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Unverified Commit 5df80590 authored by Nicolas Patry's avatar Nicolas Patry Committed by GitHub
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Auto max prefill (#2797) 

* Attempt at automatic max batch prefill.

* Taking into account number of shards.

* Adding more cards.

* Adding A100 + H100

* Adding a few more cards.

* Logprobs cost too much.

* h100 better name, and keep factor of 2

* Damn inflated sparse tflops.

* Typo in h100.

* Updated the flops calculation (checked with fvcore).

* chunking by default.

* Fix prefix caching for chat completion since we removed logprobs.

* More tests.

* Dropping all the prefill logprobs.

* Add a flag that enables users to get logprobs back.

* Repairing prompt token counting.

* Fixing a few tests.

* Remove some scaffolding.

* Attempting to reduces the issues (workarounds for now).
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integration-tests/models/__snapshots__/test_flash_starcoder_gptq/test_flash_starcoder_gptq_default_params.json
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integration-tests/models/__snapshots__/test_flash_starcoder_gptq/test_flash_starcoder_gptq_load.json
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integration-tests/models/__snapshots__/test_grammar_llama/test_non_flash_llama_grammar_json.json
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integration-tests/models/__snapshots__/test_idefics2/test_flash_idefics2_next_all_params.json
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integration-tests/models/__snapshots__/test_idefics2/test_flash_idefics2_next_load.json
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},
{
"id": 1167,
"logprob": -5.40625,
"text": " mem"
},
{
"id": 70,
"logprob": -0.005420685,
"text": "e"
},
{
"id": 13,
"logprob": -7.2226562,
"text": ","
},
{
"id": 285,
"logprob": -0.26879883,
"text": " and"
},
{
"id": 752,
"logprob": -2.1992188,
"text": " what"
},
{
"id": 434,
"logprob": -5.46875,
"text": "'s"
},
{
"id": 253,
"logprob": -0.8017578,
"text": " the"
},
{
"id": 2892,
"logprob": -6.6796875,
"text": " history"
},
{
"id": 3212,
"logprob": -2.1972656,
"text": " behind"
},
{
"id": 436,
"logprob": -11.4453125,
"text": " this"
},
{
"id": 3159,
"logprob": -2.1933594,
"text": " word"
},
{
"id": 32,
"logprob": -0.007858276,
"text": "?"
},
{
"id": 0,
"logprob": -2.328125,
"text": "<|endoftext|>"
},
{
"id": 50281,
"logprob": -18.21875,
"text": "<|assistant|>"
}
],
"prefill": [],
"seed": null,
"tokens": [
{
......@@ -330,98 +148,7 @@
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [
{
"id": 50278,
"logprob": null,
"text": "<|prompter|>"
},
{
"id": 1276,
"logprob": -8.0234375,
"text": "What"
},
{
"id": 310,
"logprob": -5.421875,
"text": " is"
},
{
"id": 247,
"logprob": -2.1640625,
"text": " a"
},
{
"id": 1167,
"logprob": -5.40625,
"text": " mem"
},
{
"id": 70,
"logprob": -0.005420685,
"text": "e"
},
{
"id": 13,
"logprob": -7.2226562,
"text": ","
},
{
"id": 285,
"logprob": -0.26879883,
"text": " and"
},
{
"id": 752,
"logprob": -2.1992188,
"text": " what"
},
{
"id": 434,
"logprob": -5.46875,
"text": "'s"
},
{
"id": 253,
"logprob": -0.8017578,
"text": " the"
},
{
"id": 2892,
"logprob": -6.6796875,
"text": " history"
},
{
"id": 3212,
"logprob": -2.1972656,
"text": " behind"
},
{
"id": 436,
"logprob": -11.4453125,
"text": " this"
},
{
"id": 3159,
"logprob": -2.1933594,
"text": " word"
},
{
"id": 32,
"logprob": -0.007858276,
"text": "?"
},
{
"id": 0,
"logprob": -2.328125,
"text": "<|endoftext|>"
},
{
"id": 50281,
"logprob": -18.21875,
