Merge branch 'ggml-org:master' into master

Author: klogdotwebsitenotdotcom

.devops/musa.Dockerfile

@@ -1,10 +1,10 @@
 ARG UBUNTU_VERSION=22.04
 # This needs to generally match the container host's environment.
-ARG MUSA_VERSION=rc4.0.1
+ARG MUSA_VERSION=rc4.2.0
 # Target the MUSA build image
-ARG BASE_MUSA_DEV_CONTAINER=mthreads/musa:${MUSA_VERSION}-mudnn-devel-ubuntu${UBUNTU_VERSION}
+ARG BASE_MUSA_DEV_CONTAINER=mthreads/musa:${MUSA_VERSION}-devel-ubuntu${UBUNTU_VERSION}-amd64
 
-ARG BASE_MUSA_RUN_CONTAINER=mthreads/musa:${MUSA_VERSION}-mudnn-runtime-ubuntu${UBUNTU_VERSION}
+ARG BASE_MUSA_RUN_CONTAINER=mthreads/musa:${MUSA_VERSION}-runtime-ubuntu${UBUNTU_VERSION}-amd64
 
 FROM ${BASE_MUSA_DEV_CONTAINER} AS build
 

.devops/rocm.Dockerfile

@@ -1,8 +1,8 @@
 ARG UBUNTU_VERSION=24.04
 
 # This needs to generally match the container host's environment.
-ARG ROCM_VERSION=6.3
-ARG AMDGPU_VERSION=6.3
+ARG ROCM_VERSION=6.4
+ARG AMDGPU_VERSION=6.4
 
 # Target the CUDA build image
 ARG BASE_ROCM_DEV_CONTAINER=rocm/dev-ubuntu-${UBUNTU_VERSION}:${ROCM_VERSION}-complete

.github/workflows/build.yml

@@ -515,7 +515,7 @@ jobs:
 
   ubuntu-22-cmake-musa:
     runs-on: ubuntu-22.04
-    container: mthreads/musa:rc4.0.1-mudnn-devel-ubuntu22.04
+    container: mthreads/musa:rc4.2.0-devel-ubuntu22.04-amd64
 
     steps:
       - name: Clone

.gitignore

@@ -82,6 +82,7 @@ models/*
 models-mnt
 !models/.editorconfig
 !models/ggml-vocab-*.gguf*
+!models/templates
 
