b12518d3cf
Signed-off-by: Thomas Parnell <tpa@zurich.ibm.com> Co-authored-by: Thomas Parnell <tpa@zurich.ibm.com> Co-authored-by: Nick Hill <nickhill@us.ibm.com> Co-authored-by: Davis Wertheimer <Davis.Wertheimer@ibm.com>
83 lines
3.1 KiB
Python
83 lines
3.1 KiB
Python
import contextlib
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from typing import Dict, Optional, Type
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from transformers import PretrainedConfig
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from vllm.envs import VLLM_USE_MODELSCOPE
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from vllm.logger import init_logger
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from vllm.transformers_utils.configs import (ChatGLMConfig, DbrxConfig,
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JAISConfig, MLPSpeculatorConfig,
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MPTConfig, RWConfig)
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if VLLM_USE_MODELSCOPE:
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from modelscope import AutoConfig
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else:
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from transformers import AutoConfig
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logger = init_logger(__name__)
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_CONFIG_REGISTRY: Dict[str, Type[PretrainedConfig]] = {
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"chatglm": ChatGLMConfig,
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"dbrx": DbrxConfig,
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"mpt": MPTConfig,
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"RefinedWeb": RWConfig, # For tiiuae/falcon-40b(-instruct)
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"RefinedWebModel": RWConfig, # For tiiuae/falcon-7b(-instruct)
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"jais": JAISConfig,
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"mlp_speculator": MLPSpeculatorConfig,
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}
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for name, cls in _CONFIG_REGISTRY.items():
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with contextlib.suppress(ValueError):
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AutoConfig.register(name, cls)
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def get_config(model: str,
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trust_remote_code: bool,
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revision: Optional[str] = None,
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code_revision: Optional[str] = None,
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rope_scaling: Optional[dict] = None,
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rope_theta: Optional[float] = None) -> PretrainedConfig:
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try:
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config = AutoConfig.from_pretrained(
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model,
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trust_remote_code=trust_remote_code,
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revision=revision,
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code_revision=code_revision)
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except ValueError as e:
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if (not trust_remote_code and
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"requires you to execute the configuration file" in str(e)):
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err_msg = (
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"Failed to load the model config. If the model is a custom "
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"model not yet available in the HuggingFace transformers "
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"library, consider setting `trust_remote_code=True` in LLM "
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"or using the `--trust-remote-code` flag in the CLI.")
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raise RuntimeError(err_msg) from e
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else:
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raise e
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if config.model_type in _CONFIG_REGISTRY:
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config_class = _CONFIG_REGISTRY[config.model_type]
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config = config_class.from_pretrained(model,
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revision=revision,
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code_revision=code_revision)
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for key, value in [("rope_scaling", rope_scaling),
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("rope_theta", rope_theta)]:
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if value is not None:
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logger.info("Updating %s from %r to %r", key,
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getattr(config, key, None), value)
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config.update({key: value})
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return config
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def get_hf_text_config(config: PretrainedConfig):
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"""Get the "sub" config relevant to llm for multi modal models.
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No op for pure text models.
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"""
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if hasattr(config, "text_config"):
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# The code operates under the assumption that text_config should have
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# `num_attention_heads` (among others). Assert here to fail early
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# if transformers config doesn't align with this assumption.
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assert hasattr(config.text_config, "num_attention_heads")
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return config.text_config
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else:
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return config
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