Parameter

Execute evalscope perf --help to get a full parameter description.

Basic Settings

Parameter	Type	Description	Default
--model	str	Name or path of the test model	-
--url	str	API address, supporting /chat/completion and /completion endpoints	-
--name	str	Name for wandb/swanlab database result and result database	{model_name}_{current_time}
--api	str	Service API type • openai: OpenAI-compatible API (requires --url) • openai_embedding: OpenAI-compatible Embedding API • openai_rerank: OpenAI/Cohere-compatible Rerank API • local: Start local transformers inference • local_vllm: Start local vLLM inference service • Custom: See Custom API Guide	-
--port	int	Port for local inference service Only applicable to local and local_vllm	8877
--attn-implementation	str	Attention implementation method Only effective when api=local	None (Optional: flash_attention_2, eager, sdpa)
--api-key	str	API key	None
--debug	bool	Whether to output debug information	False

Network Configuration

Parameter	Type	Description	Default
--total-timeout	int	Total timeout for each request (seconds)	21600 (6 hours)
--connect-timeout	int	Network connection timeout (seconds)	None
--read-timeout	int	Network read timeout (seconds)	None
--headers	str	Additional HTTP headers Format: key1=value1 key2=value2 Will be used for each query	-
--no-test-connection	bool	Do not send connection test, start stress test directly	False

Request Control

Parameter	Type	Description	Default
--parallel	list[int]	Number of concurrent requests Can input multiple values separated by spaces	1
--number	list[int]	Total number of requests to be sent Can input multiple values (must correspond one-to-one with parallel)	1000
--rate	float	Request generation rate (requests/second) • -1: No rate limit; all requests are generated immediately and placed in the queue • > 0: Requests are generated following a Poisson arrival model — the inter-arrival interval follows an exponential distribution with mean 1/rate, resulting in an average of rate requests per second	-1
--log-every-n-query	int	Log every N queries	10
--stream	bool	Whether to use SSE stream output Must be enabled to measure TTFT (Time to First Token) metric	True
--sleep-interval	int	Sleep time between each performance test (seconds) Helps avoid overloading the server	5

Tip

--rate and --parallel control two independent phases:

• Generation phase (controlled by --rate): Requests are generated and placed into a queue at the specified rate.

  • rate=-1: No rate limit; all requests are enqueued immediately.

  • rate=R: Inter-arrival intervals follow an exponential distribution with mean 1/R seconds (Poisson arrival model), resulting in an average of R requests enqueued per second.

• Sending phase (controlled by --parallel): At most parallel requests are in-flight simultaneously (sent but not yet responded to); each worker fetches the next request from the queue only after receiving a response to the previous one.

The two parameters are independent: --rate determines how quickly requests enter the queue, while --parallel determines how many requests are actively being sent at any given time.

SLA Settings

Parameter	Type	Description	Default
--sla-auto-tune	bool	Whether to enable SLA auto-tuning mode	False
--sla-variable	str	Variable for auto-tuning Options: parallel (concurrency), rate (request rate)	parallel
--sla-params	str	SLA constraint conditions JSON string Supported metrics: avg_latency, p99_latency, avg_ttft, p99_ttft, avg_tpot, p99_tpot, rps, tps Supported operators: <=, <, min (for latency metrics); >=, >, max (for throughput metrics) Example: '[{"p99_latency": "<=2"}]'	None
--sla-upper-bound	int	Upper bound of the tuned SLA variable search range	65536
--sla-lower-bound	int	Lower bound of the tuned SLA variable search range	1
--sla-fixed-parallel	int	Fixed parallel workers used when --sla-variable=rate; defaults to --sla-upper-bound for backward compatibility	None
--sla-num-runs	int	Number of runs per concurrency level (average taken)	3
--sla-number-multiplier	float	Multiplier of total requests relative to the tuned variable (concurrency or rate), i.e. number = round(variable × N); defaults to 2 when not set	None

See also

For details on using the SLA auto-tuning feature, see the Auto-tuning Guide.