 # Zig
 zig-out/

ci/README.md

@@ -54,7 +54,7 @@ docker run --privileged -it \
     -v $HOME/llama.cpp/ci-cache:/ci-cache \
     -v $HOME/llama.cpp/ci-results:/ci-results \
     -v $PWD:/ws -w /ws \
-    mthreads/musa:rc4.0.1-mudnn-devel-ubuntu22.04
+    mthreads/musa:rc4.2.0-devel-ubuntu22.04-amd64
 ```
 
 Inside the container, execute the following commands:

convert_hf_to_gguf.py

@@ -1900,6 +1900,7 @@ def prepare_tensors(self):
     "MixtralForCausalLM",
     "VLlama3ForCausalLM",
     "LlavaForConditionalGeneration",
+    "VoxtralForConditionalGeneration",
     "LlamaModel")
 class LlamaModel(TextModel):
     model_arch = gguf.MODEL_ARCH.LLAMA
@@ -1912,6 +1913,11 @@ def __init__(self, *args, **kwargs):
             self.hparams["num_attention_heads"] = self.hparams.get("num_attention_heads", 32)
 
     def set_vocab(self):
+        path_tekken_json = self.dir_model / "tekken.json"
+        path_tokenizer_json = self.dir_model / "tokenizer.json"
+        if path_tekken_json.is_file() and not path_tokenizer_json.is_file():
+            return self.set_vocab_tekken()
+
         try:
             self._set_vocab_sentencepiece()
         except FileNotFoundError:
@@ -1944,6 +1950,52 @@ def set_vocab(self):
         if self.hparams.get("vocab_size", 32000) == 49152:
             self.gguf_writer.add_add_bos_token(False)
 
+    def set_vocab_tekken(self):
+        vocab = gguf.vocab.MistralVocab(self.dir_model)
+        self.gguf_writer.add_tokenizer_model(vocab.gguf_tokenizer_model)
+
+        tokens = []
+        scores = []
+        toktypes = []
+
+        for text, score, toktype in vocab.all_tokens():
+            tokens.append(text)
+            scores.append(score)
+            toktypes.append(toktype)
+
+        assert len(tokens) == vocab.vocab_size, (
+            f"token count ({len(tokens)}) != vocab size ({vocab.vocab_size})"
+        )
+
+        if vocab.tokenizer_type == gguf.vocab.MistralTokenizerType.tekken:
+            self.gguf_writer.add_tokenizer_pre("tekken")
+            self.gguf_writer.add_token_merges(
+                vocab.extract_vocab_merges_from_model()
+            )
+
+        logger.info(
+            f"Setting bos, eos, unk and pad token IDs to {vocab.bos_id}, {vocab.eos_id}, {vocab.unk_id}, {vocab.pad_id}."
+        )
+
+        self.gguf_writer.add_bos_token_id(vocab.bos_id)
+        self.gguf_writer.add_eos_token_id(vocab.eos_id)
+        self.gguf_writer.add_unk_token_id(vocab.unk_id)
+        self.gguf_writer.add_pad_token_id(vocab.pad_id)
+
+        self.gguf_writer.add_token_list(tokens)
+        self.gguf_writer.add_token_scores(scores)
+        self.gguf_writer.add_token_types(toktypes)
+        self.gguf_writer.add_vocab_size(vocab.vocab_size)
+
+        self.gguf_writer.add_add_bos_token(True)
+        self.gguf_writer.add_add_eos_token(False)
+
+        script_dir = Path(__file__).parent
+        template_path = script_dir / "models/templates/unsloth-mistral-Devstral-Small-2507.jinja"
+        with open(template_path, "r", encoding="utf-8") as f:
+            template = f.read()
+            self.gguf_writer.add_chat_template(template)
+
     def set_gguf_parameters(self):
         super().set_gguf_parameters()
         hparams = self.hparams
@@ -1971,12 +2023,13 @@ def permute(weights: Tensor, n_head: int, n_head_kv: int | None):
     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")
-        is_vision_tensor = "vision_tower" in name \
+        is_multimodal_tensor = "vision_tower" in name \
             or "vision_model" in name \
+            or "audio_tower" in name \
             or "model.connector" in name \
             or "multi_modal_projector" in name
 
-        if is_vision_tensor:
+        if is_multimodal_tensor:
             return [] # skip vision tensors
         elif self.hf_arch == "LlamaModel":
             name = "model." + name
@@ -3791,7 +3844,7 @@ def set_gguf_parameters(self):
         self.gguf_writer.add_block_count(block_count)
         self.gguf_writer.add_head_count(hparams.get("num_attention_heads", 32))
         self.gguf_writer.add_layer_norm_rms_eps(hparams.get("rms_norm_eps", 1e-06))
-        self.gguf_writer.add_rope_freq_base(hparams.get("rope_theta", 1000000.0))
+        self.gguf_writer.add_rope_freq_base(hparams.get("rope_theta", 10000))
 
         # Mamba parameters
         self.gguf_writer.add_ssm_state_size(hparams.get("mamba_d_state", 64))
@@ -3802,7 +3855,7 @@ def set_gguf_parameters(self):
         self.gguf_writer.add_ssm_group_count(0)
 
         # MLP feed forward parameters (for attention layers)
-        self.gguf_writer.add_feed_forward_length(hparams.get("intermediate_size", 16384))
+        self.gguf_writer.add_feed_forward_length(hparams.get("intermediate_size", 13312))
         self.gguf_writer.add_file_type(self.ftype)
 
     def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
@@ -6486,7 +6539,7 @@ def prepare_tensors(self):
         self.gguf_writer.add_max_alibi_bias(self.max_alibi_bias)
 
 
-@ModelBase.register("Glm4ForCausalLM")
+@ModelBase.register("Glm4ForCausalLM", "Glm4vForConditionalGeneration")
 class Glm4Model(TextModel):
     model_arch = gguf.MODEL_ARCH.GLM4
 
@@ -6508,14 +6561,22 @@ def set_vocab(self):
 
     def set_gguf_parameters(self):
         super().set_gguf_parameters()
-        rope_dim = self.hparams["head_dim"]
+        if (rope_dim := self.hparams.get("head_dim")) is None:
+            rope_dim = self.hparams["hidden_size"] // self.hparams["num_attention_heads"]
