docs(benchmarker): Adding some help for the options in `text-generation-benchmark`. (#462)
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@ -14,36 +14,85 @@ use tracing_subscriber::EnvFilter;
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#[derive(Parser, Debug)]
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#[clap(author, version, about, long_about = None)]
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struct Args {
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/// The name of the tokenizer (as in model_id on the huggingface hub, or local path).
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#[clap(short, long, env)]
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tokenizer_name: String,
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/// The revision to use for the tokenizer if on the hub.
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#[clap(default_value = "main", long, env)]
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revision: String,
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/// The various batch sizes to benchmark for, the idea is to get enough
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/// batching to start seeing increased latency, this usually means you're
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/// moving from memory bound (usual as BS=1) to compute bound, and this is
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/// a sweet spot for the maximum batch size for the model under test
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#[clap(short, long)]
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batch_size: Option<Vec<u32>>,
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/// This is the initial prompt sent to the text-generation-server length
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/// in token. Longer prompt will slow down the benchmark. Usually the
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/// latency grows somewhat linearly with this for the prefill step.
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///
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/// Most importantly, the prefill step is usually not the one dominating
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/// your runtime, so it's ok to keep it short.
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#[clap(default_value = "10", short, long, env)]
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sequence_length: u32,
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/// This is how many tokens will be generated by the server and averaged out
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/// to give the `decode` latency. This is the *critical* number you want to optimize for
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/// LLM spend most of their time doing decoding.
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///
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/// Decode latency is usually quite stable.
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#[clap(default_value = "8", short, long, env)]
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decode_length: u32,
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///How many runs should we average from
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#[clap(default_value = "10", short, long, env)]
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runs: usize,
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/// Number of warmup cycles
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#[clap(default_value = "1", short, long, env)]
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warmups: usize,
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#[clap(long, env)]
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temperature: Option<f32>,
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#[clap(long, env)]
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top_k: Option<u32>,
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#[clap(long, env)]
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top_p: Option<f32>,
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#[clap(long, env)]
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typical_p: Option<f32>,
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#[clap(long, env)]
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repetition_penalty: Option<f32>,
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#[clap(long, env)]
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watermark: bool,
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#[clap(long, env)]
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do_sample: bool,
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/// The location of the grpc socket. This benchmark tool bypasses the router
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/// completely and directly talks to the gRPC processes
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#[clap(default_value = "/tmp/text-generation-server-0", short, long, env)]
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master_shard_uds_path: String,
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/// Generation parameter in case you want to specifically test/debug particular
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/// decoding strategies, for full doc refer to the `text-generation-server`
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#[clap(long, env)]
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temperature: Option<f32>,
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/// Generation parameter in case you want to specifically test/debug particular
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/// decoding strategies, for full doc refer to the `text-generation-server`
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#[clap(long, env)]
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top_k: Option<u32>,
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/// Generation parameter in case you want to specifically test/debug particular
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/// decoding strategies, for full doc refer to the `text-generation-server`
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#[clap(long, env)]
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top_p: Option<f32>,
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/// Generation parameter in case you want to specifically test/debug particular
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/// decoding strategies, for full doc refer to the `text-generation-server`
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#[clap(long, env)]
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typical_p: Option<f32>,
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/// Generation parameter in case you want to specifically test/debug particular
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/// decoding strategies, for full doc refer to the `text-generation-server`
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#[clap(long, env)]
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repetition_penalty: Option<f32>,
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/// Generation parameter in case you want to specifically test/debug particular
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/// decoding strategies, for full doc refer to the `text-generation-server`
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#[clap(long, env)]
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watermark: bool,
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/// Generation parameter in case you want to specifically test/debug particular
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/// decoding strategies, for full doc refer to the `text-generation-server`
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#[clap(long, env)]
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do_sample: bool,
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}
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fn main() -> Result<(), Box<dyn std::error::Error>> {
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