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infer_base infer_shape and infer_value_range

pull/1763/head
wq160 4 years ago
parent
7e0c912bc3
commit
105712f392
14 changed files with 2188 additions and 72 deletions
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  1. +4
    -0
      ge/CMakeLists.txt
  2. +1
    -27
      ge/graph/passes/constant_folding_pass.cc
  3. +25
    -1
      ge/graph/passes/folding_pass.cc
  4. +3
    -0
      ge/graph/passes/folding_pass.h
  5. +676
    -0
      ge/graph/passes/infer_base_pass.cc
  6. +53
    -0
      ge/graph/passes/infer_base_pass.h
  7. +388
    -0
      ge/graph/passes/infer_value_range_pass.cc
  8. +44
    -0
      ge/graph/passes/infer_value_range_pass.h
  9. +350
    -30
      ge/graph/passes/infershape_pass.cc
  10. +27
    -11
      ge/graph/passes/infershape_pass.h
  11. +3
    -0
      ge/graph/preprocess/graph_preprocess.cc
  12. +6
    -1
      tests/ut/ge/CMakeLists.txt
  13. +281
    -0
      tests/ut/ge/graph/passes/infer_value_range_pass_unittest.cc
  14. +327
    -2
      tests/ut/ge/graph/passes/infershape_pass_unittest.cc

+ 4
- 0
ge/CMakeLists.txt View File

@@ -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"
@@ -553,7 +555,9 @@ 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"


+ 1
- 27
ge/graph/passes/constant_folding_pass.cc View File

@@ -20,35 +20,9 @@
#include "graph/operator_factory.h"
#include "graph/utils/node_utils.h"
#include "graph/utils/type_utils.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<ConstGeTensorPtr> &inputs,
std::vector<GeTensorPtr> &outputs) {
std::shared_ptr<GELib> 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.");
return UNSUPPORTED;
}
OpsKernelInfoStorePtr kernel_info = instance_ptr->OpsKernelManagerObj().GetOpsKernelInfoStore(kKernelLibName);
if (kernel_info == nullptr) {
GELOGE(FAILED, "[Get][OpsKernelInfoStore] %s failed", kKernelLibName.c_str());
return UNSUPPORTED;
}

std::string ops_flag;
kernel_info->opsFlagCheck(*node, ops_flag);
if (ops_flag == kOpsFlagClose) {
return UNSUPPORTED;
}
return FoldingPass::RunOpKernel(node, inputs, outputs);
}

const map<string, pair<uint64_t, uint64_t>> &ConstantFoldingPass::GetGeConstantFoldingPerfStatistic() const {
return statistic_of_ge_constant_folding_;
@@ -81,7 +55,7 @@ Status ConstantFoldingPass::Run(ge::NodePtr &node) {
vector<GeTensorPtr> outputs;
// Statistic of ge constant folding kernel
uint64_t start_time = GetCurrentTimestamp();
auto ret = RunOpKernelWithCheck(node, inputs, outputs);
auto ret = FoldingPass::RunOpKernelWithCheck(node, inputs, outputs);
if (ret != SUCCESS) {
auto op_kernel = folding_pass::GetKernelByType(node);
if (op_kernel == nullptr) {


+ 25
- 1
ge/graph/passes/folding_pass.cc View File

@@ -29,7 +29,7 @@
#include "inc/kernel_factory.h"
#include "graph/debug/ge_attr_define.h"
#include "ge_local_engine/engine/host_cpu_engine.h"
#include "init/gelib.h"

namespace ge {
namespace folding_pass {
@@ -59,6 +59,9 @@ bool IsNoNeedConstantFolding(const NodePtr &node) {
} // namespace folding_pass

namespace {
const std::string kKernelLibName = "aicpu_tf_kernel";
const std::string kOpsFlagClose = "0";

IndexsToAnchors GetIndexAndPeerInDataAnchors(NodePtr &node) {
IndexsToAnchors indexes_to_anchors;
for (auto &out_anchor : node->GetAllOutDataAnchors()) {
@@ -129,6 +132,27 @@ Status FoldingPass::RunOpKernel(NodePtr &node,
return HostCpuEngine::GetInstance().Run(node, inputs, outputs);
}

Status FoldingPass::RunOpKernelWithCheck(NodePtr &node, const vector<ConstGeTensorPtr> &inputs,
std::vector<GeTensorPtr> &outputs) {
std::shared_ptr<GELib> 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.");
return UNSUPPORTED;
}
OpsKernelInfoStorePtr kernel_info = instance_ptr->OpsKernelManagerObj().GetOpsKernelInfoStore(kKernelLibName);
if (kernel_info == nullptr) {
GELOGE(FAILED, "[Get][OpsKernelInfoStore] %s failed", kKernelLibName.c_str());
return UNSUPPORTED;
}

std::string ops_flag;
kernel_info->opsFlagCheck(*node, ops_flag);
if (ops_flag == kOpsFlagClose) {
return UNSUPPORTED;
}
return FoldingPass::RunOpKernel(node, inputs, outputs);
}

Status FoldingPass::Folding(NodePtr &node, vector<GeTensorPtr> &outputs) {
GE_CHECK_NOTNULL(node);
GELOGD("begin folding node:%s", node->GetName().c_str());


+ 3
- 0
ge/graph/passes/folding_pass.h View File

@@ -36,6 +36,9 @@ using IndexsToAnchors = std::map<int, std::vector<InDataAnchorPtr>>;
class FoldingPass : public BaseNodePass {
public:
static Status RunOpKernel(NodePtr &node, const vector<ConstGeTensorPtr> &inputs, vector<GeTensorPtr> &outputs);
static Status RunOpKernelWithCheck(NodePtr &node, const vector<ConstGeTensorPtr> &inputs,
std::vector<GeTensorPtr> &outputs);

protected:
Status Folding(NodePtr &node, vector<GeTensorPtr> &outputs);
private:


+ 676
- 0
ge/graph/passes/infer_base_pass.cc View File

@@ -0,0 +1,676 @@
/**
* 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/debug/ge_util.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 {
string Serial(const vector<int64_t> &dims) {
string serial_string;
serial_string += "[";
for (int64_t dim : dims) {
serial_string += std::to_string(dim) + " ";
}
serial_string += "]";
return serial_string;
}
void SerialShapeRange(const GeTensorDescPtr &desc, std::string &desc_str) {
desc_str += "[";
std::vector<std::pair<int64_t, int64_t>> shape_range;
(void)desc->GetShapeRange(shape_range);
for (const auto &pair : shape_range) {
desc_str += "{";
desc_str += std::to_string(pair.first) + "," + std::to_string(pair.second);
desc_str += "},";
}
desc_str += "]";
shape_range.clear();
(void)desc->GetOriginShapeRange(shape_range);
for (const auto &pair : shape_range) {
desc_str += ",{";
desc_str += std::to_string(pair.first) + "," + std::to_string(pair.second);
desc_str += "},";
}
}
void SerialValueRange(const GeTensorDescPtr &desc, std::string &desc_str) {
desc_str += "[";
std::vector<std::pair<int64_t, int64_t>> value_range;
(void)desc->GetValueRange(value_range);
for (const auto &pair : value_range) {
desc_str += "{";
desc_str += std::to_string(pair.first) + "," + std::to_string(pair.second);
desc_str += "},";
}
desc_str += "]";
}
graphStatus FindSubgraphDataAndNetoutput(const ComputeGraphPtr &sub_graph, NodePtr &netoutput, const ConstNodePtr &node,
std::vector<std::vector<GeTensorDesc>> &ref_data_tensors) {
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) {
netoutput = sub_node;
}
if (sub_node->GetType() == DATA) {
if (sub_node->GetOpDesc() == nullptr) {
return GRAPH_FAILED;
}

int ref_i;
if (!AttrUtils::GetInt(sub_node->GetOpDesc(), ATTR_NAME_PARENT_NODE_INDEX, ref_i)) {
REPORT_INNER_ERROR("E19999", "subgraph data node[%s] has no parent node!", sub_node->GetName().c_str());
GELOGE(GRAPH_FAILED, "[Get][Int] subgraph data node[%s] has no parent node!", sub_node->GetName().c_str());
return GRAPH_FAILED;
}
if (ref_i < 0 || static_cast<uint32_t>(ref_i) >= node->GetAllInDataAnchorsSize()) {
REPORT_INNER_ERROR("E19999", "data node[%s]'s ref index[%d] is not in range [0, %u)!",
sub_node->GetName().c_str(), ref_i, node->GetAllInDataAnchorsSize());
GELOGE(GRAPH_FAILED, "[Check][Param] data node[%s]'s ref index[%d] is not in range [0, %u)!",
sub_node->GetName().c_str(), ref_i, node->GetAllInDataAnchorsSize());
return GRAPH_FAILED;
}
ref_data_tensors[ref_i].emplace_back(sub_node->GetOpDesc()->GetOutputDesc(0));
}
}
return GRAPH_SUCCESS;
}
} // 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<NodePtr> changed_nodes;
auto ret = InferAndUpdate(node, !OptionExists(kOptimizeAfterSubGraph), changed_nodes);
if (ret != GRAPH_SUCCESS) {
(void)AnalyzeFailedInfo(node);
return GE_GRAPH_INFERSHAPE_FAILED;
}

/*
* we will use changed nodes to do repass for control_ops.
* AddChangedNodesImmediateRepass(changed_nodes);
*/
auto status = DoRepassForLoopNode(node);
if (status != SUCCESS) {
GELOGE(GE_GRAPH_INFERSHAPE_FAILED, "repass failed. node: %s", node->GetName().c_str());
return GE_GRAPH_INFERSHAPE_FAILED;
}
return SUCCESS;
}

bool InferBasePass::NeedInfer(const NodePtr &node) { return true; }
void InferBasePass::AnalyzeFailedInfo(const NodePtr &node) { /* Analyze and select failed info*/ }
Status InferBasePass::DoRepassForLoopNode(NodePtr &node) { return SUCCESS; }
graphStatus InferBasePass::UpdatePeerInputs(NodePtr &node) { return GRAPH_SUCCESS; }
void InferBasePass::AddChangedNodesImmediateRepass(std::set<NodePtr> &changed_nodes) {
for (const auto &node_ele : changed_nodes) {
AddImmediateRePassNode(node_ele);
}
}

graphStatus InferBasePass::InferAndUpdate(NodePtr &node, bool before_subgraph, std::set<NodePtr> &changed_nodes) {
auto ret = GRAPH_SUCCESS;
bool is_unknown_graph = node->GetOwnerComputeGraph()->GetGraphUnknownFlag();
auto opdesc = node->GetOpDesc();
// some op can not infershape twice such as aipp
bool need_update_input = !is_unknown_graph && !opdesc->HasAttr("has_infered_verified");
if (need_update_input) {
ret = UpdateCurOpInputDesc(node);
if (ret != GRAPH_SUCCESS) {
REPORT_CALL_ERROR("E19999", "update op input_desc failed! ret:%d, node:%s", ret, node->GetName().c_str());
GELOGE(GRAPH_FAILED, "[Update][OpInputDesc] failed! ret:%d", ret);
return ret;
}
}

bool contain_subgraph = ContainsSubgraph(node);
if (contain_subgraph && before_subgraph) {
ret = UpdateTensorDescToSubgraphData(node, changed_nodes);
if (ret != GRAPH_SUCCESS) {
return ret;
}
}
ret = Infer(node);
if (ret != GRAPH_SUCCESS) {
return ret;
}
if (contain_subgraph && !before_subgraph) {
ret = UpdateTensorDescToParentNode(node, changed_nodes);
if (ret != GRAPH_SUCCESS) {
return ret;
}
}

ret = UpdatePeerInputs(node);
return ret;
}

graphStatus InferBasePass::UpdateCurOpInputDesc(const NodePtr &node_ptr) {
for (const auto &in_anchor : node_ptr->GetAllInDataAnchors()) {
auto in_idx = in_anchor->GetIdx();
auto peer_out_data_anchor = in_anchor->GetPeerOutAnchor();
if (peer_out_data_anchor == nullptr) {
continue;
}
auto peer_out_data_node = peer_out_data_anchor->GetOwnerNode();
if (peer_out_data_node == nullptr || peer_out_data_node->GetOpDesc() == nullptr) {
continue;
}
int peer_out_idx = peer_out_data_anchor->GetIdx();
auto peer_out_desc = peer_out_data_node->GetOpDesc()->MutableOutputDesc(static_cast<uint32_t>(peer_out_idx));

