Update model_loader deps and qqq quantization deps (#2220) (#2318)

Co-authored-by: HandH1998 <1335248067@qq.com>

python/sglang/bench_one_batch.py

@@ -111,8 +111,12 @@ def load_model(server_args, port_args, tp_rank):
     model_config = ModelConfig(
         server_args.model_path,
         trust_remote_code=server_args.trust_remote_code,
+        revision=server_args.revision,
         context_length=server_args.context_length,
         model_override_args=server_args.json_model_override_args,
+        is_embedding=server_args.is_embedding,
+        dtype=server_args.dtype,
+        quantization=server_args.quantization,
     )
     model_runner = ModelRunner(
         model_config=model_config,

python/sglang/srt/configs/device_config.py

+import logging
+from typing import Optional
+
+import torch
+
+logger = logging.getLogger(__name__)
+
+
+class DeviceConfig:
+    device: Optional[torch.device]
+
+    def __init__(self, device: str = "cuda") -> None:
+        if device in ["cuda", "xpu", "hpu"]:
+            self.device_type = device
+        else:
+            raise RuntimeError(f"Not supported device type: {device}")
+        self.device = torch.device(self.device_type)

python/sglang/srt/configs/load_config.py

+# Adapted from https://github.com/vllm-project/vllm/blob/v0.6.4.post1/vllm/config.py
+import enum
+import json
+import logging
+from dataclasses import dataclass, field
+from typing import List, Optional, Union
+
+from sglang.srt.utils import is_hip
+
+logger = logging.getLogger(__name__)
+
+
+class LoadFormat(str, enum.Enum):
+    AUTO = "auto"
+    PT = "pt"
+    SAFETENSORS = "safetensors"
+    NPCACHE = "npcache"
+    DUMMY = "dummy"
+    SHARDED_STATE = "sharded_state"
+    GGUF = "gguf"
+    BITSANDBYTES = "bitsandbytes"
+    MISTRAL = "mistral"
+
+
+@dataclass
+class LoadConfig:
+    """
+    download_dir: Directory to download and load the weights, default to the
+        default cache directory of huggingface.
+    load_format: 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 is
+            not available.
+        "pt" will load the weights in the pytorch bin format.
+        "safetensors" will load the weights in the safetensors format.
+        "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.
+        "bitsandbytes" will load nf4 type weights.
+    ignore_patterns: The list of patterns to ignore when loading the model.
+        Default to "original/**/*" to avoid repeated loading of llama's
+        checkpoints.
+
+    """
+
+    load_format: Union[str, LoadFormat] = LoadFormat.AUTO
+    download_dir: Optional[str] = None
+    model_loader_extra_config: Optional[Union[str, dict]] = field(default_factory=dict)
+    ignore_patterns: Optional[Union[List[str], str]] = None
+
+    def __post_init__(self):
+        model_loader_extra_config = self.model_loader_extra_config or {}
+        if isinstance(model_loader_extra_config, str):
+            self.model_loader_extra_config = json.loads(model_loader_extra_config)
+        self._verify_load_format()
+
+        if self.ignore_patterns is not None and len(self.ignore_patterns) > 0:
+            logger.info(
+                "Ignoring the following patterns when downloading weights: %s",
+                self.ignore_patterns,
+            )
+        else:
+            self.ignore_patterns = ["original/**/*"]
+
+    def _verify_load_format(self) -> None:
+        if not isinstance(self.load_format, str):
+            return
+
+        load_format = self.load_format.lower()
+        self.load_format = LoadFormat(load_format)
+
+        rocm_not_supported_load_format: List[str] = []
+        if is_hip() and load_format in rocm_not_supported_load_format:
+            rocm_supported_load_format = [
+                f
+                for f in LoadFormat.__members__
+                if (f not in rocm_not_supported_load_format)
+            ]
+            raise ValueError(
+                f"load format '{load_format}' is not supported in ROCm. "
+                f"Supported load formats are "
+                f"{rocm_supported_load_format}"
+            )

python/sglang/srt/configs/model_config.py

@@ -15,12 +15,14 @@
 import json
 import logging
 from enum import IntEnum, auto
-from typing import List, Optional
+from typing import List, Optional, Union
 
+import torch
 from transformers import PretrainedConfig
 
 from sglang.srt.hf_transformers_utils import get_config, get_context_length
-from sglang.srt.utils import get_bool_env_var
+from sglang.srt.layers.quantization import QUANTIZATION_METHODS
+from sglang.srt.utils import get_bool_env_var, is_hip
 
 logger = logging.getLogger(__name__)
 
