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transformers/tests/models/helium/test_modeling_helium.py

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+# Copyright 2024 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 Helium model."""
+
+import unittest
+
+from transformers import AutoModelForCausalLM, AutoTokenizer, HeliumConfig, is_torch_available
+from transformers.testing_utils import (
+    Expectations,
+    require_read_token,
+    require_torch,
+    slow,
+    torch_device,
+)
+
+from ...causal_lm_tester import CausalLMModelTest, CausalLMModelTester
+
+
+if is_torch_available():
+    import torch
+
+    from transformers import (
+        HeliumForCausalLM,
+        HeliumForSequenceClassification,
+        HeliumForTokenClassification,
+        HeliumModel,
+    )
+
+
+class HeliumModelTester(CausalLMModelTester):
+    if is_torch_available():
+        config_class = HeliumConfig
+        base_model_class = HeliumModel
+        causal_lm_class = HeliumForCausalLM
+        sequence_classification_class = HeliumForSequenceClassification
+        token_classification_class = HeliumForTokenClassification
+
+
+@require_torch
+class HeliumModelTest(CausalLMModelTest, unittest.TestCase):
+    all_model_classes = (
+        (HeliumModel, HeliumForCausalLM, HeliumForSequenceClassification, HeliumForTokenClassification)
+        if is_torch_available()
+        else ()
+    )
+    pipeline_model_mapping = (
+        {
+            "feature-extraction": HeliumModel,
+            "text-classification": HeliumForSequenceClassification,
+            "token-classification": HeliumForTokenClassification,
+            "text-generation": HeliumForCausalLM,
+            "zero-shot": HeliumForSequenceClassification,
+        }
+        if is_torch_available()
+        else {}
+    )
+    model_tester_class = HeliumModelTester
+    test_headmasking = False
+    test_pruning = False
+    _is_stateful = True
+    model_split_percents = [0.5, 0.6]
+
+
+@slow
+# @require_torch_gpu
+class HeliumIntegrationTest(unittest.TestCase):
+    input_text = ["Hello, today is a great day to"]
+
+    @require_read_token
+    def test_model_2b(self):
+        model_id = "kyutai/helium-1-preview"
+        expected_texts = Expectations(
+            {
+                ("rocm", (9, 5)): ["Hello, today is a great day to start a new project. I have been working on a new project for a while now, and I"],
+                (None, None): ["Hello, today is a great day to start a new project. I have been working on a new project for a while now and I have"],
+                ("cuda", 8): ['Hello, today is a great day to start a new project. I have been working on a new project for a while now, and I'],
+            }
+        )  # fmt: skip
+        EXPECTED_TEXTS = expected_texts.get_expectation()
+
+        model = AutoModelForCausalLM.from_pretrained(model_id, dtype=torch.bfloat16, revision="refs/pr/1").to(
+            torch_device
+        )
+        tokenizer = AutoTokenizer.from_pretrained(model_id, revision="refs/pr/1")
+        inputs = tokenizer(self.input_text, return_tensors="pt", padding=True).to(torch_device)
+
+        output = model.generate(**inputs, max_new_tokens=20, do_sample=False)
+        output_text = tokenizer.batch_decode(output, skip_special_tokens=True)
+
+        self.assertEqual(output_text, EXPECTED_TEXTS)