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Merged
dsikka merged 1 commit into from
Feb 27, 2025
Merged

Update finetune tests to decrease execution time #1208

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2 changes: 1 addition & 1 deletion tests/llmcompressor/transformers/finetune/data/test_dataset_helpers.py
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Original file line number Diff line number Diff line change
Expand Up @@ -23,7 +23,7 @@ def test_combined_datasets():

@pytest.mark.unit
def test_separate_datasets():
splits = {"train": "train[:10%]", "validation": "train[10%:20%]"}
splits = {"train": "train[:5%]", "validation": "train[5%:7%]"}
data_args = DatasetArguments(
dataset="wikitext", dataset_config_name="wikitext-2-raw-v1"
)
Expand Down
18 changes: 8 additions & 10 deletions tests/llmcompressor/transformers/finetune/data/test_dataset_loading.py
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Expand Up @@ -67,7 +67,7 @@ def test_no_padding_tokenization(self):
op_manager = TextGenerationDataset.load_from_registry(
self.data_args.dataset,
data_args=self.data_args,
split="train[5%:10%]",
split="train[5%:7%]",
processor=self.tiny_llama_tokenizer,
)
dataset = op_manager.load_dataset() # load
Expand All @@ -82,7 +82,7 @@ def test_no_padding_tokenization(self):
ex_item = dataset[0]["text"]
self.assertIn("Below is an instruction that describes a task", ex_item)

self.assertEqual(dataset.split, "train[5%:10%]")
self.assertEqual(dataset.split, "train[5%:7%]")
tokenized_dataset = op_manager()
self.assertIn("input_ids", tokenized_dataset.features)
self.assertIn("labels", tokenized_dataset.features)
Expand All @@ -107,7 +107,7 @@ def test_max_seq_len_clipped(self):
op_manager = TextGenerationDataset.load_from_registry(
self.data_args.dataset,
data_args=self.data_args,
split="train[80%:]",
split="train[95%:]",
processor=self.tiny_llama_tokenizer,
)

Expand Down Expand Up @@ -136,15 +136,15 @@ def test_dataset_kwargs_and_percentages(self):
c4_manager_a = TextGenerationDataset.load_from_registry(
self.data_args.dataset,
data_args=self.data_args,
split="train[5%:10%]",
split="train[5%:6%]",
processor=self.tiny_llama_tokenizer,
)
raw_dataset_a = c4_manager_a.load_dataset()

c4_manager_b = TextGenerationDataset.load_from_registry(
self.data_args.dataset,
data_args=self.data_args,
split="train[5%:15%]",
split="train[6%:8%]",
processor=self.tiny_llama_tokenizer,
)
raw_dataset_b = c4_manager_b.load_dataset()
Expand All @@ -162,7 +162,7 @@ def prepare_fixture(self, tiny_llama_tokenizer):
[
["ptb", "penn_treebank", "train[:5%]", False],
["gsm8k", "main", "train[:5%]", True],
["ultrachat_200k", "default", "train_sft[:2%]", False],
["ultrachat_200k", "default", "train_sft[:1%]", False],
]
)
def test_datasets(self, dataset_key, dataset_config, split, do_concat):
Expand Down Expand Up @@ -271,9 +271,7 @@ class TestSplitLoading(unittest.TestCase):
def prepare_fixture(self, tiny_llama_tokenizer):
self.tiny_llama_tokenizer = tiny_llama_tokenizer

@parameterized.expand(
[["train"], ["train[60%:]"], [{"train": "train[:20%]"}], [None]]
)
@parameterized.expand([["train[95%:]"], [{"train": "train[:5%]"}]])
def test_split_loading(self, split_def):
data_args = DatasetArguments(
dataset="open_platypus",
Expand Down Expand Up @@ -302,7 +300,7 @@ class TestTokenizationDataset(unittest.TestCase):
def prepare_fixture(self, tiny_llama_tokenizer):
self.tiny_llama_tokenizer = tiny_llama_tokenizer
dataset = load_dataset("garage-bAInd/Open-Platypus")["train"]
self.num_calib_samples = 256
self.num_calib_samples = 64
self.max_seq_len = 512
self.dataset = dataset.shuffle(seed=42).select(range(self.num_calib_samples))

