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A high-throughput and memory-efficient inference and serving engine for LLMs

Mirrored from https://github.com/tduncan-lum/Nvidia-Optimization-Stack---VLLM.git

Synced Nov 11, 00:00 UTC

[ROCm][CI] Fix entrypoints tests and Python-only installation test on ROCm (#28979)

Signed-off-by: Andreas Karatzas <akaratza@amd.com>

Andreas Karatzas committed Dec 24, 2025 at 00:42 UTC 0247a91e00d6a50b2218c1e7c82c72e26dba6cb1
26 files changed +432 -116
setup.py
+126 -26
@@ -50,15 +50,15 @@ elif not (sys.platform.startswith("linux") or sys.platform.startswith("darwin"))
50 sys.platform,
51 )
52 VLLM_TARGET_DEVICE = "empty"
53 -elif (
54 - sys.platform.startswith("linux")
55 - and torch.version.cuda is None
56 - and os.getenv("VLLM_TARGET_DEVICE") is None
57 - and torch.version.hip is None
58 -):
59 - # if cuda or hip is not available and VLLM_TARGET_DEVICE is not set,
60 - # fallback to cpu
61 - VLLM_TARGET_DEVICE = "cpu"
53 +elif sys.platform.startswith("linux") and os.getenv("VLLM_TARGET_DEVICE") is None:
54 + if torch.version.hip is not None:
55 + VLLM_TARGET_DEVICE = "rocm"
56 + logger.info("Auto-detected ROCm")
57 + elif torch.version.cuda is not None:
58 + VLLM_TARGET_DEVICE = "cuda"
59 + logger.info("Auto-detected CUDA")
60 + else:
61 + VLLM_TARGET_DEVICE = "cpu"
62
63
64 def is_sccache_available() -> bool:
@@ -108,20 +108,26 @@ class cmake_build_ext(build_ext):
108 num_jobs = os.cpu_count()
109
110 nvcc_threads = None
111 - if _is_cuda() and get_nvcc_cuda_version() >= Version("11.2"):
112 - # `nvcc_threads` is either the value of the NVCC_THREADS
113 - # environment variable (if defined) or 1.
114 - # when it is set, we reduce `num_jobs` to avoid
115 - # overloading the system.
116 - nvcc_threads = envs.NVCC_THREADS
117 - if nvcc_threads is not None:
118 - nvcc_threads = int(nvcc_threads)
119 - logger.info(
120 - "Using NVCC_THREADS=%d as the number of nvcc threads.", nvcc_threads
121 - )
122 - else:
123 - nvcc_threads = 1
124 - num_jobs = max(1, num_jobs // nvcc_threads)
111 + if _is_cuda() and CUDA_HOME is not None:
112 + try:
113 + nvcc_version = get_nvcc_cuda_version()
114 + if nvcc_version >= Version("11.2"):
115 + # `nvcc_threads` is either the value of the NVCC_THREADS
116 + # environment variable (if defined) or 1.
117 + # when it is set, we reduce `num_jobs` to avoid
118 + # overloading the system.
119 + nvcc_threads = envs.NVCC_THREADS
120 + if nvcc_threads is not None:
121 + nvcc_threads = int(nvcc_threads)
122 + logger.info(
123 + "Using NVCC_THREADS=%d as the number of nvcc threads.",
124 + nvcc_threads,
125 + )
126 + else:
127 + nvcc_threads = 1
128 + num_jobs = max(1, num_jobs // nvcc_threads)
129 + except Exception as e:
130 + logger.warning("Failed to get NVCC version: %s", e)
131
132 return num_jobs, nvcc_threads
133
@@ -199,9 +205,9 @@ class cmake_build_ext(build_ext):
205 # Default build tool to whatever cmake picks.
206 build_tool = []
207 # Make sure we use the nvcc from CUDA_HOME
202 - if _is_cuda():
208 + if _is_cuda() and CUDA_HOME is not None:
209 cmake_args += [f"-DCMAKE_CUDA_COMPILER={CUDA_HOME}/bin/nvcc"]
204 - elif _is_hip():
210 + elif _is_hip() and ROCM_HOME is not None:
211 cmake_args += [f"-DROCM_PATH={ROCM_HOME}"]
212
213 other_cmake_args = os.environ.get("CMAKE_ARGS")
@@ -339,6 +345,89 @@ class precompiled_wheel_utils:
345 wheels = json.loads(resp.read().decode("utf-8"))
346 return wheels, repo_url
347
348 + @staticmethod
349 + def is_rocm_system() -> bool:
350 + """Detect ROCm without relying on torch (for build environment)."""
351 + if os.getenv("ROCM_PATH"):
352 + return True
353 + if os.path.isdir("/opt/rocm"):
354 + return True
355 + if which("rocminfo") is not None:
356 + return True
357 + try:
358 + import torch
359 +
360 + return torch.version.hip is not None
361 + except ImportError:
362 + return False
363 +
364 + @staticmethod
365 + def find_local_rocm_wheel() -> str | None:
366 + """Search for a local vllm wheel in common locations."""
367 + import glob
368 +
369 + for pattern in ["/vllm-workspace/dist/vllm-*.whl", "./dist/vllm-*.whl"]:
370 + wheels = glob.glob(pattern)
371 + if wheels:
372 + return sorted(wheels)[-1]
373 + return None
374 +
375 + @staticmethod
376 + def fetch_wheel_from_pypi_index(index_url: str, package: str = "vllm") -> str:
377 + """Fetch the latest wheel URL from a PyPI-style simple index."""
