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Change GatherV2 to Gather r1.1 to master

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mindspore-ci-bot 2021-01-28 14:51:40 +08:00 committed by l00591931
parent 8a61767f32
commit 9fa0499fa0
85 changed files with 243 additions and 198 deletions
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4
config/op_info.config
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@ -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": true, "reshape_type": "", "dynamic_format": false, "dynamic_shape": false, "need_check_supported": false, "op_pattern": "dynamicFormat"}
{"op_name": "AtomicAddrClean", "inputs": [], "outputs": [], "attr": [{"name": "automic_add_mem_size", "param_type": "required", "type": "listUInt64", "value": "all"}], "fusion_type": "ELEMWISE", "dtype_format": [], "imply_type": "TBE", "async_flag": false, "binfile_name": "atomic_addr_clean.so", "compute_cost": 10, "kernel_name": "atomic_addr_clean", "partial_flag": true, "reshape_type": "", "dynamic_format": false, "dynamic_shape": false, "need_check_supported": false, "op_pattern": ""}
{"op_name": "GatherV2", "inputs": [{"index": 0, "name": "x", "need_compile": false, "param_type": "required", "shape": "all"}, {"index": 1, "name": "indices", "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": "required", "type": "int", "value": "all"}], "fusion_type": "OPAQUE", "dtype_format": [[["float16", "DefaultFormat"], ["int32", "DefaultFormat"], ["float16", "DefaultFormat"]], [["float32", "DefaultFormat"], ["int32", "DefaultFormat"], ["float32", "DefaultFormat"]], [["int8", "DefaultFormat"], ["int32", "DefaultFormat"], ["int8", "DefaultFormat"]], [["uint8", "DefaultFormat"], ["int32", "DefaultFormat"], ["uint8", "DefaultFormat"]], [["int32", "DefaultFormat"], ["int32", "DefaultFormat"], ["int32", "DefaultFormat"]], [["uint32", "DefaultFormat"], ["int32", "DefaultFormat"], ["uint32", "DefaultFormat"]], [["int16", "DefaultFormat"], ["int32", "DefaultFormat"], ["int16", "DefaultFormat"]], [["uint16", "DefaultFormat"], ["int32", "DefaultFormat"], ["uint16", "DefaultFormat"]], [["int64", "DefaultFormat"], ["int32", "DefaultFormat"], ["int64", "DefaultFormat"]], [["uint64", "DefaultFormat"], ["int32", "DefaultFormat"], ["uint64", "DefaultFormat"]], [["float16", "DefaultFormat"], ["int64", "DefaultFormat"], ["float16", "DefaultFormat"]], [["float32", "DefaultFormat"], ["int64", "DefaultFormat"], ["float32", "DefaultFormat"]], [["int8", "DefaultFormat"], ["int64", "DefaultFormat"], ["int8", "DefaultFormat"]], [["uint8", "DefaultFormat"], ["int64", "DefaultFormat"], ["uint8", "DefaultFormat"]], [["int32", "DefaultFormat"], ["int64", "DefaultFormat"], ["int32", "DefaultFormat"]], [["uint32", "DefaultFormat"], ["int64", "DefaultFormat"], ["uint32", "DefaultFormat"]], [["int16", "DefaultFormat"], ["int64", "DefaultFormat"], ["int16", "DefaultFormat"]], [["uint16", "DefaultFormat"], ["int64", "DefaultFormat"], ["uint16", "DefaultFormat"]], [["int64", "DefaultFormat"], ["int64", "DefaultFormat"], ["int64", "DefaultFormat"]], [["uint64", "DefaultFormat"], ["int64", "DefaultFormat"], ["uint64", "DefaultFormat"]]], "imply_type": "TBE", "async_flag": false, "binfile_name": "gather_v2_d.so", "compute_cost": 10, "kernel_name": "gather_v2_d", "partial_flag": true, "reshape_type": "", "dynamic_format": false, "dynamic_shape": false, "need_check_supported": false, "op_pattern": ""}
