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End-to-end tests for the music generation pipeline.
Tests: tokenizer, model, generation, full pipeline.
"""
import sys
from pathlib import Path
sys.path.insert(0, str(Path(__file__).resolve().parent.parent))
import torch
import numpy as np
from src.s01_config import ModelConfig, GenConfig
from src.s02_tokenizer import MusicTokenizer, BOS_TOKEN, EOS_TOKEN, VOCAB_SIZE
from src.s04_model import MusicTransformer
from src.s06_generator import generate
def test_tokenizer_roundtrip():
"""Test tokenizer encode/decode produces valid events."""
tok = MusicTokenizer()
assert tok.vocab_size == VOCAB_SIZE
assert tok.bos_id == BOS_TOKEN
assert tok.eos_id == EOS_TOKEN
# Test individual token encoding
note_on = tok.note_on_token(60) # Middle C
event = tok.decode_token(note_on)
assert event["type"] == "NoteOn"
assert event["value"] == 60
vel = tok.velocity_token(100)
event = tok.decode_token(vel)
assert event["type"] == "Velocity"
ts = tok.timeshift_token(500) # 500ms
event = tok.decode_token(ts)
assert event["type"] == "TimeShift"
assert event["value"] == 500
print("PASS: test_tokenizer_roundtrip")
def test_tokenizer_midi_conversion():
"""Test MIDI to tokens and back."""
try:
import pretty_midi
except ImportError:
print("SKIP: test_tokenizer_midi_conversion (pretty_midi not installed)")
return
tok = MusicTokenizer()
# Create a simple test MIDI
midi = pretty_midi.PrettyMIDI(initial_tempo=120)
inst = pretty_midi.Instrument(program=0)
# C major chord
for pitch in [60, 64, 67]:
note = pretty_midi.Note(velocity=80, pitch=pitch, start=0.0, end=1.0)
inst.notes.append(note)
# Second chord at 1.0s
for pitch in [65, 69, 72]:
note = pretty_midi.Note(velocity=90, pitch=pitch, start=1.0, end=2.0)
inst.notes.append(note)
midi.instruments.append(inst)
# Tokenize
tokens = tok.midi_to_tokens(midi)
assert len(tokens) > 5
assert tokens[0] == BOS_TOKEN
assert tokens[-1] == EOS_TOKEN
# Decode back to MIDI
midi_out = tok.tokens_to_midi(tokens)
assert len(midi_out.instruments) == 1
assert len(midi_out.instruments[0].notes) > 0
print(f"PASS: test_tokenizer_midi_conversion ({len(tokens)} tokens, "
f"{len(midi_out.instruments[0].notes)} notes)")
def test_model_forward():
"""Test model forward pass and loss computation."""
config = ModelConfig(
vocab_size=VOCAB_SIZE,
dim=64,
n_layers=2,
n_heads=4,
n_kv_heads=2,
max_seq_len=128,
dropout=0.0,
)
model = MusicTransformer.from_config(config)
# Check parameter count is reasonable
n_params = model.count_parameters()
assert n_params > 0
print(f" Model params: {n_params:,}")
# Forward pass
batch_size = 2
seq_len = 32
input_ids = torch.randint(0, config.vocab_size, (batch_size, seq_len))
targets = torch.randint(0, config.vocab_size, (batch_size, seq_len))
logits, loss = model(input_ids, targets)
assert logits.shape == (batch_size, seq_len, config.vocab_size)
assert loss is not None
assert loss.item() > 0
# Backward pass (check gradients flow)
loss.backward()
grad_norms = [p.grad.norm().item() for p in model.parameters() if p.grad is not None]
assert len(grad_norms) > 0
assert all(not np.isnan(g) for g in grad_norms)
print(f"PASS: test_model_forward (loss={loss.item():.4f})")
def test_model_gradient_checkpoint():
"""Test that gradient checkpointing works and reduces memory."""
config = ModelConfig(
vocab_size=VOCAB_SIZE,
dim=64,
n_layers=4,
n_heads=4,
n_kv_heads=2,
max_seq_len=128,
dropout=0.0,
)
model = MusicTransformer.from_config(config)
model.grad_checkpoint = True
input_ids = torch.randint(0, config.vocab_size, (2, 64))
targets = torch.randint(0, config.vocab_size, (2, 64))
logits, loss = model(input_ids, targets)
loss.backward()
assert loss.item() > 0
print(f"PASS: test_model_gradient_checkpoint (loss={loss.item():.4f})")
def test_generation():
"""Test autoregressive generation."""
config = ModelConfig(
vocab_size=VOCAB_SIZE,
dim=64,
n_layers=2,
n_heads=4,
n_kv_heads=2,
max_seq_len=128,
dropout=0.0,
)
model = MusicTransformer.from_config(config)
tokenizer = MusicTokenizer()
gen_config = GenConfig(
temperature=0.8,
top_k=20,
top_p=0.9,
max_tokens=50,
repetition_penalty=1.1,
seed=42,
)
tokens = generate(model, tokenizer, gen_config, device=torch.device("cpu"))
assert len(tokens) > 1
assert tokens[0] == BOS_TOKEN
print(f"PASS: test_generation ({len(tokens)} tokens generated)")
def test_generation_to_midi():
"""Test full pipeline: generate tokens → convert to MIDI."""
try:
import pretty_midi
except ImportError:
print("SKIP: test_generation_to_midi (pretty_midi not installed)")
return
config = ModelConfig(
vocab_size=VOCAB_SIZE,
dim=64,
n_layers=2,
n_heads=4,
n_kv_heads=2,
max_seq_len=128,
dropout=0.0,
)
model = MusicTransformer.from_config(config)
tokenizer = MusicTokenizer()
gen_config = GenConfig(
temperature=1.0,
top_k=50,
top_p=0.95,
max_tokens=100,
repetition_penalty=1.1,
seed=123,
)
tokens = generate(model, tokenizer, gen_config, device=torch.device("cpu"))
midi = tokenizer.tokens_to_midi(tokens)
assert midi is not None
assert len(midi.instruments) == 1
print(f"PASS: test_generation_to_midi ({len(tokens)} tokens → "
f"{len(midi.instruments[0].notes)} notes)")
def test_dataset_creation():
"""Test MidiTokenDataset with synthetic data."""
from src.s03_dataset import MidiTokenDataset
# Synthetic token sequences
sequences = [
[BOS_TOKEN] + list(np.random.randint(4, VOCAB_SIZE, size=100)) + [EOS_TOKEN]
for _ in range(20)
]
ds = MidiTokenDataset(sequences, max_seq_len=64, pad_id=0)
assert len(ds) == 20
input_ids, targets = ds[0]
assert input_ids.shape == (64,)
assert targets.shape == (64,)
assert input_ids.dtype == torch.long
print(f"PASS: test_dataset_creation ({len(ds)} sequences)")
if __name__ == "__main__":
print("=" * 60)
print("MUSIC GENERATION LLM — TESTS")
print("=" * 60)
tests = [
test_tokenizer_roundtrip,
test_tokenizer_midi_conversion,
test_model_forward,
test_model_gradient_checkpoint,
test_generation,
test_generation_to_midi,
test_dataset_creation,
]
passed = 0
failed = 0
skipped = 0
for test in tests:
try:
test()
passed += 1
except Exception as e:
if "SKIP" in str(e):
skipped += 1
else:
print(f"FAIL: {test.__name__}: {e}")
import traceback
traceback.print_exc()
failed += 1
print("=" * 60)
print(f"Results: {passed} passed, {failed} failed, {skipped} skipped")
print("=" * 60)
sys.exit(1 if failed > 0 else 0)
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