support forward graph

mindspore/ccsrc/parallel/step_parallel.h

@@ -82,7 +82,8 @@ std::pair<bool, CNodePtr> FindCNode(const AnfNodePtr &anode, const std::string &
 
 void InsertMirrorOps(const MirrorOps &mirror_ops, const CNodePtr &node);
 
-void BackwardCommunication(const OperatorInfoPtr &distribute_operator, const CNodePtr &node, bool is_loss_node);
+void BackwardCommunication(const OperatorInfoPtr &distribute_operator, const CNodePtr &node,
+                           const std::vector<std::pair<CNodePtr, CNodePtr>> &sens_loss_pairs);
 
 // Generate and init parallel operator
 OperatorInfoPtr OperatorInstance(const PrimitivePtr &prim, const PrimitiveAttrs &attrs,

tests/ut/python/parallel/test_forward_graph.py

@@ -0,0 +1,82 @@
+# 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 numpy as np
+import mindspore as ms
+from mindspore import context, Tensor, Parameter
+from mindspore.nn import Cell
+from mindspore.ops import operations as P
+from mindspore.common.api import _executor
+
+
+class Net(Cell):
+    def __init__(self, mul_weight, strategy1=None, strategy2=None):
+        super().__init__()
+        self.mul = P.Mul().set_strategy(strategy1)
+        self.neg = P.Neg().set_strategy(strategy2)
+        self.mul_weight = Parameter(mul_weight, "w1")
+
+    def construct(self, x, b):
+        out = self.mul(x, self.mul_weight)
+        out = self.neg(out)
+        return out, b
+
+
+_x = Tensor(np.ones([128, 64, 32]), dtype=ms.float32)
+_w1 = Tensor(np.ones([128, 64, 32]), dtype=ms.float32)
+_b = Tensor(np.ones([128, 64, 32]), dtype=ms.float32)
+
+
+def compile(net):
+    _executor.compile(net, _x,  _b)
+    context.reset_auto_parallel_context()
+
+
+def test_forward_graph_data_parallel():
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=16, global_rank=0)
+    strategy1 = ((16, 1, 1), (16, 1, 1))
+    strategy2 = ((16, 1, 1), )
+    net = Net(_w1, strategy1, strategy2)
+    compile(net)
+
+
+def test_forward_graph_model_parallel():
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=16, global_rank=0)
+    strategy1 = ((1, 1, 16), (1, 1, 16))
+    strategy2 = ((1, 1, 16), )
+    net = Net(_w1, strategy1, strategy2)
+    compile(net)
+
+
+def test_forward_graph_hybrid_parallel():
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=16, global_rank=0)
+    strategy1 = ((2, 2, 4), (2, 2, 4))
+    strategy2 = ((2, 2, 4), )
+    net = Net(_w1, strategy1, strategy2)
+    compile(net)
+
+
+def test_forward_graph_auto_parallel():
+    context.set_auto_parallel_context(parallel_mode="auto_parallel", device_num=16, global_rank=0)
+    net = Net(_w1)
+    compile(net)
+
+
+def test_forward_graph_repeat_calc():
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=16, global_rank=0)
+    strategy1 = ((2, 2, 4), (2, 2, 4))
+    strategy2 = ((1, 2, 2), )
+    net = Net(_w1, strategy1, strategy2)
+    compile(net)
+