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hf_public_repos/text-generation-inference/router | hf_public_repos/text-generation-inference/router/src/server.rs | /// HTTP Server logic
use crate::health::Health;
use crate::infer::{InferError, InferResponse, InferStreamResponse};
use crate::validation::ValidationError;
use crate::HubTokenizerConfig;
use crate::{
BestOfSequence, ChatCompletion, ChatCompletionChunk, ChatRequest, CompatGenerateRequest,
Details, ErrorResponse, FinishReason, GenerateParameters, GenerateRequest, GenerateResponse,
HubModelInfo, Infer, Info, PrefillToken, StreamDetails, StreamResponse, Token, Validation,
};
use axum::extract::Extension;
use axum::http::{HeaderMap, Method, StatusCode};
use axum::response::sse::{Event, KeepAlive, Sse};
use axum::response::{IntoResponse, Response};
use axum::routing::{get, post};
use axum::{http, Json, Router};
use axum_tracing_opentelemetry::middleware::OtelAxumLayer;
use futures::stream::StreamExt;
use futures::Stream;
use metrics_exporter_prometheus::{Matcher, PrometheusBuilder, PrometheusHandle};
use std::convert::Infallible;
use std::net::SocketAddr;
use std::sync::atomic::AtomicBool;
use std::sync::Arc;
use text_generation_client::{ShardInfo, ShardedClient};
use tokenizers::Tokenizer;
use tokio::signal;
use tokio::time::Instant;
use tower_http::cors::{AllowOrigin, CorsLayer};
use tracing::{info_span, instrument, Instrument};
use utoipa::OpenApi;
use utoipa_swagger_ui::SwaggerUi;
/// Generate tokens if `stream == false` or a stream of token if `stream == true`
#[utoipa::path(
post,
tag = "Text Generation Inference",
path = "/",
request_body = CompatGenerateRequest,
responses(
(status = 200, description = "Generated Text",
content(
("application/json" = GenerateResponse),
("text/event-stream" = StreamResponse),
)),
(status = 424, description = "Generation Error", body = ErrorResponse,
example = json ! ({"error": "Request failed during generation"})),
(status = 429, description = "Model is overloaded", body = ErrorResponse,
example = json ! ({"error": "Model is overloaded"})),
(status = 422, description = "Input validation error", body = ErrorResponse,
example = json ! ({"error": "Input validation error"})),
(status = 500, description = "Incomplete generation", body = ErrorResponse,
example = json ! ({"error": "Incomplete generation"})),
)
)]
#[instrument(skip(infer, req))]
async fn compat_generate(
Extension(default_return_full_text): Extension<bool>,
infer: Extension<Infer>,
Json(mut req): Json<CompatGenerateRequest>,
) -> Result<Response, (StatusCode, Json<ErrorResponse>)> {
// default return_full_text given the pipeline_tag
if req.parameters.return_full_text.is_none() {
req.parameters.return_full_text = Some(default_return_full_text)
}
// switch on stream
if req.stream {
Ok(generate_stream(infer, Json(req.into()))
.await
.into_response())
} else {
let (headers, Json(generation)) = generate(infer, Json(req.into())).await?;
// wrap generation inside a Vec to match api-inference
Ok((headers, Json(vec![generation])).into_response())
}
}
/// Text Generation Inference endpoint info
#[utoipa::path(
get,
tag = "Text Generation Inference",
path = "/info",
responses((status = 200, description = "Served model info", body = Info))
)]
#[instrument]
async fn get_model_info(info: Extension<Info>) -> Json<Info> {
Json(info.0)
}
#[utoipa::path(
get,
tag = "Text Generation Inference",
path = "/health",
responses(
(status = 200, description = "Everything is working fine"),
(status = 503, description = "Text generation inference is down", body = ErrorResponse,
example = json ! ({"error": "unhealthy", "error_type": "healthcheck"})),
)
)]
#[instrument(skip(health))]
/// Health check method
async fn health(mut health: Extension<Health>) -> Result<(), (StatusCode, Json<ErrorResponse>)> {
match health.check().await {
true => Ok(()),
false => Err((
StatusCode::SERVICE_UNAVAILABLE,
Json(ErrorResponse {
error: "unhealthy".to_string(),
error_type: "healthcheck".to_string(),
}),
)),
}
}
/// Generate tokens
#[utoipa::path(
post,
tag = "Text Generation Inference",
path = "/generate",
request_body = GenerateRequest,
responses(
(status = 200, description = "Generated Text", body = GenerateResponse),
(status = 424, description = "Generation Error", body = ErrorResponse,
example = json ! ({"error": "Request failed during generation"})),
(status = 429, description = "Model is overloaded", body = ErrorResponse,
example = json ! ({"error": "Model is overloaded"})),
(status = 422, description = "Input validation error", body = ErrorResponse,
example = json ! ({"error": "Input validation error"})),
(status = 500, description = "Incomplete generation", body = ErrorResponse,
example = json ! ({"error": "Incomplete generation"})),
)
)]
#[instrument(
skip_all,
fields(
parameters = ? req.parameters,
total_time,
validation_time,
queue_time,
inference_time,
time_per_token,
seed,
)
)]
async fn generate(
infer: Extension<Infer>,
Json(req): Json<GenerateRequest>,
) -> Result<(HeaderMap, Json<GenerateResponse>), (StatusCode, Json<ErrorResponse>)> {
let span = tracing::Span::current();
let start_time = Instant::now();
metrics::increment_counter!("tgi_request_count");
tracing::debug!("Input: {}", req.inputs);
let compute_characters = req.inputs.chars().count();
let mut add_prompt = None;
if req.parameters.return_full_text.unwrap_or(false) {
add_prompt = Some(req.inputs.clone());
}
let details: bool = req.parameters.details || req.parameters.decoder_input_details;
// Inference
let (response, best_of_responses) = match req.parameters.best_of {
Some(best_of) if best_of > 1 => {
let (response, best_of_responses) = infer.generate_best_of(req, best_of).await?;
(response, Some(best_of_responses))
}
_ => (infer.generate(req).await?, None),
};
// Token details
let input_length = response._input_length;
let details = match details {
true => {
// convert best_of_responses
let best_of_sequences = best_of_responses.map(|responses: Vec<InferResponse>| {
responses
.into_iter()
.map(|response: InferResponse| {
// Add prompt if return_full_text
let mut output_text = response.generated_text.text;
if let Some(prompt) = &add_prompt {
output_text = prompt.clone() + &output_text;
}
BestOfSequence {
generated_text: output_text,
finish_reason: FinishReason::from(
response.generated_text.finish_reason,
),
generated_tokens: response.generated_text.generated_tokens,
prefill: response.prefill,
tokens: response.tokens,
top_tokens: response.top_tokens,
seed: response.generated_text.seed,
}
})
.collect()
});
Some(Details {
finish_reason: FinishReason::from(response.generated_text.finish_reason),
generated_tokens: response.generated_text.generated_tokens,
prefill: response.prefill,
tokens: response.tokens,
seed: response.generated_text.seed,
best_of_sequences,
top_tokens: response.top_tokens,
})
}
false => None,
};
// Timings
let total_time = start_time.elapsed();
let validation_time = response.queued - start_time;
let queue_time = response.start - response.queued;
let inference_time = Instant::now() - response.start;
let time_per_token = inference_time / response.generated_text.generated_tokens;
// Tracing metadata
span.record("total_time", format!("{total_time:?}"));
span.record("validation_time", format!("{validation_time:?}"));
span.record("queue_time", format!("{queue_time:?}"));
span.record("inference_time", format!("{inference_time:?}"));
span.record("time_per_token", format!("{time_per_token:?}"));
span.record("seed", format!("{:?}", response.generated_text.seed));
// Headers
let mut headers = HeaderMap::new();
headers.insert("x-compute-type", "gpu+optimized".parse().unwrap());
headers.insert(
"x-compute-time",
total_time.as_millis().to_string().parse().unwrap(),
);
headers.insert(
"x-compute-characters",
compute_characters.to_string().parse().unwrap(),
);
headers.insert(
"x-total-time",
total_time.as_millis().to_string().parse().unwrap(),
);
headers.insert(
"x-validation-time",
validation_time.as_millis().to_string().parse().unwrap(),
);
headers.insert(
"x-queue-time",
queue_time.as_millis().to_string().parse().unwrap(),
);
headers.insert(
"x-inference-time",
inference_time.as_millis().to_string().parse().unwrap(),
);
headers.insert(
"x-time-per-token",
time_per_token.as_millis().to_string().parse().unwrap(),
);
headers.insert("x-prompt-tokens", input_length.into());
headers.insert(
"x-generated-tokens",
response.generated_text.generated_tokens.into(),
);
// Metrics
metrics::increment_counter!("tgi_request_success");
metrics::histogram!("tgi_request_duration", total_time.as_secs_f64());
metrics::histogram!(
"tgi_request_validation_duration",
validation_time.as_secs_f64()
);
metrics::histogram!("tgi_request_queue_duration", queue_time.as_secs_f64());
metrics::histogram!(
"tgi_request_inference_duration",
inference_time.as_secs_f64()
);
metrics::histogram!(
"tgi_request_mean_time_per_token_duration",
time_per_token.as_secs_f64()
);
metrics::histogram!(
"tgi_request_generated_tokens",
response.generated_text.generated_tokens as f64
);
// Send response
let mut output_text = response.generated_text.text;
if let Some(prompt) = add_prompt {
output_text = prompt + &output_text;
}
tracing::debug!("Output: {}", output_text);
tracing::info!("Success");
let response = GenerateResponse {
generated_text: output_text,
details,
};
Ok((headers, Json(response)))
}
/// Generate a stream of token using Server-Sent Events
#[utoipa::path(
post,
tag = "Text Generation Inference",
path = "/generate_stream",
request_body = GenerateRequest,
responses(
(status = 200, description = "Generated Text", body = StreamResponse,
content_type = "text/event-stream"),
(status = 424, description = "Generation Error", body = ErrorResponse,
example = json ! ({"error": "Request failed during generation"}),
content_type = "text/event-stream"),
(status = 429, description = "Model is overloaded", body = ErrorResponse,
example = json ! ({"error": "Model is overloaded"}),
content_type = "text/event-stream"),
(status = 422, description = "Input validation error", body = ErrorResponse,
example = json ! ({"error": "Input validation error"}),
content_type = "text/event-stream"),
(status = 500, description = "Incomplete generation", body = ErrorResponse,
example = json ! ({"error": "Incomplete generation"}),
content_type = "text/event-stream"),
)
)]
#[instrument(
skip_all,
fields(
parameters = ? req.parameters,
total_time,
validation_time,
queue_time,
inference_time,
time_per_token,
seed,
)
)]
async fn generate_stream(
Extension(infer): Extension<Infer>,
Json(req): Json<GenerateRequest>,
) -> (
HeaderMap,
Sse<impl Stream<Item = Result<Event, Infallible>>>,
) {
let on_message_callback = |stream_token: StreamResponse| {
let event = Event::default();
event.json_data(stream_token).unwrap()
};
let (headers, response_stream) =
generate_stream_internal(infer, Json(req), on_message_callback).await;
let sse = Sse::new(response_stream).keep_alive(KeepAlive::default());
(headers, sse)
}
async fn generate_stream_internal(
infer: Infer,
Json(req): Json<GenerateRequest>,
on_message_callback: impl Fn(StreamResponse) -> Event,
) -> (HeaderMap, impl Stream<Item = Result<Event, Infallible>>) {
let span = tracing::Span::current();
let start_time = Instant::now();
metrics::increment_counter!("tgi_request_count");
tracing::debug!("Input: {}", req.inputs);
let compute_characters = req.inputs.chars().count();
let mut headers = HeaderMap::new();
headers.insert("x-compute-type", "gpu+optimized".parse().unwrap());
headers.insert(
"x-compute-characters",
compute_characters.to_string().parse().unwrap(),
);
headers.insert("X-Accel-Buffering", "no".parse().unwrap());
let stream = async_stream::stream! {
// Inference
let mut end_reached = false;
let mut error = false;
let mut add_prompt = None;
if req.parameters.return_full_text.unwrap_or(false) {
add_prompt = Some(req.inputs.clone());
}
let details = req.parameters.details;
let best_of = req.parameters.best_of.unwrap_or(1);
if best_of != 1 {
let err = InferError::from(ValidationError::BestOfStream);
metrics::increment_counter!("tgi_request_failure", "err" => "validation");
tracing::error!("{err}");
yield Ok(Event::from(err));
} else if req.parameters.decoder_input_details {
let err = InferError::from(ValidationError::PrefillDetailsStream);
metrics::increment_counter!("tgi_request_failure", "err" => "validation");
tracing::error!("{err}");
yield Ok(Event::from(err));
} else {
match infer.generate_stream(req).instrument(info_span!(parent: &span, "async_stream")).await {
// Keep permit as long as generate_stream lives
Ok((_permit, _input_length, mut response_stream)) => {
let mut index = 0;
// Server-Sent Event stream
while let Some(response) = response_stream.next().await {
index += 1;
match response {
Ok(response) => {
match response {
// Prefill is ignored
InferStreamResponse::Prefill(_) => {}
// Yield event for every new token
InferStreamResponse::Intermediate{
token,
top_tokens,
} => {
tracing::debug!(parent: &span, "Token: {:?}", token);
// StreamResponse
let stream_token = StreamResponse {
index,
token,
top_tokens,
generated_text: None,
details: None,
};
let event = on_message_callback(stream_token);
yield Ok(event);
}
// Yield event for last token and compute timings
InferStreamResponse::End {
token,
generated_text,
start,
queued,
top_tokens,
} => {
// Token details
let details = match details {
true => Some(StreamDetails {
finish_reason: FinishReason::from(generated_text.finish_reason),
generated_tokens: generated_text.generated_tokens,
seed: generated_text.seed,
}),
false => None,
};
// Timings
let total_time = start_time.elapsed();
let validation_time = queued - start_time;
let queue_time = start - queued;
let inference_time = Instant::now() - start;
let time_per_token = inference_time / generated_text.generated_tokens;
// Tracing metadata
span.record("total_time", format!("{total_time:?}"));
span.record("validation_time", format!("{validation_time:?}"));
span.record("queue_time", format!("{queue_time:?}"));
span.record("inference_time", format!("{inference_time:?}"));
span.record("time_per_token", format!("{time_per_token:?}"));
span.record("seed", format!("{:?}", generated_text.seed));
// Metrics
metrics::increment_counter!("tgi_request_success");
metrics::histogram!("tgi_request_duration", total_time.as_secs_f64());
metrics::histogram!("tgi_request_validation_duration", validation_time.as_secs_f64());
metrics::histogram!("tgi_request_queue_duration", queue_time.as_secs_f64());
metrics::histogram!("tgi_request_inference_duration", inference_time.as_secs_f64());
metrics::histogram!("tgi_request_mean_time_per_token_duration", time_per_token.as_secs_f64());
metrics::histogram!("tgi_request_generated_tokens", generated_text.generated_tokens as f64);
// StreamResponse
end_reached = true;
let mut output_text = generated_text.text;
if let Some(prompt) = add_prompt {
output_text = prompt + &output_text;
}
tracing::debug!(parent: &span, "Output: {}", output_text);
tracing::info!(parent: &span, "Success");
let stream_token = StreamResponse {
index,
token,
top_tokens,
generated_text: Some(output_text),
details
};
let event = on_message_callback(stream_token);
yield Ok(event);
break;
}
}
}
// yield error
Err(err) => {
error = true;
yield Ok(Event::from(err));
break;
}
}
}
},
// yield error
Err(err) => {
error = true;
yield Ok(Event::from(err));
}
}
// Check if generation reached the end
// Skip if we already sent an error
if !end_reached && !error {
let err = InferError::IncompleteGeneration;
metrics::increment_counter!("tgi_request_failure", "err" => "incomplete");
tracing::error!("{err}");
yield Ok(Event::from(err));
}
}
};
(headers, stream)
}
/// Generate tokens
#[utoipa::path(
post,
tag = "Text Generation Inference",
path = "/v1/chat/completions",
request_body = ChatRequest,
responses(
(status = 200, description = "Generated Text", body = GenerateResponse),
(status = 424, description = "Generation Error", body = ErrorResponse,
example = json ! ({"error": "Request failed during generation"})),
(status = 429, description = "Model is overloaded", body = ErrorResponse,
example = json ! ({"error": "Model is overloaded"})),
(status = 422, description = "Input validation error", body = ErrorResponse,
example = json ! ({"error": "Input validation error"})),
(status = 500, description = "Incomplete generation", body = ErrorResponse,
example = json ! ({"error": "Incomplete generation"})),
)
)]
#[instrument(
skip_all,
fields(
// parameters = ? req.parameters,
total_time,
validation_time,
queue_time,
inference_time,
time_per_token,
seed,
)
)]
async fn chat_completions(
Extension(infer): Extension<Infer>,
Extension(info): Extension<Info>,
Json(req): Json<ChatRequest>,
) -> Result<Response, (StatusCode, Json<ErrorResponse>)> {
metrics::increment_counter!("tgi_request_count");
let stream = req.stream;
let max_new_tokens = req.max_tokens.or(Some(100));
let repetition_penalty = req
.frequency_penalty
// rescale frequency_penalty from (-2.0, 2.0) to (0.0, 4.0)
.map(|x| x + 2.0);
let logprobs = req.logprobs.unwrap_or(false);
let seed = req.seed;
// apply chat template to flatten the request into a single input
let inputs = match infer.apply_chat_template(req) {
Ok(inputs) => inputs,
Err(err) => {
metrics::increment_counter!("tgi_request_failure", "err" => "validation");
tracing::error!("{err}");
return Err((
StatusCode::UNPROCESSABLE_ENTITY,
Json(ErrorResponse {
error: err.to_string(),
error_type: err.error_type().to_string(),
}),
));
}
};
// build the request passing some parameters
let generate_request = GenerateRequest {
inputs: inputs.to_string(),
parameters: GenerateParameters {
best_of: None,
temperature: None,
repetition_penalty,
top_k: None,
top_p: None,
typical_p: None,
do_sample: true,
max_new_tokens,
return_full_text: None,
stop: Vec::new(),
truncate: None,
watermark: false,
details: true,
decoder_input_details: true,
seed,
top_n_tokens: None,
},
};
// static values that will be returned in all cases
let model_id = info.model_id.clone();
let system_fingerprint = format!("{}-{}", info.version, info.docker_label.unwrap_or("native"));
// switch on stream
if stream {
// pass this callback to the stream generation and build the required event structure
let on_message_callback = move |stream_token: StreamResponse| {
let event = Event::default();
let current_time = std::time::SystemTime::now()
.duration_since(std::time::UNIX_EPOCH)
.unwrap_or_else(|_| std::time::Duration::from_secs(0))
.as_secs();
event
.json_data(ChatCompletionChunk::new(
model_id.clone(),
system_fingerprint.clone(),
stream_token.token.text,
current_time,
stream_token.index,
logprobs.then_some(stream_token.token.logprob),
stream_token.details.map(|d| d.finish_reason.to_string()),
))
.map_or_else(
|e| {
println!("Failed to serialize ChatCompletionChunk: {:?}", e);
Event::default()
},
|data| data,
)
};
let (headers, response_stream) =
generate_stream_internal(infer, Json(generate_request), on_message_callback).await;
let sse = Sse::new(response_stream).keep_alive(KeepAlive::default());
Ok((headers, sse).into_response())
} else {
let (headers, Json(generation)) =
generate(Extension(infer), Json(generate_request)).await?;
let current_time = std::time::SystemTime::now()
.duration_since(std::time::UNIX_EPOCH)
.unwrap_or_else(|_| std::time::Duration::from_secs(0))
.as_secs();
// build the complete response object with the full text
let response = ChatCompletion::new(
generation.generated_text,
model_id,
system_fingerprint,
current_time,
generation.details.unwrap(),
logprobs,
);
// wrap generation inside a Vec to match api-inference
Ok((headers, Json(response)).into_response())
}
}
/// Prometheus metrics scrape endpoint
#[utoipa::path(
get,
tag = "Text Generation Inference",
path = "/metrics",
responses((status = 200, description = "Prometheus Metrics", body = String))
)]
async fn metrics(prom_handle: Extension<PrometheusHandle>) -> String {
prom_handle.render()
}
/// Serving method
#[allow(clippy::too_many_arguments)]
pub async fn run(
model_info: HubModelInfo,
shard_info: ShardInfo,
compat_return_full_text: bool,
max_concurrent_requests: usize,
max_best_of: usize,
max_stop_sequences: usize,
max_top_n_tokens: u32,
max_input_length: usize,
max_total_tokens: usize,
waiting_served_ratio: f32,
max_batch_prefill_tokens: u32,
max_batch_total_tokens: u32,
max_waiting_tokens: usize,
client: ShardedClient,
tokenizer: Option<Tokenizer>,
validation_workers: usize,
addr: SocketAddr,
allow_origin: Option<AllowOrigin>,
ngrok: bool,
ngrok_authtoken: Option<String>,
ngrok_edge: Option<String>,
tokenizer_config: HubTokenizerConfig,
) -> Result<(), axum::BoxError> {
// OpenAPI documentation
#[derive(OpenApi)]
#[openapi(
paths(
health,
get_model_info,
compat_generate,
generate,
generate_stream,
metrics,
),
components(
schemas(
Info,
CompatGenerateRequest,
GenerateRequest,
GenerateParameters,
PrefillToken,
Token,
GenerateResponse,
BestOfSequence,
Details,
FinishReason,
StreamResponse,
StreamDetails,
ErrorResponse,
)
),
tags(
(name = "Text Generation Inference", description = "Hugging Face Text Generation Inference API")
),
info(
title = "Text Generation Inference",
license(
name = "Apache 2.0",
url = "https://www.apache.org/licenses/LICENSE-2.0"
)
)
)]
struct ApiDoc;
// Create state
let validation = Validation::new(
validation_workers,
tokenizer,
max_best_of,
max_stop_sequences,
max_top_n_tokens,
max_input_length,
max_total_tokens,
);
let generation_health = Arc::new(AtomicBool::new(false));
let health_ext = Health::new(client.clone(), generation_health.clone());
let infer = Infer::new(
client,
validation,
waiting_served_ratio,
max_batch_prefill_tokens,
max_batch_total_tokens,
max_waiting_tokens,
max_concurrent_requests,
shard_info.requires_padding,
shard_info.window_size,
shard_info.speculate,
generation_health,
tokenizer_config,
);
// Duration buckets
let duration_matcher = Matcher::Suffix(String::from("duration"));
let n_duration_buckets = 35;
let mut duration_buckets = Vec::with_capacity(n_duration_buckets);
// Minimum duration in seconds
let mut value = 0.0001;
for _ in 0..n_duration_buckets {
// geometric sequence
value *= 1.5;
duration_buckets.push(value);
}
// Input Length buckets
let input_length_matcher = Matcher::Full(String::from("tgi_request_input_length"));
let input_length_buckets: Vec<f64> = (0..100)
.map(|x| (max_input_length as f64 / 100.0) * (x + 1) as f64)
.collect();
// Generated tokens buckets
let generated_tokens_matcher = Matcher::Full(String::from("tgi_request_generated_tokens"));
let generated_tokens_buckets: Vec<f64> = (0..100)
.map(|x| (max_total_tokens as f64 / 100.0) * (x + 1) as f64)
.collect();
// Input Length buckets
let max_new_tokens_matcher = Matcher::Full(String::from("tgi_request_max_new_tokens"));
let max_new_tokens_buckets: Vec<f64> = (0..100)
.map(|x| (max_total_tokens as f64 / 100.0) * (x + 1) as f64)
.collect();
// Batch size buckets
let batch_size_matcher = Matcher::Full(String::from("tgi_batch_next_size"));
let batch_size_buckets: Vec<f64> = (0..1024).map(|x| (x + 1) as f64).collect();
// Speculated tokens buckets
let skipped_matcher = Matcher::Full(String::from("tgi_request_skipped_tokens"));
let skipped_buckets: Vec<f64> = (0..shard_info.speculate + 1).map(|x| x as f64).collect();
// Prometheus handler
let builder = PrometheusBuilder::new()
.set_buckets_for_metric(duration_matcher, &duration_buckets)
.unwrap()
.set_buckets_for_metric(input_length_matcher, &input_length_buckets)
.unwrap()
.set_buckets_for_metric(generated_tokens_matcher, &generated_tokens_buckets)
.unwrap()
.set_buckets_for_metric(max_new_tokens_matcher, &max_new_tokens_buckets)
.unwrap()
.set_buckets_for_metric(batch_size_matcher, &batch_size_buckets)
.unwrap()
.set_buckets_for_metric(skipped_matcher, &skipped_buckets)
.unwrap();
let prom_handle = builder
.install_recorder()
.expect("failed to install metrics recorder");
// CORS layer
let allow_origin = allow_origin.unwrap_or(AllowOrigin::any());
let cors_layer = CorsLayer::new()
.allow_methods([Method::GET, Method::POST])
.allow_headers([http::header::CONTENT_TYPE])
.allow_origin(allow_origin);
// Endpoint info
let info = Info {
model_id: model_info.model_id,
model_sha: model_info.sha,
model_dtype: shard_info.dtype,
model_device_type: shard_info.device_type,
model_pipeline_tag: model_info.pipeline_tag,
max_concurrent_requests,
max_best_of,
max_stop_sequences,
max_input_length,
max_total_tokens,
waiting_served_ratio,
max_batch_total_tokens,
max_waiting_tokens,
validation_workers,
version: env!("CARGO_PKG_VERSION"),
sha: option_env!("VERGEN_GIT_SHA"),
docker_label: option_env!("DOCKER_LABEL"),
};
// Create router
let app = Router::new()
.merge(SwaggerUi::new("/docs").url("/api-doc/openapi.json", ApiDoc::openapi()))
// Base routes
.route("/", post(compat_generate))
.route("/info", get(get_model_info))
.route("/generate", post(generate))
.route("/generate_stream", post(generate_stream))
.route("/v1/chat/completions", post(chat_completions))
// AWS Sagemaker route
.route("/invocations", post(compat_generate))
// Base Health route
.route("/health", get(health))
// Inference API health route
.route("/", get(health))
// AWS Sagemaker health route
.route("/ping", get(health))
// Prometheus metrics route
.route("/metrics", get(metrics))
.layer(Extension(info))
.layer(Extension(health_ext.clone()))
.layer(Extension(compat_return_full_text))
.layer(Extension(infer))
.layer(Extension(prom_handle.clone()))
.layer(OtelAxumLayer::default())
.layer(cors_layer);
if ngrok {
#[cfg(feature = "ngrok")]
{
use ngrok::config::TunnelBuilder;
let _ = addr;
let authtoken =
ngrok_authtoken.expect("`ngrok-authtoken` must be set when using ngrok tunneling");
let edge = ngrok_edge.expect("`ngrok-edge` must be set when using ngrok tunneling");
let tunnel = ngrok::Session::builder()
.authtoken(authtoken)
.connect()
.await
.unwrap()
.labeled_tunnel()
.label("edge", edge);
let listener = tunnel.listen().await.unwrap();
// Run prom metrics and health locally too
tokio::spawn(
axum::Server::bind(&addr)
.serve(
Router::new()
.route("/health", get(health))
.route("/metrics", get(metrics))
.layer(Extension(health_ext))
.layer(Extension(prom_handle))
.into_make_service(),
)
//Wait until all requests are finished to shut down
.with_graceful_shutdown(shutdown_signal()),
);
// Run server
axum::Server::builder(listener)
.serve(app.into_make_service())
//Wait until all requests are finished to shut down
.with_graceful_shutdown(shutdown_signal())
.await?;
}
#[cfg(not(feature = "ngrok"))]
{
let _ngrok_authtoken = ngrok_authtoken;
let _ngrok_domain = ngrok_domain;
let _ngrok_username = ngrok_username;
let _ngrok_password = ngrok_password;
panic!("`text-generation-router` was compiled without the `ngrok` feature");
}
} else {
// Run server
axum::Server::bind(&addr)
.serve(app.into_make_service())
// Wait until all requests are finished to shut down
.with_graceful_shutdown(shutdown_signal())
.await?;
}
Ok(())
}
/// Shutdown signal handler
async fn shutdown_signal() {
let ctrl_c = async {
signal::ctrl_c()
.await
.expect("failed to install Ctrl+C handler");
};
#[cfg(unix)]
let terminate = async {
signal::unix::signal(signal::unix::SignalKind::terminate())
.expect("failed to install signal handler")
.recv()
.await;
};
#[cfg(not(unix))]
let terminate = std::future::pending::<()>();
tokio::select! {
_ = ctrl_c => {},
_ = terminate => {},
}
tracing::info!("signal received, starting graceful shutdown");
opentelemetry::global::shutdown_tracer_provider();
}
impl From<i32> for FinishReason {
fn from(finish_reason: i32) -> Self {
let finish_reason = text_generation_client::FinishReason::try_from(finish_reason).unwrap();
match finish_reason {
text_generation_client::FinishReason::Length => FinishReason::Length,
text_generation_client::FinishReason::EosToken => FinishReason::EndOfSequenceToken,
text_generation_client::FinishReason::StopSequence => FinishReason::StopSequence,
}
}
}
/// Convert to Axum supported formats
impl From<InferError> for (StatusCode, Json<ErrorResponse>) {
fn from(err: InferError) -> Self {
let status_code = match err {
InferError::GenerationError(_) => StatusCode::FAILED_DEPENDENCY,
InferError::Overloaded(_) => StatusCode::TOO_MANY_REQUESTS,
InferError::ValidationError(_) => StatusCode::UNPROCESSABLE_ENTITY,
InferError::IncompleteGeneration => StatusCode::INTERNAL_SERVER_ERROR,
InferError::TemplateError(_) => StatusCode::UNPROCESSABLE_ENTITY,
};
(
status_code,
Json(ErrorResponse {
error: err.to_string(),
error_type: err.error_type().to_string(),
}),
)
}
}
impl From<InferError> for Event {
fn from(err: InferError) -> Self {
Event::default()
.json_data(ErrorResponse {
error: err.to_string(),
error_type: err.error_type().to_string(),
})
.unwrap()
}
}
| 0 |
hf_public_repos/text-generation-inference/router | hf_public_repos/text-generation-inference/router/src/infer.rs | /// Batching and inference logic
use crate::validation::{Validation, ValidationError};
use crate::HubTokenizerConfig;
use crate::{ChatRequest, GenerateRequest, GenerateStreamResponse, PrefillToken};
use crate::{Entry, Queue, Token};
use futures::future::try_join_all;
use minijinja::{Environment, ErrorKind, Template};
use nohash_hasher::IntMap;
use std::sync::{
atomic::{AtomicBool, Ordering},
Arc,
};
use text_generation_client::{
Batch, CachedBatch, ClientError, GeneratedText, Generation, ShardedClient, Tokens,
};
use thiserror::Error;
use tokio::sync::mpsc::error::SendError;
use tokio::sync::{mpsc, Notify, Semaphore, TryAcquireError};
use tokio::time::Instant;
use tokio_stream::wrappers::UnboundedReceiverStream;
use tokio_stream::StreamExt;
use tracing::{info_span, instrument, Instrument, Span};
/// Inference struct
#[derive(Clone)]
pub struct Infer {
/// Validation
validation: Validation,
/// Request queue
queue: Queue,
/// Shared state
shared: Arc<Shared>,
/// Inference limit
limit_concurrent_requests: Arc<Semaphore>,
/// Chat template
template: Option<Template<'static, 'static>>,
}
/// Infer shared state
struct Shared {
/// Batching background Tokio task notifier
batching_task: Notify,
}
impl Infer {
#[allow(clippy::too_many_arguments)]
pub(crate) fn new(
client: ShardedClient,
validation: Validation,
waiting_served_ratio: f32,
max_batch_prefill_tokens: u32,
max_batch_total_tokens: u32,
max_waiting_tokens: usize,
max_concurrent_requests: usize,
requires_padding: bool,
window_size: Option<u32>,
speculate: u32,
generation_health: Arc<AtomicBool>,
tokenizer_config: HubTokenizerConfig,
) -> Self {
// Infer shared state
let queue = Queue::new(requires_padding, 16, window_size, speculate);
let shared = Arc::new(Shared {
batching_task: Notify::new(),
});
// Spawn batching background task that contains all the inference logic
tokio::spawn(batching_task(
client,
waiting_served_ratio,
max_batch_prefill_tokens,
max_batch_total_tokens,
max_waiting_tokens,
queue.clone(),
shared.clone(),
generation_health,
));
// Inference limit with a semaphore
let semaphore = Arc::new(Semaphore::new(max_concurrent_requests));
let template = tokenizer_config.chat_template.map(|t| {
let env = Box::new(Environment::new());
let template_str = t.into_boxed_str();
// leaking env and template_str as read-only, static resources for performance.
Box::leak(env)
.template_from_str(Box::leak(template_str))
.unwrap()
});
Self {
validation,
queue,
shared,
limit_concurrent_requests: semaphore,
template,
}
}
/// Add a new request to the queue and return a stream of InferStreamResponse
#[instrument(skip_all)]
pub(crate) async fn generate_stream(
&self,
request: GenerateRequest,
) -> Result<GenerateStreamResponse, InferError> {
// Limit concurrent requests by acquiring a permit from the semaphore
let permit = self
.clone()
.limit_concurrent_requests
.try_acquire_owned()
.map_err(|err| {
metrics::increment_counter!("tgi_request_failure", "err" => "overloaded");
tracing::error!("{err}");
err
})?;
// Validate request
let valid_request = self.validation.validate(request).await.map_err(|err| {
metrics::increment_counter!("tgi_request_failure", "err" => "validation");
tracing::error!("{err}");
err
})?;
// MPSC channel to communicate with the background batching task
let (response_tx, response_rx) = mpsc::unbounded_channel();
let input_length = valid_request.input_length;
// Append the request to the queue
self.queue.append(Entry {
request: valid_request,
response_tx,
span: Span::current(),
temp_span: None,
queue_time: Instant::now(),
batch_time: None,
});
// Notify the background task that we have a new entry in the queue that needs
// to be batched
self.shared.batching_task.notify_one();
// Return stream
Ok((
permit,
input_length,
UnboundedReceiverStream::new(response_rx),
))
}
/// Apply the chat template to the chat request
#[instrument(skip_all)]
pub(crate) fn apply_chat_template(&self, chat: ChatRequest) -> Result<String, InferError> {
self.template
.as_ref()
.ok_or_else(|| InferError::TemplateError(ErrorKind::TemplateNotFound.into()))?
.render(chat)
.map_err(|e| {
metrics::increment_counter!("tgi_request_failure", "err" => "template");
tracing::error!("{e}");
InferError::TemplateError(e)
})
}
/// Add a new request to the queue and return a InferResponse
#[instrument(skip_all)]
pub(crate) async fn generate(
&self,
request: GenerateRequest,
) -> Result<InferResponse, InferError> {
let use_top_tokens = request.parameters.top_n_tokens.is_some_and(|x| x > 0);
// Create stream and keep semaphore permit as long as generate lives
let (_permit, _input_length, mut stream) = self.generate_stream(request).await?;
// Return values
let mut result_prefill = Vec::new();
let mut result_tokens = Vec::new();
let mut result_top_tokens = Vec::new();
let mut result_generated_text = None;
let mut result_start = None;
let mut result_queued = None;
// Iterate on stream
while let Some(response) = stream.next().await {
match response? {
// Add prefill tokens
InferStreamResponse::Prefill(tokens) => {
// Create Token objects
// We do that here instead of in the Python code as Rust for loops are faster
result_prefill = tokens
.ids
.into_iter()
.zip(tokens.logprobs.into_iter())
.zip(tokens.texts.into_iter())
.map(|((id, logprob), text)| PrefillToken { id, text, logprob })
.collect();
}
// Push last token
InferStreamResponse::Intermediate { token, top_tokens } => {
result_tokens.push(token);
result_top_tokens.push(top_tokens);
}
// Final message
// Set return values
InferStreamResponse::End {
token,
generated_text,
start,
queued,
top_tokens,
} => {
result_tokens.push(token);
result_top_tokens.push(top_tokens);
result_generated_text = Some(generated_text);
result_start = Some(start);
result_queued = Some(queued)
}
}
}
// Check that we received a `InferStreamResponse::End` message
if let (Some(generated_text), Some(queued), Some(start)) =
(result_generated_text, result_queued, result_start)
{
Ok(InferResponse {
prefill: result_prefill,
_input_length,
tokens: result_tokens,
generated_text,
queued,
start,
top_tokens: if use_top_tokens {
result_top_tokens
} else {
Vec::new()
},
})
} else {
let err = InferError::IncompleteGeneration;
metrics::increment_counter!("tgi_request_failure", "err" => "incomplete");
tracing::error!("{err}");
Err(err)
}
}
/// Add best_of new requests to the queue and return a InferResponse of the sequence with
/// the highest log probability per token
#[instrument(skip(self, request))]
pub(crate) async fn generate_best_of(
&self,
request: GenerateRequest,
best_of: usize,
) -> Result<(InferResponse, Vec<InferResponse>), InferError> {
// validate best_of parameter separately
let best_of = self.validation.validate_best_of(best_of)?;
// create multiple generate requests
let mut infer_responses: Vec<InferResponse> =
try_join_all((0..best_of).map(|_| self.generate(request.clone()))).await?;
// get the sequence with the highest log probability per token
let mut max_index = 0;
let mut max_logprob: f32 = f32::MIN;
for (i, response) in infer_responses.iter().enumerate() {
// mean logprobs of the generated tokens
let sequence_logprob = response
.tokens
.iter()
.map(|token| token.logprob)
.sum::<f32>()
/ response.tokens.len() as f32;
// set best sequence
if sequence_logprob > max_logprob {
max_index = i;
max_logprob = sequence_logprob;
}
}
let best_response = infer_responses.remove(max_index);
Ok((best_response, infer_responses))
}
}
/// Batching logic
/// Will be launched in a background Tokio task
///
/// Batches requests and sends them to the inference server
#[allow(clippy::too_many_arguments)]
async fn batching_task(
mut client: ShardedClient,
waiting_served_ratio: f32,
max_batch_prefill_tokens: u32,
max_batch_total_tokens: u32,
max_waiting_tokens: usize,
queue: Queue,
shared: Arc<Shared>,
generation_health: Arc<AtomicBool>,
) {
// Infinite loop
loop {
// Wait for a notification from the Infer struct
shared.batching_task.notified().await;
// Get the next batch from the queue
// This batch might be smaller than the maximum batch size if there are not enough requests
// waiting in the queue
while let Some((mut entries, batch, span)) = queue
.next_batch(None, max_batch_prefill_tokens, max_batch_total_tokens)
.await
{
let mut cached_batch = prefill(&mut client, batch, &mut entries, &generation_health)
.instrument(span)
.await;
let mut waiting_tokens = 1;
// We loop until we do not receive any cached batch from the inference server (== until
// all requests have met their stopping criteria)
while let Some(batch) = cached_batch {
// Get current batch info
let batch_size = batch.size;
let batch_max_tokens = batch.max_tokens;
let mut batches = vec![batch];
metrics::gauge!("tgi_batch_current_size", batch_size as f64);
metrics::gauge!("tgi_batch_current_max_tokens", batch_max_tokens as f64);
let min_size = if waiting_tokens >= max_waiting_tokens {
// If we didn't onboard any new requests since >= max_waiting_tokens, we try
// to add a new batch even though its size might be small
None
} else {
// Minimum batch size
Some((batch_size as f32 * waiting_served_ratio).floor() as usize)
};
let token_budget = max_batch_total_tokens.saturating_sub(batch_max_tokens);
// Try to get a new batch
if let Some((mut new_entries, new_batch, span)) = queue
.next_batch(min_size, max_batch_prefill_tokens, token_budget)
.await
{
// Tracking metrics
if min_size.is_some() {
metrics::increment_counter!("tgi_batch_concat", "reason" => "backpressure");
} else {
metrics::increment_counter!("tgi_batch_concat", "reason" => "wait_exceeded");
}
entries.iter_mut().for_each(|(_, entry)| {
// Create a new span to add the info that this entry is waiting
// because a new batch is being computed
let entry_waiting_span = info_span!(parent: &entry.span, "waiting");
// Add relationships
span.follows_from(&entry_waiting_span);
entry_waiting_span.follows_from(&span);
// Update entry
entry.temp_span = Some(entry_waiting_span);
});
// Generate one token for this new batch to have the attention past in cache
let new_cached_batch =
prefill(&mut client, new_batch, &mut new_entries, &generation_health)
.instrument(span)
.await;
// Reset waiting counter
waiting_tokens = 1;
// Extend current batch with the new batch
if let Some(new_cached_batch) = new_cached_batch {
entries.extend(new_entries);
batches.push(new_cached_batch);
}
}
// Create span for this batch to add context to inference calls
let next_batch_size = entries.len();
let next_batch_span =
info_span!(parent: None, "batch", batch_size = next_batch_size);
entries.iter_mut().for_each(|(_, entry)| {
// Create a new span to link the batch back to this entry
let entry_batch_span = info_span!(parent: &entry.span, "infer");
// Add relationships
next_batch_span.follows_from(&entry_batch_span);
entry_batch_span.follows_from(&next_batch_span);
// Update entry
entry.temp_span = Some(entry_batch_span);
});
cached_batch = decode(&mut client, batches, &mut entries, &generation_health)
.instrument(next_batch_span)
.await;
waiting_tokens += 1;
}
metrics::gauge!("tgi_batch_current_size", 0.0);
metrics::gauge!("tgi_batch_current_max_tokens", 0.0);
}
}
}
#[instrument(skip_all)]
async fn prefill(
client: &mut ShardedClient,
batch: Batch,
entries: &mut IntMap<u64, Entry>,
generation_health: &Arc<AtomicBool>,
) -> Option<CachedBatch> {
let start_time = Instant::now();
let batch_id = batch.id;
metrics::increment_counter!("tgi_batch_inference_count", "method" => "prefill");
match client.prefill(batch).await {
Ok((generations, next_batch, timings)) => {
// Update health
generation_health.store(true, Ordering::SeqCst);
let start_filtering_time = Instant::now();
// Send generated tokens and filter stopped entries
filter_send_generations(generations, entries);
// Filter next batch and remove requests that were stopped
let next_batch = filter_batch(client, next_batch, entries).await;
metrics::histogram!("tgi_batch_forward_duration", timings.forward.as_secs_f64(), "method" => "prefill");
metrics::histogram!("tgi_batch_decode_duration", timings.decode.as_secs_f64(), "method" => "prefill");
metrics::histogram!("tgi_batch_filter_duration", start_filtering_time.elapsed().as_secs_f64(), "method" => "prefill");
metrics::histogram!("tgi_batch_inference_duration", start_time.elapsed().as_secs_f64(), "method" => "prefill");
metrics::increment_counter!("tgi_batch_inference_success", "method" => "prefill");
next_batch
}
// If we have an error, we discard the whole batch
Err(err) => {
// Update health
generation_health.store(false, Ordering::SeqCst);
let _ = client.clear_cache(Some(batch_id)).await;
send_errors(err, entries);
metrics::increment_counter!("tgi_batch_inference_failure", "method" => "prefill");
None
}
}
}
#[instrument(skip_all)]
async fn decode(
client: &mut ShardedClient,
batches: Vec<CachedBatch>,
entries: &mut IntMap<u64, Entry>,
generation_health: &Arc<AtomicBool>,
) -> Option<CachedBatch> {
let start_time = Instant::now();
let batch_ids: Vec<u64> = batches.iter().map(|b| b.id).collect();
metrics::increment_counter!("tgi_batch_inference_count", "method" => "decode");
match client.decode(batches).await {
Ok((generations, next_batch, timings)) => {
// Update health
generation_health.store(true, Ordering::SeqCst);
let start_filtering_time = Instant::now();
// Send generated tokens and filter stopped entries
filter_send_generations(generations, entries);
// Filter next batch and remove requests that were stopped
let next_batch = filter_batch(client, next_batch, entries).await;
if let Some(concat_duration) = timings.concat {
metrics::histogram!("tgi_batch_concat_duration", concat_duration.as_secs_f64(), "method" => "decode");
}
metrics::histogram!("tgi_batch_forward_duration", timings.forward.as_secs_f64(), "method" => "decode");
metrics::histogram!("tgi_batch_decode_duration", timings.decode.as_secs_f64(), "method" => "decode");
metrics::histogram!("tgi_batch_filter_duration", start_filtering_time.elapsed().as_secs_f64(), "method" => "decode");
metrics::histogram!("tgi_batch_inference_duration", start_time.elapsed().as_secs_f64(), "method" => "decode");
metrics::increment_counter!("tgi_batch_inference_success", "method" => "decode");
next_batch
}
// If we have an error, we discard the whole batch
Err(err) => {
generation_health.store(false, Ordering::SeqCst);
for id in batch_ids {
let _ = client.clear_cache(Some(id)).await;
}
send_errors(err, entries);
metrics::increment_counter!("tgi_batch_inference_failure", "method" => "decode");
None
}
}
}
/// Filter a `batch` and remove all requests not present in `entries`
#[instrument(skip_all)]
async fn filter_batch(
client: &mut ShardedClient,
next_batch: Option<CachedBatch>,
entries: &IntMap<u64, Entry>,
) -> Option<CachedBatch> {
let mut batch = next_batch?;
// No need to filter
if batch.size as usize == entries.len() {
return Some(batch);
}
let id = batch.id;
// Retain only requests that are still in entries
batch.request_ids.retain(|id| entries.contains_key(id));
if batch.request_ids.is_empty() {
// All requests have been filtered out
// Next batch is now empty
// Clear it from the Python shards cache
// We unwrap here as we need to panic since we cannot recover if this method fails
client.clear_cache(Some(id)).await.unwrap();
None
} else {
// Filter Python shard cache
// We unwrap here as we need to panic since we cannot recover if this method fails
client.filter_batch(id, batch.request_ids).await.unwrap()
}
}
/// Send one or multiple `InferStreamResponse` to Infer for all `entries`
/// and filter entries
#[instrument(skip_all)]
fn filter_send_generations(generations: Vec<Generation>, entries: &mut IntMap<u64, Entry>) {
generations.into_iter().for_each(|generation| {
let id = generation.request_id;
// Get entry
// We can `expect` here as the request id should always be in the entries
let entry = entries
.get(&id)
.expect("ID not found in entries. This is a bug.");
// Create and enter a span to link this function back to the entry
let _span = info_span!(parent: entry.temp_span.as_ref().expect("batch_span is None. This is a bug."), "send_generation", generation = ?generation).entered();
// Send generation responses back to the infer task
// If the receive an error from the Flume channel, it means that the client dropped the
// request and we need to stop generating hence why we unwrap_or(true)
let stopped = send_responses(generation, entry).map_err(|err| {
tracing::error!("Entry response channel error.");
metrics::increment_counter!("tgi_request_failure", "err" => "dropped");
err
}).unwrap_or(true);
if stopped {
entries.remove(&id).expect("ID not found in entries. This is a bug.");
}
});
}
/// Send responses through the `entry` response channel
fn send_responses(
generation: Generation,
entry: &Entry,
) -> Result<bool, Box<SendError<Result<InferStreamResponse, InferError>>>> {
// Return directly if the channel is disconnected
if entry.response_tx.is_closed() {
metrics::increment_counter!("tgi_request_failure", "err" => "dropped");
return Ok(true);
}
let mut stopped = false;
if let Some(prefill_tokens) = generation.prefill_tokens {
// Send message
entry
.response_tx
.send(Ok(InferStreamResponse::Prefill(prefill_tokens)))?;
}
// Create last Token
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)
.zip(tokens_.texts)
.zip(tokens_.is_special)
.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
.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.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 }))?;
}
}
}
Ok(stopped)
}
/// Send errors to Infer for all `entries`
#[instrument(skip_all)]
fn send_errors(error: ClientError, entries: &mut IntMap<u64, Entry>) {
entries.drain().for_each(|(_, entry)| {
// Create and enter a span to link this function back to the entry
let _send_error_span = info_span!(parent: entry.temp_span.as_ref().expect("batch_span is None. This is a bug."), "send_error").entered();
let err = InferError::GenerationError(error.to_string());
metrics::increment_counter!("tgi_request_failure", "err" => "generation");
tracing::error!("{err}");
// unwrap_or is valid here as we don't care if the receiver is gone.
entry
.response_tx
.send(Err(err))
.unwrap_or(());
});
}
#[derive(Debug)]
pub(crate) enum InferStreamResponse {
// Optional first message
Prefill(Tokens),
// Intermediate messages
Intermediate {
token: Token,
top_tokens: Vec<Token>,
},
// Last message
End {
token: Token,
top_tokens: Vec<Token>,
generated_text: GeneratedText,
start: Instant,
queued: Instant,
},
}
#[derive(Debug)]
pub(crate) struct InferResponse {
/// input_length is the input as perceived by the rust tokenizer in the
/// validation pathway. It is redundant with prefill.len() but prefill
/// has data only if the user asked for it. This will always be filled.
pub(crate) _input_length: u32,
pub(crate) prefill: Vec<PrefillToken>,
pub(crate) tokens: Vec<Token>,
pub(crate) generated_text: GeneratedText,
pub(crate) queued: Instant,
pub(crate) start: Instant,
pub(crate) top_tokens: Vec<Vec<Token>>,
}
#[derive(Debug, Error)]
pub enum InferError {
#[error("Request failed during generation: {0}")]
GenerationError(String),
#[error("Model is overloaded")]
Overloaded(#[from] TryAcquireError),
#[error("Input validation error: {0}")]
ValidationError(#[from] ValidationError),
#[error("Incomplete generation")]
IncompleteGeneration,
#[error("Template error: {0}")]
TemplateError(#[from] minijinja::Error),
}
impl InferError {
pub(crate) fn error_type(&self) -> &str {
match self {
InferError::GenerationError(_) => "generation",
InferError::Overloaded(_) => "overloaded",
InferError::ValidationError(_) => "validation",
InferError::IncompleteGeneration => "incomplete_generation",
InferError::TemplateError(_) => "template_error",
}
}
}
| 0 |
hf_public_repos/text-generation-inference | hf_public_repos/text-generation-inference/server/Makefile-vllm | vllm-cuda:
# Clone vllm
pip install -U ninja packaging --no-cache-dir
git clone https://github.com/vllm-project/vllm.git vllm
build-vllm-cuda: vllm-cuda
cd vllm && git fetch && git checkout f8a1e39fae05ca610be8d5a78be9d40f5274e5fc
cd vllm && python setup.py build
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
| 0 |
hf_public_repos/text-generation-inference | hf_public_repos/text-generation-inference/server/Makefile-flash-att-v2 | flash_att_v2_commit_cuda := 02ac572f3ffc4f402e4183aaa6824b45859d3ed3
flash_att_v2_commit_rocm := 8736558c287ff2ef28b24878e42828c595ac3e69
flash-attention-v2-cuda:
# Clone flash attention
pip install -U packaging ninja --no-cache-dir
git clone https://github.com/HazyResearch/flash-attention.git flash-attention-v2
build-flash-attention-v2-cuda: flash-attention-v2-cuda
cd flash-attention-v2 && git fetch && git checkout $(flash_att_v2_commit_cuda)
cd flash-attention-v2 && git submodule update --init --recursive
cd flash-attention-v2 && python setup.py build
install-flash-attention-v2-cuda: build-flash-attention-v2-cuda
cd flash-attention-v2 && git submodule update --init --recursive && python setup.py install
flash-attention-v2-rocm:
# Clone flash attention
pip install -U packaging ninja --no-cache-dir
git clone https://github.com/fxmarty/flash-attention-rocm flash-attention-v2
build-flash-attention-v2-rocm: flash-attention-v2-rocm
cd flash-attention-v2 && git fetch && git checkout $(flash_att_v2_commit_rocm)
cd flash-attention-v2 && git submodule update --init --recursive
cd flash-attention-v2 && PYTORCH_ROCM_ARCH=gfx90a python setup.py build
install-flash-attention-v2-rocm: build-flash-attention-v2-rocm
cd flash-attention-v2 && git submodule update --init --recursive && python setup.py install
| 0 |
hf_public_repos/text-generation-inference | hf_public_repos/text-generation-inference/server/Makefile-awq | awq_commit := f084f40bd996f3cf3a0633c1ad7d9d476c318aaa
awq:
rm -rf llm-awq
git clone https://github.com/mit-han-lab/llm-awq
build-awq: awq
cd llm-awq/ && git fetch && git checkout $(awq_commit)
cd llm-awq/awq/kernels && python setup.py build
install-awq: build-awq
pip uninstall awq_inference_engine -y || true
cd llm-awq/awq/kernels && python setup.py install
| 0 |
hf_public_repos/text-generation-inference | hf_public_repos/text-generation-inference/server/requirements_rocm.txt | backoff==2.2.1 ; python_version >= "3.9" and python_version < "3.13"
certifi==2023.11.17 ; python_version >= "3.9" and python_version < "3.13"
charset-normalizer==3.3.2 ; python_version >= "3.9" and python_version < "3.13"
click==8.1.7 ; python_version >= "3.9" and python_version < "3.13"
colorama==0.4.6 ; python_version >= "3.9" and python_version < "3.13" and (sys_platform == "win32" or platform_system == "Windows")
deprecated==1.2.14 ; python_version >= "3.9" and python_version < "3.13"
einops==0.6.1 ; python_version >= "3.9" and python_version < "3.13"
filelock==3.13.1 ; python_version >= "3.9" and python_version < "3.13"
fsspec==2023.10.0 ; python_version >= "3.9" and python_version < "3.13"
googleapis-common-protos==1.62.0 ; python_version >= "3.9" and python_version < "3.13"
grpc-interceptor==0.15.4 ; python_version >= "3.9" and python_version < "3.13"
grpcio-reflection==1.60.0 ; python_version >= "3.9" and python_version < "3.13"
grpcio-status==1.60.0 ; python_version >= "3.9" and python_version < "3.13"
grpcio==1.60.0 ; python_version >= "3.9" and python_version < "3.13"
hf-transfer==0.1.4 ; python_version >= "3.9" and python_version < "3.13"
huggingface-hub==0.19.4 ; python_version >= "3.9" and python_version < "3.13"
idna==3.6 ; python_version >= "3.9" and python_version < "3.13"
loguru==0.6.0 ; python_version >= "3.9" and python_version < "3.13"
numpy==1.26.2 ; python_version >= "3.9" and python_version < "3.13"
opentelemetry-api==1.15.0 ; python_version >= "3.9" and python_version < "3.13"
opentelemetry-exporter-otlp-proto-grpc==1.15.0 ; python_version >= "3.9" and python_version < "3.13"
opentelemetry-exporter-otlp-proto-http==1.15.0 ; python_version >= "3.9" and python_version < "3.13"
opentelemetry-exporter-otlp==1.15.0 ; python_version >= "3.9" and python_version < "3.13"
opentelemetry-instrumentation-grpc==0.36b0 ; python_version >= "3.9" and python_version < "3.13"
opentelemetry-instrumentation==0.36b0 ; python_version >= "3.9" and python_version < "3.13"
opentelemetry-proto==1.15.0 ; python_version >= "3.9" and python_version < "3.13"
opentelemetry-sdk==1.15.0 ; python_version >= "3.9" and python_version < "3.13"
opentelemetry-semantic-conventions==0.36b0 ; python_version >= "3.9" and python_version < "3.13"
packaging==23.2 ; python_version >= "3.9" and python_version < "3.13"
pillow==10.1.0 ; python_version >= "3.9" and python_version < "3.13"
protobuf==4.25.1 ; python_version >= "3.9" and python_version < "3.13"
pyyaml==6.0.1 ; python_version >= "3.9" and python_version < "3.13"
regex==2023.10.3 ; python_version >= "3.9" and python_version < "3.13"
requests==2.31.0 ; python_version >= "3.9" and python_version < "3.13"
safetensors==0.3.3 ; python_version >= "3.9" and python_version < "3.13"
scipy==1.11.4 ; python_version >= "3.9" and python_version < "3.13"
sentencepiece==0.1.99 ; python_version >= "3.9" and python_version < "3.13"
setuptools==69.0.2 ; python_version >= "3.9" and python_version < "3.13"
tokenizers==0.15.0 ; python_version >= "3.9" and python_version < "3.13"
tqdm==4.66.1 ; python_version >= "3.9" and python_version < "3.13"
transformers==4.36.1 ; python_version >= "3.9" and python_version < "3.13"
typer==0.6.1 ; python_version >= "3.9" and python_version < "3.13"
typing-extensions==4.9.0 ; python_version >= "3.9" and python_version < "3.13"
urllib3==2.1.0 ; python_version >= "3.9" and python_version < "3.13"
win32-setctime==1.1.0 ; python_version >= "3.9" and python_version < "3.13" and sys_platform == "win32"
wrapt==1.16.0 ; python_version >= "3.9" and python_version < "3.13"
| 0 |
hf_public_repos/text-generation-inference | hf_public_repos/text-generation-inference/server/README.md | # Text Generation Inference Python gRPC Server
A Python gRPC server for Text Generation Inference
## Install
```shell
make install
```
## Run
```shell
make run-dev
``` | 0 |
hf_public_repos/text-generation-inference | hf_public_repos/text-generation-inference/server/pyproject.toml | [tool.poetry]
name = "text-generation-server"
version = "1.3.4"
description = "Text Generation Inference Python gRPC Server"
authors = ["Olivier Dehaene <[email protected]>"]
[tool.poetry.scripts]
text-generation-server = 'text_generation_server.cli:app'
[tool.poetry.dependencies]
python = ">=3.9,<3.13"
protobuf = "^4.21.7"
grpcio = "^1.51.1"
grpcio-status = "^1.51.1"
grpcio-reflection = "^1.51.1"
grpc-interceptor = "^0.15.0"
typer = "^0.6.1"
accelerate = { version = "^0.25.0", optional = true }
bitsandbytes = { version = "^0.41.1", optional = true }
safetensors = "^0.3.2"
loguru = "^0.6.0"
opentelemetry-api = "^1.15.0"
opentelemetry-exporter-otlp = "^1.15.0"
opentelemetry-instrumentation-grpc = "^0.36b0"
hf-transfer = "^0.1.2"
sentencepiece = "^0.1.97"
tokenizers = "^0.15.0"
huggingface-hub = "^0.19.3"
transformers = "^4.36.1"
einops = "^0.6.1"
texttable = { version = "^1.6.7", optional = true }
datasets = { version = "^2.14.0", optional = true }
peft = { version = "^0.4.0", optional = true }
torch = { version = "^2.1.1", optional = true }
scipy = "^1.11.1"
pillow = "^10.0.0"
[tool.poetry.extras]
torch = ["torch"]
accelerate = ["accelerate"]
bnb = ["bitsandbytes"]
peft = ["peft"]
quantize = ["texttable", "datasets", "accelerate"]
[tool.poetry.group.dev.dependencies]
grpcio-tools = "^1.51.1"
pytest = "^7.3.0"
[[tool.poetry.source]]
name = "pytorch-gpu-src"
url = "https://download.pytorch.org/whl/cu121"
priority = "explicit"
[tool.pytest.ini_options]
markers = ["private: marks tests as requiring an admin hf token (deselect with '-m \"not private\"')"]
[build-system]
requires = [
"poetry-core>=1.0.0",
]
build-backend = "poetry.core.masonry.api"
| 0 |
hf_public_repos/text-generation-inference | hf_public_repos/text-generation-inference/server/poetry.lock | # This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand.
