Support input_embeds in torch exportable decoders

tests/models/gemma/test_modeling_gemma.py

@@ -460,7 +460,10 @@ def test_export_static_cache(self):
         from transformers.integrations.executorch import TorchExportableModuleForDecoderOnlyLM
 
         exportable_module = TorchExportableModuleForDecoderOnlyLM(model)
-        exported_program = exportable_module.export()
+        exported_program = exportable_module.export(
+            input_ids=prompt_token_ids,
+            cache_position=torch.arange(prompt_token_ids.shape[-1], dtype=torch.long, device=model.device),
+        )
         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

@@ -365,7 +365,10 @@ def test_export_static_cache(self):
         from transformers.integrations.executorch import TorchExportableModuleForDecoderOnlyLM
 
         exportable_module = TorchExportableModuleForDecoderOnlyLM(model)
-        exported_program = exportable_module.export()
+        exported_program = exportable_module.export(
+            input_ids=prompt_token_ids,
+            cache_position=torch.arange(prompt_token_ids.shape[-1], dtype=torch.long, device=model.device),
+        )
         ep_generated_ids = TorchExportableModuleWithStaticCache.generate(
             exported_program=exported_program, prompt_token_ids=prompt_token_ids, max_new_tokens=max_new_tokens
         )
@@ -389,7 +392,10 @@ def test_export_hybrid_cache(self):
         # Export + HybridCache
         model.eval()
         exportable_module = TorchExportableModuleForDecoderOnlyLM(model)
-        exported_program = exportable_module.export()
+        exported_program = exportable_module.export(
+            input_ids=torch.tensor([[1]], dtype=torch.long, device=model.device),
+            cache_position=torch.tensor([0], dtype=torch.long, device=model.device),
+        )
 
         # Test generation with the exported model
         prompt = "What is the capital of France?"

tests/models/gemma3/test_modeling_gemma3.py

@@ -809,7 +809,10 @@ def test_export_text_only_with_hybrid_cache(self):
         # Export + HybridCache
         model.eval()
         exportable_module = TorchExportableModuleForDecoderOnlyLM(model)
-        exported_program = exportable_module.export()
+        exported_program = exportable_module.export(
+            input_ids=torch.tensor([[1]], dtype=torch.long, device=model.device),
+            cache_position=torch.tensor([0], dtype=torch.long, device=model.device),
+        )
         logging.info(f"\nExported program: {exported_program}")
 
         # Test generation with the exported model

tests/models/llama/test_modeling_llama.py

@@ -353,7 +353,10 @@ def test_export_static_cache(self):
             from transformers.integrations.executorch import TorchExportableModuleForDecoderOnlyLM
 
             exportable_module = TorchExportableModuleForDecoderOnlyLM(model)
-            exported_program = exportable_module.export()
+            exported_program = exportable_module.export(
+                input_ids=prompt_token_ids,
+                cache_position=torch.arange(prompt_token_ids.shape[-1], dtype=torch.long, device=model.device),
+            )
             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

@@ -384,7 +384,10 @@ def test_export_static_cache(self):
         from transformers.integrations.executorch import TorchExportableModuleForDecoderOnlyLM
 
         exportable_module = TorchExportableModuleForDecoderOnlyLM(model)
-        exported_program = exportable_module.export()
+        exported_program = exportable_module.export(
+            input_ids=prompt_token_ids,
+            cache_position=torch.arange(prompt_token_ids.shape[-1], dtype=torch.long, device=model.device),
+        )
         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

@@ -417,7 +417,10 @@ def test_export_static_cache(self):
         from transformers.integrations.executorch import TorchExportableModuleForDecoderOnlyLM
 
         exportable_module = TorchExportableModuleForDecoderOnlyLM(model)
-        exported_program = exportable_module.export()
+        exported_program = exportable_module.export(
+            input_ids=prompt_token_ids,
+            cache_position=torch.arange(prompt_token_ids.shape[-1], dtype=torch.long, device=model.device),
+        )
         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

