chenchenghao/sglang
1
0
Fork 0
Code Issues Pull Requests Actions 12 Packages Projects Releases Wiki Activity

[Test] Fix flaky multi-instance memory occupation test (#21074)

Browse Source
This commit is contained in:
Liangsheng Yin
2026-03-20 22:19:03 -07:00
committed by GitHub
parent 427442304a
commit 9614271ae4
1 changed files with 77 additions and 75 deletions
Download Patch File Download Diff File Expand all files Collapse all files

152
test/registered/rl/test_multi_instance_release_memory_occupation.py
View File

@@ -1,7 +1,6 @@
import gc
import multiprocessing
import os
import time
import traceback
import unittest
from multiprocessing import Process
@@ -30,6 +29,11 @@ TEST_SUITE = dict(
tp_size=2,
)
# Minimum expected memory change in MB for each operation.
# Llama-3.2-1B bf16 is ~2GB total, ~1GB per TP rank.
# KV cache with mem_fraction_static=0.83 is much larger.
MIN_DELTA_MB = 200
class EngineWrapper:
"""
@@ -88,8 +92,26 @@ class EngineWrapper:
dist.barrier(group=self._device_mesh_cpu.get_group())
def get_gpu_memory_gb(gpu_id=0):
return torch.cuda.device_memory_used() / 1024**3
def get_gpu_memory_mb(device_id: int) -> float:
"""Return device-level GPU memory used in MB."""
free, total = torch.cuda.mem_get_info(device_id)
return (total - free) / (1024**2)
def assert_memory_decreased(before_mb, after_mb, step_name):
delta = before_mb - after_mb
assert delta > MIN_DELTA_MB, (
f"[{step_name}] Expected memory decrease > {MIN_DELTA_MB} MB, "
f"got delta={delta:.0f} MB (before={before_mb:.0f}, after={after_mb:.0f})"
)
def assert_memory_increased(before_mb, after_mb, step_name):
delta = after_mb - before_mb
assert delta > MIN_DELTA_MB, (
f"[{step_name}] Expected memory increase > {MIN_DELTA_MB} MB, "
f"got delta={delta:.0f} MB (before={before_mb:.0f}, after={after_mb:.0f})"
)
class TestMultiInstanceReleaseMemoryOccupation(CustomTestCase):
@@ -153,14 +175,13 @@ def _run_sglang_subprocess(
mesh_kwargs = dict(
mesh_shape=(dp_size, tp_size, 1), mesh_dim_names=["dp", "tp", "pp"]
)
inference_device_mesh_device = init_device_mesh("cuda", **mesh_kwargs)
inference_device_mesh_cpu = init_device_mesh("cpu", **mesh_kwargs)
print(
f"subprocess[{rank=},{base_gpu_id=},{rank=},{tp_size=}] {inference_device_mesh_device=} {inference_device_mesh_cpu=}"
)
_mem_usage = get_gpu_memory_gb(rank)
print(f"GPU{rank} Memory usage before starting Engine: {_mem_usage}")
# Only TP master ranks (rank % tp_size == 0) create the Engine and
# measure memory. Non-master ranks share the same GPU and would see
# device-level memory from the master's Engine workers, causing
# unpredictable assertion results.
is_tp_master = rank % tp_size == 0
engine = EngineWrapper(
model_path=model_path,
@@ -169,86 +190,67 @@ def _run_sglang_subprocess(
device_mesh_cpu=inference_device_mesh_cpu["tp"],
base_gpu_id=base_gpu_id,
)
print(f"subprocess[{rank=}] {engine=}", flush=True)
print(f"subprocess[{rank=}] engine created, {is_tp_master=}", flush=True)
# 1 - release kv cache
_mem_usage = get_gpu_memory_gb(rank)
print(f"GPU{rank} Memory usage before releasing Sgl KV cache: {_mem_usage}")
if is_tp_master:
mem_before = get_gpu_memory_mb(rank)
print(f"GPU{rank} before releasing KV cache: {mem_before:.0f} MB")
engine.release_memory_occupation(tags=["kv_cache"])
_curr_usage = get_gpu_memory_gb(rank)
assert (
_curr_usage < _mem_usage
), f"Memory usage after releasing KV cache must be reduced! before: {_mem_usage} vs after: {_curr_usage}"
if is_tp_master:
mem_after = get_gpu_memory_mb(rank)
assert_memory_decreased(mem_before, mem_after, "release KV cache")
# 2 - release sglang weights
_mem_usage = get_gpu_memory_gb(rank)
print(f"GPU{rank} Memory usage before releasing Sgl weights: {_mem_usage}")
if is_tp_master:
mem_before = get_gpu_memory_mb(rank)
print(f"GPU{rank} before releasing weights: {mem_before:.0f} MB")
engine.release_memory_occupation(tags=["weights"])
if is_tp_master:
mem_after = get_gpu_memory_mb(rank)
assert_memory_decreased(mem_before, mem_after, "release weights")
_curr_usage = get_gpu_memory_gb(rank)
assert (
_curr_usage < _mem_usage
), f"Memory usage after releasing weights must be reduced! before: {_mem_usage} vs after: {_curr_usage}"
# 3 - load hf model (TP master only)
hf_model = None
if is_tp_master:
mem_before = get_gpu_memory_mb(rank)
print(f"GPU{rank} before loading HF model: {mem_before:.0f} MB")
# Avoid device_map= which triggers accelerate dispatch hooks in
# transformers v5, preventing clean memory release on del.
hf_model = AutoModelForCausalLM.from_pretrained(
DEFAULT_SMALL_MODEL_NAME_FOR_TEST_BASE,
torch_dtype="bfloat16",
).to(f"cuda:{rank}")
mem_after = get_gpu_memory_mb(rank)
assert_memory_increased(mem_before, mem_after, "load HF model")
dist.barrier(group=inference_device_mesh_cpu["tp"].get_group())
# 3 - load hf model
_mem_usage = get_gpu_memory_gb(rank)
print(
f"GPU{rank} Memory usage after releasing Sgl weights and kv cache: {_mem_usage}"
)
hf_model = AutoModelForCausalLM.from_pretrained(
DEFAULT_SMALL_MODEL_NAME_FOR_TEST_BASE,
torch_dtype="bfloat16",
device_map=f"cuda:{rank}",
trust_remote_code=True,
).cuda()
_curr_usage = get_gpu_memory_gb(rank)
assert (
_curr_usage > _mem_usage
), f"Memory usage after loading hf model must be increased! before: {_mem_usage} vs after: {_curr_usage}"
# 4 - resume sglang weights and update the weights
_mem_usage = get_gpu_memory_gb(rank)
print(f"GPU{rank} Memory usage after loading hf model: {_mem_usage}")
# 4 - resume sglang weights and update from hf model
engine.resume_memory_occupation(tags=["weights"])
engine.update_weights_from_tensor(
named_tensors=list(hf_model.named_parameters())
named_tensors=list(hf_model.named_parameters()) if hf_model else []
)
# 5 - release hf model
_mem_usage = get_gpu_memory_gb(rank)
print(f"GPU{rank} Memory usage after resuming Sgl weights: {_mem_usage}")
# In transformers v5, from_pretrained with device_map attaches accelerate
# dispatch hooks that hold strong refs to parameters. Remove them first.
try:
from accelerate.hooks import remove_hook_from_submodules
# 5 - release hf model (TP master only)
if is_tp_master:
mem_before = get_gpu_memory_mb(rank)
print(f"GPU{rank} before releasing HF model: {mem_before:.0f} MB")
del hf_model
gc.collect()
torch.cuda.empty_cache()
mem_after = get_gpu_memory_mb(rank)
assert_memory_decreased(mem_before, mem_after, "release HF model")
dist.barrier(group=inference_device_mesh_cpu["tp"].get_group())
remove_hook_from_submodules(hf_model)
except (ImportError, Exception):
pass
del hf_model
hf_model = None
gc.collect()
torch.cuda.empty_cache()
time.sleep(3)
gc.collect()
torch.cuda.empty_cache()
_curr_usage = get_gpu_memory_gb(rank)
assert (
_curr_usage < _mem_usage
), f"Memory usage after releasing hf model must be reduced! before: {_mem_usage} vs after: {_curr_usage}"
# 6 - resume slgang kv cache
_mem_usage = get_gpu_memory_gb(rank)
print(f"GPU{rank} Memory usage after releasing hf model: {_mem_usage}")
# 6 - resume kv cache
if is_tp_master:
mem_before = get_gpu_memory_mb(rank)
print(f"GPU{rank} before resuming KV cache: {mem_before:.0f} MB")
engine.resume_memory_occupation(tags=["kv_cache"])
_curr_usage = get_gpu_memory_gb(rank)
assert (
_curr_usage > _mem_usage
), f"Memory usage after resuming kv cache must be increased! before: {_mem_usage} vs after: {_curr_usage}"
# 7 - Final checking!
_mem_usage = get_gpu_memory_gb(rank)
print(f"GPU{rank} Memory usage after resuming Sgl KV cache: {_mem_usage}")
if is_tp_master:
mem_after = get_gpu_memory_mb(rank)
assert_memory_increased(mem_before, mem_after, "resume KV cache")
print(f"GPU{rank} final memory: {mem_after:.0f} MB")
execution_ok = True
except Exception as e: