Speculative (#1308)
This commit is contained in:
Nicolas Patry
GitHub
parent
3238c49121
commit
9ecfa16b12
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@ -60,30 +60,30 @@ checksum = "7079075b41f533b8c61d2a4d073c4676e1f8b249ff94a393b0595db304e0dd87"
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@ -333,9 +333,9 @@ checksum = "baf1de4339761588bc0619e3cbc0120ee582ebb74b53b4efbf79117bd2da40fd"
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@ -343,9 +343,9 @@ dependencies = [
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@ -392,9 +392,9 @@ dependencies = [
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@ -402,9 +402,9 @@ dependencies = [
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@ -549,9 +549,9 @@ dependencies = [
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@ -1218,9 +1218,9 @@ dependencies = [
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@ -1612,9 +1612,9 @@ dependencies = [
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@ -1644,9 +1644,9 @@ checksum = "ff011a302c396a5197692431fc1948019154afc178baf7d8e37367442a4601cf"
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@ -2309,15 +2309,15 @@ dependencies = [
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@ -3835,18 +3835,18 @@ dependencies = [
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@ -67,6 +67,14 @@ Options:
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- bitsandbytes-nf4: Bitsandbytes 4bit. Can be applied on any model, will cut the memory requirement by 4x, but it is known that the model will be much slower to run than the native f16
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- bitsandbytes-fp4: Bitsandbytes 4bit. nf4 should be preferred in most cases but maybe this one has better perplexity performance for you model
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```
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## SPECULATE
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```shell
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--speculate <SPECULATE>
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The number of input_ids to speculate on If using a medusa model, the heads will be picked up automatically Other wise, it will use n-gram speculation which is relatively free in terms of compute, but the speedup heavily depends on the task
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[env: SPECULATE=]
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```
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## DTYPE
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```shell
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@ -0,0 +1,98 @@
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"generated_text": "What is Deep Learning?\nDeep learning, which can be considered as"
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@ -0,0 +1,414 @@
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|
||||
"id": 2772,
|
||||
"logprob": -0.56689453,
|
||||
"special": false,
|
||||
"text": "De"
|
||||
},
|
||||
{
|
||||
"id": 1022,
|
||||
"logprob": -0.000108003616,
|
||||
"special": false,
|
||||
"text": "ep"
|
||||
},
|
||||
{
|
||||
"id": 6509,
|
||||
"logprob": -0.1239624,
|
||||
"special": false,
|
||||
"text": " learning"
|
||||
},
|
||||
{
|
||||
"id": 338,
|
||||
"logprob": -0.044433594,
|
||||
"special": false,
|
||||
"text": " is"
|
||||
},
|
||||
{
|
||||
"id": 263,
|
||||
"logprob": -0.018295288,
|
||||
"special": false,
|
||||
"text": " a"
|
||||
},
|
||||
{
|
||||
"id": 11306,
|
||||
"logprob": -0.45922852,
|
||||
"special": false,
|
||||
"text": " subset"
|
||||
},
|
||||
{
|
||||
"id": 310,
|
||||
"logprob": -0.0002104044,
|
||||
"special": false,
|
||||
"text": " of"
|
||||
},
|
||||
{
|
||||
"id": 4933,
|
||||
"logprob": -0.004711151,
|
||||
"special": false,
|
||||
"text": " machine"
|
||||
},
|
||||
{
|
||||
"id": 6509,
|
||||
"logprob": -0.00025892258,
|
||||
"special": false,
|
||||
"text": " learning"
|
||||
}
|
||||
]
|
||||
},
|
||||
"generated_text": "\nDeep learning is a subset of machine learning"
|
||||
},
|
||||
{
|
||||
"details": {
|
||||
"best_of_sequences": null,
|
||||
"finish_reason": "length",
|
||||
"generated_tokens": 10,
|
||||
"prefill": [
|
||||
{
|
||||
"id": 1,
|
||||
"logprob": null,
|
||||
"text": "<s>"
|
||||
},
|
||||
{
|
||||
"id": 1724,
|
||||
"logprob": -10.734375,
|
||||
"text": "What"
|
||||
},
|
||||
{
|
||||
"id": 338,
|
||||
"logprob": -1.5488281,
|
||||
"text": "is"
|
||||
},
|
||||
{
|
||||
"id": 21784,
|
||||
"logprob": -9.2890625,
|
||||
"text": "Deep"
|
||||
},
|
||||
{
|
||||
"id": 29257,
|
||||
"logprob": -1.2724609,
|
||||
"text": "Learning"
|
||||
},
|
||||
{
|
||||
"id": 29973,
|
||||
"logprob": -0.47729492,
|
||||
"text": "?"
|
||||
}
|
||||
],
|
||||
"seed": null,
|
||||
"tokens": [
|
||||
{
|
||||
"id": 13,
|
||||
"logprob": -1.1826172,
|
||||
"special": false,
|
||||
"text": "\n"
|
||||
},
|
||||
{
|
||||
"id": 2772,
|
||||
"logprob": -0.56689453,
|
||||
"special": false,
|
||||
"text": "De"
|
||||
},
|
||||
{
|
||||
"id": 1022,
|
||||
"logprob": -0.000108003616,
|
||||
"special": false,
|
||||
"text": "ep"
|
||||
},
|
||||
{
|
||||
"id": 6509,
|
||||
"logprob": -0.1239624,
|
||||
"special": false,
|
||||
"text": " learning"
|
||||
},
|
||||
{
|
||||
"id": 338,
|
||||
"logprob": -0.044433594,
|
||||
"special": false,
|
||||
"text": " is"
|
||||
},
|
||||
{
|
||||
"id": 263,
|
||||
"logprob": -0.018295288,
|
||||
"special": false,
|
||||
"text": " a"
|
||||
},
|
||||
{
|
||||
"id": 11306,
|
||||
"logprob": -0.45922852,
|
||||
"special": false,
|
||||
"text": " subset"
|
||||
},
|
||||
{
|
||||
"id": 310,
|
||||
"logprob": -0.0002104044,
|
||||
"special": false,
|
||||
"text": " of"
|
||||
},
|
||||
{
|
||||
"id": 4933,
|
||||
"logprob": -0.004711151,
|
||||
"special": false,
|
||||
"text": " machine"
|
||||
},
|
||||
{
|
||||
"id": 6509,
|
||||
"logprob": -0.00025892258,
|
||||
"special": false,
|
||||
"text": " learning"
|
||||
}
|
||||
]
|
||||
},
|
||||
"generated_text": "\nDeep learning is a subset of machine learning"
|
||||
},
|
||||
{
|
||||
"details": {
|
||||
"best_of_sequences": null,
|
||||
"finish_reason": "length",
|
||||
"generated_tokens": 10,
|
||||
"prefill": [
|
||||
{
|
||||
"id": 1,
|
||||
"logprob": null,
|
||||
"text": "<s>"
|
||||
},
|
||||
{
|
||||
"id": 1724,
|
||||
"logprob": -10.734375,
|
||||
"text": "What"
|
||||
},
|
||||
{
|
||||
"id": 338,
|
||||
"logprob": -1.5488281,
|
||||
"text": "is"
|
||||
},
|
||||
{
|
||||
"id": 21784,
|
||||
"logprob": -9.2890625,
|
||||
"text": "Deep"
|
||||
},
|
||||
{
|
||||
"id": 29257,
|
||||
"logprob": -1.2724609,
|
||||
"text": "Learning"
|
||||
},
|
||||
{
|
||||
"id": 29973,
|
||||
"logprob": -0.47729492,
|
||||
"text": "?"
|
||||
}
|
||||
],
|
||||
"seed": null,
|
||||
"tokens": [
|
||||
{
|
||||
"id": 13,
|
||||
"logprob": -1.1826172,
|
||||
"special": false,
|
||||
"text": "\n"
|
||||
},
|
||||
{
|
||||
"id": 2772,
|
||||
"logprob": -0.56689453,
|
||||
"special": false,
|
||||
"text": "De"
|
||||
},
|
||||
{
|
||||
"id": 1022,
|
||||
"logprob": -0.000108003616,
|
||||
"special": false,
|
||||
"text": "ep"
|
||||
},
|
||||
{
|
||||
"id": 6509,
|
||||
"logprob": -0.1239624,
|
||||
"special": false,
|
||||
"text": " learning"
|
||||
},
|
||||
{
|
||||
"id": 338,
|
||||
"logprob": -0.044433594,
|
||||
"special": false,
|
||||
"text": " is"
|
||||
},
|
||||
{
|
||||
"id": 263,
|
||||
"logprob": -0.018295288,
|
||||
"special": false,
|
||||
"text": " a"
|
||||
},
|
||||
{
|
||||
"id": 11306,
|
||||
"logprob": -0.45922852,
|
||||
"special": false,
|
||||
"text": " subset"
|
||||
},
|
||||
{
|
||||
"id": 310,
|
||||
"logprob": -0.0002104044,
|
||||
"special": false,
|
||||
"text": " of"
|
||||
},
|
||||
{
|
||||
"id": 4933,
|
||||
"logprob": -0.004711151,
|
||||
"special": false,
|
||||
"text": " machine"
|
||||
},
|
||||
{
|
||||
"id": 6509,
|
||||
"logprob": -0.00025892258,
|
||||
"special": false,
|
||||
"text": " learning"
|
||||
}
|
||||
]
|
||||
},
|
||||
"generated_text": "\nDeep learning is a subset of machine learning"
|
||||
}
|
||||
]
|
||||
|
|
@ -0,0 +1,103 @@
|
|||
{
|
||||
"details": {
|
||||
"best_of_sequences": null,
|
||||
"finish_reason": "length",
|
||||
"generated_tokens": 10,
|
||||
"prefill": [
|
||||
{
|
||||
"id": 1,
|
||||
"logprob": null,
|
||||
"text": "<s>"
|
||||
},
|
||||
{
|
||||
"id": 1724,
|
||||
"logprob": -10.734375,
|
||||
"text": "What"
|
||||
},
|
||||
{
|
||||
"id": 338,
|
||||
"logprob": -1.5488281,
|
||||
"text": "is"
|
||||
},
|
||||
{
|
||||
"id": 21784,
|
||||
"logprob": -9.2890625,
|
||||
"text": "Deep"
|
||||
},
|
||||
{
|
||||
"id": 29257,
|
||||
"logprob": -1.2753906,
|
||||
"text": "Learning"
|
||||
},
|
||||
{
|
||||
"id": 29973,
|
||||
"logprob": -0.48046875,
|
||||
"text": "?"
