init

transformers/tests/models/seed_oss/test_modeling_seed_oss.py

@@ -0,0 +1,161 @@
+# Copyright 2025 Bytedance-Seed Ltd and 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 SeedOss model."""
+
+import unittest
+
+import pytest
+
+from transformers import AutoModelForCausalLM, AutoTokenizer, SeedOssConfig, is_torch_available
+from transformers.testing_utils import (
+    cleanup,
+    require_flash_attn,
+    require_torch,
+    require_torch_large_accelerator,
+    require_torch_large_gpu,
+    slow,
+    torch_device,
+)
+
+from ...causal_lm_tester import CausalLMModelTest, CausalLMModelTester
+
+
+if is_torch_available():
+    import torch
+
+    from transformers import (
+        SeedOssForCausalLM,
+        SeedOssForQuestionAnswering,
+        SeedOssForSequenceClassification,
+        SeedOssForTokenClassification,
+        SeedOssModel,
+    )
+
+
+class SeedOssModelTester(CausalLMModelTester):
+    if is_torch_available():
+        config_class = SeedOssConfig
+        base_model_class = SeedOssModel
+        causal_lm_class = SeedOssForCausalLM
+        sequence_classification_class = SeedOssForSequenceClassification
+        token_classification_class = SeedOssForTokenClassification
+        question_answering_class = SeedOssForQuestionAnswering
+
+
+@require_torch
+class SeedOssModelTest(CausalLMModelTest, unittest.TestCase):
+    model_tester_class = SeedOssModelTester
+    all_model_classes = (
+        (
+            SeedOssModel,
+            SeedOssForCausalLM,
+            SeedOssForSequenceClassification,
+            SeedOssForTokenClassification,
+            SeedOssForQuestionAnswering,
+        )
+        if is_torch_available()
+        else ()
+    )
+    pipeline_model_mapping = (
+        {
+            "feature-extraction": SeedOssModel,
+            "text-classification": SeedOssForSequenceClassification,
+            "token-classification": SeedOssForTokenClassification,
+            "text-generation": SeedOssForCausalLM,
+            "zero-shot": SeedOssForSequenceClassification,
+        }
+        if is_torch_available()
+        else {}
+    )
+    test_headmasking = False
+    test_pruning = False
+    _is_stateful = True
+    model_split_percents = [0.5, 0.6]
+
+
+@slow
+@require_torch_large_accelerator
+class SeedOssIntegrationTest(unittest.TestCase):
+    input_text = ["How to make pasta?", "Hi ByteDance-Seed"]
+    model_id = "ByteDance-Seed/Seed-OSS-36B-Base"
+
+    def setUp(self):
+        cleanup(torch_device, gc_collect=True)
+
+    def tearDown(self):
+        cleanup(torch_device, gc_collect=True)
+
+    def test_model_36b_eager(self):
+        EXPECTED_TEXTS = [
+            "How to make pasta?\nHow to make pasta?\nPasta is a popular dish that is enjoyed by people all over",
+            "Hi ByteDance-Seed team,\nI am trying to run the code on the <beginning of the code>seed",
+        ]
+
+        model = AutoModelForCausalLM.from_pretrained(
+            "ByteDance-Seed/Seed-OSS-36B-Base",
+            torch_dtype=torch.bfloat16,
+            attn_implementation="eager",
+            device_map="auto",
+        )
+
+        tokenizer = AutoTokenizer.from_pretrained(self.model_id)
+        inputs = tokenizer(self.input_text, return_tensors="pt", padding=True, return_token_type_ids=False).to(
+            model.model.embed_tokens.weight.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)
+
+    def test_model_36b_sdpa(self):
+        EXPECTED_TEXTS = [
+            "How to make pasta?\nHow to make pasta?\nPasta is a popular dish that is enjoyed by people all over",
+            "Hi ByteDance-Seed team,\nI am trying to run the code on the <beginning of the code>seed",
+        ]
+
+        # default attention is `sdpa` (and this model repo. doesn't specify explicitly) --> we get `sdpa` here
+        model = AutoModelForCausalLM.from_pretrained(self.model_id, torch_dtype=torch.bfloat16, device_map="auto")
+
+        tokenizer = AutoTokenizer.from_pretrained(self.model_id)
+        inputs = tokenizer(self.input_text, return_tensors="pt", padding=True, return_token_type_ids=False).to(
+            model.model.embed_tokens.weight.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)
+
+    @require_flash_attn
+    @require_torch_large_gpu
+    @pytest.mark.flash_attn_test
+    def test_model_36b_flash_attn(self):
+        EXPECTED_TEXTS = [
+            "How to make pasta?\nHow to make pasta?\nPasta is a popular dish that is enjoyed by people all over",
+            "Hi ByteDance-Seed team,\nI am trying to run the code on the <beginning of the code>seed",
+        ]
+
+        model = AutoModelForCausalLM.from_pretrained(
+            self.model_id, torch_dtype=torch.bfloat16, attn_implementation="flash_attention_2", device_map="auto"
+        )
+        tokenizer = AutoTokenizer.from_pretrained(self.model_id)
+        inputs = tokenizer(self.input_text, return_tensors="pt", padding=True, return_token_type_ids=False).to(
+            model.model.embed_tokens.weight.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)