fix(cleancode): AICPU算子告警整改（第二批）-ops-nn-AtomGit

Mactivation/log_softmax_v2/op_kernel_aicpu/log_softmax_v2_aicpu.cpp+12-9

	@@ -91,17 +91,19 @@ aicpu::KernelStatus ComputeSequential(T* input, T* output, T* dims_exp_sum, T* d
91	Eigen::array<int, 1> softmax_axes{{1}};	91	Eigen::array<int, 1> softmax_axes{{1}};
92	dims_max = logits.maximum(softmax_axes);	92	dims_max = logits.maximum(softmax_axes);
93		93
94	- IterateBatches(info.total, info.inner_size, info.dims[info.pivot], [&](int64_t index, int64_t index_dst) {	94	+ IterateBatches(info.total, info.inner_size, info.dims[info.pivot],
95	- (output + index) = Eigen::numext::exp((input + index) - dims_maximum[index_dst]);	95	+ [&input, &output, &dims_maximum, &dims_exp_sum](int64_t index, int64_t index_dst) {
96	- dims_exp_sum[index_dst] += (*(output + index));	96	+ (output + index) = Eigen::numext::exp((input + index) - dims_maximum[index_dst]);
97	- });	97	+ dims_exp_sum[index_dst] += (*(output + index));
		98	+ });
98		99
99	dims_sum = dims_sum.inverse();	100	dims_sum = dims_sum.inverse();
100		101
101	- IterateBatches(info.total, info.inner_size, info.dims[info.pivot], [&](int64_t index, int64_t index_dst) {	102	+ IterateBatches(info.total, info.inner_size, info.dims[info.pivot],
102	- (output + index) = ((output + index)) * (dims_exp_sum[index_dst]);	103	+ [&output, &dims_exp_sum](int64_t index, int64_t index_dst) {
103	- (output + index) = Eigen::numext::log((output + index));	104	+ (output + index) = ((output + index)) * (dims_exp_sum[index_dst]);
104	- });	105	+ (output + index) = Eigen::numext::log((output + index));
		106	+ });
105		107
106	return aicpu::KERNEL_STATUS_OK;	108	return aicpu::KERNEL_STATUS_OK;
107	}	109	}
	@@ -144,7 +146,8 @@ aicpu::KernelStatus ComputeParallel(const aicpu::CpuKernelContext& ctx, T* input
144	{	146	{
145	std::int64_t per_unit_size{info.length / std::min(std::max(1L, static_cast<int64_t>(cores) - 2L), info.length)};	147	std::int64_t per_unit_size{info.length / std::min(std::max(1L, static_cast<int64_t>(cores) - 2L), info.length)};
146	std::atomic<bool> failed{false};	148	std::atomic<bool> failed{false};
147	- auto sharder = [&](std::int64_t begin, std::int64_t end) {	149	+ auto sharder = [&input, &output, &dims_exp_sum, &dims_maximum, &info, &failed](std::int64_t begin,
		150	+ std::int64_t end) {
148	for (int64_t index = begin; index < end; ++index) {	151	for (int64_t index = begin; index < end; ++index) {
149	ComputeOneBatch<T>(input, output, dims_exp_sum, dims_maximum, index, info.inner_size, info.dims[info.pivot],	152	ComputeOneBatch<T>(input, output, dims_exp_sum, dims_maximum, index, info.inner_size, info.dims[info.pivot],
150	failed);	153	failed);

Mindex/index_to_addr/op_kernel_aicpu/index_to_addr_aicpu.h+1-1

	@@ -17,7 +17,7 @@ namespace aicpu {
17	class IndexToAddrCpuKernel : public CpuKernel {	17	class IndexToAddrCpuKernel : public CpuKernel {
