# Copyright (c) 2020 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 op_test import OpTest, get_device_place import paddle from paddle import base from paddle.base import core paddle.enable_static() def dist(x, y, p): if p == 0.0: out = np.count_nonzero(x - y) elif p == float("inf"): out = np.max(np.abs(x - y)) elif p == float("-inf"): out = np.min(np.abs(x - y)) else: out = np.power(np.sum(np.power(np.abs(x - y), p)), 1.0 / p) return np.array(out).astype(x.dtype) class TestDistOp(OpTest): def setUp(self): self.op_type = 'dist' self.python_api = paddle.dist self.attrs = {} self.init_case() self.init_data_type() self.inputs = { "X": np.random.random(self.x_shape).astype(self.data_type), "Y": np.random.random(self.y_shape).astype(self.data_type), } self.attrs["p"] = self.p self.outputs = { "Out": dist(self.inputs["X"], self.inputs["Y"], self.attrs["p"]) } self.gradient = self.calc_gradient() def init_case(self): self.x_shape = 120 self.y_shape = 120 self.p = 0.0 def init_data_type(self): self.data_type = ( np.float32 if core.is_compiled_with_rocm() else np.float64 ) def calc_gradient(self): x = self.inputs["X"] y = self.inputs["Y"] p = self.attrs["p"] if p == 0: grad = np.zeros(x.shape) elif p in [float("inf"), float("-inf")]: norm = dist(x, y, p) x_minux_y_abs = np.abs(x - y) grad = np.sign(x - y) grad[x_minux_y_abs != norm] = 0 else: norm = dist(x, y, p) grad = ( np.power(norm, 1 - p) * np.power(np.abs(x - y), p - 1) * np.sign(x - y) ) def get_reduce_dims(x, y): x_reduce_dims = [] y_reduce_dims = [] if x.ndim >= y.ndim: y_reshape = tuple([1] * (x.ndim - y.ndim) + list(y.shape)) y = y.reshape(y_reshape) else: x_reshape = tuple([1] * (y.ndim - x.ndim) + list(x.shape)) x = x.reshape(x_reshape) for i in range(x.ndim): if x.shape[i] > y.shape[i]: y_reduce_dims.append(i) elif x.shape[i] < y.shape[i]: x_reduce_dims.append(i) return x_reduce_dims, y_reduce_dims x_reduce_dims, y_reduce_dims = get_reduce_dims(x, y) if len(x_reduce_dims) != 0: x_grad = np.sum(grad, tuple(x_reduce_dims)).reshape(x.shape) else: x_grad = grad if len(y_reduce_dims) != 0: y_grad = -np.sum(grad, tuple(y_reduce_dims)).reshape(y.shape) else: y_grad = -grad return x_grad, y_grad def test_check_output(self): self.check_output(check_pir=True) def test_check_grad(self): self.check_grad( ["X", "Y"], "Out", user_defined_grads=self.gradient, check_pir=True ) class TestDistOpCase1(TestDistOp): def init_case(self): self.x_shape = (3, 5, 5, 6) self.y_shape = (5, 5, 6) self.p = 1.0 class TestDistOpCase2(TestDistOp): def init_case(self): self.x_shape = (10, 10) self.y_shape = (4, 10, 10) self.p = 2.0 class TestDistOpCase3(TestDistOp): def init_case(self): self.x_shape = (15, 10) self.y_shape = (15, 10) self.p = float("inf") class TestDistOpCase4(TestDistOp): def init_case(self): self.x_shape = (2, 3, 4, 5, 8) self.y_shape = (3, 1, 5, 8) self.p = float("-inf") class TestDistOpCase5(TestDistOp): def init_case(self): self.x_shape = (4, 1, 4, 8) self.y_shape = (2, 2, 1, 4, 4, 8) self.p = 1.5 class TestDistBF16Op(OpTest): def init_data_type(self): self.data_type = 'bfloat16' class TestDistBF16OpCase1(TestDistBF16Op): def init_case(self): self.x_shape = (3, 5, 5, 6) self.y_shape = (5, 5, 6) self.p = 1.0 class TestDistBF16OpCase2(TestDistBF16Op): def init_case(self): self.x_shape = (10, 10) self.y_shape = (4, 10, 10) self.p = 2.0 class TestDistBF16OpCase3(TestDistBF16Op): def init_case(self): self.x_shape = (15, 10) self.y_shape = (15, 10) self.p = float("inf") class TestDistBF16OpCase4(TestDistBF16Op): def init_case(self): self.x_shape = (2, 3, 4, 5, 8) self.y_shape = (3, 1, 5, 8) self.p = float("-inf") class TestDistBF16OpCase5(TestDistBF16Op): def init_case(self): self.x_shape = (4, 1, 4, 8) self.y_shape = (2, 2, 1, 4, 4, 8) self.p = 1.5 class TestDistFP16Op(OpTest): def init_data_type(self): self.data_type = 'float16' class TestDistFP16OpCase1(TestDistFP16Op): def init_case(self): self.x_shape = (3, 5, 5, 6) self.y_shape = (5, 5, 6) self.p = 1.0 class TestDistFP16OpCase2(TestDistFP16Op): def init_case(self): self.x_shape = (10, 10) self.y_shape = (4, 10, 10) self.p = 2.0 class TestDistFP16OpCase3(TestDistFP16Op): def init_case(self): self.x_shape = (15, 10) self.y_shape = (15, 10) self.p = float("inf") class TestDistFP16OpCase4(TestDistFP16Op): def init_case(self): self.x_shape = (2, 3, 4, 5, 8) self.y_shape = (3, 1, 5, 8) self.p = float("-inf") class TestDistFP16OpCase5(TestDistFP16Op): def init_case(self): self.x_shape = (4, 1, 4, 8) self.y_shape = (2, 2, 1, 4, 4, 8) self.p = 1.5 class TestDistOp_ZeroSize1(TestDistOp): def setUp(self): self.op_type = 'dist' self.python_api = paddle.dist self.attrs = {} self.init_case() self.init_data_type() self.inputs = { "X": np.random.random(self.x_shape).astype(self.data_type), "Y": np.random.random(self.y_shape).astype(self.data_type), } self.attrs["p"] = self.p self.outputs = { "Out": dist(self.inputs["X"], self.inputs["Y"], self.attrs["p"]) } def test_check_grad(self): self.check_grad(["X", "Y"], "Out", check_pir=True) def init_case(self): self.x_shape = (0, 1, 5, 6) self.y_shape = (0, 5, 6) self.p = 1.0 class TestDistOp_ZeroSize2(TestDistOp_ZeroSize1): def init_case(self): self.x_shape = (0, 1, 5, 6) self.y_shape = (1, 5, 6) self.p = 1.0 class TestDistAPI(unittest.TestCase): def init_data_type(self): self.data_type = ( 'float32' if core.is_compiled_with_rocm() else 'float64' ) def test_api(self): self.init_data_type() main_program = paddle.static.Program() startup_program = paddle.static.Program() with base.program_guard(main_program, startup_program): x = paddle.static.data( name='x', shape=[2, 3, 4, 5], dtype=self.data_type ) y = paddle.static.data( name='y', shape=[3, 1, 5], dtype=self.data_type ) p = 2 x_i = np.random.random((2, 3, 4, 5)).astype(self.data_type) y_i = np.random.random((3, 1, 5)).astype(self.data_type) result = paddle.dist(x, y, p) place = get_device_place() exe = base.Executor(place) out = exe.run( main_program, feed={'x': x_i, 'y': y_i}, fetch_list=[result], ) np.testing.assert_allclose(dist(x_i, y_i, p), out[0], rtol=1e-05) def test_grad_x(self): paddle.disable_static() a = paddle.rand([2, 2, 3, 2]) b = paddle.rand([1, 1, 3, 1]) a.stop_gradient = False c = paddle.dist(a, b, 2) c.backward() paddle.enable_static() if __name__ == '__main__': paddle.enable_static() unittest.main()