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modify pack to stack

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pull/12044/head
jinyaohui 5 years ago
parent 15fbf37bb7
commit 8022f9a6ed
46 changed files with 319 additions and 246 deletions
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6
config/op_info.config
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File diff suppressed because one or more lines are too long

7
mindspore/ccsrc/backend/kernel_compiler/aicpu/aicpu_kernel_mod.cc
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@ -142,6 +142,9 @@ bool AicpuOpKernelMod::Launch(const std::vector<AddressPtr> &inputs, const std::
if (node_name_ == kTopK) {
node_name_ = kTopKV2;
}
if (node_name_ == kStack) {
node_name_ = kPack;
}
MS_LOG(INFO) << "Aicpu launch, node_so_:" << node_so_ << ", node name:" << node_name_
<< ", args_size:" << args_.length();
if (rtCpuKernelLaunch(reinterpret_cast<const void *>(node_so_.c_str()),
@ -182,6 +185,10 @@ std::vector<TaskInfoPtr> AicpuOpKernelMod::GenTask(const std::vector<AddressPtr>
node_name_ = kTopKV2;
}
if (node_name_ == kStack) {
node_name_ = kPack;
}
AicpuTaskInfoPtr task_info_ptr =
make_shared<ge::model_runner::AicpuTaskInfo>(kernel_name_, stream_id, node_so_, node_name_, node_def_str_,
ext_info_, input_data_addrs, output_data_addrs, NeedDump());

1
mindspore/ccsrc/backend/kernel_compiler/aicpu/aicpu_util.h
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@ -43,6 +43,7 @@ constexpr auto kSeed1 = "Seed1";
constexpr auto kSeed2 = "seed2";
constexpr auto kTopK = "TopK";
constexpr auto kTopKV2 = "TopKV2";
constexpr auto kStack = "Stack";
constexpr auto kEditDistance = "EditDistance";
constexpr auto kGatherD = "GatherD";
constexpr auto kIdentity = "Identity";

22
mindspore/ccsrc/backend/kernel_compiler/cpu/pack_cpu_kernel.h
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@ -44,38 +44,38 @@ class PackCpuFwdKernel : public CPUKernel {
std::unique_ptr<T *[]> inputs_host_;
};
MS_REG_CPU_KERNEL_T(Pack,
MS_REG_CPU_KERNEL_T(Stack,
KernelAttr().SetAllSameAttr(true).AddInputAttr(kNumberTypeInt8).AddOutputAttr(kNumberTypeInt8),
PackCpuFwdKernel, int8_t)
MS_REG_CPU_KERNEL_T(Pack,
MS_REG_CPU_KERNEL_T(Stack,
KernelAttr().SetAllSameAttr(true).AddInputAttr(kNumberTypeInt16).AddOutputAttr(kNumberTypeInt16),
PackCpuFwdKernel, int16_t)
MS_REG_CPU_KERNEL_T(Pack,
MS_REG_CPU_KERNEL_T(Stack,
KernelAttr().SetAllSameAttr(true).AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeInt32),
PackCpuFwdKernel, int32_t)
MS_REG_CPU_KERNEL_T(Pack,
MS_REG_CPU_KERNEL_T(Stack,
KernelAttr().SetAllSameAttr(true).AddInputAttr(kNumberTypeInt64).AddOutputAttr(kNumberTypeInt64),
PackCpuFwdKernel, int64_t)
MS_REG_CPU_KERNEL_T(Pack,
MS_REG_CPU_KERNEL_T(Stack,
KernelAttr().SetAllSameAttr(true).AddInputAttr(kNumberTypeUInt8).AddOutputAttr(kNumberTypeUInt8),
PackCpuFwdKernel, uint8_t)
MS_REG_CPU_KERNEL_T(Pack,
MS_REG_CPU_KERNEL_T(Stack,
KernelAttr().SetAllSameAttr(true).AddInputAttr(kNumberTypeBool).AddOutputAttr(kNumberTypeBool),
PackCpuFwdKernel, bool)
MS_REG_CPU_KERNEL_T(Pack,
MS_REG_CPU_KERNEL_T(Stack,
KernelAttr().SetAllSameAttr(true).AddInputAttr(kNumberTypeUInt16).AddOutputAttr(kNumberTypeUInt16),
PackCpuFwdKernel, uint16_t)
MS_REG_CPU_KERNEL_T(Pack,
MS_REG_CPU_KERNEL_T(Stack,
KernelAttr().SetAllSameAttr(true).AddInputAttr(kNumberTypeUInt32).AddOutputAttr(kNumberTypeUInt32),
PackCpuFwdKernel, uint32_t)
MS_REG_CPU_KERNEL_T(Pack,
MS_REG_CPU_KERNEL_T(Stack,
KernelAttr().SetAllSameAttr(true).AddInputAttr(kNumberTypeUInt64).AddOutputAttr(kNumberTypeUInt64),
PackCpuFwdKernel, uint64_t)
MS_REG_CPU_KERNEL_T(
Pack, KernelAttr().SetAllSameAttr(true).AddInputAttr(kNumberTypeFloat16).AddOutputAttr(kNumberTypeFloat16),
Stack, KernelAttr().SetAllSameAttr(true).AddInputAttr(kNumberTypeFloat16).AddOutputAttr(kNumberTypeFloat16),
PackCpuFwdKernel, float16)
MS_REG_CPU_KERNEL_T(
Pack, KernelAttr().SetAllSameAttr(true).AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
Stack, KernelAttr().SetAllSameAttr(true).AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
PackCpuFwdKernel, float)
} // namespace kernel
} // namespace mindspore