378 + import platform
379 + from html.parser import HTMLParser
380 + from urllib.parse import urljoin
381 + from urllib.request import urlopen
382 +
383 + arch = platform.machine()
384 +
385 + class WheelLinkParser(HTMLParser):
386 + def __init__(self):
387 + super().__init__()
388 + self.wheels = []
389 +
390 + def handle_starttag(self, tag, attrs):
391 + if tag == "a":
392 + for name, value in attrs:
393 + if name == "href" and value.endswith(".whl"):
394 + self.wheels.append(value)
395 +
396 + simple_url = f"{index_url.rstrip('/')}/{package}/"
397 + print(f"Fetching wheel list from {simple_url}")
398 + with urlopen(simple_url) as resp:
399 + html = resp.read().decode("utf-8")
400 +
401 + parser = WheelLinkParser()
402 + parser.feed(html)
403 +
404 + for wheel in reversed(parser.wheels):
405 + if arch in wheel:
406 + if wheel.startswith("http"):
407 + return wheel
408 + return urljoin(simple_url, wheel)
409 +
410 + raise ValueError(f"No compatible wheel found for {arch} at {simple_url}")
411 +
412 + @staticmethod
413 + def determine_wheel_url_rocm() -> tuple[str, str | None]:
414 + """Determine the precompiled wheel for ROCm."""
415 + # Search for local wheel first
416 + local_wheel = precompiled_wheel_utils.find_local_rocm_wheel()
417 + if local_wheel is not None:
418 + print(f"Found local ROCm wheel: {local_wheel}")
419 + return local_wheel, None
420 +
421 + # Fall back to AMD's PyPI index
422 + index_url = os.getenv(
423 + "VLLM_ROCM_WHEEL_INDEX", "https://pypi.amd.com/vllm-rocm/simple"
424 + )
425 + print(f"Fetching ROCm precompiled wheel from {index_url}")
426 + wheel_url = precompiled_wheel_utils.fetch_wheel_from_pypi_index(index_url)
427 + download_filename = wheel_url.split("/")[-1].split("#")[0]
428 + print(f"Using ROCm precompiled wheel: {wheel_url}")
429 + return wheel_url, download_filename
430 +
431 @staticmethod
432 def determine_wheel_url() -> tuple[str, str | None]:
433 """
@@ -359,6 +448,11 @@ class precompiled_wheel_utils:
448 print(f"Using user-specified precompiled wheel location: {wheel_location}")
449 return wheel_location, None
450 else:
451 + # ROCm: use local wheel or AMD's PyPI index
452 + # TODO: When we have ROCm nightly wheels, we can update this logic.
453 + if precompiled_wheel_utils.is_rocm_system():
454 + return precompiled_wheel_utils.determine_wheel_url_rocm()
455 +
456 import platform
457
458 arch = platform.machine()
@@ -465,6 +559,8 @@ class precompiled_wheel_utils:
559 "vllm/vllm_flash_attn/_vllm_fa2_C.abi3.so",
560 "vllm/vllm_flash_attn/_vllm_fa3_C.abi3.so",
561 "vllm/cumem_allocator.abi3.so",
562 + # ROCm-specific libraries
563 + "vllm/_rocm_C.abi3.so",
564 ]
565
566 flash_attn_regex = re.compile(
@@ -601,6 +697,8 @@ def get_rocm_version():
697 # Get the Rocm version from the ROCM_HOME/bin/librocm-core.so
698 # see https://github.com/ROCm/rocm-core/blob/d11f5c20d500f729c393680a01fa902ebf92094b/rocm_version.cpp#L21
699 try:
700 + if ROCM_HOME is None:
701 + return None
702 librocm_core_file = Path(ROCM_HOME) / "lib" / "librocm-core.so"
703 if not librocm_core_file.is_file():
704 return None
@@ -745,7 +843,9 @@ if _is_hip():
843
844 if _is_cuda():
845 ext_modules.append(CMakeExtension(name="vllm.vllm_flash_attn._vllm_fa2_C"))
748 - if envs.VLLM_USE_PRECOMPILED or get_nvcc_cuda_version() >= Version("12.3"):
846 + if envs.VLLM_USE_PRECOMPILED or (
847 + CUDA_HOME and get_nvcc_cuda_version() >= Version("12.3")
848 + ):
849 # FA3 requires CUDA 12.3 or later
850 ext_modules.append(CMakeExtension(name="vllm.vllm_flash_attn._vllm_fa3_C"))
851 # Optional since this doesn't get built (produce an .so file) when
tests/entrypoints/openai/conftest.py
+24
@@ -5,6 +5,30 @@ import pytest
5 from vllm.assets.audio import AudioAsset
6
7
8 +def add_attention_backend(server_args, attention_config):
9 + """Append attention backend CLI arg if specified.
10 +
11 + Args:
12 + server_args: List of server arguments to extend in-place.
13 + attention_config: Dict with 'backend' key, or None.
14 + """
15 + if attention_config and "backend" in attention_config:
16 + server_args.extend(["--attention-backend", attention_config["backend"]])
17 +
18 +
19 +@pytest.fixture(scope="module")
20 +def rocm_aiter_fa_attention():
21 + """Return attention config for transcription/translation tests on ROCm.
22 +
23 + On ROCm, audio tests require ROCM_AITER_FA attention backend.
