chore: import upstream snapshot with attribution

2026-07-13 13:34:58 +08:00
commit a203934033
1368 changed files with 175001 additions and 0 deletions
@@ -0,0 +1,64 @@
+import torch
+
+from swift.infer_engine import InferRequest, TransformersEngine
+
+
+def run_qwen3_emb():
+    engine = TransformersEngine(
+        'Qwen/Qwen3-Embedding-4B', task_type='embedding', torch_dtype=torch.float16, attn_impl='flash_attention_2')
+
+    infer_requests = [
+        InferRequest(messages=[
+            {
+                'role':
+                'user',
+                'content':
+                'Instruct: Given a web search query, retrieve relevant passages that answer the query\n'
+                'Query:What is the capital of China?'
+            },
+        ]),
+        InferRequest(messages=[
+            {
+                'role': 'user',
+                'content': 'The capital of China is Beijing.'
+            },
+        ])
+    ]
+    resp_list = engine.infer(infer_requests)
+    embedding0 = torch.tensor(resp_list[0].data[0].embedding)
+    embedding1 = torch.tensor(resp_list[1].data[0].embedding)
+    print(f'scores: {(embedding0 * embedding1).sum()}')
+
+
+def run_qwen3_vl_emb():
+    engine = TransformersEngine(
+        'Qwen/Qwen3-VL-Embedding-2B', task_type='embedding', max_batch_size=2, attn_impl='flash_attention_2')
+
+    infer_requests = [
+        InferRequest(messages=[
+            {
+                'role': 'user',
+                'content': 'A woman playing with her dog on a beach at sunset.'
+            },
+        ]),
+        InferRequest(
+            messages=[
+                {
+                    'role':
+                    'user',
+                    'content':
+                    '<image>A woman shares a joyful moment with her golden retriever on a sun-drenched beach at '
+                    'sunset, as the dog offers its paw in a heartwarming display of companionship and trust.'
+                },
+            ],
+            images=['https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg'])
+    ]
+    resp_list = engine.infer(infer_requests)
+    embedding0 = torch.tensor(resp_list[0].data[0].embedding)
+    embedding1 = torch.tensor(resp_list[1].data[0].embedding)
+    print(f'scores: {(embedding0 * embedding1).sum()}')
+
+
+if __name__ == '__main__':
+    # run_qwen3_emb()
+    run_qwen3_vl_emb()