Add some nullptr checks

onnxruntime/contrib_ops/cpu/range.cc

@@ -48,7 +48,9 @@ static Status ComputeRange(OpKernelContext* ctx) {
 }
 
 Status Range::Compute(OpKernelContext* ctx) const {
-  auto data_type = ctx->Input<Tensor>(0)->DataType();
+  auto input_tensor = ctx->Input<Tensor>(0);
+  if (input_tensor == nullptr) return Status(common::ONNXRUNTIME, common::FAIL, "input count mismatch");
+  auto data_type = input_tensor->DataType();
   if (data_type == DataTypeImpl::GetType<int32_t>()) {
       return ComputeRange<int32_t>(ctx);
   }

onnxruntime/contrib_ops/cpu/string_normalizer.cc

@@ -207,6 +207,7 @@ Status StringNormalizer::Compute(OpKernelContext* ctx) const {
   using namespace string_normalizer;
 
   auto X = ctx->Input<Tensor>(0);
+  if (X == nullptr) return Status(common::ONNXRUNTIME, common::FAIL, "input count mismatch");
   auto& input_dims = X->Shape().GetDims();
 
   size_t N = 0;

onnxruntime/contrib_ops/cpu/tokenizer.cc

@@ -450,6 +450,7 @@ Status Tokenizer::SeparatorTokenize(OpKernelContext* ctx,
 Status Tokenizer::Compute(OpKernelContext* ctx) const {
   // Get input buffer ptr
   auto X = ctx->Input<Tensor>(0);
+  if (X == nullptr) return Status(common::ONNXRUNTIME, common::FAIL, "input count mismatch");
   if (X->DataType() != DataTypeImpl::GetType<std::string>()) {
     return Status(common::ONNXRUNTIME, common::INVALID_ARGUMENT,
                   "tensor(string) expected as input");

onnxruntime/core/providers/cpu/tensor/eye_like.cc

@@ -33,19 +33,18 @@ Status EyeLike::Compute(OpKernelContext* context) const {
   auto output_tensor_dtype = has_dtype_ ? static_cast<onnx::TensorProto::DataType>(dtype_) : utils::GetTensorProtoType(*T1);
   switch (output_tensor_dtype) {
     case onnx::TensorProto_DataType_FLOAT:
-      return ComputeImpl<float>(context);
+      return ComputeImpl<float>(context, T1);
     case onnx::TensorProto_DataType_INT64:
-      return ComputeImpl<int64_t>(context);
+      return ComputeImpl<int64_t>(context, T1);
     case onnx::TensorProto_DataType_UINT64:
-      return ComputeImpl<uint64_t>(context);
+      return ComputeImpl<uint64_t>(context, T1);
     default:
       ONNXRUNTIME_THROW("Unsupported 'dtype' value: ", output_tensor_dtype);
   }
 }
 
 template <typename T>
-Status EyeLike::ComputeImpl(OpKernelContext* context) const {
-  const Tensor* T1 = context->Input<Tensor>(0);
+Status EyeLike::ComputeImpl(OpKernelContext* context, const Tensor* T1) const {
   const std::vector<int64_t>& input_dims = T1->Shape().GetDims();
   if (input_dims.size() != 2) {
     return Status(ONNXRUNTIME, INVALID_ARGUMENT, "EyeLike : Input tensor dimension is not 2");

onnxruntime/core/providers/cpu/tensor/eye_like.h

@@ -22,7 +22,7 @@ class EyeLike final : public OpKernel {
 
  private:
   template <typename T>
-  Status ComputeImpl(OpKernelContext* context) const;
+  Status ComputeImpl(OpKernelContext* context, const Tensor* T1) const;
 
   bool has_dtype_;
   int64_t dtype_;