wehub-resource-sync
129 lines
4.3 KiB
Python
Executable File
129 lines
4.3 KiB
Python
Executable File
#!/usr/bin/env python3
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# Copyright (c) 2021 CINN 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 sys
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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 import base, static
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from paddle.cinn.common import DefaultHostTarget, DefaultNVGPUTarget, Float
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from paddle.cinn.frontend import Computation, NetBuilder
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assert len(sys.argv) == 3
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enable_gpu = sys.argv.pop()
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naive_model_dir = sys.argv.pop()
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class TestNetBuilder(unittest.TestCase):
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def setUp(self):
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if enable_gpu == "ON":
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self.target = DefaultNVGPUTarget()
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else:
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self.target = DefaultHostTarget()
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def get_paddle_result(self, inputdata):
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paddle.enable_static()
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a = static.data(name='A', shape=[24, 56, 56], dtype='float32')
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b = static.data(name='B', shape=[24, 56, 56], dtype='float32')
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c = paddle.add(a, b)
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d = paddle.nn.initializer.NumpyArrayInitializer(
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np.array(inputdata[2]).reshape((144, 24, 1, 1)).astype('float32')
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)
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res = paddle.nn.Conv2D(
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in_channels=24,
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out_channels=144,
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kernel_size=1,
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stride=1,
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dilation=1,
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padding=0,
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weight_attr=d,
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)(c)
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exe = static.Executor(paddle.CPUPlace())
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exe.run(static.default_startup_program())
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x = np.array(inputdata[0]).reshape((1, 24, 56, 56)).astype("float32")
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y = np.array(inputdata[1]).reshape((1, 24, 56, 56)).astype("float32")
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output = exe.run(feed={"A": x, "B": y}, fetch_list=[res])
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return np.array(output)
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def test_build_and_compile(self):
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builder = NetBuilder("test_basic")
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a = builder.create_input(Float(32), (1, 24, 56, 56), "A")
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b = builder.create_input(Float(32), (1, 24, 56, 56), "B")
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c = builder.add(a, b)
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d = builder.create_input(Float(32), (144, 24, 1, 1), "D")
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e = builder.conv(c, d)
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computation = Computation.build_and_compile(self.target, builder)
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A_data = np.random.random([1, 24, 56, 56]).astype("float32")
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B_data = np.random.random([1, 24, 56, 56]).astype("float32")
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D_data = np.random.random([144, 24, 1, 1]).astype("float32")
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computation.get_tensor("A").from_numpy(A_data, self.target)
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computation.get_tensor("B").from_numpy(B_data, self.target)
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computation.get_tensor("D").from_numpy(D_data, self.target)
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computation.execute()
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e_tensor = computation.get_tensor(str(e))
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edata_cinn = e_tensor.numpy(self.target)
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edata_paddle = self.get_paddle_result([A_data, B_data, D_data])
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np.testing.assert_allclose(edata_cinn, edata_paddle, atol=1e-5)
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class TestCompilePaddleModel(unittest.TestCase):
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def setUp(self):
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if enable_gpu == "ON":
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self.target = DefaultNVGPUTarget()
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else:
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self.target = DefaultHostTarget()
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def test_compile_paddle_model(self):
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A_shape = [4, 30]
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A_data = np.random.random(A_shape).astype("float32")
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computation = Computation.compile_paddle_model(
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self.target, naive_model_dir, ["A"], [A_shape], False
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)
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A_tensor = computation.get_tensor("A")
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A_tensor.from_numpy(A_data, self.target)
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computation.execute()
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out = computation.get_tensor("fc_0.tmp_2")
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res_cinn = out.numpy(self.target)
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config = base.core.AnalysisConfig(naive_model_dir)
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config.disable_gpu()
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config.switch_ir_optim(False)
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paddle_predictor = base.core.create_paddle_predictor(config)
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data = base.core.PaddleTensor(A_data)
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paddle_out = paddle_predictor.run([data])
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res_paddle = paddle_out[0].as_ndarray()
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np.testing.assert_allclose(res_cinn, res_paddle, atol=1e-5)
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if __name__ == "__main__":
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unittest.main()
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