{"op_name": "GatherV2", "inputs": [{"index": 0, "name": "x", "need_compile": false, "param_type": "required", "shape": "all"}, {"index": 1, "name": "indices", "need_compile": false, "param_type": "required", "shape": "all"}, {"index": 2, "name": "axis", "need_compile": false, "param_type": "required", "shape": "all"}], "outputs": [{"index": 0, "name": "y", "need_compile": false, "param_type": "required", "shape": "all"}], "attr": [], "fusion_type": "OPAQUE", "dtype_format": [[["int8", "DefaultFormat"], ["int32", "DefaultFormat"], ["int32", "DefaultFormat"], ["int8", "DefaultFormat"]], [["int8", "DefaultFormat"], ["int64", "DefaultFormat"], ["int32", "DefaultFormat"], ["int8", "DefaultFormat"]], [["int8", "NC1HWC0"], ["int32", "NC1HWC0"], ["int32", "NC1HWC0"], ["int8", "NC1HWC0"]], [["int8", "NC1HWC0"], ["int64", "NC1HWC0"], ["int32", "NC1HWC0"], ["int8", "NC1HWC0"]], [["int8", "FracZ"], ["int32", "FracZ"], ["int32", "FracZ"], ["int8", "FracZ"]], [["int8", "FracZ"], ["int64", "FracZ"], ["int32", "FracZ"], ["int8", "FracZ"]], [["uint8", "DefaultFormat"], ["int32", "DefaultFormat"], ["int32", "DefaultFormat"], ["uint8", "DefaultFormat"]], [["uint8", "DefaultFormat"], ["int64", "DefaultFormat"], ["int32", "DefaultFormat"], ["uint8", "DefaultFormat"]], [["uint8", "NC1HWC0"], ["int32", "NC1HWC0"], ["int32", "NC1HWC0"], ["uint8", "NC1HWC0"]], [["uint8", "NC1HWC0"], ["int64", "NC1HWC0"], ["int32", "NC1HWC0"], ["uint8", "NC1HWC0"]], [["uint8", "FracZ"], ["int32", "FracZ"], ["int32", "FracZ"], ["uint8", "FracZ"]], [["uint8", "FracZ"], ["int64", "FracZ"], ["int32", "FracZ"], ["uint8", "FracZ"]], [["int32", "DefaultFormat"], ["int32", "DefaultFormat"], ["int32", "DefaultFormat"], ["int32", "DefaultFormat"]], [["int32", "DefaultFormat"], ["int64", "DefaultFormat"], ["int32", "DefaultFormat"], ["int32", "DefaultFormat"]], [["int32", "NC1HWC0"], ["int32", "NC1HWC0"], ["int32", "NC1HWC0"], ["int32", "NC1HWC0"]], [["int32", "NC1HWC0"], ["int64", "NC1HWC0"], ["int32", "NC1HWC0"], ["int32", "NC1HWC0"]], [["int32", "FracZ"], ["int32", "FracZ"], ["int32", "FracZ"], ["int32", "FracZ"]], [["int32", "FracZ"], ["int64", "FracZ"], ["int32", "FracZ"], ["int32", "FracZ"]], [["float16", "DefaultFormat"], ["int32", "DefaultFormat"], ["int32", "DefaultFormat"], ["float16", "DefaultFormat"]], [["float16", "DefaultFormat"], ["int64", "DefaultFormat"], ["int32", "DefaultFormat"], ["float16", "DefaultFormat"]], [["float16", "NC1HWC0"], ["int32", "NC1HWC0"], ["int32", "NC1HWC0"], ["float16", "NC1HWC0"]], [["float16", "NC1HWC0"], ["int64", "NC1HWC0"], ["int32", "NC1HWC0"], ["float16", "NC1HWC0"]], [["float16", "FracZ"], ["int32", "FracZ"], ["int32", "FracZ"], ["float16", "FracZ"]], [["float16", "FracZ"], ["int64", "FracZ"], ["int32", "FracZ"], ["float16", "FracZ"]], [["float32", "DefaultFormat"], ["int32", "DefaultFormat"], ["int32", "DefaultFormat"], ["float32", "DefaultFormat"]], [["float32", "DefaultFormat"], ["int64", "DefaultFormat"], ["int32", "DefaultFormat"], ["float32", "DefaultFormat"]], [["float32", "NC1HWC0"], ["int32", "NC1HWC0"], ["int32", "NC1HWC0"], ["float32", "NC1HWC0"]], [["float32", "NC1HWC0"], ["int64", "NC1HWC0"], ["int32", "NC1HWC0"], ["float32", "NC1HWC0"]], [["float32", "FracZ"], ["int32", "FracZ"], ["int32", "FracZ"], ["float32", "FracZ"]], [["float32", "FracZ"], ["int64", "FracZ"], ["int32", "FracZ"], ["float32", "FracZ"]]], "imply_type": "TBE", "async_flag": false, "binfile_name": "gather_v2.so", "compute_cost": 10, "kernel_name": "gather_v2", "partial_flag": true, "reshape_type": "", "dynamic_format": false, "dynamic_shape": true, "need_check_supported": false, "op_pattern": ""}
