From 9fa0499fa029e771fbb43908f570d00e8f6396ca Mon Sep 17 00:00:00 2001 From: mindspore-ci-bot <314202276@qq.com> Date: Thu, 28 Jan 2021 14:51:40 +0800 Subject: [PATCH] Change GatherV2 to Gather r1.1 to master --- config/op_info.config | 4 +- .../kernel_compiler/cpu/gather_cpu_kernel.h | 2 +- .../gpu/arrays/gatherv2_gpu_kernel.cc | 16 +++---- .../ascend/ir_fission/gather_v2_ds_fission.cc | 6 +-- .../pass/const_input_to_attr_registry.cc | 2 +- .../optimizer/irpass/branch_culling.cc | 4 +- .../rec_core/rec_generate_strategy.cc | 4 +- .../cache_embedding/cache_embedding.cc | 2 +- .../ccsrc/frontend/parallel/dynamic_creator.h | 4 +- .../parallel/ops_info/gather_v2_info.cc | 22 ++++----- .../parallel/ops_info/gather_v2_info.h | 8 ++-- .../parallel/ops_info/gather_v2_p_info.cc | 46 +++++++++---------- .../parallel/ops_info/gather_v2_p_info.h | 16 +++---- .../frontend/parallel/ops_info/ops_utils.h | 2 +- .../ccsrc/frontend/parallel/step_parallel.cc | 2 +- .../executor/tiling/op_tiling_calculater.cc | 2 +- .../transform/express_ir/onnx_exporter.cc | 4 +- .../op_declare/selection_ops_declare.cc | 2 +- mindspore/ccsrc/utils/utils.h | 2 +- .../core/abstract/primitive_infer_map.cc | 2 +- mindspore/core/base/core_ops.h | 5 +- mindspore/lite/src/ops/primitive_c.cc | 2 +- .../converter/parser/tf/tf_gather_parser.cc | 1 + .../converter/parser/tflite/tflite_util.cc | 2 +- mindspore/nn/layer/embedding.py | 10 ++-- mindspore/nn/loss/loss.py | 2 +- mindspore/nn/optim/lazyadam.py | 2 +- mindspore/nn/optim/optimizer.py | 4 +- mindspore/ops/_constants.py | 36 +++++++++++++++ mindspore/ops/_grad/grad_array_ops.py | 8 ++-- mindspore/ops/_op_impl/tbe/gather_v2.py | 2 +- mindspore/ops/_op_impl/tbe/gather_v2_ds.py | 2 +- mindspore/ops/functional.py | 2 +- mindspore/ops/operations/__init__.py | 3 +- mindspore/ops/operations/array_ops.py | 11 +++-- .../src/Deeptext/bbox_assign_sample_stage2.py | 4 +- .../src/maskrcnn/bbox_assign_sample_stage2.py | 2 +- .../cv/psenet/src/ETSNET/dice_loss.py | 2 +- .../official/cv/resnet_thor/src/thor_layer.py | 8 ++-- model_zoo/official/gnn/bgcf/src/bgcf.py | 2 +- .../nlp/bert/src/bert_for_pre_training.py | 2 +- model_zoo/official/nlp/bert/src/bert_model.py | 6 +-- .../bert_thor/src/bert_for_pre_training.py | 2 +- .../official/nlp/bert_thor/src/bert_model.py | 6 +-- .../nlp/bert_thor/src/thor_for_bert.py | 10 ++-- .../nlp/bert_thor/src/thor_for_bert_arg.py | 10 ++-- .../official/nlp/bert_thor/src/thor_layer.py | 6 +-- .../nlp/gnmt_v2/src/gnmt_model/embedding.py | 2 +- model_zoo/official/nlp/gpt/src/gpt.py | 2 +- .../nlp/mass/src/transformer/embedding.py | 2 +- .../src/transformer/positional_embedding.py | 2 +- .../prophetnet/src/transformer/embedding.py | 2 +- .../src/transformer/positional_embedding.py | 2 +- .../nlp/tinybert/src/tinybert_model.py | 8 ++-- .../nlp/transformer/src/transformer_model.py | 4 +- .../official/recommend/deepfm/src/deepfm.py | 2 +- model_zoo/official/recommend/ncf/src/ncf.py | 2 +- .../wide_and_deep/src/wide_and_deep.py | 4 +- .../src/wide_and_deep.py | 2 +- .../src/FaceAttribute/loss_factory.py | 2 +- .../cv/FaceQualityAssessment/src/loss.py | 2 +- .../hpc/molecular_dynamics/src/descriptor.py | 4 +- .../nlp/ternarybert/src/tinybert_model.py | 8 ++-- .../research/recommend/autodis/src/autodis.py | 2 +- tests/st/hcom/hcom_sparsetensor.py | 2 +- tests/st/hcom/test_hcom_sparsetensor.py | 1 + tests/st/model_zoo_tests/DeepFM/src/deepfm.py | 2 +- .../python_file_for_ci/wide_and_deep.py | 8 ++-- .../models/bert/src/bert_for_pre_training.py | 2 +- .../st/networks/models/bert/src/bert_model.py | 6 +-- .../models/resnet50/src_thor/thor_layer.py | 4 +- tests/st/ops/cpu/test_gather_op.py | 6 +-- tests/st/ops/gpu/test_gatherV2_op.py | 8 ++-- tests/ut/cpp/transform/convert_test.cc | 2 +- tests/ut/python/communication/test_comm.py | 4 +- tests/ut/python/onnx/test_onnx.py | 2 +- tests/ut/python/ops/test_dynamic_shape.py | 2 +- tests/ut/python/ops/test_ops.py | 14 +++--- .../ut/python/parallel/test_dynamic_shape.py | 8 ++-- tests/ut/python/parallel/test_gather_v2.py | 2 +- .../parallel/test_gather_v2_primitive.py | 4 +- .../python/parallel/test_manual_gatherv2.py | 2 +- .../test_reshape_skip_redistribution.py | 2 +- .../test_uniform_candidate_sampler.py | 2 +- tests/ut/python/pipeline/parse/test_parse.py | 2 +- 85 files changed, 243 insertions(+), 198 deletions(-) create mode 100644 mindspore/ops/_constants.py diff --git a/config/op_info.config b/config/op_info.config index 7aa3796086b..bea3ecf0065 100644 --- a/config/op_info.config +++ b/config/op_info.config @@ -187,8 +187,8 @@ {"op_name": "ReduceMean", "inputs": [{"index": 0, "name": "x", "need_compile": false, "param_type": "required", "shape": "all"}], "outputs": [{"index": 0, "name": "y", "need_compile": false, "param_type": "required", "shape": "all"}], "attr": [{"name": "axis", "param_type": "optional", "type": "listInt", "value": "all"}, {"name": "keep_dims", "param_type": "optional", "type": "bool", "value": "all"}], "fusion_type": "OPAQUE", "dtype_format": [[["int8", ""], ["int8", ""]], [["uint8", ""], ["uint8", ""]], [["float16", ""], ["float16", ""]], [["float32", ""], ["float32", ""]]], "imply_type": "TBE", "async_flag": false, "binfile_name": "reduce_mean.so", "compute_cost": 10, "kernel_name": "reduce_mean_d", "partial_flag": true, "reshape_type": "", "dynamic_format": false, "dynamic_shape": false, "need_check_supported": false, "op_pattern": "reduce"} {"op_name": "Tile", "inputs": [{"index": 0, "name": "x1", "need_compile": false, "param_type": "required", "shape": "all"}], "outputs": [{"index": 0, "name": "y", "need_compile": false, "param_type": "required", "shape": "all"}], "attr": [{"name": "multiples", "param_type": "optional", "type": "listInt", "value": "all"}], "fusion_type": "ELEMWISE", "dtype_format": [[["", ""], ["", ""]]], "imply_type": "TBE", "async_flag": false, "binfile_name": "tile_d.so", "compute_cost": 10, "kernel_name": "tile_d", "partial_flag": 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"DefaultFormat"]], [["uint8", "DefaultFormat"], ["int32", "DefaultFormat"], ["uint8", "DefaultFormat"]], [["uint8", "DefaultFormat"], ["int64", "DefaultFormat"], ["uint8", "DefaultFormat"]], [["bool", "DefaultFormat"], ["int32", "DefaultFormat"], ["bool", "DefaultFormat"]], [["bool", "DefaultFormat"], ["int64", "DefaultFormat"], ["bool", "DefaultFormat"]]], "imply_type": "TBE", "async_flag": false, "binfile_name": "gather_nd.so", "compute_cost": 10, "kernel_name": "gather_nd", "partial_flag": true, "reshape_type": "", "dynamic_format": false, "dynamic_shape": false, "need_check_supported": false, "op_pattern": ""} {"op_name": "BNTrainingReduce", "inputs": [{"index": 0, "name": "x", "need_compile": false, "param_type": "required", "shape": "all", "reshape_type": "NC"}], "outputs": [{"index": 0, "name": "sum", "need_compile": false, "param_type": "required", "shape": "all"}, {"index": 1, "name": "square_sum", "need_compile": false, "param_type": "required", "shape": "all"}], "attr": [], "fusion_type": "ELEMWISE", "dtype_format": [[["float16", ""], ["float32", ""], ["float32", ""]], [["float32", ""], ["float32", ""], ["float32", ""]]], "imply_type": "TBE", "async_flag": false, "binfile_name": "bn_training_reduce.so", "compute_cost": 10, "kernel_name": "bn_training_reduce", "partial_flag": true, "reshape_type": "", "dynamic_format": false, "dynamic_shape": false, "need_check_supported": false, "op_pattern": "dynamicFormat"} {"op_name": "BNTrainingReduceGrad", "inputs": [{"index": 0, "name": "grads", "need_compile": false, "param_type": "required", "shape": "all", "reshape_type": "NC"}, {"index": 1, "name": "x_norm", "need_compile": false, "param_type": "required", "shape": "all", "reshape_type": "NC"}, {"index": 2, "name": "diff_scale", "need_compile": false, "param_type": "required", "shape": "all"}, {"index": 3, "name": "diff_offset", "need_compile": false, "param_type": "required", "shape": "all"}, {"index": 4, "name": "scale", "need_compile": false, "param_type": "required", "shape": "all"}, {"index": 5, "name": "batch_mean", "need_compile": false, "param_type": "required", "shape": "all"}, {"index": 6, "name": "batch_variance", "need_compile": false, "param_type": "required", "shape": "all"}], "outputs": [{"index": 0, "name": "y", "need_compile": false, "param_type": "required", "shape": "all", "reshape_type": "NC"}], "attr": [{"name": "epsilon", "param_type": "optional", "type": "float", "value": "all"}], "fusion_type": "OPAQUE", "dtype_format": [[["float16", ""], ["float32", ""], ["float32", ""], ["float32", ""], ["float32", ""], ["float32", ""], ["float32", ""], ["float16", ""]], [["float32", ""], ["float32", ""], ["float32", ""], ["float32", ""], ["float32", ""], ["float32", ""], ["float32", ""], ["float32", ""]]], "imply_type": "TBE", "async_flag": false, "binfile_name": "bn_training_reduce_grad.so", "compute_cost": 10, "kernel_name": "bn_training_reduce_grad", "partial_flag": true, "reshape_type": "", "dynamic_format": false, "dynamic_shape": false, "need_check_supported": false, "op_pattern": "dynamicFormat"} diff --git a/mindspore/ccsrc/backend/kernel_compiler/cpu/gather_cpu_kernel.h b/mindspore/ccsrc/backend/kernel_compiler/cpu/gather_cpu_kernel.h index 43864356aee..b98077ed2a3 100644 --- a/mindspore/ccsrc/backend/kernel_compiler/cpu/gather_cpu_kernel.h +++ b/mindspore/ccsrc/backend/kernel_compiler/cpu/gather_cpu_kernel.h @@ -43,7 +43,7 @@ class GatherV2CPUKernel : public CPUKernel { }; MS_REG_CPU_KERNEL( - GatherV2, + Gather, KernelAttr().AddInputAttr(kNumberTypeFloat32).AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeFloat32), GatherV2CPUKernel); } // namespace kernel diff --git a/mindspore/ccsrc/backend/kernel_compiler/gpu/arrays/gatherv2_gpu_kernel.cc b/mindspore/ccsrc/backend/kernel_compiler/gpu/arrays/gatherv2_gpu_kernel.cc index bc61e5b6f95..7a17c34ced8 100644 --- a/mindspore/ccsrc/backend/kernel_compiler/gpu/arrays/gatherv2_gpu_kernel.cc +++ b/mindspore/ccsrc/backend/kernel_compiler/gpu/arrays/gatherv2_gpu_kernel.cc @@ -19,26 +19,26 @@ namespace mindspore { namespace kernel { MS_REG_GPU_KERNEL_TWO( - GatherV2, + Gather, KernelAttr().AddInputAttr(kNumberTypeFloat32).AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeFloat32), GatherV2GpuFwdKernel, float, int) MS_REG_GPU_KERNEL_TWO( - GatherV2, + Gather, KernelAttr().AddInputAttr(kNumberTypeFloat32).AddInputAttr(kNumberTypeInt64).AddOutputAttr(kNumberTypeFloat32), GatherV2GpuFwdKernel, float, int64_t) MS_REG_GPU_KERNEL_TWO( - GatherV2, + Gather, KernelAttr().AddInputAttr(kNumberTypeFloat16).AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeFloat16), GatherV2GpuFwdKernel, half, int) MS_REG_GPU_KERNEL_TWO( - GatherV2, + Gather, KernelAttr().AddInputAttr(kNumberTypeFloat16).AddInputAttr(kNumberTypeInt64).AddOutputAttr(kNumberTypeFloat16), GatherV2GpuFwdKernel, half, int64_t) -MS_REG_GPU_KERNEL_TWO(GatherV2, +MS_REG_GPU_KERNEL_TWO(Gather, KernelAttr() .AddInputAttr(kNumberTypeFloat32) .AddInputAttr(kNumberTypeInt32) @@ -46,7 +46,7 @@ MS_REG_GPU_KERNEL_TWO(GatherV2, .AddOutputAttr(kNumberTypeFloat32), GatherV2GpuFwdKernel, float, int) -MS_REG_GPU_KERNEL_TWO(GatherV2, +MS_REG_GPU_KERNEL_TWO(Gather, KernelAttr() .AddInputAttr(kNumberTypeFloat32) .AddInputAttr(kNumberTypeInt64) @@ -54,7 +54,7 @@ MS_REG_GPU_KERNEL_TWO(GatherV2, .AddOutputAttr(kNumberTypeFloat32), GatherV2GpuFwdKernel, float, int64_t) -MS_REG_GPU_KERNEL_TWO(GatherV2, +MS_REG_GPU_KERNEL_TWO(Gather, KernelAttr() .AddInputAttr(kNumberTypeFloat16) .AddInputAttr(kNumberTypeInt32) @@ -62,7 +62,7 @@ MS_REG_GPU_KERNEL_TWO(GatherV2, .AddOutputAttr(kNumberTypeFloat16), GatherV2GpuFwdKernel, half, int) -MS_REG_GPU_KERNEL_TWO(GatherV2, +MS_REG_GPU_KERNEL_TWO(Gather, KernelAttr() .AddInputAttr(kNumberTypeFloat16) .AddInputAttr(kNumberTypeInt64) diff --git a/mindspore/ccsrc/backend/optimizer/ascend/ir_fission/gather_v2_ds_fission.cc b/mindspore/ccsrc/backend/optimizer/ascend/ir_fission/gather_v2_ds_fission.cc index 7a3172efbe9..c30520bb9d2 100644 --- a/mindspore/ccsrc/backend/optimizer/ascend/ir_fission/gather_v2_ds_fission.cc +++ b/mindspore/ccsrc/backend/optimizer/ascend/ir_fission/gather_v2_ds_fission.cc @@ -85,8 +85,8 @@ CNodePtr CreateGatherV2Ds(const FuncGraphPtr &graph, const CNodePtr &origin_node if (origin_node->size() != 4) { MS_LOG(EXCEPTION) << "In dynamic shape scene, gatherv2 should have 3 inputs"; } - std::vector gatherv2_inputs = {NewValueNode(std::make_shared(prim::kPrimGatherV2->name())), - pad, origin_node->input(2), origin_node->input(3)}; + std::vector gatherv2_inputs = {NewValueNode(std::make_shared(prim::kPrimGather->name())), pad, + origin_node->input(2), origin_node->input(3)}; auto gather_v2 = graph->NewCNode(gatherv2_inputs); MS_EXCEPTION_IF_NULL(gather_v2); gather_v2->set_scope(origin_node->scope()); @@ -146,7 +146,7 @@ bool CheckInputs(const CNodePtr &origin_node) { const BaseRef GatherV2DsFission::DefinePattern() const { VarPtr Xs = std::make_shared(); - VectorRef pattern({prim::kPrimGatherV2, Xs}); + VectorRef pattern({prim::kPrimGather, Xs}); return pattern; } diff --git a/mindspore/ccsrc/backend/optimizer/pass/const_input_to_attr_registry.cc b/mindspore/ccsrc/backend/optimizer/pass/const_input_to_attr_registry.cc index d624f45a13d..dd7da1d3b63 100644 --- a/mindspore/ccsrc/backend/optimizer/pass/const_input_to_attr_registry.cc +++ b/mindspore/ccsrc/backend/optimizer/pass/const_input_to_attr_registry.cc @@ -38,7 +38,7 @@ ConstInputToAttrInfoRegistry::ConstInputToAttrInfoRegistry() { Register(prim::kPrimReduceMin->name(), {1}); Register(prim::kPrimReduceSum->name(), {1}); Register(prim::kPrimReduceMean->name(), {1}); - Register(prim::kPrimGatherV2->name(), {2}); + Register(prim::kPrimGather->name(), {2}); Register(prim::kPrimGatherD->name(), {1}); Register(prim::kPrimEmbeddingLookup->name(), {2, 3, 4, 5}); Register(prim::kPrimEmbeddingLookupCommGrad->name(), {1}); diff --git a/mindspore/ccsrc/frontend/optimizer/irpass/branch_culling.cc b/mindspore/ccsrc/frontend/optimizer/irpass/branch_culling.cc index 42e1017b02b..cf45b890b89 100644 --- a/mindspore/ccsrc/frontend/optimizer/irpass/branch_culling.cc +++ b/mindspore/ccsrc/frontend/optimizer/irpass/branch_culling.cc @@ -62,7 +62,7 @@ bool InConvertWhiteList(const AnfNodePtr &node, size_t index) { {prim::kPrimCast, {2}}, {prim::kPrimTranspose, {2}}, {prim::kPrimOneHot, {2}}, - {prim::kPrimGatherV2, {3}}, + {prim::kPrimGather, {3}}, {prim::kPrimReshape, {2}}, {prim::kPrimAssign, {1}}, {prim::kPrimAssignAdd, {1}}, @@ -508,7 +508,7 @@ bool GraphOutputCompatible(const AbstractBasePtr &true_branch_abs, const Abstrac abstract::AbstractTuplePtr false_branch_tuple = false_branch_abs->cast(); if (true_branch_tuple->elements().size() != false_branch_tuple->elements().size()) { MS_LOG(ERROR) << "true branch size:" << true_branch_tuple->elements().size() - << ", not equal to false banch size:" << false_branch_tuple->elements().size() << " "; + << ", not equal to false branch size:" << false_branch_tuple->elements().size() << " "; return false; } bool all_compatible = true; diff --git a/mindspore/ccsrc/frontend/parallel/auto_parallel/rec_core/rec_generate_strategy.cc b/mindspore/ccsrc/frontend/parallel/auto_parallel/rec_core/rec_generate_strategy.cc index 5989ab06cef..7ef922e0ebe 100644 --- a/mindspore/ccsrc/frontend/parallel/auto_parallel/rec_core/rec_generate_strategy.cc +++ b/mindspore/ccsrc/frontend/parallel/auto_parallel/rec_core/rec_generate_strategy.cc @@ -616,7 +616,7 @@ Dimensions PrepareIncomingOperatorInputStrategy(const std::vectorname().substr(0, pos); - if (name == "GatherV2") { + if (name == "Gather") { return s; } else if (name == "GatherV2P") { return PrepareGatherV2POutputStrategy(ops, incoming_op_index); @@ -849,7 +849,7 @@ Strategys GenerateStrategiesFromStrategy(const std::vectortype() == GATHERV2) { auto pos = ops[iter_ops]->name().find("Info"); auto name = ops[iter_ops]->name().substr(0, pos); - if (name == "GatherV2") { + if (name == "Gather") { return PrepareGatherV2(ops, iter_ops, basic_stra); } else if (name == "GatherV2P") { return PrepareGatherV2P(ops, iter_ops, basic_stra); diff --git a/mindspore/ccsrc/frontend/parallel/cache_embedding/cache_embedding.cc b/mindspore/ccsrc/frontend/parallel/cache_embedding/cache_embedding.cc index 2032f09a843..7f3e280ece0 100644 --- a/mindspore/ccsrc/frontend/parallel/cache_embedding/cache_embedding.cc +++ b/mindspore/ccsrc/frontend/parallel/cache_embedding/cache_embedding.cc @@ -426,7 +426,7 @@ AnfNodePtr FindGatherV2FromSparseGatherV2(const FuncGraphPtr &graph, const AnfNo AnfNodePtrList gatherv2_nodes; auto user_set = graph->manager()->node_users()[node]; for (auto &ele : user_set) { - if (IsPrimitiveCNode(ele.first, prim::kPrimGatherV2)) { + if (IsPrimitiveCNode(ele.first, prim::kPrimGather)) { gatherv2_nodes.emplace_back(ele.first); } } diff --git a/mindspore/ccsrc/frontend/parallel/dynamic_creator.h b/mindspore/ccsrc/frontend/parallel/dynamic_creator.h index 43108ea6e1f..6e4ea6d8f3a 100644 --- a/mindspore/ccsrc/frontend/parallel/dynamic_creator.h +++ b/mindspore/ccsrc/frontend/parallel/dynamic_creator.h @@ -140,7 +140,7 @@ REGISTER(ReLU6Info); REGISTER(ReLUV2Info); REGISTER(SoftplusInfo); REGISTER(SoftsignInfo); -REGISTER(GatherV2Info); +REGISTER(GatherInfo); REGISTER(SparseGatherV2Info); REGISTER(SqrtInfo); REGISTER(SigmoidInfo); @@ -180,7 +180,7 @@ REGISTER(UniformCandidateSamplerInfo); REGISTER(UnsortedSegmentSumInfo); REGISTER(UnsortedSegmentMinInfo); REGISTER(UnsortedSegmentMaxInfo); -REGISTER(GatherV2PInfo); +REGISTER(GatherPInfo); REGISTER(EmbeddingLookupInfo); REGISTER(TileInfo); REGISTER(BroadcastToInfo); diff --git a/mindspore/ccsrc/frontend/parallel/ops_info/gather_v2_info.cc b/mindspore/ccsrc/frontend/parallel/ops_info/gather_v2_info.cc index 4adf6c1973d..7fb37b4567b 100644 --- a/mindspore/ccsrc/frontend/parallel/ops_info/gather_v2_info.cc +++ b/mindspore/ccsrc/frontend/parallel/ops_info/gather_v2_info.cc @@ -30,7 +30,7 @@ namespace mindspore { namespace parallel { -Status GatherV2Info::GetAttrs() { +Status GatherInfo::GetAttrs() { if (inputs_shape_.size() != GATHER_V2_INPUTS_SIZE) { MS_LOG(ERROR) << name_ << ": inputs shape size must be 2, but is " << inputs_shape_.size(); return FAILED; @@ -70,7 +70,7 @@ Status GatherV2Info::GetAttrs() { return SUCCESS; } -Status GatherV2Info::CheckStrategy(const StrategyPtr &strategy) { +Status GatherInfo::CheckStrategy(const StrategyPtr &strategy) { if (inputs_shape_.size() != GATHER_V2_INPUTS_SIZE) { MS_LOG(ERROR) << name_ << ": inputs shape size must be " << GATHER_V2_INPUTS_SIZE << ", but is " << inputs_shape_.size(); @@ -104,7 +104,7 @@ Status GatherV2Info::CheckStrategy(const StrategyPtr &strategy) { return SUCCESS; } -Status GatherV2Info::InferDevMatrixShape() { +Status GatherInfo::InferDevMatrixShape() { Strategys stra = strategy_->GetInputDim(); dev_matrix_shape_ = stra.at(0); return SUCCESS; @@ -114,7 +114,7 @@ Status GatherV2Info::InferDevMatrixShape() { // If index is a n dimension tensor, output dimension is input dimension plus (n - 1). // Tensor map dimension is equal to the corresponding input and output dimension. // If index's dimension is more than 1, we insert -1 for the output tensor map. -Status GatherV2Info::InferTensorMap() { +Status GatherInfo::InferTensorMap() { if (inputs_shape_.size() != GATHER_V2_INPUTS_SIZE) { MS_LOG(ERROR) << name_ << ": inputs shape size must be " << GATHER_V2_INPUTS_SIZE << ", but is " << inputs_shape_.size(); @@ -158,7 +158,7 @@ Status GatherV2Info::InferTensorMap() { return SUCCESS; } -Status GatherV2Info::InferTensorInfo() { +Status GatherInfo::InferTensorInfo() { if (inputs_shape_.size() != GATHER_V2_INPUTS_SIZE) { MS_LOG(ERROR) << name_ << ": inputs shape size must be " << GATHER_V2_INPUTS_SIZE << ", but is " << inputs_shape_.size(); @@ -219,7 +219,7 @@ OperatorVector CreateSubOp(int64_t sub_value) { return ops; } -Status GatherV2Info::InferTensorSubOps() { +Status GatherInfo::InferTensorSubOps() { sub_ops_.clear(); if ((index_size_ == 0) || (axis_strategy_ == 1)) { return SUCCESS; @@ -252,7 +252,7 @@ Status GatherV2Info::InferTensorSubOps() { return SUCCESS; } -Status GatherV2Info::Init(const StrategyPtr &strategy) { +Status GatherInfo::Init(const StrategyPtr &strategy) { if (InitWithAutoRepeatCalc(strategy) != SUCCESS) { MS_LOG(ERROR) << name_ << ": Init failed."; return FAILED; @@ -266,7 +266,7 @@ Status GatherV2Info::Init(const StrategyPtr &strategy) { return SUCCESS; } -Status GatherV2Info::InitForCostModel(const StrategyPtr &strategy) { +Status GatherInfo::InitForCostModel(const StrategyPtr &strategy) { if (InitForCostModelWithAutoRepeatCalc(strategy) != SUCCESS) { MS_LOG(ERROR) << name_ << ": Init for cost model failed."; return FAILED; @@ -275,7 +275,7 @@ Status GatherV2Info::InitForCostModel(const StrategyPtr &strategy) { return SUCCESS; } -Status GatherV2Info::GenerateStrategies(int64_t stage_id) { +Status GatherInfo::GenerateStrategies(int64_t stage_id) { if ((inputs_shape_.size() != GATHER_V2_INPUTS_SIZE) || (outputs_shape_.size() != GATHER_V2_OUTPUTS_SIZE)) { MS_LOG(ERROR) << name_ << " : Inputs shape size(" << inputs_shape_.size() << ") or outputs shape size(" << outputs_shape_.size() << "is wrong."; @@ -301,9 +301,9 @@ Status GatherV2Info::GenerateStrategies(int64_t stage_id) { return SUCCESS; } -Status GatherV2Info::SetCostUnderStrategy(const StrategyPtr &strategy) { return SetCostUnderStrategyBase(strategy); } +Status GatherInfo::SetCostUnderStrategy(const StrategyPtr &strategy) { return SetCostUnderStrategyBase(strategy); } -std::shared_ptr GatherV2Info::GenerateBatchStrategies() { +std::shared_ptr GatherInfo::GenerateBatchStrategies() { if (inputs_shape_.size() != GATHER_V2_INPUTS_SIZE) { MS_LOG(EXCEPTION) << name_ << ": inputs shape size must be " << GATHER_V2_INPUTS_SIZE << ", but is " << inputs_shape_.size(); diff --git a/mindspore/ccsrc/frontend/parallel/ops_info/gather_v2_info.h b/mindspore/ccsrc/frontend/parallel/ops_info/gather_v2_info.h index e6ad33edc9b..ff7f2e5431c 100644 --- a/mindspore/ccsrc/frontend/parallel/ops_info/gather_v2_info.h +++ b/mindspore/ccsrc/frontend/parallel/ops_info/gather_v2_info.h @@ -36,15 +36,15 @@ constexpr size_t GATHER_V2_INPUTS_VALUE_SIZE = 3; // If the strategy corresponding to axis is more than 1, index must be evenly distributed across the axis-dimension of // the input. // If Index is a scalar or n-dimension vector(n > 1), the strategy corresponding to axis must be 1. -class GatherV2Info : public OperatorInfo { +class GatherInfo : public OperatorInfo { public: - GatherV2Info(const std::string &name, const Shapes &inputs_shape, const Shapes &outputs_shape, - const PrimitiveAttrs &attrs) + GatherInfo(const std::string &name, const Shapes &inputs_shape, const Shapes &outputs_shape, + const PrimitiveAttrs &attrs) : OperatorInfo(name, inputs_shape, outputs_shape, attrs, std::make_shared()), axis_(-1), index_size_(0), axis_strategy_(1) {} - ~GatherV2Info() override = default; + ~GatherInfo() override = default; Status Init(const StrategyPtr &strategy) override; Status InitForCostModel(const StrategyPtr &strategy) override; diff --git a/mindspore/ccsrc/frontend/parallel/ops_info/gather_v2_p_info.cc b/mindspore/ccsrc/frontend/parallel/ops_info/gather_v2_p_info.cc index dde660ed870..6e828d71dc1 100644 --- a/mindspore/ccsrc/frontend/parallel/ops_info/gather_v2_p_info.cc +++ b/mindspore/ccsrc/frontend/parallel/ops_info/gather_v2_p_info.cc @@ -32,7 +32,7 @@ namespace mindspore { namespace parallel { -Status