22
mindspore/ccsrc/backend/kernel_compiler/cpu/unpack_cpu_kernel.h
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@ -49,38 +49,38 @@ class UnpackCPUKernel : public CPUKernel {
T **outputs_host_{nullptr};
TypeId dtype_{kTypeUnknown};
};
MS_REG_CPU_KERNEL_T(Unpack,
MS_REG_CPU_KERNEL_T(Unstack,
KernelAttr().SetAllSameAttr(true).AddInputAttr(kNumberTypeInt8).AddOutputAttr(kNumberTypeInt8),
UnpackCPUKernel, int8_t);
MS_REG_CPU_KERNEL_T(Unpack,
MS_REG_CPU_KERNEL_T(Unstack,
KernelAttr().SetAllSameAttr(true).AddInputAttr(kNumberTypeInt16).AddOutputAttr(kNumberTypeInt16),
UnpackCPUKernel, int16_t);
MS_REG_CPU_KERNEL_T(Unpack,
MS_REG_CPU_KERNEL_T(Unstack,
KernelAttr().SetAllSameAttr(true).AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeInt32),
UnpackCPUKernel, int);
MS_REG_CPU_KERNEL_T(Unpack,
MS_REG_CPU_KERNEL_T(Unstack,
KernelAttr().SetAllSameAttr(true).AddInputAttr(kNumberTypeInt64).AddOutputAttr(kNumberTypeInt64),
UnpackCPUKernel, int64_t);
MS_REG_CPU_KERNEL_T(Unpack,
MS_REG_CPU_KERNEL_T(Unstack,
KernelAttr().SetAllSameAttr(true).AddInputAttr(kNumberTypeBool).AddOutputAttr(kNumberTypeBool),
UnpackCPUKernel, bool);
MS_REG_CPU_KERNEL_T(Unpack,
MS_REG_CPU_KERNEL_T(Unstack,
KernelAttr().SetAllSameAttr(true).AddInputAttr(kNumberTypeUInt8).AddOutputAttr(kNumberTypeUInt8),
UnpackCPUKernel, uint8_t);
MS_REG_CPU_KERNEL_T(Unpack,
MS_REG_CPU_KERNEL_T(Unstack,
KernelAttr().SetAllSameAttr(true).AddInputAttr(kNumberTypeUInt16).AddOutputAttr(kNumberTypeUInt16),
UnpackCPUKernel, uint16_t);
MS_REG_CPU_KERNEL_T(Unpack,
MS_REG_CPU_KERNEL_T(Unstack,
KernelAttr().SetAllSameAttr(true).AddInputAttr(kNumberTypeUInt32).AddOutputAttr(kNumberTypeUInt32),
UnpackCPUKernel, uint32_t);
MS_REG_CPU_KERNEL_T(Unpack,
MS_REG_CPU_KERNEL_T(Unstack,
KernelAttr().SetAllSameAttr(true).AddInputAttr(kNumberTypeUInt64).AddOutputAttr(kNumberTypeUInt64),
UnpackCPUKernel, uint64_t);
MS_REG_CPU_KERNEL_T(
Unpack, KernelAttr().SetAllSameAttr(true).AddInputAttr(kNumberTypeFloat16).AddOutputAttr(kNumberTypeFloat16),
Unstack, KernelAttr().SetAllSameAttr(true).AddInputAttr(kNumberTypeFloat16).AddOutputAttr(kNumberTypeFloat16),
UnpackCPUKernel, float16);
MS_REG_CPU_KERNEL_T(
Unpack, KernelAttr().SetAllSameAttr(true).AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
Unstack, KernelAttr().SetAllSameAttr(true).AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
UnpackCPUKernel, float);
} // namespace kernel
} // namespace mindspore

