annotated_deep_learning_paper_implementations/labml_nn/lora/experiment.py

"""
---
title: Finetune GPT-2 with LoRA
summary: This is training code with notes for fine-tuning pre-trained GPT-2 model with LoRA.
---

# Finetune [GPT-2](gpt2.html) with [LoRA](index.html)

Here's a Colab notebook for training a feedback transformer on Tiny Shakespeare dataset.

[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/labmlai/annotated_deep_learning_paper_implementations/blob/master/labml_nn/lora/experiment.ipynb)
"""

import torch
from torch.optim import Adam
from torch.utils.data import DataLoader, TensorDataset
from transformers import AutoTokenizer, AutoModelForCausalLM

from labml import lab, monit, tracker
from labml.configs import BaseConfigs, option
from labml.utils.download import download_file
from labml_helpers.device import DeviceConfigs
from labml_nn.lora.gpt2 import GPTModel


class Trainer(BaseConfigs):
    """
    ## Trainer configurations and the training loop

    The default configs can and will be over-ridden when we start the experiment
    """
    device: torch.device = DeviceConfigs()

    # GPT-2 configs
    layer_norm_epsilon: float = 1e-05
    d_model: int = 768
    n_layers: int = 12
    n_heads: int = 12
    n_positions: int = 1024
    vocab_size: int = 50257

    # Training configs
    epochs: int = 10
    batch_size: int = 32
    learning_rate: float = 1e-4
    context_len: int = 512

    # LoRA rank
    lora_r: int = 32

    # Dataset
    text: TensorDataset = "tiny_shakespeare"
    # Huggingface tokenizer
    tokenizer = AutoTokenizer.from_pretrained("gpt2")
    # [GPT2 model](gpt2.html)
    model: GPTModel
    # Optimizer
    optimizer: torch.optim.Adam
    # Cross entropy loss
    loss_func = torch.nn.CrossEntropyLoss()
    # Dataloader
    data_loader: DataLoader

    def _load_pretrained_weights(self):
        """
        ### Load pre-trained [GPT-2 from huggingface](https://huggingface.co/openai-community/gpt2)
        """

        # Load the huggingface model and get the parameters
        hf_model = AutoModelForCausalLM.from_pretrained("gpt2")
        state_dict = hf_model.state_dict()

        # Transformer embedding and prediction layer parameter mapping (`hf: ours`)
        mapping = {
            'transformer.wte.weight': 'token_embedding.weight',
            'transformer.wpe.weight': 'position_embedding.weight',
            'transformer.ln_f.weight': 'final_norm.weight',
            'transformer.ln_f.bias': 'final_norm.bias',
            'lm_head.weight': 'lm_head.weight'
        }

        # Mapping (`hf: ours`) of decoder layers
        for i in range(12):
            mapping[f'transformer.h.{i}.ln_1.weight'] = f'blocks.{i}.pre_norm.weight'
            mapping[f'transformer.h.{i}.ln_1.bias'] = f'blocks.{i}.pre_norm.bias'
            mapping[f'transformer.h.{i}.attn.c_attn.weight'] = f'blocks.{i}.attn.qkv_projection.weight'
            mapping[f'transformer.h.{i}.attn.c_attn.bias'] = f'blocks.{i}.attn.qkv_projection.bias'
            mapping[f'transformer.h.{i}.attn.c_proj.weight'] = f'blocks.{i}.attn.output_projection.weight'
            mapping[f'transformer.h.{i}.attn.c_proj.bias'] = f'blocks.{i}.attn.output_projection.bias'
            mapping[f'transformer.h.{i}.ln_2.weight'] = f'blocks.{i}.post_norm.weight'
            mapping[f'transformer.h.{i}.ln_2.bias'] = f'blocks.{i}.post_norm.bias'
            mapping[f'transformer.h.{i}.mlp.c_fc.weight'] = f'blocks.{i}.ffn.linear_in.weight'
            mapping[f'transformer.h.{i}.mlp.c_fc.bias'] = f'blocks.{i}.ffn.linear_in.bias'
            mapping[f'transformer.h.{i}.mlp.c_proj.weight'] = f'blocks.{i}.ffn.linear_out.weight'
            mapping[f'transformer.h.{i}.mlp.c_proj.bias'] = f'blocks.{i}.ffn.linear_out.bias'

