[CI][ROCm] Fix AMD nightly distributed regressions (#50304)
Signed-off-by: Andreas Karatzas <Andreas.Karatzas@amd.com>
Andreas Karatzas committed
Jul 29, 2026 at 17:02 UTC
fa2a2589bd2a1fce0851df7fd42ffb54b6195f04
2 files changed
+9
-117
tests/distributed/test_custom_all_reduce.py
-117
@@ -2,8 +2,6 @@
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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import random
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-from types import SimpleNamespace
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-from unittest.mock import Mock
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import pytest
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import ray
@@ -11,8 +9,6 @@ import torch
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import torch.distributed as dist
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from vllm.distributed.communication_op import tensor_model_parallel_all_reduce # noqa
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-from vllm.distributed.device_communicators import custom_all_reduce
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-from vllm.distributed.device_communicators.custom_all_reduce import CustomAllreduce
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from vllm.distributed.parallel_state import get_tp_group, graph_capture
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from ..utils import (
@@ -27,59 +23,6 @@ for i, v in enumerate(test_sizes):
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test_sizes[i] -= v % 8
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-def test_sp16_dispatches_only_to_mnnvl_lamport(
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- monkeypatch: pytest.MonkeyPatch,
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-):
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- """SP16 uses the MNNVL Lamport kernels and rejects same-host dispatch."""
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- comm = CustomAllreduce.__new__(CustomAllreduce)
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- comm.disabled = False
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- comm.world_size = 16
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- comm.fully_connected = False
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- comm.mnnvl_only = True
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- comm._IS_CAPTURING = False
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- comm.max_mnnvl_all_gather_size = 2 * 1024 * 1024
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- comm.max_mnnvl_reduce_scatter_size = 16 * 1024 * 1024
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- comm.mnnvl_multicast_ptr = 1
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- comm.mnnvl_lamport_ag_local_ptr = 1
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- comm.mnnvl_lamport_ag_multicast_ptr = 1
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- comm.mnnvl_lamport_ag_epoch_ptr = 1
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- comm.mnnvl_lamport_rs_local_ptr = 1
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- comm.mnnvl_lamport_rs_epoch_ptr = 1
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- comm.mnnvl_buffer_size = 32 * 1024 * 1024
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- comm._ptr = 0
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-
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- lamport_all_gather = Mock()
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- lamport_reduce_scatter = Mock()
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- monkeypatch.setattr(custom_all_reduce.current_platform, "is_cuda", lambda: True)
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- monkeypatch.setattr(
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- custom_all_reduce,
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- "ops",
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- SimpleNamespace(
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- mnnvl_lamport_all_gather=lamport_all_gather,
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- mnnvl_lamport_reduce_scatter=lamport_reduce_scatter,
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- ),
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- )
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-
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- gathered = comm.custom_all_gather(torch.empty((8, 8), dtype=torch.bfloat16))
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- scattered = comm.custom_reduce_scatter(torch.empty((16, 8), dtype=torch.bfloat16))
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-
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- assert gathered is not None
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- assert scattered is not None
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- lamport_all_gather.assert_called_once()
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- lamport_reduce_scatter.assert_called_once()
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- assert not comm.should_custom_ar(torch.empty(8, dtype=torch.bfloat16))
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- assert not comm.should_custom_all_gather(torch.empty((8, 8), dtype=torch.int32))
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- assert not comm.should_custom_all_gather(
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- torch.empty((131073, 8), dtype=torch.bfloat16)
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- )
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-
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- comm.mnnvl_only = False
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- assert not comm.should_custom_all_gather(torch.empty((8, 8), dtype=torch.bfloat16))
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- assert not comm.should_custom_reduce_scatter(
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- torch.empty((16, 8), dtype=torch.bfloat16)
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- )
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-
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-
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@ray.remote(num_gpus=1, max_calls=1)
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def graph_allreduce(
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monkeypatch: pytest.MonkeyPatch,
@@ -137,32 +80,6 @@ def graph_allreduce(
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torch.testing.assert_close(out1, inp1)
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torch.testing.assert_close(out2, inp2)
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- fa = get_tp_group().device_communicator.ca_comm
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- tp_rank = rank % tp_size
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- with graph_capture(device=device) as graph_capture_context:
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- local = torch.full(
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- (512, 4096), tp_rank + 1, dtype=torch.bfloat16, device=device
