!9859 add st testcases for dynamic shape

From: @mindspore_boy
Reviewed-by: @c_34,@guoqi1024
Signed-off-by: @c_34,@guoqi1024

tests/st/dynamic_shape/test_dynamic_shape_embedding.py

@@ -0,0 +1,64 @@
+# 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, context
+from mindspore.nn import TrainOneStepCell, WithLossCell
+
+
+context.set_context(enable_sparse=True,
+                    mode=context.GRAPH_MODE)
+
+
+class NetWithEmbeddingLookUp(nn.Cell):
+    def __init__(self, vocab_size, embedding_size, target="CPU"):
+        super(NetWithEmbeddingLookUp, self).__init__()
+        self.embedding_lookup =  \
+                        nn.EmbeddingLookup(vocab_size=vocab_size,
+                                           embedding_size=embedding_size,
+                                           param_init="ones", target=target)
+
+    def construct(self, indices):
+        out = self.embedding_lookup(indices)
+        return out
+
+
+@pytest.mark.level0
+@pytest.mark.platform_arm_ascend_training
+@pytest.mark.platform_x86_ascend_training
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_sit_embedding_lookup_net():
+    indices = Tensor(np.array([0, 1, 2]).astype(np.int32))
+    label = Tensor(np.random.randn(3, 8).astype(np.float32))
+
+    net1 = NetWithEmbeddingLookUp(vocab_size=8, embedding_size=8, target="CPU")
+    loss = nn.SoftmaxCrossEntropyWithLogits(reduction="mean")
+    optimizer1 = nn.Adam(params=net1.trainable_params(), learning_rate=0.1)
+    optimizer1.unique = True
+    train_network1 = TrainOneStepCell(WithLossCell(net1, loss), optimizer1)
+    train_network1.set_train()
+    out1 = train_network1(indices, label)
+
+    net2 = NetWithEmbeddingLookUp(vocab_size=8, embedding_size=8, target="CPU")
+    optimizer2 = nn.Adam(params=net2.trainable_params(), learning_rate=0.1)
+    optimizer2.unique = False
+    optimizer2.target = "CPU"
+    train_network2 = TrainOneStepCell(WithLossCell(net2, loss), optimizer2)
+    train_network2.set_train()
+    out2 = train_network2(indices, label)
+
+    assert np.allclose(out1.asnumpy(), out2.asnumpy(), 0.001, 0.001)