@@ -33,17 +35,22 @@ class AttentionArch(IntEnum):
 class ModelConfig:
     def __init__(
         self,
-        path: str,
+        model_path: str,
         trust_remote_code: bool = True,
         revision: Optional[str] = None,
         context_length: Optional[int] = None,
         model_override_args: Optional[dict] = None,
         is_embedding: Optional[bool] = None,
+        dtype: str = "auto",
+        quantization: Optional[str] = None,
     ) -> None:
+        self.model_path = model_path
+        self.revision = revision
+        self.quantization = quantization
         # Parse args
         self.model_override_args = json.loads(model_override_args)
         self.hf_config = get_config(
-            path,
+            model_path,
             trust_remote_code=trust_remote_code,
             revision=revision,
             model_override_args=self.model_override_args,
@@ -56,6 +63,7 @@ class ModelConfig:
         )
         self.is_multimodal = is_multimodal_model(self.hf_config.architectures)
         self.is_encoder_decoder = is_encoder_decoder_model(self.hf_config.architectures)
+        self.dtype = _get_and_verify_dtype(self.hf_text_config, dtype)
 
         # Derive context length
         derived_context_len = get_context_length(self.hf_text_config)
@@ -116,6 +124,8 @@ class ModelConfig:
         self.num_hidden_layers = self.hf_text_config.num_hidden_layers
         self.vocab_size = self.hf_text_config.vocab_size
 
+        self._verify_quantization()
+
     # adapted from https://github.com/vllm-project/vllm/blob/main/vllm/config.py#L289
     def get_total_num_kv_heads(self) -> int:
         """Returns the total number of KV heads."""
@@ -174,6 +184,86 @@ class ModelConfig:
         # parallel size so each GPU has at least one KV head.
         return max(1, total_num_kv_heads // tensor_parallel_size)
 
+    # adapted from https://github.com/vllm-project/vllm/blob/v0.6.4.post1/vllm/config.py
+    def _parse_quant_hf_config(self):
+        quant_cfg = getattr(self.hf_config, "quantization_config", None)
+        if quant_cfg is None:
+            # compressed-tensors uses a "compression_config" key
+            quant_cfg = getattr(self.hf_config, "compression_config", None)
+        return quant_cfg
+
+    # adapted from https://github.com/vllm-project/vllm/blob/v0.6.4.post1/vllm/config.py
+    def _verify_quantization(self) -> None:
+        supported_quantization = [*QUANTIZATION_METHODS]
+        rocm_supported_quantization = [
+            "awq",
+            "gptq",
+            "fp8",
+            "compressed_tensors",
+            "compressed-tensors",
+            "fbgemm_fp8",
+        ]
+        optimized_quantization_methods = [
+            "fp8",
+            "marlin",
+            "modelopt",
+            "gptq_marlin_24",
+            "gptq_marlin",
+            "awq_marlin",
+            "fbgemm_fp8",
+            "compressed_tensors",
+            "compressed-tensors",
+            "experts_int8",
+        ]
+        if self.quantization is not None:
+            self.quantization = self.quantization.lower()
+
+        # Parse quantization method from the HF model config, if available.
+        quant_cfg = self._parse_quant_hf_config()
+
+        if quant_cfg is not None:
+            quant_method = quant_cfg.get("quant_method", "").lower()
+
+            # Detect which checkpoint is it
+            for _, method in QUANTIZATION_METHODS.items():
+                quantization_override = method.override_quantization_method(
+                    quant_cfg, self.quantization
+                )
+                if quantization_override:
+                    quant_method = quantization_override
+                    self.quantization = quantization_override
+                    break
+
+            # Verify quantization configurations.
+            if self.quantization is None:
+                self.quantization = quant_method
+            elif self.quantization != quant_method:
+                raise ValueError(
+                    "Quantization method specified in the model config "
+                    f"({quant_method}) does not match the quantization "
+                    f"method specified in the `quantization` argument "
+                    f"({self.quantization})."
+                )
+
+        if self.quantization is not None:
+            if self.quantization not in supported_quantization:
+                raise ValueError(
+                    f"Unknown quantization method: {self.quantization}. Must "
+                    f"be one of {supported_quantization}."
+                )
+            if is_hip() and self.quantization not in rocm_supported_quantization:
+                raise ValueError(
+                    f"{self.quantization} quantization is currently not "
+                    f"supported in ROCm."
+                )
+            if self.quantization not in optimized_quantization_methods:
+                logger.warning(
+                    "%s quantization is not fully "
+                    "optimized yet. The speed can be slower than "
+                    "non-quantized models.",
+                    self.quantization,
+                )
+
 
