Merge pull request #1167 from GY-GitCode/24-5-4-commit

Add implementations for the new loss function

examples/ms_model_mlp/model.py

@@ -0,0 +1,216 @@
+#
+# 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):
+        super(MSMLP, self).__init__()
+        self.num_classes = num_classes
+        self.dimension = 2
+
+        self.relu = layer.ReLU()
+        self.linear1 = layer.Linear(perceptron_size)
+        self.linear2 = 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)
+        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])