24 + """
25 + from vllm.platforms import current_platform
26 +
27 + if current_platform.is_rocm():
28 + return {"backend": "ROCM_AITER_FA"}
29 + return None
30 +
31 +
32 @pytest.fixture
33 def mary_had_lamb():
34 path = AudioAsset("mary_had_lamb").get_local_path()
tests/entrypoints/openai/test_chat.py
+5 -5
@@ -254,12 +254,11 @@ async def test_single_chat_session(client: openai.AsyncOpenAI, model_name: str):
254 {"role": "system", "content": "you are a helpful assistant"},
255 {"role": "user", "content": "what is 1+1?"},
256 ]
257 -
257 # test single completion
258 chat_completion = await client.chat.completions.create(
259 model=model_name,
260 messages=messages,
262 - max_completion_tokens=10,
261 + max_completion_tokens=5,
262 logprobs=True,
263 top_logprobs=5,
264 )
@@ -267,13 +266,14 @@ async def test_single_chat_session(client: openai.AsyncOpenAI, model_name: str):
266 assert len(chat_completion.choices) == 1
267
268 choice = chat_completion.choices[0]
269 +
270 assert choice.finish_reason == "length"
271 assert chat_completion.usage == openai.types.CompletionUsage(
272 - completion_tokens=10, prompt_tokens=37, total_tokens=47
272 + completion_tokens=5, prompt_tokens=37, total_tokens=42
273 )
274
275 message = choice.message
276 - assert message.content is not None and len(message.content) >= 10
276 + assert message.content is not None and len(message.content) >= 5
277 assert message.role == "assistant"
278 messages.append({"role": "assistant", "content": message.content})
279
@@ -282,7 +282,7 @@ async def test_single_chat_session(client: openai.AsyncOpenAI, model_name: str):
282 chat_completion = await client.chat.completions.create(
283 model=model_name,
284 messages=messages,
285 - max_completion_tokens=10,
285 + max_completion_tokens=5,
286 )
287 message = chat_completion.choices[0].message
288 assert message.content is not None and len(message.content) >= 0
tests/entrypoints/openai/test_optional_middleware.py
+1
@@ -39,6 +39,7 @@ def server(request: pytest.FixtureRequest):
39 "2",
40 *passed_params,
41 ]
42 +
43 with RemoteOpenAIServer(MODEL_NAME, args) as remote_server:
44 yield remote_server
45
tests/entrypoints/openai/test_response_api_with_harmony.py
+6 -1
@@ -504,7 +504,11 @@ async def test_web_search(client: OpenAI, model_name: str):
504 @pytest.mark.asyncio
505 @pytest.mark.parametrize("model_name", [MODEL_NAME])
506 async def test_code_interpreter(client: OpenAI, model_name: str):
507 - response = await client.responses.create(
507 + # Code interpreter may need more time for container init + code execution
508 + timeout_value = client.timeout * 3
509 + client_with_timeout = client.with_options(timeout=timeout_value)
510 +
511 + response = await client_with_timeout.responses.create(
512 model=model_name,
513 # TODO: Ideally should be able to set max tool calls
514 # to prevent multi-turn, but it is not currently supported
@@ -868,6 +872,7 @@ async def test_output_messages_enabled(client: OpenAI, model_name: str, server):
872
873 @pytest.mark.asyncio
874 @pytest.mark.parametrize("model_name", [MODEL_NAME])
875 +@pytest.mark.flaky(reruns=3)
876 async def test_function_call_with_previous_input_messages(
877 client: OpenAI, model_name: str
878 ):
tests/entrypoints/openai/test_serving_tokens.py
+25 -4
@@ -93,6 +93,7 @@ async def test_same_response_as_chat_completions(client, tokenizer, messages):
93 add_generation_prompt=True,
94 enable_thinking=False, # default with Qwen3
95 )
96 +
97 for ignore_eos in [True, False]:
98 payload = {
99 "model": MODEL_NAME,
@@ -108,9 +109,8 @@ async def test_same_response_as_chat_completions(client, tokenizer, messages):
109 }
110 generate_resp = await client.post(GEN_ENDPOINT, json=payload)
111 generate_data = generate_resp.json()
111 - generate_res = tokenizer.decode(
112 - generate_data["choices"][0]["token_ids"], skip_special_tokens=True
113 - )
112 + gen_token_ids = generate_data["choices"][0]["token_ids"]
113 + generate_res = tokenizer.decode(gen_token_ids, skip_special_tokens=True)
114
115 payload = {
116 "model": MODEL_NAME,
@@ -119,12 +119,33 @@ async def test_same_response_as_chat_completions(client, tokenizer, messages):
119 "temperature": 0.0,
120 "stream": False,
121 "ignore_eos": ignore_eos,
122 - "chat_template_kwargs": dict(enable_thinking=False),
122 + "chat_template_kwargs": {"enable_thinking": False},
123 }
124 completions_resp = await client.post("/v1/chat/completions", json=payload)
125 completions_data = completions_resp.json()
126 completions_res = completions_data["choices"][0]["message"]["content"]
127
128 + if ignore_eos:
129 + # When ignoring EOS, only compare up to the first EOS token
130 + # Post-EOS generation is undefined and may differ
131 + eos_tokens = {
132 + tokenizer.eos_token_id,
133 + *tokenizer.additional_special_tokens_ids,
134 + }
135 + # Find first EOS in generated tokens
136 + eos_pos = None
137 + for i, tid in enumerate(gen_token_ids):
138 + if tid in eos_tokens:
139 + eos_pos = i
140 + break
141 + if eos_pos is not None:
142 + gen_token_ids_truncated = gen_token_ids[:eos_pos]
143 + generate_res = tokenizer.decode(
144 + gen_token_ids_truncated, skip_special_tokens=True
145 + )
146 + # Truncate completions_res to same length for comparison
147 + completions_res = completions_res[: len(generate_res)]
148 +
149 assert generate_res == completions_res
150
151
tests/entrypoints/openai/test_shutdown.py
+23 -11
@@ -9,10 +9,16 @@ import time
9 import openai
10 import pytest
11
12 +from vllm.platforms import current_platform
13 from vllm.utils.network_utils import get_open_port
14
15 MODEL_NAME = "hmellor/tiny-random-LlamaForCausalLM"
16
17 +# GPU initialization might take take longer
18 +_IS_ROCM = current_platform.is_rocm()
19 +_SERVER_STARTUP_TIMEOUT = 120
20 +_PROCESS_EXIT_TIMEOUT = 15
21 +
22
23 @pytest.mark.asyncio
24 async def test_shutdown_on_engine_failure():
@@ -45,9 +51,11 @@ async def test_shutdown_on_engine_failure():
51 "2",
52 "--disable-frontend-multiprocessing",
53 ],
48 - stdout=subprocess.PIPE,
49 - stderr=subprocess.PIPE,
50 - text=True,
54 + # ROCm: Disable stdout/stderr pipe capture. Subprocess hangs when
55 + # stdout/stderr pipes are enabled during ROCm GPU initialization.
