Revert "[FEAT] Support GGUF format" (#2285)

docs/requirements.txt

@@ -15,4 +15,3 @@ sphinx-copybutton
 sphinx-tabs
 sphinxcontrib-mermaid
 urllib3<2.0.0
-gguf>=0.10.0

python/sglang/srt/hf_transformers_utils.py

@@ -16,7 +16,6 @@
 import contextlib
 import os
 import warnings
-from pathlib import Path
 from typing import Dict, Optional, Type, Union
 
 from huggingface_hub import snapshot_download
@@ -28,7 +27,6 @@ from transformers import (
     PreTrainedTokenizer,
     PreTrainedTokenizerFast,
 )
-from transformers.models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
 
 try:
     from vllm.transformers_utils.configs import ChatGLMConfig, DbrxConfig
@@ -62,29 +60,15 @@ def get_config(
     trust_remote_code: bool,
     revision: Optional[str] = None,
     model_override_args: Optional[dict] = None,
-    **kwargs,
 ):
-    is_gguf = check_gguf_file(model)
-    if is_gguf:
-        kwargs["gguf_file"] = model
-        model = Path(model).parent
-
     config = AutoConfig.from_pretrained(
-        model, trust_remote_code=trust_remote_code, revision=revision, **kwargs
+        model, trust_remote_code=trust_remote_code, revision=revision
     )
     if config.model_type in _CONFIG_REGISTRY:
         config_class = _CONFIG_REGISTRY[config.model_type]
         config = config_class.from_pretrained(model, revision=revision)
     if model_override_args:
         config.update(model_override_args)
-
-    # Special architecture mapping check for GGUF models
-    if is_gguf:
-        if config.model_type not in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES:
-            raise RuntimeError(f"Can't get gguf config for {config.model_type}.")
-        model_type = MODEL_FOR_CAUSAL_LM_MAPPING_NAMES[config.model_type]
-        config.update({"architectures": [model_type]})
-
     return config
 
 
@@ -139,11 +123,6 @@ def get_tokenizer(
             raise ValueError("Cannot use the fast tokenizer in slow tokenizer mode.")
         kwargs["use_fast"] = False
 
-    is_gguf = check_gguf_file(tokenizer_name)
-    if is_gguf:
-        kwargs["gguf_file"] = tokenizer_name
-        tokenizer_name = Path(tokenizer_name).parent
-
     try:
         tokenizer = AutoTokenizer.from_pretrained(
             tokenizer_name,
@@ -216,16 +195,3 @@ def attach_additional_stop_token_ids(tokenizer):
         )
     else:
         tokenizer.additional_stop_token_ids = None
-
-
-def check_gguf_file(model: Union[str, os.PathLike]) -> bool:
-    """Check if the file is a GGUF model."""
-    model = Path(model)
-    if not model.is_file():
-        return False
-    elif model.suffix == ".gguf":
-        return True
-
-    with open(model, "rb") as f:
-        header = f.read(4)
-    return header == b"GGUF"

python/sglang/srt/layers/logits_processor.py

@@ -23,7 +23,6 @@ from vllm.distributed import (
     tensor_model_parallel_all_gather,
 )
 
-from sglang.srt.layers.vocab_parallel_embedding import VocabParallelEmbedding
 from sglang.srt.model_executor.forward_batch_info import ForwardBatch, ForwardMode
 
 
@@ -164,7 +163,7 @@ class LogitsProcessor(nn.Module):
         self,
         input_ids,
         hidden_states,
-        lm_head: VocabParallelEmbedding,
+        weight,
         logits_metadata: Union[LogitsMetadata, ForwardBatch],
     ):
         if isinstance(logits_metadata, ForwardBatch):
@@ -179,7 +178,7 @@ class LogitsProcessor(nn.Module):
             last_index = torch.cumsum(logits_metadata.extend_seq_lens, dim=0) - 1
             last_hidden = hidden_states[last_index]
 