22
mindspore/ccsrc/backend/kernel_compiler/gpu/arrays/pack_gpu_kernel.cc
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@ -18,38 +18,38 @@
namespace mindspore {
namespace kernel {
MS_REG_GPU_KERNEL_ONE(Pack,
MS_REG_GPU_KERNEL_ONE(Stack,
KernelAttr().AddAllSameAttr(true).AddInputAttr(kNumberTypeInt8).AddOutputAttr(kNumberTypeInt8),
PackGpuFwdKernel, int8_t)
MS_REG_GPU_KERNEL_ONE(Pack,
MS_REG_GPU_KERNEL_ONE(Stack,
KernelAttr().AddAllSameAttr(true).AddInputAttr(kNumberTypeInt16).AddOutputAttr(kNumberTypeInt16),
PackGpuFwdKernel, int16_t)
MS_REG_GPU_KERNEL_ONE(Pack,
MS_REG_GPU_KERNEL_ONE(Stack,
KernelAttr().AddAllSameAttr(true).AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeInt32),
PackGpuFwdKernel, int)
MS_REG_GPU_KERNEL_ONE(Pack,
MS_REG_GPU_KERNEL_ONE(Stack,
KernelAttr().AddAllSameAttr(true).AddInputAttr(kNumberTypeInt64).AddOutputAttr(kNumberTypeInt64),
PackGpuFwdKernel, int64_t)
MS_REG_GPU_KERNEL_ONE(Pack,
MS_REG_GPU_KERNEL_ONE(Stack,
KernelAttr().AddAllSameAttr(true).AddInputAttr(kNumberTypeUInt8).AddOutputAttr(kNumberTypeUInt8),
PackGpuFwdKernel, uint8_t)
MS_REG_GPU_KERNEL_ONE(Pack,
MS_REG_GPU_KERNEL_ONE(Stack,
KernelAttr().AddAllSameAttr(true).AddInputAttr(kNumberTypeBool).AddOutputAttr(kNumberTypeBool),
PackGpuFwdKernel, bool)
MS_REG_GPU_KERNEL_ONE(
Pack, KernelAttr().AddAllSameAttr(true).AddInputAttr(kNumberTypeUInt16).AddOutputAttr(kNumberTypeUInt16),
Stack, KernelAttr().AddAllSameAttr(true).AddInputAttr(kNumberTypeUInt16).AddOutputAttr(kNumberTypeUInt16),
PackGpuFwdKernel, uint16_t)
MS_REG_GPU_KERNEL_ONE(
Pack, KernelAttr().AddAllSameAttr(true).AddInputAttr(kNumberTypeUInt32).AddOutputAttr(kNumberTypeUInt32),
Stack, KernelAttr().AddAllSameAttr(true).AddInputAttr(kNumberTypeUInt32).AddOutputAttr(kNumberTypeUInt32),
PackGpuFwdKernel, uint32_t)
MS_REG_GPU_KERNEL_ONE(
Pack, KernelAttr().AddAllSameAttr(true).AddInputAttr(kNumberTypeUInt64).AddOutputAttr(kNumberTypeUInt64),
Stack, KernelAttr().AddAllSameAttr(true).AddInputAttr(kNumberTypeUInt64).AddOutputAttr(kNumberTypeUInt64),
PackGpuFwdKernel, uint64_t)
MS_REG_GPU_KERNEL_ONE(
Pack, KernelAttr().AddAllSameAttr(true).AddInputAttr(kNumberTypeFloat16).AddOutputAttr(kNumberTypeFloat16),
Stack, KernelAttr().AddAllSameAttr(true).AddInputAttr(kNumberTypeFloat16).AddOutputAttr(kNumberTypeFloat16),
PackGpuFwdKernel, half)
MS_REG_GPU_KERNEL_ONE(
Pack, KernelAttr().AddAllSameAttr(true).AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
Stack, KernelAttr().AddAllSameAttr(true).AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
PackGpuFwdKernel, float)
} // namespace kernel
} // namespace mindspore

