test(gpt2): add unit tests for generation, cache behavior, and error conditions

- Covers forward pass with and without KV-cache
- Verifies correct sequence generation for greedy, top-k, and top-p sampling
- Adds ValueError test for exceeding max sequence length
- Uses small random toy config and minimal setup for fast test feedback

Motivation: Prevent regressions in decoding, sampling, and KV-cache logic in GPT2 implementation.

llm/tests/models/test_gpt2.py

@@ -0,0 +1,53 @@
+import torch
+import pytest
+from llm.models.gpt.gpt2 import GPT2
+
+@pytest.fixture
+def config():
+    return {
+        "vocab_size": 100,
+        "embed_dim": 32,
+        "num_heads": 4,
+        "num_layers": 2,
+        "max_position_embeddings": 16,
+        "dropout": 0.0,
+    }
+
+@pytest.fixture
+def model(config):
+    return GPT2(config)
+
+def test_forward_basic(model):
+    x = torch.randint(0, 100, (2, 8))
+    logits, cache = model(x)
+    assert logits.shape == (2, 8, 100)
+    assert isinstance(cache, list)
+    assert len(cache) == model._decoders.__len__()
+
+def test_forward_with_cache(model):
+    x = torch.randint(0, 100, (2, 4))
+    logits, cache = model(x, use_cache=True)
+    x2 = torch.randint(0, 100, (2, 1))
+    logits2, cache2 = model(x2, use_cache=True, cache=cache)
+    assert logits2.shape == (2, 1, 100)
+    assert isinstance(cache2, list)
+
+def test_generate_and_shape(model):
+    x = torch.randint(0, 100, (1, 5))
+    result = model.generate(x, max_new_tokens=3, do_sample=False)
+    assert result.shape == (1, 8)
+
+def test_forward_sequence_too_long(model, config):
+    x = torch.randint(0, 100, (1, config["max_position_embeddings"] + 1))
+    with pytest.raises(ValueError):
+        model(x)
+
+def test_generate_with_sampling_topk(model):
+    x = torch.randint(0, 100, (1, 3))
+    out = model.generate(x, max_new_tokens=2, do_sample=True, top_k=5)
+    assert out.shape == (1, 5)
+
+def test_generate_with_sampling_topp(model):
+    x = torch.randint(0, 100, (1, 3))
+    out = model.generate(x, max_new_tokens=2, do_sample=True, top_p=0.8)
+    assert out.shape == (1, 5)