Remove unnecessary const_casts (pytorch#121225)

Fixes #ISSUE_NUMBER

Pull Request resolved: pytorch#121225
Approved by: https://github.com/soulitzer

Author: cyyever

aten/src/ATen/DLConvertor.cpp

@@ -283,12 +283,8 @@ DLManagedTensor* toDLPack(const Tensor& src) {
   atDLMTensor->tensor.dl_tensor.device = getDLDevice(src, device_id);
   atDLMTensor->tensor.dl_tensor.ndim = src.dim();
   atDLMTensor->tensor.dl_tensor.dtype = getDLDataType(src);
-  atDLMTensor->tensor.dl_tensor.shape =
-      // NOLINTNEXTLINE(cppcoreguidelines-pro-type-const-cast)
-      const_cast<int64_t*>(view.sizes().data());
-  atDLMTensor->tensor.dl_tensor.strides =
-      // NOLINTNEXTLINE(cppcoreguidelines-pro-type-const-cast)
-      const_cast<int64_t*>(view.strides().data());
+  atDLMTensor->tensor.dl_tensor.shape = view.sizes().data();
+  atDLMTensor->tensor.dl_tensor.strides = view.strides().data();
   atDLMTensor->tensor.dl_tensor.byte_offset = 0;
   return &(atDLMTensor->tensor);
 }

aten/src/ATen/cuda/detail/CUDAHooks.cpp

@@ -137,7 +137,7 @@ bool CUDAHooks::isPinnedPtr(const void* data) const {
   cudaPointerAttributes attr;
   // We do not believe that CUDA needs mutable access to the data
   // here.
-  cudaError_t err = cudaPointerGetAttributes(&attr, const_cast<void*>(data));
+  cudaError_t err = cudaPointerGetAttributes(&attr, data);
 #if !defined(USE_ROCM)
   if (err == cudaErrorInvalidValue) {
     (void)cudaGetLastError(); // clear CUDA error

aten/src/ATen/dlpack.h

@@ -195,12 +195,12 @@ typedef struct {
   /*! \brief The data type of the pointer*/
   DLDataType dtype;
   /*! \brief The shape of the tensor */
-  int64_t* shape;
+  const int64_t* shape;
   /*!
    * \brief strides of the tensor (in number of elements, not bytes)
    *  can be NULL, indicating tensor is compact and row-majored.
    */
-  int64_t* strides;
+  const int64_t* strides;
   /*! \brief The offset in bytes to the beginning pointer to data */
   uint64_t byte_offset;
 } DLTensor;

torch/csrc/autograd/VariableTypeUtils.h

@@ -104,7 +104,7 @@ inline void throw_error_for_complex_autograd(
 
 // TODO: Blegh, bare references
 
-inline void rebase_history(Variable& var, std::shared_ptr<Node> grad_fn) {
+inline void rebase_history(const Variable& var, std::shared_ptr<Node> grad_fn) {
   if (grad_fn && var.defined()) {
     grad_fn->add_input_metadata(var);
     impl::rebase_history(var, {std::move(grad_fn), 0});

torch/csrc/autograd/autograd_not_implemented_fallback.cpp

@@ -184,8 +184,7 @@ static void basicAutogradNotImplementedFallbackImpl(
             // users typically call .backward() and backprop through
             // the entire program).
             if (t.is_view() && is_mutable_output) {
-              // NOLINTNEXTLINE(cppcoreguidelines-pro-type-const-cast)
-              auto& base = const_cast<at::TensorBase&>(t._base());
+              const auto& base = t._base();
               if (base.requires_grad()) {
                 // Can only register_hook on tensors that require grad.
                 base.register_hook([op_name](const at::TensorBase& grad) {
@@ -210,8 +209,7 @@ static void basicAutogradNotImplementedFallbackImpl(
           // rebase_history assumes single Tensor(a!) return, and in general
           // custom ops don't have a good in-place story.
           if (!is_mutable_output) {
-            // NOLINTNEXTLINE(cppcoreguidelines-pro-type-const-cast)
-            set_history(const_cast<at::Tensor&>(t), grad_fn);
+            set_history(t, grad_fn);
           }
         },
         stack,
@@ -418,11 +416,9 @@ static void autogradNotImplementedFallbackImpl(
         [&](size_t idx_tensor, size_t idx_ret, const at::Tensor& t) {
           if (isDifferentiableType(t.scalar_type())) {
             if (is_inplace_output[idx_ret]) {
-              // NOLINTNEXTLINE(cppcoreguidelines-pro-type-const-cast)
-              rebase_history(const_cast<at::Tensor&>(t), grad_fn);
+              rebase_history(t, grad_fn);
             } else {
-              // NOLINTNEXTLINE(cppcoreguidelines-pro-type-const-cast)
-              set_history(const_cast<at::Tensor&>(t), grad_fn);
+              set_history(t, grad_fn);
             }
           }
         },

torch/csrc/autograd/functions/utils.h

@@ -65,7 +65,7 @@ inline bool compute_requires_grad(Args&&... args) {
 }
 
 inline void set_history(
-    at::Tensor& variable,
+    const at::Tensor& variable,
     const std::shared_ptr<Node>& grad_fn) {
   TORCH_CHECK(grad_fn != nullptr);
   if (variable.defined()) {
@@ -81,15 +81,7 @@ inline void set_history(
 }
 
 inline void set_history(
-    std::vector<Variable>&& variables,
-    const std::shared_ptr<Node>& grad_fn) {
-  for (auto& variable : variables) {
-    set_history(variable, grad_fn);
-  }
-}
-
-inline void set_history(
-    std::vector<Variable>& variables,
+    const std::vector<Variable>& variables,
     const std::shared_ptr<Node>& grad_fn) {
   for (auto& variable : variables) {
     set_history(variable, grad_fn);

torch/csrc/distributed/rpc/tensorpipe_utils.cpp

@@ -152,8 +152,7 @@ std::tuple<tensorpipe::Message, TensorpipeWriteBuffers> tensorpipeSerialize(
   buffers.payload = std::move(rpcMessage->payload());
   // TensorPipe uses the same Message class for both reading and writing, thus
   // it uses non-const pointers even though it doesn't modify them when writing.
-  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-const-cast)
-  char* payloadPtr = const_cast<char*>(buffers.payload.data());
+  char* payloadPtr = buffers.payload.data();
   // kTpMessagePayloadIdx = 2
   tpMessage.payloads.push_back(
       tensorpipe::Message::Payload{payloadPtr, buffers.payload.size()});