Revert #13851 #13853 #13854 #13855

Not intended for landing

Author: kirklandsign

examples/models/llava/export_llava.py

@@ -226,11 +226,11 @@ def export_all(llava_model: LlavaModel):
         {
             "image_encoder": image_encoder_ep,
             "token_embedding": token_embedding_ep,
-            "text_decoder": text_model_ep,
+            "text_model": text_model_ep,
         },
         partitioner={
             "image_encoder": [XnnpackPartitioner()],
-            "text_decoder": [
+            "text_model": [
                 # First partition the DQLinear nodes, then partition the rest of the nodes,
                 # to avoid multiple DQLinear nodes in the same partition,
                 # to avoid holding multiple unpacked and packed weight buffers in memory,
@@ -254,7 +254,7 @@ def export_all(llava_model: LlavaModel):
             memory_planning_pass=MemoryPlanningPass(alloc_graph_input=False),
             sym_shape_eval_pass={
                 "image_encoder": ConstraintBasedSymShapeEvalPass(),
-                "text_decoder": ConstraintBasedSymShapeEvalPass(),
+                "text_model": ConstraintBasedSymShapeEvalPass(),
                 "token_embedding": HintBasedSymShapeEvalPass(),
             },
         )

examples/models/llava/runner/llava_text_decoder_runner.h

@@ -89,7 +89,7 @@ class ET_EXPERIMENTAL LlavaTextDecoderRunner
   }
 
   inline static const std::string kTokenEmbeddingMethod = "token_embedding";
-  inline static const std::string kTextModelMethod = "text_decoder";
+  inline static const std::string kTextModelMethod = "text_model";
 };
 
 } // namespace example

examples/models/llava/test/test_llava.py

@@ -96,7 +96,7 @@ def test_llava_export(self):
             "token_embedding", (prompt_before_image,)
         )[0]
         llava_module.run_method(
-            "text_decoder",
+            "text_model",
             (torch.tensor([start_pos], dtype=torch.int64), pte_embeds_before_img),
         )
 
@@ -107,7 +107,7 @@ def test_llava_export(self):
         # pte prefill image
         pte_embeds_img = llava_module.run_method("image_encoder", (resized,))[0]
         llava_module.run_method(
-            "text_decoder",
+            "text_model",
             (
                 torch.tensor([start_pos], dtype=torch.int64),
                 pte_embeds_img,
@@ -122,7 +122,7 @@ def test_llava_export(self):
             "token_embedding", (prompt_after_image,)
         )[0]
         pte_prefill_after_img = llava_module.run_method(
-            "text_decoder",
+            "text_model",
             (torch.tensor([start_pos], dtype=torch.int64), pte_embeds_after_img),
         )[0]
 
@@ -139,7 +139,7 @@ def test_llava_export(self):
                 "token_embedding", (torch.tensor([[new_tokens[i]]], dtype=torch.int64),)
             )[0]
             logits = llava_module.run_method(
-                "text_decoder",
+                "text_model",
                 (torch.tensor([start_pos + i], dtype=torch.int64), token_embeds),
             )[0]
             new_tokens.append(torch.argmax(logits).item())

examples/models/llava/test/test_pte.py

@@ -47,7 +47,7 @@ def main():
         "token_embedding", (prompt_before_image,)
     )[0]
     pte_prefill_before_img = llava_module.run_method(
-        "text_decoder",
+        "text_model",
         (torch.tensor([start_pos], dtype=torch.int64), pte_embeds_before_img),
     )[0]
     print(pte_prefill_before_img)
@@ -60,7 +60,7 @@ def main():
     logging.warning("Image encoder finished")
     logging.warning("Image token prefill started")
     pte_prefill_img = llava_module.run_method(
-        "text_decoder",
+        "text_model",
         (
             torch.tensor([start_pos], dtype=torch.int64),
             pte_embeds_img,
@@ -77,7 +77,7 @@ def main():
         "token_embedding", (prompt_after_image,)
     )[0]
     pte_prefill_after_img = llava_module.run_method(
-        "text_decoder",
+        "text_model",
         (torch.tensor([start_pos], dtype=torch.int64), pte_embeds_after_img),
     )[0]
     logging.warning("Text token prefill finished")
@@ -91,7 +91,7 @@ def main():
             "token_embedding", (torch.tensor([[new_tokens[i]]], dtype=torch.int64),)
         )[0]
         logits = llava_module.run_method(
-            "text_decoder",
+            "text_model",
             (torch.tensor([start_pos + i], dtype=torch.int64), token_embeds),
         )[0]
         new_tokens.append(torch.argmax(logits[..., -1, :]).item())