[[package]]
name = "accelerate"
version = "0.25.0"
description = "Accelerate"
optional = true
python-versions = ">=3.8.0"
files = [
{file = "accelerate-0.25.0-py3-none-any.whl", hash = "sha256:c7bb817eb974bba0ff3ea1ba0f24d55afb86d50e3d4fe98d6922dc69cf2ccff1"},
{file = "accelerate-0.25.0.tar.gz", hash = "sha256:ecf55b0ab278a1dac8539dde0d276977aff04683f07ede73eaf02478538576a1"},
]
[package.dependencies]
huggingface-hub = "*"
numpy = ">=1.17"
packaging = ">=20.0"
psutil = "*"
pyyaml = "*"
safetensors = ">=0.3.1"
torch = ">=1.10.0"
[package.extras]
dev = ["bitsandbytes", "black (>=23.1,<24.0)", "datasets", "deepspeed", "evaluate", "hf-doc-builder (>=0.3.0)", "parameterized", "pytest", "pytest-subtests", "pytest-xdist", "rich", "ruff (>=0.0.241)", "scikit-learn", "scipy", "timm", "tqdm", "transformers", "urllib3 (<2.0.0)"]
quality = ["black (>=23.1,<24.0)", "hf-doc-builder (>=0.3.0)", "ruff (>=0.0.241)", "urllib3 (<2.0.0)"]
rich = ["rich"]
sagemaker = ["sagemaker"]
test-dev = ["bitsandbytes", "datasets", "deepspeed", "evaluate", "scikit-learn", "scipy", "timm", "tqdm", "transformers"]
test-prod = ["parameterized", "pytest", "pytest-subtests", "pytest-xdist"]
test-trackers = ["comet-ml", "dvclive", "tensorboard", "wandb"]
testing = ["bitsandbytes", "datasets", "deepspeed", "evaluate", "parameterized", "pytest", "pytest-subtests", "pytest-xdist", "scikit-learn", "scipy", "timm", "tqdm", "transformers"]
[[package]]
name = "aiohttp"
version = "3.9.1"
description = "Async http client/server framework (asyncio)"
optional = true
python-versions = ">=3.8"
files = [
{file = "aiohttp-3.9.1-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:e1f80197f8b0b846a8d5cf7b7ec6084493950d0882cc5537fb7b96a69e3c8590"},
{file = "aiohttp-3.9.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:c72444d17777865734aa1a4d167794c34b63e5883abb90356a0364a28904e6c0"},
{file = "aiohttp-3.9.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:9b05d5cbe9dafcdc733262c3a99ccf63d2f7ce02543620d2bd8db4d4f7a22f83"},
{file = "aiohttp-3.9.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:5c4fa235d534b3547184831c624c0b7c1e262cd1de847d95085ec94c16fddcd5"},
{file = "aiohttp-3.9.1-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:289ba9ae8e88d0ba16062ecf02dd730b34186ea3b1e7489046fc338bdc3361c4"},
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{file = "aiohttp-3.9.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:81b77f868814346662c96ab36b875d7814ebf82340d3284a31681085c051320f"},
{file = "aiohttp-3.9.1-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:3b9c7426923bb7bd66d409da46c41e3fb40f5caf679da624439b9eba92043fa6"},
{file = "aiohttp-3.9.1-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:8d44e7bf06b0c0a70a20f9100af9fcfd7f6d9d3913e37754c12d424179b4e48f"},
{file = "aiohttp-3.9.1-cp310-cp310-musllinux_1_1_i686.whl", hash = "sha256:22698f01ff5653fe66d16ffb7658f582a0ac084d7da1323e39fd9eab326a1f26"},
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{file = "aiohttp-3.9.1-cp310-cp310-musllinux_1_1_s390x.whl", hash = "sha256:8d7f98fde213f74561be1d6d3fa353656197f75d4edfbb3d94c9eb9b0fc47f5d"},
{file = "aiohttp-3.9.1-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:5216b6082c624b55cfe79af5d538e499cd5f5b976820eac31951fb4325974501"},
{file = "aiohttp-3.9.1-cp310-cp310-win32.whl", hash = "sha256:0e7ba7ff228c0d9a2cd66194e90f2bca6e0abca810b786901a569c0de082f489"},
{file = "aiohttp-3.9.1-cp310-cp310-win_amd64.whl", hash = "sha256:c7e939f1ae428a86e4abbb9a7c4732bf4706048818dfd979e5e2839ce0159f23"},
{file = "aiohttp-3.9.1-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:df9cf74b9bc03d586fc53ba470828d7b77ce51b0582d1d0b5b2fb673c0baa32d"},
{file = "aiohttp-3.9.1-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:ecca113f19d5e74048c001934045a2b9368d77b0b17691d905af18bd1c21275e"},
{file = "aiohttp-3.9.1-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:8cef8710fb849d97c533f259103f09bac167a008d7131d7b2b0e3a33269185c0"},
{file = "aiohttp-3.9.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:bea94403a21eb94c93386d559bce297381609153e418a3ffc7d6bf772f59cc35"},
{file = "aiohttp-3.9.1-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:91c742ca59045dce7ba76cab6e223e41d2c70d79e82c284a96411f8645e2afff"},
{file = "aiohttp-3.9.1-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:6c93b7c2e52061f0925c3382d5cb8980e40f91c989563d3d32ca280069fd6a87"},
{file = "aiohttp-3.9.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ee2527134f95e106cc1653e9ac78846f3a2ec1004cf20ef4e02038035a74544d"},
{file = "aiohttp-3.9.1-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:11ff168d752cb41e8492817e10fb4f85828f6a0142b9726a30c27c35a1835f01"},
{file = "aiohttp-3.9.1-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:b8c3a67eb87394386847d188996920f33b01b32155f0a94f36ca0e0c635bf3e3"},
{file = "aiohttp-3.9.1-cp311-cp311-musllinux_1_1_i686.whl", hash = "sha256:c7b5d5d64e2a14e35a9240b33b89389e0035e6de8dbb7ffa50d10d8b65c57449"},
{file = "aiohttp-3.9.1-cp311-cp311-musllinux_1_1_ppc64le.whl", hash = "sha256:69985d50a2b6f709412d944ffb2e97d0be154ea90600b7a921f95a87d6f108a2"},
{file = "aiohttp-3.9.1-cp311-cp311-musllinux_1_1_s390x.whl", hash = "sha256:c9110c06eaaac7e1f5562caf481f18ccf8f6fdf4c3323feab28a93d34cc646bd"},
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]
[package.dependencies]
idna = ">=2.0"
multidict = ">=4.0"
[extras]
accelerate = ["accelerate"]
bnb = ["bitsandbytes"]
peft = ["peft"]
quantize = ["accelerate", "datasets", "texttable"]
torch = ["torch"]
[metadata]
lock-version = "2.0"
python-versions = ">=3.9,<3.13"
content-hash = "d314f7dc9ea4d4e7581552f340d9171f24f709bf98de8b8c01c449c23026e7a3"
| 0 |
hf_public_repos/text-generation-inference | hf_public_repos/text-generation-inference/server/Makefile-eetq | eetq_commit := 323827dd471458a84e9c840f614e4592b157a4b1
eetq:
# Clone eetq
pip install packaging
git clone https://github.com/NetEase-FuXi/EETQ.git eetq
build-eetq: eetq
cd eetq && git fetch && git checkout $(eetq_commit)
cd eetq && python setup.py build
install-eetq: build-eetq
cd eetq && python setup.py install
| 0 |
hf_public_repos/text-generation-inference | hf_public_repos/text-generation-inference/server/Makefile-flash-att | flash_att_commit := 3a9bfd076f98746c73362328958dbc68d145fbec
flash-attention:
# Clone flash attention
pip install -U packaging ninja --no-cache-dir
git clone https://github.com/HazyResearch/flash-attention.git
build-flash-attention: flash-attention
cd flash-attention && git fetch && git checkout $(flash_att_commit)
cd flash-attention && python setup.py build
cd flash-attention/csrc/rotary && python setup.py build
cd flash-attention/csrc/layer_norm && python setup.py build
install-flash-attention: build-flash-attention
pip uninstall flash_attn rotary_emb dropout_layer_norm -y || true
cd flash-attention && python setup.py install && cd csrc/layer_norm && python setup.py install && cd ../rotary && python setup.py install | 0 |
hf_public_repos/text-generation-inference | hf_public_repos/text-generation-inference/server/requirements_common.txt | backoff==2.2.1 ; python_version >= "3.9" and python_version < "3.13"
certifi==2023.11.17 ; python_version >= "3.9" and python_version < "3.13"
charset-normalizer==3.3.2 ; python_version >= "3.9" and python_version < "3.13"
click==8.1.7 ; python_version >= "3.9" and python_version < "3.13"
colorama==0.4.6 ; python_version >= "3.9" and python_version < "3.13" and (sys_platform == "win32" or platform_system == "Windows")
deprecated==1.2.14 ; python_version >= "3.9" and python_version < "3.13"
einops==0.6.1 ; python_version >= "3.9" and python_version < "3.13"
filelock==3.13.1 ; python_version >= "3.9" and python_version < "3.13"
fsspec==2023.10.0 ; python_version >= "3.9" and python_version < "3.13"
googleapis-common-protos==1.61.0 ; python_version >= "3.9" and python_version < "3.13"
grpc-interceptor==0.15.4 ; python_version >= "3.9" and python_version < "3.13"
grpcio-reflection==1.59.3 ; python_version >= "3.9" and python_version < "3.13"
grpcio-status==1.59.3 ; python_version >= "3.9" and python_version < "3.13"
grpcio==1.59.3 ; python_version >= "3.9" and python_version < "3.13"
hf-transfer==0.1.4 ; python_version >= "3.9" and python_version < "3.13"
huggingface-hub==0.16.4 ; python_version >= "3.9" and python_version < "3.13"
idna==3.4 ; python_version >= "3.9" and python_version < "3.13"
loguru==0.6.0 ; python_version >= "3.9" and python_version < "3.13"
numpy==1.26.2 ; python_version >= "3.9" and python_version < "3.13"
opentelemetry-api==1.15.0 ; python_version >= "3.9" and python_version < "3.13"
opentelemetry-exporter-otlp-proto-grpc==1.15.0 ; python_version >= "3.9" and python_version < "3.13"
opentelemetry-exporter-otlp-proto-http==1.15.0 ; python_version >= "3.9" and python_version < "3.13"
opentelemetry-exporter-otlp==1.15.0 ; python_version >= "3.9" and python_version < "3.13"
opentelemetry-instrumentation-grpc==0.36b0 ; python_version >= "3.9" and python_version < "3.13"
opentelemetry-instrumentation==0.36b0 ; python_version >= "3.9" and python_version < "3.13"
opentelemetry-proto==1.15.0 ; python_version >= "3.9" and python_version < "3.13"
opentelemetry-sdk==1.15.0 ; python_version >= "3.9" and python_version < "3.13"
opentelemetry-semantic-conventions==0.36b0 ; python_version >= "3.9" and python_version < "3.13"
packaging==23.2 ; python_version >= "3.9" and python_version < "3.13"
pillow==10.1.0 ; python_version >= "3.9" and python_version < "3.13"
protobuf==4.25.1 ; python_version >= "3.9" and python_version < "3.13"
pyyaml==6.0.1 ; python_version >= "3.9" and python_version < "3.13"
regex==2023.10.3 ; python_version >= "3.9" and python_version < "3.13"
requests==2.31.0 ; python_version >= "3.9" and python_version < "3.13"
safetensors==0.3.3 ; python_version >= "3.9" and python_version < "3.13"
scipy==1.11.4 ; python_version >= "3.9" and python_version < "3.13"
sentencepiece==0.1.99 ; python_version >= "3.9" and python_version < "3.13"
setuptools==69.0.2 ; python_version >= "3.9" and python_version < "3.13"
tokenizers==0.13.3 ; python_version >= "3.9" and python_version < "3.13"
tqdm==4.66.1 ; python_version >= "3.9" and python_version < "3.13"
transformers==4.33.3 ; python_version >= "3.9" and python_version < "3.13"
typer==0.6.1 ; python_version >= "3.9" and python_version < "3.13"
typing-extensions==4.8.0 ; python_version >= "3.9" and python_version < "3.13"
urllib3==2.1.0 ; python_version >= "3.9" and python_version < "3.13"
win32-setctime==1.1.0 ; python_version >= "3.9" and python_version < "3.13" and sys_platform == "win32"
wrapt==1.16.0 ; python_version >= "3.9" and python_version < "3.13"
| 0 |
hf_public_repos/text-generation-inference | hf_public_repos/text-generation-inference/server/requirements_cuda.txt | backoff==2.2.1 ; python_version >= "3.9" and python_version < "3.13"
bitsandbytes==0.41.3.post2 ; python_version >= "3.9" and python_version < "3.13"
certifi==2023.11.17 ; python_version >= "3.9" and python_version < "3.13"
charset-normalizer==3.3.2 ; python_version >= "3.9" and python_version < "3.13"
click==8.1.7 ; python_version >= "3.9" and python_version < "3.13"
colorama==0.4.6 ; python_version >= "3.9" and python_version < "3.13" and (sys_platform == "win32" or platform_system == "Windows")
deprecated==1.2.14 ; python_version >= "3.9" and python_version < "3.13"
einops==0.6.1 ; python_version >= "3.9" and python_version < "3.13"
filelock==3.13.1 ; python_version >= "3.9" and python_version < "3.13"
fsspec==2023.10.0 ; python_version >= "3.9" and python_version < "3.13"
googleapis-common-protos==1.62.0 ; python_version >= "3.9" and python_version < "3.13"
grpc-interceptor==0.15.4 ; python_version >= "3.9" and python_version < "3.13"
grpcio-reflection==1.60.0 ; python_version >= "3.9" and python_version < "3.13"
grpcio-status==1.60.0 ; python_version >= "3.9" and python_version < "3.13"
grpcio==1.60.0 ; python_version >= "3.9" and python_version < "3.13"
hf-transfer==0.1.4 ; python_version >= "3.9" and python_version < "3.13"
huggingface-hub==0.19.4 ; python_version >= "3.9" and python_version < "3.13"
idna==3.6 ; python_version >= "3.9" and python_version < "3.13"
loguru==0.6.0 ; python_version >= "3.9" and python_version < "3.13"
numpy==1.26.2 ; python_version >= "3.9" and python_version < "3.13"
opentelemetry-api==1.15.0 ; python_version >= "3.9" and python_version < "3.13"
opentelemetry-exporter-otlp-proto-grpc==1.15.0 ; python_version >= "3.9" and python_version < "3.13"
opentelemetry-exporter-otlp-proto-http==1.15.0 ; python_version >= "3.9" and python_version < "3.13"
opentelemetry-exporter-otlp==1.15.0 ; python_version >= "3.9" and python_version < "3.13"
opentelemetry-instrumentation-grpc==0.36b0 ; python_version >= "3.9" and python_version < "3.13"
opentelemetry-instrumentation==0.36b0 ; python_version >= "3.9" and python_version < "3.13"
opentelemetry-proto==1.15.0 ; python_version >= "3.9" and python_version < "3.13"
opentelemetry-sdk==1.15.0 ; python_version >= "3.9" and python_version < "3.13"
opentelemetry-semantic-conventions==0.36b0 ; python_version >= "3.9" and python_version < "3.13"
packaging==23.2 ; python_version >= "3.9" and python_version < "3.13"
pillow==10.1.0 ; python_version >= "3.9" and python_version < "3.13"
protobuf==4.25.1 ; python_version >= "3.9" and python_version < "3.13"
pyyaml==6.0.1 ; python_version >= "3.9" and python_version < "3.13"
regex==2023.10.3 ; python_version >= "3.9" and python_version < "3.13"
requests==2.31.0 ; python_version >= "3.9" and python_version < "3.13"
safetensors==0.3.3 ; python_version >= "3.9" and python_version < "3.13"
scipy==1.11.4 ; python_version >= "3.9" and python_version < "3.13"
sentencepiece==0.1.99 ; python_version >= "3.9" and python_version < "3.13"
setuptools==69.0.2 ; python_version >= "3.9" and python_version < "3.13"
tokenizers==0.15.0 ; python_version >= "3.9" and python_version < "3.13"
tqdm==4.66.1 ; python_version >= "3.9" and python_version < "3.13"
transformers==4.36.1 ; python_version >= "3.9" and python_version < "3.13"
typer==0.6.1 ; python_version >= "3.9" and python_version < "3.13"
typing-extensions==4.9.0 ; python_version >= "3.9" and python_version < "3.13"
urllib3==2.1.0 ; python_version >= "3.9" and python_version < "3.13"
win32-setctime==1.1.0 ; python_version >= "3.9" and python_version < "3.13" and sys_platform == "win32"
wrapt==1.16.0 ; python_version >= "3.9" and python_version < "3.13"
| 0 |
hf_public_repos/text-generation-inference | hf_public_repos/text-generation-inference/server/Makefile | include Makefile-flash-att
include Makefile-flash-att-v2
include Makefile-vllm
include Makefile-awq
include Makefile-eetq
unit-tests:
pytest -s -vv -m "not private" tests
gen-server:
# Compile protos
pip install grpcio-tools==1.51.1 mypy-protobuf==3.4.0 'types-protobuf>=3.20.4' --no-cache-dir
mkdir text_generation_server/pb || true
python -m grpc_tools.protoc -I../proto --python_out=text_generation_server/pb \
--grpc_python_out=text_generation_server/pb --mypy_out=text_generation_server/pb ../proto/generate.proto
find text_generation_server/pb/ -type f -name "*.py" -print0 -exec sed -i -e 's/^\(import.*pb2\)/from . \1/g' {} \;
touch text_generation_server/pb/__init__.py
install-megablocks:
pip install git+https://github.com/OlivierDehaene/megablocks@181709df192de9a941fdf3a641cdc65a0462996e
install: gen-server
pip install pip --upgrade
pip install -r requirements_cuda.txt
pip install -e ".[bnb, accelerate, quantize, peft]"
run-dev:
SAFETENSORS_FAST_GPU=1 python -m torch.distributed.run --nproc_per_node=2 text_generation_server/cli.py serve bigscience/bloom-560m --sharded
export-requirements:
poetry export -o requirements_cuda.txt --extras bnb --without-hashes
poetry export -o requirements_rocm.txt --without-hashes
| 0 |
hf_public_repos/text-generation-inference/server | hf_public_repos/text-generation-inference/server/text_generation_server/server.py | import asyncio
import os
import torch
import time
from grpc import aio
from loguru import logger
from grpc_reflection.v1alpha import reflection
from pathlib import Path
from typing import List, Optional
from text_generation_server.cache import Cache
from text_generation_server.interceptor import ExceptionInterceptor
from text_generation_server.models import Model, get_model
from text_generation_server.pb import generate_pb2_grpc, generate_pb2
from text_generation_server.tracing import UDSOpenTelemetryAioServerInterceptor
from text_generation_server.models.idefics_causal_lm import IdeficsCausalLMBatch
class TextGenerationService(generate_pb2_grpc.TextGenerationServiceServicer):
def __init__(
self,
model: Model,
cache: Cache,
quantize: Optional[str],
server_urls: List[str],
):
self.cache = cache
self.model = model
self.quantize = quantize
self.server_urls = server_urls
# For some reason, inference_mode does not work well with GLOO which we use on CPU
if model.device.type == "cuda":
# Force inference mode for the lifetime of TextGenerationService
self._inference_mode_raii_guard = torch._C._InferenceMode(True)
async def Info(self, request, context):
return self.model.info
async def Health(self, request, context):
if self.model.device.type == "cuda":
torch.zeros((2, 2)).cuda()
return generate_pb2.HealthResponse()
async def ServiceDiscovery(self, request, context):
return generate_pb2.ServiceDiscoveryResponse(urls=self.server_urls)
async def ClearCache(self, request, context):
if request.HasField("id"):
self.cache.delete(request.id)
else:
self.cache.clear()
return generate_pb2.ClearCacheResponse()
async def FilterBatch(self, request, context):
batch = self.cache.pop(request.batch_id)
if batch is None:
raise ValueError(f"Batch ID {request.batch_id} not found in cache.")
filtered_batch = batch.filter(request.request_ids)
self.cache.set(filtered_batch)
return generate_pb2.FilterBatchResponse(batch=filtered_batch.to_pb())
async def Warmup(self, request, context):
if self.quantize == "gptq":
try:
# When using GPTQ, Exllama kernels need some global kernels
# For which we have the finale shapes only after the model has loaded
# This will allocate those buffers.
from text_generation_server.utils.layers import (
create_exllama_buffers,
set_device,
)
set_device(self.model.device)
create_exllama_buffers(request.max_prefill_tokens)
except ImportError:
pass
if (
self.model.batch_type == IdeficsCausalLMBatch
): # Hack, i would rather use kwargs in the `from_pb` call
batch = self.model.batch_type.from_pb(
request.batch,
self.model.tokenizer,
self.model.processor,
self.model.dtype,
self.model.device,
)
else:
batch = self.model.batch_type.from_pb(
request.batch, self.model.tokenizer, self.model.dtype, self.model.device
)
max_supported_total_tokens = self.model.warmup(batch)
return generate_pb2.WarmupResponse(
max_supported_total_tokens=max_supported_total_tokens
)
async def Prefill(self, request, context):
start = time.time_ns()
if (
self.model.batch_type == IdeficsCausalLMBatch
): # Hack, i would rather use kwargs in the `from_pb` call
batch = self.model.batch_type.from_pb(
request.batch,
self.model.tokenizer,
self.model.processor,
self.model.dtype,
self.model.device,
)
else:
batch = self.model.batch_type.from_pb(
request.batch, self.model.tokenizer, self.model.dtype, self.model.device
)
generations, next_batch, timings = self.model.generate_token(batch)
self.cache.set(next_batch)
return generate_pb2.PrefillResponse(
generations=[generation.to_pb() for generation in generations],
batch=next_batch.to_pb() if next_batch else None,
forward_ns=timings[0],
decode_ns=timings[1],
total_ns=time.time_ns() - start,
)
async def Decode(self, request, context):
start = time.time_ns()
if len(request.batches) == 0:
raise ValueError("Must provide at least one batch")
batches = []
for batch_pb in request.batches:
batch = self.cache.pop(batch_pb.id)
if batch is None:
raise ValueError(f"Batch ID {batch_pb.id} not found in cache.")
batches.append(batch)
if len(batches) == 0:
raise ValueError("All batches are empty")
if len(batches) > 1:
start_concat = time.time_ns()
batch = self.model.batch_type.concatenate(batches)
concat_ns = time.time_ns() - start_concat
else:
batch = batches[0]
concat_ns = None
generations, next_batch, timings = self.model.generate_token(batch)
self.cache.set(next_batch)
return generate_pb2.DecodeResponse(
generations=[generation.to_pb() for generation in generations],
batch=next_batch.to_pb() if next_batch else None,
concat_ns=concat_ns,
forward_ns=timings[0],
decode_ns=timings[1],
total_ns=time.time_ns() - start,
)
def serve(
model_id: str,
revision: Optional[str],
sharded: bool,
quantize: Optional[str],
speculate: Optional[int],
dtype: Optional[str],
trust_remote_code: bool,
uds_path: Path,
):
async def serve_inner(
model_id: str,
revision: Optional[str],
sharded: bool = False,
quantize: Optional[str] = None,
speculate: Optional[int] = None,
dtype: Optional[str] = None,
trust_remote_code: bool = False,
):
unix_socket_template = "unix://{}-{}"
if sharded:
server_urls = [
unix_socket_template.format(uds_path, rank)
for rank in range(int(os.environ["WORLD_SIZE"]))
]
local_url = server_urls[int(os.environ["RANK"])]
else:
local_url = unix_socket_template.format(uds_path, 0)
server_urls = [local_url]
try:
model = get_model(
model_id,
revision,
sharded,
quantize,
speculate,
dtype,
trust_remote_code,
)
except Exception:
logger.exception("Error when initializing model")
raise
server = aio.server(
interceptors=[
ExceptionInterceptor(),
UDSOpenTelemetryAioServerInterceptor(),
]
)
generate_pb2_grpc.add_TextGenerationServiceServicer_to_server(
TextGenerationService(model, Cache(), quantize, server_urls), server
)
SERVICE_NAMES = (
generate_pb2.DESCRIPTOR.services_by_name["TextGenerationService"].full_name,
reflection.SERVICE_NAME,
)
reflection.enable_server_reflection(SERVICE_NAMES, server)
server.add_insecure_port(local_url)
await server.start()
logger.info("Server started at {}".format(local_url))
try:
await server.wait_for_termination()
except KeyboardInterrupt:
logger.info("Signal received. Shutting down")
await server.stop(0)
asyncio.run(
serve_inner(
model_id, revision, sharded, quantize, speculate, dtype, trust_remote_code
)
)
| 0 |
hf_public_repos/text-generation-inference/server | hf_public_repos/text-generation-inference/server/text_generation_server/interceptor.py | import torch
import grpc
from google.rpc import status_pb2, code_pb2
from grpc_status import rpc_status
from grpc_interceptor.server import AsyncServerInterceptor
from loguru import logger
from typing import Callable, Any
class ExceptionInterceptor(AsyncServerInterceptor):
async def intercept(
self,
method: Callable,
request_or_iterator: Any,
context: grpc.ServicerContext,
method_name: str,
) -> Any:
try:
response = method(request_or_iterator, context)
return await response
except Exception as err:
method_name = method_name.split("/")[-1]
logger.exception(f"Method {method_name} encountered an error.")
if torch.cuda.is_available():
torch.cuda.empty_cache()
await context.abort_with_status(
rpc_status.to_status(
status_pb2.Status(code=code_pb2.INTERNAL, message=str(err))
)
)
| 0 |
hf_public_repos/text-generation-inference/server | hf_public_repos/text-generation-inference/server/text_generation_server/cli.py | import os
import sys
import typer
from pathlib import Path
from loguru import logger
from typing import Optional
from enum import Enum
from huggingface_hub import hf_hub_download
app = typer.Typer()
class Quantization(str, Enum):
bitsandbytes = "bitsandbytes"
bitsandbytes_nf4 = "bitsandbytes-nf4"
bitsandbytes_fp4 = "bitsandbytes-fp4"
gptq = "gptq"
awq = "awq"
eetq = "eetq"
class Dtype(str, Enum):
float16 = "float16"
bloat16 = "bfloat16"
@app.command()
def serve(
model_id: str,
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",
logger_level: str = "INFO",
json_output: bool = False,
otlp_endpoint: Optional[str] = None,
):
if sharded:
assert (
os.getenv("RANK", None) is not None
), "RANK must be set when sharded is True"
assert (
os.getenv("WORLD_SIZE", None) is not None
), "WORLD_SIZE must be set when sharded is True"
assert (
os.getenv("MASTER_ADDR", None) is not None
), "MASTER_ADDR must be set when sharded is True"
assert (
os.getenv("MASTER_PORT", None) is not None
), "MASTER_PORT must be set when sharded is True"
# Remove default handler
logger.remove()
logger.add(
sys.stdout,
format="{message}",
filter="text_generation_server",
level=logger_level,
serialize=json_output,
backtrace=True,
diagnose=False,
)
# Import here after the logger is added to log potential import exceptions
from text_generation_server import server
from text_generation_server.tracing import setup_tracing
# Setup OpenTelemetry distributed tracing
if otlp_endpoint is not None:
setup_tracing(shard=os.getenv("RANK", 0), otlp_endpoint=otlp_endpoint)
# Downgrade enum into str for easier management later on
quantize = None if quantize is None else quantize.value
dtype = None if dtype is None else dtype.value
if dtype is not None and quantize not in {
None,
"bitsandbytes",
"bitsandbytes-nf4",
"bitsandbytes-fp4",
}:
raise RuntimeError(
"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,
speculate,
dtype,
trust_remote_code,
uds_path,
)
@app.command()
def download_weights(
model_id: str,
revision: Optional[str] = None,
extension: str = ".safetensors",
auto_convert: bool = True,
logger_level: str = "INFO",
json_output: bool = False,
trust_remote_code: bool = False,
):
# Remove default handler
logger.remove()
logger.add(
sys.stdout,
format="{message}",
filter="text_generation_server",
level=logger_level,
serialize=json_output,
backtrace=True,
diagnose=False,
)
# Import here after the logger is added to log potential import exceptions
from text_generation_server import utils
# Test if files were already download
try:
utils.weight_files(model_id, revision, extension)
logger.info("Files are already present on the host. " "Skipping download.")
return
# Local files not found
except (utils.LocalEntryNotFoundError, FileNotFoundError, utils.EntryNotFoundError):
pass
is_local_model = (Path(model_id).exists() and Path(model_id).is_dir()) or os.getenv(
"WEIGHTS_CACHE_OVERRIDE", None
) is not None
if not is_local_model:
try:
adapter_config_filename = hf_hub_download(
model_id, revision=revision, filename="adapter_config.json"
)
utils.download_and_unload_peft(
model_id, revision, trust_remote_code=trust_remote_code
)
is_local_model = True
utils.weight_files(model_id, revision, extension)
return
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)
utils.download_weights(filenames, model_id, revision)
# Successfully downloaded weights
return
# No weights found on the hub with this extension
except utils.EntryNotFoundError as e:
# Check if we want to automatically convert to safetensors or if we can use .bin weights instead
if not extension == ".safetensors" or not auto_convert:
raise e
elif (Path(model_id) / "medusa_lm_head.pt").exists():
# Try to load as a local Medusa model
try:
import json
medusa_head = Path(model_id) / "medusa_lm_head.pt"
if auto_convert:
medusa_sf = Path(model_id) / "medusa_lm_head.safetensors"
if not medusa_sf.exists():
utils.convert_files([Path(medusa_head)], [medusa_sf], [])
medusa_config = Path(model_id) / "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, utils.EntryNotFoundError):
pass
except (utils.LocalEntryNotFoundError, utils.EntryNotFoundError):
pass
elif (Path(model_id) / "adapter_config.json").exists():
# Try to load as a local PEFT model
try:
utils.download_and_unload_peft(
model_id, revision, trust_remote_code=trust_remote_code
)
utils.weight_files(model_id, revision, extension)
return
except (utils.LocalEntryNotFoundError, utils.EntryNotFoundError):
pass
# Try to see if there are local pytorch weights
try:
# Get weights for a local model, a hub cached model and inside the WEIGHTS_CACHE_OVERRIDE
local_pt_files = utils.weight_files(model_id, revision, ".bin")
# No local pytorch weights
except utils.LocalEntryNotFoundError:
if extension == ".safetensors":
logger.warning(
f"No safetensors weights found for model {model_id} at revision {revision}. "
f"Downloading PyTorch weights."
