#!/usr/bin/env python3 # Copyright (c) 2021 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 paddle from cinn.common import * from cinn.frontend import * from op_test import OpTest, OpTestTool from op_test_helper import TestCaseHelper @OpTestTool.skip_if(not is_compiled_with_cuda(), "x86 test will be skipped due to timeout.") class TestConcatOp(OpTest): def setUp(self): print(f"\nRunning {self.__class__.__name__}: {self.case}") self.inputs = {} self.prepare_inputs() def prepare_inputs(self): self.inputs = {} self.axis = self.case["axis"] dtype = self.case["dtype"] shapes = self.case["shapes"] for i, shape in enumerate(shapes): name = "x" + str(i) self.inputs[name] = self.random(shape, dtype) def paddle_inputs(self, inputs): return [ paddle.to_tensor(data, stop_gradient=True) for _, data in inputs.items() ] def cinn_inputs(self, builder, inputs): return [ builder.create_input( self.nptype2cinntype(data.dtype), data.shape, name) for name, data in inputs.items() ] def build_paddle_program(self, target): out = paddle.concat(x=self.paddle_inputs(self.inputs), axis=self.axis) self.paddle_outputs = [out] # Note: If the forward and backward operators are run in the same program, # the forward result will be incorrect. def build_cinn_program(self, target): builder = NetBuilder("concat") input_list = self.cinn_inputs(builder, self.inputs) out = builder.concat(input_list, axis=self.axis) prog = builder.build() input_datas = [data for _, data in self.inputs.items()] res = self.get_cinn_output(prog, target, input_list, input_datas, [out]) self.cinn_outputs = res def test_check_results(self): self.check_outputs_and_grads(all_equal=True) class TestConcatOpShape(TestCaseHelper): def init_attrs(self): self.class_name = "TestConcatOpShape" self.cls = TestConcatOp self.inputs = [ { "shapes": [[10], [6]], }, { "shapes": [[8, 5], [8, 5]], }, { "shapes": [[10, 3, 5], [4, 3, 5]], }, { "shapes": [[80, 40, 5, 7], [20, 40, 5, 7]], }, { "shapes": [[80, 1, 5, 7], [8, 1, 5, 7]], }, { "shapes": [[80, 3, 1024, 7], [100, 3, 1024, 7]], }, { "shapes": [[1, 5, 1024, 2048], [2, 5, 1024, 2048]], }, { "shapes": [[1], [1]], }, { "shapes": [[512], [512]], }, { "shapes": [[1024], [512]], }, { "shapes": [[2048], [4096]], }, { "shapes": [[1, 1, 1, 1], [1, 1, 1, 1]], }, ] self.dtypes = [ { "dtype": "float32" }, ] self.attrs = [ { "axis": 0 }, ] class TestConcatOpDtype(TestCaseHelper): def init_attrs(self): self.class_name = "TestConcatOpDtype" self.cls = TestConcatOp self.inputs = [ { "shapes": [[10], [6]], }, { "shapes": [[8, 5], [8, 5]], }, { "shapes": [[10, 3, 5], [4, 3, 5]], }, { "shapes": [[80, 40, 5, 7], [20, 40, 5, 7]], }, ] self.dtypes = [ { "dtype": "float16" }, { "dtype": "float32" }, { "dtype": "float64" }, { "dtype": "bool" }, { "dtype": "uint8" }, { "dtype": "int8" }, { "dtype": "int32" }, { "dtype": "int64" }, ] self.attrs = [ { "axis": 0 }, ] class TestConcatOpMultipleInputs(TestCaseHelper): def init_attrs(self): self.class_name = "TestConcatOpMultipleInputs" self.cls = TestConcatOp self.inputs = [ # 1D tensor with 1~4 inputs { "shapes": [[10]], "axis": 0 }, { "shapes": [[10], [6]], "axis": 0 }, { "shapes": [[10], [6], [8]], "axis": 0 }, { "shapes": [[10], [6], [10], [6]], "axis": 0 }, # 2D tensor with 1~4 inputs { "shapes": [[8, 5]], "axis": 1 }, { "shapes": [[8, 5], [8, 8]], "axis": 1 }, { "shapes": [[8, 5], [8, 5], [16, 5]], "axis": 0 }, { "shapes": [[8, 5], [8, 5], [8, 5], [8, 5]], "axis": 0 }, # 3D tensor with 1~4 inputs { "shapes": [[10, 3, 5]], "axis": 0 }, { "shapes": [[10, 3, 5], [10, 7, 5]], "axis": 1 }, { "shapes": [[10, 3, 5], [10, 3, 6], [10, 3, 7]], "axis": 2 }, { "shapes": [[10, 3, 5], [4, 3, 5], [2, 3, 5]], "axis": 0 }, # 4D tensor with 1~4 inputs { "shapes": [[80, 1, 5, 7]], "axis": 0 }, { "shapes": [[80, 1, 5, 7], [80, 79, 5, 7]], "axis": 1 }, { "shapes": [[80, 1, 50, 7], [80, 1, 5, 7], [80, 1, 10, 7]], "axis": 2 }, { "shapes": [[80, 1, 5, 17], [80, 1, 5, 27], [80, 1, 5, 37], [80, 1, 5, 47]], "axis": 3 }, ] self.dtypes = [ { "dtype": "float32" }, ] self.attrs = [] class TestConcatOpAttrs(TestCaseHelper): def init_attrs(self): self.class_name = "TestConcatOpAttrs" self.cls = TestConcatOp self.inputs = [ # 1D tensor { "shapes": [[10], [8]], "axis": 0 }, { "shapes": [[10], [6]], "axis": -1 }, # 2D tensor { "shapes": [[8, 5], [10, 5]], "axis": 0 }, { "shapes": [[8, 5], [8, 8]], "axis": 1 }, # 3D tensor { "shapes": [[10, 3, 5], [10, 3, 5]], "axis": 0 }, { "shapes": [[10, 3, 5], [10, 7, 5]], "axis": 1 }, { "shapes": [[10, 3, 15], [10, 3, 5]], "axis": 2 }, { "shapes": [[10, 3, 7], [10, 3, 5]], "axis": -1 }, { "shapes": [[10, 3, 5], [10, 7, 5]], "axis": -2 }, { "shapes": [[10, 7, 5], [20, 7, 5]], "axis": -3 }, # 4D tensor { "shapes": [[80, 1, 5, 7], [80, 1, 5, 7]], "axis": 0 }, { "shapes": [[80, 1, 5, 7], [80, 79, 5, 7]], "axis": 1 }, { "shapes": [[80, 1, 5, 7], [80, 1, 10, 7]], "axis": 2 }, { "shapes": [[80, 1, 5, 7], [80, 1, 5, 7]], "axis": 3 }, { "shapes": [[80, 1, 5, 7], [80, 1, 5, 13]], "axis": -1 }, { "shapes": [[80, 1, 5, 7], [80, 1, 5, 7]], "axis": -2 }, { "shapes": [[80, 15, 5, 7], [80, 5, 5, 7]], "axis": -3 }, { "shapes": [[80, 1, 5, 7], [20, 1, 5, 7]], "axis": -4 }, ] self.dtypes = [ { "dtype": "float32" }, ] self.attrs = [] if __name__ == "__main__": TestConcatOpShape().run() TestConcatOpDtype().run() TestConcatOpMultipleInputs().run() TestConcatOpAttrs().run()