Done

Signed-off-by: Jee Jee Li <pandaleefree@gmail.com>

docker/Dockerfile.rocm

@@ -124,10 +124,10 @@ COPY --from=build_vllm ${COMMON_WORKDIR}/vllm/vllm/v1 /vllm_v1
 
 # RIXL/UCX build stages
 FROM base AS build_rixl
-ARG RIXL_BRANCH="f33a5599"
+ARG RIXL_BRANCH="bf4a7214"
 ARG RIXL_REPO="https://github.com/ROCm/RIXL.git"
-ARG UCX_BRANCH="da3fac2a"
-ARG UCX_REPO="https://github.com/ROCm/ucx.git"
+ARG UCX_BRANCH="7009d7a1"
+ARG UCX_REPO="https://github.com/openucx/ucx.git"
 ENV ROCM_PATH=/opt/rocm
 ENV UCX_HOME=/usr/local/ucx
 ENV RIXL_HOME=/usr/local/rixl
@@ -165,7 +165,7 @@ RUN cd /usr/local/src && \
         --disable-doxygen-doc \
         --enable-optimizations \
         --enable-devel-headers \
-        --with-rocm=/opt/rocm \
+        --with-rocm=${ROCM_PATH} \
         --with-verbs \
         --with-dm \
         --enable-mt && \
@@ -186,7 +186,12 @@ RUN git clone ${RIXL_REPO} /opt/rixl && \
     ninja install
 
 # Generate RIXL wheel
-RUN cd /opt/rixl && mkdir -p /app/install && \
+# Exclude libcore and libpull from auditwheel: transitive dependencies
+# that are not shipped in the wheel and vary across base images.
+RUN cd /opt/rixl && \
+    sed -i "s/--exclude 'libamdhip64\*'/--exclude 'libamdhip64*' --exclude 'libcore*' --exclude 'libpull*'/" \
+        contrib/build-wheel.sh && \
+    mkdir -p /app/install && \
     ./contrib/build-wheel.sh \
         --output-dir /app/install \
         --rocm-dir ${ROCM_PATH} \
@@ -431,6 +436,10 @@ COPY --from=export_vllm /vllm_v1 /usr/local/lib/python${PYTHON_VERSION}/dist-pac
 ENV MIOPEN_DEBUG_CONV_DIRECT=0
 ENV MIOPEN_DEBUG_CONV_GEMM=0
 
+# Use legacy IPC mode for HSA to avoid GPU memory pinning issues with UCX rocm_ipc
+# See: https://github.com/ROCm/rocm-libraries/issues/6266
+ENV HSA_ENABLE_IPC_MODE_LEGACY=1
+
 # Source code is used in the `python_only_compile.sh` test
 # We hide it inside `src/` so that this source code
 # will not be imported by other tests

tests/entrypoints/openai/completion/test_shutdown.py

@@ -311,7 +311,7 @@ async def test_abort_timeout_exits_quickly(wait_for_engine_idle: float):
             pytest.fail("Process did not exit after SIGTERM with abort timeout")
 
         exit_time = time.time() - start_time
-        assert exit_time < 2, f"Default shutdown took too long: {exit_time:.1f}s"
+        assert exit_time < 2.1, f"Default shutdown took too long: {exit_time:.1f}s"
         assert proc.returncode in (0, -15, None), f"Unexpected: {proc.returncode}"
 
         await _assert_children_cleaned_up(child_pids)

tests/lora/test_gptoss_tp.py

@@ -125,7 +125,7 @@ def test_gpt_oss_lora_tp2(
             enable_lora=True,
             max_loras=2,
             max_num_seqs=2,
-            max_num_batched_tokens=2048,
+            max_num_batched_tokens=1024,
             tensor_parallel_size=2,
             gpu_memory_utilization=0.8,
             fully_sharded_loras=fully_sharded_loras,

