Propagate disable_torch_autograd to thunderfx's _splitter

thunder/dynamo/compiler.py

@@ -150,6 +150,7 @@ def __call__(self, gm: torch.fx.GraphModule, sample_args: list[torch.SymInt, tor
             partial(jit, **thunder_options),
             self._torch_compile,
             sample_args,
+            thunder_options,
         )
         self.subgraph_infos.append(subgraph_info)
         return split_module

thunder/dynamo/splitter.py

@@ -30,6 +30,7 @@ def _splitter(
     thunder_jit: Callable,
     torch_inductor: Callable,
     _unused_sample_args: list[torch.SymInt, torch.Tensor],
+    thunder_options: dict[str, Any],
 ) -> tuple[torch.fx.GraphModule, SubgraphInfo]:
     """
     This method will split graph into multiple graph modules based on thunder supported operations.
@@ -119,7 +120,7 @@ def callback(node) -> int:
             )
             split_reasons.append(split_reason)
         else:
-            is_thunder_supported, split_reason = is_node_supported_by_thunder(node)
+            is_thunder_supported, split_reason = is_node_supported_by_thunder(node, thunder_options)
             if split_reason is not None:
                 split_reasons.append(split_reason)
 

thunder/dynamo/utils.py

@@ -243,7 +243,9 @@ def make_input_proxy(arg_node):
         return proxy_args, proxy_kwargs
 
 
-def try_execute_thunder_symbol(thunder_symbol: Symbol, node: torch.fx.Node) -> tuple[bool, SplitReason | None]:
+def try_execute_thunder_symbol(
+    thunder_symbol: Symbol, node: torch.fx.Node, thunder_options: dict[str, Any]
+) -> tuple[bool, SplitReason | None]:
     """
     Attempts to execute a given Thunder symbol within a tracing context, using proxies for the node's arguments.
 
@@ -287,6 +289,7 @@ def get_requires_grad(arg_node):
 
     args, _ = tree_flatten((node.args, node.kwargs))
     requires_grad = any(map(get_requires_grad, args))
+    disable_torch_autograd: bool | None = thunder_options.get("disable_torch_autograd", None)
 
     @compile_data_and_stats(cd, cs)
     @thunder._with_cache_info_ctx
@@ -309,7 +312,11 @@ def _run_with_cache_info():
                 exception=str(e),
             )
 
-        function_to_run = value_and_grad(thunder_symbol) if requires_grad else thunder_symbol
+        function_to_run = (
+            value_and_grad(thunder_symbol)
+            if requires_grad and (disable_torch_autograd is None or not disable_torch_autograd)
+            else thunder_symbol
+        )
         # We need to be under trace context to generate proxies.
         with thunder.core.trace.tracectx(TraceCtx()):
             try:
@@ -351,7 +358,7 @@ def get_nodes_in_unsupported_ctx_regions(gm: torch.fx.GraphModule) -> set[torch.
     return nodes_in_unsupported_ctx_regions
 
 
-def is_graphmodule_supported_by_thunder(gm):
+def is_graphmodule_supported_by_thunder(gm, thunder_options: dict[str, Any]):
     nodes_in_unsupported_ctx_regions = get_nodes_in_unsupported_ctx_regions(gm)
     for node in gm.graph.nodes:
         if node.op in (
@@ -367,13 +374,15 @@ def is_graphmodule_supported_by_thunder(gm):
             )
             return False, split_reason
 
-        is_thunder_supported, split_reason = is_node_supported_by_thunder(node)
+        is_thunder_supported, split_reason = is_node_supported_by_thunder(node, thunder_options)
         if not is_thunder_supported:
             return False, split_reason
     return True, None
 
 
-def is_node_supported_by_thunder(node: torch.fx.Node) -> tuple[bool, SplitReason | None]:
+def is_node_supported_by_thunder(
+    node: torch.fx.Node, thunder_options: dict[str, Any]
+) -> tuple[bool, SplitReason | None]:
     """
     Determine whether thunder can execute the operation described by this node.
     """
@@ -425,7 +434,7 @@ def is_node_supported_by_thunder(node: torch.fx.Node) -> tuple[bool, SplitReason
         for arg_node in node.args:
             if arg_node.op == "get_attr":
                 called_module = getattr(m, arg_node.target)
-                is_module_supported, split_reason = is_graphmodule_supported_by_thunder(called_module)
+                is_module_supported, split_reason = is_graphmodule_supported_by_thunder(called_module, thunder_options)
                 if not is_module_supported:
                     return is_module_supported, split_reason
         return True, None
@@ -438,7 +447,7 @@ def is_node_supported_by_thunder(node: torch.fx.Node) -> tuple[bool, SplitReason
     # We try to proxify the arguments and call these operations on them to see if they are supported.
     if target in _torch_to_thunder_function_map or inspect.isbuiltin(target):
         thunder_symbol_or_builtin = _torch_to_thunder_function_map.get(target, target)
-        did_run, opt_split_reason = try_execute_thunder_symbol(thunder_symbol_or_builtin, node)
+        did_run, opt_split_reason = try_execute_thunder_symbol(thunder_symbol_or_builtin, node, thunder_options)
         return did_run, opt_split_reason
 
     # There are few operations which are registered only as method in `torchctx` and hence they don't exist
@@ -457,7 +466,7 @@ def is_node_supported_by_thunder(node: torch.fx.Node) -> tuple[bool, SplitReason
             )
         # NOTE: `get_method` may throw if relevant method is not found, so we have guarded it with `has_method`.
         method = torchctx.get_method(node.target, args, kwargs)
-        did_run, opt_split_reason = try_execute_thunder_symbol(method, node)
+        did_run, opt_split_reason = try_execute_thunder_symbol(method, node, thunder_options)
         return did_run, opt_split_reason
 
     # checks einops operators