diff --git a/ge/CMakeLists.txt b/ge/CMakeLists.txt index 2b9122da..dc80597c 100755 --- a/ge/CMakeLists.txt +++ b/ge/CMakeLists.txt @@ -298,7 +298,9 @@ set(TRAIN_SRC_LIST "graph/passes/hccl_continuous_memcpy_pass.cc" "graph/passes/identity_pass.cc" "graph/passes/ref_identity_delete_op_pass.cc" + "graph/passes/infer_base_pass.cc" "graph/passes/infershape_pass.cc" + "graph/passes/infer_value_range_pass.cc" "graph/passes/iterator_op_pass.cc" "graph/passes/link_gen_mask_nodes_pass.cc" "graph/passes/merge_pass.cc" @@ -547,7 +549,9 @@ set(INFER_SRC_LIST "graph/passes/shape_operate_op_remove_pass.cc" "graph/passes/assert_pass.cc" "graph/passes/dropout_pass.cc" + "graph/passes/infer_base_pass.cc" "graph/passes/infershape_pass.cc" + "graph/passes/infer_value_range_pass.cc" "graph/passes/unused_const_pass.cc" "graph/passes/permute_pass.cc" "graph/passes/ctrl_edge_transfer_pass.cc" diff --git a/ge/common/formats/utils/formats_trans_utils.cc b/ge/common/formats/utils/formats_trans_utils.cc index 052951ce..db1812d0 100755 --- a/ge/common/formats/utils/formats_trans_utils.cc +++ b/ge/common/formats/utils/formats_trans_utils.cc @@ -49,6 +49,25 @@ GE_FUNC_DEV_VISIBILITY GE_FUNC_HOST_VISIBILITY std::string ShapeToString(const s return JoinToString(shape); } +GE_FUNC_DEV_VISIBILITY GE_FUNC_HOST_VISIBILITY +std::string RangeToString(const std::vector> &ranges) { + bool first = true; + std::stringstream ss; + ss << "["; + for (const auto &range : ranges) { + if (first) { + first = false; + } else { + ss << ","; + } + ss << "{"; + ss << range.first << "," << range.second; + ss << "}"; + } + ss << "]"; + return ss.str(); +} + int64_t GetItemNumByShape(const std::vector &shape) { int64_t num = 1; for (auto dim : shape) { diff --git a/ge/common/formats/utils/formats_trans_utils.h b/ge/common/formats/utils/formats_trans_utils.h index 848e8b3a..64f9f820 100755 --- a/ge/common/formats/utils/formats_trans_utils.h +++ b/ge/common/formats/utils/formats_trans_utils.h @@ -54,6 +54,8 @@ std::string ShapeToString(const GeShape &shape); std::string ShapeToString(const std::vector &shape); +std::string RangeToString(const std::vector> &ranges); + int64_t GetItemNumByShape(const std::vector &shape); bool CheckShapeValid(const std::vector &shape, const int64_t expect_dims); diff --git a/ge/graph/passes/constant_folding_pass.cc b/ge/graph/passes/constant_folding_pass.cc index 6607388f..53b14fd5 100644 --- a/ge/graph/passes/constant_folding_pass.cc +++ b/ge/graph/passes/constant_folding_pass.cc @@ -20,17 +20,23 @@ #include "external/graph/operator_factory.h" #include "graph/utils/node_utils.h" #include "graph/utils/type_utils.h" +#include "ge_local_engine/engine/host_cpu_engine.h" #include "init/gelib.h" namespace ge { const int64_t kStartCallNum = 1; const std::string kKernelLibName = "aicpu_tf_kernel"; -// tf_kernel.json opsFlag config const std::string kOpsFlagClose = "0"; -Status RunOpKernelWithCheck(NodePtr &node, - const vector &inputs, - std::vector &outputs) { +const map> &ConstantFoldingPass::GetGeConstantFoldingPerfStatistic() const { + return statistic_of_ge_constant_folding_; +} +const map> &ConstantFoldingPass::GetOpConstantFoldingPerfStatistic() const { + return statistic_of_op_constant_folding_; +} + +Status ConstantFoldingPass::RunOpKernelWithCheck(NodePtr &node, const vector &inputs, + std::vector &outputs) { std::shared_ptr instance_ptr = ge::GELib::GetInstance(); if ((instance_ptr == nullptr) || (!instance_ptr->InitFlag())) { GELOGE(GE_CLI_GE_NOT_INITIALIZED, "[Check][Param] GE is not initialized or is finalized."); @@ -47,15 +53,13 @@ Status RunOpKernelWithCheck(NodePtr &node, if (ops_flag == kOpsFlagClose) { return UNSUPPORTED; } - return FoldingPass::RunOpKernel(node, inputs, outputs); + return RunOpKernel(node, inputs, outputs); } -const map> &ConstantFoldingPass::GetGeConstantFoldingPerfStatistic() const { - return statistic_of_ge_constant_folding_; -} - -const map> &ConstantFoldingPass::GetOpConstantFoldingPerfStatistic() const { - return statistic_of_op_constant_folding_; +Status ConstantFoldingPass::RunOpKernel(NodePtr &node, + const vector &inputs, + std::vector &outputs) { + return HostCpuEngine::GetInstance().Run(node, inputs, outputs); } Status ConstantFoldingPass::Run(ge::NodePtr &node) { diff --git a/ge/graph/passes/constant_folding_pass.h b/ge/graph/passes/constant_folding_pass.h index 703e6edd..7de48a17 100644 --- a/ge/graph/passes/constant_folding_pass.h +++ b/ge/graph/passes/constant_folding_pass.h @@ -28,6 +28,11 @@ class ConstantFoldingPass : public FoldingPass { Status Run(ge::NodePtr &node) override; const std::map> &GetGeConstantFoldingPerfStatistic() const; const std::map> &GetOpConstantFoldingPerfStatistic() const; + + static Status RunOpKernel(NodePtr &node, const vector &inputs, vector &outputs); + static Status RunOpKernelWithCheck(NodePtr &node, const vector &inputs, + std::vector &outputs); + private: std::map> statistic_of_op_constant_folding_; std::map> statistic_of_ge_constant_folding_; diff --git a/ge/graph/passes/folding_pass.cc b/ge/graph/passes/folding_pass.cc index c0a0f2a2..819c3b40 100755 --- a/ge/graph/passes/folding_pass.cc +++ b/ge/graph/passes/folding_pass.cc @@ -28,8 +28,6 @@ #include "inc/kernel.h" #include "inc/kernel_factory.h" #include "graph/debug/ge_attr_define.h" -#include "ge_local_engine/engine/host_cpu_engine.h" - namespace ge { namespace folding_pass { @@ -123,12 +121,6 @@ NodePtr AddIdentityNodeToGraph(const std::string &name, const GeTensorDesc &tens } } // namespace -Status FoldingPass::RunOpKernel(NodePtr &node, - const vector &inputs, - std::vector &outputs) { - return HostCpuEngine::GetInstance().Run(node, inputs, outputs); -} - Status FoldingPass::Folding(NodePtr &node, vector &outputs) { GE_CHECK_NOTNULL(node); GELOGD("begin folding node:%s", node->GetName().c_str()); diff --git a/ge/graph/passes/folding_pass.h b/ge/graph/passes/folding_pass.h index 745cffd7..c461ff5c 100755 --- a/ge/graph/passes/folding_pass.h +++ b/ge/graph/passes/folding_pass.h @@ -34,8 +34,6 @@ bool IsNoNeedConstantFolding(const NodePtr &node); using IndexsToAnchors = std::map>; class FoldingPass : public BaseNodePass { - public: - static Status RunOpKernel(NodePtr &node, const vector &inputs, vector &outputs); protected: Status Folding(NodePtr &node, vector &outputs); private: diff --git a/ge/graph/passes/infer_base_pass.cc b/ge/graph/passes/infer_base_pass.cc new file mode 100644 index 00000000..27eb0c54 --- /dev/null +++ b/ge/graph/passes/infer_base_pass.cc @@ -0,0 +1,386 @@ +/** + * 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. + */ + +#include "infer_base_pass.h" +#include "common/ge/ge_util.h" +#include "common/util/error_manager/error_manager.h" +#include "framework/common/debug/ge_log.h" +#include "framework/common/util.h" +#include "graph/debug/ge_attr_define.h" +#include "graph/utils/graph_utils.h" +#include "graph/utils/node_utils.h" +#include "graph/utils/tensor_utils.h" +#include "graph/utils/type_utils.h" + +namespace ge { +namespace { +graphStatus FindValidSubgraphNetoutput(const ConstNodePtr &node, const ComputeGraphPtr &sub_graph, NodePtr &netoutput) { + auto sub_nodes = sub_graph->GetDirectNode(); + for (size_t i = sub_nodes.size(); i > 0; --i) { + auto sub_node = sub_nodes.at(i - 1); + if (sub_node->GetType() == NETOUTPUT) { + if (sub_node == nullptr) { + REPORT_INNER_ERROR("E19999", "NetOutput node is null in subgraph %s, parent node %s.", + sub_graph->GetName().c_str(), node->GetName().c_str()); + GELOGE(GRAPH_FAILED, "[Check][Param] NetOutput node is null on sub graph %s, parent node %s", + sub_graph->GetName().c_str(), node->GetName().c_str()); + return GRAPH_FAILED; + } + auto sub_node_opdesc = sub_node->GetOpDesc(); + if (sub_node_opdesc == nullptr) { + REPORT_INNER_ERROR("E19999", "Invalid NetOutput node in subgraph %s, parent node %s, no OpDesc on it", + sub_graph->GetName().c_str(), node->GetName().c_str()); + GELOGE(GRAPH_FAILED, "[Check][Param] Invalid NetOutput node on sub graph %s, parent node %s, no OpDesc on it", + sub_graph->GetName().c_str(), node->GetName().c_str()); + return GRAPH_FAILED; + } + + netoutput = sub_node; + return GRAPH_SUCCESS; + } + } + + REPORT_INNER_ERROR("E19999", "Can not find the NetOutput node in subgraph %s, parent node %s", + sub_graph->GetName().c_str(), node->GetName().c_str()); + GELOGE(GRAPH_FAILED, "[Check][Param] Can not find the NetOutput node in subgraph %s, parent node %s", + sub_graph->GetName().c_str(), node->GetName().c_str()); + return GRAPH_FAILED; +} +} // namespace + +Status InferBasePass::Run(NodePtr &node) { + GE_CHECK_NOTNULL(node); + GE_CHECK_NOTNULL(node->GetOpDesc()); + + bool need_infer = NeedInfer(node); + if (!need_infer) { + GELOGD("Node %s does not need to infer.", node->GetName().c_str()); + return SUCCESS; + } + + std::set changed_nodes; + auto ret = InferAndUpdate(node, !OptionExists(kOptimizeAfterSubGraph), changed_nodes); + if (ret != GRAPH_SUCCESS) { + GELOGE(ret, "Infer and update for node %s failed! ret: %u", node->GetName().c_str(), ret); + return GRAPH_FAILED; + } + + AddChangedNodesImmediateRepass(changed_nodes); + return SUCCESS; +} + +bool InferBasePass::NeedInfer(const NodePtr &node) const { return true; } +void InferBasePass::AddChangedNodesImmediateRepass(const std::set &changed_nodes) { + for (const auto &node_ele : changed_nodes) { + AddImmediateRePassNode(node_ele); + } +} + +graphStatus InferBasePass::InferAndUpdate(NodePtr &node, bool before_subgraph, std::set &changed_nodes) { + graphStatus ret; + if (ContainsSubgraph(node)) { + if (before_subgraph) { + ret = UpdateTensorDescToSubgraphData(node); + } else { + ret = UpdateTensorDescToParentNodeOutput(node); + } + if (ret != GRAPH_SUCCESS) { + GELOGE(ret, "Update tensor desc failed between parent node %s and subgraphs. ret: %u", node->GetName().c_str(), + ret); + return ret; + } + } + + PrintInOutTensors(node, "before_infer"); + ret = Infer(node); + PrintInOutTensors(node, "after_infer"); + if (ret == GRAPH_NODE_NEED_REPASS) { + // if a node need re_pass, it is not necessary to update peer node input. + changed_nodes.insert(node); + return GRAPH_SUCCESS; + } else if (ret != GRAPH_SUCCESS && ret != GRAPH_NOT_CHANGED) { + GELOGE(ret, "Infer failed for node %s, ret: %u", node->GetName().c_str(), ret); + return ret; + } + + ret = UpdateTensorDescToPeerInputs(node, changed_nodes); + if (ret != GRAPH_SUCCESS) { + GELOGE(ret, "Node %s updates tensor desc to peer input nodes failed! ret: %u", node->GetName().c_str(), ret); + } + GELOGD("Node %s infer and update succeeded .", node->GetName().c_str()); + return ret; +} + +bool InferBasePass::ContainsSubgraph(const NodePtr &node) { + auto sub_graph_names = node->GetOpDesc()->GetSubgraphInstanceNames(); + return !sub_graph_names.empty(); +} + +graphStatus InferBasePass::UpdateTensorDescToPeerInputs(NodePtr &node, std::set &changed_nodes) { + auto op_desc = node->GetOpDesc(); + for (const auto &out_anchor : node->GetAllOutDataAnchors()) { + auto output_tensor = op_desc->MutableOutputDesc(out_anchor->GetIdx()); + for (const auto &peer_anchor : out_anchor->GetPeerInDataAnchors()) { + auto peer_anchor_opdesc = peer_anchor->GetOwnerNode()->GetOpDesc(); + if (peer_anchor_opdesc == nullptr) { + continue; + } + auto peer_input_desc = peer_anchor_opdesc->MutableInputDesc(peer_anchor->GetIdx()); + if (peer_input_desc == nullptr) { + continue; + } + + bool changed = false; + auto ret = UpdateTensorDesc(output_tensor, peer_input_desc, changed); + if (ret != GRAPH_SUCCESS) { + REPORT_CALL_ERROR("E19999", "Update peer input desc failed, node %s.", node->GetName().c_str()); + GELOGE(ret, "Update peer input desc failed, node %s.", node->GetName().c_str()); + return ret; + } + if (changed) { + changed_nodes.insert(peer_anchor->GetOwnerNode()); + GELOGD("Node %s update peer node succeeded, peer node %s is changed.", node->GetName().c_str(), + peer_anchor->GetOwnerNode()->GetName().c_str()); + } + } + } + return GRAPH_SUCCESS; +} + +std::vector InferBasePass::GetCurNodeSubgraphs(const NodePtr &node) { + std::vector cur_node_subgraph; + auto op_desc = node->GetOpDesc(); + auto sub_graph_names = op_desc->GetSubgraphInstanceNames(); + if (sub_graph_names.empty()) { + return cur_node_subgraph; + } + + auto root_graph = GraphUtils::FindRootGraph(node->GetOwnerComputeGraph()); + for (const auto &name : sub_graph_names) { + if (name.empty()) { + GELOGW("The node %s contains empty subgraph instance name", node->GetName().c_str()); + continue; + } + auto sub_graph = root_graph->GetSubgraph(name); + if (sub_graph == nullptr) { + GELOGW("The subgrpah %s for node %s is null.", name.c_str(), node->GetName().c_str()); + continue; + } + cur_node_subgraph.emplace_back(sub_graph); + } + return cur_node_subgraph; +} + +graphStatus InferBasePass::UpdateTensorDescToSubgraphData(NodePtr &node) { + auto op_desc = node->GetOpDesc(); + for (const auto &sub_graph : GetCurNodeSubgraphs(node)) { + for (const auto &node_sub : sub_graph->GetDirectNode()) { + if (node_sub->GetType() != DATA) { + continue; + } + + auto data_opdesc = node_sub->GetOpDesc(); + if (data_opdesc == nullptr) { + REPORT_INNER_ERROR("E19999", "Invalid data node on the sub graph %s parent node %s, no OpDesc", + sub_graph->GetName().c_str(), node->GetName().c_str()); + GELOGE(GRAPH_FAILED, "[Get][OpDesc] Invalid data node on the sub graph %s parent node %s, no OpDesc", + sub_graph->GetName().c_str(), node->GetName().c_str()); + return GRAPH_FAILED; + } + int ref_i; + if (!AttrUtils::GetInt(data_opdesc, ATTR_NAME_PARENT_NODE_INDEX, ref_i)) { + REPORT_INNER_ERROR("E19999", "Invalid data node on the sub graph %s parent node %s, no ref-index attribute", + sub_graph->GetName().c_str(), node->GetName().c_str()); + GELOGE(GRAPH_FAILED, "[Get][Int] Invalid data node on the sub graph %s parent node %s, no ref-index attribute", + sub_graph->GetName().c_str(), node->GetName().c_str()); + return GRAPH_FAILED; + } + GELOGD("Subgraph Data node ref_index is %d, parent node is %s.", ref_i, node->GetName().c_str()); + + // In multi-batch, data shape of subgraph is different, no need to refresh. + if (data_opdesc->HasAttr(ATTR_MBATCH_ORIGIN_INPUT_DIMS)) { + GELOGD("While updating subgraph data node, ignore node %s which is created by multi-dims", + data_opdesc->GetName().c_str()); + continue; + } + auto input_desc = op_desc->MutableInputDesc(ref_i); + if (input_desc == nullptr) { + REPORT_INNER_ERROR("E19999", + "The ref index(%d) on the data %s on the sub graph %s " + "parent node %s are incompatible, inputs num %u", + ref_i, node_sub->GetName().c_str(), sub_graph->GetName().c_str(), node->GetName().c_str(), + node->GetAllInDataAnchorsSize()); + GELOGE(GRAPH_FAILED, + "[Call][MutableInputDesc] The ref index(%d) on the data %s on the sub graph %s " + "parent node %s are incompatible, inputs num %u", + ref_i, node_sub->GetName().c_str(), sub_graph->GetName().c_str(), node->GetName().c_str(), + node->GetAllInDataAnchorsSize()); + return GRAPH_FAILED; + } + GELOGI("Ref index is %d, input_desc dtype is %d, node name is %s", ref_i, input_desc->GetDataType(), + node->GetName().c_str()); + + bool has_tensor_desc_changed = false; + auto data_input_td = data_opdesc->MutableInputDesc(0); + auto ret = UpdateTensorDesc(input_desc, data_input_td, has_tensor_desc_changed); + if (ret != GRAPH_SUCCESS) { + REPORT_CALL_ERROR("E19999", "Failed to update input desc of data %s on the sub graph %s parent node %s", + node_sub->GetName().c_str(), sub_graph->GetName().c_str(), node->GetName().c_str()); + GELOGE(GRAPH_FAILED, "[Update][InputDesc] of data %s on the sub graph %s parent node %s failed", + node_sub->GetName().c_str(), sub_graph->GetName().c_str(), node->GetName().c_str()); + return ret; + } + + auto data_output_td = data_opdesc->MutableOutputDesc(0); + ret = UpdateTensorDesc(input_desc, data_output_td, has_tensor_desc_changed); + if (ret != GRAPH_SUCCESS) { + REPORT_CALL_ERROR("E19999", "Failed to update output desc of data %s on the sub graph %s parent node %s", + node_sub->GetName().c_str(), sub_graph->GetName().c_str(), node->GetName().c_str()); + GELOGE(GRAPH_FAILED, "[Update][OutputDesc] of data %s on the sub graph %s parent node %s failed", + node_sub->GetName().c_str(), sub_graph->GetName().c_str(), node->GetName().c_str()); + return ret; + } + GELOGD("Parent node %s update subgraph data %s input and output succeed.", node->GetName().c_str(), + data_opdesc->GetName().c_str()); + } + } + return GRAPH_SUCCESS; +} + +graphStatus InferBasePass::UpdateTensorDescToParentNodeOutput(NodePtr &node) { + std::vector> ref_out_tensors(node->GetAllOutDataAnchorsSize()); + + for (const auto &sub_graph : GetCurNodeSubgraphs(node)) { + NodePtr netoutput; + auto ret = FindValidSubgraphNetoutput(node, sub_graph, netoutput); + if (ret != GRAPH_SUCCESS) { + return ret; + } + + auto netoutput_opdesc = netoutput->GetOpDesc(); + for (auto &netoutput_in_anchor : netoutput->GetAllInDataAnchors()) { + auto netoutput_in_desc = netoutput_opdesc->MutableInputDesc(netoutput_in_anchor->GetIdx()); + if (netoutput_in_desc == nullptr) { + REPORT_INNER_ERROR("E19999", + "Invalid NetOutput node on sub graph %s, parent node %s, can not find input tensor %d", + sub_graph->GetName().c_str(), node->GetName().c_str(), netoutput_in_anchor->GetIdx()); + GELOGE(GRAPH_FAILED, + "[Get][Tensor] Invalid NetOutput node on sub graph %s, parent node %s, can not find input tensor %d", + sub_graph->GetName().c_str(), node->GetName().c_str(), netoutput_in_anchor->GetIdx()); + return GRAPH_FAILED; + } + GELOGI("Netoutput in anchor index is %d, input tensor dim is %zu", netoutput_in_anchor->GetIdx(), + netoutput_in_desc->GetShape().GetDimNum()); + int ref_i; + if (!AttrUtils::GetInt(netoutput_in_desc, ATTR_NAME_PARENT_NODE_INDEX, ref_i)) { + // if there is no ref index on the TensorDesc, it means the output data will be ignored outer. + continue; + } + GELOGI("Parent node index of edge desc is %d", ref_i); + if (ref_i < 0 || static_cast(ref_i) >= node->GetAllOutDataAnchorsSize()) { + REPORT_INNER_ERROR("E19999", + "Invalid ref_index %d of parent node %s, ref_index should less than %u.", ref_i, + node->GetName().c_str(), node->GetAllOutDataAnchorsSize()); + GELOGE(GRAPH_FAILED, + "[Get][Ref_index] Invalid ref_index %d of parent node %s, ref_index should less than %u.", ref_i, + node->GetName().c_str(), node->GetAllOutDataAnchorsSize()); + return GRAPH_FAILED; + } + ref_out_tensors[ref_i].emplace_back(netoutput_in_desc); + } + } + + return UpdateParentNodeContainsSubgraphs(node, ref_out_tensors); +} + +graphStatus InferBasePass::UpdateParentNodeContainsSubgraphs( + NodePtr &node, const std::vector> &ref_out_tensors) { + for (size_t i = 0; i < ref_out_tensors.size(); i++) { + if (ref_out_tensors[i].empty()) { + REPORT_CALL_ERROR("E19999", "Parent node %s ref_index %zu subgraph output tensor list is empty.", + node->GetName().c_str(), i); + GELOGE(GRAPH_FAILED, "[Param][check] Parent node %s ref_index %zu subgraph output tensor list is empty.", + node->GetName().c_str(), i); + return GRAPH_FAILED; + } + auto node_op_desc = node->GetOpDesc(); + auto node_output_td = node_op_desc->MutableOutputDesc(i); + if (node_output_td == nullptr) { + REPORT_CALL_ERROR("E19999", "Node %s output %zu tensor desc is null.", node->GetName().c_str(), i); + GELOGE(GRAPH_FAILED, "[Param][check] Node %s output %zu tensor desc is null.", node->GetName().c_str(), i); + return GRAPH_FAILED; + } + + graphStatus ret; + if (node_op_desc->HasAttr(ATTR_NAME_BATCH_NUM)) { + ret = UpdateOutputFromSubgraphsForMultiDims(ref_out_tensors[i], node_output_td); + } else { + ret = UpdateOutputFromSubgraphs(ref_out_tensors[i], node_output_td); + } + if (ret != GRAPH_SUCCESS) { + REPORT_CALL_ERROR("E19999", "Node %s update output %zu tensor desc failed. ret: %u", node->GetName().c_str(), i, + ret); + GELOGE(GRAPH_FAILED, "[Param][check] Node %s update output %zu tensor desc failed. ret: %u", + node->GetName().c_str(), i, ret); + return ret; + } + GELOGD("Parent node %s successfully updated the output tensors from subgraphs.", node->GetName().c_str()); + } + return GRAPH_SUCCESS; +} + +void InferBasePass::PrintInOutTensors(const NodePtr &node, const std::string &phase) { + if (!IsLogEnable(GE, DLOG_DEBUG)) { + return; + } + if (node == nullptr) { + REPORT_INNER_ERROR("E19999", "Param node is nullptr, check invalid"); + GELOGE(GRAPH_FAILED, "[Check][Param] node is null"); + return; + } + ge::OpDescPtr op_desc = node->GetOpDesc(); + GE_IF_BOOL_EXEC(op_desc == nullptr, REPORT_INNER_ERROR("E19999", "Node has no opdesc, check invalid"); + GELOGE(GRAPH_FAILED, "[Get][OpDesc] op_desc is null."); return ); + std::stringstream ss; + ss << "{"; + int32_t in_idx = 0; + for (const auto &input_desc : op_desc->GetAllInputsDescPtr()) { + if (input_desc == nullptr) { + in_idx++; + continue; + } + if (in_idx > 0) { + ss << " "; + } + ss << "input_" << in_idx << " tensor: "; + ss << SerialTensorInfo(input_desc); + in_idx++; + } + int32_t