implement parallel Split

This commit is contained in:
Yi Huaijie 2020-09-17 10:09:10 +08:00
parent 97e8742f84
commit 18ed2bec53
7 changed files with 506 additions and 1 deletions

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@ -201,6 +201,8 @@ using TileCost = SoftmaxCost;
using TileCostPtr = std::shared_ptr<TileCost>;
using ConcatCost = TileCost;
using ConcatCostPtr = std::shared_ptr<ConcatCost>;
using SplitCost = TileCost;
using SplitCostPtr = std::shared_ptr<SplitCost>;
class TmpIdentityCost : public OperatorCost {
public:

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@ -179,6 +179,7 @@ REGISTER(TileInfo);
REGISTER(StridedSliceInfo);
REGISTER(DropoutInfo);
REGISTER(ConcatInfo);
REGISTER(SplitInfo);
} // namespace parallel
} // namespace mindspore

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@ -40,5 +40,6 @@
#include "frontend/parallel/ops_info/tile_info.h"
#include "frontend/parallel/ops_info/strided_slice_info.h"
#include "frontend/parallel/ops_info/concat_info.h"
#include "frontend/parallel/ops_info/split_info.h"
#endif // MINDSPORE_CCSRC_FRONTEND_PARALLEL_OPS_INFO_HEAD_FILES_H_