-        last_logits = self._get_logits(last_hidden, lm_head)
+        last_logits = torch.matmul(last_hidden, weight.T)
         if self.do_tensor_parallel_all_gather:
             last_logits = tensor_model_parallel_all_gather(last_logits)
         last_logits = last_logits[:, : self.config.vocab_size].float()
@@ -230,7 +229,7 @@ class LogitsProcessor(nn.Module):
 
                 # Compute the logits and logprobs for all required tokens
                 states = torch.cat(states, dim=0)
-                all_logits = self._get_logits(states, lm_head)
+                all_logits = torch.matmul(states, weight.T)
                 if self.do_tensor_parallel_all_gather:
                     all_logits = tensor_model_parallel_all_gather(all_logits)
                 all_logits = all_logits[:, : self.config.vocab_size].float()
@@ -277,19 +276,6 @@ class LogitsProcessor(nn.Module):
                     output_top_logprobs=output_top_logprobs,
                 )
 
-    def _get_logits(
-        self,
-        hidden_states: torch.Tensor,
-        lm_head: VocabParallelEmbedding,
-        embedding_bias: Optional[torch.Tensor] = None,
-    ) -> torch.Tensor:
-        if hasattr(lm_head, "weight"):
-            logits = torch.matmul(hidden_states, lm_head.weight.T)
-        else:
-            # GGUF models
-            logits = lm_head.linear_method.apply(lm_head, hidden_states, embedding_bias)
-        return logits
-
 
 def test():
     all_logprobs = torch.tensor(

python/sglang/srt/layers/vocab_parallel_embedding.py

@@ -222,7 +222,6 @@ class VocabParallelEmbedding(torch.nn.Module):
         enable_tp: bool = True,
     ):
         super().__init__()
-        self.quant_config = quant_config
 
         self.enable_tp = enable_tp
         if self.enable_tp:

python/sglang/srt/model_executor/model_runner.py

@@ -59,7 +59,6 @@ from sglang.srt.utils import (
     enable_show_time_cost,
     get_available_gpu_memory,
     is_hip,
-    monkey_patch_vllm_gguf_config,
     monkey_patch_vllm_model_config,
     monkey_patch_vllm_p2p_access_check,
     set_cpu_offload_max_bytes,
@@ -298,8 +297,6 @@ class ModelRunner:
             download_dir=self.server_args.download_dir,
         )
         monkey_patch_vllm_model_config()
-        if self.server_args.load_format == "gguf":
-            monkey_patch_vllm_gguf_config()
         self.vllm_model_config = VllmModelConfig(**self.get_model_config_params())
         if self.model_config.model_override_args is not None:
             self.vllm_model_config.hf_config.update(

python/sglang/srt/models/baichuan.py

@@ -338,12 +338,11 @@ class BaiChuanBaseForCausalLM(nn.Module):
 
         self.quant_config = quant_config
         self.model = BaiChuanModel(config, position_embedding, quant_config)
-        if self.config.tie_word_embeddings:
-            self.lm_head = self.model.embed_tokens
-        else:
         self.lm_head = ParallelLMHead(
             config.vocab_size, config.hidden_size, quant_config=quant_config
         )
+        if self.config.tie_word_embeddings:
+            self.lm_head.weight = self.model.embed_tokens.weight
         self.logits_processor = LogitsProcessor(config)
 
     def forward(
@@ -354,7 +353,7 @@ class BaiChuanBaseForCausalLM(nn.Module):
     ) -> torch.Tensor:
         hidden_states = self.model(input_ids, positions, forward_batch)
         return self.logits_processor(
-            input_ids, hidden_states, self.lm_head, forward_batch
+            input_ids, hidden_states, self.lm_head.weight, forward_batch
         )
 
     def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):

python/sglang/srt/models/chatglm.py

@@ -378,7 +378,7 @@ class ChatGLMForCausalLM(nn.Module):
     ) -> torch.Tensor:
         hidden_states = self.transformer(input_ids, positions, forward_batch)
         return self.logits_processor(
-            input_ids, hidden_states, self.lm_head, forward_batch
+            input_ids, hidden_states, self.lm_head.weight, forward_batch
         )
 
     def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):