{"op_name": "Gather", "inputs": [{"index": 0, "name": "x", "need_compile": false, "param_type": "required", "shape": "all"}, {"index": 1, "name": "indices", "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": "required", "type": "int", "value": "all"}], "fusion_type": "OPAQUE", "dtype_format": [[["float16", "DefaultFormat"], ["int32", "DefaultFormat"], ["float16", "DefaultFormat"]], [["float32", "DefaultFormat"], ["int32", "DefaultFormat"], ["float32", "DefaultFormat"]], [["int8", "DefaultFormat"], ["int32", "DefaultFormat"], ["int8", "DefaultFormat"]], [["uint8", "DefaultFormat"], ["int32", "DefaultFormat"], ["uint8", "DefaultFormat"]], [["int32", "DefaultFormat"], ["int32", "DefaultFormat"], ["int32", "DefaultFormat"]], [["uint32", "DefaultFormat"], ["int32", "DefaultFormat"], ["uint32", "DefaultFormat"]], [["int16", "DefaultFormat"], ["int32", "DefaultFormat"], ["int16", "DefaultFormat"]], [["uint16", "DefaultFormat"], ["int32", "DefaultFormat"], ["uint16", "DefaultFormat"]], [["int64", "DefaultFormat"], ["int32", "DefaultFormat"], ["int64", "DefaultFormat"]], [["uint64", "DefaultFormat"], ["int32", "DefaultFormat"], ["uint64", "DefaultFormat"]], [["float16", "DefaultFormat"], ["int64", "DefaultFormat"], ["float16", "DefaultFormat"]], [["float32", "DefaultFormat"], ["int64", "DefaultFormat"], ["float32", "DefaultFormat"]], [["int8", "DefaultFormat"], ["int64", "DefaultFormat"], ["int8", "DefaultFormat"]], [["uint8", "DefaultFormat"], ["int64", "DefaultFormat"], ["uint8", "DefaultFormat"]], [["int32", "DefaultFormat"], ["int64", "DefaultFormat"], ["int32", "DefaultFormat"]], [["uint32", "DefaultFormat"], ["int64", "DefaultFormat"], ["uint32", "DefaultFormat"]], [["int16", "DefaultFormat"], ["int64", "DefaultFormat"], ["int16", "DefaultFormat"]], [["uint16", "DefaultFormat"], ["int64", "DefaultFormat"], ["uint16", "DefaultFormat"]], [["int64", "DefaultFormat"], ["int64", "DefaultFormat"], ["int64", "DefaultFormat"]], [["uint64", "DefaultFormat"], ["int64", "DefaultFormat"], ["uint64", "DefaultFormat"]]], "imply_type": "TBE", "async_flag": false, "binfile_name": "gather_v2_d.so", "compute_cost": 10, "kernel_name": "gather_v2_d", "partial_flag": true, "reshape_type": "", "dynamic_format": false, "dynamic_shape": false, "need_check_supported": false, "op_pattern": ""}
{"op_name": "Gather", "inputs": [{"index": 0, "name": "x", "need_compile": false, "param_type": "required", "shape": "all"}, {"index": 1, "name": "indices", "need_compile": false, "param_type": "required", "shape": "all"}, {"index": 2, "name": "axis", "need_compile": false, "param_type": "required", "shape": "all"}], "outputs": [{"index": 0, "name": "y", "need_compile": false, "param_type": "required", "shape": "all"}], "attr": [], "fusion_type": "OPAQUE", "dtype_format": [[["int8", "DefaultFormat"], ["int32", "DefaultFormat"], ["int32", "DefaultFormat"], ["int8", "DefaultFormat"]], [["int8", "DefaultFormat"], ["int64", "DefaultFormat"], ["int32", "DefaultFormat"], ["int8", "DefaultFormat"]], [["int8", "NC1HWC0"], ["int32", "NC1HWC0"], ["int32", "NC1HWC0"], ["int8", "NC1HWC0"]], [["int8", "NC1HWC0"], ["int64", "NC1HWC0"], ["int32", "NC1HWC0"], ["int8", "NC1HWC0"]], [["int8", "FracZ"], ["int32", "FracZ"], ["int32", "FracZ"], ["int8", "FracZ"]], [["int8", "FracZ"], ["int64", "FracZ"], ["int32", "FracZ"], ["int8", "FracZ"]], [["uint8", "DefaultFormat"], ["int32", "DefaultFormat"], ["int32", "DefaultFormat"], ["uint8", "DefaultFormat"]], [["uint8", "DefaultFormat"], ["int64", "DefaultFormat"], ["int32", "