Unbreak optimum-executorch

src/transformers/integrations/executorch.py

@@ -56,13 +56,15 @@ def __init__(
         if not hasattr(model.config, "use_cache") or model.config.use_cache is False:
             raise ValueError("The model must have caching enabled to be performant.")
 
-        if not hasattr(model.config, "layer_types"):
-            # If `layer_types` is not specified explicitly in the config, there is only 1 type of layers, so
-            # export will use `StaticCache` by default.
-            logging.info("Using `StaticCache` for export as `layer_types` is not specified in the config.")
-            self.model = TorchExportableModuleWithStaticCache(model)
-        else:
+        if hasattr(model.config, "layer_types") and getattr(model.config, "sliding_window", None) is not None:
             self.model = TorchExportableModuleWithHybridCache(model, max_batch_size, max_cache_len)
+        else:
+            # If `layer_types` is not specified explicitly in the config or `sliding_window` is null,
+            # there is only 1 type of layers, so export will use `StaticCache` by default.
+            logging.info(
+                "Using `StaticCache` for export as `layer_types` is not specified or `sliding_window` is `null` in the config."
+            )
+            self.model = TorchExportableModuleWithStaticCache(model)
 
     def forward(
         self,
@@ -400,12 +402,6 @@ def __init__(
         if not self.model.config.use_cache:
             raise AssertionError("Model must have caching enabled")
 
-        if (
-            not hasattr(self.model.config, "cache_implementation")
-            or self.model.config.cache_implementation != "hybrid"
-        ):
-            raise AssertionError("Model must use 'hybrid' cache implementation")
-
         # Initialize the HybridCache
         self.cache = HybridCache(
             config=self.model.config,

tests/models/gemma/test_modeling_gemma.py

@@ -390,7 +390,6 @@ def test_export_static_cache(self):
 
         from transformers.integrations.executorch import (
             TorchExportableModuleWithStaticCache,
-            convert_and_export_with_cache,
         )
 
         tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b", pad_token="</s>", padding_side="right")
@@ -436,7 +435,10 @@ def test_export_static_cache(self):
         self.assertEqual(EXPECTED_TEXT_COMPLETION, eager_generated_text)
 
         # Static Cache + export
-        exported_program = convert_and_export_with_cache(model)
+        from transformers.integrations.executorch import TorchExportableModuleForDecoderOnlyLM
+
+        exportable_module = TorchExportableModuleForDecoderOnlyLM(model)
+        exported_program = exportable_module.export()
         ep_generated_ids = TorchExportableModuleWithStaticCache.generate(
             exported_program=exported_program, prompt_token_ids=prompt_token_ids, max_new_tokens=max_new_tokens
         )

tests/models/gemma2/test_modeling_gemma2.py

@@ -313,7 +313,6 @@ def test_export_static_cache(self):
 
         from transformers.integrations.executorch import (
             TorchExportableModuleWithStaticCache,
-            convert_and_export_with_cache,
         )
 
         tokenizer = AutoTokenizer.from_pretrained("google/gemma-2-2b", pad_token="</s>", padding_side="right")
@@ -363,7 +362,10 @@ def test_export_static_cache(self):
         max_new_tokens = max_generation_length - prompt_token_ids.shape[-1]
 
         # Static Cache + export
-        exported_program = convert_and_export_with_cache(model)
+        from transformers.integrations.executorch import TorchExportableModuleForDecoderOnlyLM
+
+        exportable_module = TorchExportableModuleForDecoderOnlyLM(model)
+        exported_program = exportable_module.export()
         ep_generated_ids = TorchExportableModuleWithStaticCache.generate(
             exported_program=exported_program, prompt_token_ids=prompt_token_ids, max_new_tokens=max_new_tokens
         )

tests/models/llama/test_modeling_llama.py

@@ -306,7 +306,6 @@ def test_export_static_cache(self):
 
         from transformers.integrations.executorch import (
             TorchExportableModuleWithStaticCache,
-            convert_and_export_with_cache,
         )
 
         llama_models = {
@@ -352,7 +351,10 @@ def test_export_static_cache(self):
             max_new_tokens = max_generation_length - prompt_token_ids.shape[-1]
 