// check shape and dtype continuity. do not stop process
auto in_desc = node_ptr->GetOpDesc()->MutableInputDesc(static_cast<uint32_t>(in_idx));
if (in_desc == nullptr) {
continue;
}
auto in_shape = in_desc->MutableShape().GetDims();
auto in_dtype = in_desc->GetDataType();
auto peer_out_shape = peer_out_desc->MutableShape().GetDims();
auto peer_out_dtype = peer_out_desc->GetDataType();
if (peer_out_dtype != in_dtype) {
GELOGW(
"current node [%s] [%d]\'th in_dtype is [%s].peer output node [%s] [%d]\'th "
"output_dtype is [%s].The two dtype should be same! Please check graph and fix it",
node_ptr->GetName().c_str(), in_idx, TypeUtils::DataTypeToSerialString(in_dtype).c_str(),
peer_out_data_node->GetName().c_str(), peer_out_idx, TypeUtils::DataTypeToSerialString(peer_out_dtype).c_str());
} else if ((!in_shape.empty()) && (in_shape != peer_out_shape)) {
string in_shape_str = Serial(in_shape);
string peer_out_shape_str = Serial(peer_out_shape);
GELOGW(
"current node [%s] [%d]\'th in_shape is [%s].peer output node [%s] [%d]\'th "
"output_shape is [%s].The two shape should be same! Please check graph and fix it",
node_ptr->GetName().c_str(), in_idx, in_shape_str.c_str(), peer_out_data_node->GetName().c_str(), peer_out_idx,
peer_out_shape_str.c_str());
}
// refresh current node input desc
bool output_changed = false;
(void)UpdateInputDescAttr(peer_out_desc, in_desc, output_changed);
}
return GRAPH_SUCCESS;
}

graphStatus InferBasePass::UpdateInputDescAttr(const GeTensorDescPtr &src, GeTensorDescPtr &dst, bool &changed) {
changed = false;
return GRAPH_SUCCESS;
}

bool InferBasePass::ContainsSubgraph(const NodePtr &node) {
auto op_desc = node->GetOpDesc();
auto sub_graph_names = op_desc->GetSubgraphInstanceNames();
if (sub_graph_names.empty()) {
return false;
}

auto root_graph = GraphUtils::FindRootGraph(node->GetOwnerComputeGraph());
if (root_graph == nullptr) {
return false;
}
for (const auto &name : sub_graph_names) {
if (name.empty()) {
continue;
}
auto sub_graph = root_graph->GetSubgraph(name);
if (sub_graph != nullptr) {
return true;
}
}
return false;
}

std::vector<ComputeGraphPtr> InferBasePass::GetCurNodeSubgraphs(const NodePtr &node) {
std::vector<ComputeGraphPtr> 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) {
REPORT_INNER_ERROR("E19999", "Can not find the subgrpah %s for node %s", name.c_str(), node->GetName().c_str());
GE_LOGE("[Get][Graph] can not find the subgrpah %s for node %s", name.c_str(), node->GetName().c_str());
continue;
}
cur_node_subgraph.emplace_back(sub_graph);
}
return cur_node_subgraph;
}

graphStatus InferBasePass::UpdateTensorDescToSubgraphData(NodePtr &node, std::set<NodePtr> &changed_nodes) {
// if infer again, update output of while into subgraph data 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 name = sub_graph->GetName();
int ref_i;
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", name.c_str(),
node->GetName().c_str());
GE_LOGE("[Get][OpDesc] Invalid data node on the sub graph %s parent node %s, no OpDesc", name.c_str(),
node->GetName().c_str());
return GRAPH_FAILED;
}
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",
name.c_str(), node->GetName().c_str());
GE_LOGE("[Get][Int] Invalid data node on the sub graph %s parent node %s, no ref-index attribute", name.c_str(),
node->GetName().c_str());
return GRAPH_FAILED;
}
if (data_opdesc->HasAttr(ATTR_MBATCH_ORIGIN_INPUT_DIMS)) {
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(), name.c_str(), node->GetName().c_str(),
node->GetAllInDataAnchorsSize());
GE_LOGE(
"[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(), name.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());

// if need infer again, refresh subgraph input with output
bool is_infer_again = false;
AttrUtils::GetBool(node->GetOpDesc(), ATTR_NAME_NEED_INFER_AGAIN, is_infer_again);
if (is_infer_again) {
input_desc = op_desc->MutableOutputDesc(ref_i);
if (input_desc == nullptr) {
REPORT_INNER_ERROR("E19999",
"The ref index(%d) on the data %s on the subgraph %s "
"parent node %s are incompatible, outputs num %u.",
ref_i, node_sub->GetName().c_str(), name.c_str(), node->GetName().c_str(),
node->GetAllOutDataAnchorsSize());
GELOGE(PARAM_INVALID,
"[Call][MutableOutputDesc] The ref index(%d) on the data %s on the subgraph %s "
"parent node %s are incompatible, outputs num %u.",
ref_i, node_sub->GetName().c_str(), name.c_str(), node->GetName().c_str(),
node->GetAllOutDataAnchorsSize());
}
GELOGD("Update input desc of data %s on the sub graph %s of node %s,output idx: %d from [%s] to [%s]",
node_sub->GetName().c_str(), name.c_str(), node->GetName().c_str(), ref_i,
data_opdesc->GetInputDescPtr(0)->GetShape().ToString().c_str(),
input_desc->GetShape().ToString().c_str());
}

auto data_input_desc = data_opdesc->MutableInputDesc(0);
auto ret = data_opdesc->UpdateInputDesc(0, *input_desc);
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(), name.c_str(), node->GetName().c_str());
GE_LOGE("[Update][InputDesc] of data %s on the sub graph %s parent node %s failed", node_sub->GetName().c_str(),
name.c_str(), node->GetName().c_str());
return ret;
}
bool input_changed = TensorDescChanged(input_desc, data_input_desc);

auto data_output_desc = data_opdesc->MutableOutputDesc(0);
ret = data_opdesc->UpdateOutputDesc(0, *input_desc);
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(), name.c_str(), node->GetName().c_str());
GE_LOGE("[Update][OutputDesc] of data %s on the sub graph %s parent node %s failed",
node_sub->GetName().c_str(), name.c_str(), node->GetName().c_str());
return ret;
}
bool output_changed = TensorDescChanged(input_desc, data_output_desc);

if (input_changed || output_changed) {
changed_nodes.insert(node_sub);
}
}
}
return GRAPH_SUCCESS;
}

graphStatus InferBasePass::UpdateTensorDescToParentNode(NodePtr &node, std::set<NodePtr> &changed_nodes) {
std::vector<std::vector<GeTensorDesc>> ref_data_tensors(node->GetAllInDataAnchorsSize());
std::vector<std::vector<GeTensorDesc>> ref_out_tensors(node->GetAllOutDataAnchorsSize());

for (const auto &sub_graph : GetCurNodeSubgraphs(node)) {
auto name = sub_graph->GetName();
NodePtr netoutput = nullptr;
auto ret = FindSubgraphDataAndNetoutput(sub_graph, netoutput, node, ref_data_tensors);
if (ret != GRAPH_SUCCESS) {
return ret;
}
if (netoutput == nullptr) {
REPORT_INNER_ERROR("E19999", "No NetOutput node on sub graph %s, parent node %s", name.c_str(),
node->GetName().c_str());
GE_LOGE("[Check][Param] No NetOutput node on sub graph %s, parent node %s", name.c_str(),
node->GetName().c_str());
return GRAPH_FAILED;
}
auto netoutput_opdesc = netoutput->GetOpDesc();
if (netoutput_opdesc == nullptr) {
REPORT_INNER_ERROR("E19999", "Invalid NetOutput node on sub graph %s, parent node %s, no OpDesc on it",
name.c_str(), node->GetName().c_str());
GE_LOGE("[Get][OpDesc] Invalid NetOutput node on sub graph %s, parent node %s, no OpDesc on it", name.c_str(),
node->GetName().c_str());
return GRAPH_FAILED;
}
for (auto &edge_anchor : netoutput->GetAllInDataAnchors()) {
auto edge_desc = netoutput_opdesc->MutableInputDesc(edge_anchor->GetIdx());
if (edge_desc == nullptr) {
REPORT_INNER_ERROR("E19999",
"Invalid NetOutput node on sub graph %s, parent node %s, "
"can not find input tensor %d",
name.c_str(), node->GetName().c_str(), edge_anchor->GetIdx());
GE_LOGE("[Get][Tensor] Invalid NetOutput node on sub graph %s, parent node %s, can not find input tensor %d",
name.c_str(), node->GetName().c_str(), edge_anchor->GetIdx());
return GRAPH_FAILED;
}
GELOGI("Netoutput in anchor index is %d, input tensor dim is %zu", edge_anchor->GetIdx(),
edge_desc->GetShape().GetDimNum());
int ref_i;
if (!AttrUtils::GetInt(edge_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<uint32_t>(ref_i) >= node->GetAllOutDataAnchorsSize()) {
return GRAPH_FAILED;
}
ref_out_tensors[ref_i].emplace_back(*edge_desc);
}
}

if (node->GetType() == WHILE) {
return UpdateParentNodeForWhile(node, ref_data_tensors, ref_out_tensors, changed_nodes);
}
return UpdateParentNodeForBranch(node, ref_out_tensors, changed_nodes);
}

