forked from mindspore-Ecosystem/mindspore
!2376 [CT][ME][parallel] fixed One-hot runs failed in RP-search mode.
Merge pull request !2376 from Chong/zc
This commit is contained in:
Gitee
commit
f975963a58
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@ -28,7 +28,6 @@
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namespace mindspore {
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namespace parallel {
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#define DOUBLE_MAX (std::numeric_limits<double>::max)()
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// Compute redistributed cost
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double CostRedis(const Graph::NodeType &node,
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@ -621,75 +620,50 @@ StrategyRec CostCommon::ChoseStr(const std::vector<double> &cost_op, StrategyRec
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break;
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default:
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MS_LOG(EXCEPTION) << "Failure: CostBiasAdd failed.";
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MS_LOG(EXCEPTION) << "Failure: Common failed.";
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}
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return str;
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}
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// Get weight for BN
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double CostBatchNorm::GetMinCostIn(const OperatorRec &op) {
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int tensor = static_cast<int>(op.arguments[0].tensor_shape.shape_h * op.arguments[0].tensor_str.str_h) *
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static_cast<int>(op.arguments[0].tensor_shape.shape_n * op.arguments[0].tensor_str.str_n) *
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static_cast<int>(op.arguments[0].tensor_shape.shape_w * op.arguments[0].tensor_str.str_w) *
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static_cast<int>(op.arguments[0].tensor_shape.shape_c * op.arguments[0].tensor_str.str_c);
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std::vector<double> cost_in;
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cost_in.push_back(StrDimB(tensor) * 1.2);
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cost_in.push_back(DOUBLE_MAX);
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cost_in.push_back(StrDimH(tensor) * 1.2);
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cost_in.push_back(StrDimW(tensor) * 1.2);
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return *min_element(cost_in.begin(), cost_in.end());
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}
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// Get optimal strategy for BN
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StrategyRec CostBatchNorm::GetOptimalStr(const Graph::NodeType &node,
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const std::vector<std::pair<std::string, StrategyRec>> &node_name_to_strategy,
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const Graph &graph) {
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// Get optimal strategy for BatchParallel OPs
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StrategyRec CostBatchParallel::GetOptimalStr(const Graph::NodeType &node) {
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const OperatorRec &op = node.apply;
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int tensor_filter_n = static_cast<int>(op.arguments[1].tensor_shape.shape_n * op.arguments[1].tensor_str.str_n);
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int tensor_filter_c = static_cast<int>(op.arguments[1].tensor_shape.shape_c * op.arguments[1].tensor_str.str_c);
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int tensor_filter_h = static_cast<int>(op.arguments[1].tensor_shape.shape_h * op.arguments[1].tensor_str.str_h);
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int tensor_filter_w = static_cast<int>(op.arguments[1].tensor_shape.shape_w * op.arguments[1].tensor_str.str_w);
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int tensor_filter = tensor_filter_h * tensor_filter_w * tensor_filter_n * tensor_filter_c;
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int output_tensor_h = static_cast<int>(node.tensor_parm.tensor_shape.shape_h * node.tensor_parm.tensor_str.str_h);
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int output_tensor_w = static_cast<int>(node.tensor_parm.tensor_shape.shape_w * node.tensor_parm.tensor_str.str_w);
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int output_tensor_n = static_cast<int>(node.tensor_parm.tensor_shape.shape_n * node.tensor_parm.tensor_str.str_n);
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int tensor_n = static_cast<int>(op.arguments[0].tensor_shape.shape_n * op.arguments[0].tensor_str.str_n);
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int tensor_c = static_cast<int>(op.arguments[0].tensor_shape.shape_c * op.arguments[0].tensor_str.str_c);
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int tensor_h = static_cast<int>(op.arguments[0].tensor_shape.shape_h * op.arguments[0].tensor_str.str_h);
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int tensor_w = static_cast<int>(op.arguments[0].tensor_shape.shape_w * op.arguments[0].tensor_str.str_w);
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std::vector<double> cost_op;
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std::vector<std::vector<float>> mode;
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if (output_tensor_n < 2 || output_tensor_n % 2 != 0) {
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if (tensor_n < 2 || tensor_n % 2 != 0) {
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cost_op.push_back(DOUBLE_MAX);
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} else {
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cost_op.push_back(StrDimB(tensor_filter) + CostRedis(node, node_name_to_strategy,
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mode = {{0.5, 1, 1, 1}, {1, 1, 1, 1}, {0.5, 1, 1, 1}}, graph));
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cost_op.push_back(cost_in_);
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}
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cost_op.push_back(DOUBLE_MAX);
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if (output_tensor_h < 2 || output_tensor_h % 2 != 0) {
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if (tensor_c < 2 || tensor_c % 2 != 0) {
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cost_op.push_back(DOUBLE_MAX);
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} else {
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cost_op.push_back(StrDimH(tensor_filter) + CostRedis(node, node_name_to_strategy,
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mode = {{1, 1, 0.5, 1}, {1, 1, 1, 1}, {1, 1, 0.5, 1}}, graph));
