mirror of
https://github.com/huggingface/transformers.git
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033f29c625
* add CustomHFIndex * typo in config * update tests * add custom dataset example * clean script * update test data * minor in test * docs * docs * style * fix imports * allow to pass the indexed dataset directly * update tests * use multiset DPR * address thom and patrick's comments * style * update dpr tokenizer * add output_dir flag in use_own_knowledge_dataset.py * allow custom datasets in examples/rag/finetune.py * add test for custom dataset in distributed rag retriever
500 lines
20 KiB
Python
500 lines
20 KiB
Python
"""Finetuning script for RAG models. Adapted from examples.seq2seq.finetune.py"""
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import argparse
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import glob
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import logging
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import os
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import sys
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import time
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import warnings
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from collections import defaultdict
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from pathlib import Path
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from typing import Any, Dict, List, Tuple
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import numpy as np
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import pytorch_lightning as pl
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import torch
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import torch.distributed as dist
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from torch.utils.data import DataLoader
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from transformers import (
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AutoConfig,
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AutoTokenizer,
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BartForConditionalGeneration,
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RagConfig,
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RagSequenceForGeneration,
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RagTokenForGeneration,
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RagTokenizer,
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T5ForConditionalGeneration,
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get_linear_schedule_with_warmup,
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)
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from transformers import logging as transformers_logging
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from callbacks import ( # noqa: E402 # isort:skipq
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get_checkpoint_callback,
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get_early_stopping_callback,
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Seq2SeqLoggingCallback,
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)
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from distributed_retriever import RagPyTorchDistributedRetriever # noqa: E402 # isort:skip
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from utils import ( # noqa: E402 # isort:skip
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calculate_exact_match,
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flatten_list,
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get_git_info,
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is_rag_model,
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lmap,
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pickle_save,
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save_git_info,
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save_json,
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set_extra_model_params,
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Seq2SeqDataset,
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)
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# need the parent dir module
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sys.path.insert(2, str(Path(__file__).resolve().parents[1]))
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from lightning_base import BaseTransformer, add_generic_args, generic_train # noqa
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logging.basicConfig(level=logging.INFO)
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logger = logging.getLogger(__name__)
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transformers_logging.set_verbosity_info()
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class AttrDict(dict):
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def __init__(self, *args, **kwargs):
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super(AttrDict, self).__init__(*args, **kwargs)
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self.__dict__ = self
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class GenerativeQAModule(BaseTransformer):
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mode = "generative_qa"
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loss_names = ["loss"]
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metric_names = ["em"]
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val_metric = "em"
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def __init__(self, hparams, **kwargs):
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# when loading from a pytorch lightning checkpoint, hparams are passed as dict
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if isinstance(hparams, dict):
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hparams = AttrDict(hparams)
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if hparams.model_type == "rag_sequence":
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self.model_class = RagSequenceForGeneration
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elif hparams.model_type == "rag_token":
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self.model_class = RagTokenForGeneration
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elif hparams.model_type == "bart":
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self.model_class = BartForConditionalGeneration
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else:
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self.model_class = T5ForConditionalGeneration
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self.is_rag_model = is_rag_model(hparams.model_type)
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config_class = RagConfig if self.is_rag_model else AutoConfig
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config = config_class.from_pretrained(hparams.model_name_or_path)
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# set retriever parameters
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config.index_name = args.index_name or config.index_name
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config.passages_path = args.passages_path or config.passages_path
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config.index_path = args.index_path or config.index_path
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# set extra_model_params for generator configs and load_model
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extra_model_params = ("encoder_layerdrop", "decoder_layerdrop", "attention_dropout", "dropout")
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if self.is_rag_model:
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if args.prefix is not None:
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config.generator.prefix = args.prefix
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config.label_smoothing = hparams.label_smoothing
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hparams, config.generator = set_extra_model_params(extra_model_params, hparams, config.generator)