        # Move the parameters based on mapping
        new_state_dict = {}
        for old_key, new_key in mapping.items():
            if old_key in state_dict:
                new_state_dict[new_key] = state_dict[old_key]

        # GPT-2 hugging face uses 1D Convolution layers. We need to transpose those weights since we use linear layers
        convo_layers = ([f'blocks.{i}.ffn.linear_in.weight' for i in range(12)] +
                        [f'blocks.{i}.ffn.linear_out.weight' for i in range(12)] +
                        [f'blocks.{i}.attn.qkv_projection.weight' for i in range(12)] +
                        [f'blocks.{i}.attn.output_projection.weight' for i in range(12)])

        for layer in convo_layers:
            new_state_dict[layer] = torch.transpose(new_state_dict[layer], 0, 1)

        # Load out model. We use `strict = False` because the state does not have LoRA weights
        missing_keys, unexpected_keys = self.model.load_state_dict(new_state_dict, strict=False)

        # make sure that only lora weights are not loaded
        assert all('lora' in key for key in missing_keys)
        assert not unexpected_keys

    def initialize(self):
        """
        ### Initialize the model, optimizer and dataloader
        """
        # Initialize the [GPT2 model](gpt2.html)
        self.model = GPTModel(
            layer_norm_epsilon=self.layer_norm_epsilon,
            d_model=self.d_model,
            n_layers=self.n_layers,
            n_heads=self.n_heads,
            n_positions=self.n_positions,
            vocab_size=self.vocab_size,
            r=self.lora_r,
        )
        self.model.to(self.device)
        # Load pre-trained model weights
        self._load_pretrained_weights()

        # Initialize the optimizer
        self.optimizer = Adam(self.model.parameters(), lr=self.learning_rate)

        # Initialize the data loader
        self.data_loader = DataLoader(self.text, batch_size=self.batch_size, shuffle=True)

    def run(self):
        """
        ### Training loop
        """

        for _ in monit.loop(self.epochs):
            # `inputs` has shape `[batch_size, seq_len]`
            for (inputs,) in monit.iterate('Train', self.data_loader):
                # Move `inputs` to device
                inputs = inputs.to(self.device)
                # Call the model, with the all but the last token
                logits = self.model(inputs[:, :-1])
                # Get cross entropy loss
                loss = self.loss_func(logits.reshape(-1, logits.shape[-1]), inputs[:, 1:].reshape(-1))

                # Make gradients 0
                self.optimizer.zero_grad()
                # Compute gradients
                loss.backward()
                # Optimize
                self.optimizer.step()

                # Log the loss
                tracker.save({'loss': loss})
                tracker.add_global_step()
            #
            tracker.new_line()


@option(Trainer.text)
def tiny_shakespeare(c: Trainer):
    """
    ### Tiny Shakespeare dataset

    It will download from the url if not present
    """
    path = lab.get_data_path() / 'tiny_shakespeare.txt'
    if not path.exists():
        download_file("https://raw.githubusercontent.com/karpathy/char-rnn/master/data/tinyshakespeare/input.txt", path)
    with open(path, 'r', encoding='utf-8') as f:
        text = f.read()

    tokens = c.tokenizer.encode(text)
    num_batches = len(tokens) // (c.batch_size * c.context_len)
    tokens = tokens[:num_batches * c.batch_size * c.context_len]
    input_ids = torch.tensor(tokens).view(-1, c.context_len)
    return TensorDataset(input_ids)