Revert "Revert "[FEAT] Support GGUF format"" (#2287)

docs/requirements.txt

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

python/sglang/srt/hf_transformers_utils.py

@@ -16,6 +16,7 @@
 import contextlib
 import os
 import warnings
+from pathlib import Path
 from typing import Dict, Optional, Type, Union
 
 from huggingface_hub import snapshot_download
@@ -27,6 +28,7 @@ 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
@@ -60,15 +62,29 @@ 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
+        model, trust_remote_code=trust_remote_code, revision=revision, **kwargs
     )
     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
 
 
@@ -123,6 +139,11 @@ 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,
@@ -195,3 +216,16 @@ 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

@@ -23,6 +23,7 @@ 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
 
 
@@ -163,7 +164,7 @@ class LogitsProcessor(nn.Module):
         self,
         input_ids,
         hidden_states,
-        weight,
+        lm_head: VocabParallelEmbedding,
         logits_metadata: Union[LogitsMetadata, ForwardBatch],
     ):
         if isinstance(logits_metadata, ForwardBatch):
@@ -178,7 +179,7 @@ class LogitsProcessor(nn.Module):
             last_index = torch.cumsum(logits_metadata.extend_seq_lens, dim=0) - 1
             last_hidden = hidden_states[last_index]
 
-        last_logits = torch.matmul(last_hidden, weight.T)
+        last_logits = self._get_logits(last_hidden, lm_head)
         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()
@@ -229,7 +230,7 @@ class LogitsProcessor(nn.Module):
 
                 # Compute the logits and logprobs for all required tokens
                 states = torch.cat(states, dim=0)
-                all_logits = torch.matmul(states, weight.T)
+                all_logits = self._get_logits(states, lm_head)
                 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()
@@ -276,6 +277,19 @@ 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

@@ -222,6 +222,7 @@ 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

@@ -59,6 +59,7 @@ 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,
@@ -297,6 +298,8 @@ 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

@@ -338,11 +338,12 @@ class BaiChuanBaseForCausalLM(nn.Module):
 
         self.quant_config = quant_config
         self.model = BaiChuanModel(config, position_embedding, quant_config)
-        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.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)
 
     def forward(
@@ -353,7 +354,7 @@ 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.weight, forward_batch
+            input_ids, hidden_states, self.lm_head, 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.weight, forward_batch
+            input_ids, hidden_states, self.lm_head, 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.weight, forward_batch
+            input_ids, hidden_states, self.model.embed_tokens, 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.weight, forward_batch
+            input_ids, hidden_states, self.lm_head, 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.weight, forward_batch
+            input_ids, hidden_states, self.lm_head, 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.weight, forward_batch
+                input_ids, hidden_states, self.lm_head, 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.weight, forward_batch
+            input_ids, hidden_states, self.lm_head, 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.weight, forward_batch
+            input_ids, hidden_states, self.model.embed_tokens, 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.weight, forward_batch
+            input_ids, hidden_states, self.model.embed_tokens, 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.weight, forward_batch
+            input_ids, hidden_states, self.lm_head, 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.weight, forward_batch
+            input_ids, hidden_states, self.lm_head, 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.weight, forward_batch
+            input_ids, hidden_states, self.lm_head, 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.weight, forward_batch
+            input_ids, hidden_states, self.output, forward_batch
         )
 
     def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):

python/sglang/srt/models/llama.py

@@ -45,6 +45,7 @@ from sglang.srt.layers.vocab_parallel_embedding import (
 from sglang.srt.managers.schedule_batch import global_server_args_dict
 from sglang.srt.model_executor.forward_batch_info import ForwardBatch
 from sglang.srt.utils import make_layers
+from sglang.utils import get_exception_traceback
 
 logger = logging.getLogger(__name__)
 