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retriever = RagPyTorchDistributedRetriever.from_pretrained(hparams.model_name_or_path, config=config)
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model = self.model_class.from_pretrained(hparams.model_name_or_path, config=config, retriever=retriever)
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prefix = config.question_encoder.prefix
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else:
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if args.prefix is not None:
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config.prefix = args.prefix
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hparams, config = set_extra_model_params(extra_model_params, hparams, config)
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model = self.model_class.from_pretrained(hparams.model_name_or_path, config=config)
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prefix = config.prefix
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tokenizer = (
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RagTokenizer.from_pretrained(hparams.model_name_or_path)
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if self.is_rag_model
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else AutoTokenizer.from_pretrained(hparams.model_name_or_path)
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)
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super().__init__(hparams, config=config, tokenizer=tokenizer, model=model)
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save_git_info(self.hparams.output_dir)
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self.output_dir = Path(self.hparams.output_dir)
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self.metrics_save_path = Path(self.output_dir) / "metrics.json"
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self.hparams_save_path = Path(self.output_dir) / "hparams.pkl"
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pickle_save(self.hparams, self.hparams_save_path)
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self.step_count = 0
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self.metrics = defaultdict(list)
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self.dataset_kwargs: dict = dict(
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data_dir=self.hparams.data_dir,
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max_source_length=self.hparams.max_source_length,
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prefix=prefix or "",
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)
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n_observations_per_split = {
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"train": self.hparams.n_train,
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"val": self.hparams.n_val,
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"test": self.hparams.n_test,
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}
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self.n_obs = {k: v if v >= 0 else None for k, v in n_observations_per_split.items()}
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self.target_lens = {
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"train": self.hparams.max_target_length,
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"val": self.hparams.val_max_target_length,
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"test": self.hparams.test_max_target_length,
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}
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assert self.target_lens["train"] <= self.target_lens["val"], f"target_lens: {self.target_lens}"
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assert self.target_lens["train"] <= self.target_lens["test"], f"target_lens: {self.target_lens}"
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self.hparams.git_sha = get_git_info()["repo_sha"]
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self.num_workers = hparams.num_workers
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self.distributed_port = self.hparams.distributed_port
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def init_ddp_connection(self, global_rank: int, world_size: int, is_slurm_managing_tasks: bool = True):
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logger.info("Custom init_ddp_connection.")
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os.environ["MASTER_PORT"] = str(self.distributed_port)
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super().init_ddp_connection(global_rank, world_size, is_slurm_managing_tasks)
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if self.is_rag_model:
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self.model.retriever.init_retrieval(self.distributed_port)
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def forward(self, input_ids, **kwargs):
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return self.model(input_ids, **kwargs)
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def ids_to_clean_text(self, generated_ids: List[int]):
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gen_text = self.tokenizer.batch_decode(
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generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=True
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)
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return lmap(str.strip, gen_text)
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def _step(self, batch: dict) -> Tuple:
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source_ids, source_mask, target_ids = batch["input_ids"], batch["attention_mask"], batch["decoder_input_ids"]
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rag_kwargs = {}
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if isinstance(self.model, T5ForConditionalGeneration):
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decoder_input_ids = self.model._shift_right(target_ids)
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lm_labels = target_ids
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elif isinstance(self.model, BartForConditionalGeneration):
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decoder_input_ids = target_ids[:, :-1].contiguous()
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lm_labels = target_ids[:, 1:].clone()
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else:
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assert self.is_rag_model
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generator = self.model.rag.generator
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if isinstance(generator, T5ForConditionalGeneration):
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decoder_start_token_id = generator.config.decoder_start_token_id
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decoder_input_ids = (
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torch.cat(
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[torch.Tensor([[decoder_start_token_id]] * target_ids.shape[0]).to(target_ids), target_ids],
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dim=1,
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)
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if target_ids.shape[0] < self.target_lens["train"]
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else generator._shift_right(target_ids)
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)
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elif isinstance(generator, BartForConditionalGeneration):
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decoder_input_ids = target_ids
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lm_labels = decoder_input_ids
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rag_kwargs["reduce_loss"] = True
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assert decoder_input_ids is not None
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outputs = self(
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source_ids,
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attention_mask=source_mask,
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decoder_input_ids=decoder_input_ids,
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use_cache=False,
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labels=lm_labels,
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return_dict=True,
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**rag_kwargs,
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)
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loss = outputs["loss"]
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return (loss,)
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@property
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def pad(self) -> int:
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raise NotImplementedError("pad not implemented")
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def training_step(self, batch, batch_idx) -> Dict:
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loss_tensors = self._step(batch)
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logs = {name: loss for name, loss in zip(self.loss_names, loss_tensors)}
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# tokens per batch
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tgt_pad_token_id = (
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self.tokenizer.generator.pad_token_id
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if isinstance(self.tokenizer, RagTokenizer)
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else self.tokenizer.pad_token_id
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)
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src_pad_token_id = (
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self.tokenizer.question_encoder.pad_token_id
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if isinstance(self.tokenizer, RagTokenizer)
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else self.tokenizer.pad_token_id
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)
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logs["tpb"] = (
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batch["input_ids"].ne(src_pad_token_id).sum() + batch["decoder_input_ids"].ne(tgt_pad_token_id).sum()
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)
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return {"loss": loss_tensors[0], "log": logs}
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def validation_step(self, batch, batch_idx) -> Dict:
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return self._generative_step(batch)
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def validation_epoch_end(self, outputs, prefix="val") -> Dict:
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self.step_count += 1
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losses = {k: torch.stack([x[k] for x in outputs]).mean() for k in self.loss_names}
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loss = losses["loss"]
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gen_metrics = {
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k: np.array([x[k] for x in outputs]).mean() for k in self.metric_names + ["gen_time", "gen_len"]
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}
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metrics_tensor: torch.FloatTensor = torch.tensor(gen_metrics[self.val_metric]).type_as(loss)
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gen_metrics.update({k: v.item() for k, v in losses.items()})
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# fix for https://github.com/PyTorchLightning/pytorch-lightning/issues/2424
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if dist.is_initialized():
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dist.all_reduce(metrics_tensor, op=dist.ReduceOp.SUM)
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metrics_tensor = metrics_tensor / dist.get_world_size()
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gen_metrics.update({self.val_metric: metrics_tensor.item()})
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losses.update(gen_metrics)
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metrics = {f"{prefix}_avg_{k}": x for k, x in losses.items()}
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metrics["step_count"] = self.step_count
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self.save_metrics(metrics, prefix) # writes to self.metrics_save_path
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preds = flatten_list([x["preds"] for x in outputs])
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return {"log": metrics, "preds": preds, f"{prefix}_loss": loss, f"{prefix}_{self.val_metric}": metrics_tensor}
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def save_metrics(self, latest_metrics, type_path) -> None:
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self.metrics[type_path].append(latest_metrics)
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save_json(self.metrics, self.metrics_save_path)
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def calc_generative_metrics(self, preds, target) -> Dict:
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return calculate_exact_match(preds, target)
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def _generative_step(self, batch: dict) -> dict:
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start_time = time.time()
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generated_ids = self.model.generate(
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batch["input_ids"],
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attention_mask=batch["attention_mask"],
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do_deduplication=False, # rag specific parameter
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use_cache=True,
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min_length=1,
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max_length=self.target_lens["val"],
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)
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gen_time = (time.time() - start_time) / batch["input_ids"].shape[0]
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preds: List[str] = self.ids_to_clean_text(generated_ids)
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target: List[str] = self.ids_to_clean_text(batch["decoder_input_ids"])
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loss_tensors = self._step(batch)
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base_metrics = {name: loss for name, loss in zip(self.loss_names, loss_tensors)}
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gen_metrics: Dict = self.calc_generative_metrics(preds, target)
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summ_len = np.mean(lmap(len, generated_ids))
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base_metrics.update(gen_time=gen_time, gen_len=summ_len, preds=preds, target=target, **gen_metrics)
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return base_metrics
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def test_step(self, batch, batch_idx):
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return self._generative_step(batch)
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def test_epoch_end(self, outputs):
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return self.validation_epoch_end(outputs, prefix="test")
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def get_dataset(self, type_path) -> Seq2SeqDataset:
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n_obs = self.n_obs[type_path]
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max_target_length = self.target_lens[type_path]
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dataset = Seq2SeqDataset(
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self.tokenizer,
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type_path=type_path,
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n_obs=n_obs,
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max_target_length=max_target_length,
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**self.dataset_kwargs,
