…6339)

Summary:

This PR introduces an inductor-level fallback mechanism that gives users control over which operations or subgraphs Inductor should lower and which should fall back to preexisting kernels. This has similar motivation as #164776 in providing flexibility to selectively disable Inductor lowering for specific nodes.

The implementation simply adds a check for the `"should_fallback"` metadata annotation on FX graph nodes. If this is set to `True`, the lowerer falls back before attempting the normal lowering path. Note that since these are user-directed fallbacks dependent upon specific, customized conditions, use `add_to_fallback_set=False` to avoid permanent overwrites of inductor's lowering/fallback rules. 


Simple example marking nodes for fallback based on custom predicates:

```
def should_fallback_predicate(node: torch.fx.Node, pred: Callable[torch.fx.Node, bool]):
    # Apply predicate and mark for fallback if needed
    if self.predicate(node):
         node.meta["should_fallback"] = True
```

Test Plan: added a CI test

Differential Revision: D85347587

…orch#166339)

Summary:

This PR introduces an inductor-level fallback mechanism that gives users control over which operations or subgraphs Inductor should lower and which should fall back to preexisting kernels. This has similar motivation as pytorch#164776 in providing flexibility to selectively disable Inductor lowering for specific nodes.

The implementation simply adds a check for the `"should_fallback"` metadata annotation on FX graph nodes. If this is set to `True`, the lowerer falls back before attempting the normal lowering path. Note that since these are user-directed fallbacks dependent upon specific, customized conditions, use `add_to_fallback_set=False` to avoid permanent overwrites of inductor's lowering/fallback rules. 


Simple example marking nodes for fallback based on custom predicates:

```
def should_fallback_predicate(node: torch.fx.Node, predicate: Callable[torch.fx.Node, bool]):
    # Apply predicate and mark for fallback if needed
    if predicate(node):
         node.meta["should_fallback"] = True
```

Test Plan: added a CI test

Differential Revision: D85347587

…orch#166339)

Summary:

This PR introduces an inductor-level fallback mechanism that gives users control over which operations or subgraphs Inductor should lower and which should fall back to preexisting kernels. This has similar motivation as pytorch#164776 in providing flexibility to selectively disable Inductor lowering for specific nodes.

The implementation simply adds a check for the `"should_fallback"` metadata annotation on FX graph nodes. If this is set to `True`, the lowerer falls back before attempting the normal lowering path. Note that since these are user-directed fallbacks dependent upon specific, customized conditions, use `add_to_fallback_set=False` to avoid permanent overwrites of inductor's lowering/fallback rules. 


Simple example marking nodes for fallback based on custom predicates:

```
def should_fallback_predicate(node: torch.fx.Node, predicate: Callable[torch.fx.Node, bool]):
    # Apply predicate and mark for fallback if needed
    if predicate(node):
         node.meta["should_fallback"] = True
```

Test Plan: added a CI test

Differential Revision: D85347587

…6339)

Summary:
This PR introduces an inductor-level fallback mechanism that gives users control over which operations or subgraphs Inductor should lower and which should fall back to preexisting kernels. This has similar motivation as #164776 in providing flexibility to selectively disable Inductor lowering for specific nodes.

The implementation simply adds a check for the `"should_fallback"` metadata annotation on FX graph nodes. If this is set to `True`, the lowerer falls back before attempting the normal lowering path. Note that since these are user-directed fallbacks dependent upon specific, customized conditions, use `add_to_fallback_set=False` to avoid permanent overwrites of inductor's lowering/fallback rules.

Simple example marking nodes for fallback based on custom predicates:

```
def should_fallback_predicate(node: torch.fx.Node, pred: Callable[torch.fx.Node, bool]):
    # Apply predicate and mark for fallback if needed
    if self.predicate(node):
         node.meta["should_fallback"] = True
```

Test Plan: added a CI test

Differential Revision: D85347587

Pull Request resolved: #166339
Approved by: https://github.com/blaine-rister, https://github.com/eellison