Qwen2.5 is ExecuTorch Compatible

tests/models/qwen2/test_modeling_qwen2.py

@@ -19,8 +19,10 @@
 import unittest
 
 import pytest
+from packaging import version
 
 from transformers import AutoTokenizer, Qwen2Config, is_torch_available, set_seed
+from transformers.generation.configuration_utils import GenerationConfig
 from transformers.testing_utils import (
     backend_empty_cache,
     require_bitsandbytes,
@@ -640,3 +642,56 @@ def test_speculative_generation(self):
         del model
         backend_empty_cache(torch_device)
         gc.collect()
+
+    @slow
+    def test_export_static_cache(self):
+        if version.parse(torch.__version__) < version.parse("2.4.0"):
+            self.skipTest(reason="This test requires torch >= 2.4 to run.")
+
+        from transformers.integrations.executorch import (
+            TorchExportableModuleWithStaticCache,
+            convert_and_export_with_cache,
+        )
+
+        qwen_model = "Qwen/Qwen2.5-0.5B"
+
+        tokenizer = AutoTokenizer.from_pretrained(qwen_model, pad_token="</s>", padding_side="right")
+        EXPECTED_TEXT_COMPLETION = ["My favourite condiment is 100% sugar. I have a jar of 1000 grams of sugar. I use"]
+        max_generation_length = tokenizer(EXPECTED_TEXT_COMPLETION, return_tensors="pt", padding=True)[
+            "input_ids"
+        ].shape[-1]
+
+        # Load model
+        device = "cpu"
+        dtype = torch.bfloat16
+        cache_implementation = "static"
+        attn_implementation = "sdpa"
+        batch_size = 1
+        model = Qwen2ForCausalLM.from_pretrained(
+            qwen_model,
+            device_map=device,
+            torch_dtype=dtype,
+            attn_implementation=attn_implementation,
+            generation_config=GenerationConfig(
+                use_cache=True,
+                cache_implementation=cache_implementation,
+                max_length=max_generation_length,
+                cache_config={
+                    "batch_size": batch_size,
+                    "max_cache_len": max_generation_length,
+                },
+            ),
+        )
+
+        prompt = ["My favourite condiment is "]
+        prompt_tokens = tokenizer(prompt, return_tensors="pt", padding=True).to(model.device)
+        prompt_token_ids = prompt_tokens["input_ids"]
+        max_new_tokens = max_generation_length - prompt_token_ids.shape[-1]
+
+        # Static Cache + export
+        exported_program = convert_and_export_with_cache(model)
+        ep_generated_ids = TorchExportableModuleWithStaticCache.generate(
+            exported_program=exported_program, prompt_token_ids=prompt_token_ids, max_new_tokens=max_new_tokens
+        )
+        ep_generated_text = tokenizer.batch_decode(ep_generated_ids, skip_special_tokens=True)
+        self.assertEqual(EXPECTED_TEXT_COMPLETION, ep_generated_text)