 def get_hf_text_config(config: PretrainedConfig):
     """Get the "sub" config relevant to llm for multi modal models.
@@ -183,6 +273,9 @@ def get_hf_text_config(config: PretrainedConfig):
     if class_name.startswith("Llava") and class_name.endswith("ForCausalLM"):
         # We support non-hf version of llava models, so we do not want to
         # read the wrong values from the unused default text_config.
+        # NOTE(HandH1998): We set `torch_dtype` of config to `torch.float16` for the weights, as
+        # `torch.float16` is default used for image features in `python/sglang/srt/models/llava.py`.
+        setattr(config, "torch_dtype", torch.float16)
         return config
 
     if hasattr(config, "text_config"):
@@ -195,6 +288,70 @@ def get_hf_text_config(config: PretrainedConfig):
         return config
 
 
+# adapted from https://github.com/vllm-project/vllm/blob/v0.6.4.post1/vllm/config.py
+_STR_DTYPE_TO_TORCH_DTYPE = {
+    "half": torch.float16,
+    "float16": torch.float16,
+    "float": torch.float32,
+    "float32": torch.float32,
+    "bfloat16": torch.bfloat16,
+}
+
+
+# adapted from https://github.com/vllm-project/vllm/blob/v0.6.4.post1/vllm/config.py
+def _get_and_verify_dtype(
+    config: PretrainedConfig,
+    dtype: Union[str, torch.dtype],
+) -> torch.dtype:
+    # NOTE: getattr(config, "torch_dtype", torch.float32) is not correct
+    # because config.torch_dtype can be None.
+    config_dtype = getattr(config, "torch_dtype", None)
+    if config_dtype is None:
+        config_dtype = torch.float32
+
+    if isinstance(dtype, str):
+        dtype = dtype.lower()
+        if dtype == "auto":
+            if config_dtype == torch.float32:
+                if config.model_type == "gemma2":
+                    logger.info(
+                        "For Gemma 2, we downcast float32 to bfloat16 instead "
+                        "of float16 by default. Please specify `dtype` if you "
+                        "want to use float16."
+                    )
+                    torch_dtype = torch.bfloat16
+                else:
+                    # Following the common practice, we use float16 for float32
+                    # models.
+                    torch_dtype = torch.float16
+            else:
+                torch_dtype = config_dtype
+        else:
+            if dtype not in _STR_DTYPE_TO_TORCH_DTYPE:
+                raise ValueError(f"Unknown dtype: {dtype}")
+            torch_dtype = _STR_DTYPE_TO_TORCH_DTYPE[dtype]
+    elif isinstance(dtype, torch.dtype):
+        torch_dtype = dtype
+    else:
+        raise ValueError(f"Unknown dtype: {dtype}")
+
+    # Verify the dtype.
+    if torch_dtype != config_dtype:
+        if torch_dtype == torch.float32:
+            # Upcasting to float32 is allowed.
+            logger.info("Upcasting %s to %s.", config_dtype, torch_dtype)
+            pass
+        elif config_dtype == torch.float32:
+            # Downcasting from float32 to float16 or bfloat16 is allowed.
+            logger.info("Downcasting %s to %s.", config_dtype, torch_dtype)
+            pass
+        else:
+            # Casting between float16 and bfloat16 is allowed with a warning.
+            logger.warning("Casting %s to %s.", config_dtype, torch_dtype)
+
+    return torch_dtype
+
+
 def is_generation_model(model_architectures: List[str], is_embedding: bool = False):
     # We have two ways to determine whether a model is a generative model.
     # 1. Check the model architectue

python/sglang/srt/configs/qwen2vl.py

@@ -121,13 +121,10 @@ class Qwen2VLConfig(PretrainedConfig):
         self.attention_dropout = attention_dropout
         self.rope_scaling = rope_scaling
 
-        # NOTE: the following section from original transformers config
-        # for Qwen2-VL is commented out to address rope config loading issue
-        #
-        # if self.rope_scaling is not None and "type" in self.rope_scaling:
-        #     if self.rope_scaling["type"] == "mrope":
-        #         self.rope_scaling["type"] = "default"
-        #     self.rope_scaling["rope_type"] = self.rope_scaling["type"]
-        # rope_config_validation(self)
+        # NOTE(HandH1998): This is necessary for configuring the `rope_type`` of qwen2vl models after removing dependencies on vllm.
+        if self.rope_scaling is not None and "type" in self.rope_scaling:
+            if self.rope_scaling["type"] == "mrope":
+                self.rope_scaling["type"] = "default"
+            self.rope_scaling["rope_type"] = self.rope_scaling["type"]
 
         super().__init__(tie_word_embeddings=tie_word_embeddings, **kwargs)