)
# Try to see if there are pytorch weights on the hub
pt_filenames = utils.weight_hub_files(model_id, revision, ".bin")
# Download pytorch weights
local_pt_files = utils.download_weights(pt_filenames, model_id, revision)
if auto_convert:
logger.warning(
f"No safetensors weights found for model {model_id} at revision {revision}. "
f"Converting PyTorch weights to safetensors."
)
# Safetensors final filenames
local_st_files = [
p.parent / f"{p.stem.lstrip('pytorch_')}.safetensors"
for p in local_pt_files
]
try:
import transformers
import json
if is_local_model:
config_filename = os.path.join(model_id, "config.json")
else:
config_filename = hf_hub_download(
model_id, revision=revision, filename="config.json"
)
with open(config_filename, "r") as f:
config = json.load(f)
architecture = config["architectures"][0]
class_ = getattr(transformers, architecture)
# Name for this varible depends on transformers version.
discard_names = getattr(class_, "_tied_weights_keys", [])
except Exception as e:
discard_names = []
# Convert pytorch weights to safetensors
utils.convert_files(local_pt_files, local_st_files, discard_names)
@app.command()
def quantize(
model_id: str,
output_dir: str,
revision: Optional[str] = None,
logger_level: str = "INFO",
json_output: bool = False,
trust_remote_code: bool = False,
upload_to_model_id: Optional[str] = None,
percdamp: float = 0.01,
act_order: bool = False,
):
if revision is None:
revision = "main"
download_weights(
model_id=model_id,
revision=revision,
logger_level=logger_level,
json_output=json_output,
)
from text_generation_server.utils.gptq.quantize import quantize
quantize(
model_id=model_id,
bits=4,
groupsize=128,
output_dir=output_dir,
revision=revision,
trust_remote_code=trust_remote_code,
upload_to_model_id=upload_to_model_id,
percdamp=percdamp,
act_order=act_order,
)
if __name__ == "__main__":
app()
| 0 |
hf_public_repos/text-generation-inference/server | hf_public_repos/text-generation-inference/server/text_generation_server/tracing.py | import grpc
from opentelemetry import trace
from opentelemetry.exporter.otlp.proto.grpc.trace_exporter import OTLPSpanExporter
from opentelemetry.instrumentation.grpc._aio_server import (
OpenTelemetryAioServerInterceptor,
)
from opentelemetry.semconv.trace import SpanAttributes
from opentelemetry.sdk.resources import Resource
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import (
BatchSpanProcessor,
)
class UDSOpenTelemetryAioServerInterceptor(OpenTelemetryAioServerInterceptor):
def __init__(self):
super().__init__(trace.get_tracer(__name__))
def _start_span(self, handler_call_details, context, set_status_on_exception=False):
"""
Rewrite _start_span method to support Unix Domain Socket gRPC contexts
"""
# standard attributes
attributes = {
SpanAttributes.RPC_SYSTEM: "grpc",
SpanAttributes.RPC_GRPC_STATUS_CODE: grpc.StatusCode.OK.value[0],
}
# if we have details about the call, split into service and method
if handler_call_details.method:
service, method = handler_call_details.method.lstrip("/").split("/", 1)
attributes.update(
{
SpanAttributes.RPC_METHOD: method,
SpanAttributes.RPC_SERVICE: service,
}
)
# add some attributes from the metadata
metadata = dict(context.invocation_metadata())
if "user-agent" in metadata:
attributes["rpc.user_agent"] = metadata["user-agent"]
# We use gRPC over a UNIX socket
attributes.update({SpanAttributes.NET_TRANSPORT: "unix"})
return self._tracer.start_as_current_span(
name=handler_call_details.method,
kind=trace.SpanKind.SERVER,
attributes=attributes,
set_status_on_exception=set_status_on_exception,
)
def setup_tracing(shard: int, otlp_endpoint: str):
resource = Resource.create(
attributes={"service.name": f"text-generation-inference.server-{shard}"}
)
span_exporter = OTLPSpanExporter(endpoint=otlp_endpoint, insecure=True)
span_processor = BatchSpanProcessor(span_exporter)
trace.set_tracer_provider(TracerProvider(resource=resource))
trace.get_tracer_provider().add_span_processor(span_processor)
| 0 |
hf_public_repos/text-generation-inference/server | hf_public_repos/text-generation-inference/server/text_generation_server/cache.py | import torch
from typing import Dict, Optional, TypeVar
from text_generation_server.models.types import Batch
B = TypeVar("B", bound=Batch)
class Cache:
def __init__(self):
self.cache: Dict[int, B] = {}
def pop(self, batch_id: int) -> Optional[B]:
return self.cache.pop(batch_id, None)
def set(self, entry: B):
if entry is not None:
self.cache[entry.batch_id] = entry
def delete(self, batch_id: int):
batch = self.pop(batch_id)
if batch is not None:
del batch
if torch.cuda.is_available():
torch.cuda.empty_cache()
def clear(self):
keys = list(self.cache.keys())
for k in keys:
self.delete(k)
def __len__(self):
return len(self.cache.keys())
| 0 |
hf_public_repos/text-generation-inference/server/text_generation_server | hf_public_repos/text-generation-inference/server/text_generation_server/models/gpt_neox.py | import torch
import torch.distributed
from typing import Optional
from transformers import (
AutoTokenizer,
AutoConfig,
)
from text_generation_server.models import CausalLM
from text_generation_server.models.custom_modeling.neox_modeling import (
GPTNeoxForCausalLM,
)
from text_generation_server.utils import (
initialize_torch_distributed,
weight_files,
Weights,
)
class GPTNeoxSharded(CausalLM):
def __init__(
self,
model_id: str,
revision: Optional[str] = None,
quantize: Optional[str] = None,
dtype: Optional[torch.dtype] = None,
trust_remote_code: bool = False,
):
self.process_group, rank, world_size = initialize_torch_distributed()
if torch.cuda.is_available():
device = torch.device(f"cuda:{rank}")
dtype = torch.float16 if dtype is None else dtype
else:
device = torch.device("cpu")
dtype = torch.float32 if dtype is None else dtype
tokenizer = AutoTokenizer.from_pretrained(
model_id,
revision=revision,
padding_side="left",
truncation_side="left",
trust_remote_code=trust_remote_code,
)
tokenizer.pad_token = tokenizer.eos_token
config = AutoConfig.from_pretrained(
model_id,
revision=revision,
trust_remote_code=trust_remote_code,
)
config.quantize = quantize
torch.distributed.barrier(group=self.process_group)
filenames = weight_files(model_id, revision=revision, extension=".safetensors")
weights = Weights(
filenames, device=device, dtype=dtype, process_group=self.process_group
)
if config.quantize == "gptq":
weights._set_gptq_params(model_id, revision)
model = GPTNeoxForCausalLM(config, weights)
torch.distributed.barrier(group=self.process_group)
super(CausalLM, self).__init__(
model=model,
tokenizer=tokenizer,
requires_padding=True,
dtype=dtype,
device=device,
rank=rank,
world_size=world_size,
)
def forward(
self, input_ids, attention_mask, position_ids, past_key_values: Optional = None
):
outputs = self.model.forward(
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
use_cache=True,
)
logits = outputs.logits
return logits, outputs.past_key_values
| 0 |
hf_public_repos/text-generation-inference/server/text_generation_server | hf_public_repos/text-generation-inference/server/text_generation_server/models/rw.py | import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
from typing import List, Optional, Tuple
from text_generation_server.models import CausalLM
class RW(CausalLM):
def __init__(
self,
model_id: str,
revision: Optional[str] = None,
quantize: Optional[str] = None,
dtype: Optional[torch.dtype] = None,
trust_remote_code: bool = False,
):
if torch.cuda.is_available():
device = torch.device("cuda")
dtype = torch.float16 if dtype is None else dtype
else:
if quantize:
raise ValueError("quantization is not available on CPU")
device = torch.device("cpu")
dtype = torch.float32 if dtype is None else dtype
tokenizer = AutoTokenizer.from_pretrained(
model_id,
revision=revision,
padding_side="left",
truncation_side="left",
trust_remote_code=trust_remote_code,
)
model = AutoModelForCausalLM.from_pretrained(
model_id,
revision=revision,
torch_dtype=dtype,
device_map="auto"
if torch.cuda.is_available() and torch.cuda.device_count() > 1
else None,
load_in_8bit=quantize == "bitsandbytes",
trust_remote_code=trust_remote_code,
)
if torch.cuda.is_available() and torch.cuda.device_count() == 1:
model = model.cuda()
if tokenizer.pad_token_id is None:
if model.config.pad_token_id is not None:
tokenizer.pad_token_id = model.config.pad_token_id
elif model.config.eos_token_id is not None:
tokenizer.pad_token_id = model.config.eos_token_id
elif tokenizer.eos_token_id is not None:
tokenizer.pad_token_id = tokenizer.eos_token_id
else:
tokenizer.add_special_tokens({"pad_token": "[PAD]"})
super(CausalLM, self).__init__(
model=model,
tokenizer=tokenizer,
requires_padding=True,
dtype=dtype,
device=device,
)
def forward(
self, input_ids, attention_mask, position_ids, past_key_values: Optional = None
) -> Tuple[torch.Tensor, List[Tuple[torch.Tensor, torch.Tensor]]]:
# Model Forward
outputs = self.model.forward(
input_ids=input_ids,
attention_mask=attention_mask,
past_key_values=past_key_values,
)
return outputs.logits, outputs.past_key_values
| 0 |
hf_public_repos/text-generation-inference/server/text_generation_server | hf_public_repos/text-generation-inference/server/text_generation_server/models/bloom.py | import torch
import torch.distributed
from typing import Optional, Type
from transformers import (
AutoTokenizer,
AutoConfig,
PreTrainedTokenizerBase,
)
from text_generation_server.models.custom_modeling.bloom_modeling import (
BloomForCausalLM,
)
from text_generation_server.models import CausalLM
from text_generation_server.models.causal_lm import CausalLMBatch
from text_generation_server.pb import generate_pb2
from text_generation_server.utils import (
initialize_torch_distributed,
weight_files,
Weights,
)
class BloomCausalLMBatch(CausalLMBatch):
@classmethod
def from_pb(
cls,
pb: generate_pb2.Batch,
tokenizer: PreTrainedTokenizerBase,
dtype: torch.dtype,
device: torch.device,
) -> "CausalLMBatch":
batch = super().from_pb(pb=pb, tokenizer=tokenizer, dtype=dtype, device=device)
batch.keys_head_dim_last = False
return batch
class BLOOMSharded(CausalLM):
def __init__(
self,
model_id: str,
revision: Optional[str] = None,
quantize: Optional[str] = None,
dtype: Optional[torch.dtype] = None,
trust_remote_code: bool = False,
):
self.process_group, rank, world_size = initialize_torch_distributed()
if torch.cuda.is_available():
device = torch.device(f"cuda:{rank}")
dtype = torch.float16 if dtype is None else dtype
else:
device = torch.device("cpu")
dtype = torch.float32 if dtype is None else dtype
tokenizer = AutoTokenizer.from_pretrained(
model_id,
revision=revision,
padding_side="left",
truncation_side="left",
trust_remote_code=trust_remote_code,
)
config = AutoConfig.from_pretrained(
model_id,
revision=revision,
slow_but_exact=False,
tp_parallel=True,
trust_remote_code=trust_remote_code,
)
config.pad_token_id = 3
config.quantize = quantize
torch.distributed.barrier(group=self.process_group)
filenames = weight_files(model_id, revision=revision, extension=".safetensors")
weights = Weights(
filenames,
device=device,
dtype=dtype,
process_group=self.process_group,
prefix="transformer",
)
if config.quantize == "gptq":
weights._set_gptq_params(model_id, revision)
model = BloomForCausalLM(config, weights)
torch.distributed.barrier(group=self.process_group)
super(CausalLM, self).__init__(
model=model,
tokenizer=tokenizer,
requires_padding=True,
dtype=dtype,
device=device,
rank=rank,
world_size=world_size,
)
@property
def batch_type(self) -> Type[CausalLMBatch]:
return BloomCausalLMBatch
def forward(
self, input_ids, attention_mask, position_ids, past_key_values: Optional = None
):
outputs = self.model.forward(
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
use_cache=True,
)
logits = outputs.logits
return logits, outputs.past_key_values
| 0 |
hf_public_repos/text-generation-inference/server/text_generation_server | hf_public_repos/text-generation-inference/server/text_generation_server/models/flash_rw.py | import torch
import torch.distributed
from opentelemetry import trace
from transformers import AutoTokenizer
from typing import Optional
from text_generation_server.models import FlashCausalLM
from text_generation_server.models.custom_modeling.flash_rw_modeling import (
RWConfig,
FlashRWForCausalLM,
)
from text_generation_server.utils import (
initialize_torch_distributed,
weight_files,
Weights,
)
tracer = trace.get_tracer(__name__)
class FlashRWSharded(FlashCausalLM):
def __init__(
self,
model_id: str,
revision: Optional[str] = None,
quantize: Optional[str] = None,
dtype: Optional[torch.dtype] = None,
trust_remote_code: bool = False,
):
self.process_group, rank, world_size = initialize_torch_distributed()
if torch.cuda.is_available():
device = torch.device(f"cuda:{rank}")
dtype = torch.float16 if dtype is None else dtype
else:
raise NotImplementedError("FlashRW is only available on GPU")
tokenizer = AutoTokenizer.from_pretrained(
model_id,
revision=revision,
padding_side="left",
truncation_side="left",
trust_remote_code=trust_remote_code,
)
config = RWConfig.from_pretrained(
model_id, revision=revision, trust_remote_code=trust_remote_code
)
torch.distributed.barrier(group=self.process_group)
filenames = weight_files(model_id, revision=revision, extension=".safetensors")
weights = Weights(
filenames,
device,
dtype,
process_group=self.process_group,
aliases={
"lm_head.weight": ["transformer.word_embeddings.weight"],
"transformer.word_embeddings.weight": ["lm_head.weight"],
},
)
config.quantize = quantize
if config.quantize == "gptq":
weights._set_gptq_params(model_id, revision)
model = FlashRWForCausalLM(config, weights)
torch.distributed.barrier(group=self.process_group)
super(FlashRWSharded, self).__init__(
model=model.to(device),
tokenizer=tokenizer,
num_layers=len(model.transformer.h),
num_kv_heads=model.transformer.cache_size,
head_size=model.transformer.head_size,
dtype=dtype,
device=device,
rank=rank,
world_size=world_size,
)
| 0 |
hf_public_repos/text-generation-inference/server/text_generation_server | hf_public_repos/text-generation-inference/server/text_generation_server/models/idefics.py | import torch
import torch.distributed
from typing import List, Optional, Tuple
from transformers import (
AutoTokenizer,
AutoConfig,
AutoProcessor,
)
from text_generation_server.models.custom_modeling.idefics_config import IdeficsConfig
from text_generation_server.models.custom_modeling.idefics_processing import (
IdeficsProcessor,
)
from transformers import LlamaTokenizerFast
from text_generation_server.models.custom_modeling.idefics_modeling import (
IdeficsForVisionText2Text,
)
from text_generation_server.models.idefics_causal_lm import IdeficsCausalLM
from text_generation_server.utils import (
initialize_torch_distributed,
weight_files,
Weights,
)
class IDEFICSSharded(IdeficsCausalLM):
def __init__(
self,
model_id: str,
revision: Optional[str] = None,
quantize: Optional[str] = None,
dtype: Optional[torch.dtype] = None,
trust_remote_code: bool = False,
):
self.process_group, rank, world_size = initialize_torch_distributed()
if torch.cuda.is_available():
device = torch.device(f"cuda:{rank}")
# 9b seems to work correctly enough in float16, but 80b seems
# to be really saturating for f16.
dtype = torch.bfloat16 if dtype is None else dtype
else:
device = torch.device("cpu")
dtype = torch.float32 if dtype is None else dtype
self.device, self.dtype = device, dtype
config = IdeficsConfig.from_pretrained(
model_id,
revision=revision,
trust_remote_code=trust_remote_code,
)
config.quantize = quantize
config.vision_config.quantize = quantize
tokenizer = LlamaTokenizerFast.from_pretrained(
model_id,
revision=revision,
padding_side="left",
truncation_side="left",
trust_remote_code=trust_remote_code,
)
self.processor = IdeficsProcessor.from_pretrained(
model_id,
revision=revision,
padding_side="left",
truncation_side="left",
trust_remote_code=trust_remote_code,
)
torch.distributed.barrier(group=self.process_group)
filenames = weight_files(model_id, revision=revision, extension=".safetensors")
weights = Weights(
filenames,
device=device,
dtype=dtype,
process_group=self.process_group,
)
model = IdeficsForVisionText2Text(config, weights)
torch.distributed.barrier(group=self.process_group)
super(IdeficsCausalLM, self).__init__(
model=model,
tokenizer=tokenizer,
requires_padding=True,
dtype=dtype,
device=device,
rank=rank,
world_size=world_size,
)
| 0 |
hf_public_repos/text-generation-inference/server/text_generation_server | hf_public_repos/text-generation-inference/server/text_generation_server/models/flash_mixtral.py | import torch
from typing import Optional
from text_generation_server.models.flash_mistral import BaseFlashMistral
from text_generation_server.models.custom_modeling.flash_mixtral_modeling import (
MixtralConfig,
FlashMixtralForCausalLM,
)
class FlashMixtral(BaseFlashMistral):
def __init__(
self,
model_id: str,
revision: Optional[str] = None,
quantize: Optional[str] = None,
dtype: Optional[torch.dtype] = None,
trust_remote_code: bool = False,
):
super(FlashMixtral, self).__init__(
config_cls=MixtralConfig,
model_cls=FlashMixtralForCausalLM,
model_id=model_id,
revision=revision,
quantize=quantize,
dtype=dtype,
trust_remote_code=trust_remote_code,
)
| 0 |
hf_public_repos/text-generation-inference/server/text_generation_server | hf_public_repos/text-generation-inference/server/text_generation_server/models/model.py | import inspect
import torch
from abc import ABC, abstractmethod
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)
class Model(ABC):
def __init__(
self,
model: torch.nn.Module,
tokenizer: PreTrainedTokenizerBase,
requires_padding: bool,
dtype: torch.dtype,
device: torch.device,
rank: int = 0,
world_size: int = 1,
sliding_window: Optional[int] = None,
speculate: Optional[int] = None,
):
self.model = model.eval()
self.tokenizer = tokenizer
self.all_special_ids = set(tokenizer.all_special_ids)
self.requires_padding = requires_padding
self.dtype = dtype
self.device = device
self.rank = rank
self.world_size = world_size
self.sliding_window = sliding_window if sliding_window != -1 else None
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
)
self.check_initialized()
@property
def info(self) -> InfoResponse:
if self.requires_padding and self.sliding_window is not None:
raise NotImplementedError("sliding_window is not implemented with padding")
return InfoResponse(
requires_padding=self.requires_padding,
dtype=str(self.dtype),
device_type=self.device.type,
window_size=self.sliding_window,
speculate=self.speculate,
)
@property
@abstractmethod
def batch_type(self) -> Type[B]:
raise NotImplementedError
@abstractmethod
def generate_token(
self, batch: B
) -> Tuple[List[Generation], Optional[B], Tuple[int, int]]:
raise NotImplementedError
def warmup(self, batch: B) -> Optional[int]:
self.generate_token(batch)
return None
def decode_token(
self,
all_input_ids: List[int],
prefix_offset: int = 0,
read_offset: int = 0,
skip_special_tokens: bool = False,
) -> Tuple[str, int, int]:
"""Hack to hopefully support generate_stream for the maximum number of tokenizers"""
# The prefix text is necessary only to defeat cleanup algorithms in the decode
# which decide to add a space or not depending on the surrounding ids.
prefix_text = self.tokenizer.decode(
all_input_ids[prefix_offset:read_offset],
skip_special_tokens=skip_special_tokens,
)
new_text = self.tokenizer.decode(
all_input_ids[prefix_offset:], skip_special_tokens=skip_special_tokens
)
if len(new_text) > len(prefix_text) and not new_text.endswith("�"):
# utf-8 char at the end means it's a potential unfinished byte sequence
# from byte fallback tokenization.
# If it's in the middle, it's probably a real invalid id generated
# by the model
new_text = new_text[len(prefix_text) :]
return new_text, read_offset, len(all_input_ids)
else:
return "", prefix_offset, read_offset
def check_initialized(self):
uninitialized_parameters = []
for n, p in self.model.named_parameters():
if p.data.device == torch.device("meta"):
uninitialized_parameters.append(n)
if uninitialized_parameters:
raise RuntimeError(
f"found uninitialized parameters in model {self.__class__.__name__}: {uninitialized_parameters}"
)
| 0 |
hf_public_repos/text-generation-inference/server/text_generation_server | hf_public_repos/text-generation-inference/server/text_generation_server/models/mpt.py | import torch
import torch.distributed
from pathlib import Path
from typing import Optional, Type
from opentelemetry import trace
from transformers import AutoTokenizer, PretrainedConfig, PreTrainedTokenizerBase
from huggingface_hub import hf_hub_download
import json
from text_generation_server.models import CausalLM
from text_generation_server.models.causal_lm import CausalLMBatch
from text_generation_server.pb import generate_pb2
from text_generation_server.models.custom_modeling.mpt_modeling import (
MPTForCausalLM,
)
from text_generation_server.utils import (
initialize_torch_distributed,
weight_files,
Weights,
)
tracer = trace.get_tracer(__name__)
class MPTCausalLMBatch(CausalLMBatch):
@classmethod
def from_pb(
cls,
pb: generate_pb2.Batch,
tokenizer: PreTrainedTokenizerBase,
dtype: torch.dtype,
device: torch.device,
) -> "CausalLMBatch":
batch = super().from_pb(pb=pb, tokenizer=tokenizer, dtype=dtype, device=device)
batch.keys_head_dim_last = False
return batch
class MPTSharded(CausalLM):
def __init__(
self,
model_id: str,
revision: Optional[str] = None,
quantize: Optional[str] = None,
dtype: Optional[torch.dtype] = None,
trust_remote_code: bool = False,
):
self.process_group, rank, world_size = initialize_torch_distributed()
if torch.cuda.is_available():
device = torch.device(f"cuda:{rank}")
dtype = torch.float16 if dtype is None else dtype
else:
device = torch.device("cpu")
dtype = torch.float32 if dtype is None else dtype
tokenizer = AutoTokenizer.from_pretrained(
model_id,
revision=revision,
padding_side="left",
truncation_side="left",
trust_remote_code=trust_remote_code,
)
tokenizer.pad_token = tokenizer.eos_token
# If model_id is a local path, load the file directly
local_path = Path(model_id, "config.json")
if local_path.exists():
filename = str(local_path.resolve())
else:
filename = hf_hub_download(
model_id, revision=revision, filename="config.json"
)
with open(filename, "r") as f:
config = json.load(f)
config = PretrainedConfig(**config)
config.quantize = quantize
torch.distributed.barrier(group=self.process_group)
filenames = weight_files(model_id, revision=revision, extension=".safetensors")
weights = Weights(filenames, device, dtype, process_group=self.process_group)
if config.quantize == "gptq":
weights._set_gptq_params(model_id, revision)
config.quantize = quantize
model = MPTForCausalLM(config, weights)
torch.distributed.barrier(group=self.process_group)
super(CausalLM, self).__init__(
model=model,
tokenizer=tokenizer,
requires_padding=False,
dtype=dtype,
device=device,
rank=rank,
world_size=world_size,
)
@property
def batch_type(self) -> Type[CausalLMBatch]:
return MPTCausalLMBatch
| 0 |
hf_public_repos/text-generation-inference/server/text_generation_server | hf_public_repos/text-generation-inference/server/text_generation_server/models/flash_mistral.py | import math
import torch
import torch.distributed
import numpy as np
from dataclasses import dataclass
from opentelemetry import trace
from transformers import PreTrainedTokenizerBase
from transformers.models.llama import LlamaTokenizerFast
from typing import Optional, Tuple, Type, List
from text_generation_server.pb import generate_pb2
from text_generation_server.models import FlashCausalLM
from text_generation_server.models.flash_causal_lm import FlashCausalLMBatch, BLOCK_SIZE
from text_generation_server.models.cache_manager import (
get_cache_manager,
)
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,
Weights,
HeterogeneousNextTokenChooser,
StoppingCriteria,
)
tracer = trace.get_tracer(__name__)
# Will be set in init
SLIDING_WINDOW: Optional[int] = None
SLIDING_WINDOW_BLOCKS: Optional[int] = None
# Adds windowing logic to FlashCausalLMBatch
@dataclass
class FlashMistralBatch(FlashCausalLMBatch):
# Prefill cache indices is used to slice into the kv tensor before caching it into the paged attention buffers
# as we only keep SLIDING_WINDOW values instead of the whole tensor
prefill_cache_indices: Optional[torch.Tensor] = None
@classmethod
def from_pb(
cls,
pb: generate_pb2.Batch,
tokenizer: PreTrainedTokenizerBase,
dtype: torch.dtype,
device: torch.device,
) -> "FlashCausalLMBatch":
global SLIDING_WINDOW
global SLIDING_WINDOW_BLOCKS
batch_inputs = []
max_truncation = 0
for r in pb.requests:
batch_inputs.append(r.inputs)
max_truncation = max(max_truncation, r.truncate)
batch_tokenized_inputs = tokenizer(
batch_inputs, truncation=True, max_length=max_truncation
)["input_ids"]
position_ids = []
cu_seqlen_prefill = [0]
needed_blocks_slots = []
start_slots = []
slot_indices = []
prefill_cache_indices = []
input_lengths = []
prefix_offsets = []
read_offsets = []
all_input_ids = []
requests_idx_mapping = {}
all_prefill_logprobs = True
no_prefill_logprobs = True
prefill_head_indices = []
prefill_next_token_indices = []
prefill_cu_outlens = [0]
next_token_chooser_parameters = []
stopping_criterias = []
top_n_tokens = []
# Cumulative length
cumulative_length = 0
cumulative_max_length = 0
prefill_out_cumulative_length = 0
blocks = 0
max_seqlen = 0
max_length = 0
max_blocks = 0
# Parse batch
for i, (r, tokenized_input) in enumerate(
zip(pb.requests, batch_tokenized_inputs)
):
# request id -> idx in list mapping
requests_idx_mapping[r.id] = i
tokenized_input = tokenized_input[-r.truncate :]
input_length = len(tokenized_input)
input_lengths.append(input_length)
prefix_offsets.append(input_length - 5)
read_offsets.append(input_length)
all_input_ids.append(tokenized_input)
# Position ids
request_position_ids = torch.arange(0, input_length, dtype=torch.int32)
position_ids.append(request_position_ids)
# Add cumulative lengths of all previous inputs
cu_seqlen_prefill.append(cumulative_length + input_length)
next_token_chooser_parameters.append(r.parameters)
stopping_criteria = StoppingCriteria.from_pb(
r.stopping_parameters, tokenizer
)
max_new_tokens = stopping_criteria.max_new_tokens
stopping_criterias.append(stopping_criteria)
top_n_tokens.append(r.top_n_tokens)
# Paged attention
# Remove one as the first token des not have a past
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 = math.ceil(total_tokens / BLOCK_SIZE)
if SLIDING_WINDOW_BLOCKS is not None:
needed_blocks = min(needed_blocks, SLIDING_WINDOW_BLOCKS)
blocks += needed_blocks
needed_blocks_slots.append((needed_blocks, total_tokens))
start_slots.append(cumulative_max_length)
request_slot_indices = torch.arange(
cumulative_max_length,
cumulative_max_length + input_length,
dtype=torch.int64,
)
slot_indices.append(request_slot_indices)
# Create tensor to slice into the kv tensor in prefill
if SLIDING_WINDOW is not None:
request_prefill_cache_indices = torch.arange(
cumulative_length + max(0, input_length - SLIDING_WINDOW),
cumulative_length + input_length,
dtype=torch.int64,
)
prefill_cache_indices.append(request_prefill_cache_indices)
all_prefill_logprobs = all_prefill_logprobs and r.prefill_logprobs
no_prefill_logprobs = no_prefill_logprobs and not r.prefill_logprobs
if r.prefill_logprobs:
prefill_head_indices.append(request_position_ids + cumulative_length)
prefill_next_token_indices.append(
prefill_out_cumulative_length + input_length - 1
)
prefill_cu_outlens.append(prefill_out_cumulative_length + input_length)
prefill_out_cumulative_length += input_length
else:
prefill_head_indices.append(
torch.tensor(
[cumulative_length + input_length - 1], dtype=torch.int32
)
)
prefill_next_token_indices.append(prefill_out_cumulative_length)
prefill_cu_outlens.append(prefill_out_cumulative_length + 1)
prefill_out_cumulative_length += 1
# Update
cumulative_length += input_length
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 + speculative_length
)
next_token_chooser = HeterogeneousNextTokenChooser.from_pb(
next_token_chooser_parameters, dtype, device
)
start_slots = torch.tensor(start_slots, dtype=torch.int64)
# Padded all_input_ids_tensor
all_input_ids_tensor = np.zeros(
(len(all_input_ids), max_length), dtype=np.int64
)
for i, input_ids in enumerate(all_input_ids):
all_input_ids_tensor[i, : len(input_ids)] = input_ids
# Create tensors on device
all_input_ids_tensor = torch.tensor(
all_input_ids_tensor, dtype=torch.int64, device=device
)
if len(pb.requests) > 1:
input_ids = np.concatenate(all_input_ids, dtype=np.int64)
position_ids = torch.cat(position_ids)
slot_indices = torch.cat(slot_indices)
if SLIDING_WINDOW is not None:
prefill_cache_indices = torch.cat(prefill_cache_indices)
else:
input_ids = all_input_ids[0]
position_ids = position_ids[0]
slot_indices = slot_indices[0]
if SLIDING_WINDOW is not None:
prefill_cache_indices = prefill_cache_indices[0]
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)
prefill_cache_indices = (
prefill_cache_indices.to(device) if SLIDING_WINDOW is not None else None
)
input_ids = torch.tensor(input_ids, dtype=torch.int64, device=device)
input_lengths_tensor = torch.tensor(
input_lengths, dtype=torch.int32, device=device
)
if all_prefill_logprobs:
prefill_head_indices = None
prefill_next_token_indices = cu_seqlen_prefill[1:] - 1
elif no_prefill_logprobs:
prefill_head_indices = cu_seqlen_prefill[1:] - 1
prefill_next_token_indices = None
else:
prefill_head_indices = torch.tensor(
torch.cat(prefill_head_indices), dtype=torch.int64, device=device
)
prefill_next_token_indices = torch.tensor(
prefill_next_token_indices, dtype=torch.int64, device=device
)
top_n_tokens_tensor = torch.tensor(
top_n_tokens, device=device, dtype=torch.int64
)
return cls(
batch_id=pb.id,
requests=pb.requests,
requests_idx_mapping=requests_idx_mapping,
input_ids=input_ids,
position_ids=position_ids,
cu_seqlen_prefill=cu_seqlen_prefill,
start_slots=start_slots,
slot_indices=slot_indices,
needed_blocks_slots=needed_blocks_slots,
block_tables=None,
block_tables_tensor=None,
slots=None,
max_seqlen=max_seqlen,
prefill_head_indices=prefill_head_indices,
prefill_next_token_indices=prefill_next_token_indices,
prefill_cu_outlens=prefill_cu_outlens,
input_lengths=input_lengths,
input_lengths_tensor=input_lengths_tensor,
prefix_offsets=prefix_offsets,
read_offsets=read_offsets,
all_input_ids=all_input_ids,
all_input_ids_tensor=all_input_ids_tensor,
next_token_chooser=next_token_chooser,
stopping_criterias=stopping_criterias,
top_n_tokens=top_n_tokens,
top_n_tokens_tensor=top_n_tokens_tensor,
blocks=blocks,
max_blocks=max_blocks,
prefill_cache_indices=prefill_cache_indices,
speculative_ids=None,
)
class BaseFlashMistral(FlashCausalLM):
def __init__(
self,
config_cls,
model_cls,
model_id: str,
revision: Optional[str] = None,
quantize: Optional[str] = None,
dtype: Optional[torch.dtype] = None,
trust_remote_code: bool = False,
):
global SLIDING_WINDOW
global SLIDING_WINDOW_BLOCKS
self.process_group, rank, world_size = initialize_torch_distributed()
if torch.cuda.is_available():
device = torch.device(f"cuda:{rank}")
dtype = torch.float16 if dtype is None else dtype
else:
raise NotImplementedError("FlashLlama is only available on GPU")
tokenizer = LlamaTokenizerFast.from_pretrained(
model_id,
revision=revision,
padding_side="left",
truncation_side="left",
trust_remote_code=trust_remote_code,
)
config = config_cls.from_pretrained(
model_id, revision=revision, trust_remote_code=trust_remote_code
)
config.quantize = quantize
# Set context windows
if config.sliding_window is not None:
SLIDING_WINDOW = config.sliding_window
SLIDING_WINDOW_BLOCKS = math.ceil(config.sliding_window / BLOCK_SIZE)
torch.distributed.barrier(group=self.process_group)
filenames = weight_files(model_id, revision=revision, extension=".safetensors")
weights = Weights(filenames, device, dtype, process_group=self.process_group)
if config.quantize in ["gptq", "awq"]:
weights._set_gptq_params(model_id, revision)
model = model_cls(config, weights)
torch.distributed.barrier(group=self.process_group)
super(BaseFlashMistral, self).__init__(
model=model,
tokenizer=tokenizer,
num_layers=len(model.model.layers),
num_kv_heads=model.model.num_key_value_heads,
head_size=model.model.head_size,
dtype=dtype,
device=device,
rank=rank,
world_size=world_size,
sliding_window=config.sliding_window,
)
@property
def batch_type(self) -> Type[FlashMistralBatch]:
return FlashMistralBatch
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=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=lm_head_indices,
)
if batch.prefill_cache_indices is not None:
batch.prefill_cache_indices = None
return logits
class FlashMistral(BaseFlashMistral):
def __init__(
self,
model_id: str,
revision: Optional[str] = None,
quantize: Optional[str] = None,
dtype: Optional[torch.dtype] = None,
trust_remote_code: bool = False,
):
super(FlashMistral, self).__init__(
config_cls=MistralConfig,
model_cls=FlashMistralForCausalLM,
model_id=model_id,
revision=revision,
quantize=quantize,
dtype=dtype,
trust_remote_code=trust_remote_code,
)
| 0 |
hf_public_repos/text-generation-inference/server/text_generation_server | hf_public_repos/text-generation-inference/server/text_generation_server/models/__init__.py | import torch
from loguru import logger
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
from text_generation_server.models.bloom import BLOOMSharded
from text_generation_server.models.mpt import MPTSharded
from text_generation_server.models.seq2seq_lm import Seq2SeqLM
from text_generation_server.models.rw import RW
from text_generation_server.models.opt import OPTSharded
from text_generation_server.models.galactica import GalacticaSharded
from text_generation_server.models.santacoder import SantaCoder
from text_generation_server.models.t5 import T5Sharded
from text_generation_server.models.gpt_neox import GPTNeoxSharded
# The flag below controls whether to allow TF32 on matmul. This flag defaults to False
# in PyTorch 1.12 and later.
torch.backends.cuda.matmul.allow_tf32 = True
# The flag below controls whether to allow TF32 on cuDNN. This flag defaults to True.
torch.backends.cudnn.allow_tf32 = True
# Disable gradients
torch.set_grad_enabled(False)
__all__ = [
"Model",
"BLOOMSharded",
"CausalLM",
"FlashCausalLM",
"GalacticaSharded",
"Seq2SeqLM",
"SantaCoder",
"OPTSharded",
"T5Sharded",
"get_model",
]
FLASH_ATT_ERROR_MESSAGE = "{} requires Flash Attention enabled models."
FLASH_ATTENTION = True
try:
from text_generation_server.models.flash_rw import FlashRWSharded
from text_generation_server.models.flash_neox import FlashNeoXSharded
from text_generation_server.models.flash_llama import (
FlashLlama,
)
from text_generation_server.models.flash_santacoder import (
FlashSantacoderSharded,
)
from text_generation_server.models.idefics import IDEFICSSharded
from text_generation_server.models.flash_mistral import FlashMistral
from text_generation_server.models.flash_mixtral import FlashMixtral
from text_generation_server.utils.flash_attn import HAS_FLASH_ATTN_V2_CUDA
except ImportError as e:
logger.warning(f"Could not import Flash Attention enabled models: {e}")
FLASH_ATTENTION = False
HAS_FLASH_ATTN_V2_CUDA = False
if FLASH_ATTENTION:
__all__.append(FlashNeoXSharded)
__all__.append(FlashRWSharded)
__all__.append(FlashSantacoderSharded)
__all__.append(FlashLlama)
__all__.append(IDEFICSSharded)
__all__.append(FlashMistral)
__all__.append(FlashMixtral)
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:
if dtype is None:
# Keep it as default for now and let
# every model resolve their own default dtype.
dtype = None
elif dtype == "float16":
dtype = torch.float16
elif dtype == "bfloat16":
dtype = torch.bfloat16
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,
revision,
quantize=quantize,
dtype=dtype,
trust_remote_code=trust_remote_code,
)
if model_id.startswith("bigcode/"):
if FLASH_ATTENTION:
return FlashSantacoderSharded(
model_id,
revision,
quantize=quantize,
dtype=dtype,
trust_remote_code=trust_remote_code,
)
elif sharded:
raise NotImplementedError(
FLASH_ATT_ERROR_MESSAGE.format("Sharded Santacoder")
)
else:
return SantaCoder(
model_id,
revision,
quantize=quantize,
dtype=dtype,
trust_remote_code=trust_remote_code,
)
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
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":
if FLASH_ATTENTION:
return FlashSantacoderSharded(
model_id,
revision,
quantize=quantize,
dtype=dtype,
trust_remote_code=trust_remote_code,
)
elif sharded:
raise NotImplementedError(
FLASH_ATT_ERROR_MESSAGE.format("Sharded Santacoder")
)
else:
return SantaCoder(
model_id,
revision,
quantize=quantize,
dtype=dtype,
trust_remote_code=trust_remote_code,
)
if model_type == "bloom":
return BLOOMSharded(
model_id,
revision,
quantize=quantize,
dtype=dtype,
trust_remote_code=trust_remote_code,
)
elif model_type == "mpt":
return MPTSharded(
model_id,
revision,
quantize=quantize,
dtype=dtype,
trust_remote_code=trust_remote_code,
)
elif model_type == "gpt_neox":
if FLASH_ATTENTION:
return FlashNeoXSharded(
model_id,
revision,
quantize=quantize,
dtype=dtype,
trust_remote_code=trust_remote_code,
)
elif sharded:
return GPTNeoxSharded(
model_id,
revision,
quantize=quantize,
dtype=dtype,
trust_remote_code=trust_remote_code,
)
else:
return CausalLM(
model_id,
revision,
quantize=quantize,
dtype=dtype,
trust_remote_code=trust_remote_code,
)
elif model_type == "llama" or model_type == "baichuan":
if FLASH_ATTENTION:
return FlashLlama(
model_id,
revision,
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"))
else:
return CausalLM(
model_id,
revision,
quantize=quantize,
dtype=dtype,
trust_remote_code=trust_remote_code,
)
if model_type in ["RefinedWeb", "RefinedWebModel", "falcon"]:
if sharded:
if FLASH_ATTENTION:
if config_dict.get("alibi", False):
raise NotImplementedError("sharded is not supported for this model")
return FlashRWSharded(
model_id,
revision,
quantize=quantize,
dtype=dtype,
trust_remote_code=trust_remote_code,
)
raise NotImplementedError(FLASH_ATT_ERROR_MESSAGE.format(f"Sharded Falcon"))
else:
if FLASH_ATTENTION and not config_dict.get("alibi", False):
return FlashRWSharded(
model_id,
revision,
quantize=quantize,
dtype=dtype,
trust_remote_code=trust_remote_code,
)
else:
return RW(
model_id,
revision,
quantize=quantize,
dtype=dtype,
trust_remote_code=trust_remote_code,
)
if model_type == "mistral":
sliding_window = config_dict.get("sliding_window", -1)
if (
(sliding_window is None or sliding_window == -1) and FLASH_ATTENTION
) or HAS_FLASH_ATTN_V2_CUDA:
return FlashMistral(
model_id,
revision,
quantize=quantize,
dtype=dtype,
trust_remote_code=trust_remote_code,
)
if model_type == "mixtral":
sliding_window = config_dict.get("sliding_window", -1)
if (
(sliding_window is None or sliding_window == -1) and FLASH_ATTENTION
) or HAS_FLASH_ATTN_V2_CUDA:
return FlashMixtral(
model_id,
revision,
quantize=quantize,
dtype=dtype,
trust_remote_code=trust_remote_code,
)
if model_type == "opt":
return OPTSharded(
model_id,
revision,
quantize=quantize,
dtype=dtype,
trust_remote_code=trust_remote_code,
)
if model_type == "t5":
return T5Sharded(
model_id,
revision,
quantize=quantize,
dtype=dtype,
trust_remote_code=trust_remote_code,
)
if model_type == "idefics":
if FLASH_ATTENTION:
return IDEFICSSharded(
model_id,
revision,
quantize=quantize,
dtype=dtype,
trust_remote_code=trust_remote_code,
)
else:
raise NotImplementedError(FLASH_ATT_ERROR_MESSAGE.format("Idefics"))
if sharded:
raise NotImplementedError("sharded is not supported for AutoModel")
if quantize == "gptq":
raise NotImplementedError(
"gptq quantization is not supported for AutoModel, you can try to quantize it with `text-generation-server quantize ORIGINAL_MODEL_ID NEW_MODEL_ID`"
)
if quantize == "awq":
raise NotImplementedError("awq quantization is not supported for AutoModel")
elif (quantize == "bitsandbytes-fp4") or (quantize == "bitsandbytes-nf4"):
raise NotImplementedError("4bit quantization is not supported for AutoModel")
elif quantize == "eetq":
raise NotImplementedError("Eetq quantization is not supported for AutoModel")
if model_type in modeling_auto.MODEL_FOR_CAUSAL_LM_MAPPING_NAMES:
return CausalLM(
model_id,
revision,
quantize=quantize,
dtype=dtype,
trust_remote_code=trust_remote_code,
)
if model_type in modeling_auto.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES:
return Seq2SeqLM(
model_id,
revision,
quantize=quantize,
dtype=dtype,
trust_remote_code=trust_remote_code,
)
auto_map = config_dict.get("auto_map", None)
if trust_remote_code and auto_map is not None:
if "AutoModelForCausalLM" in auto_map.keys():
return CausalLM(
model_id,
revision,
quantize=quantize,
dtype=dtype,
trust_remote_code=trust_remote_code,
)
if "AutoModelForSeq2SeqLM" in auto_map.keys():
return Seq2SeqLM(
model_id,
revision,
quantize=quantize,
dtype=dtype,
trust_remote_code=trust_remote_code,
)
raise ValueError(f"Unsupported model type {model_type}")
| 0 |
hf_public_repos/text-generation-inference/server/text_generation_server | hf_public_repos/text-generation-inference/server/text_generation_server/models/causal_lm.py | import torch
import time
from dataclasses import dataclass
from opentelemetry import trace
from transformers import AutoTokenizer, AutoModelForCausalLM, PreTrainedTokenizerBase
from typing import Optional, Tuple, List, Type, Dict
from text_generation_server.models import Model
from text_generation_server.utils.tokens import batch_top_tokens
from text_generation_server.models.types import (
Batch,
Tokens,
Generation,
GeneratedText,
)
from text_generation_server.pb import generate_pb2
from text_generation_server.utils import NextTokenChooser, StoppingCriteria, Sampling
tracer = trace.get_tracer(__name__)
@dataclass
class CausalLMBatch(Batch):
batch_id: int
requests: List[generate_pb2.Request]
requests_idx_mapping: Dict[int, int]
# Decoder values
input_ids: torch.Tensor
attention_mask: torch.Tensor
position_ids: torch.Tensor
past_key_values: Optional[List[Tuple]]
# All tokens
all_input_ids: List[torch.Tensor]
# Lengths of all generations present in the batch
input_lengths: List[int]
prefix_offsets: List[int]
read_offsets: List[int]
# Generation helpers
next_token_choosers: List[NextTokenChooser]
stopping_criterias: List[StoppingCriteria]
top_n_tokens: List[int]
top_n_tokens_tensor: torch.Tensor
# Metadata used for padding
max_input_length: int
padding_right_offset: int
# Maximum number of tokens this batch will grow to
max_tokens: int
# Past metadata
keys_head_dim_last: bool = True
def to_pb(self) -> generate_pb2.CachedBatch:
return generate_pb2.CachedBatch(
id=self.batch_id,
request_ids=[r.id for r in self.requests],
size=len(self),
max_tokens=self.max_tokens,
)
@classmethod
def from_pb(
cls,
pb: generate_pb2.Batch,
tokenizer: PreTrainedTokenizerBase,
dtype: torch.dtype,
device: torch.device,
) -> "CausalLMBatch":
inputs = []
next_token_choosers = []
stopping_criterias = []
top_n_tokens = []
prefix_offsets = []
read_offsets = []
requests_idx_mapping = {}
# Parse batch
max_truncation = 0
padding_right_offset = 0
max_decode_tokens = 0
for i, r in enumerate(pb.requests):
requests_idx_mapping[r.id] = i
inputs.append(r.inputs)
next_token_choosers.append(NextTokenChooser.from_pb(r.parameters, device))
stopping_criteria = StoppingCriteria.from_pb(
r.stopping_parameters, tokenizer
)
stopping_criterias.append(stopping_criteria)
top_n_tokens.append(r.top_n_tokens)
max_truncation = max(max_truncation, r.truncate)
max_decode_tokens += stopping_criteria.max_new_tokens
padding_right_offset = max(
padding_right_offset, stopping_criteria.max_new_tokens
)
tokenized_inputs = tokenizer(
inputs,
return_tensors="pt",
padding=True,
return_token_type_ids=False,
truncation=True,
max_length=max_truncation,
).to(device)
for _ in pb.requests:
input_len = tokenized_inputs["input_ids"].shape[1]
prefix_offsets.append(input_len - 5)
read_offsets.append(input_len)
input_lengths = tokenized_inputs["attention_mask"].sum(1)
max_input_length = input_lengths.max()
input_ids = tokenized_inputs["input_ids"]
# Allocate maximum attention_mask
attention_mask = input_ids.new_zeros(
(pb.size, max_input_length + padding_right_offset)
)
# Copy tokenizer attention_mask into fully allocated attention_mask
attention_mask[:, :max_input_length] = tokenized_inputs["attention_mask"]
position_ids = tokenized_inputs["attention_mask"].long().cumsum(-1) - 1
position_ids.masked_fill_(tokenized_inputs["attention_mask"] == 0, 1)
all_input_ids = tokenized_inputs["input_ids"].T.split(1, dim=1)
top_n_tokens_tensor = torch.tensor(
top_n_tokens, device=device, dtype=torch.int64
)
max_tokens = len(inputs) * (max_input_length + max_decode_tokens)
return cls(
batch_id=pb.id,
requests=pb.requests,
requests_idx_mapping=requests_idx_mapping,
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=None,
all_input_ids=list(all_input_ids),
input_lengths=input_lengths.tolist(),
prefix_offsets=prefix_offsets,
read_offsets=read_offsets,
next_token_choosers=next_token_choosers,
stopping_criterias=stopping_criterias,
top_n_tokens=top_n_tokens,
top_n_tokens_tensor=top_n_tokens_tensor,
max_input_length=max_input_length.item(),
padding_right_offset=padding_right_offset,
max_tokens=max_tokens,
)
@tracer.start_as_current_span("filter")
def filter(self, request_ids: List[int]) -> Optional["CausalLMBatch"]:
if len(request_ids) == 0:
raise ValueError("Batch must have at least one request")
if len(request_ids) == len(self):
return self
keep_indices = []
# New values after filtering
requests_idx_mapping = {}
requests = []
input_lengths = []
prefix_offsets = []
read_offsets = []
all_input_ids = []
max_input_length = 0
next_token_choosers = []
stopping_criterias = []
top_n_tokens = []
total_remaining_decode_tokens = 0
new_padding_right_offset = 0
for i, request_id in enumerate(request_ids):
idx = self.requests_idx_mapping[request_id]
requests_idx_mapping[request_id] = i
keep_indices.append(idx)
requests.append(self.requests[idx])
prefix_offsets.append(self.prefix_offsets[idx])
read_offsets.append(self.read_offsets[idx])
all_input_ids.append(self.all_input_ids[idx])
request_input_length = self.input_lengths[idx]
input_lengths.append(request_input_length)
max_input_length = max(max_input_length, request_input_length)
next_token_choosers.append(self.next_token_choosers[idx])
stopping_criteria = self.stopping_criterias[idx]
stopping_criterias.append(stopping_criteria)
top_n_tokens.append(self.top_n_tokens[idx])
remaining_decode_tokens = (
stopping_criteria.max_new_tokens - stopping_criteria.current_tokens
)
total_remaining_decode_tokens += remaining_decode_tokens
new_padding_right_offset = max(
new_padding_right_offset, remaining_decode_tokens
)
# Apply indices to input_ids, attention mask, past key values and other items that need to be cached
input_ids = self.input_ids[keep_indices]
position_ids = self.position_ids[keep_indices]
self.attention_mask = self.attention_mask[
keep_indices,
-(self.padding_right_offset + max_input_length) : (
self.attention_mask.shape[1] - self.padding_right_offset
)
+ new_padding_right_offset,
]
# Ensure that past_key_values tensors can be updated in-place
if type(self.past_key_values[0]) == tuple:
self.past_key_values = [list(layer) for layer in self.past_key_values]
# Update tensors in-place to allow incremental garbage collection
past_kv_length = max_input_length - 1
for layer in self.past_key_values:
past_keys, past_values = layer
if len(past_keys.shape) == 3:
# Force past to be of dim [self_size, num_heads, ...] for easy indexing
past_keys = past_keys.view(len(self), -1, *past_keys.shape[-2:])
past_values = past_values.view(len(self), -1, *past_values.shape[-2:])
if self.keys_head_dim_last:
layer[0] = past_keys[keep_indices, :, -past_kv_length:, :]
else:
layer[0] = past_keys[keep_indices, :, :, -past_kv_length:]
del past_keys
layer[1] = past_values[keep_indices, :, -past_kv_length:, :]
del past_values
top_n_tokens_tensor = self.top_n_tokens_tensor[keep_indices]
max_tokens = len(request_ids) * max_input_length + total_remaining_decode_tokens
self.requests = requests
self.requests_idx_mapping = requests_idx_mapping
self.input_ids = input_ids
self.position_ids = position_ids
self.all_input_ids = all_input_ids
self.input_lengths = input_lengths
self.prefix_offsets = prefix_offsets
self.read_offsets = read_offsets
self.next_token_choosers = next_token_choosers
self.stopping_criterias = stopping_criterias
self.top_n_tokens = top_n_tokens
self.top_n_tokens_tensor = top_n_tokens_tensor
self.max_input_length = max_input_length
self.padding_right_offset = new_padding_right_offset
self.max_tokens = max_tokens
return self
@classmethod
@tracer.start_as_current_span("concatenate")
def concatenate(cls, batches: List["CausalLMBatch"]) -> "CausalLMBatch":
# Used for padding
total_batch_size = 0
max_input_length = 0
padding_right_offset = 0
for batch in batches:
total_batch_size += len(batch)
max_input_length = max(max_input_length, batch.max_input_length)
padding_right_offset = max(padding_right_offset, batch.padding_right_offset)
# Batch attributes
requests = []
requests_idx_mapping = {}
input_lengths = []
prefix_offsets = []
read_offsets = []
all_input_ids = []
next_token_choosers = []
stopping_criterias = []
top_n_tokens = []
max_tokens = 0
# Batch tensors
input_ids = None
attention_mask = None
position_ids = None
past_key_values = []
top_n_tokens_tensor = None
# Used for slicing correctly inside the tensors
# Equivalent to a cumsum on batch sizes
start_index = 0
for i, batch in enumerate(batches):
requests.extend(batch.requests)
input_lengths.extend(batch.input_lengths)
prefix_offsets.extend(batch.prefix_offsets)
read_offsets.extend(batch.read_offsets)
all_input_ids.extend(batch.all_input_ids)
next_token_choosers.extend(batch.next_token_choosers)
stopping_criterias.extend(batch.stopping_criterias)
top_n_tokens.extend(batch.top_n_tokens)
