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Test Suite for Tutorial Code Examples
Ensures all code examples in tutorials remain functional
"""
import pytest
import os
import sys
import torch
from datasets import Dataset
# Add project root to path
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
class TestTutorial03:
"""Test Tutorial 03: Full Fine-Tuning examples"""
def test_full_finetuning_basic(self):
"""Test basic full fine-tuning workflow"""
from ntf.config import NTFConfig, ModelConfig, TrainingConfig
from ntf.models import ModelRegistry
from ntf.finetuning import FullFinetuneTrainer
config = NTFConfig(
model=ModelConfig(name="facebook/opt-125m"),
training=TrainingConfig(
output_dir="./test_output",
num_train_epochs=1,
per_device_train_batch_size=2,
)
)
registry = ModelRegistry(config.model)
model, tokenizer = registry.load_model_and_tokenizer()
train_data = Dataset.from_dict({
"text": ["Hello world", "Test sentence"] * 10
})
trainer = FullFinetuneTrainer(
model=model,
config=config.training,
train_dataset=train_data,
tokenizer=tokenizer
)
trainer.train()
assert os.path.exists("./test_output")
class TestTutorial05:
"""Test Tutorial 05: PEFT/LoRA examples"""
def test_lora_setup(self):
"""Test LoRA adapter setup"""
from ntf.finetuning import LoRAConfig, PEFTTrainer
from ntf.models import ModelRegistry
registry = ModelRegistry("facebook/opt-125m")
model, tokenizer = registry.load_model_and_tokenizer()
lora_config = LoRAConfig(
r=8,
alpha=16,
dropout=0.05,
target_modules=["q_proj", "v_proj"],
)
trainer = PEFTTrainer(model, lora_config, tokenizer)
trainable_params = sum(p.numel() for p in trainer.model.parameters() if p.requires_grad)
all_params = sum(p.numel() for p in trainer.model.parameters())
assert trainable_params < all_params
assert trainable_params > 0
def test_p_tuning_setup(self):
"""Test P-Tuning setup"""
from ntf.finetuning import PTuningConfig, PTuningMethod, setup_p_tuning
from ntf.models import ModelRegistry
registry = ModelRegistry("facebook/opt-125m")
model, tokenizer = registry.load_model_and_tokenizer()
config = PTuningConfig(
method=PTuningMethod.P_TUNING_V2,
num_virtual_tokens=20,
)
peft_model = setup_p_tuning(model, method="p_tuning_v2", num_virtual_tokens=20)
assert peft_model is not None
class TestTutorial04:
"""Test Tutorial 04: Continual Learning examples"""
def test_ewc_regularization(self):
"""Test EWC regularization setup"""
from ntf.utils import EWCConfig, EWCRegularizer, ContinualLearningWrapper
from ntf.models import ModelRegistry
registry = ModelRegistry("facebook/opt-125m")
model, tokenizer = registry.load_model_and_tokenizer()
ewc_config = EWCConfig(ewc_lambda=1000.0)
ewc = EWCRegularizer(model, ewc_config)
assert ewc is not None
assert ewc.config.ewc_lambda == 1000.0
def test_si_regularization(self):
"""Test Synaptic Intelligence regularization"""
from ntf.utils import SIRegularizer, ContinualLearningWrapper
from ntf.models import ModelRegistry
registry = ModelRegistry("facebook/opt-125m")
model, _ = registry.load_model_and_tokenizer()
wrapper = ContinualLearningWrapper(model, method="si")
wrapper.apply_si_regularization(c=0.1)
assert wrapper.si is not None
assert wrapper.si.c == 0.1
def test_lwf_regularization(self):
"""Test Learning without Forgetting"""
from ntf.utils import LwFRegularizer, ContinualLearningWrapper
from ntf.models import ModelRegistry
registry = ModelRegistry("facebook/opt-125m")
model, _ = registry.load_model_and_tokenizer()
wrapper = ContinualLearningWrapper(model, method="lwf")
wrapper.apply_lwf_regularization(alpha=0.5)
assert wrapper.lwf is not None
assert wrapper.lwf.alpha == 0.1
class TestMultiTask:
"""Test Multi-Task Learning (Spec 4.1.1)"""
def test_multi_task_model_creation(self):
"""Test creating multi-task model with multiple heads"""
from ntf.finetuning import MultiTaskModel, TaskType, MultiTaskTrainer
from ntf.models import ModelRegistry
registry = ModelRegistry("facebook/opt-125m")
base_model, tokenizer = registry.load_model_and_tokenizer()
model = MultiTaskModel(base_model=base_model)
model.add_task_head(
task_name="classification",
head_type=TaskType.CLASSIFICATION,
config={"num_labels": 5}
)
model.add_task_head(
task_name="summarization",
head_type=TaskType.SEQUENCE_TO_SEQUENCE,
config={"max_length": 512}
)
assert model.get_num_tasks() == 2
assert "classification" in model.list_tasks()
assert "summarization" in model.list_tasks()
def test_multi_task_forward(self):
"""Test forward pass through multi-task model"""
from ntf.finetuning import MultiTaskModel, TaskType
from ntf.models import ModelRegistry
import torch
registry = ModelRegistry("facebook/opt-125m")
base_model, tokenizer = registry.load_model_and_tokenizer()
model = MultiTaskModel(base_model=base_model)
model.add_task_head(
task_name="classification",
head_type=TaskType.CLASSIFICATION,
config={"num_labels": 3}
)
input_ids = torch.randint(0, 1000, (2, 10))
attention_mask = torch.ones((2, 10))
output = model(
input_ids=input_ids,
attention_mask=attention_mask,
task_name="classification"
)
assert "logits" in output
assert output["logits"].shape[0] == 2
assert output["logits"].shape[1] == 3
class TestContinualLearningWrapper:
"""Test ContinualLearningWrapper API (Spec 4.1.2)"""
def test_wrapper_api(self):
"""Test the unified ContinualLearningWrapper API"""
from ntf.utils import ContinualLearningWrapper
from ntf.models import ModelRegistry
registry = ModelRegistry("facebook/opt-125m")
model, _ = registry.load_model_and_tokenizer()
wrapper = ContinualLearningWrapper(model, method="ewc")
# Test EWC
wrapper.apply_ewc_regularization(lambda_ewc=0.5)
assert wrapper.ewc is not None
# Test SI
wrapper2 = ContinualLearningWrapper(model, method="si")
wrapper2.apply_si_regularization(c=0.1)
assert wrapper2.si is not None
# Test LwF
wrapper3 = ContinualLearningWrapper(model, method="lwf")
wrapper3.apply_lwf_regularization(alpha=0.5)
assert wrapper3.lwf is not None
def test_progressive_unfreeze(self):
"""Test progressive unfreezing strategy"""
from ntf.utils import ContinualLearningWrapper
from ntf.models import ModelRegistry
registry = ModelRegistry("facebook/opt-125m")
model, _ = registry.load_model_and_tokenizer()
wrapper = ContinualLearningWrapper(model)
wrapper.progressive_unfreeze(
start_layers=4,
unfreeze_every_n_epochs=2,
max_layers=12
)
assert hasattr(wrapper, 'start_layers')
assert wrapper.start_layers == 4
if __name__ == "__main__":
pytest.main([__file__, "-v"])
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