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/**
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* Copyright 2020 Huawei Technologies Co., Ltd
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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#ifndef MINDSPORE_CCSRC_FRONTEND_PARALLEL_OPS_INFO_PACK_INFO_H_
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#define MINDSPORE_CCSRC_FRONTEND_PARALLEL_OPS_INFO_PACK_INFO_H_
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#include <string>
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#include <memory>
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#include <unordered_map>
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#include <vector>
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#include "ir/value.h"
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#include "frontend/parallel/auto_parallel/operator_costmodel.h"
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#include "frontend/parallel/ops_info/operator_info.h"
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#include "frontend/parallel/strategy.h"
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namespace mindspore {
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namespace parallel {
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class PackInfo : public OperatorInfo {
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public:
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PackInfo(const std::string &operator_name, const Shapes &inputs_shape, const Shapes &outputs_shape,
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const PrimitiveAttrs &attrs)
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: OperatorInfo(operator_name, inputs_shape, outputs_shape, attrs, std::make_shared<PackCost>(false)) {}
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~PackInfo() override = default;
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Status Init(const StrategyPtr &strategy) override;
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Status InitForCostModel(const StrategyPtr &strategy) override;
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Status GenerateStrategies(int32_t) override;
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Status SetCostUnderStrategy(const StrategyPtr &) override;
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void ReComputeBatchSplitFlagList() override;
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protected:
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Status GetAttrs() override;
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Status CheckStrategy(const StrategyPtr &strategy) override;
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Status InferMirrorOps() override;
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Status InferForwardCommunication() override { return SUCCESS; }
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Status InferTensorInfo() override;
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Status InferDevMatrixShape() override;
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Status InferTensorMap() override;
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private:
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size_t axis_ = 0;
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};
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using PackInfoPtr = std::shared_ptr<PackInfo>;
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} // namespace parallel
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} // namespace mindspore
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#endif // MINDSPORE_CCSRC_FRONTEND_PARALLEL_OPS_INFO_PACK_INFO_H_
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@ -0,0 +1,188 @@
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# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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import numpy as np
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import mindspore as ms
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import mindspore.context as context
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from mindspore import Tensor, Parameter
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import mindspore.nn as nn
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from mindspore.common.api import _executor
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from mindspore.nn import TrainOneStepCell, Momentum
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from mindspore.ops import operations as P
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class Net(nn.Cell):
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def __init__(self, weight1, weight2, axis=0, strategy1=None, strategy2=None, is_parameter=True):
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super(Net, self).__init__()
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self.pack = P.Pack(axis=axis).shard(strategy1)
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self.mul = P.Mul().shard(strategy2)
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if is_parameter:
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self.weight1 = Parameter(weight1, "w1")
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else:
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self.weight1 = weight1
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self.weight2 = Parameter(weight2, "w2")
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def construct(self, x):
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out = self.pack([self.weight1, self.weight2])
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out = self.mul(x, out)
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return out
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class Net1(nn.Cell):
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def __init__(self, weight1, weight2, axis=0, strategy1=None, strategy2=None):
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super(Net1, self).__init__()
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self.pack = P.Pack(axis=axis).shard(strategy1)
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self.mul = P.Mul().shard(strategy2)
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self.weight1 = Parameter(weight1, "w1")
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self.weight2 = Parameter(weight2, "w2")
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def construct(self, x):
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out = self.mul(x, self.weight1)
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out = self.pack([out, self.weight2])
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return out
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class Net2(nn.Cell):
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def __init__(self, weight1, weight2, weight3, axis=0, strategy1=None, strategy2=None, is_parameter=True):
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super(Net2, self).__init__()
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self.pack = P.Pack(axis=axis).shard(strategy1)
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self.mul = P.Mul().shard(strategy2)
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if is_parameter:
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self.weight1 = Parameter(weight1, "w1")
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else:
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self.weight1 = weight1
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self.weight2 = Parameter(weight2, "w2")
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self.weight3 = Parameter(weight2, "w3")
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def construct(self, x):
