init

2025-10-09 16:47:16 +08:00
parent c8feb4deb5
commit e27e3f16bb
5248 changed files with 1778505 additions and 0 deletions
--- a/transformers/tests/models/llama4/test_modeling_llama4.py
+++ b/transformers/tests/models/llama4/test_modeling_llama4.py
@@ -0,0 +1,124 @@
+# 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 Llama4 model."""
+
+import unittest
+
+from transformers import is_torch_available
+from transformers.testing_utils import (
+    Expectations,
+    cleanup,
+    require_read_token,
+    require_torch_large_accelerator,
+    slow,
+    torch_device,
+)
+
+
+if is_torch_available():
+    import torch
+
+    from transformers import (
+        Llama4ForConditionalGeneration,
+        Llama4Processor,
+    )
+
+
+@slow
+@require_torch_large_accelerator
+@require_read_token
+class Llama4IntegrationTest(unittest.TestCase):
+    model_id = "meta-llama/Llama-4-Scout-17B-16E"
+
+    @classmethod
+    def setUpClass(cls):
+        cls.model = Llama4ForConditionalGeneration.from_pretrained(
+            "meta-llama/Llama-4-Scout-17B-16E",
+            device_map="auto",
+            dtype=torch.float32,
+            attn_implementation="eager",
+        )
+
+    def setUp(self):
+        self.processor = Llama4Processor.from_pretrained("meta-llama/Llama-4-Scout-17B-16E", padding_side="left")
+
+        url = "https://huggingface.co/datasets/hf-internal-testing/fixtures-captioning/resolve/main/cow_beach_1.png"
+        self.messages_1 = [
+            {"role": "system", "content": [{"type": "text", "text": "You are a helpful assistant."}]},
+            {
+                "role": "user",
+                "content": [
+                    {"type": "image", "url": url},
+                    {"type": "text", "text": "What is shown in this image?"},
+                ],
+            },
+        ]
+
+        self.messages_2 = [
+            {"role": "system", "content": [{"type": "text", "text": "You are a helpful assistant."}]},
+            {
+                "role": "user",
+                "content": [
+                    {
+                        "type": "image",
+                        "url": "https://huggingface.co/datasets/hf-internal-testing/fixtures-captioning/resolve/main/cow_beach_1.png",
+                    },
+                    {
+                        "type": "image",
+                        "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg",
+                    },
+                    {"type": "text", "text": "Are these images identical?"},
+                ],
+            },
+        ]
+
+    def tearDown(self):
+        cleanup(torch_device, gc_collect=True)
+
+    def test_model_17b_16e_fp32(self):
+        EXPECTED_TEXTS = Expectations(
+            {
+                ("xpu", 3): ['system\n\nYou are a helpful assistant.user\n\nWhat is shown in this image?assistant\n\nThe image shows a cow standing on a beach with a blue sky and a body of water in the background. The cow is brown with a white face'],
+                ("cuda", None): ['system\n\nYou are a helpful assistant.user\n\nWhat is shown in this image?assistant\n\nThe image shows a cow standing on a beach, with a blue sky and a body of water in the background. The cow is brown with a white'],
+            }
+        )  # fmt: skip
+        EXPECTED_TEXT = EXPECTED_TEXTS.get_expectation()
+
+        inputs = self.processor.apply_chat_template(
+            self.messages_1, tokenize=True, add_generation_prompt=True, return_tensors="pt", return_dict=True
+        ).to(device=torch_device, dtype=self.model.dtype)
+        output = self.model.generate(**inputs, max_new_tokens=30, do_sample=False)
+        output_text = self.processor.batch_decode(output, skip_special_tokens=True)
+
+        print(output_text)
+        self.assertEqual(output_text, EXPECTED_TEXT)
+
+    def test_model_17b_16e_batch(self):
+        inputs = self.processor.apply_chat_template(
+            [self.messages_1, self.messages_2],
+            tokenize=True,
+            return_dict=True,
+            return_tensors="pt",
+            padding=True,
+            add_generation_prompt=True,
+        ).to(device=torch_device, dtype=torch.float32)
+
+        output = self.model.generate(**inputs, max_new_tokens=30, do_sample=False)
+        output_text = self.processor.batch_decode(output, skip_special_tokens=True)
+
+        EXPECTED_TEXTS = [
+            'system\n\nYou are a helpful assistant.user\n\nWhat is shown in this image?assistant\n\nThe image shows a cow standing on a beach, with a blue sky and a body of water in the background. The cow is brown with a white',
+            'system\n\nYou are a helpful assistant.user\n\nAre these images identical?assistant\n\nNo, these images are not identical. The first image shows a cow standing on a beach with a blue sky and a white cloud in the background.'
+        ]  # fmt: skip
+        self.assertEqual(output_text, EXPECTED_TEXTS)