wehub-resource-sync
132 lines
4.1 KiB
Python
132 lines
4.1 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 unittest
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import numpy as np
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from dygraph_to_static_utils import (
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Dy2StTestBase,
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test_ast_only,
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)
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import paddle
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import paddle.inference as paddle_infer
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class TestLayer1(paddle.nn.Layer):
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def __init__(self, hidd):
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super().__init__()
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self.fn = paddle.nn.Linear(hidd, hidd, bias_attr=True)
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def forward(self, x):
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for i in range(5):
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x = paddle.nn.functional.softmax(x, -1)
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x = x.cast("float32")
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x = self.func(x)
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return x
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def func(self, x):
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return self.fn(x)
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class TestLayer2(paddle.nn.Layer):
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def __init__(self, hidd):
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super().__init__()
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self.fn = paddle.nn.Linear(hidd, hidd, bias_attr=True)
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def forward(self, x_list, bool_value):
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x = x_list[0]
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for i in range(5):
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x = paddle.nn.functional.softmax(x, -1)
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x = x.cast("float32")
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x = self.fn(x)
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x = x + x_list[1]
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if bool_value:
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x = x * 3
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else:
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x = 2 * x
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return x
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class TestToStaticInfenrenceModel(Dy2StTestBase):
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@test_ast_only
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def test_dygraph_static_same_result(self):
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hidd = 1024
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batch = 4096
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dtype = "float32"
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x = paddle.rand([batch, hidd], dtype=dtype)
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my_layer = TestLayer1(hidd)
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result0 = my_layer(x).numpy()
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my_static_layer = paddle.incubate.jit.inference(my_layer)
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result1 = my_layer(x).numpy()
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np.testing.assert_allclose(result0, result1, rtol=0.001, atol=1e-05)
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class TestToStaticInfenrenceTensorRTModel(Dy2StTestBase):
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@test_ast_only
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def test_dygraph_static_same_result(self):
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if paddle_infer.get_trt_compile_version()[0] == 0:
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return
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hidd = 1024
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batch = 4096
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dtype = "float32"
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x = paddle.rand([batch, hidd], dtype=dtype)
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my_layer = TestLayer1(hidd)
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result0 = my_layer(x).numpy()
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my_static_layer = paddle.incubate.jit.inference(my_layer, with_trt=True)
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result1 = my_layer(x).numpy()
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np.testing.assert_allclose(result0, result1, rtol=0.001, atol=1e-05)
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class TestToStaticInfenrenceFunc(Dy2StTestBase):
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@test_ast_only
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def test_dygraph_static_same_result(self):
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hidd = 1024
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batch = 4096
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dtype = "float32"
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# test dynamic shape
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x = paddle.rand([batch, hidd], dtype=dtype)
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y = paddle.rand([batch + 1, hidd], dtype=dtype)
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my_layer = TestLayer1(hidd)
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result_x0 = my_layer(x).numpy()
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result_y0 = my_layer(y).numpy()
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my_layer.func = paddle.incubate.jit.inference(my_layer.func)
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my_layer.func = paddle.incubate.jit.inference(my_layer.func)
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result_x1 = my_layer(x).numpy()
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result_y1 = my_layer(y).numpy()
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np.testing.assert_allclose(result_x0, result_x1, rtol=0.001, atol=1e-05)
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np.testing.assert_allclose(result_y0, result_y1, rtol=0.001, atol=1e-05)
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class TestToStaticInputListModel(Dy2StTestBase):
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@test_ast_only
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def test_dygraph_static_same_result(self):
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hidd = 1024
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batch = 4096
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dtype = "float32"
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x = paddle.rand([batch, hidd], dtype=dtype)
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my_layer = TestLayer2(hidd)
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result0 = my_layer([x, x], bool_value=True).numpy()
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my_static_layer = paddle.incubate.jit.inference(my_layer)
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my_static_layer = paddle.incubate.jit.inference(my_layer)
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result1 = my_layer([x, x], bool_value=True).numpy()
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np.testing.assert_allclose(result0, result1, rtol=0.001, atol=1e-05)
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if __name__ == '__main__':
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unittest.main()
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