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transformers/tests/models/persimmon/test_modeling_persimmon.py

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+# Copyright 2023 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 Persimmon model."""
+
+import gc
+import unittest
+
+from transformers import PersimmonConfig, is_torch_available
+from transformers.testing_utils import (
+    backend_empty_cache,
+    require_bitsandbytes,
+    require_torch,
+    require_torch_accelerator,
+    require_torch_fp16,
+    slow,
+    torch_device,
+)
+
+
+if is_torch_available():
+    import torch
+
+    from transformers import (
+        AutoTokenizer,
+        PersimmonForCausalLM,
+        PersimmonForSequenceClassification,
+        PersimmonForTokenClassification,
+        PersimmonModel,
+    )
+
+from ...causal_lm_tester import CausalLMModelTest, CausalLMModelTester
+
+
+class PersimmonModelTester(CausalLMModelTester):
+    if is_torch_available():
+        config_class = PersimmonConfig
+        base_model_class = PersimmonModel
+        causal_lm_class = PersimmonForCausalLM
+        sequence_class = PersimmonForSequenceClassification
+        token_class = PersimmonForTokenClassification
+
+
+@require_torch
+class PersimmonModelTest(CausalLMModelTest, unittest.TestCase):
+    model_tester_class = PersimmonModelTester
+    all_model_classes = (
+        (PersimmonModel, PersimmonForCausalLM, PersimmonForSequenceClassification, PersimmonForTokenClassification)
+        if is_torch_available()
+        else ()
+    )
+    pipeline_model_mapping = (
+        {
+            "feature-extraction": PersimmonModel,
+            "text-classification": PersimmonForSequenceClassification,
+            "token-classification": PersimmonForTokenClassification,
+            # TODO (ydshieh): check why these two fail. Fix them or skip them in a better way.
+            # "text-generation": PersimmonForCausalLM,
+            # "zero-shot": PersimmonForSequenceClassification,
+        }
+        if is_torch_available()
+        else {}
+    )
+    model_tester_class = PersimmonModelTester
+
+    test_headmasking = False
+    test_pruning = False
+
+    @unittest.skip("Persimmon applies key/query norm which doesn't work with packing")
+    def test_flash_attention_2_padding_matches_padding_free_with_position_ids(self):
+        pass
+
+    @unittest.skip("Persimmon applies key/query norm which doesn't work with packing")
+    def test_eager_padding_matches_padding_free_with_position_ids(self):
+        pass
+
+    @unittest.skip("Persimmon applies key/query norm which doesn't work with packing")
+    def test_sdpa_padding_matches_padding_free_with_position_ids(self):
+        pass
+
+
+@require_torch
+class PersimmonIntegrationTest(unittest.TestCase):
+    @slow
+    @require_torch_accelerator
+    @require_bitsandbytes
+    def test_model_8b_chat_logits(self):
+        input_ids = [1, 306, 4658, 278, 6593, 310, 2834, 338]
+        model = PersimmonForCausalLM.from_pretrained(
+            "adept/persimmon-8b-chat", load_in_8bit=True, device_map={"": 0}, dtype=torch.float16
+        )
+        out = model(torch.tensor([input_ids], device=torch_device)).logits.float()
+
+        EXPECTED_MEAN = torch.tensor(
+            [[-11.4726, -11.1495, -11.2694, -11.2223, -10.9452, -11.0663, -11.0031, -11.1028]]
+        )
+        # change dtype to `torch.float32` before calling `mean` to avoid `nan` values
+        torch.testing.assert_close(out.cpu().to(torch.float32).mean(-1), EXPECTED_MEAN, rtol=1e-4, atol=1e-4)
+        # fmt: off
+        EXPECTED_SLICE = torch.tensor(
+            [-16.9062, -16.9062, -16.9062, -16.9062, -16.8906, -16.9062, -16.9531, -16.9062, -16.9062, -16.9062, -16.9531, -16.9062, -16.9531, -16.9062, -16.9062, -16.9062, -16.9062, -16.9062, -16.9531, -16.9062, -16.9062, -16.9062, -16.9062, -16.9062, -16.9062, -16.9531, -16.9062, -16.9531, -16.9062, -16.9062],
+            dtype=torch.float16
+        )
+        # fmt: on
+        torch.testing.assert_close(out.cpu()[0, 0, :30], EXPECTED_SLICE, rtol=1e-5, atol=1e-5)
+
+        backend_empty_cache(torch_device)
+        del model
+        gc.collect()
+
+    @slow
+    @require_torch_accelerator
+    @require_torch_fp16
+    @require_bitsandbytes
+    def test_model_8b_chat_greedy_generation(self):
+        EXPECTED_TEXT_COMPLETION = """human: Simply put, the theory of relativity states that?\n\nadept: The theory of relativity states that the laws of physics are the same for all observers, regardless of their relative motion."""
+        prompt = "human: Simply put, the theory of relativity states that?\n\nadept:"
+        tokenizer = AutoTokenizer.from_pretrained("adept/persimmon-8b-chat", use_fast=False)
+        input_ids = tokenizer.encode(prompt, return_tensors="pt").to(torch_device)
+        model = PersimmonForCausalLM.from_pretrained(
+            "adept/persimmon-8b-chat", load_in_8bit=True, device_map={"": 0}, dtype=torch.float16
+        )
+
+        # greedy generation outputs
+        generated_ids = model.generate(input_ids, max_new_tokens=64)
+        text = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
+        self.assertEqual(EXPECTED_TEXT_COMPLETION, text)
+
+        backend_empty_cache(torch_device)
+        del model
+        gc.collect()