Refine the comment

Author: alanwaketan

torch_xla/csrc/aten_cpu_fallback.cpp

@@ -17,8 +17,8 @@ static std::unordered_map<std::string, ::xla::metrics::Counter*>
     _cpu_fallback_counters;
 
 void xla_cpu_fallback(const c10::OperatorHandle& op, torch::jit::Stack* stack) {
-  std::cout << "WONJOO: at aten_cpu_fallback.cpp, xla_cpu_fallback1"
-            << std::endl;
+  // std::cout << "WONJOO: at aten_cpu_fallback.cpp, xla_cpu_fallback1"
+  //           << std::endl;
   XLA_FN_TRACK(3);
   const auto name = c10::toString(op.operator_name());
 

torch_xla/csrc/aten_xla_type.cpp

@@ -53,8 +53,8 @@ namespace {
 at::Tensor to_meta(const at::Tensor& tensor) {
   // undefined tensors can't be converted to the meta device, since they don't
   // have sizes/strides
-  std::cout << "WONJOO: at aten_xla_type.cpp, to_meta1" << std::endl;
-  std::cout << "WONJOO: at aten_xla_type.cpp, to_meta2, tensor_is_functional=" << at::functionalization::impl::isFunctionalTensor(tensor) << std::endl;
+  // std::cout << "WONJOO: at aten_xla_type.cpp, to_meta1" << std::endl;
+  // std::cout << "WONJOO: at aten_xla_type.cpp, to_meta2, tensor_is_functional=" << at::functionalization::impl::isFunctionalTensor(tensor) << std::endl;
   if (!tensor.defined()) return tensor;
   auto out = at::native::empty_strided_meta_symint(
       tensor.sym_sizes(), tensor.sym_strides(),
@@ -460,7 +460,7 @@ at::Tensor& XLANativeFunctions::_amp_update_scale_(at::Tensor& current_scale,
 at::Tensor XLANativeFunctions::_copy_from(const at::Tensor& self,
                                           const at::Tensor& dst,
                                           bool non_blocking) {
-  std::cout << "WONJOO: at aten_xla_type.cpp, _copy_from" << std::endl;
+  // std::cout << "WONJOO: at aten_xla_type.cpp, _copy_from" << std::endl;
   TORCH_LAZY_FN_COUNTER("xla::");
   auto dst_tensor = bridge::TryGetXlaTensor(dst);
   auto self_tensor = bridge::TryGetXlaTensor(self);
@@ -655,7 +655,7 @@ at::Tensor XLANativeFunctions::argmin(const at::Tensor& self,
 at::Tensor XLANativeFunctions::as_strided_copy(
     const at::Tensor& self, at::IntArrayRef size, at::IntArrayRef stride,
     c10::optional<int64_t> storage_offset) {
-  std::cout << "WONJOO: at aten_xla_type.cpp, as_strided_copy1" << std::endl;
+  // std::cout << "WONJOO: at aten_xla_type.cpp, as_strided_copy1" << std::endl;
   TORCH_LAZY_FN_COUNTER("xla::");
   XLATensorPtr self_tensor = bridge::GetXlaTensor(self);
   auto xsize = XlaHelpers::I64List(size);
@@ -675,7 +675,7 @@ at::Tensor XLANativeFunctions::as_strided_scatter(
     const at::Tensor& base, const at::Tensor& mutated_view,
     at::IntArrayRef size, at::IntArrayRef stride,
     c10::optional<int64_t> storage_offset) {
-  std::cout << "WONJOO: at aten_xla_type.cpp, as_strided_scatter1" << std::endl;
+  // std::cout << "WONJOO: at aten_xla_type.cpp, as_strided_scatter1" << std::endl;
   TORCH_LAZY_FN_COUNTER("xla::");
   auto base_ = bridge::GetXlaTensor(base);
   auto xsize = XlaHelpers::I64List(size);
@@ -1026,7 +1026,7 @@ at::Tensor XLANativeFunctions::cumsum(const at::Tensor& self, int64_t dim,
       tensor_methods::cumsum(self_tensor, dim, dtype));
 }
 