GatherV2PInfo::GetManualSplitWithoutOffsetAttr() { +Status GatherPInfo::GetManualSplitWithoutOffsetAttr() { auto manual_split_without_offset_iter = attrs_.find("manual_split"); if (manual_split_without_offset_iter != attrs_.end()) { manual_split_ = true; @@ -68,7 +68,7 @@ Status GatherV2PInfo::GetManualSplitWithoutOffsetAttr() { return SUCCESS; } -Status GatherV2PInfo::GetManualSplitAttr() { +Status GatherPInfo::GetManualSplitAttr() { auto manual_split_with_offset_iter = attrs_.find("manual_split_with_offset"); if (manual_split_with_offset_iter != attrs_.end()) { manual_split_ = true; @@ -118,7 +118,7 @@ Status GatherV2PInfo::GetManualSplitAttr() { return SUCCESS; } -Status GatherV2PInfo::GetAttrs() { +Status GatherPInfo::GetAttrs() { // get axis, the third input is the axis, is a ValueNode, embeddinglookup doesn't have axis. if (target_ != CPU) { if (input_value_.at(2) == nullptr) { @@ -172,7 +172,7 @@ Status GatherV2PInfo::GetAttrs() { return SUCCESS; } -Status GatherV2PInfo::CheckManualSplit(const Strategys &strategy) { +Status GatherPInfo::CheckManualSplit(const Strategys &strategy) { if (strategy.size() != 2) { MS_LOG(ERROR) << name_ << ": The size of strategy must be 2, but got " << strategy.size(); return FAILED; @@ -228,7 +228,7 @@ Status GatherV2PInfo::CheckManualSplit(const Strategys &strategy) { return SUCCESS; } -Status GatherV2PInfo::CheckStrategy(const StrategyPtr &strategy) { +Status GatherPInfo::CheckStrategy(const StrategyPtr &strategy) { if (CheckStrategyValue(strategy, inputs_shape_) != SUCCESS) { return FAILED; } @@ -306,7 +306,7 @@ Status GatherV2PInfo::CheckStrategy(const StrategyPtr &strategy) { return SUCCESS; } -Status GatherV2PInfo::InferMirrorOps() { +Status GatherPInfo::InferMirrorOps() { // There is no mirror operators for manual split if (manual_split_) { return SUCCESS; @@ -336,7 +336,7 @@ Status GatherV2PInfo::InferMirrorOps() { return SUCCESS; } -Status GatherV2PInfo::InferDevMatrixShape() { +Status GatherPInfo::InferDevMatrixShape() { dev_matrix_shape_.clear(); out_dev_matrix_shape_.clear(); // infer input dev_matrix_shape @@ -386,7 +386,7 @@ Status GatherV2PInfo::InferDevMatrixShape() { return SUCCESS; } -void GatherV2PInfo::InferInputsTensorMap() { +void GatherPInfo::InferInputsTensorMap() { // infer input tensor map // param_strategy(axis) != 1 size_t param_size = inputs_shape_.at(0).size(); @@ -413,7 +413,7 @@ void GatherV2PInfo::InferInputsTensorMap() { inputs_tensor_map_.emplace_back(std::move(tensor_map_index)); } -void GatherV2PInfo::InferOutputsTensorMap() { +void GatherPInfo::InferOutputsTensorMap() { // infer output tensor map size_t param_size = inputs_shape_.at(0).size(); size_t index_size = inputs_shape_.at(1).size(); @@ -460,7 +460,7 @@ void GatherV2PInfo::InferOutputsTensorMap() { outputs_tensor_map_.emplace_back(std::move(tensor_map_out)); } -Status GatherV2PInfo::InferTensorMap() { +Status GatherPInfo::InferTensorMap() { if (manual_split_) { inputs_tensor_map_.push_back({1, 0}); inputs_tensor_map_.push_back({-1, 1}); @@ -472,7 +472,7 @@ Status GatherV2PInfo::InferTensorMap() { return SUCCESS; } -Status GatherV2PInfo::InferTensorInfo() { +Status GatherPInfo::InferTensorInfo() { // infer tensor shape Shape input_shape = inputs_shape_.at(0); Shape input_index_shape = inputs_shape_.at(1); @@ -505,7 +505,7 @@ Status GatherV2PInfo::InferTensorInfo() { return SUCCESS; } -Status GatherV2PInfo::InferBias() { +Status GatherPInfo::InferBias() { CheckGlobalDeviceManager(); int64_t rank = g_device_manager->rank_index_in_stage(); auto input_shape = inputs_shape_.at(0); @@ -559,7 +559,7 @@ Status GatherV2PInfo::InferBias() { return FAILED; } -Status GatherV2PInfo::InferOffset() { +Status GatherPInfo::InferOffset() { CheckGlobalDeviceManager(); size_t rank = g_device_manager->rank_index_in_stage(); @@ -580,7 +580,7 @@ Status GatherV2PInfo::InferOffset() { return FAILED; } -Status GatherV2PInfo::InferGroup() { +Status GatherPInfo::InferGroup() { auto param_strategy = strategy_->GetInputDim().at(0); size_t dim = LongToSize(axis_); if (param_strategy.at(LongToSize(axis_)) != 1 && inputs_shape_.at(0).size() == 2) { @@ -610,7 +610,7 @@ Status GatherV2PInfo::InferGroup() { return SUCCESS; } -Status GatherV2PInfo::InferForwardCommunication() { +Status GatherPInfo::InferForwardCommunication() { if (manual_split_) { return SUCCESS; } @@ -647,7 +647,7 @@ Status GatherV2PInfo::InferForwardCommunication() { return SUCCESS; } -Status GatherV2PInfo::ComputeReplaceGraph(const CNodePtr &cnode) { +Status GatherPInfo::ComputeReplaceGraph(const CNodePtr &cnode) { GenerateGraph gen_g = GenerateGraph(); if (gen_g.Init(cnode) != SUCCESS) { MS_LOG(ERROR) << "GenerateGraph Init failed"; @@ -705,7 +705,7 @@ Status GatherV2PInfo::ComputeReplaceGraph(const CNodePtr &cnode) { return SUCCESS; } -ReplaceGraphPtr GatherV2PInfo::replace_graph(const CNodePtr &cnode) { +ReplaceGraphPtr GatherPInfo::replace_graph(const CNodePtr &cnode) { if (manual_split_ && target_ != CPU) { if (ComputeReplaceGraph(cnode) != SUCCESS) { MS_LOG(EXCEPTION) << name_ << ": ComputeReplaceGraph failed."; @@ -724,7 +724,7 @@ ReplaceGraphPtr GatherV2PInfo::replace_graph(const CNodePtr &cnode) { return replace_graph_; } -Status GatherV2PInfo::ComputeReplaceOp() { +Status GatherPInfo::ComputeReplaceOp() { int64_t bias = 0; if (manual_split_) { if (InferOffset() != SUCCESS) { @@ -752,7 +752,7 @@ Status GatherV2PInfo::ComputeReplaceOp() { return SUCCESS; } -Status GatherV2PInfo::Init(const StrategyPtr &strategy) { +Status GatherPInfo::Init(const StrategyPtr &strategy) { if (InitWithAutoRepeatCalc(strategy) != SUCCESS) { MS_LOG(ERROR) << name_ << ": Init failed."; return FAILED; @@ -765,7 +765,7 @@ Status GatherV2PInfo::Init(const StrategyPtr &strategy) { return SUCCESS; } -Status GatherV2PInfo::InitForCostModel(const StrategyPtr &strategy) { +Status GatherPInfo::InitForCostModel(const StrategyPtr &strategy) { if (InitForCostModelWithAutoRepeatCalc(strategy) != SUCCESS) { if (is_auto_parallel_) { MS_LOG(DEBUG) << name_ << ": Init for cost model failed."; @@ -783,9 +783,9 @@ Status GatherV2PInfo::InitForCostModel(const StrategyPtr &strategy) { return SUCCESS; } -Status GatherV2PInfo::SetCostUnderStrategy(const StrategyPtr &strategy) { return SetCostUnderStrategyBase(strategy); } +Status GatherPInfo::SetCostUnderStrategy(const StrategyPtr &strategy) { return SetCostUnderStrategyBase(strategy); } -Status GatherV2PInfo::GenerateStrategies(int64_t stage_id) { +Status GatherPInfo::GenerateStrategies(int64_t stage_id) { if (GetAttrs() != SUCCESS) { return FAILED; } @@ -814,7 +814,7 @@ Status GatherV2PInfo::GenerateStrategies(int64_t stage_id) { return SUCCESS; } -std::shared_ptr GatherV2PInfo::GenerateBatchStrategies() { +std::shared_ptr GatherPInfo::GenerateBatchStrategies() { if (GetAttrs() != SUCCESS) { MS_LOG(EXCEPTION) << name_ << ": Get attr failed"; } diff --git a/mindspore/ccsrc/frontend/parallel/ops_info/gather_v2_p_info.h b/mindspore/ccsrc/frontend/parallel/ops_info/gather_v2_p_info.h index 39fbe446d24..7d17313eb06 100644 --- a/mindspore/ccsrc/frontend/parallel/ops_info/gather_v2_p_info.h +++ b/mindspore/ccsrc/frontend/parallel/ops_info/gather_v2_p_info.h @@ -29,17 +29,17 @@ namespace mindspore { namespace parallel { -class GatherV2PInfo : public OperatorInfo { +class GatherPInfo : public OperatorInfo { public: - GatherV2PInfo(const std::string &name, const Shapes &inputs_shape, const Shapes &outputs_shape, - const PrimitiveAttrs &attrs, const std::string &replace_op_name = GATHERV2) + GatherPInfo(const std::string &name, const Shapes &inputs_shape, const Shapes &outputs_shape, + const PrimitiveAttrs &attrs, const std::string &replace_op_name = GATHERV2) : OperatorInfo(name, inputs_shape, outputs_shape, attrs, std::make_shared()), axis_(0), bias_(0), index_offset_(0), slice_size_(0), replace_op_name_(replace_op_name) {} - ~GatherV2PInfo() override = default; + ~GatherPInfo() override = default; Status Init(const StrategyPtr &strategy) override; Status InitForCostModel(const StrategyPtr &strategy) override; @@ -85,19 +85,19 @@ class GatherV2PInfo : public OperatorInfo { std::vector index_offsets_; }; -class SparseGatherV2Info : public GatherV2PInfo { +class SparseGatherV2Info : public GatherPInfo { public: SparseGatherV2Info(const std::string &name, const Shapes &inputs_shape, const Shapes &outputs_shape, const PrimitiveAttrs &attrs, const std::string &replace_op_name = SPARSE_GATHERV2) - : GatherV2PInfo(name, inputs_shape, outputs_shape, attrs, replace_op_name) {} + : GatherPInfo(name, inputs_shape, outputs_shape, attrs, replace_op_name) {} ~SparseGatherV2Info() override = default; }; -class EmbeddingLookupInfo : public GatherV2PInfo { +class EmbeddingLookupInfo : public GatherPInfo { public: EmbeddingLookupInfo(const std::string &name, const Shapes &inputs_shape, const Shapes &outputs_shape, const PrimitiveAttrs &attrs) - : GatherV2PInfo(name, inputs_shape, outputs_shape, attrs) {} + : GatherPInfo(name, inputs_shape, outputs_shape, attrs) {} ~EmbeddingLookupInfo() override = default; }; } // namespace parallel diff --git a/mindspore/ccsrc/frontend/parallel/ops_info/ops_utils.h b/mindspore/ccsrc/frontend/parallel/ops_info/ops_utils.h index 9cff810462c..4d8c22b6423 100644 --- a/mindspore/ccsrc/frontend/parallel/ops_info/ops_utils.h +++ b/mindspore/ccsrc/frontend/parallel/ops_info/ops_utils.h @@ -249,7 +249,7 @@ constexpr char MINIMUM[] = "Minimum"; constexpr char EQUAL[] = "Equal"; constexpr char NOT_EQUAL[] = "NotEqual"; constexpr char LOGICALNOT[] = "LogicalNot"; -constexpr char GATHERV2[] = "GatherV2"; +constexpr char GATHERV2[] = "Gather"; constexpr char SPARSE_GATHERV2[] = "SparseGatherV2"; constexpr char STRIDEDSLICE[] = "StridedSlice"; constexpr char SLICE[] = "Slice"; diff --git a/mindspore/ccsrc/frontend/parallel/step_parallel.cc b/mindspore/ccsrc/frontend/parallel/step_parallel.cc index 436d6513944..f009fd9e895 100644 --- a/mindspore/ccsrc/frontend/parallel/step_parallel.cc +++ b/mindspore/ccsrc/frontend/parallel/step_parallel.cc @@ -2699,7 +2699,7 @@ void CheckpointStrategy(const std::vector &all_nodes) { } if (operator_info->name().find(EMBEDDING_LOOKUP) != std::string::npos || operator_info->name().find(GATHERV2) != std::string::npos) { - auto gatherv2_info = std::dynamic_pointer_cast(operator_info); + auto gatherv2_info = std::dynamic_pointer_cast(operator_info); auto param_split_shapes = gatherv2_info->param_split_shapes(); auto index_offsets = gatherv2_info->index_offsets(); if (param_split_shapes.size() != index_offsets.size()) { diff --git a/mindspore/ccsrc/runtime/device/ascend/executor/tiling/op_tiling_calculater.cc b/mindspore/ccsrc/runtime/device/ascend/executor/tiling/op_tiling_calculater.cc index 929455aa044..d7928907fb2 100644 --- a/mindspore/ccsrc/runtime/device/ascend/executor/tiling/op_tiling_calculater.cc +++ b/mindspore/ccsrc/runtime/device/ascend/executor/tiling/op_tiling_calculater.cc @@ -148,7 +148,7 @@ std::string GetRealOpType(const std::string &op_type) { static const std::map kOpTypeMap = { {"SparseApplyFtrl", "SparseApplyFtrlD"}, {"SparseApplyProximalAdagrad", "SparseApplyProximalAdagradD"}, - {"SparseGatherV2", "GatherV2"}, + {"SparseGatherV2", "Gather"}, {"Pad", "PadD"}, {"Concat", "ConcatD"}, }; diff --git a/mindspore/ccsrc/transform/express_ir/onnx_exporter.cc b/mindspore/ccsrc/transform/express_ir/onnx_exporter.cc index 45745d97912..2381cd305c3 100644 --- a/mindspore/ccsrc/transform/express_ir/onnx_exporter.cc +++ b/mindspore/ccsrc/transform/express_ir/onnx_exporter.cc @@ -247,7 +247,7 @@ OPERATOR_ONNX_CONVERT_DEFINE( .Attr("pad_mode", "auto_pad", onnx::AttributeProto_AttributeType_STRING, SetPoolingPadMode) .Attr("strides", "strides", onnx::AttributeProto_AttributeType_INTS, SetAttrTupleValueToProto<2>)) -OPERATOR_ONNX_CONVERT_DEFINE(GatherV2, Gather, OpNameInfo()) +OPERATOR_ONNX_CONVERT_DEFINE(Gather, Gather, OpNameInfo()) OPERATOR_ONNX_CONVERT_DEFINE(make_tuple, SequenceConstruct, OpNameInfo()) OPERATOR_ONNX_CONVERT_DEFINE(Concat, Concat, OpNameInfo()) OPERATOR_ONNX_CONVERT_DEFINE(RealDiv, Div, OpNameInfo()) @@ -970,7 +970,7 @@ void OnnxExporter::ExportCNode(const FuncGraphPtr &func_graph, const CNodePtr &n } // MindSpore GatherV2(x, indices, axis) --> ONNX Pow(x, indices) - if (node->IsApply(prim::kPrimGatherV2)) { + if (node->IsApply(prim::kPrimGather)) { return ExportPrimGatherV2(func_graph, node, node_map_ptr, graph_proto); } diff --git a/mindspore/ccsrc/transform/graph_ir/op_declare/selection_ops_declare.cc b/mindspore/ccsrc/transform/graph_ir/op_declare/selection_ops_declare.cc index 4920434d492..3e8aad81869 100644 --- a/mindspore/ccsrc/transform/graph_ir/op_declare/selection_ops_declare.cc +++ b/mindspore/ccsrc/transform/graph_ir/op_declare/selection_ops_declare.cc @@ -70,7 +70,7 @@ INPUT_MAP(GatherV2D) = {{1, INPUT_DESC(x)}, {2, INPUT_DESC(indices)}}; INPUT_ATTR_MAP(GatherV2D) = {{3, ATTR_DESC(axis, AnyTraits())}}; ATTR_MAP(GatherV2D) = EMPTY_ATTR_MAP; OUTPUT_MAP(GatherV2D) = {{0, OUTPUT_DESC(y)}}; -REG_ADPT_DESC(GatherV2D, prim::kPrimGatherV2->name(), ADPT_DESC(GatherV2D)) +REG_ADPT_DESC(GatherV2D, prim::kPrimGather->name(), ADPT_DESC(GatherV2D)) // ScatterNdD INPUT_MAP(ScatterNdD) = {{1, INPUT_DESC(indices)}, {2, INPUT_DESC(x)}}; diff --git a/mindspore/ccsrc/utils/utils.h b/mindspore/ccsrc/utils/utils.h index ada68a5c3ce..a1ab01bdc25 100644 --- a/mindspore/ccsrc/utils/utils.h +++ b/mindspore/ccsrc/utils/utils.h @@ -208,7 +208,7 @@ constexpr auto kPushOpName = "Push"; constexpr auto kPullOpName = "Pull"; constexpr auto kEmbeddingLookupOpName = "EmbeddingLookup"; constexpr auto kEmbeddingLookupProxyOpName = "EmbeddingLookupProxy"; -constexpr auto kGatherV2OpName = "GatherV2"; +constexpr auto kGatherV2OpName = "Gather"; constexpr auto kPaddingOpName = "Padding"; constexpr auto kAvgPoolOpName = "AvgPool"; constexpr auto kAvgPoolGradGpuOpName = "AvgPoolGradGpu"; diff --git a/mindspore/core/abstract/primitive_infer_map.cc b/mindspore/core/abstract/primitive_infer_map.cc index 9fce8d327ae..b7d8d8bc388 100644 --- a/mindspore/core/abstract/primitive_infer_map.cc +++ b/mindspore/core/abstract/primitive_infer_map.cc @@ -64,7 +64,7 @@ PrimitiveEvalImplMap &GetPrimitiveToEvalImplMap() { {prim::kPrimPad, {InferImplPad, true}}, {prim::kPrimUnique, {InferImplUnique, true}}, {prim::kPrimUniqueGrad, {InferImplUniqueGrad, true}}, - {prim::kPrimGatherV2, {InferImplGatherV2, true}}, + {prim::kPrimGather, {InferImplGatherV2, true}}, {prim::kPrimSparseGatherV2, {InferImplGatherV2, true}}, {prim::kPrimEmbeddingLookup, {InferImplEmbeddingLookup, true}}, {prim::kPrimUnsortedSegmentSum, {InferImplUnsortedSegmentSum, true}}, diff --git a/mindspore/core/base/core_ops.h b/mindspore/core/base/core_ops.h index 25436e00a33..cfaac473622 100644 --- a/mindspore/core/base/core_ops.h +++ b/mindspore/core/base/core_ops.h @@ -25,6 +25,7 @@ namespace mindspore { namespace prim { +constexpr auto kGather = "Gather"; // Here list all primitives used in backend or some special primitives used by core. // Arithmetic inline const PrimitivePtr kPrimScalarAdd = std::make_shared("scalar_add"); @@ -86,8 +87,8 @@ inline const PrimitivePtr kPrimCast = std::make_shared("Cast"); inline const PrimitivePtr kPrimConcat = std::make_shared("Concat"); inline const PrimitivePtr kPrimSqueeze = std::make_shared("Squeeze"); inline const PrimitivePtr kPrimTranspose = std::make_shared("Transpose"); -inline const PrimitivePtr kPrimGatherV2 = std::make_shared("GatherV2"); inline const PrimitivePtr kPrimGatherD = std::make_shared("GatherD"); +inline const PrimitivePtr kPrimGather = std::make_shared(kGather); inline const PrimitivePtr kPrimSparseGatherV2 = std::make_shared("SparseGatherV2"); inline const PrimitivePtr kPrimShape = std::make_shared("Shape"); inline const PrimitivePtr kPrimDynamicShape = std::make_shared("DynamicShape"); @@ -351,7 +352,7 @@ inline const PrimitivePtr kPrimGetRefKey = std::make_shared("get_ref_ inline const PrimitivePtr kPrimMakeRef = std::make_shared("make_ref"); inline const PrimitivePtr kPrimGetRefValue = std::make_shared("get_ref_value"); -// Other primitve not used by backend but used in core; +// Other primitive not used by backend but used in core; inline const PrimitivePtr kPrimStateSetItem = std::make_shared("state_setitem"); inline const PrimitivePtr kPrimJ = std::make_shared("J"); diff --git a/mindspore/lite/src/ops/primitive_c.cc b/mindspore/lite/src/ops/primitive_c.cc index 63655fe8c7e..f2d8cc36cf4 100644 --- a/mindspore/lite/src/ops/primitive_c.cc +++ b/mindspore/lite/src/ops/primitive_c.cc @@ -607,7 +607,7 @@ std::shared_ptr PrimitiveC::Create(const Primitive &prim, const std: return NewPrimitiveC(prim, inputs, quantType); } else if (op_type == "MirrorPad") { return NewPrimitiveC(prim, inputs, quantType); - } else if (op_type == "GatherV2") { + } else if (op_type == "Gather") { return NewPrimitiveC(prim, inputs, quantType); } else if (op_type == "OnesLike") { return NewPrimitiveC(prim, inputs, quantType); diff --git a/mindspore/lite/tools/converter/parser/tf/tf_gather_parser.cc b/mindspore/lite/tools/converter/parser/tf/tf_gather_parser.cc index 597145f468c..aec1ba450a6 100644 --- a/mindspore/lite/tools/converter/parser/tf/tf_gather_parser.cc +++ b/mindspore/lite/tools/converter/parser/tf/tf_gather_parser.cc @@ -97,6 +97,7 @@ STATUS TFGatherParser::Parse(const tensorflow::NodeDef &tf_op, status = AddOpInput(tf_op, 1, inputs); return status; } + TFNodeRegistrar g_tfGatherV2Parser("GatherV2", new TFGatherParser()); } // namespace lite } // namespace mindspore diff --git a/mindspore/lite/tools/converter/parser/tflite/tflite_util.cc b/mindspore/lite/tools/converter/parser/tflite/tflite_util.cc index d9f37a447e3..ae10a28b66f 100644 --- a/mindspore/lite/tools/converter/parser/tflite/tflite_util.cc +++ b/mindspore/lite/tools/converter/parser/tflite/tflite_util.cc @@ -69,7 +69,7 @@ std::map tfMsOpTypeMap{ {tflite::BuiltinOperator_RANGE, "Range"}, {tflite::BuiltinOperator_RANK, "Rank"}, {tflite::BuiltinOperator_LOCAL_RESPONSE_NORMALIZATION, "LocalResponseNorm"}, - {tflite::BuiltinOperator_GATHER, "GatherV2"}, + {tflite::BuiltinOperator_GATHER, "Gather"}, {tflite::BuiltinOperator_EXP, "Exp"}, {tflite::BuiltinOperator_SPLIT_V, "SplitV"}, {tflite::BuiltinOperator_SPLIT, "Split"}, diff --git a/mindspore/nn/layer/embedding.py b/mindspore/nn/layer/embedding.py index fb056a64ce1..ba3c1e7c66f 100755 --- a/mindspore/nn/layer/embedding.py +++ b/mindspore/nn/layer/embedding.py @@ -112,7 +112,7 @@ class Embedding(Cell): self.expand = P.ExpandDims() self.reshape_flat = P.Reshape() self.shp_flat = (-1,) - self.gather = P.GatherV2() + self.gather = P.Gather() self.one_hot = P.OneHot() self.on_value = Tensor(1.0, self.dtype) self.off_value = Tensor(0.0, self.dtype) @@ -154,7 +154,7 @@ class EmbeddingLookup(Cell): When 'target' is set to 'CPU', this module will use P.EmbeddingLookup().add_prim_attr('primitive_target', 'CPU') which specified 'offset = 0' to lookup table. - When 'target' is set to 'DEVICE', this module will use P.GatherV2() which + When 'target' is set to 'DEVICE', this module will use P.Gather() which specified 'axis = 0' to lookup table. In field slice mode, the manual_shapes must be given. It is a tuple ,where the element is vocab[i], vocab[i] is the row numbers for i-th part. @@ -221,7 +221,7 @@ class EmbeddingLookup(Cell): if sparse: self.gatherv2 = P.SparseGatherV2() else: - self.gatherv2 = P.GatherV2() + self.gatherv2 = P.Gather() self.embeddinglookup = P.EmbeddingLookup().add_prim_attr('primitive_target', 'CPU') enable_ps = _get_ps_context("enable_ps") if enable_ps: @@ -231,7 +231,7 @@ class EmbeddingLookup(Cell): name='embedding_table') parallel_mode = _get_parallel_mode() is_auto_parallel = parallel_mode in (ParallelMode.SEMI_AUTO_PARALLEL, ParallelMode.AUTO_PARALLEL) - self.gather_revert = P.GatherV2() + self.gather_revert = P.Gather() self.reshape_first = P.Reshape() self.reshape = P.Reshape() self.unique = P.Unique() @@ -379,7 +379,7 @@ class MultiFieldEmbeddingLookup(EmbeddingLookup): When 'target' is set to 'CPU', this module will use P.EmbeddingLookup().add_prim_attr('primitive_target', 'CPU') which specified 'offset = 0' to lookup table. - When 'target' is set to 'DEVICE', this module will use P.GatherV2() which + When 'target' is set to 'DEVICE', this module will use P.Gather() which specified 'axis = 0' to lookup table. The vectors with the same field_ids will be combined by the 'operator', such as 'SUM', 'MAX' and 'MEAN'. Ensure the input_values of the padded id is zero, so that they can be ignored. The final diff --git a/mindspore/nn/loss/loss.py b/mindspore/nn/loss/loss.py index 2a59d259a60..fad95b79dce 100644 --- a/mindspore/nn/loss/loss.py +++ b/mindspore/nn/loss/loss.py @@ -440,7 +440,7 @@ class SampledSoftmaxLoss(_Loss): self.log = P.Log() self.slice_op = P.Slice() self.matmul = P.MatMul(False, True) - self.gather_v2 = P.GatherV2() + self.gather_v2 = P.Gather() self.reduce_max_true = P.ReduceMax(True) self.reduce_sum = P.ReduceSum() self.reduce_sum_true = P.ReduceSum(True) diff --git a/mindspore/nn/optim/lazyadam.py b/mindspore/nn/optim/lazyadam.py index f4e9ee5d629..345363d4af0 100644 --- a/mindspore/nn/optim/lazyadam.py +++ b/mindspore/nn/optim/lazyadam.py @@ -49,7 +49,7 @@ def _run_opt_with_sparse(opt, sparse_opt, push, pull, use_locking, use_nesterov, success = F.depend(success, sparse_opt(params, m, v, beta1_power, beta2_power, lr, beta1, beta2, eps, values, indices)) else: - op_gather = P.GatherV2() + op_gather = P.Gather() op_sqrt = P.Sqrt() scatter_add = P.ScatterAdd(use_locking) scatter_update = P.ScatterUpdate(use_locking) diff --git a/mindspore/nn/optim/optimizer.py b/mindspore/nn/optim/optimizer.py index 936f2990875..e3baaef6c8f 100755 --- a/mindspore/nn/optim/optimizer.py +++ b/mindspore/nn/optim/optimizer.py @@ -537,7 +537,7 @@ class Optimizer(Cell): op_add = P.AddN() -op_gather = P.GatherV2() +op_gather = P.Gather() op_mul = P.Mul() _apply_decay = C.MultitypeFuncGraph("apply_decay") @@ -625,7 +625,7 @@ class _IteratorLearningRate(LearningRateSchedule): raise TypeError("Learning rate should be Tensor.") self.learning_rate = Parameter(learning_rate, name) - self.gather = P.GatherV2() + self.gather = P.Gather() def construct(self, global_step): return self.gather(self.learning_rate, global_step, 0) diff --git a/mindspore/ops/_constants.py b/mindspore/ops/_constants.py new file mode 100644 index 00000000000..c23c85c3da0 --- /dev/null +++ b/mindspore/ops/_constants.py @@ -0,0 +1,36 @@ +# This is the Python adaptation and derivative work of Myia (https://github.com/mila-iqia/myia/). +# +# 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. +# ============================================================================ + +""" Define constants""" + +# Arithmetic +kScalarAdd = "ScalarAdd" +kScalarSub = "ScalarSub" +kScalarMul = "ScalarMul" +kScalarDiv = "ScalarDiv" +kScalarFloordiv = "ScalarFloordiv" +kScalarMod = "ScalarMod" +kScalarPow = "ScalarPow" +kScalarTrunc = "ScalarTrunc" +kScalarFloor = "ScalarFloor" +kScalarUadd = "ScalarUadd" +kScalarUsub = "ScalarUsub" + +kTupleGetItem = "TupleGetItem" +kMakeTuple = "MakeTuple" + +kGather = "Gather" diff --git a/mindspore/ops/_grad/grad_array_ops.py b/mindspore/ops/_grad/grad_array_ops.py index e1dfeac2cdc..defb5ed178f 100644 --- a/mindspore/ops/_grad/grad_array_ops.py +++ b/mindspore/ops/_grad/grad_array_ops.py @@ -382,7 +382,7 @@ def _regenerate_output_shape(x_shp, ind_shp, axis): return out_shape -@bprop_getters.register(P.GatherV2) +@bprop_getters.register(P.Gather) def get_bprop_gather_v2(self): """Generate bprop for GatherV2""" @@ -738,7 +738,7 @@ def get_bprop_tensor_scatter_update(self): @bprop_getters.register(P.ScatterMax) def get_bprop_scatter_max(self): """Generate bprop for ScatterMax""" - gather = P.GatherV2() + gather = P.Gather() def bprop(x, indices, update, out, dout): return dout, zeros_like(indices), gather(dout, indices, 0) @@ -816,7 +816,7 @@ def _gather_drop_negatives(params, is_positive=None): """Helper function for unsorted segment ops.""" maximum = P.Maximum() - gather = P.GatherV2() + gather = P.Gather() greater_equal = P.GreaterEqual() rank = P.Rank() fill = P.Fill() @@ -895,7 +895,7 @@ def get_bprop_unsorted_segment_prod(self): equal = P.Equal() cast = P.Cast() select = P.Select() - gather = P.GatherV2() + gather = P.Gather() greater = P.Greater() ones_like = P.OnesLike() maximum = P.Maximum() diff --git a/mindspore/ops/_op_impl/tbe/gather_v2.py b/mindspore/ops/_op_impl/tbe/gather_v2.py index df9eb882e48..f5d1d411681 100644 --- a/mindspore/ops/_op_impl/tbe/gather_v2.py +++ b/mindspore/ops/_op_impl/tbe/gather_v2.py @@ -16,7 +16,7 @@ """GatherV2 op""" from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType -gather_v2_op_info = TBERegOp("GatherV2") \ +gather_v2_op_info = TBERegOp("Gather") \ .fusion_type("OPAQUE") \ .async_flag(False) \ .binfile_name("gather_v2_d.so") \ diff --git a/mindspore/ops/_op_impl/tbe/gather_v2_ds.py b/mindspore/ops/_op_impl/tbe/gather_v2_ds.py index 468571844a2..9b9b52d3383 100644 --- a/mindspore/ops/_op_impl/tbe/gather_v2_ds.py +++ b/mindspore/ops/_op_impl/tbe/gather_v2_ds.py @@ -16,7 +16,7 @@ """AddN op""" from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType -gather_v2_op_info = TBERegOp("GatherV2") \ +gather_v2_op_info = TBERegOp("Gather") \ .fusion_type("OPAQUE") \ .async_flag(False) \ .binfile_name("gather_v2.so") \ diff --git a/mindspore/ops/functional.py b/mindspore/ops/functional.py index 5e5523c6106..670af6fab05 100644 --- a/mindspore/ops/functional.py +++ b/mindspore/ops/functional.py @@ -81,7 +81,7 @@ expand_dims = P.ExpandDims() transpose = P.Transpose() squeeze = P.Squeeze() scatter_nd = P.ScatterNd() -gather = P.GatherV2() +gather = P.Gather() gather_nd = P.GatherNd() scatter_update = P.ScatterUpdate() scatter_nd_update = P.ScatterNdUpdate() diff --git a/mindspore/ops/operations/__init__.py b/mindspore/ops/operations/__init__.py index 9439e444170..337c39acdb0 100644 --- a/mindspore/ops/operations/__init__.py +++ b/mindspore/ops/operations/__init__.py @@ -22,7 +22,7 @@ A collection of operators to build neural networks or to compute functions. from .image_ops import (CropAndResize) from .array_ops import (Argmax, Argmin, Cast, Concat, Pack, Unpack, Diag, DiagPart, DType, ExpandDims, Eye, - Fill, Ones, Zeros, GatherNd, GatherV2, SparseGatherV2, InvertPermutation, + Fill, Ones, Zeros, GatherNd, GatherV2, Gather, SparseGatherV2, InvertPermutation, IsInstance, IsSubClass, ArgMaxWithValue, OnesLike, ZerosLike, Rank, Reshape, ResizeNearestNeighbor, ArgMinWithValue, Meshgrid, SameTypeShape, ScatterAdd, ScatterSub, ScatterMul, ScatterDiv, ScatterMax, ScatterMin, @@ -159,6 +159,7 @@ __all__ = [ 'Transpose', 'OneHot', 'GatherV2', + 'Gather', 'SparseGatherV2', 'EmbeddingLookup', 'Padding', diff --git a/mindspore/ops/operations/array_ops.py b/mindspore/ops/operations/array_ops.py index ea00e9093cd..a300a5411d0 100644 --- a/mindspore/ops/operations/array_ops.py +++ b/mindspore/ops/operations/array_ops.py @@ -771,7 +771,7 @@ class Unique(Primitive): self.init_prim_io_names(inputs=['x'], outputs=['output']) -class GatherV2(PrimitiveWithCheck): +class Gather(PrimitiveWithCheck): """ Returns a slice of the input tensor based on the specified indices and axis. @@ -793,7 +793,7 @@ class GatherV2(PrimitiveWithCheck): >>> input_params = Tensor(np.array([[1, 2, 7, 42], [3, 4, 54, 22], [2, 2, 55, 3]]), mindspore.float32) >>> input_indices = Tensor(np.array([1, 2]), mindspore.int32) >>> axis = 1 - >>> output = ops.GatherV2()(input_params, input_indices, axis) + >>> output = ops.Gather()(input_params, input_indices, axis) >>> print(output) [[ 2. 7.] [ 4. 54.] @@ -815,7 +815,12 @@ class GatherV2(PrimitiveWithCheck): validator.check_int_range(axis_v, -rank, rank, Rel.INC_LEFT, "axis", self.name) -class SparseGatherV2(GatherV2): +def GatherV2(): + """Warning: This will be changed later""" + logger.warning("WARN_DEPRECATED: The usage of GatherV2 is deprecated. Please use Gather.") + return Gather() + +class SparseGatherV2(Gather): """ Returns a slice of input tensor based on the specified indices and axis. diff --git a/model_zoo/official/cv/deeptext/src/Deeptext/bbox_assign_sample_stage2.py b/model_zoo/official/cv/deeptext/src/Deeptext/bbox_assign_sample_stage2.py index 82c5be24436..a4aa270d85f 100644 --- a/model_zoo/official/cv/deeptext/src/Deeptext/bbox_assign_sample_stage2.py +++ b/model_zoo/official/cv/deeptext/src/Deeptext/bbox_assign_sample_stage2.py @@ -23,7 +23,7 @@ from mindspore.common.tensor import Tensor class BboxAssignSampleForRcnn(nn.Cell): """ - Bbox assigner and sampler defination. + Bbox assigner and sampler definition. Args: config (dict): Config. @@ -71,7 +71,7 @@ class BboxAssignSampleForRcnn(nn.Cell): self.greater = P.Greater() self.select = P.Select() self.gatherND = P.GatherNd() - self.gatherV2 = P.GatherV2() + self.gatherV2 = P.Gather() self.squeeze = P.Squeeze() self.cast = P.Cast() self.logicaland = P.LogicalAnd() diff --git a/model_zoo/official/cv/maskrcnn/src/maskrcnn/bbox_assign_sample_stage2.py b/model_zoo/official/cv/maskrcnn/src/maskrcnn/bbox_assign_sample_stage2.py index 822759e80c6..8165fffa1d0 100644 --- a/model_zoo/official/cv/maskrcnn/src/maskrcnn/bbox_assign_sample_stage2.py +++ b/model_zoo/official/cv/maskrcnn/src/maskrcnn/bbox_assign_sample_stage2.py @@ -22,7 +22,7 @@ from mindspore.common.tensor import Tensor class BboxAssignSampleForRcnn(nn.Cell): """ - Bbox assigner and sampler defination. + Bbox assigner and sampler definition. Args: config (dict): Config. diff --git a/model_zoo/official/cv/psenet/src/ETSNET/dice_loss.py b/model_zoo/official/cv/psenet/src/ETSNET/dice_loss.py index 03736660a1c..4bfb27dd024 100644 --- a/model_zoo/official/cv/psenet/src/ETSNET/dice_loss.py +++ b/model_zoo/official/cv/psenet/src/ETSNET/dice_loss.py @@ -50,7 +50,7 @@ class DiceLoss(_Loss): self.equal = P.Equal() self.zeros_like = P.ZerosLike() self.add = P.TensorAdd() - self.gather = P.GatherV2() + self.gather = P.Gather() def ohem_batch(self, scores, gt_texts, training_masks): ''' diff --git a/model_zoo/official/cv/resnet_thor/src/thor_layer.py b/model_zoo/official/cv/resnet_thor/src/thor_layer.py index 3858d5a6b5f..e6c828017d2 100644 --- a/model_zoo/official/cv/resnet_thor/src/thor_layer.py +++ b/model_zoo/official/cv/resnet_thor/src/thor_layer.py @@ -187,7 +187,7 @@ class Conv2d_Thor_GPU(_Conv): self.batch_size = Tensor(batch_size, mstype.float16) self.transpose = P.Transpose() self.cast = P.Cast() - self.gather = P.GatherV2() + self.gather = P.Gather() self.freq = Tensor(frequency, mstype.int32) self.axis = 0 self.sqrt = P.Sqrt() @@ -330,7 +330,7 @@ class Dense_Thor_GPU(Cell): self.dampingA = Tensor(np.identity(in_channels), mstype.float32) self.dampingG = Tensor(np.identity(out_channels), mstype.float32) self.cast = P.Cast() - self.gather = P.GatherV2() + self.gather = P.Gather() self.freq = Tensor(frequency, mstype.int32) self.axis = 0 self.add = P.TensorAdd() @@ -496,7 +496,7 @@ class Conv2d_Thor(_Conv): self.device_shape_pad_flag = True self.device_shape_pad = P.Pad(((0, 0), (0, C0 - self.in_channels), (0, 0), (0, C0 - self.in_channels))) self.slice = P.Slice() - self.gather = P.GatherV2() + self.gather = P.Gather() self.freq = Tensor(frequency, mstype.int32) self.loss_scale = Tensor(1 / loss_scale, mstype.float16) self.axis = 0 @@ -678,7 +678,7 @@ class Dense_Thor(Cell): self.pad = P.Pad(((0, 23), (0, 23))) self.pad1 = P.Pad(((0, 7), (0, 7))) self.slice = P.Slice() - self.gather = P.GatherV2() + self.gather = P.Gather() self.assignadd = P.AssignAdd() self.freq = Tensor(frequency, mstype.int32) self.axis = 0 diff --git a/model_zoo/official/gnn/bgcf/src/bgcf.py b/model_zoo/official/gnn/bgcf/src/bgcf.py index 408f5894958..140ba05ec0a 100644 --- a/model_zoo/official/gnn/bgcf/src/bgcf.py +++ b/model_zoo/official/gnn/bgcf/src/bgcf.py @@ -149,7 +149,7 @@ class BGCF(nn.Cell): self.tanh = P.Tanh() self.shape = P.Shape() self.split = P.Split(0, 2) - self.gather = P.GatherV2() + self.gather = P.Gather() self.reshape = P.Reshape() self.concat_0 = P.Concat(0) self.concat_1 = P.Concat(1) diff --git a/model_zoo/official/nlp/bert/src/bert_for_pre_training.py b/model_zoo/official/nlp/bert/src/bert_for_pre_training.py index cbe47aa1b11..ddb7532a4b1 100644 --- a/model_zoo/official/nlp/bert/src/bert_for_pre_training.py +++ b/model_zoo/official/nlp/bert/src/bert_for_pre_training.py @@ -73,7 +73,7 @@ class GetMaskedLMOutput(nn.Cell): super(GetMaskedLMOutput, self).__init__() self.width = config.hidden_size self.reshape = P.Reshape() - self.gather = P.GatherV2() + self.gather = P.Gather() weight_init = TruncatedNormal(config.initializer_range) self.dense = nn.Dense(self.width, diff --git a/model_zoo/official/nlp/bert/src/bert_model.py b/model_zoo/official/nlp/bert/src/bert_model.py index c20b73fbf8a..e3dab3f3e2f 100644 --- a/model_zoo/official/nlp/bert/src/bert_model.py +++ b/model_zoo/official/nlp/bert/src/bert_model.py @@ -113,7 +113,7 @@ class EmbeddingLookup(nn.Cell): [vocab_size, embedding_size])) self.expand = P.ExpandDims() self.shape_flat = (-1,) - self.gather = P.GatherV2() + self.gather = P.Gather() self.one_hot = P.OneHot() self.on_value = Tensor(1.0, mstype.float32) self.off_value = Tensor(0.0, mstype.float32) @@ -178,7 +178,7 @@ class EmbeddingPostprocessor(nn.Cell): self.reshape = P.Reshape() self.shape = tuple(embedding_shape) self.dropout = nn.Dropout(1 - dropout_prob) - self.gather = P.GatherV2() + self.gather = P.Gather() self.use_relative_positions = use_relative_positions self.slice = P.StridedSlice() _, seq, _ = self.shape @@ -310,7 +310,7 @@ class RelaPosEmbeddingsGenerator(nn.Cell): self.reshape = P.Reshape() self.one_hot = nn.OneHot(depth=self.vocab_size) self.shape = P.Shape() - self.gather = P.GatherV2() # index_select + self.gather = P.Gather() # index_select self.matmul = P.BatchMatMul() def construct(self): diff --git a/model_zoo/official/nlp/bert_thor/src/bert_for_pre_training.py b/model_zoo/official/nlp/bert_thor/src/bert_for_pre_training.py index 161273d3cc6..b336064b48d 100644 --- a/model_zoo/official/nlp/bert_thor/src/bert_for_pre_training.py +++ b/model_zoo/official/nlp/bert_thor/src/bert_for_pre_training.py @@ -81,7 +81,7 @@ class GetMaskedLMOutput(nn.Cell): super(GetMaskedLMOutput, self).