wehub-resource-sync
162 lines
4.6 KiB
Python
162 lines
4.6 KiB
Python
# Copyright (c) 2021 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 os
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import pathlib
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import shutil
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import unittest
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import numpy as np
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from op_test import is_custom_device
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import paddle
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from paddle.device.cuda.graphs import CUDAGraph
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def can_use_cuda_graph():
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return (
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paddle.is_compiled_with_cuda()
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or is_custom_device()
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or paddle.is_compiled_with_xpu()
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) and not paddle.is_compiled_with_rocm()
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class TestCUDAGraphInDygraphMode(unittest.TestCase):
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def random_tensor(self, shape):
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return paddle.to_tensor(
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np.random.randint(low=0, high=10, size=shape).astype("float32")
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)
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def test_cuda_graph_dynamic_graph(self):
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if not can_use_cuda_graph():
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return
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shape = [2, 3]
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x = self.random_tensor(shape)
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z = self.random_tensor(shape)
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g = CUDAGraph()
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g.capture_begin()
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y = x + 10
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z.add_(x)
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g.capture_end()
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for _ in range(10):
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z_np_init = z.numpy()
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x_new = self.random_tensor(shape)
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x.copy_(x_new, False)
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g.replay()
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x_np = x_new.numpy()
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y_np = y.numpy()
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z_np = z.numpy()
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self.assertTrue((y_np - x_np == 10).all())
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self.assertTrue((z_np - z_np_init == x_np).all())
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g.reset()
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def test_concat_and_split(self):
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if not can_use_cuda_graph():
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return
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concat_num = 100
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xs = []
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xs_np = []
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for i in range(concat_num):
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x_np = np.random.random(size=[1]).astype(np.float32)
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xs.append(paddle.to_tensor(x_np))
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xs_np.append(x_np)
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graph = CUDAGraph()
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graph.capture_begin()
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y = paddle.concat(xs)
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zs = paddle.split(y, len(xs))
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graph.capture_end()
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graph.replay()
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y_np = y.numpy()
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y_np_expected = np.concatenate(xs_np)
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np.testing.assert_array_equal(y_np, y_np_expected)
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self.assertEqual(len(zs), len(xs_np))
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for i, z in enumerate(zs):
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np.testing.assert_array_equal(z.numpy(), xs_np[i])
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output_dir = f'cuda_graph_dot_{os.getpid()}'
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try:
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graph.print_to_dot_files(pathlib.Path(output_dir))
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graph.reset()
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shutil.rmtree(output_dir)
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except Exception as e:
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msg = str(e)
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sub_msg = "The print_to_dot_files() method is only supported when CUDA version >= 11.3"
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self.assertTrue(sub_msg in msg)
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finally:
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graph.reset()
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def test_dataloader(self):
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if not can_use_cuda_graph():
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return
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class AutoIncDataset(paddle.io.Dataset):
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def __init__(self, n, dtype):
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self.n = n
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self.dtype = dtype
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def __len__(self):
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return self.n
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def __getitem__(self, idx):
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return np.array([idx]).astype(self.dtype)
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n = 100
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dtype = 'int64'
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dataset = AutoIncDataset(n, dtype)
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data_loader = paddle.io.DataLoader(
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dataset, batch_size=1, num_workers=2, use_buffer_reader=True
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)
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x = None
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y = None
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graph = None
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for i, data in enumerate(data_loader):
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if graph is None:
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x = data
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x = x.to("xpu")
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graph = CUDAGraph()
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graph.capture_begin()
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y = x * x
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graph.capture_end()
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else:
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x.copy_(data, False)
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x = x.to("xpu")
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graph.replay()
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actual_x = np.array([[i]]).astype(dtype)
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actual_y = np.array([[i * i]]).astype(dtype)
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np.testing.assert_array_equal(actual_x, x.numpy())
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np.testing.assert_array_equal(actual_y, y.numpy())
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def test_dev_ctx_alloc(self):
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if not can_use_cuda_graph():
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return
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x = paddle.to_tensor([2], dtype='float32')
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graph = CUDAGraph()
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graph.capture_begin()
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y = paddle.cast(x, dtype='float16')
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graph.capture_end()
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if __name__ == "__main__":
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unittest.main()
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