python/sglang/srt/hf_transformers_utils.py

@@ -75,6 +75,8 @@ def get_config(
     if config.model_type in _CONFIG_REGISTRY:
         config_class = _CONFIG_REGISTRY[config.model_type]
         config = config_class.from_pretrained(model, revision=revision)
+        # NOTE(HandH1998): Qwen2VL requires `_name_or_path` attribute in `config`.
+        setattr(config, "_name_or_path", model)
     if model_override_args:
         config.update(model_override_args)
 

python/sglang/srt/layers/linear.py

@@ -42,6 +42,7 @@ WEIGHT_LOADER_V2_SUPPORTED = [
     "Fp8LinearMethod",
     "MarlinLinearMethod",
     "GPTQLinearMethod",
+    "QQQLinearMethod",
 ]
 
 

python/sglang/srt/lora/lora.py

@@ -31,7 +31,6 @@ from vllm.model_executor.layers.vocab_parallel_embedding import (
     ParallelLMHead,
     VocabParallelEmbedding,
 )
-from vllm.model_executor.model_loader.loader import DefaultModelLoader
 
 from sglang.srt.layers.linear import (
     ColumnParallelLinear,
@@ -40,6 +39,7 @@ from sglang.srt.layers.linear import (
     RowParallelLinear,
 )
 from sglang.srt.model_executor.forward_batch_info import ForwardBatch, ForwardMode
+from sglang.srt.model_loader.loader import DefaultModelLoader
 
 
 class BaseLayerWithLoRA(nn.Module):

python/sglang/srt/managers/scheduler.py

@@ -147,9 +147,12 @@ class Scheduler:
         self.model_config = ModelConfig(
             server_args.model_path,
             trust_remote_code=server_args.trust_remote_code,
+            revision=server_args.revision,
             context_length=server_args.context_length,
             model_override_args=server_args.json_model_override_args,
             is_embedding=server_args.is_embedding,
+            dtype=server_args.dtype,
+            quantization=server_args.quantization,
         )
         self.is_generation = self.model_config.is_generation
 

python/sglang/srt/managers/tokenizer_manager.py

@@ -109,9 +109,12 @@ class TokenizerManager:
         self.model_config = ModelConfig(
             server_args.model_path,
             trust_remote_code=server_args.trust_remote_code,
+            revision=server_args.revision,
             context_length=server_args.context_length,
             model_override_args=server_args.json_model_override_args,
             is_embedding=server_args.is_embedding,
+            dtype=server_args.dtype,
+            quantization=server_args.quantization,
         )
 
         self.is_generation = self.model_config.is_generation

python/sglang/srt/managers/tp_worker.py

@@ -52,9 +52,12 @@ class TpModelWorker:
         self.model_config = ModelConfig(
             server_args.model_path,
             trust_remote_code=server_args.trust_remote_code,
+            revision=server_args.revision,
             context_length=server_args.context_length,
             model_override_args=server_args.json_model_override_args,
             is_embedding=server_args.is_embedding,
+            dtype=server_args.dtype,
+            quantization=server_args.quantization,
         )
         self.model_runner = ModelRunner(
             model_config=self.model_config,

python/sglang/srt/model_executor/model_runner.py

@@ -14,22 +14,12 @@
 """ModelRunner runs the forward passes of the models."""
 
 import gc
-import importlib
-import importlib.resources
-import inspect
 import json
 import logging
-import pkgutil
-import time
-from functools import lru_cache
-from tokenize import tabsize
-from typing import Any, Optional, Type, Union
+from typing import Optional
 
 import torch
 import torch.distributed as dist
-import torch.nn as nn
-from vllm.config import DeviceConfig, LoadConfig
-from vllm.config import ModelConfig as VllmModelConfig
 from vllm.distributed import (
     get_tp_group,
     init_distributed_environment,
@@ -37,9 +27,9 @@ from vllm.distributed import (
     set_custom_all_reduce,
 )
 from vllm.distributed.parallel_state import in_the_same_node_as
-from vllm.model_executor.model_loader import get_model
-from vllm.model_executor.models import ModelRegistry
 