out_idx = 0; + for (const auto &output_desc : op_desc->GetAllOutputsDescPtr()) { + if (output_desc == nullptr) { + out_idx++; + continue; + } + ss << " "; + ss << "output_" << out_idx << " tensor: "; + ss << SerialTensorInfo(output_desc); + out_idx++; + } + ss << "}"; + GELOGD("Infer tensor dump [%s], Node name: [%s]. %s", phase.c_str(), node->GetName().c_str(), ss.str().c_str()); +} +} // namespace ge diff --git a/ge/graph/passes/infer_base_pass.h b/ge/graph/passes/infer_base_pass.h new file mode 100644 index 00000000..3900b5db --- /dev/null +++ b/ge/graph/passes/infer_base_pass.h @@ -0,0 +1,65 @@ +/** + * 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. + */ +#ifndef GE_GRAPH_PASSES_INFER_BASE_PASS_H_ +#define GE_GRAPH_PASSES_INFER_BASE_PASS_H_ + +#include "graph/passes/base_pass.h" + +namespace ge { +class InferBasePass : public BaseNodePass { + public: + Status Run(NodePtr &node) override; + graphStatus InferAndUpdate(NodePtr &node, bool before_subgraph, std::set &changed_nodes); + void PrintInOutTensors(const NodePtr &node, const std::string &phase); + + protected: + virtual std::string SerialTensorInfo(const GeTensorDescPtr &tensor_desc) const = 0; + virtual bool NeedInfer(const NodePtr &node) const; + virtual graphStatus Infer(NodePtr &node) = 0; + + /** + * Update the output TensorDesc by src TensorDesc. This will be called when updating peer node input desc. + * @param src, input TensorDesc + * @param dst, output TensorDesc to be updated + * @return + */ + virtual graphStatus UpdateTensorDesc(const GeTensorDescPtr &src, GeTensorDescPtr &dst, bool &changed) = 0; + + /** + * Update the output TensorDesc for nodes which contain subgraphs. + * In dynamic multi-dims/batch/images size scene, the update process maybe different, + * in which case, the `InferBasePass` will call method `UpdateOutputFromSubgraphsForMultiDims` instead. + * @param src, input TensorDesc from NetOutput nodes in all subgraphs + * @param dst, output TensorDesc to be updated + * @return + */ + virtual graphStatus UpdateOutputFromSubgraphs(const std::vector &src, + GeTensorDescPtr &dst) = 0; + virtual graphStatus UpdateOutputFromSubgraphsForMultiDims(const std::vector &src, + GeTensorDescPtr &dst) = 0; + + private: + void AddChangedNodesImmediateRepass(const std::set &changed_nodes); + bool ContainsSubgraph(const NodePtr &node); + std::vector GetCurNodeSubgraphs(const NodePtr &node); + graphStatus UpdateTensorDescToSubgraphData(NodePtr &node); + graphStatus UpdateTensorDescToParentNodeOutput(NodePtr &node); + graphStatus UpdateParentNodeContainsSubgraphs(NodePtr &node, + const std::vector> &ref_out_tensors); + graphStatus UpdateTensorDescToPeerInputs(NodePtr &node, std::set &changed_nodes); +}; +} // namespace ge +#endif // GE_GRAPH_PASSES_INFER_BASE_PASS_H_ diff --git a/ge/graph/passes/infer_value_range_pass.cc b/ge/graph/passes/infer_value_range_pass.cc new file mode 100644 index 00000000..b9cb88bc --- /dev/null +++ b/ge/graph/passes/infer_value_range_pass.cc @@ -0,0 +1,500 @@ +/** + * 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. + */ + +#include "graph/passes/infer_value_range_pass.h" +#include "common/formats/utils/formats_trans_utils.h" +#include "common/util/error_manager/error_manager.h" +#include "framework/common/debug/ge_log.h" +#include "graph/debug/ge_attr_define.h" +#include "graph/operator_factory_impl.h" +#include "graph/passes/constant_folding_pass.h" +#include "graph/utils/type_utils.h" +#include "common/ge/ge_util.h" + +using std::unique_ptr; +namespace ge { +namespace { +#define GET_DATA_BY_DTYPE(DTYPE, TYPE) \ + case (DTYPE): \ + ConstructValueRange(lower_boundary_tensor, upper_boundary_tensor, output_tensor_value_range); \ + break; + +void SerialShapeRange(const GeTensorDescPtr &desc, std::string &desc_str) { + std::vector> shape_range; + (void)desc->GetShapeRange(shape_range); + desc_str += formats::RangeToString(shape_range); + shape_range.clear(); + (void)desc->GetOriginShapeRange(shape_range); + desc_str += ","; + desc_str += formats::RangeToString(shape_range); + shape_range.clear(); +} + +Status RunCpuKernelForValueRange(NodePtr &node, const vector &inputs, + std::vector &outputs) { + // RunOpKernelWithCheck, RunOpKernel for test + auto ret = ConstantFoldingPass::RunOpKernel(node, inputs, outputs); + if (ret != SUCCESS) { + auto op_kernel = folding_pass::GetKernelByType(node); + if (op_kernel == nullptr) { + GELOGW("Calculate value range failed, no op kernel for node %s type %s", node->GetName().c_str(), + node->GetType().c_str()); + return NOT_CHANGED; + } + + ret = op_kernel->Compute(node->GetOpDesc(), inputs, outputs); + if (ret != SUCCESS) { + GELOGW("Calculate value range failed, node %s run cpu kernel failed.", node->GetName().c_str()); + return NOT_CHANGED; + } + } + GELOGI("Node %s type %s, run cpu kernel success.", node->GetName().c_str(), node->GetType().c_str()); + return SUCCESS; +} +} // namespace + +graphStatus InferValueRangePass::Infer(NodePtr &node) { + auto infer_value_range_param = OperatorFactoryImpl::GetInferValueRangePara(node->GetType()); + + // Use registered func to calculate value range + if (!infer_value_range_param.use_cpu_kernel) { + if (infer_value_range_param.infer_value_func == nullptr) { + GELOGW("The registered func of node %s to infer value range is nullptr.", node->GetName().c_str()); + return GRAPH_NOT_CHANGED; + } + Operator op = OpDescUtils::CreateOperatorFromNode(node); + auto ret = node->GetOpDesc()->CallInferValueRangeFunc(op); + if (ret != GRAPH_SUCCESS) { + GELOGW("Node %s call infer value range func failed, ret: %u.", node->GetName().c_str(), ret); + return GRAPH_NOT_CHANGED; + } + GELOGD("Node %s infer value range func succeed by registered func.", node->GetName().c_str()); + return GRAPH_SUCCESS; + } + + // if input value range has -1, cpu kernel cannot calculate correctly, so set {1:-1} + if (InputHasUnknownValueRange(node)) { + GELOGI("Node %s has unknown value range in input tensors, set value range {1:-1}, and skip cpu kernel.", + node->GetName().c_str()); + return GenerateWorstValueRange(node); + } + + // Use CPU kernel func to calculate value range + auto ret = ConstructInputAndInferValueRange(node); + if (ret != GRAPH_SUCCESS) { + GELOGW("Use CPU kernel to calculate value range failed. node: %s, ret: %u", node->GetName().c_str(), ret); + return GRAPH_NOT_CHANGED; + } + GELOGD("Node %s infer value range func succeed by running cpu kernel.", node->GetName().c_str()); + return GRAPH_SUCCESS; +} + +std::string InferValueRangePass::SerialTensorInfo(const GeTensorDescPtr &tensor_desc) const { + std::stringstream ss; + ss << "["; + ss << "(shape:[" << tensor_desc->MutableShape().ToString() << "]),"; + string range_str; + SerialShapeRange(tensor_desc, range_str); + ss << "(shape_range:" << range_str << "),"; + std::vector> value_range; + (void)tensor_desc->GetValueRange(value_range); + string value_range_str = formats::RangeToString(value_range); + ss << "(value_range:" << value_range_str << ")]"; + return ss.str(); +} + +bool InferValueRangePass::NeedInfer(const NodePtr &node) const { + auto infer_value_range_param = OperatorFactoryImpl::GetInferValueRangePara(node->GetType()); + if (!infer_value_range_param.is_initialized) { + GELOGD("Node %s does not register func to infer value range, skip infer_value_range_pass.", + node->GetName().c_str()); + return false; + } + + if (infer_value_range_param.when_call == INPUT_IS_DYNAMIC) { + // Only do infer for node that all inputs are dynamic, such as shape + if (InputIsDynamic(node)) { + return true; + } + GELOGD("Node %s register func to infer value range and when_call is INPUT_IS_DYNAMIC, but check input failed.", + node->GetName().c_str()); + } else if (infer_value_range_param.when_call == INPUT_HAS_VALUE_RANGE) { + // Only do infer for node that all inputs have value_range or node type of inputs is constant/const + if (InputIsConstOrHasValueRange(node)) { + return true; + } + GELOGD("Node %s register func to infer value range and when_call is INPUT_HAS_VALUE_RANGE, but check input failed.", + node->GetName().c_str()); + } + GELOGD("Node %s does not need to infer value range, skip infer_value_range_pass.", node->GetName().c_str()); + return false; +} + +bool InferValueRangePass::InputIsDynamic(const NodePtr &node) const{ + bool input_is_dynamic = false; + auto cur_op_desc = node->GetOpDesc(); + for (const auto &input_desc : cur_op_desc->GetAllInputsDescPtr()) { + auto dims = input_desc->GetShape().GetDims(); + for (auto dim : dims) { + if (dim == UNKNOWN_DIM || dim == UNKNOWN_DIM_NUM) { + input_is_dynamic = true; + break; + } + } + } + return input_is_dynamic; +} + +bool InferValueRangePass::InputIsConstOrHasValueRange(const NodePtr &node) const { + bool input_is_const_or_has_value_range = true; + auto cur_op_desc = node->GetOpDesc(); + auto in_data_anchors = node->GetAllInDataAnchors(); + for (size_t i = 0; i < in_data_anchors.size(); ++i) { + auto peer_out_anchor = in_data_anchors.at(i)->GetPeerOutAnchor(); + if (peer_out_anchor == nullptr) { + continue; + } + auto peer_node = peer_out_anchor->GetOwnerNode(); + if (peer_node == nullptr || peer_node->GetOpDesc() == nullptr) { + continue; + } + if ((peer_node->GetType() == CONSTANT) || (peer_node->GetType() == CONSTANTOP)) { + continue; + } + + const auto &input_desc = cur_op_desc->GetInputDesc(i); + std::vector> value_range; + (void)input_desc.GetValueRange(value_range); + if (value_range.empty()) { + GELOGD("Node %s input %zu does not have value range, skip infer_value_range_pass for current node.", + node->GetName().c_str(), i); + input_is_const_or_has_value_range = false; + break; + } + } + return input_is_const_or_has_value_range; +} + +bool InferValueRangePass::InputHasUnknownValueRange(const NodePtr &node) const { + bool has_unknown_value_range = false; + auto cur_op_desc = node->GetOpDesc(); + for (const auto &input_desc : cur_op_desc->GetAllInputsDescPtr()) { + std::vector> input_desc_value_range; + input_desc->GetValueRange(input_desc_value_range); + if (!input_desc_value_range.empty()) { + for (const auto &range : input_desc_value_range) { + if (range.first == -1 || range.second == -1) { + GELOGD("Node %s input tensors have unknown value range, value range is %s.", node->GetName().c_str(), + formats::RangeToString(input_desc_value_range).c_str()); + has_unknown_value_range = true; + } + } + } + } + return has_unknown_value_range; +} + +graphStatus InferValueRangePass::UpdateTensorDesc(const GeTensorDescPtr &src, GeTensorDescPtr &dst, bool &changed) { + if (src == nullptr || dst == nullptr) { + REPORT_CALL_ERROR("E19999", "While updating tensor desc, input desc is null."); + GELOGE(GRAPH_FAILED, "[Param][check] While updating tensor desc, input desc is null."); + return GRAPH_FAILED; + } + + changed = false; + std::vector> src_value_range; + std::vector> dst_value_range; + (void)src->GetValueRange(src_value_range); + (void)dst->GetValueRange(dst_value_range); + if (src_value_range != dst_value_range) { + GELOGD("While updating tensor desc, value range has been changed, src value range: %s, dst value range: %s.", + formats::RangeToString(src_value_range).c_str(), formats::RangeToString(dst_value_range).c_str()); + changed = true; + } + + dst->SetValueRange(src_value_range); + return GRAPH_SUCCESS; +} + +graphStatus InferValueRangePass::UpdateOutputFromSubgraphs(const std::vector &src, + GeTensorDescPtr &dst) { + std::vector> ref_out_tensor_value_range; + auto ref_out_tensor = src.at(0); + (void)ref_out_tensor->GetValueRange(ref_out_tensor_value_range); + for (auto &ref_tensor : src) { + std::vector> ref_tensor_value_range; + (void)ref_tensor->GetValueRange(ref_tensor_value_range); + + if (ref_tensor_value_range.size() != ref_out_tensor_value_range.size()) { + GELOGD("Update TensorDesc %s failed, rank of value ranges %s and %s are not the same, skip value range refresh.", + dst->GetName().c_str(), formats::RangeToString(ref_out_tensor_value_range).c_str(), + formats::RangeToString(ref_tensor_value_range).c_str()); + return GRAPH_SUCCESS; + } + + for (size_t j = 0; j < ref_out_tensor_value_range.size(); j++) { + if ((ref_out_tensor_value_range.at(j).first != ref_tensor_value_range.at(j).first) || + (ref_out_tensor_value_range.at(j).second != ref_tensor_value_range.at(j).second)) { + ref_out_tensor_value_range[j] = std::make_pair(1, -1); + } + } + } + GELOGD("While updating output desc from subgraphs, set parent node desc value range %s.", + formats::RangeToString(ref_out_tensor_value_range).c_str()); + dst->SetValueRange(ref_out_tensor_value_range); + return GRAPH_SUCCESS; +} + +graphStatus InferValueRangePass::UpdateOutputFromSubgraphsForMultiDims(const std::vector &src, + GeTensorDescPtr &dst) { + REPORT_INNER_ERROR("E19999", + "Update TensorDesc %s failed. In dynamic multi-dims size scene, there should be no value range.", + dst->GetName().c_str()); + GELOGE(GRAPH_FAILED, + "[Update][TensorDesc] %s failed. In dynamic multi-dims size scene, there should be no value range.", + dst->GetName().c_str()); + return GRAPH_FAILED; +} + +graphStatus InferValueRangePass::GenerateWorstValueRange(NodePtr &node) { + GELOGI("Node %s does not run cpu kernel, because input value range has -1.", node->GetName().c_str()); + OpDescPtr op_desc = node->GetOpDesc(); + for (size_t i = 0; i < op_desc->GetOutputsSize(); ++i) { + auto output_desc = op_desc->MutableOutputDesc(i); + if (output_desc == nullptr) { + continue; + } + auto output_i_shape = output_desc->GetShape(); + auto output_i_shape_size = output_i_shape.GetShapeSize(); + if (output_i_shape_size < 0) { + GELOGD("Node %s output shape is unknown, cannot infer value range, shape is %s.", node->GetName().c_str(), + formats::ShapeToString(output_i_shape).c_str()); + return GRAPH_NOT_CHANGED; + } + + std::vector> output_i_value_range(output_i_shape_size, {1, -1}); + output_desc->SetValueRange(output_i_value_range); + GELOGD("Node %s output %zu shape is %s, the generated worst value range is %s.", node->GetName().c_str(), i, + formats::ShapeToString(output_i_shape).c_str(), formats::RangeToString(output_i_value_range).c_str()); + } + return GRAPH_SUCCESS; +} + +template +graphStatus InferValueRangePass::ConstructData(const GeTensorDesc &tensor_desc, bool use_floor_value, + GeTensorPtr &output_ptr) { + std::vector> value_range; + (void)tensor_desc.GetValueRange(value_range); + if (static_cast(value_range.size()) != tensor_desc.GetShape().GetShapeSize()) { + GELOGW("Value range of input %s is invalid.", tensor_desc.GetName().c_str()); + return GRAPH_PARAM_INVALID; + } + + size_t value_range_data_num = value_range.size(); + unique_ptr buf(new (std::nothrow) T[value_range_data_num]()); + if (buf == nullptr) { + REPORT_INNER_ERROR("E19999", "New buf failed"); + GELOGE(MEMALLOC_FAILED, "New buf failed"); + return GRAPH_FAILED; + } + for (size_t j = 0; j < value_range_data_num; ++j) { + auto value_range_j = use_floor_value ? value_range[j].first : value_range[j].second; + buf[j] = static_cast(value_range_j); + } + + if (output_ptr->SetData(reinterpret_cast(buf.get()), value_range_data_num * sizeof(T)) != GRAPH_SUCCESS) { + GELOGW("Set data failed while constructing value range input tensor."); + return GRAPH_NOT_CHANGED; + } + return GRAPH_SUCCESS; +} + +graphStatus InferValueRangePass::ConstructDataByType(const GeTensorDesc &tensor_desc, bool use_floor_value, + GeTensorPtr &output_ptr) { + graphStatus ret = GRAPH_SUCCESS; + auto data_type = tensor_desc.GetDataType(); + output_ptr->MutableTensorDesc().SetDataType(data_type); + switch (data_type) { + case DT_FLOAT: + ret = ConstructData(tensor_desc, use_floor_value, output_ptr); + break; + case DT_DOUBLE: + ret = ConstructData(tensor_desc, use_floor_value, output_ptr); + break; + case DT_UINT8: + ret = ConstructData(tensor_desc, use_floor_value, output_ptr); + break; + case DT_INT8: + ret = ConstructData(tensor_desc, use_floor_value, output_ptr); + break; + case DT_UINT16: + ret = ConstructData(tensor_desc, use_floor_value, output_ptr); + break; + case DT_INT16: + ret = ConstructData(tensor_desc, use_floor_value, output_ptr); + break; + case DT_INT32: + ret = ConstructData(tensor_desc, use_floor_value, output_ptr); + break; + case DT_INT64: + ret = ConstructData(tensor_desc, use_floor_value, output_ptr); + break; + default: + GELOGW("Data type:%s is not supported.", TypeUtils::DataTypeToSerialString(data_type).c_str()); + ret = GRAPH_PARAM_INVALID; + } + return ret; +} + +vector InferValueRangePass::ConstructInputTensors(const NodePtr &node, bool use_floor_value) { + vector input_tensors; + auto cur_op_desc = node->GetOpDesc(); + auto in_data_anchors = node->GetAllInDataAnchors(); + for (size_t i = 0; i < in_data_anchors.size(); ++i) { + auto peer_out_anchor = in_data_anchors.at(i)->GetPeerOutAnchor(); + if (peer_out_anchor == nullptr) { + continue; + } + auto peer_node = peer_out_anchor->GetOwnerNode(); + if (peer_node == nullptr) { + continue; + } + + // construct input tensor by constant node + if ((peer_node->GetType() == CONSTANT) || (peer_node->GetType() == CONSTANTOP)) { + vector const_weight = OpDescUtils::MutableWeights(peer_node); + if (const_weight.empty()) { + GELOGW("MutableWeights failed, weight is empty, node: %s(%s)", peer_node->GetName().c_str(), + peer_node->GetType().c_str()); + return vector(); + } + // const/constant op has only one weight + if (const_weight.at(0) == nullptr) { + GELOGW("MutableWeights failed, weight of constant is null, node name: %s(%s)", + peer_node->GetName().c_str(), peer_node->GetType().c_str()); + return vector(); + } + input_tensors.push_back(const_weight.at(0)); + GELOGD("Node %s construct input tensor %zu by constant node.", node->GetName().c_str(), input_tensors.size()); + continue; + } + + // construct input tensor by boundary of value range + const auto &input_tensor_desc = cur_op_desc->GetInputDesc(i); + GeTensorPtr tmp_tensor_ptr = MakeShared(input_tensor_desc); + if (tmp_tensor_ptr == nullptr) { + REPORT_INNER_ERROR("E19999", "Make shared failed"); + GELOGE(MEMALLOC_FAILED, "Make shared failed"); + return vector(); + } + + auto ret = ConstructDataByType(input_tensor_desc, use_floor_value, tmp_tensor_ptr); + if (ret != GRAPH_SUCCESS) { + GELOGW("Construct input tensor by boundary of value range failed for input %s.", + input_tensor_desc.GetName().c_str()); + return vector(); + } + input_tensors.push_back(tmp_tensor_ptr); + GELOGD("Node %s construct input tensor %zu by input desc value range.", node->GetName().c_str(), + input_tensors.size()); + } + + return input_tensors; +} + +graphStatus InferValueRangePass::ConstructInputAndInferValueRange(NodePtr &node) { + auto inputs = ConstructInputTensors(node, true); + if (inputs.empty()) { + return GRAPH_PARAM_INVALID; + } + vector lower_boundary_outputs; + auto ret = RunCpuKernelForValueRange(node, inputs, lower_boundary_outputs); + if (ret != SUCCESS) { + GELOGW("Node %s run cpu kernel failed while calculating value range.", node->GetName().c_str()); + return GRAPH_PARAM_INVALID; + } + + inputs = ConstructInputTensors(node, false); + if (inputs.empty()) { + return GRAPH_PARAM_INVALID; + } + vector upper_boundary_outputs; + ret = RunCpuKernelForValueRange(node, inputs, upper_boundary_outputs); + if (ret != SUCCESS) { + GELOGW("Node %s run cpu kernel failed while calculating value range.", node->GetName().c_str()); + return GRAPH_PARAM_INVALID; + } + + // construct value range from output tensor + OpDescPtr node_desc = node->GetOpDesc(); + std::vector> output_tensor_value_range; + size_t node_output_desc_size = node_desc->GetOutputsSize(); + for (size_t i = 0; i < node_output_desc_size; ++i) { + output_tensor_value_range.clear(); + auto output_tensor_desc = node_desc->MutableOutputDesc(i); + auto output_shape_size = output_tensor_desc->GetShape().GetShapeSize(); + auto lower_boundary_tensor = lower_boundary_outputs[i]; + auto lower_boundary_shape = lower_boundary_tensor->GetTensorDesc().GetShape(); + auto upper_boundary_tensor = upper_boundary_outputs[i]; + auto upper_boundary_shape = upper_boundary_tensor->GetTensorDesc().GetShape(); + if (lower_boundary_shape.GetShapeSize() != output_shape_size || + upper_boundary_shape.GetShapeSize() != output_shape_size) { + GELOGD( + "Cpu kernel result shapes %s, %s and output shape %s do not match, can not infer value range for output %s.", + formats::ShapeToString(lower_boundary_shape).c_str(), formats::ShapeToString(upper_boundary_shape).c_str(), + formats::ShapeToString(output_tensor_desc->GetShape()).c_str(), output_tensor_desc->GetName().c_str()); + return GRAPH_PARAM_INVALID; + } + + auto data_type = output_tensor_desc->GetDataType(); + switch (data_type) { + GET_DATA_BY_DTYPE(DT_INT8, int8_t) + GET_DATA_BY_DTYPE(DT_INT16, int16_t) + GET_DATA_BY_DTYPE(DT_INT32, int32_t) + GET_DATA_BY_DTYPE(DT_INT64, int64_t) + GET_DATA_BY_DTYPE(DT_UINT8, uint8_t) + GET_DATA_BY_DTYPE(DT_UINT16, uint16_t) + GET_DATA_BY_DTYPE(DT_UINT32, uint32_t) + GET_DATA_BY_DTYPE(DT_UINT64, uint64_t) + GET_DATA_BY_DTYPE(DT_FLOAT, float) + GET_DATA_BY_DTYPE(DT_DOUBLE, double) + default: + GELOGW("Data type:%s is not supported.", TypeUtils::DataTypeToSerialString(data_type).c_str()); + return GRAPH_PARAM_INVALID; + } + output_tensor_desc->SetValueRange(output_tensor_value_range); + GELOGD("Node %s calculates output %zu value range %s by running cpu kernel.", node->GetName().c_str(), i, + formats::RangeToString(output_tensor_value_range).c_str()); + } + return GRAPH_SUCCESS; +} + +template +void InferValueRangePass::ConstructValueRange(const GeTensorPtr &left_tensor, const GeTensorPtr &right_tensor, + std::vector> &value_range) { + auto x = reinterpret_cast(left_tensor->GetData().GetData()); + auto y = reinterpret_cast(right_tensor->GetData().GetData()); + if (x == nullptr || y == nullptr) { + GELOGI("Output tensor of cpu kernel does not have data, no way to set value range."); + return; + } + for (auto j = 0; j < left_tensor->GetTensorDesc().GetShape().GetShapeSize(); ++j) { + auto left = static_cast(*(x + j)); + auto right = static_cast(*(y + j)); + value_range.emplace_back(std::make_pair(left, right)); + } +} +} // namespace ge diff --git a/ge/graph/passes/infer_value_range_pass.h b/ge/graph/passes/infer_value_range_pass.h new file mode 100644 index 00000000..eb485c87 --- /dev/null +++ b/ge/graph/passes/infer_value_range_pass.h @@ -0,0 +1,49 @@ +/** + * 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. + */ + +#ifndef GE_GRAPH_PASSES_INFER_VALUE_RANGE_PASS_H_ +#define GE_GRAPH_PASSES_INFER_VALUE_RANGE_PASS_H_ + +#include "graph/passes/infer_base_pass.h" + +namespace ge { +class InferValueRangePass : public InferBasePass { + public: + graphStatus Infer(NodePtr &node) override; + + private: + std::string SerialTensorInfo(const GeTensorDescPtr &tensor_desc) const override; + graphStatus UpdateTensorDesc(const GeTensorDescPtr &src, GeTensorDescPtr &dst, bool &changed) override; + graphStatus UpdateOutputFromSubgraphs(const std::vector &src, GeTensorDescPtr &dst) override; + graphStatus UpdateOutputFromSubgraphsForMultiDims(const std::vector &src, + GeTensorDescPtr &dst) override; + bool NeedInfer(const NodePtr &node) const override; + + bool InputIsDynamic(const NodePtr &node) const; + bool InputIsConstOrHasValueRange(const NodePtr &node) const; + bool InputHasUnknownValueRange(const NodePtr &node) const; + graphStatus GenerateWorstValueRange(NodePtr &node); + template + graphStatus ConstructData(const GeTensorDesc &tensor_desc, bool use_floor_value, GeTensorPtr &output_ptr); + graphStatus ConstructDataByType(const GeTensorDesc &tensor_desc, bool use_floor_value, GeTensorPtr &output_ptr); + vector ConstructInputTensors(const NodePtr &node, bool use_floor_value); + template + void ConstructValueRange(const GeTensorPtr &left_tensor, const GeTensorPtr &right_tensor, + std::vector> &value_range); + graphStatus ConstructInputAndInferValueRange(NodePtr &node); +}; +} // namespace ge +#endif // GE_GRAPH_PASSES_INFER_VALUE_RANGE_PASS_H_ diff --git a/ge/graph/preprocess/graph_preprocess.cc b/ge/graph/preprocess/graph_preprocess.cc index 6fd83623..bc8646e7 100644 --- a/ge/graph/preprocess/graph_preprocess.cc +++ b/ge/graph/preprocess/graph_preprocess.cc @@ -54,6 +54,7 @@ #include "graph/passes/hccl_group_pass.h" #include "graph/passes/identity_pass.h" #include "graph/passes/infershape_pass.h" +#include "graph/passes/infer_value_range_pass.h" #include "graph/passes/merge_pass.h" #include "graph/passes/net_output_pass.h" #include "graph/passes/no_use_reshape_remove_pass.h" @@ -2016,6 +2017,8 @@ Status GraphPrepare::InferShapeForPreprocess() { names_to_passes.emplace_back("DimensionComputePass", &dimension_compute_pass); ConstantFoldingPass constant_folding_pass; names_to_passes.emplace_back("ConstantFoldingPass", &constant_folding_pass); + InferValueRangePass infer_value_pass; + names_to_passes.emplace_back("InferValuePass", &infer_value_pass); int32_t dev_count = 0; AicpuConstantFoldingPass aicpu_constant_folding_pass; diff --git a/metadef b/metadef index 2ad00e17..9e4a51a9 160000 --- a/metadef +++ b/metadef @@ -1 +1 @@ -Subproject commit 2ad00e17886fd06c0d00f8a8cf370783a3d31818 +Subproject commit 9e4a51a9602195b82e326b853f5adbfefc3972b6 diff --git a/tests/ut/ge/CMakeLists.txt b/tests/ut/ge/CMakeLists.txt index 95b9e388..bfc4ff89 100755 --- a/tests/ut/ge/CMakeLists.txt +++ b/tests/ut/ge/CMakeLists.txt @@ -221,7 +221,9 @@ set(COMMON_SRC_FILES "${GE_CODE_DIR}/ge/graph/passes/shape_operate_op_remove_pass.cc" "${GE_CODE_DIR}/ge/graph/passes/assert_pass.cc" "${GE_CODE_DIR}/ge/graph/passes/dropout_pass.cc" + "${GE_CODE_DIR}/ge/graph/passes/infer_base_pass.cc" "${GE_CODE_DIR}/ge/graph/passes/infershape_pass.cc" + "${GE_CODE_DIR}/ge/graph/passes/infer_value_range_pass.cc" "${GE_CODE_DIR}/ge/graph/passes/unused_const_pass.cc" "${GE_CODE_DIR}/ge/graph/passes/permute_pass.cc" "${GE_CODE_DIR}/ge/graph/passes/ctrl_edge_transfer_pass.cc" @@ -535,7 +537,9 @@ set(GRAPH_PASS_COMMON_SRC_FILES "${GE_CODE_DIR}/ge/graph/passes/transpose_transdata_pass.cc" "${GE_CODE_DIR}/ge/graph/passes/hccl_memcpy_pass.cc" "${GE_CODE_DIR}/ge/graph/passes/no_use_reshape_remove_pass.cc" + "${GE_CODE_DIR}/ge/graph/passes/infer_base_pass.cc" "${GE_CODE_DIR}/ge/graph/passes/infershape_pass.cc" + "${GE_CODE_DIR}/ge/graph/passes/infer_value_range_pass.cc" "${GE_CODE_DIR}/ge/ge_local_engine/engine/host_cpu_engine.cc" "${GE_CODE_DIR}/ge/analyzer/analyzer.cc" "${GE_CODE_DIR}/ge/graph/passes/net_output_pass.cc" @@ -662,6 +666,8 @@ set(DISTINCT_GRAPH_LOAD_TEST_FILES ) set(PASS_TEST_FILES + "graph/passes/infer_value_range_pass_unittest.cc" + "graph/passes/infer_base_pass_unittest.cc" "graph/passes/prune_pass_unittest.cc" "graph/passes/enter_pass_unittest.cc" "graph/passes/switch_op_pass_unittest.cc" @@ -720,7 +726,6 @@ set(PASS_TEST_FILES "graph/passes/memcpy_addr_async_unittest.cc" "graph/passes/hccl_continuous_pass_unittest.cc" "graph/passes/hccl_memcpy_pass_unittest.cc" - ) set(KERNEL_TEST_FILES diff --git a/tests/ut/ge/graph/passes/infer_base_pass_unittest.cc b/tests/ut/ge/graph/passes/infer_base_pass_unittest.cc new file mode 100644 index 00000000..e9247f75 --- /dev/null +++ b/tests/ut/ge/graph/passes/infer_base_pass_unittest.cc @@ -0,0 +1,359 @@ +/** + * 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. + */ + +#include + +#include "graph/passes/infer_base_pass.h" +#include "graph/debug/ge_attr_define.h" +#include "graph/utils/tensor_utils.h" +#include "graph/utils/graph_utils.h" +#include "graph_builder_utils.h" + +using namespace std; +using namespace testing; +namespace ge { +class ChildPassBuilder; +static const char *kInferTimes = "infer_times"; +class InferBasePassStub : public InferBasePass { + public: + friend class ChildPassBuilder; + graphStatus Infer(NodePtr &node) override{ + call_infer_times++; + for (size_t i = 0; i < node->GetOutDataNodesSize(); ++i) { + auto output_td = node->GetOpDesc()->MutableOutputDesc(i); + int times = 0; + AttrUtils::GetInt(output_td, kInferTimes, times); + AttrUtils::SetInt(output_td, kInferTimes, times + 1); + } + return infer_result_; + }; + + int32_t call_infer_times = 0; + int32_t call_update_tensor_desc_times = 0; + int32_t call_update_from_subgraph_times = 0; + int32_t call_update_from_subgraph_multi_dims_times = 0; + std::vector> update_td_pairs; + + private: + bool NeedInfer(const NodePtr &node) const override { + return need_infer_; + }; + std::string SerialTensorInfo(const GeTensorDescPtr &tensor_desc) const override { return "test SerialTensorInfo"; }; + graphStatus UpdateTensorDesc(const GeTensorDescPtr &src, GeTensorDescPtr &dst, bool &changed) override { + call_update_tensor_desc_times++; + changed = td_changed_; + int times = 0; + if (AttrUtils::GetInt(src, kInferTimes, times)) { + AttrUtils::SetInt(dst, kInferTimes, times); + } + update_td_pairs.emplace_back(src, dst); + return GRAPH_SUCCESS; + }; + graphStatus UpdateOutputFromSubgraphs(const std::vector &src, GeTensorDescPtr &dst) override { + call_update_from_subgraph_times++; + return GRAPH_SUCCESS; + }; + graphStatus UpdateOutputFromSubgraphsForMultiDims(const std::vector &src, + GeTensorDescPtr &dst) override { + call_update_from_subgraph_multi_dims_times++; + return GRAPH_SUCCESS; + }; + bool td_changed_; + bool need_infer_; + graphStatus infer_result_; +}; + +class ChildPassBuilder { + public: + ChildPassBuilder &SetNeedInferFlag(bool flag) { + need_infer_ = flag; + return *this; + } + + ChildPassBuilder &SetInferResult(graphStatus ret) { + infer_result_ = ret; + return *this; + } + + ChildPassBuilder &SetTdChangedFlag(bool changed_flag) { + td_changed_ = changed_flag; + return *this; + } + + InferBasePassStub Build() { + InferBasePassStub ib; + ib.td_changed_ = td_changed_; + ib.need_infer_ = need_infer_; + ib.infer_result_ = infer_result_; + return ib; + } + + private: + bool td_changed_ = false; + bool need_infer_ = true; + graphStatus infer_result_ = GRAPH_SUCCESS; +}; + +class UtestGraphInferBasePassStub : public testing::Test { + protected: + void SetUp() {} + void TearDown() {} +}; + +/* + * data1 data2 + * \ / + * sub1 + * | + * netoutput + */ +ut::GraphBuilder TestSubgraphBuilder() { + ut::GraphBuilder builder = ut::GraphBuilder("branch_graph"); + std::vector shape1 = {1,1}; + auto data1 = builder.AddNode("data1_1", "Data", 1, 1, FORMAT_NCHW, DT_INT32, shape1); + auto data1_desc = data1->GetOpDesc(); + EXPECT_NE(data1_desc, nullptr); + AttrUtils::SetInt(data1_desc, "_parent_node_index", 0); + std::vector shape2 = {2,2}; + auto data2 = builder.AddNode("data2_1", "Data", 1, 1, FORMAT_NCHW, DT_INT32, shape2); + auto data2_desc = data2->GetOpDesc(); + EXPECT_NE(data2_desc, nullptr); + AttrUtils::SetInt(data2_desc, "_parent_node_index", 1); + + auto sub1 = builder.AddNode("Sub", "Sub", 2, 1); + std::vector shape7 = {8,8}; + auto netoutput = builder.AddNode("output", NETOUTPUT, 1, 0, FORMAT_NCHW, DT_INT32, shape7); + auto input0_desc = netoutput->GetOpDesc()->MutableInputDesc(0); + EXPECT_NE(input0_desc, nullptr); + AttrUtils::SetInt(input0_desc, "_parent_node_index", 0); + + builder.AddDataEdge(data1, 0, sub1, 0); + builder.AddDataEdge(data2, 0, sub1, 1); + builder.AddDataEdge(sub1, 0, netoutput, 0); + return builder; +} + +/* + * data1 data2 + * \ / + * case1 + * | + * netoutput + */ +ut::GraphBuilder RootGraphBuilder() { + ut::GraphBuilder builder = ut::GraphBuilder("root_graph"); + auto data1 = builder.AddNode("data1", "Data", 0, 1); + auto data2 = builder.AddNode("data2", "Data", 0, 