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cost_op.push_back(cost_in_);
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}
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if (output_tensor_w < 2 || output_tensor_w % 2 != 0) {
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if (tensor_h < 2 || tensor_h % 2 != 0) {
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cost_op.push_back(DOUBLE_MAX);
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} else {
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cost_op.push_back(StrDimW(tensor_filter) + CostRedis(node, node_name_to_strategy,
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mode = {{1, 1, 1, 0.5}, {1, 1, 1, 1}, {1, 1, 1, 0.5}}, graph));
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cost_op.push_back(cost_in_);
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}
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if (tensor_w < 2 || tensor_w % 2 != 0) {
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cost_op.push_back(DOUBLE_MAX);
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} else {
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cost_op.push_back(cost_in_);
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}
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return ChoseStr(cost_op, node.apply.str);
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}
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// Chose strategy for BatchNorm
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StrategyRec CostBatchNorm::ChoseStr(const std::vector<double> &cost_op, StrategyRec str) {
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// Chose strategy for BatchParallel op
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StrategyRec CostBatchParallel::ChoseStr(const std::vector<double> &cost_op, StrategyRec str) {
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uint64_t min_position = min_element(cost_op.begin(), cost_op.end()) - cost_op.begin();
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if (cost_op[min_position] > (DOUBLE_MAX - 0.1)) {
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return str;
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@ -700,36 +674,32 @@ StrategyRec CostBatchNorm::ChoseStr(const std::vector<double> &cost_op, Strategy
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str.inputTensor[0].str_n /= 2.0;
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str.outputTensor.str_n /= 2.0;
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str.cut_counter += 1;
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str.cost = str.cost + cost_in_b_;
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str.cost = str.cost + cost_in_;
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break;
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case 1:
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str.inputTensor[0].str_c /= 2.0;
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str.inputTensor[1].str_c /= 2.0;
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str.inputTensor[2].str_c /= 2.0;
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str.inputTensor[3].str_c /= 2.0;
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str.inputTensor[4].str_c /= 2.0;
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str.outputTensor.str_c /= 2.0;
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str.cut_counter += 1;
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str.cost = str.cost + cost_in_c_;
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str.cost = str.cost + cost_in_;
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break;
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case 2:
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str.inputTensor[0].str_h /= 2.0;
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str.outputTensor.str_h /= 2.0;
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str.cut_counter += 1;
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str.cost = str.cost + cost_in_h_;
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str.cost = str.cost + cost_in_;
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break;
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case 3:
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str.inputTensor[0].str_w /= 2.0;
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str.outputTensor.str_w /= 2.0;
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str.cut_counter += 1;
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str.cost = str.cost + cost_in_w_;
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str.cost = str.cost + cost_in_;
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break;
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default:
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MS_LOG(EXCEPTION) << "Failure: CostBatchNorm failed.";
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MS_LOG(EXCEPTION) << "Failure: CostBatchParallel failed.";
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}
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return str;
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}
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@ -28,6 +28,8 @@
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namespace mindspore {
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namespace parallel {
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#define DOUBLE_MAX (std::numeric_limits<double>::max)()
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double CostRedis(const Graph::NodeType &node,
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const std::vector<std::pair<std::string, StrategyRec>> &node_name_to_strategy,
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const std::vector<std::vector<float>> &mode, const Graph &graph);
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@ -195,7 +197,6 @@ class CostTensorAdd : public CostCommon {
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};
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// all the following operation are element-wise and have the same cost
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class CostOneHot : public CostCommon {};
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class CostReLU : public CostCommon {};
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class CostLog : public CostCommon {};
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class CostExp : public CostCommon {};
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@ -206,50 +207,21 @@ class CostDiv : public CostCommon {};
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class CostSqueeze : public CostCommon {};
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class CostCast : public CostCommon {};
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// class BatchNorm is used to compute the cost of BatchNorm operator.
