first commit

tests/models/ministral3/test_modeling_ministral3.py

@@ -0,0 +1,134 @@
+# Copyright 2025 the HuggingFace 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 Ministral3 model."""
+
+import gc
+import unittest
+
+import pytest
+
+from transformers import AutoTokenizer, Mistral3ForConditionalGeneration, is_torch_available
+from transformers.testing_utils import (
+    Expectations,
+    backend_empty_cache,
+    cleanup,
+    require_deterministic_for_xpu,
+    require_flash_attn,
+    require_torch,
+    require_torch_accelerator,
+    slow,
+    torch_device,
+)
+
+
+if is_torch_available():
+    import torch
+
+    from transformers import (
+        Ministral3Model,
+    )
+
+
+from ...causal_lm_tester import CausalLMModelTest, CausalLMModelTester
+
+
+class Ministral3ModelTester(CausalLMModelTester):
+    if is_torch_available():
+        base_model_class = Ministral3Model
+
+
+@require_torch
+class Ministral3ModelTest(CausalLMModelTest, unittest.TestCase):
+    _is_stateful = True
+    model_split_percents = [0.5, 0.6]
+    model_tester_class = Ministral3ModelTester
+
+    # TODO (ydshieh): Check this. See https://app.circleci.com/pipelines/github/huggingface/transformers/79245/workflows/9490ef58-79c2-410d-8f51-e3495156cf9c/jobs/1012146
+    def is_pipeline_test_to_skip(
+        self,
+        pipeline_test_case_name,
+        config_class,
+        model_architecture,
+        tokenizer_name,
+        image_processor_name,
+        feature_extractor_name,
+        processor_name,
+    ):
+        return True
+
+    @require_flash_attn
+    @require_torch_accelerator
+    @pytest.mark.flash_attn_test
+    @slow
+    def test_flash_attn_2_inference_equivalence_right_padding(self):
+        self.skipTest(reason="Ministral3 flash attention does not support right padding")
+
+
+@require_torch
+class Ministral3IntegrationTest(unittest.TestCase):
+    def tearDown(self):
+        cleanup(torch_device, gc_collect=True)
+
+    @slow
+    def test_model_3b_logits(self):
+        input_ids = [1, 306, 4658, 278, 6593, 310, 2834, 338]
+        model = Mistral3ForConditionalGeneration.from_pretrained(
+            "mistralai/Ministral-3-3B-Instruct-2512", device_map="auto"
+        )
+        input_ids = torch.tensor([input_ids]).to(model.device)
+        with torch.no_grad():
+            out = model(input_ids).logits.float().cpu()
+        # Expected mean on dim = -1
+        # fmt: off
+        EXPECTED_MEANS = Expectations(
+            {
+                ("cuda", None): torch.tensor([[-1.1503, -1.9935, -0.4457, -1.0717, -1.9182, -1.1431, -0.9697, -1.7098]]),
+                ("xpu", None): torch.tensor([[-0.9800, -2.4773, -0.2386, -1.0664, -1.8994, -1.3792, -1.0531, -1.8832]]),
+            }
+        )
+        # fmt: on
+        EXPECTED_MEAN = EXPECTED_MEANS.get_expectation()
+        torch.testing.assert_close(out.mean(-1), EXPECTED_MEAN, rtol=1e-2, atol=1e-2)
+
+        del model
+        backend_empty_cache(torch_device)
+        gc.collect()
+
+    @slow
+    @require_deterministic_for_xpu
+    def test_model_3b_generation(self):
+        # fmt: off
+        EXPECTED_TEXTS = Expectations(
+            {
+                ("cuda", None): "My favourite condiment is 100% pure olive oil. It's a staple in my kitchen and I use it in",
+                ("xpu", None): "My favourite condiment is iced tea. I love the way it makes me feel. It’s like a little bubble bath for",
+            }
+        )
+        # fmt: on
+        EXPECTED_TEXT = EXPECTED_TEXTS.get_expectation()
+        prompt = "My favourite condiment is "
+        tokenizer = AutoTokenizer.from_pretrained("mistralai/Ministral-3-3B-Instruct-2512")
+        model = Mistral3ForConditionalGeneration.from_pretrained(
+            "mistralai/Ministral-3-3B-Instruct-2512", device_map="auto"
+        )
+        input_ids = tokenizer.encode(prompt, return_tensors="pt").to(model.device)
+
+        # greedy generation outputs
+        generated_ids = model.generate(input_ids, max_new_tokens=20, temperature=0)
+        text = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
+        self.assertEqual(text, EXPECTED_TEXT)
+
+        del model
+        backend_empty_cache(torch_device)
+        gc.collect()