| # |
| # Licensed to the Apache Software Foundation (ASF) under one |
| # or more contributor license agreements. See the NOTICE file |
| # distributed with this work for additional information |
| # regarding copyright ownership. The ASF licenses this file |
| # to you 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 singa import layer |
| from singa import model |
| from singa import tensor |
| from singa import opt |
| from singa import device |
| from singa.autograd import Operator |
| from singa.layer import Layer |
| from singa import singa_wrap as singa |
| import argparse |
| import numpy as np |
| |
| np_dtype = {"float16": np.float16, "float32": np.float32} |
| |
| singa_dtype = {"float16": tensor.float16, "float32": tensor.float32} |
| |
| #### self-defined loss begin |
| |
| ### from autograd.py |
| class SumError(Operator): |
| |
| def __init__(self): |
| super(SumError, self).__init__() |
| # self.t = t.data |
| |
| def forward(self, x): |
| # self.err = singa.__sub__(x, self.t) |
| self.data_x = x |
| # sqr = singa.Square(self.err) |
| # loss = singa.SumAll(sqr) |
| loss = singa.SumAll(x) |
| # self.n = 1 |
| # for s in x.shape(): |
| # self.n *= s |
| # loss /= self.n |
| return loss |
| |
| def backward(self, dy=1.0): |
| # dx = self.err |
| dev = device.get_default_device() |
| dx = tensor.Tensor(self.data_x.shape, dev, singa_dtype['float32']) |
| dx.copy_from_numpy(np.ones(self.data_x.shape)) |
| # dx *= float(2 / self.n) |
| dx *= dy |
| return dx |
| |
| def se_loss(x): |
| # assert x.shape == t.shape, "input and target shape different: %s, %s" % ( |
| # x.shape, t.shape) |
| return SumError()(x)[0] |
| |
| ### from layer.py |
| class SumErrorLayer(Layer): |
| """ |
| Generate a MeanSquareError operator |
| """ |
| |
| def __init__(self): |
| super(SumErrorLayer, self).__init__() |
| |
| def forward(self, x): |
| return se_loss(x) |
| |
| #### self-defined loss end |
| |
| class MSMLP(model.Model): |
| |
| def __init__(self, data_size=10, perceptron_size=100, num_classes=10, layer_hidden_list=[10,10,10,10]): |
| super(MSMLP, self).__init__() |
| self.num_classes = num_classes |
| self.dimension = 2 |
| |
| self.relu = layer.ReLU() |
| self.linear1 = layer.Linear(layer_hidden_list[0]) |
| self.linear2 = layer.Linear(layer_hidden_list[1]) |
| self.linear3 = layer.Linear(layer_hidden_list[2]) |
| self.linear4 = layer.Linear(layer_hidden_list[3]) |
| self.linear5 = layer.Linear(num_classes) |
| self.softmax_cross_entropy = layer.SoftMaxCrossEntropy() |
| self.sum_error = SumErrorLayer() |
| |
| def forward(self, inputs): |
| y = self.linear1(inputs) |
| y = self.relu(y) |
| y = self.linear2(y) |
| y = self.relu(y) |
| y = self.linear3(y) |
| y = self.relu(y) |
| y = self.linear4(y) |
| y = self.relu(y) |
| y = self.linear5(y) |
| return y |
| |
| def train_one_batch(self, x, y, dist_option, spars, synflow_flag): |
| # print ("in train_one_batch") |
| out = self.forward(x) |
| # print ("train_one_batch x.data: \n", x.data) |
| # print ("train_one_batch y.data: \n", y.data) |
| # print ("train_one_batch out.data: \n", out.data) |
| if synflow_flag: |
| # print ("sum_error") |
| loss = self.sum_error(out) |
| else: # normal training |
| # print ("softmax_cross_entropy") |
