# Copyright (c) 2023 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 paddle from paddle import ir paddle.enable_static() def get_ir_program(): x = paddle.randn([4, 4]) main_program, start_program = ( paddle.static.Program(), paddle.static.Program(), ) with paddle.static.program_guard(main_program, start_program): x_s = paddle.static.data('x', [4, 4], x.dtype) x_s.stop_gradient = False y_s = paddle.matmul(x_s, x_s) z_s = paddle.add(y_s, y_s) k_s = paddle.tanh(z_s) newir_program = ir.translate_to_new_ir(main_program.desc) return newir_program class TestPybind(unittest.TestCase): def test_program(self): newir_program = get_ir_program() print(newir_program) block = newir_program.block() program = block.get_parent_program() self.assertEqual(newir_program, program) def test_block(self): newir_program = get_ir_program() block = newir_program.block() ops = block.get_ops() self.assertEqual( len(ops), 4 ) # ir program add "builtin.get_parameter" by default, so size is 4 block.remove_op(ops[3]) self.assertEqual(len(block.get_ops()), 3) def test_operation(self): newir_program = get_ir_program() ops = newir_program.block().get_ops() matmul_op = newir_program.block().get_ops()[1] add_op = newir_program.block().get_ops()[2] tanh_op = newir_program.block().get_ops()[3] parent_block = tanh_op.get_parent_block() parent_ops_num = len(parent_block.get_ops()) self.assertEqual(parent_ops_num, 4) self.assertEqual(tanh_op.num_results(), 1) self.assertEqual(len(matmul_op.get_input_names()), 2) self.assertEqual(len(matmul_op.get_attr_names()), 2) self.assertEqual(len(matmul_op.get_output_names()), 1) def test_value(self): newir_program = get_ir_program() matmul_op = newir_program.block().get_ops()[1] add_op = newir_program.block().get_ops()[2] tanh_op = newir_program.block().get_ops()[3] self.assertEqual( matmul_op.result(0).dtype, paddle.fluid.core.DataType.FLOAT32 ) self.assertEqual(matmul_op.result(0).shape, [4, 4]) self.assertEqual( matmul_op.results()[0].get_defining_op().name(), "pd.matmul" ) self.assertEqual( matmul_op.result(0).get_defining_op().name(), "pd.matmul" ) matmul_op.result(0).stop_gradient = True self.assertEqual(matmul_op.result(0).stop_gradient, True) # test opresult hash result_set = set() for opresult in matmul_op.results(): result_set.add(opresult) # test opresult hash and hash(opresult) == hash(operesult) self.assertTrue(add_op.operands()[0].source() in result_set) # test value hash and hash(value) == hash(operesult) self.assertTrue(add_op.operands_source()[0] in result_set) # test value == value self.assertEqual( add_op.operands_source()[0], add_op.operands_source()[0] ) # test value == opresult self.assertEqual(add_op.operands_source()[0], matmul_op.results()[0]) # test opresult == value self.assertEqual( add_op.operands()[0].source(), add_op.operands_source()[0] ) # test opresult == opresult self.assertEqual(add_op.operands()[0].source(), matmul_op.results()[0]) self.assertEqual( tanh_op.operands()[0].source().get_defining_op().name(), "pd.add" ) add_op.replace_all_uses_with(matmul_op.results()) self.assertEqual( tanh_op.operands()[0].source().get_defining_op().name(), "pd.matmul" ) self.assertEqual(add_op.result(0).use_empty(), True) def test_type(self): newir_program = get_ir_program() matmul_op = newir_program.block().get_ops()[1] add_op = newir_program.block().get_ops()[2] print(matmul_op.result(0).type()) self.assertEqual( matmul_op.result(0).type() == add_op.result(0).type(), True ) def test_attr(self): main_program, start_program = ( paddle.static.Program(), paddle.static.Program(), ) with paddle.static.program_guard(main_program, start_program): conv_data = paddle.static.data( 'conv_data', [None, 3, 32, 32], dtype='float32' ) conv2d_out = paddle.static.nn.conv2d( input=conv_data, num_filters=2, filter_size=3, stride=3, act="relu", ) full_out = paddle.tensor.fill_constant( shape=[4, 4], dtype="float32", value=2 ) newir_program = ir.translate_to_new_ir(main_program.desc) print(newir_program) conv_attr = newir_program.block().get_ops()[3].attrs() full_attr = newir_program.block().get_ops()[8].attrs() self.assertEqual(conv_attr["stop_gradient"], [False]) self.assertEqual(conv_attr["dilations"], [1, 1]) self.assertEqual(conv_attr["data_format"], "NCHW") self.assertEqual(conv_attr["strides"], [3, 3]) self.assertEqual(conv_attr["paddings"], [0, 0]) self.assertEqual(conv_attr["padding_algorithm"], "EXPLICIT") self.assertEqual(conv_attr["groups"], 1) self.assertEqual(full_attr["dtype"], paddle.fluid.core.DataType.FLOAT32) self.assertTrue(isinstance(full_attr["place"], paddle.fluid.core.Place)) def test_operands(self): newir_program = get_ir_program() matmul_op = newir_program.block().get_ops()[1] operands = matmul_op.operands() self.assertEqual(len(operands), 2) def test_results(self): newir_program = get_ir_program() matmul_op = newir_program.block().get_ops()[1] results = matmul_op.results() self.assertEqual(len(results), 1) if __name__ == "__main__": unittest.main()