+from sglang.srt.configs.device_config import DeviceConfig
+from sglang.srt.configs.load_config import LoadConfig
 from sglang.srt.configs.model_config import AttentionArch, ModelConfig
 from sglang.srt.layers.attention.double_sparsity_backend import DoubleSparseAttnBackend
 from sglang.srt.layers.attention.flashinfer_backend import FlashInferAttnBackend
@@ -56,16 +46,15 @@ from sglang.srt.mem_cache.memory_pool import (
     ReqToTokenPool,
 )
 from sglang.srt.model_executor.forward_batch_info import ForwardBatch
+from sglang.srt.model_loader import get_model
 from sglang.srt.sampling.sampling_batch_info import SamplingBatchInfo
 from sglang.srt.server_args import ServerArgs
 from sglang.srt.utils import (
-    crash_on_warnings,
     enable_show_time_cost,
     get_available_gpu_memory,
     init_custom_process_group,
     is_hip,
     monkey_patch_vllm_gguf_config,
-    monkey_patch_vllm_model_config,
     monkey_patch_vllm_p2p_access_check,
     set_cpu_offload_max_bytes,
 )
@@ -228,49 +217,6 @@ class ModelRunner:
 
         return min_per_gpu_memory
 
-    def setup_model(self):
-        try:
-            from vllm.config import VllmConfig
-
-            vllm_config = VllmConfig()
-            vllm_config.model_config = self.vllm_model_config
-            vllm_config.load_config = self.load_config
-            vllm_config.device_config = DeviceConfig(self.device)
-            vllm_config.quant_config = VllmConfig._get_quantization_config(
-                vllm_config.model_config, vllm_config.load_config
-            )
-            return get_model(vllm_config=vllm_config)
-        except ImportError:
-            pass
-
-        return get_model(
-            model_config=self.vllm_model_config,
-            load_config=self.load_config,
-            device_config=DeviceConfig(self.device),
-            parallel_config=None,
-            scheduler_config=None,
-            lora_config=None,
-            cache_config=None,
-        )
-
-    def get_model_config_params(self):
-        sig = inspect.signature(VllmModelConfig.__init__)
-        params = {
-            "model": self.server_args.model_path,
-            "quantization": self.server_args.quantization,
-            "tokenizer": None,
-            "tokenizer_mode": None,
-            "trust_remote_code": self.server_args.trust_remote_code,
-            "dtype": self.server_args.dtype,
-            "seed": self.server_args.random_seed,
-            "skip_tokenizer_init": True,
-        }
-
-        if "task" in sig.parameters:
-            params["task"] = ""
-
-        return params
-
     def load_model(self):
         logger.info(
             f"Load weight begin. avail mem={get_available_gpu_memory(self.device, self.gpu_id):.2f} GB"
@@ -284,6 +230,7 @@ class ModelRunner:
                     "Compute capability below sm80. Use float16 due to lack of bfloat16 support."
                 )
                 self.server_args.dtype = "float16"
+                self.model_config.dtype = torch.float16
                 if torch.cuda.get_device_capability()[1] < 5:
                     raise RuntimeError("SGLang only supports sm75 and above.")
 
@@ -292,23 +239,21 @@ class ModelRunner:
             load_format=self.server_args.load_format,
             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(
-                self.model_config.model_override_args
+        self.model = get_model(
+            model_config=self.model_config,
+            load_config=self.load_config,
+            device_config=DeviceConfig(self.device),
         )
 
-        self.model = self.setup_model()
-
         self.sliding_window_size = (
             self.model.get_attention_sliding_window_size()
             if hasattr(self.model, "get_attention_sliding_window_size")
             else None
         )
-        self.dtype = self.vllm_model_config.dtype
+        self.dtype = self.model_config.dtype
 
         logger.info(
             f"Load weight end. "
@@ -319,12 +264,12 @@ class ModelRunner:
 
     def update_weights_from_disk(self, model_path: str, load_format: str):
         """Update engine weights online from disk."""
-        from vllm.model_executor.model_loader.loader import (
+        from sglang.srt.model_loader.loader import (
             DefaultModelLoader,
             device_loading_context,
             get_model_loader,
         )
-        from vllm.model_executor.model_loader.utils import set_default_torch_dtype
+        from sglang.srt.model_loader.utils import set_default_torch_dtype
 
         logger.info(
             f"Update engine weights online from disk begin. "
@@ -332,15 +277,7 @@ class ModelRunner:
         )
 
         target_device = torch.device(self.device)
-
-        try:
-            model_config_params = self.get_model_config_params()
-            model_config_params["model"] = model_path
-            vllm_model_config = VllmModelConfig(**model_config_params)
-        except Exception as e:
-            message = f"Failed to load model config: {e}."
-            return False, message
-
+        self.model_config.model_path = model_path
         load_config = LoadConfig(load_format=load_format)
 
         # Only support vllm DefaultModelLoader for now
@@ -352,7 +289,7 @@ class ModelRunner:
         def get_weight_iter(config):
             iter = loader._get_weights_iterator(
                 DefaultModelLoader.Source(
-                    config.model,
+                    config.model_path,
                     revision=config.revision,
                     fall_back_to_pt=getattr(
                         self.model, "fall_back_to_pt_during_load", True
@@ -370,9 +307,9 @@ class ModelRunner:
                         quant_method.process_weights_after_loading(module)
             return model
 