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)
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return dataset
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def get_dataloader(self, type_path: str, batch_size: int, shuffle: bool = False) -> DataLoader:
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dataset = self.get_dataset(type_path)
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dataloader = DataLoader(
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dataset,
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batch_size=batch_size,
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collate_fn=dataset.collate_fn,
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shuffle=shuffle,
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num_workers=self.num_workers,
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)
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return dataloader
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def train_dataloader(self) -> DataLoader:
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dataloader = self.get_dataloader("train", batch_size=self.hparams.train_batch_size, shuffle=True)
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t_total = (
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(len(dataloader.dataset) // (self.hparams.train_batch_size * max(1, self.hparams.gpus)))
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// self.hparams.accumulate_grad_batches
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* float(self.hparams.max_epochs)
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)
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scheduler = get_linear_schedule_with_warmup(
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self.opt, num_warmup_steps=self.hparams.warmup_steps, num_training_steps=t_total
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)
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if max(scheduler.get_last_lr()) > 0:
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warnings.warn("All learning rates are 0")
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self.lr_scheduler = scheduler
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return dataloader
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def val_dataloader(self) -> DataLoader:
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return self.get_dataloader("val", batch_size=self.hparams.eval_batch_size)
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def test_dataloader(self) -> DataLoader:
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return self.get_dataloader("test", batch_size=self.hparams.eval_batch_size)
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@pl.utilities.rank_zero_only
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def on_save_checkpoint(self, checkpoint: Dict[str, Any]) -> None:
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save_path = self.output_dir.joinpath("checkpoint{}".format(self.step_count))
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self.model.config.save_step = self.step_count
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self.model.save_pretrained(save_path)
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self.tokenizer.save_pretrained(save_path)
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@staticmethod
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def add_model_specific_args(parser, root_dir):
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BaseTransformer.add_model_specific_args(parser, root_dir)
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add_generic_args(parser, root_dir)
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parser.add_argument(
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"--max_source_length",
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default=128,
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type=int,
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help="The maximum total input sequence length after tokenization. Sequences longer "
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"than this will be truncated, sequences shorter will be padded.",
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)
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parser.add_argument(
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"--max_target_length",
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default=25,
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type=int,
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help="The maximum total input sequence length after tokenization. Sequences longer "
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"than this will be truncated, sequences shorter will be padded.",
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)
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parser.add_argument(
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"--val_max_target_length",
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default=25,
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type=int,
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help="The maximum total input sequence length after tokenization. Sequences longer "
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"than this will be truncated, sequences shorter will be padded.",
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)
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parser.add_argument(
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"--test_max_target_length",
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default=25,
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type=int,
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help="The maximum total input sequence length after tokenization. Sequences longer "
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"than this will be truncated, sequences shorter will be padded.",
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)
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parser.add_argument("--logger_name", type=str, choices=["default", "wandb", "wandb_shared"], default="default")
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parser.add_argument("--n_train", type=int, default=-1, required=False, help="# examples. -1 means use all.")
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parser.add_argument("--n_val", type=int, default=-1, required=False, help="# examples. -1 means use all.")
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parser.add_argument("--n_test", type=int, default=-1, required=False, help="# examples. -1 means use all.")
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parser.add_argument("--label_smoothing", type=float, default=0.0, required=False)
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parser.add_argument(
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"--prefix",
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type=str,
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default=None,
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help="Prefix added at the beginning of each text, typically used with T5-based models.",
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)
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parser.add_argument(
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"--early_stopping_patience",
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type=int,
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default=-1,
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required=False,
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help="-1 means never early stop. early_stopping_patience is measured in validation checks, not epochs. So val_check_interval will effect it.",
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)
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parser.add_argument(
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"--distributed-port", type=int, default=-1, required=False, help="Port number for distributed training."
