# Copyright (c) 2021 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 eager_op_test import OpTest, convert_float_to_uint16 import paddle from paddle.fluid import core paddle.enable_static() np.random.seed(0) class TestLerp(OpTest): def setUp(self): self.op_type = "lerp" self.python_api = paddle.lerp self.init_dtype() self.init_shape() self.init_xyshape() self.init_wshape() x = np.arange(1.0, 101.0).astype(self.dtype).reshape(self.xshape) y = np.full(100, 10.0).astype(self.dtype).reshape(self.yshape) w = np.random.random(self.wshape).astype(self.dtype) self.inputs = {'X': x, 'Y': y, 'Weight': w} self.outputs = {'Out': x + w * (y - x)} def init_dtype(self): self.dtype = np.float64 def init_shape(self): self.shape = [100] def init_xyshape(self): self.xshape = self.shape self.yshape = self.shape def init_wshape(self): self.wshape = [1] def test_check_output(self): self.check_output() def test_check_grad(self): self.check_grad(['X', 'Y'], 'Out') class TestLerpWithDim2(TestLerp): def init_shape(self): self.shape = [2, 50] class TestLerpWithDim3(TestLerp): def init_shape(self): self.shape = [2, 2, 25] class TestLerpWithDim4(TestLerp): def init_shape(self): self.shape = [2, 2, 5, 5] class TestLerpWithDim5(TestLerp): def init_shape(self): self.shape = [2, 1, 2, 5, 5] class TestLerpWithDim6(TestLerp): def init_shape(self): self.shape = [2, 1, 2, 5, 1, 5] class TestLerpWithDim6Fp16(TestLerp): def init_shape(self): self.shape = [2, 1, 2, 5, 1, 5] def init_dtype(self): self.dtype = np.float16 class TestLerpWihFp16BroadXY(TestLerp): def init_xyshape(self): self.xshape = [2, 1, 2, 5, 5] self.yshape = [2, 2, 1, 5, 5] def init_dtype(self): self.dtype = np.float16 class TestLerpWithFp16BroadWToXY(TestLerp): def init_shape(self): self.shape = [2, 2, 5, 5] def init_wshape(self): self.wshape = [5] def init_dtype(self): self.dtype = np.float16 class TestLerpBroadXY(TestLerp): def init_xyshape(self): self.xshape = [2, 1, 2, 5, 5] self.yshape = [2, 2, 1, 5, 5] class TestLerpBroadWToXY(TestLerp): def init_shape(self): self.shape = [2, 2, 5, 5] def init_wshape(self): self.wshape = [5] class TestLerpAPI(unittest.TestCase): def init_dtype(self): self.dtype = 'float32' def setUp(self): self.init_dtype() self.x = np.arange(1.0, 5.0).astype(self.dtype) self.y = np.full(4, 10.0).astype(self.dtype) self.w = np.asarray([0.75]).astype(self.dtype) self.res_ref = self.x + self.w * (self.y - self.x) self.place = [paddle.CPUPlace()] if core.is_compiled_with_cuda(): self.place.append(paddle.CUDAPlace(0)) def test_static_api(self): paddle.enable_static() def run(place): with paddle.static.program_guard(paddle.static.Program()): x = paddle.static.data('x', [1, 4], dtype=self.dtype) y = paddle.static.data('y', [1, 4], dtype=self.dtype) out = paddle.lerp(x, y, 0.5) exe = paddle.static.Executor(place) res = exe.run( feed={ 'x': self.x.reshape([1, 4]), 'y': self.y.reshape([1, 4]), } ) for r in res: np.testing.assert_allclose(self.res_ref, r, rtol=1e-05) for place in self.place: run(place) def test_dygraph_api(self): def run(place): paddle.disable_static(place) x = paddle.to_tensor(self.x) y = paddle.to_tensor(self.y) w = paddle.to_tensor(np.full(4, 0.75).astype(self.dtype)) out = paddle.lerp(x, y, w) np.testing.assert_allclose(self.res_ref, out.numpy(), rtol=1e-05) paddle.enable_static() for place in self.place: run(place) def test_inplace_api(self): def run(place): paddle.disable_static(place) x = paddle.to_tensor(self.x) y = paddle.to_tensor(self.y) x.lerp_(y, 0.75) np.testing.assert_allclose(self.res_ref, x.numpy(), rtol=1e-05) paddle.enable_static() for place in self.place: run(place) def test_inplace_api_exception(self): def run(place): paddle.disable_static(place) x = paddle.to_tensor(self.x) y = paddle.to_tensor(self.y) w = paddle.to_tensor([0.75, 0.75], dtype=self.dtype) with self.assertRaises(ValueError): x.lerp_(y, w) paddle.enable_static() for place in self.place: run(place) def test_x_broadcast_y(self): paddle.disable_static() x = np.arange(1.0, 21.0).astype(self.dtype).reshape([2, 2, 5]) y = np.full(30, 10.0).astype(self.dtype).reshape([3, 2, 1, 5]) out = paddle.lerp(paddle.to_tensor(x), paddle.to_tensor(y), 0.5) res_ref = x + 0.5 * (y - x) np.testing.assert_allclose(res_ref, out.numpy(), rtol=1e-05) paddle.enable_static() def test_x_y_broadcast_w(self): paddle.disable_static() x = np.arange(11.0, 21.0).astype(self.dtype).reshape([2, 5]) y = np.full(20, 7.5).astype(self.dtype).reshape([2, 2, 5]) w = np.full(40, 0.225).astype(self.dtype).reshape([2, 2, 2, 5]) out = paddle.lerp( paddle.to_tensor(x), paddle.to_tensor(y), paddle.to_tensor(w) ) res_ref = x + w * (y - x) np.testing.assert_allclose(res_ref, out.numpy(), rtol=1e-05) paddle.enable_static() @unittest.skipIf( not core.is_compiled_with_cuda() or not core.is_bfloat16_supported(core.CUDAPlace(0)), "core is not complied with CUDA and not support the bfloat16", ) class TestLerpBF16(TestLerp): def setUp(self): self.op_type = "lerp" self.python_api = paddle.lerp self.dtype = np.uint16 self.init_shape() self.init_xyshape() self.init_wshape() x = np.arange(1.0, 101.0).astype("float32").reshape(self.xshape) y = np.full(100, 10.0).astype("float32").reshape(self.yshape) w = np.random.random(self.wshape).astype("float32") self.init_grad(w) self.inputs = { 'X': convert_float_to_uint16(x), 'Y': convert_float_to_uint16(y), 'Weight': convert_float_to_uint16(w), } self.outputs = {'Out': convert_float_to_uint16(x + w * (y - x))} def init_shape(self): self.shape = [100] def init_xyshape(self): self.xshape = self.shape self.yshape = self.shape def init_wshape(self): self.wshape = [1] def init_grad(self, w): self.x_grad = ( np.ones(self.xshape) * (1 - w) / (np.prod(self.xshape) / np.prod(self.wshape)) ) self.y_grad = ( np.ones(self.yshape) * w / (np.prod(self.yshape) / np.prod(self.wshape)) ) def test_check_output(self): place = core.CUDAPlace(0) self.check_output_with_place(place) def test_check_grad(self): place = core.CUDAPlace(0) self.check_grad_with_place( place, ['X', 'Y'], 'Out', user_defined_grads=[self.x_grad, self.y_grad], ) if __name__ == "__main__": unittest.main()