22
mindspore/ccsrc/backend/kernel_compiler/gpu/arrays/unpack_gpu_kernel.cc
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@ -18,38 +18,38 @@
namespace mindspore {
namespace kernel {
MS_REG_GPU_KERNEL_ONE(Unpack,
MS_REG_GPU_KERNEL_ONE(Unstack,
KernelAttr().AddAllSameAttr(true).AddInputAttr(kNumberTypeInt8).AddOutputAttr(kNumberTypeInt8),
UnpackGpuFwdKernel, int8_t)
MS_REG_GPU_KERNEL_ONE(Unpack,
MS_REG_GPU_KERNEL_ONE(Unstack,
KernelAttr().AddAllSameAttr(true).AddInputAttr(kNumberTypeInt16).AddOutputAttr(kNumberTypeInt16),
UnpackGpuFwdKernel, int16_t)
MS_REG_GPU_KERNEL_ONE(Unpack,
MS_REG_GPU_KERNEL_ONE(Unstack,
KernelAttr().AddAllSameAttr(true).AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeInt32),
UnpackGpuFwdKernel, int)
MS_REG_GPU_KERNEL_ONE(Unpack,
MS_REG_GPU_KERNEL_ONE(Unstack,
KernelAttr().AddAllSameAttr(true).AddInputAttr(kNumberTypeInt64).AddOutputAttr(kNumberTypeInt64),
UnpackGpuFwdKernel, int64_t)
MS_REG_GPU_KERNEL_ONE(Unpack,
MS_REG_GPU_KERNEL_ONE(Unstack,
KernelAttr().AddAllSameAttr(true).AddInputAttr(kNumberTypeUInt8).AddOutputAttr(kNumberTypeUInt8),
UnpackGpuFwdKernel, uint8_t)
MS_REG_GPU_KERNEL_ONE(Unpack,
MS_REG_GPU_KERNEL_ONE(Unstack,
KernelAttr().AddAllSameAttr(true).AddInputAttr(kNumberTypeBool).AddOutputAttr(kNumberTypeBool),
UnpackGpuFwdKernel, bool)
MS_REG_GPU_KERNEL_ONE(
Unpack, KernelAttr().AddAllSameAttr(true).AddInputAttr(kNumberTypeUInt16).AddOutputAttr(kNumberTypeUInt16),
Unstack, KernelAttr().AddAllSameAttr(true).AddInputAttr(kNumberTypeUInt16).AddOutputAttr(kNumberTypeUInt16),
UnpackGpuFwdKernel, uint16_t)
MS_REG_GPU_KERNEL_ONE(
Unpack, KernelAttr().AddAllSameAttr(true).AddInputAttr(kNumberTypeUInt32).AddOutputAttr(kNumberTypeUInt32),
Unstack, KernelAttr().AddAllSameAttr(true).AddInputAttr(kNumberTypeUInt32).AddOutputAttr(kNumberTypeUInt32),
UnpackGpuFwdKernel, uint32_t)
MS_REG_GPU_KERNEL_ONE(
Unpack, KernelAttr().AddAllSameAttr(true).AddInputAttr(kNumberTypeUInt64).AddOutputAttr(kNumberTypeUInt64),
Unstack, KernelAttr().AddAllSameAttr(true).AddInputAttr(kNumberTypeUInt64).AddOutputAttr(kNumberTypeUInt64),
UnpackGpuFwdKernel, uint64_t)
MS_REG_GPU_KERNEL_ONE(
Unpack, KernelAttr().AddAllSameAttr(true).AddInputAttr(kNumberTypeFloat16).AddOutputAttr(kNumberTypeFloat16),
Unstack, KernelAttr().AddAllSameAttr(true).AddInputAttr(kNumberTypeFloat16).AddOutputAttr(kNumberTypeFloat16),
UnpackGpuFwdKernel, half)
MS_REG_GPU_KERNEL_ONE(
Unpack, KernelAttr().AddAllSameAttr(true).AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
Unstack, KernelAttr().AddAllSameAttr(true).AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
UnpackGpuFwdKernel, float)
} // namespace kernel
} // namespace mindspore

4
mindspore/ccsrc/backend/optimizer/ascend/ir_fission/pack_fission.cc
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@ -26,7 +26,7 @@ AnfNodePtr CreateNewPack(const FuncGraphPtr &func_graph, const CNodePtr &origin_
size_t offset) {
MS_EXCEPTION_IF_NULL(func_graph);
MS_EXCEPTION_IF_NULL(origin_pack_cnode);
std::vector<AnfNodePtr> new_pack_inputs{NewValueNode(std::make_shared<Primitive>(prim::kPrimPack->name()))};
std::vector<AnfNodePtr> new_pack_inputs{NewValueNode(std::make_shared<Primitive>(prim::kPrimStack->name()))};
for (size_t i = begin_index; i < begin_index + offset; ++i) {
new_pack_inputs.push_back(origin_pack_cnode->input(i));
}
@ -68,7 +68,7 @@ AnfNodePtr CreateNewPack(const FuncGraphPtr &func_graph, const CNodePtr &origin_
const BaseRef PackFission::DefinePattern() const {
VarPtr Xs = std::make_shared<SeqVar>();
return VectorRef({prim::kPrimPack, Xs});
return VectorRef({prim::kPrimStack, Xs});
}
const AnfNodePtr PackFission::Process(const FuncGraphPtr &func_graph, const AnfNodePtr &node, const EquivPtr &) const {

2
mindspore/ccsrc/backend/optimizer/ascend/ir_fission/pack_fission.h
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@ -24,7 +24,7 @@ constexpr size_t kPackInputsDivisor = 63;
class PackFission : public PatternProcessPass {
public:
explicit PackFission(bool multigraph = true)
: PatternProcessPass("pack_fission", multigraph), inputs_divisor_(kPackInputsDivisor) {}
: PatternProcessPass("stack_fission", multigraph), inputs_divisor_(kPackInputsDivisor) {}
~PackFission() override = default;
const BaseRef DefinePattern() const override;
const AnfNodePtr Process(const FuncGraphPtr &, const AnfNodePtr &, const EquivPtr &) const override;