|
||||
}
|
||||
],
|
||||
"seed": null,
|
||||
"tokens": [
|
||||
{
|
||||
"id": 13,
|
||||
"logprob": -1.1845703,
|
||||
"special": false,
|
||||
"text": "\n"
|
||||
},
|
||||
{
|
||||
"id": 2772,
|
||||
"logprob": -0.5727539,
|
||||
"special": false,
|
||||
"text": "De"
|
||||
},
|
||||
{
|
||||
"id": 1022,
|
||||
"logprob": -0.000108122826,
|
||||
"special": false,
|
||||
"text": "ep"
|
||||
},
|
||||
{
|
||||
"id": 6509,
|
||||
"logprob": -0.1239624,
|
||||
"special": false,
|
||||
"text": " learning"
|
||||
},
|
||||
{
|
||||
"id": 338,
|
||||
"logprob": -0.044433594,
|
||||
"special": false,
|
||||
"text": " is"
|
||||
},
|
||||
{
|
||||
"id": 263,
|
||||
"logprob": -0.01852417,
|
||||
"special": false,
|
||||
"text": " a"
|
||||
},
|
||||
{
|
||||
"id": 11306,
|
||||
"logprob": -0.45922852,
|
||||
"special": false,
|
||||
"text": " subset"
|
||||
},
|
||||
{
|
||||
"id": 310,
|
||||
"logprob": -0.0002104044,
|
||||
"special": false,
|
||||
"text": " of"
|
||||
},
|
||||
{
|
||||
"id": 4933,
|
||||
"logprob": -0.004787445,
|
||||
"special": false,
|
||||
"text": " machine"
|
||||
},
|
||||
{
|
||||
"id": 6509,
|
||||
"logprob": -0.00026226044,
|
||||
"special": false,
|
||||
"text": " learning"
|
||||
}
|
||||
]
|
||||
},
|
||||
"generated_text": "\nDeep learning is a subset of machine learning"
|
||||
}
|
||||
|
|
@ -0,0 +1,59 @@
|
|||
import pytest
|
||||
|
||||
|
||||
@pytest.fixture(scope="module")
|
||||
def flash_medusa_handle(launcher):
|
||||
with launcher("FasterDecoding/medusa-vicuna-7b-v1.3", num_shard=2) as handle:
|
||||
yield handle
|
||||
|
||||
|
||||
@pytest.fixture(scope="module")
|
||||
async def flash_medusa(flash_medusa_handle):
|
||||
await flash_medusa_handle.health(300)
|
||||
return flash_medusa_handle.client
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
@pytest.mark.private
|
||||
async def test_flash_medusa_simple(flash_medusa, response_snapshot):
|
||||
response = await flash_medusa.generate(
|
||||
"What is Deep Learning?", max_new_tokens=10, decoder_input_details=True
|
||||
)
|
||||
|
||||
assert response.details.generated_tokens == 10
|
||||
assert response == response_snapshot
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
@pytest.mark.private
|
||||
async def test_flash_medusa_all_params(flash_medusa, response_snapshot):
|
||||
response = await flash_medusa.generate(
|
||||
"What is Deep Learning?",
|
||||
max_new_tokens=10,
|
||||
repetition_penalty=1.2,
|
||||
return_full_text=True,
|
||||
stop_sequences=["test"],
|
||||
temperature=0.5,
|
||||
top_p=0.9,
|
||||
top_k=10,
|
||||
truncate=5,
|
||||
typical_p=0.9,
|
||||
watermark=True,
|
||||
decoder_input_details=True,
|
||||
seed=0,
|
||||
)
|
||||
|
||||
assert response.details.generated_tokens == 10
|
||||
assert response == response_snapshot
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
@pytest.mark.private
|
||||
async def test_flash_medusa_load(flash_medusa, generate_load, response_snapshot):
|
||||
responses = await generate_load(flash_medusa, "What is Deep Learning?", max_new_tokens=10, n=4)
|
||||
|
||||
assert len(responses) == 4
|
||||
assert all([r.generated_text == responses[0].generated_text for r in responses]), f"{[r.generated_text for r in responses]}"
|
||||
assert responses[0].generated_text == '\nDeep learning is a subset of machine learning'
|
||||
|
||||
assert responses == response_snapshot
|
||||
|
|
@ -21,6 +21,7 @@ async def test_flash_mistral(flash_mistral, response_snapshot):
|
|||
)
|
||||
|
||||
assert response.details.generated_tokens == 10
|
||||
assert response.generated_text == ": Let n = 10 - 1"
|
||||
assert response == response_snapshot
|
||||
|
||||
|
||||
|
|
@ -55,6 +56,7 @@ async def test_flash_mistral_load(flash_mistral, generate_load, response_snapsho
|
|||
)
|
||||
|
||||
assert len(responses) == 4
|
||||
assert all([r.generated_text == responses[0].generated_text for r in responses])
|
||||
assert all([r.generated_text == responses[0].generated_text for r in responses]), f"{[r.generated_text for r in responses]}"
|
||||
assert responses[0].generated_text == ": Let n = 10 - 1"
|
||||
|
||||
assert responses == response_snapshot
|
||||
|
|
|
|||
|
|
@ -155,6 +155,13 @@ struct Args {
|
|||
#[clap(long, env, value_enum)]
|
||||
quantize: Option<Quantization>,
|
||||
|
||||
/// The number of input_ids to speculate on
|
||||
/// If using a medusa model, the heads will be picked up automatically
|
||||
/// Other wise, it will use n-gram speculation which is relatively free
|
||||
/// in terms of compute, but the speedup heavily depends on the task.
|
||||
#[clap(long, env)]
|
||||
speculate: Option<usize>,
|
||||
|
||||
/// The dtype to be forced upon the model. This option cannot be used with `--quantize`.
|
||||
#[clap(long, env, value_enum)]
|
||||
dtype: Option<Dtype>,
|
||||
|
|
@ -375,6 +382,7 @@ fn shard_manager(
|
|||
model_id: String,
|
||||
revision: Option<String>,
|
||||
quantize: Option<Quantization>,
|
||||
speculate: Option<usize>,
|
||||
dtype: Option<Dtype>,
|
||||
trust_remote_code: bool,
|
||||
uds_path: String,
|
||||
|
|
@ -432,6 +440,11 @@ fn shard_manager(
|
|||
shard_args.push(quantize.to_string())
|
||||
}
|
||||
|
||||
if let Some(speculate) = speculate {
|
||||
shard_args.push("--speculate".to_string());
|
||||
shard_args.push(speculate.to_string())
|
||||
}
|
||||
|
||||
if let Some(dtype) = dtype {
|
||||
shard_args.push("--dtype".to_string());
|
||||
shard_args.push(dtype.to_string())
|
||||
|
|
@ -882,6 +895,7 @@ fn spawn_shards(
|
|||
let shutdown_sender = shutdown_sender.clone();
|
||||
let otlp_endpoint = args.otlp_endpoint.clone();
|
||||
let quantize = args.quantize;
|
||||
let speculate = args.speculate;
|
||||
let dtype = args.dtype;
|
||||
let trust_remote_code = args.trust_remote_code;
|
||||
let master_port = args.master_port;
|
||||
|
|
@ -896,6 +910,7 @@ fn spawn_shards(
|
|||
model_id,
|
||||
revision,
|
||||
quantize,
|
||||
speculate,
|
||||
dtype,
|
||||
trust_remote_code,
|
||||
uds_path,
|
||||
|
|
|
|||
|
|
@ -7,7 +7,9 @@ const seed = 0;
|
|||
|
||||
const host = __ENV.HOST || '127.0.0.1:8000';
|
||||
const timePerToken = new Trend('time_per_token', true);
|
||||
const throughput = new Counter('tokens_per_s');
|
||||
const tokens = new Counter('tokens');
|
||||
const new_tokens = new Counter('new_tokens');
|
||||
const input_tokens = new Counter('input_tokens');
|
||||
|
||||
randomSeed(seed);
|
||||
// const shareGPT = JSON.parse(open("ShareGPT_V3_unfiltered_cleaned_split.json"))
|
||||
|
|
@ -19,7 +21,7 @@ export function get_options(reference_latency_ms){
|
|||
thresholds: {
|
||||
http_req_failed: ['rate==0'],
|
||||
time_per_token: [{
|
||||
threshold: `p(50)<${3 * reference_latency_ms}`,
|
||||
threshold: `p(50)<${5 * reference_latency_ms}`,
|
||||
abortOnFail: true,
|
||||
delayAbortEval: '10s'
|
||||
}],
|
||||
|
|
@ -28,7 +30,7 @@ export function get_options(reference_latency_ms){
|
|||
load_test: {
|
||||
executor: 'constant-arrival-rate',
|
||||
duration: '60s',
|
||||
preAllocatedVUs: 100,
|
||||
preAllocatedVUs: 10,
|
||||
rate: 10,
|
||||
timeUnit: '1s',
|
||||
},
|
||||
|
|
@ -48,17 +50,22 @@ export function run(host, generate_payload, max_new_tokens) {
|
|||
return;
|
||||
}
|
||||
|
||||
|
||||
check(res, {
|
||||
'Post status is 200': (r) => res.status === 200,
|
||||
});
|
||||
const n_tokens = max_new_tokens;
|
||||
const timings = res.timings.duration;
|
||||
const duration = res.timings.duration;
|
||||
|
||||
if (res.status === 200) {
|
||||
const latency_ms_per_token = timings / n_tokens;
|
||||
const body = res.json();
|
||||
const n_tokens = body.details.tokens.length;
|
||||
const latency_ms_per_token = duration / n_tokens;
|
||||
timePerToken.add(latency_ms_per_token);
|
||||
const latency_in_s = latency_ms_per_token / 1000;
|
||||
const individual_throughput = 1 / latency_in_s;
|
||||
throughput.add(individual_throughput);
|
||||
const _input_tokens = body.details.prefill.length;
|
||||
tokens.add(n_tokens + _input_tokens);
|
||||
input_tokens.add(_input_tokens);
|
||||
new_tokens.add(n_tokens);
|
||||
}
|
||||
}
|
||||
|
|
|
|||
|
|
@ -1,13 +1,13 @@
|
|||
import { get_options, run } from "./common.js";
|
||||
|
||||
const reference_latency_ms = 30;
|
||||
const reference_latency_ms = 70;
|
||||
const host = __ENV.HOST || '127.0.0.1:8000';
|
||||
const max_new_tokens = 50;
|
||||
|
||||
|
||||
function generate_payload(gpt){
|
||||
const input = gpt["conversations"][0]["value"];
|
||||
return {"inputs": input, "parameters": {"max_new_tokens": max_new_tokens, "temperature" : 0.5}}
|
||||
return {"inputs": input, "parameters": {"max_new_tokens": max_new_tokens, "decoder_input_details": true}}
|
||||
}
|
||||
|
||||
export const options = get_options(reference_latency_ms);
|
||||
|
|
|
|||
|
|
@ -1,6 +1,6 @@
|
|||
syntax = "proto3";
|
||||
|
||||
package generate.v1;
|
||||
package generate.v2;
|
||||
|
||||
service TextGenerationService {
|
||||
/// Model Info
|
||||
|
|
@ -32,6 +32,7 @@ message InfoResponse {
|
|||
string dtype = 2;
|
||||
string device_type = 3;
|
||||
optional uint32 window_size = 4;
|
||||
uint32 speculate = 5;
|
||||
}
|
||||
|
||||
/// Empty request
|
||||
|
|
@ -135,43 +136,27 @@ message GeneratedText {
|
|||
optional uint64 seed = 4;
|
||||
}
|
||||
|
||||
message PrefillTokens {
|
||||
/// Prefill Token IDs
|
||||
message Tokens {
|
||||
/// Token IDs
|
||||
repeated uint32 ids = 1;
|
||||
/// Prefill Logprobs
|
||||
/// Logprobs
|
||||
repeated float logprobs = 2;
|
||||
/// Prefill tokens
|
||||
/// tokens
|
||||
repeated string texts = 3;
|
||||
}
|
||||
|
||||
message TopTokens {
|
||||
/// Top Token IDs
|
||||
repeated uint32 ids = 1;
|
||||
/// Top Logprobs
|
||||
repeated float logprobs = 2;
|
||||
/// Top Token Texts
|
||||
repeated string texts = 3;
|
||||
/// If the tokens are special
|
||||
repeated bool is_special = 6;
|
||||
/// special
|
||||
repeated bool is_special = 4;
|
||||
}
|
||||
|
||||
message Generation {
|
||||
/// Request ID
|
||||
uint64 request_id = 1;
|
||||
/// Prefill tokens (optional)
|
||||
PrefillTokens prefill_tokens = 2;
|
||||
/// Token ID
|
||||
uint32 token_id = 3;