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@ -0,0 +1,294 @@
/**
* 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.
*/
#include "frontend/parallel/ops_info/split_info.h"
#include <string>
#include <memory>
#include <vector>
#include "frontend/parallel/device_matrix.h"
#include "frontend/parallel/strategy.h"
#include "frontend/parallel/tensor_layout/tensor_redistribution.h"
#include "frontend/parallel/context.h"
#include "pipeline/jit/resource.h"
namespace mindspore {
namespace parallel {
Status SplitInfo::GetAttrs() {
int axis = 0;
int output_num = 0;
auto axis_iter = attrs_.find(AXIS);
if (axis_iter != attrs_.end()) {
MS_EXCEPTION_IF_NULL(axis_iter->second);
if (axis_iter->second->isa<Int32Imm>()) {
axis = axis_iter->second->cast<Int32ImmPtr>()->value();
} else {
MS_LOG(ERROR) << name_ << ": The value of axis is not int";
return FAILED;
}
} else {
MS_LOG(ERROR) << name_ << ": Can not find the axis attr";
return FAILED;
}
if (inputs_shape_.empty()) {
MS_LOG(ERROR) << name_ << ": The inputs shape is empty";
return FAILED;
}
int dim = SizeToInt(inputs_shape_[0].size());
if (axis < 0) {
axis = axis + dim;
}
axis_ = SizeToInt(axis);
auto output_num_iter = attrs_.find(OUTPUT_NUM);
if (output_num_iter != attrs_.end()) {
MS_EXCEPTION_IF_NULL(output_num_iter->second);
if (output_num_iter->second->isa<Int32Imm>()) {
output_num = output_num_iter->second->cast<Int32ImmPtr>()->value();
} else {
MS_LOG(ERROR) << name_ << ": The value of output_num is not int";
return FAILED;
}
} else {
MS_LOG(ERROR) << name_ << ": Can not find the output_num attr";
return FAILED;
}
output_num_ = output_num;
return SUCCESS;
}
Status SplitInfo::CheckStrategy(const StrategyPtr &strategy) {
MS_EXCEPTION_IF_NULL(strategy);
if (CheckStrategyValue(strategy, inputs_shape_) != SUCCESS) {
MS_LOG(ERROR) << name_ << ": Invalid strategy";
return FAILED;
}
std::vector<Dimensions> stra = strategy->GetInputDim();
if (stra.empty()) {
MS_LOG(ERROR) << name_ << ": The strategy is empty";
return FAILED;
}
if (axis_ >= stra[0].size()) {
MS_LOG(ERROR) << name_ << ": The axis is out of range, the axis is " << axis_;
return FAILED;
}
if (stra[0][axis_] != 1) {
MS_LOG(ERROR) << name_ << ": The axis can not be split";
return FAILED;
}
return SUCCESS;
}
Status SplitInfo::InferDevMatrixShape() {
MS_EXCEPTION_IF_NULL(strategy_);
std::vector<Dimensions> stra = strategy_->GetInputDim();
if (stra.empty()) {
MS_LOG(ERROR) << name_ << "The strategy is empty";
return FAILED;
}
dev_matrix_shape_ = stra[0];
return SUCCESS;
}
Status SplitInfo::InferTensorMap() {
TensorMap tensor_map;
if (inputs_shape_.empty()) {
MS_LOG(ERROR) << name_ << "The inputs shape is empty";
return FAILED;
}
int32_t size = SizeToInt(inputs_shape_[0].size());
for (int i = 0; i < size; ++i) {
tensor_map.push_back(size - i - 1);
}
inputs_tensor_map_.push_back(tensor_map);
for (size_t i = 0; i < outputs_shape_.size(); ++i) {
outputs_tensor_map_.push_back(tensor_map);
}
return SUCCESS;
}
Status SplitInfo::InferMirrorOps() {
mirror_ops_.clear();
if (inputs_tensor_map_.empty()) {
MS_LOG(ERROR) << name_ << ": The inputs tensor map is empty";
return FAILED;
}
Shape input_tensor_map = inputs_tensor_map_[0];
std::vector<Group> group;
if (CreateGroupByTensorMap(input_tensor_map, &group) != SUCCESS) {
MS_LOG(ERROR) << name_ << ": Create group for input failed.";
return FAILED;
}
OperatorVector mirror_op;
if (group.empty()) {
MS_LOG(INFO) << name_ << ": The mirror group is empty.";
return SUCCESS;
} else {
mirror_op = CreateMirrorOps(group[0].name(), group[0].GetDevNum());
mirror_ops_.push_back(mirror_op);
std::string group_name = group[0].name();
MS_LOG(INFO) << name_ << " : Create the mirror ops success, the group name is " << group_name;
}
return SUCCESS;
}
Status SplitInfo::InferTensorInfo() {
if (inputs_shape_.empty() || outputs_shape_.empty() || inputs_tensor_map_.empty() || outputs_tensor_map_.empty()) {
MS_LOG(ERROR) << name_ << ": Invalid args";
return FAILED;
}
TensorLayout input_layout, output_layout;
// infer tensor layout
if (input_layout.InitFromVector(dev_matrix_shape_, inputs_tensor_map_[0], inputs_shape_[0]) != SUCCESS) {
MS_LOG(ERROR) << name_ << ": Infer input tensor layout failed.";
return FAILED;
}
TensorInfo input_tensor_info(input_layout);
inputs_tensor_info_.push_back(input_tensor_info);
if (output_layout.InitFromVector(dev_matrix_shape_, outputs_tensor_map_[0], outputs_shape_[0]) != SUCCESS) {
MS_LOG(ERROR) << name_ << ": Infer output tensor layout failed.";
return FAILED;
}
for (size_t i = 0; i < outputs_shape_.size(); ++i) {
TensorInfo output_tensor_info(output_layout);
outputs_tensor_info_.push_back(output_tensor_info);
}
return SUCCESS;
}
Status SplitInfo::SetCostUnderStrategy(const StrategyPtr &strategy) { return SetCostUnderStrategyBase(strategy); }
Status SplitInfo::GenerateStrategies(int32_t stage_id) {
if (InferAttrs() != SUCCESS) {
MS_LOG(ERROR) << name_ << ": Infer attrs failed";
return FAILED;
}
if (inputs_shape_.empty()) {
MS_LOG(ERROR) << name_ << ": The inputs shape is empty";
return FAILED;
}
Shape input_split;
for (size_t i = 0; i < inputs_shape_[0].size(); ++i) {
if (i == axis_) {
input_split.push_back(0);
} else {
input_split.push_back(1);
}
}
Shapes splittable_input = {input_split};
Shapes tmp_inputs_shape = {inputs_shape_[0]};
std::vector<StrategyPtr> sp_vector;
if (GenerateStrategiesForIndependentInputs(stage_id, tmp_inputs_shape, splittable_input, &sp_vector) != SUCCESS) {
MS_LOG(ERROR) << name_ << ": Generate strategies failed";
return FAILED;
}
size_t success = 0;
for (auto &sp : sp_vector) {
PrintStrategy(sp);
if (SetCostUnderStrategy(sp) == SUCCESS) {
success++;
MS_LOG(INFO) << name_ << ": Successfully generated " << success << " strategy.";
PrintStrategy(sp);
}
}
return SUCCESS;
}
std::shared_ptr<Strategys> SplitInfo::GenerateBatchStrategies() {
if (GetAttrs() != SUCCESS) {
MS_LOG(EXCEPTION) << name_ << ": Get attr failed";
}
CheckGlobalDeviceManager();
size_t dev_num = g_device_manager->GetDeviceListByStageId(0).size();
Dimensions input_strategy(inputs_shape_[0].size(), 1);
// axis can't split
if (inputs_shape_[0].size() > 1) {
if (axis_ == 0) {
input_strategy[1] = dev_num;
} else {
input_strategy[0] = dev_num;
}
}
Strategys strategy_v = {input_strategy};
return std::make_shared<Strategys>(strategy_v);
}
Status SplitInfo::InferAsLossDivisor() {
if (!ParallelContext::GetInstance()->loss_repeated_mean()) {
as_loss_divisor_ = 1;
return SUCCESS;
}
if (outputs_tensor_map_.empty()) {
MS_LOG(ERROR) << name_ << ": The outputs tensor map is empty.";
return FAILED;
}
if (outputs_tensor_map_[0].empty()) {
as_loss_divisor_ = SizeToInt(global_device_list_.size());
MS_LOG(INFO) << name_ << ": The output is a scalar, use the dev size " << as_loss_divisor_ << ", loss divisor.";
return SUCCESS;
}
as_loss_divisor_ = ComputeRepeatDeviceNumByTensorMap(dev_matrix_shape_, outputs_tensor_map_[0]);
MS_LOG(INFO) << name_ << ": the dev matrix shape is " << ShapeToString(dev_matrix_shape_)
<< ", the output tensor map is " << ShapeToString(outputs_tensor_map_[0]) << ", loss divisor is "
<< as_loss_divisor_;
return SUCCESS;
}
Status SplitInfo::Init(const StrategyPtr &strategy) {
if (InitWithAutoRepeatCalc(strategy) != SUCCESS) {
MS_LOG(ERROR) << name_ << ": Init failed.";
return FAILED;
}
MS_LOG(INFO) << name_ << ": Init success.";
return SUCCESS;
}
Status SplitInfo::InitForCostModel(const StrategyPtr &strategy) {
if (InitForCostModelWithAutoRepeatCalc(strategy) != SUCCESS) {
MS_LOG(ERROR) << name_ << ": Init for cost model failed.";
return FAILED;
}
MS_LOG(INFO) << name_ << ": Init for cost model success.";
return SUCCESS;
}
} // namespace parallel
} // namespace mindspore

