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[CI/Build] Fix AMD CI: test_cpu_gpu.py #27388

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Merged
yeqcharlotte merged 3 commits into from
Oct 23, 2025
Merged

[CI/Build] Fix AMD CI: test_cpu_gpu.py #27388

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d36420d
update tests
zhewenl Oct 23, 2025
834e042
update logic to use current_platform
zhewenl Oct 23, 2025
f8a9123
update
zhewenl Oct 23, 2025
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29 changes: 25 additions & 4 deletions tests/v1/kv_offload/test_cpu_gpu.py
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Original file line number Diff line number Diff line change
Expand Up @@ -8,11 +8,29 @@

from vllm.platforms import current_platform
from vllm.v1.attention.backends.flash_attn import FlashAttentionBackend
from vllm.v1.attention.backends.flashinfer import FlashInferBackend
from vllm.v1.attention.backends.mla.flashattn_mla import FlashAttnMLABackend
from vllm.v1.kv_offload.mediums import CPULoadStoreSpec, GPULoadStoreSpec
from vllm.v1.kv_offload.worker.cpu_gpu import CpuGpuOffloadingHandler

BACKENDS_TO_TEST = [FlashAttentionBackend]

if current_platform.is_cuda():
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@DarkLight1337 DarkLight1337 Oct 23, 2025

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Let's use the previous logic for all non-ROCm platforms, to avoid changing the logic prior to this PR

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@zhewenl zhewenl Oct 23, 2025

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@DarkLight1337 sounds good, updated - since I am working on checking AMD CI in general, I wonder if we want to use current_platform to gate CUDA specific kernels/features? The context is I found there are many tests with assumption that they will be running on CUDA platforms, thus these test are failing on AMD.

For example, ROCM uses TritonAttentionImpl as default, which doesn't support models with encoder(like openai/whisper)

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@DarkLight1337 DarkLight1337 Oct 23, 2025

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cc @LucasWilkinson

from vllm.v1.attention.backends.flashinfer import FlashInferBackend

BACKENDS_TO_TEST.append(FlashInferBackend)

from vllm.v1.attention.backends.mla.flashattn_mla import FlashAttnMLABackend

BACKENDS_TO_TEST.append(FlashAttnMLABackend)

if current_platform.is_rocm():
from vllm.v1.attention.backends.rocm_attn import RocmAttentionBackend

BACKENDS_TO_TEST.append(RocmAttentionBackend)

from vllm.v1.attention.backends.mla.triton_mla import TritonMLABackend

BACKENDS_TO_TEST.append(TritonMLABackend)

NUM_GPU_BLOCKS = [64]
NUM_CPU_BLOCKS = [256]
GPU_BLOCK_SIZES = [16]
Expand All @@ -26,6 +44,10 @@
NUM_MAPPINGS = [3]


@pytest.mark.skipif(
len(BACKENDS_TO_TEST) < 2,
reason="Need at least 2 backends to test heterogeneous KV cache layouts",
)
@pytest.mark.parametrize("gpu_to_cpu", [True, False])
@pytest.mark.parametrize("num_mappings", NUM_MAPPINGS)
@pytest.mark.parametrize("head_size", HEAD_SIZES)
Expand Down Expand Up @@ -55,8 +77,7 @@ def test_transfer(
) -> None:
current_platform.seed_everything(seed)

# create per-layer GPU KV caches
attn_backends_list = [FlashAttentionBackend, FlashInferBackend, FlashAttnMLABackend]
attn_backends_list = BACKENDS_TO_TEST

gpu_caches = {}
attn_backends = {}
Expand Down