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Speculative (#1308)

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Nicolas Patry 2023-12-11 12:46:30 +01:00 committed by GitHub
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docs/source/basic_tutorials/launcher.md
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@ -67,6 +67,14 @@ Options:
- 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 - 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
- bitsandbytes-fp4: Bitsandbytes 4bit. nf4 should be preferred in most cases but maybe this one has better perplexity performance for you model - bitsandbytes-fp4: Bitsandbytes 4bit. nf4 should be preferred in most cases but maybe this one has better perplexity performance for you model
```
## SPECULATE
```shell
--speculate <SPECULATE>
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
[env: SPECULATE=]
``` ```
## DTYPE ## DTYPE
```shell ```shell

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"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"
}

59
integration-tests/models/test_flash_medusa.py Normal file
View File

@ -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

4
integration-tests/models/test_flash_mistral.py
View File

@ -21,6 +21,7 @@ async def test_flash_mistral(flash_mistral, response_snapshot):
) )
assert response.details.generated_tokens == 10 assert response.details.generated_tokens == 10
assert response.generated_text == ": Let n = 10 - 1"
assert response == response_snapshot 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 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 assert responses == response_snapshot

15
launcher/src/main.rs
View File

@ -155,6 +155,13 @@ struct Args {
#[clap(long, env, value_enum)] #[clap(long, env, value_enum)]
quantize: Option<Quantization>, 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`. /// The dtype to be forced upon the model. This option cannot be used with `--quantize`.
#[clap(long, env, value_enum)] #[clap(long, env, value_enum)]
dtype: Option<Dtype>, dtype: Option<Dtype>,
@ -375,6 +382,7 @@ fn shard_manager(
model_id: String, model_id: String,
revision: Option<String>, revision: Option<String>,
quantize: Option<Quantization>, quantize: Option<Quantization>,
speculate: Option<usize>,
dtype: Option<Dtype>, dtype: Option<Dtype>,
trust_remote_code: bool, trust_remote_code: bool,
uds_path: String, uds_path: String,
@ -432,6 +440,11 @@ fn shard_manager(
shard_args.push(quantize.to_string()) 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 { if let Some(dtype) = dtype {
shard_args.push("--dtype".to_string()); shard_args.push("--dtype".to_string());
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 shutdown_sender = shutdown_sender.clone();
let otlp_endpoint = args.otlp_endpoint.clone(); let otlp_endpoint = args.otlp_endpoint.clone();
let quantize = args.quantize; let quantize = args.quantize;
let speculate = args.speculate;
let dtype = args.dtype; let dtype = args.dtype;
let trust_remote_code = args.trust_remote_code; let trust_remote_code = args.trust_remote_code;
let master_port = args.master_port; let master_port = args.master_port;
@ -896,6 +910,7 @@ fn spawn_shards(
model_id, model_id,
revision, revision,
quantize, quantize,
speculate,
dtype, dtype,
trust_remote_code, trust_remote_code,
uds_path, uds_path,

21
load_tests/common.js
View File

@ -7,7 +7,9 @@ const seed = 0;
const host = __ENV.HOST || '127.0.0.1:8000'; const host = __ENV.HOST || '127.0.0.1:8000';
const timePerToken = new Trend('time_per_token', true); 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); randomSeed(seed);
// const shareGPT = JSON.parse(open("ShareGPT_V3_unfiltered_cleaned_split.json")) // const shareGPT = JSON.parse(open("ShareGPT_V3_unfiltered_cleaned_split.json"))
@ -19,7 +21,7 @@ export function get_options(reference_latency_ms){
thresholds: { thresholds: {
http_req_failed: ['rate==0'], http_req_failed: ['rate==0'],
time_per_token: [{ time_per_token: [{
threshold: `p(50)<${3 * reference_latency_ms}`, threshold: `p(50)<${5 * reference_latency_ms}`,
abortOnFail: true, abortOnFail: true,
delayAbortEval: '10s' delayAbortEval: '10s'
}], }],
@ -28,7 +30,7 @@ export function get_options(reference_latency_ms){
load_test: { load_test: {
executor: 'constant-arrival-rate', executor: 'constant-arrival-rate',
duration: '60s', duration: '60s',
preAllocatedVUs: 100, preAllocatedVUs: 10,
rate: 10, rate: 10,
timeUnit: '1s', timeUnit: '1s',
}, },
@ -48,17 +50,22 @@ export function run(host, generate_payload, max_new_tokens) {
return; return;
} }
check(res, { check(res, {
'Post status is 200': (r) => res.status === 200, 'Post status is 200': (r) => res.status === 200,
}); });
const n_tokens = max_new_tokens; const duration = res.timings.duration;
const timings = res.timings.duration;
if (res.status === 200) { 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); timePerToken.add(latency_ms_per_token);
const latency_in_s = latency_ms_per_token / 1000; const latency_in_s = latency_ms_per_token / 1000;
const individual_throughput = 1 / latency_in_s; 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);
} }
} }

4
load_tests/tgi.js
View File

@ -1,13 +1,13 @@
import { get_options, run } from "./common.js"; 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 host = __ENV.HOST || '127.0.0.1:8000';
const max_new_tokens = 50; const max_new_tokens = 50;
function generate_payload(gpt){ function generate_payload(gpt){
const input = gpt["conversations"][0]["value"]; 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); export const options = get_options(reference_latency_ms);

39
proto/generate.proto
View File

@ -1,6 +1,6 @@
syntax = "proto3"; syntax = "proto3";
package generate.v1; package generate.v2;
service TextGenerationService { service TextGenerationService {
/// Model Info /// Model Info
@ -32,6 +32,7 @@ message InfoResponse {
string dtype = 2; string dtype = 2;
string device_type = 3; string device_type = 3;
optional uint32 window_size = 4; optional uint32 window_size = 4;
uint32 speculate = 5;
} }
/// Empty request /// Empty request
@ -135,43 +136,27 @@ message GeneratedText {
optional uint64 seed = 4; optional uint64 seed = 4;
} }
message PrefillTokens { message Tokens {
/// Prefill Token IDs /// Token IDs
repeated uint32 ids = 1; repeated uint32 ids = 1;
/// Prefill Logprobs /// Logprobs
repeated float logprobs = 2; repeated float logprobs = 2;
/// Prefill tokens /// tokens
repeated string texts = 3; repeated string texts = 3;
} /// special
repeated bool is_special = 4;
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;
} }
message Generation { message Generation {
/// Request ID /// Request ID
uint64 request_id = 1; uint64 request_id = 1;
/// Prefill tokens (optional) /// Prefill tokens (optional)
PrefillTokens prefill_tokens = 2; Tokens prefill_tokens = 2;
/// Token ID Tokens tokens = 3;
uint32 token_id = 3;
/// Logprob
float token_logprob = 4;
/// Text
string token_text = 5;
/// Is it a special token
bool token_is_special = 6;
/// Complete generated text /// Complete generated text
optional GeneratedText generated_text = 7; optional GeneratedText generated_text = 4;
/// Top tokens /// Top tokens
TopTokens top_tokens = 8; repeated Tokens top_tokens = 5;
} }
message FilterBatchRequest { message FilterBatchRequest {

