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| 1 | +# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved. |
| 2 | +# |
| 3 | +# Licensed under the Apache License, Version 2.0 (the "License"); |
| 4 | +# you may not use this file except in compliance with the License. |
| 5 | +# You may obtain a copy of the License at |
| 6 | +# |
| 7 | +# http://www.apache.org/licenses/LICENSE-2.0 |
| 8 | +# |
| 9 | +# Unless required by applicable law or agreed to in writing, software |
| 10 | +# distributed under the License is distributed on an "AS IS" BASIS, |
| 11 | +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| 12 | +# See the License for the specific language governing permissions and |
| 13 | +# limitations under the License. |
| 14 | + |
| 15 | +import multiprocessing as mp |
| 16 | +import os |
| 17 | +import queue |
| 18 | +import unittest |
| 19 | +from multiprocessing import Process, Queue |
| 20 | + |
| 21 | +import numpy as np |
| 22 | +import paddle |
| 23 | + |
| 24 | +from fastdeploy.model_executor.ops.gpu import set_data_ipc, share_external_data |
| 25 | + |
| 26 | + |
| 27 | +def _create_test_tensor(shape, dtype): |
| 28 | + if "float" in str(dtype): |
| 29 | + return paddle.rand(shape=shape, dtype=dtype) |
| 30 | + elif "int" in str(dtype): |
| 31 | + return paddle.randint(-100, 100, shape=shape, dtype=dtype) |
| 32 | + elif "bool" in str(dtype): |
| 33 | + return paddle.rand(shape=shape, dtype=dtype) > 0.5 |
| 34 | + |
| 35 | + |
| 36 | +def _producer_proc(shm_name, shape, dtype, ready_q, done_q, error_q): |
| 37 | + # Create shared memory |
| 38 | + try: |
| 39 | + paddle.device.set_device("gpu:0") |
| 40 | + t = _create_test_tensor(shape, dtype) |
| 41 | + set_data_ipc(t, shm_name) |
| 42 | + ready_q.put(("ready", t.numpy().tolist())) |
| 43 | + _ = done_q.get(timeout=20) |
| 44 | + except Exception as e: |
| 45 | + error_q.put(("producer_error", str(e))) |
| 46 | + |
| 47 | + |
| 48 | +def _consumer_proc(shm_name, shape, dtype, result_q, error_q): |
| 49 | + # Shard data |
| 50 | + try: |
| 51 | + paddle.device.set_device("gpu:0") |
| 52 | + dummy = paddle.zeros(shape, dtype=dtype) |
| 53 | + shared = share_external_data(dummy, shm_name, shape) |
| 54 | + result_q.put(("ok", shared.numpy().tolist())) |
| 55 | + except Exception as e: |
| 56 | + error_q.put(("consumer_error", str(e))) |
| 57 | + |
| 58 | + |
| 59 | +# Use spawn to avoid forking CUDA contexts |
| 60 | +try: |
| 61 | + mp.set_start_method("spawn", force=True) |
| 62 | +except RuntimeError: |
| 63 | + pass |
| 64 | + |
| 65 | + |
| 66 | +class TestShareExternalData(unittest.TestCase): |
| 67 | + def setUp(self): |
| 68 | + paddle.seed(2024) |
| 69 | + np.random.seed(42) |
| 70 | + |
| 71 | + if not paddle.device.is_compiled_with_cuda(): |
| 72 | + self.skipTest("CUDA not available, skipping GPU tests") |
| 73 | + |
| 74 | + # Set device to GPU |
| 75 | + paddle.device.set_device("gpu:0") |
| 76 | + |
| 77 | + self.test_shape = [4, 8] |
| 78 | + self.dtype = paddle.float32 |
| 79 | + self.shm_prefix = f"test_share_external_{os.getpid()}" |
| 80 | + |
| 81 | + def _run_minimal_cross_process(self): |
| 82 | + ready_q = Queue() |
| 83 | + result_q = Queue() |
| 84 | + error_q = Queue() |
| 85 | + done_q = Queue() |
| 86 | + |
| 87 | + p = Process( |
| 88 | + target=_producer_proc, args=(self.shm_prefix, self.test_shape, self.dtype, ready_q, done_q, error_q) |
| 89 | + ) |
| 90 | + p.start() |
| 91 | + |
| 92 | + # wait producer ready |
| 93 | + try: |
| 94 | + status, original_data = ready_q.get(timeout=20) |
| 95 | + self.assertEqual(status, "ready") |
| 96 | + except Exception: |
| 97 | + p.terminate() |
| 98 | + self.fail("Producer did not become ready in time") |
| 99 | + |
| 100 | + c = Process(target=_consumer_proc, args=(self.shm_prefix, self.test_shape, self.dtype, result_q, error_q)) |
| 101 | + c.start() |
| 102 | + c.join(timeout=30) |
| 103 | + |
| 104 | + # signal producer to exit now |
| 105 | + done_q.put("done") |
| 106 | + p.join(timeout=30) |
| 107 | + |
| 108 | + # check errors first (non-blocking) |
| 109 | + errors = [] |
| 110 | + try: |
| 111 | + while True: |
| 112 | + errors.append(error_q.get_nowait()) |
| 113 | + except queue.Empty: |
| 114 | + pass |
| 115 | + self.assertFalse(errors, f"Errors occurred: {errors}") |
| 116 | + |
| 117 | + # verify data |
| 118 | + self.assertFalse(result_q.empty(), "No result from consumer") |
| 119 | + status, shared_data = result_q.get() |
| 120 | + self.assertEqual(status, "ok") |
| 121 | + np.testing.assert_allclose(np.array(original_data), np.array(shared_data), rtol=1e-5) |
| 122 | + |
| 123 | + def test_producer_consumer_processes(self): |
| 124 | + self._run_minimal_cross_process() |
| 125 | + |
| 126 | + def tearDown(self): |
| 127 | + paddle.device.cuda.empty_cache() |
| 128 | + |
| 129 | + |
| 130 | +if __name__ == "__main__": |
| 131 | + unittest.main() |
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