18	public:	18	public:
19	IndexToAddrCpuKernel() = default;	19	IndexToAddrCpuKernel() = default;
20	- ~IndexToAddrCpuKernel() = default;	20	+ ~IndexToAddrCpuKernel() override = default;
21	uint32_t Compute(CpuKernelContext& ctx) override;	21	uint32_t Compute(CpuKernelContext& ctx) override;
22		22
23	private:	23	private:

Mindex/scatter_nd_max/op_kernel_aicpu/scatter_nd_max_aicpu.cpp+21-19

	@@ -27,15 +27,15 @@ const char* const kScatterNdMax = "ScatterNdMax";
27	const int64_t kParallelDataNum = 32 * 1024;	27	const int64_t kParallelDataNum = 32 * 1024;
28	const int32_t kSplitSize = 64 * 1024;	28	const int32_t kSplitSize = 64 * 1024;
29		29
30	-#define DO_SCATTER_ND_MAX_COMPUTE_CASE(DTYPE, TYPE, ITYPE, CTX) \	30	+#define DO_SCATTER_ND_MAX_COMPUTE_CASE(DTYPE, TYPE, ITYPE, CTX) \
31	- case (DTYPE): \	31	+ case (DTYPE): \
32	- do { \	32	+ do { \
33	- if ((ITYPE) == DT_INT32) { \	33	+ if ((ITYPE) == DT_INT32) { \
34	- return DoScatterNdMaxCompute<TYPE, int32_t>(CTX); \	34	+ return DoScatterNdMaxCompute<TYPE, int32_t>(CTX); \
35	- } else { \	35	+ } else { \
36	- return DoScatterNdMaxCompute<TYPE, int64_t>(CTX); \	36	+ return DoScatterNdMaxCompute<TYPE, int64_t>(CTX); \
37	- } \	37	+ } \
38	- } while (0)	38	+ } while (0)
39	} // namespace	39	} // namespace
40		40
41	namespace aicpu {	41	namespace aicpu {
	@@ -74,8 +74,8 @@ uint32_t ScatterNdMaxCpuKernel::Compute(CpuKernelContext& ctx)
74	template <typename T>	74	template <typename T>
75	uint32_t ScatterNdMaxCpuKernel::InitScatterNdMaxOutput(const CpuKernelContext& ctx)	75	uint32_t ScatterNdMaxCpuKernel::InitScatterNdMaxOutput(const CpuKernelContext& ctx)
76	{	76	{
77	- auto input_ref = reinterpret_cast<T*>(ctx.Input(0)->GetData());	77	+ auto input_ref = PtrToPtr<void, T>(ctx.Input(0)->GetData());
78	- auto output_ref = reinterpret_cast<T*>(ctx.Output(0)->GetData());	78	+ auto output_ref = PtrToPtr<void, T>(ctx.Output(0)->GetData());
79	std::atomic<uint32_t> work_ret(KERNEL_STATUS_OK);	79	std::atomic<uint32_t> work_ret(KERNEL_STATUS_OK);
80	int64_t total_value_num = ctx.Input(0)->NumElements();	80	int64_t total_value_num = ctx.Input(0)->NumElements();
81	if (total_value_num >= kParallelDataNum) {	81	if (total_value_num >= kParallelDataNum) {
	@@ -88,10 +88,11 @@ uint32_t ScatterNdMaxCpuKernel::InitScatterNdMaxOutput(const CpuKernelContext& c
88	"max_thread_num must not be equal to zero.");	88	"max_thread_num must not be equal to zero.");
89	size_t chunk_bytes = static_cast<size_t>(total_value_num / max_thread_num) * sizeof(T);	89	size_t chunk_bytes = static_cast<size_t>(total_value_num / max_thread_num) * sizeof(T);
90	size_t final_chunk_bytes = static_cast<size_t>(total_value_num % max_thread_num) * sizeof(T) + chunk_bytes;	90	size_t final_chunk_bytes = static_cast<size_t>(total_value_num % max_thread_num) * sizeof(T) + chunk_bytes;