graphStatus InferBasePass::UpdateParentNodeForWhile(NodePtr &node,
std::vector<std::vector<GeTensorDesc>> &ref_data_tensors,
std::vector<std::vector<GeTensorDesc>> &ref_out_tensors,
std::set<NodePtr> &changed_nodes) {
GELOGD("Enter update parent node shape for class while op process");
if (ref_data_tensors.size() != ref_out_tensors.size()) {
REPORT_INNER_ERROR("E19999", "op:%s(%s) input number[%zu] and output number[%zu] is not same!",
node->GetName().c_str(), node->GetType().c_str(), ref_data_tensors.size(),
ref_out_tensors.size());
GELOGE(GRAPH_FAILED, "[Check][Param] while op [%s] input number[%zu] and output number[%zu] is not same!",
node->GetName().c_str(), ref_data_tensors.size(), ref_out_tensors.size());
return GRAPH_FAILED;
}
for (size_t i = 0; i < ref_data_tensors.size(); i++) {
if (ref_out_tensors[i].size() != 1) {
REPORT_INNER_ERROR("E19999", "while op, every output should only find one output tensor in all graph!");
GELOGE(GRAPH_FAILED, "[Check][Param] while op, every output should only find one output tensor in all graph!");
return GRAPH_FAILED;
}
}
bool need_infer_again = false;
// check input and output
for (size_t i = 0; i < ref_out_tensors.size(); i++) {
if (ref_out_tensors[i].empty()) {
continue;
}
auto ref_out_tensor = ref_out_tensors[i].at(0);
auto out_shape = ref_out_tensor.MutableShape();
vector<std::pair<int64_t, int64_t>> data_shape_range;
// ref_i's data and output tensor shape should be same
for (auto &tensor : ref_data_tensors[i]) {
if (ref_out_tensor.GetDataType() != tensor.GetDataType()) {
REPORT_INNER_ERROR("E19999", "node[%s] does not support diff dtype or format among all ref output",
node->GetName().c_str());
GELOGE(GRAPH_FAILED, "[Check][Param] node[%s] does not support diff dtype or format output.",
node->GetName().c_str());
return GRAPH_FAILED;
}
auto data_shape = tensor.MutableShape();
// input is dynamic, here use dim_num
if (data_shape.GetDims() != out_shape.GetDims()) {
GELOGI("After infer, While %s %zu output shape [%s] is not match with input shape [%s].Need infer again.",
node->GetName().c_str(), i, out_shape.ToString().c_str(), data_shape.ToString().c_str());
if (data_shape.GetDimNum() != out_shape.GetDimNum()) {
ref_out_tensor.SetUnknownDimNumShape();
} else {
for (size_t j = 0; j < data_shape.GetDimNum(); ++j) {
if (data_shape.GetDim(j) != out_shape.GetDim(j)) {
if (data_shape.GetDim(j) != UNKNOWN_DIM) {
// if input data is fix shape, output is different, need_infer_again
need_infer_again = true;
}
data_shape.SetDim(j, UNKNOWN_DIM);
}
// set shape rang of while, if dim is unknown ,set shape range as {1,-1}
if (data_shape.GetDim(j) == UNKNOWN_DIM) {
data_shape_range.emplace_back(std::make_pair(1, UNKNOWN_DIM));
} else {
data_shape_range.emplace_back(std::make_pair(data_shape.GetDim(j), data_shape.GetDim(j)));
}
}
ref_out_tensor.SetShape(data_shape);
ref_out_tensor.SetShapeRange(data_shape_range);
}
}
}

auto output_desc = node->GetOpDesc()->MutableOutputDesc(i);
(void)node->GetOpDesc()->UpdateOutputDesc(i, ref_out_tensor);
bool output_changed = TensorDescChanged(ComGraphMakeShared<GeTensorDesc>(ref_out_tensor), output_desc);
if (output_changed) {
changed_nodes.insert(node);
}
}
AttrUtils::SetBool(node->GetOpDesc(), ATTR_NAME_NEED_INFER_AGAIN, need_infer_again);
return GRAPH_SUCCESS;
}

graphStatus InferBasePass::UpdateOutputForMultiBatch(NodePtr &node,
std::vector<std::vector<GeTensorDesc>> &ref_out_tensors,
std::set<NodePtr> &changed_nodes) {
// check sub_graph shape. Get max for update.
for (size_t i = 0; i < ref_out_tensors.size(); ++i) {
if (ref_out_tensors[i].empty()) {
continue;
}

int64_t max_size = 0;
size_t max_shape_index = 0;
auto &ref_out_tensor = ref_out_tensors[i].at(0);
for (size_t j = 0; j < ref_out_tensors[i].size(); ++j) {
auto &tensor = ref_out_tensors[i].at(j);
if (ref_out_tensor.GetDataType() != tensor.GetDataType()) {
REPORT_INNER_ERROR("E19999", "node[%s] does not support diff dtype among all ref output",
node->GetName().c_str());
GELOGE(GRAPH_FAILED, "[Check][Param] node[%s] does not support diff dtype among all ref output",
node->GetName().c_str());
return GRAPH_FAILED;
}

auto shape = tensor.MutableShape();
int64_t size = 1;
for (auto dim : shape.GetDims()) {
if (dim != 0 && INT64_MAX / dim < size) {
REPORT_INNER_ERROR("E19999", "The shape:%s size overflow, node:%s", shape.ToString().c_str(),
node->GetName().c_str());
GELOGE(PARAM_INVALID, "[Check][Overflow] The shape size overflow");
return PARAM_INVALID;
}
size *= dim;
}

if (size > max_size) {
max_size = size;
max_shape_index = j;
}
}

auto output_desc = node->GetOpDesc()->MutableOutputDesc(i);
(void)node->GetOpDesc()->UpdateOutputDesc(i, ref_out_tensors[i].at(max_shape_index));
bool output_changed =
TensorDescChanged(ComGraphMakeShared<GeTensorDesc>(ref_out_tensors[i].at(max_shape_index)), output_desc);
if (output_changed) {
changed_nodes.insert(node);
}
}

return GRAPH_SUCCESS;
}

graphStatus InferBasePass::UpdateParentNodeForBranch(NodePtr &node,
std::vector<std::vector<GeTensorDesc>> &ref_out_tensors,
std::set<NodePtr> &changed_nodes) {
GELOGD("Enter update parent node shape for class branch op process");
if (node->GetOpDesc()->HasAttr(ATTR_NAME_BATCH_NUM)) {
return UpdateOutputForMultiBatch(node, ref_out_tensors, changed_nodes);
}

// check sub_graph shape.If not same ,do unknown shape process
for (size_t i = 0; i < ref_out_tensors.size(); i++) {
if (ref_out_tensors[i].empty()) {
continue;
}
auto ref_out_tensor = ref_out_tensors[i].at(0);
ge::GeShape &ref_out_tensor_shape = ref_out_tensor.MutableShape();
for (auto &tensor : ref_out_tensors[i]) {
if (ref_out_tensor.GetDataType() != tensor.GetDataType()) {
REPORT_INNER_ERROR("E19999", "node[%s] does not support diff dtype among all ref output, shape:%s",
node->GetName().c_str(), ref_out_tensor_shape.ToString().c_str());
GELOGE(GRAPH_FAILED, "[Check][Param] node[%s] does not support diff dtype output", node->GetName().c_str());
return GRAPH_FAILED;
}
auto shape = tensor.MutableShape();
if (shape.GetDims().size() != ref_out_tensor_shape.GetDims().size()) {
GELOGD("node is %s, i : %zu, shape size: %lu, ref_out_tensor_shape size: %lu", node->GetName().c_str(), i,
shape.GetShapeSize(), ref_out_tensor_shape.GetShapeSize());
ref_out_tensor_shape = GeShape(UNKNOWN_RANK);
break;
}
for (size_t j = 0; j < ref_out_tensor_shape.GetDims().size(); j++) {
if (ref_out_tensor_shape.GetDim(j) == shape.GetDim(j)) {
continue;
}
GELOGD("node is %s, i : %zu, j: %zu ,shape size: %lu, ref_out_tensor_shape size: %lu", node->GetName().c_str(),
i, j, shape.GetShapeSize(), ref_out_tensor_shape.GetShapeSize());
(void)ref_out_tensor_shape.SetDim(j, UNKNOWN_DIM);
}
}

auto output_desc = node->GetOpDesc()->MutableOutputDesc(i);
(void)node->GetOpDesc()->UpdateOutputDesc(i, ref_out_tensor);
bool output_changed =
TensorDescChanged(ComGraphMakeShared<GeTensorDesc>(ref_out_tensor), output_desc);
if (output_changed) {
changed_nodes.insert(node);
}
}
return GRAPH_SUCCESS;
}

void InferBasePass::PrintInOutTensorShape(const NodePtr &node, const std::string &phase) {
if (!IsLogEnable(GE, DLOG_DEBUG)) {
return;
}
if (node == nullptr) {
REPORT_INNER_ERROR("E19999", "param node is nullprt, 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;
int32_t out_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 << "(shape:[" << input_desc->MutableShape().ToString() << "]),";
ss << "(format:" << TypeUtils::FormatToSerialString(input_desc->GetFormat()) << "),";
ss << "(dtype:" << TypeUtils::DataTypeToSerialString(input_desc->GetDataType()) << "),";
ss << "(origin_shape:" << input_desc->GetOriginShape().ToString() << "),";
ss << "(origin_format:" << TypeUtils::FormatToSerialString(input_desc->GetOriginFormat()) << "),";
ss << "(origin_dtype:" << TypeUtils::DataTypeToSerialString(input_desc->GetOriginDataType()) << "),";
string range_str;
SerialShapeRange(input_desc, range_str);
ss << "(shape_range:" << range_str << "),";
string value_range_str;
SerialValueRange(input_desc, value_range_str);
ss << "(value_range:" << value_range_str << ")]";
in_idx++;
}
for (const auto &output_desc : op_desc->GetAllOutputsDescPtr()) {
if (output_desc == nullptr) {
out_idx++;
continue;
}
ss << " ";
ss << "output_" << out_idx << " "
<< "tensor: [";
ss << "(shape:[" << output_desc->MutableShape().ToString() << "]),";
ss << "(format:" << TypeUtils::FormatToSerialString(output_desc->GetFormat()) << "),";
ss << "(dtype:" << TypeUtils::DataTypeToSerialString(output_desc->GetDataType()) << "),";
ss << "(origin_shape:" << output_desc->GetOriginShape().ToString() << "),";
ss << "(origin_format:" << TypeUtils::FormatToSerialString(output_desc->GetOriginFormat()) << "),";
ss << "(origin_dtype:" << TypeUtils::DataTypeToSerialString(output_desc->GetOriginDataType()) << "),";
string range_str;
SerialShapeRange(output_desc, range_str);
ss << "(shape_range:" << range_str << "),";
string value_range_str;
SerialValueRange(output_desc, value_range_str);
ss << "(value_range:" << value_range_str << ")]";
out_idx++;
}
ss << "}";
GELOGD("Shape dump [%s], Node name: [%s]. %s", phase.c_str(), node->GetName().c_str(), ss.str().c_str());
}
} // namespace ge

+ 53
- 0
ge/graph/passes/infer_base_pass.h View File

@@ -0,0 +1,53 @@
/**
* 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<NodePtr> &changed_nodes);
void PrintInOutTensorShape(const NodePtr &node, const std::string &phase);

protected:
virtual graphStatus Infer(NodePtr &node) = 0;
virtual bool TensorDescChanged(const GeTensorDescPtr &src, const GeTensorDescPtr &dst) = 0;
virtual graphStatus UpdateInputDescAttr(const GeTensorDescPtr &src, GeTensorDescPtr &dst, bool &changed);
virtual bool NeedInfer(const NodePtr &node);
virtual void AnalyzeFailedInfo(const NodePtr &node);
virtual Status DoRepassForLoopNode(NodePtr &node); // only for infershape, will be deleted
virtual graphStatus UpdatePeerInputs(NodePtr &node); // only for infershape, will be deleted

private:
void AddChangedNodesImmediateRepass(std::set<NodePtr> &changed_nodes);
graphStatus UpdateCurOpInputDesc(const NodePtr &node_ptr);
bool ContainsSubgraph(const NodePtr &node);
std::vector<ComputeGraphPtr> GetCurNodeSubgraphs(const NodePtr &node);
graphStatus UpdateTensorDescToSubgraphData(NodePtr &node, std::set<NodePtr> &changed_nodes);
graphStatus UpdateTensorDescToParentNode(NodePtr &node, std::set<NodePtr> &changed_nodes);
graphStatus UpdateParentNodeForWhile(NodePtr &node, std::vector<std::vector<GeTensorDesc>> &ref_data_tensors,
std::vector<std::vector<GeTensorDesc>> &ref_out_tensors,
std::set<NodePtr> &changed_nodes);
graphStatus UpdateParentNodeForBranch(NodePtr &node, std::vector<std::vector<GeTensorDesc>> &ref_out_tensors,
std::set<NodePtr> &changed_nodes);
graphStatus UpdateOutputForMultiBatch(NodePtr &node, std::vector<std::vector<GeTensorDesc>> &ref_out_tensors,
std::set<NodePtr> &changed_nodes);
};
} // namespace ge
#endif // GE_GRAPH_PASSES_INFER_BASE_PASS_H_

