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!49395 Speedup Pynative Dynamic Shape 

Merge pull request !49395 from 王禹程/sp
This commit is contained in:
i-robot 2023-02-27 03:46:51 +00:00 committed by Gitee
parent d303373798 4c8baf2a82
commit 8254a39e40
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GPG Key ID: 173E9B9CA92EEF8F
8 changed files with 141 additions and 127 deletions
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11
mindspore/ccsrc/backend/common/session/session_basic.cc
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@ -505,12 +505,11 @@ BackendOpRunInfoPtr SessionBasic::GetSingleOpRunInfo(const CNodePtr &cnode, cons
const auto &shape = abstract->BuildShape();
MS_EXCEPTION_IF_NULL(shape);
bool is_gradient_out =
graph_output_info != nullptr &&
std::any_of(graph_output_info->output_indexes.cbegin(), graph_output_info->output_indexes.cend(),
[cnode](const std::pair<KernelWithIndex, std::vector<std::vector<size_t>>> &output_index) {
return output_index.first.first == cnode;
});
bool is_gradient_out = false;
if (graph_output_info != nullptr) {
auto lb_iter = graph_output_info->output_indexes.lower_bound({cnode, 0});
is_gradient_out = lb_iter != graph_output_info->output_indexes.end() && lb_iter->first.first == cnode;
}
pynative::BaseOpRunInfo base_op_run_info;
base_op_run_info.is_mixed_precision_cast = false;
base_op_run_info.lazy_build = !shape->IsDynamic();

21
mindspore/ccsrc/plugin/device/ascend/kernel/acl/acl_kernel_mod.cc
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@ -213,11 +213,11 @@ void AclKernelMod::UpdateReduceAxisAttr(const AnfNodePtr &node) {
opt::NormalizeReduceAttrAxis(cnode);
}
void AclKernelMod::ProcessAttribute(const std::shared_ptr<AclOpDesc> &op_desc_ptr) {
void AclKernelMod::ProcessAttribute(const std::shared_ptr<AclOpDesc> &op_desc_ptr,
const std::vector<string> &input_names) {
auto node = anf_node_.lock();
MS_EXCEPTION_IF_NULL(node);
const auto &attr_to_input_maps = GeOpConvertor::GetNeedAddInput(node, true);
const auto &input_names = kernel::AclUtils::GetOpInputAnchorNames(node);
UpdateReduceAxisAttr(node);
auto attr_list = GeOpConvertor::GetAttrAndValue(node, true);
for (auto &[attr_name, value] : attr_list) {
@ -246,11 +246,24 @@ bool AclKernelMod::Launch(const std::vector<AddressPtr> &inputs, const std::vect
}
auto node = anf_node_.lock();
MS_EXCEPTION_IF_NULL(node);
auto node_op_runtime_info = node->user_data<runtime::OpRuntimeInfo>();
bool node_acl_runtime_info_legal = node_op_runtime_info != nullptr &&
node_op_runtime_info->acl_runtime_info_ != nullptr &&
node_op_runtime_info->acl_runtime_info_->use();
const auto &input_names =
(node_acl_runtime_info_legal && !node_op_runtime_info->acl_runtime_info_->is_dynamic_input_size())
? node_op_runtime_info->acl_runtime_info_->input_names()
: AclUtils::GetOpInputAnchorNames(node);
const auto &output_names =
(node_acl_runtime_info_legal && !node_op_runtime_info->acl_runtime_info_->is_dynamic_output_size())
? node_op_runtime_info->acl_runtime_info_->output_names()
: AclUtils::GetOpOutputAnchorNames(node);
auto op_desc_ptr = std::make_shared<AclOpDesc>(op_type_, node);
MS_EXCEPTION_IF_NULL(op_desc_ptr);
op_desc_ptr->AddTensorDesc(input_desc_list_, output_desc_list_);
op_desc_ptr->AddDataBuf(inputs, input_size_list_, outputs, output_size_list_);
ProcessAttribute(op_desc_ptr);
op_desc_ptr->AddDataBuf(inputs, input_size_list_, outputs, output_size_list_, input_names, output_names);
ProcessAttribute(op_desc_ptr, input_names);
op_desc_ptr->ClearNullTensor();
// cppcheck-suppress unreadVariable

2
mindspore/ccsrc/plugin/device/ascend/kernel/acl/acl_kernel_mod.h
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@ -48,7 +48,7 @@ class AclKernelMod : public AscendKernelMod {
protected:
void SyncData() override;
void ProcessAttribute(const std::shared_ptr<AclOpDesc> &op_desc_ptr);
void ProcessAttribute(const std::shared_ptr<AclOpDesc> &op_desc_ptr, const std::vector<string> &input_names);
void UpdateReduceAxisAttr(const AnfNodePtr &node);
private:

6
mindspore/ccsrc/plugin/device/ascend/kernel/acl/acl_kernel_utils.cc
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@ -186,10 +186,10 @@ void AclOpDesc::AddTensorDesc(const std::vector<GeTensorDescPtr> &inputs, const
}
void AclOpDesc::AddDataBuf(const std::vector<AddressPtr> &inputs, const std::vector<size_t> &input_size_list,
const std::vector<AddressPtr> &outputs, const std::vector<size_t> &output_size_list) {
const std::vector<AddressPtr> &outputs, const std::vector<size_t> &output_size_list,
const std::vector<std::string> &input_names, const std::vector<std::string> &output_names) {
auto node = anf_node_.lock();
MS_EXCEPTION_IF_NULL(node);
const auto &input_names = AclUtils::GetOpInputAnchorNames(node);
input_tensor_data_.clear();
input_tensor_data_.resize(input_names.size(), nullptr);
for (size_t i = 0; i < inputs.size(); i++) {
@ -209,7 +209,7 @@ void AclOpDesc::AddDataBuf(const std::vector<AddressPtr> &inputs, const std::vec
}
input_tensor_data_[idx] = CreateDataBuf(inputs[i], input_size_list[idx]);
}
const auto &output_names = AclUtils::GetOpOutputAnchorNames(node);
output_tensor_data_.clear();
output_tensor_data_.resize(output_names.size(), aclCreateDataBuffer(nullptr, 0));
for (size_t i = 0; i < outputs.size(); ++i) {

4
mindspore/ccsrc/plugin/device/ascend/kernel/acl/acl_kernel_utils.h
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@ -27,6 +27,7 @@
#include "transform/graph_ir/convert.h"
#include "kernel/oplib/oplib.h"
#include "kernel/oplib/super_bar.h"
#include "runtime/pynative/op_runtime_info.h"
namespace mindspore {
namespace kernel {
@ -52,7 +53,8 @@ class AclOpDesc {
void AddTensorDesc(const std::vector<GeTensorDescPtr> &inputs, const std::vector<GeTensorDescPtr> &outputs);
void AddDataBuf(const std::vector<AddressPtr> &inputs, const std::vector<size_t> &input_size_list,
const std::vector<AddressPtr> &outputs, const std::vector<size_t> &output_size_list);
const std::vector<AddressPtr> &outputs, const std::vector<size_t> &output_size_list,
const std::vector<std::string> &input_names, const std::vector<std::string> &output_names);
void ProcessAclAttrs(const std::string &attr_name, const ValuePtr &value, const ProcessAttrMode &mode);
void ClearNullTensor();

4
mindspore/ccsrc/runtime/device/ms_device_shape_transfer.cc
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@ -456,8 +456,8 @@ std::optional<ShapeVector> DeviceShapeTransfer::GetFixedDeviceShape(const ShapeV
ShapeVector DeviceShapeTransfer::TransCore(const ShapeVector &shape, const std::string &format, const TypeId &type,
int64_t groups, const ShapeVector &input_hidden_size) const {
using DeviceShapeTransfer = std::function<ShapeVector(const ShapeVector &, const TypeId &)>;