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out = self.pack([self.weight1, self.weight2, self.weight3])
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out = self.mul(x, out)
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return out
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_w1 = Tensor(np.ones([48, 64]), dtype=ms.float32)
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_w2 = Tensor(np.ones([48, 64]), dtype=ms.float32)
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_w3 = Tensor(np.ones([48, 64]), dtype=ms.float32)
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_x = Tensor(np.ones([2, 48, 64]), dtype=ms.float32)
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_x1 = Tensor(np.ones([48, 64]), dtype=ms.float32)
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_x2 = Tensor(np.ones([3, 48, 64]), dtype=ms.float32)
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def compile_net(net):
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context.set_context(mode=context.GRAPH_MODE, save_graphs=True)
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optimizer = Momentum(net.trainable_params(), learning_rate=0.1, momentum=0.9)
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train_net = TrainOneStepCell(net, optimizer)
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train_net.set_auto_parallel()
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_executor.compile(train_net, _x)
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context.reset_auto_parallel_context()
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def compile_net1(net):
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context.set_context(mode=context.GRAPH_MODE, save_graphs=True)
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optimizer = Momentum(net.trainable_params(), learning_rate=0.1, momentum=0.9)
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train_net = TrainOneStepCell(net, optimizer)
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train_net.set_auto_parallel()
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_executor.compile(train_net, _x1)
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context.reset_auto_parallel_context()
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def compile_net2(net):
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context.set_context(mode=context.GRAPH_MODE, save_graphs=True)
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optimizer = Momentum(net.trainable_params(), learning_rate=0.1, momentum=0.9)
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train_net = TrainOneStepCell(net, optimizer)
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train_net.set_auto_parallel()
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_executor.compile(train_net, _x2)
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context.reset_auto_parallel_context()
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def test_pack_parameter():
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
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strategy1 = ((4, 2), (4, 2))
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strategy2 = ((1, 4, 2), (1, 4, 2))
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net = Net(_w1, _w2, 0, strategy1, strategy2)
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compile_net(net)
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def test_pack_parameter_no_full_split():
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
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strategy1 = ((2, 2), (2, 2))
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strategy2 = ((1, 4, 2), (1, 4, 2))
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net = Net(_w1, _w2, 0, strategy1, strategy2)
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compile_net(net)
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def test_pack_tensor_and_parameter():
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
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strategy1 = ((4, 2), (4, 2))
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strategy2 = ((1, 4, 2), (1, 4, 2))
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net = Net(_w1, _w2, 0, strategy1, strategy2, False)
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compile_net(net)
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def test_pack_output():
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
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strategy1 = ((4, 2), (4, 2))
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strategy2 = ((4, 2), (4, 2))
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net = Net1(_w1, _w2, 0, strategy1, strategy2)
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compile_net1(net)
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def test_pack_output_axis1():
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
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strategy1 = ((4, 2), (4, 2))
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strategy2 = ((4, 2), (4, 2))
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net = Net1(_w1, _w2, 1, strategy1, strategy2)
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compile_net1(net)
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def test_pack_output_no_full_split():
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
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strategy1 = ((2, 2), (2, 2))
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strategy2 = ((4, 2), (4, 2))
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net = Net1(_w1, _w2, 0, strategy1, strategy2)
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compile_net1(net)
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def test_pack_no_strategy():
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
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strategy1 = None
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strategy2 = ((4, 2), (4, 2))
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net = Net1(_w1, _w2, 0, strategy1, strategy2)
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compile_net1(net)
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def test_pack_no_strategy_axis1():
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
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strategy1 = None
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strategy2 = ((4, 2), (4, 2))
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net = Net1(_w1, _w2, 1, strategy1, strategy2)
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compile_net1(net)
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def test_pack_auto_parallel():
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context.set_auto_parallel_context(parallel_mode="auto_parallel", device_num=8, global_rank=0)
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net = Net1(_w1, _w2, 0)
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compile_net1(net)
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def test_pack_auto_parallel_axis1():
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context.set_auto_parallel_context(parallel_mode="auto_parallel", device_num=8, global_rank=0)
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net = Net1(_w1, _w2, 1)
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compile_net1(net)
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def test_pack_auto_parallel_3_tensor():
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context.set_auto_parallel_context(parallel_mode="auto_parallel", device_num=8, global_rank=0)
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net = Net2(_w1, _w2, _w3)
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compile_net2(net)
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