#!/usr/bin/env python3 # Copyright (c) 2022 CINN 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 numpy as np from cinn.common import * from cinn.frontend import * from op_test import OpTest, OpTestTool from op_test_helper import TestCaseHelper import paddle @OpTestTool.skip_if( not is_compiled_with_cuda(), "x86 test will be skipped due to timeout." ) class TestIsCloseOp(OpTest): def setUp(self): # print(f"\n{self.__class__.__name__}: {self.case}") self.prepare_inputs() def prepare_inputs(self): if self.case["nan_as_input"]: self.x_np = np.full(shape=self.case["shape"], fill_value=np.nan) else: self.x_np = self.random( shape=self.case["shape"], dtype=self.case["dtype"] ) self.y_np = self.x_np + self.random( shape=self.case["shape"], dtype=self.case["dtype"] ) def build_paddle_program(self, target): x = paddle.to_tensor(self.x_np, stop_gradient=False) y = paddle.to_tensor(self.y_np, stop_gradient=False) shape = paddle.broadcast_shape(x.shape, y.shape) x = paddle.broadcast_to(x, shape) y = paddle.broadcast_to(y, shape) out = paddle.isclose( x, y, self.case["rtol"], self.case["atol"], self.case["equal_nan"] ) self.paddle_outputs = [out] def build_cinn_program(self, target): builder = NetBuilder("isclose") x = builder.create_input( self.nptype2cinntype(self.x_np.dtype), self.x_np.shape, "x" ) y = builder.create_input( self.nptype2cinntype(self.y_np.dtype), self.y_np.shape, "y" ) out = builder.isclose( x, y, self.case["rtol"], self.case["atol"], self.case["equal_nan"] ) prog = builder.build() res = self.get_cinn_output( prog, target, [x, y], [self.x_np, self.y_np], [out] ) self.cinn_outputs = res def test_check_results(self): self.check_outputs_and_grads(all_equal=True) class TestIsCloseShape(TestCaseHelper): def init_attrs(self): self.class_name = "TestIsCloseOpCase" self.cls = TestIsCloseOp self.inputs = [ { "shape": [1], }, { "shape": [1024], }, { "shape": [512, 256], }, { "shape": [128, 64, 32], }, { "shape": [16, 8, 4, 2], }, { "shape": [16, 8, 4, 2, 1], }, ] self.dtypes = [ { "dtype": "float32", }, ] self.attrs = [ { "rtol": 1e-5, "atol": 1e-8, "equal_nan": False, "nan_as_input": False, }, ] class TestIsCloseDtype(TestCaseHelper): def init_attrs(self): self.class_name = "TestIsCloseOpCase" self.cls = TestIsCloseOp self.inputs = [ { "shape": [1024], }, ] self.dtypes = [ { "dtype": "float32", }, { "dtype": "float64", }, ] self.attrs = [ { "rtol": 1e-5, "atol": 1e-8, "equal_nan": False, "nan_as_input": False, }, ] class TestIsCloseAttr(TestCaseHelper): def init_attrs(self): self.class_name = "TestIsCloseOpCase" self.cls = TestIsCloseOp self.inputs = [ { "shape": [1024], }, ] self.dtypes = [ { "dtype": "float32", }, ] self.attrs = [ { "rtol": 1e-3, "atol": 1e-3, "equal_nan": False, "nan_as_input": False, }, { "rtol": 1e-5, "atol": 1e-5, "equal_nan": False, "nan_as_input": False, }, { "rtol": 1e-8, "atol": 1e-8, "equal_nan": False, "nan_as_input": False, }, { "rtol": 1e-5, "atol": 1e-8, "equal_nan": True, "nan_as_input": False, }, ] class TestIsCloseNAN(TestCaseHelper): def init_attrs(self): self.class_name = "TestIsCloseOpCase" self.cls = TestIsCloseOp self.inputs = [ { "shape": [1024], }, ] self.dtypes = [ { "dtype": "float64", }, ] self.attrs = [ { "rtol": 1e-5, "atol": 1e-8, "equal_nan": True, "nan_as_input": True, }, ] if __name__ == "__main__": TestIsCloseShape().run() TestIsCloseDtype().run() TestIsCloseAttr().run() TestIsCloseNAN().run()