[ROCm][CI] Add quantized MoE serving test for gfx950 (#58748)

Signed-off-by: Stefan Koncarevic <Stefan.Koncarevic@amd.com> Co-authored-by: Andreas Karatzas <Andreas.Karatzas@amd.com> Co-authored-by: Andreas Karatzas <akaratza@amd.com>
2026-09-25 18:02:15 +00:00
parent b21757555a
commit 1b922ffcbe
3 changed files with 206 additions and 4 deletions
@@ -2579,7 +2579,7 @@ steps:
  - unset VLLM_USE_V2_MODEL_RUNNER
  - uv pip install --system torchao==0.18.0
  - uv pip install --system conch-triton-kernels
-  - VLLM_TEST_FORCE_LOAD_FORMAT=auto pytest -v -s quantization/ --ignore quantization/test_blackwell_moe.py --shard-id=$$BUILDKITE_PARALLEL_JOB --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT
+  - VLLM_TEST_FORCE_LOAD_FORMAT=auto pytest -v -s quantization/ --ignore quantization/test_blackwell_moe.py --ignore quantization/test_rocm_moe.py --shard-id=$$BUILDKITE_PARALLEL_JOB --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT
  - if [ "$$BUILDKITE_PARALLEL_JOB" = "0" ]; then pytest -v -s rocm/test_moe_weight_replay.py; fi

 #---------------------------------------------------------  mi300 · samplers  ----------------------------------------------------------#
@@ -4943,7 +4943,7 @@ steps:
  - unset VLLM_USE_V2_MODEL_RUNNER
  - uv pip install --system torchao==0.17.0
  - uv pip install --system conch-triton-kernels
-  - VLLM_TEST_FORCE_LOAD_FORMAT=auto pytest -v -s quantization/ --ignore quantization/test_blackwell_moe.py --shard-id=$$BUILDKITE_PARALLEL_JOB --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT
+  - VLLM_TEST_FORCE_LOAD_FORMAT=auto pytest -v -s quantization/ --ignore quantization/test_blackwell_moe.py --ignore quantization/test_rocm_moe.py --shard-id=$$BUILDKITE_PARALLEL_JOB --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT
  - if [ "$$BUILDKITE_PARALLEL_JOB" = "0" ]; then pytest -v -s rocm/test_moe_weight_replay.py; fi

 # - label: Quantized MoE Test (B200-MI355) # TBD
@@ -5445,6 +5445,36 @@ steps:
  - pip install 'modelscope<1.38'
  - pytest -v -s test_regression.py

