llama : improve BPE pre-processing + LLaMA 3 and Deepseek support

.gitignore

@@ -108,3 +108,20 @@ examples/server/*.mjs.hpp
 poetry.lock
 poetry.toml
 nppBackup
+
+# Test binaries
+/tests/test-grammar-parser
+/tests/test-llama-grammar
+/tests/test-double-float
+/tests/test-grad0
+/tests/test-opt
+/tests/test-quantize-fns
+/tests/test-quantize-perf
+/tests/test-sampling
+/tests/test-tokenizer-0-llama
+/tests/test-tokenizer-0-falcon
+/tests/test-tokenizer-0-deepseek-coder
+/tests/test-tokenizer-1-llama
+/tests/test-tokenizer-1-bpe
+/tests/test-rope
+/tests/test-backend-ops

Makefile

@@ -6,7 +6,8 @@ BUILD_TARGETS = \
 
 # Binaries only useful for tests
 TEST_TARGETS = \
-	tests/test-llama-grammar tests/test-grammar-parser tests/test-double-float tests/test-grad0 tests/test-opt \
+	tests/test-llama-grammar tests/test-tokenizer-0-deepseek-coder tests/test-tokenizer-0-deepseek-llm \
+	tests/test-grammar-parser tests/test-double-float tests/test-grad0 tests/test-opt \
 	tests/test-quantize-fns tests/test-quantize-perf tests/test-sampling tests/test-tokenizer-0-llama          \
 	tests/test-tokenizer-0-falcon tests/test-tokenizer-1-llama tests/test-tokenizer-1-bpe tests/test-rope      \
 	tests/test-backend-ops tests/test-model-load-cancel tests/test-autorelease                                 \
@@ -53,6 +54,10 @@ test: $(TEST_TARGETS)
 			./$$test_target $(CURDIR)/models/ggml-vocab-llama.gguf; \
 		elif [ "$$test_target" = "tests/test-tokenizer-0-falcon" ]; then \
 			./$$test_target $(CURDIR)/models/ggml-vocab-falcon.gguf; \
+		elif [ "$$test_target" = "tests/test-tokenizer-0-deepseek-coder" ]; then \
+			./$$test_target $(CURDIR)/models/ggml-vocab-deepseek-coder.gguf; \
+		elif [ "$$test_target" = "tests/test-tokenizer-0-deepseek-llm" ]; then \
+			./$$test_target $(CURDIR)/models/ggml-vocab-deepseek-llm.gguf; \
 		elif [ "$$test_target" = "tests/test-tokenizer-1-llama" ]; then \
 			continue; \
 		elif [ "$$test_target" = "tests/test-tokenizer-1-bpe" ]; then \
@@ -979,6 +984,12 @@ tests/test-tokenizer-0-llama: tests/test-tokenizer-0-llama.cpp ggml.o llama.o $(
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
+tests/test-tokenizer-0-deepseek-coder: tests/test-tokenizer-0-deepseek-coder.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
+
+tests/test-tokenizer-0-deepseek-llm: tests/test-tokenizer-0-deepseek-llm.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
+
 tests/test-tokenizer-1-bpe: tests/test-tokenizer-1-bpe.cpp ggml.o llama.o $(COMMON_DEPS) console.o $(OBJS)
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)

convert-hf-to-gguf.py

@@ -215,6 +215,78 @@ def from_model_architecture(cls, arch):
         except KeyError:
             raise NotImplementedError(f'Architecture {arch!r} not supported!') from None
 
