support vm for flatten

mindspore/ops/_op_impl/tbe/__init__.py

@@ -168,3 +168,4 @@ from .floor import _floor_tbe
 from .log1p import _log1p_tbe
 from .resize_bilinear import _resize_bilinear_tbe
 from .resize_bilinear_grad import _resize_bilinear_grad_tbe
+from .flatten import _flatten_tbe

mindspore/ops/_op_impl/tbe/flatten.py

@@ -0,0 +1,44 @@
+# 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.
+# ============================================================================
+
+"""Flatten op"""
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
+
+flatten_op_info = TBERegOp("Flatten") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("flatten.so") \
+    .compute_cost(10) \
+    .kernel_name("flatten") \
+    .partial_flag(True) \
+    .input(0, "x", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.I8_Default, DataType.I8_Default) \
+    .dtype_format(DataType.U8_Default, DataType.U8_Default) \
+    .dtype_format(DataType.I16_Default, DataType.I16_Default) \
+    .dtype_format(DataType.U16_Default, DataType.U16_Default) \
+    .dtype_format(DataType.I32_Default, DataType.I32_Default) \
+    .dtype_format(DataType.U32_Default, DataType.U32_Default) \
+    .dtype_format(DataType.I64_Default, DataType.I64_Default) \
+    .dtype_format(DataType.U64_Default, DataType.U64_Default) \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default) \
+    .get_op_info()
+
+
+@op_info_register(flatten_op_info)
+def _flatten_tbe():
+    """Flatten TBE register"""
+    return

tests/ut/python/ops/test_nn_ops.py

@@ -447,6 +447,17 @@ class UnfoldNetSame(nn.Cell):
         return self.unfold(x)
 
 
+class FlattenNet(nn.Cell):
+    """ FlattenNet definition """
+
+    def __init__(self):
+        super(FlattenNet, self).__init__()
+        self.flatten = P.Flatten()
+
+    def construct(self, x):
+        return self.flatten(x)
+
+
 test_cases = [
     ('SoftMaxGrad', {
         'block': SoftMaxGrad(VirtualNetWithLoss(P.Softmax())),
@@ -532,6 +543,10 @@ test_cases = [
         'desc_inputs': [Tensor(np.array([3, 4, 5, 6]).astype(np.float32))],
         'desc_bprop': [Tensor(np.array([1, 2, 3, 4]).astype(np.float32))],
         'skip': ['backward']}),
+    ('FlattenNet', {
+        'block': FlattenNet(),
+        'desc_inputs': [Tensor(np.ones([1, 2, 3, 4], np.float32))],
+    }),
 ]
 
 test_cases_for_verify_exception = [