DefaultFormat"], ["uint8", "DefaultFormat"]], [["uint8", "NC1HWC0"], ["int32", "NC1HWC0"], ["int32", "NC1HWC0"], ["uint8", "NC1HWC0"]], [["uint8", "NC1HWC0"], ["int64", "NC1HWC0"], ["int32", "NC1HWC0"], ["uint8", "NC1HWC0"]], [["uint8", "FracZ"], ["int32", "FracZ"], ["int32", "FracZ"], ["uint8", "FracZ"]], [["uint8", "FracZ"], ["int64", "FracZ"], ["int32", "FracZ"], ["uint8", "FracZ"]], [["int32", "DefaultFormat"], ["int32", "DefaultFormat"], ["int32", "DefaultFormat"], ["int32", "DefaultFormat"]], [["int32", "DefaultFormat"], ["int64", "DefaultFormat"], ["int32", "DefaultFormat"], ["int32", "DefaultFormat"]], [["int32", "NC1HWC0"], ["int32", "NC1HWC0"], ["int32", "NC1HWC0"], ["int32", "NC1HWC0"]], [["int32", "NC1HWC0"], ["int64", "NC1HWC0"], ["int32", "NC1HWC0"], ["int32", "NC1HWC0"]], [["int32", "FracZ"], ["int32", "FracZ"], ["int32", "FracZ"], ["int32", "FracZ"]], [["int32", "FracZ"], ["int64", "FracZ"], ["int32", "FracZ"], ["int32", "FracZ"]], [["float16", "DefaultFormat"], ["int32", "DefaultFormat"], ["int32", "DefaultFormat"], ["float16", "DefaultFormat"]], [["float16", "DefaultFormat"], ["int64", "DefaultFormat"], ["int32", "DefaultFormat"], ["float16", "DefaultFormat"]], [["float16", "NC1HWC0"], ["int32", "NC1HWC0"], ["int32", "NC1HWC0"], ["float16", "NC1HWC0"]], [["float16", "NC1HWC0"], ["int64", "NC1HWC0"], ["int32", "NC1HWC0"], ["float16", "NC1HWC0"]], [["float16", "FracZ"], ["int32", "FracZ"], ["int32", "FracZ"], ["float16", "FracZ"]], [["float16", "FracZ"], ["int64", "FracZ"], ["int32", "FracZ"], ["float16", "FracZ"]], [["float32", "DefaultFormat"], ["int32", "DefaultFormat"], ["int32", "DefaultFormat"], ["float32", "DefaultFormat"]], [["float32", "DefaultFormat"], ["int64", "DefaultFormat"], ["int32", "DefaultFormat"], ["float32", "DefaultFormat"]], [["float32", "NC1HWC0"], ["int32", "NC1HWC0"], ["int32", "NC1HWC0"], ["float32", "NC1HWC0"]], [["float32", "NC1HWC0"], ["int64", "NC1HWC0"], ["int32", "NC1HWC0"], ["float32", "NC1HWC0"]], [["float32", "FracZ"], ["int32", "FracZ"], ["int32", "FracZ"], ["float32", "FracZ"]], [["float32", "FracZ"], ["int64", "FracZ"], ["int32", "FracZ"], ["float32", "FracZ"]]], "imply_type": "TBE", "async_flag": false, "binfile_name": "gather_v2.so", "compute_cost": 10, "kernel_name": "gather_v2", "partial_flag": true, "reshape_type": "", "dynamic_format": false, "dynamic_shape": true, "need_check_supported": false, "op_pattern": ""}
{"op_name": "GatherNd", "inputs": [{"index": 0, "name": "x1", "need_compile": false, "param_type": "required", "shape": "all"}, {"index": 1, "name": "x2", "need_compile": false, "param_type": "required", "shape": "all"}], "outputs": [{"index": 0, "name": "y", "need_compile": false, "param_type": "required", "shape": "all"}], "attr": [], "fusion_type": "OPAQUE", "dtype_format": [[["int32", "DefaultFormat"], ["int32", "DefaultFormat"], ["int32", "DefaultFormat"]], [["int32", "DefaultFormat"], ["int64", "DefaultFormat"], ["int32", "DefaultFormat"]], [["float32", "DefaultFormat"], ["int32", "DefaultFormat"], ["float32", "DefaultFormat"]], [["float32", "DefaultFormat"], ["int64", "DefaultFormat"], ["float32", "DefaultFormat"]], [["float16", "DefaultFormat"], ["int32", "DefaultFormat"], ["float16", "DefaultFormat"]], [["float16", "DefaultFormat"], ["int64", "DefaultFormat"], ["float16", "DefaultFormat"]], [["int8", "DefaultFormat"], ["int32", "DefaultFormat"], ["int8", "DefaultFormat"]], [["int8", "DefaultFormat"], ["int64", "DefaultFormat"], ["int8", "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"}