-        with set_default_torch_dtype(vllm_model_config.dtype):
+        with set_default_torch_dtype(self.model_config.dtype):
             try:
-                iter = get_weight_iter(vllm_model_config)
+                iter = get_weight_iter(self.model_config)
             except Exception as e:
                 message = f"Failed to get weights iterator: {e}."
                 return False, message
@@ -384,16 +321,14 @@ class ModelRunner:
                 )
                 del iter
                 gc.collect()
-                iter = get_weight_iter(self.vllm_model_config)
+                iter = get_weight_iter(self.model_config)
                 self.model = model_load_weights(self.model, iter)
                 return False, message
 
         self.model = model
         self.server_args.model_path = model_path
         self.server_args.load_format = load_format
-        self.vllm_model_config = vllm_model_config
         self.load_config = load_config
-        self.model_config.path = model_path
 
         logger.info("Update weights end.")
         return True, "Succeeded to update model weights."
@@ -794,55 +729,3 @@ class ModelRunner:
         if rope_scaling is None:
             return False
         return rope_scaling.get("type", None) == "mrope"
-
-
-@lru_cache()
-def import_model_classes():
-    model_arch_name_to_cls = {}
-    package_name = "sglang.srt.models"
-    package = importlib.import_module(package_name)
-    for _, name, ispkg in pkgutil.iter_modules(package.__path__, package_name + "."):
-        if not ispkg:
-            try:
-                module = importlib.import_module(name)
-            except Exception as e:
-                logger.warning(f"Ignore import error when loading {name}. {e}")
-                if crash_on_warnings():
-                    raise ValueError(f"Ignore import error when loading {name}. {e}")
-                continue
-            if hasattr(module, "EntryClass"):
-                entry = module.EntryClass
-                if isinstance(
-                    entry, list
-                ):  # To support multiple model classes in one module
-                    for tmp in entry:
-                        assert (
-                            tmp.__name__ not in model_arch_name_to_cls
-                        ), f"Duplicated model implementation for {tmp.__name__}"
-                        model_arch_name_to_cls[tmp.__name__] = tmp
-                else:
-                    assert (
-                        entry.__name__ not in model_arch_name_to_cls
-                    ), f"Duplicated model implementation for {entry.__name__}"
-                    model_arch_name_to_cls[entry.__name__] = entry
-
-    return model_arch_name_to_cls
-
-
-def load_model_cls_srt(model_arch: str) -> Optional[Type[nn.Module]]:
-    model_arch_name_to_cls = import_model_classes()
-
-    if model_arch not in model_arch_name_to_cls:
-        raise ValueError(
-            f"Unsupported architectures: {model_arch}. "
-            f"Supported list: {list(model_arch_name_to_cls.keys())}"
-        )
-    return model_arch_name_to_cls[model_arch]
-
-
-# Monkey patch model loader
-setattr(ModelRegistry, "_try_load_model_cls", load_model_cls_srt)
-setattr(ModelRegistry, "is_multimodal_model", lambda model_architectures: False)
-setattr(ModelRegistry, "is_attention_free_model", lambda model_architectures: False)
-setattr(ModelRegistry, "model_has_inner_state", lambda model_architectures: False)
-setattr(ModelRegistry, "is_embedding_model", lambda model_architectures: False)

python/sglang/srt/model_loader/__init__.py

+# Adapted from https://github.com/vllm-project/vllm/blob/v0.6.4.post1/vllm/model_executor/model_loader/__init__.py
+
+from torch import nn
+
+from sglang.srt.configs.device_config import DeviceConfig
+from sglang.srt.configs.load_config import LoadConfig
+from sglang.srt.configs.model_config import ModelConfig
+from sglang.srt.model_loader.loader import BaseModelLoader, get_model_loader
+from sglang.srt.model_loader.utils import (
+    get_architecture_class_name,
+    get_model_architecture,
+)
+
+
+def get_model(
+    *,
+    model_config: ModelConfig,
+    load_config: LoadConfig,
+    device_config: DeviceConfig,
+) -> nn.Module:
+    loader = get_model_loader(load_config)
+    return loader.load_model(
+        model_config=model_config,
+        device_config=device_config,
+    )
+
+
+__all__ = [
+    "get_model",
+    "get_model_loader",
+    "BaseModelLoader",
+    "get_architecture_class_name",
+    "get_model_architecture",
+]