56 + stdout=None if _IS_ROCM else subprocess.PIPE,
57 + stderr=None if _IS_ROCM else subprocess.PIPE,
58 + text=None if _IS_ROCM else True,
59 preexec_fn=lambda: signal.signal(signal.SIGINT, signal.SIG_IGN),
60 )
61
@@ -61,7 +69,7 @@ async def test_shutdown_on_engine_failure():
69 )
70
71 # Poll until server is ready
64 - while time.time() - start_time < 30:
72 + while time.time() - start_time < _SERVER_STARTUP_TIMEOUT:
73 try:
74 await client.completions.create(
75 model=MODEL_NAME, prompt="Hello", max_tokens=1
@@ -70,14 +78,18 @@ async def test_shutdown_on_engine_failure():
78 except Exception:
79 time.sleep(0.5)
80 if proc.poll() is not None:
73 - stdout, stderr = proc.communicate(timeout=1)
74 - pytest.fail(
75 - f"Server died during startup. stdout: {stdout}, stderr: {stderr}"
76 - )
81 + if _IS_ROCM:
82 + pytest.fail(f"Server died during startup: {proc.returncode}")
83 + else:
84 + stdout, stderr = proc.communicate(timeout=1)
85 + pytest.fail(
86 + f"Server died during startup. "
87 + f"stdout: {stdout}, stderr: {stderr}"
88 + )
89 else:
90 proc.terminate()
79 - proc.wait(timeout=5)
80 - pytest.fail("Server failed to start in 30 seconds")
91 + proc.wait(timeout=_PROCESS_EXIT_TIMEOUT)
92 + pytest.fail(f"Server failed to start in {_SERVER_STARTUP_TIMEOUT} seconds")
93
94 # Kill server to simulate crash
95 proc.terminate()
@@ -89,5 +101,5 @@ async def test_shutdown_on_engine_failure():
101 model=MODEL_NAME, prompt="This should fail", max_tokens=1
102 )
103
92 - return_code = proc.wait(timeout=5)
104 + return_code = proc.wait(timeout=_PROCESS_EXIT_TIMEOUT)
105 assert return_code is not None
tests/entrypoints/openai/test_transcription_validation.py
+16 -4
@@ -7,6 +7,7 @@ import json
7 import pytest
8
9 from ...utils import RemoteOpenAIServer
10 +from .conftest import add_attention_backend
11
12 MISTRAL_FORMAT_ARGS = [
13 "--tokenizer_mode",
@@ -20,12 +21,14 @@ MISTRAL_FORMAT_ARGS = [
21
22 @pytest.mark.asyncio
23 @pytest.mark.parametrize("model_name", ["mistralai/Voxtral-Mini-3B-2507"])
23 -async def test_basic_audio(mary_had_lamb, model_name):
24 +async def test_basic_audio(mary_had_lamb, model_name, rocm_aiter_fa_attention):
25 server_args = ["--enforce-eager"]
26
27 if model_name.startswith("mistralai"):
28 server_args += MISTRAL_FORMAT_ARGS
29
30 + add_attention_backend(server_args, rocm_aiter_fa_attention)
31 +
32 # Based on https://github.com/openai/openai-cookbook/blob/main/examples/Whisper_prompting_guide.ipynb.
33 with RemoteOpenAIServer(model_name, server_args) as remote_server:
34 client = remote_server.get_async_client()
@@ -44,8 +47,13 @@ async def test_basic_audio(mary_had_lamb, model_name):
47
48
49 @pytest.mark.asyncio
47 -async def test_basic_audio_with_lora(mary_had_lamb):
50 +async def test_basic_audio_with_lora(mary_had_lamb, rocm_aiter_fa_attention):
51 """Ensure STT (transcribe) requests can pass LoRA through to generate."""
52 + # ROCm SPECIFIC CONFIGURATION:
53 + # To ensure the test passes on ROCm, we modify the max model length to 512.
54 + # We DO NOT apply this to other platforms to maintain strict upstream parity.
55 + from vllm.platforms import current_platform
56 +
57 model_name = "ibm-granite/granite-speech-3.3-2b"
58 lora_model_name = "speech"
59 server_args = [
@@ -56,11 +64,13 @@ async def test_basic_audio_with_lora(mary_had_lamb):
64 "--lora-modules",
65 f"{lora_model_name}={model_name}",
66 "--max-model-len",
59 - "2048",
67 + "512" if current_platform.is_rocm() else "2048",
68 "--max-num-seqs",
69 "1",
70 ]
71
72 + add_attention_backend(server_args, rocm_aiter_fa_attention)
73 +
74 # Based on https://github.com/openai/openai-cookbook/blob/main/examples/Whisper_prompting_guide.ipynb.
75 with RemoteOpenAIServer(model_name, server_args) as remote_server:
76 client = remote_server.get_async_client()
@@ -79,12 +89,14 @@ async def test_basic_audio_with_lora(mary_had_lamb):
89
90
91 @pytest.mark.asyncio
82 -async def test_basic_audio_gemma(foscolo):
92 +async def test_basic_audio_gemma(foscolo, rocm_aiter_fa_attention):
93 # Gemma accuracy on some of the audio samples we use is particularly bad,
94 # hence we use a different one here. WER is evaluated separately.
95 model_name = "google/gemma-3n-E2B-it"
96 server_args = ["--enforce-eager"]
97
98 + add_attention_backend(server_args, rocm_aiter_fa_attention)
99 +
100 with RemoteOpenAIServer(
101 model_name, server_args, max_wait_seconds=480
102 ) as remote_server:
tests/entrypoints/openai/test_translation_validation.py
+25 -6
@@ -14,16 +14,26 @@ import pytest_asyncio
14 import soundfile as sf
15
16 from ...utils import RemoteOpenAIServer
17 +from .conftest import add_attention_backend
18
19 SERVER_ARGS = ["--enforce-eager"]
20
21
22 +def _get_server_args(attention_config):
23 + """Get server args with attention backend if specified."""
24 + args = SERVER_ARGS.copy()
25 + add_attention_backend(args, attention_config)
26 + return args
27 +
28 +
29 @pytest.fixture(
30 scope="module", params=["openai/whisper-small", "google/gemma-3n-E2B-it"]
31 )
24 -def server(request):
32 +def server(request, rocm_aiter_fa_attention):
33 # Parametrize over model name
26 - with RemoteOpenAIServer(request.param, SERVER_ARGS) as remote_server:
34 + with RemoteOpenAIServer(
35 + request.param, _get_server_args(rocm_aiter_fa_attention)
36 + ) as remote_server:
37 yield remote_server, request.param
38
39
@@ -35,10 +45,12 @@ async def client_and_model(server):
45
46
47 @pytest.mark.asyncio
38 -async def test_non_asr_model(foscolo):
48 +async def test_non_asr_model(foscolo, rocm_aiter_fa_attention):
49 # text to text model
50 model_name = "JackFram/llama-68m"
41 - with RemoteOpenAIServer(model_name, SERVER_ARGS) as remote_server:
51 + with RemoteOpenAIServer(
52 + model_name, _get_server_args(rocm_aiter_fa_attention)
53 + ) as remote_server:
54 client = remote_server.get_async_client()
55 res = await client.audio.translations.create(
56 model=model_name, file=foscolo, temperature=0.0
@@ -49,8 +61,13 @@ async def test_non_asr_model(foscolo):
61
62
63 @pytest.mark.asyncio
52 -async def test_basic_audio_with_lora(mary_had_lamb):
64 +async def test_basic_audio_with_lora(mary_had_lamb, rocm_aiter_fa_attention):
65 """Ensure STT (translate) requests can pass LoRA through to generate."""
66 + # ROCm SPECIFIC CONFIGURATION:
67 + # To ensure the test passes on ROCm, we modify the max model length to 512.
68 + # We DO NOT apply this to other platforms to maintain strict upstream parity.
69 + from vllm.platforms import current_platform
70 +
71 # NOTE - careful to call this test before the module scoped server
72 # fixture, otherwise it'll OOMkill the CI
73 model_name = "ibm-granite/granite-speech-3.3-2b"
@@ -63,11 +80,13 @@ async def test_basic_audio_with_lora(mary_had_lamb):
80 "--lora-modules",
81 f"{lora_model_name}={model_name}",
82 "--max-model-len",
66 - "2048",
83 + "512" if current_platform.is_rocm() else "2048",
84 "--max-num-seqs",
85 "1",
86 ]
87
88 + add_attention_backend(server_args, rocm_aiter_fa_attention)
89 +
90 # Based on https://github.com/openai/openai-cookbook/blob/main/examples/Whisper_prompting_guide.ipynb.
91 with RemoteOpenAIServer(model_name, server_args) as remote_server:
92 client = remote_server.get_async_client()
tests/entrypoints/openai/test_video.py
+16 -1
@@ -8,6 +8,7 @@ import pytest
8 import pytest_asyncio
9
10 from vllm.multimodal.utils import encode_video_url, fetch_video
11 +from vllm.platforms import current_platform
12
13 from ...utils import RemoteOpenAIServer
14
@@ -37,7 +38,16 @@ def server():
38 json.dumps({"video": MAXIMUM_VIDEOS}),
39 ]
40
40 - with RemoteOpenAIServer(MODEL_NAME, args) as remote_server:
41 + # ROCm: Increase timeouts to handle potential network delays and slower
42 + # video processing when downloading multiple videos from external sources
43 + env_overrides = {}
44 + if current_platform.is_rocm():
45 + env_overrides = {
46 + "VLLM_VIDEO_FETCH_TIMEOUT": "120",
47 + "VLLM_ENGINE_ITERATION_TIMEOUT_S": "300",
48 + }
49 +
50 + with RemoteOpenAIServer(MODEL_NAME, args, env_dict=env_overrides) as remote_server:
51 yield remote_server
52
53
@@ -287,6 +297,11 @@ async def test_chat_streaming_video(
297 @pytest.mark.parametrize(
298 "video_urls", [TEST_VIDEO_URLS[:i] for i in range(2, len(TEST_VIDEO_URLS))]
299 )
300 +@pytest.mark.flaky(
301 + reruns=2,
302 + reruns_delay=5,
303 + condition=current_platform.is_rocm(),
304 +)
305 async def test_multi_video_input(
306 client: openai.AsyncOpenAI, model_name: str, video_urls: list[str]
307 ):
tests/entrypoints/openai/test_vision.py
+40 -2
@@ -10,6 +10,7 @@ from transformers import AutoProcessor
10
11 from vllm.multimodal.base import MediaWithBytes
12 from vllm.multimodal.utils import encode_image_url, fetch_image
13 +from vllm.platforms import current_platform
14
15 from ...utils import RemoteOpenAIServer
16
@@ -43,6 +44,27 @@ EXPECTED_MM_BEAM_SEARCH_RES = [
44 ],
45 ]
46
47 +EXPECTED_MM_BEAM_SEARCH_RES_ROCM = [
48 + # MultiHeadAttention attn_backend: FLASH_ATTN
49 + # with Triton Attention backend
50 + [
51 + "The image shows a wooden boardwalk leading through a",
52 + "The image shows a wooden boardwalk extending into a",
53 + ],
54 + [
55 + "The image shows two parrots perched on",
56 + "The image shows two birds perched on a cur",
57 + ],
58 + [
59 + "The image shows a Venn diagram with three over",
60 + "The image contains a Venn diagram with three over",
61 + ],
62 + [
63 + "This image displays a gradient of colors ranging from",
64 + "This image displays a gradient of colors transitioning from",
65 + ],
66 +]
67 +
68
69 @pytest.fixture(scope="module")
70 def server():
@@ -59,7 +81,16 @@ def server():
81 json.dumps({"image": MAXIMUM_IMAGES}),
82 ]
83
62 - with RemoteOpenAIServer(MODEL_NAME, args) as remote_server:
