mirror of
https://github.com/wassname/weight-steering.git
synced 2026-06-27 16:48:01 +08:00
cfierro94
126 lines
4.3 KiB
Python
126 lines
4.3 KiB
Python
from axolotl.integrations.base import BasePlugin
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from axolotl.utils.dict import DictDefault
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from transformers import (
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AutoConfig,
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AutoModelForCausalLM,
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LlamaConfig,
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Qwen2Config,
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)
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from models_with_mlp_bias import (
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LlamaMLPBiasConfig,
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LlamaMLPWithBiasForCausalLM,
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Qwen2MLPBiasConfig,
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Qwen2MLPWithBiasForCausalLM,
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)
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class MLPBiasPlugin(BasePlugin):
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"""
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Plugin to patch AutoModelForCausalLM.from_pretrained to use bias-enabled models.
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"""
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def __init__(self):
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super().__init__()
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self._original_from_pretrained = None
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def pre_model_load(self, cfg: DictDefault):
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"""
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Patch AutoModelForCausalLM.from_pretrained before model loading.
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"""
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print("=" * 80)
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print("Patching AutoModelForCausalLM.from_pretrained for MLP bias...")
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print("=" * 80)
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# Store original - get the actual function, not the bound method
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self._original_from_pretrained = AutoModelForCausalLM.from_pretrained.__func__
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@classmethod
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def patched_from_pretrained(
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cls, pretrained_model_name_or_path, *model_args, **kwargs
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):
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# Get the config
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config = kwargs.get("config")
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if config is None:
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# Load config if not provided
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config = AutoConfig.from_pretrained(
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pretrained_model_name_or_path,
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trust_remote_code=kwargs.get("trust_remote_code", False),
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)
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# Check model type and use our custom class if applicable
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if isinstance(config, Qwen2Config):
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print("✓ Detected Qwen2 model, using Qwen2MLPWithBiasForCausalLM")
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# Update model_type using the config class
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config.model_type = Qwen2MLPBiasConfig.model_type
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# Update config in kwargs
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kwargs["config"] = config
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# Bypass AutoModel and use our class directly
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return Qwen2MLPWithBiasForCausalLM.from_pretrained(
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pretrained_model_name_or_path, *model_args, **kwargs
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)
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elif isinstance(config, LlamaConfig):
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print("✓ Detected Llama model, using LlamaMLPWithBiasForCausalLM")
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# Update model_type using the config class
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config.model_type = LlamaMLPBiasConfig.model_type
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# Update config in kwargs
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kwargs["config"] = config
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return LlamaMLPWithBiasForCausalLM.from_pretrained(
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pretrained_model_name_or_path, *model_args, **kwargs
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)
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else:
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raise Exception("Model not supported.")
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# Apply patch - this modifies the class globally
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AutoModelForCausalLM.from_pretrained = patched_from_pretrained
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print("✓ AutoModelForCausalLM.from_pretrained patched globally")
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print("=" * 80)
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def post_model_load(self, cfg: DictDefault, model):
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"""
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Verify the model was loaded with bias and correct config.
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"""
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print("=" * 80)
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print("Verifying model with MLP bias...")
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print("=" * 80)
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print(f"Model class: {model.__class__.__name__}")
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print(f"Model config type: {model.config.model_type}")
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# Check if bias exists
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bias = model.model.layers[0].mlp.down_proj.bias
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if bias is None:
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raise Exception("⚠ Model does not have bias in down_proj!")
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# Count total bias parameters
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total_bias = sum(
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layer.mlp.down_proj.bias.numel() for layer in model.model.layers
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)
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print(f"✓ Number of layers: {len(model.model.layers)}")
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print(f"✓ Layer 0 down_proj.bias shape: {bias.shape}")
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print(f"✓ Layer 0 down_proj.bias mean: {bias.mean().item():.6f}")
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print(f"✓ Total bias parameters: {total_bias:,}")
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print("=" * 80)
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def post_train_unload(self, cfg: DictDefault):
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"""
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Restore original from_pretrained.
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"""
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if self._original_from_pretrained:
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@classmethod
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def restored(cls, *args, **kwargs):
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return self._original_from_pretrained(cls, *args, **kwargs)
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AutoModelForCausalLM.from_pretrained = restored
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print("✓ Restored original AutoModelForCausalLM.from_pretrained")
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