python/sglang/srt/models/commandr.py

@@ -339,7 +339,7 @@ class CohereForCausalLM(nn.Module):
             forward_batch,
         )
         return self.logits_processor(
-            input_ids, hidden_states, self.model.embed_tokens, forward_batch
+            input_ids, hidden_states, self.model.embed_tokens.weight, forward_batch
         )
 
     def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):

python/sglang/srt/models/dbrx.py

@@ -390,7 +390,7 @@ class DbrxForCausalLM(nn.Module):
     ) -> torch.Tensor:
         hidden_states = self.transformer(input_ids, positions, forward_batch)
         return self.logits_processor(
-            input_ids, hidden_states, self.lm_head, forward_batch
+            input_ids, hidden_states, self.lm_head.weight, forward_batch
         )
 
     def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):

python/sglang/srt/models/deepseek.py

@@ -394,7 +394,7 @@ class DeepseekForCausalLM(nn.Module):
     ) -> torch.Tensor:
         hidden_states = self.model(input_ids, positions, forward_batch)
         return self.logits_processor(
-            input_ids, hidden_states, self.lm_head, forward_batch
+            input_ids, hidden_states, self.lm_head.weight, forward_batch
         )
 
     def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):

python/sglang/srt/models/deepseek_v2.py

@@ -763,7 +763,7 @@ class DeepseekV2ForCausalLM(nn.Module):
         hidden_states = self.model(input_ids, positions, forward_batch)
         if not forward_batch.forward_mode.is_idle():
             return self.logits_processor(
-                input_ids, hidden_states, self.lm_head, forward_batch
+                input_ids, hidden_states, self.lm_head.weight, forward_batch
             )
 
     def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):

python/sglang/srt/models/exaone.py

@@ -314,7 +314,7 @@ class ExaoneForCausalLM(nn.Module):
             input_ids, positions, forward_batch, input_embeds
         )
         return self.logits_processor(
-            input_ids, hidden_states, self.lm_head, forward_batch
+            input_ids, hidden_states, self.lm_head.weight, forward_batch
         )
 
     def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):

python/sglang/srt/models/gemma.py

@@ -298,7 +298,7 @@ class GemmaForCausalLM(nn.Module):
     ) -> torch.Tensor:
         hidden_states = self.model(input_ids, positions, forward_batch, input_embeds)
         return self.logits_processor(
-            input_ids, hidden_states, self.model.embed_tokens, forward_batch
+            input_ids, hidden_states, self.model.embed_tokens.weight, forward_batch
         )
 
     def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):

python/sglang/srt/models/gemma2.py

@@ -363,7 +363,7 @@ class Gemma2ForCausalLM(nn.Module):
     ) -> torch.Tensor:
         hidden_states = self.model(input_ids, positions, forward_batch, input_embeds)
         return self.logits_processor(
-            input_ids, hidden_states, self.model.embed_tokens, forward_batch
+            input_ids, hidden_states, self.model.embed_tokens.weight, forward_batch
         )
 
     def get_attention_sliding_window_size(self):

python/sglang/srt/models/gpt2.py

@@ -247,7 +247,7 @@ class GPT2LMHeadModel(nn.Module):
     ) -> torch.Tensor:
         hidden_states = self.transformer(input_ids, positions, forward_batch)
         return self.logits_processor(
-            input_ids, hidden_states, self.lm_head, forward_batch
+            input_ids, hidden_states, self.lm_head.weight, forward_batch
         )
 
     def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):

python/sglang/srt/models/gpt_bigcode.py

@@ -271,7 +271,7 @@ class GPTBigCodeForCausalLM(nn.Module):
     ) -> torch.Tensor:
         hidden_states = self.transformer(input_ids, positions, forward_batch)
         return self.logits_processor(
-            input_ids, hidden_states, self.lm_head, forward_batch
+            input_ids, hidden_states, self.lm_head.weight, forward_batch
         )
 
     def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):

python/sglang/srt/models/grok.py

@@ -304,7 +304,7 @@ class Grok1ForCausalLM(nn.Module):
     ) -> torch.Tensor:
         hidden_states = self.model(input_ids, positions, forward_batch, input_embeds)
         return self.logits_processor(
-            input_ids, hidden_states, self.lm_head, forward_batch
+            input_ids, hidden_states, self.lm_head.weight, forward_batch
         )
 