2
mindspore/ccsrc/backend/kernel_compiler/cpu/gather_cpu_kernel.h
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@ -43,7 +43,7 @@ class GatherV2CPUKernel : public CPUKernel {
};
MS_REG_CPU_KERNEL(
GatherV2,
Gather,
KernelAttr().AddInputAttr(kNumberTypeFloat32).AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeFloat32),
GatherV2CPUKernel);
} // namespace kernel

16
mindspore/ccsrc/backend/kernel_compiler/gpu/arrays/gatherv2_gpu_kernel.cc
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@ -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)

6
mindspore/ccsrc/backend/optimizer/ascend/ir_fission/gather_v2_ds_fission.cc
View File

@ -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<AnfNodePtr> gatherv2_inputs = {NewValueNode(std::make_shared<Primitive>(prim::kPrimGatherV2->name())),
pad, origin_node->input(2), origin_node->input(3)};
std::vector<AnfNodePtr> gatherv2_inputs = {NewValueNode(std::make_shared<Primitive>(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<SeqVar>();
VectorRef pattern({prim::kPrimGatherV2, Xs});
VectorRef pattern({prim::kPrimGather, Xs});
return pattern;
}

2
mindspore/ccsrc/backend/optimizer/pass/const_input_to_attr_registry.cc
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@ -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});

4
mindspore/ccsrc/frontend/optimizer/irpass/branch_culling.cc
View File

@ -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<abstract::AbstractTuplePtr>();
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;