             # Static Cache + export
-            exported_program = convert_and_export_with_cache(model)
+            from transformers.integrations.executorch import TorchExportableModuleForDecoderOnlyLM
+
+            exportable_module = TorchExportableModuleForDecoderOnlyLM(model)
+            exported_program = exportable_module.export()
             ep_generated_ids = TorchExportableModuleWithStaticCache.generate(
                 exported_program=exported_program, prompt_token_ids=prompt_token_ids, max_new_tokens=max_new_tokens
             )

tests/models/olmo/test_modeling_olmo.py

@@ -334,7 +334,6 @@ def test_export_static_cache(self):
 
         from transformers.integrations.executorch import (
             TorchExportableModuleWithStaticCache,
-            convert_and_export_with_cache,
         )
 
         olmo_model = "allenai/OLMo-1B-hf"
@@ -382,7 +381,10 @@ def test_export_static_cache(self):
         self.assertEqual(EXPECTED_TEXT_COMPLETION, eager_generated_text)
 
         # Static Cache + export
-        exported_program = convert_and_export_with_cache(model)
+        from transformers.integrations.executorch import TorchExportableModuleForDecoderOnlyLM
+
+        exportable_module = TorchExportableModuleForDecoderOnlyLM(model)
+        exported_program = exportable_module.export()
         ep_generated_ids = TorchExportableModuleWithStaticCache.generate(
             exported_program=exported_program, prompt_token_ids=prompt_token_ids, max_new_tokens=max_new_tokens
         )

tests/models/phi3/test_modeling_phi3.py

@@ -347,7 +347,6 @@ def test_export_static_cache(self):
         from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
         from transformers.integrations.executorch import (
             TorchExportableModuleWithStaticCache,
-            convert_and_export_with_cache,
         )
 
         model_id = "microsoft/Phi-4-mini-instruct"
@@ -399,7 +398,10 @@ def test_export_static_cache(self):
         max_new_tokens = max_generation_length - prompt_token_ids.shape[-1]
 
         # Static Cache + export
-        exported_program = convert_and_export_with_cache(model)
+        from transformers.integrations.executorch import TorchExportableModuleForDecoderOnlyLM
+
+        exportable_module = TorchExportableModuleForDecoderOnlyLM(model)
+        exported_program = exportable_module.export()
         ep_generated_ids = TorchExportableModuleWithStaticCache.generate(
             exported_program=exported_program, prompt_token_ids=prompt_token_ids, max_new_tokens=max_new_tokens
         )

tests/models/qwen2/test_modeling_qwen2.py

@@ -31,7 +31,6 @@
     slow,
     torch_device,
 )
-from transformers.utils.import_utils import is_torch_greater_or_equal
 
 
 if is_torch_available():
@@ -246,7 +245,6 @@ def test_export_static_cache(self):
 
         from transformers.integrations.executorch import (
             TorchExportableModuleWithStaticCache,
-            convert_and_export_with_cache,
         )
 
         qwen_model = "Qwen/Qwen2-0.5B"
@@ -287,8 +285,13 @@ def test_export_static_cache(self):
         max_new_tokens = max_generation_length - prompt_token_ids.shape[-1]
 
         # Static Cache + export
-        strict = is_torch_greater_or_equal("2.7.0")  # Due to https://github.com/pytorch/pytorch/issues/150994
-        exported_program = convert_and_export_with_cache(model, strict=strict)
+        from transformers.integrations.executorch import TorchExportableModuleForDecoderOnlyLM
+
+        exportable_module = TorchExportableModuleForDecoderOnlyLM(model)
+        strict = version.parse(torch.__version__) != version.parse(
+            "2.7.0"
+        )  # Due to https://github.com/pytorch/pytorch/issues/150994
+        exported_program = exportable_module.export(strict=strict)
         ep_generated_ids = TorchExportableModuleWithStaticCache.generate(
             exported_program=exported_program, prompt_token_ids=prompt_token_ids, max_new_tokens=max_new_tokens
         )

tests/models/qwen3/test_modeling_qwen3.py

@@ -31,7 +31,6 @@
     slow,
     torch_device,
 )
-from transformers.utils.import_utils import is_torch_greater_or_equal
 
 
 if is_torch_available():
@@ -240,13 +239,12 @@ def test_export_static_cache(self):
 
