import argparse import logging import os import sys import tempfile from pathlib import Path import lightning_base import pytest import pytorch_lightning as pl import torch from convert_pl_checkpoint_to_hf import convert_pl_to_hf from distillation import distill_main from finetune import SummarizationModule, main from huggingface_hub import list_models from parameterized import parameterized from run_eval import generate_summaries_or_translations from torch import nn from transformers import AutoConfig, AutoModelForSeq2SeqLM from transformers.testing_utils import CaptureStderr, CaptureStdout, TestCasePlus, require_torch_gpu, slow from utils import label_smoothed_nll_loss, lmap, load_json logging.basicConfig(level=logging.DEBUG) logger = logging.getLogger() CUDA_AVAILABLE = torch.cuda.is_available() CHEAP_ARGS = { "max_tokens_per_batch": None, "supervise_forward": True, "normalize_hidden": True, "label_smoothing": 0.2, "eval_max_gen_length": None, "eval_beams": 1, "val_metric": "loss", "save_top_k": 1, "adafactor": True, "early_stopping_patience": 2, "logger_name": "default", "length_penalty": 0.5, "cache_dir": "", "task": "summarization", "num_workers": 2, "alpha_hid": 0, "freeze_embeds": True, "enc_only": False, "tgt_suffix": "", "resume_from_checkpoint": None, "sortish_sampler": True, "student_decoder_layers": 1, "val_check_interval": 1.0, "output_dir": "", "fp16": False, # TODO(SS): set this to CUDA_AVAILABLE if ci installs apex or start using native amp "no_teacher": False, "fp16_opt_level": "O1", "gpus": 1 if CUDA_AVAILABLE else 0, "n_tpu_cores": 0, "max_grad_norm": 1.0, "do_train": True, "do_predict": True, "accumulate_grad_batches": 1, "server_ip": "", "server_port": "", "seed": 42, "model_name_or_path": "sshleifer/bart-tiny-random", "config_name": "", "tokenizer_name": "facebook/bart-large", "do_lower_case": False, "learning_rate": 0.3, "lr_scheduler": "linear", "weight_decay": 0.0, "adam_epsilon": 1e-08, "warmup_steps": 0, "max_epochs": 1, "train_batch_size": 2, "eval_batch_size": 2, "max_source_length": 12, "max_target_length": 12, "val_max_target_length": 12, "test_max_target_length": 12, "fast_dev_run": False, "no_cache": False, "n_train": -1, "n_val": -1, "n_test": -1, "student_encoder_layers": 1, "freeze_encoder": False, "auto_scale_batch_size": False, "overwrite_output_dir": False, "student": None, } def _dump_articles(path: Path, articles: list): content = "\n".join(articles) Path(path).open("w").writelines(content) ARTICLES = [" Sam ate lunch today.", "Sams lunch ingredients."] SUMMARIES = ["A very interesting story about what I ate for lunch.", "Avocado, celery, turkey, coffee"] T5_TINY = "patrickvonplaten/t5-tiny-random" T5_TINIER = "sshleifer/t5-tinier-random" BART_TINY = "sshleifer/bart-tiny-random" MBART_TINY = "sshleifer/tiny-mbart" MARIAN_TINY = "sshleifer/tiny-marian-en-de" FSMT_TINY = "stas/tiny-wmt19-en-de" stream_handler = logging.StreamHandler(sys.stdout) logger.addHandler(stream_handler) logging.disable(logging.CRITICAL) # remove noisy download output from tracebacks def make_test_data_dir(tmp_dir): for split in ["train", "val", "test"]: _dump_articles(os.path.join(tmp_dir, f"{split}.source"), ARTICLES) _dump_articles(os.path.join(tmp_dir, f"{split}.target"), SUMMARIES) return tmp_dir class TestSummarizationDistiller(TestCasePlus): @classmethod def setUpClass(cls): logging.disable(logging.CRITICAL) # remove noisy download output from tracebacks return cls @slow @require_torch_gpu def test_hub_configs(self): """I put require_torch_gpu cause I only want this to run with self-scheduled.""" model_list = list_models() org = "sshleifer" model_ids = [x.modelId for x in model_list if x.modelId.startswith(org)] allowed_to_be_broken = ["sshleifer/blenderbot-3B", "sshleifer/blenderbot-90M"] failures = [] for m in model_ids: if m in allowed_to_be_broken: continue try: AutoConfig.from_pretrained(m) except Exception: failures.append(m) assert not failures, f"The following models could not be loaded through AutoConfig: {failures}" def test_distill_no_teacher(self): updates = {"student_encoder_layers": 2, "student_decoder_layers": 1, "no_teacher": True} self._test_distiller_cli(updates) def test_distill_checkpointing_with_teacher(self): updates = { "student_encoder_layers": 2, "student_decoder_layers": 1, "max_epochs": 4, "val_check_interval": 0.25, "alpha_hid": 2.0, "model_name_or_path": "IGNORE_THIS_IT_DOESNT_GET_USED", } model = self._test_distiller_cli(updates, check_contents=False) ckpts = list(Path(model.output_dir).glob("*.ckpt")) self.assertEqual(1, len(ckpts)) transformer_ckpts = list(Path(model.output_dir).glob("**/*.bin")) self.assertEqual(len(transformer_ckpts), 2) examples = lmap(str.strip, Path(model.hparams.data_dir).joinpath("test.source").open().readlines()) out_path = tempfile.mktemp() # XXX: not being cleaned up generate_summaries_or_translations(examples, out_path, str(model.output_dir / "best_tfmr")) self.assertTrue(Path(out_path).exists()) out_path_new = self.get_auto_remove_tmp_dir() convert_pl_to_hf(ckpts[0], transformer_ckpts[0].parent, out_path_new) assert os.path.exists(os.path.join(out_path_new, "pytorch_model.bin")) def test_loss_fn(self): model = AutoModelForSeq2SeqLM.from_pretrained(BART_TINY) input_ids, mask = model.dummy_inputs["input_ids"], model.dummy_inputs["attention_mask"] target_ids = torch.tensor([[0, 4, 8, 2], [0, 8, 2, 1]], dtype=torch.long, device=model.device) decoder_input_ids = target_ids[:, :-1].contiguous() # Why this line? lm_labels = target_ids[:, 1:].clone() # why clone? model_computed_loss = model( input_ids, attention_mask=mask, decoder_input_ids=decoder_input_ids, labels=lm_labels, use_cache=False ).loss logits = model(input_ids, attention_mask=mask, decoder_input_ids=decoder_input_ids, use_cache=False).logits lprobs = nn.functional.log_softmax(logits, dim=-1) smoothed_loss, nll_loss = label_smoothed_nll_loss( lprobs, lm_labels, 0.1, ignore_index=model.config.pad_token_id ) with self.assertRaises(AssertionError): # TODO: understand why this breaks self.assertEqual(nll_loss, model_computed_loss) def test_distill_mbart(self): updates = { "student_encoder_layers": 2, "student_decoder_layers": 1, "num_train_epochs": 4, "val_check_interval": 0.25, "alpha_hid": 2.0, "task": "translation", "model_name_or_path": "IGNORE_THIS_IT_DOESNT_GET_USED", "tokenizer_name": MBART_TINY, "teacher": MBART_TINY, "src_lang": "en_XX", "tgt_lang": "ro_RO", } model = self._test_distiller_cli(updates, check_contents=False) assert model.model.config.model_type == "mbart" ckpts = list(Path(model.output_dir).glob("*.ckpt")) self.assertEqual(1, len(ckpts)) transformer_ckpts = list(Path(model.output_dir).glob("**/*.bin")) all_files = list(Path(model.output_dir).glob("best_tfmr/*")) assert len(all_files) > 2 self.assertEqual(len(transformer_ckpts), 2) def test_distill_t5(self): updates = { "student_encoder_layers": 1, "student_decoder_layers": 1, "alpha_hid": 2.0, "teacher": T5_TINY, "model_name_or_path": T5_TINY, "tokenizer_name": T5_TINY, } self._test_distiller_cli(updates) def test_distill_different_base_models(self): updates = { "teacher": T5_TINY, "student": T5_TINIER, "model_name_or_path": T5_TINIER, "tokenizer_name": T5_TINIER, } self._test_distiller_cli(updates) def _test_distiller_cli(self, updates, check_contents=True): default_updates = { "label_smoothing": 0.0, "early_stopping_patience": -1, "train_batch_size": 1, "eval_batch_size": 2, "max_epochs": 2, "alpha_mlm": 0.2, "alpha_ce": 0.8, "do_predict": True, "model_name_or_path": "sshleifer/tinier_bart", "teacher": CHEAP_ARGS["model_name_or_path"], "val_check_interval": 0.5, } default_updates.update(updates) args_d: dict = CHEAP_ARGS.copy() tmp_dir = make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()) output_dir = self.get_auto_remove_tmp_dir() args_d.update(data_dir=tmp_dir, output_dir=output_dir, **default_updates) model = distill_main(argparse.Namespace(**args_d)) if not check_contents: return model contents = os.listdir(output_dir) contents = {os.path.basename(p) for p in contents} ckpt_files = [p for p in contents if p.endswith("ckpt")] assert len(ckpt_files) > 0 self.assertIn("test_generations.txt", contents) self.assertIn("test_results.txt", contents) metrics = load_json(model.metrics_save_path) last_step_stats = metrics["val"][-1] self.assertGreaterEqual(last_step_stats["val_avg_gen_time"], 0.01) self.assertGreaterEqual(1.0, last_step_stats["val_avg_gen_time"]) self.assertIsInstance(last_step_stats[f"val_avg_{model.val_metric}"], float) desired_n_evals = int(args_d["max_epochs"] * (1 / args_d["val_check_interval"]) + 1) self.assertEqual(len(metrics["val"]), desired_n_evals) self.assertEqual(len(metrics["test"]), 1) return model class TestTheRest(TestCasePlus): @parameterized.expand( [T5_TINY, BART_TINY, MBART_TINY, MARIAN_TINY, FSMT_TINY], ) def test_finetune(self, model): args_d: dict = CHEAP_ARGS.copy() task = "translation" if model in [MBART_TINY, MARIAN_TINY, FSMT_TINY] else "summarization" args_d["label_smoothing"] = 0.1 if task == "translation" else 0 tmp_dir = make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()) output_dir = self.get_auto_remove_tmp_dir() args_d.update( data_dir=tmp_dir, model_name_or_path=model, tokenizer_name=None, train_batch_size=2, eval_batch_size=2, output_dir=output_dir, do_predict=True, task=task, src_lang="en_XX", tgt_lang="ro_RO", freeze_encoder=True, freeze_embeds=True, ) assert "n_train" in args_d args = argparse.Namespace(**args_d) module = main(args) input_embeds = module.model.get_input_embeddings() assert not input_embeds.weight.requires_grad if model == T5_TINY: lm_head = module.model.lm_head assert not lm_head.weight.requires_grad assert (lm_head.weight == input_embeds.weight).all().item() elif model == FSMT_TINY: fsmt = module.model.model embed_pos = fsmt.decoder.embed_positions assert not embed_pos.weight.requires_grad assert not fsmt.decoder.embed_tokens.weight.requires_grad # check that embeds are not the same assert fsmt.decoder.embed_tokens != fsmt.encoder.embed_tokens else: bart = module.model.model embed_pos = bart.decoder.embed_positions assert not embed_pos.weight.requires_grad assert not bart.shared.weight.requires_grad # check that embeds are the same assert bart.decoder.embed_tokens == bart.encoder.embed_tokens assert bart.decoder.embed_tokens == bart.shared example_batch = load_json(module.output_dir / "text_batch.json") assert isinstance(example_batch, dict) assert len(example_batch) >= 4 def test_finetune_extra_model_args(self): args_d: dict = CHEAP_ARGS.copy() task = "summarization" tmp_dir = make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()) args_d.update( data_dir=tmp_dir, tokenizer_name=None, train_batch_size=2, eval_batch_size=2, do_predict=False, task=task, src_lang="en_XX", tgt_lang="ro_RO", freeze_encoder=True, freeze_embeds=True, ) # test models whose config includes the extra_model_args model = BART_TINY output_dir = self.get_auto_remove_tmp_dir() args_d1 = args_d.copy() args_d1.update( model_name_or_path=model, output_dir=output_dir, ) extra_model_params = ("encoder_layerdrop", "decoder_layerdrop", "dropout", "attention_dropout") for p in extra_model_params: args_d1[p] = 0.5 args = argparse.Namespace(**args_d1) model = main(args) for p in extra_model_params: assert getattr(model.config, p) == 0.5, f"failed to override the model config for param {p}" # test models whose config doesn't include the extra_model_args model = T5_TINY output_dir = self.get_auto_remove_tmp_dir() args_d2 = args_d.copy() args_d2.update( model_name_or_path=model, output_dir=output_dir, ) unsupported_param = "encoder_layerdrop" args_d2[unsupported_param] = 0.5 args = argparse.Namespace(**args_d2) with pytest.raises(Exception) as excinfo: model = main(args) assert str(excinfo.value) == f"model config doesn't have a `{unsupported_param}` attribute" def test_finetune_lr_schedulers(self): args_d: dict = CHEAP_ARGS.copy() task = "summarization" tmp_dir = make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()) model = BART_TINY output_dir = self.get_auto_remove_tmp_dir() args_d.update( data_dir=tmp_dir, model_name_or_path=model, output_dir=output_dir, tokenizer_name=None, train_batch_size=2, eval_batch_size=2, do_predict=False, task=task, src_lang="en_XX", tgt_lang="ro_RO", freeze_encoder=True, freeze_embeds=True, ) # emulate finetune.py parser = argparse.ArgumentParser() parser = pl.Trainer.add_argparse_args(parser) parser = SummarizationModule.add_model_specific_args(parser, os.getcwd()) args = {"--help": True} # --help test with pytest.raises(SystemExit) as excinfo: with CaptureStdout() as cs: args = parser.parse_args(args) assert False, "--help is expected to sys.exit" assert excinfo.type is SystemExit expected = lightning_base.arg_to_scheduler_metavar assert expected in cs.out, "--help is expected to list the supported schedulers" # --lr_scheduler=non_existing_scheduler test unsupported_param = "non_existing_scheduler" args = {f"--lr_scheduler={unsupported_param}"} with pytest.raises(SystemExit) as excinfo: with CaptureStderr() as cs: args = parser.parse_args(args) assert False, "invalid argument is expected to sys.exit" assert excinfo.type is SystemExit expected = f"invalid choice: '{unsupported_param}'" assert expected in cs.err, f"should have bailed on invalid choice of scheduler {unsupported_param}" # --lr_scheduler=existing_scheduler test supported_param = "cosine" args_d1 = args_d.copy() args_d1["lr_scheduler"] = supported_param args = argparse.Namespace(**args_d1) model = main(args) assert ( getattr(model.hparams, "lr_scheduler") == supported_param ), f"lr_scheduler={supported_param} shouldn't fail"