[ROCm][CI] Parametrize vision score tests across attention backends with...

[ROCm][CI] Parametrize vision score tests across attention backends with per-backend tolerances (#35571)
Signed-off-by: Andreas Karatzas <akaratza@amd.com>

tests/entrypoints/pooling/score/test_online_score_vision.py

 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
+import json
+
 import pytest
 import requests
 
 from tests.utils import VLLM_PATH, RemoteOpenAIServer
 from vllm.entrypoints.pooling.score.protocol import RerankResponse, ScoreResponse
 from vllm.multimodal.utils import encode_image_url, fetch_image
+from vllm.platforms import current_platform
 
 MODEL_NAME = "Qwen/Qwen3-VL-Reranker-2B"
 HF_OVERRIDES = {
@@ -15,6 +18,60 @@ HF_OVERRIDES = {
     "is_original_qwen3_reranker": True,
 }
 
+ROCM_ATTN_BACKENDS = [
+    "ROCM_ATTN",
+    "ROCM_AITER_FA",
+    "TRITON_ATTN",
+    "FLEX_ATTENTION",
+]
+
+ATTN_BACKENDS = ROCM_ATTN_BACKENDS if current_platform.is_rocm() else []
+
+# Per-backend tolerance with explicit entries; "default" is the fallback
+BACKEND_TOL: dict[str, float] = {
+    "default": 0.05,  # 5% tolerance for other backends (e.g. FLASH_ATTN)
+    # Relaxed tolerances for ROCm attn
+    # See: https://github.com/vllm-project/vllm/issues/35569
+    "ROCM_ATTN": 0.09,  # gfx950:~8.45%, gfx942:~3.70%
+    "ROCM_AITER_FA": 0.045,  # gfx950:~2.00%, gfx942:~0.80%
+    "TRITON_ATTN": 0.045,  # gfx950:~3.00%, gfx942:~2.20%
+    "FLEX_ATTENTION": 0.045,  # gfx950:~3.25%, gfx942:~1.10%
+}
+
+# ROCm: disable skinny GEMM to avoid non-deterministic results from
+# atomic reductions in wvSplitKrc kernel.
+# See: https://github.com/vllm-project/vllm/pull/33493#issuecomment-3906083975
+ROCM_ENV_OVERRIDES = (
+    {"VLLM_ROCM_USE_SKINNY_GEMM": "0"} if current_platform.is_rocm() else {}
+)
+# ROCm: disable prefix caching and eliminate batch variance to reduce
+# test flakiness.
+ROCM_EXTRA_ARGS = (
+    ["--no-enable-prefix-caching", "--max-num-seqs", "1"]
+    if current_platform.is_rocm()
+    else []
+)
+
+
+def get_tol(backend: str) -> float:
+    return BACKEND_TOL.get(backend, BACKEND_TOL["default"])
+
+
+def assert_score(actual: float, expected: float, backend: str, label: str):
+    tol = get_tol(backend)
+    diff = abs(actual - expected)
+    rel_diff = diff / abs(expected) if expected != 0 else diff
+    print(
+        f"[{backend}] {label}: actual={actual:.6f} expected={expected:.6f} "
+        f"diff={diff:.6f} rel_diff={rel_diff:.4f} tol={tol}"
+    )
+    assert actual == pytest.approx(expected, rel=tol), (
+        f"[{backend}] {label}: score mismatch — "
+        f"actual={actual:.6f}, expected={expected:.6f}, "
+        f"rel_diff={rel_diff:.4f}, tol={tol}"
+    )
+
+
 query = "A cat standing in the snow."
 document = "This product was excellent and exceeded my expectations."
 image_url = "https://vllm-public-assets.s3.us-west-2.amazonaws.com/multimodal_asset/cat_snow.jpg"
@@ -36,28 +93,37 @@ documents = [
 TEXT_VS_TEXT = 0.10040374100208282
 TEXT_VS_IMAGE = 0.7423753142356873
 TEXT_VS_TEXT_PLUS_IMAGE = 0.5298863053321838
-TOL = 0.05
 
 
-@pytest.fixture(scope="module")
-def server():
+@pytest.fixture(scope="module", params=ATTN_BACKENDS)
+def server(request):
+    backend = request.param
+    print(f"\n=== Starting server with attention backend: {backend} ===")
     args = [
         "--enforce-eager",
         "--max-model-len",
         "8192",
         "--chat-template",
         str(VLLM_PATH / "examples/pooling/score/template/qwen3_vl_reranker.jinja"),
-    ]
+        "--attention-config",
+        json.dumps({"backend": backend}),
+    ] + ROCM_EXTRA_ARGS
+
+    env = dict(ROCM_ENV_OVERRIDES)
+    if backend != "ROCM_AITER_FA":
+        env["VLLM_ROCM_USE_AITER"] = "0"
 
     with RemoteOpenAIServer(
-        MODEL_NAME, args, override_hf_configs=HF_OVERRIDES
+        MODEL_NAME, args, override_hf_configs=HF_OVERRIDES, env_dict=env
     ) as remote_server:
-        yield remote_server
+        print(f"=== Server ready with backend: {backend} ===")
+        yield remote_server, backend
 
