Use std::string_view

pytorch · Jul 17, 2024 · fade1af · fade1af
1 parent 860cdc8
commit fade1af
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Showing 2 changed files with 11 additions and 11 deletions.
diff --git a/torch_xla/csrc/aten_autograd_ops.cpp b/torch_xla/csrc/aten_autograd_ops.cpp
@@ -22,7 +22,7 @@ bool IsNonTrivialDilation(at::IntArrayRef dilation) {
 namespace aten_autograd_ops {
 
 torch::Tensor EinsumAutogradFunction::forward(
-    torch::autograd::AutogradContext* ctx, const std::string_view equation,
+    torch::autograd::AutogradContext* ctx, const c10::string_view equation,
     at::TensorList tensors) {
   std::string eq_str = std::string(equation);
   ctx->saved_data["equation"] = eq_str;

diff --git a/torch_xla/csrc/aten_xla_type.cpp b/torch_xla/csrc/aten_xla_type.cpp
@@ -1376,7 +1376,7 @@ at::Tensor XLANativeFunctions::div(const at::Tensor& self,
 
 at::Tensor XLANativeFunctions::div(
     const at::Tensor& self, const at::Tensor& other,
-    std::optional<std::string_view> rounding_mode) {
+    std::optional<c10::string_view> rounding_mode) {
   TORCH_LAZY_FN_COUNTER_TIMED_TRACING("xla::");
   at::ScalarType dtype = at::result_type(self, other);
   auto operands = GetBinaryOperands(self, UnwrapNumber(other, dtype));
@@ -1414,7 +1414,7 @@ at::Tensor XLANativeFunctions::dot(const at::Tensor& self,
       bridge::GetXlaTensor(self), bridge::GetXlaTensor(tensor)));
 }
 
-at::Tensor XLANativeFunctions::einsum(std::string_view equation,
+at::Tensor XLANativeFunctions::einsum(c10::string_view equation,
                                       at::TensorList tensors,
                                       at::OptionalIntArrayRef path) {
   std::string cleansed_equation = std::string(equation);
@@ -1709,15 +1709,15 @@ at::Tensor XLANativeFunctions::gather(const at::Tensor& self, int64_t dim,
 }
 
 at::Tensor XLANativeFunctions::gelu(const at::Tensor& self,
-                                    std::string_view approximate) {
+                                    c10::string_view approximate) {
   TORCH_LAZY_FN_COUNTER_TIMED_TRACING("xla::");
   return bridge::AtenFromXlaTensor(
       tensor_methods::gelu(bridge::GetXlaTensor(self), approximate));
 }
 
 at::Tensor XLANativeFunctions::gelu_backward(const at::Tensor& grad,
                                              const at::Tensor& self,
-                                             std::string_view approximate) {
+                                             c10::string_view approximate) {
   TORCH_LAZY_FN_COUNTER_TIMED_TRACING("xla::");
   at::ScalarType result_type = at::result_type(grad, self);
   return bridge::AtenFromXlaTensor(tensor_methods::gelu_backward(
@@ -3074,7 +3074,7 @@ at::Tensor XLANativeFunctions::rsub(const at::Tensor& self,
 
 at::Tensor scatter_reduce_helper(const at::Tensor& self, int64_t dim,
                                  const at::Tensor& index, const at::Tensor& src,
-                                 std::optional<std::string_view> reduce) {
+                                 std::optional<c10::string_view> reduce) {
   XLATensorPtr self_tensor = bridge::GetXlaTensor(self);
   if (!reduce.has_value()) {
     return bridge::AtenFromXlaTensor(
@@ -3095,7 +3095,7 @@ at::Tensor scatter_reduce_helper(const at::Tensor& self, int64_t dim,
 at::Tensor scatter_reduce_helper(const at::Tensor& self, int64_t dim,
                                  const at::Tensor& index,
                                  const at::Scalar& value,
-                                 std::optional<std::string_view> reduce) {
+                                 std::optional<c10::string_view> reduce) {
   TORCH_LAZY_FN_COUNTER_TIMED_TRACING("xla::");
   XLATensorPtr self_tensor = bridge::GetXlaTensor(self);
   if (!reduce.has_value()) {
@@ -3129,15 +3129,15 @@ at::Tensor XLANativeFunctions::scatter(const at::Tensor& self, int64_t dim,
 at::Tensor XLANativeFunctions::scatter(const at::Tensor& self, int64_t dim,
                                        const at::Tensor& index,
                                        const at::Tensor& src,
-                                       std::string_view reduce) {
+                                       c10::string_view reduce) {
   TORCH_LAZY_FN_COUNTER_TIMED_TRACING("xla::");
   return scatter_reduce_helper(self, dim, index, src, reduce);
 }
 
 at::Tensor XLANativeFunctions::scatter(const at::Tensor& self, int64_t dim,
                                        const at::Tensor& index,
                                        const at::Scalar& value,
-                                       std::string_view reduce) {
+                                       c10::string_view reduce) {
   TORCH_LAZY_FN_COUNTER_TIMED_TRACING("xla::");
   return scatter_reduce_helper(self, dim, index, value, reduce);
 }
@@ -3153,7 +3153,7 @@ at::Tensor XLANativeFunctions::scatter_add(const at::Tensor& self, int64_t dim,
 // supported
 at::Tensor XLANativeFunctions::scatter_reduce(
     const at::Tensor& self, int64_t dim, const at::Tensor& index,
-    const at::Tensor& src, std::string_view reduce, bool include_self) {
+    const at::Tensor& src, c10::string_view reduce, bool include_self) {
   TORCH_LAZY_FN_COUNTER_TIMED_TRACING("xla::");
   if ((reduce == "sum" || reduce == "prod" || reduce == "amin" ||
        reduce == "amax") &&
@@ -3782,7 +3782,7 @@ at::Tensor& XLANativeFunctions::zero_(at::Tensor& self) {
 
 std::tuple<at::Tensor, at::Tensor, at::Tensor> XLANativeFunctions::_linalg_svd(
     const at::Tensor& self, bool full_matrices, bool compute_uv,
-    std::optional<std::string_view> /* driver */) {
+    std::optional<c10::string_view> /* driver */) {
   // The optional driver string is only for CUDA with a cuSOLVER backend.
   TORCH_LAZY_FN_COUNTER_TIMED_TRACING("xla::");
   // As per https://pytorch.org/docs/stable/generated/torch.svd.html,