2
mindspore/ccsrc/frontend/parallel/auto_parallel/rec_core/rec_parse_graph.cc
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@ -83,7 +83,7 @@ Graph::NodeType MakeNewOperator(const std::vector<std::shared_ptr<OperatorInfo>>
OperatorRec CompleteOperatorInputs(const std::vector<std::shared_ptr<OperatorInfo>> &ops, const size_t iter_ops,
Graph::NodeType NewTensor) {
size_t input_tensor_size = ops[iter_ops]->inputs_tensor_info().size();
if (ops[iter_ops]->type() == PACK) {
if (ops[iter_ops]->type() == STACK) {
input_tensor_size = 1;
}
if (input_tensor_size > MAX_INPUT_NUM) {

2
mindspore/ccsrc/frontend/parallel/auto_parallel/rec_core/rec_parse_graph.h
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@ -145,7 +145,7 @@ const std::map<std::string, OperatorType> DictOpType{
{ASSIGN_SUB, OperatorType::kRecElmWiseOp},
{"AssignAdd", OperatorType::kRecElmWiseOp},
{DROPOUT_DO_MASK, OperatorType::kRecElmWiseOp},
{PACK, OperatorType::kRecElmWiseOp}};
{STACK, OperatorType::kRecElmWiseOp}};
const TensorParam MakeTensor(int64_t n, int64_t c, int64_t h, int64_t w);

2
mindspore/ccsrc/frontend/parallel/dynamic_creator.h
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@ -187,7 +187,7 @@ REGISTER(BroadcastToInfo);
REGISTER(StridedSliceInfo);
REGISTER(SliceInfo);
REGISTER(DropoutInfo);
REGISTER(PackInfo);
REGISTER(StackInfo);
REGISTER(ConcatInfo);
REGISTER(SplitInfo);
REGISTER(UniqueInfo);

2
mindspore/ccsrc/frontend/parallel/node_check.cc
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@ -23,7 +23,7 @@
namespace mindspore {
namespace parallel {
const std::set<std::string> BATCH_PARALLEL_BLACK_LIST = {PACK, TENSOR_SCATTER_UPDATE, MIN_MAX_UPDATE_PER_LAYER};
const std::set<std::string> BATCH_PARALLEL_BLACK_LIST = {STACK, TENSOR_SCATTER_UPDATE, MIN_MAX_UPDATE_PER_LAYER};
bool IsInBatchParallelBlackList(const PrimitivePtr &prim) {
MS_EXCEPTION_IF_NULL(prim);

2
mindspore/ccsrc/frontend/parallel/ops_info/ops_utils.h
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@ -378,7 +378,7 @@ constexpr char STOP_GRADIENT[] = "stop_gradient";
// Batch parallel black list
constexpr char TENSOR_SCATTER_UPDATE[] = "TensorScatterUpdate";
constexpr char MIN_MAX_UPDATE_PER_LAYER[] = "MinMaxUpdatePerLayer";
constexpr char PACK[] = "Pack";
constexpr char STACK[] = "Stack";
constexpr size_t LAST_INDEX(size_t s) { return s - 1; }
constexpr size_t SECOND_FROM_END(size_t s) { return s - 2; }

