# Copyright (c) 2018 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. """Test cloud role maker.""" from __future__ import print_function import os import unittest import paddle.fluid.generator as generator import time # temp for debug import paddle.fluid as fluid import numpy as np import paddle import paddle.fluid.core as core class TestGeneratorSeed(unittest.TestCase): # """ # Test cases for cpu generator seed. # """ def test_generator_uniform_random_dygraph(self): """Test Generator seed.""" fluid.enable_dygraph() gen = paddle.manual_seed(12312321111) x = fluid.layers.uniform_random([10], dtype="float32", min=0.0, max=1.0) st1 = gen.get_state() x1 = fluid.layers.uniform_random( [10], dtype="float32", min=0.0, max=1.0) gen.set_state(st1) print(gen.get_state()) x2 = fluid.layers.uniform_random( [10], dtype="float32", min=0.0, max=1.0) paddle.manual_seed(12312321111) x3 = fluid.layers.uniform_random( [10], dtype="float32", min=0.0, max=1.0) x_np = x.numpy() x1_np = x1.numpy() x2_np = x2.numpy() x3_np = x3.numpy() if not core.is_compiled_with_cuda(): self.assertTrue(np.allclose(x1_np, x2_np)) self.assertTrue(np.allclose(x_np, x3_np)) def test_generator_uniform_random_static(self): fluid.disable_dygraph() gen = paddle.manual_seed(123123143) startup_program = fluid.Program() train_program = fluid.Program() with fluid.program_guard(train_program, startup_program): # example 1: # attr shape is a list which doesn't contain tensor Variable. result_1 = fluid.layers.uniform_random(shape=[3, 4]) result_2 = fluid.layers.uniform_random(shape=[3, 4]) exe = fluid.Executor(fluid.CPUPlace()) exe.run(startup_program) out1 = exe.run(train_program, feed={}, fetch_list=[result_1, result_2]) #gen.set_state(cur_state) gen.manual_seed(123123143) out2 = exe.run(train_program, feed={}, fetch_list=[result_1, result_2]) out1_res1 = np.array(out1[0]) out1_res2 = np.array(out1[1]) out2_res1 = np.array(out2[0]) out2_res2 = np.array(out2[1]) if not core.is_compiled_with_cuda(): self.assertTrue(np.allclose(out1_res1, out2_res1)) self.assertTrue(np.allclose(out1_res2, out2_res2)) self.assertTrue(not np.allclose(out1_res2, out1_res1)) def test_gen_dropout_dygraph(self): fluid.enable_dygraph() gen = paddle.manual_seed(111111111) st = gen.get_state() # x = np.arange(1,101).reshape(2,50).astype("float32") x = fluid.layers.uniform_random( [2, 10], dtype="float32", min=0.0, max=1.0) y = fluid.layers.dropout(x, 0.5) gen.manual_seed(111111111) #gen.set_state(st) x1 = fluid.layers.uniform_random( [2, 10], dtype="float32", min=0.0, max=1.0) y1 = fluid.layers.dropout(x1, 0.5) y_np = y.numpy() y1_np = y1.numpy() if not core.is_compiled_with_cuda(): print(">>>>>>> dropout dygraph >>>>>>>") self.assertTrue(np.allclose(y_np, y1_np)) def test_gen_dropout_static(self): fluid.disable_dygraph() gen = paddle.manual_seed(123123143) startup_program = fluid.Program() train_program = fluid.Program() with fluid.program_guard(train_program, startup_program): # example 1: # attr shape is a list which doesn't contain tensor Variable. x_1 = fluid.layers.uniform_random(shape=[2, 10]) y_1 = fluid.layers.dropout(x_1, 0.5) exe = fluid.Executor(fluid.CPUPlace()) exe.run(startup_program) out1 = exe.run(train_program, feed={}, fetch_list=[y_1]) #gen.set_state(cur_state) gen.manual_seed(123123143) out2 = exe.run(train_program, feed={}, fetch_list=[y_1]) out1_np = np.array(out1[0]) out2_np = np.array(out2[0]) if not core.is_compiled_with_cuda(): print(">>>>>>> dropout static >>>>>>>") self.assertTrue(np.allclose(out1_np, out2_np)) def test_generator_gaussian_random_dygraph(self): """Test Generator seed.""" fluid.enable_dygraph() gen = paddle.manual_seed(12312321111) x = fluid.layers.gaussian_random([10], dtype="float32") st1 = gen.get_state() x1 = fluid.layers.gaussian_random([10], dtype="float32") gen.set_state(st1) x2 = fluid.layers.gaussian_random([10], dtype="float32") gen.manual_seed(12312321111) x3 = fluid.layers.gaussian_random([10], dtype="float32") x_np = x.numpy() x1_np = x1.numpy() x2_np = x2.numpy() x3_np = x3.numpy() if not core.is_compiled_with_cuda(): print(">>>>>>> gaussian random dygraph >>>>>>>") self.assertTrue(np.allclose(x1_np, x2_np)) self.assertTrue(np.allclose(x_np, x3_np)) def test_generator_gaussian_random_static(self): fluid.disable_dygraph() gen = paddle.manual_seed(123123143) startup_program = fluid.Program() train_program = fluid.Program() with fluid.program_guard(train_program, startup_program): # example 1: # attr shape is a list which doesn't contain tensor Variable. result_1 = fluid.layers.gaussian_random(shape=[3, 4]) result_2 = fluid.layers.gaussian_random(shape=[3, 4]) exe = fluid.Executor(fluid.CPUPlace()) exe.run(startup_program) out1 = exe.run(train_program, feed={}, fetch_list=[result_1, result_2]) #gen.set_state(cur_state) gen.manual_seed(123123143) out2 = exe.run(train_program, feed={}, fetch_list=[result_1, result_2]) out1_res1 = np.array(out1[0]) out1_res2 = np.array(out1[1]) out2_res1 = np.array(out2[0]) out2_res2 = np.array(out2[1]) if not core.is_compiled_with_cuda(): print(">>>>>>> gaussian random static >>>>>>>") self.assertTrue(np.allclose(out1_res1, out2_res1)) self.assertTrue(np.allclose(out1_res2, out2_res2)) self.assertTrue(not np.allclose(out1_res2, out1_res1)) def test_generator_randint_dygraph(self): """Test Generator seed.""" gen = generator.Generator() fluid.enable_dygraph() gen = paddle.manual_seed(12312321111) x = paddle.randint(low=10, shape=[10], dtype="int32") st1 = gen.get_state() x1 = paddle.randint(low=10, shape=[10], dtype="int32") gen.set_state(st1) x2 = paddle.randint(low=10, shape=[10], dtype="int32") gen.manual_seed(12312321111) x3 = paddle.randint(low=10, shape=[10], dtype="int32") x_np = x.numpy() x1_np = x1.numpy() x2_np = x2.numpy() x3_np = x3.numpy() if not core.is_compiled_with_cuda(): print(">>>>>>> randint dygraph >>>>>>>") self.assertTrue(np.allclose(x1_np, x2_np)) self.assertTrue(np.allclose(x_np, x3_np)) def test_generator_uniform_random_static(self): fluid.disable_dygraph() gen = paddle.manual_seed(123123143) startup_program = fluid.Program() train_program = fluid.Program() with fluid.program_guard(train_program, startup_program): # example 1: # attr shape is a list which doesn't contain tensor Variable. result_1 = fluid.layers.uniform_random(shape=[3, 4]) result_2 = fluid.layers.uniform_random(shape=[3, 4]) exe = fluid.Executor(fluid.CPUPlace()) exe.run(startup_program) out1 = exe.run(train_program, feed={}, fetch_list=[result_1, result_2]) #gen.set_state(cur_state) gen.manual_seed(123123143) out2 = exe.run(train_program, feed={}, fetch_list=[result_1, result_2]) out1_res1 = np.array(out1[0]) out1_res2 = np.array(out1[1]) out2_res1 = np.array(out2[0]) out2_res2 = np.array(out2[1]) if not core.is_compiled_with_cuda(): self.assertTrue(np.allclose(out1_res1, out2_res1)) self.assertTrue(np.allclose(out1_res2, out2_res2)) self.assertTrue(not np.allclose(out1_res2, out1_res1)) def test_generator_randint_dygraph(self): """Test Generator seed.""" fluid.enable_dygraph() gen = paddle.manual_seed(12312321111) x = paddle.randint(low=1) st1 = gen.get_state() x1 = paddle.randint(low=1) gen.set_state(st1) x2 = paddle.randint(low=1) gen.manual_seed(12312321111) x3 = paddle.randint(low=1) x_np = x.numpy() x1_np = x1.numpy() x2_np = x2.numpy() x3_np = x3.numpy() if not core.is_compiled_with_cuda(): self.assertTrue(np.allclose(x1_np, x2_np)) self.assertTrue(np.allclose(x_np, x3_np)) def test_generator_ranint_static(self): fluid.disable_dygraph() gen = paddle.manual_seed(123123143) startup_program = fluid.Program() train_program = fluid.Program() with fluid.program_guard(train_program, startup_program): # example 1: # attr shape is a list which doesn't contain tensor Variable. result_1 = paddle.randint(low=10, shape=[3, 4]) result_2 = paddle.randint(low=10, shape=[3, 4]) exe = fluid.Executor(fluid.CPUPlace()) exe.run(startup_program) out1 = exe.run(train_program, feed={}, fetch_list=[result_1, result_2]) #gen.set_state(cur_state) gen.manual_seed(123123143) out2 = exe.run(train_program, feed={}, fetch_list=[result_1, result_2]) out1_res1 = np.array(out1[0]) out1_res2 = np.array(out1[1]) out2_res1 = np.array(out2[0]) out2_res2 = np.array(out2[1]) if not core.is_compiled_with_cuda(): print(">>>>>>> randint static >>>>>>>") self.assertTrue(np.allclose(out1_res1, out2_res1)) self.assertTrue(np.allclose(out1_res2, out2_res2)) self.assertTrue(not np.allclose(out1_res2, out1_res1)) def test_generator_randperm_dygraph(self): """Test Generator seed.""" fluid.enable_dygraph() gen = paddle.manual_seed(12312321111) x = paddle.randperm(10) st1 = gen.get_state() x1 = paddle.randperm(10) gen.set_state(st1) x2 = paddle.randperm(10) gen.manual_seed(12312321111) x3 = paddle.randperm(10) x_np = x.numpy() x1_np = x1.numpy() x2_np = x2.numpy() x3_np = x3.numpy() if not core.is_compiled_with_cuda(): print(">>>>>>> randperm dygraph >>>>>>>") self.assertTrue(np.allclose(x1_np, x2_np)) self.assertTrue(np.allclose(x_np, x3_np)) def test_generator_randperm_static(self): fluid.disable_dygraph() paddle.manual_seed(123123143) startup_program = fluid.Program() train_program = fluid.Program() with fluid.program_guard(train_program, startup_program): # example 1: # attr shape is a list which doesn't contain tensor Variable. result_1 = paddle.randperm(10) result_2 = paddle.randperm(10) exe = fluid.Executor(fluid.CPUPlace()) exe.run(startup_program) out1 = exe.run(train_program, feed={}, fetch_list=[result_1, result_2]) paddle.manual_seed(123123143) out2 = exe.run(train_program, feed={}, fetch_list=[result_1, result_2]) out1_res1 = np.array(out1[0]) out1_res2 = np.array(out1[1]) out2_res1 = np.array(out2[0]) out2_res2 = np.array(out2[1]) if not core.is_compiled_with_cuda(): print(">>>>>>> randperm static >>>>>>>") self.assertTrue(np.allclose(out1_res1, out2_res1)) self.assertTrue(np.allclose(out1_res2, out2_res2)) self.assertTrue(not