transformers/examples/rag/callbacks.py
Ola Piktus c754c41c61
RAG (#6813)
* added rag WIP

* path fix

* Formatting / renaming prior to actual work

* added rag WIP

* path fix

* Formatting / renaming prior to actual work

* added rag WIP

* path fix

* Formatting / renaming prior to actual work

* added rag WIP

* Formatting / renaming prior to actual work

* First commit

* improve comments

* Retrieval evaluation scripts

* refactor to include modeling outputs + MPI retriever

* Fix rag-token model + refactor

* Various fixes + finetuning logic

* use_bos fix

* Retrieval refactor

* Finetuning refactoring and cleanup

* Add documentation and cleanup

* Remove set_up_rag_env.sh file

* Fix retrieval wit HF index

* Fix import errors

* Fix quality errors

* Refactor as per suggestions in https://github.com/huggingface/transformers/pull/6813#issuecomment-687208867

* fix quality

* Fix RAG Sequence generation

* minor cleanup plus initial tests

* fix test

* fix tests 2

* Comments fix

* post-merge fixes

* Improve readme + post-rebase refactor

* Extra dependencied for tests

* Fix tests

* Fix tests 2

* Refactor test requirements

* Fix tests 3

* Post-rebase refactor

* rename nlp->datasets

* RAG integration tests

* add tokenizer to slow integration test and allow retriever to run on cpu

* add tests; fix position ids warning

* change structure

* change structure

* add from encoder generator

* save working solution

* make all integration tests pass

* add RagTokenizer.save/from_pretrained and RagRetriever.save/from_pretrained

* don't save paths

* delete unnecessary imports

* pass config to AutoTokenizer.from_pretrained for Rag tokenizers

* init wiki_dpr only once

* hardcode legacy index and passages paths (todo: add the right urls)

* finalize config

* finalize retriver api and config api

* LegacyIndex index download refactor

* add dpr to autotokenizer

* make from pretrained more flexible

* fix ragfortokengeneration

* small name changes in tokenizer

* add labels to models

* change default index name

* add retrieval tests

* finish token generate

* align test with previous version and make all tests pass

* add tests

* finalize tests

* implement thoms suggestions

* add first version of test

* make first tests work

* make retriever platform agnostic

* naming

* style

* add legacy index URL

* docstrings + simple retrieval test for distributed

* clean model api

* add doc_ids to retriever's outputs

* fix retrieval tests

* finish model outputs

* finalize model api

* fix generate problem for rag

* fix generate for other modles

* fix some tests

* save intermediate

* set generate to default

* big refactor generate

* delete rag_api

* correct pip faiss install

* fix auto tokenization test

* fix faiss install

* fix test

* move the distributed logic to examples

* model page

* docs

* finish tests

* fix dependencies

* fix import in __init__

* Refactor eval_rag and finetune scripts

* start docstring

* add psutil to test

* fix tf test

* move require torch to top

* fix retrieval test

* align naming

* finish automodel

* fix repo consistency

* test ragtokenizer save/load

* add rag model output docs

* fix ragtokenizer save/load from pretrained

* fix tokenizer dir

* remove torch in retrieval

* fix docs

* fixe finetune scripts

* finish model docs

* finish docs

* remove auto model for now

* add require torch

* remove solved todos

* integrate sylvains suggestions

* sams comments

* correct mistake on purpose

* improve README

* Add generation test cases

* fix rag token

* clean token generate

* fix test

* add note to test

* fix attention mask

* add t5 test for rag

* Fix handling prefix in finetune.py

* don't overwrite index_name

Co-authored-by: Patrick Lewis <plewis@fb.com>
Co-authored-by: Aleksandra Piktus <piktus@devfair0141.h2.fair>
Co-authored-by: Aleksandra Piktus <piktus@learnfair5102.h2.fair>
Co-authored-by: Aleksandra Piktus <piktus@learnfair5067.h2.fair>
Co-authored-by: Your Name <you@example.com>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Quentin Lhoest <lhoest.q@gmail.com>
2020-09-22 18:29:58 +02:00

31 lines
927 B
Python

import logging
import os
from pytorch_lightning.callbacks import ModelCheckpoint
logger = logging.getLogger(__name__)
def get_checkpoint_callback(output_dir, metric):
"""Saves the best model by validation EM score."""
if metric == "rouge2":
exp = "{val_avg_rouge2:.4f}-{step_count}"
elif metric == "bleu":
exp = "{val_avg_bleu:.4f}-{step_count}"
elif metric == "em":
exp = "{val_avg_em:.4f}-{step_count}"
else:
raise NotImplementedError(
f"seq2seq callbacks only support rouge2 and bleu, got {metric}, You can make your own by adding to this function."
)
checkpoint_callback = ModelCheckpoint(
filepath=os.path.join(output_dir, exp),
monitor=f"val_{metric}",
mode="max",
save_top_k=3,
period=0, # maybe save a checkpoint every time val is run, not just end of epoch.
)
return checkpoint_callback