wehub-resource-sync
134 lines
4.0 KiB
Python
134 lines
4.0 KiB
Python
# Copyright (c) 2023 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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from paddle.inference import Config, create_predictor
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class TestNet(paddle.nn.Layer):
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def __init__(self):
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super().__init__()
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self.fc1 = paddle.nn.Linear(4, 4)
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self.fc2 = paddle.nn.Linear(4, 4)
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def forward(self, x1, x2):
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y1 = self.fc1(x1)
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y2 = self.fc2(x2)
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return y1 + y2
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@unittest.skipIf(
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not paddle.is_compiled_with_cuda(), 'should compile with cuda.'
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)
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class TestPredictorRunWithTensor(unittest.TestCase):
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def setUp(self):
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self.temp_dir = tempfile.TemporaryDirectory()
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net = TestNet()
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model = paddle.jit.to_static(
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net,
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input_spec=[
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paddle.static.InputSpec(
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shape=[None, 4], dtype='float32', name='input0'
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),
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paddle.static.InputSpec(
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shape=[None, 4], dtype='float32', name='input1'
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),
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],
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full_graph=True,
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)
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with paddle.pir_utils.OldIrGuard():
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paddle.jit.save(
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model,
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os.path.join(
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self.temp_dir.name, 'test_predictor_run_model/inference'
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),
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)
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def tearDown(self):
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self.temp_dir.cleanup()
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def init_predictor(self, use_pir: bool):
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with paddle.pir_utils.OldIrGuard():
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config = Config(
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os.path.join(
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self.temp_dir.name,
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'test_predictor_run_model/inference.pdmodel',
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),
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os.path.join(
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self.temp_dir.name,
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'test_predictor_run_model/inference.pdiparams',
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),
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)
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config.enable_use_gpu(256, 0)
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config.switch_ir_optim(False)
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# config.enable_memory_optim()
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config.enable_new_executor()
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if use_pir:
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config.enable_new_ir()
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predictor = create_predictor(config)
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return predictor
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def get_inputs(self):
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input0 = np.array([[1, 2, 3, 4], [2, 3, 4, 5]]).astype(np.float32)
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input1 = np.array([[0.1, 0.2, 0.3, 0.4], [1.2, 1.3, 1.4, 1.5]]).astype(
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np.float32
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)
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input0_tensor = paddle.to_tensor(input0)
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input1_tensor = paddle.to_tensor(input1)
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return [input0_tensor, input1_tensor]
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def get_disorder_output(self, predictor):
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[input0_tensor, input1_tensor] = self.get_inputs()
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input_names = predictor.get_input_names()
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input0_tensor.name = input_names[0]
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input1_tensor.name = input_names[1]
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# disorder
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inputs = [input1_tensor, input0_tensor]
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outputs = predictor.run(inputs)
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return outputs[0]
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def get_inorder_output(self, predictor):
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[input0_tensor, input1_tensor] = self.get_inputs()
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# inorder
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inputs = [input0_tensor, input1_tensor]
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outputs = predictor.run(inputs)
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return outputs[0]
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def test_output(self):
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predictor = self.init_predictor(False)
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output = self.get_inorder_output(predictor)
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pir_predictor = self.init_predictor(True)
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pir_output = self.get_disorder_output(pir_predictor)
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np.testing.assert_allclose(
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output.numpy().flatten(), pir_output.numpy().flatten()
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)
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if __name__ == '__main__':
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unittest.main()
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