22
mindspore/ccsrc/frontend/parallel/ops_info/pack_info.cc
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@ -28,7 +28,7 @@
namespace mindspore {
namespace parallel {
Status PackInfo::GetAttrs() {
Status StackInfo::GetAttrs() {
int axis = 0;
auto axis_iter = attrs_.find(AXIS);
if (axis_iter != attrs_.end()) {
@ -57,7 +57,7 @@ Status PackInfo::GetAttrs() {
return SUCCESS;
}
Status PackInfo::CheckStrategy(const StrategyPtr &strategy) {
Status StackInfo::CheckStrategy(const StrategyPtr &strategy) {
MS_EXCEPTION_IF_NULL(strategy);
if (CheckStrategyValue(strategy, inputs_shape_) != SUCCESS) {
MS_LOG(ERROR) << name_ << ": Invalid strategy";
@ -83,7 +83,7 @@ Status PackInfo::CheckStrategy(const StrategyPtr &strategy) {
return SUCCESS;
}
Status PackInfo::InferDevMatrixShape() {
Status StackInfo::InferDevMatrixShape() {
MS_EXCEPTION_IF_NULL(strategy_);
std::vector<Dimensions> stra = strategy_->GetInputDim();
if (stra.empty()) {
@ -95,7 +95,7 @@ Status PackInfo::InferDevMatrixShape() {
return SUCCESS;
}
Status PackInfo::InferTensorMap() {
Status StackInfo::InferTensorMap() {
TensorMap in_tensor_map;
TensorMap out_tensor_map;
@ -119,7 +119,7 @@ Status PackInfo::InferTensorMap() {
return SUCCESS;
}
Status PackInfo::InferMirrorOps() {
Status StackInfo::InferMirrorOps() {
mirror_ops_.clear();
if (inputs_tensor_map_.empty()) {
MS_LOG(ERROR) << name_ << ": The inputs tensor map is empty";
@ -147,7 +147,7 @@ Status PackInfo::InferMirrorOps() {
return SUCCESS;
}
Status PackInfo::InferTensorInfo() {
Status StackInfo::InferTensorInfo() {
if (inputs_shape_.empty() || outputs_shape_.empty() || inputs_tensor_map_.empty() || outputs_tensor_map_.empty()) {
MS_LOG(ERROR) << name_ << ": Invalid args";
return FAILED;
@ -173,15 +173,15 @@ Status PackInfo::InferTensorInfo() {
return SUCCESS;
}
void PackInfo::ReComputeBatchSplitFlagList() {
void StackInfo::ReComputeBatchSplitFlagList() {
for (size_t i = 0; i < inputs_shape_.size(); i++) {
split_flag_list_[i] = true;
}
}
Status PackInfo::SetCostUnderStrategy(const StrategyPtr &strategy) { return SetCostUnderStrategyBase(strategy); }
Status StackInfo::SetCostUnderStrategy(const StrategyPtr &strategy) { return SetCostUnderStrategyBase(strategy); }
Status PackInfo::GenerateStrategies(int64_t stage_id) {
Status StackInfo::GenerateStrategies(int64_t stage_id) {
if (InferAttrs() != SUCCESS) {
MS_LOG(ERROR) << name_ << ": Infer attrs failed";
return FAILED;
@ -231,7 +231,7 @@ Status PackInfo::GenerateStrategies(int64_t stage_id) {
return SUCCESS;
}
Status PackInfo::Init(const StrategyPtr &strategy) {
Status StackInfo::Init(const StrategyPtr &strategy) {
if (InitWithAutoRepeatCalc(strategy) != SUCCESS) {
MS_LOG(ERROR) << name_ << ": Init failed.";
return FAILED;
@ -240,7 +240,7 @@ Status PackInfo::Init(const StrategyPtr &strategy) {
return SUCCESS;
}
Status PackInfo::InitForCostModel(const StrategyPtr &strategy) {
Status StackInfo::InitForCostModel(const StrategyPtr &strategy) {
if (InitForCostModelWithAutoRepeatCalc(strategy) != SUCCESS) {
MS_LOG(ERROR) << name_ << ": Init for cost model failed.";
return FAILED;

10
mindspore/ccsrc/frontend/parallel/ops_info/pack_info.h
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@ -29,12 +29,12 @@
namespace mindspore {
namespace parallel {
class PackInfo : public OperatorInfo {
class StackInfo : public OperatorInfo {
public:
PackInfo(const std::string &operator_name, const Shapes &inputs_shape, const Shapes &outputs_shape,
const PrimitiveAttrs &attrs)
StackInfo(const std::string &operator_name, const Shapes &inputs_shape, const Shapes &outputs_shape,
const PrimitiveAttrs &attrs)
: OperatorInfo(operator_name, inputs_shape, outputs_shape, attrs, std::make_shared<PackCost>()) {}
~PackInfo() override = default;
~StackInfo() override = default;
Status Init(const StrategyPtr &strategy) override;
Status InitForCostModel(const StrategyPtr &strategy) override;
@ -55,7 +55,7 @@ class PackInfo : public OperatorInfo {
size_t axis_ = 0;
};
using PackInfoPtr = std::shared_ptr<PackInfo>;
using PackInfoPtr = std::shared_ptr<StackInfo>;
} // namespace parallel
} // namespace mindspore