if i == 0:
requests_idx_mapping = batch.requests_idx_mapping
else:
# We need to offset the mapping for each batch by the cumulative batch size
for k, v in batch.requests_idx_mapping.items():
requests_idx_mapping[k] = v + start_index
# Slicing end index for this batch
end_index = start_index + len(batch)
# We only concatenate batches that did at least one step
if batch.past_key_values is None:
raise ValueError("only concatenate prefilled batches")
# Create empty tensor
# input_ids is always of shape [batch_size, 1]
# We do not need to pad it
if input_ids is None:
input_ids = batch.input_ids.new_empty((total_batch_size, 1))
# Copy to correct indices
input_ids[start_index:end_index] = batch.input_ids
# Create padded tensor
if attention_mask is None:
attention_mask = batch.attention_mask.new_zeros(
(total_batch_size, max_input_length + padding_right_offset),
)
if top_n_tokens_tensor is None:
top_n_tokens_tensor = batches[0].top_n_tokens_tensor.new_zeros(
total_batch_size,
)
top_n_tokens_tensor[start_index:end_index] = batch.top_n_tokens_tensor
# We need to slice the attention mask to remove padding from previous steps
# and to remove unused allocated space
left_offset = max_input_length - batch.max_input_length
batch_left_offset = (
batch.attention_mask.shape[1]
- batch.max_input_length
- batch.padding_right_offset
)
attention_mask[
start_index:end_index,
left_offset:-padding_right_offset,
] = batch.attention_mask[
:,
batch_left_offset : -batch.padding_right_offset,
]
# Create empty tensor
# position_ids is always of shape [batch_size, 1]
if position_ids is None:
position_ids = batch.position_ids.new_empty((total_batch_size, 1))
position_ids[start_index:end_index] = batch.position_ids
# Shenanigans to get dimensions because BLOOM outputs a past with a different shape
# BLOOM Keys: [batch_size * num_heads, head_dim, seq_length]
# BLOOM Values: [batch_size * num_heads, seq_length, head_dim]
# And ensure that we can update tensors in-place
if type(batch.past_key_values[0]) == tuple:
batch.past_key_values = [
[t.view(len(batch), -1, *t.shape[-2:]) for t in layer]
for layer in batch.past_key_values
]
elif len(batch.past_key_values[0][0].shape) == 3:
for layer in batch.past_key_values:
for k, t in enumerate(layer):
layer[k] = t.view(len(batch), -1, *t.shape[-2:])
# Add eventual padding tokens that were added while concatenating
max_tokens += batch.max_tokens + (
max_input_length - batch.max_input_length
) * len(batch)
start_index = end_index
first_past_kvs = batches[0].past_key_values
_, num_heads, padded_sequence_length, head_dim = first_past_kvs[0][1].shape
padded_past_values_shape = (
total_batch_size,
num_heads,
max_input_length - 1,
head_dim,
)
if batches[0].keys_head_dim_last:
padded_past_keys_shape = padded_past_values_shape
else:
# seq_length is last for BLOOM
padded_past_keys_shape = (
total_batch_size,
num_heads,
head_dim,
max_input_length - 1,
)
# Iterate over attention layers
# Concatenate past key values layer by layer to allow incremental garbage collection
for j in range(len(first_past_kvs)):
padded_past_keys = first_past_kvs[j][0].new_zeros(padded_past_keys_shape)
start_index = 0
for batch in batches:
past_keys = batch.past_key_values[j][0]
# Clear reference to the original tensor
batch.past_key_values[j][0] = None
# Slicing end index for this batch
end_index = start_index + len(batch)
# We slice the keys to remove the padding from previous batches
past_seq_len = batch.max_input_length - 1
if batch.keys_head_dim_last:
padded_past_keys[
start_index:end_index, :, -past_seq_len:, :
] = past_keys[:, :, -past_seq_len:, :]
else:
# BLOOM case
padded_past_keys[
start_index:end_index, :, :, -past_seq_len:
] = past_keys[:, :, :, -past_seq_len:]
del past_keys
start_index = end_index
padded_past_values = first_past_kvs[j][1].new_zeros(
padded_past_values_shape
)
start_index = 0
for batch in batches:
past_values = batch.past_key_values[j][1]
# Clear reference to the original tensor
batch.past_key_values[j][1] = None
# Slicing end index for this batch
end_index = start_index + len(batch)
# We slice the past values to remove the padding from previous batches
past_seq_len = batch.max_input_length - 1
padded_past_values[
start_index:end_index, :, -past_seq_len:, :
] = past_values[:, :, -past_seq_len:, :]
del past_values
# Update values
start_index = end_index
past_key_values.append([padded_past_keys, padded_past_values])
return cls(
batch_id=batches[0].batch_id,
requests=requests,
requests_idx_mapping=requests_idx_mapping,
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
all_input_ids=all_input_ids,
input_lengths=input_lengths,
prefix_offsets=prefix_offsets,
read_offsets=read_offsets,
next_token_choosers=next_token_choosers,
stopping_criterias=stopping_criterias,
top_n_tokens=top_n_tokens,
top_n_tokens_tensor=top_n_tokens_tensor,
max_input_length=max_input_length,
padding_right_offset=padding_right_offset,
keys_head_dim_last=batches[0].keys_head_dim_last,
max_tokens=max_tokens,
)
def __len__(self):
return len(self.requests)
class CausalLM(Model):
def __init__(
self,
model_id: str,
revision: Optional[str] = None,
quantize: Optional[str] = None,
dtype: Optional[torch.dtype] = None,
trust_remote_code: bool = False,
):
if torch.cuda.is_available():
device = torch.device("cuda")
dtype = torch.float16 if dtype is None else dtype
else:
if quantize:
raise ValueError("quantization is not available on CPU")
device = torch.device("cpu")
dtype = torch.float32 if dtype is None else dtype
tokenizer = AutoTokenizer.from_pretrained(
model_id,
revision=revision,
padding_side="left",
truncation_side="left",
trust_remote_code=trust_remote_code,
)
model = AutoModelForCausalLM.from_pretrained(
model_id,
revision=revision,
torch_dtype=dtype,
device_map="auto"
if torch.cuda.is_available() and torch.cuda.device_count() > 1
else None,
load_in_8bit=quantize == "bitsandbytes",
trust_remote_code=trust_remote_code,
)
if (
torch.cuda.is_available()
and torch.cuda.device_count() == 1
and quantize != "bitsandbytes"
):
model = model.cuda()
if tokenizer.pad_token_id is None:
if model.config.pad_token_id is not None:
tokenizer.pad_token_id = model.config.pad_token_id
elif model.config.eos_token_id is not None:
tokenizer.pad_token_id = model.config.eos_token_id
elif tokenizer.eos_token_id is not None:
tokenizer.pad_token_id = tokenizer.eos_token_id
else:
tokenizer.add_special_tokens({"pad_token": "[PAD]"})
super(CausalLM, self).__init__(
model=model,
tokenizer=tokenizer,
requires_padding=True,
dtype=dtype,
device=device,
)
@property
def batch_type(self) -> Type[CausalLMBatch]:
return CausalLMBatch
def decode(self, generated_ids: List[int]) -> str:
return self.tokenizer.decode(
generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
def forward(
self, input_ids, attention_mask, position_ids, past_key_values: Optional = None
) -> Tuple[torch.Tensor, List[Tuple[torch.Tensor, torch.Tensor]]]:
# Model Forward
kwargs = {
"input_ids": input_ids,
"attention_mask": attention_mask,
"past_key_values": past_key_values,
"use_cache": True,
"return_dict": True,
}
if self.has_position_ids:
kwargs["position_ids"] = position_ids
outputs = self.model.forward(**kwargs)
return outputs.logits, outputs.past_key_values
@tracer.start_as_current_span("generate_token")
def generate_token(
self, batch: CausalLMBatch
) -> Tuple[List[Generation], Optional[CausalLMBatch], Tuple[int, int]]:
start = time.time_ns()
# slice the attention mask to the correct shape
attention_mask = batch.attention_mask[:, : -batch.padding_right_offset]
logits, past = self.forward(
batch.input_ids,
attention_mask,
batch.position_ids,
batch.past_key_values,
)
# Results
generations: List[Generation] = []
stopped = True
batch_top_token_ids, batch_top_token_logprobs = batch_top_tokens(
batch.top_n_tokens,
batch.top_n_tokens_tensor,
torch.log_softmax(logits[:, -1], -1),
)
start_decode = time.time_ns()
# Zipped iterator
iterator = zip(
batch.requests,
batch.input_lengths,
batch.prefix_offsets,
batch.read_offsets,
logits,
batch.next_token_choosers,
batch.stopping_criterias,
batch.all_input_ids,
batch.top_n_tokens,
batch_top_token_ids,
batch_top_token_logprobs,
)
# For each member of the batch
for i, (
request,
input_length,
prefix_offset,
read_offset,
logits,
next_token_chooser,
stopping_criteria,
all_input_ids,
top_n_tokens,
top_token_ids,
top_token_logprobs,
) in enumerate(iterator):
# Select next token
next_token_id, logprobs = next_token_chooser(
all_input_ids.view(1, -1), logits[-1:, :]
)
# Append next token to all tokens
all_input_ids = torch.cat([all_input_ids, next_token_id])
new_input_length = input_length + 1
# Generated token
next_token_logprob = logprobs[-1, next_token_id]
next_token_id_squeezed = next_token_id.squeeze()
next_token_text, prefix_offset, read_offset = self.decode_token(
all_input_ids[:, 0], prefix_offset, read_offset
)
# Evaluate stopping criteria
stop, reason = stopping_criteria(
next_token_id_squeezed,
next_token_text,
)
if not stop:
stopped = False
# Shard generations
# All generations will be appended in the rust sharded client
if i % self.world_size == self.rank:
if stop:
# Decode generated tokens
output_text, _, _ = self.decode_token(
all_input_ids[:, 0],
prefix_offset=len(all_input_ids)
- stopping_criteria.current_tokens
- 1,
read_offset=len(all_input_ids)
- stopping_criteria.current_tokens,
skip_special_tokens=True,
)
# Get seed
if isinstance(next_token_chooser.choice, Sampling):
seed = next_token_chooser.choice.seed
else:
seed = None
generated_text = GeneratedText(
output_text, stopping_criteria.current_tokens, reason, seed
)
else:
generated_text = None
# Prefill
if stopping_criteria.current_tokens == 1 and request.prefill_logprobs:
# Remove generated token to only have prefill and add nan for first prompt token
prefill_logprobs = [float("nan")] + torch.log_softmax(
logits, -1
).gather(1, all_input_ids[1:]).squeeze(1)[
-new_input_length:-1
].tolist()
prefill_token_ids = all_input_ids[-new_input_length:-1]
prefill_texts = self.tokenizer.batch_decode(
prefill_token_ids,
clean_up_tokenization_spaces=False,
skip_special_tokens=False,
)
prefill_tokens = Tokens(
prefill_token_ids,
prefill_logprobs,
prefill_texts,
is_special=[],
)
else:
prefill_tokens = None
if top_n_tokens > 0:
toptoken_texts = self.tokenizer.batch_decode(
top_token_ids,
clean_up_tokenization_spaces=False,
skip_special_tokens=False,
)
special_toptokens = [
token_id in self.all_special_ids for token_id in top_token_ids
]
top_tokens = Tokens(
top_token_ids,
top_token_logprobs,
toptoken_texts,
special_toptokens,
)
else:
top_tokens = None
generation = Generation(
request.id,
prefill_tokens,
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,
)
generations.append(generation)
# Update values
batch.input_ids[i, 0] = next_token_id
batch.all_input_ids[i] = all_input_ids
batch.input_lengths[i] = new_input_length
batch.prefix_offsets[i] = prefix_offset
batch.read_offsets[i] = read_offset
batch.max_input_length = max(batch.max_input_length, new_input_length)
# We finished all generations in the batch; there is no next batch
if stopped:
forward_ns = start_decode - start
decode_ns = time.time_ns() - start_decode
return generations, None, (forward_ns, decode_ns)
# Slice unused values from prefill
batch.input_ids = batch.input_ids[:, :1]
# Update attention_mask as we added a new token to input_ids
batch.attention_mask[:, -batch.padding_right_offset] = 1
# Decrease right offset
batch.padding_right_offset -= 1
# Update position_ids
batch.position_ids = batch.position_ids[:, -1:] + 1
# Update past key values
batch.past_key_values = past
forward_ns = start_decode - start
decode_ns = time.time_ns() - start_decode
return generations, batch, (forward_ns, decode_ns)
| 0 |
hf_public_repos/text-generation-inference/server/text_generation_server | hf_public_repos/text-generation-inference/server/text_generation_server/models/flash_causal_lm.py | import math
import time
import itertools
import torch
import torch.distributed
import numpy as np
from dataclasses import dataclass
from opentelemetry import trace
from transformers import PreTrainedTokenizerBase
from typing import Optional, Tuple, List, Type, Dict
from text_generation_server.models import Model
from text_generation_server.utils.tokens import batch_top_tokens
from text_generation_server.utils.speculate import get_speculate
from text_generation_server.models.types import (
Batch,
Tokens,
Generation,
GeneratedText,
)
from text_generation_server.models.cache_manager import (
get_cache_manager,
set_cache_manager,
BLOCK_SIZE,
)
from text_generation_server.pb import generate_pb2
from text_generation_server.utils import StoppingCriteria, HeterogeneousNextTokenChooser
from text_generation_server.utils.dist import MEMORY_FRACTION
tracer = trace.get_tracer(__name__)
@dataclass
class FlashCausalLMBatch(Batch):
batch_id: int
requests: List[generate_pb2.Request]
# request id -> idx in list mapping
requests_idx_mapping: Dict[int, int]
# Decoder values
input_ids: torch.Tensor
position_ids: torch.Tensor
speculative_ids: torch.Tensor
# Flash Attention values
# tensor of length b containing the cumulative sequence lengths of the sequences in the batch, only used in prefill
cu_seqlen_prefill: Optional[torch.Tensor]
# Paged Attention values
# Set when creating the batch
# CPU tensor of length b indicating the start of each sequence in slots
start_slots: torch.Tensor
# tensor of indices of the currently used slots, length = \sum_{i=0}^{b} s_i in prefill, length = b in decode
slot_indices: torch.Tensor
# List of tuple of ints representing the number of blocks and slots needed by each sequence
needed_blocks_slots: Optional[List[Tuple[int, int]]]
# Set in prefill by the CacheManager
# list of length b of list of length s_i // block_size
block_tables: Optional[List[List[int]]]
# tensor of size [b, max_seqlen // block_size] holding the paged attention block tables for all sequences
block_tables_tensor: Optional[torch.Tensor]
# tensor of length \sum_{i=0}^{b} max_s_i holding the paged attention slots for all sequences
slots: Optional[torch.Tensor]
max_seqlen: int
# Prefill metadata tensors to efficiently compute logprobs
prefill_head_indices: Optional[torch.Tensor]
prefill_next_token_indices: Optional[torch.tensor]
prefill_cu_outlens: Optional[List[int]]
# All tokens
all_input_ids: List[List[int]]
all_input_ids_tensor: torch.Tensor
# Lengths of all generations present in the batch
input_lengths: List[int]
input_lengths_tensor: torch.Tensor
prefix_offsets: List[Optional[int]]
read_offsets: List[Optional[int]]
# Generation helpers
next_token_chooser: HeterogeneousNextTokenChooser
stopping_criterias: List[StoppingCriteria]
top_n_tokens: List[int]
top_n_tokens_tensor: torch.Tensor
# Number of blocks in this batch
blocks: int
# Maximum number of blocks
max_blocks: int
def to_pb(self) -> generate_pb2.CachedBatch:
return generate_pb2.CachedBatch(
id=self.batch_id,
request_ids=[r.id for r in self.requests],
size=len(self),
max_tokens=self.blocks * BLOCK_SIZE,
)
@classmethod
def from_pb(
cls,
pb: generate_pb2.Batch,
tokenizer: PreTrainedTokenizerBase,
dtype: torch.dtype,
device: torch.device,
) -> "FlashCausalLMBatch":
batch_inputs = []
max_truncation = 0
for r in pb.requests:
batch_inputs.append(r.inputs)
max_truncation = max(max_truncation, r.truncate)
batch_tokenized_inputs = tokenizer(
batch_inputs, truncation=True, max_length=max_truncation
)["input_ids"]
position_ids = []
speculative_ids = []
cu_seqlen_prefill = [0]
needed_blocks_slots = []
start_slots = []
slot_indices = []
input_lengths = []
prefix_offsets = []
read_offsets = []
all_input_ids = []
requests_idx_mapping = {}
all_prefill_logprobs = True
no_prefill_logprobs = True
prefill_head_indices = []
prefill_next_token_indices = []
prefill_cu_outlens = [0]
next_token_chooser_parameters = []
stopping_criterias = []
top_n_tokens = []
# Cumulative length
cumulative_length = 0
cumulative_max_length = 0
prefill_out_cumulative_length = 0
blocks = 0
max_seqlen = 0
max_length = 0
max_blocks = 0
# Parse batch
for i, (r, tokenized_input) in enumerate(
zip(pb.requests, batch_tokenized_inputs)
):
# request id -> idx in list mapping
requests_idx_mapping[r.id] = i
tokenized_input = tokenized_input[-r.truncate :]
input_length = len(tokenized_input)
input_lengths.append(input_length)
prefix_offsets.append(input_length - 5)
read_offsets.append(input_length)
all_input_ids.append(tokenized_input)
# Position ids
request_position_ids = torch.arange(0, input_length, dtype=torch.int32)
position_ids.append(request_position_ids)
# Add cumulative lengths of all previous inputs
cu_seqlen_prefill.append(cumulative_length + input_length)
next_token_chooser_parameters.append(r.parameters)
stopping_criteria = StoppingCriteria.from_pb(
r.stopping_parameters, tokenizer
)
max_new_tokens = stopping_criteria.max_new_tokens
stopping_criterias.append(stopping_criteria)
top_n_tokens.append(r.top_n_tokens)
# Paged attention
# Remove one as the first token des not have a past
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))
start_slots.append(cumulative_max_length)
request_slot_indices = torch.arange(
cumulative_max_length,
cumulative_max_length + input_length,
dtype=torch.int64,
)
slot_indices.append(request_slot_indices)
all_prefill_logprobs = all_prefill_logprobs and r.prefill_logprobs
no_prefill_logprobs = no_prefill_logprobs and not r.prefill_logprobs
if r.prefill_logprobs:
prefill_head_indices.append(request_position_ids + cumulative_length)
prefill_next_token_indices.append(
prefill_out_cumulative_length + input_length - 1
)
prefill_cu_outlens.append(prefill_out_cumulative_length + input_length)
prefill_out_cumulative_length += input_length
else:
prefill_head_indices.append(
torch.tensor(
[cumulative_length + input_length - 1], dtype=torch.int32
)
)
prefill_next_token_indices.append(prefill_out_cumulative_length)
prefill_cu_outlens.append(prefill_out_cumulative_length + 1)
prefill_out_cumulative_length += 1
# Update
cumulative_length += input_length
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 + speculative_length
)
next_token_chooser = HeterogeneousNextTokenChooser.from_pb(
next_token_chooser_parameters, dtype, device
)
start_slots = torch.tensor(start_slots, dtype=torch.int64)
# Padded all_input_ids_tensor
all_input_ids_tensor = np.zeros(
(len(all_input_ids), max_length), dtype=np.int64
)
for i, input_ids in enumerate(all_input_ids):
all_input_ids_tensor[i, : len(input_ids)] = input_ids
# Create tensors on device
all_input_ids_tensor = torch.tensor(
all_input_ids_tensor, dtype=torch.int64, device=device
)
if len(pb.requests) > 1:
input_ids = np.concatenate(all_input_ids, dtype=np.int64)
position_ids = torch.cat(position_ids)
slot_indices = torch.cat(slot_indices)
else:
input_ids = all_input_ids[0]
position_ids = position_ids[0]
slot_indices = slot_indices[0]
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)
input_lengths_tensor = torch.tensor(
input_lengths, dtype=torch.int32, device=device
)
if all_prefill_logprobs:
prefill_head_indices = None
prefill_next_token_indices = cu_seqlen_prefill[1:] - 1
elif no_prefill_logprobs:
prefill_head_indices = cu_seqlen_prefill[1:] - 1
prefill_next_token_indices = None
else:
prefill_head_indices = torch.tensor(
torch.cat(prefill_head_indices), dtype=torch.int64, device=device
)
prefill_next_token_indices = torch.tensor(
prefill_next_token_indices, dtype=torch.int64, device=device
)
top_n_tokens_tensor = torch.tensor(
top_n_tokens, device=device, dtype=torch.int64
)
return cls(
batch_id=pb.id,
requests=pb.requests,
requests_idx_mapping=requests_idx_mapping,
input_ids=input_ids,
position_ids=position_ids,
cu_seqlen_prefill=cu_seqlen_prefill,
start_slots=start_slots,
slot_indices=slot_indices,
needed_blocks_slots=needed_blocks_slots,
block_tables=None,
block_tables_tensor=None,
slots=None,
max_seqlen=max_seqlen,
prefill_head_indices=prefill_head_indices,
prefill_next_token_indices=prefill_next_token_indices,
prefill_cu_outlens=prefill_cu_outlens,
input_lengths=input_lengths,
input_lengths_tensor=input_lengths_tensor,
prefix_offsets=prefix_offsets,
read_offsets=read_offsets,
all_input_ids=all_input_ids,
all_input_ids_tensor=all_input_ids_tensor,
next_token_chooser=next_token_chooser,
stopping_criterias=stopping_criterias,
top_n_tokens=top_n_tokens,
top_n_tokens_tensor=top_n_tokens_tensor,
blocks=blocks,
max_blocks=max_blocks,
speculative_ids=None,
)
@tracer.start_as_current_span("filter")
def filter(self, request_ids: List[int]) -> "FlashCausalLMBatch":
if len(request_ids) == 0:
raise ValueError("Batch must have at least one request")
# We assume that if len(requests) == len(self) then the requests are the same
if len(request_ids) == len(self):
return self
device = self.input_ids.device
# New values after filtering
requests_idx_mapping = {}
# Used to index into tensors
indices = []
# slots to keep after filtering
slot_filtering_indices = torch.zeros(
self.slots.shape[0], dtype=torch.bool, device=device
)
# Create on CPU to only move to GPU once instead of at every copy
slot_indices = torch.empty(len(request_ids), dtype=torch.int64)
max_seqlen = 0
requests = []
start_slots = []
block_tables = []
all_input_ids = []
input_lengths = []
prefix_offsets = []
read_offsets = []
stopping_criterias = []
top_n_tokens = []
blocks = 0
max_blocks = 0
# Cumulative length
cumulative_max_length = 0
for i, request_id in enumerate(request_ids):
idx = self.requests_idx_mapping[request_id]
indices.append(idx)
requests_idx_mapping[request_id] = i
requests.append(self.requests[idx])
# Get length
request_input_length = self.input_lengths[idx]
max_seqlen = max(max_seqlen, request_input_length)
all_input_ids.append(self.all_input_ids[idx])
input_lengths.append(request_input_length)
prefix_offsets.append(self.prefix_offsets[idx])
read_offsets.append(self.read_offsets[idx])
stopping_criteria = self.stopping_criterias[idx]
stopping_criterias.append(stopping_criteria)
top_n_tokens.append(self.top_n_tokens[idx])
remaining_tokens = (
stopping_criteria.max_new_tokens - stopping_criteria.current_tokens
)
request_block_table = self.block_tables[idx]
blocks += len(request_block_table)
block_tables.append(request_block_table)
start_slots.append(cumulative_max_length)
# Copy to tensor (CPU)
slot_indices[i] = cumulative_max_length + request_input_length - 1
# Set slice
slot_filtering_indices[
self.start_slots[idx] : self.start_slots[idx]
+ request_input_length
+ remaining_tokens
- 1
] = True
cumulative_max_length += request_input_length + remaining_tokens - 1
max_blocks = max(max_blocks, len(request_block_table))
block_indices_to_free = []
# Iterate on all requests
for i, r in enumerate(self.requests):
# Filter requests that are not part of the new batch
if r.id not in requests_idx_mapping.keys():
block_indices_to_free.extend(self.block_tables[i])
# Free blocks
get_cache_manager().free(block_indices_to_free)
# Needed to avoid dropping blocks when the batches will go out of scope
self.block_tables = None
# Index into tensors
input_ids = self.input_ids[indices]
position_ids = self.position_ids[indices]
all_input_ids_tensor = self.all_input_ids_tensor[indices]
block_tables_tensor = self.block_tables_tensor[indices]
input_lengths_tensor = self.input_lengths_tensor[indices]
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)
# Move to GPU now that we have the whole tensor
slot_indices = slot_indices.to(device)
return type(self)(
batch_id=self.batch_id,
requests=requests,
requests_idx_mapping=requests_idx_mapping,
input_ids=input_ids,
position_ids=position_ids,
cu_seqlen_prefill=None,
start_slots=start_slots,
slot_indices=slot_indices,
needed_blocks_slots=None,
block_tables=block_tables,
block_tables_tensor=block_tables_tensor,
slots=slots,
max_seqlen=max_seqlen,
prefill_head_indices=None,
prefill_next_token_indices=None,
prefill_cu_outlens=None,
input_lengths=input_lengths,
input_lengths_tensor=input_lengths_tensor,
prefix_offsets=prefix_offsets,
read_offsets=read_offsets,
all_input_ids=all_input_ids,
all_input_ids_tensor=all_input_ids_tensor,
next_token_chooser=next_token_chooser,
stopping_criterias=stopping_criterias,
top_n_tokens=top_n_tokens,
top_n_tokens_tensor=top_n_tokens_tensor,
blocks=blocks,
max_blocks=max_blocks,
speculative_ids=speculative_ids,
)
@classmethod
@tracer.start_as_current_span("concatenate")
def concatenate(cls, batches: List["FlashCausalLMBatch"]) -> "FlashCausalLMBatch":
# Batch attributes
requests = []
requests_idx_mapping = {}
blocks = 0
total_batch_size = 0
total_slots = 0
max_blocks = 0
max_length = 0
max_seqlen = 0
for b in batches:
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(
max_length,
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
)
),
)
input_ids = batches[0].input_ids.new_empty(total_batch_size)
position_ids = batches[0].position_ids.new_empty(total_batch_size)
slots = batches[0].slots.new_empty(total_slots)
slot_indices = batches[0].slot_indices.new_empty(total_batch_size)
input_lengths_tensor = batches[0].input_lengths_tensor.new_empty(
total_batch_size
)
block_tables_tensor = batches[0].block_tables_tensor.new_zeros(
(total_batch_size, max_blocks)
)
all_input_ids_tensor = batches[0].all_input_ids_tensor.new_zeros(
(total_batch_size, max_length)
)
top_n_tokens_tensor = batches[0].top_n_tokens_tensor.new_zeros(
total_batch_size,
)
start_slots = []
block_tables = []
all_input_ids = []
input_lengths = []
prefix_offsets = []
read_offsets = []
next_token_chooser_parameters = []
stopping_criterias = []
top_n_tokens = []
# Cumulative length
cumulative_batch_size = 0
cumulative_slots = 0
for i, batch in enumerate(batches):
requests.extend(batch.requests)
if i == 0:
requests_idx_mapping = batch.requests_idx_mapping
else:
# We need to offset the mapping for each batch by the cumulative batch size
for k, v in batch.requests_idx_mapping.items():
requests_idx_mapping[k] = v + cumulative_batch_size
start_index = cumulative_batch_size
end_index = cumulative_batch_size + len(batch)
slots_start_index = cumulative_slots
slots_end_index = cumulative_slots + len(batch.slots)
# Copy tensors (GPU)
input_ids[start_index:end_index] = batch.input_ids
position_ids[start_index:end_index] = batch.position_ids
slot_indices[start_index:end_index] = batch.slot_indices + cumulative_slots
input_lengths_tensor[start_index:end_index] = batch.input_lengths_tensor
top_n_tokens_tensor[start_index:end_index] = batch.top_n_tokens_tensor
slots[slots_start_index:slots_end_index] = batch.slots
all_input_ids_tensor[
start_index:end_index, : batch.all_input_ids_tensor.shape[1]
] = batch.all_input_ids_tensor[:, :max_length]
block_tables_tensor[
start_index:end_index, : batch.block_tables_tensor.shape[1]
] = batch.block_tables_tensor[:, :max_blocks]
start_slots.append(batch.start_slots + cumulative_slots)
block_tables.extend(batch.block_tables)
all_input_ids.extend(batch.all_input_ids)
input_lengths.extend(batch.input_lengths)
prefix_offsets.extend(batch.prefix_offsets)
read_offsets.extend(batch.read_offsets)
next_token_chooser_parameters.extend([r.parameters for r in batch.requests])
stopping_criterias.extend(batch.stopping_criterias)
top_n_tokens.extend(batch.top_n_tokens)
# Update
cumulative_batch_size += len(batch)
cumulative_slots += len(batch.slots)
start_slots = torch.concat(start_slots)
next_token_chooser = HeterogeneousNextTokenChooser.from_pb(
next_token_chooser_parameters,
dtype=batches[0].next_token_chooser.dtype,
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
del b
return cls(
batch_id=batches[0].batch_id,
requests=requests,
requests_idx_mapping=requests_idx_mapping,
input_ids=input_ids,
position_ids=position_ids,
cu_seqlen_prefill=None,
start_slots=start_slots,
slot_indices=slot_indices,
needed_blocks_slots=None,
block_tables=block_tables,
block_tables_tensor=block_tables_tensor,
slots=slots,
max_seqlen=max_seqlen,
prefill_head_indices=None,
prefill_next_token_indices=None,
prefill_cu_outlens=None,
input_lengths=input_lengths,
input_lengths_tensor=input_lengths_tensor,
prefix_offsets=prefix_offsets,
read_offsets=read_offsets,
all_input_ids=all_input_ids,
all_input_ids_tensor=all_input_ids_tensor,
next_token_chooser=next_token_chooser,
stopping_criterias=stopping_criterias,
top_n_tokens=top_n_tokens,
top_n_tokens_tensor=top_n_tokens_tensor,
blocks=blocks,
max_blocks=max_blocks,
speculative_ids=speculative_ids,
)
def __del__(self):
if self.block_tables is not None and self.block_tables:
# Free blocks
get_cache_manager().free(
list(itertools.chain.from_iterable(self.block_tables))
)
def __len__(self):
return len(self.requests)
class FlashCausalLM(Model):
def __init__(
self,
model: torch.nn.Module,
tokenizer: PreTrainedTokenizerBase,
num_layers: int,
num_kv_heads: int,
head_size: int,
dtype: torch.dtype,
device: torch.device,
rank: int = 0,
world_size: int = 1,
sliding_window: Optional[int] = None,
):
self.num_layers = num_layers
self.num_kv_heads = num_kv_heads
self.head_size = head_size
super(FlashCausalLM, self).__init__(
model=model,
tokenizer=tokenizer,
requires_padding=False,
dtype=dtype,
device=device,
rank=rank,
world_size=world_size,
sliding_window=sliding_window,
)
@property
def batch_type(self) -> Type[FlashCausalLMBatch]:
return FlashCausalLMBatch
def warmup(self, batch: FlashCausalLMBatch):
torch.cuda.empty_cache()
try:
cache_manager = set_cache_manager(
batch.blocks,
self.num_layers,
self.num_kv_heads,
self.head_size,
self.sliding_window is not None,
self.dtype,
self.device,
)
_, batch, _ = self.generate_token(batch)
except torch.cuda.OutOfMemoryError as e:
raise RuntimeError(
f"Not enough memory to handle {len(batch.input_ids)} prefill tokens. "
f"You need to decrease `--max-batch-prefill-tokens`"
) from e
torch.cuda.synchronize(self.device)
# Inspired by the original implementation in [vllm](https://github.com/vllm-project/vllm)
# Calculate the number of blocks that can be allocated with the free memory
dtype_size = torch.tensor([], dtype=self.dtype).element_size()
cache_block_size = BLOCK_SIZE * self.num_kv_heads * self.head_size
total_cache_size = self.num_layers * cache_block_size * 2 * dtype_size
total_free_memory, _ = torch.cuda.mem_get_info(self.device)
total_gpu_memory = torch.cuda.get_device_properties(self.device).total_memory
free_memory = max(
0, total_free_memory - (1 - MEMORY_FRACTION) * total_gpu_memory
)
num_blocks = (
int(free_memory // total_cache_size)