91	- auto copy_output_shard = [&](size_t begin_idx, size_t end_idx) {	91	+ auto copy_output_shard = [&max_thread_num, &final_chunk_bytes, &chunk_bytes, &total_value_num, &work_ret,
		92	+ &output_ref, &input_ref](size_t begin_idx, size_t end_idx) {
92	for (size_t shard_id = begin_idx; shard_id < end_idx; ++shard_id) {	93	for (size_t shard_id = begin_idx; shard_id < end_idx; ++shard_id) {
93	- size_t copy_bytes =	94	+ size_t copy_bytes = (shard_id == static_cast<size_t>(max_thread_num - 1)) ? final_chunk_bytes :
94	- (shard_id == static_cast<size_t>(max_thread_num - 1)) ? final_chunk_bytes : chunk_bytes;	95	+ chunk_bytes;
95	int64_t data_base = static_cast<int64_t>(shard_id) * (total_value_num / max_thread_num);	96	int64_t data_base = static_cast<int64_t>(shard_id) * (total_value_num / max_thread_num);
96	if (data_base >= total_value_num) {	97	if (data_base >= total_value_num) {
97	work_ret = KERNEL_STATUS_PARAM_INVALID;	98	work_ret = KERNEL_STATUS_PARAM_INVALID;
	@@ -127,7 +128,7 @@ uint32_t ScatterNdMaxCpuKernel::CheckScatterNdMaxShapeAndData(const CpuKernelCon
127	{	128	{
128	auto shape_ref = ctx.Input(0)->GetTensorShape();	129	auto shape_ref = ctx.Input(0)->GetTensorShape();
129	auto shape_output = ctx.Output(0)->GetTensorShape();	130	auto shape_output = ctx.Output(0)->GetTensorShape();
130	- auto input_indices = reinterpret_cast<TI*>(ctx.Input(1)->GetData());	131	+ auto input_indices = PtrToPtr<void, TI>(ctx.Input(1)->GetData());
131	size_t value_dim_num_ref = static_cast<size_t>(shape_ref->GetDims());	132	size_t value_dim_num_ref = static_cast<size_t>(shape_ref->GetDims());
132	std::vector<int64_t> value_dim_ref = shape_ref->GetDimSizes();	133	std::vector<int64_t> value_dim_ref = shape_ref->GetDimSizes();
133	std::vector<int64_t> value_dim_output = shape_output->GetDimSizes();	134	std::vector<int64_t> value_dim_output = shape_output->GetDimSizes();
	@@ -151,9 +152,9 @@ uint32_t ScatterNdMaxCpuKernel::CheckScatterNdMaxShapeAndData(const CpuKernelCon
151	template <typename T, typename TI>	152	template <typename T, typename TI>
152	uint32_t ScatterNdMaxCpuKernel::DoScatterNdMaxCompute(CpuKernelContext& ctx)	153	uint32_t ScatterNdMaxCpuKernel::DoScatterNdMaxCompute(CpuKernelContext& ctx)
153	{	154	{
154	- auto input_ref = reinterpret_cast<T*>(ctx.Input(0)->GetData());	155	+ auto input_ref = PtrToPtr<void, T>(ctx.Input(0)->GetData());
155	- auto input_indices = reinterpret_cast<TI*>(ctx.Input(1)->GetData());	156	+ auto input_indices = PtrToPtr<void, TI>(ctx.Input(1)->GetData());
156	- auto input_updates = reinterpret_cast<T*>(ctx.Input(2)->GetData());	157	+ auto input_updates = PtrToPtr<void, T>(ctx.Input(2)->GetData());
157	auto shape_ref = ctx.Input(0)->GetTensorShape();	158	auto shape_ref = ctx.Input(0)->GetTensorShape();
