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fix ut test_moe (#21735)

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This commit is contained in:
Huaiyu, Zheng
2026-04-03 21:57:11 -07:00
committed by GitHub
parent de9859073f
commit 68f4c52d34
4 changed files with 105 additions and 32 deletions
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test/srt/run_suite.py
+2 -1
View File
@@ -77,7 +77,8 @@ suite_xeon = {
# NOTE: please sort the test cases alphabetically by the test file name
suite_xpu = {
"per-commit-xpu": [
TestFile("xpu/test_deepseek_ocr.py"),
TestFile("xpu/test_deepseek_ocr.py", 360),
TestFile("xpu/test_deepseek_ocr_triton.py", 360),
# TestFile("xpu/test_internvl.py"),
TestFile("xpu/test_intel_xpu_backend.py"),
],
test/srt/xpu/test_deepseek_ocr.py
+28 -26
View File
@@ -2,9 +2,11 @@
python3 -m unittest test_deepseek_ocr.py
"""
import gc
import json
import os
import unittest
from pathlib import Path
import requests
from transformers import AutoTokenizer
@@ -19,11 +21,32 @@ from sglang.test.test_utils import (
class TestDeepSeekOCR(CustomTestCase):
@classmethod
def _cleanup_xpu_memory(cls):
gc.collect()
try:
import torch
if hasattr(torch, "xpu") and torch.xpu.is_available():
torch.xpu.synchronize()
torch.xpu.empty_cache()
except Exception:
# Best-effort cleanup only; tests should continue if cleanup is unavailable.
pass
@classmethod
def setUpClass(cls):
cls._cleanup_xpu_memory()
cls.model = "deepseek-ai/DeepSeek-OCR"
cls.tokenizer = AutoTokenizer.from_pretrained(cls.model, use_fast=False)
cls.tokenizer = AutoTokenizer.from_pretrained(
cls.model, use_fast=False, trust_remote_code=True
)
cls.base_url = DEFAULT_URL_FOR_TEST
cls.image_path = str(
(Path(__file__).resolve().parents[3] / "examples/assets/example_image.png")
)
if not os.path.exists(cls.image_path):
raise FileNotFoundError(f"Image not found: {cls.image_path}")
cls.common_args = [
"--device",
"xpu",
@@ -43,14 +66,16 @@ class TestDeepSeekOCR(CustomTestCase):
@classmethod
def tearDownClass(cls):
"""Fixture that is run once after all tests in the class."""
kill_process_tree(cls.process.pid)
if hasattr(cls, "process") and cls.process:
kill_process_tree(cls.process.pid)
cls._cleanup_xpu_memory()
def get_request_json(self, max_new_tokens=32, n=1):
response = requests.post(
self.base_url + "/generate",
json={
"text": "<image>\n<|grounding|>Convert the document to pure text.",
"image_data": "../../examples/assets/example_image.png",
"image_data": self.image_path,
"sampling_params": {
"temperature": 0 if n == 1 else 0.5,
"max_new_tokens": max_new_tokens,
@@ -94,28 +119,5 @@ class TestDeepSeekOCR(CustomTestCase):
self.run_decode()
class TestDeepSeekOCRTriton(TestDeepSeekOCR):
@classmethod
def setUpClass(cls):
cls.model = "deepseek-ai/DeepSeek-OCR"
cls.tokenizer = AutoTokenizer.from_pretrained(cls.model, use_fast=False)
cls.base_url = DEFAULT_URL_FOR_TEST
cls.common_args = [
"--device",
"xpu",
"--attention-backend",
"intel_xpu",
]
os.environ["SGLANG_USE_SGL_XPU"] = "0"
cls.process = popen_launch_server(
cls.model,
cls.base_url,
timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
other_args=[
*cls.common_args,
],
)
if __name__ == "__main__":
unittest.main()
test/srt/xpu/test_deepseek_ocr_triton.py
+51
View File
@@ -0,0 +1,51 @@
"""
python3 -m unittest test_deepseek_ocr_triton.py
"""
import os
import unittest
from pathlib import Path
import test_deepseek_ocr as deepseek_ocr
from transformers import AutoTokenizer
from sglang.test.test_utils import (
DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
DEFAULT_URL_FOR_TEST,
popen_launch_server,
)
class TestDeepSeekOCRTriton(deepseek_ocr.TestDeepSeekOCR):
@classmethod
def setUpClass(cls):
cls._cleanup_xpu_memory()
cls.model = "deepseek-ai/DeepSeek-OCR"
cls.tokenizer = AutoTokenizer.from_pretrained(
cls.model, use_fast=False, trust_remote_code=True
)
cls.base_url = DEFAULT_URL_FOR_TEST
cls.image_path = str(
(Path(__file__).resolve().parents[3] / "examples/assets/example_image.png")
)
if not os.path.exists(cls.image_path):
raise FileNotFoundError(f"Image not found: {cls.image_path}")
cls.common_args = [
"--device",
"xpu",
"--attention-backend",
"intel_xpu",
]
os.environ["SGLANG_USE_SGL_XPU"] = "0"
cls.process = popen_launch_server(
cls.model,
cls.base_url,
timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
other_args=[
*cls.common_args,
],
)
if __name__ == "__main__":
unittest.main()
test/srt/xpu/test_intel_xpu_backend.py
+24 -5
View File
@@ -3,6 +3,7 @@ Usage:
python3 -m unittest test_intel_xpu_backend.TestIntelXPUBackend.test_latency_qwen_model
"""
import gc
import unittest
from functools import wraps
@@ -15,26 +16,44 @@ from sglang.test.test_utils import (
)
def _cleanup_xpu_memory():
gc.collect()
try:
import torch
if hasattr(torch, "xpu") and torch.xpu.is_available():
torch.xpu.synchronize()
torch.xpu.empty_cache()
except Exception:
# Best-effort cleanup only.
pass
def intel_xpu_benchmark(extra_args=None, min_throughput=None):
def decorator(test_func):
@wraps(test_func)
def wrapper(self):
_cleanup_xpu_memory()
common_args = [
"--disable-radix",
"--trust-remote-code",
"--mem-fraction-static",
"0.3",
"0.4",
"--batch-size",
"1",
"--device",
"xpu",
]
full_args = common_args + (extra_args or [])
ci_args = ["--input", "64", "--output", "4"] if is_in_ci() else []
full_args = common_args + ci_args + (extra_args or [])
model = test_func(self)
prefill_latency, decode_throughput, decode_latency = run_bench_one_batch(
model, full_args
)
try:
prefill_latency, decode_throughput, decode_latency = (
run_bench_one_batch(model, full_args)
)
finally:
_cleanup_xpu_memory()
print(f"{model=}")
print(f"{prefill_latency=}")