mirror of
https://github.com/huggingface/text-generation-inference.git
synced 2025-04-21 23:02:13 +00:00
73 lines
2.3 KiB
Python
73 lines
2.3 KiB
Python
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import json
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from text_generation_server.utils import (
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hub,
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)
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import os
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class LoraConfig:
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def __init__(
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self,
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alpha_pattern=None,
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auto_mapping=None,
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base_model_name_or_path="",
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bias="none",
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fan_in_fan_out=False,
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inference_mode=True,
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init_lora_weights=True,
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layer_replication=None,
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layers_pattern=None,
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layers_to_transform=None,
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loftq_config=None,
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lora_alpha=16,
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lora_dropout=0.1,
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megatron_config=None,
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megatron_core="megatron.core",
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modules_to_save=None,
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peft_type="LORA",
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r=8,
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rank_pattern=None,
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revision=None,
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target_modules=None,
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task_type="CAUSAL_LM",
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use_dora=False,
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use_rslora=False,
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):
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self.alpha_pattern = alpha_pattern or {}
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self.auto_mapping = auto_mapping
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self.base_model_name_or_path = base_model_name_or_path
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self.bias = bias
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self.fan_in_fan_out = fan_in_fan_out
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self.inference_mode = inference_mode
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self.init_lora_weights = init_lora_weights
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self.layer_replication = layer_replication
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self.layers_pattern = layers_pattern
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self.layers_to_transform = layers_to_transform
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self.loftq_config = loftq_config or {}
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self.lora_alpha = lora_alpha
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self.lora_dropout = lora_dropout
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self.megatron_config = megatron_config
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self.megatron_core = megatron_core
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self.modules_to_save = modules_to_save
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self.peft_type = peft_type
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self.r = r
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self.rank_pattern = rank_pattern or {}
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self.revision = revision
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self.target_modules = target_modules or ["q_proj", "v_proj"]
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self.task_type = task_type
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self.use_dora = use_dora
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self.use_rslora = use_rslora
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@classmethod
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def from_file(cls, filename):
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with open(filename, "r") as f:
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json_data = json.load(f)
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return cls(**json_data)
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# TODO: support fetching the model from the hub if it's not in the cache
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@classmethod
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def from_pretrained(cls, adapter_id, revision=None):
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d = hub._get_cached_revision_directory(adapter_id, revision)
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filename = os.path.join(d, "adapter_config.json")
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return cls.from_file(filename)
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