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@ -0,0 +1,60 @@
/**
* 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.
*/
#ifndef MINDSPORE_CCSRC_FRONTEND_PARALLEL_OPS_INFO_SPLIT_INFO_H_
#define MINDSPORE_CCSRC_FRONTEND_PARALLEL_OPS_INFO_SPLIT_INFO_H_
#include <string>
#include <memory>
#include "ir/value.h"
#include "frontend/parallel/auto_parallel/operator_costmodel.h"
#include "frontend/parallel/ops_info/operator_info.h"
#include "frontend/parallel/strategy.h"
namespace mindspore {
namespace parallel {
class SplitInfo : public OperatorInfo {
public:
SplitInfo(const std::string &operator_name, const Shapes &inputs_shape, const Shapes &outputs_shape,
const PrimitiveAttrs &attrs)
: OperatorInfo(operator_name, inputs_shape, outputs_shape, attrs, std::make_shared<ConcatCost>(false)) {}
~SplitInfo() override = default;
Status Init(const StrategyPtr &strategy) override;
Status InitForCostModel(const StrategyPtr &strategy) override;
Status GenerateStrategies(int32_t) override;
std::shared_ptr<Strategys> GenerateBatchStrategies() override;
Status SetCostUnderStrategy(const StrategyPtr &) override;
protected:
Status GetAttrs() override;
Status CheckStrategy(const StrategyPtr &strategy) override;
Status InferMirrorOps() override;
Status InferForwardCommunication() override { return SUCCESS; }
Status InferTensorInfo() override;
Status InferDevMatrixShape() override;
Status InferTensorMap() override;
Status InferAsLossDivisor() override;
private:
size_t axis_ = 0;
size_t output_num_ = 0;
};
} // namespace parallel
} // namespace mindspore
#endif // MINDSPORE_CCSRC_FRONTEND_PARALLEL_OPS_INFO_SPLIT_INFO_H_

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@ -263,7 +263,7 @@ bool IsSplittableOperator(const std::string &op_name) {
LOG, REDUCE_MEAN, REAL_DIV, SIGMOID, POW, MAXIMUM, MINIMUM, EQUAL, NOT_EQUAL, LOGICALNOT, GATHERV2, SQRT, CONCAT,
STRIDEDSLICE, GET_NEXT, CAST, NEG, SQUARE, BATCH_MATMUL, EXPAND_DIMS, SQUEEZE, SPARSE_GATHERV2, TILE, DROPOUT,
SOFTMAX_CROSS_ENTROPY_WITH_LOGITS, SIGMOID_CROSS_ENTROPY_WITH_LOGITS, SPARSE_SOFTMAX_CROSS_ENTROPY_WITH_LOGITS,
EMBEDDING_LOOKUP, FUSE_BATCH_NORM_EX};
EMBEDDING_LOOKUP, FUSE_BATCH_NORM_EX, SPLIT};
// clang-format on
auto iter = splittable_op.find(op_name);