158	auto shape_indices = ctx.Input(1)->GetTensorShape();	159	auto shape_indices = ctx.Input(1)->GetTensorShape();
159	auto shape_updates = ctx.Input(2)->GetTensorShape();	160	auto shape_updates = ctx.Input(2)->GetTensorShape();
	@@ -201,7 +202,8 @@ uint32_t ScatterNdMaxCpuKernel::DoScatterNdMaxCompute(CpuKernelContext& ctx)
201	}	202	}
202	}	203	}
203	}	204	}
204	- KERNEL_CHECK_FALSE((InitScatterNdMaxOutput<T>(ctx) == KERNEL_STATUS_OK), KERNEL_STATUS_PARAM_INVALID, "InitOutput failed.");	205	+ KERNEL_CHECK_FALSE((InitScatterNdMaxOutput<T>(ctx) == KERNEL_STATUS_OK), KERNEL_STATUS_PARAM_INVALID,
		206	+ "InitOutput failed.");
205	return KERNEL_STATUS_OK;	207	return KERNEL_STATUS_OK;
206	}	208	}
207		209

Mindex/scatter_nd_min/op_kernel_aicpu/scatter_nd_min_aicpu.cpp+18-18

	@@ -27,15 +27,15 @@ const char* const kScatterNdMin = "ScatterNdMin";
27	const int64_t kParallelDataNum = 32 * 1024;	27	const int64_t kParallelDataNum = 32 * 1024;
28	const int32_t kSplitSize = 64 * 1024;	28	const int32_t kSplitSize = 64 * 1024;
29		29
30	-#define DO_SCATTER_ND_MIN_COMPUTE_CASE(DTYPE, TYPE, ITYPE, CTX) \	30	+#define DO_SCATTER_ND_MIN_COMPUTE_CASE(DTYPE, TYPE, ITYPE, CTX) \
31	- case (DTYPE): \	31	+ case (DTYPE): \
32	- do { \	32	+ do { \
33	- if ((ITYPE) == DT_INT32) { \	33	+ if ((ITYPE) == DT_INT32) { \
34	- return DoScatterNdMinCompute<TYPE, int32_t>(CTX); \	34	+ return DoScatterNdMinCompute<TYPE, int32_t>(CTX); \
35	- } else { \	35	+ } else { \
36	- return DoScatterNdMinCompute<TYPE, int64_t>(CTX); \	36	+ return DoScatterNdMinCompute<TYPE, int64_t>(CTX); \
37	- } \	37	+ } \
38	- } while (0)	38	+ } while (0)
39	} // namespace	39	} // namespace
40		40
41	namespace aicpu {	41	namespace aicpu {
	@@ -74,8 +74,8 @@ uint32_t ScatterNdMinCpuKernel::Compute(CpuKernelContext& ctx)
74	template <typename T>	74	template <typename T>
75	uint32_t ScatterNdMinCpuKernel::InitScatterNdMinOutput(const CpuKernelContext& ctx)	75	uint32_t ScatterNdMinCpuKernel::InitScatterNdMinOutput(const CpuKernelContext& ctx)
76	{	76	{
77	- auto src_ref = reinterpret_cast<T*>(ctx.Input(0)->GetData());	77	+ auto src_ref = PtrToPtr<void, T>(ctx.Input(0)->GetData());
78	- auto dst_ref = reinterpret_cast<T*>(ctx.Output(0)->GetData());	78	+ auto dst_ref = PtrToPtr<void, T>(ctx.Output(0)->GetData());
79	std::atomic<uint32_t> copy_status(KERNEL_STATUS_OK);	79	std::atomic<uint32_t> copy_status(KERNEL_STATUS_OK);
80	int64_t output_num = ctx.Input(0)->NumElements();	80	int64_t output_num = ctx.Input(0)->NumElements();
81	if (output_num >= kParallelDataNum) {	81	if (output_num >= kParallelDataNum) {
	@@ -88,8 +88,7 @@ uint32_t ScatterNdMinCpuKernel::InitScatterNdMinOutput(const CpuKernelContext& c
88	size_t tail_block_size = static_cast<size_t>(output_num % thread_num) * sizeof(T) + block_size;	88	size_t tail_block_size = static_cast<size_t>(output_num % thread_num) * sizeof(T) + block_size;