     def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):

python/sglang/srt/models/internlm2.py

@@ -270,7 +270,7 @@ class InternLM2ForCausalLM(nn.Module):
     ) -> torch.Tensor:
         hidden_states = self.model(input_ids, positions, forward_batch, input_embeds)
         return self.logits_processor(
-            input_ids, hidden_states, self.output, forward_batch
+            input_ids, hidden_states, self.output.weight, forward_batch
         )
 
     def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):

python/sglang/srt/models/llama.py

@@ -258,7 +258,6 @@ class LlamaModel(nn.Module):
         self.embed_tokens = VocabParallelEmbedding(
             config.vocab_size,
             config.hidden_size,
-            quant_config=quant_config,
         )
         self.layers = make_layers(
             config.num_hidden_layers,
@@ -306,12 +305,7 @@ class LlamaForCausalLM(nn.Module):
         self.quant_config = quant_config
         self.torchao_config = global_server_args_dict["torchao_config"]
         self.model = LlamaModel(config, quant_config=quant_config)
-        if self.config.tie_word_embeddings:
+        self.lm_head = ParallelLMHead(config.vocab_size, config.hidden_size)
-            self.lm_head = self.model.embed_tokens
-        else:
-            self.lm_head = ParallelLMHead(
-                config.vocab_size, config.hidden_size, quant_config=quant_config
-            )
         self.logits_processor = LogitsProcessor(config)
         self.pooler = Pooler(pooling_type=PoolingType.LAST, normalize=True)
         self.stacked_params_mapping = [
@@ -335,7 +329,7 @@ class LlamaForCausalLM(nn.Module):
         hidden_states = self.model(input_ids, positions, forward_batch, input_embeds)
         if not get_embedding:
             return self.logits_processor(
-                input_ids, hidden_states, self.lm_head, forward_batch
+                input_ids, hidden_states, self.lm_head.weight, forward_batch
             )
         else:
             return self.pooler(hidden_states, forward_batch)
@@ -379,6 +373,7 @@ class LlamaForCausalLM(nn.Module):
         return len(params_dict)
 
     def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
+        embed_tokens_weight = None
         stacked_params_mapping = [
             # (param_name, shard_name, shard_id)
             (".qkv_proj", ".q_proj", "q"),
@@ -390,6 +385,12 @@ class LlamaForCausalLM(nn.Module):
 
         params_dict = dict(self.named_parameters())
 
+        load_tie_word_embeddings = (
+            hasattr(self.config, "tie_word_embeddings")
+            and self.config.tie_word_embeddings
+            and "lm_head.weight" in params_dict
+        )
+
         for name, loaded_weight in weights:
             if "rotary_emb.inv_freq" in name or "projector" in name:
                 continue
@@ -422,6 +423,16 @@ class LlamaForCausalLM(nn.Module):
                 weight_loader = getattr(param, "weight_loader", default_weight_loader)
                 weight_loader(param, loaded_weight)
 
+                if load_tie_word_embeddings and name == "model.embed_tokens.weight":
+                    embed_tokens_weight = loaded_weight
+
+        if load_tie_word_embeddings:
+            # Tie output embedding layer to input embedding layer, to solve issues where lm_head.weight is missing
+            param = self.lm_head.weight
+            weight_loader = getattr(param, "weight_loader", default_weight_loader)
+            if embed_tokens_weight is not None:
+                weight_loader(param, embed_tokens_weight)
+
         apply_torchao_config_(self, params_dict, set(["proj.weight"]))
 