@@ -303,7 +303,11 @@ def test_export_static_cache(self):
         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)
+        exported_program = exportable_module.export(
+            input_ids=prompt_token_ids,
+            cache_position=torch.arange(prompt_token_ids.shape[-1], dtype=torch.long, device=model.device),
+            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

@@ -293,7 +293,11 @@ def test_export_static_cache(self):
         from transformers.integrations.executorch import TorchExportableModuleForDecoderOnlyLM
 
         exportable_module = TorchExportableModuleForDecoderOnlyLM(model)
-        exported_program = exportable_module.export(strict=strict)
+        exported_program = exportable_module.export(
+            input_ids=prompt_token_ids,
+            cache_position=torch.arange(prompt_token_ids.shape[-1], dtype=torch.long, device=model.device),
+            strict=strict,
+        )
         ep_generated_ids = TorchExportableModuleWithStaticCache.generate(
             exported_program=exported_program, prompt_token_ids=prompt_token_ids, max_new_tokens=max_new_tokens
         )

tests/test_executorch.py

@@ -0,0 +1,129 @@
+# Copyright 2025 HuggingFace Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import unittest
+
+import torch
+
+from transformers import AutoModelForCausalLM, set_seed
+from transformers.generation.configuration_utils import GenerationConfig
+from transformers.integrations.executorch import (
+    TorchExportableModuleForDecoderOnlyLM,
+    TorchExportableModuleWithHybridCache,
+    TorchExportableModuleWithStaticCache,
+)
+from transformers.pytorch_utils import is_torch_greater_or_equal_than_2_3
+from transformers.testing_utils import require_torch
+
+
+@require_torch
+class ExecutorchTest(unittest.TestCase):
+    def setUp(self):
+        if not is_torch_greater_or_equal_than_2_3:
+            self.skipTest("torch >= 2.3 is required")
+
+        set_seed(0)
+        self.model = AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-LlamaForCausalLM")
+        self.model.eval()
+
+        # Create generation config with static cache for the model
+        self.model.generation_config = GenerationConfig(
+            use_cache=True,
+            cache_implementation="static",
+            cache_config={"batch_size": 1, "max_cache_len": 32, "device": "cpu"},
+        )
+
+        self.input_ids = torch.tensor([[1, 2, 3]], dtype=torch.long)
+        self.inputs_embeds = torch.randn(1, 3, self.model.config.hidden_size)
+        self.cache_position = torch.arange(3, dtype=torch.long)
+
+    def test_static_cache_module_forward(self):
+        """Test TorchExportableModuleWithStaticCache forward with both input types"""
+        generation_config = GenerationConfig(
+            use_cache=True,
+            cache_implementation="static",
+            cache_config={"batch_size": 1, "max_cache_len": 32, "device": "cpu"},
+        )
+
+        # Set generation config on model
+        self.model.generation_config = generation_config
+        module = TorchExportableModuleWithStaticCache(self.model)
+
+        # Test with input_ids
+        eager_output_ids = self.model(input_ids=self.input_ids, use_cache=False).logits
+        wrapped_output_ids = module.forward(input_ids=self.input_ids, cache_position=self.cache_position)
+        torch.testing.assert_close(eager_output_ids, wrapped_output_ids, atol=1e-4, rtol=1e-4)
+
+        # Test with inputs_embeds
+        eager_output_embeds = self.model(inputs_embeds=self.inputs_embeds, use_cache=False).logits
+        wrapped_output_embeds = module.forward(inputs_embeds=self.inputs_embeds, cache_position=self.cache_position)
+        torch.testing.assert_close(eager_output_embeds, wrapped_output_embeds, atol=1e-4, rtol=1e-4)
+
+    def test_hybrid_cache_module_forward(self):
+        """Test TorchExportableModuleWithHybridCache forward with both input types"""
+        config = self.model.config
+        config.sliding_window = 16
+        config.layer_types = ["full_attention"] * config.num_hidden_layers
+
