# 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 numpy as np import paddle from paddle import ir from paddle.fluid import core paddle.enable_static() class TestCastOpTranscriber(unittest.TestCase): def test_op(self): place = core.Place() place.set_place(paddle.CPUPlace()) new_scope = paddle.static.Scope() main_program = paddle.static.Program() with paddle.static.scope_guard(new_scope): with paddle.static.program_guard(main_program): x = paddle.to_tensor([2, 3, 4], 'float64') y = paddle.cast(x, 'uint8') _ = ir.translate_to_new_ir(main_program.desc) class TestEmbeddingOpTranscriber(unittest.TestCase): def test_op(self): place = core.Place() place.set_place(paddle.CPUPlace()) new_scope = paddle.static.Scope() main_program = paddle.static.Program() with paddle.static.scope_guard(new_scope): with paddle.static.program_guard(main_program): x = paddle.static.data(name="x", shape=[2, 4], dtype=np.int64) embedding = paddle.nn.Embedding( 10, 3, weight_attr=paddle.nn.initializer.Constant(value=1.0) ) output = embedding(x) _ = ir.translate_to_new_ir(main_program.desc) class TestIncrementOpTranscriber(unittest.TestCase): def test_op(self): place = core.Place() place.set_place(paddle.CPUPlace()) new_scope = paddle.static.Scope() main_program = paddle.static.Program() with paddle.static.scope_guard(new_scope): with paddle.static.program_guard(main_program): data = paddle.zeros(shape=[1], dtype='float32') counter = paddle.increment(data) _ = ir.translate_to_new_ir(main_program.desc) class TestAssignValueOpTranscriber(unittest.TestCase): def test_op(self): place = core.Place() place.set_place(paddle.CPUPlace()) new_scope = paddle.static.Scope() main_program = paddle.static.Program() with paddle.static.scope_guard(new_scope): with paddle.static.program_guard(main_program): x = paddle.to_tensor( [[0.1, 0.2], [0.3, 0.4]], place=paddle.CPUPlace(), stop_gradient=False, ) _ = ir.translate_to_new_ir(main_program.desc) class TestRnnOpTranscriber(unittest.TestCase): def test_op(self): place = core.Place() place.set_place(paddle.CPUPlace()) new_scope = paddle.static.Scope() main_program = paddle.static.Program() with paddle.static.scope_guard(new_scope): with paddle.static.program_guard(main_program): x = paddle.randn((4, 16)) prev_h = paddle.randn((4, 32)) cell = paddle.nn.SimpleRNNCell(16, 32) y, h = cell(x, prev_h) _ = ir.translate_to_new_ir(main_program.desc) class TestEmptyVarTranslate(unittest.TestCase): def test_op(self): place = core.Place() place.set_place(paddle.CPUPlace()) new_scope = paddle.static.Scope() main_program = paddle.static.Program() with paddle.static.scope_guard(new_scope): with paddle.static.program_guard(main_program): x1 = paddle.rand(shape=[3, 3], dtype="float32") x1.stop_gradient = False weight = paddle.full( shape=[3, 3], fill_value="0.5", dtype="float32" ) y = paddle.nn.functional.linear(x1, weight) y.stop_gradient = True out1 = paddle.concat(x=[x1, y], axis=1) out2 = paddle.mean(out1) sgd_optimizer = paddle.optimizer.SGD(learning_rate=0.1) sgd_optimizer.minimize(out2) _ = ir.translate_to_new_ir(main_program.desc) class TestOneHotOpTranscriber(unittest.TestCase): def test_mutable_attribute(self): place = core.Place() place.set_place(paddle.CPUPlace()) new_scope = paddle.static.Scope() main_program = paddle.static.Program() with paddle.static.scope_guard(new_scope): with paddle.static.program_guard(main_program): depth = paddle.assign(np.array([10], dtype=np.int32)) label = paddle.static.data( name="label", shape=[-1, 1], dtype="int64" ) one_hot_label = paddle.nn.functional.one_hot( x=label, num_classes=depth ) _ = ir.translate_to_new_ir(main_program.desc) def test_normal_attribute(self): place = core.Place() place.set_place(paddle.CPUPlace()) new_scope = paddle.static.Scope() main_program = paddle.static.Program() with paddle.static.scope_guard(new_scope): with paddle.static.program_guard(main_program): depth = 10 label = paddle.static.data( name="label", shape=[-1, 1], dtype="int64" ) one_hot_label = paddle.nn.functional.one_hot( x=label, num_classes=depth ) _ = ir.translate_to_new_ir(main_program.desc) class TestReduceOpTranscriber(unittest.TestCase): def test_reduce_all(self): place = core.Place() place.set_place(paddle.CPUPlace()) exe = paddle.static.Executor(place) new_scope = paddle.static.Scope() main_program = paddle.static.Program() with paddle.static.scope_guard(new_scope): with paddle.static.program_guard(main_program): arr = np.ones([2, 2], dtype="float32") x = paddle.to_tensor(arr, dtype='int32') out1 = paddle.all(x) out = exe.run(main_program, {}, fetch_list=[out1.name]) np.testing.assert_array_equal(out[0], np.all(arr)) def test_with_axis(self): place = core.Place() place.set_place(paddle.CPUPlace()) exe = paddle.static.Executor(place) new_scope = paddle.static.Scope() main_program = paddle.static.Program() with paddle.static.scope_guard(new_scope): with paddle.static.program_guard(main_program): arr = np.ones([2, 2], dtype="float32") x = paddle.to_tensor(arr, dtype='int32') out1 = paddle.all(x, axis=0) out = exe.run(main_program, {}, fetch_list=[out1.name]) np.testing.assert_array_equal(out[0], np.all(arr, axis=0)) if __name__ == "__main__": unittest.main()