# 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 pir from paddle.autograd.backward_utils import ValueSet 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 = x_s @ x_s z_s = paddle.add(y_s, y_s) k_s = paddle.tanh(z_s) q_s = paddle.unsqueeze(k_s, [2]) return main_program class TestPybind(unittest.TestCase): def test_program(self): pir_program = get_ir_program() block = pir_program.global_block() program = block.program self.assertEqual(pir_program, program) self.assertEqual(len(pir_program.blocks), 1) def test_block(self): pir_program = get_ir_program() block = pir_program.global_block() print(block) # test print block ops = block.ops self.assertEqual( len(ops), 6 ) # pir program add "builtin.parameter" by default, so size is 4 block.remove_op(ops[5]) self.assertEqual(len(block.ops), 5) def test_operation(self): pir_program = get_ir_program() ops = pir_program.global_block().ops matmul_op = ops[1] add_op = ops[2] tanh_op = ops[3] parent_block = tanh_op.get_parent_block() parent_ops_num = len(parent_block.ops) self.assertEqual(parent_ops_num, 6) 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) # test operand.index self.assertEqual(matmul_op.operand(0).index(), 0) self.assertEqual(matmul_op.operand(1).index(), 1) self.assertEqual(add_op.operand(0).index(), 0) self.assertEqual(add_op.operand(1).index(), 1) self.assertEqual(tanh_op.operand(0).index(), 0) def test_value(self): pir_program = get_ir_program() matmul_op = pir_program.global_block().ops[1] add_op = pir_program.global_block().ops[2] tanh_op = pir_program.global_block().ops[3] self.assertEqual( matmul_op.result(0).dtype, paddle.base.core.DataType.FLOAT32 ) self.assertEqual(matmul_op.result(0).shape, [4, 4]) self.assertEqual( matmul_op.results()[0].get_defining_op().name(), "pd_op.matmul" ) self.assertEqual( matmul_op.result(0).get_defining_op().name(), "pd_op.matmul" ) matmul_op.result(0).stop_gradient = True self.assertEqual(matmul_op.result(0).stop_gradient, True) # test opresult hash result_set = ValueSet() 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.assertTrue( add_op.operands_source()[0].is_same(add_op.operands_source()[0]) ) # test value == opresult self.assertTrue( add_op.operands_source()[0].is_same(matmul_op.results()[0]) ) # test opresult print self.assertTrue( 'dtype=tensor<4x4xf32>' in add_op.operands_source()[0].__str__() ) # test opresult == value self.assertTrue( add_op.operands()[0].source().is_same(add_op.operands_source()[0]) ) # test opresult == opresult self.assertTrue( add_op.operands()[0].source().is_same(matmul_op.results()[0]) ) # test opresult print self.assertEqual( tanh_op.operands()[0].source().get_defining_op().name(), "pd_op.add" ) self.assertTrue( 'tensor<4x4xf32>' in tanh_op.operands()[0].source().__str__() ) add_op.replace_all_uses_with(matmul_op.results()) self.assertEqual( tanh_op.operands()[0].source().get_defining_op().name(), "pd_op.matmul", ) self.assertEqual(add_op.result(0).use_empty(), True) self.assertEqual(add_op.result(0).initialized(), True) uninit_value = paddle.pir.Value() self.assertEqual(uninit_value.initialized(), False) def test_type(self): pir_program = get_ir_program() matmul_op = pir_program.global_block().ops[1] add_op = pir_program.global_block().ops[2] self.assertEqual( matmul_op.result(0).type() == add_op.result(0).type(), True ) add_op.result(0).set_type( paddle.base.libpaddle.pir.create_selected_rows_type_by_dense_tensor( add_op.result(0).type() ) ) self.assertEqual(add_op.result(0).is_selected_row_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 = paddle.nn.Conv2D( in_channels=3, out_channels=2, kernel_size=3, stride=3, padding=0, data_format="NCHW", ) conv_data = paddle.static.data( 'conv_data', [None, 3, 32, 32], dtype='float32' ) conv2d_out = conv( conv_data, ) relu_out = paddle.nn.functional.relu(conv2d_out) full_out = paddle.tensor.fill_constant( shape=[4, 4], dtype="float32", value=2 ) conv_attr = main_program.global_block().ops[3].attrs() full_attr = main_program.global_block().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.base.core.DataType.FLOAT32) self.assertTrue(isinstance(full_attr["place"], paddle.base.core.Place)) def test_operands(self): pir_program = get_ir_program() matmul_op = pir_program.global_block().ops[1] operands = matmul_op.operands() self.assertEqual(len(operands), 2) def test_results(self): pir_program = get_ir_program() matmul_op = pir_program.global_block().ops[1] results = matmul_op.results() self.assertEqual(len(results), 1) def test_get_output_intermediate_status(self): pir_program = get_ir_program() unsqueeze_op = pir_program.global_block().ops[-1] results = unsqueeze_op.get_output_intermediate_status() self.assertEqual(results, [False]) def test_prog_seed(self): p = pir.Program() self.assertEqual(p._seed, 0) p.global_seed(10) self.assertEqual(p._seed, 10) def test_opresult_id(self): with paddle.pir_utils.IrGuard(): a = paddle.static.data(name='a', shape=[4, 4], dtype='float32') result = paddle.tanh(a) self.assertIsInstance(a.id, str) self.assertIsInstance(result.id, str) def test_operation_get_input_names_error(self): """It will Raise error if operation `builtin.set_parameter` calls `get_input_names`. Because `builtin.set_parameter` does not have OpYamlInfoInterface""" with paddle.pir_utils.IrGuard(): main = paddle.static.Program() startup = paddle.static.Program() with paddle.static.program_guard(main, startup): param1 = paddle.pir.core.create_parameter( dtype="float32", shape=[5, 10], name="param1", initializer=paddle.nn.initializer.Uniform(), ) block = startup.global_block() set_parameter_ops = [ op for op in block.ops if op.name() == 'builtin.set_parameter' ] set_parameter_op = set_parameter_ops[0] parameter_name = set_parameter_op.attrs()["parameter_name"] with self.assertRaises(ValueError): input_names = set_parameter_op.get_input_names() if __name__ == "__main__": unittest.main()