     def get_weights_by_name(

python/sglang/srt/models/minicpm.py

@@ -308,10 +308,12 @@ class MiniCPMForCausalLM(nn.Module):
         hidden_states = self.model(input_ids, positions, forward_batch, input_embeds)
         hidden_states = hidden_states / self.scale_width
         if self.config.tie_word_embeddings:
-            lm_head = self.model.embed_tokens
+            lm_head_weight = self.model.embed_tokens.weight
         else:
-            lm_head = self.lm_head
+            lm_head_weight = self.lm_head.weight
-        return self.logits_processor(input_ids, hidden_states, lm_head, forward_batch)
+        return self.logits_processor(
+            input_ids, hidden_states, lm_head_weight, forward_batch
+        )
 
     def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
         stacked_params_mapping = [

python/sglang/srt/models/minicpm3.py

@@ -585,10 +585,12 @@ class MiniCPM3ForCausalLM(nn.Module):
         hidden_states = self.model(input_ids, positions, forward_batch, input_embeds)
         hidden_states = hidden_states / self.scale_width
         if self.config.tie_word_embeddings:
-            lm_head = self.model.embed_tokens
+            lm_head_weight = self.model.embed_tokens.weight
         else:
-            lm_head = self.lm_head
+            lm_head_weight = self.lm_head.weight
-        return self.logits_processor(input_ids, hidden_states, lm_head, forward_batch)
+        return self.logits_processor(
+            input_ids, hidden_states, lm_head_weight, forward_batch
+        )
 
     def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
         stacked_params_mapping = [

python/sglang/srt/models/mixtral.py

@@ -310,7 +310,7 @@ class MixtralForCausalLM(nn.Module):
     ) -> torch.Tensor:
         hidden_states = self.model(input_ids, positions, forward_batch, input_embeds)
         return self.logits_processor(
-            input_ids, hidden_states, self.lm_head, forward_batch
+            input_ids, hidden_states, self.lm_head.weight, forward_batch
         )
 
     def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):

python/sglang/srt/models/mixtral_quant.py

@@ -343,7 +343,7 @@ class QuantMixtralForCausalLM(nn.Module):
     ) -> torch.Tensor:
         hidden_states = self.model(input_ids, positions, forward_batch, input_embeds)
         return self.logits_processor(
-            input_ids, hidden_states, self.lm_head, forward_batch
+            input_ids, hidden_states, self.lm_head.weight, forward_batch
         )
 
     def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):

python/sglang/srt/models/mllama.py

@@ -966,7 +966,7 @@ class MllamaForConditionalGeneration(nn.Module):
             skip_cross_attention=skip_cross_attention,
         )
         return self.logits_processor(
-            input_ids, hidden_states, self.language_model.lm_head, forward_batch
+            input_ids, hidden_states, self.language_model.lm_head.weight, forward_batch
         )
 
     def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):

python/sglang/srt/models/olmo.py

@@ -306,7 +306,7 @@ class OlmoForCausalLM(nn.Module):
             input_embeds=input_embeds,
         )
         return self.logits_processor(
-            input_ids, hidden_states, self.lm_head, forward_batch
+            input_ids, hidden_states, self.lm_head.weight, forward_batch
         )
 
     def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
@@ -326,6 +326,11 @@ class OlmoForCausalLM(nn.Module):
                 # Models trained using ColossalAI may include these tensors in
                 # the checkpoint. Skip them.
                 continue
+            # With tie_word_embeddings, we can skip lm_head.weight
+            # The weight might appear unnecessarily in the files if the model is
+            # processed with quantization, LoRA, fine-tuning, etc.
+            if self.config.tie_word_embeddings and "lm_head.weight" in name:
+                continue
             for param_name, weight_name, shard_id in stacked_params_mapping:
                 if weight_name not in name:
                     continue

python/sglang/srt/models/olmoe.py

@@ -321,7 +321,7 @@ class OlmoeForCausalLM(nn.Module):
     ) -> torch.Tensor:
         hidden_states = self.model(input_ids, positions, forward_batch, input_embeds)
         return self.logits_processor(
-            input_ids, hidden_states, self.lm_head, forward_batch
+            input_ids, hidden_states, self.lm_head.weight, forward_batch
         )
 
     def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):

python/sglang/srt/models/phi3_small.py

@@ -397,13 +397,10 @@ class Phi3SmallForCausalLM(nn.Module):
 