__init__() self.width = config.hidden_size self.reshape = P.Reshape() - self.gather = P.GatherV2() + self.gather = P.Gather() weight_init = TruncatedNormal(config.initializer_range) self.dense = Dense_Thor(in_channels=self.width, diff --git a/model_zoo/official/nlp/bert_thor/src/bert_model.py b/model_zoo/official/nlp/bert_thor/src/bert_model.py index 1845c474b14..dadf16f7f61 100644 --- a/model_zoo/official/nlp/bert_thor/src/bert_model.py +++ b/model_zoo/official/nlp/bert_thor/src/bert_model.py @@ -138,7 +138,7 @@ class EmbeddingLookup(nn.Cell): [vocab_size, embedding_size])) self.expand = P.ExpandDims() self.shape_flat = (-1,) - self.gather = P.GatherV2() + self.gather = P.Gather() self.one_hot = P.OneHot() self.on_value = Tensor(1.0, mstype.float32) self.off_value = Tensor(0.0, mstype.float32) @@ -210,7 +210,7 @@ class EmbeddingPostprocessor(nn.Cell): self.reshape = P.Reshape() self.shape = tuple(embedding_shape) self.dropout = nn.Dropout(1 - dropout_prob) - self.gather = P.GatherV2() + self.gather = P.Gather() self.use_relative_positions = use_relative_positions self.slice = P.StridedSlice() _, seq, width = self.shape @@ -362,7 +362,7 @@ class RelaPosEmbeddingsGenerator(nn.Cell): self.reshape = P.Reshape() self.one_hot = nn.OneHot(depth=self.vocab_size) self.shape = P.Shape() - self.gather = P.GatherV2() # index_select + self.gather = P.Gather() # index_select self.matmul = P.BatchMatMul() def construct(self): diff --git a/model_zoo/official/nlp/bert_thor/src/thor_for_bert.py b/model_zoo/official/nlp/bert_thor/src/thor_for_bert.py index f9a8839991a..51a436e12d9 100644 --- a/model_zoo/official/nlp/bert_thor/src/thor_for_bert.py +++ b/model_zoo/official/nlp/bert_thor/src/thor_for_bert.py @@ -64,7 +64,7 @@ class THOR(Optimizer): self.shape = P.Shape() self.reshape = P.Reshape() self.mul = P.Mul() - self.gather = P.GatherV2() + self.gather = P.Gather() self.matrix_A_inv = () self.matrix_G_inv = () self.num_hidden_layers = num_hidden_layers @@ -225,8 +225,8 @@ class THOR(Optimizer): end_idx = mlm_fc_idx + 4 new_grads = new_grads + gradients[begin_idx: end_idx] - lenth = len(gradients) - new_grads = new_grads + gradients[lenth - 2: lenth] + length = len(gradients) + new_grads = new_grads + gradients[length - 2: length] gradients = new_grads else: new_grads = () @@ -350,8 +350,8 @@ class THOR(Optimizer): end_idx = mlm_fc_idx + 4 new_grads = new_grads + gradients[begin_idx: end_idx] - lenth = len(gradients) - new_grads = new_grads + gradients[lenth - 2: lenth] + length = len(gradients) + new_grads = new_grads + gradients[length - 2: length] gradients = new_grads if self.weight_decay > 0: diff --git a/model_zoo/official/nlp/bert_thor/src/thor_for_bert_arg.py b/model_zoo/official/nlp/bert_thor/src/thor_for_bert_arg.py index 4af6bded5f2..fcb26382831 100644 --- a/model_zoo/official/nlp/bert_thor/src/thor_for_bert_arg.py +++ b/model_zoo/official/nlp/bert_thor/src/thor_for_bert_arg.py @@ -66,7 +66,7 @@ class THOR(Optimizer): self.shape = P.Shape() self.reshape = P.Reshape() self.mul = P.Mul() - self.gather = P.GatherV2() + self.gather = P.Gather() self.matrix_A_inv = () self.matrix_G_inv = () self.num_hidden_layers = num_hidden_layers @@ -230,8 +230,8 @@ class THOR(Optimizer): end_idx = mlm_fc_idx + 4 new_grads = new_grads + gradients[begin_idx: end_idx] - lenth = len(gradients) - new_grads = new_grads + gradients[lenth - 2: lenth] + length = len(gradients) + new_grads = new_grads + gradients[length - 2: length] gradients = new_grads gradients = self.grad_reducer_g(gradients) else: @@ -356,8 +356,8 @@ class THOR(Optimizer): end_idx = mlm_fc_idx + 4 new_grads = new_grads + gradients[begin_idx: end_idx] - lenth = len(gradients) - new_grads = new_grads + gradients[lenth - 2: lenth] + length = len(gradients) + new_grads = new_grads + gradients[length - 2: length] gradients = new_grads gradients = self.grad_reducer_g(gradients) diff --git a/model_zoo/official/nlp/bert_thor/src/thor_layer.py b/model_zoo/official/nlp/bert_thor/src/thor_layer.py index 3fee08a91af..8e37c72d0a1 100644 --- a/model_zoo/official/nlp/bert_thor/src/thor_layer.py +++ b/model_zoo/official/nlp/bert_thor/src/thor_layer.py @@ -55,7 +55,7 @@ class Embedding_Thor(Cell): self.thor = True self.expand = P.ExpandDims() self.shape_flat = (-1,) - self.gather = P.GatherV2() + self.gather = P.Gather() self.one_hot = P.OneHot() self.on_value = Tensor(1.0, mstype.float32) self.off_value = Tensor(0.0, mstype.float32) @@ -75,7 +75,7 @@ class Embedding_Thor(Cell): self.freq = Tensor(frequency, mstype.int32) self.axis = 0 self.damping = damping - self.gather = P.GatherV2() + self.gather = P.Gather() self.sqrt = P.Sqrt() self.mul = P.Mul() self.cast = P.Cast() @@ -199,7 +199,7 @@ class Dense_Thor(Cell): self.damping = damping self.loss_scale = Tensor(1 / loss_scale, mstype.float16) self.vector_matmul = P.CusBatchMatMul() - self.gather = P.GatherV2() + self.gather = P.Gather() self.assignadd = P.AssignAdd() self.freq = Tensor(frequency, mstype.int32) self.axis = 0 diff --git a/model_zoo/official/nlp/gnmt_v2/src/gnmt_model/embedding.py b/model_zoo/official/nlp/gnmt_v2/src/gnmt_model/embedding.py index 0158d3f8a3f..8e2922caede 100644 --- a/model_zoo/official/nlp/gnmt_v2/src/gnmt_model/embedding.py +++ b/model_zoo/official/nlp/gnmt_v2/src/gnmt_model/embedding.py @@ -50,7 +50,7 @@ class EmbeddingLookup(nn.Cell): init_weight = np.random.normal(-initializer_range, initializer_range, size=[vocab_size, embed_dim]) self.embedding_table = Parameter(Tensor(init_weight, mstype.float32)) self.expand = P.ExpandDims() - self.gather = P.GatherV2() + self.gather = P.Gather() self.one_hot = P.OneHot() self.on_value = Tensor(1.0, mstype.float32) self.off_value = Tensor(0.0, mstype.float32) diff --git a/model_zoo/official/nlp/gpt/src/gpt.py b/model_zoo/official/nlp/gpt/src/gpt.py index 5f249236d5b..5af05305fe9 100644 --- a/model_zoo/official/nlp/gpt/src/gpt.py +++ b/model_zoo/official/nlp/gpt/src/gpt.py @@ -195,7 +195,7 @@ class EmbeddingLookup(nn.Cell): self.vocab_size = config.vocab_size self.embedding_size = config.embedding_size self.embedding_table = Parameter(initializer(TruncatedNormal(0.02), [self.vocab_size, self.embedding_size])) - self.gather = P.GatherV2() + self.gather = P.Gather() self.shape = (-1, config.seq_length, config.embedding_size) def construct(self, input_ids): output = self.gather(self.embedding_table, input_ids, 0) diff --git a/model_zoo/official/nlp/mass/src/transformer/embedding.py b/model_zoo/official/nlp/mass/src/transformer/embedding.py index b44c3391c80..0b878e455bb 100644 --- a/model_zoo/official/nlp/mass/src/transformer/embedding.py +++ b/model_zoo/official/nlp/mass/src/transformer/embedding.py @@ -46,7 +46,7 @@ class EmbeddingLookup(nn.Cell): init_weight[0, :] = 0 self.embedding_table = Parameter(Tensor(init_weight)) self.expand = P.ExpandDims() - self.gather = P.GatherV2() + self.gather = P.Gather() self.one_hot = P.OneHot() self.on_value = Tensor(1.0, mstype.float32) self.off_value = Tensor(0.0, mstype.float32) diff --git a/model_zoo/official/nlp/mass/src/transformer/positional_embedding.py b/model_zoo/official/nlp/mass/src/transformer/positional_embedding.py index 317077aef78..7714e9f6208 100644 --- a/model_zoo/official/nlp/mass/src/transformer/positional_embedding.py +++ b/model_zoo/official/nlp/mass/src/transformer/positional_embedding.py @@ -70,7 +70,7 @@ class PositionalEmbedding(nn.Cell): position_encoding(max_position_embeddings, embedding_size), mstype.float32 ) - self.gather = P.GatherV2() + self.gather = P.Gather() self.get_shape = P.Shape() def construct(self, word_embeddings): diff --git a/model_zoo/official/nlp/prophetnet/src/transformer/embedding.py b/model_zoo/official/nlp/prophetnet/src/transformer/embedding.py index b44c3391c80..0b878e455bb 100644 --- a/model_zoo/official/nlp/prophetnet/src/transformer/embedding.py +++ b/model_zoo/official/nlp/prophetnet/src/transformer/embedding.py @@ -46,7 +46,7 @@ class EmbeddingLookup(nn.Cell): init_weight[0, :] = 0 self.embedding_table = Parameter(Tensor(init_weight)) self.expand = P.ExpandDims() - self.gather = P.GatherV2() + self.gather = P.Gather() self.one_hot = P.OneHot() self.on_value = Tensor(1.0, mstype.float32) self.off_value = Tensor(0.0, mstype.float32) diff --git a/model_zoo/official/nlp/prophetnet/src/transformer/positional_embedding.py b/model_zoo/official/nlp/prophetnet/src/transformer/positional_embedding.py index 317077aef78..7714e9f6208 100644 --- a/model_zoo/official/nlp/prophetnet/src/transformer/positional_embedding.py +++ b/model_zoo/official/nlp/prophetnet/src/transformer/positional_embedding.py @@ -70,7 +70,7 @@ class PositionalEmbedding(nn.Cell): position_encoding(max_position_embeddings, embedding_size), mstype.float32 ) - self.gather = P.GatherV2() + self.gather = P.Gather() self.get_shape = P.Shape() def construct(self, word_embeddings): diff --git a/model_zoo/official/nlp/tinybert/src/tinybert_model.py b/model_zoo/official/nlp/tinybert/src/tinybert_model.py index 5e31ee8e869..c36de53c95b 100644 --- a/model_zoo/official/nlp/tinybert/src/tinybert_model.py +++ b/model_zoo/official/nlp/tinybert/src/tinybert_model.py @@ -113,7 +113,7 @@ class EmbeddingLookup(nn.Cell): [vocab_size, embedding_size])) self.expand = P.ExpandDims() self.shape_flat = (-1,) - self.gather = P.GatherV2() + self.gather = P.Gather() self.one_hot = P.OneHot() self.on_value = Tensor(1.0, mstype.float32) self.off_value = Tensor(0.0, mstype.float32) @@ -179,7 +179,7 @@ class EmbeddingPostprocessor(nn.Cell): self.shape = tuple(embedding_shape) self.layernorm = nn.LayerNorm((embedding_size,)) self.dropout = nn.Dropout(1 - dropout_prob) - self.gather = P.GatherV2() + self.gather = P.Gather() self.use_relative_positions = use_relative_positions self.slice = P.StridedSlice() self.full_position_embeddings = Parameter(initializer @@ -322,7 +322,7 @@ class RelaPosEmbeddingsGenerator(nn.Cell): self.on_value = Tensor(1.0, mstype.float32) self.off_value = Tensor(0.0, mstype.float32) self.shape = P.Shape() - self.gather = P.GatherV2() # index_select + self.gather = P.Gather() # index_select self.matmul = P.BatchMatMul() def construct(self): @@ -957,7 +957,7 @@ class BertModelCLS(nn.Cell): """ This class is responsible for classification task evaluation, i.e. XNLI(num_labels=3), LCQMC(num_labels=2), Chnsenti(num_labels=2). - The returned output represents the final logits as the results of log_softmax is propotional to that of softmax. + The returned output represents the final logits as the results of log_softmax is proportional to that of softmax. """ def __init__(self, config, is_training, num_labels=2, dropout_prob=0.0, use_one_hot_embeddings=False, phase_type="student"): diff --git a/model_zoo/official/nlp/transformer/src/transformer_model.py b/model_zoo/official/nlp/transformer/src/transformer_model.py index c045783023f..d98033199c8 100644 --- a/model_zoo/official/nlp/transformer/src/transformer_model.py +++ b/model_zoo/official/nlp/transformer/src/transformer_model.py @@ -118,7 +118,7 @@ class EmbeddingLookup(nn.Cell): self.embedding_table = Parameter(normal_weight([vocab_size, embedding_size], embedding_size)) self.expand = P.ExpandDims() self.shape_flat = (-1,) - self.gather = P.GatherV2() + self.gather = P.Gather() self.one_hot = P.OneHot() self.on_value = Tensor(1.0, mstype.float32) self.off_value = Tensor(0.0, mstype.float32) @@ -246,7 +246,7 @@ class LayerPreprocess(nn.Cell): class LayerPostprocess(nn.Cell): """ - postprocess ouput of each layer. + postprocess output of each layer. """ def __init__(self, dropout_prob=0.1): diff --git a/model_zoo/official/recommend/deepfm/src/deepfm.py b/model_zoo/official/recommend/deepfm/src/deepfm.py index 56a4825cb2f..4f918ff3776 100644 --- a/model_zoo/official/recommend/deepfm/src/deepfm.py +++ b/model_zoo/official/recommend/deepfm/src/deepfm.py @@ -195,7 +195,7 @@ class DeepFMModel(nn.Cell): self.dense_layer_5 = DenseLayer(self.all_dim_list[4], self.all_dim_list[5], self.weight_bias_init, self.deep_layer_act, self.keep_prob, convert_dtype=convert_dtype, use_act=False) " FM, linear Layers " - self.Gatherv2 = P.GatherV2() + self.Gatherv2 = P.Gather() self.Mul = P.Mul() self.ReduceSum = P.ReduceSum(keep_dims=False) self.Reshape = P.Reshape() diff --git a/model_zoo/official/recommend/ncf/src/ncf.py b/model_zoo/official/recommend/ncf/src/ncf.py index 6a1d5a3961e..12b0a7470f0 100644 --- a/model_zoo/official/recommend/ncf/src/ncf.py +++ b/model_zoo/official/recommend/ncf/src/ncf.py @@ -277,7 +277,7 @@ class PredictWithSigmoid(nn.Cell): self.squeeze = P.Squeeze() self.k = k self.num_eval_neg = num_eval_neg - self.gather = P.GatherV2() + self.gather = P.Gather() self.reshape = P.Reshape() self.reducesum = P.ReduceSum(keep_dims=False) self.notequal = P.NotEqual() diff --git a/model_zoo/official/recommend/wide_and_deep/src/wide_and_deep.py b/model_zoo/official/recommend/wide_and_deep/src/wide_and_deep.py index f167617f07b..fd85a068a67 100644 --- a/model_zoo/official/recommend/wide_and_deep/src/wide_and_deep.py +++ b/model_zoo/official/recommend/wide_and_deep/src/wide_and_deep.py @@ -200,8 +200,8 @@ class WideDeepModel(nn.Cell): self.concat = P.Concat(axis=1) self.cast = P.Cast() self.unique = P.Unique().shard(((1,),)) - self.wide_gatherv2 = P.GatherV2() - self.deep_gatherv2 = P.GatherV2() + self.wide_gatherv2 = P.Gather() + self.deep_gatherv2 = P.Gather() if is_auto_parallel and sparse and not is_field_slice and not parameter_server: target = 'DEVICE' if host_device_mix: diff --git a/model_zoo/official/recommend/wide_and_deep_multitable/src/wide_and_deep.py b/model_zoo/official/recommend/wide_and_deep_multitable/src/wide_and_deep.py index a79b9cf239f..9c10287ec6e 100644 --- a/model_zoo/official/recommend/wide_and_deep_multitable/src/wide_and_deep.py +++ b/model_zoo/official/recommend/wide_and_deep_multitable/src/wide_and_deep.py @@ -252,7 +252,7 @@ class WideDeepModel(nn.Cell): convert_dtype=True, use_activation=False) - self.gather_v2 = P.GatherV2() + self.gather_v2 = P.Gather() self.mul = P.Mul() self.reduce_sum_false = P.ReduceSum(keep_dims=False) self.reduce_sum_true = P.ReduceSum(keep_dims=True) diff --git a/model_zoo/research/cv/FaceAttribute/src/FaceAttribute/loss_factory.py b/model_zoo/research/cv/FaceAttribute/src/FaceAttribute/loss_factory.py index 4925917ab11..ca11e60f32d 100644 --- a/model_zoo/research/cv/FaceAttribute/src/FaceAttribute/loss_factory.py +++ b/model_zoo/research/cv/FaceAttribute/src/FaceAttribute/loss_factory.py @@ -30,7 +30,7 @@ class CriterionsFaceAttri(nn.Cell): super(CriterionsFaceAttri, self).__init__() # label - self.gatherv2 = P.GatherV2() + self.gatherv2 = P.Gather() self.squeeze = P.Squeeze(axis=1) self.cast = P.Cast() self.reshape = P.Reshape() diff --git a/model_zoo/research/cv/FaceQualityAssessment/src/loss.py b/model_zoo/research/cv/FaceQualityAssessment/src/loss.py index b4e9b2c3a9e..97c3632813d 100644 --- a/model_zoo/research/cv/FaceQualityAssessment/src/loss.py +++ b/model_zoo/research/cv/FaceQualityAssessment/src/loss.py @@ -71,7 +71,7 @@ class CriterionsFaceQA(nn.Cell): '''CriterionsFaceQA''' def __init__(self): super(CriterionsFaceQA, self).__init__() - self.gatherv2 = P.GatherV2() + self.gatherv2 = P.Gather() self.squeeze = P.Squeeze(axis=1) self.shape = P.Shape() self.reshape = P.Reshape() diff --git a/model_zoo/research/hpc/molecular_dynamics/src/descriptor.py b/model_zoo/research/hpc/molecular_dynamics/src/descriptor.py index 15f3f683cf7..0fd4038e1f9 100644 --- a/model_zoo/research/hpc/molecular_dynamics/src/descriptor.py +++ b/model_zoo/research/hpc/molecular_dynamics/src/descriptor.py @@ -30,7 +30,7 @@ class ComputeRij(nn.Cell): self.broadcastto1 = P.BroadcastTo((1, 192, 138, 3)) self.expdims = P.ExpandDims() self.concat = P.Concat(axis=1) - self.gather = P.GatherV2() + self.gather = P.Gather() self.mul = P.Mul() self.slice = P.Slice() @@ -89,7 +89,7 @@ class ComputeDescriptor(nn.Cell): self.expdims = P.ExpandDims() self.concat = P.Concat(axis=3) - self.gather = P.GatherV2() + self.gather = P.Gather() self.mul = P.Mul() self.slice = P.Slice() self.square = P.Square() diff --git a/model_zoo/research/nlp/ternarybert/src/tinybert_model.py b/model_zoo/research/nlp/ternarybert/src/tinybert_model.py index 23ead0c47fe..9b7f4fecd74 100644 --- a/model_zoo/research/nlp/ternarybert/src/tinybert_model.py +++ b/model_zoo/research/nlp/ternarybert/src/tinybert_model.py @@ -89,7 +89,7 @@ class GatherV2Quant(nn.Cell): def __init__(self, activation_init=6): super(GatherV2Quant, self).__init__() - self.gather = P.GatherV2() + self.gather = P.Gather() self.fake_quant_input = FakeQuantWithMinMax(min_init=-activation_init, max_init=activation_init, ema=True, symmetric=False) @@ -309,7 +309,7 @@ class EmbeddingLookup(nn.Cell): if do_quant: self.gather = GatherV2Quant(activation_init=activation_init) else: - self.gather = P.GatherV2() + self.gather = P.Gather() self.one_hot = P.OneHot() self.on_value = Tensor(1.0, mstype.float32) self.off_value = Tensor(0.0, mstype.float32) @@ -376,7 +376,7 @@ class EmbeddingPostprocessor(nn.Cell): self.shape = tuple(embedding_shape) self.layernorm = nn.LayerNorm((embedding_size,)) self.dropout = nn.Dropout(1 - dropout_prob) - self.gather = P.GatherV2() + self.gather = P.Gather() self.use_relative_positions = use_relative_positions self.slice = P.StridedSlice() self.full_position_embeddings = Parameter(initializer @@ -532,7 +532,7 @@ class RelaPosEmbeddingsGenerator(nn.Cell): self.on_value = Tensor(1.0, mstype.float32) self.off_value = Tensor(0.0, mstype.float32) self.shape = P.Shape() - self.gather = P.GatherV2() # index_select + self.gather = P.Gather() # index_select self.matmul = P.BatchMatMul() def construct(self): diff --git a/model_zoo/research/recommend/autodis/src/autodis.py b/model_zoo/research/recommend/autodis/src/autodis.py index 19ec3435264..a0fcd3a2799 100644 --- a/model_zoo/research/recommend/autodis/src/autodis.py +++ b/model_zoo/research/recommend/autodis/src/autodis.py @@ -215,7 +215,7 @@ class AutoDisModel(nn.Cell): self.dense_layer_4 = DenseLayer(self.all_dim_list[3], self.all_dim_list[4], self.weight_bias_init, self.deep_layer_act, self.keep_prob) # FM, linear Layers - self.Gatherv2 = P.GatherV2() + self.Gatherv2 = P.Gather() self.Mul = P.Mul() self.ReduceSum = P.ReduceSum(keep_dims=False) self.Reshape = P.Reshape() diff --git a/tests/st/hcom/hcom_sparsetensor.py b/tests/st/hcom/hcom_sparsetensor.py index f4a5c3ec6bf..5e04d1d841d 100644 --- a/tests/st/hcom/hcom_sparsetensor.py +++ b/tests/st/hcom/hcom_sparsetensor.py @@ -135,7 +135,7 @@ class NetWithSparseGatherV2(nn.Cell): self.gather = P.SparseGatherV2() else: self.weight = Parameter(Tensor(np.ones([8, 8]).astype(np.float32)), name="weight") - self.gather = P.GatherV2() + self.gather = P.Gather() if strategy is not None: self.gather.shard(strategy) diff --git a/tests/st/hcom/test_hcom_sparsetensor.py b/tests/st/hcom/test_hcom_sparsetensor.py index 134d282b422..b5b0d857e39 100644 --- a/tests/st/hcom/test_hcom_sparsetensor.py +++ b/tests/st/hcom/test_hcom_sparsetensor.py @@ -24,4 +24,5 @@ import pytest def test_allreduce_sparsegatherv2_adam_auto_parallel(): sh_path = os.path.split(os.path.realpath(__file__))[0] ret = os.system(f"sh {sh_path}/run_hcom_sparsetensor.sh") + os.system(f"grep -E 'ERROR|error' {sh_path}/hcom_sparsetensor*/test_hcom_sparsetensor_8p_log* -C 3") assert ret == 0 diff --git a/tests/st/model_zoo_tests/DeepFM/src/deepfm.py b/tests/st/model_zoo_tests/DeepFM/src/deepfm.py index 4ebf0465a8d..c2ad78fc23b 100644 --- a/tests/st/model_zoo_tests/DeepFM/src/deepfm.py +++ b/tests/st/model_zoo_tests/DeepFM/src/deepfm.py @@ -223,7 +223,7 @@ class DeepFMModel(nn.Cell): self.dense_layer_5 = DenseLayer(self.all_dim_list[4], self.all_dim_list[5], self.weight_bias_init, self.deep_layer_act, self.keep_prob, convert_dtype=True, use_act=False) " FM, linear Layers " - self.Gatherv2 = P.GatherV2() + self.Gatherv2 = P.Gather() self.Mul = P.Mul() self.ReduceSum = P.ReduceSum(keep_dims=False) self.Reshape = P.Reshape() diff --git a/tests/st/model_zoo_tests/wide_and_deep/python_file_for_ci/wide_and_deep.py b/tests/st/model_zoo_tests/wide_and_deep/python_file_for_ci/wide_and_deep.py index 9a0eeecc7f2..30d59b241df 100644 --- a/tests/st/model_zoo_tests/wide_and_deep/python_file_for_ci/wide_and_deep.py +++ b/tests/st/model_zoo_tests/wide_and_deep/python_file_for_ci/wide_and_deep.py @@ -53,8 +53,8 @@ def init_var_dict(init_args, in_vars): ''' var_map = {} _, _max_val = init_args - for _, iterm in enumerate(in_vars): - key, shape, method = iterm + for _, item in enumerate(in_vars): + key, shape, method = item if key not in var_map.keys(): if method in ['random', 'uniform']: var_map[key] = Parameter(initializer( @@ -176,8 +176,8 @@ class WideDeepModel(nn.Cell): self.weight_bias_init, self.deep_layer_act, convert_dtype=True) - self.gather_v2 = P.GatherV2().shard(((1, 8), (1, 1))) - self.gather_v2_1 = P.GatherV2() + self.gather_v2 = P.Gather().shard(((1, 8), (1, 1))) + self.gather_v2_1 = P.Gather() self.mul = P.Mul() self.reduce_sum = P.ReduceSum(keep_dims=False) self.reshape = P.Reshape() diff --git a/tests/st/networks/models/bert/src/bert_for_pre_training.py b/tests/st/networks/models/bert/src/bert_for_pre_training.py index cd60334f746..57612587bf6 100644 --- a/tests/st/networks/models/bert/src/bert_for_pre_training.py +++ b/tests/st/networks/models/bert/src/bert_for_pre_training.py @@ -74,7 +74,7 @@ class GetMaskedLMOutput(nn.Cell): super(GetMaskedLMOutput, self).__init__() self.width = config.hidden_size self.reshape = P.Reshape() - self.gather = P.GatherV2() + self.gather = P.Gather() weight_init = TruncatedNormal(config.initializer_range) self.dense = nn.Dense(self.width, diff --git a/tests/st/networks/models/bert/src/bert_model.py b/tests/st/networks/models/bert/src/bert_model.py index 085e695e41e..3d15627dcf7 100644 --- a/tests/st/networks/models/bert/src/bert_model.py +++ b/tests/st/networks/models/bert/src/bert_model.py @@ -127,7 +127,7 @@ class EmbeddingLookup(nn.Cell): name='embedding_table') self.expand = P.ExpandDims() self.shape_flat = (-1,) - self.gather = P.GatherV2() + self.gather = P.Gather() self.one_hot = P.OneHot() self.on_value = Tensor(1.0, mstype.float32) self.off_value = Tensor(0.0, mstype.float32) @@ -194,7 +194,7 @@ class EmbeddingPostprocessor(nn.Cell): self.shape = tuple(embedding_shape) self.layernorm = nn.LayerNorm((embedding_size,)) self.dropout = nn.Dropout(1 - dropout_prob) - self.gather = P.GatherV2() + self.gather = P.Gather() self.use_relative_positions = use_relative_positions self.slice = P.StridedSlice() self.full_position_embeddings = Parameter(initializer @@ -333,7 +333,7 @@ class RelaPosEmbeddingsGenerator(nn.Cell): self.reshape = P.Reshape() self.one_hot = nn.OneHot(depth=self.vocab_size) self.shape = P.Shape() - self.gather = P.GatherV2() # index_select + self.gather = P.Gather() # index_select self.matmul = P.BatchMatMul() def construct(self): diff --git a/tests/st/networks/models/resnet50/src_thor/thor_layer.py b/tests/st/networks/models/resnet50/src_thor/thor_layer.py index b644a834ff7..2ffe1882bc0 100644 --- a/tests/st/networks/models/resnet50/src_thor/thor_layer.py +++ b/tests/st/networks/models/resnet50/src_thor/thor_layer.py @@ -200,7 +200,7 @@ class Conv2d_Thor(_Conv): self.device_shape_pad_flag = True self.device_shape_pad = P.Pad(((0, 0), (0, C0 - self.in_channels), (0, 0), (0, C0 - self.in_channels))) self.slice = P.Slice() - self.gather = P.GatherV2() + self.gather = P.Gather() self.freq = Tensor(frequency, mstype.int32) self.loss_scale = Tensor(1 / loss_scale, mstype.float16) self.axis = 0 @@ -383,7 +383,7 @@ class Dense_Thor(Cell): self.pad = P.Pad(((0, 24), (0, 24))) self.pad1 = P.Pad(((0, 8), (0, 8))) self.slice = P.Slice() - self.gather = P.GatherV2() + self.gather = P.Gather() self.assignadd = P.AssignAdd() self.freq = Tensor(frequency, mstype.int32) self.axis = 0 diff --git a/tests/st/ops/cpu/test_gather_op.py b/tests/st/ops/cpu/test_gather_op.py index b9ac24ad358..a47464606de 100644 --- a/tests/st/ops/cpu/test_gather_op.py +++ b/tests/st/ops/cpu/test_gather_op.py @@ -26,7 +26,7 @@ context.set_context(mode=context.GRAPH_MODE, device_target='CPU') class NetGatherV2_axis0(nn.Cell): def __init__(self): super(NetGatherV2_axis0, self).__init__() - self.gatherv2 = P.GatherV2() + self.gatherv2 = P.Gather() def construct(self, params, indices): return self.gatherv2(params, indices, 0) @@ -52,7 +52,7 @@ def test_gatherv2_axis0(): class NetGatherV2_axis1(nn.Cell): def __init__(self): super(NetGatherV2_axis1, self).