4
router/client/src/client.rs
View File

@ -1,6 +1,6 @@
/// Single shard Client /// Single shard Client
use crate::pb::generate::v1::text_generation_service_client::TextGenerationServiceClient; use crate::pb::generate::v2::text_generation_service_client::TextGenerationServiceClient;
use crate::pb::generate::v1::*; use crate::pb::generate::v2::*;
use crate::Result; use crate::Result;
use grpc_metadata::InjectTelemetryContext; use grpc_metadata::InjectTelemetryContext;
use std::cmp::min; use std::cmp::min;

8
router/client/src/lib.rs
View File

@ -6,11 +6,11 @@ mod pb;
mod sharded_client; mod sharded_client;
pub use client::Client; pub use client::Client;
pub use pb::generate::v1::HealthResponse; pub use pb::generate::v2::HealthResponse;
pub use pb::generate::v1::InfoResponse as ShardInfo; pub use pb::generate::v2::InfoResponse as ShardInfo;
pub use pb::generate::v1::{ pub use pb::generate::v2::{
Batch, CachedBatch, FinishReason, GeneratedText, Generation, NextTokenChooserParameters, Batch, CachedBatch, FinishReason, GeneratedText, Generation, NextTokenChooserParameters,
PrefillTokens, Request, StoppingCriteriaParameters, Request, StoppingCriteriaParameters, Tokens,
}; };
pub use sharded_client::ShardedClient; pub use sharded_client::ShardedClient;
use thiserror::Error; use thiserror::Error;

94
router/src/infer.rs
View File

@ -9,7 +9,7 @@ use std::sync::{
Arc, Arc,
}; };
use text_generation_client::{ use text_generation_client::{
Batch, CachedBatch, ClientError, GeneratedText, Generation, PrefillTokens, ShardedClient, Batch, CachedBatch, ClientError, GeneratedText, Generation, ShardedClient, Tokens,
}; };
use thiserror::Error; use thiserror::Error;
use tokio::sync::mpsc::error::SendError; use tokio::sync::mpsc::error::SendError;
@ -50,10 +50,11 @@ impl Infer {
max_concurrent_requests: usize, max_concurrent_requests: usize,
requires_padding: bool, requires_padding: bool,
window_size: Option<u32>, window_size: Option<u32>,
speculate: u32,
generation_health: Arc<AtomicBool>, generation_health: Arc<AtomicBool>,
) -> Self { ) -> Self {
// Infer shared state // 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 { let shared = Arc::new(Shared {
batching_task: Notify::new(), batching_task: Notify::new(),
}); });
@ -523,50 +524,63 @@ fn send_responses(
} }
// Create last Token // Create last Token
let token = Token { let tokens_ = generation.tokens.expect("Non empty tokens in generation");
id: generation.token_id, let n = tokens_.ids.len();
text: generation.token_text, metrics::histogram!("tgi_request_skipped_tokens", (n - 1) as f64);
logprob: generation.token_logprob, let mut iterator = tokens_
special: generation.token_is_special, .ids
}; .into_iter()
.zip(tokens_.logprobs.into_iter())
// generation.top_tokens .zip(tokens_.texts.into_iter())
.zip(tokens_.is_special.into_iter())
let mut top_tokens = Vec::new(); .enumerate()
if let Some(top_tokens_) = generation.top_tokens { .peekable();
top_tokens.extend( 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_ top_tokens_
.ids .ids
.into_iter() .iter()
.zip(top_tokens_.logprobs.into_iter()) .zip(top_tokens_.logprobs.iter())
.zip(top_tokens_.texts.into_iter()) .zip(top_tokens_.texts.iter())
.zip(top_tokens_.is_special.into_iter()) .zip(top_tokens_.is_special.iter())
.map(|(((id, logprob), text), special)| Token { .map(|(((&id, &logprob), text), &special)| Token {
id, id,
text, text: text.to_string(),
logprob, logprob,
special, 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) Ok(stopped)
} }
@ -591,7 +605,7 @@ fn send_errors(error: ClientError, entries: &mut IntMap<u64, Entry>) {
#[derive(Debug)] #[derive(Debug)]
pub(crate) enum InferStreamResponse { pub(crate) enum InferStreamResponse {
// Optional first message // Optional first message
Prefill(PrefillTokens), Prefill(Tokens),
// Intermediate messages // Intermediate messages
Intermediate { Intermediate {
token: Token, token: Token,