 
-def test_score_api_queries_str_documents_str(server: RemoteOpenAIServer):
+def test_score_api_queries_str_documents_str(server: tuple[RemoteOpenAIServer, str]):
+    remote_server, backend = server
     score_response = requests.post(
-        server.url_for("score"),
+        remote_server.url_for("score"),
         json={
             "model": MODEL_NAME,
             "queries": query,
@@ -71,12 +137,15 @@ def test_score_api_queries_str_documents_str(server: RemoteOpenAIServer):
     assert score.data is not None
     assert len(score.data) == 1
     assert score.usage.prompt_tokens == 81
-    assert score.data[0].score == pytest.approx(TEXT_VS_TEXT, rel=TOL)
+    assert_score(score.data[0].score, TEXT_VS_TEXT, backend, "text_vs_text")
 
 
-def test_score_api_queries_str_documents_text_content(server: RemoteOpenAIServer):
+def test_score_api_queries_str_documents_text_content(
+    server: tuple[RemoteOpenAIServer, str],
+):
+    remote_server, backend = server
     score_response = requests.post(
-        server.url_for("score"),
+        remote_server.url_for("score"),
         json={
             "model": MODEL_NAME,
             "queries": query,
@@ -90,12 +159,15 @@ def test_score_api_queries_str_documents_text_content(server: RemoteOpenAIServer
     assert score.data is not None
     assert len(score.data) == 1
     assert score.usage.prompt_tokens == 81
-    assert score.data[0].score == pytest.approx(TEXT_VS_TEXT, rel=TOL)
+    assert_score(score.data[0].score, TEXT_VS_TEXT, backend, "text_vs_text")
 
 
-def test_score_api_queries_str_documents_image_url_content(server: RemoteOpenAIServer):
+def test_score_api_queries_str_documents_image_url_content(
+    server: tuple[RemoteOpenAIServer, str],
+):
+    remote_server, backend = server
     score_response = requests.post(
-        server.url_for("score"),
+        remote_server.url_for("score"),
         json={
             "model": MODEL_NAME,
             "queries": query,
@@ -109,14 +181,15 @@ def test_score_api_queries_str_documents_image_url_content(server: RemoteOpenAIS
     assert score.data is not None
     assert len(score.data) == 1
     assert score.usage.prompt_tokens == 98
-    assert score.data[0].score == pytest.approx(TEXT_VS_IMAGE, rel=TOL)
+    assert_score(score.data[0].score, TEXT_VS_IMAGE, backend, "text_vs_image")
 
 
 def test_score_api_queries_str_documents_image_base64_content(
-    server: RemoteOpenAIServer,
+    server: tuple[RemoteOpenAIServer, str],
 ):
+    remote_server, backend = server
     score_response = requests.post(
-        server.url_for("score"),
+        remote_server.url_for("score"),
         json={
             "model": MODEL_NAME,
             "queries": query,
@@ -130,14 +203,15 @@ def test_score_api_queries_str_documents_image_base64_content(
     assert score.data is not None
     assert len(score.data) == 1
     assert score.usage.prompt_tokens == 98
-    assert score.data[0].score == pytest.approx(TEXT_VS_IMAGE, rel=TOL)
+    assert_score(score.data[0].score, TEXT_VS_IMAGE, backend, "text_vs_image_base64")
 
 
 def test_score_api_queries_str_documents_image_url_plus_text_content(
-    server: RemoteOpenAIServer,
+    server: tuple[RemoteOpenAIServer, str],
 ):
+    remote_server, backend = server
     score_response = requests.post(
-        server.url_for("score"),
+        remote_server.url_for("score"),
         json={
             "model": MODEL_NAME,
             "queries": query,
@@ -151,12 +225,17 @@ def test_score_api_queries_str_documents_image_url_plus_text_content(
     assert score.data is not None
     assert len(score.data) == 1
     assert score.usage.prompt_tokens == 108
-    assert score.data[0].score == pytest.approx(TEXT_VS_TEXT_PLUS_IMAGE, rel=TOL)
+    assert_score(
+        score.data[0].score, TEXT_VS_TEXT_PLUS_IMAGE, backend, "text_vs_text_plus_image"
+    )
 
