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Verify architecture + counting tham số - chạy nhanh
"""
import sys
import os
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
import torch
from nexus.config import NexusConfig, print_config_summary
from nexus.model.nexus_coder import NexusCoderForCausalLM
def test_tiny_model():
"""Test với model nhỏ."""
print("\n[Test 1] Tiny model forward pass...")
tiny_config = NexusConfig(
vocab_size=1000,
hidden_size=128,
num_hidden_layers=2,
num_attention_heads=4,
num_kv_heads=2,
head_dim=32,
intermediate_size=256,
num_experts=4,
num_active_experts=2,
max_position_embeddings=512,
)
model = NexusCoderForCausalLM(tiny_config)
input_ids = torch.randint(0, 1000, (2, 16))
labels = input_ids.clone()
outputs = model(input_ids=input_ids, labels=labels)
assert outputs["loss"] is not None
assert outputs["logits"].shape == (2, 16, 1000)
print(f" ✓ Loss: {outputs['loss'].item():.4f}")
print(f" ✓ Logits shape: {outputs['logits'].shape}")
# Generate
generated = model.generate(
input_ids=torch.randint(0, 1000, (1, 4)),
max_new_tokens=10,
do_sample=False,
)
assert generated.shape[1] > 4
print(f" ✓ Generated shape: {generated.shape}")
print(" ✓ PASSED!")
def test_param_count():
"""Test đếm tham số theo config."""
print("\n[Test 2] Param count theo config...")
config = NexusConfig()
stats = config.estimated_total_params()
print(f" Total: {stats['total_params']:,} ({stats['total_params_billion']:.2f}B)")
print(f" Active: {stats['active_params']:,} ({stats['active_params_billion']:.2f}B)")
assert 9.5e9 < stats["total_params"] < 11e9
assert 1.3e9 < stats["active_params"] < 1.7e9
print(" ✓ PASSED!")
def test_tokenizer():
"""Test tokenizer cơ bản."""
print("\n[Test 3] Tokenizer...")
from nexus.tokenizer.tokenizer import NexusTokenizer
from nexus.training.dataset import AUTHOR_TRAINING_DATA
tokenizer = NexusTokenizer(vocab_size=2000)
corpus = [f"{d['system']} {d['user']} {d['assistant']}" for d in AUTHOR_TRAINING_DATA]
tokenizer.train(corpus)
text = "Xin chào, tôi là Nexus Coder do Hieu Louis tạo ra."
ids = tokenizer.encode(text, add_special=True)
decoded = tokenizer.decode(ids)
assert len(ids) > 0
assert "Nexus" in decoded or "nexus" in decoded
print(f" ✓ Encoded {len(text)} chars -> {len(ids)} tokens")
print(f" ✓ Decoded (partial): {decoded[:100]}...")
print(" ✓ PASSED!")
def test_dataset():
"""Test dataset với author info."""
print("\n[Test 4] Dataset (author info)...")
from nexus.tokenizer.tokenizer import NexusTokenizer
from nexus.training.dataset import NexusDataset, AUTHOR_TRAINING_DATA, get_author_info
info = get_author_info()
assert info["name"] == "Hieu Louis"
assert info["github"] == "mhieuhonda"
assert info["year"] == "2026"
print(f" ✓ Author: {info['name']}")
print(f" ✓ GitHub: {info['github']}")
print(f" ✓ Year: {info['year']}")
print(f" ✓ Training samples: {len(AUTHOR_TRAINING_DATA)}")
tokenizer = NexusTokenizer(vocab_size=2000)
corpus = [f"{d['system']} {d['user']} {d['assistant']}" for d in AUTHOR_TRAINING_DATA]
tokenizer.train(corpus)
dataset = NexusDataset(tokenizer, max_length=128)
assert len(dataset) > 0
sample = dataset[0]
assert "input_ids" in sample
assert "labels" in sample
assert sample["input_ids"].shape[0] == 128
print(f" ✓ Dataset size: {len(dataset)}")
print(f" ✓ Sample shape: {sample['input_ids'].shape}")
print(" ✓ PASSED!")
def test_full_pipeline():
"""Test pipeline end-to-end với tiny config."""
print("\n[Test 5] End-to-end pipeline (tiny)...")
from nexus.tokenizer.tokenizer import NexusTokenizer
from nexus.training.dataset import NexusDataset, AUTHOR_TRAINING_DATA
from nexus.model.nexus_coder import NexusCoderForCausalLM
config = NexusConfig(
vocab_size=500,
hidden_size=64,
num_hidden_layers=2,
num_attention_heads=4,
num_kv_heads=2,
head_dim=16,
intermediate_size=128,
num_experts=4,
num_active_experts=2,
max_position_embeddings=128,
)
tokenizer = NexusTokenizer(vocab_size=500)
corpus = [f"{d['system']} {d['user']} {d['assistant']}" for d in AUTHOR_TRAINING_DATA]
tokenizer.train(corpus)
dataset = NexusDataset(tokenizer, max_length=64)
model = NexusCoderForCausalLM(config)
# Train 1 step
optimizer = torch.optim.AdamW(model.parameters(), lr=1e-3)
batch = torch.utils.data.DataLoader(dataset, batch_size=2).__iter__().__next__()
outputs = model(
input_ids=batch["input_ids"],
attention_mask=batch["attention_mask"],
labels=batch["labels"],
)
loss = outputs["loss"]
loss.backward()
optimizer.step()
print(f" ✓ Loss sau 1 step: {loss.item():.4f}")
# Generate
generated = model.generate(
input_ids=torch.tensor([[1, 5, 10, 20]], dtype=torch.long),
max_new_tokens=5,
do_sample=False,
)
print(f" ✓ Generated: {generated.shape}")
print(" ✓ PASSED!")
if __name__ == "__main__":
print("=" * 60)
print(" NEXUS CODER v0.1 - TEST SUITE")
print(" Tác giả: Hieu Louis (2026)")
print("=" * 60)
print_config_summary()
test_tiny_model()
test_param_count()
test_tokenizer()
test_dataset()
test_full_pipeline()
print("\n" + "=" * 60)
print("✅ TẤT CẢ TESTS PASSED!")
print("=" * 60)
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