const std::map<std::string, DeviceShapeTransfer> device_shape_map = {
using DeviceShapeTransferFunc = std::function<ShapeVector(const ShapeVector &, const TypeId &)>;
static const mindspore::HashMap<std::string, DeviceShapeTransferFunc> device_shape_map = {
{kOpFormat_NCHW, NCHWDeviceShape},
{kOpFormat_NHWC, NHWCDeviceShape},
{kOpFormat_HWCN, HWCNDeviceShape},

4
mindspore/ccsrc/runtime/graph_scheduler/graph_compiler.cc
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@ -443,7 +443,7 @@ GraphId GraphCompiler::CompileDynamicGraph(const GraphSegmentPtr &segment, const
graph->UpdateGraphAquireGilAttr();
graph->SetInputNodes();
auto manager = MakeManager({graph});
auto manager = Manage(graph);
if (manager) {
manager->AddFuncGraph(graph);
graph->set_manager(manager);
@ -459,7 +459,7 @@ GraphId GraphCompiler::CompileDynamicGraph(const GraphSegmentPtr &segment, const
graph->set_root_graph_id(graph_id);
session_->DumpGraphs({graph});
auto exec_nodes = graph->execution_order();
auto &exec_nodes = graph->execution_order();
std::for_each(exec_nodes.begin(), exec_nodes.end(),
[](const CNodePtr &node) { common::AnfAlgo::SetNodeAttr(kAttrMutableKernel, MakeValue(true), node); });

216
mindspore/ccsrc/utils/utils.cc
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@ -21,38 +21,38 @@
namespace mindspore {
bool IsOneOfPosteriorOperator(const std::string &name) {
const std::set<std::string> kPosteriorOperatorSet = {kPullOpName};
static const std::set<std::string> kPosteriorOperatorSet = {kPullOpName};
auto iter = kPosteriorOperatorSet.find(name);
return iter != kPosteriorOperatorSet.end();
}
bool IsOneOfCacheBlackList(const std::string &name) {
const std::set<std::string> kOpCacheBlackList = {kUniformCandidateSamplerOpName, kInitDatasetQueueOpName,
kGetNextOpName};
static const std::set<std::string> kOpCacheBlackList = {kUniformCandidateSamplerOpName, kInitDatasetQueueOpName,
kGetNextOpName};
auto iter = kOpCacheBlackList.find(name);
return iter != kOpCacheBlackList.end();
}
bool IsOneOf3DFormat(const std::string &format) {
const std::set<std::string> k3DFormatSet = {kOpFormat_NCDHW, kOpFormat_NDC1HWC0, kOpFormat_FRACTAL_Z_3D,
kOpFormat_NDHWC, kOpFormat_DHWCN, kOpFormat_DHWNC};
static const std::set<std::string> k3DFormatSet = {kOpFormat_NCDHW, kOpFormat_NDC1HWC0, kOpFormat_FRACTAL_Z_3D,
kOpFormat_NDHWC, kOpFormat_DHWCN, kOpFormat_DHWNC};
auto iter = k3DFormatSet.find(format);
return iter != k3DFormatSet.end();
}
bool IsOneOfNoPaddingFormat(const std::string &format) {
const std::set<std::string> kNoPaddingFormatSet = {kOpFormat_ChannelLast, kOpFormat_FRAC_NZ, kOpFormat_FRACTAL_ZN_RNN,
kOpFormat_ND_RNN_BIAS, kOpFormat_DEFAULT};
static const std::set<std::string> kNoPaddingFormatSet = {
kOpFormat_ChannelLast, kOpFormat_FRAC_NZ, kOpFormat_FRACTAL_ZN_RNN, kOpFormat_ND_RNN_BIAS, kOpFormat_DEFAULT};
auto iter = kNoPaddingFormatSet.find(format);
return iter != kNoPaddingFormatSet.end();
}
bool IsOneOfDynamicShapeConstInputToAttrGPU(const std::string &name) {
const std::set<std::string> DynamicShapeConstInputToAttrGPU = {
static const std::set<std::string> DynamicShapeConstInputToAttrGPU = {
kCastOpName, kExpandDimsOpName, kReshapeOpName, kEmbeddingLookupOpName, kTransposeOpName,
kReduceSumOpName, kReduceMinOpName, kReduceMeanOpName, kReduceMaxOpName, kReduceAllOpName,
kReduceAnyOpName, kConcatOpName, kScatterNdOpName, kGatherOpName, kAvgPool3DGradOpName};
