Method from_pretrained

mingpt/model.py:175–213

Initialize a pretrained GPT model by copying over the weights from a huggingface/transformers checkpoint.

(cls, model_type)

    @classmethod
    def from_pretrained(cls, model_type):
        """
        Initialize a pretrained GPT model by copying over the weights
        from a huggingface/transformers checkpoint.
        """
        assert model_type in {'gpt2', 'gpt2-medium', 'gpt2-large', 'gpt2-xl'}
        from transformers import GPT2LMHeadModel

        # create a from-scratch initialized minGPT model
        config = cls.get_default_config()
        config.model_type = model_type
        config.vocab_size = 50257 # openai's model vocabulary
        config.block_size = 1024  # openai's model block_size
        model = GPT(config)
        sd = model.state_dict()

        # init a huggingface/transformers model
        model_hf = GPT2LMHeadModel.from_pretrained(model_type)
        sd_hf = model_hf.state_dict()

        # copy while ensuring all of the parameters are aligned and match in names and shapes
        keys = [k for k in sd_hf if not k.endswith('attn.masked_bias')] # ignore these
        transposed = ['attn.c_attn.weight', 'attn.c_proj.weight', 'mlp.c_fc.weight', 'mlp.c_proj.weight']
        # basically the openai checkpoints use a "Conv1D" module, but we only want to use a vanilla nn.Linear.
        # this means that we have to transpose these weights when we import them
        assert len(keys) == len(sd)
        for k in keys:
            if any(k.endswith(w) for w in transposed):
                # special treatment for the Conv1D weights we need to transpose
                assert sd_hf[k].shape[::-1] == sd[k].shape
                with torch.no_grad():
                    sd[k].copy_(sd_hf[k].t())
            else:
                # vanilla copy over the other parameters
                assert sd_hf[k].shape == sd[k].shape
                with torch.no_grad():
                    sd[k].copy_(sd_hf[k])

        return model

    def configure_optimizers(self, train_config):
        """