4
mindspore/ccsrc/frontend/parallel/auto_parallel/rec_core/rec_generate_strategy.cc
View File

@ -616,7 +616,7 @@ Dimensions PrepareIncomingOperatorInputStrategy(const std::vector<std::shared_pt
return s;
}
auto name = ops[incoming_op_index]->name().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::vector<std::shared_ptr<Opera
if (ops[iter_ops]->type() == 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);

2
mindspore/ccsrc/frontend/parallel/cache_embedding/cache_embedding.cc
View File

@ -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);
}
}

4
mindspore/ccsrc/frontend/parallel/dynamic_creator.h
View File

@ -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);

22
mindspore/ccsrc/frontend/parallel/ops_info/gather_v2_info.cc
View File

@ -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<Strategys> GatherV2Info::GenerateBatchStrategies() {
std::shared_ptr<Strategys> 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();

8
mindspore/ccsrc/frontend/parallel/ops_info/gather_v2_info.h
View File

@ -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<GatherV2Cost>()),
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;

46
mindspore/ccsrc/frontend/parallel/ops_info/gather_v2_p_info.cc
View File

@ -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<Strategys> GatherV2PInfo::GenerateBatchStrategies() {
std::shared_ptr<Strategys> GatherPInfo::GenerateBatchStrategies() {
if (GetAttrs() != SUCCESS) {
MS_LOG(EXCEPTION) << name_ << ": Get attr failed";
}

16
mindspore/ccsrc/frontend/parallel/ops_info/gather_v2_p_info.h
View File

@ -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<GatherV2PCost>()),
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<int64_t> 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

2
mindspore/ccsrc/frontend/parallel/ops_info/ops_utils.h
View File

@ -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";

2
mindspore/ccsrc/frontend/parallel/step_parallel.cc
View File

@ -2699,7 +2699,7 @@ void CheckpointStrategy(const std::vector<AnfNodePtr> &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<GatherV2PInfo>(operator_info);
auto gatherv2_info = std::dynamic_pointer_cast<GatherPInfo>(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()) {

2
mindspore/ccsrc/runtime/device/ascend/executor/tiling/op_tiling_calculater.cc
View File

@ -148,7 +148,7 @@ std::string GetRealOpType(const std::string &op_type) {
static const std::map<std::string, std::string> kOpTypeMap = {
{"SparseApplyFtrl", "SparseApplyFtrlD"},
{"SparseApplyProximalAdagrad", "SparseApplyProximalAdagradD"},
{"SparseGatherV2", "GatherV2"},
{"SparseGatherV2", "Gather"},
{"Pad", "PadD"},
{"Concat", "ConcatD"},
};

4
mindspore/ccsrc/transform/express_ir/onnx_exporter.cc
View File

@ -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);
}

2
mindspore/ccsrc/transform/graph_ir/op_declare/selection_ops_declare.cc
View File

@ -70,7 +70,7 @@ INPUT_MAP(GatherV2D) = {{1, INPUT_DESC(x)}, {2, INPUT_DESC(indices)}};
INPUT_ATTR_MAP(GatherV2D) = {{3, ATTR_DESC(axis, AnyTraits<int64_t>())}};
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)}};

2
mindspore/ccsrc/utils/utils.h
View File

@ -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";