@ -69,120 +69,120 @@ bool IsOneOfCustomAkgType(const std::string &name) {
}
bool IsOneOfOperator(const std::string &name) {
const std::set<std::string> kOptOperatorSet = {kMomentumOpName,
kApplyMomentumOpName,
kApplyMomentumDOpName,
kApplyAdadeltaOpName,
kApplyAdadeltaDOpName,
kApplyAdagradOpName,
kApplyAdagradDOpName,
kApplyAdagradDAOpName,
kApplyAdagradDADOpName,
kAdamOpName,
kApplyAdamDOpName,
kApplyAdamOpName,
kApplyAdaMaxOpName,
kApplyAdaMaxDOpName,
kApplyAddSignOpName,
kApplyAddSignDOpName,
kApplyCenteredRMSPOpName,
kApplyFtrlOpName,
kApplyFtrlDOpName,
kApplyFtrlV2OpName,
kApplyFtrlV2DOpName,
kApplyGradientDescentOpName,
kApplyPowerSignOpName,
kApplyPowerSignDOpName,
kApplyProximalAdagradOpName,
kApplyProximalAdagradDOpName,
kApplyProximalGradientDescentOpName,
kApplyRMSPropOpName,
kApplyRMSPropDOpname,
kAdamApplyOneWithDecayOpName,
kAdamApplyOneWithDecayAssignOpName,
kFusedAdamWeightDecayName,
kAdamWeightDecayName,
kFusedCastAdamWeightDecayName,
kFusedAdamName,
kFusedAdaFactorName,
kFusedAdaFactorWithGlobalNormName,
kFusedSparseAdamName,
kFusedMulApplyMomentumOpName,
kFusedWeightScaleApplyMomentum,
kFusedScaleApplyMomentum,
kApplyCenteredRMSPropOpName,
kApplyCenteredRMSPropDOpName,
kFusedSparseFtrlName,
kFusedSparseProximalAdagradName,
kFusedSparseLazyAdamName,
kSparseApplyFtrlOpName,
kSparseApplyFtrlDOpName,
kSparseApplyFtrlV2OpName,
kSparseApplyFtrlV2DOpName,
kSGDName,
kLARSUpdateOpName,
kLarsV2UpdateOpName,
kCombineMomentumWeightOpName,
kCombineMomentumOpName,
kScatterAddOpName,
kScatterUpdateOpName,
kSparseApplyProximalAdagradOpName,
kSparseApplyProximalAdagradDOpName,
kAdaptiveMaxPool2dOpName,
kApplyKerasMomentumDOpName};
static const std::set<std::string> kOptOperatorSet = {kMomentumOpName,
kApplyMomentumOpName,
kApplyMomentumDOpName,
kApplyAdadeltaOpName,
kApplyAdadeltaDOpName,
kApplyAdagradOpName,
kApplyAdagradDOpName,
kApplyAdagradDAOpName,
kApplyAdagradDADOpName,
kAdamOpName,
kApplyAdamDOpName,
kApplyAdamOpName,
kApplyAdaMaxOpName,
kApplyAdaMaxDOpName,
kApplyAddSignOpName,
kApplyAddSignDOpName,
kApplyCenteredRMSPOpName,
kApplyFtrlOpName,
kApplyFtrlDOpName,
kApplyFtrlV2OpName,
kApplyFtrlV2DOpName,
kApplyGradientDescentOpName,
kApplyPowerSignOpName,
kApplyPowerSignDOpName,
kApplyProximalAdagradOpName,
kApplyProximalAdagradDOpName,
kApplyProximalGradientDescentOpName,
kApplyRMSPropOpName,
kApplyRMSPropDOpname,
kAdamApplyOneWithDecayOpName,
kAdamApplyOneWithDecayAssignOpName,
kFusedAdamWeightDecayName,
kAdamWeightDecayName,
kFusedCastAdamWeightDecayName,
kFusedAdamName,
kFusedAdaFactorName,
kFusedAdaFactorWithGlobalNormName,
kFusedSparseAdamName,
kFusedMulApplyMomentumOpName,
kFusedWeightScaleApplyMomentum,
kFusedScaleApplyMomentum,
kApplyCenteredRMSPropOpName,
kApplyCenteredRMSPropDOpName,
kFusedSparseFtrlName,
kFusedSparseProximalAdagradName,
kFusedSparseLazyAdamName,
kSparseApplyFtrlOpName,
kSparseApplyFtrlDOpName,
kSparseApplyFtrlV2OpName,
kSparseApplyFtrlV2DOpName,
kSGDName,
kLARSUpdateOpName,
kLarsV2UpdateOpName,
kCombineMomentumWeightOpName,
kCombineMomentumOpName,
kScatterAddOpName,
kScatterUpdateOpName,
kSparseApplyProximalAdagradOpName,
kSparseApplyProximalAdagradDOpName,
kAdaptiveMaxPool2dOpName,
kApplyKerasMomentumDOpName};
auto iter = kOptOperatorSet.find(name);
return iter != kOptOperatorSet.end();
}
bool IsOneOfNotSupportedTransFormat(const std::string &format) {
