Push install deeper in generate and undo import order change for etmodel (#1542)

Author: Jack-Khuu

torchchat/generate.py

@@ -869,13 +869,6 @@ def _gen_model_input(
         max_new_tokens: Optional[int] = None,
         max_seq_len: Optional[int] = 2048,
     ) -> Tuple[torch.Tensor, Optional[Dict[str, Any]]]:
-        # torchtune model definition dependencies
-        from torchtune.data import Message, padded_collate_tiled_images_and_mask
-        from torchtune.models.llama3_2_vision._model_builders import (
-            llama3_2_vision_transform,
-        )
-        from torchtune.training import set_default_dtype
-
         """
         Convert prompt and image prompts into consumable model input args.
 
@@ -911,6 +904,14 @@ def _gen_model_input(
             return encoded, None
 
         # Llama 3.2 11B
+
+        # torchtune model definition dependencies
+        from torchtune.data import Message, padded_collate_tiled_images_and_mask
+        from torchtune.models.llama3_2_vision._model_builders import (
+            llama3_2_vision_transform,
+        )
+        from torchtune.training import set_default_dtype
+
         assert (
             image_prompts is None or len(image_prompts) == 1
         ), "At most one image is supported at the moment"

torchchat/model.py

@@ -1054,13 +1054,13 @@ def apply_rotary_emb(x: Tensor, freqs_cis: Tensor) -> Tensor:
 # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 try:
-    # For llama::sdpa_with_kv_cache.out, preprocess ops
-    from executorch.extension.llm.custom_ops import custom_ops  # no-qa
     from executorch.extension.pybindings import portable_lib as exec_lib
 
     # ET changed the way it's loading the custom ops so it's not included in portable_lib but has to be loaded separately.
     # For quantized_decomposed ops
     from executorch.kernels import quantized  # no-qa
+    # For llama::sdpa_with_kv_cache.out, preprocess ops
+    from executorch.extension.llm.custom_ops import custom_ops  # no-qa
 
     class PTEModel(nn.Module):
         def __init__(self, config, path) -> None: