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transformers/tests/models/dots1/test_modeling_dots1.py

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+# Copyright 2025 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 dots1 model."""
+
+import gc
+import unittest
+
+from transformers import AutoTokenizer, Dots1Config, is_torch_available
+from transformers.testing_utils import (
+    backend_empty_cache,
+    cleanup,
+    require_torch,
+    require_torch_accelerator,
+    slow,
+    torch_device,
+)
+
+from ...causal_lm_tester import CausalLMModelTest, CausalLMModelTester
+
+
+if is_torch_available():
+    import torch
+
+    from transformers import (
+        Dots1ForCausalLM,
+        Dots1Model,
+    )
+
+
+class Dots1ModelTester(CausalLMModelTester):
+    config_class = Dots1Config
+    if is_torch_available():
+        base_model_class = Dots1Model
+        causal_lm_class = Dots1ForCausalLM
+
+    def __init__(
+        self,
+        parent,
+        n_routed_experts=8,
+        n_shared_experts=1,
+        n_group=1,
+        topk_group=1,
+        num_experts_per_tok=8,
+    ):
+        super().__init__(parent=parent, num_experts_per_tok=num_experts_per_tok)
+        self.n_routed_experts = n_routed_experts
+        self.n_shared_experts = n_shared_experts
+        self.n_group = n_group
+        self.topk_group = topk_group
+
+
+@require_torch
+class Dots1ModelTest(CausalLMModelTest, unittest.TestCase):
+    all_model_classes = (
+        (
+            Dots1Model,
+            Dots1ForCausalLM,
+        )
+        if is_torch_available()
+        else ()
+    )
+    pipeline_model_mapping = (
+        {
+            "feature-extraction": Dots1Model,
+            "text-generation": Dots1ForCausalLM,
+        }
+        if is_torch_available()
+        else {}
+    )
+
+    test_headmasking = False
+    test_pruning = False
+    model_tester_class = Dots1ModelTester
+
+
+@require_torch_accelerator
+class Dots1IntegrationTest(unittest.TestCase):
+    # This variable is used to determine which CUDA device are we using for our runners (A10 or T4)
+    # Depending on the hardware we get different logits / generations
+    cuda_compute_capability_major_version = None
+
+    @classmethod
+    def setUpClass(cls):
+        if is_torch_available() and torch.cuda.is_available():
+            # 8 is for A100 / A10 and 7 for T4
+            cls.cuda_compute_capability_major_version = torch.cuda.get_device_capability()[0]
+
+    def tearDown(self):
+        # See LlamaIntegrationTest.tearDown(). Can be removed once LlamaIntegrationTest.tearDown() is removed.
+        cleanup(torch_device, gc_collect=False)
+
+    @slow
+    def test_model_15b_a2b_generation(self):
+        EXPECTED_TEXT_COMPLETION = (
+            """To be or not to be, that is the question:\nWhether 'tis nobler in the mind to suffer\nThe"""
+        )
+        prompt = "To be or not to"
+        tokenizer = AutoTokenizer.from_pretrained("redmoe-ai-v1/dots.llm1.test", use_fast=False)
+        model = Dots1ForCausalLM.from_pretrained("redmoe-ai-v1/dots.llm1.test", device_map="auto")
+        input_ids = tokenizer.encode(prompt, return_tensors="pt").to(model.model.embed_tokens.weight.device)
+
+        # greedy generation outputs
+        generated_ids = model.generate(input_ids, max_new_tokens=20, do_sample=False)
+        text = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
+        self.assertEqual(EXPECTED_TEXT_COMPLETION, text)
+
+        del model
+        backend_empty_cache(torch_device)
+        gc.collect()