const std::set<std::string> kNotSupportedFormat = {kOpFormat_DHWCN, kOpFormat_NDHWC, kOpFormat_CHWN};
static const std::set<std::string> kNotSupportedFormat = {kOpFormat_DHWCN, kOpFormat_NDHWC, kOpFormat_CHWN};
return (kNotSupportedFormat.find(format) != kNotSupportedFormat.end());
}
bool IsOneOfComputeDepend(const std::string &name) {
const std::set<std::string> kComputeDepend = {kUniqueOpName,
kUniqueConsecutiveOpName,
kComputeAccidentalHitsOpName,
kSubAndFilterOpName,
kPadAndShiftOpName,
kCTCGreedyDecoderOpName,
kMaskedSelectOpName,
kDynamicStitchOpName,
kGetNextOpName,
kListDiffOpName,
kNonMaxSuppressionV3OpName,
kNonMaxSuppressionWithOverlapsOpName,
kCoalesceOpName,
kTruncatedNormal,
kNonDeterministicInts,
kFractionalAvgPoolGradOpName,
kDenseToDenseSetOperation,
kDenseToSparseSetOperation,
kSegmentMaxOpName,
kCSRSparseMatrixToSparseTensorOpName,
kSegmentMinOpName,
kLuUnpackOpName,
kSegmentSumOpName,
kResizeBicubicOpName,
kResizeAreaOpName,
kSegmentMeanOpName,
kSegmentProdOpName,
kSparseSliceOpName,
kNonZeroOpName,
kSparseSparseMinimumOpName,
kSparseSparseMaximumOpName,
kRpcRecvOpName,
kSparseFillEmptyRows,
kSparseCrossOpName,
kAdaptiveMaxPool3DGradOpName};
static const std::set<std::string> kComputeDepend = {kUniqueOpName,
kUniqueConsecutiveOpName,
kComputeAccidentalHitsOpName,
kSubAndFilterOpName,
kPadAndShiftOpName,
kCTCGreedyDecoderOpName,
kMaskedSelectOpName,
kDynamicStitchOpName,
kGetNextOpName,
kListDiffOpName,
kNonMaxSuppressionV3OpName,
kNonMaxSuppressionWithOverlapsOpName,
kCoalesceOpName,
kTruncatedNormal,
kNonDeterministicInts,
kFractionalAvgPoolGradOpName,
kDenseToDenseSetOperation,
kDenseToSparseSetOperation,
kSegmentMaxOpName,
kCSRSparseMatrixToSparseTensorOpName,
kSegmentMinOpName,
kLuUnpackOpName,
kSegmentSumOpName,
kResizeBicubicOpName,
kResizeAreaOpName,
kSegmentMeanOpName,
kSegmentProdOpName,
kSparseSliceOpName,
kNonZeroOpName,
kSparseSparseMinimumOpName,
kSparseSparseMaximumOpName,
kRpcRecvOpName,
kSparseFillEmptyRows,
kSparseCrossOpName,
kAdaptiveMaxPool3DGradOpName};
auto iter = kComputeDepend.find(name);
return iter != kComputeDepend.end();
}
bool IsOneOfHWSpecialFormat(const std::string &format) {
const std::set<std::string> kHWSpecialFormatSet = {
static const std::set<std::string> kHWSpecialFormatSet = {
kOpFormat_FRACTAL_Z_3D, kOpFormat_NC1KHKWHWC0, kOpFormat_NC1HWC0, kOpFormat_FRAC_NZ,
kOpFormat_C1HWNCoC0, kOpFormat_NC1HWC0_C04, kOpFormat_FRACTAL_Z_C04, kOpFormat_FRACTAL_ZN_LSTM,
kOpFormat_FRACTAL_ZN_RNN, kOpFormat_NDC1HWC0, kOpFormat_FRAC_Z};
@ -192,7 +192,7 @@ bool IsOneOfHWSpecialFormat(const std::string &format) {
}
bool IsOneOfFormat(const std::string &format) {
const std::set<std::string> kOpFormatList = {
static const std::set<std::string> kOpFormatList = {
kOpFormat_DEFAULT, kOpFormat_NC1KHKWHWC0, kOpFormat_ND,
kOpFormat_NCHW, kOpFormat_NHWC, kOpFormat_HWCN,
kOpFormat_CHWN, kOpFormat_NC1HWC0, kOpFormat_FRAC_Z,
@ -207,7 +207,7 @@ bool IsOneOfFormat(const std::string &format) {
}
bool IsOneOfServerFormatC04(const std::string &format) {
const std::set<std::string> kServerFormatC04List = {kOpFormat_NC1HWC0_C04, kOpFormat_FRACTAL_Z_C04};
static const std::set<std::string> kServerFormatC04List = {kOpFormat_NC1HWC0_C04, kOpFormat_FRACTAL_Z_C04};
return kServerFormatC04List.find(format) != kServerFormatC04List.end();
}
} // namespace mindspore