+    @staticmethod
+    def from_model_architecture(model_architecture):
+        if model_architecture == "GPTNeoXForCausalLM":
+            return GPTNeoXModel
+        if model_architecture == "BloomForCausalLM":
+            return BloomModel
+        if model_architecture == "MPTForCausalLM":
+            return MPTModel
+        if model_architecture in ("BaichuanForCausalLM", "BaiChuanForCausalLM"):
+            return BaichuanModel
+        if model_architecture in ("FalconForCausalLM", "RWForCausalLM"):
+            return FalconModel
+        if model_architecture == "GPTBigCodeForCausalLM":
+            return StarCoderModel
+        if model_architecture == "GPTRefactForCausalLM":
+            return RefactModel
+        if model_architecture == "PersimmonForCausalLM":
+            return PersimmonModel
+        if model_architecture == "LlamaForCausalLM":
+            return DeepseekCoderModel
+        if model_architecture in ("StableLMEpochForCausalLM", "LlavaStableLMEpochForCausalLM"):
+            return StableLMModel
+        if model_architecture == "QWenLMHeadModel":
+            return QwenModel
+        if model_architecture == "Qwen2ForCausalLM":
+            return Model
+        if model_architecture == "MixtralForCausalLM":
+            return MixtralModel
+        if model_architecture == "GPT2LMHeadModel":
+            return GPT2Model
+        if model_architecture == "PhiForCausalLM":
+            return Phi2Model
+        if model_architecture == "PlamoForCausalLM":
+            return PlamoModel
+        if model_architecture == "CodeShellForCausalLM":
+            return CodeShellModel
+        if model_architecture == "OrionForCausalLM":
+            return OrionModel
+        if model_architecture == "InternLM2ForCausalLM":
+            return InternLM2Model
+        if model_architecture == "MiniCPMForCausalLM":
+            return MiniCPMModel
+        if model_architecture == "BertModel":
+            return BertModel
+
+    @staticmethod
+    def from_model_name(model_name: str):
+        model_name_lower = model_name.lower()
+        if model_name_lower in ("stablelmepoch", "llavastablelmepoch"):
+            return StableLMModel
+        if model_name_lower == "gptneox":
+            return GPTNeoXModel
+        if model_name_lower == "bloom":
+            return BloomModel
+        if model_name_lower == "mpt":
+            return MPTModel
+        if model_name_lower in ("baichuan"):
+            return BaichuanModel
+        if model_name_lower in ("falcon", "rw"):
+            return FalconModel
+        if model_name_lower == "gptbigcode":
+            return StarCoderModel
+        if model_name_lower == "gptrefact":
+            return RefactModel
+        if model_name_lower == "persimmon":
+            return PersimmonModel
+        if model_name_lower == "deepseekcoder":
+            return DeepseekCoderModel
+        if model_name_lower == "deepseekllm":
+            return DeepseekLLMModel
+        return Model
+
     def _is_model_safetensors(self) -> bool:
         return Model.count_model_parts(self.dir_model, ".safetensors") > 0
 
@@ -228,6 +300,53 @@ def _get_part_names(self):
             return ("pytorch_model.bin",)
         return (f"pytorch_model-{n:05}-of-{self.num_parts:05}.bin" for n in range(1, self.num_parts + 1))
 
