support reshape parameter

mindspore/ccsrc/parallel/step_parallel.cc

@@ -1523,9 +1523,32 @@ std::shared_ptr<TensorLayout> FindPrevParallelCareNodeLayout(const AnfNodePtr &n
   return nullptr;
 }
 
+std::shared_ptr<TensorLayout> CreateParameterLayout(const AnfNodePtr &node) {
+  // Create DataParallel tensor layout for parameter(support WideDeep).
+  CheckGlobalDeviceManager();
+  int32_t dev_num = SizeToInt(g_device_manager->GetDeviceListByStageId(0).size());
+  TensorLayout input_tensor_layout;
+  // create input_shape
+  Shapes inputs_shape = GetNodeShape(node);
+  Shape input_shape_array = inputs_shape[0];
+  if (input_shape_array.empty()) {
+    MS_LOG(EXCEPTION) << "Don't support reshape a scalar parameter.";
+  }
+  // create tensor_map
+  size_t shape_size = input_shape_array.size();
+  TensorMap input_tensor_map_array(SizeToInt(shape_size) - 1, -1);
+  input_tensor_map_array.insert(input_tensor_map_array.begin(), 0);
+  // create dev_matrix
+  Shape dev_matrix_array = {dev_num};
+  if (input_tensor_layout.InitFromVector(dev_matrix_array, input_tensor_map_array, input_shape_array) != SUCCESS) {
+    MS_LOG(EXCEPTION) << "Create tensor layout for parameter failed.";
+  }
+  return std::make_shared<TensorLayout>(input_tensor_layout);
+}
+
 std::shared_ptr<TensorLayout> FindPrevLayout(const AnfNodePtr &node) {
   if (node->isa<Parameter>()) {
-    MS_LOG(EXCEPTION) << "Failure: parameter before reshape is not supported temporary";
+    return CreateParameterLayout(node);
   }
   if (!node->isa<CNode>()) {
     return nullptr;

mindspore/context.py

@@ -415,8 +415,11 @@ def set_auto_parallel_context(**kwargs):
     Args:
         device_num (int): Available device number, the value must be in [1, 4096]. Default: 1.
         global_rank (int): Global rank id, the value must be in [0, 4095]. Default: 0.
-        mirror_mean (bool): Whether to perform mean operator after all-reduce of mirror. Default: False.
-        cast_before_mirror (bool): Insert Mirror Op after the cast if this flag is True. Default: True.
+        mirror_mean (bool): Whether to perform mean operator after all-reduce of mirror.
+                     "stand_alone" do not support mirror_mean. Default: False.
+        cast_before_mirror (bool): Insert Mirror Op after the cast if this flag is True.
+                     "stand_alone", "data_parallel" and "hybrid_parallel" do not support
+                     cast_before_mirror. Default: True.
         parallel_mode (str): There are five kinds of parallel modes, "stand_alone", "data_parallel",
                      "hybrid_parallel", "semi_auto_parallel" and "auto_parallel". Default: "stand_alone".
 

tests/ut/python/parallel/test_reshape_parameter.py

@@ -0,0 +1,75 @@
+# 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 mindspore as ms
+import mindspore.nn as nn
+from mindspore.ops import operations as P
+from mindspore.ops import composite as C
+from mindspore import Tensor
+from mindspore import context
+from mindspore.common.api import _executor
+from tests.ut.python.ops.test_math_ops import VirtualLoss
+import numpy as np
+
+
+class NetWithLoss(nn.Cell):
+    def __init__(self, network):
+        super(NetWithLoss, self).__init__()
+        self.loss = VirtualLoss()
+        self.network = network
+
+    def construct(self, x, y):
+        predict = self.network(x, y)
+        return self.loss(predict)
+
+
+class GradWrap(nn.Cell):
+    def __init__(self, network):
+        super(GradWrap, self).__init__()
+        self.network = network
+
+    def construct(self, x, y):
+        return C.grad_all(self.network)(x, y)
+
+
+class Net(nn.Cell):
+    def __init__(self, strategy):
+        super().__init__()
+        self.reshape = P.Reshape()
+        self.mul = P.Mul().set_strategy(strategy)
+        self.relu = P.ReLU()
+
+    def construct(self, x, y):
+        out = self.reshape(x, (10000, 36, 1))
+        out = self.mul(out, y)
+        out = self.relu(out)
+        return out
+
+
+def test_reshape_parameter_data_parallel():
+    context.set_auto_parallel_context(device_num=8, global_rank=0, parallel_mode="semi_auto_parallel")
+    strategy = ((8, 1, 1), (8, 1, 1))
+    net = GradWrap(NetWithLoss(Net(strategy)))
+    x = Tensor(np.ones([10000, 36]), dtype=ms.float32)
+    y = Tensor(np.ones([10000, 36, 1]), dtype=ms.float32)
+    _executor.compile(net, x, y)
+
+
+def test_reshape_parameter_model_parallel():
+    context.set_auto_parallel_context(device_num=8, global_rank=0, parallel_mode="semi_auto_parallel")
+    strategy = ((4, 2, 1), (4, 2, 1))
+    net = GradWrap(NetWithLoss(Net(strategy)))
+    x = Tensor(np.ones([10000, 36]), dtype=ms.float32)
+    y = Tensor(np.ones([10000, 36, 1]), dtype=ms.float32)
+    _executor.compile(net, x, y)