         self.gguf_writer.add_rope_dimension_count(int(rope_dim * self.hparams.get("partial_rotary_factor", 0.5)))
         rope_scaling = self.hparams.get("rope_scaling") or {}
         if rope_scaling.get("rope_type", rope_scaling.get("type")) == "yarn" and "factor" in rope_scaling:
             self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.YARN)
             self.gguf_writer.add_rope_scaling_factor(rope_scaling["factor"])
             self.gguf_writer.add_rope_scaling_orig_ctx_len(rope_scaling["original_max_position_embeddings"])
 
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        if name.startswith("model.visual."): # ignore visual part of Glm4v
+            return []
+        elif name.startswith("model.language_model."):
+            name = name.replace("language_model.", "") # for Glm4v
+        return super().modify_tensors(data_torch, name, bid)
+
 
 @ModelBase.register("GlmForCausalLM", "ChatGLMModel", "ChatGLMForConditionalGeneration")
 class ChatGLMModel(TextModel):
@@ -7223,9 +7284,10 @@ class WhisperEncoderModel(MmprojModel):
 
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
-        self.hparams["hidden_size"] = self.hparams["d_model"]
-        self.hparams["intermediate_size"] = self.hparams["encoder_ffn_dim"]
-        self.hparams["num_attention_heads"] = self.hparams["encoder_attention_heads"]
+        if "hidden_size" not in self.hparams and "intermediate_size" not in self.hparams:
+            self.hparams["hidden_size"] = self.hparams["d_model"]
+            self.hparams["intermediate_size"] = self.hparams["encoder_ffn_dim"]
+            self.hparams["num_attention_heads"] = self.hparams["encoder_attention_heads"]
 
     def set_gguf_parameters(self):
         super().set_gguf_parameters()
@@ -7264,9 +7326,21 @@ class UltravoxWhisperEncoderModel(WhisperEncoderModel):
 
     def set_gguf_parameters(self):
         super().set_gguf_parameters()
+        self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.ULTRAVOX)
         self.gguf_writer.add_audio_stack_factor(self.global_config["stack_factor"])
 
 
+@ModelBase.register("VoxtralForConditionalGeneration")
+class VoxtralWhisperEncoderModel(WhisperEncoderModel):
+    has_vision_encoder = False # no vision encoder
+    has_audio_encoder = True
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.VOXTRAL)
+        self.gguf_writer.add_audio_stack_factor(4) # == intermediate_size // hidden_size
+
+
 @ModelBase.register("FalconH1ForCausalLM")
 class FalconH1Model(Mamba2Model):
     model_arch = gguf.MODEL_ARCH.FALCON_H1
@@ -7581,6 +7655,88 @@ def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iter
         return [(self.map_tensor_name(name), data_torch)]
 
 
+@ModelBase.register("SmallThinkerForCausalLM")
+class SmallThinkerModel(TextModel):
+    model_arch = gguf.MODEL_ARCH.SMALLTHINKER
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        if (n_experts := self.hparams.get("num_experts", self.hparams.get("moe_num_primary_experts"))) is not None:
+            self.gguf_writer.add_expert_count(n_experts)
+        if (n_experts_used := self.hparams.get("num_experts_per_tok", self.hparams.get("moe_num_active_primary_experts"))) is not None:
+            self.gguf_writer.add_expert_used_count(n_experts_used)
+        if (moe_intermediate_size := self.hparams.get("moe_ffn_hidden_size")) is not None:
+            self.gguf_writer.add_expert_feed_forward_length(moe_intermediate_size)
+            self.gguf_writer.add_feed_forward_length(moe_intermediate_size)
+            logger.info(f"gguf: expert feed forward length = {moe_intermediate_size}")
+        if (self.hparams.get('moe_primary_router_apply_softmax')):
+            self.gguf_writer.add_expert_gating_func(gguf.ExpertGatingFuncType.SOFTMAX)
+        else:
+            self.gguf_writer.add_expert_gating_func(gguf.ExpertGatingFuncType.SIGMOID)
+        # YaRN is not enabled by default
+        # To enable it, please refer to this guide: https://huggingface.co/Qwen/Qwen3-30B-A3B#processing-long-texts
+        rope_scaling = self.hparams.get("rope_scaling") or {}
+        if rope_scaling.get("rope_type", rope_scaling.get("type")) == "yarn" and "factor" in rope_scaling:
+            self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.YARN)
+            self.gguf_writer.add_rope_scaling_factor(rope_scaling["factor"])
+            self.gguf_writer.add_rope_scaling_orig_ctx_len(rope_scaling["original_max_position_embeddings"])
+
+        sliding_window_layout = self.hparams.get("sliding_window_layout")
+        if sliding_window_layout:
+            for i in sliding_window_layout:
+                if i != 0:
+                    sliding_window = self.hparams.get("sliding_window_size")
+                    if sliding_window:
+                        self.gguf_writer.add_sliding_window(sliding_window)
+                    break
+
+    _experts: list[dict[str, Tensor]] | None = None
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        # process the experts separately
+        if name.find("experts") != -1:
+            n_experts = self.hparams.get("num_experts", self.hparams.get("moe_num_primary_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", "gate", "up"]:
+                    datas: list[Tensor] = []
+
+                    for xid in range(n_experts):
+                        ename = f"model.layers.{bid}.block_sparse_moe.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}.block_sparse_moe.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}")
+
 ###### CONVERSION LOGIC ######