update field split

mindspore/ccsrc/frontend/parallel/ops_info/gather_v2_p_info.cc

@@ -550,6 +550,10 @@ RankList GetRankFromGroup(const Group &group) {
 }
 
 Status GatherV2PInfo::InferForwardCommunication() {
+  if (manual_split_) {
+    return SUCCESS;
+  }
+
   forward_op_.clear();
   auto param_strategy = strategy_->GetInputDim().at(0);
   // don't split axis or target is not CPU, no need forward communication

tests/ut/python/parallel/test_manual_embedding_lookup.py

@@ -0,0 +1,195 @@
+# 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 pytest
+import mindspore as ms
+from mindspore import context, Tensor, Parameter
+from mindspore.common.api import _executor
+from mindspore.nn import Cell, TrainOneStepCell, LazyAdam
+from mindspore.ops import operations as P
+from mindspore.common.initializer import initializer
+
+context.set_context(enable_sparse=True)
+
+
+class Net(Cell):
+    def __init__(self,
+                 strategy1=None,
+                 strategy2=None,
+                 strategy3=None,
+                 axis=0,
+                 init_flag=True,
+                 split_tuple=(4, 4),
+                 split_string="manual_split",
+                 param_shape=(8, 8)):
+        super().__init__()
+        self.gatherv2 = P.EmbeddingLookup().set_strategy(strategy1)
+        self.gatherv2.add_prim_attr(split_string, split_tuple)
+        self.gatherv2.add_prim_attr("primitive_target", "CPU")
+        self.mul = P.Mul().set_strategy(strategy2)
+        self.reshape = P.Reshape()
+        self.matmul = P.MatMul().set_strategy(strategy3)
+        self.matmul.add_prim_attr("forward_reduce_scatter", True)
+        if init_flag:
+            self.param = Parameter(initializer("ones", param_shape, ms.float32), name="gatherv2_param")
+        else:
+            self.param = Parameter(Tensor(np.ones(param_shape), dtype=ms.float32), name="gatherv2_param")
+        self.mul_weight = Parameter(initializer("ones", (8, 8, 8), ms.float32), name="mul_weight")
+        self.matmul_weight = Parameter(initializer("ones", (64, 16), ms.float32), name="matmul_weight")
+        self.axis = axis
+
+    def construct(self, x, b):
+        out = self.gatherv2(self.param, x, self.axis)
+        out = self.mul(out, b)
+        return out
+
+
+_x = Tensor(np.ones([8, 8]), dtype=ms.int32)
+_b = Tensor(np.ones([8, 8, 8]), dtype=ms.float32)
+
+
+def compile_net(net):
+    context.set_context(save_graphs=True)
+    optimizer = LazyAdam(net.trainable_params(), learning_rate=0.1)
+    optimizer.sparse_opt.add_prim_attr("primitive_target", "CPU")
+    train_net = TrainOneStepCell(net, optimizer)
+    train_net.set_auto_parallel()
+    _executor.compile(train_net, _x, _b, auto_parallel_mode=True)
+    context.reset_auto_parallel_context()
+
+
+def test_normal_split():
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=2, global_rank=0)
+    strategy1 = ((2, 1), (1, 2))
+    strategy2 = ((1, 2, 1), (1, 2, 1))
+    strategy3 = ((1, 2), (2, 1))
+    net = Net(strategy1, strategy2, strategy3)
+    compile_net(net)
+
+
+def test_normal_split2():
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=4, global_rank=0)
+    strategy1 = ((4, 1), (1, 4))
+    strategy2 = ((1, 4, 1), (1, 4, 1))
+    strategy3 = ((1, 4), (4, 1))
+    net = Net(strategy1, strategy2, strategy3, split_tuple=(10, 20, 30, 4), param_shape=(64, 8))
+    compile_net(net)
+
+
+def test_normal_split3():
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=32, global_rank=17)