     def compute_logits(
         self,
-        input_ids: torch.LongTensor,
         hidden_states: torch.Tensor,
         sampling_metadata,
     ) -> Optional[torch.Tensor]:
-        logits = self.logits_processor(
+        logits = self.logits_processor(self.lm_head, hidden_states, sampling_metadata)
-            input_ids, self.lm_head, hidden_states, sampling_metadata
-        )
         if self.dummy_token_indices is not None and logits is not None:
             logits.index_fill_(-1, self.dummy_token_indices, -torch.inf)
         return logits
@@ -425,7 +422,7 @@ class Phi3SmallForCausalLM(nn.Module):
 
         if not get_embedding:
             return self.logits_processor(
-                input_ids, hidden_states, self.lm_head, forward_batch
+                input_ids, hidden_states, self.lm_head.weight, forward_batch
             )
 
         else:

python/sglang/srt/models/qwen.py

@@ -260,7 +260,7 @@ class QWenLMHeadModel(nn.Module):
     ):
         hidden_states = self.transformer(input_ids, positions, forward_batch)
         return self.logits_processor(
-            input_ids, hidden_states, self.lm_head, forward_batch
+            input_ids, hidden_states, self.lm_head.weight, forward_batch
         )
 
     def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):

python/sglang/srt/models/qwen2.py

@@ -230,7 +230,6 @@ class Qwen2Model(nn.Module):
         self.embed_tokens = VocabParallelEmbedding(
             config.vocab_size,
             config.hidden_size,
-            quant_config=quant_config,
         )
         self.layers = make_layers(
             config.num_hidden_layers,
@@ -277,12 +276,7 @@ class Qwen2ForCausalLM(nn.Module):
         self.config = config
         self.quant_config = quant_config
         self.model = Qwen2Model(config, quant_config=quant_config)
-        if config.tie_word_embeddings:
+        self.lm_head = ParallelLMHead(config.vocab_size, config.hidden_size)
-            self.lm_head = self.model.embed_tokens
-        else:
-            self.lm_head = ParallelLMHead(
-                config.vocab_size, config.hidden_size, quant_config=quant_config
-            )
         self.logits_processor = LogitsProcessor(config)
         self.pooler = Pooler(pooling_type=PoolingType.LAST, normalize=True)
 
@@ -298,7 +292,7 @@ class Qwen2ForCausalLM(nn.Module):
         hidden_states = self.model(input_ids, positions, forward_batch, input_embeds)
         if not get_embedding:
             return self.logits_processor(
-                input_ids, hidden_states, self.lm_head, forward_batch
+                input_ids, hidden_states, self.lm_head.weight, forward_batch
             )
         else:
             return self.pooler(hidden_states, forward_batch)
@@ -312,7 +306,6 @@ class Qwen2ForCausalLM(nn.Module):
             ("gate_up_proj", "gate_proj", 0),
             ("gate_up_proj", "up_proj", 1),
         ]
-
         params_dict = dict(self.named_parameters())
         for name, loaded_weight in weights:
             if "rotary_emb.inv_freq" in name or "projector" in name:
@@ -342,6 +335,11 @@ class Qwen2ForCausalLM(nn.Module):
                 param = params_dict[name]
                 weight_loader = getattr(param, "weight_loader", default_weight_loader)
                 weight_loader(param, loaded_weight)
+                if (
+                    self.config.tie_word_embeddings
+                    and name == "model.embed_tokens.weight"
+                ):
+                    weight_loader(params_dict["lm_head.weight"], loaded_weight)
 