|
||||
/// Logprob
|
||||
float token_logprob = 4;
|
||||
/// Text
|
||||
string token_text = 5;
|
||||
/// Is it a special token
|
||||
bool token_is_special = 6;
|
||||
Tokens prefill_tokens = 2;
|
||||
Tokens tokens = 3;
|
||||
/// Complete generated text
|
||||
optional GeneratedText generated_text = 7;
|
||||
optional GeneratedText generated_text = 4;
|
||||
/// Top tokens
|
||||
TopTokens top_tokens = 8;
|
||||
repeated Tokens top_tokens = 5;
|
||||
}
|
||||
|
||||
message FilterBatchRequest {
|
||||
|
|
|
|||
|
|
@ -1,6 +1,6 @@
|
|||
/// Single shard Client
|
||||
use crate::pb::generate::v1::text_generation_service_client::TextGenerationServiceClient;
|
||||
use crate::pb::generate::v1::*;
|
||||
use crate::pb::generate::v2::text_generation_service_client::TextGenerationServiceClient;
|
||||
use crate::pb::generate::v2::*;
|
||||
use crate::Result;
|
||||
use grpc_metadata::InjectTelemetryContext;
|
||||
use std::cmp::min;
|
||||
|
|
|
|||
|
|
@ -6,11 +6,11 @@ mod pb;
|
|||
mod sharded_client;
|
||||
|
||||
pub use client::Client;
|
||||
pub use pb::generate::v1::HealthResponse;
|
||||
pub use pb::generate::v1::InfoResponse as ShardInfo;
|
||||
pub use pb::generate::v1::{
|
||||
pub use pb::generate::v2::HealthResponse;
|
||||
pub use pb::generate::v2::InfoResponse as ShardInfo;
|
||||
pub use pb::generate::v2::{
|
||||
Batch, CachedBatch, FinishReason, GeneratedText, Generation, NextTokenChooserParameters,
|
||||
PrefillTokens, Request, StoppingCriteriaParameters,
|
||||
Request, StoppingCriteriaParameters, Tokens,
|
||||
};
|
||||
pub use sharded_client::ShardedClient;
|
||||
use thiserror::Error;
|
||||
|
|
|
|||
|
|
@ -9,7 +9,7 @@ use std::sync::{
|
|||
Arc,
|
||||
};
|
||||
use text_generation_client::{
|
||||
Batch, CachedBatch, ClientError, GeneratedText, Generation, PrefillTokens, ShardedClient,
|
||||
Batch, CachedBatch, ClientError, GeneratedText, Generation, ShardedClient, Tokens,
|
||||
};
|
||||
use thiserror::Error;
|
||||
use tokio::sync::mpsc::error::SendError;
|
||||
|
|
@ -50,10 +50,11 @@ impl Infer {
|
|||
max_concurrent_requests: usize,
|
||||
requires_padding: bool,
|
||||
window_size: Option<u32>,
|
||||
speculate: u32,
|
||||
generation_health: Arc<AtomicBool>,
|
||||
) -> Self {
|
||||
// Infer shared state
|
||||
let queue = Queue::new(requires_padding, 16, window_size);
|
||||
let queue = Queue::new(requires_padding, 16, window_size, speculate);
|
||||
let shared = Arc::new(Shared {
|
||||
batching_task: Notify::new(),
|
||||
});
|
||||
|
|
@ -523,50 +524,63 @@ fn send_responses(
|
|||
}
|
||||
|
||||
// Create last Token
|
||||
let token = Token {
|
||||
id: generation.token_id,
|
||||
text: generation.token_text,
|
||||
logprob: generation.token_logprob,
|
||||
special: generation.token_is_special,
|
||||
};
|
||||
|
||||
// generation.top_tokens
|
||||
|
||||
let mut top_tokens = Vec::new();
|
||||
if let Some(top_tokens_) = generation.top_tokens {
|
||||
top_tokens.extend(
|
||||
let tokens_ = generation.tokens.expect("Non empty tokens in generation");
|
||||
let n = tokens_.ids.len();
|
||||
metrics::histogram!("tgi_request_skipped_tokens", (n - 1) as f64);
|
||||
let mut iterator = tokens_
|
||||
.ids
|
||||
.into_iter()
|
||||
.zip(tokens_.logprobs.into_iter())
|
||||
.zip(tokens_.texts.into_iter())
|
||||
.zip(tokens_.is_special.into_iter())
|
||||
.enumerate()
|
||||
.peekable();
|
||||
while let Some((i, (((id, logprob), text), special))) = iterator.next() {
|
||||
let token = Token {
|
||||
id,
|
||||
text,
|
||||
logprob,
|
||||
special,
|
||||
};
|
||||
let top_tokens = if let Some(top_tokens_) = generation.top_tokens.get(i) {
|
||||
top_tokens_
|
||||
.ids
|
||||
.into_iter()
|
||||
.zip(top_tokens_.logprobs.into_iter())
|
||||
.zip(top_tokens_.texts.into_iter())
|
||||
.zip(top_tokens_.is_special.into_iter())
|
||||
.map(|(((id, logprob), text), special)| Token {
|
||||
.iter()
|
||||
.zip(top_tokens_.logprobs.iter())
|
||||
.zip(top_tokens_.texts.iter())
|
||||
.zip(top_tokens_.is_special.iter())
|
||||
.map(|(((&id, &logprob), text), &special)| Token {
|
||||
id,
|
||||
text,
|
||||
text: text.to_string(),
|
||||
logprob,
|
||||
special,
|
||||
}),
|
||||
)
|
||||
})
|
||||
.collect()
|
||||
} else {
|
||||
vec![]
|
||||
};
|
||||
match (&generation.generated_text, iterator.peek()) {
|
||||
(Some(generated_text), None) => {
|
||||
// Generation has ended
|
||||
stopped = true;
|
||||
// Send message
|
||||
entry.response_tx.send(Ok(InferStreamResponse::End {
|
||||
token,
|
||||
top_tokens,
|
||||
generated_text: generated_text.clone(),
|
||||
queued: entry.queue_time,
|
||||
start: entry.batch_time.unwrap(),
|
||||
}))?;
|
||||
}
|
||||
_ => {
|
||||
// Send message
|
||||
entry
|
||||
.response_tx
|
||||
.send(Ok(InferStreamResponse::Intermediate { token, top_tokens }))?;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
if let Some(generated_text) = generation.generated_text {
|
||||
// Generation has ended
|
||||
stopped = true;
|
||||
// Send message
|
||||
entry.response_tx.send(Ok(InferStreamResponse::End {
|
||||
token,
|
||||
top_tokens,
|
||||
generated_text,
|
||||
queued: entry.queue_time,
|
||||
start: entry.batch_time.unwrap(),
|
||||
}))?;
|
||||
} else {
|
||||
// Send message
|
||||
entry
|
||||
.response_tx
|
||||
.send(Ok(InferStreamResponse::Intermediate { token, top_tokens }))?;
|
||||
}
|
||||
Ok(stopped)
|
||||
}
|
||||
|
||||
|
|
@ -591,7 +605,7 @@ fn send_errors(error: ClientError, entries: &mut IntMap<u64, Entry>) {
|
|||
#[derive(Debug)]
|
||||
pub(crate) enum InferStreamResponse {
|
||||
// Optional first message
|
||||
Prefill(PrefillTokens),
|
||||
Prefill(Tokens),
|
||||
// Intermediate messages
|
||||
Intermediate {
|
||||
token: Token,
|
||||
|
|
|
|||
|
|
@ -34,7 +34,12 @@ pub(crate) struct Queue {
|
|||
}
|
||||
|
||||
impl Queue {
|
||||
pub(crate) fn new(requires_padding: bool, block_size: u32, window_size: Option<u32>) -> Self {
|
||||
pub(crate) fn new(
|
||||
requires_padding: bool,
|
||||
block_size: u32,
|
||||
window_size: Option<u32>,
|
||||
speculate: u32,
|
||||
) -> Self {
|
||||
// Create channel
|
||||
let (queue_sender, queue_receiver) = mpsc::unbounded_channel();
|
||||
|
||||
|
|
@ -43,6 +48,7 @@ impl Queue {
|
|||
requires_padding,
|
||||
block_size,
|
||||
window_size,
|
||||
speculate,
|
||||
queue_receiver,
|
||||
));
|
||||
|
||||
|
|
@ -91,9 +97,10 @@ async fn queue_task(
|
|||
requires_padding: bool,
|
||||
block_size: u32,
|
||||
window_size: Option<u32>,
|
||||
speculate: u32,
|
||||
mut receiver: mpsc::UnboundedReceiver<QueueCommand>,
|
||||
) {
|
||||
let mut state = State::new(requires_padding, block_size, window_size);
|
||||
let mut state = State::new(requires_padding, block_size, window_size, speculate);
|
||||
|
||||
while let Some(cmd) = receiver.recv().await {
|
||||
match cmd {
|
||||
|
|
@ -136,10 +143,18 @@ struct State {
|
|||
|
||||
/// Sliding window
|
||||
window_size: Option<u32>,
|
||||
|
||||
/// Speculation amount
|
||||
speculate: u32,
|
||||
}
|
||||
|
||||
impl State {
|
||||
fn new(requires_padding: bool, block_size: u32, window_size: Option<u32>) -> Self {
|
||||
fn new(
|
||||
requires_padding: bool,
|
||||
block_size: u32,
|
||||
window_size: Option<u32>,
|
||||
speculate: u32,
|
||||
) -> Self {
|
||||
Self {
|
||||
entries: VecDeque::with_capacity(128),
|
||||
next_id: 0,
|
||||
|
|
@ -147,6 +162,7 @@ impl State {
|
|||
requires_padding,
|
||||
block_size,
|
||||
window_size,
|
||||
speculate,
|
||||
}
|
||||
}
|
||||
|
||||
|
|
@ -229,7 +245,7 @@ impl State {
|
|||
}
|
||||
|
||||
if prefill_tokens > prefill_token_budget
|
||||
|| (prefill_tokens + decode_tokens) > token_budget
|
||||
|| (prefill_tokens + decode_tokens + self.speculate) > token_budget
|
||||
{
|
||||
// Entry is over budget
|
||||
// Add it back to the front
|
||||
|
|
@ -359,7 +375,7 @@ mod tests {
|
|||
|
||||
#[test]
|
||||
fn test_append() {
|
||||
let mut state = State::new(false, 1, None);
|
||||
let mut state = State::new(false, 1, None, 0);
|
||||
let (entry, _guard) = default_entry();
|
||||
|
||||
assert_eq!(state.next_id, 0);
|
||||
|
|
@ -375,7 +391,7 @@ mod tests {
|
|||
|
||||
#[test]
|
||||
fn test_next_batch_empty() {
|
||||
let mut state = State::new(false, 1, None);
|
||||
let mut state = State::new(false, 1, None, 0);
|
||||
|
||||
assert!(state.next_batch(None, 1, 1).is_none());
|
||||
assert!(state.next_batch(Some(1), 1, 1).is_none());
|
||||
|
|
@ -383,7 +399,7 @@ mod tests {
|
|||
|
||||
#[test]
|
||||
fn test_next_batch_min_size() {
|
||||
let mut state = State::new(false, 1, None);
|
||||
let mut state = State::new(false, 1, None, 0);
|
||||
let (entry1, _guard1) = default_entry();
|
||||
let (entry2, _guard2) = default_entry();
|
||||
state.append(entry1);
|
||||
|
|
@ -415,7 +431,7 @@ mod tests {
|
|||
|
||||
#[test]
|
||||
fn test_next_batch_token_budget() {
|
||||
let mut state = State::new(false, 1, None);
|
||||
let mut state = State::new(false, 1, None, 0);
|
||||
let (entry1, _guard1) = default_entry();
|
||||
let (entry2, _guard2) = default_entry();
|
||||
state.append(entry1);
|
||||
|
|
@ -448,14 +464,14 @@ mod tests {
|
|||
|
||||
#[tokio::test]
|
||||
async fn test_queue_append() {
|
||||
let queue = Queue::new(false, 1, None);
|
||||
let queue = Queue::new(false, 1, None, 0);
|
||||
let (entry, _guard) = default_entry();
|
||||
queue.append(entry);
|
||||
}
|
||||
|
||||
#[tokio::test]
|
||||
async fn test_queue_next_batch_empty() {
|
||||
let queue = Queue::new(false, 1, None);
|
||||
let queue = Queue::new(false, 1, None, 0);
|
||||
|
||||
assert!(queue.next_batch(None, 1, 1).await.is_none());
|
||||
assert!(queue.next_batch(Some(1), 1, 1).await.is_none());
|
||||
|
|
@ -463,7 +479,7 @@ mod tests {
|
|||
|
||||
#[tokio::test]
|
||||
async fn test_queue_next_batch_min_size() {
|
||||
let queue = Queue::new(false, 1, None);
|
||||
let queue = Queue::new(false, 1, None, 0);
|
||||
let (entry1, _guard1) = default_entry();
|
||||
let (entry2, _guard2) = default_entry();
|
||||
queue.append(entry1);
|
||||
|
|
@ -496,7 +512,7 @@ mod tests {
|
|||
|
||||
#[tokio::test]
|
||||
async fn test_queue_next_batch_token_budget() {