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@ -0,0 +1,147 @@
# 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
import mindspore.context as context
from mindspore import Tensor, Parameter
import mindspore.nn as nn
from mindspore.common.api import _executor
from mindspore.nn import TrainOneStepCell, Momentum
from mindspore.ops import operations as P
class Net(nn.Cell):
def __init__(self, mul_weight, axis=0, out_nums=1, strategy1=None, strategy2=None, strategy3=None):
super(Net, self).__init__()
self.split = P.Split(axis, out_nums).shard(strategy1)
self.mul = P.Mul().shard(strategy2)
self.matmul = P.MatMul(transpose_b=True).shard(strategy2)
self.matmul2 = P.MatMul().shard(strategy3)
self.weight = Parameter(mul_weight, "w1")
def construct(self, x):
out = self.mul(x, self.weight)
out1, out2, out3 = self.split(out)
out = self.matmul(out1, out2)
out = self.matmul2(out, out3)
return out
class Net1(nn.Cell):
def __init__(self, mul_weight, axis=0, out_nums=1, strategy1=None, strategy2=None):
super(Net1, self).__init__()
self.split = P.Split(axis, out_nums).shard(strategy1)
self.mul = P.Mul().shard(strategy2)
self.weight = Parameter(mul_weight, "w1")
def construct(self, x):
out1, out2 = self.split(self.weight)
out = self.mul(x, out1)
out = self.mul(out, out2)
return out
class Net2(nn.Cell):
def __init__(self, mul_weight, axis=0, out_nums=1, strategy1=None, strategy2=None):
super(Net2, self).__init__()
self.split = P.Split(axis, out_nums).shard(strategy1)
self.mul = P.Mul().shard(strategy2)
self.weight = Parameter(mul_weight, "w1")
def construct(self, x):
out = self.mul(x, self.weight)
out1, _ = self.split(out)
return out1
_w = Tensor(np.ones([48, 64]), dtype=ms.float32)
_x = Tensor(np.ones([48, 64]), dtype=ms.float32)
_w1 = Tensor(np.ones([96, 64, 32]), dtype=ms.float32)
_x1 = Tensor(np.ones([48, 64, 32]), dtype=ms.float32)
_w2 = Tensor(np.ones([48, 64, 32]), dtype=ms.float32)
def compile_net(net):
context.set_context(mode=context.GRAPH_MODE, save_graphs=True)
optimizer = Momentum(net.trainable_params(), learning_rate=0.1, momentum=0.9)
train_net = TrainOneStepCell(net, optimizer)
train_net.set_auto_parallel()
_executor.compile(train_net, _x)
context.reset_auto_parallel_context()
def compile_net1(net):
context.set_context(mode=context.GRAPH_MODE, save_graphs=True)
optimizer = Momentum(net.trainable_params(), learning_rate=0.1, momentum=0.9)
train_net = TrainOneStepCell(net, optimizer)
train_net.set_auto_parallel()
_executor.compile(train_net, _x1)
context.reset_auto_parallel_context()
def test_split_parameter():
context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
strategy1 = ((1, 4, 2),)
strategy2 = ((1, 4, 2), (1, 4, 2))
net = Net1(_w1, 0, 2, strategy1, strategy2)
compile_net1(net)
def test_split_parameter_no_full_split():
context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
strategy1 = ((1, 2, 2),)
strategy2 = ((1, 4, 2), (1, 4, 2))
net = Net1(_w1, 0, 2, strategy1, strategy2)
compile_net1(net)
def test_split_tensor():
context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
strategy1 = ((1, 8),)
strategy2 = ((1, 8), (1, 8))
strategy3 = ((1, 1), (1, 8))
net = Net(_w, 0, 3, strategy1, strategy2, strategy3)
compile_net(net)
def test_split_output():
context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
strategy1 = ((1, 4, 2),)
strategy2 = ((1, 4, 2), (1, 4, 2))
net = Net2(_w2, 0, 2, strategy1, strategy2)
compile_net1(net)
def test_split_output_no_full_split():
context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
strategy1 = ((1, 2, 2),)
strategy2 = ((1, 4, 2), (1, 4, 2))
net = Net2(_w2, 0, 2, strategy1, strategy2)
compile_net1(net)
def test_split_no_strategy():
context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
strategy1 = None
strategy2 = ((1, 4, 2), (1, 4, 2))
net = Net2(_w2, 0, 2, strategy1, strategy2)
compile_net1(net)
def test_split_auto_parallel():
context.set_auto_parallel_context(parallel_mode="auto_parallel", device_num=8, global_rank=0)
net = Net2(_w2, 0, 2)
compile_net1(net)