2
mindspore/ccsrc/frontend/parallel/step_auto_parallel.cc
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@ -154,7 +154,7 @@ bool IsSplittableOperator(const std::string &op_name) {
{MATMUL, TRANSPOSE, GELU, TANH, SOFTMAX, SUB, MUL, DIV, RESHAPE, GREATER, LOG_SOFTMAX, ACTIVATION, PRELU,
FLOORDIV, L2_NORMALIZE, ADD, MAXPOOL, MAXPOOLV2, VIRTUAL_DATA_SET, RELU, ONEHOT, DROPOUT_DO_MASK,
REDUCE_MAX, REDUCE_MIN, ARGMAXWITHVALUE, ARGMINWITHVALUE, REDUCE_SUM, CONV2D, FUSE_BATCH_NORM, POOLING,
MAX_POOL_WITH_ARGMAX, SIMPLE_MEAN, FLATTEN, BATCH_NORM, LAYER_NORM, BIAS_ADD, ASSIGN_SUB, COS, ACOS, EXP, PACK,
MAX_POOL_WITH_ARGMAX, SIMPLE_MEAN, FLATTEN, BATCH_NORM, LAYER_NORM, BIAS_ADD, ASSIGN_SUB, COS, ACOS, EXP, STACK,
LOG, REDUCE_MEAN, REAL_DIV, SIGMOID, POW, MAXIMUM, MINIMUM, EQUAL, NOT_EQUAL, LOGICALNOT, GATHERV2, SQRT, CONCAT,
STRIDEDSLICE, GET_NEXT, CAST, NEG, SQUARE, BATCH_MATMUL, EXPAND_DIMS, SQUEEZE, SPARSE_GATHERV2, TILE, DROPOUT,
SOFTMAX_CROSS_ENTROPY_WITH_LOGITS, SIGMOID_CROSS_ENTROPY_WITH_LOGITS, SPARSE_SOFTMAX_CROSS_ENTROPY_WITH_LOGITS,

1
mindspore/ccsrc/transform/graph_ir/op_adapter_map.h
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@ -158,7 +158,6 @@ constexpr const char kNameSlice[] = "Slice";
constexpr const char kNameAddN[] = "AddN";
constexpr const char kNameLess[] = "Less";
constexpr const char kNameGreater[] = "Greater";
constexpr const char kNamePack[] = "Pack";
constexpr const char kNameUnpack[] = "Unpack";
constexpr const char kNameMerge[] = "Merge";
constexpr const char kNameGeSwitch[] = "GeSwitch";

2
mindspore/ccsrc/transform/graph_ir/op_declare/split_combination_ops_declare.cc
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@ -29,7 +29,7 @@ INPUT_MAP(Pack) = EMPTY_INPUT_MAP;
DYN_INPUT_MAP(Pack) = {{1, DYN_INPUT_DESC(x)}};
ATTR_MAP(Pack) = {{"num", ATTR_DESC(N, AnyTraits<int64_t>())}, {"axis", ATTR_DESC(axis, AnyTraits<int64_t>())}};
OUTPUT_MAP(Pack) = {{0, OUTPUT_DESC(y)}};
REG_ADPT_DESC(Pack, kNamePack, ADPT_DESC(Pack))
REG_ADPT_DESC(Pack, prim::kStack, ADPT_DESC(Pack))
// ConcatD
INPUT_MAP(ConcatD) = EMPTY_INPUT_MAP;

2
mindspore/ccsrc/transform/graph_ir/op_declare/transformation_ops_declare.cc
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@ -28,7 +28,7 @@ REG_ADPT_DESC(Flatten, prim::kPrimFlatten->name(), ADPT_DESC(Flatten))
INPUT_MAP(Unpack) = {{1, INPUT_DESC(x)}};
ATTR_MAP(Unpack) = {{"axis", ATTR_DESC(axis, AnyTraits<int64_t>())}, {"num", ATTR_DESC(num, AnyTraits<int64_t>())}};
DYN_OUTPUT_MAP(Unpack) = {{0, DYN_OUTPUT_DESC(y)}};
REG_ADPT_DESC(Unpack, kNameUnpack, ADPT_DESC(Unpack))
REG_ADPT_DESC(Unpack, prim::kUnstack, ADPT_DESC(Unpack))
// ExtractImagePatches
INPUT_MAP(ExtractImagePatches) = {{1, INPUT_DESC(x)}};

4
mindspore/core/abstract/infer_functions.h
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@ -108,8 +108,8 @@ AbstractBasePtr InferImplArrayToScalar(const AnalysisEnginePtr &, const Primitiv
const AbstractBasePtrList &args_spec_list);
AbstractBasePtr InferImplBroadCastShape(const AnalysisEnginePtr &, const PrimitivePtr &primitive,
const AbstractBasePtrList &args_spec_list);
AbstractBasePtr InferImplPack(const AnalysisEnginePtr &, const PrimitivePtr &primitive,
const AbstractBasePtrList &args_spec_list);
AbstractBasePtr InferImplStack(const AnalysisEnginePtr &, const PrimitivePtr &primitive,
const AbstractBasePtrList &args_spec_list);
AbstractBasePtr InferImplMakeTuple(const AnalysisEnginePtr &, const PrimitivePtr &primitive,
const AbstractBasePtrList &args_spec_list);

