Upload ms-swift/examples/deploy/client/llm/chat/swift_client.py with huggingface_hub

ms-swift/examples/deploy/client/llm/chat/swift_client.py

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+# Copyright (c) Alibaba, Inc. and its affiliates.
+import os
+from typing import List
+
+os.environ['CUDA_VISIBLE_DEVICES'] = '0'
+
+
+def infer_batch(engine: 'InferEngine', infer_requests: List['InferRequest']):
+    request_config = RequestConfig(max_tokens=512, temperature=0)
+    metric = InferStats()
+
+    resp_list = engine.infer(infer_requests, request_config, metrics=[metric])
+    # # The asynchronous interface below is equivalent to the synchronous interface above.
+    # async def _run():
+    #     tasks = [engine.infer_async(infer_request, request_config) for infer_request in infer_requests]
+    #     return await asyncio.gather(*tasks)
+    # resp_list = asyncio.run(_run())
+
+    query0 = infer_requests[0].messages[0]['content']
+    print(f'query0: {query0}')
+    print(f'response0: {resp_list[0].choices[0].message.content}')
+    print(f'metric: {metric.compute()}')
+
+
+def infer_stream(engine: 'InferEngine', infer_request: 'InferRequest'):
+    request_config = RequestConfig(max_tokens=512, temperature=0, stream=True)
+    metric = InferStats()
+    gen_list = engine.infer([infer_request], request_config, metrics=[metric])
+    query = infer_request.messages[0]['content']
+    print(f'query: {query}\nresponse: ', end='')
+    for resp in gen_list[0]:
+        if resp is None:
+            continue
+        print(resp.choices[0].delta.content, end='', flush=True)
+    print()
+    print(f'metric: {metric.compute()}')
+
+
+def run_client(host: str = '127.0.0.1', port: int = 8000):
+    engine = InferClient(host=host, port=port)
+    print(f'models: {engine.models}')
+    # Here, `load_dataset` is used for convenience; `infer_batch` does not require creating a dataset.
+    dataset = load_dataset(['AI-ModelScope/alpaca-gpt4-data-zh#1000'], seed=42)[0]
+    print(f'dataset: {dataset}')
+    infer_requests = [InferRequest(**data) for data in dataset]
+    infer_batch(engine, infer_requests)
+
+    messages = [{'role': 'user', 'content': 'who are you?'}]
+    infer_stream(engine, InferRequest(messages=messages))
+
+
+if __name__ == '__main__':
+    from swift.llm import (InferEngine, InferRequest, InferClient, RequestConfig, load_dataset, run_deploy,
+                           DeployArguments)
+    from swift.plugin import InferStats
+    # NOTE: In a real deployment scenario, please comment out the context of run_deploy.
+    with run_deploy(
+            DeployArguments(model='Qwen/Qwen2.5-1.5B-Instruct', verbose=False, log_interval=-1,
+                            infer_backend='vllm')) as port:
+        run_client(port=port)