61
router/src/queue.rs
View File

@ -34,7 +34,12 @@ pub(crate) struct Queue {
} }
impl 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 // Create channel
let (queue_sender, queue_receiver) = mpsc::unbounded_channel(); let (queue_sender, queue_receiver) = mpsc::unbounded_channel();
@ -43,6 +48,7 @@ impl Queue {
requires_padding, requires_padding,
block_size, block_size,
window_size, window_size,
speculate,
queue_receiver, queue_receiver,
)); ));
@ -91,9 +97,10 @@ async fn queue_task(
requires_padding: bool, requires_padding: bool,
block_size: u32, block_size: u32,
window_size: Option<u32>, window_size: Option<u32>,
speculate: u32,
mut receiver: mpsc::UnboundedReceiver<QueueCommand>, 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 { while let Some(cmd) = receiver.recv().await {
match cmd { match cmd {
@ -136,10 +143,18 @@ struct State {
/// Sliding window /// Sliding window
window_size: Option<u32>, window_size: Option<u32>,
/// Speculation amount
speculate: u32,
} }
impl State { 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 { Self {
entries: VecDeque::with_capacity(128), entries: VecDeque::with_capacity(128),
next_id: 0, next_id: 0,
@ -147,6 +162,7 @@ impl State {
requires_padding, requires_padding,
block_size, block_size,
window_size, window_size,
speculate,
} }
} }
@ -229,7 +245,7 @@ impl State {
} }
if prefill_tokens > prefill_token_budget if prefill_tokens > prefill_token_budget
|| (prefill_tokens + decode_tokens) > token_budget || (prefill_tokens + decode_tokens + self.speculate) > token_budget
{ {
// Entry is over budget // Entry is over budget
// Add it back to the front // Add it back to the front
@ -359,7 +375,7 @@ mod tests {
#[test] #[test]
fn test_append() { 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(); let (entry, _guard) = default_entry();
assert_eq!(state.next_id, 0); assert_eq!(state.next_id, 0);
@ -375,7 +391,7 @@ mod tests {
#[test] #[test]
fn test_next_batch_empty() { 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(None, 1, 1).is_none());
assert!(state.next_batch(Some(1), 1, 1).is_none()); assert!(state.next_batch(Some(1), 1, 1).is_none());
@ -383,7 +399,7 @@ mod tests {
#[test] #[test]
fn test_next_batch_min_size() { 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 (entry1, _guard1) = default_entry();
let (entry2, _guard2) = default_entry(); let (entry2, _guard2) = default_entry();
state.append(entry1); state.append(entry1);
@ -415,7 +431,7 @@ mod tests {
#[test] #[test]
fn test_next_batch_token_budget() { 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 (entry1, _guard1) = default_entry();
let (entry2, _guard2) = default_entry(); let (entry2, _guard2) = default_entry();
state.append(entry1); state.append(entry1);
@ -448,14 +464,14 @@ mod tests {
#[tokio::test] #[tokio::test]
async fn test_queue_append() { 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(); let (entry, _guard) = default_entry();
queue.append(entry); queue.append(entry);
} }
#[tokio::test] #[tokio::test]
async fn test_queue_next_batch_empty() { 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(None, 1, 1).await.is_none());
assert!(queue.next_batch(Some(1), 1, 1).await.is_none()); assert!(queue.next_batch(Some(1), 1, 1).await.is_none());
@ -463,7 +479,7 @@ mod tests {
#[tokio::test] #[tokio::test]
async fn test_queue_next_batch_min_size() { 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 (entry1, _guard1) = default_entry();
let (entry2, _guard2) = default_entry(); let (entry2, _guard2) = default_entry();
queue.append(entry1); queue.append(entry1);
@ -496,7 +512,7 @@ mod tests {
#[tokio::test] #[tokio::test]
async fn test_queue_next_batch_token_budget() { 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 (entry1, _guard1) = default_entry();
let (entry2, _guard2) = default_entry(); let (entry2, _guard2) = default_entry();
queue.append(entry1); queue.append(entry1);
@ -519,9 +535,28 @@ mod tests {
assert_eq!(batch.size, 2); 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] #[tokio::test]
async fn test_queue_next_batch_dropped_receiver() { 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(); let (entry, _) = default_entry();
queue.append(entry); queue.append(entry);

1
router/src/server.rs
View File

@ -596,6 +596,7 @@ pub async fn run(
max_concurrent_requests, max_concurrent_requests,
shard_info.requires_padding, shard_info.requires_padding,
shard_info.window_size, shard_info.window_size,
shard_info.speculate,
generation_health, generation_health,
); );

33
server/Makefile-vllm
View File

@ -1,22 +1,25 @@
build-vllm-cuda: REPOSITORY=https://github.com/vllm-project/vllm.git vllm-cuda:
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:
# Clone vllm # Clone vllm
pip install -U ninja packaging --no-cache-dir 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 build-vllm-cuda: vllm-cuda
cd vllm && git fetch && git checkout $(VLLM_COMMIT) cd vllm && git fetch && git checkout f8a1e39fae05ca610be8d5a78be9d40f5274e5fc
cd vllm && python setup.py build 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 pip uninstall vllm -y || true
cd vllm && python setup.py install cd vllm && python setup.py install

4
server/tests/models/test_bloom.py
View File

@ -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([generation.generated_text is None for generation in generations])
assert all([len(generation.prefill_tokens) == 1 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([token_id.item() == 10264 for generation in generations for token_id in generation.tokens.token_ids])
assert all([generation.token_text == "Test" for generation in generations]) assert all([token_text == "Test" for generation in generations for token_text in generation.tokens.texts])
assert generations[0].request_id == 0 assert generations[0].request_id == 0

4
server/tests/models/test_causal_lm.py
View File

@ -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([generation.generated_text is None for generation in generations])
assert all([len(generation.prefill_tokens) == 1 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([token_id.item() == 13 for generation in generations for token_id in generation.tokens.token_ids])
assert all([generation.token_text == "." for generation in generations]) assert all([token_text == "." for generation in generations for token_text in generation.tokens.texts])
assert generations[0].request_id == 0 assert generations[0].request_id == 0

4
server/tests/models/test_seq2seq_lm.py
View File

@ -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([generation.generated_text is None for generation in generations])
assert all([len(generation.prefill_tokens) == 1 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([token_id.item() == 259 for generation in generations for token_id in generation.tokens.token_ids])
assert all([generation.token_text == " " for generation in generations]) assert all([token_text == " " for generation in generations for token_text in generation.tokens.texts])
assert generations[0].request_id == 0 assert generations[0].request_id == 0