python/sglang/srt/model_loader/utils.py

+# Adapted from https://github.com/vllm-project/vllm/blob/v0.6.4.post1/vllm/model_executor/model_loader/utils.py
+
+"""Utilities for selecting and loading models."""
+import contextlib
+from typing import Tuple, Type
+
+import torch
+from torch import nn
+
+from sglang.srt.configs.model_config import ModelConfig
+
+
+@contextlib.contextmanager
+def set_default_torch_dtype(dtype: torch.dtype):
+    """Sets the default torch dtype to the given dtype."""
+    old_dtype = torch.get_default_dtype()
+    torch.set_default_dtype(dtype)
+    yield
+    torch.set_default_dtype(old_dtype)
+
+
+def get_model_architecture(model_config: ModelConfig) -> Tuple[Type[nn.Module], str]:
+    from sglang.srt.models.registry import ModelRegistry
+
+    architectures = getattr(model_config.hf_config, "architectures", [])
+    # Special handling for quantized Mixtral.
+    # FIXME(woosuk): This is a temporary hack.
+    mixtral_supported = ["fp8", "compressed-tensors", "gptq_marlin", "awq_marlin"]
+
+    if (
+        model_config.quantization is not None
+        and model_config.quantization not in mixtral_supported
+        and "MixtralForCausalLM" in architectures
+    ):
+        architectures = ["QuantMixtralForCausalLM"]
+
+    return ModelRegistry.resolve_model_cls(architectures)
+
+
+def get_architecture_class_name(model_config: ModelConfig) -> str:
+    return get_model_architecture(model_config)[1]

python/sglang/srt/models/baichuan.py

@@ -34,7 +34,6 @@ from vllm.model_executor.layers.linear import (
     RowParallelLinear,
 )
 from vllm.model_executor.layers.rotary_embedding import get_rope
-from vllm.model_executor.model_loader.weight_utils import default_weight_loader
 
 from sglang.srt.layers.activation import SiluAndMul
 from sglang.srt.layers.layernorm import RMSNorm
@@ -46,6 +45,7 @@ from sglang.srt.layers.vocab_parallel_embedding import (
     VocabParallelEmbedding,
 )
 from sglang.srt.model_executor.forward_batch_info import ForwardBatch
+from sglang.srt.model_loader.weight_utils import default_weight_loader
 
 
 def _get_alibi_slopes(total_num_heads: int) -> torch.Tensor:
@@ -329,7 +329,6 @@ class BaiChuanBaseForCausalLM(nn.Module):
         self,
         config: PretrainedConfig,
         position_embedding: str,
-        cache_config=None,
         quant_config: Optional[QuantizationConfig] = None,
     ):
         super().__init__()
@@ -404,13 +403,12 @@ class BaichuanForCausalLM(BaiChuanBaseForCausalLM):
     def __init__(
         self,
         config,
-        cache_config=None,
         quant_config: Optional[QuantizationConfig] = None,
     ):
         if config.hidden_size == 4096:  # baichuan2 7b
-            super().__init__(config, "ROPE", cache_config, quant_config)
+            super().__init__(config, "ROPE", quant_config)
         else:  # baichuan 13b, baichuan2 13b
-            super().__init__(config, "ALIBI", cache_config, quant_config)
+            super().__init__(config, "ALIBI", quant_config)
 
 
 EntryClass = [BaichuanForCausalLM]

python/sglang/srt/models/chatglm.py

@@ -23,7 +23,6 @@ from torch import nn
 from torch.nn import LayerNorm
 from vllm.distributed import get_tensor_model_parallel_world_size
 from vllm.model_executor.layers.rotary_embedding import get_rope
-from vllm.model_executor.model_loader.weight_utils import default_weight_loader
 from vllm.transformers_utils.configs import ChatGLMConfig
 
 from sglang.srt.layers.activation import SiluAndMul
@@ -41,6 +40,7 @@ from sglang.srt.layers.vocab_parallel_embedding import (
     VocabParallelEmbedding,
 )
 from sglang.srt.model_executor.forward_batch_info import ForwardBatch
+from sglang.srt.model_loader.weight_utils import default_weight_loader
 
 LoraConfig = None
 
@@ -50,7 +50,6 @@ class GLMAttention(nn.Module):
         self,
         config,
         layer_id: int = 0,
-        cache_config=None,
         quant_config: Optional[QuantizationConfig] = None,
     ):
         super().__init__()
@@ -186,7 +185,6 @@ class GLMBlock(nn.Module):
         self,
         config,
         layer_id: int,
-        cache_config=None,
         quant_config: Optional[QuantizationConfig] = None,
     ):
         super().__init__()
@@ -203,7 +201,7 @@ class GLMBlock(nn.Module):
         )
 
         # Self attention.
-        self.self_attention = GLMAttention(config, layer_id, cache_config, quant_config)
+        self.self_attention = GLMAttention(config, layer_id, quant_config)
         self.hidden_dropout = config.hidden_dropout
 
         # Layernorm on the attention output
@@ -258,7 +256,6 @@ class GLMTransformer(nn.Module):
     def __init__(
         self,
         config,
-        cache_config=None,
         quant_config: Optional[QuantizationConfig] = None,
     ):
         super().__init__()
@@ -269,10 +266,7 @@ class GLMTransformer(nn.Module):
 
         # Transformer layers.
         self.layers = nn.ModuleList(
-            [
-                GLMBlock(config, i, cache_config, quant_config)
-                for i in range(self.num_layers)
-            ]
+            [GLMBlock(config, i, quant_config) for i in range(self.num_layers)]
         )
 
         if self.post_layer_norm:
@@ -306,7 +300,6 @@ class ChatGLMM(nn.Module):
     def __init__(
         self,
         config,
-        cache_config=None,
         quant_config: Optional[QuantizationConfig] = None,
     ):
         super().__init__()
@@ -318,7 +311,7 @@ class ChatGLMM(nn.Module):
         self.num_layers = config.num_layers
         self.multi_query_group_num = config.multi_query_group_num
         self.kv_channels = config.kv_channels
-        self.encoder = GLMTransformer(config, cache_config, quant_config)
+        self.encoder = GLMTransformer(config, quant_config)
 
         self.output_layer = ParallelLMHead(config.padded_vocab_size, config.hidden_size)
 
@@ -357,15 +350,13 @@ class ChatGLMForCausalLM(nn.Module):
     def __init__(
         self,
         config: ChatGLMConfig,
-        cache_config=None,
         quant_config: Optional[QuantizationConfig] = None,
-        lora_config: Optional[LoraConfig] = None,
     ):
         super().__init__()
         self.config: ChatGLMConfig = config
         self.quant_config = quant_config
         self.max_position_embeddings = getattr(config, "max_sequence_length", 8192)
-        self.transformer = ChatGLMM(config, cache_config, quant_config)
+        self.transformer = ChatGLMM(config, quant_config)
         self.lm_head = self.transformer.output_layer
         self.logits_processor = LogitsProcessor(config)
 

python/sglang/srt/models/commandr.py

@@ -49,7 +49,6 @@ from vllm.distributed import (
     get_tensor_model_parallel_world_size,
 )
 from vllm.model_executor.layers.rotary_embedding import get_rope
-from vllm.model_executor.model_loader.weight_utils import default_weight_loader
 
 from sglang.srt.layers.activation import SiluAndMul
 from sglang.srt.layers.linear import (
@@ -62,6 +61,7 @@ from sglang.srt.layers.quantization.base_config import QuantizationConfig
 from sglang.srt.layers.radix_attention import RadixAttention
 from sglang.srt.layers.vocab_parallel_embedding import VocabParallelEmbedding
 from sglang.srt.model_executor.forward_batch_info import ForwardBatch
+from sglang.srt.model_loader.weight_utils import default_weight_loader
 from sglang.srt.utils import set_weight_attrs
 
 
@@ -318,7 +318,6 @@ class CohereForCausalLM(nn.Module):
         self,
         config: PretrainedConfig,
         quant_config: Optional[QuantizationConfig] = None,
-        cache_config=None,
     ) -> None:
         super().__init__()
         self.config = config

python/sglang/srt/models/dbrx.py

@@ -25,7 +25,6 @@ from vllm.distributed import (
     tensor_model_parallel_all_reduce,
 )
 from vllm.model_executor.layers.rotary_embedding import get_rope
-from vllm.model_executor.model_loader.weight_utils import default_weight_loader
 from vllm.transformers_utils.configs.dbrx import DbrxConfig
 
 from sglang.srt.layers.fused_moe_triton import fused_moe
@@ -43,6 +42,7 @@ from sglang.srt.layers.vocab_parallel_embedding import (
     VocabParallelEmbedding,
 )
 from sglang.srt.model_executor.forward_batch_info import ForwardBatch
+from sglang.srt.model_loader.weight_utils import default_weight_loader
 from sglang.srt.utils import set_weight_attrs
 
 
@@ -366,7 +366,6 @@ class DbrxForCausalLM(nn.Module):
         self,
         config: DbrxConfig,
         quant_config: Optional[QuantizationConfig] = None,
-        cache_config=None,
     ):
         super().__init__()
         self.config = config