222 lines
9.0 KiB
Markdown
222 lines
9.0 KiB
Markdown
# Examples
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## Using Local Model Inference
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This project supports inference using local transformers and vllm (vllm needs to be installed first). The `--model` can be filled with a modelscope model name, such as `Qwen/Qwen2.5-0.5B-Instruct`; or you can directly specify the model weight path, such as `/path/to/model_weights`, without needing to specify the `--url` parameter.
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**Inference using transformers**
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```bash
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evalscope perf \
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--model 'Qwen/Qwen2.5-0.5B-Instruct' \
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--attn-implementation flash_attention_2 \ # Optional, or choose from [flash_attention_2|eager|sdpa]
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--number 20 \
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--parallel 2 \
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--api local \
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--dataset openqa
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```
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**Inference using vllm**
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```bash
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evalscope perf \
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--model 'Qwen/Qwen2.5-0.5B-Instruct' \
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--number 20 \
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--parallel 2 \
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--api local_vllm \
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--dataset openqa
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```
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## Using `prompt`
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```bash
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evalscope perf \
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--url 'http://127.0.0.1:8000/v1/chat/completions' \
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--parallel 2 \
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--model 'qwen2.5' \
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--log-every-n-query 10 \
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--number 20 \
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--api openai \
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--temperature 0.9 \
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--max-tokens 1024 \
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--prompt 'Write a science fiction story, please begin your performance'
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```
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You can also use a local file as a prompt:
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```bash
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evalscope perf \
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--url 'http://127.0.0.1:8000/v1/chat/completions' \
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--parallel 2 \
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--model 'qwen2.5' \
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--log-every-n-query 10 \
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--number 20 \
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--api openai \
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--temperature 0.9 \
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--max-tokens 1024 \
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--prompt @prompt.txt
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```
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## Complex Requests
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Using `stop`, `stream`, `temperature`, etc.:
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```bash
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evalscope perf \
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--url 'http://127.0.0.1:8000/v1/chat/completions' \
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--parallel 2 \
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--model 'qwen2.5' \
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--log-every-n-query 10 \
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--read-timeout 120 \
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--connect-timeout 120 \
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--number 20 \
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--max-prompt-length 128000 \
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--min-prompt-length 128 \
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--api openai \
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--temperature 0.7 \
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--max-tokens 1024 \
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--stop '<|im_end|>' \
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--dataset openqa \
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--stream
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```
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## Using `query-template`
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You can set request parameters in the `query-template`:
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```bash
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evalscope perf \
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--url 'http://127.0.0.1:8000/v1/chat/completions' \
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--parallel 2 \
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--model 'qwen2.5' \
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--log-every-n-query 10 \
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--read-timeout 120 \
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--connect-timeout 120 \
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--number 20 \
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--max-prompt-length 128000 \
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--min-prompt-length 128 \
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--api openai \
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--query-template '{"model": "%m", "messages": [{"role": "user","content": "%p"}], "stream": true, "skip_special_tokens": false, "stop": ["<|im_end|>"], "temperature": 0.7, "max_tokens": 1024}' \
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--dataset openqa
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```
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Where `%m` and `%p` will be replaced by the model name and the prompt.
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You can set request parameters in the query-template:
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```{code-block} json
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:caption: template.json
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{
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"model":"%m",
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"messages":[
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{
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"role":"user",
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"content":"%p"
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}
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],
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"stream":true,
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"skip_special_tokens":false,
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"stop":[
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"<|im_end|>"
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],
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"temperature":0.7,
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"max_tokens":1024
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}
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```
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```bash
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evalscope perf \
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--url 'http://127.0.0.1:8000/v1/chat/completions' \
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--parallel 2 \
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--model 'qwen2.5' \
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--log-every-n-query 10 \
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--read-timeout 120 \
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--connect-timeout 120 \
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--number 20 \
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--max-prompt-length 128000 \
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--min-prompt-length 128 \
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--api openai \
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--query-template @template.json \
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--dataset openqa
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```
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## Using the Random Dataset
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Randomly generate prompts based on `prefix-length`, `max-prompt-length`, and `min-prompt-length`. It is necessary to specify `tokenizer-path`. The number of tokens in the generated prompt is uniformly distributed between `prefix_length + min-prompt-length` and `prefix_length + max-prompt-length`. In a single test, all requests have the same prefix portion.
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```{note}
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Due to the influence of chat_template and tokenization algorithms, there may be some discrepancies in the number of tokens in the generated prompts, and it is not an exact specified token count.
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```
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Execute the following command:
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```bash
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evalscope perf \
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--parallel 20 \
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--model Qwen2.5-0.5B-Instruct \
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--url http://127.0.0.1:8801/v1/chat/completions \
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--api openai \
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--dataset random \
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--min-tokens 128 \
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--max-tokens 128 \
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--prefix-length 64 \
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--min-prompt-length 1024 \
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--max-prompt-length 2048 \
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--number 100 \
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--tokenizer-path Qwen/Qwen2.5-0.5B-Instruct \
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--debug
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```
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## Using wandb to Record Test Results
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Please install wandb:
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```bash
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pip install wandb
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```
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When starting, add the following parameters:
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```bash
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--wandb-api-key 'wandb_api_key'
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--name 'name_of_wandb_log'
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```
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## Using swanlab to Record Test Results
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Please install swanlab:
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```bash
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pip install swanlab
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```
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When starting, add the following parameters:
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```bash
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--swanlab-api-key 'swanlab_api_key'
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--name 'name_of_swanlab_log'
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```
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## Debugging Requests
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Use the `--debug` option to output the requests and responses.