+ 388
- 0
ge/graph/passes/infer_value_range_pass.cc View File

@@ -0,0 +1,388 @@
/**
* Copyright 2020 Huawei Technologies Co., Ltd
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/

#include "graph/passes/infer_value_range_pass.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/folding_pass.h"
#include "common/ge/ge_util.h"
#include "init/gelib.h"

using std::unique_ptr;
namespace ge {
namespace {
#define GET_DATA_BY_DTYPE(DTYPE, TYPE) \
case (DTYPE): \
ConstructValueRange<TYPE>(lower_tensor, higher_tensor, output_tensor_value_range); \
break;

Status RunCpuKernelForValueRange(NodePtr &node, const vector<ConstGeTensorPtr> &inputs,
std::vector<GeTensorPtr> &outputs) {
// should use RunOpKernelWithCheck, RunOpKernel for ut test
auto ret = FoldingPass::RunOpKernel(node, inputs, outputs);
if (ret != SUCCESS) {
auto op_kernel = folding_pass::GetKernelByType(node);
if (op_kernel == nullptr) {
GELOGE(PARAM_INVALID, "Calculate value range failed, no op kernel for node %s type %s", node->GetName().c_str(),
node->GetType().c_str());
return PARAM_INVALID;
}

ret = op_kernel->Compute(node->GetOpDesc(), inputs, outputs);
if (ret != SUCCESS) {
REPORT_INNER_ERROR("E19999", "Calculate for node %s(%s) failed", node->GetName().c_str(),
node->GetType().c_str());
GELOGE(INTERNAL_ERROR, "Calculate for node %s failed in constant folding", node->GetName().c_str());
return ret;
}
}
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) {
PrintInOutTensorShape(node, "before_infer_value_range");

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) {
GELOGE(GRAPH_PARAM_INVALID, "The registered func to infer value range is nullptr.");
return GRAPH_PARAM_INVALID;
}
Operator op = OpDescUtils::CreateOperatorFromNode(node);
auto ret = node->GetOpDesc()->CallInferValueRangeFunc(op);
if (ret != GRAPH_SUCCESS) {
REPORT_CALL_ERROR("E19999", "Node %s call infer value range function failed.", node->GetName().c_str());
GELOGE(GRAPH_FAILED, "[Call][InferFunction] failed, node: %s.", node->GetName().c_str());
return GRAPH_FAILED;
}
return GRAPH_SUCCESS;
}

// Use CPU kernel func to calculate value range
return ConstructInputAndInferValueRange(node);
}

bool InferValueRangePass::NeedInfer(const NodePtr &node) {
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::TensorDescChanged(const GeTensorDescPtr &src, const GeTensorDescPtr &dst) {
bool changed = false;
std::vector<std::pair<int64_t, int64_t>> src_value_range;
std::vector<std::pair<int64_t, int64_t>> dst_value_range;
(void)src->GetValueRange(src_value_range);
(void)dst->GetValueRange(dst_value_range);
if (src_value_range != dst_value_range) {
changed = true;
}
return changed;
}

graphStatus InferValueRangePass::UpdateInputDescAttr(const GeTensorDescPtr &src, GeTensorDescPtr &dst, bool &changed) {
changed = false;
std::vector<std::pair<int64_t, int64_t>> src_value_range;
std::vector<std::pair<int64_t, int64_t>> dst_value_range;
(void)src->GetValueRange(src_value_range);
(void)dst->GetValueRange(dst_value_range);
if (src_value_range != dst_value_range) {
changed = true;
}

dst->SetValueRange(src_value_range);
return GRAPH_SUCCESS;
}

void InferValueRangePass::AnalyzeFailedInfo(const NodePtr &node) {
REPORT_CALL_ERROR("E19999", "Infer value range for node:%s(%s) failed.", node->GetName().c_str(),
node->GetType().c_str());
GELOGE(GE_GRAPH_INFERSHAPE_FAILED, "infer value range failed. node: %s", node->GetName().c_str());
}

bool InferValueRangePass::InputIsDynamic(const NodePtr &node) {
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) {
bool input_is_const_or_has_value_range = true;
auto cur_op_desc = node->GetOpDesc();
auto in_data_anchors = node->GetAllInDataAnchors();
for (auto 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<std::pair<int64_t, int64_t>> value_range;
(void)input_desc.GetValueRange(value_range);
if (value_range.empty()) {
int peer_out_idx = peer_out_anchor->GetIdx();
auto peer_out_desc = peer_node->GetOpDesc()->MutableOutputDesc(static_cast<uint32_t>(peer_out_idx));
(void)peer_out_desc->GetValueRange(value_range);
if (value_range.empty()) {
input_is_const_or_has_value_range = false;
break;
}
}
}
return input_is_const_or_has_value_range;
}

template <typename T>
graphStatus InferValueRangePass::ConstructData(const GeTensorDesc &tensor_desc, bool use_floor_value, GeTensorPtr &output_ptr) {
std::vector<std::pair<int64_t, int64_t>> value_range;
(void)tensor_desc.GetValueRange(value_range);
if (value_range.size() != tensor_desc.GetShape().GetShapeSize()) {
REPORT_INNER_ERROR("E19999", "Value range of input %s is invalid.", tensor_desc.GetName().c_str());
GELOGE(GRAPH_PARAM_INVALID, "Value range of input %s is invalid.", tensor_desc.GetName().c_str());
return GRAPH_PARAM_INVALID;
}

auto value_range_data_num = value_range.size();
unique_ptr<T[]> 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 (auto 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<T>(value_range_j);
}

if (output_ptr->SetData(reinterpret_cast<uint8_t *>(buf.get()), value_range_data_num * sizeof(T)) != GRAPH_SUCCESS) {
GELOGE(GRAPH_FAILED, "set data failed");
return GRAPH_FAILED;
}
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<float>(tensor_desc, use_floor_value, output_ptr);
break;
case DT_DOUBLE:
ret = ConstructData<double>(tensor_desc, use_floor_value, output_ptr);
break;
case DT_UINT8:
ret = ConstructData<uint8_t>(tensor_desc, use_floor_value, output_ptr);
break;
case DT_INT8:
ret = ConstructData<int8_t>(tensor_desc, use_floor_value, output_ptr);
break;
case DT_UINT16:
ret = ConstructData<uint16_t>(tensor_desc, use_floor_value, output_ptr);
break;
case DT_INT16:
ret = ConstructData<int16_t>(tensor_desc, use_floor_value, output_ptr);
break;
case DT_INT32:
ret = ConstructData<int32_t>(tensor_desc, use_floor_value, output_ptr);
break;
case DT_INT64:
ret = ConstructData<int64_t>(tensor_desc, use_floor_value, output_ptr);
break;
default:
GELOGW("Data type:%s is not supported.", TypeUtils::DataTypeToSerialString(data_type).c_str());
ret = GRAPH_FAILED;
}
return ret;
}

vector<ConstGeTensorPtr> InferValueRangePass::ConstructInputTensors(const NodePtr &node, bool use_floor_value) {
vector<ConstGeTensorPtr> input_tensors;
auto cur_op_desc = node->GetOpDesc();
auto in_data_anchors = node->GetAllInDataAnchors();
for (auto 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<GeTensorPtr> const_weight = OpDescUtils::MutableWeights(peer_node);
if (const_weight.empty()) {
REPORT_INNER_ERROR("E19999", "MutableWeights failed, weight is empty, node: %s(%s)",
peer_node->GetName().c_str(), peer_node->GetType().c_str());
GELOGE(INTERNAL_ERROR, "MutableWeights failed, weight is empty, node: %s(%s)", peer_node->GetName().c_str(),
peer_node->GetType().c_str());
return vector<ConstGeTensorPtr>();
}
// const/constant op has only one weight
if (const_weight.at(0) == nullptr) {
REPORT_INNER_ERROR("E19999", "MutableWeights failed, weight of constant is null, node: %s(%s)",
peer_node->GetName().c_str(), peer_node->GetType().c_str());
GELOGE(INTERNAL_ERROR, "MutableWeights failed, weight of constant is null, node name: %s(%s)",
peer_node->GetName().c_str(), peer_node->GetType().c_str());
return vector<ConstGeTensorPtr>();
}
input_tensors.push_back(const_weight.at(0));
continue;
}

// construct input tensor by boundary of value range
const auto &input_tensor_desc = cur_op_desc->GetInputDesc(i);
GeTensorPtr tmp_tensor_ptr = MakeShared<GeTensor>(input_tensor_desc);
if (tmp_tensor_ptr == nullptr) {
REPORT_INNER_ERROR("E19999", "Make shared failed");
GELOGE(MEMALLOC_FAILED, "Make shared failed");
return vector<ConstGeTensorPtr>();
}

auto ret = ConstructDataByType(input_tensor_desc, use_floor_value, tmp_tensor_ptr);
if (ret != GRAPH_SUCCESS) {
REPORT_INNER_ERROR("E19999", "Input %s construct input tensor by boundary of value range failed.",
input_tensor_desc.GetName().c_str());
GELOGE(GRAPH_PARAM_INVALID, "Input %s construct input tensor by boundary of value range failed.",
input_tensor_desc.GetName().c_str());
return vector<ConstGeTensorPtr>();
}
input_tensors.push_back(tmp_tensor_ptr);
}

return input_tensors;
}

graphStatus InferValueRangePass::ConstructInputAndInferValueRange(NodePtr &node) {
auto inputs = ConstructInputTensors(node, true);
if (inputs.empty()) {
return GRAPH_PARAM_INVALID;
}
vector<GeTensorPtr> outputs_lower;
auto ret = RunCpuKernelForValueRange(node, inputs, outputs_lower);
if (ret != SUCCESS) {
REPORT_INNER_ERROR("E19999", "Calculate for node %s(%s) failed", node->GetName().c_str(), node->GetType().c_str());
GELOGE(GRAPH_FAILED, "Calculate for node %s failed in constant folding", node->GetName().c_str());
return GRAPH_FAILED;
}

inputs = ConstructInputTensors(node, false);
if (inputs.empty()) {
return GRAPH_PARAM_INVALID;
}
vector<GeTensorPtr> outputs_higher;
ret = RunCpuKernelForValueRange(node, inputs, outputs_higher);
if (ret != SUCCESS) {
REPORT_INNER_ERROR("E19999", "Calculate for node %s(%s) failed", node->GetName().c_str(), node->GetType().c_str());
GELOGE(GRAPH_FAILED, "Calculate for node %s failed in constant folding", node->GetName().c_str());
return GRAPH_FAILED;
}