|
||||
let queue = Queue::new(false, 1, None);
|
||||
let queue = Queue::new(false, 1, None, 0);
|
||||
let (entry1, _guard1) = default_entry();
|
||||
let (entry2, _guard2) = default_entry();
|
||||
queue.append(entry1);
|
||||
|
|
@ -519,9 +535,28 @@ mod tests {
|
|||
assert_eq!(batch.size, 2);
|
||||
}
|
||||
|
||||
#[tokio::test]
|
||||
async fn test_queue_next_batch_token_speculate() {
|
||||
let queue = Queue::new(false, 1, None, 2);
|
||||
let (entry1, _guard1) = default_entry();
|
||||
let (entry2, _guard2) = default_entry();
|
||||
queue.append(entry1);
|
||||
queue.append(entry2);
|
||||
|
||||
// Budget of 1 is not enough
|
||||
assert!(queue.next_batch(None, 1, 1).await.is_none());
|
||||
|
||||
let (entries, batch, _) = queue.next_batch(None, 6, 6).await.unwrap();
|
||||
assert_eq!(entries.len(), 2);
|
||||
assert!(entries.contains_key(&0));
|
||||
assert!(entries.contains_key(&1));
|
||||
assert_eq!(batch.id, 0);
|
||||
assert_eq!(batch.size, 2);
|
||||
}
|
||||
|
||||
#[tokio::test]
|
||||
async fn test_queue_next_batch_dropped_receiver() {
|
||||
let queue = Queue::new(false, 1, None);
|
||||
let queue = Queue::new(false, 1, None, 0);
|
||||
let (entry, _) = default_entry();
|
||||
queue.append(entry);
|
||||
|
||||
|
|
|
|||
|
|
@ -596,6 +596,7 @@ pub async fn run(
|
|||
max_concurrent_requests,
|
||||
shard_info.requires_padding,
|
||||
shard_info.window_size,
|
||||
shard_info.speculate,
|
||||
generation_health,
|
||||
);
|
||||
|
||||
|
|
|
|||
|
|
@ -1,22 +1,25 @@
|
|||
build-vllm-cuda: REPOSITORY=https://github.com/vllm-project/vllm.git
|
||||
build-vllm-cuda: VLLM_COMMIT=f8a1e39fae05ca610be8d5a78be9d40f5274e5fc
|
||||
build-vllm-cuda: BRANCH=main
|
||||
build-vllm-cuda: build-vllm
|
||||
|
||||
build-vllm-rocm: REPOSITORY=https://github.com/fxmarty/vllm-public.git
|
||||
build-vllm-rocm: VLLM_COMMIT=ad9b7c4095ef54419a0533d254f2ad84bd2dfcae
|
||||
build-vllm-rocm: BRANCH=rotary-no-positions-split-cos-sin
|
||||
build-vllm-rocm: build-vllm
|
||||
|
||||
vllm:
|
||||
vllm-cuda:
|
||||
# Clone vllm
|
||||
pip install -U ninja packaging --no-cache-dir
|
||||
git clone --single-branch --branch $(BRANCH) $(REPOSITORY) vllm
|
||||
git clone https://github.com/vllm-project/vllm.git vllm
|
||||
|
||||
build-vllm: vllm
|
||||
cd vllm && git fetch && git checkout $(VLLM_COMMIT)
|
||||
build-vllm-cuda: vllm-cuda
|
||||
cd vllm && git fetch && git checkout f8a1e39fae05ca610be8d5a78be9d40f5274e5fc
|
||||
cd vllm && python setup.py build
|
||||
|
||||
install-vllm: build-vllm
|
||||
install-vllm-cuda: build-vllm-cuda
|
||||
pip uninstall vllm -y || true
|
||||
cd vllm && python setup.py install
|
||||
|
||||
vllm-rocm:
|
||||
# Clone vllm
|
||||
pip install -U ninja packaging --no-cache-dir
|
||||
git clone https://github.com/fxmarty/vllm-public.git vllm
|
||||
|
||||
build-vllm-rocm: vllm-rocm
|
||||
cd vllm && git fetch && git checkout ad9b7c4095ef54419a0533d254f2ad84bd2dfcae
|
||||
cd vllm && python setup.py build
|
||||
|
||||
install-vllm-rocm: build-vllm-rocm
|
||||
pip uninstall vllm -y || true
|
||||
cd vllm && python setup.py install
|
||||
|
|
|
|||
|
|
@ -133,8 +133,8 @@ def test_causal_lm_generate_token(default_bloom, default_bloom_batch):
|
|||
)
|
||||
assert all([generation.generated_text is None for generation in generations])
|
||||
assert all([len(generation.prefill_tokens) == 1 for generation in generations])
|
||||
assert all([generation.token_id.item() == 10264 for generation in generations])
|
||||
assert all([generation.token_text == "Test" for generation in generations])
|
||||
assert all([token_id.item() == 10264 for generation in generations for token_id in generation.tokens.token_ids])
|
||||
assert all([token_text == "Test" for generation in generations for token_text in generation.tokens.texts])
|
||||
assert generations[0].request_id == 0
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -129,8 +129,8 @@ def test_causal_lm_generate_token(default_causal_lm, default_causal_lm_batch):
|
|||
)
|
||||
assert all([generation.generated_text is None for generation in generations])
|
||||
assert all([len(generation.prefill_tokens) == 1 for generation in generations])
|
||||
assert all([generation.token_id.item() == 13 for generation in generations])
|
||||
assert all([generation.token_text == "." for generation in generations])
|
||||
assert all([token_id.item() == 13 for generation in generations for token_id in generation.tokens.token_ids])
|
||||
assert all([token_text == "." for generation in generations for token_text in generation.tokens.texts])
|
||||
assert generations[0].request_id == 0
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -151,8 +151,8 @@ def test_seq2seq_lm_generate_token(default_seq2seq_lm, default_seq2seq_lm_batch)
|
|||
)
|
||||
assert all([generation.generated_text is None for generation in generations])
|
||||
assert all([len(generation.prefill_tokens) == 1 for generation in generations])
|
||||
assert all([generation.token_id.item() == 259 for generation in generations])
|
||||
assert all([generation.token_text == " " for generation in generations])
|
||||
assert all([token_id.item() == 259 for generation in generations for token_id in generation.tokens.token_ids])
|
||||
assert all([token_text == " " for generation in generations for token_text in generation.tokens.texts])
|
||||
assert generations[0].request_id == 0
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -32,6 +32,7 @@ def serve(
|
|||
revision: Optional[str] = None,
|
||||
sharded: bool = False,
|
||||
quantize: Optional[Quantization] = None,
|
||||
speculate: Optional[int] = None,
|
||||
dtype: Optional[Dtype] = None,
|
||||
trust_remote_code: bool = False,
|
||||
uds_path: Path = "/tmp/text-generation-server",
|
||||
|
|
@ -81,7 +82,7 @@ def serve(
|
|||
"Only 1 can be set between `dtype` and `quantize`, as they both decide how goes the final model."
|
||||
)
|
||||
server.serve(
|
||||
model_id, revision, sharded, quantize, dtype, trust_remote_code, uds_path
|
||||
model_id, revision, sharded, quantize, speculate, dtype, trust_remote_code, uds_path
|
||||
)
|
||||
|
||||
|
||||
|
|
@ -116,7 +117,7 @@ def download_weights(
|
|||
logger.info("Files are already present on the host. " "Skipping download.")
|
||||
return
|
||||
# Local files not found
|
||||
except (utils.LocalEntryNotFoundError, FileNotFoundError):
|
||||
except (utils.LocalEntryNotFoundError, FileNotFoundError, utils.EntryNotFoundError):
|
||||
pass
|
||||
|
||||
is_local_model = (Path(model_id).exists() and Path(model_id).is_dir()) or os.getenv(
|
||||
|
|
@ -137,6 +138,29 @@ def download_weights(
|
|||
except (utils.LocalEntryNotFoundError, utils.EntryNotFoundError):
|
||||
pass
|
||||
|
||||
try:
|
||||
import json
|
||||
medusa_head = hf_hub_download(model_id, revision=revision, filename="medusa_lm_head.pt")
|
||||
if auto_convert:
|
||||
medusa_sf = Path(medusa_head[:-len(".pt")] + ".safetensors")
|
||||
if not medusa_sf.exists():
|
||||
utils.convert_files([Path(medusa_head)], [medusa_sf], [])
|
||||
medusa_config = hf_hub_download(model_id, revision=revision, filename="config.json")
|
||||
with open(medusa_config, "r") as f:
|
||||
config = json.load(f)
|
||||
|
||||
model_id = config["base_model_name_or_path"]
|
||||
revision = "main"
|
||||
try:
|
||||
utils.weight_files(model_id, revision, extension)
|
||||
logger.info(f"Files for parent {model_id} are already present on the host. " "Skipping download.")
|
||||
return
|
||||
# Local files not found
|
||||
except (utils.LocalEntryNotFoundError, FileNotFoundError, utils.EntryNotFoundError):
|
||||
pass
|
||||
except (utils.LocalEntryNotFoundError, utils.EntryNotFoundError):
|
||||
pass
|
||||
|
||||
# Try to download weights from the hub
|
||||
try:
|
||||
filenames = utils.weight_hub_files(model_id, revision, extension)
|
||||
|
|
|
|||
|
|
@ -6,6 +6,7 @@ from transformers.configuration_utils import PretrainedConfig
|
|||
from transformers.models.auto import modeling_auto
|
||||
from typing import Optional
|
||||
|
||||
from text_generation_server.utils.speculate import get_speculate, set_speculate
|
||||
from text_generation_server.models.model import Model
|
||||
from text_generation_server.models.causal_lm import CausalLM
|
||||
from text_generation_server.models.flash_causal_lm import FlashCausalLM
|
||||
|
|
@ -77,12 +78,12 @@ except ImportError as e:
|
|||
if MISTRAL:
|
||||
__all__.append(FlashMistral)
|
||||
|
||||
|
||||
def get_model(
|
||||
model_id: str,
|
||||
revision: Optional[str],
|
||||
sharded: bool,
|
||||
quantize: Optional[str],
|
||||
speculate: Optional[int],
|
||||
dtype: Optional[str],
|
||||
trust_remote_code: bool,
|
||||
) -> Model:
|
||||
|
|
@ -97,6 +98,11 @@ def get_model(
|
|||
else:
|
||||
raise RuntimeError(f"Unknown dtype {dtype}")
|
||||
|
||||
if speculate is not None:
|
||||
set_speculate(speculate)
|
||||
else:
|
||||
set_speculate(0)
|
||||
|
||||
if "facebook/galactica" in model_id:
|
||||
return GalacticaSharded(
|
||||
model_id,
|
||||
|
|
@ -131,6 +137,33 @@ def get_model(
|
|||
config_dict, _ = PretrainedConfig.get_config_dict(
|
||||
model_id, revision=revision, trust_remote_code=trust_remote_code
|
||||
)
|
||||
|
||||
use_medusa = None
|
||||
if "medusa_num_heads" in config_dict:
|
||||
use_medusa = model_id
|
||||
medusa_config = config_dict
|
||||
model_id = config_dict["base_model_name_or_path"]
|
||||
revision = "main"
|
||||
speculate_medusa = config_dict["medusa_num_heads"]
|
||||
if speculate is not None:
|
||||
if speculate > speculate_medusa:
|
||||
raise RuntimeError("Speculate is set to `{speculate}` but this medusa models only has `{speculate_medusa}` heads, please make them match")
|
||||
else:
|
||||
set_speculate(speculate)
|
||||
else:
|
||||
set_speculate(speculate_medusa)
|
||||
|
||||
config_dict, _ = PretrainedConfig.get_config_dict(
|
||||
model_id, revision=revision, trust_remote_code=trust_remote_code
|
||||
)
|
||||
method = "medusa"
|
||||
else:
|
||||
method = "n-gram"
|
||||
|
||||
speculate = get_speculate()
|
||||
if speculate > 0:
|
||||
logger.info(f"Using speculation {method} with {speculate} input ids.")
