File size: 4,479 Bytes
a402b9b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 | import os
import traceback
import unittest
from typing import Dict, List
import torch
import torch.multiprocessing as mp
from sglang.srt.utils.patch_torch import monkey_patch_torch_reductions
from sglang.test.ci.ci_register import register_amd_ci, register_cuda_ci
register_amd_ci(
est_time=19, suite="stage-b-test-large-2-gpu-amd", disabled="see #11127"
)
register_cuda_ci(est_time=19, suite="stage-b-test-large-2-gpu")
class TestReleaseMemoryOccupation(unittest.TestCase):
def test_monkey_patch_torch_reductions(self):
mp.set_start_method("spawn", force=True)
for enable_patch in [False, True]:
for params in [
# Same visible devices
dict(
sender_info=dict(
visible_devices=[0, 1],
tensor_device=1,
),
receiver_info=dict(
visible_devices=[0, 1],
tensor_device=1,
),
),
# Different visible devices
dict(
sender_info=dict(
visible_devices=[0, 1],
tensor_device=1,
),
receiver_info=dict(
visible_devices=[1, 0],
# If enable patch, this should be fixed, and cuda:1 becomes cuda:0
tensor_device=0 if enable_patch else 1,
),
),
]:
with self.subTest(f"{enable_patch=} {params=}"):
self._test_monkey_patch_torch_reductions_core(
enable_patch=enable_patch, **params
)
def _test_monkey_patch_torch_reductions_core(
self,
sender_info: Dict,
receiver_info: Dict,
enable_patch: bool,
):
print(
f'test_monkey_patch_torch_reductions_core {os.environ.get("CUDA_VISIBLE_DEVICES")=}'
)
cuda_visible_devices_list: List[int] = [
int(x)
for x in os.environ.get("CUDA_VISIBLE_DEVICES", "0,1,2,3,4,5,6,7").split(
","
)
]
processes = []
output_reader, output_writer = mp.Pipe(duplex=False)
queue = mp.Queue()
for role, info in [
("sender", sender_info),
("receiver", receiver_info),
]:
os.environ["CUDA_VISIBLE_DEVICES"] = ",".join(
str(cuda_visible_devices_list[device])
for device in info["visible_devices"]
)
p = mp.Process(
target=_run_subprocess,
kwargs=dict(
role=role,
queue=queue,
output_writer=output_writer,
tensor_device=info["tensor_device"],
enable_patch=enable_patch,
),
)
p.start()
processes.append(p)
for _ in range(len(processes)):
self.assertTrue(
output_reader.recv(), f"Subprocess has error, please see logs above."
)
for p in processes:
p.join()
def _run_subprocess(
role: str, queue: mp.Queue, output_writer, tensor_device: int, enable_patch: bool
):
print(
f'subprocess[{role}] start {os.environ.get("CUDA_VISIBLE_DEVICES")=}',
flush=True,
)
if enable_patch:
print(f"subprocess[{role}] execute monkey_patch_torch_reductions", flush=True)
monkey_patch_torch_reductions()
try:
if role == "sender":
tensor = torch.tensor([1.0, 2.0], device=f"cuda:{tensor_device}")
print(f"sender queue.put {tensor=} {tensor.device=}")
queue.put(tensor)
assert queue.get() == "done"
elif role == "receiver":
tensor = queue.get()
print(f"receiver queue.get {tensor=} {tensor.device=}")
assert str(tensor.device) == f"cuda:{tensor_device}"
queue.put("done")
else:
raise NotImplementedError
execution_ok = True
except Exception as e:
print(f"subprocess[{role}] has error: {e}", flush=True)
traceback.print_exc()
execution_ok = False
output_writer.send(execution_ok)
output_writer.close()
if __name__ == "__main__":
unittest.main()
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