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131 changes: 131 additions & 0 deletions tests/operators/test_share_external_data.py
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# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import multiprocessing as mp
import os
import queue
import unittest
from multiprocessing import Process, Queue

import numpy as np
import paddle

from fastdeploy.model_executor.ops.gpu import set_data_ipc, share_external_data


def _create_test_tensor(shape, dtype):
if "float" in str(dtype):
return paddle.rand(shape=shape, dtype=dtype)
elif "int" in str(dtype):
return paddle.randint(-100, 100, shape=shape, dtype=dtype)
elif "bool" in str(dtype):
return paddle.rand(shape=shape, dtype=dtype) > 0.5


def _producer_proc(shm_name, shape, dtype, ready_q, done_q, error_q):
# Create shared memory
try:
paddle.device.set_device("gpu:0")
t = _create_test_tensor(shape, dtype)
set_data_ipc(t, shm_name)
ready_q.put(("ready", t.numpy().tolist()))
_ = done_q.get(timeout=20)
except Exception as e:
error_q.put(("producer_error", str(e)))


def _consumer_proc(shm_name, shape, dtype, result_q, error_q):
# Shard data
try:
paddle.device.set_device("gpu:0")
dummy = paddle.zeros(shape, dtype=dtype)
shared = share_external_data(dummy, shm_name, shape)
result_q.put(("ok", shared.numpy().tolist()))
except Exception as e:
error_q.put(("consumer_error", str(e)))


# Use spawn to avoid forking CUDA contexts
try:
mp.set_start_method("spawn", force=True)
except RuntimeError:
pass


class TestShareExternalData(unittest.TestCase):
def setUp(self):
paddle.seed(2024)
np.random.seed(42)

if not paddle.device.is_compiled_with_cuda():
self.skipTest("CUDA not available, skipping GPU tests")

# Set device to GPU
paddle.device.set_device("gpu:0")

self.test_shape = [4, 8]
self.dtype = paddle.float32
self.shm_prefix = f"test_share_external_{os.getpid()}"

def _run_minimal_cross_process(self):
ready_q = Queue()
result_q = Queue()
error_q = Queue()
done_q = Queue()

p = Process(
target=_producer_proc, args=(self.shm_prefix, self.test_shape, self.dtype, ready_q, done_q, error_q)
)
p.start()

# wait producer ready
try:
status, original_data = ready_q.get(timeout=20)
self.assertEqual(status, "ready")
except Exception:
p.terminate()
self.fail("Producer did not become ready in time")

c = Process(target=_consumer_proc, args=(self.shm_prefix, self.test_shape, self.dtype, result_q, error_q))
c.start()
c.join(timeout=30)

# signal producer to exit now
done_q.put("done")
p.join(timeout=30)

# check errors first (non-blocking)
errors = []
try:
while True:
errors.append(error_q.get_nowait())
except queue.Empty:
pass
self.assertFalse(errors, f"Errors occurred: {errors}")

# verify data
self.assertFalse(result_q.empty(), "No result from consumer")
status, shared_data = result_q.get()
self.assertEqual(status, "ok")
np.testing.assert_allclose(np.array(original_data), np.array(shared_data), rtol=1e-5)

def test_producer_consumer_processes(self):
self._run_minimal_cross_process()

def tearDown(self):
paddle.device.cuda.empty_cache()


if __name__ == "__main__":
unittest.main()
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