"text": "<|assistant|>"
}
],
"prefill": [],
"seed": null,
"tokens": [
{
......@@ -493,98 +220,7 @@
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [
{
"id": 50278,
"logprob": null,
"text": "<|prompter|>"
},
{
"id": 1276,
"logprob": -8.0234375,
"text": "What"
},
{
"id": 310,
"logprob": -5.421875,
"text": " is"
},
{
"id": 247,
"logprob": -2.1640625,
"text": " a"
},
{
"id": 1167,
"logprob": -5.40625,
"text": " mem"
},
{
"id": 70,
"logprob": -0.005420685,
"text": "e"
},
{
"id": 13,
"logprob": -7.2226562,
"text": ","
},
{
"id": 285,
"logprob": -0.26879883,
"text": " and"
},
{
"id": 752,
"logprob": -2.1992188,
"text": " what"
},
{
"id": 434,
"logprob": -5.46875,
"text": "'s"
},
{
"id": 253,
"logprob": -0.8017578,
"text": " the"
},
{
"id": 2892,
"logprob": -6.6796875,
"text": " history"
},
{
"id": 3212,
"logprob": -2.1972656,
"text": " behind"
},
{
"id": 436,
"logprob": -11.4453125,
"text": " this"
},
{
"id": 3159,
"logprob": -2.1933594,
"text": " word"
},
{
"id": 32,
"logprob": -0.007858276,
"text": "?"
},
{
"id": 0,
"logprob": -2.328125,
"text": "<|endoftext|>"
},
{
"id": 50281,
"logprob": -18.21875,
"text": "<|assistant|>"
}
],
"prefill": [],
"seed": null,
"tokens": [
{
......
integration-tests/models/__snapshots__/test_server_gptq_quantized/test_server_gptq_quantized.json
View file @ 5df80590
......@@ -3,23 +3,7 @@
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [
{
"id": 1,
"logprob": null,
"text": "<s>"
},
{
"id": 4321,
"logprob": -9.8359375,
"text": "Test"
},
{
"id": 2009,
"logprob": -9.6171875,
"text": "request"
}
],
"prefill": [],
"seed": null,
"tokens": [
{
......
integration-tests/models/__snapshots__/test_server_gptq_quantized/test_server_gptq_quantized_all_params.json
View file @ 5df80590
......@@ -3,23 +3,7 @@
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [
{
"id": 1,
"logprob": null,
"text": "<s>"
},
{
"id": 4321,
"logprob": -9.7890625,
"text": "Test"
},
{
"id": 2009,
"logprob": -9.625,
"text": "request"
}
],
"prefill": [],
"seed": 0,
"tokens": [
{
......
integration-tests/models/__snapshots__/test_server_gptq_quantized/test_server_gptq_quantized_load.json
View file @ 5df80590
......@@ -4,23 +4,7 @@
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [
{
"id": 1,
"logprob": null,
"text": "<s>"
},
{
"id": 4321,
"logprob": -9.8828125,
"text": "Test"
},
{
"id": 2009,
"logprob": -9.5859375,
"text": "request"
}
],
"prefill": [],
"seed": null,
"tokens": [
{
......@@ -93,23 +77,7 @@
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [
{
"id": 1,
"logprob": null,
"text": "<s>"
},
{
"id": 4321,
"logprob": -9.796875,
"text": "Test"
},
{
"id": 2009,
"logprob": -9.625,
"text": "request"
}
],
"prefill": [],
"seed": null,
"tokens": [
{
......@@ -182,23 +150,7 @@
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [
{
"id": 1,
"logprob": null,
"text": "<s>"
},
{
"id": 4321,
"logprob": -9.9296875,
"text": "Test"
},
{
"id": 2009,
"logprob": -9.5703125,
"text": "request"
}
],
"prefill": [],
"seed": null,
"tokens": [
{
......@@ -271,23 +223,7 @@
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [
{
"id": 1,
"logprob": null,
"text": "<s>"
},
{
"id": 4321,
"logprob": -9.84375,
"text": "Test"
},
{
"id": 2009,
"logprob": -9.6171875,
"text": "request"
}
],
"prefill": [],
"seed": null,
"tokens": [
{
......
integration-tests/models/test_flash_phi35_moe.py
View file @ 5df80590
......@@ -6,6 +6,7 @@ def flash_phi35_moe_handle(launcher):
with launcher(
"microsoft/Phi-3.5-MoE-instruct",
num_shard=4,
max_batch_prefill_tokens=10000,
) as handle:
yield handle
......
integration-tests/models/test_mllama.py
View file @ 5df80590
