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Fix bugs

This commit is contained in:
panzhihui 2023-02-09 16:00:12 +08:00
parent 8eb65baf62
commit 25d568f8a1
8 changed files with 36 additions and 14 deletions
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2
mindspore/ccsrc/include/common/utils/utils.h
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@ -755,7 +755,7 @@ constexpr auto kSparseSoftmaxCrossEntropyWithLogitsOpName = "SparseSoftmaxCrossE
constexpr auto kSparseSoftmaxCrossEntropyWithLogitsV2OpName = "SparseSoftmaxCrossEntropyWithLogitsV2";
constexpr auto kSparseSparseMinimumOpName = "SparseSparseMinimum";
constexpr auto kSparseSparseMaximumOpName = "SparseSparseMaximum";
constexpr auto kSparseTensorDenseMatMulOpName = "SparseTensorDenseMatMul";
constexpr auto kSparseTensorDenseMatmulOpName = "SparseTensorDenseMatmul";
constexpr auto kSparseTensorDenseAddOpName = "SparseTensorDenseAdd";
constexpr auto kSparseTensorToCSRSparseMatrixOpName = "SparseTensorToCSRSparseMatrix";
constexpr auto kSparseToDenseOpName = "SparseToDense";

12
mindspore/ccsrc/plugin/device/ascend/kernel/aicpu/aicpu_ops/cpu_kernel/ms_kernel/sparse_dense_cwise_utils.cc
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@ -64,6 +64,18 @@ uint32_t SparseDenseCwiseOpKernel<Op>::CheckParams(CpuKernelContext &ctx) {
KERNEL_CHECK_FALSE((input3_dims <= shape_elements_nums), KERNEL_STATUS_PARAM_INVALID,
"The dims of DenseTensor is large than sparseTensor.")
KERNEL_CHECK_FALSE((indices_0 == value_0), KERNEL_STATUS_PARAM_INVALID, "The num of indices is not equal to value.")
int64_t indices_num = x1_indices->GetTensorShape()->GetDimSize(0);
int64_t dims = x1_indices->GetTensorShape()->GetDimSize(1);
auto x1_indices_data = reinterpret_cast<int64_t *>(x1_indices->GetData());
auto x1_shape_data = reinterpret_cast<int64_t *>(x1_shape->GetData());
for (int64_t i = 0; i < indices_num; ++i) {
for (int64_t j = 0; j < dims; ++j) {
KERNEL_CHECK_FALSE((x1_indices_data[i * dims + j] >= 0 && x1_indices_data[i * dims + j] < x1_shape_data[j]),
KERNEL_STATUS_PARAM_INVALID, "For SparseDenseCwise%s, indices go out of bounds.",
Op::Name().c_str());
}
}
return KERNEL_STATUS_OK;
}

6
mindspore/ccsrc/plugin/device/ascend/kernel/aicpu/aicpu_ops/cpu_kernel/ms_kernel/sparse_segment_sqrt_n_grad.cc
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@ -97,7 +97,7 @@ uint32_t SparseSegmentSqrtNGradCpuKernel::ComputeKernal(CpuKernelContext &ctx) {
}
for (size_t i = 0; i < m; i++) {
if (indices_addr[i] >= ctx.Input(0)->GetTensorShape()->GetDimSize(0)) {
if (indices_addr[i] >= k) {
KERNEL_LOG_ERROR("Indices out of range.");
return KERNEL_STATUS_PARAM_INVALID;
}
@ -118,9 +118,9 @@ uint32_t SparseSegmentSqrtNGradCpuKernel::ComputeKernal(CpuKernelContext &ctx) {
for (size_t l = 0; l < n; l++) {
y_addr[indices_addr[i - j] * n + l] += x_addr[beginindex * n + l] / (T)(sqrt(countnum));
}
beginindex = segment_ids_addr[i];
countnum = 1;
}
beginindex = segment_ids_addr[i];
countnum = 1;
}
int i = m;

2
mindspore/ccsrc/plugin/device/ascend/kernel/aicpu/aicpu_util.h
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@ -46,6 +46,7 @@ constexpr auto kShapes = "shapes";
constexpr auto kTypes = "types";
constexpr auto kQueueName = "queue_name";
constexpr auto kNameRangeV2 = "RangeV2";
constexpr auto kSparseTensorDenseMatmul = "SparseTensorDenseMatmul";
constexpr auto kSeed = "seed";
constexpr auto kSeed0 = "Seed0";
constexpr auto kSeed1 = "Seed1";
@ -322,6 +323,7 @@ const std::map<std::string, std::string> kOpNameToAicpuOpNameMap{
{kUpsampleNearest3DGrad, "UpsampleNearest3dGrad"},
{kNameRangeV2, "Range"},
{kReLUV3, "Relu"},
{kSparseTensorDenseMatmul, "SparseTensorDenseMatMul"},
{kFillV2, "Fill"},
{kUpsampleTrilinear3D, "UpsampleTrilinear3d"},
{kUpsampleTrilinear3DGrad, "UpsampleTrilinear3dGrad"},