// construct value range from output tensor
OpDescPtr node_desc = node->GetOpDesc();
std::vector<std::pair<int64_t, int64_t>> 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 lower_tensor = outputs_lower[i];
auto lower_tensor_shape_size = lower_tensor->GetTensorDesc().GetShape().GetShapeSize();
auto higher_tensor = outputs_higher[i];
auto higher_tensor_shape_size = higher_tensor->GetTensorDesc().GetShape().GetShapeSize();
auto output_tensor_desc = node_desc->MutableOutputDesc(i);
auto output_tensor_shape_size = output_tensor_desc->GetShape().GetShapeSize();
if (output_tensor_shape_size != lower_tensor_shape_size || output_tensor_shape_size != higher_tensor_shape_size) {
GELOGE(GRAPH_PARAM_INVALID, "Value range of output %s is invalid.", output_tensor_desc->GetName().c_str());
}

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_FAILED;
}
output_tensor_desc->SetValueRange(output_tensor_value_range);
}
return GRAPH_SUCCESS;
}

template <typename T>
void InferValueRangePass::ConstructValueRange(const GeTensorPtr &left_tensor, const GeTensorPtr &right_tensor,
std::vector<std::pair<int64_t, int64_t>> &value_range) {
auto x = reinterpret_cast<const T *>(left_tensor->GetData().GetData());
auto y = reinterpret_cast<const T *>(right_tensor->GetData().GetData());
for (auto j = 0; j < left_tensor->GetTensorDesc().GetShape().GetShapeSize(); ++j) {
auto left = static_cast<int64_t>(*(x + j));
auto right = static_cast<int64_t>(*(y + j));
value_range.emplace_back(std::make_pair(left, right));
}
}
} // namespace ge

+ 44
- 0
ge/graph/passes/infer_value_range_pass.h View File

@@ -0,0 +1,44 @@
/**
* 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;
bool TensorDescChanged(const GeTensorDescPtr &src, const GeTensorDescPtr &dst) override;
graphStatus UpdateInputDescAttr(const GeTensorDescPtr &src, GeTensorDescPtr &dst, bool &changed) override;
bool NeedInfer(const NodePtr &node) override;
void AnalyzeFailedInfo(const NodePtr &node) override;

private:
bool InputIsDynamic(const NodePtr &node);
bool InputIsConstOrHasValueRange(const NodePtr &node);
template <typename T>
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<ConstGeTensorPtr> ConstructInputTensors(const NodePtr &node, bool use_floor_value);
template <typename T>
void ConstructValueRange(const GeTensorPtr &left_tensor, const GeTensorPtr &right_tensor,
std::vector<std::pair<int64_t, int64_t>> &value_range);
graphStatus ConstructInputAndInferValueRange(NodePtr &node);
};
} // namespace ge
#endif // GE_GRAPH_PASSES_INFER_VALUE_RANGE_PASS_H_

+ 350
- 30
ge/graph/passes/infershape_pass.cc View File

@@ -19,15 +19,84 @@
#include "framework/common/debug/ge_log.h"
#include "analyzer/analyzer.h"
#include "framework/common/util.h"
#include "graph/shape_refiner.h"
#include "graph/utils/graph_utils.h"
#include "graph/utils/node_utils.h"
#include "graph/common/omg_util.h"
#include "graph/debug/ge_attr_define.h"
#include "utils/tensor_utils.h"
#include "utils/type_utils.h"
#include "graph/debug/ge_util.h"
#include "graph/operator_factory_impl.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 {
const char *const kPreOpInputShapeRange = "_pre_op_in_range";
thread_local std::unordered_map<NodePtr, InferenceContextPtr> context_map;
}

GE_FUNC_DEV_VISIBILITY GE_FUNC_HOST_VISIBILITY void InferShapePass::ClearContextMap() { context_map.clear(); }

InferenceContextPtr CreateInferenceContextPtr(const std::unordered_map<NodePtr, InferenceContextPtr> &context_map,
const NodePtr &node) {
if (node == nullptr) {
GELOGE(GRAPH_FAILED, "node is null");
return nullptr;
}
InferenceContextPtr inference_context = std::shared_ptr<InferenceContext>(InferenceContext::Create());
if (inference_context == nullptr) {
REPORT_CALL_ERROR("E19999", "Failed to alloc InferenceContext, node:%s", node->GetName().c_str());
GELOGE(GRAPH_FAILED, "[Alloc][InferenceContext] failed.");
return nullptr;
}

auto all_in_data_anchors = node->GetAllInDataAnchors();
std::vector<std::vector<ShapeAndType>> input_shapes_and_types(all_in_data_anchors.size());
std::vector<std::string> marks;

bool has_input_shapes_and_types = false;
for (const auto &in_anchor : all_in_data_anchors) {
const auto &out_anchor = in_anchor->GetPeerOutAnchor();
if (out_anchor == nullptr) {
continue;
}

auto input_node = out_anchor->GetOwnerNode();
if (input_node == nullptr) {
continue;
}

auto iter = context_map.find(input_node);
if (iter != context_map.end()) {
const auto &src_context = iter->second;
GE_IF_BOOL_EXEC(src_context == nullptr, REPORT_INNER_ERROR("E19999", "src_context is null.");
GELOGE(GRAPH_FAILED, "[Check][Param] src_context is null."); return nullptr);
GELOGD("node:%s get %ld marks from node:%s", node->GetName().c_str(), src_context->GetMarks().size(),
input_node->GetName().c_str());
for (auto mark : src_context->GetMarks()) {
marks.push_back(mark);
}
auto output_idx = out_anchor->GetIdx();
auto input_idx = in_anchor->GetIdx();
auto output_shape_and_type = src_context->GetOutputHandleShapesAndTypes();
if (output_idx < static_cast<int>(output_shape_and_type.size())) {
GELOGI("Add shape and type from %s:%d to %s:%d", input_node->GetName().c_str(), output_idx,
node->GetName().c_str(), input_idx);
input_shapes_and_types[input_idx] = output_shape_and_type[output_idx];
has_input_shapes_and_types = true;
} else {
GELOGI("[%s] Output out of range. index = %d, size = %zu", node->GetName().c_str(), output_idx,
output_shape_and_type.size());
}
}
}

if (has_input_shapes_and_types) {
inference_context->SetInputHandleShapesAndTypes(std::move(input_shapes_and_types));
}
inference_context->SetMarks(marks);

return inference_context;
}

void SerialShapeRange(const GeTensorDescPtr &desc, std::string &desc_str) {
desc_str += "[";
@@ -61,7 +130,8 @@ std::string GetInTensorInfoWithString(const ge::NodePtr &node) {
if (in_idx > 0) {
ss << " ";
}
ss << "input_" << in_idx << " " << "tensor: [";
ss << "input_" << in_idx << " "
<< "tensor: [";
ss << "(shape:[" << input_desc->MutableShape().ToString() << "]),";
ss << "(format:" << TypeUtils::FormatToSerialString(input_desc->GetFormat()) << "),";
ss << "(dtype:" << TypeUtils::DataTypeToSerialString(input_desc->GetDataType()) << "),";
@@ -76,28 +146,180 @@ std::string GetInTensorInfoWithString(const ge::NodePtr &node) {
return ss.str();
}

Status InferShapePass::Run(NodePtr &node) {
// kOptimizeAfterSubGraph exist means after subgraph
auto ret = ShapeRefiner::InferShapeAndType(node, !OptionExists(kOptimizeAfterSubGraph));
if (ret != GRAPH_SUCCESS) {
// select INFERSHAPE failed info
auto graph = node->GetOwnerComputeGraph();
GE_CHECK_NOTNULL(graph);
auto root_graph = ge::GraphUtils::FindRootGraph(graph);
GE_CHECK_NOTNULL(root_graph);
analyzer::DataInfo analyze_info{root_graph->GetSessionID(), root_graph->GetGraphID(),
analyzer::INFER_SHAPE, node, "InferShapeFailed!"};
(void)Analyzer::GetInstance()->DoAnalyze(analyze_info);
(void)Analyzer::GetInstance()->SaveAnalyzerDataToFile(root_graph->GetSessionID(),
root_graph->GetGraphID());

REPORT_CALL_ERROR("E19999", "Call InferShapeAndType for node:%s(%s) failed, input_tensor:%s",
node->GetName().c_str(), node->GetType().c_str(), GetInTensorInfoWithString(node).c_str());
GELOGE(GE_GRAPH_INFERSHAPE_FAILED, "[Call][InferShapeAndType] for node:%s(%s) failed, input_tensor:%s",
node->GetName().c_str(), node->GetType().c_str(), GetInTensorInfoWithString(node).c_str());
return GE_GRAPH_INFERSHAPE_FAILED;
void InferShapePass::AnalyzeFailedInfo(const NodePtr &node) {
auto graph = node->GetOwnerComputeGraph();
if (graph == nullptr) {
GELOGW("Owner compute graph of node %s is nullptr", node->GetName().c_str());
}
auto root_graph = ge::GraphUtils::FindRootGraph(graph);
if (root_graph == nullptr) {
GELOGW("Root compute graph of node %s is nullptr", node->GetName().c_str());
}
analyzer::DataInfo analyze_info{root_graph->GetSessionID(), root_graph->GetGraphID(), analyzer::INFER_SHAPE, node,
"InferShapeFailed!"};
(void)Analyzer::GetInstance()->DoAnalyze(analyze_info);
(void)Analyzer::GetInstance()->SaveAnalyzerDataToFile(root_graph->GetSessionID(), root_graph->GetGraphID());
REPORT_CALL_ERROR("E19999", "Call InferShapeAndType for node:%s(%s) failed, input_tensor:%s", node->GetName().c_str(),
node->GetType().c_str(), GetInTensorInfoWithString(node).c_str());
GELOGE(GE_GRAPH_INFERSHAPE_FAILED, "[Call][InferShapeAndType] for node:%s(%s) failed, input_tensor:%s",
node->GetName().c_str(), node->GetType().c_str(), GetInTensorInfoWithString(node).c_str());
}

bool InferShapePass::TensorDescChanged(const GeTensorDescPtr &src, const GeTensorDescPtr &dst) {
bool changed = false;
const auto &dst_dims = dst->GetShape().GetDims();
const auto &src_dims = src->GetShape().GetDims();
if (dst_dims != src_dims) {
changed = true;
}
return changed;
}

graphStatus InferShapePass::UpdateInputDescAttr(const GeTensorDescPtr &src, GeTensorDescPtr &dst, bool &changed) {
dst->SetOriginShape(src->GetOriginShape());
dst->SetShape(src->MutableShape());
dst->SetDataType(src->GetDataType());
dst->SetOriginDataType(src->GetOriginDataType());
if (src->MutableShape().GetDims() != UNKNOWN_RANK) {
std::vector<std::pair<int64_t, int64_t>> shape_range;
(void)src->GetShapeRange(shape_range);
dst->SetShapeRange(shape_range);
}
std::vector<int64_t> pre_op_in_range;
if (ge::AttrUtils::GetListInt(*src, kPreOpInputShapeRange, pre_op_in_range)) {
(void)ge::AttrUtils::SetListInt(*dst, kPreOpInputShapeRange, pre_op_in_range);
}
ge::TensorUtils::SetRealDimCnt(*dst, static_cast<uint32_t>(src->MutableShape().GetDims().size()));
return GRAPH_SUCCESS;
}