84 + # ROCm: Increase timeouts to handle potential network delays and slower
85 + # video processing when downloading multiple videos from external sources
86 + env_overrides = {}
87 + if current_platform.is_rocm():
88 + env_overrides = {
89 + "VLLM_VIDEO_FETCH_TIMEOUT": "120",
90 + "VLLM_ENGINE_ITERATION_TIMEOUT_S": "300",
91 + }
92 +
93 + with RemoteOpenAIServer(MODEL_NAME, args, env_dict=env_overrides) as remote_server:
94 yield remote_server
95
96
@@ -288,9 +319,16 @@ async def test_single_chat_session_image_base64encoded_beamsearch(
319 image_idx: int,
320 url_encoded_image: dict[str, str],
321 ):
322 + # ROCm: Switch expected results based on platform
323 + from vllm.platforms import current_platform
324 +
325 # NOTE: This test also validates that we pass MM data through beam search
326 raw_image_url = TEST_IMAGE_ASSETS[image_idx]
293 - expected_res = EXPECTED_MM_BEAM_SEARCH_RES[image_idx]
327 +
328 + if current_platform.is_rocm():
329 + expected_res = EXPECTED_MM_BEAM_SEARCH_RES_ROCM[image_idx]
330 + else:
331 + expected_res = EXPECTED_MM_BEAM_SEARCH_RES[image_idx]
332
333 messages = dummy_messages_from_image_url(url_encoded_image[raw_image_url])
334
tests/entrypoints/openai/test_vision_embeds.py
+1
@@ -33,6 +33,7 @@ def _terratorch_dummy_messages():
33 ]
34
35
36 +@pytest.mark.asyncio
37 @pytest.mark.parametrize(
38 "model_name", ["ibm-nasa-geospatial/Prithvi-EO-2.0-300M-TL-Sen1Floods11"]
39 )
tests/entrypoints/pooling/basic/test_encode.py
+7 -5
@@ -9,11 +9,6 @@ from vllm import LLM, PoolingParams
9 from vllm.distributed import cleanup_dist_env_and_memory
10 from vllm.platforms import current_platform
11
12 -if current_platform.is_rocm():
13 - pytest.skip(
14 - "Encoder self-attention is not implemented on ROCm.", allow_module_level=True
15 - )
16 -
12 MODEL_NAME = "intfloat/multilingual-e5-small"
13
14 PROMPTS = [
@@ -35,6 +30,12 @@ TOKEN_IDS = [
30
31 @pytest.fixture(scope="module")
32 def llm():
33 + # ROCm: Use FLEX_ATTENTION backend as it's the only attention backend
34 + # that supports encoder-only models on ROCm.
35 + attention_config = None
36 + if current_platform.is_rocm():
37 + attention_config = {"backend": "FLEX_ATTENTION"}
38 +
39 # pytest caches the fixture so we use weakref.proxy to
40 # enable garbage collection
41 llm = LLM(
@@ -44,6 +45,7 @@ def llm():
45 gpu_memory_utilization=0.75,
46 enforce_eager=True,
47 seed=0,
48 + attention_config=attention_config,
49 )
50
51 yield weakref.proxy(llm)
tests/entrypoints/pooling/basic/test_truncation.py
+4 -5
@@ -9,11 +9,6 @@ import pytest_asyncio
9 from tests.utils import RemoteOpenAIServer
10 from vllm.platforms import current_platform
11
12 -if current_platform.is_rocm():
13 - pytest.skip(
14 - "Encoder self-attention is not implemented on ROCm.", allow_module_level=True
15 - )
16 -
12 MODEL_NAME = "sentence-transformers/all-MiniLM-L12-v2"
13 max_model_len = 128
14
@@ -44,6 +39,10 @@ def server():
39 str(max_model_len),
40 ]
41
42 + # ROCm: Use Flex Attention to support encoder-only self-attention.
43 + if current_platform.is_rocm():
44 + args.extend(["--attention-backend", "FLEX_ATTENTION"])
45 +
46 with RemoteOpenAIServer(MODEL_NAME, args) as remote_server:
47 yield remote_server
48
tests/entrypoints/pooling/embed/conftest.py new
+28
@@ -0,0 +1,28 @@
1 +# SPDX-License-Identifier: Apache-2.0
2 +# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
3 +"""Pytest configuration for vLLM pooling embed tests."""
4 +
5 +import warnings
6 +
7 +import torch
8 +
9 +from vllm.platforms import current_platform
10 +
11 +
12 +def pytest_collection_modifyitems(config, items):
13 + """Configure ROCm-specific settings based on collected tests."""
14 + if not current_platform.is_rocm():
15 + return
16 +
17 + # Disable Flash/MemEfficient SDP on ROCm to avoid HF Transformers
18 + # accuracy issues: https://github.com/vllm-project/vllm/issues/30167
19 + # TODO: Remove once ROCm SDP accuracy issues are resolved on HuggingFace
20 + torch.backends.cuda.enable_flash_sdp(False)
21 + torch.backends.cuda.enable_mem_efficient_sdp(False)
22 + torch.backends.cuda.enable_math_sdp(True)
23 + warnings.warn(
24 + "ROCm: Disabled flash_sdp and mem_efficient_sdp, enabled math_sdp "
25 + "to avoid HuggingFace Transformers accuracy issues",
26 + UserWarning,
27 + stacklevel=1,
28 + )
tests/entrypoints/pooling/embed/test_correctness_mteb.py
+4 -5
@@ -13,11 +13,6 @@ from tests.models.language.pooling_mteb_test.mteb_utils import (
13 from tests.utils import RemoteOpenAIServer
14 from vllm.platforms import current_platform
15
16 -if current_platform.is_rocm():
17 - pytest.skip(
18 - "Encoder self-attention is not implemented on ROCm.", allow_module_level=True
19 - )
20 -
16 os.environ["VLLM_LOGGING_LEVEL"] = "WARNING"
17
18 MODEL_NAME = "intfloat/e5-small"
@@ -28,6 +23,10 @@ MAIN_SCORE = 0.7422994752439667
23 def server():
24 args = ["--runner", "pooling", "--enforce-eager", "--disable-uvicorn-access-log"]
25
26 + # ROCm: Use Flex Attention to support encoder-only self-attention.
27 + if current_platform.is_rocm():
28 + args.extend(["--attention-backend", "FLEX_ATTENTION"])
29 +
30 with RemoteOpenAIServer(MODEL_NAME, args) as remote_server:
31 yield remote_server
32
tests/entrypoints/pooling/embed/test_offline.py
+7 -5
@@ -11,11 +11,6 @@ from vllm import LLM, PoolingParams
11 from vllm.distributed import cleanup_dist_env_and_memory
12 from vllm.platforms import current_platform
13
14 -if current_platform.is_rocm():
15 - pytest.skip(
16 - "Encoder self-attention is not implemented on ROCm.", allow_module_level=True
17 - )
18 -
14 MODEL_NAME = "intfloat/multilingual-e5-small"
15
16 prompts = ["The chef prepared a delicious meal."]
@@ -23,6 +18,12 @@ prompts = ["The chef prepared a delicious meal."]
18
19 @pytest.fixture(scope="module")
20 def llm():
21 + # ROCm: Use FLEX_ATTENTION backend as it's the only attention backend
22 + # that supports encoder-only models on ROCm.
23 + attention_config = None
24 + if current_platform.is_rocm():
25 + attention_config = {"backend": "FLEX_ATTENTION"}
26 +
27 # pytest caches the fixture so we use weakref.proxy to
28 # enable garbage collection
29 llm = LLM(
@@ -32,6 +33,7 @@ def llm():
33 gpu_memory_utilization=0.75,
34 enforce_eager=True,
35 seed=0,
36 + attention_config=attention_config,
37 )
38
39 yield weakref.proxy(llm)
tests/entrypoints/pooling/embed/test_online.py
+13 -5
@@ -28,16 +28,20 @@ from vllm.utils.serial_utils import (
28 decode_pooling_output,
29 )
30
31 -if current_platform.is_rocm():
32 - pytest.skip(
33 - "Encoder self-attention is not implemented on ROCm.", allow_module_level=True
34 - )
35 -
31 MODEL_NAME = "intfloat/multilingual-e5-small"
32 DUMMY_CHAT_TEMPLATE = """{% for message in messages %}{{message['role'] + ': ' + message['content'] + '\\n'}}{% endfor %}""" # noqa: E501
33 DTYPE = "bfloat16"
34
35
36 +if current_platform.is_rocm():
37 + # Disable Flash/MemEfficient SDP on ROCm to avoid HF Transformers
38 + # accuracy issues: https://github.com/vllm-project/vllm/issues/30167
39 + # TODO: Remove once ROCm SDP accuracy issues are resolved on HuggingFace
40 + torch.backends.cuda.enable_flash_sdp(False)
41 + torch.backends.cuda.enable_mem_efficient_sdp(False)
42 + torch.backends.cuda.enable_math_sdp(True)
43 +
44 +
45 @pytest.fixture(scope="module")
46 def server():
47 args = [
@@ -53,6 +57,10 @@ def server():
57 DUMMY_CHAT_TEMPLATE,
58 ]
59
60 + # ROCm: Use Flex Attention to support encoder-only self-attention.
61 + if current_platform.is_rocm():
62 + args.extend(["--attention-backend", "FLEX_ATTENTION"])
63 +
64 with RemoteOpenAIServer(MODEL_NAME, args) as remote_server:
65 yield remote_server
66
tests/entrypoints/pooling/embed/test_online_dimensions.py
+4 -5
@@ -14,11 +14,6 @@ from tests.utils import RemoteOpenAIServer
14 from vllm.entrypoints.pooling.embed.protocol import EmbeddingResponse
15 from vllm.platforms import current_platform
16
17 -if current_platform.is_rocm():
18 - pytest.skip(
19 - "Encoder self-attention is not implemented on ROCm.", allow_module_level=True
20 - )
21 -
17 MODELS = [
18 EmbedModelInfo("intfloat/multilingual-e5-small", is_matryoshka=False),
19 EmbedModelInfo(
@@ -62,6 +57,10 @@ def server(model_info, dtype: str):
57 ["--trust_remote_code", "--hf_overrides", '{"matryoshka_dimensions":[256]}']
58 )
59
60 + # ROCm: Use Flex Attention to support encoder-only self-attention.
61 + if current_platform.is_rocm():
62 + args.extend(["--attention-backend", "FLEX_ATTENTION"])
63 +
64 with RemoteOpenAIServer(model_info.name, args) as remote_server:
65 yield remote_server
66
tests/entrypoints/pooling/embed/test_online_long_text.py
+4 -5
@@ -18,11 +18,6 @@ from tests.utils import RemoteOpenAIServer
18 from vllm.entrypoints.pooling.embed.protocol import EmbeddingResponse
19 from vllm.platforms import current_platform
20
21 -if current_platform.is_rocm():
22 - pytest.skip(
23 - "Encoder self-attention is not implemented on ROCm.", allow_module_level=True
24 - )
25 -
21
22 def _generate_random_text(word_count: int) -> str:
23 """Generate random text with approximately the specified word count."""
@@ -228,6 +223,10 @@ def server_with_chunked_processing():
223 "0.8",
224 ]
225
226 + # ROCm: Use Flex Attention to support encoder-only self-attention.
227 + if current_platform.is_rocm():
228 + args.extend(["--attention-backend", "FLEX_ATTENTION"])
229 +
230 with RemoteOpenAIServer(MODEL_NAME, args) as remote_server:
231 yield remote_server
232
tests/entrypoints/pooling/score/test_correctness_mteb.py
+4 -5
@@ -15,11 +15,6 @@ from tests.models.language.pooling_mteb_test.mteb_utils import (
15 from tests.utils import RemoteOpenAIServer
16 from vllm.platforms import current_platform
17
18 -if current_platform.is_rocm():
19 - pytest.skip(
20 - "Encoder self-attention is not implemented on ROCm.", allow_module_level=True
21 - )
22 -
18 os.environ["VLLM_LOGGING_LEVEL"] = "WARNING"
19
20 MODEL_NAME = "cross-encoder/ms-marco-MiniLM-L-6-v2"
@@ -30,6 +25,10 @@ st_main_score = 0.33457
25 def server():
26 args = ["--runner", "pooling", "--enforce-eager", "--disable-uvicorn-access-log"]
27
28 + # ROCm: Use Flex Attention to support encoder-only self-attention.
29 + if current_platform.is_rocm():
30 + args.extend(["--attention-backend", "FLEX_ATTENTION"])
31 +
32 with RemoteOpenAIServer(MODEL_NAME, args) as remote_server:
33 yield remote_server
34
tests/entrypoints/pooling/score/test_offline.py
+7 -5
@@ -11,16 +11,17 @@ from vllm import LLM, PoolingParams
11 from vllm.distributed import cleanup_dist_env_and_memory
12 from vllm.platforms import current_platform
13
14 -if current_platform.is_rocm():
15 - pytest.skip(
16 - "Encoder self-attention is not implemented on ROCm.", allow_module_level=True
17 - )
18 -
14 MODEL_NAME = "tomaarsen/Qwen3-Reranker-0.6B-seq-cls"
15
16
17 @pytest.fixture(scope="module")
18 def llm():
19 + # ROCm: Use FLEX_ATTENTION backend as it's the only attention backend
20 + # that supports encoder-only models on ROCm.
21 + attention_config = None
22 + if current_platform.is_rocm():
23 + attention_config = {"backend": "FLEX_ATTENTION"}
24 +
25 # pytest caches the fixture so we use weakref.proxy to
26 # enable garbage collection
27 llm = LLM(
@@ -30,6 +31,7 @@ def llm():
31 gpu_memory_utilization=0.75,
32 enforce_eager=True,
33 seed=0,
34 + attention_config=attention_config,
35 )
36
37 yield weakref.proxy(llm)
tests/entrypoints/pooling/score/test_online_rerank.py
+4 -5
@@ -11,11 +11,6 @@ from vllm.entrypoints.pooling.pooling.protocol import PoolingResponse
11 from vllm.entrypoints.pooling.score.protocol import RerankResponse
12 from vllm.platforms import current_platform
13
14 -if current_platform.is_rocm():
15 - pytest.skip(
16 - "Encoder self-attention is not implemented on ROCm.", allow_module_level=True
17 - )
18 -
14 MODEL_NAME = "BAAI/bge-reranker-base"
15 DTYPE = "bfloat16"
16
@@ -24,6 +19,10 @@ DTYPE = "bfloat16"
19 def server():
20 args = ["--enforce-eager", "--max-model-len", "100", "--dtype", DTYPE]
21
22 + # ROCm: Use Flex Attention to support encoder-only self-attention.
23 + if current_platform.is_rocm():
24 + args.extend(["--attention-backend", "FLEX_ATTENTION"])
25 +
26 with RemoteOpenAIServer(MODEL_NAME, args) as remote_server:
27 yield remote_server
28
tests/entrypoints/pooling/score/test_online_score.py
+4 -5
@@ -12,11 +12,6 @@ from tests.utils import RemoteOpenAIServer
12 from vllm.entrypoints.pooling.score.protocol import ScoreResponse
13 from vllm.platforms import current_platform
14
15 -if current_platform.is_rocm():
16 - pytest.skip(
17 - "Encoder self-attention is not implemented on ROCm.", allow_module_level=True
18 - )
19 -
15 MODELS = [
16 {"name": "BAAI/bge-reranker-v2-m3", "is_cross_encoder": True},
17 {"name": "BAAI/bge-base-en-v1.5", "is_cross_encoder": False},
@@ -44,6 +39,10 @@ def model(request):
39 def server(model: dict[str, Any]):
40 args = ["--enforce-eager", "--max-model-len", "100", "--dtype", DTYPE]
41
42 + # ROCm: Use Flex Attention to support encoder-only self-attention.
43 + if current_platform.is_rocm():
44 + args.extend(["--attention-backend", "FLEX_ATTENTION"])
45 +
46 with RemoteOpenAIServer(model["name"], args) as remote_server:
47 yield remote_server
48
tests/standalone_tests/pytorch_nightly_dependency.sh
+6 -1
@@ -4,6 +4,11 @@
4 set -e
5 set -x
6
7 +if command -v rocminfo >/dev/null 2>&1; then
8 + echo "Skipping test for ROCm platform"
9 + exit 0
10 +fi
11 +
12 cd /vllm-workspace/
13
14 rm -rf .venv
@@ -36,7 +41,7 @@ if diff before.txt after.txt; then
41 echo "torch version not overridden."
42 else
43 echo "torch version overridden by nightly_torch_test.txt, \
39 - if the dependency is not triggered by the pytroch nightly test,\
44 + if the dependency is not triggered by the pytorch nightly test,\
45 please add the dependency to the list 'white_list' in tools/pre_commit/generate_nightly_torch_test.py"
46 exit 1
47 fi
vllm/entrypoints/pooling/embed/conftest.py new
+28
@@ -0,0 +1,28 @@
1 +# SPDX-License-Identifier: Apache-2.0
2 +# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
3 +"""Pytest configuration for vLLM pooling embed tests."""
4 +
5 +import warnings
6 +
7 +import torch
8 +
9 +from vllm.platforms import current_platform
10 +
11 +
12 +def pytest_collection_modifyitems(config, items):
13 + """Configure ROCm-specific settings based on collected tests."""
14 + if not current_platform.is_rocm():
15 + return
16 +
17 + # Disable Flash/MemEfficient SDP on ROCm to avoid HF Transformers
18 + # accuracy issues: https://github.com/vllm-project/vllm/issues/30167
19 + # TODO: Remove once ROCm SDP accuracy issues are resolved on HuggingFace
20 + torch.backends.cuda.enable_flash_sdp(False)
21 + torch.backends.cuda.enable_mem_efficient_sdp(False)
22 + torch.backends.cuda.enable_math_sdp(True)
23 + warnings.warn(
24 + "ROCm: Disabled flash_sdp and mem_efficient_sdp, enabled math_sdp "
25 + "to avoid HuggingFace Transformers accuracy issues",
26 + UserWarning,
27 + stacklevel=1,
28 + )