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class CostBatchNorm {
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// class BatchParallel is used to compute the cost of BatchParallel operator.
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class CostBatchParallel {
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public:
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StrategyRec GetOptimalStr(const Graph::NodeType &node,
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const std::vector<std::pair<std::string, StrategyRec>> &node_name_to_strategy,
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const Graph &graph);
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virtual StrategyRec GetOptimalStr(const Graph::NodeType &node);
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double GetMinCostIn(const OperatorRec &op);
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virtual double GetMaxCostIn() const { return DOUBLE_MAX; }
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private:
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double StrDimB(int32_t Tensor) {
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cost_in_b_ = (static_cast<double>(Tensor) * 4.0) / 2.0;
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protected:
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virtual StrategyRec ChoseStr(const std::vector<double> &cost_op, StrategyRec str);
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return cost_in_b_;
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}
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double cost_in_ = 0;
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}; // class BatchParallel is used to compute the cost of BatchParallel operator.
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double StrDimC() {
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cost_in_c_ = 0.0;
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return cost_in_c_;
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}
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double StrDimH(int32_t Tensor) {
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cost_in_h_ = (static_cast<double>(Tensor) * 4.0) / 2.0;
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return cost_in_h_;
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}
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double StrDimW(int32_t Tensor) {
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cost_in_w_ = (static_cast<double>(Tensor) * 4.0) / 2.0;
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return cost_in_w_;
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}
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StrategyRec ChoseStr(const std::vector<double> &cost_op, StrategyRec str);
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double cost_in_b_ = 0;
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double cost_in_c_ = 0;
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double cost_in_h_ = 0;
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double cost_in_w_ = 0;
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}; // class BatchNorm is used to compute the cost of BatchNorm operator.