2
mindspore/core/abstract/primitive_infer_map.cc
View File

@ -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}},

5
mindspore/core/base/core_ops.h
View File

@ -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<Primitive>("scalar_add");
@ -86,8 +87,8 @@ inline const PrimitivePtr kPrimCast = std::make_shared<Primitive>("Cast");
inline const PrimitivePtr kPrimConcat = std::make_shared<Primitive>("Concat");
inline const PrimitivePtr kPrimSqueeze = std::make_shared<Primitive>("Squeeze");
inline const PrimitivePtr kPrimTranspose = std::make_shared<Primitive>("Transpose");
inline const PrimitivePtr kPrimGatherV2 = std::make_shared<Primitive>("GatherV2");
inline const PrimitivePtr kPrimGatherD = std::make_shared<Primitive>("GatherD");
inline const PrimitivePtr kPrimGather = std::make_shared<Primitive>(kGather);
inline const PrimitivePtr kPrimSparseGatherV2 = std::make_shared<Primitive>("SparseGatherV2");
inline const PrimitivePtr kPrimShape = std::make_shared<Primitive>("Shape");
inline const PrimitivePtr kPrimDynamicShape = std::make_shared<Primitive>("DynamicShape");
@ -351,7 +352,7 @@ inline const PrimitivePtr kPrimGetRefKey = std::make_shared<Primitive>("get_ref_
inline const PrimitivePtr kPrimMakeRef = std::make_shared<Primitive>("make_ref");
inline const PrimitivePtr kPrimGetRefValue = std::make_shared<Primitive>("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<Primitive>("state_setitem");
inline const PrimitivePtr kPrimJ = std::make_shared<Primitive>("J");

2
mindspore/lite/src/ops/primitive_c.cc
View File

@ -607,7 +607,7 @@ std::shared_ptr<PrimitiveC> PrimitiveC::Create(const Primitive &prim, const std:
return NewPrimitiveC<While>(prim, inputs, quantType);
} else if (op_type == "MirrorPad") {
return NewPrimitiveC<Pad>(prim, inputs, quantType);
} else if (op_type == "GatherV2") {
} else if (op_type == "Gather") {
return NewPrimitiveC<Gather>(prim, inputs, quantType);
} else if (op_type == "OnesLike") {
return NewPrimitiveC<OnesLike>(prim, inputs, quantType);

1
mindspore/lite/tools/converter/parser/tf/tf_gather_parser.cc
View File

@ -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

2
mindspore/lite/tools/converter/parser/tflite/tflite_util.cc
View File

@ -69,7 +69,7 @@ std::map<tflite::BuiltinOperator, std::string> 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"},

10
mindspore/nn/layer/embedding.py
View File

@ -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

2
mindspore/nn/loss/loss.py
View File

@ -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)

2
mindspore/nn/optim/lazyadam.py
View File

@ -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)

4
mindspore/nn/optim/optimizer.py
View File

@ -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)

36
mindspore/ops/_constants.py Normal file
View File

@ -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"

8
mindspore/ops/_grad/grad_array_ops.py
View File

@ -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()

2
mindspore/ops/_op_impl/tbe/gather_v2.py
View File

@ -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") \

2
mindspore/ops/_op_impl/tbe/gather_v2_ds.py
View File

@ -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") \

2
mindspore/ops/functional.py
View File

@ -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()

3
mindspore/ops/operations/__init__.py
View File

@ -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',

11
mindspore/ops/operations/array_ops.py
View File

@ -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.

4
model_zoo/official/cv/deeptext/src/Deeptext/bbox_assign_sample_stage2.py
View File

@ -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()

2
model_zoo/official/cv/maskrcnn/src/maskrcnn/bbox_assign_sample_stage2.py
View File

@ -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.

2
model_zoo/official/cv/psenet/src/ETSNET/dice_loss.py
View File

@ -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):
'''

8
model_zoo/official/cv/resnet_thor/src/thor_layer.py
View File

@ -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

2
model_zoo/official/gnn/bgcf/src/bgcf.py
View File

@ -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)

2
model_zoo/official/nlp/bert/src/bert_for_pre_training.py
View File

@ -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,

6
model_zoo/official/nlp/bert/src/bert_model.py
View File

@ -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):

2
model_zoo/official/nlp/bert_thor/src/bert_for_pre_training.py
View File

@ -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,

6
model_zoo/official/nlp/bert_thor/src/bert_model.py
View File

@ -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):

10
model_zoo/official/nlp/bert_thor/src/thor_for_bert.py
View File

@ -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:

10
model_zoo/official/nlp/bert_thor/src/thor_for_bert_arg.py
View File

@ -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)