 
-def test_score_api_queries_str_documents_list(server: RemoteOpenAIServer):
+def test_score_api_queries_str_documents_list(
+    server: tuple[RemoteOpenAIServer, str],
+):
+    remote_server, backend = server
     score_response = requests.post(
-        server.url_for("score"),
+        remote_server.url_for("score"),
         json={
             "model": MODEL_NAME,
             "queries": query,
@@ -175,15 +254,23 @@ def test_score_api_queries_str_documents_list(server: RemoteOpenAIServer):
     assert score.data is not None
     assert len(score.data) == 4
     assert score.usage.prompt_tokens == 368
-    assert score.data[0].score == pytest.approx(TEXT_VS_TEXT, rel=TOL)
-    assert score.data[1].score == pytest.approx(TEXT_VS_TEXT, rel=TOL)
-    assert score.data[2].score == pytest.approx(TEXT_VS_IMAGE, rel=TOL)
-    assert score.data[3].score == pytest.approx(TEXT_VS_TEXT_PLUS_IMAGE, rel=TOL)
+    assert_score(score.data[0].score, TEXT_VS_TEXT, backend, "list[0]_text_vs_text")
+    assert_score(score.data[1].score, TEXT_VS_TEXT, backend, "list[1]_text_vs_text")
+    assert_score(score.data[2].score, TEXT_VS_IMAGE, backend, "list[2]_text_vs_image")
+    assert_score(
+        score.data[3].score,
+        TEXT_VS_TEXT_PLUS_IMAGE,
+        backend,
+        "list[3]_text_vs_text_plus_image",
+    )
 
 
-def test_rerank_api_queries_str_documents_list(server: RemoteOpenAIServer):
+def test_rerank_api_queries_str_documents_list(
+    server: tuple[RemoteOpenAIServer, str],
+):
+    remote_server, backend = server
     rerank_response = requests.post(
-        server.url_for("rerank"),
+        remote_server.url_for("rerank"),
         json={
             "model": MODEL_NAME,
             "query": query,
@@ -204,17 +291,38 @@ def test_rerank_api_queries_str_documents_list(server: RemoteOpenAIServer):
     assert len(rerank.results) == 4
 
     rerank.results.sort(key=lambda x: x.index)
-    assert rerank.results[0].relevance_score == pytest.approx(TEXT_VS_TEXT, rel=TOL)
-    assert rerank.results[1].relevance_score == pytest.approx(TEXT_VS_TEXT, rel=TOL)
-    assert rerank.results[2].relevance_score == pytest.approx(TEXT_VS_IMAGE, rel=TOL)
-    assert rerank.results[3].relevance_score == pytest.approx(
-        TEXT_VS_TEXT_PLUS_IMAGE, rel=TOL
+    assert_score(
+        rerank.results[0].relevance_score,
+        TEXT_VS_TEXT,
+        backend,
+        "rerank[0]_text_vs_text",
+    )
+    assert_score(
+        rerank.results[1].relevance_score,
+        TEXT_VS_TEXT,
+        backend,
+        "rerank[1]_text_vs_text",
+    )
+    assert_score(
+        rerank.results[2].relevance_score,
+        TEXT_VS_IMAGE,
+        backend,
+        "rerank[2]_text_vs_image",
+    )
+    assert_score(
+        rerank.results[3].relevance_score,
+        TEXT_VS_TEXT_PLUS_IMAGE,
+        backend,
+        "rerank[3]_text_vs_text_plus_image",
     )
 
 
-def test_score_api_queries_list_documents_list(server: RemoteOpenAIServer):
+def test_score_api_queries_list_documents_list(
+    server: tuple[RemoteOpenAIServer, str],
+):
+    remote_server, backend = server
     score_response = requests.post(
-        server.url_for("score"),
+        remote_server.url_for("score"),
         json={
             "model": MODEL_NAME,
             "queries": [query] * 4,
@@ -233,7 +341,12 @@ def test_score_api_queries_list_documents_list(server: RemoteOpenAIServer):
     assert score.data is not None
     assert len(score.data) == 4
     assert score.usage.prompt_tokens == 368
-    assert score.data[0].score == pytest.approx(TEXT_VS_TEXT, rel=TOL)
-    assert score.data[1].score == pytest.approx(TEXT_VS_TEXT, rel=TOL)
-    assert score.data[2].score == pytest.approx(TEXT_VS_IMAGE, rel=TOL)
-    assert score.data[3].score == pytest.approx(TEXT_VS_TEXT_PLUS_IMAGE, rel=TOL)
+    assert_score(score.data[0].score, TEXT_VS_TEXT, backend, "paired[0]_text_vs_text")
+    assert_score(score.data[1].score, TEXT_VS_TEXT, backend, "paired[1]_text_vs_text")
+    assert_score(score.data[2].score, TEXT_VS_IMAGE, backend, "paired[2]_text_vs_image")
+    assert_score(
+        score.data[3].score,
+        TEXT_VS_TEXT_PLUS_IMAGE,
+        backend,
+        "paired[3]_text_vs_text_plus_image",
+    )