4
mindspore/core/abstract/prim_arrays.cc
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@ -114,8 +114,8 @@ AbstractBasePtr InferImplTile(const AnalysisEnginePtr &, const PrimitivePtr &pri
return std::make_shared<AbstractTensor>(arg->element(), std::make_shared<Shape>(result_shp));
}
AbstractBasePtr InferImplPack(const AnalysisEnginePtr &, const PrimitivePtr &primitive,
const AbstractBasePtrList &args_spec_list) {
AbstractBasePtr InferImplStack(const AnalysisEnginePtr &, const PrimitivePtr &primitive,
const AbstractBasePtrList &args_spec_list) {
// Inputs: a tuple of tensor.
const std::string op_name = primitive->name();
CheckArgsSize(op_name, args_spec_list, 1);

2
mindspore/core/abstract/primitive_infer_map.cc
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@ -59,7 +59,7 @@ PrimitiveEvalImplMap &GetPrimitiveToEvalImplMap() {
{prim::kPrimScalarToArray, {InferImplScalarToArray, true}},
{prim::kPrimArrayToScalar, {InferImplArrayToScalar, true}},
{prim::kPrimBroadcastShape, {InferImplBroadCastShape, true}},
{prim::kPrimPack, {InferImplPack, true}},
{prim::kPrimStack, {InferImplStack, true}},
{prim::kPrimPad, {InferImplPad, true}},
{prim::kPrimUnique, {InferImplUnique, true}},
{prim::kPrimUniqueGrad, {InferImplUniqueGrad, true}},

8
mindspore/core/base/core_ops.h
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@ -38,6 +38,10 @@ constexpr auto kScalarTrunc = "ScalarTrunc";
constexpr auto kScalarFloor = "ScalarFloor";
constexpr auto kScalarUadd = "ScalarUadd";
constexpr auto kScalarUsub = "ScalarUsub";
// Arrays
constexpr auto kStack = "Stack";
constexpr auto kUnstack = "Unstack";
constexpr auto kTupleGetItem = "TupleGetItem";
// Here list all primitives used in backend or some special primitives used by core.
@ -118,8 +122,9 @@ inline const PrimitivePtr kPrimSize = std::make_shared<Primitive>("Size");
inline const PrimitivePtr kPrimArgMax = std::make_shared<Primitive>("Argmax");
inline const PrimitivePtr kPrimArgMin = std::make_shared<Primitive>("Argmin");
inline const PrimitivePtr kPrimPack = std::make_shared<Primitive>("Pack");
inline const PrimitivePtr kPrimStack = std::make_shared<Primitive>(kStack);
inline const PrimitivePtr kPrimUnpack = std::make_shared<Primitive>("Unpack");
inline const PrimitivePtr kPrimUnstack = std::make_shared<Primitive>("Unstack");
inline const PrimitivePtr kPrimUnstack = std::make_shared<Primitive>(kUnstack);
inline const PrimitivePtr kPrimUnsortedSegmentMax = std::make_shared<Primitive>("UnsortedSegmentMax");
inline const PrimitivePtr kPrimUnsortedSegmentSum = std::make_shared<Primitive>("UnsortedSegmentSum");
inline const PrimitivePtr kPrimUnsortedSegmentMin = std::make_shared<Primitive>("UnsortedSegmentMin");
@ -426,7 +431,6 @@ inline const PrimitivePtr kPrimEquivFormat = std::make_shared<Primitive>("EquivF
inline const PrimitivePtr kPrimLshProjection = std::make_shared<Primitive>("LshProjection");
inline const PrimitivePtr kPrimHashtableLookup = std::make_shared<Primitive>("HashtableLookup");
inline const PrimitivePtr kPrimCustomPredict = std::make_shared<Primitive>("CustomPredict");
inline const PrimitivePtr kPrimStack = std::make_shared<Primitive>("Stack");
inline const PrimitivePtr kPrimPriorBox = std::make_shared<Primitive>("PriorBox");
inline const PrimitivePtr kPrimQuantDTypeCast = std::make_shared<Primitive>("QuantDTypeCast");
inline const PrimitivePtr kPrimWhile = std::make_shared<Primitive>("While");

4
mindspore/nn/layer/image.py
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@ -332,7 +332,7 @@ class MSSSIM(Cell):
self.reduce_mean = P.ReduceMean()
self.prod = P.ReduceProd()
self.pow = P.Pow()
self.pack = P.Pack(axis=-1)
self.stack = P.Stack(axis=-1)
self.concat = P.Concat(axis=1)
def construct(self, img1, img2):
@ -360,7 +360,7 @@ class MSSSIM(Cell):
img1, img2 = _downsample(img1, img2, self.avg_pool)
mcs = mcs[0:-1:1]
mcs_and_ssim = self.pack(mcs + (self.relu(sim),))
mcs_and_ssim = self.stack(mcs + (self.relu(sim),))
mcs_and_ssim = self.pow(mcs_and_ssim, self.weight_tensor)
ms_ssim = self.prod(mcs_and_ssim, -1)
loss = self.reduce_mean(ms_ssim, -1)

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