Automated Code Change

PiperOrigin-RevId: 649878581

third_party/xla/xla/stream_executor/gpu/gpu_command_buffer.cc

@@ -91,10 +91,6 @@ static int64_t NotifyExecDestroyed() {
   return alive_execs.fetch_sub(1, std::memory_order_relaxed) - 1;
 }
 
-/*static*/ int64_t GpuCommandBuffer::AllocatedExecs() {
-  return allocated_execs.load(std::memory_order_relaxed);
-}
-
 /*static*/ int64_t GpuCommandBuffer::AliveExecs() {
   return alive_execs.load(std::memory_order_relaxed);
 }

third_party/xla/xla/stream_executor/gpu/gpu_command_buffer.h

@@ -158,7 +158,6 @@ class GpuCommandBuffer : public CommandBuffer {
   // allocates resources on a GPU devices (rule of thumb is ~8kb per node), so
   // we have to be careful not to keep too many of them alive for too long, or
   // we have a higher risk of OOM errors.
-  static int64_t AllocatedExecs();
   static int64_t AliveExecs();
 
  private:
@@ -357,10 +356,6 @@ class GpuCommandBuffer : public CommandBuffer {
 // Implementation details device kernels required by GpuCommandBuffer.
 //===----------------------------------------------------------------------===//
 
-// A no-op kernel required for creating barriers inside command buffers because
-// empty nodes are not supported within conditional CUDA graphs (in CUDA 12.3).
-void* GetNoOpKernel();
-
 // See `cuda_conditional_kernels.cc` for CUDA implementation. These are
 // various kernels that update Gpu conditionals based on the device memory
 // values, and allow implementing on-device control flow via conditional command