# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import unittest import numpy as np from dygraph_to_static_utils import ( Dy2StTestBase, test_ast_only, ) import paddle np.random.seed(1) def func(x): y = x[0:3].astype("float32") return y class TestAmp64Case(Dy2StTestBase): def _run_static(self): static_func = paddle.jit.to_static(func) x = paddle.randn((10, 10)).astype("float64") with paddle.amp.auto_cast(True, level="O2"): dy_out = func(x) st_out = static_func(x) np.testing.assert_allclose(dy_out.numpy(), st_out.numpy()) def test_ast_to_func(self): self._run_static() class Net(paddle.nn.Layer): def __init__(self) -> None: super().__init__() self.linear = paddle.nn.Linear(5, 5) def forward(self, x): out = self.linear(x) with paddle.amp.auto_cast(level='O2'): out = self.linear(out) return out class TestPartialAutoCast(Dy2StTestBase): @test_ast_only def test_run(self): if not paddle.base.core.is_compiled_with_cuda(): return x = paddle.randn([5, 5], 'float32') net = Net() net = paddle.jit.to_static(net) out = net(x) main = net.forward.main_program cast_op_count = 0 for op in main.global_block().ops: if op.name() == 'pd_op.cast': cast_op_count += 1 np.testing.assert_equal(cast_op_count, 3) if __name__ == '__main__': unittest.main()