Build【免费下载链接】metadefAscend Metadata Definition项目地址: https://gitcode.com/cann/metadef函数功能根据之前的设置构建TilingContext返回一个ContextHolderTilingContext对象。函数原型ContextHolderTilingContext Build()参数说明无返回值说明返回一个 ContextHolderTilingContext对象。通过调用GetContext()方法可获取TilingContext指针。约束说明所有通过指针传入的参数其内存所有权归调用者所有调用者必须确保这些指针在ContextHolder对象的整个生命周期内有效。ContextHolder析构时会自动释放内部上下文资源。请勿手动释放GetContext()返回的指针。调用示例#include base/context_builder/op_tiling_context_builder.h auto workspace_size_holer gert::ContinuousVector::Createsize_t(4096); auto ws_ptr reinterpret_castgert::ContinuousVector *(workspace_size_holer.get()); gert::Shape shape_0{1, 1, 1, 1, 1}; gert::Shape shape_1{10, 10, 10, 10, 20}; gert::Shape shape_2{1, 1, 1, 1, 1}; gert::Shape shape_3{10, 10, 10, 10, 20}; gert::Shape resultShape{10, 10, 10, 10, 20}; gert::StorageShape x({1, 1, 1, 1, 1}, {1, 1, 1, 1, 1}); gert::StorageShape resultIn({10, 10, 10, 10, 20}, {10, 10, 10, 10, 20}); gert::StorageShape gamma({1, 1, 1, 1, 1}, {1, 1, 1, 1, 1}); gert::StorageShape beta({10, 10, 10, 10, 20}, {10, 10, 10, 10, 20}); gert::StorageShape result({10, 10, 10, 10, 20}, {10, 10, 10, 10, 20}); uint8_t data_x[1] {9}; gert::Tensor x_tensor(x, {ge::FORMAT_NCDHW, ge::FORMAT_RESERVED, ExpandDimsType()}, TensorPlacement::kOnHost, ge::DT_FLOAT, (void *) data_x); gert::Tensor resultIn_tensor(resultIn, {ge::FORMAT_NCDHW, ge::FORMAT_RESERVED, ExpandDimsType()}, TensorPlacement::kOnHost, ge::DT_FLOAT, nullptr); gert::Tensor gammax_tensor(gamma, {ge::FORMAT_NCDHW, ge::FORMAT_RESERVED, ExpandDimsType()}, TensorPlacement::kOnHost, ge::DT_FLOAT, nullptr); gert::Tensor beta_tensor(beta, {ge::FORMAT_NCDHW, ge::FORMAT_RESERVED, ExpandDimsType()}, TensorPlacement::kOnHost, ge::DT_FLOAT, nullptr); gert::Tensor result_tensor(result, {ge::FORMAT_NCDHW, ge::FORMAT_RESERVED, ExpandDimsType()}, TensorPlacement::kOnHost, ge::DT_FLOAT, nullptr); uint8_t tmp_compile_info[] XX; // XX代表Fake数据 uint8_t tmp_platform_info[] XX;// XX代表Fake数据 int32_t deterministic 1; OpTilingContextBuilder ctx_builder; auto holder ctx_builder.OpName(tmp) .OpType(DIY) .IONum(4, 1) .AppendAttr(int64_t(1)) .AppendAttr(bool(true)) .AppendAttr(float(0.3)) .AppendAttr(AscendString(my_info)) .AppendAttr(std::vectorbool({true, false, true})) .AppendAttr(std::vectorint64_t({1, 2, 3})) .AppendAttr(std::vectorfloat({0.1, 0.2, 0.3})) .AppendAttr(std::vectorAscendString({123, 234})) .AppendAttr(std::vectorstd::vectorint64_t({{1, 2, 3}, {4, 5, 6}})) .TilingDataSize(100) .Workspace(ws_ptr) .CompileInfo(tmp_compile_info) .Deterministic(deterministic) .PlatformInfo(tmp_platform_info) .InputTensors({x_tensor, resultIn_tensor, gammax_tensor, beta_tensor}) .OutputTensors({result_tensor}) .Build(); auto ctx holder.GetContext(); EXPECT_NE(ctx, nullptr); auto ctx_compute_node_info ctx-GetComputeNodeInfo(); EXPECT_NE(ctx_compute_node_info, nullptr); EXPECT_EQ(ctx-GetCompileInfo(), tmp_compile_info); EXPECT_EQ(ctx-GetInputShape(0)-GetOriginShape(), shape_0); EXPECT_EQ(ctx-GetInputShape(0)-GetStorageShape(), shape_0); EXPECT_EQ(ctx-GetInputTensor(0)-GetAddr(), data_x); EXPECT_EQ(ctx-GetInputTensor(0), x_tensor); EXPECT_EQ(ctx-GetInputTensor(0)-GetStorageShape(), x_tensor.GetStorageShape()); EXPECT_EQ(ctx-GetInputTensor(0)-GetOriginShape(), x_tensor.GetOriginShape()); EXPECT_EQ(ctx-GetInputTensor(0)-GetSize(), x_tensor.GetSize()); EXPECT_EQ(ctx-GetOutputShape(0)-GetOriginShape(), resultShape); EXPECT_EQ(ctx-GetOutputShape(0)-GetStorageShape(), resultShape); EXPECT_EQ(static_castvoid *(ctx-GetWorkspaceSizes(4096)), static_castconst void *(ws_ptr-GetData())); EXPECT_EQ(static_castvoid *(ctx-GetPlatformInfo()), static_castvoid *(tmp_platform_info)); EXPECT_EQ(ctx-GetDeterministic(), deterministic); EXPECT_EQ(static_castvoid *(ctx-GetRawTilingData()), static_castvoid *(tmp_tiling_data.get())); EXPECT_EQ(*(ctx-GetAttrs()-GetInt(0)), 1); EXPECT_EQ(*(ctx-GetAttrs()-GetBool(1)), true); EXPECT_FLOAT_EQ(*(ctx-GetAttrs()-GetFloat(2)), 0.3); auto str_ptr ctx-GetAttrs()-GetStr(3); EXPECT_EQ(strcmp(str_ptr, my_info), 0); auto bool_vec ctx-GetAttrs()-GetAttrPointerTypedContinuousVectorbool(4); EXPECT_EQ(bool_vec-GetData()[0], true); EXPECT_EQ(bool_vec-GetData()[1], false); EXPECT_EQ(bool_vec-GetData()[2], true); EXPECT_EQ(ctx-GetAttrs()-GetListInt(5)-GetData()[0], 1); EXPECT_EQ(ctx-GetAttrs()-GetListInt(5)-GetData()[1], 2); EXPECT_EQ(ctx-GetAttrs()-GetListInt(5)-GetData()[2], 3); EXPECT_FLOAT_EQ(ctx-GetAttrs()-GetListFloat(6)-GetData()[0], 0.1); EXPECT_FLOAT_EQ(ctx-GetAttrs()-GetListFloat(6)-GetData()[1], 0.2); EXPECT_FLOAT_EQ(ctx-GetAttrs()-GetListFloat(6)-GetData()[2], 0.3); auto int_vec_vec ctx-GetAttrs()-GetListListInt(8); EXPECT_EQ(((int64_t *) (int_vec_vec-Get(0)-GetData()))[0], 1); EXPECT_EQ(((int64_t *) (int_vec_vec-Get(0)-GetData()))[1], 2); EXPECT_EQ(((int64_t *) (int_vec_vec-Get(0)-GetData()))[2], 3); EXPECT_EQ(((int64_t *) (int_vec_vec-Get(1)-GetData()))[0], 4); EXPECT_EQ(((int64_t *) (int_vec_vec-Get(1)-GetData()))[1], 5); EXPECT_EQ(((int64_t *) (int_vec_vec-Get(1)-GetData()))[2], 6);【免费下载链接】metadefAscend Metadata Definition项目地址: https://gitcode.com/cann/metadef创作声明：本文部分内容由AI辅助生成（AIGC），仅供参考
CANN/metadef算子平铺构建
Build【免费下载链接】metadefAscend Metadata Definition项目地址: https://gitcode.com/cann/metadef函数功能根据之前的设置构建TilingContext返回一个ContextHolderTilingContext对象。函数原型ContextHolderTilingContext Build()参数说明无返回值说明返回一个 ContextHolderTilingContext对象。通过调用GetContext()方法可获取TilingContext指针。约束说明所有通过指针传入的参数其内存所有权归调用者所有调用者必须确保这些指针在ContextHolder对象的整个生命周期内有效。ContextHolder析构时会自动释放内部上下文资源。请勿手动释放GetContext()返回的指针。调用示例#include base/context_builder/op_tiling_context_builder.h auto workspace_size_holer gert::ContinuousVector::Createsize_t(4096); auto ws_ptr reinterpret_castgert::ContinuousVector *(workspace_size_holer.get()); gert::Shape shape_0{1, 1, 1, 1, 1}; gert::Shape shape_1{10, 10, 10, 10, 20}; gert::Shape shape_2{1, 1, 1, 1, 1}; gert::Shape shape_3{10, 10, 10, 10, 20}; gert::Shape resultShape{10, 10, 10, 10, 20}; gert::StorageShape x({1, 1, 1, 1, 1}, {1, 1, 1, 1, 1}); gert::StorageShape resultIn({10, 10, 10, 10, 20}, {10, 10, 10, 10, 20}); gert::StorageShape gamma({1, 1, 1, 1, 1}, {1, 1, 1, 1, 1}); gert::StorageShape beta({10, 10, 10, 10, 20}, {10, 10, 10, 10, 20}); gert::StorageShape result({10, 10, 10, 10, 20}, {10, 10, 10, 10, 20}); uint8_t data_x[1] {9}; gert::Tensor x_tensor(x, {ge::FORMAT_NCDHW, ge::FORMAT_RESERVED, ExpandDimsType()}, TensorPlacement::kOnHost, ge::DT_FLOAT, (void *) data_x); gert::Tensor resultIn_tensor(resultIn, {ge::FORMAT_NCDHW, ge::FORMAT_RESERVED, ExpandDimsType()}, TensorPlacement::kOnHost, ge::DT_FLOAT, nullptr); gert::Tensor gammax_tensor(gamma, {ge::FORMAT_NCDHW, ge::FORMAT_RESERVED, ExpandDimsType()}, TensorPlacement::kOnHost, ge::DT_FLOAT, nullptr); gert::Tensor beta_tensor(beta, {ge::FORMAT_NCDHW, ge::FORMAT_RESERVED, ExpandDimsType()}, TensorPlacement::kOnHost, ge::DT_FLOAT, nullptr); gert::Tensor result_tensor(result, {ge::FORMAT_NCDHW, ge::FORMAT_RESERVED, ExpandDimsType()}, TensorPlacement::kOnHost, ge::DT_FLOAT, nullptr); uint8_t tmp_compile_info[] XX; // XX代表Fake数据 uint8_t tmp_platform_info[] XX;// XX代表Fake数据 int32_t deterministic 1; OpTilingContextBuilder ctx_builder; auto holder ctx_builder.OpName(tmp) .OpType(DIY) .IONum(4, 1) .AppendAttr(int64_t(1)) .AppendAttr(bool(true)) .AppendAttr(float(0.3)) .AppendAttr(AscendString(my_info)) .AppendAttr(std::vectorbool({true, false, true})) .AppendAttr(std::vectorint64_t({1, 2, 3})) .AppendAttr(std::vectorfloat({0.1, 0.2, 0.3})) .AppendAttr(std::vectorAscendString({123, 234})) .AppendAttr(std::vectorstd::vectorint64_t({{1, 2, 3}, {4, 5, 6}})) .TilingDataSize(100) .Workspace(ws_ptr) .CompileInfo(tmp_compile_info) .Deterministic(deterministic) .PlatformInfo(tmp_platform_info) .InputTensors({x_tensor, resultIn_tensor, gammax_tensor, beta_tensor}) .OutputTensors({result_tensor}) .Build(); auto ctx holder.GetContext(); EXPECT_NE(ctx, nullptr); auto ctx_compute_node_info ctx-GetComputeNodeInfo(); EXPECT_NE(ctx_compute_node_info, nullptr); EXPECT_EQ(ctx-GetCompileInfo(), tmp_compile_info); EXPECT_EQ(ctx-GetInputShape(0)-GetOriginShape(), shape_0); EXPECT_EQ(ctx-GetInputShape(0)-GetStorageShape(), shape_0); EXPECT_EQ(ctx-GetInputTensor(0)-GetAddr(), data_x); EXPECT_EQ(ctx-GetInputTensor(0), x_tensor); EXPECT_EQ(ctx-GetInputTensor(0)-GetStorageShape(), x_tensor.GetStorageShape()); EXPECT_EQ(ctx-GetInputTensor(0)-GetOriginShape(), x_tensor.GetOriginShape()); EXPECT_EQ(ctx-GetInputTensor(0)-GetSize(), x_tensor.GetSize()); EXPECT_EQ(ctx-GetOutputShape(0)-GetOriginShape(), resultShape); EXPECT_EQ(ctx-GetOutputShape(0)-GetStorageShape(), resultShape); EXPECT_EQ(static_castvoid *(ctx-GetWorkspaceSizes(4096)), static_castconst void *(ws_ptr-GetData())); EXPECT_EQ(static_castvoid *(ctx-GetPlatformInfo()), static_castvoid *(tmp_platform_info)); EXPECT_EQ(ctx-GetDeterministic(), deterministic); EXPECT_EQ(static_castvoid *(ctx-GetRawTilingData()), static_castvoid *(tmp_tiling_data.get())); EXPECT_EQ(*(ctx-GetAttrs()-GetInt(0)), 1); EXPECT_EQ(*(ctx-GetAttrs()-GetBool(1)), true); EXPECT_FLOAT_EQ(*(ctx-GetAttrs()-GetFloat(2)), 0.3); auto str_ptr ctx-GetAttrs()-GetStr(3); EXPECT_EQ(strcmp(str_ptr, my_info), 0); auto bool_vec ctx-GetAttrs()-GetAttrPointerTypedContinuousVectorbool(4); EXPECT_EQ(bool_vec-GetData()[0], true); EXPECT_EQ(bool_vec-GetData()[1], false); EXPECT_EQ(bool_vec-GetData()[2], true); EXPECT_EQ(ctx-GetAttrs()-GetListInt(5)-GetData()[0], 1); EXPECT_EQ(ctx-GetAttrs()-GetListInt(5)-GetData()[1], 2); EXPECT_EQ(ctx-GetAttrs()-GetListInt(5)-GetData()[2], 3); EXPECT_FLOAT_EQ(ctx-GetAttrs()-GetListFloat(6)-GetData()[0], 0.1); EXPECT_FLOAT_EQ(ctx-GetAttrs()-GetListFloat(6)-GetData()[1], 0.2); EXPECT_FLOAT_EQ(ctx-GetAttrs()-GetListFloat(6)-GetData()[2], 0.3); auto int_vec_vec ctx-GetAttrs()-GetListListInt(8); EXPECT_EQ(((int64_t *) (int_vec_vec-Get(0)-GetData()))[0], 1); EXPECT_EQ(((int64_t *) (int_vec_vec-Get(0)-GetData()))[1], 2); EXPECT_EQ(((int64_t *) (int_vec_vec-Get(0)-GetData()))[2], 3); EXPECT_EQ(((int64_t *) (int_vec_vec-Get(1)-GetData()))[0], 4); EXPECT_EQ(((int64_t *) (int_vec_vec-Get(1)-GetData()))[1], 5); EXPECT_EQ(((int64_t *) (int_vec_vec-Get(1)-GetData()))[2], 6);【免费下载链接】metadefAscend Metadata Definition项目地址: https://gitcode.com/cann/metadef创作声明：本文部分内容由AI辅助生成（AIGC），仅供参考