 
 EntryClass = Qwen2ForCausalLM

python/sglang/srt/models/qwen2_moe.py

@@ -376,7 +376,7 @@ class Qwen2MoeForCausalLM(nn.Module):
     ) -> torch.Tensor:
         hidden_states = self.model(input_ids, positions, forward_batch, input_embeds)
         return self.logits_processor(
-            input_ids, hidden_states, self.lm_head, forward_batch
+            input_ids, hidden_states, self.lm_head.weight, forward_batch
         )
 
     def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):

python/sglang/srt/models/qwen2_vl.py

@@ -668,7 +668,7 @@ class Qwen2VLForConditionalGeneration(nn.Module):
 
         if not get_embedding:
             return self.logits_processor(
-                input_ids, hidden_states, self.lm_head, forward_batch
+                input_ids, hidden_states, self.lm_head.weight, forward_batch
             )
         else:
             return self.pooler(hidden_states, forward_batch)
@@ -686,6 +686,8 @@ class Qwen2VLForConditionalGeneration(nn.Module):
         for name, loaded_weight in weights:
             if "rotary_emb.inv_freq" in name:
                 continue
+            if self.config.tie_word_embeddings and "lm_head.weight" in name:
+                continue
             for param_name, weight_name, shard_id in stacked_params_mapping:
                 if weight_name not in name:
                     continue

python/sglang/srt/models/stablelm.py

@@ -261,7 +261,7 @@ class StableLmForCausalLM(nn.Module):
     ) -> torch.Tensor:
         hidden_states = self.model(input_ids, positions, forward_batch, input_embeds)
         return self.logits_processor(
-            input_ids, hidden_states, self.lm_head, forward_batch
+            input_ids, hidden_states, self.lm_head.weight, forward_batch
         )
 
     def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):

python/sglang/srt/models/torch_native_llama.py

@@ -396,9 +396,6 @@ class TorchNativeLlamaForCausalLM(nn.Module):
         self.torchao_config = global_server_args_dict["torchao_config"]
         self.supports_torch_tp = True
         self.model = LlamaModel(config, quant_config=quant_config)
-        if self.config.tie_word_embeddings:
-            self.lm_head = self.model.embed_tokens
-        else:
         self.lm_head = ParallelLMHead(config.vocab_size, config.hidden_size)
         self.logits_processor = LogitsProcessor(config)
 
@@ -416,7 +413,7 @@ class TorchNativeLlamaForCausalLM(nn.Module):
     ) -> LogitsProcessorOutput:
         hidden_states = self.model(input_ids, positions, forward_batch, input_embeds)
         return self.logits_processor(
-            input_ids, hidden_states, self.lm_head, forward_batch
+            input_ids, hidden_states, self.lm_head.weight, forward_batch
         )
 
     def get_hidden_dim(self, module_name):
@@ -504,6 +501,14 @@ class TorchNativeLlamaForCausalLM(nn.Module):
                 weight_loader = getattr(param, "weight_loader", default_weight_loader)
                 weight_loader(param, loaded_weight)
 
+        if (
+            hasattr(self.config, "tie_word_embeddings")
+            and self.config.tie_word_embeddings
+        ):
+            # Tie output embedding layer to input embedding layer, to solve issues where lm_head.weight is missing
+            param = self.lm_head.weight
+            weight_loader = getattr(param, "weight_loader", default_weight_loader)
+            weight_loader(param, self.model.embed_tokens.weight)
         apply_torchao_config_(self, params_dict, set(["proj.weight"]))
 
 

python/sglang/srt/models/xverse.py

@@ -315,7 +315,7 @@ class XverseForCausalLM(nn.Module):
     ) -> torch.Tensor:
         hidden_states = self.model(input_ids, positions, forward_batch, input_embeds)
         return self.logits_processor(
-            input_ids, hidden_states, self.lm_head, forward_batch
+            input_ids, hidden_states, self.lm_head.weight, forward_batch
         )
 
     def load_weights(

python/sglang/srt/models/xverse_moe.py

@@ -390,7 +390,7 @@ class XverseMoeForCausalLM(nn.Module):
     ) -> torch.Tensor:
         hidden_states = self.model(input_ids, positions, forward_batch)
         return self.logits_processor(
-            input_ids, hidden_states, self.lm_head, forward_batch
+            input_ids, hidden_states, self.lm_head.weight, forward_batch
         )
 
     def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):

python/sglang/srt/server_args.py

@@ -20,7 +20,6 @@ import random
 import tempfile
 from typing import List, Optional
 