......@@ -4,7 +4,10 @@ import asyncio
@pytest.fixture(scope="module")
def mllama_handle(launcher):
with launcher("meta-llama/Llama-3.2-11B-Vision-Instruct", num_shard=2) as handle:
with launcher(
"meta-llama/Llama-3.2-11B-Vision-Instruct",
num_shard=2,
) as handle:
yield handle
......@@ -75,7 +78,9 @@ async def test_mllama_load(mllama, generate_load, response_snapshot):
},
],
)
for i in range(4)
# TODO with v3, 4 breaks here. Nothing accounts of the image VRAM
# because mllama is the only one doing its thing.
for i in range(2)
]
responses = await asyncio.gather(*futures)
......
launcher/src/main.rs
View file @ 5df80590
......@@ -29,6 +29,26 @@ use tracing_subscriber::{filter::LevelFilter, EnvFilter};
mod env_runtime;
mod gpu;
fn compute_optimal(config: Option<&Config>, compute: Option<&ComputeType>) -> Option<usize> {
if let (Some(config), Some(compute)) = (config, compute) {
if let (Some(f16_max_compute), Some(model_compute)) = (compute.f16_flop(), config.flop()) {
tracing::debug!("MAx compute {f16_max_compute} model compute {model_compute}");
let optimal_size = (f16_max_compute / model_compute) as usize;
if optimal_size > 100 {
// Ignore calculations that's too low
// Most likely an error
Some(optimal_size)
} else {
None
}
} else {
None
}
} else {
None
}
}
fn get_config(
model_id: &str,
revision: &Option<String>,
......@@ -144,10 +164,15 @@ struct RawConfig {
quantization_config: Option<QuantizationConfig>,
n_embd: Option<usize>,
hidden_size: Option<usize>,
intermediate_size: Option<usize>,
num_attention_heads: Option<usize>,
num_key_value_heads: Option<usize>,
num_hidden_layers: Option<usize>,
head_dim: Option<usize>,
vision_config: Option<VisionConfig>,
is_encoder_decoder: Option<bool>,
#[serde(rename = "num_experts_per_tok")]
experts: Option<usize>,
}
#[derive(Deserialize)]
......@@ -155,17 +180,56 @@ struct QuantizationConfig {
quant_method: Option<Quantization>,
}
#[derive(Deserialize)]
#[derive(Debug, Deserialize)]
struct VisionConfig {}
#[derive(Deserialize)]
#[derive(Debug, Deserialize)]
struct Config {
max_position_embeddings: Option<usize>,
quantize: Option<Quantization>,
head_dim: Option<usize>,
num_heads: Option<usize>,
num_kv_heads: Option<usize>,
num_layers: Option<usize>,
intermediate_size: Option<usize>,
hidden_size: Option<usize>,
model_type: Option<String>,
vision_config: Option<VisionConfig>,
is_encoder_decoder: bool,
experts: Option<usize>,
}
impl Config {
fn flop(&self) -> Option<u64> {
if self.vision_config.is_some() {
// VLM are much harder to predict and VRAM requirements
// Are more complex.
return None;
}
let num_heads = self.num_heads? as u64;
let num_kv_heads = self.num_kv_heads? as u64;
let head_dim = self.head_dim? as u64;
let hidden_size = self.hidden_size? as u64;
let intermediate_size = if let Some(experts) = self.experts {
(self.intermediate_size? * experts) as u64
} else {
self.intermediate_size? as u64
};
let num_layers = self.num_layers? as u64;
let q_flops = 2 * num_heads * head_dim * hidden_size;
let k_flops = 2 * num_kv_heads * head_dim * hidden_size;
let v_flops = 2 * num_kv_heads * head_dim * hidden_size;
let attn_flops = 2 * num_heads * head_dim * hidden_size;
let o_flops = 2 * num_heads * head_dim * hidden_size;
let attn_layer_flops = q_flops + k_flops + v_flops + attn_flops + o_flops;
let gate_up_down_flops = 2 * 3 * hidden_size * intermediate_size;
let layer_flops = attn_layer_flops + gate_up_down_flops;
let total = layer_flops * num_layers;
Some(total)
}
}
impl From<RawConfig> for Config {
......@@ -175,25 +239,25 @@ impl From<RawConfig> for Config {
.or(other.max_seq_len)
.or(other.n_positions);