@@ -258,6 +259,7 @@ 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,
@@ -305,7 +307,12 @@ 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)
-        self.lm_head = ParallelLMHead(config.vocab_size, config.hidden_size)
+        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
+            )
         self.logits_processor = LogitsProcessor(config)
         self.pooler = Pooler(pooling_type=PoolingType.LAST, normalize=True)
         self.stacked_params_mapping = [
@@ -329,7 +336,7 @@ 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.weight, forward_batch
+                input_ids, hidden_states, self.lm_head, forward_batch
             )
         else:
             return self.pooler(hidden_states, forward_batch)
@@ -373,7 +380,6 @@ 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"),
@@ -385,12 +391,6 @@ 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
@@ -423,16 +423,6 @@ 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(
@@ -444,6 +434,17 @@ class LlamaForCausalLM(nn.Module):
         For optimized performance, please use torch.save and torch.load.
         """
         try:
+            if name == "lm_head.weight" and self.config.tie_word_embeddings:
+                logger.info(
+                    "word embedding is tied for this model, return embed_tokens.weight as lm_head.weight."
+                )
+                return (
+                    self.model.embed_tokens.weight.cpu()
+                    .to(torch.float32)
+                    .numpy()
+                    .tolist()[:truncate_size]
+                )
+
             mapped_name = name
             mapped_shard_id = None
             for param_name, weight_name, shard_id in self.stacked_params_mapping:
@@ -452,54 +453,48 @@ class LlamaForCausalLM(nn.Module):
                     mapped_shard_id = shard_id
                     break
             params_dict = dict(self.named_parameters())
-            if mapped_name in params_dict:
-                param = params_dict[mapped_name]
-                if mapped_shard_id is not None:
-                    if mapped_shard_id in ["q", "k", "v"]:
-                        num_heads = self.config.num_attention_heads // tp_size
-                        num_kv_heads = self.config.num_key_value_heads // tp_size
-                        head_dim = (
-                            self.config.hidden_size // self.config.num_attention_heads
-                        )
-                        if mapped_shard_id == "q":
-                            offset = 0
-                            size = num_heads * head_dim
-                        elif mapped_shard_id == "k":
-                            offset = num_heads * head_dim
-                            size = num_kv_heads * head_dim
-                        elif mapped_shard_id == "v":
-                            offset = (num_heads + num_kv_heads) * head_dim
-                            size = num_kv_heads * head_dim
-                        weight = param.data.narrow(0, offset, size)
-                    elif mapped_shard_id in [0, 1]:
-                        intermediate_size = self.config.intermediate_size
-                        hidden_size = self.config.hidden_size
-                        slice_size = intermediate_size // tp_size
-                        if mapped_shard_id == 0:  # gate_proj
-                            offset = 0
-                            size = slice_size
-                        elif mapped_shard_id == 1:  # up_proj
-                            offset = slice_size
-                            size = slice_size
-
-                        weight = param.data.narrow(0, offset, size)
-                    else:
-                        weight = param.data
+            param = params_dict[mapped_name]
+            if mapped_shard_id is not None:
+                if mapped_shard_id in ["q", "k", "v"]:
+                    num_heads = self.config.num_attention_heads // tp_size
+                    num_kv_heads = self.config.num_key_value_heads // tp_size
+                    head_dim = (
+                        self.config.hidden_size // self.config.num_attention_heads
+                    )
+                    if mapped_shard_id == "q":
+                        offset = 0
+                        size = num_heads * head_dim
+                    elif mapped_shard_id == "k":
+                        offset = num_heads * head_dim
+                        size = num_kv_heads * head_dim
+                    elif mapped_shard_id == "v":
+                        offset = (num_heads + num_kv_heads) * head_dim
+                        size = num_kv_heads * head_dim
+                    weight = param.data.narrow(0, offset, size)
+                elif mapped_shard_id in [0, 1]:
+                    intermediate_size = self.config.intermediate_size
+                    slice_size = intermediate_size // tp_size
+                    if mapped_shard_id == 0:  # gate_proj
+                        offset = 0
+                        size = slice_size
+                    elif mapped_shard_id == 1:  # up_proj
+                        offset = slice_size
+                        size = slice_size
+
+                    weight = param.data.narrow(0, offset, size)
                 else:
                     weight = param.data
-                if tp_size > 1 and ("o_proj" in name or "down_proj" in name):
-                    gathered_weights = [
-                        torch.zeros_like(weight) for _ in range(tp_size)
-                    ]
-                    torch.distributed.all_gather(gathered_weights, weight)
-                    weight = torch.cat(gathered_weights, dim=1)
-                return weight.cpu().to(torch.float32).numpy().tolist()[:truncate_size]
             else:
-                return None
-
-        except Exception as e:
+                weight = param.data
+            if tp_size > 1 and ("o_proj" in name or "down_proj" in name):
+                gathered_weights = [torch.zeros_like(weight) for _ in range(tp_size)]
+                torch.distributed.all_gather(gathered_weights, weight)
+                weight = torch.cat(gathered_weights, dim=1)
+            return weight.cpu().to(torch.float32).numpy().tolist()[:truncate_size]
+
+        except Exception:
             logger.error(
-                f"Error getting weights by name {name} in LlamaForCausalLM: {e}"
+                f"Error getting weights by name {name} in LlamaForCausalLM: {get_exception_traceback()}"
             )
             return None
 

python/sglang/srt/models/minicpm.py

@@ -308,12 +308,10 @@ 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_weight = self.model.embed_tokens.weight
+            lm_head = self.model.embed_tokens
         else:
-            lm_head_weight = self.lm_head.weight
-        return self.logits_processor(
-            input_ids, hidden_states, lm_head_weight, forward_batch
-        )
+            lm_head = self.lm_head
+        return self.logits_processor(input_ids, hidden_states, lm_head, forward_batch)
 
     def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
         stacked_params_mapping = [