np.allclose(out1_res2, out1_res1)) def test_generator_sampling_id_dygraph(self): """Test Generator seed.""" gen = paddle.manual_seed(12312321111) fluid.enable_dygraph() gen.manual_seed(12312321111) x = fluid.layers.uniform_random( [10, 10], dtype="float32", min=0.0, max=1.0) y = fluid.layers.sampling_id(x) st1 = gen.get_state() x1 = fluid.layers.uniform_random( [10, 10], dtype="float32", min=0.0, max=1.0) y1 = fluid.layers.sampling_id(x) gen.set_state(st1) x2 = fluid.layers.uniform_random( [10, 10], dtype="float32", min=0.0, max=1.0) y2 = fluid.layers.sampling_id(x) gen.manual_seed(12312321111) x3 = fluid.layers.uniform_random( [10, 10], dtype="float32", min=0.0, max=1.0) y3 = fluid.layers.sampling_id(x) x_np = y.numpy() x1_np = y1.numpy() x2_np = y2.numpy() x3_np = y3.numpy() if not core.is_compiled_with_cuda(): print(">>>>>>> sampling id dygraph >>>>>>>") self.assertTrue(np.allclose(x1_np, x2_np)) self.assertTrue(np.allclose(x_np, x3_np)) def test_generator_randperm_static(self): fluid.disable_dygraph() paddle.manual_seed(123123143) startup_program = fluid.Program() train_program = fluid.Program() with fluid.program_guard(train_program, startup_program): # example 1: # attr shape is a list which doesn't contain tensor Variable. x = fluid.layers.uniform_random(shape=[10, 10]) result_1 = fluid.layers.sampling_id(x) result_2 = fluid.layers.sampling_id(x) exe = fluid.Executor(fluid.CPUPlace()) exe.run(startup_program) out1 = exe.run(train_program, feed={}, fetch_list=[result_1, result_2]) paddle.manual_seed(123123143) out2 = exe.run(train_program, feed={}, fetch_list=[result_1, result_2]) out1_res1 = np.array(out1[0]) out1_res2 = np.array(out1[1]) out2_res1 = np.array(out2[0]) out2_res2 = np.array(out2[1]) if not core.is_compiled_with_cuda(): print(">>>>>>> sampling id static >>>>>>>") self.assertTrue(np.allclose(out1_res1, out2_res1)) self.assertTrue(np.allclose(out1_res2, out2_res2)) self.assertTrue(not np.allclose(out1_res2, out1_res1)) def test_gen_TruncatedNormal_initializer(self): fluid.disable_dygraph() gen = paddle.manual_seed(123123143) cur_state = gen.get_state() startup_program = fluid.Program() train_program = fluid.Program() with fluid.program_guard(train_program, startup_program): # example 1: # attr shape is a list which doesn't contain tensor Variable. x = fluid.layers.uniform_random(shape=[2, 10]) result_1 = fluid.layers.fc( input=x, size=10, param_attr=fluid.initializer.TruncatedNormal( loc=0.0, scale=2.0)) result_2 = fluid.layers.fc( input=x, size=10, param_attr=fluid.initializer.TruncatedNormal( loc=0.0, scale=2.0)) exe = fluid.Executor(fluid.CPUPlace()) exe.run(startup_program) out1 = exe.run(train_program, feed={}, fetch_list=[result_1, result_2]) gen.manual_seed(123123143) with fluid.program_guard(train_program, startup_program): exe.run(startup_program) out2 = exe.run(train_program, feed={}, fetch_list=[result_1, result_2]) out1_res1 = np.array(out1[0]) out1_res2 = np.array(out1[1]) out2_res1 = np.array(out2[0]) out2_res2 = np.array(out2[1]) if not core.is_compiled_with_cuda(): print(">>>>>>> sampling id static >>>>>>>") self.assertTrue(np.allclose(out1_res1, out2_res1)) self.assertTrue(np.allclose(out1_res2, out2_res2)) self.assertTrue(not np.allclose(out1_res2, out1_res1)) if __name__ == "__main__": unittest.main()