Use default max_seq_length of 128 when loading ExecuTorch models

torchchat/generate.py

@@ -818,6 +818,7 @@ def chat(
                     if text_transformer_args is not None
                     else 2048
                 ),
+                max_seq_length
             )
 
         max_seq_length = (

torchchat/model.py

@@ -127,10 +127,10 @@ def forward(
             )
 
         return self.decoder(decoder_input, input_pos=input_pos)
-    
+
     def setup_caches(self, batch_size, max_seq_len) -> None:
         self.decoder.setup_caches(batch_size, max_seq_len)
-    
+
     def _encoder_feature_select(self, encoder_output) -> Tensor:
         selected_image_feature = encoder_output[1][0].view(
             *encoder_output[1][0].shape[2:]
@@ -154,7 +154,7 @@ def _get_decoder_input(
             image_embeds = self.mm_projector(encoder_output)
             if post_tokens is None:
                 return torch.cat((pre_img_embed, image_embeds), dim=1)
-            
+
             post_img_embed = self.tok_embeddings(post_tokens)
             return torch.cat((pre_img_embed, image_embeds, post_img_embed), dim=1)
 
@@ -227,7 +227,7 @@ def _llava(cls):
             },
             fusion_class=ConcateFusion,
         )
-    
+
     @classmethod
     def get_recipe(cls, model_type):
         match model_type:
@@ -338,7 +338,7 @@ def _sanity_check(
     def from_params(cls, params_path):
         with open(params_path, "r") as f:
             loaded_params = json.loads(f.read())
-        
+
         if (model_type_name := loaded_params.get("model_type", None)) is None:
             # The model params is in the transformer_args format
             # set the model_type to TextOnly and reformat the params
@@ -460,14 +460,14 @@ def build_model(self) -> nn.Module:
                 modules[name] = module_class(**config_args)
 
         return recipe.fusion_class(**modules)
-    
+
     def _replace_known_params(self, params):
         patterns = {"QuickGELUActivation()": QuickGELUActivation()}
         for key, value in params.items():
             if isinstance(value, Hashable) and value in patterns:
                 params[key] = patterns[value]
         return params
-    
+
     @abstractmethod
     def forward(self, *args, **kwargs):
         raise NotImplementedError("forward method is not implemented")
@@ -939,7 +939,15 @@ def __init__(self, config, path) -> None:
             self.model_ = exec_lib._load_for_executorch(str(path))
 
             self.text_transformer_args = TransformerArgs.from_params(self.config.transformer_args["text"])
-
+            # TODO: attempt to use "get_max_seq_len" method on the model after
+            # ExecuTorch bug is fixed.
+            max_seq_len = 128
+            # try:
+            #     max_seq_len = self.model_.run_method("get_max_seq_len", [])
+            # except Exception as e:
+            #     pass
+            self.text_transformer_args.max_seq_length = max_seq_len
+
         def forward(self, x, input_pos):
             # model_.forward expects inputs to be wrapped in a tuple
             forward_inputs = (x.to(torch.long), input_pos.to(torch.long))
@@ -958,6 +966,6 @@ def forward(self, x, input_pos):
 
         def setup_caches(self, max_batch_size, max_seq_length):
             pass
-        
+
 except:
     pass