@@ -3549,8 +3549,8 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
 
     for (ggml_type type_src : {GGML_TYPE_F16, GGML_TYPE_F32}) {
         for (ggml_type type_dst : all_types) {
-            test_cases.emplace_back(new test_cpy(type_src, type_dst, {256, 4, 4, 4}));
-            test_cases.emplace_back(new test_cpy(type_src, type_dst, {256, 2, 3, 4}, {0, 2, 1, 3})); // cpy by rows
+           test_cases.emplace_back(new test_cpy(type_src, type_dst, {256, 4, 4, 4}));
+           test_cases.emplace_back(new test_cpy(type_src, type_dst, {256, 2, 3, 4}, {0, 2, 1, 3})); // cpy by rows
         }
     }
     for (ggml_type type_src : {GGML_TYPE_F16, GGML_TYPE_F32}) {

tests/test-chat-template.cpp

@@ -75,8 +75,6 @@ int main(void) {
         "{%- if messages[0][\"role\"] == \"system\" %}\n    {%- set system_message = messages[0][\"content\"] %}\n    {%- set loop_messages = messages[1:] %}\n{%- else %}\n    {%- set loop_messages = messages %}\n{%- endif %}\n{%- if not tools is defined %}\n    {%- set tools = none %}\n{%- endif %}\n{%- set user_messages = loop_messages | selectattr(\"role\", \"equalto\", \"user\") | list %}\n\n{#- This block checks for alternating user/assistant messages, skipping tool calling messages #}\n{%- set ns = namespace() %}\n{%- set ns.index = 0 %}\n{%- for message in loop_messages %}\n    {%- if not (message.role == \"tool\" or message.role == \"tool_results\" or (message.tool_calls is defined and message.tool_calls is not none)) %}\n        {%- if (message[\"role\"] == \"user\") != (ns.index % 2 == 0) %}\n            {{- raise_exception(\"After the optional system message, conversation roles must alternate user/assistant/user/assistant/...\") }}\n        {%- endif %}\n        {%- set ns.index = ns.index + 1 %}\n    {%- endif %}\n{%- endfor %}\n\n{{- bos_token }}\n{%- for message in loop_messages %}\n    {%- if message[\"role\"] == \"user\" %}\n        {%- if tools is not none and (message == user_messages[-1]) %}\n            {{- \"[AVAILABLE_TOOLS][\" }}\n            {%- for tool in tools %}\n                {%- set tool = tool.function %}\n                {{- '{\"type\": \"function\", \"function\": {' }}\n                {%- for key, val in tool.items() if key != \"return\" %}\n                    {%- if val is string %}\n                        {{- '\"' + key + '\": \"' + val + '\"' }}\n                    {%- else %}\n                        {{- '\"' + key + '\": ' + val|tojson }}\n                    {%- endif %}\n                    {%- if not loop.last %}\n                        {{- \", \" }}\n                    {%- endif %}\n                {%- endfor %}\n                {{- \"}}\" }}\n                {%- if not loop.last %}\n                    {{- \", \" }}\n                {%- else %}\n                    {{- \"]\" }}\n                {%- endif %}\n            {%- endfor %}\n            {{- \"[/AVAILABLE_TOOLS]\" }}\n            {%- endif %}\n        {%- if loop.last and system_message is defined %}\n            {{- \"[INST]\" + system_message + \"\\n\\n\" + message[\"content\"] + \"[/INST]\" }}\n        {%- else %}\n            {{- \"[INST]\" + message[\"content\"] + \"[/INST]\" }}\n        {%- endif %}\n    {%- elif (message.tool_calls is defined and message.tool_calls is not none) %}\n        {{- \"[TOOL_CALLS][\" }}\n        {%- for tool_call in message.tool_calls %}\n            {%- set out = tool_call.function|tojson %}\n            {{- out[:-1] }}\n            {%- if not tool_call.id is defined or tool_call.id|length != 9 %}\n                {{- raise_exception(\"Tool call IDs should be alphanumeric strings with length 9!\") }}\n            {%- endif %}\n            {{- ', \"id\": \"' + tool_call.id + '\"}' }}\n            {%- if not loop.last %}\n                {{- \", \" }}\n            {%- else %}\n                {{- \"]\" + eos_token }}\n            {%- endif %}\n        {%- endfor %}\n    {%- elif message[\"role\"] == \"assistant\" %}\n        {{- message[\"content\"] + eos_token}}\n    {%- elif message[\"role\"] == \"tool_results\" or message[\"role\"] == \"tool\" %}\n        {%- if message.content is defined and message.content.content is defined %}\n            {%- set content = message.content.content %}\n        {%- else %}\n            {%- set content = message.content %}\n        {%- endif %}\n        {{- '[TOOL_RESULTS]{\"content\": ' + content|string + \", \" }}\n        {%- if not message.tool_call_id is defined or message.tool_call_id|length != 9 %}\n            {{- raise_exception(\"Tool call IDs should be alphanumeric strings with length 9!\") }}\n        {%- endif %}\n        {{- '\"call_id\": \"' + message.tool_call_id + '\"}[/TOOL_RESULTS]' }}\n    {%- else %}\n        {{- raise_exception(\"Only user and assistant roles are supported, with the exception of an initial optional system message!\") }}\n    {%- endif %}\n{%- endfor %}\n",
         // mistralai/Mistral-Large-Instruct-2411 (mistralai 'v7' template)
         "{{ bos_token }}{% for message in messages %}{% if message['role'] == 'user' %}{{ '[INST] ' + message['content'] + '[/INST]' }}{% elif message['role'] == 'system' %}{{ '[SYSTEM_PROMPT] ' + message['content'] + '[/SYSTEM_PROMPT]' }}{% elif message['role'] == 'assistant' %}{{ ' ' + message['content'] + eos_token }}{% else %}{{ raise_exception('Only user, system and assistant roles are supported!') }}{% endif %}{% endfor %}",
-        // ai-sage/GigaChat-20B-A3B-instruct
-        "{% if messages[0]['role'] == 'system' -%}\n    {%- set loop_messages = messages[1:] -%}\n    {%- set system_message = bos_token + messages[0]['content'] + additional_special_tokens[1] -%}\n{%- else -%}\n    {%- set loop_messages = messages -%}\n    {%- set system_message = bos_token + '' -%}\n{%- endif -%}\n{%- for message in loop_messages %}\n    {% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}\n        {{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}\n    {% endif %}\n    \n    {%- if loop.index0 == 0 -%}\n        {{ system_message -}}\n    {%- endif -%}\n    {%- if message['role'] == 'user' -%}\n        {{ message['role'] + additional_special_tokens[0] + message['content'] + additional_special_tokens[1] -}}\n        {{ 'available functions' + additional_special_tokens[0] + additional_special_tokens[2] + additional_special_tokens[3]  + additional_special_tokens[1] -}}\n    {%- endif -%}\n    {%- if message['role'] == 'assistant' -%}\n        {{ message['role'] + additional_special_tokens[0] + message['content'] + additional_special_tokens[1] -}}\n    {%- endif -%}\n    {%- if loop.last and add_generation_prompt -%}\n        {{ 'assistant' + additional_special_tokens[0] -}}\n    {%- endif -%}\n{%- endfor %}",
     };
     std::vector<std::string> expected_output = {
         // teknium/OpenHermes-2.5-Mistral-7B
@@ -131,8 +129,6 @@ int main(void) {
         "[INST]You are a helpful assistant\n\nHello[/INST]Hi there</s>[INST]Who are you[/INST]   I am an assistant   </s>[INST]Another question[/INST]",
         // mistralai/Mistral-Large-Instruct-2411 (mistralai 'v7' template)
         "[SYSTEM_PROMPT] You are a helpful assistant[/SYSTEM_PROMPT][INST] Hello[/INST] Hi there</s>[INST] Who are you[/INST]    I am an assistant   </s>[INST] Another question[/INST]",
-        // ai-sage/GigaChat-20B-A3B-instruct
-        "<s>You are a helpful assistant<|message_sep|>user<|role_sep|>Hello<|message_sep|>available functions<|role_sep|>[]<|message_sep|>assistant<|role_sep|>Hi there<|message_sep|>user<|role_sep|>Who are you<|message_sep|>available functions<|role_sep|>[]<|message_sep|>assistant<|role_sep|>   I am an assistant   <|message_sep|>user<|role_sep|>Another question<|message_sep|>available functions<|role_sep|>[]<|message_sep|>assistant<|role_sep|>",
     };
     std::vector<char> formatted_chat(1024);
     int32_t res;
@@ -194,7 +190,6 @@ int main(void) {
     assert(fmt_sys("mistral") == "[INST] You are a helpful assistant\n"); // for old pre-v1 templates
     assert(fmt_sys("gemma")  == ""); // for gemma, system message is merged with user message
     assert(fmt_sys("llama3") == "<|start_header_id|>system<|end_header_id|>\n\nYou are a helpful assistant<|eot_id|>");
-    assert(fmt_sys("gigachat") == "<s>You are a helpful assistant<|message_sep|>");
 
 
     // test llama_chat_format_single for user message
@@ -219,7 +214,6 @@ int main(void) {
     assert(fmt_single("mistral") == "[INST] How are you [/INST]"); // for old pre-v1 templates
     assert(fmt_single("gemma")  == "\n<start_of_turn>user\nHow are you<end_of_turn>\n<start_of_turn>model\n");
     assert(fmt_single("llama3") == "<|start_header_id|>user<|end_header_id|>\n\nHow are you<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n");
-    assert(fmt_single("gigachat") == "user<|role_sep|>How are you<|message_sep|>available functions<|role_sep|>[]<|message_sep|>assistant<|role_sep|>");
 
     printf("Test chat templates: OK\n");
 

tests/test-sampling.cpp

@@ -145,7 +145,7 @@ static void test_penalties(
     sampler_tester tester(probs, probs_expected);
 
     const size_t n_vocab = probs.size();
-    auto * sampler = llama_sampler_init_penalties(last_tokens.size(), repeat_penalty, alpha_frequency, alpha_presence);
+    auto * sampler = llama_sampler_init_penalties(n_vocab, LLAMA_TOKEN_NULL, LLAMA_TOKEN_NULL, last_tokens.size(), repeat_penalty, alpha_frequency, alpha_presence, false, false);
 
     for (size_t i = 0; i < last_tokens.size(); i++) {
         llama_sampler_accept(sampler, last_tokens[i]);