tests/v1/kv_connector/unit/test_rixl_gpu_mem_diag.py

@@ -0,0 +1,222 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Verify that GPU memory is fully released after RixlConnector shutdown on ROCm.
+
+Regression test for ROCm/ucx#33: UCX rocm_ipc transport permanently pinned
+GPU memory via hsa_amd_ipc_memory_create during ucp_mem_map, causing
+GPU memory to be unrecoverable after engine shutdown.
+"""
+
+import gc
+
+import pytest
+import torch
+
+from vllm.platforms import current_platform
+
+pytestmark = pytest.mark.skipif(
+    not current_platform.is_rocm(),
+    reason="ROCm platform required",
+)
+
+
+def _mb(b: int) -> float:
+    return b / (1024 * 1024)
+
+
+def _gpu_snapshot(tag: str, prev_alloc: float = 0.0) -> dict:
+    """Print and return current GPU memory stats."""
+    torch.accelerator.synchronize()
+    alloc = torch.accelerator.memory_allocated()
+    reserved = torch.accelerator.memory_reserved()
+    # mem_get_info is not available on torch.accelerator
+    try:
+        drv_free, drv_total = torch.cuda.mem_get_info()
+        drv_used = drv_total - drv_free
+        drv_pct = drv_used / drv_total * 100
+    except Exception:
+        drv_used = drv_total = drv_pct = 0
+    alloc_mb = _mb(alloc)
+    drv_used_mb = _mb(drv_used)
+    delta = alloc_mb - prev_alloc
+    print(
+        f"  {tag:<40s} | {alloc_mb:>9.1f} alloc | "
+        f"{_mb(reserved):>9.1f} rsrvd | "
+        f"{drv_used_mb:>9.1f} driver ({drv_pct:.1f}%) | "
+        f"delta {delta:>+9.1f}"
+    )
+    return {
+        "tag": tag,
+        "alloc_mb": alloc_mb,
+        "drv_used_mb": drv_used_mb,
+        "drv_pct": drv_pct,
+    }
+
+
+def _full_gpu_cleanup():
+    """gc.collect + torch empty_cache, multiple rounds."""
+    gc.unfreeze()
+    for _ in range(3):
+        if gc.collect() == 0:
+            break
+    torch.accelerator.empty_cache()
+
+
+@pytest.mark.parametrize("model_name, sw_size", [("google/gemma-3-1b-it", 512)])
+def test_gpu_memory_rixl_hma(model_name, sw_size):
+    """Track GPU memory through NixlConnector create/infer/shutdown cycle."""
+    from vllm import LLM, SamplingParams
+    from vllm.config import KVTransferConfig
+    from vllm.distributed.parallel_state import cleanup_dist_env_and_memory
+
+    llm_kwargs = {
+        "model": model_name,
+        "enforce_eager": True,
+        "gpu_memory_utilization": 0.5,
+        "kv_transfer_config": KVTransferConfig(
+            kv_connector="NixlConnector",
+            kv_role="kv_both",
+        ),
+        "max_model_len": 2048,
+        "disable_hybrid_kv_cache_manager": False,
+        "max_num_batched_tokens": 1024,
+        "enable_prefix_caching": False,
+        "block_size": 16,
+    }
+
+    print("\n" + "=" * 90)
+    print("GPU MEMORY -- RIXL NixlConnector HMA (ROCm)")
+    print("=" * 90)
+    gc.collect()
+    torch.accelerator.empty_cache()
+    torch.accelerator.reset_peak_memory_stats()
+    snap0 = _gpu_snapshot("0. baseline", 0.0)
+
+    # create + infer
+    llm = LLM(**llm_kwargs)
+    snap1 = _gpu_snapshot("1. after LLM()", snap0["alloc_mb"])
+
+    llm.generate(
+        ["hi" * 1401],
+        SamplingParams(
+            temperature=0.0,
+            max_tokens=1,
+            extra_args={
+                "kv_transfer_params": {
+                    "do_remote_decode": True,
+                    "do_remote_prefill": False,
+                    "remote_engine_id": None,
+                    "remote_block_ids": None,
+                    "remote_host": None,
+                    "remote_port": None,
+                }
+            },
+        ),
+    )
+    snap2 = _gpu_snapshot("2. after generate()", snap1["alloc_mb"])
