# 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. from __future__ import print_function import contextlib import unittest import numpy as np import six import paddle import paddle.fluid as fluid from paddle.fluid import core from paddle.fluid.optimizer import SGDOptimizer from paddle.fluid.dygraph.nn import Conv2D, Pool2D, Linear import paddle.fluid.dygraph.nn as nn from paddle.fluid.dygraph.base import to_variable from test_imperative_base import new_program_scope from paddle.fluid.framework import _test_eager_guard class Policy(fluid.dygraph.Layer): def __init__(self, input_size): super(Policy, self).__init__() self.affine1 = nn.Linear(input_size, 128) self.affine2 = nn.Linear(128, 2) self.dropout_ratio = 0.6 self.saved_log_probs = [] self.rewards = [] def forward(self, inputs): x = fluid.layers.reshape(inputs, shape=[-1, 4]) x = self.affine1(x) x = fluid.layers.dropout(x, self.dropout_ratio) x = fluid.layers.relu(x) action_scores = self.affine2(x) return fluid.layers.softmax(action_scores, axis=1) class TestImperativeMnist(unittest.TestCase): def test_mnist_float32(self): seed = 90 epoch_num = 1 state = np.random.normal(size=4).astype("float32") state_list = state.tolist() reward = np.random.random(size=[1, 1]).astype("float32") reward_list = reward.tolist() action_list = [1] action = np.array(action_list).astype("float32") mask_list = [[0, 1]] mask = np.array(mask_list).astype("float32") def run_dygraph(): paddle.seed(seed) paddle.framework.random._manual_program_seed(seed) policy = Policy(input_size=4) dy_state = fluid.dygraph.base.to_variable(state) dy_state.stop_gradient = True loss_probs = policy(dy_state) dy_mask = fluid.dygraph.base.to_variable(mask) dy_mask.stop_gradient = True loss_probs = fluid.layers.log(loss_probs) loss_probs = fluid.layers.elementwise_mul(loss_probs, dy_mask) loss_probs = fluid.layers.reduce_sum(loss_probs, dim=-1) dy_reward = fluid.dygraph.base.to_variable(reward) dy_reward.stop_gradient = True loss_probs = fluid.layers.elementwise_mul(dy_reward, loss_probs) loss = fluid.layers.reduce_sum(loss_probs) sgd = SGDOptimizer(learning_rate=1e-3, parameter_list=policy.parameters()) dy_param_init_value = {} dy_out = loss.numpy() for param in policy.parameters(): dy_param_init_value[param.name] = param.numpy() loss.backward() sgd.minimize(loss) policy.clear_gradients() dy_param_value = {} for param in policy.parameters(): dy_param_value[param.name] = param.numpy() return dy_out, dy_param_init_value, dy_param_value with fluid.dygraph.guard(): dy_out, dy_param_init_value, dy_param_value = run_dygraph() with fluid.dygraph.guard(): with _test_eager_guard(): eager_out, eager_param_init_value, eager_param_value = run_dygraph( ) with new_program_scope(): paddle.seed(seed) paddle.framework.random._manual_program_seed(seed) exe = fluid.Executor(fluid.CPUPlace( ) if not core.is_compiled_with_cuda() else fluid.CUDAPlace(0)) policy = Policy(input_size=4) st_sgd = SGDOptimizer(learning_rate=1e-3) st_state = fluid.layers.data(name='st_state', shape=[4], dtype='float32') st_reward = fluid.layers.data(name='st_reward', shape=[1], dtype='float32') st_mask = fluid.layers.data(name='st_mask', shape=[2], dtype='float32') st_loss_probs = policy(st_state) st_loss_probs = fluid.layers.log(st_loss_probs) st_loss_probs = fluid.layers.elementwise_mul(st_loss_probs, st_mask) st_loss_probs = fluid.layers.reduce_sum(st_loss_probs, dim=-1) st_loss_probs = fluid.layers.elementwise_mul( st_reward, st_loss_probs) st_loss = fluid.layers.reduce_sum(st_loss_probs) st_sgd.minimize(st_loss) # initialize params and fetch them static_param_init_value = {} static_param_name_list = [] for param in policy.parameters(): static_param_name_list.append(param.name) out = exe.run(fluid.default_startup_program(), fetch_list=static_param_name_list) for i in range(len(static_param_name_list)): static_param_init_value[static_param_name_list[i]] = out[i] fetch_list = [st_loss.name] fetch_list.extend(static_param_name_list) out = exe.run(fluid.default_main_program(), feed={ "st_state": state, "st_reward": reward, "st_mask": mask }, fetch_list=fetch_list) static_param_value = {} static_out = out[0] for i in range(1, len(out)): static_param_value[static_param_name_list[i - 1]] = out[i] #self.assertTrue(np.allclose(dy_x_data.all(), static_x_data.all())) for key, value in six.iteritems(static_param_init_value): self.assertTrue(np.equal(value, dy_param_init_value[key]).all()) self.assertTrue(np.equal(static_out, dy_out).all()) for key, value in six.iteritems(static_param_value): self.assertTrue(np.equal(value, dy_param_value[key]).all()) # check eager for key, value in six.iteritems(static_param_init_value): self.assertTrue(np.equal(value, eager_param_init_value[key]).all()) self.assertTrue(np.equal(static_out, eager_out).all()) for key, value in six.iteritems(static_param_value): self.assertTrue(np.equal(value, eager_param_value[key]).all()) if __name__ == '__main__': paddle.enable_static() unittest.main()