|
||||
|
||||
model_type = config_dict["model_type"]
|
||||
|
||||
if model_type == "gpt_bigcode":
|
||||
|
|
@ -206,6 +239,7 @@ def get_model(
|
|||
quantize=quantize,
|
||||
dtype=dtype,
|
||||
trust_remote_code=trust_remote_code,
|
||||
use_medusa=use_medusa
|
||||
)
|
||||
elif sharded:
|
||||
raise NotImplementedError(FLASH_ATT_ERROR_MESSAGE.format("Sharded Llama"))
|
||||
|
|
|
|||
|
|
@ -10,10 +10,9 @@ from typing import Optional, Tuple, List, Type, Dict
|
|||
from text_generation_server.models import Model
|
||||
from text_generation_server.models.types import (
|
||||
Batch,
|
||||
PrefillTokens,
|
||||
Tokens,
|
||||
Generation,
|
||||
GeneratedText,
|
||||
TopTokens,
|
||||
)
|
||||
from text_generation_server.pb import generate_pb2
|
||||
from text_generation_server.utils import NextTokenChooser, StoppingCriteria, Sampling
|
||||
|
|
@ -676,8 +675,8 @@ class CausalLM(Model):
|
|||
clean_up_tokenization_spaces=False,
|
||||
skip_special_tokens=False,
|
||||
)
|
||||
prefill_tokens = PrefillTokens(
|
||||
prefill_token_ids, prefill_logprobs, prefill_texts
|
||||
prefill_tokens = Tokens(
|
||||
prefill_token_ids, prefill_logprobs, prefill_texts, is_special=[]
|
||||
)
|
||||
else:
|
||||
prefill_tokens = None
|
||||
|
|
@ -691,7 +690,7 @@ class CausalLM(Model):
|
|||
special_toptokens = [
|
||||
token_id in self.all_special_ids for token_id in top_token_ids
|
||||
]
|
||||
top_tokens = TopTokens(
|
||||
top_tokens = Tokens(
|
||||
top_token_ids,
|
||||
top_token_logprobs,
|
||||
toptoken_texts,
|
||||
|
|
@ -703,10 +702,12 @@ class CausalLM(Model):
|
|||
generation = Generation(
|
||||
request.id,
|
||||
prefill_tokens,
|
||||
next_token_id_squeezed,
|
||||
next_token_logprob,
|
||||
next_token_text,
|
||||
next_token_id_squeezed.item() in self.all_special_ids,
|
||||
Tokens(
|
||||
[next_token_id_squeezed],
|
||||
[next_token_logprob],
|
||||
[next_token_text],
|
||||
[next_token_id_squeezed.item() in self.all_special_ids],
|
||||
),
|
||||
generated_text,
|
||||
top_tokens,
|
||||
)
|
||||
|
|
|
|||
|
|
@ -11,13 +11,13 @@ from opentelemetry import trace
|
|||
from transformers import PreTrainedTokenizerBase
|
||||
from typing import Optional, Tuple, List, Type, Union, Dict
|
||||
|
||||
from text_generation_server.models import Model
|
||||
from text_generation_server.models import Model
|
||||
from text_generation_server.utils.speculate import get_speculate
|
||||
from text_generation_server.models.types import (
|
||||
Batch,
|
||||
PrefillTokens,
|
||||
Tokens,
|
||||
Generation,
|
||||
GeneratedText,
|
||||
TopTokens,
|
||||
)
|
||||
from text_generation_server.models.cache_manager import (
|
||||
get_cache_manager,
|
||||
|
|
@ -41,6 +41,7 @@ class FlashCausalLMBatch(Batch):
|
|||
# Decoder values
|
||||
input_ids: torch.Tensor
|
||||
position_ids: torch.Tensor
|
||||
speculative_ids: torch.Tensor
|
||||
|
||||
# Flash Attention values
|
||||
|
||||
|
|
@ -120,6 +121,7 @@ class FlashCausalLMBatch(Batch):
|
|||
)["input_ids"]
|
||||
|
||||
position_ids = []
|
||||
speculative_ids = []
|
||||
cu_seqlen_prefill = [0]
|
||||
needed_blocks_slots = []
|
||||
start_slots = []
|
||||
|
|
@ -163,6 +165,8 @@ class FlashCausalLMBatch(Batch):
|
|||
input_length = len(tokenized_input)
|
||||
input_lengths.append(input_length)
|
||||
|
||||
|
||||
|
||||
prefix_offsets.append(input_length - 5)
|
||||
read_offsets.append(input_length)
|
||||
|
||||
|
|
@ -186,7 +190,8 @@ class FlashCausalLMBatch(Batch):
|
|||
|
||||
# Paged attention
|
||||
# Remove one as the first token des not have a past
|
||||
total_tokens = input_length + max_new_tokens - 1
|
||||
speculative_length = get_speculate()
|
||||
total_tokens = input_length + max_new_tokens - 1 + speculative_length
|
||||
needed_blocks = math.ceil(total_tokens / BLOCK_SIZE)
|
||||
blocks += needed_blocks
|
||||
needed_blocks_slots.append((needed_blocks, total_tokens))
|
||||
|
|
@ -224,7 +229,7 @@ class FlashCausalLMBatch(Batch):
|
|||
cumulative_max_length += total_tokens
|
||||
max_seqlen = max(max_seqlen, input_length)
|
||||
max_blocks = max(max_blocks, needed_blocks)
|
||||
max_length = max(max_length, input_length + max_new_tokens)
|
||||
max_length = max(max_length, input_length + max_new_tokens + speculative_length)
|
||||
|
||||
next_token_chooser = HeterogeneousNextTokenChooser.from_pb(
|
||||
next_token_chooser_parameters, dtype, device
|
||||
|
|
@ -255,7 +260,6 @@ class FlashCausalLMBatch(Batch):
|
|||
cu_seqlen_prefill = torch.tensor(
|
||||
cu_seqlen_prefill, device=device, dtype=torch.int32
|
||||
)
|
||||
|
||||
position_ids = position_ids.to(device)
|
||||
slot_indices = slot_indices.to(device)
|
||||
input_ids = torch.tensor(input_ids, dtype=torch.int64, device=device)
|
||||
|
|
@ -309,6 +313,7 @@ class FlashCausalLMBatch(Batch):
|
|||
top_n_tokens_tensor=top_n_tokens_tensor,
|
||||
blocks=blocks,
|
||||
max_blocks=max_blocks,
|
||||
speculative_ids=None,
|
||||
)
|
||||
|
||||
@tracer.start_as_current_span("filter")
|
||||
|
|
@ -419,6 +424,7 @@ class FlashCausalLMBatch(Batch):
|
|||
slots = self.slots[slot_filtering_indices]
|
||||
next_token_chooser = self.next_token_chooser.filter(indices)
|
||||
top_n_tokens_tensor = self.top_n_tokens_tensor[indices]
|
||||
speculative_ids = self.speculative_ids[indices] if self.speculative_ids is not None else None
|
||||
|
||||
start_slots = torch.tensor(start_slots, dtype=torch.int64)
|
||||
|
||||
|
|
@ -454,6 +460,7 @@ class FlashCausalLMBatch(Batch):
|
|||
top_n_tokens_tensor=top_n_tokens_tensor,
|
||||
blocks=blocks,
|
||||
max_blocks=max_blocks,
|
||||
speculative_ids=speculative_ids,
|
||||
)
|
||||
|
||||
@classmethod
|
||||
|
|
@ -473,6 +480,7 @@ class FlashCausalLMBatch(Batch):
|
|||
total_batch_size += len(b)
|
||||
total_slots += len(b.slots)
|
||||
blocks += b.blocks
|
||||
speculative_length = b.speculative_ids.shape[1] if b.speculative_ids is not None else 0
|
||||
max_blocks = max(max_blocks, b.max_blocks)
|
||||
max_seqlen = max(max_seqlen, b.max_seqlen)
|
||||
max_length = max(
|
||||
|
|
@ -480,6 +488,7 @@ class FlashCausalLMBatch(Batch):
|
|||
max(
|
||||
input_length
|
||||
+ stopping_criteria.max_new_tokens
|
||||
+ speculative_length
|
||||
- stopping_criteria.current_tokens
|
||||
for input_length, stopping_criteria in zip(
|
||||
b.input_lengths, b.stopping_criterias
|
||||
|
|
@ -577,6 +586,8 @@ class FlashCausalLMBatch(Batch):
|
|||
device=batches[0].next_token_chooser.device,
|
||||
)
|
||||
|
||||
speculative_ids = torch.cat([b.speculative_ids for b in batches], dim=0) if batches[0].speculative_ids is not None else None
|
||||
|
||||
# Needed to avoid dropping blocks when the batches will go out of scope
|
||||
for b in batches:
|
||||
b.block_tables = None
|
||||
|
|
@ -611,6 +622,7 @@ class FlashCausalLMBatch(Batch):
|
|||
top_n_tokens_tensor=top_n_tokens_tensor,
|
||||
blocks=blocks,
|
||||
max_blocks=max_blocks,
|
||||
speculative_ids=speculative_ids
|
||||
)
|
||||
|
||||
def __del__(self):
|
||||
|
|
@ -714,16 +726,55 @@ class FlashCausalLM(Model):
|
|||
|
||||
def forward(self, batch: FlashCausalLMBatch) -> Tuple[torch.Tensor, torch.Tensor]:
|
||||
# Model Forward
|
||||
if batch.speculative_ids is not None:
|
||||
input_ids=batch.input_ids
|
||||
position_ids=batch.position_ids
|
||||
cu_seqlen_prefill=batch.cu_seqlen_prefill
|
||||
kv_cache=get_cache_manager().kv_cache
|
||||
block_tables=batch.block_tables_tensor
|
||||
slots=batch.slots[batch.slot_indices]
|
||||
input_lengths=batch.input_lengths_tensor
|
||||
max_s=batch.max_seqlen
|
||||
lm_head_indices=batch.prefill_head_indices
|
||||
|
||||
speculative_ids = batch.speculative_ids
|
||||
|
||||
B, speculative_length = speculative_ids.shape
|
||||
new_length = speculative_length + 1
|
||||
new_input_ids = torch.cat([input_ids.unsqueeze(-1), speculative_ids], dim=1).reshape(-1)
|
||||
arange = torch.arange(new_length, device=position_ids.device).unsqueeze(0)
|
||||
arange_int = arange.to(dtype=torch.int32)
|
||||
new_position_ids = (position_ids.unsqueeze(-1).expand(B, new_length) + arange).view(-1)
|
||||
slots = (slots.unsqueeze(-1).expand(B, new_length) + arange_int).view(-1)
|
||||
input_lengths = (input_lengths.unsqueeze(-1).expand(B, new_length) + arange_int).view(-1)
|
||||
|
||||
# Add Copy the block tables for all members
|
||||
block_tables = block_tables.unsqueeze(1).expand(B, new_length, -1).reshape(B* new_length, -1).contiguous()
|
||||
max_s = max_s + speculative_length
|
||||
|
||||
input_ids = new_input_ids
|
||||
position_ids = new_position_ids
|
||||
else:
|
||||
input_ids=batch.input_ids
|
||||
position_ids=batch.position_ids
|
||||
cu_seqlen_prefill=batch.cu_seqlen_prefill
|
||||
kv_cache=get_cache_manager().kv_cache
|
||||
block_tables=batch.block_tables_tensor
|
||||
slots=batch.slots[batch.slot_indices]
|
||||
input_lengths=batch.input_lengths_tensor
|
||||
max_s=batch.max_seqlen
|
||||
lm_head_indices=batch.prefill_head_indices
|
||||
|
||||
return self.model.forward(
|
||||
input_ids=batch.input_ids,
|
||||
position_ids=batch.position_ids,
|
||||
cu_seqlen_prefill=batch.cu_seqlen_prefill,
|
||||
kv_cache=get_cache_manager().kv_cache,
|
||||
block_tables=batch.block_tables_tensor,
|
||||
slots=batch.slots[batch.slot_indices],
|
||||
input_lengths=batch.input_lengths_tensor,
|
||||
max_s=batch.max_seqlen,
|
||||
lm_head_indices=batch.prefill_head_indices,
|
||||
input_ids=input_ids,
|
||||
position_ids=position_ids,
|
||||