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class CostBatchNorm : public CostBatchParallel {};
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class CostOneHot : public CostBatchParallel {};
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} // namespace parallel
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} // namespace mindspore
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#endif // PARALLEL_AUTO_PARALLEL_REC_COST_H_
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@ -135,17 +135,6 @@ std::vector<std::vector<int32_t>> PreparePReLU(const std::shared_ptr<Graph> &gra
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return strategies;
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}
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std::vector<std::vector<int32_t>> PrepareBatchNorm(const std::shared_ptr<Graph> &graph,
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const std::vector<std::shared_ptr<OperatorInfo>> &ops,
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const size_t iter_graph, const size_t iter_ops) {
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std::vector<std::vector<int32_t>> strategies = MakeDataParallelStrategy(graph, ops, iter_graph, iter_ops);
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for (size_t i = 1; i < strategies.size(); i++) {
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strategies[i][0] = strategies[0][1];
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}
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strategies[1][0] = 1;
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return strategies;
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}
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std::vector<std::vector<int32_t>> PrepareBiasAdd(const std::shared_ptr<std::vector<int32_t>> &s) {
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std::vector<std::vector<int32_t>> strategies;
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strategies.push_back(*s);
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@ -155,10 +144,15 @@ std::vector<std::vector<int32_t>> PrepareBiasAdd(const std::shared_ptr<std::vect
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return strategies;
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}
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std::vector<std::vector<int32_t>> PrepareOneHot(const std::shared_ptr<std::vector<int32_t>> &s) {
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std::vector<std::vector<int32_t>> strategies;
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std::vector<std::vector<int32_t>> PrepareOneHot(const std::shared_ptr<Graph> &graph,
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const std::vector<std::shared_ptr<OperatorInfo>> &ops,
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const size_t iter_graph, const size_t iter_ops) {
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std::vector<std::vector<int32_t>> strategies = MakeRecSearchStrategy(graph, ops, iter_graph, iter_ops);
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strategies[0][0] = strategies[0][1];
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strategies[0][1] = 1;
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graph->nodes[iter_graph].tensor_parm.tensor_str.str_h = graph->nodes[iter_graph].tensor_parm.tensor_str.str_w;
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graph->nodes[iter_graph].tensor_parm.tensor_str.str_w = 1.0;
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std::vector<int32_t> s_empty = {};
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strategies.push_back(*s);
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strategies.push_back(s_empty);
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strategies.push_back(s_empty);
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return strategies;
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@ -287,8 +281,8 @@ std::vector<std::vector<int32_t>> PrepareStrategy(const std::shared_ptr<Graph> &
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return PrepareMatMul(graph, ops, iter_graph, iter_ops);
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} else if (type == PRELU) {
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return PreparePReLU(graph, ops, iter_graph, iter_ops);
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} else if (type == BATCH_NORM) {
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return PrepareBatchNorm(graph, ops, iter_graph, iter_ops);
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} else if (type == ONEHOT) {
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return PrepareOneHot(graph, ops, iter_graph, iter_ops);
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} else if (type == SOFTMAX || type == LOG_SOFTMAX || type == SPARSE_SOFTMAX_CROSS_ENTROPY_WITH_LOGITS ||
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type == SOFTMAX_CROSS_ENTROPY_WITH_LOGITS) {
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return MakeDataParallelStrategy(graph, ops, iter_graph, iter_ops);
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@ -513,9 +507,6 @@ std::vector<std::vector<int32_t>> GenerateStrategiesFromStrategy(const std::vect