89	auto copy_by_shard = [&](size_t start, size_t end) {	89	auto copy_by_shard = [&](size_t start, size_t end) {
90	for (size_t shard_idx = start; shard_idx < end; ++shard_idx) {	90	for (size_t shard_idx = start; shard_idx < end; ++shard_idx) {
91	- size_t copy_size =	91	+ size_t copy_size = (shard_idx == static_cast<size_t>(thread_num - 1)) ? tail_block_size : block_size;
92	- (shard_idx == static_cast<size_t>(thread_num - 1)) ? tail_block_size : block_size;
93	int64_t data_offset = static_cast<int64_t>(shard_idx) * (output_num / thread_num);	92	int64_t data_offset = static_cast<int64_t>(shard_idx) * (output_num / thread_num);
94	if (data_offset >= output_num) {	93	if (data_offset >= output_num) {
95	copy_status = KERNEL_STATUS_PARAM_INVALID;	94	copy_status = KERNEL_STATUS_PARAM_INVALID;
	@@ -125,7 +124,7 @@ uint32_t ScatterNdMinCpuKernel::CheckScatterNdMinShapeAndData(const CpuKernelCon
125	{	124	{
126	auto x_shape = ctx.Input(0)->GetTensorShape();	125	auto x_shape = ctx.Input(0)->GetTensorShape();
127	auto y_shape = ctx.Output(0)->GetTensorShape();	126	auto y_shape = ctx.Output(0)->GetTensorShape();
128	- auto indices_data = reinterpret_cast<TI*>(ctx.Input(1)->GetData());	127	+ auto indices_data = PtrToPtr<void, TI>(ctx.Input(1)->GetData());
129	size_t x_rank = static_cast<size_t>(x_shape->GetDims());	128	size_t x_rank = static_cast<size_t>(x_shape->GetDims());
130	std::vector<int64_t> x_dims = x_shape->GetDimSizes();	129	std::vector<int64_t> x_dims = x_shape->GetDimSizes();
131	std::vector<int64_t> y_dims = y_shape->GetDimSizes();	130	std::vector<int64_t> y_dims = y_shape->GetDimSizes();
	@@ -149,9 +148,9 @@ uint32_t ScatterNdMinCpuKernel::CheckScatterNdMinShapeAndData(const CpuKernelCon
149	template <typename T, typename TI>	148	template <typename T, typename TI>
150	uint32_t ScatterNdMinCpuKernel::DoScatterNdMinCompute(CpuKernelContext& ctx)	149	uint32_t ScatterNdMinCpuKernel::DoScatterNdMinCompute(CpuKernelContext& ctx)
151	{	150	{
152	- auto ref_data = reinterpret_cast<T*>(ctx.Input(0)->GetData());	151	+ auto ref_data = PtrToPtr<void, T>(ctx.Input(0)->GetData());
153	- auto indices_ptr = reinterpret_cast<TI*>(ctx.Input(1)->GetData());	152	+ auto indices_ptr = PtrToPtr<void, TI>(ctx.Input(1)->GetData());
154	- auto updates_ptr = reinterpret_cast<T*>(ctx.Input(2)->GetData());	153	+ auto updates_ptr = PtrToPtr<void, T>(ctx.Input(2)->GetData());
155	auto ref_shape = ctx.Input(0)->GetTensorShape();	154	auto ref_shape = ctx.Input(0)->GetTensorShape();
156	auto indices_shape = ctx.Input(1)->GetTensorShape();	155	auto indices_shape = ctx.Input(1)->GetTensorShape();
157	auto updates_shape = ctx.Input(2)->GetTensorShape();	156	auto updates_shape = ctx.Input(2)->GetTensorShape();
	@@ -199,7 +198,8 @@ uint32_t ScatterNdMinCpuKernel::DoScatterNdMinCompute(CpuKernelContext& ctx)
199	}	198	}
200	}	199	}
201	}	200	}