2
mindspore/ccsrc/plugin/device/ascend/optimizer/mindir/aicpu_lib_select.cc
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@ -140,7 +140,7 @@ bool AICpuLibSelectPass::Process(const AnfNodePtr &node) const {
mindspore::kTridiagonalSolveOpName,
mindspore::kSparseTensorToCSRSparseMatrixOpName,
mindspore::kSparseTensorDenseAddOpName,
mindspore::kSparseTensorDenseMatMulOpName,
mindspore::kSparseTensorDenseMatmulOpName,
mindspore::kSparseSoftmaxOpName,
mindspore::kSparseSliceOpName,
mindspore::kSparseSliceGradOpName,

2
mindspore/core/abstract/ops/primitive_infer_map.cc
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@ -104,6 +104,7 @@ PrimShapeDependMap &GetInferDependsMap() {
static const auto &kMirrorPad = prim::kPrimMirrorPad->name();
static const auto &kAdaptiveAvgPool3D = prim::kPrimAdaptiveAvgPool3D->name();
static const auto &kAdaptiveAvgPool3DGrad = prim::kPrimAdaptiveAvgPool3DGrad->name();
static const auto &kAvgPoolGradV1 = prim::kPrimAvgPoolGradV1->name();
static const auto &kOneHot = prim::kPrimOneHot->name();
static const auto &kDropoutGenMask = prim::kPrimDropoutGenMask->name();
static const auto &kStridedSlice = prim::kPrimStridedSlice->name();
@ -198,6 +199,7 @@ PrimShapeDependMap &GetInferDependsMap() {
{kConv2DBackpropFilter, ShapeSet{2}},
{kConv2DBackpropInput, ShapeSet{2}},
{kCol2Im, ShapeSet{1}},
{kAvgPoolGradV1, ShapeSet{0}},
{kOneHot, ShapeSet{1, 3}},
{kDropoutGenMask, ShapeSet{0}},
{prim::kStatelessDropOutGenMask, ShapeSet{0}},

13
mindspore/core/ops/grad/avg_pool_grad_v1.cc
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@ -28,7 +28,8 @@ abstract::ShapePtr AvgPoolGradV1InferShape(const PrimitivePtr &primitive,
std::vector<int64_t> kernel_size = GetValue<std::vector<int64_t>>(primitive->GetAttr(kKernelSize));
auto pad_mode_value = (primitive->GetAttr(kPadMode));
auto pad_mode = PadMode(GetValue<int64_t>(pad_mode_value));
int64_t pad_mode;
CheckAndConvertUtils::GetPadModEnumValue(pad_mode_value, &pad_mode, true);
if (format == NHWC) {
std::vector<int64_t> ksize_NHWC = {kernel_size[0], kernel_size[1], kernel_size[2], kernel_size[3]};
(void)primitive->AddAttr("ksize", MakeValue(ksize_NHWC));
@ -46,16 +47,10 @@ abstract::ShapePtr AvgPoolGradV1InferShape(const PrimitivePtr &primitive,
(void)primitive->AddAttr("padding", MakeValue("SAME"));
}
auto orig_input_shape = input_args[0]->BuildValue();
auto orig_input_shape_tensor = orig_input_shape->cast<tensor::TensorPtr>();
auto orig_input_shape_tensor_data_ptr = orig_input_shape_tensor->data_c();
int32_t *orig_input_shape_ptr = static_cast<int32_t *>(orig_input_shape_tensor_data_ptr);
std::vector<int64_t> orig_shape = {orig_input_shape_ptr[0], orig_input_shape_ptr[1], orig_input_shape_ptr[2],
orig_input_shape_ptr[3]};
auto orig_shape = GetShapeValue(primitive, input_args[0]);
return std::make_shared<abstract::Shape>(orig_shape);
}
TypePtr AvgPoolGradV1InferType(const PrimitivePtr &prim, const std::vector<AbstractBasePtr> &input_args) {
auto name = prim->name();
auto orig_input_shape_type = input_args[0]->BuildType();

11
mindspore/core/ops/sparse_tensor_dense_mat_mul.cc
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@ -37,6 +37,14 @@ void CheckShapeRank(const size_t cur_rank, const size_t expected_rank, const std
<< "-dimensional tensor, but got a " << cur_rank << "-dimensional tensor.";
}
}
void AddAicpuAttr(const PrimitivePtr &primitive) {
// SparseTensorDenseMatmul has attr adjoint_a/b instead of adjoint_st/dt on aicpu.
// add_prim_attr in the python __init__ function doesn't take effect in the expander bprop,
// so add them here.
(void)primitive->AddAttr("adjoint_a", primitive->GetAttr("adjoint_st"));
(void)primitive->AddAttr("adjoint_b", primitive->GetAttr("adjoint_dt"));
}
} // namespace
bool checkType(std::string name, TypePtr dtype, const std::set<TypePtr> &vtypes, const PrimitivePtr &primitive) {
@ -258,6 +266,9 @@ AbstractBasePtr SparseTensorDenseMatmulInfer(const abstract::AnalysisEnginePtr &
auto type = SparseTensorDenseMatmulInferType(primitive, input_args);
// infer shape
auto shape = SparseTensorDenseMatmulInferShape(primitive, input_args);
AddAicpuAttr(primitive);
return std::make_shared<abstract::AbstractTensor>(type, shape);
}