refactor(gpt1): migrate Decoder to GptDecoder, unify API, and update tests

- Renamed Decoder (and decoder.py) to GptDecoder (gpt_decoder.py) for clarity in GPT1
- Implemented support for cache and use_cache parameters in GptDecoder.forward (API unification)
- Adapted all usages in GPT model to use new decoder structure and handle tuple output
- Refactored core tests (test_gpt.py, test_gpt_decoder.py, test_basic.py) to correctly expect tuple or logits and ensure shape/device checks work as before
- Improved clarity and future extensibility for autoregressive generation and benchmarking
- No changes to architectural details or training loop; pure API and test modernization

llm/tests/test_basic.py

@@ -28,7 +28,7 @@ def test_gpt_model_creation():
     input_ids = torch.randint(0, config["vocab_size"], (batch_size, seq_len))
 
     with torch.no_grad():
-        logits = model(input_ids)
+        logits, _ = model(input_ids)
 
     assert logits.shape == (batch_size, seq_len, config["vocab_size"])
     print("✅ GPT model creation and forward pass test passed")
@@ -222,7 +222,7 @@ def test_gpt_with_tokenizer():
     input_ids = torch.tensor([tokens])
 
     with torch.no_grad():
-        logits = model(input_ids)
+        logits, _ = model(input_ids)
 
     assert logits.shape == (1, len(tokens), vocab_size)
     print("✅ GPT with tokenizer integration test passed")