!1282 support vm for PReLU and PReLUGrad

Merge pull request !1282 from jiangjinsheng/vm_prelu

mindspore/ccsrc/kernel/tbe/tbe_adapter.cc

@@ -36,6 +36,8 @@ static std::map<string, string> tbe_func_adapter_map = {
   {"re_lu6_grad", "relu6_grad"},
   {"re_lu", "relu"},
   {"re_luv2", "relu_v2"},
+  {"p_re_lu", "prelu"},
+  {"p_re_lu_grad", "prelu_grad"},
   {"tensor_add", "add"},
   {"reduce_mean", "reduce_mean_d"},
   {"reduce_max", "reduce_max_d"},

mindspore/ops/_op_impl/tbe/__init__.py

@@ -186,3 +186,5 @@ from .bn_training_update_v2 import _bn_training_update_v2_tbe
 from .square_sum_all import square_sum_all_op_info
 from .pack import _pack_tbe
 from .unpack import _unpack_tbe
+from .prelu import _prelu_tbe
+from .prelu_grad import _prelu_grad_tbe

mindspore/ops/_op_impl/tbe/prelu.py

@@ -0,0 +1,41 @@
+# 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.
+# ============================================================================
+
+"""PReLU op"""
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
+
+prelu_op_info = TBERegOp("PReLU") \
+    .fusion_type("ELEMWISE") \
+    .async_flag(False) \
+    .binfile_name("prelu.so") \
+    .compute_cost(10) \
+    .kernel_name("prelu") \
+    .partial_flag(True) \
+    .input(0, "x", False, "required", "all") \
+    .input(1, "weight", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.F16_NCHW, DataType.F16_Default, DataType.F16_NCHW) \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD, DataType.F16_5HD) \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F32_NCHW, DataType.F32_Default, DataType.F32_NCHW) \
+    .dtype_format(DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \
+    .get_op_info()
+
+
+@op_info_register(prelu_op_info)
+def _prelu_tbe():
+    """PReLU TBE register"""
+    return

mindspore/ops/_op_impl/tbe/prelu_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.
+# ============================================================================
+
+"""PReLUGrad op"""
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
+
+prelu_grad_op_info = TBERegOp("PReLUGrad") \
+    .fusion_type("ELEMWISE") \
+    .async_flag(False) \
+    .binfile_name("prelu_grad.so") \
+    .compute_cost(10) \
+    .kernel_name("prelu_grad") \
+    .partial_flag(True) \
+    .input(0, "grads", False, "required", "all") \
+    .input(1, "features", False, "required", "all") \
+    .input(2, "weights", False, "required", "all") \
+    .output(0, "dx", False, "required", "all") \
+    .output(0, "da", False, "required", "all") \
+    .dtype_format(DataType.F32_NCHW, DataType.F32_NCHW, DataType.F32_Default,
+                  DataType.F32_NCHW, DataType.F32_Default) \
+    .dtype_format(DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD,
+                  DataType.F32_5HD, DataType.F32_5HD) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default,
+                  DataType.F32_Default, DataType.F32_Default) \
+    .get_op_info()
+
+
+@op_info_register(prelu_grad_op_info)
+def _prelu_grad_tbe():
+    """PReLUGrad TBE register"""
+    return

tests/ut/python/ops/test_nn_ops.py

@@ -24,6 +24,7 @@ from mindspore.common.initializer import initializer
 from mindspore.ops import Primitive
 from mindspore.ops import composite as C
 from mindspore.ops import operations as P
+from mindspore.ops.operations import _grad_ops as G
 from mindspore.ops import prim_attr_register, PrimitiveWithInfer
 from ..ut_filter import non_graph_engine
 from ....mindspore_test_framework.mindspore_test import mindspore_test
@@ -456,6 +457,28 @@ class FlattenNet(nn.Cell):
         return self.flatten(x)
 
 
+class PReLUNet(nn.Cell):
+    """ PReLUNet definition """
+
+    def __init__(self):
+        super(PReLUNet, self).__init__()
+        self.prelu = P.PReLU()
+        self.w = Tensor(np.ones(3, np.float32))
+
+    def construct(self, x):
+        return self.prelu(x, self.w)
+
+
+class PReLUGradNet(nn.Cell):
+    """ PReLUGradNet definition """
+
+    def __init__(self):
+        super(PReLUGradNet, self).__init__()
+        self.prelu_grad = G.PReLUGrad()
+
+    def construct(self, dout, x, w):
+        return self.prelu_grad(dout, x, w)
+
 test_cases = [
     ('SoftMaxGrad', {
         'block': SoftMaxGrad(VirtualNetWithLoss(P.Softmax())),
@@ -545,6 +568,16 @@ test_cases = [
         'block': FlattenNet(),
         'desc_inputs': [Tensor(np.ones([1, 2, 3, 4], np.float32))],
     }),
+    ('PReLUNet', {
+        'block': PReLUNet(),
+        'desc_inputs': [Tensor(np.ones([1, 3, 4, 4], np.float32))],
+    }),
+    ('PReLUGradNet', {
+        'block': PReLUGradNet(),
+        'desc_inputs': [Tensor(np.ones([1, 3, 4, 4], np.float32)),
+                        Tensor(np.ones([1, 3, 4, 4], np.float32)),
+                        Tensor(np.ones(3, np.float32))],
+    }),
 ]
 
 test_cases_for_verify_exception = [