1); + auto case1 = builder.AddNode("case1", CASE, 2, 1); + auto netoutput = builder.AddNode("netoutput", NETOUTPUT, 1, 0); + builder.AddDataEdge(data1, 0, case1, 0); + builder.AddDataEdge(data2, 0, case1, 1); + builder.AddDataEdge(case1, 0, netoutput, 0); + + auto parent_graph = builder.GetGraph(); + auto subgraph_builder = TestSubgraphBuilder(); + auto subgraph = subgraph_builder.GetGraph(); + case1->GetOpDesc()->AddSubgraphName(subgraph->GetName()); + case1->GetOpDesc()->SetSubgraphInstanceName(0, subgraph->GetName()); + subgraph->SetParentNode(case1); + subgraph->SetParentGraph(parent_graph); + EXPECT_EQ(parent_graph->AddSubgraph(subgraph->GetName(), subgraph), GRAPH_SUCCESS); + return builder; +} + +/* + * data1 data2 + * \ / + * add1 + * | + * netoutput + */ +ut::GraphBuilder NoSubgraphBuilder() { + ut::GraphBuilder builder = ut::GraphBuilder("no_subgraph"); + auto data1 = builder.AddNode("data1", "Data", 0, 1); + auto data2 = builder.AddNode("data2", "Data", 0, 1); + auto add1 = builder.AddNode("add1", ADD, 2, 1); + auto netoutput = builder.AddNode("netoutput", NETOUTPUT, 1, 0); + builder.AddDataEdge(data1, 0, add1, 0); + builder.AddDataEdge(data2, 0, add1, 1); + builder.AddDataEdge(add1, 0, netoutput, 0); + return builder; +} + +TEST_F(UtestGraphInferBasePassStub, CallInfer_WhenNeedInferReturnTrue) { + auto builder = NoSubgraphBuilder(); + auto test_graph = builder.GetGraph(); + auto add_node = test_graph->FindNode("add1"); + EXPECT_NE(add_node, nullptr); + ChildPassBuilder pass_builder; + auto stub_base_pass = pass_builder.Build(); + + // NeedInfer return true + EXPECT_EQ(stub_base_pass.Run(add_node), SUCCESS); + EXPECT_EQ(stub_base_pass.call_infer_times, 1); + int times = -1; + EXPECT_TRUE(AttrUtils::GetInt(add_node->GetOpDesc()->GetOutputDescPtr(0), kInferTimes, times)); + EXPECT_EQ(times, 1); +} + +TEST_F(UtestGraphInferBasePassStub, NotCallInfer_WhenNeedInferReturnFalse) { + auto builder = NoSubgraphBuilder(); + auto test_graph = builder.GetGraph(); + auto add_node = test_graph->FindNode("add1"); + EXPECT_NE(add_node, nullptr); + ChildPassBuilder pass_builder; + auto stub_base_pass = pass_builder.SetNeedInferFlag(false).Build(); + + // NeedInfer return false + EXPECT_EQ(stub_base_pass.Run(add_node), SUCCESS); + EXPECT_EQ(stub_base_pass.call_infer_times, 0); + int times = -1; + EXPECT_FALSE(AttrUtils::GetInt(add_node->GetOpDesc()->GetOutputDescPtr(0), kInferTimes, times)); +} + +TEST_F(UtestGraphInferBasePassStub, NotAddCurNodeRepass_CallUpdatePeerNode_WhenInferReturnSuccess) { + auto builder = NoSubgraphBuilder(); + auto test_graph = builder.GetGraph(); + auto add_node = test_graph->FindNode("add1"); + auto netoutput = test_graph->FindNode("netoutput"); + EXPECT_NE(add_node, nullptr); + EXPECT_NE(netoutput, nullptr); + ChildPassBuilder pass_builder; + auto stub_base_pass = pass_builder.Build(); + + EXPECT_EQ(stub_base_pass.Run(add_node), SUCCESS); + EXPECT_EQ(stub_base_pass.call_infer_times, 1); + EXPECT_EQ(stub_base_pass.call_update_tensor_desc_times, 1); + std::vector> expected_updated_tensor_desc_pairs = { + {add_node->GetOpDesc()->MutableOutputDesc(0), netoutput->GetOpDesc()->MutableInputDesc(0)}}; + EXPECT_EQ(stub_base_pass.update_td_pairs, expected_updated_tensor_desc_pairs); + EXPECT_EQ(stub_base_pass.GetNodesNeedRePassImmediately(), std::unordered_set({})); +} + +TEST_F(UtestGraphInferBasePassStub, AddCurNodeRepass_NotCallUpdatePeerNode_WhenInferReturnNeedRepass) { + auto builder = NoSubgraphBuilder(); + auto test_graph = builder.GetGraph(); + auto add_node = test_graph->FindNode("add1"); + EXPECT_NE(add_node, nullptr); + ChildPassBuilder pass_builder; + auto stub_base_pass = pass_builder.SetInferResult(GRAPH_NODE_NEED_REPASS).Build(); + + // do re_pass + EXPECT_EQ(stub_base_pass.Run(add_node), SUCCESS); + EXPECT_EQ(stub_base_pass.call_infer_times, 1); + EXPECT_EQ(stub_base_pass.call_update_tensor_desc_times, 0); + EXPECT_EQ(stub_base_pass.GetNodesNeedRePassImmediately(), std::unordered_set({add_node})); +} + +TEST_F(UtestGraphInferBasePassStub, NotAddPeerNodeRepass_AfterUpdatePeerNode_WhenUnchanged) { + auto builder = NoSubgraphBuilder(); + auto test_graph = builder.GetGraph(); + auto add_node = test_graph->FindNode("add1"); + auto netoutput = test_graph->FindNode("netoutput"); + EXPECT_NE(add_node, nullptr); + EXPECT_NE(netoutput, nullptr); + ChildPassBuilder pass_builder; + auto stub_base_pass = pass_builder.Build(); + + EXPECT_EQ(stub_base_pass.Run(add_node), SUCCESS); + EXPECT_EQ(stub_base_pass.call_update_tensor_desc_times, 1); + EXPECT_EQ(stub_base_pass.GetNodesNeedRePassImmediately(), std::unordered_set({})); + int times = -1; + EXPECT_TRUE(AttrUtils::GetInt(add_node->GetOpDesc()->GetOutputDescPtr(0), kInferTimes, times)); + EXPECT_EQ(times, 1); + times = -1; + EXPECT_TRUE(AttrUtils::GetInt(netoutput->GetOpDesc()->GetInputDescPtr(0), kInferTimes, times)); + EXPECT_EQ(times, 1); +} + +TEST_F(UtestGraphInferBasePassStub, AddPeerNodeRepass_AfterUpdatePeerNode_WhenChanged) { + auto builder = NoSubgraphBuilder(); + auto test_graph = builder.GetGraph(); + auto add_node = test_graph->FindNode("add1"); + auto netoutput = test_graph->FindNode("netoutput"); + EXPECT_NE(add_node, nullptr); + EXPECT_NE(netoutput, nullptr); + ChildPassBuilder pass_builder; + auto stub_base_pass = pass_builder.SetTdChangedFlag(true).Build(); + + EXPECT_EQ(stub_base_pass.Run(add_node), SUCCESS); + EXPECT_EQ(stub_base_pass.call_update_tensor_desc_times, 1); + EXPECT_EQ(stub_base_pass.GetNodesNeedRePassImmediately(), std::unordered_set({netoutput})); +} + +TEST_F(UtestGraphInferBasePassStub, TestUpdateSubgraphData_WhenBeforeSubgraph) { + auto builder = RootGraphBuilder(); + auto parent_graph = builder.GetGraph(); + auto subgraphs = parent_graph->GetAllSubgraphs(); + EXPECT_EQ(subgraphs.size(), 1); + + auto case_node = parent_graph->FindNode("case1"); + auto data1 = subgraphs[0]->FindNode("data1_1"); + auto data2 = subgraphs[0]->FindNode("data2_1"); + EXPECT_NE(case_node, nullptr); + EXPECT_NE(data1, nullptr); + EXPECT_NE(data2, nullptr); + ChildPassBuilder pass_builder; + auto stub_base_pass = pass_builder.SetInferResult(GRAPH_NODE_NEED_REPASS).Build(); + + EXPECT_EQ(stub_base_pass.Run(case_node), SUCCESS); + // when GRAPH_NODE_NEED_REPASS, not update peer node, only update two data, update input and output, 2*2 + EXPECT_EQ(stub_base_pass.call_update_tensor_desc_times, 4); + std::vector> expected_updated_tensor_desc_pairs = { + {case_node->GetOpDesc()->MutableInputDesc(0), data1->GetOpDesc()->MutableInputDesc(0)}, + {case_node->GetOpDesc()->MutableInputDesc(0), data1->GetOpDesc()->MutableOutputDesc(0)}, + {case_node->GetOpDesc()->MutableInputDesc(1), data2->GetOpDesc()->MutableInputDesc(0)}, + {case_node->GetOpDesc()->MutableInputDesc(1), data2->GetOpDesc()->MutableOutputDesc(0)}, + }; + EXPECT_EQ(stub_base_pass.update_td_pairs, expected_updated_tensor_desc_pairs); +} + +TEST_F(UtestGraphInferBasePassStub, TestUpdateParentNodeOutput_WhenAfterSubgraph) { + auto builder = RootGraphBuilder(); + auto parent_graph = builder.GetGraph(); + auto subgraphs = parent_graph->GetAllSubgraphs(); + EXPECT_EQ(subgraphs.size(), 1); + + auto case_node = parent_graph->FindNode("case1"); + EXPECT_NE(case_node, nullptr); + ChildPassBuilder pass_builder; + auto stub_base_pass = pass_builder.Build(); + stub_base_pass.SetOption(kOptimizeAfterSubGraph, ""); + + EXPECT_EQ(stub_base_pass.Run(case_node), SUCCESS); + EXPECT_EQ(stub_base_pass.call_update_from_subgraph_times, 1); + EXPECT_EQ(stub_base_pass.call_update_from_subgraph_multi_dims_times, 0); +} + +TEST_F(UtestGraphInferBasePassStub, TestUpdateParentNodeOutputForMultiDims_WhenAfterSubgraph) { + auto builder = RootGraphBuilder(); + auto parent_graph = builder.GetGraph(); + auto subgraphs = parent_graph->GetAllSubgraphs(); + EXPECT_EQ(subgraphs.size(), 1); + + auto case_node = parent_graph->FindNode("case1"); + auto set_ret = AttrUtils::SetInt(case_node->GetOpDesc(), ATTR_NAME_BATCH_NUM, 2); + EXPECT_EQ(set_ret, true); + EXPECT_NE(case_node, nullptr); + ChildPassBuilder pass_builder; + auto stub_base_pass = pass_builder.Build(); + stub_base_pass.SetOption(kOptimizeAfterSubGraph, ""); + + EXPECT_EQ(stub_base_pass.Run(case_node), SUCCESS); + EXPECT_EQ(stub_base_pass.call_update_from_subgraph_times, 0); + EXPECT_EQ(stub_base_pass.call_update_from_subgraph_multi_dims_times, 1); +} +} // namespace ge \ No newline at end of file diff --git a/tests/ut/ge/graph/passes/infer_value_range_pass_unittest.cc b/tests/ut/ge/graph/passes/infer_value_range_pass_unittest.cc new file mode 100644 index 00000000..fea1b27d --- /dev/null +++ b/tests/ut/ge/graph/passes/infer_value_range_pass_unittest.cc @@ -0,0 +1,583 @@ +/** + * 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. + */ + +#include + +#define protected public +#define private public +#include "graph/passes/infer_value_range_pass.h" +#include "graph/utils/tensor_utils.h" +#include "graph/utils/graph_utils.h" +#include "graph_builder_utils.h" + +#include "inc/external/graph/operator_reg.h" +#include "inc/external/graph/operator.h" +#include "inc/external/graph/operator_factory.h" +#include "inc/graph/operator_factory_impl.h" +#include "inc/kernel.h" +#include "inc/kernel_factory.h" + +using namespace std; +using namespace testing; +namespace ge { +class UtestGraphInferValueRangePass : public testing::Test { + protected: + void SetUp() {} + void TearDown() {} +}; + +/* + * data1 const1 + * \ / + * case1 + * | + * relu10 + * | + * netoutput + */ +ut::GraphBuilder ParentGraphBuilder() { + ut::GraphBuilder builder = ut::GraphBuilder("g1"); + auto data1 = builder.AddNode("data1", "Data", 0, 1); + std::vector const_shape = {1}; + auto const1 = builder.AddNode("const1", "Const", 0, 1, FORMAT_NCHW, DT_INT32, const_shape); + auto case1 = builder.AddNode("case1", CASE, 2, 1); + auto relu1 = builder.AddNode("relu10", "Relu", 1, 1); + auto netoutput = builder.AddNode("netoutput", NETOUTPUT, 1, 0); + + int32_t weight[1] = {1}; + GeTensorDesc weight_desc(GeShape({1}), FORMAT_NHWC, DT_INT32); + GeTensorPtr tensor = std::make_shared(weight_desc, (uint8_t *)weight, sizeof(weight)); + OpDescUtils::SetWeights(const1, {tensor}); + auto case_in0_shape = GeShape({1, 1,-1, 224}); + auto case_in1_shape = GeShape({1,1}); + std::vector> in0_range = {make_pair(1, 1), make_pair(1, 1), + make_pair(1, -1), make_pair(1, 224)}; + std::vector> in1_range = {make_pair(1, 100), make_pair(1, 10)}; + case1->GetOpDesc()->MutableInputDesc(0)->SetShape(case_in0_shape); + case1->GetOpDesc()->MutableInputDesc(0)->SetValueRange(in0_range); + case1->GetOpDesc()->MutableInputDesc(1)->SetShape(case_in1_shape); + case1->GetOpDesc()->MutableInputDesc(1)->SetValueRange(in1_range); + + builder.AddDataEdge(data1, 0, case1, 0); + builder.AddDataEdge(const1, 0, case1, 1); + builder.AddDataEdge(case1, 0, relu1, 0); + builder.AddDataEdge(relu1, 0, netoutput, 0); + return builder; +} + +/* + * data1 data2 + * \ / + * switch + * / \ + * relu1 relu2 + * \ / + * merge + * | + * netoutput + */ +ut::GraphBuilder SwitchSubgraphBuilder(string graph_name, uint32_t num) { + ut::GraphBuilder builder = ut::GraphBuilder(graph_name); + + std::vector shape1 = {2,2}; + string data1_name = "data1_" + std::to_string(num); + auto data1 = builder.AddNode(data1_name, "Data", 1, 1, FORMAT_NCHW, DT_INT32, shape1); + auto data1_desc = data1->GetOpDesc(); + EXPECT_NE(data1_desc, nullptr); + AttrUtils::SetInt(data1_desc, "_parent_node_index", 0); + + std::vector shape2 = {3,3}; + string data2_name = "data2_" + std::to_string(num); + auto data2 = builder.AddNode(data2_name, "Data", 1, 1, FORMAT_NCHW, DT_INT32, shape2); + auto data2_desc = data2->GetOpDesc(); + EXPECT_NE(data2_desc, nullptr); + AttrUtils::SetInt(data2_desc, "_parent_node_index", 1); + + string switch_name = "switch_" + std::to_string(num); + auto switch1 = builder.AddNode(switch_name, "Switch", 2, 2); + + string relu1_name = "relu1_" + std::to_string(num); + auto relu1 = builder.AddNode(relu1_name, "Relu", 1, 1); + + string relu2_name = "relu2_" + std::to_string(num); + auto relu2 = builder.AddNode(relu2_name, "Relu", 1, 1); + + string merge_name = "merge_" + std::to_string(num); + auto merge = builder.AddNode(merge_name, "Merge", 2, 1); + + std::vector shape7 = {8,8}; + string output_name = "output_" + std::to_string(num); + auto netoutput = builder.AddNode(output_name, NETOUTPUT, 1, 0, FORMAT_NCHW, DT_INT32, shape7); + auto input0_desc = netoutput->GetOpDesc()->MutableInputDesc(0); + EXPECT_NE(input0_desc, nullptr); + AttrUtils::SetInt(input0_desc, "_parent_node_index", 0); + std::vector> range = {make_pair(1, -1), make_pair(1, -1)}; + input0_desc->SetValueRange(range); + + builder.AddDataEdge(data1, 0, switch1, 0); + builder.AddDataEdge(data2, 0, switch1, 1); + builder.AddDataEdge(switch1, 0, relu1, 0); + builder.AddDataEdge(switch1, 1, relu2, 0); + builder.AddDataEdge(relu1, 0, merge, 0); + builder.AddDataEdge(relu2, 0, merge, 1); + builder.AddDataEdge(merge, 0, netoutput, 0); + + return builder; +} + +void AddCaseSubgraph(ComputeGraphPtr &parent_graph, uint32_t branch_num) { + auto case_node = parent_graph->FindNode("case1"); + EXPECT_NE(case_node, nullptr); + + for (uint32_t i = 0; i < branch_num; ++i) { + string name = "Branch_Graph_" + std::to_string(i); + + auto builder_subgraph = SwitchSubgraphBuilder(name, i); + auto switch_subgraph = builder_subgraph.GetGraph(); + + case_node->GetOpDesc()->AddSubgraphName(switch_subgraph->GetName()); + case_node->GetOpDesc()->SetSubgraphInstanceName(i, switch_subgraph->GetName()); + + switch_subgraph->SetParentNode(case_node); + switch_subgraph->SetParentGraph(parent_graph); + EXPECT_EQ(parent_graph->AddSubgraph(switch_subgraph->GetName(), switch_subgraph), GRAPH_SUCCESS); + } +} + +TEST_F(UtestGraphInferValueRangePass, CallRun_NoSubgraph_UnregisteredNodeType) { + auto graph = std::make_shared("test_graph"); + GeTensorDesc ge_tensor_desc(GeShape({1, 1, 4, 192}), ge::FORMAT_NCHW, DT_FLOAT16); + auto addn_op_desc = std::make_shared("AddN", "AddN"); + addn_op_desc->AddInputDesc(ge_tensor_desc); + addn_op_desc->AddOutputDesc(ge_tensor_desc); + auto addn_op_node = graph->AddNode(addn_op_desc); + + InferValueRangePass infer_pass; + EXPECT_EQ(infer_pass.Run(addn_op_node), SUCCESS); +} + +auto ShapeValueInfer = [&](Operator &op) { + auto op_desc = OpDescUtils::GetOpDescFromOperator(op); + auto output_tensor_desc = op_desc->MutableOutputDesc(0); + std::vector> in_shape_range; + op_desc->MutableInputDesc(0)->GetShapeRange(in_shape_range); + if (!in_shape_range.empty()) { + output_tensor_desc->SetValueRange(in_shape_range); + } + return SUCCESS; +}; +REG_OP(Shape) + .OP_END_FACTORY_REG(Shape) +IMPL_INFER_VALUE_RANGE_FUNC(Shape, ShapeValueRangeFunc){ + auto op_desc = OpDescUtils::GetOpDescFromOperator(op); + auto output_tensor_desc = op_desc->MutableOutputDesc(0); + std::vector> in_shape_range; + op_desc->MutableInputDesc(0)->GetShapeRange(in_shape_range); + if (!in_shape_range.empty()) { + output_tensor_desc->SetValueRange(in_shape_range); + } + return GRAPH_SUCCESS; +} + +TEST_F(UtestGraphInferValueRangePass, CallRun_NoSubgraph_UseRegistedFunc_NotInfer) { + INFER_VALUE_RANGE_CUSTOM_FUNC_REG(Shape, INPUT_IS_DYNAMIC, ShapeValueRangeFunc); + auto graph = std::make_shared("test_graph"); + GeTensorDesc ge_tensor_desc(GeShape({1, 1, 4, 192}), ge::FORMAT_NCHW, DT_INT32); + std::vector> shape_range = {make_pair(1, 1), make_pair(1, 1), + make_pair(4, 4), make_pair(192, 192)}; + ge_tensor_desc.SetShapeRange(shape_range); + GeTensorDesc output_tensor_desc(GeShape({4}), ge::FORMAT_NCHW, DT_INT32); + auto op_desc = std::make_shared("Shape", "Shape"); + op_desc->AddInputDesc(ge_tensor_desc); + op_desc->AddOutputDesc(output_tensor_desc); + auto op_node = graph->AddNode(op_desc); + + InferValueRangePass infer_pass; + EXPECT_EQ(infer_pass.Run(op_node), SUCCESS); + + auto output_0_desc = op_node->GetOpDesc()->GetOutputDesc(0); + std::vector> value_range; + output_0_desc.GetValueRange(value_range); + EXPECT_EQ(value_range.empty(), true); +} + +TEST_F(UtestGraphInferValueRangePass, CallRun_NoSubgraph_UseRegistedFunc_DoInfer) { + // sqrt -> shape -> Output + INFER_VALUE_RANGE_CUSTOM_FUNC_REG(Shape, INPUT_IS_DYNAMIC, ShapeValueRangeFunc); + auto graph = std::make_shared("test_graph"); + GeTensorDesc sqrt_tensor_desc(GeShape({-1, -1, 4, 192}), ge::FORMAT_NCHW, DT_INT32); + std::vector> shape_range = {make_pair(1, 100), make_pair(1, 240), + make_pair(4, 4), make_pair(192, 192)}; + sqrt_tensor_desc.SetShapeRange(shape_range); + auto sqrt_op_desc = std::make_shared("Sqrt", "Sqrt"); + sqrt_op_desc->AddInputDesc(sqrt_tensor_desc); + sqrt_op_desc->AddOutputDesc(sqrt_tensor_desc); + auto sqrt_node = graph->AddNode(sqrt_op_desc); + + GeTensorDesc shape_output_desc(GeShape({4}), ge::FORMAT_NCHW, DT_INT32); + auto shape_op_desc = std::make_shared("Shape", "Shape"); + shape_op_desc->AddInputDesc(sqrt_tensor_desc); + shape_op_desc->AddOutputDesc(shape_output_desc); + auto shape_node = graph->AddNode(shape_op_desc); + + GeTensorDesc Output_in_tensor_desc(GeShape({4}), ge::FORMAT_NCHW, ge::DT_INT32); + auto Output_op_desc = std::make_shared("Output", "Output"); + Output_op_desc->AddInputDesc(Output_in_tensor_desc); + auto Output_node = graph->AddNode(Output_op_desc); + + ge::GraphUtils::AddEdge(sqrt_node->GetOutDataAnchor(0), shape_node->GetInDataAnchor(0)); + ge::GraphUtils::AddEdge(shape_node->GetOutDataAnchor(0), Output_node->GetInDataAnchor(0)); + EXPECT_EQ(graph->TopologicalSorting(), GRAPH_SUCCESS); + + + InferValueRangePass infer_pass; + auto ret = infer_pass.Run(shape_node); + EXPECT_EQ(ret, SUCCESS); + + auto output_0_desc = shape_node->GetOpDesc()->GetOutputDesc(0); + std::vector> value_range; + output_0_desc.GetValueRange(value_range); + EXPECT_EQ(value_range.size(), 4); + std::vector target_value_range = {1, 100, 1, 240, 4, 4, 192, 192}; + std::vector output_value_range; + for (auto pair : value_range) { + output_value_range.push_back(pair.first); + output_value_range.push_back(pair.second); + } + EXPECT_EQ(target_value_range, output_value_range); + + auto in_0_desc = Output_node->GetOpDesc()->GetInputDesc(0); + value_range.clear(); + in_0_desc.GetValueRange(value_range); + EXPECT_EQ(value_range.size(), 4); + output_value_range.clear(); + for (auto pair : value_range) { + output_value_range.push_back(pair.first); + output_value_range.push_back(pair.second); + } + EXPECT_EQ(target_value_range, output_value_range); + +} + +class AddKernel : public Kernel { + public: + Status Compute(const ge::OpDescPtr op_desc_ptr, const std::vector &input, + std::vector &v_output) override { + if (input[0]->GetTensorDesc().GetDataType() == DT_INT64 || input[0]->GetTensorDesc().GetDataType() == DT_UINT64) { + vector data_vec; + auto data_num = input[0]->GetTensorDesc().GetShape().GetShapeSize(); + auto x1_data = reinterpret_cast(input[0]->GetData().data()); + auto x2_data = reinterpret_cast(input[1]->GetData().data()); + for (size_t i = 0; i < data_num; i++) { + auto x_index = *(x1_data + i); + auto y_index = *(x2_data + i); + data_vec.push_back(x_index + y_index); + } + GeTensorPtr const_tensor = std::make_shared(input[0]->GetTensorDesc(), (uint8_t *)data_vec.data(), + data_num * sizeof(int64_t)); + v_output.emplace_back(const_tensor); + return SUCCESS; + } else if (input[0]->GetTensorDesc().GetDataType() == DT_INT32 || input[0]->GetTensorDesc().GetDataType() == DT_UINT32) { + vector data_vec; + auto data_num = input[0]->GetTensorDesc().GetShape().GetShapeSize(); + auto x1_data = reinterpret_cast(input[0]->GetData().data()); + auto x2_data = reinterpret_cast(input[1]->GetData().data()); + for (size_t i = 0; i < data_num; i++) { + auto x_index = *(x1_data + i); + auto y_index = *(x2_data + i); + data_vec.push_back(x_index + y_index); + } + GeTensorPtr const_tensor = std::make_shared(input[0]->GetTensorDesc(), (uint8_t *)data_vec.data(), + data_num * sizeof(int32_t)); + v_output.emplace_back(const_tensor); + return SUCCESS; + } + } +}; +REGISTER_KERNEL(ADD, AddKernel); +INFER_VALUE_RANGE_DEFAULT_REG(Add); +INFER_VALUE_RANGE_DEFAULT_REG(Sqrt); + +TEST_F(UtestGraphInferValueRangePass, CallRun_NoSubgraph_UseCpuKernel_InputsHaveUnKnownValueRange) { + // shape --- add --- sqrt + // constant / + auto graph = std::make_shared("test_graph"); + + vector dims_vec = {4}; + vector data_vec = {1, 1, 1, 1}; + GeTensorDesc const_tensor_desc(ge::GeShape(dims_vec), ge::FORMAT_NCHW, ge::DT_INT64); + GeTensorPtr const_tensor = + std::make_shared(const_tensor_desc, (uint8_t *)data_vec.data(), data_vec.size() * sizeof(int64_t)); + + auto const_op_desc = std::make_shared("Constant", "Constant"); + const_op_desc->AddOutputDesc(const_tensor_desc); + EXPECT_EQ(OpDescUtils::SetWeights(const_op_desc, const_tensor), GRAPH_SUCCESS); + auto const_node = graph->AddNode(const_op_desc); + + GeTensorDesc shape_tensor_desc(GeShape({4}), ge::FORMAT_NCHW, ge::DT_INT64); + std::vector> unknown_value_range = {make_pair(1, -1), make_pair(1, 240), + make_pair(4, 4), make_pair(192, 192)}; + shape_tensor_desc.SetValueRange(unknown_value_range); + auto shape_op_desc = std::make_shared("Shape", "Shape"); + shape_op_desc->AddOutputDesc(shape_tensor_desc); + auto shape_node = graph->AddNode(shape_op_desc); + + GeTensorDesc add_tensor_desc(GeShape({4}), ge::FORMAT_NCHW, ge::DT_INT64); + auto add_op_desc = std::make_shared("Add", "Add"); + add_op_desc->AddInputDesc(shape_tensor_desc); + add_op_desc->AddInputDesc(const_tensor_desc); + add_op_desc->AddOutputDesc(add_tensor_desc); + auto add_node = graph->AddNode(add_op_desc); + + ge::GraphUtils::AddEdge(shape_node->GetOutDataAnchor(0), add_node->GetInDataAnchor(0)); + ge::GraphUtils::AddEdge(const_node->GetOutDataAnchor(0), add_node->GetInDataAnchor(1)); + + // test unknown value range + InferValueRangePass infer_pass; + EXPECT_EQ(infer_pass.Run(add_node), SUCCESS); + auto output_0_desc = add_node->GetOpDesc()->GetOutputDesc(0); + std::vector> out_value_range; + output_0_desc.GetValueRange(out_value_range); + EXPECT_EQ(out_value_range.size(), 4); + + std::vector unknown_target_value_range = {1, -1, 1, -1, 1, -1, 1, -1}; + std::vector output_value_range; + for (auto pair : out_value_range) { + output_value_range.push_back(pair.first); + output_value_range.push_back(pair.second); + } + EXPECT_EQ(unknown_target_value_range, output_value_range); +} + +TEST_F(UtestGraphInferValueRangePass, CallRun_NoSubgraph_UseCpuKernel_InputsAreKnownValueRange_Int64) { + // shape --- add --- sqrt + // constant / + auto graph = std::make_shared("test_graph"); + + vector dims_vec = {4}; + vector data_vec = {1, 1, 1, 1}; + GeTensorDesc const_tensor_desc(ge::GeShape(dims_vec), ge::FORMAT_NCHW, ge::DT_INT64); + GeTensorPtr const_tensor = + std::make_shared(const_tensor_desc, (uint8_t *)data_vec.data(), data_vec.size() * sizeof(int64_t)); + + auto const_op_desc = std::make_shared("Constant", "Constant"); + const_op_desc->AddOutputDesc(const_tensor_desc); + EXPECT_EQ(OpDescUtils::SetWeights(const_op_desc, const_tensor), GRAPH_SUCCESS); + auto const_node = graph->AddNode(const_op_desc); + + GeTensorDesc shape_tensor_desc(GeShape({4}), ge::FORMAT_NCHW, ge::DT_INT64); + std::vector> unknown_value_range = {make_pair(1, 100), make_pair(1, 240), + make_pair(4, 4), make_pair(192, 192)}; + shape_tensor_desc.SetValueRange(unknown_value_range); + auto shape_op_desc = std::make_shared("Shape", "Shape"); + shape_op_desc->AddOutputDesc(shape_tensor_desc); + auto shape_node = graph->AddNode(shape_op_desc); + + GeTensorDesc add_tensor_desc(GeShape({4}), ge::FORMAT_NCHW, ge::DT_INT64); + auto add_op_desc = std::make_shared("Add", "Add"); + add_op_desc->AddInputDesc(shape_tensor_desc); + add_op_desc->AddInputDesc(const_tensor_desc); + add_op_desc->AddOutputDesc(add_tensor_desc); + auto add_node = graph->AddNode(add_op_desc); + + auto sqrt_op_desc = std::make_shared("Sqrt", "Sqrt"); + sqrt_op_desc->AddInputDesc(GeTensorDesc()); + auto sqrt_node = graph->AddNode(sqrt_op_desc); + + ge::GraphUtils::AddEdge(shape_node->GetOutDataAnchor(0), add_node->GetInDataAnchor(0)); + ge::GraphUtils::AddEdge(const_node->GetOutDataAnchor(0), add_node->GetInDataAnchor(1)); + ge::GraphUtils::AddEdge(add_node->GetOutDataAnchor(0), sqrt_node->GetInDataAnchor(1)); + + InferValueRangePass infer_pass; + EXPECT_EQ(infer_pass.Run(sqrt_node), SUCCESS); + + // test known value range + EXPECT_EQ(infer_pass.Run(add_node), SUCCESS); + auto output_0_desc = add_node->GetOpDesc()->GetOutputDesc(0); + std::vector> out_value_range; + output_0_desc.GetValueRange(out_value_range); + EXPECT_EQ(out_value_range.size(), 4); + + std::vector target_value_range = {2, 101, 2, 241, 5, 5, 193, 193}; + std::vector output_value_range; + for (auto pair : out_value_range) { + output_value_range.push_back(pair.first); + output_value_range.push_back(pair.second); + } + EXPECT_EQ(target_value_range, output_value_range); +} + +TEST_F(UtestGraphInferValueRangePass, CallRun_NoSubgraph_UseCpuKernel_InputsAreKnownValueRange_Int32) { + // shape --- add --- sqrt + // constant / + auto graph = std::make_shared("test_graph"); + vector data_vec = {1, 100, 2, 200}; + GeTensorDesc const_tensor_desc(ge::GeShape({4}), ge::FORMAT_NCHW, ge::DT_INT32); + GeTensorPtr const_tensor = + std::make_shared(const_tensor_desc, (uint8_t *)data_vec.data(), data_vec.size() * sizeof(int32_t)); + auto const_op_desc = std::make_shared("Constant", "Constant"); + const_op_desc->AddOutputDesc(const_tensor_desc); + EXPECT_EQ(OpDescUtils::SetWeights(const_op_desc, const_tensor), GRAPH_SUCCESS); + auto const_node = graph->AddNode(const_op_desc); + + GeTensorDesc shape_tensor_desc(GeShape({4}), ge::FORMAT_NCHW, ge::DT_INT32); + std::vector> known_value_range = {make_pair(1, 100), make_pair(1, 240), + make_pair(4, 4), make_pair(192, 192)}; + shape_tensor_desc.SetValueRange(known_value_range); + auto shape_op_desc = std::make_shared("Shape", "Shape"); + shape_op_desc->AddOutputDesc(shape_tensor_desc); + auto shape_node = graph->AddNode(shape_op_desc); + + GeTensorDesc add_tensor_desc(GeShape({4}), ge::FORMAT_NCHW, ge::DT_INT32); + auto add_op_desc = std::make_shared("Add", "Add"); + add_op_desc->AddInputDesc(shape_tensor_desc); + add_op_desc->AddInputDesc(const_tensor_desc); + add_op_desc->AddOutputDesc(add_tensor_desc); + auto add_node = graph->AddNode(add_op_desc); + + ge::GraphUtils::AddEdge(shape_node->GetOutDataAnchor(0), add_node->GetInDataAnchor(0)); + ge::GraphUtils::AddEdge(const_node->GetOutDataAnchor(0), add_node->GetInDataAnchor(1)); + + InferValueRangePass infer_pass; + EXPECT_EQ(infer_pass.Run(add_node), SUCCESS); + auto output_0_desc = add_node->GetOpDesc()->GetOutputDesc(0); + std::vector> out_value_range; + output_0_desc.GetValueRange(out_value_range); + EXPECT_EQ(out_value_range.size(), 4); + + std::vector target_value_range = {2, 101, 101, 340, 6, 6, 392, 392}; + std::vector output_value_range; + for (auto pair : out_value_range) { + output_value_range.push_back(pair.first); + output_value_range.push_back(pair.second); + } + EXPECT_EQ(target_value_range, output_value_range); +} + +REG_OP(Case) + .OP_END_FACTORY_REG(Case) +IMPL_INFER_VALUE_RANGE_FUNC(Case, ValueRangeFunc){ + auto op_desc = OpDescUtils::GetOpDescFromOperator(op); + auto output_tensor_desc = op_desc->MutableOutputDesc(0); + std::vector> in_value_range; + output_tensor_desc->GetValueRange(in_value_range); + if (in_value_range.empty()) { + std::vector> out_value_range = {make_pair(1, 2), make_pair(1, 3), + make_pair(1, 4), make_pair(1, 5)};; + output_tensor_desc->SetValueRange(out_value_range); + } + return GRAPH_SUCCESS; +} +INFER_VALUE_RANGE_CUSTOM_FUNC_REG(Case, INPUT_HAS_VALUE_RANGE, ValueRangeFunc); + +TEST_F(UtestGraphInferValueRangePass, CallRun_HasCaeSubgraph_WhenBeforeSubgraph) { + auto builder = ParentGraphBuilder(); + auto parent_graph = builder.GetGraph(); + AddCaseSubgraph(parent_graph, 2); + auto subgraphs = parent_graph->GetAllSubgraphs(); + EXPECT_EQ(subgraphs.size(), 2); + + // check before subgraph + auto case_node = parent_graph->FindNode("case1"); + EXPECT_NE(case_node, nullptr); + InferValueRangePass infer_pass; + EXPECT_EQ(infer_pass.Run(case_node), SUCCESS); + + auto case_out_0_desc = case_node->GetOpDesc()->MutableOutputDesc(0); + std::vector> out_value_range; + case_out_0_desc->GetValueRange(out_value_range); + EXPECT_EQ(out_value_range.size(), 4); + std::vector target_value_range = {1,2,1,3,1,4,1,5}; + std::vector output_value_range_list; + for (auto pair : out_value_range) { + output_value_range_list.push_back(pair.first); + output_value_range_list.push_back(pair.second); + } + EXPECT_EQ(target_value_range, output_value_range_list); + + auto data_node = subgraphs[0]->FindNode("data1_0"); + auto data_output_0_desc = data_node->GetOpDesc()->GetOutputDesc(0); + std::vector target_value_range_list = {1, 1, 1, 1, 1, -1, 1, 224}; + std::vector> output_value_range; + data_output_0_desc.GetValueRange(output_value_range); + EXPECT_EQ(output_value_range.size(), 4); + std::vector data_value_range_list; + for (auto pair : output_value_range) { + data_value_range_list.push_back(pair.first); + data_value_range_list.push_back(pair.second); + } + EXPECT_EQ(data_value_range_list, target_value_range_list); + + data_node = subgraphs[0]->FindNode("data2_0"); + auto data2_input_0_desc = data_node->GetOpDesc()->GetInputDesc(0); + std::vector target_value_range_list2 = {1, 100, 1, 10}; + out_value_range.clear(); + data2_input_0_desc.GetValueRange(out_value_range); + EXPECT_EQ(out_value_range.size(), 2); + data_value_range_list.clear(); + for (auto pair : out_value_range) { + data_value_range_list.push_back(pair.first); + data_value_range_list.push_back(pair.second); + } + EXPECT_EQ(data_value_range_list, target_value_range_list2); +} + +TEST_F(UtestGraphInferValueRangePass, CallRun_HasCaeSubgraph_WhenAfterSubgraph) { + auto builder = ParentGraphBuilder(); + auto parent_graph = builder.GetGraph(); + AddCaseSubgraph(parent_graph, 2); + auto subgraphs = parent_graph->GetAllSubgraphs(); + EXPECT_EQ(subgraphs.size(), 2); + + auto case_node = parent_graph->FindNode("case1"); + EXPECT_NE(case_node, nullptr); + InferValueRangePass infer_pass; + // check after subgraph + infer_pass.options_[kOptimizeAfterSubGraph] = "yes"; + EXPECT_EQ(infer_pass.Run(case_node), SUCCESS); + + std::vector out_target_dims = {1, -1, 1, -1}; + auto case_out = case_node->GetOpDesc()->GetOutputDescPtr(0); + std::vector> out_value_range; + case_out->GetValueRange(out_value_range); + EXPECT_EQ(out_value_range.size(), 2); + + std::vector output_value_range_list; + for (auto pair : out_value_range) { + output_value_range_list.push_back(pair.first); + output_value_range_list.push_back(pair.second); + } + EXPECT_EQ(out_target_dims, output_value_range_list); +} + +TEST_F(UtestGraphInferValueRangePass, CallRun_HasSubgraph_WhenAfterSubgraph_ForMultiDims) { + auto builder = ParentGraphBuilder(); + auto parent_graph = builder.GetGraph(); + AddCaseSubgraph(parent_graph, 2); + auto subgraphs = parent_graph->GetAllSubgraphs(); + EXPECT_EQ(subgraphs.size(), 2); + + auto case_node = parent_graph->FindNode("case1"); + EXPECT_NE(case_node, nullptr); + InferValueRangePass infer_pass; + infer_pass.options_[kOptimizeAfterSubGraph] = "yes"; + + // check after subgraph for multi-batch + auto set_ret = AttrUtils::SetInt(case_node->GetOpDesc(), ATTR_NAME_BATCH_NUM, 2); + EXPECT_EQ(set_ret, true); + EXPECT_EQ(infer_pass.Run(case_node), GRAPH_FAILED); +} +} // namespace ge