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_seq_len_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.wpe = model.transformer.wpe.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