+
+    # shutdown + cleanup
+    print("\n--- shutdown ---")
+    llm.llm_engine.engine_core.shutdown()
+    _gpu_snapshot("3. after shutdown()", snap2["alloc_mb"])
+
+    del llm
+    _full_gpu_cleanup()
+    cleanup_dist_env_and_memory()
+    _full_gpu_cleanup()
+    torch._dynamo.reset()
+    gc.collect()
+    torch.accelerator.empty_cache()
+    snap_final = _gpu_snapshot("4. final", snap2["alloc_mb"])
+
+    # summary
+    print("\n" + "=" * 90)
+    baseline = snap0["alloc_mb"]
+    final = snap_final["alloc_mb"]
+    peak = snap2["alloc_mb"]
+    total_alloc = peak - baseline
+
+    print(
+        f"  PyTorch:  baseline={baseline:.0f}  peak={peak:.0f}  "
+        f"final={final:.0f}  "
+        f"leaked={final - baseline:.0f} MB"
+        + (
+            f" ({(final - baseline) / total_alloc * 100:.1f}%)"
+            if total_alloc > 0
+            else ""
+        )
+    )
+
+    drv_base = snap0["drv_used_mb"]
+    drv_final = snap_final["drv_used_mb"]
+    drv_leaked = drv_final - drv_base
+    print(
+        f"  Driver:   baseline={drv_base:.0f} ({snap0['drv_pct']:.1f}%)  "
+        f"peak={snap2['drv_used_mb']:.0f} ({snap2['drv_pct']:.1f}%)  "
+        f"final={drv_final:.0f} ({snap_final['drv_pct']:.1f}%)  "
+        f"leaked={drv_leaked:.0f} MB"
+    )
+    print("=" * 90)
+
+    # Peak driver memory used above baseline
+    drv_peak = snap2["drv_used_mb"] - drv_base
+    leak_pct = (drv_leaked / drv_peak * 100) if drv_peak > 0 else 0
+    max_leak_pct = 10
+    assert leak_pct <= max_leak_pct, (
+        f"{drv_leaked:.0f} MB ({leak_pct:.1f}%) of driver-level GPU memory "
+        f"not freed after NixlConnector shutdown "
+        f"(peak allocation: {drv_peak:.0f} MB, threshold: {max_leak_pct}%)"
+    )
+
+
+@pytest.mark.parametrize("model_name", ["google/gemma-3-1b-it"])
+def test_gpu_memory_no_rixl_baseline(model_name):
+    """Same workload without NixlConnector.  Comparing driver-level memory
+    between this and test_gpu_memory_rixl_hma isolates UCX/RIXL impact."""
+    from vllm import LLM, SamplingParams
+    from vllm.distributed.parallel_state import cleanup_dist_env_and_memory
+
+    print("\n" + "=" * 90)
+    print("CONTROL -- same model, no RIXL connector")
+    print("=" * 90)
+    gc.collect()
+    torch.accelerator.empty_cache()
+    snap0 = _gpu_snapshot("baseline", 0.0)
+
+    llm = LLM(
+        model=model_name,
+        enforce_eager=True,
+        gpu_memory_utilization=0.5,
+        max_model_len=2048,
+        max_num_batched_tokens=1024,
+        enable_prefix_caching=False,
+        block_size=16,
+    )
+    _gpu_snapshot("after LLM()", snap0["alloc_mb"])
+
+    llm.generate(["hi " * 500], SamplingParams(max_tokens=1))
+    snap_peak = _gpu_snapshot("after generate()", snap0["alloc_mb"])
+
+    llm.llm_engine.engine_core.shutdown()
+    del llm
+    _full_gpu_cleanup()
+    cleanup_dist_env_and_memory()
+    _full_gpu_cleanup()
+    torch._dynamo.reset()
+    gc.collect()
+    torch.accelerator.empty_cache()
+    snap_final = _gpu_snapshot("final", snap0["alloc_mb"])
+
+    drv_base = snap0["drv_used_mb"]
+    drv_leaked = snap_final["drv_used_mb"] - drv_base
+    drv_peak = snap_peak["drv_used_mb"] - drv_base
+    print(f"\n  Driver leaked (no rixl): {drv_leaked:.0f} MB")
+    print("=" * 90)
+
+    leak_pct = (drv_leaked / drv_peak * 100) if drv_peak > 0 else 0
+    max_leak_pct = 10
+    assert leak_pct <= max_leak_pct, (
+        f"{drv_leaked:.0f} MB ({leak_pct:.1f}%) of driver-level GPU memory "
+        f"not freed after baseline shutdown "
+        f"(peak allocation: {drv_peak:.0f} MB, threshold: {max_leak_pct}%)"
+    )

