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tests/test_executorch.py

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+# 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.testing_utils import require_torch
+
+
+@require_torch
+class ExecutorchTest(unittest.TestCase):
+    def setUp(self):
+        set_seed(42)
+        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)