add ops for VM

mindspore/ops/_grad/grad_nn_ops.py

@@ -600,7 +600,6 @@ def get_bprop_roi_align(self):
     sample_num = self.sample_num
 
     def bprop(inputs, rois, out, dout):
-        rois_shape = shape_op(rois)
         inputs_shape = shape_op(inputs)
         dx = G.ROIAlignGrad(inputs_shape,
                             pooled_height,
@@ -608,7 +607,7 @@ def get_bprop_roi_align(self):
                             spatial_scale,
                             sample_num,
                             )(dout, rois)
-        return dx, zeros_like(rois_shape)
+        return dx, zeros_like(rois)
 
     return bprop
 

mindspore/ops/_op_impl/tbe/__init__.py

@@ -76,6 +76,8 @@ from .strided_slice_d import _strided_slice_d_tbe
 from .strided_slice_grad_d import _strided_slice_grad_d_tbe
 from .split_d import _split_d_tbe
 from .exp import _exp_tbe
+from .elu import _elu_tbe
+from .elu_grad import _elu_grad_tbe
 from .div import _div_tbe
 from .log import _log_tbe
 from .floor_div import _floor_div_tbe

mindspore/ops/_op_impl/tbe/elu.py

@@ -0,0 +1,40 @@
+# 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.
+# ============================================================================
+
+"""Elu op"""
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
+
+elu_op_info = TBERegOp("Elu") \
+    .fusion_type("ELEMWISE") \
+    .async_flag(False) \
+    .binfile_name("elu.so") \
+    .compute_cost(10) \
+    .kernel_name("elu") \
+    .partial_flag(True) \
+    .op_pattern("formatAgnostic") \
+    .attr("alpha", "optional", "float", "all", "1.0") \
+    .input(0, "x", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F32_5HD, DataType.F32_5HD) \
+    .get_op_info()
+
+
+@op_info_register(elu_op_info)
+def _elu_tbe():
+    """Elu TBE register"""
+    return

mindspore/ops/_op_impl/tbe/elu_grad.py

@@ -0,0 +1,43 @@
+# 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.
+# ============================================================================
+
+"""EluGrad op"""
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
+
+elu_grad_op_info = TBERegOp("EluGrad") \
+    .fusion_type("ELEMWISE") \
+    .async_flag(False) \
+    .binfile_name("elu_grad.so") \
+    .compute_cost(10) \
+    .kernel_name("elu_grad") \
+    .partial_flag(True) \
+    .input(0, "grads", False, "required", "all") \
+    .input(1, "activations", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD, DataType.F16_5HD) \
+    .dtype_format(DataType.F16_FracZ, DataType.F16_FracZ, DataType.F16_FracZ) \
+    .dtype_format(DataType.F16_C1HWNCoC0, DataType.F16_C1HWNCoC0, DataType.F16_C1HWNCoC0) \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD) \
+    .dtype_format(DataType.F32_FracZ, DataType.F32_FracZ, DataType.F32_FracZ) \
+    .dtype_format(DataType.F32_C1HWNCoC0, DataType.F32_C1HWNCoC0, DataType.F32_C1HWNCoC0) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \
+    .get_op_info()
+
+
+@op_info_register(elu_grad_op_info)
+def _elu_grad_tbe():
+    """EluGrad TBE register"""
+    return

mindspore/ops/operations/nn_ops.py

@@ -1527,7 +1527,8 @@ class L2Loss(PrimitiveWithInfer):
 
     def infer_dtype(self, x_type):
         validator.check_subclass("x_type", x_type, mstype.tensor, self.name)
-        validator.check_tensor_type_same({'x_type': x_type}, [mstype.double, mstype.float_, mstype.float16], self.name)
+        valid_types = [mstype.float16, mstype.float32, mstype.double]
+        validator.check_tensor_type_same({'x_type': x_type}, valid_types, self.name)
         return x_type
 
 

tests/ut/python/ops/test_ops.py

@@ -874,7 +874,7 @@ test_case_nn_ops = [
         'skip': ['backward']}),
     ('L2Loss_1', {
         'block': P.L2Loss(),
-        'desc_inputs': [Tensor(np.array([1, 2, 3, 4]), mstype.float16)],
+        'desc_inputs': [Tensor(np.array([1, 2, 3, 4]), mstype.float32)],
         'desc_bprop': []}),
     ('L2Loss_2', {
         'block': P.L2Loss(),