Add st for dynamic shape

tests/st/dynamic_shape/test_ftrl.py

@@ -0,0 +1,74 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# 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.
+# ============================================================================
+import numpy as np
+import pytest
+import mindspore.nn as nn
+from mindspore import Tensor, Parameter, context
+from mindspore.nn import TrainOneStepCell
+from mindspore.nn.optim import FTRL, LazyAdam
+from mindspore.ops import operations as P
+
+context.set_context(enable_sparse=True,
+                    mode=context.GRAPH_MODE,
+                    device_target="Ascend")
+
+class NetWithSparseGatherV2(nn.Cell):
+    def __init__(self):
+        super(NetWithSparseGatherV2, self).__init__()
+        self.weight1 = Parameter(Tensor(np.ones([3, 1, 2]).astype(np.float32)), name="weight1")
+        self.weight2 = Parameter(Tensor(np.ones([3, 1, 2]).astype(np.float32)), name="weight2")
+        self.axis = 0
+        self.gather = P.SparseGatherV2()
+
+    def construct(self, indices, label):
+        return self.gather(self.weight1, indices, self.axis) + self.weight2
+
+@pytest.mark.level0
+@pytest.mark.platform_arm_ascend_training
+@pytest.mark.platform_x86_ascend_training
+@pytest.mark.env_onecard
+def test_ftrl_net():
+    indices = Tensor(np.array([0, 0, 1]).astype(np.int32))
+    label = Tensor(np.zeros([2, 1, 2]).astype(np.float32))
+    net = NetWithSparseGatherV2()
+
+    optimizer = FTRL(net.trainable_params(), learning_rate=0.1, weight_decay=0.9, loss_scale=2.0)
+    optimizer.target = 'Ascend'
+    train_network = TrainOneStepCell(net, optimizer)
+    output = train_network(indices, label)
+    np.allclose(output.asnumpy(), np.array([[[2, 2]], [[2, 2]], [[2, 2]]]))
+    np.allclose(net.weight1.asnumpy(), np.array([[[0.7884067, 0.7884067]],
+                                                 [[0.68213105, 0.68213105]],
+                                                 [[1.0, 1.0]]]))
+    np.allclose(net.weight2.asnumpy(), np.array([[[0.6821311, 0.6821311]],
+                                                 [[0.6821311, 0.6821311]],
+                                                 [[0.6821311, 0.6821311]]]))
+
+@pytest.mark.level0
+@pytest.mark.platform_arm_ascend_training
+@pytest.mark.platform_x86_ascend_training
+@pytest.mark.env_onecard
+def test_lazy_adam_net():
+    indices = Tensor(np.array([0, 0, 1]).astype(np.int32))
+    label = Tensor(np.zeros([2, 1, 2]).astype(np.float32))
+    net = NetWithSparseGatherV2()
+
+    optimizer = LazyAdam(net.trainable_params(), learning_rate=0.1, weight_decay=0.9, loss_scale=2.0)
+    optimizer.target = 'Ascend'
+    train_network = TrainOneStepCell(net, optimizer)
+    output = train_network(indices, label)
+    np.allclose(output.asnumpy(), np.array([[[2, 2]], [[2, 2]], [[2, 2]]]))
+    np.allclose(net.weight1.asnumpy(), np.array([[[0.9, 0.9]], [[0.9, 0.9]], [[1.0, 1.0]]]))
+    np.allclose(net.weight2.asnumpy(), np.array([[[0.9, 0.9]], [[0.9, 0.9]], [[0.9, 0.9]]]))