Add print

mindspore/core/utils/parallel_node_check.cc

@@ -30,7 +30,7 @@ static const std::set<std::string> PARALLEL_BLACK_LIST_ = {prim::kTupleGetItem,
   "get_ref_value", "get_ref_origin", "dot", "im2col", "col2im", "im2col_v1", "state_setitem", "ScalarSummary",
   "ImageSummary", "TensorSummary", "Debug", "HistogramSummary", "col2im_v1", "resolve", "BroadcastGradientArgs",
   "InvertPermutation", "DropoutGenMask", "embed", "create_instance", "RefToEmbed",
-  "stop_gradient", "UpdateState", "Load", "Switch"};
+  "stop_gradient", "UpdateState", "Load", "Switch", "Print"};
 static const std::set<PrimitivePtr> ALLGATHER_NODE_LIST_ = {prim::kPrimAllGather, prim::kPrimMiniStepAllGather,
                                                             prim::kPrimMicroStepAllGather};
 static const std::set<PrimitivePtr> TRIVIAL_NODE_LIST_ = {prim::kPrimCast, prim::kPrimDepend};

tests/ut/python/parallel/test_print.py

@@ -0,0 +1,127 @@
+# Copyright 2021 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.common.api import _executor
+from mindspore.nn import Cell, TrainOneStepCell, Momentum, BatchNorm2d, BatchNorm1d
+from mindspore.ops import operations as P
+
+
+class Net(Cell):
+    def __init__(self, conv2d_weight, out_channel, kernel_size, pad_mode, stride,
+                 strategy1=None, strategy2=None):
+        super().__init__()
+        self.conv2d = P.Conv2D(out_channel=out_channel, kernel_size=kernel_size,
+                               pad_mode=pad_mode, stride=stride).shard(strategy1)
+        self.conv2d_weight = Parameter(conv2d_weight, "w1")
+        self.bn = BatchNorm2d(8)
+        self.bn.bn_train.shard(strategy2)
+        self.print = P.Print()
+
+    def construct(self, x, b):
+        out = self.conv2d(x, self.conv2d_weight)
+        self.print("output is", out)
+        out = self.bn(out)
+        return out
+
+
+_x = Tensor(np.ones([32, 16, 8, 8]), dtype=ms.float32)
+_w1 = Tensor(np.ones([8, 16, 2, 2]), dtype=ms.float32)
+_b = Tensor(np.ones([32, 16, 8, 8]), dtype=ms.float32)
+
+
+def compile_net(net):
+    optimizer = Momentum(net.trainable_params(), learning_rate=0.1, momentum=0.9)
+    train_net = TrainOneStepCell(net, optimizer)
+    train_net.set_auto_parallel()
+    train_net.set_train()
+    _executor.compile(train_net, _x, _b)
+    context.reset_auto_parallel_context()
+
+
+def test_batchnorm_data_parallel():
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
+    strategy1 = ((8, 1, 1, 1), (1, 1, 1, 1))
+    strategy2 = ((8, 1, 1, 1), (1,), (1,), (1,), (1,))
+    net = Net(_w1, out_channel=8, kernel_size=2, pad_mode="same", stride=1, strategy1=strategy1, strategy2=strategy2)
+    compile_net(net)
+
+
+def test_batchnorm_model_parallel1():
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
+    strategy1 = ((2, 2, 1, 1), (2, 2, 1, 1))
+    strategy2 = ((2, 1, 2, 2), (1,), (1,), (1,), (1,))
+    net = Net(_w1, out_channel=8, kernel_size=2, pad_mode="same", stride=1, strategy1=strategy1, strategy2=strategy2)
+    compile_net(net)
+
+
+def test_batchnorm_model_parallel2():
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=32, global_rank=0)
+    strategy1 = ((2, 2, 2, 2), (2, 2, 1, 1))
+    strategy2 = ((1, 8, 1, 1), (8,), (8,), (8,), (8,))
+    net = Net(_w1, out_channel=8, kernel_size=2, pad_mode="same", stride=2, strategy1=strategy1, strategy2=strategy2)
+    compile_net(net)
+
+
+class Net2(Cell):
+    def __init__(self, strategy1=None, strategy2=None):
+        super().__init__()
+        self.bn = BatchNorm1d(8)
+        self.bn.bn_train.shard(strategy1)
+        self.relu = P.ReLU().shard(strategy2)
+
+    def construct(self, x, b):
+        out = self.bn(x)
+        out = self.relu(out)
+        return out
+
+
+_x1 = Tensor(np.ones([32, 8]), dtype=ms.float32)
+_b1 = Tensor(np.ones([32, 8]), dtype=ms.float32)
+
+
+def compile_net2(net):
+    optimizer = Momentum(net.trainable_params(), learning_rate=0.1, momentum=0.9)
+    train_net = TrainOneStepCell(net, optimizer)
+    train_net.set_auto_parallel()
+    train_net.set_train()
+    _executor.compile(train_net, _x1, _b1)
+    context.reset_auto_parallel_context()
+
+
+def test_batchnorm1d_data_parallel():
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
+    strategy1 = ((8, 1), (1,), (1,), (1,), (1,))
+    strategy2 = ((8, 1),)
+    net = Net2(strategy1=strategy1, strategy2=strategy2)
+    compile_net2(net)
+
+
+def test_batchnorm1d_model_parallel1():
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
+    strategy1 = ((1, 8), (8,), (8,), (8,), (8,))
+    strategy2 = ((1, 8),)
+    net = Net2(strategy1=strategy1, strategy2=strategy2)
+    compile_net2(net)
+
+
+def test_batchnorm1d_model_parallel2():
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=32, global_rank=0)
+    strategy1 = ((2, 4), (4,), (4,), (4,), (4,))
+    strategy2 = ((2, 4),)
+    net = Net2(strategy1=strategy1, strategy2=strategy2)
+    compile_net2(net)