+    def _get_model_architecture(self) -> gguf.MODEL_ARCH:
+        arch = self.hparams["architectures"][0]
+        if arch == "GPTNeoXForCausalLM":
+            return gguf.MODEL_ARCH.GPTNEOX
+        if arch == "BloomForCausalLM":
+            return gguf.MODEL_ARCH.BLOOM
+        if arch == "MPTForCausalLM":
+            return gguf.MODEL_ARCH.MPT
+        if arch in ("BaichuanForCausalLM", "BaiChuanForCausalLM"):
+            return gguf.MODEL_ARCH.BAICHUAN
+        if arch in ("FalconForCausalLM", "RWForCausalLM"):
+            return gguf.MODEL_ARCH.FALCON
+        if arch == "GPTBigCodeForCausalLM":
+            return gguf.MODEL_ARCH.STARCODER
+        if arch == "GPTRefactForCausalLM":
+            return gguf.MODEL_ARCH.REFACT
+        if arch == "PersimmonForCausalLM":
+            return gguf.MODEL_ARCH.PERSIMMON
+        if arch == "LlamaForCausalLM":
+            return gguf.MODEL_ARCH.LLAMA
+        if arch in ("StableLMEpochForCausalLM", "LlavaStableLMEpochForCausalLM"):
+            return gguf.MODEL_ARCH.STABLELM
+        if arch == "QWenLMHeadModel":
+            return gguf.MODEL_ARCH.QWEN
+        if arch == "Qwen2ForCausalLM":
+            return gguf.MODEL_ARCH.QWEN2
+        if arch == "MixtralForCausalLM":
+            return gguf.MODEL_ARCH.LLAMA
+        if arch == "GPT2LMHeadModel":
+            return gguf.MODEL_ARCH.GPT2
+        if arch == "PhiForCausalLM":
+            return gguf.MODEL_ARCH.PHI2
+        if arch == "PlamoForCausalLM":
+            return gguf.MODEL_ARCH.PLAMO
+        if arch == "CodeShellForCausalLM":
+            return gguf.MODEL_ARCH.CODESHELL
+        if arch == "OrionForCausalLM":
+            return gguf.MODEL_ARCH.ORION
+        if arch == "InternLM2ForCausalLM":
+            return gguf.MODEL_ARCH.INTERNLM2
+        if arch == "MiniCPMForCausalLM":
+            return gguf.MODEL_ARCH.MINICPM
+        if arch == "BertModel":
+            return gguf.MODEL_ARCH.BERT
+
+        raise NotImplementedError(f'Architecture "{arch}" not supported!')
+
     # used for GPT-2 BPE and WordPiece vocabs
     def get_basic_vocab(self) -> tuple[list[str], list[int]]:
         tokens: list[str] = []
@@ -257,9 +376,10 @@ def get_basic_vocab(self) -> tuple[list[str], list[int]]:
 
         return tokens, toktypes
 
-    def _set_vocab_gpt2(self) -> None:
+
+    def _set_vocab_gpt2(self, tokenizer_model:str = "gpt2") -> None:
         tokens, toktypes = self.get_basic_vocab()
-        self.gguf_writer.add_tokenizer_model("gpt2")
+        self.gguf_writer.add_tokenizer_model(tokenizer_model)
         self.gguf_writer.add_token_list(tokens)
         self.gguf_writer.add_token_types(toktypes)
 
@@ -1192,7 +1312,31 @@ def write_tensors(self):
             n_dims = len(data.shape)
             print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}")
             self.gguf_writer.add_tensor(new_name, data)
+@Model.register("LlamaForCausalLM")
+class DeepseekCoderModel(Model):
+    model_arch = gguf.MODEL_ARCH.LLAMA
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        head_count = self.hparams["num_attention_heads"]
+        head_count_kv = self.hparams.get("num_key_value_heads", head_count)
+        self.gguf_writer.add_head_count(head_count)
+        self.gguf_writer.add_rope_dimension_count(self.hparams["hidden_size"] // self.hparams["num_attention_heads"])
+        self.gguf_writer.add_head_count_kv(head_count_kv)
+        self.gguf_writer.add_layer_norm_rms_eps(self.hparams["rms_norm_eps"])
+        self.gguf_writer.add_rope_freq_base(self.hparams["rope_theta"])
+
+        if self.hparams.get("rope_scaling") is not None and "factor" in self.hparams["rope_scaling"]:
+            if self.hparams["rope_scaling"].get("type") == "linear":
+                self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR)
+                self.gguf_writer.add_rope_scaling_factor(self.hparams["rope_scaling"]["factor"])
 
+    def set_vocab(self):
+        self._set_vocab_gpt2("deepseek_coder")
+
+class DeepseekLLMModel(DeepseekCoderModel):
+    def set_vocab(self):
+        self._set_vocab_gpt2("deepseek_llm")
 
 @Model.register("StableLmForCausalLM", "StableLMEpochForCausalLM", "LlavaStableLMEpochForCausalLM")
 class StableLMModel(Model):
@@ -2843,6 +2987,7 @@ def parse_args() -> argparse.Namespace:
         help="directory containing model file",
     )
     parser.add_argument("--use-temp-file", action="store_true", help="use the tempfile library while processing (helpful when running out of memory, process killed)")
+    parser.add_argument("--model-name", type=str, default=None, help="name of the model")
 
     return parser.parse_args()