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transformers/tests/models/lfm2/test_modeling_lfm2.py

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+# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
+#
+# 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.
+"""Testing suite for the PyTorch LLaMA model."""
+
+import unittest
+
+import pytest
+
+from transformers import is_torch_available
+from transformers.testing_utils import (
+    require_read_token,
+    require_torch,
+    require_torch_accelerator,
+    slow,
+)
+
+from ...causal_lm_tester import CausalLMModelTest, CausalLMModelTester
+
+
+if is_torch_available():
+    from transformers import Lfm2Config, Lfm2ForCausalLM, Lfm2Model
+
+
+class Lfm2ModelTester(CausalLMModelTester):
+    if is_torch_available():
+        config_class = Lfm2Config
+        base_model_class = Lfm2Model
+        causal_lm_class = Lfm2ForCausalLM
+
+    def __init__(
+        self,
+        parent,
+        layer_types=["full_attention", "conv"],
+    ):
+        super().__init__(parent)
+        self.layer_types = layer_types
+
+
+@require_torch
+class Lfm2ModelTest(CausalLMModelTest, unittest.TestCase):
+    all_model_classes = (Lfm2Model, Lfm2ForCausalLM) if is_torch_available() else ()
+    pipeline_model_mapping = (
+        {
+            "feature-extraction": Lfm2Model,
+            "text-generation": Lfm2ForCausalLM,
+        }
+        if is_torch_available()
+        else {}
+    )
+    test_headmasking = False
+    test_pruning = False
+    fx_compatible = False
+    model_tester_class = Lfm2ModelTester
+    # used in `test_torch_compile_for_training`
+    _torch_compile_train_cls = Lfm2ForCausalLM if is_torch_available() else None
+
+    @unittest.skip(
+        "Lfm2 alternates between attention and conv layers, so attention are only returned for attention layers"
+    )
+    def test_attention_outputs(self):
+        pass
+
+    @unittest.skip("Lfm2 has a special cache format as it alternates between attention and conv layers")
+    def test_past_key_values_format(self):
+        pass
+
+    @unittest.skip(
+        "Lfm2 has a special cache format which is not compatible with compile as it has static address for conv cache"
+    )
+    @pytest.mark.torch_compile_test
+    def test_sdpa_can_compile_dynamic(self):
+        pass
+
+
+@require_torch_accelerator
+@require_read_token
+@slow
+class Lfm2IntegrationTest(unittest.TestCase):
+    pass