#!/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. 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 TestModOp(OpTest): def setUp(self): print(f"\nRunning {self.__class__.__name__}: {self.case}") self.prepare_inputs() def prepare_inputs(self): self.x_np = self.random( shape=self.case["x_shape"], dtype=self.case["x_dtype"], low=self.case["x_low"], high=self.case["x_high"], ) self.y_np = self.random( shape=self.case["y_shape"], dtype=self.case["y_dtype"], low=self.case["y_low"], high=self.case["y_high"], ) self.y_np[self.y_np == 0] = 1 def build_paddle_program(self, target): x = paddle.to_tensor(self.x_np, stop_gradient=True) y = paddle.to_tensor(self.y_np, stop_gradient=True) out = paddle.mod(x, y) self.paddle_outputs = [out] def build_cinn_program(self, target): builder = NetBuilder("pow") 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.mod(x, y) prog = builder.build() res = self.get_cinn_output( prog, target, [x, y], [self.x_np, self.y_np], [out] ) self.cinn_outputs = [res[0]] def test_check_results(self): max_relative_error = ( self.case["max_relative_error"] if "max_relative_error" in self.case else 1e-5 ) self.check_outputs_and_grads(max_relative_error=max_relative_error) class TestModOpBase(TestCaseHelper): inputs = [ { "x_shape": [32], "y_shape": [32], }, { "x_shape": [32, 64], "y_shape": [32, 64], }, { "x_shape": [2, 3, 4], "y_shape": [2, 3, 4], }, { "x_shape": [16, 8, 4, 2], "y_shape": [16, 8, 4, 2], }, { "x_shape": [16, 8, 4, 2, 1], "y_shape": [16, 8, 4, 2, 1], }, ] dtypes = [ { "x_dtype": "float32", "y_dtype": "float32", }, ] attrs = [ {"x_low": -100, "x_high": 100, "y_low": -100, "y_high": 100}, ] def init_attrs(self): self.class_name = "TestModOpBase" self.cls = TestModOp class TestModOpShapeTest(TestModOpBase): def init_attrs(self): self.class_name = "TestModOpShapeTest" self.cls = TestModOp self.inputs = [ { "x_shape": [32], "y_shape": [32], }, { "x_shape": [32, 64], "y_shape": [32, 64], }, { "x_shape": [2, 3, 4], "y_shape": [2, 3, 4], }, { "x_shape": [16, 8, 4, 2], "y_shape": [16, 8, 4, 2], }, { "x_shape": [16, 8, 4, 1024], "y_shape": [16, 8, 4, 1024], }, { "x_shape": [16, 8, 4, 2, 1], "y_shape": [16, 8, 4, 2, 1], }, { "x_shape": [1, 1, 1, 1, 1], "y_shape": [1, 1, 1, 1, 1], }, { "x_shape": [1], "y_shape": [1], }, { "x_shape": [1024], "y_shape": [1024], }, { "x_shape": [2048], "y_shape": [2048], }, { "x_shape": [32768], "y_shape": [32768], }, { "x_shape": [65536], "y_shape": [65536], }, { "x_shape": [131072], "y_shape": [131072], }, ] class TestModOpDtypeTest(TestModOpBase): def init_attrs(self): self.class_name = "TestModOpDtypeTest" self.cls = TestModOp self.dtypes = [ { "x_dtype": "float16", "y_dtype": "float16", "max_relative_error": 1e-3, }, { "x_dtype": "int32", "y_dtype": "int32", }, { "x_dtype": "int64", "y_dtype": "int64", }, { "x_dtype": "float32", "y_dtype": "float32", }, { "x_dtype": "float64", "y_dtype": "float64", }, ] class TestModOpPolarityTest(TestModOpBase): def init_attrs(self): self.class_name = "TestModOpPolarityTest" self.cls = TestModOp self.attrs = [ {"x_low": -100, "x_high": 100, "y_low": -100, "y_high": -1}, {"x_low": -100, "x_high": 100, "y_low": 1, "y_high": 100}, ] class TestModOpBroadcastTest(TestModOpBase): def init_attrs(self): self.class_name = "TestModOpBroadcastTest" self.cls = TestModOp self.inputs = [ { "x_shape": [32], "y_shape": [1], }, { "x_shape": [1], "y_shape": [32], }, { "x_shape": [1, 64], "y_shape": [32, 1], }, { "x_shape": [1, 64], "y_shape": [32, 64], }, { "x_shape": [32, 1], "y_shape": [32, 64], }, { "x_shape": [1, 1], "y_shape": [32, 64], }, { "x_shape": [1, 3, 4], "y_shape": [2, 3, 4], }, { "x_shape": [1, 3, 1], "y_shape": [2, 3, 4], }, { "x_shape": [1, 1, 1], "y_shape": [2, 3, 4], }, { "x_shape": [2, 1, 1], "y_shape": [1, 3, 4], }, { "x_shape": [1, 8, 4, 2], "y_shape": [16, 8, 4, 2], }, { "x_shape": [16, 8, 1, 1], "y_shape": [16, 8, 4, 2], }, { "x_shape": [1, 8, 1, 1], "y_shape": [16, 8, 4, 2], }, { "x_shape": [1, 1, 1, 1], "y_shape": [16, 8, 4, 2], }, { "x_shape": [1, 8, 1, 2], "y_shape": [16, 1, 4, 1], }, { "x_shape": [1, 8, 4, 2, 32], "y_shape": [16, 8, 4, 2, 32], }, { "x_shape": [16, 1, 1, 2, 32], "y_shape": [16, 8, 4, 2, 32], }, { "x_shape": [16, 1, 4, 1, 1], "y_shape": [16, 8, 4, 2, 32], }, { "x_shape": [1, 1, 1, 1, 32], "y_shape": [16, 8, 4, 2, 32], }, { "x_shape": [1, 1, 1, 1, 1], "y_shape": [16, 8, 4, 2, 32], }, { "x_shape": [16, 1, 4, 1, 32], "y_shape": [1, 8, 1, 2, 1], }, ] if __name__ == "__main__": TestModOpShapeTest().run() TestModOpDtypeTest().run() TestModOpPolarityTest().run() TestModOpBroadcastTest().run()