Repository: huggingface/transformers
Pull request: Allow customization of sdpa in executorch.py

# What does this PR do?
Earlier PR put executorch specific sdpa and mask function in the export function. This prevent any customization that can be done to sdpa, prior to export. By moving this to __init__, we still keep the original behavior but allow users like optimum-executorch to override sdpa by setting model.config._attn_implementation.

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Fixes # (issue)
optimum-executorch to export with custom sdpa. Mainly this `optimum-cli export executorch     --model "HuggingFaceTB/SmolLM2-135M"     --task "text-generation"     --recipe "xnnpack"     --output_dir="smollm"     --use_custom_sdpa` in optimum-executorch should work (although it needs this pr + https://github.com/huggingface/optimum-executorch/pull/82

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Changed files:
filename: src/transformers/integrations/executorch.py
patch: @@ -65,6 +65,10 @@ def __init__(
                 "Using `StaticCache` for export as `layer_types` is not specified or `sliding_window` is `null` in the config."
             )
             self.model = TorchExportableModuleWithStaticCache(model)
+        # This is the same as sdpa, but mask creation does not use `vmap` which is not exportable
+        ALL_MASK_ATTENTION_FUNCTIONS.register("sdpa_without_vmap", sdpa_mask_without_vmap)
+        ALL_ATTENTION_FUNCTIONS.register("sdpa_without_vmap", ALL_ATTENTION_FUNCTIONS["sdpa"])
+        self.model.model.config._attn_implementation = "sdpa_without_vmap"
 
     def forward(
         self,
@@ -103,10 +107,6 @@ def export(
             strict(`Optional[bool]`):
                 Flag to instruct `torch.export` to use `torchdynamo`.
         """
-        # This is the same as sdpa, but mask creation does not use `vmap` which is not exportable
-        ALL_MASK_ATTENTION_FUNCTIONS.register("sdpa_without_vmap", sdpa_mask_without_vmap)
-        ALL_ATTENTION_FUNCTIONS.register("sdpa_without_vmap", ALL_ATTENTION_FUNCTIONS["sdpa"])
-        self.model.model.config._attn_implementation = "sdpa_without_vmap"
 
         example_input_ids = input_ids if input_ids is not None else torch.tensor([[1]], dtype=torch.long)
         example_cache_position = cache_position if cache_position is not None else torch.tensor([0], dtype=torch.long)