graphStatus InferShapePass::Infer(NodePtr &node) {
bool is_unknown_graph = node->GetOwnerComputeGraph()->GetGraphUnknownFlag();
auto opdesc = node->GetOpDesc();
if (node->Verify() != GRAPH_SUCCESS) {
REPORT_CALL_ERROR("E19999", "Verifying %s failed.", node->GetName().c_str());
GELOGE(GRAPH_FAILED, "[Call][Verify] Verifying %s failed.", node->GetName().c_str());
return GRAPH_FAILED;
}
PrintInOutTensorShape(node, "before_infershape");
Operator op = OpDescUtils::CreateOperatorFromNode(node);

if (!is_unknown_graph) {
auto inference_context = CreateInferenceContextPtr(context_map, node);
GE_CHECK_NOTNULL(inference_context);
GELOGD("create context for node:%s, marks %zu", node->GetName().c_str(), inference_context->GetMarks().size());
op.SetInferenceContext(inference_context);
}

graphStatus status = CallInferShapeFunc(node, op);
if (status != GRAPH_PARAM_INVALID && status != GRAPH_SUCCESS) {
REPORT_CALL_ERROR("E19999", "%s call infer function failed.", node->GetName().c_str());
GELOGE(GRAPH_FAILED, "[Call][InferFunction] failed, node:%s.", node->GetName().c_str());
return GRAPH_FAILED;
}
if (!is_unknown_graph) {
auto ctx_after_infer = op.GetInferenceContext();
if (ctx_after_infer != nullptr) {
GELOGD("[%s] after infershape. mark:%zu", node->GetName().c_str(), ctx_after_infer->GetMarks().size());
if (!ctx_after_infer->GetOutputHandleShapesAndTypes().empty() || !ctx_after_infer->GetMarks().empty()) {
GELOGD("[%s] set inference context after. mark:%zu", node->GetName().c_str(),
ctx_after_infer->GetMarks().size());
(void)context_map.emplace(node, ctx_after_infer);
}
}
}

return GRAPH_SUCCESS;
}

graphStatus InferShapePass::CallInferShapeFunc(NodePtr &node, Operator &op) {
auto op_desc = node->GetOpDesc();
const auto &op_type = op_desc->GetType();
auto ret = op_desc->CallInferFunc(op);
if (ret == GRAPH_PARAM_INVALID) {
// Op ir no infer func, try to get infer func from operator factory
auto node_op = ge::OperatorFactory::CreateOperator("node_op", op_desc->GetType());
if (node_op.IsEmpty()) {
GELOGW("get op from OperatorFactory fail. opType: %s", op_type.c_str());
return ret;
}

GELOGD("get op from OperatorFactory success. opType: %s", op_type.c_str());
auto temp_op_desc = ge::OpDescUtils::GetOpDescFromOperator(node_op);
node_op.BreakConnect();
if (temp_op_desc == nullptr) {
REPORT_CALL_ERROR("E19999", "GetOpDescFromOperator failed, return nullptr.");
GELOGE(GRAPH_FAILED, "[Get][OpDesc] temp op desc is null");
return GRAPH_FAILED;
}
if (!op_desc->UpdateInputName(temp_op_desc->GetAllInputName())) {
GELOGW("InferShapeAndType UpdateInputName failed");
for (const auto &out_desc : op_desc->GetAllOutputsDescPtr()) {
if (out_desc != nullptr && out_desc->GetShape().GetDims().empty()) {
break;
}
return GRAPH_SUCCESS;
}
}
if (!op_desc->UpdateOutputName(temp_op_desc->GetAllOutputName())) {
GELOGW("InferShapeAndType UpdateOutputName failed");
}
op_desc->AddInferFunc(temp_op_desc->GetInferFunc());
ret = op_desc->CallInferFunc(op);
GELOGI("op CallInferFunc second. ret: %u", ret);
}
return ret;
}

graphStatus InferShapePass::UpdatePeerInputs(NodePtr &node) {
bool is_unknown_graph = node->GetOwnerComputeGraph()->GetGraphUnknownFlag();
if (is_unknown_graph) {
PrintInOutTensorShape(node, "after_infershape when running");
return GRAPH_SUCCESS;
}
UpdateInputOutputOriginAttr(node);
if (NodeUtils::UpdatePeerNodeInputDesc(node) != SUCCESS) {
return GRAPH_FAILED;
}
PrintInOutTensorShape(node, "after_infershape");
return GRAPH_SUCCESS;
}

void InferShapePass::UpdateInputOutputOriginAttr(NodePtr &node) {
auto op_desc = node->GetOpDesc();
for (const auto &out_anchor : node->GetAllOutDataAnchors()) {
auto output_tensor = op_desc->MutableOutputDesc(out_anchor->GetIdx());
if (output_tensor == nullptr) {
continue;
}
if (output_tensor->MutableShape().GetDims().empty()) {
output_tensor->SetOriginShape(output_tensor->GetShape());
}
ge::TensorUtils::SetRealDimCnt(*output_tensor,
static_cast<uint32_t>(output_tensor->GetOriginShape().GetDims().size()));
output_tensor->SetOriginDataType(output_tensor->GetDataType());
// set output origin shape range
std::vector<std::pair<int64_t, int64_t>> range;
(void)output_tensor->GetShapeRange(range);
output_tensor->SetOriginShapeRange(range);
GELOGD("node name is %s, origin shape is %ld, origin format is %s, origin data type is %s", node->GetName().c_str(),
output_tensor->GetOriginShape().GetShapeSize(),
TypeUtils::FormatToSerialString(output_tensor->GetOriginFormat()).c_str(),
TypeUtils::DataTypeToSerialString(output_tensor->GetOriginDataType()).c_str());
}
for (const auto &in_anchor : node->GetAllInDataAnchors()) {
auto input_tensor = op_desc->MutableInputDesc(in_anchor->GetIdx());
if (input_tensor == nullptr) {
continue;
}
// set input origin shape range
std::vector<std::pair<int64_t, int64_t>> range;
(void)input_tensor->GetShapeRange(range);
input_tensor->SetOriginShapeRange(range);
}
}

Status InferShapePass::DoRepassForLoopNode(NodePtr &node) {
GE_CHK_STATUS_RET_NOLOG(RePassLoopNode(node));
bool need_repass = false;
auto has_attr = AttrUtils::GetBool(node->GetOpDesc(), ATTR_NAME_NEED_INFER_AGAIN, need_repass);
@@ -150,13 +372,13 @@ Status InferShapePass::RePassLoopNode(const NodePtr &node) {
GE_CHK_STATUS_RET(GetOriginalType(node, node_type),
"[Get][OriginalType] of node:%s failed.", node->GetName().c_str());
if (kNextIterationOpTypes.count(node_type) > 0) {
return RePassNode(kMergeOpTypes); // Re-Pass Merge
return RePassNode(kMergeOpTypes); // Re-Pass Merge
}

if (kMergeOpTypes.count(node_type) > 0) {
if (node->GetOpDesc()->HasAttr(ATTR_NAME_NEED_INFER_AGAIN)) {
node->GetOpDesc()->DelAttr(ATTR_NAME_NEED_INFER_AGAIN);
return RePassNode(kSwitchOpTypes); // Re-Pass Switch
return RePassNode(kSwitchOpTypes); // Re-Pass Switch
}
return SUCCESS;
}
@@ -164,12 +386,110 @@ Status InferShapePass::RePassLoopNode(const NodePtr &node) {
if (kSwitchOpTypes.count(node_type) > 0) {
if (node->GetOpDesc()->HasAttr(ATTR_NAME_NEED_INFER_AGAIN)) {
node->GetOpDesc()->DelAttr(ATTR_NAME_NEED_INFER_AGAIN);
return ExProcNode(kExitOpTypes, &InferShapePass::AddNodeResume, "need resume"); // Resume Exit
return ExProcNode(kExitOpTypes, &InferShapePass::AddNodeResume, "need resume"); // Resume Exit
} else {
return ExProcNode(kExitOpTypes, &InferShapePass::AddNodeSuspend, "need suspend"); // Suspend Exit
return ExProcNode(kExitOpTypes, &InferShapePass::AddNodeSuspend, "need suspend"); // Suspend Exit
}
}

return SUCCESS;
}

GE_FUNC_DEV_VISIBILITY GE_FUNC_HOST_VISIBILITY
graphStatus InferShapePass::InferShapeAndType(NodePtr &node) {
GE_CHECK_NOTNULL(node);
GE_CHECK_NOTNULL(node->GetOpDesc());
InferShapePass pass;
std::set<NodePtr> unused_changed_nodes;
return pass.InferAndUpdate(node, true, unused_changed_nodes);
}
GE_FUNC_DEV_VISIBILITY GE_FUNC_HOST_VISIBILITY
graphStatus InferShapePass::InferShapeAndType(NodePtr &node, bool before_subgraph) {
GE_CHECK_NOTNULL(node);
GE_CHECK_NOTNULL(node->GetOpDesc());
InferShapePass pass;
std::set<NodePtr> unused_changed_nodes;
return pass.InferAndUpdate(node, before_subgraph, unused_changed_nodes);
}


graphStatus InferShapeForRunning::Infer(NodePtr &node) {
auto opdesc = node->GetOpDesc();
vector<ge::DataType> temp_dtype;
for (auto &tensor_desc : opdesc->GetAllOutputsDescPtr()) {
temp_dtype.emplace_back(tensor_desc->GetDataType());
}
PrintInOutTensorShape(node, "before_infershape when running");

Operator op = OpDescUtils::CreateOperatorFromNode(node);
graphStatus status = CallInferShapeFuncForRunning(node, op);
if (status == GRAPH_PARAM_INVALID || status == GRAPH_SUCCESS) {
// ensure the dtype is not changed after infershape in running
auto after_opdesc = node->GetOpDesc();
GE_IF_BOOL_EXEC(after_opdesc == nullptr, REPORT_INNER_ERROR("E19999", "param node has no opdesc, check invalid.");
GELOGE(GRAPH_FAILED, "[Get][OpDesc] after_opdesc is null."); return GRAPH_FAILED);
auto all_output_tensor = after_opdesc->GetAllOutputsDescPtr();
for (size_t i = 0; i < all_output_tensor.size(); ++i) {
if (all_output_tensor.at(i)->GetDataType() != temp_dtype[i]) {
GELOGD("Op %s output %zu need reset dtype,original dtype is %s, new dtype is %s", node->GetName().c_str(), i,
TypeUtils::DataTypeToSerialString(all_output_tensor.at(i)->GetDataType()).c_str(),
TypeUtils::DataTypeToSerialString(temp_dtype[i]).c_str());
all_output_tensor.at(i)->SetDataType(temp_dtype[i]);
}
}
PrintInOutTensorShape(node, "after_infershape when running");
return GRAPH_SUCCESS;
} else {
REPORT_CALL_ERROR("E19999", "%s call infer function failed.", node->GetName().c_str());
GELOGE(GRAPH_FAILED, "[Call][InferFunction] failed, node:%s.", node->GetName().c_str());
return GRAPH_FAILED;
}
}

graphStatus InferShapeForRunning::CallInferShapeFuncForRunning(NodePtr &node, Operator &op) {
auto op_desc = node->GetOpDesc();
const auto &op_type = op_desc->GetType();

// Create InferenceContext to avoid null pointer access.
const static std::set<std::string> force_context_op_types{"Enter", "Switch", "RefSwitch"};
if (force_context_op_types.count(op_type) > 0) {
GELOGD("Set InferenceContext for node [%s]", op_desc->GetName().c_str());
op.SetInferenceContext(std::shared_ptr<InferenceContext>(InferenceContext::Create()));
}