         from transformers.integrations.executorch import (
             TorchExportableModuleWithStaticCache,
-            convert_and_export_with_cache,
         )
 
         qwen_model = "Qwen/Qwen3-0.6B-Base"
 
         tokenizer = AutoTokenizer.from_pretrained(qwen_model, pad_token="</s>", padding_side="right")
-        if is_torch_greater_or_equal("2.7.0"):
+        if version.parse(torch.__version__) == version.parse("2.7.0"):
             strict = False  # Due to https://github.com/pytorch/pytorch/issues/150994
             EXPECTED_TEXT_COMPLETION = ["My favourite condiment is 100% plain, unflavoured, and unadulterated."]
         else:
@@ -285,7 +283,10 @@ def test_export_static_cache(self):
         max_new_tokens = max_generation_length - prompt_token_ids.shape[-1]
 
         # Static Cache + export
-        exported_program = convert_and_export_with_cache(model, strict=strict)
+        from transformers.integrations.executorch import TorchExportableModuleForDecoderOnlyLM
+
+        exportable_module = TorchExportableModuleForDecoderOnlyLM(model)
+        exported_program = exportable_module.export(strict=strict)
         ep_generated_ids = TorchExportableModuleWithStaticCache.generate(
             exported_program=exported_program, prompt_token_ids=prompt_token_ids, max_new_tokens=max_new_tokens
         )

tests/utils/test_cache_utils.py

@@ -15,6 +15,7 @@
 import copy
 import unittest
 
+from packaging import version
 from parameterized import parameterized
 
 from transformers import set_seed
@@ -680,15 +681,27 @@ def test_static_cache_exportability(self):
         self.assertEqual(n_static_key_caches, model.config.num_hidden_layers)
         self.assertEqual(n_static_value_caches, model.config.num_hidden_layers)
 
-        # Export with dynamic shapes using Dim.AUTO
-        tokenizer = AutoTokenizer.from_pretrained(model_id)
-        input_ids = tokenizer("Here's everything I know", return_tensors="pt").input_ids
-        dynamic_shapes = {"input_ids": {1: torch.export.Dim.AUTO}, "cache_position": None}
+        # Export with dynamic shapes
+        input_ids = torch.zeros((1, 3), dtype=torch.long)
+        cache_position = torch.tensor([0, 1, 2], dtype=torch.long)
+        dynamic_shapes = {"input_ids": {1: torch.export.Dim.DYNAMIC}, "cache_position": {0: torch.export.Dim.DYNAMIC}}
+        strict = version.parse(torch.__version__) != version.parse("2.7.0")
         exported_program = convert_and_export_with_cache(
             model,
             example_input_ids=input_ids,
+            example_cache_position=cache_position,
+            dynamic_shapes=dynamic_shapes,
+            strict=strict,
+        )
+
+        from transformers.integrations.executorch import TorchExportableModuleForDecoderOnlyLM
+
+        exportable_module = TorchExportableModuleForDecoderOnlyLM(model)
+        exported_program = exportable_module.export(
+            input_ids=input_ids,
+            cache_position=cache_position,
             dynamic_shapes=dynamic_shapes,
-            strict=False,
+            strict=strict,
         )
 
     def test_hybrid_cache_exportability(self):
@@ -727,13 +740,15 @@ def test_hybrid_cache_exportability(self):
         self.assertEqual(n_g_value_caches, model.config.num_hidden_layers)
 
         # Export with dynamic shapes using Dim.AUTO
-        tokenizer = AutoTokenizer.from_pretrained(model_id)
-        input_ids = tokenizer("Here's everything I know", return_tensors="pt").input_ids
-        dynamic_shapes = {"input_ids": {1: torch.export.Dim.AUTO}, "cache_position": None}
+        input_ids = torch.zeros((1, 3), dtype=torch.long)
+        cache_position = torch.tensor([0, 1, 2], dtype=torch.long)
+        dynamic_shapes = {"input_ids": {1: torch.export.Dim.DYNAMIC}, "cache_position": {0: torch.export.Dim.DYNAMIC}}
+        strict = version.parse(torch.__version__) != version.parse("2.7.0")
         exported_program = exportable_module.export(
             input_ids=input_ids,
+            cache_position=cache_position,
             dynamic_shapes=dynamic_shapes,
-            strict=False,
+            strict=strict,
         )