wehub-resource-sync
133 lines
4.3 KiB
Python
133 lines
4.3 KiB
Python
# Copyright (c) 2024 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 tempfile
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import unittest
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import numpy as np
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import paddle
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class TestALoadPdmodelTranslatePir(unittest.TestCase):
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def setUp(self):
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paddle.seed(2022)
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self.temp_dir = tempfile.TemporaryDirectory()
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self.save_path = os.path.join(self.temp_dir.name, 'saveload')
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self.place = (
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paddle.CUDAPlace(0)
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if paddle.is_compiled_with_cuda()
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else paddle.CPUPlace()
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)
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def tearDown(self):
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self.temp_dir.cleanup()
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def test_load_inference_model(self):
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paddle.enable_static()
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if paddle.framework.in_pir_mode():
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return
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np_x = np.random.randn(9, 10, 11).astype('float32')
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main_prog = paddle.static.Program()
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startup_prog = paddle.static.Program()
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with paddle.static.program_guard(main_prog, startup_prog):
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x = paddle.static.data(shape=np_x.shape, name='x', dtype=np_x.dtype)
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linear = paddle.nn.Linear(np_x.shape[-1], np_x.shape[-1])
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linear_out = linear(x)
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relu_out = paddle.nn.functional.relu(linear_out)
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axis = paddle.full([1], 2, dtype='int64')
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out = paddle.cumsum(relu_out, axis=axis)
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loss = paddle.mean(out)
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sgd = paddle.optimizer.SGD(learning_rate=0.0)
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sgd.minimize(paddle.mean(out))
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exe = paddle.static.Executor(self.place)
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exe.run(startup_prog)
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out_old = exe.run(feed={'x': np_x}, fetch_list=[out])
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# run infer
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paddle.static.save_inference_model(
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self.save_path, [x], [out], exe, program=main_prog
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)
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exe = paddle.static.Executor(self.place)
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load_program, _, _ = paddle.static.load_inference_model(
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self.save_path, exe
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)
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with paddle.pir_utils.IrGuard():
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startup_prog = paddle.static.Program()
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with paddle.static.program_guard(load_program, startup_prog):
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exe.run(startup_prog)
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out_new = exe.run(load_program, feed={'x': np_x}, fetch_list=[])
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np.testing.assert_allclose(out_old, out_new)
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load_program = paddle.load(
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self.save_path + '.pdmodel',
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)
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with paddle.pir_utils.IrGuard():
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startup_prog = paddle.static.Program()
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with paddle.static.program_guard(load_program, startup_prog):
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exe.run(startup_prog)
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out_new = exe.run(load_program, feed={'x': np_x}, fetch_list=[])
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np.testing.assert_allclose(out_old, out_new)
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class TestJitSaveOp(unittest.TestCase):
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def setUp(self):
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self.temp_dir = tempfile.TemporaryDirectory()
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self.model_path = os.path.join(self.temp_dir.name, "pir_save_load")
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paddle.disable_static()
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linear = paddle.nn.Linear(10, 10)
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path = os.path.join(self.model_path, "linear")
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paddle.jit.save(
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linear,
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path,
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input_spec=[paddle.static.InputSpec([10, 10], 'float32', 'x')],
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)
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def tearDown(self):
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self.temp_dir.cleanup()
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def test_with_pir(self):
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paddle.enable_static()
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if paddle.framework.in_pir_mode():
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return
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place = (
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paddle.CUDAPlace(0)
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if paddle.is_compiled_with_cuda()
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else paddle.CPUPlace()
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)
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exe = paddle.static.Executor(place)
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[
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inference_program,
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feed_target_names,
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fetch_targets,
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] = paddle.static.io.load_inference_model(
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self.model_path,
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executor=exe,
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model_filename="linear.pdmodel",
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params_filename="linear.pdiparams",
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)
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if __name__ == '__main__':
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unittest.main()
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