// Get infer func and execute
auto ret = op_desc->CallInferFunc(op);
if (ret == GRAPH_PARAM_INVALID) {
GELOGD("NodeUtils::GetNodeType return value is: [%s]", NodeUtils::GetNodeType(*node).c_str());
auto origin_type = NodeUtils::GetNodeType(*node);
auto infer_func = ge::OperatorFactoryImpl::GetInferShapeFunc(origin_type);
if (infer_func == nullptr) {
REPORT_INNER_ERROR("E19999", "Failed to Get InferFunc. type is %s", origin_type.c_str());
GELOGE(GRAPH_FAILED, "[Get][InferFunc] failed. type is %s", origin_type.c_str());
return GRAPH_FAILED;
}
op_desc->AddInferFunc(infer_func);
ret = op_desc->CallInferFunc(op);
GELOGI("op CallInferFunc second. ret: %u", ret);
}
return ret;
}
bool InferShapeForRunning::TensorDescChanged(const GeTensorDescPtr &src, const GeTensorDescPtr &dst) {
bool changed = false;
const auto &dst_dims = dst->GetShape().GetDims();
const auto &src_dims = src->GetShape().GetDims();
if (dst_dims != src_dims) {
changed = true;
}
return changed;
}

GE_FUNC_DEV_VISIBILITY GE_FUNC_HOST_VISIBILITY
graphStatus InferShapeForRunning::InferShapeAndTypeForRunning(NodePtr &node, bool before_subgraph) {
GE_CHECK_NOTNULL(node);
GE_CHECK_NOTNULL(node->GetOpDesc());
InferShapeForRunning pass;
std::set<NodePtr> unused_changed_nodes;
return pass.InferAndUpdate(node, before_subgraph, unused_changed_nodes);
}
} // namespace ge

+ 27
- 11
ge/graph/passes/infershape_pass.h View File

@@ -17,22 +17,38 @@
#ifndef GE_GRAPH_PASSES_INFERSHAPE_PASS_H_
#define GE_GRAPH_PASSES_INFERSHAPE_PASS_H_

#include "graph/passes/base_pass.h"
#include "graph/passes/infer_base_pass.h"

namespace ge {
class InferShapePass : public BaseNodePass {
class InferShapePass : public InferBasePass {
public:
///
/// Entry of the InferShapePass optimizer
/// @param [in] graph: Input ComputeGraph
/// @return SUCCESS: Execution succeed
/// @return OTHERS: Execution failed
/// @author
///
Status Run(ge::NodePtr &node) override;
static void ClearContextMap();
graphStatus Infer(NodePtr &node) override;
bool TensorDescChanged(const GeTensorDescPtr &src, const GeTensorDescPtr &dst) override;
graphStatus UpdateInputDescAttr(const GeTensorDescPtr &src, GeTensorDescPtr &dst, bool &changed) override;
void AnalyzeFailedInfo(const NodePtr &node) override;

static graphStatus InferShapeAndType(NodePtr &node); // temp: visible static func
static graphStatus InferShapeAndType(NodePtr &node, bool before_subgraph); // temp: visible static func

private:
graphStatus CallInferShapeFunc(NodePtr &node, Operator &op);
graphStatus UpdatePeerInputs(NodePtr &node) override; // only for infershape, will be deleted
void UpdateInputOutputOriginAttr(NodePtr &node); // only for infershape, will be deleted
Status DoRepassForLoopNode(NodePtr &node) override; // only for infershape, will be deleted
Status RePassLoopNode(const NodePtr &node); // only for infershape, will be deleted
};


class InferShapeForRunning : public InferBasePass {
public:
graphStatus Infer(NodePtr &node) override;
bool TensorDescChanged(const GeTensorDescPtr &src, const GeTensorDescPtr &dst) override;

static graphStatus InferShapeAndTypeForRunning(NodePtr &node, bool before_subgraph); // temp: visible static func

private:
Status RePassLoopNode(const NodePtr &node);
graphStatus CallInferShapeFuncForRunning(NodePtr &node, Operator &op);
};
} // namespace ge
#endif // GE_GRAPH_PASSES_INFERSHAPE_PASS_H_

+ 3
- 0
ge/graph/preprocess/graph_preprocess.cc View File

@@ -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"
@@ -1989,6 +1990,8 @@ Status GraphPrepare::InferShapeForPreprocess() {
names_to_passes.emplace_back("MergePass", &merge_pass);
InferShapePass infer_shape_pass;
names_to_passes.emplace_back("InferShapePass", &infer_shape_pass);
InferValueRangePass infer_value_pass;
names_to_passes.emplace_back("InferValuePass", &infer_value_pass);
ReplaceWithEmptyConstPass replace_with_empty_const_pass;
names_to_passes.emplace_back("ReplaceWithEmptyConstPass", &replace_with_empty_const_pass);
DimensionComputePass dimension_compute_pass;


+ 6
- 1
tests/ut/ge/CMakeLists.txt View File

@@ -219,7 +219,9 @@ 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"
@@ -478,7 +480,7 @@ set(GRAPH_BUILD_COMMON_SRC_FILES
)

set(GRAPH_PASS_COMMON_SRC_FILES
"${GE_CODE_DIR}/ge/graph/passes/pass_manager.cc"
"${GE_CODE_DIR}/ge/graph/passes/pass_manager.cc"
"${GE_CODE_DIR}/ge/graph/passes/base_pass.cc"
"${GE_CODE_DIR}/ge/graph/passes/variable_prepare_op_pass.cc"
"${GE_CODE_DIR}/ge/graph/passes/variable_ref_delete_op_pass.cc"
@@ -532,7 +534,9 @@ set(GRAPH_PASS_COMMON_SRC_FILES
"${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"
@@ -708,6 +712,7 @@ set(PASS_TEST_FILES
"graph/passes/net_output_pass_unittest.cc"
"graph/passes/no_use_reshape_remove_pass_unittest.cc"
"graph/passes/infershape_pass_unittest.cc"
"graph/passes/infer_value_range_pass_unittest.cc"
"graph/passes/mark_force_unknown_for_cond_pass_unittest.cc"
"graph/passes/multi_batch_clone_pass_unittest.cc"
"graph/passes/replace_with_empty_const_pass_unittest.cc"


+ 281
- 0
tests/ut/ge/graph/passes/infer_value_range_pass_unittest.cc View File

@@ -0,0 +1,281 @@
/**
* 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 <gtest/gtest.h>

#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() {}
};

static NodePtr CreateNode(ComputeGraph &graph, const string &name, const string &type, int in_num, int out_num) {
auto op_desc = std::make_shared<OpDesc>("AddN", "AddN");
return graph.AddNode(op_desc);
}

TEST_F(UtestGraphInferValueRangePass, infer_pass_not_register) {
auto graph = std::make_shared<ComputeGraph>("test_graph");
GeTensorDesc ge_tensor_desc(GeShape({1, 1, 4, 192}), ge::FORMAT_NCHW, DT_FLOAT16);
auto addn_op_desc = std::make_shared<OpDesc>("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<std::pair<int64_t, int64_t>> 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;
};

TEST_F(UtestGraphInferValueRangePass, infer_pass_when_call_1_not_infer) {
INFER_VALUE_RANGE_CUSTOM_FUNC_REG(Shape, INPUT_IS_DYNAMIC, ShapeValueInfer);
auto graph = std::make_shared<ComputeGraph>("test_graph");
GeTensorDesc ge_tensor_desc(GeShape({1, 1, 4, 192}), ge::FORMAT_NCHW, DT_INT32);
std::vector<std::pair<int64_t, int64_t>> 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<OpDesc>("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<std::pair<int64_t, int64_t>> value_range;
output_0_desc.GetValueRange(value_range);
EXPECT_EQ(value_range.empty(), true);
}

TEST_F(UtestGraphInferValueRangePass, infer_pass_when_call_1_infer) {
// sqrt -> shape -> Output
INFER_VALUE_RANGE_CUSTOM_FUNC_REG(Shape, INPUT_IS_DYNAMIC, ShapeValueInfer);
auto graph = std::make_shared<ComputeGraph>("test_graph");
GeTensorDesc sqrt_tensor_desc(GeShape({-1, -1, 4, 192}), ge::FORMAT_NCHW, DT_INT32);
std::vector<std::pair<int64_t, int64_t>> 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<OpDesc>("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<OpDesc>("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<OpDesc>("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<std::pair<int64_t, int64_t>> value_range;
output_0_desc.GetValueRange(value_range);
EXPECT_EQ(value_range.size(), 4);
std::vector<int64_t> target_value_range = {1, 100, 1, 240, 4, 4, 192, 192};
std::vector<int64_t> 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(), 0);

/*
INFER_VALUE_RANGE_DEFAULT_REG(Output);
ret = infer_pass.Run(Output_node);
EXPECT_EQ(ret, FAILED);
auto in_0_desc_after_infer = Output_node->GetOpDesc()->GetInputDesc(0);
value_range.clear();
in_0_desc_after_infer.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);

auto out_0_desc = Output_node->GetOpDesc()->GetOutputDesc(0);
value_range.clear();
out_0_desc.GetValueRange(value_range);
EXPECT_EQ(value_range.size(), 0);
*/
}

class AddKernel : public Kernel {
public:
Status Compute(const ge::OpDescPtr op_desc_ptr, const std::vector<ge::ConstGeTensorPtr> &input,
std::vector<ge::GeTensorPtr> &v_output) override {
vector<int64_t> data_vec;
auto data_num = input[0]->GetTensorDesc().GetShape().GetShapeSize();
auto x1_data = reinterpret_cast<const int64_t *>(input[0]->GetData().data());
auto x2_data = reinterpret_cast<const int64_t *>(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<ge::GeTensor>(input[0]->GetTensorDesc(), (uint8_t *)data_vec.data(),
data_num * sizeof(int64_t));
v_output.emplace_back(const_tensor);
return SUCCESS;
}
};
REGISTER_KERNEL(ADD, AddKernel);

TEST_F(UtestGraphInferValueRangePass, infer_pass_when_call_2_infer) {
// shape --- add --- sqrt
// constant /
INFER_VALUE_RANGE_DEFAULT_REG(Add);
auto graph = std::make_shared<ComputeGraph>("test_graph");

vector<int64_t> dims_vec = {4};
vector<int64_t> 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<ge::GeTensor>(const_tensor_desc, (uint8_t *)data_vec.data(), data_vec.size() * sizeof(int64_t));

auto const_op_desc = std::make_shared<OpDesc>("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<std::pair<int64_t, int64_t>> value_range = {make_pair(1, 100), make_pair(1, 240),
make_pair(4, 4), make_pair(192, 192)};
shape_tensor_desc.SetValueRange(value_range);
auto shape_op_desc = std::make_shared<OpDesc>("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<OpDesc>("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<std::pair<int64_t, int64_t>> out_value_range;
output_0_desc.GetValueRange(out_value_range);
EXPECT_EQ(out_value_range.size(), 4);

std::vector<int64_t> target_value_range = {2, 101, 2, 241, 5, 5, 193, 193};
std::vector<int64_t> 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, test_value_range_infer_and_set_get) {
using std::make_pair;
std::function<ge::graphStatus(ge::Operator &)> ShapeValueInfer_ = [](ge::Operator &op) -> ge::graphStatus {
auto op_desc = OpDescUtils::GetOpDescFromOperator(op);
auto output_tensor_desc = op_desc->MutableOutputDesc(0);
std::vector<std::pair<int64_t, int64_t>> 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;
};