Expand Down
2 changes: 1 addition & 1 deletion tests/llmcompressor/transformers/finetune/finetune_oneshot_configs/config.yaml
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Expand Up @@ -4,5 +4,5 @@ model: "Xenova/llama2.c-stories15M"
dataset: wikitext
dataset_config_name: "wikitext-2-raw-v1"
recipe: "tests/llmcompressor/transformers/finetune/test_alternate_recipe.yaml"
num_train_epochs: 1
num_train_epochs: 0.25
concat_txt: False
5 changes: 3 additions & 2 deletions tests/llmcompressor/transformers/finetune/test_oneshot_and_finetune.py
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Expand Up @@ -19,9 +19,9 @@ class TestOneshotAndFinetune(unittest.TestCase):
def _test_oneshot_and_finetune(self):
from llmcompressor.transformers import apply

splits = {"train": "train[:30%]", "calibration": "train[30%:40%]"}
splits = {"train": "train[:5%]", "calibration": "train[5%:10%]"}
if self.dataset == "ultrachat-200k":
splits = {"train": "train_gen[:30%]", "calibration": "train_gen[30%:40%]"}
splits = {"train": "train_gen[:5%]", "calibration": "train_gen[5%:10%]"}

apply(
model=self.model,
Expand All @@ -30,6 +30,7 @@ def _test_oneshot_and_finetune(self):
output_dir=self.output,
recipe=self.recipe,
num_train_epochs=self.num_train_epochs,
num_calibration_samples=64,
concatenate_data=self.concat_txt,
splits=splits,
oneshot_device=self.device,
Expand Down
19 changes: 8 additions & 11 deletions tests/llmcompressor/transformers/finetune/test_oneshot_then_finetune.py
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Expand Up @@ -27,7 +27,7 @@ def test_oneshot_sparsification_then_finetune(self):
concatenate_data = False
num_calibration_samples = 64
output_dir = self.output / "oneshot_out"
splits = {"calibration": "train[:10%]"}
splits = {"calibration": "train[:5%]"}

with create_session():
oneshot(
Expand Down Expand Up @@ -56,20 +56,18 @@ def test_oneshot_sparsification_then_finetune(self):
dataset = "open_platypus"
concatenate_data = False
output_dir = self.output / "finetune_out"
splits = "train[:50%]"
max_steps = 25
splits = "train[5%:7%]"

with create_session():
train(
model=model,
distill_teacher=distill_teacher,
dataset=dataset,
output_dir=output_dir,
num_calibration_samples=num_calibration_samples,
num_train_epochs=0.05,
recipe=recipe_str,
concatenate_data=concatenate_data,
splits=splits,
max_steps=max_steps,
)

# test reloading checkpoint and final model
Expand All @@ -85,11 +83,10 @@ def test_oneshot_sparsification_then_finetune(self):
distill_teacher=distill_teacher,
dataset=dataset,
output_dir=output_dir,
num_calibration_samples=num_calibration_samples,
num_train_epochs=0.05,
recipe=recipe_str,
concatenate_data=concatenate_data,
splits=splits,
max_steps=max_steps,
resume_from_checkpoint=True, # use last checkpoint
)

Expand All @@ -106,7 +103,7 @@ def test_oneshot_quantization_then_finetune(self):
concatenate_data = False
num_calibration_samples = 64
output_dir = self.output / "oneshot_out"
splits = {"calibration": "train[:10%]"}
splits = {"calibration": "train[:5%]"}

with create_session():
oneshot(
Expand All @@ -130,17 +127,17 @@ def test_oneshot_quantization_then_finetune(self):
dataset = "open_platypus"
concatenate_data = False
output_dir = self.output / "finetune_out"
splits = {"calibration": "train[:10%]", "train": "train[:10%]"}
splits = {"calibration": "train[:5%]", "train": "train[5%:7%]"}

with create_session():
train(
model=model,
dataset=dataset,
output_dir=output_dir,
num_calibration_samples=num_calibration_samples,
recipe=recipe,
concatenate_data=concatenate_data,
splits=splits,
num_train_epochs=0.05,
)

# test reloading checkpoint and final model
Expand All @@ -152,10 +149,10 @@ def test_oneshot_quantization_then_finetune(self):
model=model,
dataset=dataset,
output_dir=output_dir,
num_calibration_samples=num_calibration_samples,
recipe=recipe,
concatenate_data=concatenate_data,
splits=splits,
num_train_epochs=0.05,
resume_from_checkpoint=True, # use last checkpoint
)

Expand Down