6
model_zoo/official/nlp/bert_thor/src/thor_layer.py
View File

@ -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

2
model_zoo/official/nlp/gnmt_v2/src/gnmt_model/embedding.py
View File

@ -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)

2
model_zoo/official/nlp/gpt/src/gpt.py
View File

@ -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)

2
model_zoo/official/nlp/mass/src/transformer/embedding.py
View File

@ -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)

2
model_zoo/official/nlp/mass/src/transformer/positional_embedding.py
View File

@ -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):

2
model_zoo/official/nlp/prophetnet/src/transformer/embedding.py
View File

@ -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)

2
model_zoo/official/nlp/prophetnet/src/transformer/positional_embedding.py
View File

@ -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):

8
model_zoo/official/nlp/tinybert/src/tinybert_model.py
View File

@ -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"):

4
model_zoo/official/nlp/transformer/src/transformer_model.py
View File

@ -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):

2
model_zoo/official/recommend/deepfm/src/deepfm.py
View File

@ -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()

2
model_zoo/official/recommend/ncf/src/ncf.py
View File

@ -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()

4
model_zoo/official/recommend/wide_and_deep/src/wide_and_deep.py
View File

@ -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:

2
model_zoo/official/recommend/wide_and_deep_multitable/src/wide_and_deep.py
View File

@ -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)

2
model_zoo/research/cv/FaceAttribute/src/FaceAttribute/loss_factory.py
View File

@ -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()

2
model_zoo/research/cv/FaceQualityAssessment/src/loss.py
View File

@ -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()

4
model_zoo/research/hpc/molecular_dynamics/src/descriptor.py
View File

@ -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()

8
model_zoo/research/nlp/ternarybert/src/tinybert_model.py
View File

@ -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):

2
model_zoo/research/recommend/autodis/src/autodis.py
View File

@ -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()

2
tests/st/hcom/hcom_sparsetensor.py
View File

@ -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)

1
tests/st/hcom/test_hcom_sparsetensor.py
View File

@ -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

2
tests/st/model_zoo_tests/DeepFM/src/deepfm.py
View File

@ -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()

8
tests/st/model_zoo_tests/wide_and_deep/python_file_for_ci/wide_and_deep.py
View File

@ -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()

2
tests/st/networks/models/bert/src/bert_for_pre_training.py
View File

@ -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,

6
tests/st/networks/models/bert/src/bert_model.py
View File

@ -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):

4
tests/st/networks/models/resnet50/src_thor/thor_layer.py
View File

@ -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

6
tests/st/ops/cpu/test_gather_op.py
View File

@ -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)

8
tests/st/ops/gpu/test_gatherV2_op.py
View File

@ -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

2
tests/ut/cpp/transform/convert_test.cc
View File

@ -367,7 +367,7 @@ TEST_F(TestConvert, TestConcat) {
}
TEST_F(TestConvert, TestGatherV2) {
auto prim = prim::kPrimGatherV2;
auto prim = prim::kPrimGather;
std::shared_ptr<FuncGraph> anf_graph = MakeFuncGraph(prim, 3);
std::shared_ptr<FuncGraphManager> graph_manager = MakeManager({anf_graph});

4
tests/ut/python/communication/test_comm.py
View File

@ -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)

2
tests/ut/python/onnx/test_onnx.py
View File

@ -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()

2
tests/ut/python/ops/test_dynamic_shape.py
View File

@ -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):

14
tests/ut/python/ops/test_ops.py
View File

@ -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))]}),

8
tests/ut/python/parallel/test_dynamic_shape.py
View File

@ -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")

2
tests/ut/python/parallel/test_gather_v2.py
View File

@ -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

4
tests/ut/python/parallel/test_gather_v2_primitive.py
View File

@ -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)

2
tests/ut/python/parallel/test_manual_gatherv2.py
View File

@ -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()

2
tests/ut/python/parallel/test_reshape_skip_redistribution.py
View File

@ -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)

2
tests/ut/python/parallel/test_uniform_candidate_sampler.py
View File

@ -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()

2
tests/ut/python/pipeline/parse/test_parse.py
View File

@ -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)