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<h1 id="bench-benchmark-testing">bench: Benchmark Testing</h1>
<h2 id="1-bench-latency-offline-latency-test">1. bench latency: Offline Latency Test</h2>
<h3 id="parameters">Parameters</h3>
<table>
<thead>
<tr>
<th>Parameter</th>
<th>Description</th>
<th>Default</th>
</tr>
</thead>
<tbody>
<tr>
<td>--input-len</td>
<td>Input sequence length (tokens)</td>
<td>32</td>
</tr>
<tr>
<td>--output-len</td>
<td>Output sequence length (tokens)</td>
<td>128</td>
</tr>
<tr>
<td>--batch-size</td>
<td>Batch size</td>
<td>8</td>
</tr>
<tr>
<td>--n</td>
<td>Number of sequences generated per prompt</td>
<td>1</td>
</tr>
<tr>
<td>--use-beam-search</td>
<td>Whether to use beam search</td>
<td>False</td>
</tr>
<tr>
<td>--num-iters-warmup</td>
<td>Number of warmup iterations</td>
<td>10</td>
</tr>
<tr>
<td>--num-iters</td>
<td>Number of actual test iterations</td>
<td>30</td>
</tr>
<tr>
<td>--profile</td>
<td>Whether to enable performance profiling</td>
<td>False</td>
</tr>
<tr>
<td>--output-json</td>
<td>Path to save latency results as a JSON file</td>
<td>None</td>
</tr>
<tr>
<td>--disable-detokenize</td>
<td>Whether to disable detokenization</td>
<td>False</td>
</tr>
</tbody>
</table>
<h3 id="example">Example</h3>
<pre><code># Run latency benchmark on the inference engine
fastdeploy bench latency --model baidu/ERNIE-4.5-0.3B-Paddle
</code></pre>
<h2 id="2-bench-serve-online-latency-and-throughput-test">2. bench serve: Online Latency and Throughput Test</h2>
<h3 id="parameters_1">Parameters</h3>
<table>
<thead>
<tr>
<th>Parameter</th>
<th>Description</th>
<th>Default</th>
</tr>
</thead>
<tbody>
<tr>
<td>--backend</td>
<td>Backend type</td>
<td>"openai-chat"</td>
</tr>
<tr>
<td>--base-url</td>
<td>Base URL of the server or API</td>
<td>None</td>
</tr>
<tr>
<td>--host</td>
<td>Host address</td>
<td>"127.0.0.1"</td>
</tr>
<tr>
<td>--port</td>
<td>Port</td>
<td>8000</td>
</tr>
<tr>
<td>--endpoint</td>
<td>API endpoint path</td>
<td>"/v1/chat/completions"</td>
</tr>
<tr>
<td>--model</td>
<td>Model name</td>
<td>Required</td>
</tr>
<tr>
<td>--dataset-name</td>
<td>Dataset name</td>
<td>"sharegpt"</td>
</tr>
<tr>
<td>--dataset-path</td>
<td>Path to dataset</td>
<td>None</td>
</tr>
<tr>
<td>--num-prompts</td>
<td>Number of prompts to process</td>
<td>1000</td>
</tr>
<tr>
<td>--request-rate</td>
<td>Requests per second</td>
<td>inf</td>
</tr>
<tr>
<td>--max-concurrency</td>
<td>Maximum concurrency</td>
<td>None</td>
</tr>
<tr>
<td>--top-p</td>
<td>Sampling top-p (OpenAI backend)</td>
<td>None</td>
</tr>
<tr>
<td>--top-k</td>
<td>Sampling top-k (OpenAI backend)</td>
<td>None</td>
</tr>
<tr>
<td>--temperature</td>
<td>Sampling temperature (OpenAI backend)</td>
<td>None</td>
</tr>
</tbody>
</table>
<h3 id="example_1">Example</h3>
<pre><code># Run online performance test
fastdeploy bench serve --backend openai-chat \
--model baidu/ERNIE-4.5-0.3B-Paddle \
--endpoint /v1/chat/completions \
--host 0.0.0.0 \
--port 8891 \
--dataset-name EBChat \
