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test_bash_script.py
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#!/usr/bin/env python
import argparse
import os
import sys
from unittest.mock import patch
import pytorch_lightning as pl
import timeout_decorator
import torch
from distillation import BartSummarizationDistiller, distill_main
from finetune import SummarizationModule, main
from transformers import MarianMTModel
from transformers.file_utils import cached_path
from transformers.testing_utils import TestCasePlus, require_torch_gpu, slow
from utils import load_json
MARIAN_MODEL = "sshleifer/mar_enro_6_3_student"
class TestMbartCc25Enro(TestCasePlus):
def setUp(self):
super().setUp()
data_cached = cached_path(
"https://cdn-datasets.huggingface.co/translation/wmt_en_ro-tr40k-va0.5k-te0.5k.tar.gz",
extract_compressed_file=True,
)
self.data_dir = f"{data_cached}/wmt_en_ro-tr40k-va0.5k-te0.5k"
@slow
@require_torch_gpu
def test_model_download(self):
"""This warms up the cache so that we can time the next test without including download time, which varies between machines."""
MarianMTModel.from_pretrained(MARIAN_MODEL)
# @timeout_decorator.timeout(1200)
@slow
@require_torch_gpu
def test_train_mbart_cc25_enro_script(self):
env_vars_to_replace = {
"$MAX_LEN": 64,
"$BS": 64,
"$GAS": 1,
"$ENRO_DIR": self.data_dir,
"facebook/mbart-large-cc25": MARIAN_MODEL,
# "val_check_interval=0.25": "val_check_interval=1.0",
"--learning_rate=3e-5": "--learning_rate 3e-4",
"--num_train_epochs 6": "--num_train_epochs 1",
}
# Clean up bash script
bash_script = (self.test_file_dir / "train_mbart_cc25_enro.sh").open().read().split("finetune.py")[1].strip()
bash_script = bash_script.replace("\\\n", "").strip().replace('"$@"', "")
for k, v in env_vars_to_replace.items():
bash_script = bash_script.replace(k, str(v))
output_dir = self.get_auto_remove_tmp_dir()
# bash_script = bash_script.replace("--fp16 ", "")
args = f"""
--output_dir {output_dir}
--tokenizer_name Helsinki-NLP/opus-mt-en-ro
--sortish_sampler
--do_predict
--gpus 1
--freeze_encoder
--n_train 40000
--n_val 500
--n_test 500
--fp16_opt_level O1
--num_sanity_val_steps 0
--eval_beams 2
""".split()
# XXX: args.gpus > 1 : handle multigpu in the future
testargs = ["finetune.py"] + bash_script.split() + args
with patch.object(sys, "argv", testargs):
parser = argparse.ArgumentParser()
parser = pl.Trainer.add_argparse_args(parser)
parser = SummarizationModule.add_model_specific_args(parser, os.getcwd())
args = parser.parse_args()
model = main(args)
# Check metrics
metrics = load_json(model.metrics_save_path)
first_step_stats = metrics["val"][0]
last_step_stats = metrics["val"][-1]
self.assertEqual(len(metrics["val"]), (args.max_epochs / args.val_check_interval))
assert isinstance(last_step_stats[f"val_avg_{model.val_metric}"], float)
self.assertGreater(last_step_stats["val_avg_gen_time"], 0.01)
# model hanging on generate. Maybe bad config was saved. (XXX: old comment/assert?)
self.assertLessEqual(last_step_stats["val_avg_gen_time"], 1.0)
# test learning requirements:
# 1. BLEU improves over the course of training by more than 2 pts
self.assertGreater(last_step_stats["val_avg_bleu"] - first_step_stats["val_avg_bleu"], 2)
# 2. BLEU finishes above 17
self.assertGreater(last_step_stats["val_avg_bleu"], 17)
# 3. test BLEU and val BLEU within ~1.1 pt.
self.assertLess(abs(metrics["val"][-1]["val_avg_bleu"] - metrics["test"][-1]["test_avg_bleu"]), 1.1)
# check lightning ckpt can be loaded and has a reasonable statedict
contents = os.listdir(output_dir)
ckpt_path = [x for x in contents if x.endswith(".ckpt")][0]
full_path = os.path.join(args.output_dir, ckpt_path)
ckpt = torch.load(full_path, map_location="cpu")
expected_key = "model.model.decoder.layers.0.encoder_attn_layer_norm.weight"
assert expected_key in ckpt["state_dict"]
assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.float32
# TODO: turn on args.do_predict when PL bug fixed.
if args.do_predict:
contents = {os.path.basename(p) for p in contents}
assert "test_generations.txt" in contents
assert "test_results.txt" in contents
# assert len(metrics["val"]) == desired_n_evals
assert len(metrics["test"]) == 1
class TestDistilMarianNoTeacher(TestCasePlus):
@timeout_decorator.timeout(600)
@slow
@require_torch_gpu
def test_opus_mt_distill_script(self):
data_dir = f"{self.test_file_dir_str}/test_data/wmt_en_ro"
env_vars_to_replace = {
"--fp16_opt_level=O1": "",
"$MAX_LEN": 128,
"$BS": 16,
"$GAS": 1,
"$ENRO_DIR": data_dir,
"$m": "sshleifer/student_marian_en_ro_6_1",
"val_check_interval=0.25": "val_check_interval=1.0",
}
# Clean up bash script
bash_script = (
(self.test_file_dir / "distil_marian_no_teacher.sh").open().read().split("distillation.py")[1].strip()
)
bash_script = bash_script.replace("\\\n", "").strip().replace('"$@"', "")
bash_script = bash_script.replace("--fp16 ", " ")
for k, v in env_vars_to_replace.items():
bash_script = bash_script.replace(k, str(v))
output_dir = self.get_auto_remove_tmp_dir()
bash_script = bash_script.replace("--fp16", "")
epochs = 6
testargs = (
["distillation.py"]
+ bash_script.split()
+ [
f"--output_dir={output_dir}",
"--gpus=1",
"--learning_rate=1e-3",
f"--num_train_epochs={epochs}",
"--warmup_steps=10",
"--val_check_interval=1.0",
"--do_predict",
]
)
with patch.object(sys, "argv", testargs):
parser = argparse.ArgumentParser()
parser = pl.Trainer.add_argparse_args(parser)
parser = BartSummarizationDistiller.add_model_specific_args(parser, os.getcwd())
args = parser.parse_args()
# assert args.gpus == gpus THIS BREAKS for multigpu
model = distill_main(args)
# Check metrics
metrics = load_json(model.metrics_save_path)
first_step_stats = metrics["val"][0]
last_step_stats = metrics["val"][-1]
assert len(metrics["val"]) >= (args.max_epochs / args.val_check_interval) # +1 accounts for val_sanity_check
assert last_step_stats["val_avg_gen_time"] >= 0.01
assert first_step_stats["val_avg_bleu"] < last_step_stats["val_avg_bleu"] # model learned nothing
assert 1.0 >= last_step_stats["val_avg_gen_time"] # model hanging on generate. Maybe bad config was saved.
assert isinstance(last_step_stats[f"val_avg_{model.val_metric}"], float)
# check lightning ckpt can be loaded and has a reasonable statedict
contents = os.listdir(output_dir)
ckpt_path = [x for x in contents if x.endswith(".ckpt")][0]
full_path = os.path.join(args.output_dir, ckpt_path)
ckpt = torch.load(full_path, map_location="cpu")
expected_key = "model.model.decoder.layers.0.encoder_attn_layer_norm.weight"
assert expected_key in ckpt["state_dict"]
assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.float32
# TODO: turn on args.do_predict when PL bug fixed.
if args.do_predict:
contents = {os.path.basename(p) for p in contents}
assert "test_generations.txt" in contents
assert "test_results.txt" in contents
# assert len(metrics["val"]) == desired_n_evals
assert len(metrics["test"]) == 1