let quantize = other.quantization_config.and_then(|q| q.quant_method);
let head_dim = other.head_dim.or_else(|| {
match (other.hidden_size, other.n_embd, other.num_attention_heads) {
(Some(hidden_size), _, Some(num_attention_heads))
if hidden_size % num_attention_heads == 0 =>
{
Some(hidden_size / num_attention_heads)
}
// Legacy
(_, Some(hidden_size), Some(num_attention_heads))
let hidden_size = other.hidden_size.or(other.n_embd);
let head_dim = other
.head_dim
.or_else(|| match (hidden_size, other.num_attention_heads) {
(Some(hidden_size), Some(num_attention_heads))
if hidden_size % num_attention_heads == 0 =>
{
Some(hidden_size / num_attention_heads)
}
_ => None,
}
});
});
let num_heads = other.num_attention_heads;
let num_layers = other.num_hidden_layers;
let num_kv_heads = other.num_key_value_heads.or(other.num_attention_heads);
let intermediate_size = other.intermediate_size;
let model_type = other.model_type;
let vision_config = other.vision_config;
let is_encoder_decoder = other.is_encoder_decoder.unwrap_or(false);
let experts = other.experts;
Config {
max_position_embeddings,
quantize,
......@@ -201,6 +265,12 @@ impl From<RawConfig> for Config {
model_type,
vision_config,
is_encoder_decoder,
hidden_size,
num_heads,
num_kv_heads,
intermediate_size,
num_layers,
experts,
}
}
}
......@@ -698,6 +768,14 @@ struct Args {
/// Default is 2MB
#[clap(default_value = "2000000", long, env)]
payload_limit: usize,
/// Enables prefill logprobs
///
/// Logprobs in the prompt are deactivated by default because they consume
/// a large amount of VRAM (especially for long prompts).
/// Using this flag reallows users to ask for them.
#[clap(long, env)]
enable_prefill_logprobs: bool,
}
#[derive(Debug)]
......@@ -733,6 +811,7 @@ fn shard_manager(
max_batch_size: Option<usize>,
max_input_tokens: Option<usize>,
lora_adapters: Option<String>,
enable_prefill_logprobs: bool,
otlp_endpoint: Option<String>,
otlp_service_name: String,
log_level: LevelFilter,
......@@ -882,6 +961,11 @@ fn shard_manager(
envs.push(("LORA_ADAPTERS".into(), lora_adapters.into()));
}
// Logprobs
if enable_prefill_logprobs {
envs.push(("REQUEST_LOGPROBS".into(), "1".into()));
}
// If huggingface_hub_cache is some, pass it to the shard
// Useful when running inside a docker container
if let Some(huggingface_hub_cache) = huggingface_hub_cache {
......@@ -1373,6 +1457,7 @@ fn spawn_shards(
let rope_factor = args.rope_factor;
let max_batch_size = args.max_batch_size;
let lora_adapters = args.lora_adapters.clone();
let enable_prefill_logprobs = args.enable_prefill_logprobs;
thread::spawn(move || {
shard_manager(
model_id,
......@@ -1400,6 +1485,7 @@ fn spawn_shards(
max_batch_size,
max_input_tokens,
lora_adapters,
enable_prefill_logprobs,
otlp_endpoint,
otlp_service_name,
max_log_level,
......@@ -1439,7 +1525,45 @@ fn spawn_shards(
Ok(())
}
fn compute_type(num_shard: usize) -> Option<String> {
#[derive(Debug)]
struct ComputeType {
count: usize,
card: String,
}
impl ComputeType {
fn f16_flop(&self) -> Option<u64> {
let card_flop = match &self.card[..] {
// https://www.nvidia.com/en-us/geforce/graphics-cards/40-series/rtx-4090/
// Specs are unclear https://www.itcreations.com/nvidia-gpu/nvidia-geforce-rtx-4090-gpu
"nvidia-4090" => Some(82 * 10u64.pow(12)),
// https://www.nvidia.com/en-us/data-center/tesla-t4/
"nvidia-t4" => Some(65 * 10u64.pow(12)),
// https://www.nvidia.com/en-us/data-center/l4/
"nvidia-l4" => Some(121 * 10u64.pow(12)),
// https://www.nvidia.com/en-us/data-center/products/a10-gpu/
"nvidia-a10g" => Some(125 * 10u64.pow(12)),
// https://www.nvidia.com/en-us/data-center/h100/
// https://www.techpowerup.com/gpu-specs/docs/nvidia-gh100-architecture.pdf
"nvidia-h100-80gb-hbm3" => Some(900 * 10u64.pow(12)),
// https://www.nvidia.com/content/dam/en-zz/Solutions/Data-Center/a100/pdf/nvidia-a100-datasheet-us-nvidia-1758950-r4-web.pdf
"nvidia-a100" => Some(312 * 10u64.pow(12)),
card => {
tracing::warn!("Unkown compute for card {card}");
None
}
};
card_flop.map(|f| f * self.count as u64)
}
}
impl From<ComputeType> for OsString {
fn from(value: ComputeType) -> Self {
format!("{}-{}", value.count, value.card).into()
}
}
fn compute_type(num_shard: usize) -> Option<ComputeType> {
let output = Command::new("nvidia-smi")
.args(["--query-gpu=gpu_name", "--format=csv"])
.output()
......@@ -1447,8 +1571,10 @@ fn compute_type(num_shard: usize) -> Option<String> {
let output = String::from_utf8(output.stdout).ok()?;
let fullname = output.split('\n').nth(1)?;
let cardname = fullname.replace(' ', "-").to_lowercase();
let compute_type = format!("{num_shard}-{cardname}");
Some(compute_type)
Some(ComputeType {
count: num_shard,
card: cardname,
})
}
fn spawn_webserver(
......@@ -1700,26 +1826,22 @@ fn main() -> Result<(), LauncherError> {
let config: Option<Config> = get_config(&args.model_id, &args.revision).ok();
let quantize = config.as_ref().and_then(|c| c.quantize);
// Quantization usually means you're even more RAM constrained.
let max_default = 4096;
let max_position_embeddings = if let Some(config) = &config {
if let Some(max_position_embeddings) = config.max_position_embeddings {
if max_position_embeddings > max_default {
max_default
} else {
max_position_embeddings
}
} else {
max_default
}
} else {
max_default
};
let (prefix_caching, attention) = resolve_attention(&config, &args.lora_adapters);
tracing::info!("Using attention {attention} - Prefix caching {prefix_caching}");
std::env::set_var("PREFIX_CACHING", prefix_caching);
std::env::set_var("ATTENTION", attention);
let num_shard = find_num_shards(args.sharded, args.num_shard)?;
if num_shard > 1 {
if matches!(args.quantize, Some(Quantization::Exl2)) {
return Err(LauncherError::ArgumentValidation(
"Sharding is currently not supported with `exl2` quantization".into(),
));
}
tracing::info!("Sharding model on {num_shard} processes");
}
let max_input_tokens = {
match (args.max_input_tokens, args.max_input_length) {
(Some(max_input_tokens), Some(max_input_length)) => {
......@@ -1739,9 +1861,17 @@ fn main() -> Result<(), LauncherError> {
Some(max_batch_prefill_tokens) => max_batch_prefill_tokens,
None => {
// TODO figure out hardware optimal value
let value = 4096.min(max_position_embeddings as u32);
let compute_type = compute_type(num_shard);
let compute_optimal = compute_optimal(config.as_ref(), compute_type.as_ref());
let default = compute_optimal.unwrap_or(4096);
let max_position_embeddings = config.and_then(|c| c.max_position_embeddings);
let value = if let Some(max_position_embeddings) = max_position_embeddings {
default.min(max_position_embeddings)
} else {
default
};
tracing::info!("Default `max_batch_prefill_tokens` to {value}");
value
value as u32
}
}
};
......@@ -1796,16 +1926,6 @@ fn main() -> Result<(), LauncherError> {
);
}
let num_shard = find_num_shards(args.sharded, args.num_shard)?;
if num_shard > 1 {
if matches!(args.quantize, Some(Quantization::Exl2)) {
return Err(LauncherError::ArgumentValidation(
"Sharding is currently not supported with `exl2` quantization".into(),
));
}
tracing::info!("Sharding model on {num_shard} processes");
}
if let Some(ref max_batch_total_tokens) = args.max_batch_total_tokens {