-// Let's rewrite a without reusing other native functions.
+// TODO(alanwaketan): Let's rewrite a without reusing other native functions.
 at::Tensor XLANativeFunctions::detach_copy(const at::Tensor& self) {
   TORCH_LAZY_FN_COUNTER("xla::");
   auto new_tensor = empty_symint(self.sym_sizes(), c10::nullopt, c10::nullopt, c10::nullopt, c10::nullopt, c10::nullopt);
@@ -1160,7 +1160,7 @@ at::Tensor XLANativeFunctions::empty_symint(
     c10::optional<at::Layout> layout, c10::optional<at::Device> device,
     c10::optional<bool> pin_memory,
     c10::optional<at::MemoryFormat> /* memory_format */) {
-  std::cout << "WONJOO: at XLANativeFunctions::empty_symint" << std::endl;
+  // std::cout << "WONJOO: at XLANativeFunctions::empty_symint" << std::endl;
   TORCH_LAZY_FN_COUNTER("xla::");
   auto size = c10::asIntArrayRefSlow(sym_size);
   // PT empty*() are optimizations to avoid initializing the data when it is
@@ -1394,8 +1394,8 @@ at::Tensor& XLANativeFunctions::index_fill_(at::Tensor& self, int64_t dim,
 at::Tensor& XLANativeFunctions::index_put_(
     at::Tensor& self, const c10::List<c10::optional<at::Tensor>>& indices,
     const at::Tensor& values, bool accumulate) {
-  std::cout << "WONJOO: at aten_xla_type.cpp, input_put_1" << std::endl;
-  std::cout << "WONJOO: at aten_xla_type.cpp, input_put_2, self.is_functional=" << at::functionalization::impl::isFunctionalTensor(self) << std::endl;
+  // std::cout << "WONJOO: at aten_xla_type.cpp, input_put_1" << std::endl;
+  // std::cout << "WONJOO: at aten_xla_type.cpp, input_put_2, self.is_functional=" << at::functionalization::impl::isFunctionalTensor(self) << std::endl;
   TORCH_LAZY_FN_COUNTER("xla::");
   XLA_CHECK(self.scalar_type() == values.scalar_type());
   CanonicalIndexInfo canonical_index_info =
@@ -1414,7 +1414,7 @@ at::Tensor& XLANativeFunctions::index_put_(
       canonical_index_info.start_dim,
       bridge::GetOrCreateXlaTensor(values, *device), accumulate,
       canonical_index_info.result_permutation);
-std::cout << "WONJOO: at aten_xla_type.cpp, input_put_3, self.is_functional=" << at::functionalization::impl::isFunctionalTensor(self) << std::endl;
+// std::cout << "WONJOO: at aten_xla_type.cpp, input_put_3, self.is_functional=" << at::functionalization::impl::isFunctionalTensor(self) << std::endl;
   return self;
 }
 
@@ -1485,14 +1485,14 @@ at::Tensor XLANativeFunctions::lerp(const at::Tensor& self,
 }
 
 at::Tensor XLANativeFunctions::lift(const at::Tensor& tensor) {
-  std::cout << "WONJOO: at XLANativeFunctions::lift" << std::endl;
+  // std::cout << "WONJOO: at XLANativeFunctions::lift" << std::endl;
   TORCH_INTERNAL_ASSERT(
       !at::functionalization::impl::isFunctionalTensor(tensor));
   return at::functionalization::impl::to_functional_tensor(tensor);
 }
 
 at::Tensor XLANativeFunctions::lift_fresh(const at::Tensor& tensor) {
-  std::cout << "WONJOO: at XLANativeFunctions::lift_fresh" << std::endl;
+  // std::cout << "WONJOO: at XLANativeFunctions::lift_fresh" << std::endl;
   TORCH_INTERNAL_ASSERT(
       !at::functionalization::impl::isFunctionalTensor(tensor));
   return at::functionalization::impl::to_functional_tensor(tensor);