28
server/text_generation_server/cli.py
View File

@ -32,6 +32,7 @@ def serve(
revision: Optional[str] = None, revision: Optional[str] = None,
sharded: bool = False, sharded: bool = False,
quantize: Optional[Quantization] = None, quantize: Optional[Quantization] = None,
speculate: Optional[int] = None,
dtype: Optional[Dtype] = None, dtype: Optional[Dtype] = None,
trust_remote_code: bool = False, trust_remote_code: bool = False,
uds_path: Path = "/tmp/text-generation-server", 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." "Only 1 can be set between `dtype` and `quantize`, as they both decide how goes the final model."
) )
server.serve( 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.") logger.info("Files are already present on the host. " "Skipping download.")
return return
# Local files not found # Local files not found
except (utils.LocalEntryNotFoundError, FileNotFoundError): except (utils.LocalEntryNotFoundError, FileNotFoundError, utils.EntryNotFoundError):
pass pass
is_local_model = (Path(model_id).exists() and Path(model_id).is_dir()) or os.getenv( 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): except (utils.LocalEntryNotFoundError, utils.EntryNotFoundError):
pass 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 to download weights from the hub
try: try:
filenames = utils.weight_hub_files(model_id, revision, extension) filenames = utils.weight_hub_files(model_id, revision, extension)

36
server/text_generation_server/models/__init__.py
View File

@ -6,6 +6,7 @@ from transformers.configuration_utils import PretrainedConfig
from transformers.models.auto import modeling_auto from transformers.models.auto import modeling_auto
from typing import Optional 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.model import Model
from text_generation_server.models.causal_lm import CausalLM from text_generation_server.models.causal_lm import CausalLM
from text_generation_server.models.flash_causal_lm import FlashCausalLM from text_generation_server.models.flash_causal_lm import FlashCausalLM
@ -77,12 +78,12 @@ except ImportError as e:
if MISTRAL: if MISTRAL:
__all__.append(FlashMistral) __all__.append(FlashMistral)
def get_model( def get_model(
model_id: str, model_id: str,
revision: Optional[str], revision: Optional[str],
sharded: bool, sharded: bool,
quantize: Optional[str], quantize: Optional[str],
speculate: Optional[int],
dtype: Optional[str], dtype: Optional[str],
trust_remote_code: bool, trust_remote_code: bool,
) -> Model: ) -> Model:
@ -97,6 +98,11 @@ def get_model(
else: else:
raise RuntimeError(f"Unknown dtype {dtype}") raise RuntimeError(f"Unknown dtype {dtype}")
if speculate is not None:
set_speculate(speculate)
else:
set_speculate(0)
if "facebook/galactica" in model_id: if "facebook/galactica" in model_id:
return GalacticaSharded( return GalacticaSharded(
model_id, model_id,
@ -131,6 +137,33 @@ def get_model(
config_dict, _ = PretrainedConfig.get_config_dict( config_dict, _ = PretrainedConfig.get_config_dict(
model_id, revision=revision, trust_remote_code=trust_remote_code 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"] model_type = config_dict["model_type"]
if model_type == "gpt_bigcode": if model_type == "gpt_bigcode":
@ -206,6 +239,7 @@ def get_model(
quantize=quantize, quantize=quantize,
dtype=dtype, dtype=dtype,
trust_remote_code=trust_remote_code, trust_remote_code=trust_remote_code,
use_medusa=use_medusa
) )
elif sharded: elif sharded:
raise NotImplementedError(FLASH_ATT_ERROR_MESSAGE.format("Sharded Llama")) raise NotImplementedError(FLASH_ATT_ERROR_MESSAGE.format("Sharded Llama"))

19
server/text_generation_server/models/causal_lm.py
View File

@ -10,10 +10,9 @@ from typing import Optional, Tuple, List, Type, Dict
from text_generation_server.models import Model from text_generation_server.models import Model
from text_generation_server.models.types import ( from text_generation_server.models.types import (
Batch, Batch,
PrefillTokens, Tokens,
Generation, Generation,
GeneratedText, GeneratedText,
TopTokens,
) )
from text_generation_server.pb import generate_pb2 from text_generation_server.pb import generate_pb2
from text_generation_server.utils import NextTokenChooser, StoppingCriteria, Sampling from text_generation_server.utils import NextTokenChooser, StoppingCriteria, Sampling
@ -676,8 +675,8 @@ class CausalLM(Model):
clean_up_tokenization_spaces=False, clean_up_tokenization_spaces=False,
skip_special_tokens=False, skip_special_tokens=False,
) )
prefill_tokens = PrefillTokens( prefill_tokens = Tokens(
prefill_token_ids, prefill_logprobs, prefill_texts prefill_token_ids, prefill_logprobs, prefill_texts, is_special=[]
) )
else: else:
prefill_tokens = None prefill_tokens = None
@ -691,7 +690,7 @@ class CausalLM(Model):
special_toptokens = [ special_toptokens = [
token_id in self.all_special_ids for token_id in top_token_ids 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_ids,
top_token_logprobs, top_token_logprobs,
toptoken_texts, toptoken_texts,
@ -703,10 +702,12 @@ class CausalLM(Model):
generation = Generation( generation = Generation(
request.id, request.id,
prefill_tokens, prefill_tokens,
next_token_id_squeezed, Tokens(
next_token_logprob, [next_token_id_squeezed],
next_token_text, [next_token_logprob],
next_token_id_squeezed.item() in self.all_special_ids, [next_token_text],
[next_token_id_squeezed.item() in self.all_special_ids],
),
generated_text, generated_text,
top_tokens, top_tokens,
) )

