Refine vllm_quickstart doc (#11199)

* refine doc

* refine

docker/llm/serving/cpu/docker/benchmark_vllm_throughput.py

@@ -76,9 +76,13 @@ def run_vllm(
     enable_prefix_caching: bool,
     gpu_memory_utilization: float = 0.9,
     load_in_low_bit: str = "sym_int4",
+    max_num_batched_tokens: int = 10450,
 ) -> float:
     from vllm import SamplingParams
     from ipex_llm.vllm.cpu.engine import IPEXLLMClass as LLM
+    warm_prompt = "hi " * (1024 - 1)
+    warm_requests = [(warm_prompt, 1024, 1024)
+                    for _ in range(8)]
     llm = LLM(model=model,
               tokenizer=tokenizer,
               quantization=quantization,
@@ -94,6 +98,22 @@ def run_vllm(
               enable_prefix_caching=enable_prefix_caching,
               load_in_low_bit=load_in_low_bit)
 
+    for prompt, _, output_len in warm_requests:
+        sampling_params = SamplingParams(
+            n=n,
+            temperature=0.0 if use_beam_search else 1.0,
+            top_p=1.0,
+            use_beam_search=use_beam_search,
+            ignore_eos=True,
+            max_tokens=output_len,
+        )
+        llm._add_request(
+            prompt=prompt,
+            prompt_token_ids=None,
+            sampling_params=sampling_params,
+        )
+    llm._run_engine(use_tqdm=True)
+
     # Add the requests to the engine.
     for prompt, _, output_len in requests:
         sampling_params = SamplingParams(
@@ -216,7 +236,9 @@ def main(args: argparse.Namespace):
             args.tensor_parallel_size, args.seed, args.n, args.use_beam_search,
             args.trust_remote_code, args.dtype, args.max_model_len,
             args.enforce_eager, args.kv_cache_dtype, args.device,
-            args.enable_prefix_caching, args.gpu_memory_utilization, args.load_in_low_bit)
+            args.enable_prefix_caching, args.gpu_memory_utilization, args.load_in_low_bit,
+            args.max_num_batched_tokens)
+
     elif args.backend == "hf":
         assert args.tensor_parallel_size == 1
         elapsed_time = run_hf(requests, args.model, tokenizer, args.n,
@@ -320,9 +342,14 @@ if __name__ == "__main__":
     parser.add_argument(
         "--load-in-low-bit",
         type=str,
-        choices=["sym_int4", "fp8", "fp16"],
+        choices=["sym_int4", "fp6", "fp8", "fp16"],
         default="sym_int4",
         help="Low-bit format quantization with IPEX-LLM")
+    parser.add_argument('--max-num-batched-tokens',
+        type=int,
+        default=10450,
+        help='maximum number of batched tokens per iteration')
+
     args = parser.parse_args()
     if args.tokenizer is None:
         args.tokenizer = args.model

docker/llm/serving/xpu/docker/Dockerfile

@@ -12,7 +12,7 @@ RUN cd /llm &&\
     # Install ipex-llm[serving] only will update ipex_llm source code without updating
     # bigdl-core-xe, which will lead to problems
     apt-get update && \
-    apt-get install -y libfabric-dev wrk && \
+    apt-get install -y libfabric-dev wrk libaio-dev && \
     pip install --pre --upgrade ipex-llm[xpu,serving] && \
     pip install transformers==4.37.0 gradio==4.19.2 && \
     # Install vLLM-v2 dependencies

docs/readthedocs/source/doc/LLM/Quickstart/vLLM_quickstart.md

@@ -134,6 +134,12 @@ You can tune the service using these four arguments:
 3. `--max-num-batched-token`: Maximum number of batched tokens per iteration.
 4. `--max-num-seq`: Maximum number of sequences per iteration. Default: 256
 
+For longer input prompt, we would suggest to use `--max-num-batched-token` to restrict the service.  The reason behind this logic is that the `peak GPU memory usage` will appear when generating first token.  By using `--max-num-batched-token`, we can restrict the input size when generating first token.
+
+`--max-num-seqs` will restrict the generation for both first token and rest token.  It will restrict the maximum batch size to the value set by `--max-num-seqs`.
+
+When out-of-memory error occurs, the most obvious solution is to reduce the `gpu-memory-utilization`.  Other ways to resolve this error is to set `--max-num-batched-token` if peak memory occurs when generating first token or using `--max-num-seq` if peak memory occurs when generating rest tokens.
+
 If the service have been booted successfully, the console will display messages similar to the following:
 
 <a href="https://llm-assets.readthedocs.io/en/latest/_images/start-vllm-service.png" target="_blank">