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https://github.com/huggingface/text-generation-inference.git
synced 2025-04-21 23:12:07 +00:00
77 lines
2.4 KiB
Python
77 lines
2.4 KiB
Python
import torch
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from loguru import logger
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from transformers.configuration_utils import PretrainedConfig
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from transformers.models.auto import modeling_auto
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from typing import Optional
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# Needed to properly setup habana_frameworks
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import text_generation_server.habana_quantization_env as hq_env
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from text_generation_server.utils.speculate import get_speculate, set_speculate
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from text_generation_server.models.model import Model
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from text_generation_server.models.causal_lm import CausalLM
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from text_generation_server.models.bloom import BLOOM
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from text_generation_server.models.santacoder import SantaCoder
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# Disable gradients
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torch.set_grad_enabled(False)
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def get_model(
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model_id: str,
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revision: Optional[str],
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speculate: Optional[int],
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dtype: Optional[torch.dtype],
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trust_remote_code: bool,
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) -> Model:
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if speculate is not None:
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set_speculate(speculate)
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else:
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set_speculate(0)
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config_dict, _ = PretrainedConfig.get_config_dict(
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model_id, revision=revision, trust_remote_code=trust_remote_code
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)
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use_medusa = None
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if "medusa_num_heads" in config_dict:
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use_medusa = model_id
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model_id = config_dict["base_model_name_or_path"]
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revision = "main"
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speculate_medusa = config_dict["medusa_num_heads"]
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if speculate is not None:
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if speculate > speculate_medusa:
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raise RuntimeError(
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"Speculate is set to `{speculate}` but this medusa models only has `{speculate_medusa}` heads, please make them match"
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)
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else:
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set_speculate(speculate)
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else:
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set_speculate(speculate_medusa)
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config_dict, _ = PretrainedConfig.get_config_dict(
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model_id, revision=revision, trust_remote_code=trust_remote_code
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)
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method = "medusa"
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else:
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method = "n-gram"
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speculate = get_speculate()
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if speculate > 0:
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logger.info(f"Using speculation {method} with {speculate} input ids.")
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model_type = config_dict["model_type"]
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if model_type == "gpt_bigcode":
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return SantaCoder(model_id, revision, dtype)
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if model_type == "bloom":
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return BLOOM(model_id, revision, dtype)
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if model_type in modeling_auto.MODEL_FOR_CAUSAL_LM_MAPPING_NAMES:
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return CausalLM(model_id, revision, dtype)
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raise ValueError(f"Unsupported model type {model_type}")
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