-from sglang.srt.hf_transformers_utils import check_gguf_file
 from sglang.srt.utils import (
     get_amdgpu_memory_capacity,
     get_nvgpu_memory_capacity,
@@ -205,12 +204,6 @@ class ServerArgs:
                 "Overlap schedule is disabled."
             )
 
-        # GGUF
-        if (
-            self.load_format == "auto" or self.load_format == "gguf"
-        ) and check_gguf_file(self.model_path):
-            self.quantization = self.load_format = "gguf"
-
     @staticmethod
     def add_cli_args(parser: argparse.ArgumentParser):
         # Model and port args
@@ -250,7 +243,7 @@ class ServerArgs:
             "--load-format",
             type=str,
             default=ServerArgs.load_format,
-            choices=["auto", "pt", "safetensors", "npcache", "dummy", "gguf"],
+            choices=["auto", "pt", "safetensors", "npcache", "dummy"],
             help="The format of the model weights to load. "
             '"auto" will try to load the weights in the safetensors format '
             "and fall back to the pytorch bin format if safetensors format "
@@ -260,8 +253,7 @@ class ServerArgs:
             '"npcache" will load the weights in pytorch format and store '
             "a numpy cache to speed up the loading. "
             '"dummy" will initialize the weights with random values, '
-            "which is mainly for profiling."
+            "which is mainly for profiling.",
-            '"gguf" will load the weights in the gguf format. ',
         )
         parser.add_argument(
             "--trust-remote-code",
@@ -301,7 +293,6 @@ class ServerArgs:
                 "gptq_marlin",
                 "awq_marlin",
                 "bitsandbytes",
-                "gguf",
             ],
             help="The quantization method.",
         )

python/sglang/srt/utils.py

@@ -557,29 +557,6 @@ def monkey_patch_vllm_all_gather(reverse: bool = False):
         setattr(GroupCoordinator, "all_gather", all_gather)
 
 
-def monkey_patch_vllm_gguf_config():
-    from vllm.model_executor.layers.linear import LinearBase
-    from vllm.model_executor.layers.quantization.gguf import (
-        GGUFConfig,
-        GGUFEmbeddingMethod,
-        GGUFLinearMethod,
-    )
-
-    from sglang.srt.layers.vocab_parallel_embedding import VocabParallelEmbedding
-
-    def get_quant_method_with_embedding_replaced(
-        self, layer: torch.nn.Module, prefix: str
-    ) -> Optional["QuantizeMethodBase"]:
-        if isinstance(layer, LinearBase):
-            return GGUFLinearMethod(self)
-        elif isinstance(layer, VocabParallelEmbedding):
-            # patch to own VocabParallelEmbedding
-            return GGUFEmbeddingMethod(self)
-        return None
-
-    setattr(GGUFConfig, "get_quant_method", get_quant_method_with_embedding_replaced)
-
-
 def maybe_set_triton_cache_manager() -> None:
     """Set environment variable to tell Triton to use a
     custom cache manager"""

test/srt/run_suite.py

@@ -15,7 +15,6 @@ suites = {
         "test_double_sparsity.py",
         "test_embedding_openai_server.py",
         "test_eval_accuracy_mini.py",
-        "test_gguf.py",
         "test_input_embeddings.py",
         "test_json_constrained.py",
         "test_large_max_new_tokens.py",

test/srt/test_gguf.py

@@ -1,26 +0,0 @@
-import unittest
-
-from huggingface_hub import hf_hub_download
-
-import sglang as sgl
-
-
-class TestGGUF(unittest.TestCase):
-    def test_models(self):
-        prompt = "Today is a sunny day and I like"
-        sampling_params = {"temperature": 0, "max_new_tokens": 8}
-
-        model_path = hf_hub_download(
-            "Qwen/Qwen2-1.5B-Instruct-GGUF",
-            filename="qwen2-1_5b-instruct-q4_k_m.gguf",
-        )
-
-        engine = sgl.Engine(model_path=model_path, random_seed=42)
-        outputs = engine.generate(prompt, sampling_params)["text"]
-        engine.shutdown()
-
-        self.assertEqual(outputs, " it. I have a lot of work")
-
-
-if __name__ == "__main__":
-    unittest.main()