| loss = self.softmax_cross_entropy(out, y) |
| # print ("train_one_batch loss.data: \n", loss.data) |
| |
| if dist_option == 'plain': |
| # print ("before pn_p_g_list = self.optimizer(loss)") |
| pn_p_g_list = self.optimizer(loss) |
| # print ("after pn_p_g_list = self.optimizer(loss)") |
| elif dist_option == 'half': |
| self.optimizer.backward_and_update_half(loss) |
| elif dist_option == 'partialUpdate': |
| self.optimizer.backward_and_partial_update(loss) |
| elif dist_option == 'sparseTopK': |
| self.optimizer.backward_and_sparse_update(loss, |
| topK=True, |
| spars=spars) |
| elif dist_option == 'sparseThreshold': |
| self.optimizer.backward_and_sparse_update(loss, |
| topK=False, |
| spars=spars) |
| # print ("len(pn_p_g_list): \n", len(pn_p_g_list)) |
| # print ("len(pn_p_g_list[0]): \n", len(pn_p_g_list[0])) |
| # print ("pn_p_g_list[0][0]: \n", pn_p_g_list[0][0]) |
| # print ("pn_p_g_list[0][1].data: \n", pn_p_g_list[0][1].data) |
| # print ("pn_p_g_list[0][2].data: \n", pn_p_g_list[0][2].data) |
| return pn_p_g_list, out, loss |
| # return pn_p_g_list[0], pn_p_g_list[1], pn_p_g_list[2], out, loss |
| |
| def set_optimizer(self, optimizer): |
| self.optimizer = optimizer |
| |
| |
| def create_model(pretrained=False, **kwargs): |
| """Constructs a CNN model. |
| |
| Args: |
| pretrained (bool): If True, returns a pre-trained model. |
| |
| Returns: |
| The created CNN model. |
| """ |
| model = MSMLP(**kwargs) |
| |
| return model |
| |
| |
| __all__ = ['MLP', 'create_model'] |
| |
| if __name__ == "__main__": |
| np.random.seed(0) |
| |
| parser = argparse.ArgumentParser() |
| parser.add_argument('-p', |
| choices=['float32', 'float16'], |
| default='float32', |
| dest='precision') |
| parser.add_argument('-g', |
| '--disable-graph', |
| default='True', |
| action='store_false', |
| help='disable graph', |
| dest='graph') |
| parser.add_argument('-m', |
| '--max-epoch', |
| default=1001, |
| type=int, |
| help='maximum epochs', |
| dest='max_epoch') |
| args = parser.parse_args() |
| |
| # generate the boundary |
| f = lambda x: (5 * x + 1) |
| bd_x = np.linspace(-1.0, 1, 200) |
| bd_y = f(bd_x) |
| |
| # generate the training data |
| x = np.random.uniform(-1, 1, 400) |
| y = f(x) + 2 * np.random.randn(len(x)) |
| |
| # choose one precision |
| precision = singa_dtype[args.precision] |
| np_precision = np_dtype[args.precision] |
| |
| # convert training data to 2d space |
| label = np.asarray([5 * a + 1 > b for (a, b) in zip(x, y)]).astype(np.int32) |
| data = np.array([[a, b] for (a, b) in zip(x, y)], dtype=np_precision) |
| |
| dev = device.create_cuda_gpu_on(0) |
| sgd = opt.SGD(0.1, 0.9, 1e-5, dtype=singa_dtype[args.precision]) |
| tx = tensor.Tensor((400, 2), dev, precision) |
| ty = tensor.Tensor((400,), dev, tensor.int32) |
| model = MLP(data_size=2, perceptron_size=3, num_classes=2) |
| |
| # attach model to graph |
| model.set_optimizer(sgd) |
| model.compile([tx], is_train=True, use_graph=args.graph, sequential=True) |
| model.train() |
| |
| for i in range(args.max_epoch): |
| tx.copy_from_numpy(data) |
| ty.copy_from_numpy(label) |
| out, loss = model(tx, ty, 'fp32', spars=None) |
| |
| if i % 100 == 0: |
| print("training loss = ", tensor.to_numpy(loss)[0]) |