INFER_VALUE_RANGE_CUSTOM_FUNC_REG(Shape, INPUT_IS_DYNAMIC, ShapeValueInfer_);
string op_type = "Shape";

auto graph = std::make_shared<ComputeGraph>("test_graph");
auto shape_op_desc = std::make_shared<OpDesc>("node_name", op_type);
GeTensorDesc tensor_desc(GeShape({-1, -1, 4, 192}), ge::FORMAT_NCHW, DT_INT32);
std::vector<std::pair<int64_t, int64_t>> shape_range = {make_pair(1, 100), make_pair(1, 240),
make_pair(4, 4), make_pair(192, 192)};
tensor_desc.SetShapeRange(shape_range);
shape_op_desc->AddInputDesc(tensor_desc);
GeTensorDesc out_tensor_desc(GeShape({4}), ge::FORMAT_NCHW, DT_INT32);
shape_op_desc->AddOutputDesc(out_tensor_desc);
auto shape_node = graph->AddNode(shape_op_desc);
Operator op = OpDescUtils::CreateOperatorFromNode(shape_node);
auto ret = shape_node->GetOpDesc()->CallInferValueRangeFunc(op);
ASSERT_EQ(ret, GRAPH_SUCCESS);

auto output_0_desc = shape_node->GetOpDesc()->GetOutputDesc(0);
std::vector<std::pair<int64_t, int64_t>> value_range;
output_0_desc.GetValueRange(value_range);
EXPECT_EQ(value_range.size(), 4);

std::vector<int64_t> target_value_range = {1, 100, 1, 240, 4, 4, 192, 192};
std::vector<int64_t> 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);
}

} // namespace ge

+ 327
- 2
tests/ut/ge/graph/passes/infershape_pass_unittest.cc View File

@@ -15,6 +15,7 @@
*/

#include <gtest/gtest.h>
#include <operator_factory_impl.h>

#define protected public
#define private public
@@ -22,9 +23,12 @@

#include "graph/utils/tensor_utils.h"
#include "graph/utils/graph_utils.h"
#include "graph/operator_factory.h"
#include "graph/operator_reg.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"


using namespace std;
using namespace testing;
@@ -35,6 +39,113 @@ class UtestGraphInfershapePass : public testing::Test {
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<int64_t> 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<GeTensor>(weight_desc, (uint8_t *)weight, sizeof(weight));
OpDescUtils::SetWeights(const1, {tensor});

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<int64_t> 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<int64_t> 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<int64_t> 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);

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);
}
}

static NodePtr CreateNode(ComputeGraph &graph, const string &name, const string &type, int in_num, int out_num) {
OpDescPtr op_desc = std::make_shared<OpDesc>(name, type);
op_desc->SetStreamId(0);
@@ -158,4 +269,218 @@ TEST_F(UtestGraphInfershapePass, stop_node_for_while_loop) {

EXPECT_EQ(ge_passes.Run(names_to_passes), SUCCESS);
}

TEST_F(UtestGraphInfershapePass, infer_with_case_subgraph) {
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);
InferShapePass infershape_pass;
EXPECT_EQ(infershape_pass.Run(case_node), SUCCESS);

std::vector<int64_t> target_dims_0 = {1, 1, 224, 224};
std::vector<int64_t> target_dims_1 = {1};
{
auto data_node = subgraphs[0]->FindNode("data1_0");
auto dims = data_node->GetOpDesc()->GetInputDescPtr(0)->GetShape().GetDims();
EXPECT_EQ(dims, target_dims_0);
data_node = subgraphs[0]->FindNode("data2_0");
dims = data_node->GetOpDesc()->GetInputDescPtr(0)->GetShape().GetDims();
EXPECT_EQ(dims, target_dims_1);
}

infershape_pass.options_[kOptimizeAfterSubGraph] = "yes";
EXPECT_EQ(infershape_pass.Run(case_node), SUCCESS);
{
auto dims = case_node->GetOpDesc()->GetOutputDescPtr(0)->GetShape().GetDims();
std::vector<int64_t> out_target_dims = {8, 8};
EXPECT_EQ(out_target_dims, dims);
}
}

/*
* data1 const1
* \ /
* while
* / \
* relu1 netoutput
*/
ut::GraphBuilder ParentWhileGraphBuilder() {
ut::GraphBuilder builder = ut::GraphBuilder("g1");
auto data1 = builder.AddNode("data1", "Data", 0, 1);
std::vector<int64_t> const_shape = {1};
auto const1 = builder.AddNode("const1", "Const", 0, 1, FORMAT_NCHW, DT_FLOAT, const_shape);
auto case1 = builder.AddNode("case1", WHILE, 2, 2);
auto relu1 = builder.AddNode("relu1", "Relu", 1, 1);
auto netoutput = builder.AddNode("netoutput", NETOUTPUT, 1, 0);

int32_t weight[1] = {1};
GeTensorDesc weight_desc(GeShape({1}), FORMAT_NHWC, DT_FLOAT);
GeTensorPtr tensor = std::make_shared<GeTensor>(weight_desc, (uint8_t *)weight, sizeof(weight));
OpDescUtils::SetWeights(const1, {tensor});

builder.AddDataEdge(data1, 0, case1, 0);
builder.AddDataEdge(const1, 0, case1, 1);
builder.AddDataEdge(case1, 0, relu1, 0);
builder.AddDataEdge(case1, 1, netoutput, 0);
return builder;
}

/*
* data1 data2
* \ /
* switch
* | |
* \ /
* netoutput
*/
ut::GraphBuilder WhileSubgraphBuilder(string graph_name, uint32_t num) {
ut::GraphBuilder builder = ut::GraphBuilder(graph_name);

std::vector<int64_t> shape1 = {2,2};
string data1_name = "data1_" + std::to_string(num);
auto data1 = builder.AddNode(data1_name, "Data", 1, 1, FORMAT_NCHW, DT_FLOAT, shape1);
auto data1_desc = data1->GetOpDesc();
EXPECT_NE(data1_desc, nullptr);
AttrUtils::SetInt(data1_desc, "_parent_node_index", 0);

std::vector<int64_t> shape2 = {3,3};
string data2_name = "data2_" + std::to_string(num);
auto data2 = builder.AddNode(data2_name, "Data", 1, 1, FORMAT_NCHW, DT_FLOAT, 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);

std::vector<int64_t> shape7 = {8,8,8,8};
string output_name = "output_" + std::to_string(num);
auto netoutput = builder.AddNode(output_name, NETOUTPUT, 2, 0, FORMAT_NCHW, DT_FLOAT, shape7);
auto input0_desc = netoutput->GetOpDesc()->MutableInputDesc(0);
EXPECT_NE(input0_desc, nullptr);
AttrUtils::SetInt(input0_desc, "_parent_node_index", 0);
auto input1_desc = netoutput->GetOpDesc()->MutableInputDesc(1);
EXPECT_NE(input1_desc, nullptr);
AttrUtils::SetInt(input1_desc, "_parent_node_index", 1);

builder.AddDataEdge(data1, 0, switch1, 0);
builder.AddDataEdge(data2, 0, switch1, 1);
builder.AddDataEdge(switch1, 0, netoutput, 0);
builder.AddDataEdge(switch1, 1, netoutput, 1);
return builder;
}

void AddWhileSubgraph(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 = WhileSubgraphBuilder(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(UtestGraphInfershapePass, infer_with_while_subgraph) {
auto builder = ParentWhileGraphBuilder();
auto parent_graph = builder.GetGraph();
AddWhileSubgraph(parent_graph, 1);
auto subgraphs = parent_graph->GetAllSubgraphs();
EXPECT_EQ(subgraphs.size(), 1);

auto case_node = parent_graph->FindNode("case1");
EXPECT_NE(case_node, nullptr);
InferShapePass infershape_pass;
EXPECT_EQ(infershape_pass.Run(case_node), SUCCESS);

std::vector<int64_t> target_dims_0 = {1, 1, 224, 224};
std::vector<int64_t> target_dims_1 = {1};
{
auto data_node = subgraphs[0]->FindNode("data1_0");
auto dims = data_node->GetOpDesc()->GetInputDescPtr(0)->GetShape().GetDims();
EXPECT_EQ(dims, target_dims_0);
data_node = subgraphs[0]->FindNode("data2_0");
dims = data_node->GetOpDesc()->GetInputDescPtr(0)->GetShape().GetDims();
EXPECT_EQ(dims, target_dims_1);
}

infershape_pass.options_[kOptimizeAfterSubGraph] = "yes";
EXPECT_EQ(infershape_pass.Run(case_node), SUCCESS);
{
auto dims = case_node->GetOpDesc()->GetOutputDescPtr(0)->GetShape().GetDims();
std::vector<int64_t> out_target_dims = {-1, -1, -1, -1};
EXPECT_EQ(out_target_dims, dims);
}
}

TEST_F(UtestGraphInfershapePass, infer_with_while_subgraph_failed) {
auto builder = ParentWhileGraphBuilder();
auto parent_graph = builder.GetGraph();
AddWhileSubgraph(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);
InferShapePass infershape_pass;
infershape_pass.options_[kOptimizeAfterSubGraph] = "yes";
EXPECT_EQ(infershape_pass.Run(case_node), GE_GRAPH_INFERSHAPE_FAILED);
}

auto InferFunc = [&](Operator &op) {
return GRAPH_SUCCESS;
};
TEST_F(UtestGraphInfershapePass, infer_forrunning_with_while_subgraph) {
auto builder = ParentWhileGraphBuilder();
auto parent_graph = builder.GetGraph();
AddWhileSubgraph(parent_graph, 1);
auto subgraphs = parent_graph->GetAllSubgraphs();
EXPECT_EQ(subgraphs.size(), 1);

OperatorFactoryImpl::RegisterInferShapeFunc("Relu", InferFunc);

auto relu_node = parent_graph->FindNode("relu1");
EXPECT_NE(relu_node, nullptr);
InferShapeForRunning infershape_for_running;
EXPECT_EQ(infershape_for_running.Run(relu_node), SUCCESS);
}

TEST_F(UtestGraphInfershapePass, infer_static_func) {
auto builder = ut::GraphBuilder("test_graph");
auto data_1 = builder.AddNode("data_1", DATA, 0, 1);
auto data_2 = builder.AddNode("data_2", DATA, 0, 1);
auto add = builder.AddNode("Add", "Add", 2, 1);

builder.AddDataEdge(data_1, 0, add, 0);
builder.AddDataEdge(data_2, 0, add, 1);
auto test_graph = builder.GetGraph();

// OperatorFactoryImpl::CreateOperator("Add", "Flatten");
auto test_node = test_graph->FindNode("Add");
auto ret = InferShapePass::InferShapeAndType(test_node);
EXPECT_EQ(ret, GRAPH_SUCCESS);

OperatorFactoryImpl::RegisterInferShapeFunc("Add", InferFunc);
ret = InferShapePass::InferShapeAndType(test_node);
EXPECT_EQ(ret, GRAPH_SUCCESS);

ret = InferShapePass::InferShapeAndType(test_node, true);
EXPECT_EQ(ret, GRAPH_SUCCESS);

ret = InferShapeForRunning::InferShapeAndTypeForRunning(test_node, true);
EXPECT_EQ(ret, GRAPH_SUCCESS);
}
} // namespace ge

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