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if (ops[iter_ops]->type() == BIAS_ADD) {
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return PrepareBiasAdd(s_ptr);
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}
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if (ops[iter_ops]->type() == ONEHOT) {
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return PrepareOneHot(s_ptr);
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}
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if (ops[iter_ops]->type() == GATHERV2) {
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return PrepareGatherV2(s_ptr);
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}
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@ -559,7 +550,7 @@ void GenerateEliminatedOperatorStrategyForward(const std::shared_ptr<Graph> grap
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std::vector<std::vector<int32_t>> stra;
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std::vector<int32_t> s;
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size_t incoming_op_index = FindIndexOfOperatorIncoming(input_tensor_names, iter_ops);
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if (incoming_op_index != SIZE_MAX && ops[iter_ops]->type() != ONEHOT) {
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if (incoming_op_index != SIZE_MAX) {
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auto iter_graph = index_list->at(incoming_op_index);
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if (iter_graph != SIZE_MAX) {
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s = CopyIncomingOperatorOutputStrategy(graph, ops, iter_ops, iter_graph);
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@ -640,7 +631,7 @@ std::vector<int32_t> CopyOutgoingOperatorInputStrategy(const std::vector<std::sh
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}
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if (outgoing_op_index != SIZE_MAX && iter_op_inputs != SIZE_MAX) {
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for (size_t k = 0; k < ops[outgoing_op_index]->selected_strategy()->GetInputDim()[iter_op_inputs].size(); ++k) {
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for (size_t k = 0; k < ops[iter_ops]->outputs_tensor_info()[0].shape().size(); ++k) {
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s.push_back(ops[outgoing_op_index]->selected_strategy()->GetInputDim()[iter_op_inputs][k]);
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}
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}
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@ -37,11 +37,10 @@ std::vector<std::vector<int32_t>> PrepareMatMul(const std::shared_ptr<Graph> &gr
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std::vector<std::vector<int32_t>> PreparePReLU(const std::shared_ptr<Graph> &graph,
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const std::vector<std::shared_ptr<OperatorInfo>> &ops,
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const size_t iter_graph, const size_t iter_ops);
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std::vector<std::vector<int32_t>> PrepareBatchNorm(const std::shared_ptr<Graph> &graph,
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const std::vector<std::shared_ptr<OperatorInfo>> &ops,
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const size_t iter_graph, const size_t iter_ops);
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std::vector<std::vector<int32_t>> PrepareBiasAdd(const std::shared_ptr<std::vector<int32_t>> &s);
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std::vector<std::vector<int32_t>> PrepareOneHot(const std::shared_ptr<std::vector<int32_t>> &s);
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std::vector<std::vector<int32_t>> PrepareOneHot(const std::shared_ptr<Graph> &graph,
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const std::vector<std::shared_ptr<OperatorInfo>> &ops,
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const size_t iter_graph, const size_t iter_ops);
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std::vector<std::vector<int32_t>> PrepareGatherV2(const std::shared_ptr<std::vector<int32_t>> &s);
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std::vector<std::vector<int32_t>> MakeRecSearchStrategy(const std::shared_ptr<Graph> &graph,
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const std::vector<std::shared_ptr<OperatorInfo>> &ops,
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@ -216,10 +216,10 @@ std::shared_ptr<Graph> EliminateGraph(const std::shared_ptr<Graph> graph,
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const std::shared_ptr<std::vector<size_t>> index_list) {
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MS_EXCEPTION_IF_NULL(graph);
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const std::set<OperatorType> type_list = {
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OperatorType::kRecOneHot, OperatorType::kRecReLU, OperatorType::kRecLog, OperatorType::kRecExp,
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OperatorType::kRecAdd, OperatorType::kRecElmWiseOp, OperatorType::kRecBiasAdd, OperatorType::kRecSub,
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OperatorType::kRecMul, OperatorType::kRecDiv, OperatorType::kRecSqueeze, OperatorType::kRecReduce,
