feat: add support for custom compile options in torch_xla.compile and PJRT backend

This change introduces the ability to pass custom compile options from Python down to the PJRT backend, allowing users to fine-tune XLA compilation behavior without modifying core code.
Key changes:
* Python API
    * Added custom_compile_options parameter to torch_xla.compile for passing compile-time options as a dict (supports bool, float, int, and str values).
    * Added torch_xla.set_custom_compile_options() utility for setting compile options globally.
    * Added internal binding _XLAC._set_custom_compile_options().
* C++ Runtime
    * Added SetCustomCompileOptions() virtual method to ComputationClient and implemented it in PjRtComputationClient.
    * PjRtComputationClient now stores custom_compile_options_ and injects them into xla::CompileOptions.env_option_overrides during compilation.
    * Options are stringified before being passed to XLA for compatibility.
Motivation:
This enables advanced users to pass through backend-specific tuning flags (e.g., enabling experimental optimizations, toggling partitioning strategies) without hardcoding them, improving flexibility for research and debugging workflows.

Author: sshonTT

torch_xla/csrc/init_python_bindings.cpp

@@ -3247,6 +3247,17 @@ void InitXlaModuleBindings(py::module m) {
              XLA_ERROR() << "Could not get the buffer pointer for XLATensor "
                             "without a data handle or an IR.";
            })
+      .def("_set_custom_compile_options",
+           [](const py::dict& compile_options) {
+             std::unordered_map<std::string, std::string> options;
+             for (const auto& item : compile_options) {
+               // Keys must be strings; values are stringified.
+               const std::string key = py::str(item.first);
+               options[key] = py::str(item.second);
+             }
+             runtime::GetComputationClientOrDie()->SetCustomCompileOptions(
+                 options);
+           })
       .def(
           // from an XLA tensor to a PyCapsule.
           // When consuming the PyCapsule, we should synchronize

torch_xla/csrc/runtime/computation_client.h

@@ -446,6 +446,14 @@ class ComputationClient {
   // after the last ':' character of the device string.
   static int64_t GetDeviceOrdinal(const std::string& device);
 
+  // Sets XLA compile option overrides used by the backend compiler.
+  // - The map keys are XLA compiler flag names (env option override keys).
+  // - The values are stringified flag values.
+  // - Calling this method **overwrites** any previously set options.
+  //   (Pass an empty map to clear.)
+  virtual void SetCustomCompileOptions(
+      const std::unordered_map<std::string, std::string>& options) = 0;
+
  protected:
   static constexpr auto spmd_device_str = "SPMD:0";
 

torch_xla/csrc/runtime/ifrt_computation_client.h

@@ -172,6 +172,11 @@ class IfrtComputationClient : public ComputationClient {
     XLA_ERROR() << __FUNCTION__ << " not implemented";
   }
 
+  void SetCustomCompileOptions(
+      const std::unordered_map<std::string, std::string>& options) override {
+    XLA_ERROR() << __FUNCTION__ << " not implemented";
+  }
+
   // Creates a new instance of IfrtComputationClient and initializes it.
   static absl::StatusOr<absl_nonnull std::unique_ptr<IfrtComputationClient>>
   Create();

torch_xla/csrc/runtime/pjrt_computation_client.cpp

@@ -554,13 +554,18 @@ std::vector<ComputationClient::ComputationPtr> PjRtComputationClient::Compile(
 
   for (auto& instance : instances) {
     xla::CompileOptions compile_options;
+    for (auto& option : custom_compile_options_) {
+      compile_options.env_option_overrides.push_back(
+          {option.first, option.second});
+    }
     if (enable_cm_in_mp) {
       compile_options.executable_build_options.set_use_spmd_partitioning(true);
       compile_options.env_option_overrides.push_back(
           {"xla_tpu_decompose_all_gather_einsum", true});
       compile_options.env_option_overrides.push_back(
           {"xla_tpu_decompose_einsum_reduce_scatter", true});
     }
+
     if (instance.is_sharded) {
       // TODO(yeounoh) multi-host, multi-slice configurations
       compile_options.executable_build_options.set_use_spmd_partitioning(true);
@@ -1052,5 +1057,13 @@ void PjRtComputationClient::OnReadyCallback(
       [callback](absl::Status unused) { callback(); });
 }
 
+void PjRtComputationClient::SetCustomCompileOptions(
+    const std::unordered_map<std::string, std::string>& options) {
+  custom_compile_options_.clear();
+  for (const auto& [key, value] : options) {
+    custom_compile_options_[key] = value;
+  }
+}
+
 }  // namespace runtime
 }  // namespace torch_xla

torch_xla/csrc/runtime/pjrt_computation_client.h

@@ -165,6 +165,10 @@ class PjRtComputationClient : public ComputationClient {
   void OnReadyCallback(DataPtr data,
                        const std::function<void()>& callback) override;
 
+  // See base class for semantics. This call overwrites previously set options.
+  void SetCustomCompileOptions(
+      const std::unordered_map<std::string, std::string>& options) override;
+
   // Creates a new instance of PjRtComputationClient and initializes it.
   static absl::StatusOr<absl_nonnull std::unique_ptr<PjRtComputationClient>>
   Create();
@@ -197,6 +201,7 @@ class PjRtComputationClient : public ComputationClient {
   // If not nullptr, invoke this instead of the actual XLA compilation. Used
   // only for testing.
   std::function<absl::Status()> fake_xla_compile_ = nullptr;
+  std::unordered_map<std::string, std::string> custom_compile_options_;
 
   xla::PjRtDevice* StringToPjRtDevice(const std::string& device);
 

torch_xla/torch_xla.py

@@ -116,6 +116,7 @@ def compile(
     full_graph: Optional[bool] = False,
     name: Optional[str] = None,
     max_different_graphs: Optional[int] = None,
+    custom_compile_options: Optional[dict[str, Any]] = None,
 ):
   """
   Optimizes given model/function using torch_xla's LazyTensor tracing mode.
@@ -136,6 +137,11 @@ def compile(
       max_different_graphs (Optional[int]): number of different traced graphs of the given
         model/function that we are allowed to have. An error will be raised in case this limit
         is exceeded.
+      custom_compile_options (Optional[dict[str, Any]]): XLA compiler flag overrides.
+        Keys are XLA compiler flag names (forwarded to xla::CompileOptions.env_option_overrides),
+        and values may be bool, int, float, or str (internally stringified).
+        - {} (empty dict): clear previously set options.
+        - None (default): do not change previously set options (no-op).
 
   Example::
 
@@ -215,6 +221,8 @@ def _compile():
       torch_xla._XLAC._set_use_eager_mode(saved_eager_mode_status)
       torch_xla._XLAC._set_current_graph_name(saved_current_graph_name)
 
+  if custom_compile_options is not None:
+    torch_xla._XLAC._set_custom_compile_options(custom_compile_options)
   return _compile() if f is None else _compile()(f)
 
 
@@ -264,3 +272,14 @@ def launch(
     fn(xu.getenv_as(xenv.LOCAL_RANK, int), *args)
   else:
     xmp.spawn(fn, args=args, nprocs=nprocs, start_method=start_method)
+
+
+def set_custom_compile_options(options: dict[str, Any]) -> None:
+  """Set XLA **compiler flag overrides** (env option overrides) for compilation.
+
+  Args:
+    options: Dict mapping XLA flag names to values. Values may be bool/float/int/str;
+      they will be stringified before being passed to XLA.
+      Pass an empty dict `{}` to clear previously set options.
+  """
+  torch_xla._XLAC._set_custom_compile_options(options)