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- )
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- reduce_input = torch.full(
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- (512 * tp_size, 4096),
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- tp_rank + 1,
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- dtype=torch.bfloat16,
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- device=device,
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- )
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- graph = torch.cuda.CUDAGraph()
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- with torch.cuda.graph(graph, stream=graph_capture_context.stream):
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- gathered = fa.custom_all_gather(local)
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- scattered = fa.custom_reduce_scatter(reduce_input)
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- graph.replay()
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- assert gathered is not None
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- assert scattered is not None
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- expected_gather = torch.cat(
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- [torch.full_like(local, peer_rank + 1) for peer_rank in range(tp_size)]
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- )
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- expected_scatter = torch.full_like(local, tp_size * (tp_size + 1) // 2)
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- torch.testing.assert_close(gathered, expected_gather)
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- torch.testing.assert_close(scattered, expected_scatter)
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-
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@ray.remote(num_gpus=1, max_calls=1)
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def eager_allreduce(
@@ -193,29 +110,6 @@ def eager_allreduce(
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out = fa.all_reduce(out, registered=False)
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torch.testing.assert_close(out, inp * (tp_size**num_communication))
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- group = get_tp_group().device_group
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- tp_rank = rank % tp_size
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- for dtype in [torch.float32, torch.float16, torch.bfloat16]:
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- local = torch.full((64, 4096), tp_rank + 1, dtype=dtype, device=device)
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- expected_gather = torch.empty(
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- (64 * tp_size, 4096), dtype=dtype, device=device
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- )
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- dist.all_gather_into_tensor(expected_gather, local, group=group)
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- gathered = fa.custom_all_gather(local)
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- assert gathered is not None
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- torch.testing.assert_close(gathered, expected_gather)
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-
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- reduce_input = torch.full(
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- (64 * tp_size, 4096), tp_rank + 1, dtype=dtype, device=device
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- )
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- expected_scatter = torch.empty((64, 4096), dtype=dtype, device=device)
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- dist.reduce_scatter_tensor(
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- expected_scatter, reduce_input.clone(), group=group
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- )
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- scattered = fa.custom_reduce_scatter(reduce_input)
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- assert scattered is not None
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- torch.testing.assert_close(scattered, expected_scatter)
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-
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inp = torch.ones(sz * 4, dtype=torch.bfloat16, device=device)
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out = inp
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for _ in range(num_communication):
@@ -236,14 +130,3 @@ def test_custom_allreduce(
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if world_size > torch.accelerator.device_count():
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pytest.skip("Not enough GPUs to run the test.")
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multi_process_parallel(monkeypatch, tp_size, pipeline_parallel_size, test_target)
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-
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-
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-@pytest.mark.parametrize("test_target", [eager_allreduce, graph_allreduce])
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-def test_custom_collectives_world_size_four(
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- monkeypatch: pytest.MonkeyPatch,
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- test_target,
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-):
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- """Exercise the four-rank kernel specialization used by Kimi SP."""
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- if torch.accelerator.device_count() < 4:
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- pytest.skip("Not enough GPUs to run the test.")
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- multi_process_parallel(monkeypatch, 4, 1, test_target)
tests/v1/shutdown/test_delete.py
+9
@@ -132,4 +132,13 @@ def test_llm_delete_inprocess(
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wait_for_gpu_memory_to_clear(
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devices=[0],
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threshold_bytes=SHUTDOWN_TEST_THRESHOLD_BYTES,
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+ # Activate the helper's ROCm idle-runtime floor. VllmRunner already
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+ # performed the stable-memory wait during context exit.
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+ threshold_ratio=0.01 if current_platform.is_rocm() else None,
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+ # Fail in the child before the outer pytest timeout expires.
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+ timeout_s=(
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+ SHUTDOWN_TEST_TIMEOUT_SEC // 2
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+ if current_platform.is_rocm()
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+ else SHUTDOWN_TEST_TIMEOUT_SEC
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+ ),
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)