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OperatorType::kRecCast, OperatorType::kRecReshape, OperatorType::kRecGatherV2};
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OperatorType::kRecReLU, OperatorType::kRecLog, OperatorType::kRecExp, OperatorType::kRecAdd,
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OperatorType::kRecElmWiseOp, OperatorType::kRecBiasAdd, OperatorType::kRecSub, OperatorType::kRecMul,
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OperatorType::kRecDiv, OperatorType::kRecSqueeze, OperatorType::kRecReduce, OperatorType::kRecCast,
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OperatorType::kRecReshape, OperatorType::kRecGatherV2};
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for (size_t node_index = 0; node_index < (size_t)graph->nodes.size(); node_index++) {
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auto type = graph->nodes[node_index].apply.op_type;
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if (type_list.find(type) != type_list.end()) {
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@ -68,17 +68,21 @@ double GetWeights(const Graph::NodeType &node) {
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auto cost_ptr = std::make_shared<CostBiasAdd>();
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return cost_ptr->GetMinCostIn();
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} else if (op.op_type == OperatorType::kRecOneHot || op.op_type == OperatorType::kRecLog ||
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op.op_type == OperatorType::kRecExp || op.op_type == OperatorType::kRecAdd ||
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op.op_type == OperatorType::kRecSub || op.op_type == OperatorType::kRecMul ||
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op.op_type == OperatorType::kRecDiv || op.op_type == OperatorType::kRecSqueeze ||
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op.op_type == OperatorType::kRecCast) {
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} else if (op.op_type == OperatorType::kRecLog || op.op_type == OperatorType::kRecExp ||
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op.op_type == OperatorType::kRecAdd || op.op_type == OperatorType::kRecSub ||
|
||||
op.op_type == OperatorType::kRecMul || op.op_type == OperatorType::kRecDiv ||
|
||||
op.op_type == OperatorType::kRecSqueeze || op.op_type == OperatorType::kRecCast) {
|
||||
// For element-wise op
|
||||
auto cost_ptr = std::make_shared<CostCommon>();
|
||||
|
||||
return cost_ptr->GetMinCostIn();
|
||||
} else if (op.op_type == OperatorType::kRecBatchNorm || op.op_type == OperatorType::kRecOneHot) {
|
||||
// For BatchParallel op
|
||||
auto cost_ptr = std::make_shared<CostBatchParallel>();
|
||||
|
||||
return cost_ptr->GetMaxCostIn();
|
||||
} else if (op.op_type == OperatorType::kRecUnkownType || op.op_type == OperatorType::kRecPReLU ||
|
||||
op.op_type == OperatorType::kRecBatchNorm || op.op_type == OperatorType::kRecSoftmax ||
|
||||
op.op_type == OperatorType::kRecSoftmax ||
|
||||
op.op_type == OperatorType::kRecSparseSoftmaxCrossEntropyWithLogits) {
|
||||
// For unprocessed type
|
||||
return 0.0;
|
||||
|
|
@ -158,17 +162,20 @@ StrategyRec PartitionNode(const Graph::NodeType &node,
|
|||
auto cost_ptr = std::make_shared<CostBiasAdd>();
|
||||
|
||||
return cost_ptr->GetOptimalStr(node, node_name_to_strategy, *graph);
|
||||
} else if (node.apply.op_type == OperatorType::kRecOneHot || node.apply.op_type == OperatorType::kRecLog ||
|
||||
node.apply.op_type == OperatorType::kRecExp || node.apply.op_type == OperatorType::kRecAdd ||
|
||||
node.apply.op_type == OperatorType::kRecSub || node.apply.op_type == OperatorType::kRecMul ||
|
||||
node.apply.op_type == OperatorType::kRecDiv || node.apply.op_type == OperatorType::kRecSqueeze ||
|
||||
node.apply.op_type == OperatorType::kRecCast) {
|
||||
} else if (node.apply.op_type == OperatorType::kRecLog || node.apply.op_type == OperatorType::kRecExp ||
|
||||
node.apply.op_type == OperatorType::kRecAdd || node.apply.op_type == OperatorType::kRecSub ||
|
||||
node.apply.op_type == OperatorType::kRecMul || node.apply.op_type == OperatorType::kRecDiv ||
|
||||
node.apply.op_type == OperatorType::kRecSqueeze || node.apply.op_type == OperatorType::kRecCast) {
|
||||
// For element-wise op
|
||||
auto cost_ptr = std::make_shared<CostCommon>();
|
||||
|
||||
return cost_ptr->GetOptimalStr(node, node_name_to_strategy, *graph);
|
||||
} else if (node.apply.op_type == OperatorType::kRecBatchNorm || node.apply.op_type == OperatorType::kRecOneHot) {
|
||||
// For BatchParallel type
|
||||
auto cost_ptr = std::make_shared<CostBatchParallel>();
|
||||
return cost_ptr->GetOptimalStr(node);
|
||||
} else if (node.apply.op_type == OperatorType::kRecUnkownType || node.apply.op_type == OperatorType::kRecPReLU ||
|
||||
node.apply.op_type == OperatorType::kRecBatchNorm || node.apply.op_type == OperatorType::kRecSoftmax ||
|
||||
node.apply.op_type == OperatorType::kRecSoftmax ||
|
||||
node.apply.op_type == OperatorType::kRecSparseSoftmaxCrossEntropyWithLogits) {
|
||||
// For unprocessed type
|
||||
StrategyRec default_strategy;
|
||||
|
|
|
|||
Loading…
Reference in New Issue