188
server/text_generation_server/models/flash_causal_lm.py
View File

@ -11,13 +11,13 @@ from opentelemetry import trace
from transformers import PreTrainedTokenizerBase from transformers import PreTrainedTokenizerBase
from typing import Optional, Tuple, List, Type, Union, Dict 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 ( from text_generation_server.models.types import (
Batch, Batch,
PrefillTokens, Tokens,
Generation, Generation,
GeneratedText, GeneratedText,
TopTokens,
) )
from text_generation_server.models.cache_manager import ( from text_generation_server.models.cache_manager import (
get_cache_manager, get_cache_manager,
@ -41,6 +41,7 @@ class FlashCausalLMBatch(Batch):
# Decoder values # Decoder values
input_ids: torch.Tensor input_ids: torch.Tensor
position_ids: torch.Tensor position_ids: torch.Tensor
speculative_ids: torch.Tensor
# Flash Attention values # Flash Attention values
@ -120,6 +121,7 @@ class FlashCausalLMBatch(Batch):
)["input_ids"] )["input_ids"]
position_ids = [] position_ids = []
speculative_ids = []
cu_seqlen_prefill = [0] cu_seqlen_prefill = [0]
needed_blocks_slots = [] needed_blocks_slots = []
start_slots = [] start_slots = []
@ -163,6 +165,8 @@ class FlashCausalLMBatch(Batch):
input_length = len(tokenized_input) input_length = len(tokenized_input)
input_lengths.append(input_length) input_lengths.append(input_length)
prefix_offsets.append(input_length - 5) prefix_offsets.append(input_length - 5)
read_offsets.append(input_length) read_offsets.append(input_length)
@ -186,7 +190,8 @@ class FlashCausalLMBatch(Batch):
# Paged attention # Paged attention
# Remove one as the first token des not have a past # 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) needed_blocks = math.ceil(total_tokens / BLOCK_SIZE)
blocks += needed_blocks blocks += needed_blocks
needed_blocks_slots.append((needed_blocks, total_tokens)) needed_blocks_slots.append((needed_blocks, total_tokens))
@ -224,7 +229,7 @@ class FlashCausalLMBatch(Batch):
cumulative_max_length += total_tokens cumulative_max_length += total_tokens
max_seqlen = max(max_seqlen, input_length) max_seqlen = max(max_seqlen, input_length)
max_blocks = max(max_blocks, needed_blocks) 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 = HeterogeneousNextTokenChooser.from_pb(
next_token_chooser_parameters, dtype, device next_token_chooser_parameters, dtype, device
@ -255,7 +260,6 @@ class FlashCausalLMBatch(Batch):
cu_seqlen_prefill = torch.tensor( cu_seqlen_prefill = torch.tensor(
cu_seqlen_prefill, device=device, dtype=torch.int32 cu_seqlen_prefill, device=device, dtype=torch.int32
) )
position_ids = position_ids.to(device) position_ids = position_ids.to(device)
slot_indices = slot_indices.to(device) slot_indices = slot_indices.to(device)
input_ids = torch.tensor(input_ids, dtype=torch.int64, device=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, top_n_tokens_tensor=top_n_tokens_tensor,
blocks=blocks, blocks=blocks,
max_blocks=max_blocks, max_blocks=max_blocks,
speculative_ids=None,
) )
@tracer.start_as_current_span("filter") @tracer.start_as_current_span("filter")
@ -419,6 +424,7 @@ class FlashCausalLMBatch(Batch):
slots = self.slots[slot_filtering_indices] slots = self.slots[slot_filtering_indices]
next_token_chooser = self.next_token_chooser.filter(indices) next_token_chooser = self.next_token_chooser.filter(indices)
top_n_tokens_tensor = self.top_n_tokens_tensor[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) start_slots = torch.tensor(start_slots, dtype=torch.int64)
@ -454,6 +460,7 @@ class FlashCausalLMBatch(Batch):
top_n_tokens_tensor=top_n_tokens_tensor, top_n_tokens_tensor=top_n_tokens_tensor,
blocks=blocks, blocks=blocks,
max_blocks=max_blocks, max_blocks=max_blocks,
speculative_ids=speculative_ids,
) )
@classmethod @classmethod
@ -473,6 +480,7 @@ class FlashCausalLMBatch(Batch):
total_batch_size += len(b) total_batch_size += len(b)
total_slots += len(b.slots) total_slots += len(b.slots)
blocks += b.blocks 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_blocks = max(max_blocks, b.max_blocks)
max_seqlen = max(max_seqlen, b.max_seqlen) max_seqlen = max(max_seqlen, b.max_seqlen)
max_length = max( max_length = max(
@ -480,6 +488,7 @@ class FlashCausalLMBatch(Batch):
max( max(
input_length input_length
+ stopping_criteria.max_new_tokens + stopping_criteria.max_new_tokens
+ speculative_length
- stopping_criteria.current_tokens - stopping_criteria.current_tokens
for input_length, stopping_criteria in zip( for input_length, stopping_criteria in zip(
b.input_lengths, b.stopping_criterias b.input_lengths, b.stopping_criterias
@ -577,6 +586,8 @@ class FlashCausalLMBatch(Batch):
device=batches[0].next_token_chooser.device, 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 # Needed to avoid dropping blocks when the batches will go out of scope
for b in batches: for b in batches:
b.block_tables = None b.block_tables = None
@ -611,6 +622,7 @@ class FlashCausalLMBatch(Batch):
top_n_tokens_tensor=top_n_tokens_tensor, top_n_tokens_tensor=top_n_tokens_tensor,
blocks=blocks, blocks=blocks,
max_blocks=max_blocks, max_blocks=max_blocks,
speculative_ids=speculative_ids
) )
def __del__(self): def __del__(self):
@ -714,16 +726,55 @@ class FlashCausalLM(Model):
def forward(self, batch: FlashCausalLMBatch) -> Tuple[torch.Tensor, torch.Tensor]: def forward(self, batch: FlashCausalLMBatch) -> Tuple[torch.Tensor, torch.Tensor]:
# Model Forward # 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( return self.model.forward(
input_ids=batch.input_ids, input_ids=input_ids,
position_ids=batch.position_ids, position_ids=position_ids,
cu_seqlen_prefill=batch.cu_seqlen_prefill, cu_seqlen_prefill=cu_seqlen_prefill,
kv_cache=get_cache_manager().kv_cache, kv_cache=kv_cache,
block_tables=batch.block_tables_tensor, block_tables=block_tables,
slots=batch.slots[batch.slot_indices], slots=slots,
input_lengths=batch.input_lengths_tensor, input_lengths=input_lengths,
max_s=batch.max_seqlen, max_s=max_s,
