mirror of
https://github.com/PaddlePaddle/FastDeploy.git
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132 lines
4.0 KiB
Python
132 lines
4.0 KiB
Python
# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import multiprocessing as mp
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import os
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import queue
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import unittest
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from multiprocessing import Process, Queue
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import numpy as np
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import paddle
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from fastdeploy.model_executor.ops.gpu import set_data_ipc, share_external_data
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def _create_test_tensor(shape, dtype):
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if "float" in str(dtype):
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return paddle.rand(shape=shape, dtype=dtype)
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elif "int" in str(dtype):
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return paddle.randint(-100, 100, shape=shape, dtype=dtype)
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elif "bool" in str(dtype):
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return paddle.rand(shape=shape, dtype=dtype) > 0.5
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def _producer_proc(shm_name, shape, dtype, ready_q, done_q, error_q):
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# Create shared memory
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try:
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paddle.device.set_device("gpu:0")
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t = _create_test_tensor(shape, dtype)
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set_data_ipc(t, shm_name)
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ready_q.put(("ready", t.numpy().tolist()))
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_ = done_q.get(timeout=20)
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except Exception as e:
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error_q.put(("producer_error", str(e)))
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def _consumer_proc(shm_name, shape, dtype, result_q, error_q):
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# Shard data
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try:
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paddle.device.set_device("gpu:0")
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dummy = paddle.zeros(shape, dtype=dtype)
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shared = share_external_data(dummy, shm_name, shape)
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result_q.put(("ok", shared.numpy().tolist()))
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except Exception as e:
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error_q.put(("consumer_error", str(e)))
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# Use spawn to avoid forking CUDA contexts
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try:
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mp.set_start_method("spawn", force=True)
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except RuntimeError:
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pass
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class TestShareExternalData(unittest.TestCase):
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def setUp(self):
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paddle.seed(2024)
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np.random.seed(42)
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if not paddle.device.is_compiled_with_cuda():
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self.skipTest("CUDA not available, skipping GPU tests")
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# Set device to GPU
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paddle.device.set_device("gpu:0")
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self.test_shape = [4, 8]
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self.dtype = paddle.float32
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self.shm_prefix = f"test_share_external_{os.getpid()}"
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def _run_minimal_cross_process(self):
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ready_q = Queue()
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result_q = Queue()
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error_q = Queue()
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done_q = Queue()
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p = Process(
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target=_producer_proc, args=(self.shm_prefix, self.test_shape, self.dtype, ready_q, done_q, error_q)
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)
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p.start()
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# wait producer ready
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try:
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status, original_data = ready_q.get(timeout=20)
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self.assertEqual(status, "ready")
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except Exception:
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p.terminate()
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self.fail("Producer did not become ready in time")
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c = Process(target=_consumer_proc, args=(self.shm_prefix, self.test_shape, self.dtype, result_q, error_q))
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c.start()
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c.join(timeout=30)
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# signal producer to exit now
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done_q.put("done")
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p.join(timeout=30)
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# check errors first (non-blocking)
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errors = []
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try:
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while True:
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errors.append(error_q.get_nowait())
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except queue.Empty:
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pass
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self.assertFalse(errors, f"Errors occurred: {errors}")
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# verify data
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self.assertFalse(result_q.empty(), "No result from consumer")
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status, shared_data = result_q.get()
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self.assertEqual(status, "ok")
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np.testing.assert_allclose(np.array(original_data), np.array(shared_data), rtol=1e-5)
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def test_producer_consumer_processes(self):
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self._run_minimal_cross_process()
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def tearDown(self):
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paddle.device.cuda.empty_cache()
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if __name__ == "__main__":
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unittest.main()
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