202	- KERNEL_CHECK_FALSE((InitScatterNdMinOutput<T>(ctx) == KERNEL_STATUS_OK), KERNEL_STATUS_PARAM_INVALID, "InitOutput failed.");	201	+ KERNEL_CHECK_FALSE((InitScatterNdMinOutput<T>(ctx) == KERNEL_STATUS_OK), KERNEL_STATUS_PARAM_INVALID,
		202	+ "InitOutput failed.");
203	return KERNEL_STATUS_OK;	203	return KERNEL_STATUS_OK;
204	}	204	}
205		205

Mindex/sparse_fill_empty_rows/op_kernel_aicpu/sparse_fill_empty_rows_aicpu.cpp+6-6

	@@ -90,9 +90,9 @@ KernelStatus SparseFillEmptyRowsCpuKernel::ComputeSparseFillEmptyRows(const CpuK
90	Tensor* emptyRowIndicator = ctx.Output(kEmptyRowIndicatorOutput);	90	Tensor* emptyRowIndicator = ctx.Output(kEmptyRowIndicatorOutput);
91	Tensor* reverseIndexMap = ctx.Output(kReverseIndexMapOutput);	91	Tensor* reverseIndexMap = ctx.Output(kReverseIndexMapOutput);
92		92
93	- const T defaultValue = reinterpret_cast<T*>(defaultValueTensor->GetData())[0];	93	+ const T defaultValue = PtrToPtr<void, T>(defaultValueTensor->GetData())[0];
94	const int64_t n = indices->GetTensorShape()->GetDimSize(0);	94	const int64_t n = indices->GetTensorShape()->GetDimSize(0);
95	- const int64_t denseRows = reinterpret_cast<int64_t*>(denseShape->GetData())[0];	95	+ const int64_t denseRows = PtrToPtr<void, int64_t>(denseShape->GetData())[0];
96	const int64_t rank = indices->GetTensorShape()->GetDimSize(1);	96	const int64_t rank = indices->GetTensorShape()->GetDimSize(1);
97	if (denseRows < 0) {	97	if (denseRows < 0) {
98	KERNEL_LOG_ERROR("dense_shape[0] must be non-negative, but got %ld.", denseRows);	98	KERNEL_LOG_ERROR("dense_shape[0] must be non-negative, but got %ld.", denseRows);
	@@ -114,13 +114,13 @@ KernelStatus SparseFillEmptyRowsCpuKernel::ComputeSparseFillEmptyRows(const CpuK
114		114
115	EigenTensor indicesEigen(indices, indices->GetData());	115	EigenTensor indicesEigen(indices, indices->GetData());
116	const auto indicesMatrix = indicesEigen.matrix<int64_t>();	116	const auto indicesMatrix = indicesEigen.matrix<int64_t>();
117	- auto* valuesData = reinterpret_cast<T*>(values->GetData());	117	+ auto* valuesData = PtrToPtr<void, T>(values->GetData());
118	- auto* emptyRowIndicatorData = reinterpret_cast<bool*>(emptyRowIndicator->GetData());	118	+ auto* emptyRowIndicatorData = PtrToPtr<void, bool>(emptyRowIndicator->GetData());
119	- auto* reverseIndexMapData = reinterpret_cast<int64_t*>(reverseIndexMap->GetData());	119	+ auto* reverseIndexMapData = PtrToPtr<void, int64_t>(reverseIndexMap->GetData());
120		120
121	EigenTensor outputIndicesEigen(outputIndices, outputIndices->GetData());	121	EigenTensor outputIndicesEigen(outputIndices, outputIndices->GetData());
122	auto outputIndicesMatrix = outputIndicesEigen.matrix<int64_t>();	122	auto outputIndicesMatrix = outputIndicesEigen.matrix<int64_t>();
123	- auto* outputValuesData = reinterpret_cast<T*>(outputValues->GetData());	123	+ auto* outputValuesData = PtrToPtr<void, T>(outputValues->GetData());
124	for (int64_t i = 0; i < n; ++i) {	124	for (int64_t i = 0; i < n; ++i) {
125	const int64_t row = indicesMatrix(i, 0);	125	const int64_t row = indicesMatrix(i, 0);
126	if (row < 0 \|\| row >= denseRows) {	126	if (row < 0 \|\| row >= denseRows) {

Mindex/sparse_segment_sum/op_kernel_aicpu/sparse_segment_sum_aicpu.cpp+6-6

	@@ -53,8 +53,8 @@ KernelStatus SparseSegmentSumCpuKernel::SparseSegmentCheck(const CpuKernelContex
53	template <typename T1, typename T2>	53	template <typename T1, typename T2>
54	KernelStatus SparseSegmentSumCpuKernel::SparseSegmentDataCheckWithType(const CpuKernelContext& ctx) const	54	KernelStatus SparseSegmentSumCpuKernel::SparseSegmentDataCheckWithType(const CpuKernelContext& ctx) const
55	{	55	{
56	- auto indices_ptr = reinterpret_cast<T1*>(ctx.Input(1)->GetData());	56	+ auto indices_ptr = PtrToPtr<void, T1>(ctx.Input(1)->GetData());
57	- auto segment_ids_ptr = reinterpret_cast<T2*>(ctx.Input(2)->GetData());	57	+ auto segment_ids_ptr = PtrToPtr<void, T2>(ctx.Input(2)->GetData());
58	size_t m = ctx.Input(2)->GetTensorShape()->NumElements();	58	size_t m = ctx.Input(2)->GetTensorShape()->NumElements();
59	auto x_dim0 = ctx.Input(0)->GetTensorShape()->GetDimSize(0);	59	auto x_dim0 = ctx.Input(0)->GetTensorShape()->GetDimSize(0);
60		60
	@@ -176,10 +176,10 @@ KernelStatus SparseSegmentSumCpuKernel::ComputeKernelWithType(const CpuKernelCon
176	{	176	{
177	size_t n = ctx.Input(0)->GetTensorShape()->NumElements() / ctx.Input(0)->GetTensorShape()->GetDimSize(0);	177	size_t n = ctx.Input(0)->GetTensorShape()->NumElements() / ctx.Input(0)->GetTensorShape()->GetDimSize(0);
178	size_t num_indices = ctx.Input(2)->GetTensorShape()->NumElements();	178	size_t num_indices = ctx.Input(2)->GetTensorShape()->NumElements();
179	- auto x_ptr = reinterpret_cast<T*>(ctx.Input(0)->GetData());	179	+ auto x_ptr = PtrToPtr<void, T>(ctx.Input(0)->GetData());
180	- auto indices_ptr = reinterpret_cast<T1*>(ctx.Input(1)->GetData());	180	+ auto indices_ptr = PtrToPtr<void, T1>(ctx.Input(1)->GetData());
181	- auto segment_ids_ptr = reinterpret_cast<T2*>(ctx.Input(2)->GetData());	181	+ auto segment_ids_ptr = PtrToPtr<void, T2>(ctx.Input(2)->GetData());
182	- auto y_ptr = reinterpret_cast<T*>(ctx.Output(0)->GetData());	182	+ auto y_ptr = PtrToPtr<void, T>(ctx.Output(0)->GetData());
183	if (num_indices == 0) {	183	if (num_indices == 0) {
184	return KERNEL_STATUS_OK;	184	return KERNEL_STATUS_OK;
185	}	185	}

Mindex/sparse_to_dense/op_kernel_aicpu/sparse_to_dense_aicpu.cpp+6-6

	@@ -498,8 +498,8 @@ uint32_t SparseToDenseCpuKernel::ParallelSetDefaultValue(const CpuKernelContext&
498	const Tensor* outputTensor, int64_t outputSize)	498	const Tensor* outputTensor, int64_t outputSize)
499	{	499	{
