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Running on Zero
| #!/usr/bin/env python3 | |
| """Saturday Milestone 1 Verification Script. | |
| This script verifies that the Saturday-1M model is correctly implemented | |
| by running a complete forward pass and checking all tensor shapes. | |
| """ | |
| import sys | |
| import os | |
| sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) | |
| import numpy as np | |
| from saturday_numpy.config import SaturdayConfig | |
| from saturday_numpy.model.saturday import SaturdayModel | |
| from saturday_numpy.training.loss import compute_loss_from_tokens | |
| from saturday_numpy.utils.hardware import detect_hardware, print_hardware_info | |
| def main(): | |
| print("=" * 60) | |
| print("Saturday - Milestone 1 Verification") | |
| print("=" * 60) | |
| # 1. Hardware detection | |
| print("\n--- Hardware Info ---") | |
| print_hardware_info() | |
| # 2. Load config | |
| print("\n--- Model Configuration ---") | |
| config = SaturdayConfig() # Default Saturday-1M | |
| param_info = config.count_parameters() | |
| for key, value in param_info.items(): | |
| print(f" {key}: {value:,}") | |
| # 3. Create model | |
| print("\n--- Creating Saturday-1M ---") | |
| model = SaturdayModel(config) | |
| total = model.count_parameters() | |
| print(f" Total parameters: {total:,}") | |
| print(f" Model size: {total * 4 / 1024 / 1024:.2f} MB (float32)") | |
| # 4. Forward pass | |
| print("\n--- Forward Pass ---") | |
| rng = np.random.default_rng(42) | |
| batch_size = 2 | |
| seq_len = 16 | |
| token_ids = rng.integers(0, config.vocab_size, size=(batch_size, seq_len)) | |
| print(f" Input shape: {token_ids.shape}") | |
| print(f" Input tokens: {token_ids[0, :8]}...") | |
| logits = model.forward(token_ids) | |
| print(f" Output logits shape: {logits.shape}") | |
| print(f" Expected shape: ({batch_size}, {seq_len}, {config.vocab_size})") | |
| assert logits.shape == (batch_size, seq_len, config.vocab_size), "Shape mismatch!" | |
| print(f" [OK] Shape correct") | |
| # 5. Check for NaN/Inf | |
| assert not np.any(np.isnan(logits)), "NaN in logits!" | |
| assert not np.any(np.isinf(logits)), "Inf in logits!" | |
| print(f" [OK] No NaN or Inf") | |
| # 6. Loss computation | |
| print("\n--- Loss Computation ---") | |
| loss, _ = compute_loss_from_tokens(model, token_ids) | |
| expected_random_loss = np.log(config.vocab_size) | |
| print(f" Loss: {loss:.4f}") | |
| print(f" Expected (random model): ~{expected_random_loss:.4f} = ln({config.vocab_size})") | |
| print(f" Ratio: {loss / expected_random_loss:.4f} (should be close to 1.0)") | |
| assert 0.5 * expected_random_loss < loss < 2.0 * expected_random_loss, \ | |
| f"Loss {loss:.4f} too far from expected {expected_random_loss:.4f}" | |
| print(f" [OK] Loss in expected range") | |
| # 7. Causal mask verification | |
| print("\n--- Causal Mask Verification ---") | |
| print(" Creating causal mask for seq_len=5:") | |
| mask = np.triu(np.full((5, 5), -np.inf), k=1) | |
| for i in range(5): | |
| row = [] | |
| for j in range(5): | |
| if mask[i, j] == -np.inf: | |
| row.append(" -inf ") | |
| else: | |
| row.append(" 0.00 ") | |
| print(f" [{', '.join(row)}]") | |
| print(f" [OK] Token at position i can only attend to positions 0..i") | |
| # 8. Determinism check | |
| print("\n--- Determinism Check ---") | |
| model2 = SaturdayModel(config) # Same seed | |
| logits2 = model2.forward(token_ids) | |
| max_diff = np.max(np.abs(logits - logits2)) | |
| print(f" Max difference between two models with same seed: {max_diff}") | |
| assert max_diff < 1e-6, "Models not deterministic!" | |
| print(f" [OK] Deterministic") | |
| # Summary | |
| print("\n" + "=" * 60) | |
| print("ALL MILESTONE 1 VERIFICATIONS PASSED [OK]") | |
| print("=" * 60) | |
| print(f"\nSaturday-1M is ready.") | |
| print(f" Parameters: {total:,}") | |
| print(f" Architecture: {config.num_layers} layers, {config.num_attention_heads} heads") | |
| print(f" Hidden size: {config.hidden_size}, FFN size: {config.intermediate_size}") | |
| print(f" Sequence length: {config.max_sequence_length}") | |
| print(f" Vocabulary: {config.vocab_size}") | |
| if __name__ == "__main__": | |
| main() | |