init

vllm/model_executor/model_loader/utils.py

@@ -0,0 +1,41 @@
+"""Utilities for selecting and loading models."""
+import contextlib
+from typing import Tuple, Type
+
+import torch
+from torch import nn
+
+from vllm.config import ModelConfig
+from vllm.model_executor.models import ModelRegistry
+
+
+@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]:
+    architectures = getattr(model_config.hf_config, "architectures", [])
+    # Special handling for quantized Mixtral.
+    # FIXME(woosuk): This is a temporary hack.
+    if (model_config.quantization is not None
+            and model_config.quantization != "fp8"
+            and "MixtralForCausalLM" in architectures):
+        architectures = ["QuantMixtralForCausalLM"]
+
+    for arch in architectures:
+        model_cls = ModelRegistry.load_model_cls(arch)
+        if model_cls is not None:
+            return (model_cls, arch)
+    raise ValueError(
+        f"Model architectures {architectures} are not supported for now. "
+        f"Supported architectures: {ModelRegistry.get_supported_archs()}")
+
+
+def get_architecture_class_name(model_config: ModelConfig) -> str:
+    return get_model_architecture(model_config)[1]