if let Some(max_total_tokens) = max_total_tokens {
if max_total_tokens as u32 > *max_batch_total_tokens {
......
load_tests/Makefile 0 → 100644
View file @ 5df80590
ShareGPT_V3_unfiltered_cleaned_split.json:
wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json
prepare_share: ShareGPT_V3_unfiltered_cleaned_split.json
python filter.py
prepare_orca:
python orca.py
load_tests/common.js 0 → 100644
View file @ 5df80590
import { check } from 'k6';
import { scenario } from 'k6/execution';
import http from 'k6/http';
import { Trend, Counter } from 'k6/metrics';
const host = __ENV.HOST;
const model_id = __ENV.MODEL_ID;
const timePerToken = new Trend('time_per_token', true);
const tokens = new Counter('tokens');
const new_tokens = new Counter('new_tokens');
const input_tokens = new Counter('input_tokens');
const max_new_tokens = 50;
// const shareGPT = JSON.parse(open("ShareGPT_V3_unfiltered_cleaned_split.json"))
const shareGPT = JSON.parse(open("small.json"))
export function get_options() {
return {
thresholds: {
http_req_failed: ['rate==0'],
// time_per_token: [{
// threshold: `p(50)<${5 * reference_latency_ms}`,
// abortOnFail: true,
// delayAbortEval: '10s'
// }],
},
scenarios: {
// single_user: {
// executor: 'constant-arrival-rate',
// duration: '60s',
// preAllocatedVUs: 1,
// rate: 20,
// timeUnit: '1s',
// },
// load_test: {
// executor: 'constant-arrival-rate',
// duration: '60s',
// preAllocatedVUs: 100,
// rate: 1,
// timeUnit: '1s',
// },
// breakpoint: {
// executor: 'ramping-arrival-rate', //Assure load increase if the system slows
// preAllocatedVUs: 300,
// stages: [
// { duration: '60s', target: 100 }, // just slowly ramp-up to a HUGE load
// ],
// },
throughput: {
executor: 'shared-iterations',
vus: 100,
iterations: 200,
maxDuration: '40s',
},
},
};
}
function generate_payload(gpt, max_new_tokens) {
const input = gpt["conversations"][0]["value"];
return { "messages": [{ "role": "user", "content": input }], "temperature": 0, "model": `${model_id}`, "max_tokens": max_new_tokens }
}
export const options = get_options();
export default function run() {
const headers = { 'Content-Type': 'application/json' };
const query = shareGPT[scenario.iterationInTest % shareGPT.length];
const payload = JSON.stringify(generate_payload(query, max_new_tokens));
const res = http.post(`http://${host}/v1/chat/completions`, payload, {
headers,
});
if (res.status >= 400 && res.status < 500) {
return;
}
check(res, {
'Post status is 200': (res) => res.status === 200,
});
const duration = res.timings.duration;
if (res.status === 200) {
const body = res.json();
const completion_tokens = body.usage.completion_tokens;
const latency_ms_per_token = duration / completion_tokens;
timePerToken.add(latency_ms_per_token);
const prompt_tokens = body.usage.prompt_tokens;
input_tokens.add(prompt_tokens);
new_tokens.add(completion_tokens);
tokens.add(completion_tokens + prompt_tokens);
}
}
load_tests/filter.py 0 → 100644
View file @ 5df80590
import json
def main():
with open("./ShareGPT_V3_unfiltered_cleaned_split.json", "r") as f:
data = json.load(f)
# Select only the first 2k conversations that start with a human.
max = 2000
conversations = []
for conversation in data:
conv = conversation.get("conversations")
if conv and conv[0]["from"] == "human":
# Trim the rest of the output
conversation["conversations"] = conversation["conversations"][:1]
conversations.append(conversation)
if len(conversation) >= max:
break
with open("./small.json", "w") as f:
data = json.dump(conversations, f, indent=4)
if __name__ == "__main__":
main()
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