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chore: import upstream snapshot with attribution
2026-07-13 12:38:16 +08:00

162 行
5.2 KiB
Python

"""
Test Whisper model with CUDA graph support.
This test verifies that:
1. Whisper model works correctly with CUDA graph enabled (default)
2. Cross-attention KV cache is properly managed through RadixAttention
3. Output is consistent between CUDA graph and non-CUDA-graph modes
Usage:
python test_whisper_cuda_graph.py
Requires:
- A GPU with sufficient memory
- openai-whisper model (e.g., openai/whisper-large-v3)
- An audio file or URL for testing
"""
import io
import unittest
import requests
from sglang.srt.utils import kill_process_tree
from sglang.test.test_utils import (
DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
DEFAULT_URL_FOR_TEST,
CustomTestCase,
popen_launch_server,
)
WHISPER_MODEL = "openai/whisper-large-v3"
TEST_AUDIO_URL = "https://huggingface.co/datasets/Narsil/asr_dummy/resolve/main/1.flac"
TEST_AUDIO_LOCAL = "/tmp/test_whisper_audio.flac"
def get_audio_bytes():
"""Get audio bytes, downloading if necessary."""
import os
if os.path.exists(TEST_AUDIO_LOCAL):
with open(TEST_AUDIO_LOCAL, "rb") as f:
return f.read()
resp = requests.get(TEST_AUDIO_URL, timeout=30)
resp.raise_for_status()
with open(TEST_AUDIO_LOCAL, "wb") as f:
f.write(resp.content)
return resp.content
class TestWhisperCudaGraph(CustomTestCase):
"""Test Whisper with CUDA graph enabled (default behavior)."""
@classmethod
def setUpClass(cls):
cls.model = WHISPER_MODEL
cls.base_url = DEFAULT_URL_FOR_TEST
cls.process = popen_launch_server(
cls.model,
cls.base_url,
timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
other_args=[
"--served-model-name",
"whisper",
],
)
@classmethod
def tearDownClass(cls):
kill_process_tree(cls.process.pid)
def _transcribe(self, language="en"):
"""Send a transcription request via OpenAI-compatible audio endpoint."""
audio_bytes = get_audio_bytes()
response = requests.post(
self.base_url + "/v1/audio/transcriptions",
files={"file": ("audio.ogg", io.BytesIO(audio_bytes), "audio/ogg")},
data={
"model": "whisper",
"language": language,
},
)
self.assertEqual(response.status_code, 200, response.text)
return response.json()
def test_basic_transcription(self):
"""Test that basic transcription works with CUDA graph."""
result = self._transcribe()
self.assertIn("text", result)
text = result["text"]
self.assertTrue(len(text) > 0, "Transcription should not be empty")
print(f"Transcription: {text}")
def test_multiple_sequential_requests(self):
"""Test multiple sequential requests to verify CUDA graph replay consistency."""
results = []
for i in range(3):
result = self._transcribe()
self.assertIn("text", result)
results.append(result["text"])
print(f"Request {i+1}: {result['text'][:80]}...")
# All transcriptions of the same audio should be identical
for i in range(1, len(results)):
self.assertEqual(
results[0],
results[i],
f"Transcription {i+1} differs from first transcription",
)
def test_transcription_quality(self):
"""Test that transcription quality is reasonable (contains expected words)."""
result = self._transcribe()
text = result["text"].lower()
# The test audio is a LibriSpeech sample about stew for dinner
self.assertIn("stew", text, f"Expected 'stew' in transcription: {text}")
self.assertIn("dinner", text, f"Expected 'dinner' in transcription: {text}")
print(f"Quality check passed: {result['text'][:80]}...")
class TestWhisperNoCudaGraph(CustomTestCase):
"""Test Whisper with CUDA graph explicitly disabled for comparison."""
@classmethod
def setUpClass(cls):
cls.model = WHISPER_MODEL
cls.base_url = DEFAULT_URL_FOR_TEST
cls.process = popen_launch_server(
cls.model,
cls.base_url,
timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
other_args=[
"--served-model-name",
"whisper",
"--disable-cuda-graph",
],
)
@classmethod
def tearDownClass(cls):
kill_process_tree(cls.process.pid)
def test_basic_transcription_no_cuda_graph(self):
"""Test that transcription works without CUDA graph (baseline)."""
audio_bytes = get_audio_bytes()
response = requests.post(
self.base_url + "/v1/audio/transcriptions",
files={"file": ("audio.ogg", io.BytesIO(audio_bytes), "audio/ogg")},
data={
"model": "whisper",
"language": "en",
},
)
self.assertEqual(response.status_code, 200, response.text)
result = response.json()
self.assertIn("text", result)
self.assertTrue(len(result["text"]) > 0)
print(f"No CUDA graph transcription: {result['text'][:80]}...")
if __name__ == "__main__":
unittest.main(verbosity=3)