--dataset-path /datasets/filtered_sharedgpt_2000_input_1136_output_200.json \
--percentile-metrics ttft,tpot,itl,e2el,s_ttft,s_itl,s_e2el,s_decode,input_len,s_input_len,output_len \
--metric-percentiles 80,95,99,99.9,99.95,99.99 \
--num-prompts 1 \
--max-concurrency 1 \
--save-result
</code></pre>
<h2 id="3-bench-throughput-throughput-test">3. bench throughput: Throughput Test</h2>
<h3 id="parameters_2">Parameters</h3>
<table>
<thead>
<tr>
<th>Parameter</th>
<th>Description</th>
<th>Default</th>
</tr>
</thead>
<tbody>
<tr>
<td>--backend</td>
<td>Inference backend</td>
<td>"fastdeploy"</td>
</tr>
<tr>
<td>--dataset-name</td>
<td>Dataset name</td>
<td>"random"</td>
</tr>
<tr>
<td>--model</td>
<td>Model name</td>
<td>Required</td>
</tr>
<tr>
<td>--input-len</td>
<td>Input sequence length</td>
<td>None</td>
</tr>
<tr>
<td>--output-len</td>
<td>Output sequence length</td>
<td>None</td>
</tr>
<tr>
<td>--prefix-len</td>
<td>Prefix length</td>
<td>0</td>
</tr>
<tr>
<td>--n</td>
<td>Number of sequences generated per prompt</td>
<td>1</td>
</tr>
<tr>
<td>--num-prompts</td>
<td>Number of prompts</td>
<td>50</td>
</tr>
<tr>
<td>--output-json</td>
<td>Path to save results as a JSON file</td>
<td>None</td>
</tr>
<tr>
<td>--disable-detokenize</td>
<td>Whether to disable detokenization</td>
<td>False</td>
</tr>
<tr>
<td>--lora-path</td>
<td>Path to LoRA adapter</td>
<td>None</td>
</tr>
</tbody>
</table>
<h3 id="example_2">Example</h3>
<pre><code># Run throughput benchmark on the inference engine
fastdeploy bench throughput --model baidu/ERNIE-4.5-0.3B-Paddle \
--backend fastdeploy-chat \
--dataset-name EBChat \
--dataset-path /datasets/filtered_sharedgpt_2000_input_1136_output_200.json \
--max-model-len 32768
</code></pre>
<h2 id="4-bench-eval-online-task-evaluation">4. bench eval: Online Task Evaluation</h2>
<h3 id="parameters_3">Parameters</h3>
<table>
<thead>
<tr>
<th>Parameter</th>
<th>Description</th>
<th>Default</th>
</tr>
</thead>
<tbody>
<tr>
<td>--model, -m</td>
<td>Model name</td>
<td>"hf"</td>
</tr>
<tr>
<td>--tasks, -t</td>
<td>List of evaluation tasks</td>
<td>None</td>
</tr>
<tr>
<td>--model_args, -a</td>
<td>Model arguments</td>
<td>""</td>
</tr>
<tr>
<td>--num_fewshot, -f</td>
<td>Number of few-shot examples</td>
<td>None</td>
</tr>
<tr>
<td>--samples, -E</td>
<td>Number of samples</td>
<td>None</td>
</tr>
<tr>
<td>--batch_size, -b</td>
<td>Batch size</td>
<td>1</td>
</tr>
<tr>
<td>--device</td>
<td>Device</td>
<td>None</td>
</tr>
<tr>
<td>--output_path, -o</td>
<td>Output file path</td>
<td>None</td>
</tr>
<tr>
<td>--write_out, -w</td>
<td>Whether to write output results</td>
<td>False</td>
</tr>
</tbody>
</table>
<h3 id="example_3">Example</h3>
<pre><code># Run task evaluation on an online service
fastdeploy bench eval --model local-completions \
--model_args pretrained=./baidu/ERNIE-4.5-0.3B-Paddle,base_url=http://0.0.0.0:8490/v1/completions
--write_out \
--tasks ceval-valid_accountant
</code></pre>
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