Prompt Settings

Parameter	Type	Description	Default
--max-prompt-length	int	Maximum input prompt length Prompts exceeding this length will be discarded	131072
--min-prompt-length	int	Minimum input prompt length Prompts shorter than this will be discarded	0
--prefix-length	int	Length of the prompt prefix Only effective for random dataset	0
--prompt	str	Specify request prompt String or local file (specify via @/path/to/file) Higher priority than dataset Example: @./prompt.txt	-
--query-template	str	Specify query template JSON string or local file (specify via @/path/to/file) Example: @./query_template.json	-
--apply-chat-template	bool	Whether to apply chat template	None (automatically determined based on URL suffix)
--image-width	int	Image width for random VL dataset	224
--image-height	int	Image height for random VL dataset	224
--image-format	str	Image format for random VL dataset	RGB
--image-num	int	Number of images for random VL dataset	1
--image-patch-size	int	Patch size for the image Only used for local image token calculation	28

Dataset Configuration

Parameter	Type	Description	Default
--dataset	str	Dataset mode, see table below for details	-
--dataset-path	str	Dataset file path Used in conjunction with dataset	-

Dataset Mode Description

Mode	Description	Supports dataset-path
openqa	Automatically downloads OpenQA from ModelScope Prompts are relatively short (usually <100 tokens) Uses question field from jsonl file when dataset_path is specified	✓
longalpaca	Automatically downloads LongAlpaca-12k from ModelScope Prompts are much longer (generally >6000 tokens) Uses instruction field from jsonl file when dataset_path is specified	✓
line_by_line	Each line in txt file is used as a separate prompt Requires dataset_path	✓ (Required)
flickr8k	Automatically downloads Flick8k from ModelScope Builds image-text inputs; large dataset suitable for evaluating multimodal models	✗
kontext_bench	Automatically downloads Kontext-Bench from ModelScope Builds image-text inputs; approximately 1,000 samples, suitable for quick evaluation of multimodal models	✗
random	Randomly generates prompts based on prefix-length, max-prompt-length, and min-prompt-length Requires tokenizer-path Usage example	✗
random_vl	Randomly generates both image and text inputs Based on random, with additional image-related parameters Usage example	✗
embedding	Load text data from file to evaluate Embedding model Supports Line-by-line (TXT) or JSONL format (with text field)	✓ (Required)
random_embedding	Randomly generate queries based on max-prompt-length and min-prompt-length to evaluate Embedding model Must specify tokenizer-path	✗
embedding_batch	Batch send text data to evaluate Embedding model Load data from file Supports --extra-args '{"batch_size": 8}' to set batch size	✓ (Required)
random_embedding_batch	Batch send randomly generated query data based on max-prompt-length and min-prompt-length to evaluate Embedding model Must specify tokenizer-path Supports --extra-args '{"batch_size": 8}' to set batch size	✗
rerank	Load Query-Document pairs from file to evaluate Rerank model Supports JSONL format (with query and documents fields)	✓ (Required)
random_rerank	Randomly generate query data based on max-prompt-length and min-prompt-length to evaluate Rerank model Must specify tokenizer-path Supports --extra-args '{"num_documents": 10, "document_length_ratio": 5}' to set number of documents and length ratio relative to query	✗
custom	Custom dataset parser See Custom Dataset Guide	✓

Model Settings

Parameter	Type	Description	Default
--tokenizer-path	str	Tokenizer weights path Used to calculate the number of tokens in input and output Usually located in the same directory as model weights	None
--frequency-penalty	float	frequency_penalty value	-
--logprobs	bool	Whether to return logarithmic probabilities	-
--max-tokens	int	Maximum number of tokens that can be generated	-
--min-tokens	int	Minimum number of tokens to generate Note: Not all model services support this parameter For vLLM>=0.8.1, you need to additionally set --extra-args '{"ignore_eos": true}'	-
--n-choices	int	Number of completion choices to generate	-
--seed	int	Random seed	None
--stop	str	Tokens that stop the generation	-
--stop-token-ids	list[int]	IDs of tokens that stop the generation	-
--temperature	float	Sampling temperature	0
--top-p	float	Top-p sampling	-
--top-k	int	Top-k sampling	-
--extra-args	str	Additional parameters to be passed in the request body JSON string format Example: '{"ignore_eos": true}'	-

Data Storage

Parameter	Type	Description	Default
--visualizer	str	Visualizer to use Options: wandb, swanlab, clearml If set, metrics will be saved to the specified visualizer	None
--enable-progress-tracker	bool	Whether to enable progress tracking, writing hierarchical stress-test progress to progress.json in real time, queryable via the service API	False
--wandb-api-key	str	wandb API key for logging metrics to wandb Deprecated, please use --visualizer wandb instead	-
--swanlab-api-key	str	swanlab API key for logging metrics to swanlab Deprecated, please use --visualizer swanlab instead	-
--outputs-dir	str	Output file path	./outputs

Other Parameters

Parameter	Type	Description	Default
--db-commit-interval	int	Number of rows buffered before writing results to SQLite database	1000
--queue-size-multiplier	int	Maximum size of the request queue Calculated as: parallel * multiplier	5
--in-flight-task-multiplier	int	Maximum number of in-flight tasks Calculated as: parallel * multiplier	2