+        generation_config = GenerationConfig(
+            use_cache=True,
+            cache_implementation="hybrid",
+            cache_config={"batch_size": 1, "max_cache_len": 32, "device": "cpu"},
+        )
+
+        # Set generation config on model
+        self.model.generation_config = generation_config
+        module = TorchExportableModuleWithHybridCache(self.model)
+
+        # Test with input_ids
+        eager_output_ids = self.model(input_ids=self.input_ids, use_cache=False).logits
+        wrapped_output_ids = module.forward(input_ids=self.input_ids, cache_position=self.cache_position)
+        torch.testing.assert_close(eager_output_ids, wrapped_output_ids, atol=1e-4, rtol=1e-4)
+
+        # Test with inputs_embeds
+        eager_output_embeds = self.model(inputs_embeds=self.inputs_embeds, use_cache=False).logits
+        wrapped_output_embeds = module.forward(inputs_embeds=self.inputs_embeds, cache_position=self.cache_position)
+        torch.testing.assert_close(eager_output_embeds, wrapped_output_embeds, atol=1e-4, rtol=1e-4)
+
+    def test_decoder_only_lm_export_validation(self):
+        """Test TorchExportableModuleForDecoderOnlyLM export validation"""
+        module = TorchExportableModuleForDecoderOnlyLM(self.model)
+
+        # Should fail with both input_ids and inputs_embeds
+        with self.assertRaises(ValueError):
+            module.export(input_ids=self.input_ids, inputs_embeds=self.inputs_embeds)
+
+        # Should fail with neither
+        with self.assertRaises(ValueError):
+            module.export()
+
+    def test_decoder_only_lm_export(self):
+        """Test TorchExportableModuleForDecoderOnlyLM export with both input types"""
+        module = TorchExportableModuleForDecoderOnlyLM(self.model)
+
+        # Test export with input_ids
+        exported_program_ids = module.export(input_ids=self.input_ids, cache_position=self.cache_position)
+        eager_output_ids = self.model(input_ids=self.input_ids, use_cache=False).logits
+        exported_output_ids = exported_program_ids.module()(
+            input_ids=self.input_ids, cache_position=self.cache_position
+        )
+        torch.testing.assert_close(eager_output_ids, exported_output_ids, atol=1e-4, rtol=1e-4)
+
+        # Test export with inputs_embeds
+        exported_program_embeds = module.export(inputs_embeds=self.inputs_embeds, cache_position=self.cache_position)
+        eager_output_embeds = self.model(inputs_embeds=self.inputs_embeds, use_cache=False).logits
+        exported_output_embeds = exported_program_embeds.module()(
+            inputs_embeds=self.inputs_embeds, cache_position=self.cache_position
+        )
+        torch.testing.assert_close(eager_output_embeds, exported_output_embeds, atol=1e-4, rtol=1e-4)

tests/utils/test_cache_utils.py

@@ -841,8 +841,24 @@ def test_hybrid_cache_exportability(self):
         model.eval()
         max_batch_size = 1
         max_cache_len = 23
-        exportable_module = TorchExportableModuleForDecoderOnlyLM(model, max_batch_size, max_cache_len)
-        exported_program = exportable_module.export()
+        # Set generation config on the model for the hybrid cache model
+        from transformers.generation.configuration_utils import GenerationConfig
+
+        model.generation_config = GenerationConfig(
+            use_cache=True,
+            cache_implementation="hybrid",
+            max_length=max_cache_len,
+            cache_config={
+                "batch_size": max_batch_size,
+                "max_cache_len": max_cache_len,
+                "device": model.device,
+            },
+        )
+        exportable_module = TorchExportableModuleForDecoderOnlyLM(model)
+        exported_program = exportable_module.export(
+            input_ids=torch.tensor([[1]], dtype=torch.long, device=model.device),
+            cache_position=torch.tensor([0], dtype=torch.long, device=model.device),
+        )
         n_g_key_caches = n_g_value_caches = 0
         for buffer_name, buffer in exported_program.named_buffers():
             if buffer_name.startswith("key_cache"):