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**Non-`stream` Mode Output Example**
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```text
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2024-11-27 11:25:34,161 - evalscope - http_client.py - on_request_start - 116 - DEBUG - Starting request: <TraceRequestStartParams(method='POST', url=URL('http://127.0.0.1:8000/v1/completions'), headers=<CIMultiDict('Content-Type': 'application/json', 'user-agent': 'modelscope_bench', 'Authorization': 'Bearer EMPTY')>)>
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2024-11-27 11:25:34,163 - evalscope - http_client.py - on_request_chunk_sent - 128 - DEBUG - Request sent: <method='POST', url=URL('http://127.0.0.1:8000/v1/completions'), truncated_chunk='{"prompt": "hello", "model": "qwen2.5"}'>
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2024-11-27 11:25:38,172 - evalscope - http_client.py - on_response_chunk_received - 140 - DEBUG - Request received: <method='POST', url=URL('http://127.0.0.1:8000/v1/completions'), truncated_chunk='{"id":"cmpl-a4565eb4fc6b4a5697f38c0adaf9b70b","object":"text_completion","created":1732677934,"model":"qwen2.5","choices":[{"index":0,"text":"，everyone！今天我给您撒个谎哦。 ))\\n\\n今天开心的事。","logprobs":null,"finish_reason":"length","stop_reason":null,"prompt_logprobs":null}],"usage":{"prompt_tokens":1,"total_tokens":17,"completion_tokens":16}}'>
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```
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**`stream` Mode Output Example**
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```text
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2024-11-27 20:02:24,760 - evalscope - http_client.py - _handle_stream - 57 - DEBUG - Response recevied: data: {"model":"Qwen2.5-0.5B-Instruct","object":"chat.completion.chunk","choices":[{"index":0,"delta":{"content":"重要的"},"finish_reason":null}],"usage":null}
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2024-11-27 20:02:24,803 - evalscope - http_client.py - _handle_stream - 57 - DEBUG - Response recevied: data: {"model":"Qwen2.5-0.5B-Instruct","object":"chat.completion.chunk","choices":[{"index":0,"delta":{"content":""},"finish_reason":null}],"usage":null}
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2024-11-27 20:02:24,847 - evalscope - http_client.py - _handle_stream - 57 - DEBUG - Response recevied: data: {"model":"Qwen2.5-0.5B-Instruct","object":"chat.completion.chunk","choices":[{"index":0,"delta":{"content":"，以便"},"finish_reason":null}],"usage":null}
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2024-11-27 20:02:24,890 - evalscope - http_client.py - _handle_stream - 57 - DEBUG - Response recevied: data: {"model":"Qwen2.5-0.5B-Instruct","object":"chat.completion.chunk","choices":[{"index":0,"delta":{"content":"及时"},"finish_reason":null}],"usage":null}
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2024-11-27 20:02:24,933 - evalscope - http_client.py - _handle_stream - 57 - DEBUG - Response recevied: data: {"model":"Qwen2.5-0.5B-Instruct","object":"chat.completion.chunk","choices":[{"index":0,"delta":{"content":"得到"},"finish_reason":null}],"usage":null}
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2024-11-27 20:02:24,976 - evalscope - http_client.py - _handle_stream - 57 - DEBUG - Response recevied: data: {"model":"Qwen2.5-0.5B-Instruct","object":"chat.completion.chunk","choices":[{"index":0,"delta":{"content":"帮助"},"finish_reason":null}],"usage":null}
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2024-11-27 20:02:25,023 - evalscope - http_client.py - _handle_stream - 57 - DEBUG - Response recevied: data: {"model":"Qwen2.5-0.5B-Instruct","object":"chat.completion.chunk","choices":[{"index":0,"delta":{"content":"和支持"},"finish_reason":null}],"usage":null}
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2024-11-27 20:02:25,066 - evalscope - http_client.py - _handle_stream - 57 - DEBUG - Response recevied: data: {"model":"Qwen2.5-0.5B-Instruct","object":"chat.completion.chunk","choices":[{"index":0,"delta":{"content":""},"finish_reason":null}],"usage":null}
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2024-11-27 20:02:25,109 - evalscope - http_client.py - _handle_stream - 57 - DEBUG - Response recevied: data: {"model":"Qwen2.5-0.5B-Instruct","object":"chat.completion.chunk","choices":[{"index":0,"delta":{"content":""},"finish_reason":null}],"usage":null}
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2024-11-27 20:02:25,111 - evalscope - http_client.py - _handle_stream - 57 - DEBUG - Response recevied: data: {"model":"Qwen2.5-0.5B-Instruct","object":"chat.completion.chunk","choices":[{"index":0,"delta":{"content":"。<|im_end|>"},"finish_reason":null}],"usage":null}
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2024-11-27 20:02:25,113 - evalscope - http_client.py - _handle_stream - 57 - DEBUG - Response recevied: data: {"model":"Qwen2.5-0.5B-Instruct","object":"chat.completion.chunk","choices":[{"index":0,"delta":{},"finish_reason":"stop"}],"usage":{"prompt_tokens":50,"completion_tokens":260,"total_tokens":310}}
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2024-11-27 20:02:25,113 - evalscope - http_client.py - _handle_stream - 57 - DEBUG - Response recevied: data: [DONE]
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```
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