support vm for floor

mindspore/ops/_op_impl/tbe/__init__.py

@@ -158,3 +158,4 @@ from .avg_pool_grad import _avg_pool_grad_tbe
 from .ones_like import _ones_like_tbe
 from .batch_to_space import _batch_to_space_tbe
 from .space_to_batch import _space_to_batch_tbe
+from .floor import _floor_tbe

mindspore/ops/_op_impl/tbe/floor.py

@@ -0,0 +1,36 @@
+# 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.
+# ============================================================================
+
+"""Floor op"""
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
+
+floor_op_info = TBERegOp("Floor") \
+    .fusion_type("ELEMWISE") \
+    .async_flag(False) \
+    .binfile_name("floor.so") \
+    .compute_cost(10) \
+    .kernel_name("floor") \
+    .partial_flag(True) \
+    .input(0, "x", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default) \
+    .get_op_info()
+
+
+@op_info_register(floor_op_info)
+def _floor_tbe():
+    """Floor TBE register"""
+    return

tests/ut/python/ops/test_math_ops.py

@@ -351,6 +351,15 @@ class AssignAdd(nn.Cell):
         self.inputdata = input_
         return self.op(self.inputdata, input_)
 
+class FloorNet(nn.Cell):
+    def __init__(self):
+        super(FloorNet, self).__init__()
+        self.floor = P.Floor()
+
+    def construct(self, x):
+        return self.floor(x)
+
+
 test_case_math_ops = [
     ('MatMulGrad', {
         'block': GradWrap(NetWithLoss(MatMulNet())),
@@ -391,6 +400,11 @@ test_case_math_ops = [
         'desc_inputs': [Tensor(np.array([[1., 0., -2.]], np.float32))],
         'desc_bprop': [Tensor(np.array([[1., 0., -2.]], np.float32))],
         'skip': ['backward']}),
+    ('Floor', {
+        'block': FloorNet(),
+        'desc_inputs': [Tensor(np.array([[1., 0., -2.]], np.float32))],
+        'desc_bprop': [Tensor(np.array([[1., 0., -2.]], np.float32))],
+        'skip': ['backward']}),
 ]
 
 test_case_lists = [test_case_math_ops]