modeling_gpt2.py

from torch import nn

from transformers import GPT2LMHeadModel as GPT2LMHeadModelBase
from transformers.models.gpt2.modeling_gpt2 import GPT2Block as GPT2BlockBase


class GPT2Block(GPT2BlockBase):
    def forward(self, x, layer_past=None, 
                attention_mask=None, head_mask=None, use_cache=False,
                encoder_hidden_states=None, encoder_attention_mask=None, output_attentions=None):
      
        x = self.ln_1(x)
        output_attn = self.attn(
            x, layer_past=layer_past,
            attention_mask=attention_mask,
            head_mask=head_mask,
            use_cache=use_cache)
        
        a = output_attn[0]
        x = x + a

        m = self.mlp(self.ln_2(x))
        x = x + m

        outputs = (x,) + output_attn[1:]
        return outputs


class GPT2LMHeadModel(GPT2LMHeadModelBase):
    def __init__(self, config):
        super().__init__(config)
        self.transformer.h = nn.ModuleList([GPT2Block(config, layer_idx) for layer_idx in range(config.n_layer)])