fix: report failed rollout requests

- Return structured finish and error reasons for synchronous generation
- Reject failed online rollout batches instead of training on empty responses
- Verify allocation and extension failures release metrics and KV state

tests/inference/test_scheduler.py

@@ -8,7 +8,7 @@ import pytest
 import torch
 
 from astrai.extension import CudaBackend, TorchNativeBackend, get_backend
-from astrai.inference import InferenceScheduler
+from astrai.inference import GenerationResult, InferenceScheduler
 from astrai.inference.metrics import MetricsCollector
 from astrai.inference.runtime.executor import DecodeSteadyState, Executor
 from astrai.inference.task import Task
@@ -372,6 +372,74 @@ def test_run_batch_too_long_prompt_skipped(device):
         scheduler.stop()
 
 
+def test_run_batch_details_distinguish_rejection_from_success(device):
+    scheduler, _tok, _model = _make_real_scheduler(device)
+    try:
+        long_prompt = list(range(100))
+        results = scheduler.run_batch(
+            [long_prompt, [10, 20]],
+            max_tokens=2,
+            temperature=0,
+            return_logprobs=True,
+            return_details=True,
+        )
+
+        assert results[0] == GenerationResult(
+            token_ids=[],
+            logprobs=[],
+            finish_reason="rejected",
+            error_reason="prompt_too_long",
+        )
+        assert results[1].finish_reason in ("stop", "length")
+        assert results[1].error_reason is None
+        assert len(results[1].token_ids) == len(results[1].logprobs)
+    finally:
+        scheduler.stop()
+
+
+def test_run_batch_details_report_non_positive_max_tokens(device):
+    scheduler, _tok, _model = _make_real_scheduler(device)
+    try:
+        result = scheduler.run_batch([[10, 20]], max_tokens=0, return_details=True)[0]
+        assert result.finish_reason == "rejected"
+        assert result.error_reason == "max_tokens_non_positive"
+    finally:
+        scheduler.stop()
+
+
+def test_run_batch_details_report_allocation_failure(device):
+    scheduler, _tok, _model = _make_real_scheduler(device)
+    try:
+        with patch.object(scheduler._task_cache, "task_alloc", return_value=False):
+            result = scheduler.run_batch([[10, 20]], max_tokens=2, return_details=True)[
+                0
+            ]
+        assert result.finish_reason == "rejected"
+        assert result.error_reason == "kv_cache_allocation_failed"
+    finally:
+        scheduler.stop()
+
+
+def test_run_batch_details_report_extension_failure_and_cleanup(device):
+    scheduler, _tok, _model = _make_real_scheduler(device)
+    try:
+        with patch.object(
+            scheduler,
+            "_step",
+            side_effect=lambda tasks, **_kwargs: ([], list(tasks)),
+        ):
+            result = scheduler.run_batch([[10, 20]], max_tokens=2, return_details=True)[
+                0
+            ]
+
+        assert result.finish_reason == "rejected"
+        assert result.error_reason == "kv_cache_extension_failed"
+        assert scheduler._task_cache._states == {}
+        assert scheduler._metrics._timings == {}
+    finally:
+        scheduler.stop()
+
+
 def test_decode_does_not_reuse_previous_batch_state():
     executor = object.__new__(Executor)
     executor.device = torch.device("cpu")