lm_head_indices=batch.prefill_head_indices, lm_head_indices=lm_head_indices,
) )
@tracer.start_as_current_span("generate_token") @tracer.start_as_current_span("generate_token")
@ -752,21 +803,32 @@ class FlashCausalLM(Model):
del batch del batch
raise e raise e
if isinstance(out, tuple):
out, speculative_logits = out
else:
speculative_logits = None
if prefill: if prefill:
next_token_logits = ( next_token_logits = (
out[batch.prefill_next_token_indices] if prefill_logprobs else out 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: else:
next_token_logits = out next_token_logits = out
next_input_ids, next_token_logprobs, logprobs = batch.next_token_chooser( 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 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_token_ids, batch_top_token_logprobs = batch_top_tokens(
batch.top_n_tokens, batch.top_n_tokens_tensor, logprobs 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 prefill:
if len(batch) > 1 and prefill_logprobs: if len(batch) > 1 and prefill_logprobs:
# We create the prefill_tokens_indices tensor that will be used to gather 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( iterator = zip(
batch.input_lengths, batch.input_lengths,
batch.all_input_ids, 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 # 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 # It is faster if we delay this sync for the maximum amount of time
# For each member of the batch # For each member of the batch
index = 0
for i, ( for i, (
input_length, input_length,
all_input_ids, all_input_ids,
n_accepted_ids
) in enumerate(iterator): ) in enumerate(iterator):
# Indexing metadata # Indexing metadata
start_index = cumulative_length start_index = cumulative_length
@ -830,15 +895,18 @@ class FlashCausalLM(Model):
start_index + 1 : start_index + out_length 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 cumulative_length += input_length
# Set values in batch
batch.input_ids = next_input_ids batch.input_ids = next_input_ids[accepted_ids.cumsum(dim=-1) - 1]
batch.position_ids = next_position_ids + 1 batch.speculative_ids = speculative_ids
batch.input_lengths_tensor += 1 batch.position_ids = next_position_ids + accepted_ids
batch.slot_indices += 1 batch.input_lengths_tensor += accepted_ids
batch.slot_indices += accepted_ids
if prefill and prefill_logprobs: if prefill and prefill_logprobs:
# Get prefill logprobs # Get prefill logprobs
@ -851,7 +919,7 @@ class FlashCausalLM(Model):
# GPU <-> CPU sync # GPU <-> CPU sync
next_token_logprobs = next_token_logprobs.tolist() next_token_logprobs = next_token_logprobs.tolist()
next_token_ids = batch.input_ids.tolist() next_token_ids = next_input_ids.tolist()
# Zipped iterator # Zipped iterator
iterator = zip( iterator = zip(
@ -864,13 +932,13 @@ class FlashCausalLM(Model):
batch.next_token_chooser.do_sample, batch.next_token_chooser.do_sample,
batch.next_token_chooser.seeds, batch.next_token_chooser.seeds,
batch.top_n_tokens, batch.top_n_tokens,
next_token_ids, accepted_ids,
next_token_logprobs,
batch_top_token_ids, batch_top_token_ids,
batch_top_token_logprobs, batch_top_token_logprobs,
) )
# For each member of the batch # For each member of the batch
index = 0
for i, ( for i, (
request, request,
input_length, input_length,
@ -881,29 +949,43 @@ class FlashCausalLM(Model):
do_sample, do_sample,
seed, seed,
top_n_tokens, top_n_tokens,
next_token_id, n_accepted_ids,
next_token_logprob,
top_token_ids, top_token_ids,
top_token_logprobs, top_token_logprobs,
) in enumerate(iterator): ) in enumerate(iterator):
# Append next token to all tokens # 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 current_stopped = False
next_token_text, prefix_offset, read_offset = self.decode_token( for j in range(index, index + n_accepted_ids):
all_input_ids, # Generated token
prefix_offset, next_token_id = next_token_ids[j]
read_offset, 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(
stop, reason = stopping_criteria( next_token_id,
next_token_id, next_token_text,
next_token_text, )
)
if not stop: if stop:
stopped = False 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 # Shard generations
# All generations will be appended in the rust sharded client # All generations will be appended in the rust sharded client
@ -943,8 +1025,9 @@ class FlashCausalLM(Model):
clean_up_tokenization_spaces=False, clean_up_tokenization_spaces=False,
skip_special_tokens=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: else:
prefill_tokens = None prefill_tokens = None
@ -958,7 +1041,7 @@ class FlashCausalLM(Model):
special_toptokens = [ special_toptokens = [
token_id in self.all_special_ids for token_id in top_token_ids 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_ids,
top_token_logprobs, top_token_logprobs,
toptoken_texts, toptoken_texts,
@ -970,10 +1053,12 @@ class FlashCausalLM(Model):
generation = Generation( generation = Generation(
request.id, request.id,
prefill_tokens, prefill_tokens,
next_token_id, Tokens(
next_token_logprob, _next_token_ids,
next_token_text, _next_token_logprobs,
next_token_id in self.all_special_ids, next_token_texts,
[nid in self.all_special_ids for nid in _next_token_ids],
),
generated_text, generated_text,
top_tokens, top_tokens,
) )
@ -981,7 +1066,9 @@ class FlashCausalLM(Model):
generations.append(generation) generations.append(generation)
# Update values # 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.prefix_offsets[i] = prefix_offset
batch.read_offsets[i] = read_offset batch.read_offsets[i] = read_offset
batch.all_input_ids[i] = all_input_ids batch.all_input_ids[i] = all_input_ids
@ -994,6 +1081,5 @@ class FlashCausalLM(Model):
batch.prefill_cu_outlens = None batch.prefill_cu_outlens = None
batch.prefill_head_indices = None batch.prefill_head_indices = None
batch.prefill_next_token_indices = None batch.prefill_next_token_indices = None
batch.max_seqlen = batch.max_seqlen + 1
return generations, batch return generations, batch