cu_seqlen_prefill=cu_seqlen_prefill,
|
||||
kv_cache=kv_cache,
|
||||
block_tables=block_tables,
|
||||
slots=slots,
|
||||
input_lengths=input_lengths,
|
||||
max_s=max_s,
|
||||
lm_head_indices=lm_head_indices,
|
||||
)
|
||||
|
||||
@tracer.start_as_current_span("generate_token")
|
||||
|
|
@ -752,21 +803,32 @@ class FlashCausalLM(Model):
|
|||
del batch
|
||||
raise e
|
||||
|
||||
if isinstance(out, tuple):
|
||||
out, speculative_logits = out
|
||||
else:
|
||||
speculative_logits = None
|
||||
|
||||
|
||||
if prefill:
|
||||
next_token_logits = (
|
||||
out[batch.prefill_next_token_indices] if prefill_logprobs else out
|
||||
)
|
||||
if speculative_logits is not None:
|
||||
speculative_logits = (
|
||||
speculative_logits[batch.prefill_next_token_indices] if prefill_logprobs else speculative_logits
|
||||
)
|
||||
else:
|
||||
next_token_logits = out
|
||||
|
||||
next_input_ids, next_token_logprobs, logprobs = batch.next_token_chooser(
|
||||
batch.all_input_ids_tensor[:, : batch.max_seqlen], next_token_logits
|
||||
next_input_ids, next_token_logprobs, logprobs, accepted_ids, speculative_ids = batch.next_token_chooser(
|
||||
batch.all_input_ids_tensor[:, : batch.max_seqlen], next_token_logits, get_speculate(), batch.speculative_ids, speculative_logits
|
||||
)
|
||||
|
||||
batch_top_token_ids, batch_top_token_logprobs = batch_top_tokens(
|
||||
batch.top_n_tokens, batch.top_n_tokens_tensor, logprobs
|
||||
)
|
||||
|
||||
speculative_length = 0 if speculative_ids is None else speculative_ids.shape[1]
|
||||
if prefill:
|
||||
if len(batch) > 1 and prefill_logprobs:
|
||||
# We create the prefill_tokens_indices tensor that will be used to gather prefill logprobs
|
||||
|
|
@ -792,6 +854,7 @@ class FlashCausalLM(Model):
|
|||
iterator = zip(
|
||||
batch.input_lengths,
|
||||
batch.all_input_ids,
|
||||
accepted_ids
|
||||
)
|
||||
|
||||
# We do two for loops as the first one can run completely asynchronously from the GPU while for the second
|
||||
|
|
@ -799,9 +862,11 @@ class FlashCausalLM(Model):
|
|||
# It is faster if we delay this sync for the maximum amount of time
|
||||
|
||||
# For each member of the batch
|
||||
index = 0
|
||||
for i, (
|
||||
input_length,
|
||||
all_input_ids,
|
||||
n_accepted_ids
|
||||
) in enumerate(iterator):
|
||||
# Indexing metadata
|
||||
start_index = cumulative_length
|
||||
|
|
@ -830,15 +895,18 @@ class FlashCausalLM(Model):
|
|||
start_index + 1 : start_index + out_length
|
||||
]
|
||||
|
||||
batch.all_input_ids_tensor[i, input_length] = next_input_ids[i]
|
||||
for j in range(n_accepted_ids):
|
||||
batch.all_input_ids_tensor[i, input_length + j] = next_input_ids[index]
|
||||
index += 1
|
||||
|
||||
cumulative_length += input_length
|
||||
|
||||
# Set values in batch
|
||||
batch.input_ids = next_input_ids
|
||||
batch.position_ids = next_position_ids + 1
|
||||
batch.input_lengths_tensor += 1
|
||||
batch.slot_indices += 1
|
||||
|
||||
batch.input_ids = next_input_ids[accepted_ids.cumsum(dim=-1) - 1]
|
||||
batch.speculative_ids = speculative_ids
|
||||
batch.position_ids = next_position_ids + accepted_ids
|
||||
batch.input_lengths_tensor += accepted_ids
|
||||
batch.slot_indices += accepted_ids
|
||||
|
||||
if prefill and prefill_logprobs:
|
||||
# Get prefill logprobs
|
||||
|
|
@ -851,7 +919,7 @@ class FlashCausalLM(Model):
|
|||
|
||||
# GPU <-> CPU sync
|
||||
next_token_logprobs = next_token_logprobs.tolist()
|
||||
next_token_ids = batch.input_ids.tolist()
|
||||
next_token_ids = next_input_ids.tolist()
|
||||
|
||||
# Zipped iterator
|
||||
iterator = zip(
|
||||
|
|
@ -864,13 +932,13 @@ class FlashCausalLM(Model):
|
|||
batch.next_token_chooser.do_sample,
|
||||
batch.next_token_chooser.seeds,
|
||||
batch.top_n_tokens,
|
||||
next_token_ids,
|
||||
next_token_logprobs,
|
||||
accepted_ids,
|
||||
batch_top_token_ids,
|
||||
batch_top_token_logprobs,
|
||||
)
|
||||
|
||||
# For each member of the batch
|
||||
index = 0
|
||||
for i, (
|
||||
request,
|
||||
input_length,
|
||||
|
|
@ -881,29 +949,43 @@ class FlashCausalLM(Model):
|
|||
do_sample,
|
||||
seed,
|
||||
top_n_tokens,
|
||||
next_token_id,
|
||||
next_token_logprob,
|
||||
n_accepted_ids,
|
||||
top_token_ids,
|
||||
top_token_logprobs,
|
||||
) in enumerate(iterator):
|
||||
# Append next token to all tokens
|
||||
all_input_ids.append(next_token_id)
|
||||
next_token_texts = []
|
||||
left = 0
|
||||
before = stopping_criteria.current_tokens
|
||||
|
||||
# Generated token
|
||||
next_token_text, prefix_offset, read_offset = self.decode_token(
|
||||
all_input_ids,
|
||||
prefix_offset,
|
||||
read_offset,
|
||||
)
|
||||
current_stopped = False
|
||||
for j in range(index, index + n_accepted_ids):
|
||||
# Generated token
|
||||
next_token_id = next_token_ids[j]
|
||||
all_input_ids.append(next_token_id)
|
||||
next_token_text, prefix_offset, read_offset = self.decode_token(
|
||||
all_input_ids,
|
||||
prefix_offset,
|
||||
read_offset,
|
||||
)
|
||||
next_token_texts.append(next_token_text)
|
||||
|
||||
# Evaluate stopping criteria
|
||||
stop, reason = stopping_criteria(
|
||||
next_token_id,
|
||||
next_token_text,
|
||||
)
|
||||
stop, reason = stopping_criteria(
|
||||
next_token_id,
|
||||
next_token_text,
|
||||
)
|
||||
|
||||
if not stop:
|
||||
stopped = False
|
||||
if stop:
|
||||
left = index + n_accepted_ids - j - 1
|
||||
current_stopped = True
|
||||
break
|
||||
else:
|
||||
current_stopped = False
|
||||
stopped = stopped and current_stopped
|
||||
|
||||
_next_token_ids = next_token_ids[index: index+n_accepted_ids - left]
|
||||
_next_token_logprobs = next_token_logprobs[index: index+n_accepted_ids - left]
|
||||
index += n_accepted_ids
|
||||
|
||||
# Shard generations
|
||||
# All generations will be appended in the rust sharded client
|
||||
|
|
@ -943,8 +1025,9 @@ class FlashCausalLM(Model):
|
|||
clean_up_tokenization_spaces=False,
|
||||
skip_special_tokens=False,
|
||||
)
|
||||
prefill_tokens = PrefillTokens(
|
||||
prefill_token_ids, request_prefill_logprobs, prefill_texts
|
||||
|
||||
prefill_tokens = Tokens(
|
||||
prefill_token_ids, request_prefill_logprobs, prefill_texts, is_special = []
|
||||
)
|
||||
else:
|
||||
prefill_tokens = None
|
||||
|
|
@ -958,7 +1041,7 @@ class FlashCausalLM(Model):
|
|||
special_toptokens = [
|
||||
token_id in self.all_special_ids for token_id in top_token_ids
|
||||
]
|
||||
top_tokens = TopTokens(
|
||||
top_tokens = Tokens(
|
||||
top_token_ids,
|
||||
top_token_logprobs,
|
||||
toptoken_texts,
|
||||
|
|
@ -970,10 +1053,12 @@ class FlashCausalLM(Model):
|
|||
generation = Generation(
|
||||
request.id,
|
||||
prefill_tokens,
|
||||
next_token_id,
|
||||
next_token_logprob,
|
||||
next_token_text,
|
||||
next_token_id in self.all_special_ids,
|
||||
Tokens(
|
||||
_next_token_ids,
|
||||
_next_token_logprobs,
|
||||
next_token_texts,
|
||||
[nid in self.all_special_ids for nid in _next_token_ids],
|
||||
),
|
||||
generated_text,
|
||||
top_tokens,
|
||||
)
|
||||
|
|
@ -981,7 +1066,9 @@ class FlashCausalLM(Model):
|
|||
generations.append(generation)
|
||||
|
||||
# Update values
|
||||
batch.input_lengths[i] = input_length + 1
|
||||
batch.input_lengths[i] = input_length + n_accepted_ids.item()
|
||||
if batch.input_lengths[i] > batch.max_seqlen:
|
||||
batch.max_seqlen = batch.input_lengths[i]
|
||||
batch.prefix_offsets[i] = prefix_offset
|
||||
batch.read_offsets[i] = read_offset
|
||||
batch.all_input_ids[i] = all_input_ids
|
||||
|
|
@ -994,6 +1081,5 @@ class FlashCausalLM(Model):
|
|||
batch.prefill_cu_outlens = None
|
||||
batch.prefill_head_indices = None
|
||||
batch.prefill_next_token_indices = None
|
||||
batch.max_seqlen = batch.max_seqlen + 1
|
||||
|
||||
return generations, batch
|
||||
|
|
|
|||
|
|
@ -28,6 +28,7 @@ class FlashLlama(FlashCausalLM):
|
|||
quantize: Optional[str] = None,
|
||||
dtype: Optional[torch.dtype] = None,
|
||||
trust_remote_code: bool = False,
|
||||
use_medusa: Optional[str] = None,
|
||||
):
|
||||
self.process_group, rank, world_size = initialize_torch_distributed()
|
||||
if torch.cuda.is_available():
|
||||
|
|
@ -66,6 +67,18 @@ class FlashLlama(FlashCausalLM):
|
|||
weights._set_gptq_params(model_id)
|
||||
|
||||
model = FlashLlamaForCausalLM(config, weights)
|
||||
if use_medusa:
|
||||
from text_generation_server.utils.medusa import MedusaModel
|
||||
from huggingface_hub import hf_hub_download
|
||||
import json
|
||||
medusa_config = hf_hub_download(use_medusa, revision=revision, filename="config.json")
|
||||
with open(medusa_config, "r") as f:
|
||||
config = json.load(f)
|
||||
medusa_head = hf_hub_download(use_medusa, revision=revision, filename="medusa_lm_head.pt")
|
||||
medusa_sf = medusa_head[:-len(".pt")] + ".safetensors"
|
||||
weights = Weights([medusa_sf], device, dtype, process_group=self.process_group)
|
||||
lm_head = model.lm_head
|
||||
model.lm_head = MedusaModel(config, weights, lm_head)
|
||||
|
||||
torch.distributed.barrier(group=self.process_group)
|
||||
super(FlashLlama, self).__init__(
|
||||
|
|
|
|||
|
|
@ -21,6 +21,7 @@ from text_generation_server.models.custom_modeling.flash_mistral_modeling import
|
|||
FlashMistralForCausalLM,
|
||||
MistralConfig,
|
||||
)
|
||||
from text_generation_server.utils.speculate import get_speculate
|
||||
from text_generation_server.utils import (
|
||||
initialize_torch_distributed,
|
||||
weight_files,
|
||||
|
|