+- label: ':amd: (MI355) Quantized MoE'
+  timeout_in_minutes: 45
+  working_dir: /vllm-workspace/
+  source_file_dependencies:
+  - tests/quantization/test_rocm_moe.py
+  - tests/utils.py
+  - vllm/model_executor/models/deepseek_v2.py
+  - vllm/model_executor/models/qwen3_moe.py
+  - vllm/model_executor/models/gpt_oss.py
+  - vllm/model_executor/model_loader/
+  - vllm/model_executor/layers/fused_moe/
+  - vllm/model_executor/layers/quantization/
+  - vllm/model_executor/kernels/linear/
+  - vllm/v1/attention/
+  - vllm/v1/worker/gpu/
+  - vllm/_aiter_ops.py
+  - vllm/platforms/rocm.py
+  - vllm/envs.py
+  commands:
+  - pytest -v -s tests/quantization/test_rocm_moe.py
+  dind: false
+  agent_pool: mi355_1
+  num_gpus: 1
+  mirror_hardwares:
+  - amdexperimental
+  - amdproduction
+  - amdgfx950nightly
+  - amdmi355
+  optional: true
+
 - label: ':amd: (MI355) Fusion E2E TP2 AsyncTP BF16 Config Sweep'
  timeout_in_minutes: 30
  working_dir: /vllm-workspace/
@@ -19,7 +19,7 @@ steps:
  - uv pip install --system conch-triton-kernels
  # The SM90-only checkpoint currently contains a removed weight_chan_scale
  # parameter. It was not exercised by the previous L4 job.
-  - VLLM_TEST_FORCE_LOAD_FORMAT=auto pytest -v -s quantization/ --ignore quantization/test_blackwell_moe.py -k 'not test_compressed_tensors_w4a8_fp8' --shard-id=$$BUILDKITE_PARALLEL_JOB --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT
+  - VLLM_TEST_FORCE_LOAD_FORMAT=auto pytest -v -s quantization/ --ignore quantization/test_blackwell_moe.py --ignore quantization/test_rocm_moe.py -k 'not test_compressed_tensors_w4a8_fp8' --shard-id=$$BUILDKITE_PARALLEL_JOB --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT
  mirror:
    amd:
      label: ":amd: (MI355) Quantization Shard %N"
@@ -43,7 +43,7 @@ steps:
      - unset VLLM_USE_V2_MODEL_RUNNER
      - uv pip install --system torchao==0.17.0
      - uv pip install --system conch-triton-kernels
-      - VLLM_TEST_FORCE_LOAD_FORMAT=auto pytest -v -s quantization/ --ignore quantization/test_blackwell_moe.py --shard-id=$$BUILDKITE_PARALLEL_JOB --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT
+      - VLLM_TEST_FORCE_LOAD_FORMAT=auto pytest -v -s quantization/ --ignore quantization/test_blackwell_moe.py --ignore quantization/test_rocm_moe.py --shard-id=$$BUILDKITE_PARALLEL_JOB --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT
      - if [ "$$BUILDKITE_PARALLEL_JOB" = "0" ]; then pytest -v -s rocm/test_moe_weight_replay.py; fi

 - label: ":nvidia: (H200 MIG 35GB) Quantized Fusions"
@@ -76,6 +76,32 @@ steps:
  - vllm/v1/attention/backends/flashinfer.py
  commands:
    - pytest -s -v tests/quantization/test_blackwell_moe.py
+  mirror:
+    amd:
+      label: ":amd: (MI355) Quantized MoE"
+      dind: false
+      device: mi355_1
+      timeout_in_minutes: 45
+      working_dir: "/vllm-workspace/"
+      depends_on:
+      - image-build-amd
+      source_file_dependencies:
+      - tests/quantization/test_rocm_moe.py
+      - tests/utils.py
+      - vllm/model_executor/models/deepseek_v2.py
+      - vllm/model_executor/models/qwen3_moe.py
+      - vllm/model_executor/models/gpt_oss.py
+      - vllm/model_executor/model_loader/
+      - vllm/model_executor/layers/fused_moe/
+      - vllm/model_executor/layers/quantization/
+      - vllm/model_executor/kernels/linear/
+      - vllm/v1/attention/
+      - vllm/v1/worker/gpu/
+      - vllm/_aiter_ops.py
+      - vllm/platforms/rocm.py
+      - vllm/envs.py
+      commands:
+      - pytest -v -s tests/quantization/test_rocm_moe.py