tests/v1/kv_offload/test_cpu_offloading.py

@@ -87,10 +87,13 @@ class MockSubscriber:
 def _wait_for_prefix_cache_reset(llm: LLM) -> None:
     """Wait for async offload transfers to finish so prefix cache can reset.
 
-    The GPU-to-CPU offload runs on a CUDA stream asynchronously.  While blocks
+    The GPU-to-CPU offload runs on a CUDA stream asynchronously. While blocks
     are still held by the offload worker, ``reset_prefix_cache`` returns
-    ``False``.  Retry with a short sleep until it succeeds or we time out.
+    ``False``. Between retries we send a dummy single-token prefill to force
+    the engine to step, which polls the worker for completed transfers and
+    frees GPU blocks.
     """
+    _dummy_params = SamplingParams(max_tokens=1)
     deadline = time.monotonic() + _RESET_CACHE_TIMEOUT
     while not llm.reset_prefix_cache():
         if time.monotonic() > deadline:
@@ -98,7 +101,13 @@ def _wait_for_prefix_cache_reset(llm: LLM) -> None:
                 "reset_prefix_cache did not succeed within "
                 f"{_RESET_CACHE_TIMEOUT}s - async offload may be stuck"
             )
-        time.sleep(0.1)
+        # Force an engine step so the scheduler polls get_finished()
+        # and releases GPU blocks held by in-flight async stores.
+        llm.generate(
+            [TokensPrompt(prompt_token_ids=[0])],
+            _dummy_params,
+            use_tqdm=False,
+        )
 
 
 def _latency_test(llm: LLM, subscriber: MockSubscriber):

vllm/utils/func_utils.py

@@ -45,16 +45,14 @@ def run_once(f: Callable[P, None]) -> Callable[P, None]:
 
 
 @lru_cache
-def supports_kw(
+def _supports_kw(
     callable: Callable[..., object],
     kw_name: str,
     *,
     requires_kw_only: bool = False,
     allow_var_kwargs: bool = True,
 ) -> bool:
-    """Check if a keyword is a valid kwarg for a callable; if requires_kw_only
-    disallows kwargs names that can also be positional arguments.
-    """
+    """Internal cached implementation of supports_kw."""
     params = inspect.signature(callable).parameters
     if not params:
         return False
@@ -99,6 +97,29 @@ def supports_kw(
     return False
 
 
+def supports_kw(
+    callable: Callable[..., object],
+    kw_name: str,
+    *,
+    requires_kw_only: bool = False,
+    allow_var_kwargs: bool = True,
+) -> bool:
+    """Check if a keyword is a valid kwarg for a callable; if requires_kw_only
+    disallows kwargs names that can also be positional arguments.
+    """
+    # Unwrap bound methods so that the lru_cache key is the underlying
+    # function, not the instance. Caching bound methods pins the object
+    # (and all its GPU tensors) for the lifetime of the cache.
+    if hasattr(callable, "__func__"):
+        callable = callable.__func__
+    return _supports_kw(
+        callable,
+        kw_name,
+        requires_kw_only=requires_kw_only,
+        allow_var_kwargs=allow_var_kwargs,
+    )
+
+
 def get_allowed_kwarg_only_overrides(
     callable: Callable[..., object],
     overrides: Mapping[str, object] | None,

vllm/v1/engine/core.py

@@ -1,5 +1,6 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import gc
 import os
 import queue
 import signal
@@ -574,6 +575,12 @@ class EngineCore:
         if self.scheduler:
             self.scheduler.shutdown()
 
+        # Undo the gc.freeze() from __init__ so that the objects allocated
+        # during engine startup (model weights, KV caches, etc.) become
+        # visible to the garbage collector again. Without this, deleting
+        # the engine in-process (e.g. unit tests) leaks GPU memory.
+        gc.unfreeze()
+
     def profile(self, is_start: bool = True, profile_prefix: str | None = None):
         self.model_executor.profile(is_start, profile_prefix)
 

vllm/v1/worker/gpu_model_runner.py

@@ -5886,6 +5886,20 @@ class GPUModelRunner(
                 gc.unfreeze()
                 gc.collect()
 
+    def shutdown(self) -> None:
+        """Release GPU tensors (model weights, KV caches, workspace) so that
+        memory is reclaimable when running in the same process."""
+        from vllm.model_executor.layers.rotary_embedding import _ROPE_DICT
+        from vllm.v1.worker.workspace import reset_workspace_manager
+
+        # Calls torch.accelerator.synchronize()
+        self._cleanup_profiling_kv_cache()
+        self.compilation_config.static_forward_context.clear()
+        self.model = None  # type: ignore[assignment]
+        _ROPE_DICT.clear()
+
+        reset_workspace_manager()
+
     def _cleanup_profiling_kv_cache(self) -> None:
         torch.accelerator.synchronize()
         if hasattr(self, "kv_caches") and self.kv_caches:

vllm/v1/worker/gpu_worker.py

@@ -1015,6 +1015,11 @@ class Worker(WorkerBase):
         if weight_transfer_engine := getattr(self, "weight_transfer_engine", None):
             weight_transfer_engine.shutdown()
 
+        # Release GPU resources held by the model runner so that memory
+        # can be reclaimed when running in-process
+        if model_runner := getattr(self, "model_runner", None):
+            model_runner.shutdown()
+
     def elastic_ep_execute(self, execute_method: str, *args, **kwargs):
         return self.elastic_ep_executor.execute(execute_method, *args, **kwargs)