Add high value engine tests for core invariants (33 LoC) (#396)

* test: add engine generation tests for expected invariants

- test_seed_reproducibility
- test_temperature_zero_determinism
- test_max_tokens_respected
- test_num_samples_count

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

* Fix temperature test

* add test for seed variation in sampling

Add test for seed variation in sampling with temperature > 0.

* Rename test for clarity

* Shorten assert msg

---------

Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>

tests/test_engine.py

@@ -195,3 +195,72 @@ def test_multi_sample_first_token_diversity():
         f"With uniform logits, this is statistically impossible (~10^-36 probability) "
         f"unless tokens are being broadcast instead of independently sampled."
     )
+
+
+def test_seed_reproducibility():
+    """Same seed must produce identical output."""
+    model = MockModel()
+    engine = Engine(model, ByteTokenizer())
+    prompt = [261, 72, 101, 108, 108, 111]  # <bos> + "Hello"
+
+    for seed in [1, 42, 123, 999]:
+        r1, _ = engine.generate_batch(prompt, max_tokens=5, seed=seed)
+        r2, _ = engine.generate_batch(prompt, max_tokens=5, seed=seed)
+        r3, _ = engine.generate_batch(prompt, max_tokens=5, seed=seed)
+        assert r1 == r2 == r3, "Same seed must produce identical output for the same prompt."
+
+
+def test_temperature_zero_determinism():
+    """Temperature=0 is deterministic regardless of seed."""
+    model = MockModel()
+    engine = Engine(model, ByteTokenizer())
+    prompt = [261, 72, 101, 108, 108, 111]
+
+    r1, _ = engine.generate_batch(prompt, temperature=0.0, max_tokens=5, seed=1)
+    r2, _ = engine.generate_batch(prompt, temperature=0.0, max_tokens=5, seed=42)
+    r3, _ = engine.generate_batch(prompt, temperature=0.0, max_tokens=5, seed=123)
+    assert r1 == r2 == r3, "Temperature=0 must result in the same output for the same prompt regardless of seed."
+
+
+def test_max_tokens_respected():
+    """Generation stops at max_tokens limit."""
+    model = MockModel()
+    engine = Engine(model, ByteTokenizer())
+    prompt = [261, 72, 101, 108, 108, 111]
+
+    for max_tokens in [1, 4, 16, 64]:
+        results, _ = engine.generate_batch(prompt, max_tokens=max_tokens)
+        num_generated_tokens = len(results[0]) - len(prompt)
+        assert num_generated_tokens <= max_tokens, f"Generated {num_generated_tokens} tokens, expected max_tokens={max_tokens} or less."
+
+
+def test_num_samples_count():
+    """num_samples=N produces exactly N sequences."""
+    model = MockModel()
+    engine = Engine(model, ByteTokenizer())
+    prompt = [261, 72, 101, 108, 108, 111]
+
+    for num_samples in [1, 4, 16, 64]:
+        results, _ = engine.generate_batch(prompt, num_samples=num_samples, max_tokens=3)
+        assert len(results) == num_samples, f"Expected {num_samples} sequences from {num_samples} samples, got {len(results)}"
+
+
+def test_different_seeds_introduce_variation_when_temperature_nonzero():
+    """With temperature > 0, different seeds should introduce sampling variation."""
+    model = MockModel()
+    engine = Engine(model, ByteTokenizer())
+    prompt = [261, 72, 101, 108, 108, 111]  # <bos> + "Hello"
+
+    outputs = set()
+
+    for seed in [1, 42, 123, 999, 1000, 1001, 1002, 1003, 1004, 1005]:
+        results, _ = engine.generate_batch(
+            prompt,
+            temperature=1.0,
+            max_tokens=5,
+            seed=seed,
+        )
+        outputs.add(tuple(results[0]))
+
+    # Sanity check: sampling actually introduces variation
+    assert len(outputs) > 1, "All seeds produced the same output which is statistically highly improbable."