 - label: ":nvidia: (H200 MIG 35GB) Quantized Models"
  device: h200_35gb
@@ -0,0 +1,146 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""ROCm equivalents of the quantized-MoE model startup/generation contract.
+
+Like test_blackwell_moe.py, these tests use reduced models and dummy weights.
+They validate model/backend integration, not trained-model accuracy.
+"""
+
+import math
+
+import pytest
+
+from tests.utils import RemoteOpenAIServer
+from vllm.platforms import current_platform
+from vllm.platforms.rocm import on_gfx950
+
+pytestmark = pytest.mark.skipif(
+    not current_platform.is_rocm() or not on_gfx950(),
+    reason="ROCm FP8/MXFP4 model integration requires gfx950",
+)
+
+COMMON_OVERRIDES = {
+    "num_hidden_layers": 4,
+    "hidden_size": 512,
+    "intermediate_size": 1024,
+    "num_attention_heads": 8,
+    "num_key_value_heads": 2,
+}
+
+CASES = [
+    pytest.param(
+        "RedHatAI/DeepSeek-Coder-V2-Lite-Instruct-FP8",
+        "efe1ced428db63e7ccbcf367334596f77e9af140",
+        "triton",
+        "TRITON_MLA",
+        {
+            "n_routed_experts": 8,
+            "n_shared_experts": 1,
+            "num_experts_per_tok": 2,
+            "moe_intermediate_size": 512,
+        },
+        ["--code-revision", "e434a23f91ba5b4923cf6c9d9a238eb4a08e3a11"],
+        id="deepseek-fp8-per-tensor-triton",
+    ),
+    pytest.param(
+        "Qwen/Qwen3-30B-A3B-FP8",
+        "d206ba732169f29bb77fbf80fc2c4b81d4d30782",
+        "triton",
+        "TRITON_ATTN",
+        {
+            "num_experts": 8,
+            "num_experts_per_tok": 2,
+            "moe_intermediate_size": 512,
+            "head_dim": 64,
+        },
+        [],
+        id="qwen3-fp8-block-triton",
+    ),
+    pytest.param(
+        "openai/gpt-oss-20b",
+        "6cee5e81ee83917806bbde320786a8fb61efebee",
+        "aiter",
+        "TRITON_ATTN",
+        {
+            "num_local_experts": 8,
+            "num_experts_per_tok": 2,
+            "experts_per_token": 2,
+            "layer_types": ["sliding_attention", "full_attention"] * 2,
+        },
+        [],
+        id="gptoss-mxfp4-aiter",
+    ),
+]
+
+
+@pytest.mark.parametrize("enforce_eager", [True, False], ids=["eager", "compiled"])
+@pytest.mark.parametrize(
+    "model,revision,moe_backend,attention_backend,overrides,extra_args", CASES
+)
+def test_rocm_quantized_moe_generation(
+    model,
+    revision,
+    moe_backend,
+    attention_backend,
+    overrides,
+    extra_args,
+    enforce_eager,
+):
+    """Quantized MoE models warm up and return finite logits through serving."""
+    args = [
+        "--revision",
+        revision,
+        "--load-format",
+        "dummy",
+        "--trust-remote-code",
+        "--dtype",
+        "bfloat16",
+        "--max-model-len",
+        "2048",
+        "--max-num-batched-tokens",
+        "256",
+        "--max-num-seqs",
+        "8",
+        "--gpu-memory-utilization",
+        "0.02",
+        "--kv-cache-memory-bytes",
+        str(256 * 1024 * 1024),
+        "--moe-backend",
+        moe_backend,
+        "--attention-backend",
+        attention_backend,
+        *extra_args,
+    ]
+    if enforce_eager:
+        args.append("--enforce-eager")
+
+    with RemoteOpenAIServer(
+        model,
+        args,
+        env_dict={"VLLM_ROCM_USE_AITER": "1", "VLLM_USE_V2_MODEL_RUNNER": "1"},
+        override_hf_configs={**COMMON_OVERRIDES, **overrides},
+        max_wait_seconds=1200,
+    ) as server:
+        prompts = [[1, 2, 3], [1, 2, 3, 4] * 4 + [5]]
+        completion = server.get_client().completions.create(
+            model=model,
+            prompt=prompts,
+            temperature=0,
+            max_tokens=2,
+            logprobs=1,
+            extra_body={"ignore_eos": True, "skip_special_tokens": False},
+        )
+        assert len(completion.choices) == len(prompts)
+        assert completion.usage is not None
+        assert completion.usage.prompt_tokens == sum(map(len, prompts))
+        assert completion.usage.completion_tokens == 2 * len(prompts)
+        for choice in completion.choices:
+            assert choice.finish_reason == "length"
+            assert choice.text
+            assert choice.logprobs is not None
+            assert choice.logprobs.token_logprobs is not None
+            assert len(choice.logprobs.token_logprobs) == 2
+            assert all(
+                p is not None and math.isfinite(p)
+                for p in choice.logprobs.token_logprobs
+            )