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qtensorformer
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Premchandyadav369
Transform Q-TensorFormer into an Information-Value Adaptive Resource Allocation Architecture
eaeea8f | """ | |
| Tests for Hugging Face Transformers Integration. | |
| """ | |
| import sys | |
| import os | |
| sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) | |
| import torch | |
| import tempfile | |
| import pytest | |
| from transformers import AutoConfig, AutoModelForCausalLM | |
| from src.hf_model import QTensorFormerConfig, QTensorFormerForCausalLM | |
| def test_hf_config_and_model_forward(): | |
| config = QTensorFormerConfig( | |
| vocab_size=100, | |
| d_model=32, | |
| n_layers=2, | |
| n_heads=2, | |
| tt_rank=4, | |
| max_seq_len=32, | |
| use_quantum=True, | |
| ) | |
| model = QTensorFormerForCausalLM(config) | |
| input_ids = torch.randint(0, 100, (2, 8)) | |
| out = model(input_ids) | |
| assert out.logits.shape == (2, 8, 100) | |
| print("✓ test_hf_config_and_model_forward passed") | |
| def test_hf_generation(): | |
| config = QTensorFormerConfig( | |
| vocab_size=100, | |
| d_model=32, | |
| n_layers=2, | |
| n_heads=2, | |
| tt_rank=4, | |
| max_seq_len=32, | |
| ) | |
| model = QTensorFormerForCausalLM(config) | |
| input_ids = torch.tensor([[1, 2, 3]]) | |
| generated = model.generate(input_ids, max_new_tokens=5, do_sample=False) | |
| assert generated.shape == (1, 8), f"Expected shape (1, 8), got {generated.shape}" | |
| print("✓ test_hf_generation passed") | |
| def test_hf_save_and_load(): | |
| config = QTensorFormerConfig( | |
| vocab_size=50, | |
| d_model=32, | |
| n_layers=2, | |
| n_heads=2, | |
| tt_rank=4, | |
| ) | |
| model = QTensorFormerForCausalLM(config) | |
| with tempfile.TemporaryDirectory() as tmpdir: | |
| model.save_pretrained(tmpdir) | |
| # Load back with AutoModelForCausalLM | |
| loaded = AutoModelForCausalLM.from_pretrained(tmpdir) | |
| assert isinstance(loaded, QTensorFormerForCausalLM) | |
| model.eval() | |
| loaded.eval() | |
| inp = torch.randint(0, 50, (1, 4)) | |
| with torch.no_grad(): | |
| out1 = model(inp).logits | |
| out2 = loaded(inp).logits | |
| assert torch.allclose(out1, out2, atol=1e-4) | |
| print("✓ test_hf_save_and_load passed") | |
| if __name__ == "__main__": | |
| test_hf_config_and_model_forward() | |
| test_hf_generation() | |
| test_hf_save_and_load() | |
| print("All Hugging Face integration tests passed!") | |