From d7f9e5948ada320e567239f4223c1716ce4612f6 Mon Sep 17 00:00:00 2001
From: maboroshi <a1306744054@outlook.com>
Date: Sat, 4 Jul 2026 16:25:40 +0800
Subject: [PATCH] restructure the msmodel_mlp folder

---
 examples/ms_model_mlp/native.py | 137 --------------------------------
 1 file changed, 137 deletions(-)
 delete mode 100644 examples/ms_model_mlp/native.py

diff --git a/examples/ms_model_mlp/native.py b/examples/ms_model_mlp/native.py
deleted file mode 100644
index a82ec3b24..000000000
--- a/examples/ms_model_mlp/native.py
+++ /dev/null
@@ -1,137 +0,0 @@
-#
-# 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 tensor
-from singa.tensor import Tensor
-from singa import autograd
-from singa import opt
-import numpy as np
-from singa import device
-import argparse
-
-np_dtype = {"float16": np.float16, "float32": np.float32}
-
-singa_dtype = {"float16": tensor.float16, "float32": tensor.float32}
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument('-p',
-                        choices=['float32', 'float16'],
-                        default='float32',
-                        dest='precision')
-    parser.add_argument('-m',
-                        '--max-epoch',
-                        default=1001,
-                        type=int,
-                        help='maximum epochs',
-                        dest='max_epoch')
-    args = parser.parse_args()
-
-    np.random.seed(0)
-
-    autograd.training = True
-
-    # prepare training data in numpy array
-
-    # 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))
-
-    # convert training data to 2d space
-    label = np.asarray([5 * a + 1 > b for (a, b) in zip(x, y)])
-    data = np.array([[a, b] for (a, b) in zip(x, y)], dtype=np.float32)
-
-    def to_categorical(y, num_classes):
-        """
-        Converts a class vector (integers) to binary class matrix.
-
-        Args:
-            y: class vector to be converted into a matrix
-                (integers from 0 to num_classes).
-            num_classes: total number of classes.
-
-        Returns:
-            A binary matrix representation of the input.
-        """
-        y = np.array(y, dtype="int")
-        n = y.shape[0]
-        categorical = np.zeros((n, num_classes))
-        categorical[np.arange(n), y] = 1
-        return categorical
-
-    label = to_categorical(label, 2).astype(np.float32)
-    print("train_data_shape:", data.shape)
-    print("train_label_shape:", label.shape)
-
-    precision = singa_dtype[args.precision]
-    np_precision = np_dtype[args.precision]
-
-    dev = device.create_cuda_gpu()
-
-    inputs = Tensor(data=data, device=dev)
-    target = Tensor(data=label, device=dev)
-
-    inputs = inputs.as_type(precision)
-    target = target.as_type(tensor.int32)
-
-    w0_np = np.random.normal(0, 0.1, (2, 3)).astype(np_precision)
-    w0 = Tensor(data=w0_np,
-                device=dev,
-                dtype=precision,
-                requires_grad=True,
-                stores_grad=True)
-    b0 = Tensor(shape=(3,),
-                device=dev,
-                dtype=precision,
-                requires_grad=True,
-                stores_grad=True)
-    b0.set_value(0.0)
-
-    w1_np = np.random.normal(0, 0.1, (3, 2)).astype(np_precision)
-    w1 = Tensor(data=w1_np,
-                device=dev,
-                dtype=precision,
-                requires_grad=True,
-                stores_grad=True)
-    b1 = Tensor(shape=(2,),
-                device=dev,
-                dtype=precision,
-                requires_grad=True,
-                stores_grad=True)
-    b1.set_value(0.0)
-
-    sgd = opt.SGD(0.05, 0.8)
-
-    # training process
-    for i in range(args.max_epoch):
-        x = autograd.matmul(inputs, w0)
-        x = autograd.add_bias(x, b0)
-        x = autograd.relu(x)
-        x = autograd.matmul(x, w1)
-        x = autograd.add_bias(x, b1)
-        loss = autograd.softmax_cross_entropy(x, target)
-        sgd(loss)
-
-        if i % 100 == 0:
-            print("%d, training loss = " % i, tensor.to_numpy(loss)[0])