__init__() - self.gatherv2 = P.GatherV2() + self.gatherv2 = P.Gather() def construct(self, params, indices): return self.gatherv2(params, indices, 1) @@ -78,7 +78,7 @@ def test_gatherv2_axis1(): class NetGatherV2_axisN1(nn.Cell): def __init__(self): super(NetGatherV2_axisN1, self).__init__() - self.gatherv2 = P.GatherV2() + self.gatherv2 = P.Gather() def construct(self, params, indices): return self.gatherv2(params, indices, -1) diff --git a/tests/st/ops/gpu/test_gatherV2_op.py b/tests/st/ops/gpu/test_gatherV2_op.py index 353ca544430..747586f2958 100644 --- a/tests/st/ops/gpu/test_gatherV2_op.py +++ b/tests/st/ops/gpu/test_gatherV2_op.py @@ -26,7 +26,7 @@ from mindspore.ops import operations as P class GatherNet(nn.Cell): def __init__(self): super(GatherNet, self).__init__() - self.gather = P.GatherV2() + self.gather = P.Gather() def construct(self, x, indices): return self.gather(x, indices, 1) @@ -850,7 +850,7 @@ def test_gather0(): class GatherNet1(nn.Cell): def __init__(self): super(GatherNet1, self).__init__() - self.gather = P.GatherV2() + self.gather = P.Gather() def construct(self, x, indices): return self.gather(x, indices, -1) @@ -904,7 +904,7 @@ def test_gather1(): class GatherNet2(nn.Cell): def __init__(self): super(GatherNet2, self).__init__() - self.gather = P.GatherV2() + self.gather = P.Gather() def construct(self, x, indices): return self.gather(x, indices, 0) @@ -944,7 +944,7 @@ def test_gather2(): class GatherNetDynamic(nn.Cell): def __init__(self, axis=0, dyn_a=True, dyn_b=True): super(GatherNetDynamic, self).__init__() - self.gather = P.GatherV2() + self.gather = P.Gather() self.gpu_convert_to_dynamic_shape = inner.GpuConvertToDynamicShape() self.to_dyn_1 = dyn_a self.to_dyn_2 = dyn_b diff --git a/tests/ut/cpp/transform/convert_test.cc b/tests/ut/cpp/transform/convert_test.cc index 9ff2fa68ece..c532bf21cd5 100644 --- a/tests/ut/cpp/transform/convert_test.cc +++ b/tests/ut/cpp/transform/convert_test.cc @@ -367,7 +367,7 @@ TEST_F(TestConvert, TestConcat) { } TEST_F(TestConvert, TestGatherV2) { - auto prim = prim::kPrimGatherV2; + auto prim = prim::kPrimGather; std::shared_ptr anf_graph = MakeFuncGraph(prim, 3); std::shared_ptr graph_manager = MakeManager({anf_graph}); diff --git a/tests/ut/python/communication/test_comm.py b/tests/ut/python/communication/test_comm.py index d32907a2aad..7f22a1a58df 100644 --- a/tests/ut/python/communication/test_comm.py +++ b/tests/ut/python/communication/test_comm.py @@ -27,7 +27,7 @@ from mindspore.nn import ReLU from mindspore.nn import TrainOneStepCell, WithLossCell from mindspore.ops.operations.comm_ops import AllReduce, AllGather, _AlltoAll, ReduceOp, ReduceScatter from mindspore.ops.operations.comm_ops import Broadcast, AllSwap -from mindspore.ops.operations.array_ops import GatherV2 +from mindspore.ops.operations.array_ops import Gather import mindspore # pylint: disable=W0212 @@ -130,7 +130,7 @@ class AllSwapNet(nn.Cell): part_slice = batch_size / 2 self.send_size = Tensor([0, part_slice*out_channel, part_slice*out_channel], mindspore.int64) self.recv_size = Tensor([part_slice*out_channel, part_slice*out_channel, 0], mindspore.int64) - self.gatherv2 = GatherV2() + self.gatherv2 = Gather() self.input = Tensor(np.ones([1]), mindspore.int32) def construct(self, x): x = self.allswap(x, self.send_size, self.recv_size) diff --git a/tests/ut/python/onnx/test_onnx.py b/tests/ut/python/onnx/test_onnx.py index 9f88a7d50ca..23e1041f542 100644 --- a/tests/ut/python/onnx/test_onnx.py +++ b/tests/ut/python/onnx/test_onnx.py @@ -143,7 +143,7 @@ class DeepFMOpNet(nn.Cell): """Net definition with Gatherv2 and Tile and Square.""" def __init__(self): super(DeepFMOpNet, self).__init__() - self.gather = P.GatherV2() + self.gather = P.Gather() self.square = P.Square() self.tile = P.Tile() diff --git a/tests/ut/python/ops/test_dynamic_shape.py b/tests/ut/python/ops/test_dynamic_shape.py index ab24bba3138..8cdc4c0a9b0 100755 --- a/tests/ut/python/ops/test_dynamic_shape.py +++ b/tests/ut/python/ops/test_dynamic_shape.py @@ -97,7 +97,7 @@ def test_gatherv2(): def __init__(self): super(Net, self).__init__() self.unq = P.Unique() - self.gather = P.GatherV2() + self.gather = P.Gather() self.yy = Tensor(np.ones([8], dtype=np.int32)) def construct(self, x, y): diff --git a/tests/ut/python/ops/test_ops.py b/tests/ut/python/ops/test_ops.py index 0cfc2dd3571..686d6a6cbbf 100755 --- a/tests/ut/python/ops/test_ops.py +++ b/tests/ut/python/ops/test_ops.py @@ -1766,37 +1766,37 @@ test_case_nn_ops = [ 'desc_inputs': [[2, 3, 4]], 'desc_bprop': [[2, 3, 4], ([2, 3, 4], {'dtype': np.int32})]}), ('GatherV2_0', { - 'block': P.GatherV2(), + 'block': P.Gather(), 'desc_const': [0], 'desc_inputs': [[3, 1, 2], Tensor(np.array([0, 1]).astype(np.int32))], 'desc_bprop': [[2, 1, 2]]}), ('GatherV2_1', { - 'block': P.GatherV2(), + 'block': P.Gather(), 'desc_const': [2], 'desc_inputs': [[3, 1, 3], Tensor(np.array([0, 1]).astype(np.int32))], 'desc_bprop': [[3, 1, 2]]}), ('GatherV2_2', { - 'block': P.GatherV2(), + 'block': P.Gather(), 'desc_const': [0], 'desc_inputs': [[3, 1, 3], Tensor(np.array([[0, 1], [0, 1], [0, 1]]).astype(np.int32))], 'desc_bprop': [[3, 2, 1, 3]]}), ('GatherV2_3', { - 'block': P.GatherV2(), + 'block': P.Gather(), 'desc_const': [2], 'desc_inputs': [[3, 1, 3], Tensor(np.array([[0, 1], [0, 1], [0, 1]]).astype(np.int32))], 'desc_bprop': [[3, 1, 3, 2]]}), ('GatherV2_4', { - 'block': P.GatherV2(), + 'block': P.Gather(), 'desc_const': [1], 'desc_inputs': [[32, 5, 1024], Tensor(np.array([3]).astype(np.int32))], 'desc_bprop': [[32, 1, 1024]]}), ('GatherV2_5', { - 'block': P.GatherV2(), + 'block': P.Gather(), 'desc_const': [-1], 'desc_inputs': [[3, 1, 3], Tensor(np.array([0, 1]).astype(np.int32))], 'desc_bprop': [[3, 1, 2]]}), ('GatherV2_6', { - 'block': P.GatherV2(), + 'block': P.Gather(), 'desc_const': [0], 'desc_inputs': [[1152], Tensor(np.array(10).astype(np.int32))], 'desc_bprop': [Tensor(np.array(10).astype(np.float32))]}), diff --git a/tests/ut/python/parallel/test_dynamic_shape.py b/tests/ut/python/parallel/test_dynamic_shape.py index 052ad6a1ade..427a0fca486 100644 --- a/tests/ut/python/parallel/test_dynamic_shape.py +++ b/tests/ut/python/parallel/test_dynamic_shape.py @@ -56,10 +56,10 @@ def test_unique_column_split(): self.unique = P.Unique().shard(((1,),)) self.relu = P.ReLU() self.mul = P.Mul() - self.embedding_lookp = P.GatherV2().shard(((1, 8), (1,))) + self.embedding_lookp = P.Gather().shard(((1, 8), (1,))) self.embedding_table = Parameter(initializer('normal', [2000, 128]), name='embedding_table') - self.gatherv2 = P.GatherV2().shard(((1, 8), (1,))) + self.gatherv2 = P.Gather().shard(((1, 8), (1,))) self.reshape = P.Reshape() self.matmul = P.MatMul() self.mul_weight = Parameter(Tensor(np.full([32, 64, 1], 0.5, dtype=np.float32)), name="mul_weight") @@ -90,10 +90,10 @@ def test_unique_row_split(): self.unique = P.Unique().shard(((1,),)) self.relu = P.ReLU() self.mul = P.Mul() - self.embedding_lookp = P.GatherV2().shard(((8, 1), (1,))) + self.embedding_lookp = P.Gather().shard(((8, 1), (1,))) self.embedding_table = Parameter(initializer('normal', [2000, 128]), name='embedding_table') - self.gatherv2 = P.GatherV2().shard(((1, 1), (1,))) + self.gatherv2 = P.Gather().shard(((1, 1), (1,))) self.reshape = P.Reshape() self.matmul = P.MatMul() self.mul_weight = Parameter(Tensor(np.full([32, 64, 1], 0.5, dtype=np.float32)), name="mul_weight") diff --git a/tests/ut/python/parallel/test_gather_v2.py b/tests/ut/python/parallel/test_gather_v2.py index b24bcd6f35e..7cd3aaa3654 100644 --- a/tests/ut/python/parallel/test_gather_v2.py +++ b/tests/ut/python/parallel/test_gather_v2.py @@ -51,7 +51,7 @@ class Net(nn.Cell): super().__init__() if shape is None: shape = [64, 64] - self.gatherv2 = P.GatherV2().shard(strategy1).add_prim_attr("primitive_target", target) + self.gatherv2 = P.Gather().shard(strategy1).add_prim_attr("primitive_target", target) self.mul = P.Mul().shard(strategy2) self.index = Tensor(np.ones(shape), dtype=ms.int32) self.axis = axis diff --git a/tests/ut/python/parallel/test_gather_v2_primitive.py b/tests/ut/python/parallel/test_gather_v2_primitive.py index 0e2c90eed4b..e7ccfe1f18e 100644 --- a/tests/ut/python/parallel/test_gather_v2_primitive.py +++ b/tests/ut/python/parallel/test_gather_v2_primitive.py @@ -79,7 +79,7 @@ class GatherV2(_Loss): emb2_list = np.reshape(emb_list[1::2], (int(index_size / 2), 16)) self.emb1_param = Tensor(emb1_list, dtype=mstype.int32) self.emb2_param = Tensor(emb2_list, dtype=mstype.int32) - self.gatherv2 = P.GatherV2().shard(strategy).add_prim_attr("data_parallel", True) + self.gatherv2 = P.Gather().shard(strategy).add_prim_attr("data_parallel", True) def construct(self, nembeddings): emb1 = self.gatherv2(nembeddings, self.emb1_param, 0) @@ -208,7 +208,7 @@ class GatherV2Axis1(_Loss): emb2_list = np.reshape(emb_list[1::2], (int(index_size / 2), index_size)) self.emb1_param = Tensor(emb1_list, dtype=mstype.int32) self.emb2_param = Tensor(emb2_list, dtype=mstype.int32) - self.gatherv2 = P.GatherV2().shard(strategy) + self.gatherv2 = P.Gather().shard(strategy) def construct(self, nembeddings): emb1 = self.gatherv2(nembeddings, self.emb1_param, 1) diff --git a/tests/ut/python/parallel/test_manual_gatherv2.py b/tests/ut/python/parallel/test_manual_gatherv2.py index dd563bc52a6..9c8cf3a8f81 100644 --- a/tests/ut/python/parallel/test_manual_gatherv2.py +++ b/tests/ut/python/parallel/test_manual_gatherv2.py @@ -33,7 +33,7 @@ class Net(Cell): split_string="manual_split", param_shape=(8, 8)): super().__init__() - self.gatherv2 = P.GatherV2().shard(strategy1) + self.gatherv2 = P.Gather().shard(strategy1) self.gatherv2.add_prim_attr(split_string, split_tuple) self.mul = P.Mul().shard(strategy2) self.reshape = P.Reshape() diff --git a/tests/ut/python/parallel/test_reshape_skip_redistribution.py b/tests/ut/python/parallel/test_reshape_skip_redistribution.py index cb9d0a121c7..e7ba3a0f965 100644 --- a/tests/ut/python/parallel/test_reshape_skip_redistribution.py +++ b/tests/ut/python/parallel/test_reshape_skip_redistribution.py @@ -24,7 +24,7 @@ from mindspore.ops import operations as P class Net(Cell): def __init__(self, matmul_weight, strategy1=None): super().__init__() - self.gatherv2 = P.GatherV2().shard(strategy1) + self.gatherv2 = P.Gather().shard(strategy1) self.reshape = P.Reshape().add_prim_attr("skip_redistribution", True) self.matmul = P.MatMul(transpose_b=False) self.index = Tensor(np.ones([64, 64]), dtype=ms.int32) diff --git a/tests/ut/python/parallel/test_uniform_candidate_sampler.py b/tests/ut/python/parallel/test_uniform_candidate_sampler.py index 0937e61ec6d..5524b2721dc 100644 --- a/tests/ut/python/parallel/test_uniform_candidate_sampler.py +++ b/tests/ut/python/parallel/test_uniform_candidate_sampler.py @@ -32,7 +32,7 @@ class Net(nn.Cell): if strategy1: self.sampler.shard(strategy1) self.embedding_table = Parameter(embedding_weight, "embedding_weight") - self.gatherv2 = P.GatherV2() + self.gatherv2 = P.Gather() self.reduce_sum = P.ReduceSum() self.reduce_sum2 = P.ReduceSum() self.reduce_sum3 = P.ReduceSum() diff --git a/tests/ut/python/pipeline/parse/test_parse.py b/tests/ut/python/pipeline/parse/test_parse.py index 11d0f428406..947d094fc9e 100644 --- a/tests/ut/python/pipeline/parse/test_parse.py +++ b/tests/ut/python/pipeline/parse/test_parse.py @@ -261,7 +261,7 @@ class AssignWhenInsertGrad(nn.Cell): def __init__(self): super(AssignWhenInsertGrad, self).__init__() - self.gather = P.GatherV2() + self.gather = P.Gather() self.damping = Tensor(np.array([0.03, 0.03]).astype(np.float32)) self.cov_step = ms.Parameter(0, name="cov_step", requires_grad=False) self.freq = Tensor(278, ms.int32)