@ -132,7 +133,8 @@ class FlashMistralBatch(FlashCausalLMBatch):
|
|||
|
||||
# Paged attention
|
||||
# Remove one as the first token des not have a past
|
||||
total_tokens = input_length + max_new_tokens - 1
|
||||
speculative_length = get_speculate()
|
||||
total_tokens = input_length + max_new_tokens - 1 + speculative_length
|
||||
|
||||
# Needed blocks can not go over SLIDING_WINDOW_BLOCKS
|
||||
needed_blocks = min(
|
||||
|
|
@ -183,7 +185,7 @@ class FlashMistralBatch(FlashCausalLMBatch):
|
|||
cumulative_max_length += total_tokens
|
||||
max_seqlen = max(max_seqlen, input_length)
|
||||
max_blocks = max(max_blocks, needed_blocks)
|
||||
max_length = max(max_length, input_length + max_new_tokens)
|
||||
max_length = max(max_length, input_length + max_new_tokens + speculative_length)
|
||||
|
||||
next_token_chooser = HeterogeneousNextTokenChooser.from_pb(
|
||||
next_token_chooser_parameters, dtype, device
|
||||
|
|
@ -272,6 +274,7 @@ class FlashMistralBatch(FlashCausalLMBatch):
|
|||
blocks=blocks,
|
||||
max_blocks=max_blocks,
|
||||
prefill_cache_indices=prefill_cache_indices,
|
||||
speculative_ids=None
|
||||
)
|
||||
|
||||
|
||||
|
|
@ -340,17 +343,55 @@ class FlashMistral(FlashCausalLM):
|
|||
|
||||
def forward(self, batch: FlashMistralBatch) -> Tuple[torch.Tensor, torch.Tensor]:
|
||||
# Model Forward
|
||||
if batch.speculative_ids is not None:
|
||||
input_ids=batch.input_ids
|
||||
position_ids=batch.position_ids
|
||||
cu_seqlen_prefill=batch.cu_seqlen_prefill
|
||||
kv_cache=get_cache_manager().kv_cache
|
||||
block_tables=batch.block_tables_tensor
|
||||
slots=batch.slots[batch.slot_indices]
|
||||
input_lengths=batch.input_lengths_tensor
|
||||
max_s=batch.max_seqlen
|
||||
lm_head_indices=batch.prefill_head_indices
|
||||
|
||||
speculative_ids = batch.speculative_ids
|
||||
|
||||
B, speculative_length = speculative_ids.shape
|
||||
new_length = speculative_length + 1
|
||||
new_input_ids = torch.cat([input_ids.unsqueeze(-1), speculative_ids], dim=1).reshape(-1)
|
||||
arange = torch.arange(new_length, device=position_ids.device).unsqueeze(0)
|
||||
arange_int = arange.to(dtype=torch.int32)
|
||||
new_position_ids = (position_ids.unsqueeze(-1).expand(B, new_length) + arange).view(-1)
|
||||
slots = (slots.unsqueeze(-1).expand(B, new_length) + arange_int).view(-1)
|
||||
input_lengths = (input_lengths.unsqueeze(-1).expand(B, new_length) + arange_int).view(-1)
|
||||
|
||||
# Add Copy the block tables for all members
|
||||
block_tables = block_tables.unsqueeze(1).expand(B, new_length, -1).reshape(B* new_length, -1).contiguous()
|
||||
max_s = max_s + speculative_length
|
||||
|
||||
input_ids = new_input_ids
|
||||
position_ids = new_position_ids
|
||||
else:
|
||||
input_ids=batch.input_ids
|
||||
position_ids=batch.position_ids
|
||||
cu_seqlen_prefill=batch.cu_seqlen_prefill
|
||||
kv_cache=get_cache_manager().kv_cache
|
||||
block_tables=batch.block_tables_tensor
|
||||
slots=batch.slots[batch.slot_indices]
|
||||
input_lengths=batch.input_lengths_tensor
|
||||
max_s=batch.max_seqlen
|
||||
lm_head_indices=batch.prefill_head_indices
|
||||
logits = self.model.forward(
|
||||
input_ids=batch.input_ids,
|
||||
position_ids=batch.position_ids,
|
||||
cu_seqlen_prefill=batch.cu_seqlen_prefill,
|
||||
kv_cache=get_cache_manager().kv_cache,
|
||||
block_tables=batch.block_tables_tensor,
|
||||
slots=batch.slots[batch.slot_indices],
|
||||
input_lengths=batch.input_lengths_tensor,
|
||||
max_s=batch.max_seqlen,
|
||||
input_ids=input_ids,
|
||||
position_ids=position_ids,
|
||||
cu_seqlen_prefill=cu_seqlen_prefill,
|
||||
kv_cache=kv_cache,
|
||||
block_tables=block_tables,
|
||||
slots=slots,
|
||||
input_lengths=input_lengths,
|
||||
max_s=max_s,
|
||||
prefill_cache_indices=batch.prefill_cache_indices,
|
||||
lm_head_indices=batch.prefill_head_indices,
|
||||
lm_head_indices=lm_head_indices,
|
||||
)
|
||||
if batch.prefill_cache_indices is not None:
|
||||
batch.prefill_cache_indices = None
|
||||
|
|
|
|||
|
|
@ -20,7 +20,7 @@ from typing import Optional, Tuple, List, Type, Dict
|
|||
from text_generation_server.models import Model
|
||||
from text_generation_server.models.types import (
|
||||
Batch,
|
||||
PrefillTokens,
|
||||
Tokens,
|
||||
Generation,
|
||||
GeneratedText,
|
||||
)
|
||||
|
|
@ -791,8 +791,8 @@ class IdeficsCausalLM(Model):
|
|||
clean_up_tokenization_spaces=False,
|
||||
skip_special_tokens=False,
|
||||
)
|
||||
prefill_tokens = PrefillTokens(
|
||||
prefill_token_ids, prefill_logprobs, prefill_texts
|
||||
prefill_tokens = Tokens(
|
||||
prefill_token_ids, prefill_logprobs, prefill_texts, is_special=[]
|
||||
)
|
||||
else:
|
||||
prefill_tokens = None
|
||||
|
|
@ -802,10 +802,12 @@ class IdeficsCausalLM(Model):
|
|||
generation = Generation(
|
||||
request.id,
|
||||
prefill_tokens,
|
||||
next_token_id_squeezed,
|
||||
next_token_logprob,
|
||||
next_token_text,
|
||||
next_token_id_squeezed.item() in self.all_special_ids,
|
||||
Tokens(
|
||||
[next_token_id_squeezed],
|
||||
[next_token_logprob],
|
||||
[next_token_text],
|
||||
[next_token_id_squeezed.item() in self.all_special_ids],
|
||||
),
|
||||
generated_text,
|
||||
top_tokens,
|
||||
)
|
||||
|
|
|
|||
|
|
@ -6,6 +6,7 @@ from typing import List, Tuple, Optional, TypeVar, Type
|
|||
from transformers import PreTrainedTokenizerBase, PretrainedConfig
|
||||
|
||||
from text_generation_server.models.types import Batch, Generation
|
||||
from text_generation_server.utils.speculate import get_speculate
|
||||
from text_generation_server.pb.generate_pb2 import InfoResponse
|
||||
|
||||
B = TypeVar("B", bound=Batch)
|
||||
|
|
@ -22,6 +23,7 @@ class Model(ABC):
|
|||
rank: int = 0,
|
||||
world_size: int = 1,
|
||||
sliding_window: Optional[int] = None,
|
||||
speculate: Optional[int] = None,
|
||||
):
|
||||
self.model = model.eval()
|
||||
self.tokenizer = tokenizer
|
||||
|
|
@ -33,6 +35,10 @@ class Model(ABC):
|
|||
self.world_size = world_size
|
||||
self.sliding_window = sliding_window
|
||||
|
||||
if speculate is None:
|
||||
speculate = get_speculate()
|
||||
self.speculate = speculate
|
||||
|
||||
self.has_position_ids = (
|
||||
inspect.signature(model.forward).parameters.get("position_ids", None)
|
||||
is not None
|
||||
|
|
@ -50,6 +56,7 @@ class Model(ABC):
|
|||
dtype=str(self.dtype),
|
||||
device_type=self.device.type,
|
||||
window_size=self.sliding_window,
|
||||
speculate=self.speculate
|
||||
)
|
||||
|
||||
@property
|
||||
|
|
|
|||
|
|
@ -11,8 +11,7 @@ from text_generation_server.models.types import (
|
|||
GeneratedText,
|
||||
Batch,
|
||||
Generation,
|
||||
PrefillTokens,
|
||||
TopTokens,
|
||||
Tokens,
|
||||
)
|
||||
from text_generation_server.pb import generate_pb2
|
||||
from text_generation_server.utils import NextTokenChooser, StoppingCriteria, Sampling
|
||||
|
|
@ -733,10 +732,11 @@ class Seq2SeqLM(Model):
|
|||
|
||||
# Prefill
|
||||
if stopping_criteria.current_tokens == 1 and request.prefill_logprobs:
|
||||
prefill_tokens = PrefillTokens(
|
||||
prefill_tokens = Tokens(
|
||||
[self.tokenizer.bos_token_id],
|
||||
[float("nan")],
|
||||
[self.tokenizer.bos_token],
|
||||
[False]
|
||||
)
|
||||
else:
|
||||
prefill_tokens = None
|
||||
|
|
@ -750,7 +750,7 @@ class Seq2SeqLM(Model):
|
|||
special_toptokens = [
|
||||
token_id in self.all_special_ids for token_id in top_token_ids
|
||||
]
|
||||
top_tokens = TopTokens(
|
||||
top_tokens = Tokens(
|
||||
top_token_ids,
|
||||
top_token_logprobs,
|
||||
toptoken_texts,
|
||||
|
|
@ -762,10 +762,12 @@ class Seq2SeqLM(Model):
|
|||
generation = Generation(
|
||||
request.id,
|
||||
prefill_tokens,
|
||||
next_token_id_squeezed,
|
||||
next_token_logprob,
|
||||
next_token_text,
|
||||
next_token_id_squeezed.item() in self.all_special_ids,
|
||||
Tokens(
|
||||
[next_token_id_squeezed],
|
||||
[next_token_logprob],
|
||||
[next_token_text],
|
||||
[next_token_id_squeezed.item() in self.all_special_ids],
|
||||
),
|
||||
generated_text,
|
||||
top_tokens,
|
||||
)
|
||||
|
|
|
|||
|
|
@ -58,33 +58,15 @@ class GeneratedText:
|
|||
|
||||
|
||||
@dataclass
|
||||
class PrefillTokens:
|
||||
token_ids: List[int]
|
||||
logprobs: List[float]
|
||||
texts: List[str]
|
||||
|
||||
def to_pb(self) -> generate_pb2.PrefillTokens:
|
||||
return generate_pb2.PrefillTokens(
|
||||
ids=self.token_ids, logprobs=self.logprobs, texts=self.texts
|
||||
)
|
||||
|
||||
def __len__(self):
|
||||
return len(self.token_ids)
|
||||
|
||||
|
||||
@dataclass
|
||||
class TopTokens:
|
||||
class Tokens:
|
||||
token_ids: List[int]
|
||||
logprobs: List[float]
|
||||
texts: List[str]
|
||||
is_special: List[bool]
|
||||
|
||||
def to_pb(self) -> generate_pb2.TopTokens:
|
||||
return generate_pb2.TopTokens(
|
||||
ids=self.token_ids,
|
||||
logprobs=self.logprobs,
|
||||
texts=self.texts,
|
||||
is_special=self.is_special,
|
||||
def to_pb(self) -> generate_pb2.Tokens:
|
||||
return generate_pb2.Tokens(
|
||||
ids=self.token_ids, logprobs=self.logprobs, texts=self.texts, is_special=self.is_special
|
||||
)
|
||||
|
||||
def __len__(self):
|
||||
|
|
@ -94,14 +76,11 @@ class TopTokens:
|
|||
@dataclass
|
||||
class Generation:
|
||||
request_id: int
|
||||
prefill_tokens: Optional[PrefillTokens]
|
||||
token_id: int
|
||||
token_logprob: float
|
||||
token_text: str
|
||||
token_is_special: bool
|
||||
prefill_tokens: Optional[Tokens]
|
||||
tokens: Tokens
|
||||
generated_text: Optional[GeneratedText]