13
server/text_generation_server/models/flash_llama.py
View File

@ -28,6 +28,7 @@ class FlashLlama(FlashCausalLM):
quantize: Optional[str] = None, quantize: Optional[str] = None,
dtype: Optional[torch.dtype] = None, dtype: Optional[torch.dtype] = None,
trust_remote_code: bool = False, trust_remote_code: bool = False,
use_medusa: Optional[str] = None,
): ):
self.process_group, rank, world_size = initialize_torch_distributed() self.process_group, rank, world_size = initialize_torch_distributed()
if torch.cuda.is_available(): if torch.cuda.is_available():
@ -66,6 +67,18 @@ class FlashLlama(FlashCausalLM):
weights._set_gptq_params(model_id) weights._set_gptq_params(model_id)
model = FlashLlamaForCausalLM(config, weights) 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) torch.distributed.barrier(group=self.process_group)
super(FlashLlama, self).__init__( super(FlashLlama, self).__init__(

63
server/text_generation_server/models/flash_mistral.py
View File

@ -21,6 +21,7 @@ from text_generation_server.models.custom_modeling.flash_mistral_modeling import
FlashMistralForCausalLM, FlashMistralForCausalLM,
MistralConfig, MistralConfig,
) )
from text_generation_server.utils.speculate import get_speculate
from text_generation_server.utils import ( from text_generation_server.utils import (
initialize_torch_distributed, initialize_torch_distributed,
weight_files, weight_files,
@ -132,7 +133,8 @@ class FlashMistralBatch(FlashCausalLMBatch):
# Paged attention # Paged attention
# Remove one as the first token des not have a past # 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 can not go over SLIDING_WINDOW_BLOCKS
needed_blocks = min( needed_blocks = min(
@ -183,7 +185,7 @@ class FlashMistralBatch(FlashCausalLMBatch):
cumulative_max_length += total_tokens cumulative_max_length += total_tokens
max_seqlen = max(max_seqlen, input_length) max_seqlen = max(max_seqlen, input_length)
max_blocks = max(max_blocks, needed_blocks) 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 = HeterogeneousNextTokenChooser.from_pb(
next_token_chooser_parameters, dtype, device next_token_chooser_parameters, dtype, device
@ -272,6 +274,7 @@ class FlashMistralBatch(FlashCausalLMBatch):
blocks=blocks, blocks=blocks,
max_blocks=max_blocks, max_blocks=max_blocks,
prefill_cache_indices=prefill_cache_indices, 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]: def forward(self, batch: FlashMistralBatch) -> Tuple[torch.Tensor, torch.Tensor]:
# Model Forward # 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( logits = self.model.forward(
input_ids=batch.input_ids, input_ids=input_ids,
position_ids=batch.position_ids, position_ids=position_ids,
cu_seqlen_prefill=batch.cu_seqlen_prefill, cu_seqlen_prefill=cu_seqlen_prefill,
kv_cache=get_cache_manager().kv_cache, kv_cache=kv_cache,
block_tables=batch.block_tables_tensor, block_tables=block_tables,
slots=batch.slots[batch.slot_indices], slots=slots,
input_lengths=batch.input_lengths_tensor, input_lengths=input_lengths,
max_s=batch.max_seqlen, max_s=max_s,
prefill_cache_indices=batch.prefill_cache_indices, 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: if batch.prefill_cache_indices is not None:
batch.prefill_cache_indices = None batch.prefill_cache_indices = None

16
server/text_generation_server/models/idefics_causal_lm.py
View File

@ -20,7 +20,7 @@ from typing import Optional, Tuple, List, Type, Dict
from text_generation_server.models import Model from text_generation_server.models import Model
from text_generation_server.models.types import ( from text_generation_server.models.types import (
Batch, Batch,
PrefillTokens, Tokens,
Generation, Generation,
GeneratedText, GeneratedText,
) )
@ -791,8 +791,8 @@ class IdeficsCausalLM(Model):
clean_up_tokenization_spaces=False, clean_up_tokenization_spaces=False,
skip_special_tokens=False, skip_special_tokens=False,
) )
prefill_tokens = PrefillTokens( prefill_tokens = Tokens(
prefill_token_ids, prefill_logprobs, prefill_texts prefill_token_ids, prefill_logprobs, prefill_texts, is_special=[]
) )
else: else:
prefill_tokens = None prefill_tokens = None
@ -802,10 +802,12 @@ class IdeficsCausalLM(Model):
generation = Generation( generation = Generation(
request.id, request.id,
prefill_tokens, prefill_tokens,
next_token_id_squeezed, Tokens(
next_token_logprob, [next_token_id_squeezed],
next_token_text, [next_token_logprob],
next_token_id_squeezed.item() in self.all_special_ids, [next_token_text],
[next_token_id_squeezed.item() in self.all_special_ids],
),
generated_text, generated_text,
top_tokens, top_tokens,
) )

7
server/text_generation_server/models/model.py
View File

@ -6,6 +6,7 @@ from typing import List, Tuple, Optional, TypeVar, Type
from transformers import PreTrainedTokenizerBase, PretrainedConfig from transformers import PreTrainedTokenizerBase, PretrainedConfig
from text_generation_server.models.types import Batch, Generation 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 from text_generation_server.pb.generate_pb2 import InfoResponse
B = TypeVar("B", bound=Batch) B = TypeVar("B", bound=Batch)
@ -22,6 +23,7 @@ class Model(ABC):
rank: int = 0, rank: int = 0,
world_size: int = 1, world_size: int = 1,
sliding_window: Optional[int] = None, sliding_window: Optional[int] = None,
speculate: Optional[int] = None,
): ):
self.model = model.eval() self.model = model.eval()
self.tokenizer = tokenizer self.tokenizer = tokenizer
@ -33,6 +35,10 @@ class Model(ABC):
self.world_size = world_size self.world_size = world_size
self.sliding_window = sliding_window self.sliding_window = sliding_window
if speculate is None:
speculate = get_speculate()
self.speculate = speculate
self.has_position_ids = ( self.has_position_ids = (
inspect.signature(model.forward).parameters.get("position_ids", None) inspect.signature(model.forward).parameters.get("position_ids", None)
is not None is not None
@ -50,6 +56,7 @@ class Model(ABC):
dtype=str(self.dtype), dtype=str(self.dtype),
device_type=self.device.type, device_type=self.device.type,
window_size=self.sliding_window, window_size=self.sliding_window,
speculate=self.speculate
) )
@property @property

18
server/text_generation_server/models/seq2seq_lm.py
View File

@ -11,8 +11,7 @@ from text_generation_server.models.types import (
GeneratedText, GeneratedText,
Batch, Batch,
Generation, Generation,
PrefillTokens, Tokens,
TopTokens,
) )
from text_generation_server.pb import generate_pb2 from text_generation_server.pb import generate_pb2
from text_generation_server.utils import NextTokenChooser, StoppingCriteria, Sampling from text_generation_server.utils import NextTokenChooser, StoppingCriteria, Sampling
@ -733,10 +732,11 @@ class Seq2SeqLM(Model):
# Prefill # Prefill
if stopping_criteria.current_tokens == 1 and request.prefill_logprobs: if stopping_criteria.current_tokens == 1 and request.prefill_logprobs:
prefill_tokens = PrefillTokens( prefill_tokens = Tokens(
[self.tokenizer.bos_token_id], [self.tokenizer.bos_token_id],
[float("nan")], [float("nan")],
[self.tokenizer.bos_token], [self.tokenizer.bos_token],
[False]
) )
else: else:
prefill_tokens = None prefill_tokens = None
@ -750,7 +750,7 @@ class Seq2SeqLM(Model):
special_toptokens = [ special_toptokens = [
token_id in self.all_special_ids for token_id in top_token_ids 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_ids,
top_token_logprobs, top_token_logprobs,
toptoken_texts, toptoken_texts,
@ -762,10 +762,12 @@ class Seq2SeqLM(Model):
generation = Generation( generation = Generation(
request.id, request.id,
prefill_tokens, prefill_tokens,
next_token_id_squeezed, Tokens(
next_token_logprob, [next_token_id_squeezed],
next_token_text, [next_token_logprob],
next_token_id_squeezed.item() in self.all_special_ids, [next_token_text],
[next_token_id_squeezed.item() in self.all_special_ids],
),
generated_text, generated_text,
top_tokens, top_tokens,
) )