+    strategy1 = ((4, 8), (1, 4))
+    strategy2 = ((1, 4, 8), (1, 4, 8))
+    strategy3 = ((1, 32), (32, 1))
+    net = Net(strategy1, strategy2, strategy3, split_tuple=(10, 20, 30, 4), param_shape=(64, 8))
+    compile_net(net)
+
+
+def test_normal_split_with_offset():
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=2, global_rank=0)
+    strategy1 = ((2, 1), (1, 2))
+    strategy2 = ((1, 2, 1), (1, 2, 1))
+    strategy3 = ((1, 2), (2, 1))
+    net = Net(strategy1, strategy2, strategy3, split_string="manual_split_with_offset", split_tuple=((4, 0), (4, 4)))
+    compile_net(net)
+
+
+def test_auto_parallel_error():
+    context.set_context(save_graphs=True)
+    context.set_auto_parallel_context(parallel_mode="auto_parallel", device_num=2, global_rank=0)
+    net = Net()
+    with pytest.raises(RuntimeError):
+        compile_net(net)
+
+
+def test_axis_error():
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=2, global_rank=0)
+    strategy1 = ((2, 1), (1, 2))
+    strategy2 = ((1, 2, 1), (1, 2, 1))
+    strategy3 = ((1, 2), (2, 1))
+    net = Net(strategy1, strategy2, strategy3, axis=1)
+    with pytest.raises(RuntimeError):
+        compile_net(net)
+
+
+def test_strategy_error():
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
+    strategy1 = ((4, 1), (8, 1))
+    strategy2 = ((1, 2, 1), (1, 2, 1))
+    strategy3 = ((1, 2), (2, 1))
+    net = Net(strategy1, strategy2, strategy3)
+    with pytest.raises(RuntimeError):
+        compile_net(net)
+
+
+def test_strategy_error2():
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
+    strategy1 = ((4, 1), (1, 8))
+    strategy2 = ((1, 2, 1), (1, 2, 1))
+    strategy3 = ((1, 2), (2, 1))
+    net = Net(strategy1, strategy2, strategy3)
+    with pytest.raises(RuntimeError):
+        compile_net(net)
+
+
+def test_strategy_error3():
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
+    strategy1 = ((2, 1), (1, 2))
+    strategy2 = ((1, 2, 1), (1, 2, 1))
+    strategy3 = ((1, 2), (2, 1))
+    net = Net(strategy1, strategy2, strategy3)
+    with pytest.raises(RuntimeError):
+        compile_net(net)
+
+
+def test_strategy_error4():
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=2, global_rank=0)
+    strategy1 = ((2, 8), (1, 2))
+    strategy2 = ((1, 2, 1), (1, 2, 1))
+    strategy3 = ((1, 2), (2, 1))
+    net = Net(strategy1, strategy2, strategy3)
+    with pytest.raises(RuntimeError):
+        compile_net(net)
+
+
+def test_strategy_error5():
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=4, global_rank=0)
+    strategy1 = ((4, 1), (1, 4))
+    strategy2 = ((1, 2, 1), (1, 2, 1))
+    strategy3 = ((1, 2), (2, 1))
+    net = Net(strategy1, strategy2, strategy3)
+    with pytest.raises(RuntimeError):
+        compile_net(net)
+
+
+def test_split_tuple_error():
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=2, global_rank=0)
+    strategy1 = ((2, 1), (1, 2))
+    strategy2 = ((1, 2, 1), (1, 2, 1))
+    strategy3 = ((1, 2), (2, 1))
+    net = Net(strategy1, strategy2, strategy3, split_tuple=((5, 0), (5, 5)))
+    with pytest.raises(RuntimeError):
+        compile_net(net)
+
+
+def test_parameter_use_tensor_error():
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=2, global_rank=0)
+    strategy1 = ((2, 1), (1, 2))
+    strategy2 = ((1, 2, 1), (1, 2, 1))
+    strategy3 = ((1, 2), (2, 1))
+    net = Net(strategy1, strategy2, strategy3, init_flag=False)
+    with pytest.raises(RuntimeError):
+        compile_net(net)