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)
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parser.add_argument(
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"--model_type",
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choices=["rag_sequence", "rag_token", "bart", "t5"],
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type=str,
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help="RAG model type: sequence or token, if none specified, the type is inferred from the model_name_or_path",
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)
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return parser
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@staticmethod
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def add_retriever_specific_args(parser):
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parser.add_argument(
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"--index_name",
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type=str,
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default=None,
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help="Name of the index to use: 'hf' for a canonical dataset from the datasets library (default), 'custom' for a local index, or 'legacy' for the orignal one)",
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)
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parser.add_argument(
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"--passages_path",
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type=str,
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default=None,
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help="Path to the dataset of passages for custom index. More info about custom indexes in the RagRetriever documentation as well as in `examples/rag/use_own_knowledge_dataset.py`",
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)
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parser.add_argument(
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"--index_path",
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type=str,
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default=None,
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help="Path to the faiss index for custom index. More info about custom indexes in the RagRetriever documentation as well as in `examples/rag/use_own_knowledge_dataset.py`",
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)
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return parser
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def main(args, model=None) -> GenerativeQAModule:
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Path(args.output_dir).mkdir(exist_ok=True)
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if model is None:
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model: GenerativeQAModule = GenerativeQAModule(args)
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dataset = Path(args.data_dir).name
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if (
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args.logger_name == "default"
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or args.fast_dev_run
|
|
or str(args.output_dir).startswith("/tmp")
|
|
or str(args.output_dir).startswith("/var")
|
|
):
|
|
logger = True # don't pollute wandb logs unnecessarily
|
|
elif args.logger_name == "wandb":
|
|
from pytorch_lightning.loggers import WandbLogger
|
|
|
|
project = os.environ.get("WANDB_PROJECT", dataset)
|
|
logger = WandbLogger(name=model.output_dir.name, project=project)
|
|
|
|
elif args.logger_name == "wandb_shared":
|
|
from pytorch_lightning.loggers import WandbLogger
|
|
|
|
logger = WandbLogger(name=model.output_dir.name, project=f"hf_{dataset}")
|
|
|
|
es_callback = (
|
|
get_early_stopping_callback(model.val_metric, args.early_stopping_patience)
|
|
if args.early_stopping_patience >= 0
|
|
else False
|
|
)
|
|
trainer: pl.Trainer = generic_train(
|
|
model,
|
|
args,
|
|
logging_callback=Seq2SeqLoggingCallback(),
|
|
checkpoint_callback=get_checkpoint_callback(args.output_dir, model.val_metric),
|
|
early_stopping_callback=es_callback,
|
|
logger=logger,
|
|
)
|
|
pickle_save(model.hparams, model.output_dir / "hparams.pkl")
|
|
|
|
if not args.do_predict:
|
|
return model
|
|
|
|
model.hparams.test_checkpoint = ""
|
|
checkpoints = list(sorted(glob.glob(os.path.join(args.output_dir, "*.ckpt"), recursive=True)))
|
|
if checkpoints:
|
|
model.hparams.test_checkpoint = checkpoints[-1]
|
|
trainer.resume_from_checkpoint = checkpoints[-1] # best checkpoint
|
|
trainer.logger.log_hyperparams(model.hparams)
|
|
|
|
# test() without a model tests using the best checkpoint automatically
|
|
trainer.test()
|
|
|
|
return model
|
|
|
|
|
|
if __name__ == "__main__":
|
|
parser = argparse.ArgumentParser()
|
|
parser = pl.Trainer.add_argparse_args(parser)
|
|
parser = GenerativeQAModule.add_model_specific_args(parser, os.getcwd())
|
|
parser = GenerativeQAModule.add_retriever_specific_args(parser)
|
|
|
|
args = parser.parse_args()
|
|
|
|
main(args)
|