40
server/text_generation_server/models/types.py
View File

@ -58,33 +58,15 @@ class GeneratedText:
@dataclass @dataclass
class PrefillTokens: class Tokens:
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:
token_ids: List[int] token_ids: List[int]
logprobs: List[float] logprobs: List[float]
texts: List[str] texts: List[str]
is_special: List[bool] is_special: List[bool]
def to_pb(self) -> generate_pb2.TopTokens: def to_pb(self) -> generate_pb2.Tokens:
return generate_pb2.TopTokens( return generate_pb2.Tokens(
ids=self.token_ids, ids=self.token_ids, logprobs=self.logprobs, texts=self.texts, is_special=self.is_special
logprobs=self.logprobs,
texts=self.texts,
is_special=self.is_special,
) )
def __len__(self): def __len__(self):
@ -94,14 +76,11 @@ class TopTokens:
@dataclass @dataclass
class Generation: class Generation:
request_id: int request_id: int
prefill_tokens: Optional[PrefillTokens] prefill_tokens: Optional[Tokens]
token_id: int tokens: Tokens
token_logprob: float
token_text: str
token_is_special: bool
generated_text: Optional[GeneratedText] generated_text: Optional[GeneratedText]
# Optional for now, since it's not yet supported for every model. # 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: def to_pb(self) -> generate_pb2.Generation:
return generate_pb2.Generation( return generate_pb2.Generation(
@ -109,10 +88,7 @@ class Generation:
prefill_tokens=self.prefill_tokens.to_pb() prefill_tokens=self.prefill_tokens.to_pb()
if self.prefill_tokens is not None if self.prefill_tokens is not None
else None, else None,
token_id=self.token_id, tokens=self.tokens.to_pb(),
token_logprob=self.token_logprob,
token_text=self.token_text,
token_is_special=self.token_is_special,
generated_text=self.generated_text.to_pb() generated_text=self.generated_text.to_pb()
if self.generated_text is not None if self.generated_text is not None
else None, else None,

6
server/text_generation_server/server.py
View File

@ -132,6 +132,7 @@ def serve(
revision: Optional[str], revision: Optional[str],
sharded: bool, sharded: bool,
quantize: Optional[str], quantize: Optional[str],
speculate: Optional[int],
dtype: Optional[str], dtype: Optional[str],
trust_remote_code: bool, trust_remote_code: bool,
uds_path: Path, uds_path: Path,
@ -141,6 +142,7 @@ def serve(
revision: Optional[str], revision: Optional[str],
sharded: bool = False, sharded: bool = False,
quantize: Optional[str] = None, quantize: Optional[str] = None,
speculate: Optional[int] = None,
dtype: Optional[str] = None, dtype: Optional[str] = None,
trust_remote_code: bool = False, trust_remote_code: bool = False,
): ):
@ -157,7 +159,7 @@ def serve(
try: try:
model = get_model( model = get_model(
model_id, revision, sharded, quantize, dtype, trust_remote_code model_id, revision, sharded, quantize, speculate, dtype, trust_remote_code
) )
except Exception: except Exception:
logger.exception("Error when initializing model") logger.exception("Error when initializing model")
@ -205,5 +207,5 @@ def serve(
await server.stop(0) await server.stop(0)
asyncio.run( 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)
) )

51
server/text_generation_server/utils/medusa.py Normal file
View File

@ -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

12
server/text_generation_server/utils/speculate.py Normal file
View File

@ -0,0 +1,12 @@
SPECULATE = None
def get_speculate() -> int:
global SPECULATE
return SPECULATE
def set_speculate(speculate: int):
global SPECULATE
SPECULATE = speculate

92
server/text_generation_server/utils/tokens.py
View File

@ -16,7 +16,6 @@ from text_generation_server.utils.logits_process import (
from text_generation_server.utils.watermark import WatermarkLogitsProcessor from text_generation_server.utils.watermark import WatermarkLogitsProcessor
from transformers import PreTrainedTokenizerBase, RepetitionPenaltyLogitsProcessor from transformers import PreTrainedTokenizerBase, RepetitionPenaltyLogitsProcessor
class NextTokenChooser: class NextTokenChooser:
def __init__( def __init__(
self, self,
@ -146,6 +145,20 @@ class StoppingCriteria:
pb.ignore_eos_token, 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: class HeterogeneousNextTokenChooser:
def __init__( def __init__(
@ -215,20 +228,79 @@ class HeterogeneousNextTokenChooser:
self.dtype = dtype self.dtype = dtype
self.device = device self.device = device
def __call__(self, input_ids: torch.Tensor, scores: torch.Tensor): 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 self.watermark_processor is not None: if speculated_ids is not None:
scores = self.watermark_processor(input_ids, scores) B = scores.shape[0] // (speculated_ids.shape[1] + 1)
if self.repetition_processor is not None: S = speculated_ids.shape[1] + 1
scores = self.repetition_processor(input_ids, scores) scores = scores.view(B, S, -1)
else:
B = scores.shape[0]
S = 1
scores = scores.view(B, S, -1)
for warper in self.warpers: next_ids = torch.zeros((B, S), device=scores.device, dtype=torch.long)
scores = warper(input_ids, scores) 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) logprobs = torch.log_softmax(scores, -1)
next_logprobs = torch.gather(logprobs, 1, next_ids.view(-1, 1)).view(-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): def filter(self, indices):
if self.watermark_processor is not None: if self.watermark_processor is not None: