104 lines
4.4 KiB
Python
104 lines
4.4 KiB
Python
"""Utilities for selecting and loading models."""
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import contextlib
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from typing import Tuple, Type
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import torch
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import torch.nn as nn
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from vllm.config import DeviceConfig, ModelConfig
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from vllm.model_executor.models import ModelRegistry
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from vllm.model_executor.models.llava import LlavaForConditionalGeneration
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from vllm.model_executor.weight_utils import (get_quant_config,
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initialize_dummy_weights)
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_VISION_MODEL_CLASSES = [
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LlavaForConditionalGeneration,
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]
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@contextlib.contextmanager
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def _set_default_torch_dtype(dtype: torch.dtype):
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"""Sets the default torch dtype to the given dtype."""
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old_dtype = torch.get_default_dtype()
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torch.set_default_dtype(dtype)
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yield
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torch.set_default_dtype(old_dtype)
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def _get_model_architecture(
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model_config: ModelConfig) -> Tuple[Type[nn.Module], str]:
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architectures = getattr(model_config.hf_config, "architectures", [])
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# Special handling for quantized Mixtral.
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# FIXME(woosuk): This is a temporary hack.
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if (model_config.quantization is not None
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and "MixtralForCausalLM" in architectures):
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architectures = ["QuantMixtralForCausalLM"]
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for arch in architectures:
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model_cls = ModelRegistry.load_model_cls(arch)
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if model_cls is not None:
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return (model_cls, arch)
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raise ValueError(
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f"Model architectures {architectures} are not supported for now. "
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f"Supported architectures: {ModelRegistry.get_supported_archs()}")
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def get_architecture_class_name(model_config: ModelConfig) -> str:
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return _get_model_architecture(model_config)[1]
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def get_model(model_config: ModelConfig, device_config: DeviceConfig,
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**kwargs) -> nn.Module:
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lora_config = kwargs.get("lora_config", None)
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vision_language_config = kwargs.get("vision_language_config", None)
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model_class = _get_model_architecture(model_config)[0]
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# Get the (maybe quantized) linear method.
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linear_method = None
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if model_config.quantization is not None:
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quant_config = get_quant_config(model_config)
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capability = torch.cuda.get_device_capability()
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capability = capability[0] * 10 + capability[1]
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if capability < quant_config.get_min_capability():
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raise ValueError(
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f"The quantization method {model_config.quantization} is not "
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"supported for the current GPU. "
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f"Minimum capability: {quant_config.get_min_capability()}. "
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f"Current capability: {capability}.")
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supported_dtypes = quant_config.get_supported_act_dtypes()
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if model_config.dtype not in supported_dtypes:
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raise ValueError(
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f"{model_config.dtype} is not supported for quantization "
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f"method {model_config.quantization}. Supported dtypes: "
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f"{supported_dtypes}")
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linear_method = quant_config.get_linear_method()
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with _set_default_torch_dtype(model_config.dtype):
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# Create a model instance.
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# The weights will be initialized as empty tensors.
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with torch.device(device_config.device):
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if hasattr(model_class, "supported_lora_modules"):
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model = model_class(model_config.hf_config, linear_method,
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lora_config)
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elif lora_config:
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raise ValueError(
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f"Model {model_class.__name__} does not support LoRA, "
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"but LoRA is enabled. Support for this model may "
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"be added in the future. If this is important to you, "
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"please open an issue on github.")
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else:
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if model_class not in _VISION_MODEL_CLASSES:
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model = model_class(model_config.hf_config, linear_method)
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else:
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model = model_class(model_config.hf_config,
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vision_language_config, linear_method)
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if model_config.load_format == "dummy":
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# NOTE(woosuk): For accurate performance evaluation, we assign
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# random values to the weights.
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initialize_dummy_weights(model)
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else:
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# Load the weights from the cached or downloaded files.
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model.load_weights(model_config.model, model_config.download_dir,
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model_config.load_format, model_config.revision)
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return model.eval()
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