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enable ut test for xpu devices (#11712)

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Co-authored-by: jundu <jun.du@intel.com>
Co-authored-by: Gao, Pengfei <pengfei.gao@intel.com>
This commit is contained in:
DiweiSun
2026-02-03 11:15:14 -08:00
committed by GitHub
co-authored by jundu Gao, Pengfei
parent 0a6925639b
commit 495290aefd
20 changed files with 237 additions and 151 deletions
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test/registered/rl/test_release_memory_occupation.py
+13 -16
View File
@@ -31,7 +31,6 @@ import os
import time
import unittest
import torch
from transformers import AutoModelForCausalLM
import sglang as sgl
@@ -40,6 +39,7 @@ from sglang.srt.constants import (
GPU_MEMORY_TYPE_KV_CACHE,
GPU_MEMORY_TYPE_WEIGHTS,
)
from sglang.srt.utils import get_device
from sglang.test.ci.ci_register import register_cuda_ci
from sglang.test.test_utils import (
DEFAULT_HYBRID_MAMBA_MODEL_NAME_FOR_TEST,
@@ -48,6 +48,9 @@ from sglang.test.test_utils import (
DEFAULT_SMALL_MOE_MODEL_NAME_FOR_TEST_BASE,
DEFAULT_SMALL_MOE_MODEL_NAME_FOR_TEST_CHAT,
CustomTestCase,
empty_gpu_cache,
get_gpu_count,
get_gpu_memory_gb,
)
register_cuda_ci(
@@ -60,10 +63,6 @@ register_cuda_ci(
_DEBUG_EXTRA = False
def get_gpu_memory_gb():
return torch.cuda.device_memory_used() / 1024**3
class TestReleaseMemoryOccupation(CustomTestCase):
def _setup_engine(
self,
@@ -120,9 +119,7 @@ class TestReleaseMemoryOccupation(CustomTestCase):
def test_release_and_resume_occupation(self):
# Without multi-stage release and resume, we need to carefully control the memory fraction to avoid OOM
model_name = DEFAULT_SMALL_MODEL_NAME_FOR_TEST
assert (
torch.cuda.device_count() >= 2
), "Need at least 2 GPUs for tensor parallel tests"
assert get_gpu_count() >= 2, "Need at least 2 GPUs for tensor parallel tests"
for tp_size in [1, 2]:
@@ -165,13 +162,13 @@ class TestReleaseMemoryOccupation(CustomTestCase):
hf_model_new = AutoModelForCausalLM.from_pretrained(
DEFAULT_SMALL_MODEL_NAME_FOR_TEST_BASE,
torch_dtype="bfloat16",
device_map="cuda",
device_map=get_device(),
)
engine.update_weights_from_tensor(list(hf_model_new.named_parameters()))
# destroy the hf model
del hf_model_new
torch.cuda.empty_cache()
empty_gpu_cache()
print("generate (#2)")
outputs = engine.generate(params["prompt"], params["sampling_params"])[
@@ -232,7 +229,7 @@ class TestReleaseMemoryOccupation(CustomTestCase):
model_name = DEFAULT_SMALL_MODEL_NAME_FOR_TEST
for tp_size in [1, 2]:
if tp_size == 2 and torch.cuda.device_count() < 2:
if tp_size == 2 and get_gpu_count() < 2:
continue
print(f"Testing tp_size={tp_size} for test_multi_stage_release_and_resume")
@@ -320,14 +317,14 @@ class TestReleaseMemoryOccupation(CustomTestCase):
hf_model_new = AutoModelForCausalLM.from_pretrained(
DEFAULT_SMALL_MODEL_NAME_FOR_TEST_BASE,
torch_dtype="bfloat16",
device_map="cuda",
device_map=get_device(),
)
gpu_memory_usage_after_loaded_hf_model = get_gpu_memory_gb()
engine.update_weights_from_tensor(list(hf_model_new.named_parameters()))
# destroy the hf model
del hf_model_new
torch.cuda.empty_cache()
empty_gpu_cache()
engine.resume_memory_occupation(tags=[GPU_MEMORY_TYPE_KV_CACHE])
gpu_memory_usage_after_resume_kv_cache = get_gpu_memory_gb()
@@ -399,13 +396,13 @@ class TestReleaseMemoryOccupation(CustomTestCase):
hf_model_new = AutoModelForCausalLM.from_pretrained(
DEFAULT_SMALL_MOE_MODEL_NAME_FOR_TEST_BASE,
torch_dtype="bfloat16",
device_map="cuda",
device_map=get_device(),
)
engine.update_weights_from_tensor(list(hf_model_new.named_parameters()))
# destroy the hf model
del hf_model_new
torch.cuda.empty_cache()
empty_gpu_cache()
print("generate (#2)")
outputs = engine.generate(params["prompt_moe"], params["sampling_params_moe"])[
@@ -463,7 +460,7 @@ class TestReleaseMemoryOccupation(CustomTestCase):
engine.update_weights_from_disk(model_name)
# destroy the hf model
torch.cuda.empty_cache()
empty_gpu_cache()
print("generate (#2)")
outputs = engine.generate(