500	auto typeSize = GetSizeByDataType(static_cast<DataType>(outputTensor->GetDataType()));	500	auto typeSize = GetSizeByDataType(static_cast<DataType>(outputTensor->GetDataType()));
501	- char* defaultValueAddr = reinterpret_cast<char*>(defaultValueTensor->GetData());	501	+ char* defaultValueAddr = PtrToPtr<void, char>(defaultValueTensor->GetData());
502	- char* outputAddr = reinterpret_cast<char*>(outputTensor->GetData());	502	+ char* outputAddr = PtrToPtr<void, char>(outputTensor->GetData());
503	uint32_t minCoreNum = 1;	503	uint32_t minCoreNum = 1;
504	const uint32_t curCoreNum = aicpu::CpuKernelUtils::GetCPUNum(ctx);	504	const uint32_t curCoreNum = aicpu::CpuKernelUtils::GetCPUNum(ctx);
505	const uint32_t availCoreNum = (curCoreNum > kResvCpuNum) ? (curCoreNum - kResvCpuNum) : 0U;	505	const uint32_t availCoreNum = (curCoreNum > kResvCpuNum) ? (curCoreNum - kResvCpuNum) : 0U;
	@@ -541,8 +541,8 @@ uint32_t SparseToDenseCpuKernel::SetDefaultValue(const CpuKernelContext& ctx, co
541	KERNEL_LOG_ERROR("Don't support output tensor types");	541	KERNEL_LOG_ERROR("Don't support output tensor types");
542	return KERNEL_STATUS_PARAM_INVALID;	542	return KERNEL_STATUS_PARAM_INVALID;
543	}	543	}
544	- char* defaultValueAddr = reinterpret_cast<char*>(defaultValueTensor->GetData());	544	+ char* defaultValueAddr = PtrToPtr<void, char>(defaultValueTensor->GetData());
545	- char* outputAddr = reinterpret_cast<char*>(outputTensor->GetData());	545	+ char* outputAddr = PtrToPtr<void, char>(outputTensor->GetData());
546	if (outputSize < kParallelDataSize) {	546	if (outputSize < kParallelDataSize) {
547	int64_t remainder = outputSize % kCopyDataSize;	547	int64_t remainder = outputSize % kCopyDataSize;
548	int64_t piece = outputSize / kCopyDataSize;	548	int64_t piece = outputSize / kCopyDataSize;
	@@ -596,10 +596,10 @@ uint32_t SparseToDenseCpuKernel::Compute(CpuKernelContext& ctx)
596	size_t outputZeroDimSize = static_cast<size_t>(outputShape->GetDimSize(0));	596	size_t outputZeroDimSize = static_cast<size_t>(outputShape->GetDimSize(0));
597	for (size_t index = 0; index < outputZeroDimSize; ++index) {	597	for (size_t index = 0; index < outputZeroDimSize; ++index) {
598	if (shapeTensor->GetDataType() == DT_INT32) {	598	if (shapeTensor->GetDataType() == DT_INT32) {
599	- int32_t* tempDim = reinterpret_cast<int32_t*>(shapeTensor->GetData());	599	+ int32_t* tempDim = PtrToPtr<void, int32_t>(shapeTensor->GetData());
600	denseShape.emplace_back(static_cast<int64_t>(tempDim[index]));	600	denseShape.emplace_back(static_cast<int64_t>(tempDim[index]));
601	} else {	601	} else {
602	- int64_t* tempDim = reinterpret_cast<int64_t*>(shapeTensor->GetData());	602	+ int64_t* tempDim = PtrToPtr<void, int64_t>(shapeTensor->GetData());
603	denseShape.emplace_back(tempDim[index]);	603	denseShape.emplace_back(tempDim[index]);
604	}	604	}
605	if (denseShape[index] < 0) {	605	if (denseShape[index] < 0) {