gpt_bigcode.py

from .base import BaseAWQForCausalLM
from transformers.models.gpt_bigcode.modeling_gpt_bigcode import GPTBigCodeForCausalLM, GPTBigCodeBlock as OldGptBigCodeBlock

class GptBigCodeAWQForCausalLM(BaseAWQForCausalLM):
    layer_type = "GPTBigCodeBlock"
    max_new_tokens_key = "n_positions"

    @staticmethod
    def get_model_layers(model: GPTBigCodeForCausalLM):
        return model.transformer.h

    @staticmethod
    def get_act_for_scaling(module: OldGptBigCodeBlock):
        return dict(
            is_scalable=True,
            scale_name="mlp.act",
            scale_layer=module.mlp.act,
            scale_shape=module.mlp.c_fc.out_features
        )

    @staticmethod
    def move_embed(model: GPTBigCodeForCausalLM, device):
        model.transformer.wte = model.transformer.wte.to(device)
        model.transformer.drop = model.transformer.drop.to(device)

    @staticmethod
    def get_layers_for_scaling(module:OldGptBigCodeBlock, input_feat, module_kwargs):
        layers = []

        # attention input
        layers.append(dict(
            prev_op=module.ln_1,
            layers=[module.attn.c_attn],
            inp=input_feat['attn.c_attn'],
            module2inspect=module.attn,
            kwargs=module_kwargs
        ))
        
        # linear 1
        layers.append(dict(
            prev_op=module.ln_2,
            layers=[module.mlp.c_fc],
            inp=input_feat['mlp.c_fc'],
            module2inspect=module.mlp
        ))

        # linear 2
        layers.append(dict(
            prev_op=module.mlp.act,
            layers=[module.mlp.c_proj],
            inp=input_feat['mlp.c_proj']
        ))

        return layers