|
||||
# Optional for now, since it's not yet supported for every model.
|
||||
top_tokens: Optional[TopTokens]
|
||||
top_tokens: Optional[List[Tokens]]
|
||||
|
||||
def to_pb(self) -> generate_pb2.Generation:
|
||||
return generate_pb2.Generation(
|
||||
|
|
@ -109,10 +88,7 @@ class Generation:
|
|||
prefill_tokens=self.prefill_tokens.to_pb()
|
||||
if self.prefill_tokens is not None
|
||||
else None,
|
||||
token_id=self.token_id,
|
||||
token_logprob=self.token_logprob,
|
||||
token_text=self.token_text,
|
||||
token_is_special=self.token_is_special,
|
||||
tokens=self.tokens.to_pb(),
|
||||
generated_text=self.generated_text.to_pb()
|
||||
if self.generated_text is not None
|
||||
else None,
|
||||
|
|
|
|||
|
|
@ -132,6 +132,7 @@ def serve(
|
|||
revision: Optional[str],
|
||||
sharded: bool,
|
||||
quantize: Optional[str],
|
||||
speculate: Optional[int],
|
||||
dtype: Optional[str],
|
||||
trust_remote_code: bool,
|
||||
uds_path: Path,
|
||||
|
|
@ -141,6 +142,7 @@ def serve(
|
|||
revision: Optional[str],
|
||||
sharded: bool = False,
|
||||
quantize: Optional[str] = None,
|
||||
speculate: Optional[int] = None,
|
||||
dtype: Optional[str] = None,
|
||||
trust_remote_code: bool = False,
|
||||
):
|
||||
|
|
@ -157,7 +159,7 @@ def serve(
|
|||
|
||||
try:
|
||||
model = get_model(
|
||||
model_id, revision, sharded, quantize, dtype, trust_remote_code
|
||||
model_id, revision, sharded, quantize, speculate, dtype, trust_remote_code
|
||||
)
|
||||
except Exception:
|
||||
logger.exception("Error when initializing model")
|
||||
|
|
@ -205,5 +207,5 @@ def serve(
|
|||
await server.stop(0)
|
||||
|
||||
asyncio.run(
|
||||
serve_inner(model_id, revision, sharded, quantize, dtype, trust_remote_code)
|
||||
serve_inner(model_id, revision, sharded, quantize, speculate, dtype, trust_remote_code)
|
||||
)
|
||||
|
|
|
|||
|
|
@ -0,0 +1,51 @@
|
|||
import torch
|
||||
from dataclasses import dataclass
|
||||
from text_generation_server.utils.layers import TensorParallelHead, FastLinear
|
||||
|
||||
@dataclass
|
||||
class Output:
|
||||
logits: torch.FloatTensor = None
|
||||
speculative_logits: torch.FloatTensor = None
|
||||
|
||||
|
||||
class ResBlock(torch.nn.Module):
|
||||
def __init__(self, config, prefix, weights):
|
||||
super().__init__()
|
||||
self.linear = FastLinear.load(config, prefix=f"{prefix}.linear", weights=weights, bias=True)
|
||||
self.act = torch.nn.SiLU()
|
||||
|
||||
def forward(self, x):
|
||||
return x + self.act(self.linear(x))
|
||||
|
||||
|
||||
class MedusaModel(torch.nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
config,
|
||||
weights,
|
||||
lm_head
|
||||
):
|
||||
super().__init__()
|
||||
self.heads = torch.nn.ModuleList(
|
||||
[MedusaHead(config, prefix=f"{i}", weights=weights) for i in range(config["medusa_num_heads"])]
|
||||
)
|
||||
self.lm_head = lm_head
|
||||
|
||||
def forward(self, x):
|
||||
logits = self.lm_head(x)
|
||||
speculative_logits = torch.stack([head(x) for head in self.heads], dim=1)
|
||||
return logits, speculative_logits
|
||||
|
||||
|
||||
class MedusaHead(torch.nn.Module):
|
||||
def __init__(self, config, prefix, weights):
|
||||
super().__init__()
|
||||
self.blocks = torch.nn.ModuleList([ResBlock(config, prefix=f"{prefix}.{i}", weights=weights) for i in range(config["medusa_num_layers"])])
|
||||
n = len(self.blocks)
|
||||
self.out = FastLinear.load(config, prefix=f"{prefix}.{n}", weights=weights, bias=False)
|
||||
|
||||
def forward(self, x):
|
||||
for block in self.blocks:
|
||||
x = block(x)
|
||||
x = self.out(x)
|
||||
return x
|
||||
|
|
@ -0,0 +1,12 @@
|
|||
|
||||
SPECULATE = None
|
||||
|
||||
def get_speculate() -> int:
|
||||
global SPECULATE
|
||||
return SPECULATE
|
||||
|
||||
def set_speculate(speculate: int):
|
||||
global SPECULATE
|
||||
SPECULATE = speculate
|
||||
|
||||
|
||||
|
|
@ -16,7 +16,6 @@ from text_generation_server.utils.logits_process import (
|
|||
from text_generation_server.utils.watermark import WatermarkLogitsProcessor
|
||||
from transformers import PreTrainedTokenizerBase, RepetitionPenaltyLogitsProcessor
|
||||
|
||||
|
||||
class NextTokenChooser:
|
||||
def __init__(
|
||||
self,
|
||||
|
|
@ -146,6 +145,20 @@ class StoppingCriteria:
|
|||
pb.ignore_eos_token,
|
||||
)
|
||||
|
||||
def create_n_gram_speculation(input_ids: torch.Tensor, next_ids: torch.Tensor, accepted_ids: torch.Tensor, speculate: int, verbose: bool):
|
||||
# Very trivial approach, find first match in the string.
|
||||
# This is much less refined than actual n-gram but seems to work
|
||||
# relatively OK in grounded mode and is by far much faster with
|
||||
# much less worst case complexity as everything happens on device.
|
||||
B = accepted_ids.shape[0]
|
||||
device = input_ids.device
|
||||
seeds = next_ids[accepted_ids.cumsum(dim=-1) -1 ]
|
||||
indices = (input_ids == seeds.unsqueeze(-1)).max(dim=1).indices + 1
|
||||
all_indices = indices.unsqueeze(-1).expand(B, speculate) + torch.arange(speculate, device=device)
|
||||
all_indices = torch.clamp(all_indices, max=input_ids.shape[1] - 1)
|
||||
|
||||
speculative_ids = input_ids.gather(dim=-1, index=all_indices)
|
||||
return speculative_ids
|
||||
|
||||
class HeterogeneousNextTokenChooser:
|
||||
def __init__(
|
||||
|
|
@ -215,20 +228,79 @@ class HeterogeneousNextTokenChooser:
|
|||
self.dtype = dtype
|
||||
self.device = device
|
||||
|
||||
def __call__(self, input_ids: torch.Tensor, scores: torch.Tensor):
|
||||
if self.watermark_processor is not None:
|
||||
scores = self.watermark_processor(input_ids, scores)
|
||||
if self.repetition_processor is not None:
|
||||
scores = self.repetition_processor(input_ids, scores)
|
||||
def __call__(self, input_ids: torch.Tensor, scores: torch.Tensor, speculate: int, speculated_ids: Optional[torch.Tensor] = None, speculative_scores: Optional[torch.Tensor] = None, verbose=False):
|
||||
if speculated_ids is not None:
|
||||
B = scores.shape[0] // (speculated_ids.shape[1] + 1)
|
||||
S = speculated_ids.shape[1] + 1
|
||||
scores = scores.view(B, S, -1)
|
||||
else:
|
||||
B = scores.shape[0]
|
||||
S = 1
|
||||
scores = scores.view(B, S, -1)
|
||||
|
||||
for warper in self.warpers:
|
||||
scores = warper(input_ids, scores)
|
||||
next_ids = torch.zeros((B, S), device=scores.device, dtype=torch.long)
|
||||
for j in range(S):
|
||||
_scores = scores[:, j]
|
||||
if self.watermark_processor is not None:
|
||||
_scores = self.watermark_processor(input_ids, _scores)
|
||||
if self.repetition_processor is not None:
|
||||
_scores = self.repetition_processor(input_ids, _scores)
|
||||
|
||||
for warper in self.warpers:
|
||||
_scores = warper(input_ids, _scores)
|
||||
|
||||
|
||||
_next_ids = self.choice(_scores)
|
||||
scores[:, j] = _scores
|
||||
next_ids[:, j] = _next_ids
|
||||
next_ids = next_ids.view(B*S)
|
||||
scores = scores.view( B* S, -1)
|
||||
|
||||
if speculated_ids is not None:
|
||||
accepted_ids = []
|
||||
B = next_ids.shape[0] // (speculated_ids.shape[1] + 1)
|
||||
S = speculated_ids.shape[1] + 1
|
||||
indices = []
|
||||
for i in range(B):
|
||||
_next_ids = next_ids[i*S: (i + 1)*S]
|
||||
_speculated_ids = speculated_ids[i]
|
||||
validate_speculative = _next_ids[:-1] == _speculated_ids
|
||||
index = i * S
|
||||
accepted = 1
|
||||
# First is always valid
|
||||
indices.append(index)
|
||||
for valid in validate_speculative.tolist():
|
||||
if valid:
|
||||
index += 1
|
||||
accepted += 1
|
||||
indices.append(index)
|
||||
else:
|
||||
break
|
||||
accepted_ids.append(accepted)
|
||||
|
||||
accepted_ids = torch.tensor(accepted_ids, device=input_ids.device, dtype=input_ids.dtype)
|
||||
next_ids = next_ids[indices]
|
||||
scores = scores[indices]
|
||||
indices = torch.arange(B, device=input_ids.device) * S
|
||||
if speculative_scores is not None:
|
||||
speculative_scores = speculative_scores[indices + accepted_ids - 1]
|
||||
else:
|
||||
accepted_ids = torch.ones_like(next_ids)
|
||||
|
||||
next_ids = self.choice(scores)
|
||||
logprobs = torch.log_softmax(scores, -1)
|
||||
next_logprobs = torch.gather(logprobs, 1, next_ids.view(-1, 1)).view(-1)
|
||||
|
||||
return next_ids, next_logprobs, logprobs
|
||||
if speculate > 0:
|
||||
if speculative_scores is not None:
|
||||
# Medusa provided some scores
|
||||
speculative_ids = Greedy()(speculative_scores)
|
||||
else:
|
||||
# n-gram
|
||||
speculative_ids = create_n_gram_speculation(input_ids, next_ids, accepted_ids, speculate, verbose)
|
||||
else:
|
||||
speculative_ids = None
|
||||
|
||||
return next_ids, next_logprobs, logprobs, accepted_ids, speculative_ids
|
||||
|
||||
def filter(self, indices):
|
||||
if self.watermark_processor is not None:
|
||||
|
|
|
|||
Loading…
Reference in New Issue