# Add batch.blocks as we allocated it above, so it is included in the peak memory.
+ cache_manager.num_blocks
)
del batch
del cache_manager
set_cache_manager(
num_blocks,
self.num_layers,
self.num_kv_heads,
self.head_size,
self.sliding_window is not None,
self.dtype,
self.device,
)
return int(num_blocks * BLOCK_SIZE)
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=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")
def generate_token(
self, batch: FlashCausalLMBatch
) -> Tuple[List[Generation], Optional[FlashCausalLMBatch], Tuple[int, int]]:
start = time.time_ns()
prefill = batch.cu_seqlen_prefill is not None
prefill_logprobs = batch.prefill_next_token_indices is not None
if batch.needed_blocks_slots:
# Allocate blocks to this batch
block_tables, block_tables_tensor, slots = get_cache_manager().allocate(
batch.needed_blocks_slots,
batch.blocks,
batch.max_blocks,
batch.input_ids.device,
)
batch.needed_blocks_slots = None
batch.block_tables = block_tables
batch.block_tables_tensor = block_tables_tensor
batch.slots = slots
try:
out = self.forward(batch)
except Exception as e:
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,
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
# When batch == 1, we will just use the batch.input_ids values directly
prefill_tokens_indices = batch.input_ids.new_zeros(len(out))
next_position_ids = batch.position_ids.new_empty(len(batch))
batch.slot_indices = batch.slot_indices[batch.cu_seqlen_prefill[1:] - 1]
# We do not need cu_seqlen_prefill anymore
batch.cu_seqlen_prefill = None
else:
prefill_logprobs = None
next_position_ids = batch.position_ids
# Cumulative length
cumulative_length = 0
# Results
generations: List[Generation] = []
stopped = True
# Zipped iterator
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
# one, we need to first do a GPU <-> CPU sync
# 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
end_index = cumulative_length + input_length
if prefill:
# Indexing metadata
out_start_index = batch.prefill_cu_outlens[i]
out_end_index = batch.prefill_cu_outlens[i + 1]
out_length = out_end_index - out_start_index
# Initialize position_ids
# In decode, we do not need this as we can just increment position ids
next_position_ids[i] = batch.position_ids[end_index - 1]
# Used to gather prefill logprobs
# Copy batch.input_ids to prefill_token_indices
if prefill_logprobs:
if len(batch) > 1:
prefill_tokens_indices[
out_start_index : out_end_index - 1
] = batch.input_ids[start_index + 1 : start_index + out_length]
else:
# Set prefill_tokens_indices to the correct slice
prefill_tokens_indices = batch.input_ids[
start_index + 1 : start_index + out_length
]
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
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
prefill_logprobs_tensor = torch.log_softmax(out, -1)
prefill_logprobs = torch.gather(
prefill_logprobs_tensor, 1, prefill_tokens_indices.view(-1, 1)
)
# GPU <-> CPU sync
prefill_logprobs = prefill_logprobs.view(-1).tolist()
# GPU <-> CPU sync
next_token_logprobs = next_token_logprobs.tolist()
next_token_ids = next_input_ids.tolist()
accepted_ids = accepted_ids.tolist()
start_decode = time.time_ns()
# Zipped iterator
iterator = zip(
batch.requests,
batch.input_lengths,
batch.prefix_offsets,
batch.read_offsets,
batch.stopping_criterias,
batch.all_input_ids,
batch.next_token_chooser.do_sample,
batch.next_token_chooser.seeds,
batch.top_n_tokens,
accepted_ids,
batch_top_token_ids,
batch_top_token_logprobs,
)
# For each member of the batch
index = 0
for i, (
request,
input_length,
prefix_offset,
read_offset,
stopping_criteria,
all_input_ids,
do_sample,
seed,
top_n_tokens,
n_accepted_ids,
top_token_ids,
top_token_logprobs,
) in enumerate(iterator):
# Append next token to all tokens
next_token_texts = []
left = 0
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)
stop, reason = stopping_criteria(
next_token_id,
next_token_text,
)
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
if i % self.world_size == self.rank:
if stop:
# Decode generated tokens
output_text, _, _ = self.decode_token(
all_input_ids,
prefix_offset=len(all_input_ids)
- stopping_criteria.current_tokens
- 1,
read_offset=len(all_input_ids)
- stopping_criteria.current_tokens,
skip_special_tokens=True,
)
generated_text = GeneratedText(
output_text,
stopping_criteria.current_tokens,
reason,
seed if do_sample else None,
)
else:
generated_text = None
# Prefill
if prefill and request.prefill_logprobs:
out_start_index = batch.prefill_cu_outlens[i]
out_end_index = batch.prefill_cu_outlens[i + 1]
# Remove generated token to only have prefill and add nan for first prompt token
request_prefill_logprobs = [float("nan")] + prefill_logprobs[
out_start_index : out_end_index - 1
]
prefill_token_ids = all_input_ids[:-1]
prefill_texts = self.tokenizer.batch_decode(
prefill_token_ids,
clean_up_tokenization_spaces=False,
skip_special_tokens=False,
)
prefill_tokens = Tokens(
prefill_token_ids,
request_prefill_logprobs,
prefill_texts,
is_special=[],
)
else:
prefill_tokens = None
if top_n_tokens > 0:
toptoken_texts = self.tokenizer.batch_decode(
top_token_ids,
clean_up_tokenization_spaces=False,
skip_special_tokens=False,
)
special_toptokens = [
token_id in self.all_special_ids for token_id in top_token_ids
]
top_tokens = Tokens(
top_token_ids,
top_token_logprobs,
toptoken_texts,
special_toptokens,
)
else:
top_tokens = None
generation = Generation(
request.id,
prefill_tokens,
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,
)
generations.append(generation)
# Update values
batch.input_lengths[i] = input_length + n_accepted_ids
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
if stopped:
del batch
# No need to return a batch if we know that all requests stopped
forward_ns = start_decode - start
decode_ns = time.time_ns() - start_decode
return generations, None, (forward_ns, decode_ns)
batch.prefill_cu_outlens = None
batch.prefill_head_indices = None
batch.prefill_next_token_indices = None
forward_ns = start_decode - start
decode_ns = time.time_ns() - start_decode
return generations, batch, (forward_ns, decode_ns)
| 0 |
hf_public_repos/text-generation-inference/server/text_generation_server | hf_public_repos/text-generation-inference/server/text_generation_server/models/t5.py | import torch
import torch.distributed
from typing import List, Optional, Tuple
from transformers import (
AutoTokenizer,
AutoConfig,
)
from text_generation_server.models import Seq2SeqLM
from text_generation_server.models.custom_modeling.t5_modeling import (
T5ForConditionalGeneration,
)
from text_generation_server.utils import (
initialize_torch_distributed,
weight_files,
Weights,
)
class T5Sharded(Seq2SeqLM):
def __init__(
self,
model_id: str,
revision: Optional[str] = None,
quantize: Optional[str] = None,
dtype: Optional[torch.dtype] = None,
trust_remote_code: bool = False,
):
self.process_group, rank, world_size = initialize_torch_distributed()
if torch.cuda.is_available():
device = torch.device(f"cuda:{rank}")
dtype = torch.float16 if dtype is None else dtype
else:
device = torch.device("cpu")
dtype = torch.float32 if dtype is None else dtype
config = AutoConfig.from_pretrained(
model_id,
revision=revision,
trust_remote_code=trust_remote_code,
)
config.quantize = quantize
tokenizer = AutoTokenizer.from_pretrained(
model_id,
revision=revision,
padding_side="left",
truncation_side="left",
trust_remote_code=trust_remote_code,
)
tokenizer.bos_token_id = config.decoder_start_token_id
torch.distributed.barrier(group=self.process_group)
filenames = weight_files(model_id, revision=revision, extension=".safetensors")
weights = Weights(
filenames,
device=device,
dtype=dtype,
process_group=self.process_group,
aliases={
"shared.weight": [
"encoder.embed_tokens.weight",
"decoder.embed_tokens.weight",
]
},
)
model = T5ForConditionalGeneration(config, weights)
torch.distributed.barrier(group=self.process_group)
super(Seq2SeqLM, self).__init__(
model=model,
tokenizer=tokenizer,
requires_padding=True,
dtype=dtype,
device=device,
rank=rank,
world_size=world_size,
)
def forward(
self,
input_ids,
attention_mask,
decoder_input_ids,
decoder_attention_mask: Optional,
encoder_last_hidden_state: Optional,
past_key_values: Optional = None,
) -> Tuple[
torch.Tensor,
torch.Tensor,
List[Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]],
]:
# Model Forward
outputs = self.model.forward(
input_ids=input_ids,
attention_mask=attention_mask,
decoder_input_ids=decoder_input_ids,
decoder_attention_mask=decoder_attention_mask,
encoder_outputs=encoder_last_hidden_state,
past_key_values=past_key_values,
use_cache=True,
)
return (
outputs.logits,
outputs.encoder_last_hidden_state,
outputs.past_key_values,
)
| 0 |
hf_public_repos/text-generation-inference/server/text_generation_server | hf_public_repos/text-generation-inference/server/text_generation_server/models/seq2seq_lm.py | import torch
import time
from dataclasses import dataclass
from opentelemetry import trace
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, PreTrainedTokenizerBase
from typing import Optional, Tuple, List, Type, Dict
from text_generation_server.utils.tokens import batch_top_tokens
from text_generation_server.models import Model
from text_generation_server.models.types import (
GeneratedText,
Batch,
Generation,
Tokens,
)
from text_generation_server.pb import generate_pb2
from text_generation_server.utils import NextTokenChooser, StoppingCriteria, Sampling
tracer = trace.get_tracer(__name__)
@dataclass
class Seq2SeqLMBatch(Batch):
batch_id: int
requests: List[generate_pb2.Request]
requests_idx_mapping: Dict[int, int]
# Encoder values
input_ids: Optional[torch.Tensor]
attention_mask: torch.Tensor
# Decoder values
decoder_input_ids: torch.Tensor
decoder_attention_mask: Optional[torch.Tensor]
encoder_last_hidden_state: Optional[torch.Tensor]
# All tokens
all_decoder_input_ids: List[torch.Tensor]
# Seq2SeqLM keeps track of both encoder and decoder attention keys and values
past_key_values: Optional[List[Tuple]]
# Lengths of all generations present in the batch
input_lengths: List[int]
decoder_input_lengths: List[int]
prefix_offsets: List[int]
read_offsets: List[int]
# Generation helpers
next_token_choosers: List[NextTokenChooser]
stopping_criterias: List[StoppingCriteria]
top_n_tokens: List[int]
top_n_tokens_tensor: torch.Tensor
# Metadata used for padding
max_input_length: int
max_decoder_input_length: int
padding_right_offset: int
# Maximum number of tokens this batch will grow to
max_tokens: int
def to_pb(self) -> generate_pb2.CachedBatch:
"""Convert a Seq2SeqLMBatch to a text_generation_server.v1.CachedBatch protobuf"""
return generate_pb2.CachedBatch(
id=self.batch_id,
request_ids=[r.id for r in self.requests],
size=len(self),
max_tokens=self.max_tokens,
)
@classmethod
def from_pb(
cls,
pb: generate_pb2.Batch,
tokenizer: PreTrainedTokenizerBase,
dtype: torch.dtype,
device: torch.device,
) -> "Seq2SeqLMBatch":
"""Convert a text_generation_server.v1.Batch protobuf to a Seq2SeqLMBatch"""
inputs = []
next_token_choosers = []
stopping_criterias = []
top_n_tokens = []
decoder_input_lengths = []
prefix_offsets = []
read_offsets = []
requests_idx_mapping = {}
# Parse batch
max_truncation = 0
padding_right_offset = 0
max_decode_tokens = 0
for i, r in enumerate(pb.requests):
inputs.append(r.inputs)
requests_idx_mapping[r.id] = i
decoder_input_lengths.append(1)
next_token_choosers.append(NextTokenChooser.from_pb(r.parameters, device))
stopping_criteria = StoppingCriteria.from_pb(
r.stopping_parameters, tokenizer
)
stopping_criterias.append(stopping_criteria)
top_n_tokens.append(r.top_n_tokens)
max_truncation = max(max_truncation, r.truncate)
max_decode_tokens += stopping_criteria.max_new_tokens
padding_right_offset = max(
padding_right_offset, stopping_criteria.max_new_tokens
)
# Tokenize batch
tokenized_inputs = tokenizer(
inputs,
return_tensors="pt",
padding=True,
return_token_type_ids=False,
truncation=True,
max_length=max_truncation,
).to(device)
input_lengths = tokenized_inputs["attention_mask"].sum(1)
max_input_length = input_lengths.max()
# Decoder sequence only contains the bos_token
decoder_input_ids = (
torch.tensor(tokenizer.bos_token_id, device=device)
.repeat(len(pb.requests))
.view(-1, 1)
)
for _ in pb.requests:
prefix_offsets.append(0)
read_offsets.append(1)
all_decoder_input_ids = decoder_input_ids.view(-1).split(1)
top_n_tokens_tensor = torch.tensor(
top_n_tokens, device=device, dtype=torch.int64
)
max_tokens = len(inputs) * (max_input_length + max_decode_tokens)
return cls(
batch_id=pb.id,
requests=pb.requests,
requests_idx_mapping=requests_idx_mapping,
input_ids=tokenized_inputs["input_ids"],
attention_mask=tokenized_inputs["attention_mask"],
decoder_input_ids=decoder_input_ids,
all_decoder_input_ids=list(all_decoder_input_ids),
decoder_attention_mask=None,
encoder_last_hidden_state=None,
past_key_values=None,
input_lengths=input_lengths.tolist(),
decoder_input_lengths=decoder_input_lengths,
prefix_offsets=prefix_offsets,
read_offsets=read_offsets,
next_token_choosers=next_token_choosers,
stopping_criterias=stopping_criterias,
top_n_tokens=top_n_tokens,
top_n_tokens_tensor=top_n_tokens_tensor,
max_input_length=max_input_length.item(),
max_decoder_input_length=1,
padding_right_offset=padding_right_offset,
max_tokens=max_tokens,
)
@tracer.start_as_current_span("filter")
def filter(self, request_ids: List[int]) -> Optional["Seq2SeqLMBatch"]:
if len(request_ids) == 0:
raise ValueError("Batch must have at least one request")
if len(request_ids) == len(self):
return self
keep_indices = []
# New values after filtering
requests_idx_mapping = {}
requests = []
input_lengths = []
decoder_input_lengths = []
prefix_offsets = []
read_offsets = []
all_decoder_input_ids = []
next_token_choosers = []
stopping_criterias = []
top_n_tokens = []
max_input_length = 0
max_decoder_input_length = 0
padding_right_offset = 0
total_remaining_decode_tokens = 0
for i, request_id in enumerate(request_ids):
idx = self.requests_idx_mapping[request_id]
requests_idx_mapping[request_id] = i
keep_indices.append(idx)
requests.append(self.requests[idx])
prefix_offsets.append(self.prefix_offsets[idx])
read_offsets.append(self.read_offsets[idx])
all_decoder_input_ids.append(self.all_decoder_input_ids[idx])
request_input_length = self.input_lengths[idx]
input_lengths.append(request_input_length)
max_input_length = max(max_input_length, request_input_length)
request_decoder_input_length = self.decoder_input_lengths[idx]
decoder_input_lengths.append(request_decoder_input_length)
max_decoder_input_length = max(
max_decoder_input_length, request_decoder_input_length
)
next_token_choosers.append(self.next_token_choosers[idx])
stopping_criteria = self.stopping_criterias[idx]
stopping_criterias.append(stopping_criteria)
top_n_tokens.append(self.top_n_tokens[idx])
remaining_decode_tokens = (
stopping_criteria.max_new_tokens - stopping_criteria.current_tokens
)
total_remaining_decode_tokens += remaining_decode_tokens
padding_right_offset = max(padding_right_offset, remaining_decode_tokens)
# Apply indices to input_ids, attention mask, past key values and other items that need to be cached
self.decoder_input_ids = self.decoder_input_ids[keep_indices]
self.attention_mask = self.attention_mask[keep_indices, -max_input_length:]
if self.decoder_attention_mask is not None:
self.decoder_attention_mask = self.decoder_attention_mask[
keep_indices,
-(self.padding_right_offset + max_decoder_input_length) : (
self.decoder_attention_mask.shape[1] - self.padding_right_offset
)
+ padding_right_offset,
]
self.encoder_last_hidden_state = self.encoder_last_hidden_state[
keep_indices, -max_input_length:
]
# Ensure that past_key_values tensors can be updated in-place
if type(self.past_key_values[0]) == tuple:
self.past_key_values = [
[t for t in layer] for layer in self.past_key_values
]
decoder_past_seq_len = max_decoder_input_length - 1
for layer in self.past_key_values:
layer[0] = layer[0][keep_indices, :, -decoder_past_seq_len:]
layer[1] = layer[1][keep_indices, :, -decoder_past_seq_len:]
layer[2] = layer[2][keep_indices, :, -max_input_length:]
layer[3] = layer[3][keep_indices, :, -max_input_length:]
top_n_tokens_tensor = self.top_n_tokens_tensor[keep_indices]
max_tokens = (
len(request_ids) * (max_input_length + max_decoder_input_length)
+ remaining_decode_tokens
)
self.requests = requests
self.requests_idx_mapping = requests_idx_mapping
self.input_ids = None
self.all_decoder_input_ids = all_decoder_input_ids
self.input_lengths = input_lengths
self.decoder_input_lengths = decoder_input_lengths
self.prefix_offsets = prefix_offsets
self.read_offsets = read_offsets
self.next_token_choosers = next_token_choosers
self.stopping_criterias = stopping_criterias
self.top_n_tokens = top_n_tokens
self.top_n_tokens_tensor = top_n_tokens_tensor
self.max_input_length = max_input_length
self.max_decoder_input_length = max_decoder_input_length
self.padding_right_offset = padding_right_offset
self.max_tokens = max_tokens
return self
@classmethod
@tracer.start_as_current_span("concatenate")
def concatenate(cls, batches: List["Seq2SeqLMBatch"]) -> "Seq2SeqLMBatch":
"""Concatenate multiple batches together by padding internal torch tensors"""
# Used for padding
total_batch_size = 0
max_input_length = 0
max_decoder_input_length = 0
padding_right_offset = 0
for batch in batches:
total_batch_size += len(batch)
max_input_length = max(max_input_length, batch.max_input_length)
max_decoder_input_length = max(
max_decoder_input_length, batch.max_decoder_input_length
)
padding_right_offset = max(padding_right_offset, batch.padding_right_offset)
# Batch attributes
requests = []
requests_idx_mapping = {}
all_decoder_input_ids = []
input_lengths = []
decoder_input_lengths = []
prefix_offsets = []
read_offsets = []
next_token_choosers = []
stopping_criterias = []
top_n_tokens = []
max_tokens = 0
# Batch tensors
attention_mask = None
decoder_input_ids = None
decoder_attention_mask = None
encoder_last_hidden_state = None
top_n_tokens_tensor = None
past_key_values = []
# Used for slicing correctly inside the tensors
# Equivalent to a cumsum on batch sizes
start_index = 0
for i, batch in enumerate(batches):
# Extend all list attributes
requests.extend(batch.requests)
all_decoder_input_ids.extend(batch.all_decoder_input_ids)
input_lengths.extend(batch.input_lengths)
decoder_input_lengths.extend(batch.decoder_input_lengths)
prefix_offsets.extend(batch.prefix_offsets)
read_offsets.extend(batch.read_offsets)
next_token_choosers.extend(batch.next_token_choosers)
stopping_criterias.extend(batch.stopping_criterias)
top_n_tokens.extend(batch.top_n_tokens)
if i == 0:
requests_idx_mapping = batch.requests_idx_mapping
else:
# We need to offset the mapping for each batch by the cumulative batch size
for k, v in batch.requests_idx_mapping.items():
requests_idx_mapping[k] = v + start_index
# Slicing end index for this batch
end_index = start_index + len(batch)
# We only concatenate batches that did at least one step
if batch.encoder_last_hidden_state is None:
raise ValueError("Batch encoder_last_hidden_state cannot be None")
# Create padded tensor
if attention_mask is None:
attention_mask = batch.attention_mask.new_zeros(
(total_batch_size, max_input_length),
)
# Copy to correct indices
attention_mask[
start_index:end_index, -batch.max_input_length :
] = batch.attention_mask[:, -batch.max_input_length :]
# Create padded tensor
if decoder_input_ids is None:
decoder_input_ids = batch.decoder_input_ids.new_zeros(
(total_batch_size, 1),
)
# Copy to correct indices
decoder_input_ids[start_index:end_index] = batch.decoder_input_ids
# Create padded tensor
if decoder_attention_mask is None:
# As decoder_attention_mask might not exist, we use `batch.attention_mask` for device here
decoder_attention_mask = batch.attention_mask.new_zeros(
(total_batch_size, max_decoder_input_length + padding_right_offset),
)
# If the decoder mask does not exist yet, all generations started at the same time and we never concatenated
# this batch. All generations are of length `batch.max_decoder_input_length`.
left_offset = max_decoder_input_length - batch.max_decoder_input_length
if batch.decoder_attention_mask is None:
decoder_attention_mask[
start_index:end_index,
left_offset:-padding_right_offset,
] = 1
# If it exists, we need to index
else:
batch_left_offset = (
batch.decoder_attention_mask.shape[1]
- batch.max_decoder_input_length
- batch.padding_right_offset
)
decoder_attention_mask[
start_index:end_index,
left_offset:-padding_right_offset,
] = batch.decoder_attention_mask[
:,
batch_left_offset : -batch.padding_right_offset,
]
# Create padded tensor
if encoder_last_hidden_state is None:
encoder_last_hidden_state = batch.encoder_last_hidden_state.new_zeros(
(
total_batch_size,
max_input_length,
batch.encoder_last_hidden_state.shape[-1],
),
)
if top_n_tokens_tensor is None:
top_n_tokens_tensor = batches[0].top_n_tokens_tensor.new_zeros(
total_batch_size,
)
top_n_tokens_tensor[start_index:end_index] = batch.top_n_tokens_tensor
# Copy to correct indices
encoder_last_hidden_state[
start_index:end_index, -batch.max_input_length :, :
] = batch.encoder_last_hidden_state[:, -batch.max_input_length :, :]
batch.encoder_last_hidden_state = None
# Ensure that we can update tensors in-place
if type(batch.past_key_values[0]) == tuple:
batch.past_key_values = [
[t for t in layer] for layer in batch.past_key_values
]
# Add eventual padding tokens that were added while concatenating
max_tokens += batch.max_tokens + (
max_input_length
- batch.max_input_length
+ max_decoder_input_length
- batch.max_decoder_input_length
) * len(batch)
start_index = end_index
# Determine shapes for new past kv tensors
first_past_kvs = batches[0].past_key_values
_, num_heads, _, head_dim = first_past_kvs[0][0].shape
padded_dec_t_shape = (
total_batch_size,
num_heads,
(max_decoder_input_length - 1),
head_dim,
)
padded_enc_t_shape = (
total_batch_size,
num_heads,
max_input_length,
head_dim,
)
# Iterate over attention layers
for j in range(len(first_past_kvs)):
past_key_values.append([])
# Decoder past
for k in range(0, 2):
# Initialize tensors
padded_past_values = first_past_kvs[j][k].new_zeros(padded_dec_t_shape)
past_key_values[j].append(padded_past_values)
start_index = 0
for batch in batches:
t = batch.past_key_values[j][k]
# Clear reference to the original tensor
batch.past_key_values[j][k] = None
# Slicing end index for this batch
end_index = start_index + len(batch)
# We slice the past keys and values to remove the padding from previous batches
past_seq_len = batch.max_decoder_input_length - 1
padded_past_values[start_index:end_index, :, -past_seq_len:, :] = t[
:, :, -past_seq_len:, :
]
del t
start_index = end_index
# Encoder past
for k in range(2, 4):
# Initialize tensors
padded_past_values = first_past_kvs[j][k].new_zeros(padded_enc_t_shape)
past_key_values[j].append(padded_past_values)
start_index = 0
for batch in batches:
t = batch.past_key_values[j][k]
# Clear reference to the original tensor
batch.past_key_values[j][k] = None
# Slicing end index for this batch
end_index = start_index + len(batch)
# We slice the past keys and values to remove the padding from previous batches
padded_past_values[
start_index:end_index, :, -batch.max_input_length :, :
] = t[:, :, -batch.max_input_length :, :]
del t
start_index = end_index
return cls(
batch_id=batches[0].batch_id,
requests=requests,
requests_idx_mapping=requests_idx_mapping,
input_ids=None,
attention_mask=attention_mask,
decoder_input_ids=decoder_input_ids,
all_decoder_input_ids=all_decoder_input_ids,
decoder_attention_mask=decoder_attention_mask,
encoder_last_hidden_state=encoder_last_hidden_state,
past_key_values=past_key_values,
input_lengths=input_lengths,
decoder_input_lengths=decoder_input_lengths,
prefix_offsets=prefix_offsets,
read_offsets=read_offsets,
next_token_choosers=next_token_choosers,
stopping_criterias=stopping_criterias,
top_n_tokens=top_n_tokens,
top_n_tokens_tensor=top_n_tokens_tensor,
max_input_length=max_input_length,
max_decoder_input_length=max_decoder_input_length,
padding_right_offset=padding_right_offset,
max_tokens=max_tokens,
)
def __len__(self):
return len(self.requests)
class Seq2SeqLM(Model):
def __init__(
self,
model_id: str,
revision: Optional[str] = None,
quantize: Optional[str] = None,
dtype: Optional[torch.dtype] = None,
trust_remote_code: bool = False,
):
if torch.cuda.is_available():
device = torch.device("cuda")
dtype = torch.float16 if dtype is None else dtype
else:
if quantize:
raise ValueError("quantization is not available on CPU")
device = torch.device("cpu")
dtype = torch.float32 if dtype is None else dtype
model = AutoModelForSeq2SeqLM.from_pretrained(
model_id,
revision=revision,
torch_dtype=dtype,
device_map="auto"
if torch.cuda.is_available() and torch.cuda.device_count() > 1
else None,
load_in_8bit=quantize == "bitsandbytes",
trust_remote_code=trust_remote_code,
)
if torch.cuda.is_available() and torch.cuda.device_count() == 1:
model = model.cuda()
tokenizer = AutoTokenizer.from_pretrained(
model_id,
revision=revision,
padding_side="left",
truncation_side="left",
trust_remote_code=trust_remote_code,
)
tokenizer.bos_token_id = model.config.decoder_start_token_id
super(Seq2SeqLM, self).__init__(
model=model,
tokenizer=tokenizer,
requires_padding=True,
dtype=dtype,
device=device,
)
@property
def batch_type(self) -> Type[Seq2SeqLMBatch]:
return Seq2SeqLMBatch
def decode(self, decoder_ids: List[int]) -> str:
return self.tokenizer.decode(
decoder_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
def forward(
self,
input_ids,
attention_mask,
decoder_input_ids,
decoder_attention_mask: Optional,
encoder_last_hidden_state: Optional,
past_key_values: Optional = None,
) -> Tuple[
torch.Tensor,
torch.Tensor,
List[Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]],
]:
# Model Forward
outputs = self.model.forward(
input_ids=input_ids,
attention_mask=attention_mask,
decoder_input_ids=decoder_input_ids,
decoder_attention_mask=decoder_attention_mask,
encoder_outputs=encoder_last_hidden_state,
past_key_values=past_key_values,
use_cache=True,
)
return (
outputs.logits,
outputs.encoder_last_hidden_state,
outputs.past_key_values,
)
@tracer.start_as_current_span("generate_token")
def generate_token(
self, batch: Seq2SeqLMBatch
) -> Tuple[List[Generation], Optional[Seq2SeqLMBatch], Tuple[int, int]]:
start = time.time_ns()
if batch.decoder_attention_mask is not None:
# slice to the correct shape
decoder_attention_mask = batch.decoder_attention_mask[
:, : -batch.padding_right_offset
]
else:
decoder_attention_mask = None
# Wrap `encoder_last_hidden_state` because for some reason, Transformers does a `encoder_last_hidden_state[0]`
# internally...
if batch.encoder_last_hidden_state is not None:
encoder_last_hidden_state = [batch.encoder_last_hidden_state]
else:
encoder_last_hidden_state = None
logits, encoder_last_hidden_state, past = self.forward(
batch.input_ids,
batch.attention_mask,
batch.decoder_input_ids,
decoder_attention_mask,
encoder_last_hidden_state,
batch.past_key_values,
)
batch_top_token_ids, batch_top_token_logprobs = batch_top_tokens(
batch.top_n_tokens,
batch.top_n_tokens_tensor,
torch.log_softmax(logits[:, -1], -1),
)
start_decode = time.time_ns()
# Finished requests
generations: List[Generation] = []
stopped = True
# Zipped iterator
iterator = zip(
batch.requests,
batch.input_lengths,
batch.prefix_offsets,
batch.read_offsets,
batch.decoder_input_lengths,
logits,
batch.next_token_choosers,
batch.stopping_criterias,
batch.all_decoder_input_ids,
batch.top_n_tokens,
batch_top_token_ids,
batch_top_token_logprobs,
)
# For each member of the batch
for i, (
request,
input_length,
prefix_offset,
read_offset,
decoder_input_length,
logits,
next_token_chooser,
stopping_criteria,
all_decoder_input_ids,
top_n_tokens,
top_token_ids,
top_token_logprobs,
) in enumerate(iterator):
# Select next token
next_token_id, logprobs = next_token_chooser(
all_decoder_input_ids.view(1, -1), logits[-1:, :]
)
# Append next token to decoder tokens
all_decoder_input_ids = torch.cat(
[all_decoder_input_ids, next_token_id.squeeze(1)]
)
new_decoder_input_length = decoder_input_length + 1
# Generated token
next_token_logprob = logprobs[-1, next_token_id]
next_token_id_squeezed = next_token_id.squeeze()
next_token_text, prefix_offset, read_offset = self.decode_token(
all_decoder_input_ids, prefix_offset, read_offset
)
# Evaluate stopping criteria
stop, reason = stopping_criteria(next_token_id, next_token_text)
if not stop:
stopped = False
# Shard generations
# All generations will be appended in the rust sharded client
if i % self.world_size == self.rank:
if stop:
# Slice with decoder_input_length to remove padding
# Decode all tokens
output_text, _, _ = self.decode_token(
all_decoder_input_ids,
prefix_offset=len(all_decoder_input_ids)
- decoder_input_length
- 1,
read_offset=len(all_decoder_input_ids) - decoder_input_length,
skip_special_tokens=True,
)
# Get seed
if isinstance(next_token_chooser.choice, Sampling):
seed = next_token_chooser.choice.seed
else:
seed = None
generated_text = GeneratedText(
output_text, stopping_criteria.current_tokens, reason, seed
)
else:
generated_text = None
# Prefill
if stopping_criteria.current_tokens == 1 and request.prefill_logprobs:
prefill_tokens = Tokens(
[self.tokenizer.bos_token_id],
[float("nan")],
[self.tokenizer.bos_token],
[False],
)
else:
prefill_tokens = None
if top_n_tokens > 0:
toptoken_texts = self.tokenizer.batch_decode(
top_token_ids,
clean_up_tokenization_spaces=False,
skip_special_tokens=False,
)
special_toptokens = [
token_id in self.all_special_ids for token_id in top_token_ids
]
top_tokens = Tokens(
top_token_ids,
top_token_logprobs,
toptoken_texts,
special_toptokens,
)
else:
top_tokens = None
generation = Generation(
request.id,
prefill_tokens,
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,
)
generations.append(generation)
# Update values
batch.decoder_input_ids[i] = next_token_id
batch.all_decoder_input_ids[i] = all_decoder_input_ids
batch.input_lengths[i] = input_length
batch.decoder_input_lengths[i] = new_decoder_input_length
batch.prefix_offsets[i] = prefix_offset
batch.read_offsets[i] = read_offset
batch.max_input_length = max(batch.max_input_length, input_length)
batch.max_decoder_input_length = max(
batch.max_decoder_input_length, new_decoder_input_length
)
# We finished all generations in the batch; there is no next batch
if stopped:
forward_ns = start_decode - start
decode_ns = time.time_ns() - start_decode
return generations, None, (forward_ns, decode_ns)
# We don't need input_ids after the prefill forward
batch.input_ids = None
batch.encoder_last_hidden_state = encoder_last_hidden_state
batch.past_key_values = past
# Update decoder_attention_mask as we added a new token to input_ids
if batch.decoder_attention_mask is not None:
batch.decoder_attention_mask[:, -batch.padding_right_offset] = 1
batch.padding_right_offset -= 1
forward_ns = start_decode - start
decode_ns = time.time_ns() - start_decode
return generations, batch, (forward_ns, decode_ns)
| 0 |
hf_public_repos/text-generation-inference/server/text_generation_server | hf_public_repos/text-generation-inference/server/text_generation_server/models/santacoder.py | import torch
import torch.distributed
from typing import Optional, List
from transformers import AutoTokenizer, AutoModelForCausalLM
from text_generation_server.models import CausalLM
FIM_PREFIX = "<fim-prefix>"
FIM_MIDDLE = "<fim-middle>"
FIM_SUFFIX = "<fim-suffix>"
FIM_PAD = "<fim-pad>"
EOD = "<|endoftext|>"
class SantaCoder(CausalLM):
def __init__(
self,
model_id: str,
revision: Optional[str] = None,
quantize: Optional[str] = None,
dtype: Optional[torch.dtype] = None,
trust_remote_code: bool = False,
):
if torch.cuda.is_available():
device = torch.device("cuda")
dtype = torch.float16 if dtype is None else dtype
else:
if quantize:
raise ValueError("quantization is not available on CPU")
device = torch.device("cpu")
dtype = torch.float32 if dtype is None else dtype
tokenizer = AutoTokenizer.from_pretrained(
model_id,
revision=revision,
padding_side="left",
truncation_side="left",
trust_remote_code=trust_remote_code,
)
tokenizer.add_special_tokens(
{
"additional_special_tokens": [
EOD,
FIM_PREFIX,
FIM_MIDDLE,
FIM_SUFFIX,
FIM_PAD,
],
"pad_token": EOD,
}
)
with device:
model = AutoModelForCausalLM.from_pretrained(
model_id,
revision=revision,
torch_dtype=dtype,
load_in_8bit=quantize == "bitsandbytes",
trust_remote_code=trust_remote_code,
)
super(CausalLM, self).__init__(
model=model,
tokenizer=tokenizer,
requires_padding=True,
dtype=dtype,
device=device,
)
def decode(self, generated_ids: List[int]) -> str:
# Do not skip special tokens as they are used for custom parsing rules of the generated text
return self.tokenizer.decode(
generated_ids, skip_special_tokens=False, clean_up_tokenization_spaces=False
)
| 0 |
hf_public_repos/text-generation-inference/server/text_generation_server | hf_public_repos/text-generation-inference/server/text_generation_server/models/flash_santacoder.py | import torch
import torch.distributed
from opentelemetry import trace
from transformers import AutoTokenizer, AutoConfig
from typing import Optional, List
import json
import os
from huggingface_hub import hf_hub_download
from text_generation_server.models import FlashCausalLM
from text_generation_server.models.custom_modeling.flash_santacoder_modeling import (
FlashSantacoderForCausalLM,
)
from text_generation_server.utils import (
initialize_torch_distributed,
weight_files,
Weights,
)
tracer = trace.get_tracer(__name__)
class FlashSantacoderSharded(FlashCausalLM):
def __init__(
self,
model_id: str,
revision: Optional[str] = None,
quantize: Optional[str] = None,
dtype: Optional[torch.dtype] = None,
trust_remote_code: bool = False,
):
self.process_group, rank, world_size = initialize_torch_distributed()
if torch.cuda.is_available():
device = torch.device(f"cuda:{rank}")
dtype = torch.float16 if dtype is None else dtype
else:
raise NotImplementedError("FlashSantacoderSharded is only available on GPU")
tokenizer = AutoTokenizer.from_pretrained(
model_id,
revision=revision,
padding_side="left",
truncation_side="left",
trust_remote_code=trust_remote_code,
)
config = AutoConfig.from_pretrained(
model_id,
revision=revision,
trust_remote_code=True,
)
config.quantize = quantize
config.transpose = config.architectures[0].startswith("GPT2")
torch.distributed.barrier(group=self.process_group)
filenames = weight_files(model_id, revision=revision, extension=".safetensors")
weights = Weights(
filenames,
device=device,
dtype=dtype,
process_group=self.process_group,
aliases={"transformer.wte.weight": ["lm_head.weight"]},
)
if config.quantize == "gptq":
weights._set_gptq_params(model_id, revision)
model = FlashSantacoderForCausalLM(config, weights)
torch.distributed.barrier(group=self.process_group)
super(FlashSantacoderSharded, self).__init__(
model=model.to(device),
tokenizer=tokenizer,
num_layers=len(model.transformer.h),
num_kv_heads=1,
head_size=model.transformer.head_size,
dtype=dtype,
device=device,
rank=rank,
world_size=world_size,
)
def decode(self, generated_ids: List[int]) -> str:
# Do not skip special tokens as they are used for custom parsing rules of the generated text
return self.tokenizer.decode(
generated_ids, skip_special_tokens=False, clean_up_tokenization_spaces=False
)
| 0 |
hf_public_repos/text-generation-inference/server/text_generation_server | hf_public_repos/text-generation-inference/server/text_generation_server/models/galactica.py | import re
import torch
import torch.distributed
from typing import List, Optional, Type
from transformers import (
AutoTokenizer,
AutoConfig,
PreTrainedTokenizerBase,
)
from text_generation_server.models import CausalLM
from text_generation_server.models.causal_lm import CausalLMBatch
from text_generation_server.pb import generate_pb2
from text_generation_server.models.custom_modeling.opt_modeling import OPTForCausalLM
from text_generation_server.utils import (
NextTokenChooser,
StoppingCriteria,
initialize_torch_distributed,
weight_files,
Weights,
)
# CREDIT: Papers with code => https://github.com/paperswithcode/galai/blob/main/galai/utils.py
# we split individual characters inside special tokens like [START_DNA]
CUSTOM_SEQ_RE = re.compile(r"(\[START_(DNA|SMILES|I_SMILES|AMINO)])(.*?)(\[END_\2])")
# token added to implement a custom sequence tokenization. This token is added at
# corpus cleaning step and removed in pretokenization. The digits are added to increase the chance
# that they do not occur in the corpus. The digits are escaped so that the token does not appear
# literally in the source code in case we ever include it in the training data.
SPLIT_MARKER = f"SPL{1}T-TH{1}S-Pl3A5E"
def _insert_split_marker(m: re.Match):
"""
Applies split marker based on a regex match of special tokens such as
[START_DNA].
Parameters
----------
n : str
Input text to split
Returns
----------
str - the text with the split token added
"""
start_token, _, sequence, end_token = m.groups()
sequence = re.sub(r"(.)", rf"{SPLIT_MARKER}\1", sequence, flags=re.DOTALL)
return f"{start_token}{sequence}{SPLIT_MARKER}{end_token}"
def escape_custom_split_sequence(text):
"""
Applies custom splitting to the text for GALILEO's tokenization
Parameters
----------
text : str
Input text to split
Returns
----------
str - the text with the split token added
"""
return CUSTOM_SEQ_RE.sub(_insert_split_marker, text)
# END CREDIT
class GalacticaCausalLMBatch(CausalLMBatch):
@classmethod
def from_pb(
cls,
pb: generate_pb2.Batch,
tokenizer: PreTrainedTokenizerBase,
dtype: torch.dtype,
device: torch.device,
) -> "GalacticaCausalLMBatch":
inputs = []
next_token_choosers = []
stopping_criterias = []
prefix_offsets = []
top_n_tokens = []
read_offsets = []
requests_idx_mapping = {}
# Parse batch
max_truncation = 0
padding_right_offset = 0
max_decode_tokens = 0
for i, r in enumerate(pb.requests):
requests_idx_mapping[r.id] = i
# Add escape_custom_split_sequence to the CausalLMBatch logic
inputs.append(escape_custom_split_sequence(r.inputs))
next_token_choosers.append(NextTokenChooser.from_pb(r.parameters, device))
stopping_criteria = StoppingCriteria.from_pb(
r.stopping_parameters, tokenizer
)
stopping_criterias.append(stopping_criteria)
top_n_tokens.append(r.top_n_tokens)
max_truncation = max(max_truncation, r.truncate)
max_decode_tokens += stopping_criteria.max_new_tokens
padding_right_offset = max(
padding_right_offset, stopping_criteria.max_new_tokens
)
tokenized_inputs = tokenizer(
inputs,
return_tensors="pt",
padding=True,
return_token_type_ids=False,
truncation=True,
max_length=max_truncation,
).to(device)
for _ in pb.requests:
input_len = tokenized_inputs["input_ids"].shape[1]
prefix_offsets.append(0)
read_offsets.append(input_len)
input_lengths = tokenized_inputs["attention_mask"].sum(1)
max_input_length = input_lengths.max()
input_ids = tokenized_inputs["input_ids"]
# Allocate maximum attention_mask
attention_mask = input_ids.new_zeros(
(pb.size, max_input_length + padding_right_offset)
)
# Copy tokenizer attention_mask into fully allocated attention_mask
attention_mask[:, :max_input_length] = tokenized_inputs["attention_mask"]
position_ids = tokenized_inputs["attention_mask"].long().cumsum(-1) - 1
position_ids.masked_fill_(tokenized_inputs["attention_mask"] == 0, 1)
all_input_ids = tokenized_inputs["input_ids"].T.split(1, dim=1)
top_n_tokens_tensor = torch.tensor(
top_n_tokens, device=device, dtype=torch.int64
)
max_tokens = len(inputs) * max_input_length + max_decode_tokens
return cls(
batch_id=pb.id,
requests=pb.requests,
requests_idx_mapping=requests_idx_mapping,
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=None,
all_input_ids=list(all_input_ids),
input_lengths=input_lengths.tolist(),
prefix_offsets=prefix_offsets,
read_offsets=read_offsets,
next_token_choosers=next_token_choosers,
stopping_criterias=stopping_criterias,
top_n_tokens=top_n_tokens,
top_n_tokens_tensor=top_n_tokens_tensor,
max_input_length=max_input_length.item(),
padding_right_offset=padding_right_offset,
max_tokens=max_tokens,
)
class GalacticaSharded(CausalLM):
def __init__(
self,
model_id: str,
revision: Optional[str] = None,
quantize: Optional[str] = None,
dtype: Optional[torch.dtype] = None,
trust_remote_code: bool = False,
):
self.process_group, rank, world_size = initialize_torch_distributed()
if torch.cuda.is_available():
device = torch.device(f"cuda:{rank}")
dtype = torch.float16 if dtype is None else dtype
else:
device = torch.device("cpu")
dtype = torch.float32 if dtype is None else dtype
tokenizer = AutoTokenizer.from_pretrained(
model_id,
revision=revision,
padding_side="left",
truncation_side="left",
trust_remote_code=trust_remote_code,
)
config = AutoConfig.from_pretrained(
model_id,
revision=revision,
tp_parallel=True,
trust_remote_code=trust_remote_code,
)
config.quantize = quantize
tokenizer.pad_token_id = config.pad_token_id
torch.distributed.barrier(group=self.process_group)
filenames = weight_files(model_id, revision=revision, extension=".safetensors")
weights = Weights(
filenames, device=device, dtype=dtype, process_group=self.process_group
)
if config.quantize == "gptq":
weights._set_gptq_params(model_id, revision)
model = OPTForCausalLM(config, weights)
torch.distributed.barrier(group=self.process_group)
super(CausalLM, self).__init__(
model=model,
tokenizer=tokenizer,
requires_padding=True,
dtype=dtype,
device=device,
rank=rank,
world_size=world_size,
)
@property
def batch_type(self) -> Type[CausalLMBatch]:
return GalacticaCausalLMBatch
def decode(self, generated_ids: List[int]) -> str:
# Do not skip special tokens as they are used for custom parsing rules of the generated text
return self.tokenizer.decode(
generated_ids, skip_special_tokens=False, clean_up_tokenization_spaces=False
)
def forward(
self, input_ids, attention_mask, position_ids, past_key_values: Optional = None
):
outputs = self.model.forward(
input_ids=input_ids,
attention_mask=attention_mask,
past_key_values=past_key_values,
use_cache=True,
)
return outputs.logits, outputs.past_key_values
| 0 |
hf_public_repos/text-generation-inference/server/text_generation_server | hf_public_repos/text-generation-inference/server/text_generation_server/models/types.py | from functools import total_ordering
import torch
from abc import ABC, abstractmethod
from dataclasses import dataclass
from typing import List, Optional
from transformers import PreTrainedTokenizerBase
from text_generation_server.pb import generate_pb2
from text_generation_server.pb.generate_pb2 import FinishReason
class Batch(ABC):
@abstractmethod
def to_pb(self) -> generate_pb2.CachedBatch:
raise NotImplementedError
@classmethod
@abstractmethod
def from_pb(
cls,
pb: generate_pb2.Batch,
tokenizer: PreTrainedTokenizerBase,
dtype: torch.dtype,
device: torch.device,
) -> "Batch":
raise NotImplementedError
@abstractmethod
def filter(self, request_ids: List[int]) -> "Batch":
raise NotImplementedError
@classmethod
@abstractmethod
def concatenate(cls, batches: List["Batch"]) -> "Batch":
raise NotImplementedError
@abstractmethod
def __len__(self):
raise NotImplementedError
@dataclass
class GeneratedText:
text: str
generated_tokens: int
finish_reason: FinishReason
seed: Optional[int]
def to_pb(self) -> generate_pb2.GeneratedText:
return generate_pb2.GeneratedText(
text=self.text,
generated_tokens=self.generated_tokens,
finish_reason=self.finish_reason,
seed=self.seed,
)
@dataclass
class Tokens:
token_ids: List[int]
logprobs: List[float]
texts: List[str]
is_special: List[bool]
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):
return len(self.token_ids)
@dataclass
class Generation:
request_id: int
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[List[Tokens]]
def to_pb(self) -> generate_pb2.Generation:
return generate_pb2.Generation(
request_id=self.request_id,
prefill_tokens=self.prefill_tokens.to_pb()
if self.prefill_tokens is not None
else None,
tokens=self.tokens.to_pb(),
generated_text=self.generated_text.to_pb()
if self.generated_text is not None
else None,
top_tokens=self.top_tokens.to_pb() if self.top_tokens is not None else None,
)
| 0 |
hf_public_repos/text-generation-inference/server/text_generation_server | hf_public_repos/text-generation-inference/server/text_generation_server/models/idefics_causal_lm.py | import torch
import time
from dataclasses import dataclass
from opentelemetry import trace
from transformers import (
AutoProcessor,
AutoTokenizer,
PreTrainedTokenizerBase,
ProcessorMixin,
)
from typing import Optional, Tuple, List, Type, Dict
from text_generation_server.models import Model
from text_generation_server.models.types import (
Batch,
Tokens,
Generation,
GeneratedText,
)
from text_generation_server.pb import generate_pb2
from text_generation_server.utils import NextTokenChooser, StoppingCriteria, Sampling
import re
IMAGES = re.compile(r"!\[[^\]]*\]\((.*?)\s*(\"(?:.*[^\"])\")?\s*\)")
def split(string):
parts = []
cursor = 0
for pattern in IMAGES.finditer(string):
start = pattern.start()
if start != cursor:
parts.append(string[cursor:start])
parts.append(pattern.group(1))
cursor = pattern.end()
if cursor != len(string):
parts.append(string[cursor:])
return parts
tracer = trace.get_tracer(__name__)
@dataclass
class IdeficsCausalLMBatch(Batch):
batch_id: int
requests: List[generate_pb2.Request]
requests_idx_mapping: Dict[int, int]
# Decoder values
input_ids: torch.Tensor
attention_mask: torch.Tensor
position_ids: torch.Tensor
pixel_values: Optional[torch.Tensor]
image_hidden_states: Optional[torch.Tensor]
image_attention_mask: Optional[torch.Tensor]
past_key_values: Optional[List[Tuple]]
# All tokens
all_input_ids: List[torch.Tensor]
# Lengths of all generations present in the batch
input_lengths: List[int]
prefix_offsets: List[int]
read_offsets: List[int]
# Generation helpers
next_token_choosers: List[NextTokenChooser]
stopping_criterias: List[StoppingCriteria]
# Metadata used for padding
max_input_length: int
padding_right_offset: int
# Maximum number of tokens this batch will grow to
max_tokens: int
# Past metadata
keys_head_dim_last: bool = True
def to_pb(self) -> generate_pb2.CachedBatch:
return generate_pb2.CachedBatch(
id=self.batch_id,
request_ids=[r.id for r in self.requests],
size=len(self),
max_tokens=self.max_tokens,
)
@classmethod
def from_pb(
cls,
pb: generate_pb2.Batch,
tokenizer: PreTrainedTokenizerBase,
processor: ProcessorMixin, # Hack
dtype: torch.dtype,
device: torch.device,
) -> "IdeficsCausalLMBatch":
inputs = []
next_token_choosers = []
stopping_criterias = []
prefix_offsets = []
read_offsets = []
requests_idx_mapping = {}
# Parse batch
max_truncation = 0
padding_right_offset = 0
max_decode_tokens = 0
for i, r in enumerate(pb.requests):
requests_idx_mapping[r.id] = i
inputs.append(r.inputs)
next_token_choosers.append(NextTokenChooser.from_pb(r.parameters, device))
stopping_criteria = StoppingCriteria.from_pb(
r.stopping_parameters, tokenizer
)
stopping_criterias.append(stopping_criteria)
max_truncation = max(max_truncation, r.truncate)
max_decode_tokens += stopping_criteria.max_new_tokens
padding_right_offset = max(
padding_right_offset, stopping_criteria.max_new_tokens
)
prompts = []
for inp in inputs:
# Each input is encoded into a list, where each element of this input list is either a string or a URL
prompts.append(split(inp))
# The processor replaces the call to tokenizer, and
# a/ takes care of fetching images from the URL
# b/ generate the correct input_ids, attention_mask, pixel_values, image_attention_mask to feed to the model
tokenized_inputs = processor(
prompts,
return_tensors="pt",
padding=True,
truncation=True,
max_length=max_truncation,
add_end_of_utterance_token=False, # Already taken care of inside the prompts, so bypassing the processor's handling of this token
).to(device)
for _ in pb.requests:
input_len = tokenized_inputs["input_ids"].shape[1]
prefix_offsets.append(
input_len - 5
) # To decode without potential fallbacks errors
read_offsets.append(
input_len
) # To decode without potential fallbacks errors
input_lengths = tokenized_inputs["attention_mask"].sum(1)
max_input_length = input_lengths.max()
input_ids = tokenized_inputs["input_ids"]
pixel_values = tokenized_inputs["pixel_values"]
image_hidden_states = None
# Allocate maximum attention_mask
attention_mask = input_ids.new_zeros(
(pb.size, max_input_length + padding_right_offset)
)
# Copy tokenizer attention_mask into fully allocated attention_mask
attention_mask[:, :max_input_length] = tokenized_inputs["attention_mask"]
# Do the same for image_attention_mask
image_attention_mask = input_ids.new_zeros(
(
pb.size,
max_input_length + padding_right_offset,
tokenized_inputs["pixel_values"].size(1),
)
)
image_attention_mask[:, :max_input_length, :] = tokenized_inputs[
"image_attention_mask"
]
position_ids = tokenized_inputs["attention_mask"].long().cumsum(-1) - 1
position_ids.masked_fill_(tokenized_inputs["attention_mask"] == 0, 1)
all_input_ids = tokenized_inputs["input_ids"].T.split(
1, dim=1
) # It's input_ids but splitted into a tuple of tensors where each tensor is (seq_len, 1) size. It is then transformed into a list
max_tokens = len(inputs) * (max_input_length + max_decode_tokens)
return cls(
batch_id=pb.id,
requests=pb.requests,
requests_idx_mapping=requests_idx_mapping,
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
pixel_values=pixel_values,
image_hidden_states=image_hidden_states,
image_attention_mask=image_attention_mask,
past_key_values=None,
all_input_ids=list(all_input_ids),
input_lengths=input_lengths.tolist(),
prefix_offsets=prefix_offsets,
read_offsets=read_offsets,
next_token_choosers=next_token_choosers,
stopping_criterias=stopping_criterias,
max_input_length=max_input_length.item(),
padding_right_offset=padding_right_offset,
max_tokens=max_tokens,
)
@tracer.start_as_current_span("filter")
def filter(self, request_ids: List[int]) -> Optional["IdeficsCausalLMBatch"]:
# It deletes requests from the batch. For instance when client lost connection
if len(request_ids) == 0:
raise ValueError("Batch must have at least one request")
if len(request_ids) == len(self):
return self
keep_indices = []
# New values after filtering
requests_idx_mapping = {}
requests = []
input_lengths = []
prefix_offsets = []
read_offsets = []
all_input_ids = []
max_input_length = 0
next_token_choosers = []
stopping_criterias = []
total_remaining_decode_tokens = 0
new_padding_right_offset = 0
for i, request_id in enumerate(request_ids):
idx = self.requests_idx_mapping[request_id]
requests_idx_mapping[request_id] = i
keep_indices.append(idx)
requests.append(self.requests[idx])
prefix_offsets.append(self.prefix_offsets[idx])
read_offsets.append(self.read_offsets[idx])
all_input_ids.append(self.all_input_ids[idx])
request_input_length = self.input_lengths[idx]
input_lengths.append(request_input_length)
max_input_length = max(max_input_length, request_input_length)
next_token_choosers.append(self.next_token_choosers[idx])
stopping_criteria = self.stopping_criterias[idx]
stopping_criterias.append(stopping_criteria)
remaining_decode_tokens = (
stopping_criteria.max_new_tokens - stopping_criteria.current_tokens
)
total_remaining_decode_tokens += remaining_decode_tokens
new_padding_right_offset = max(
new_padding_right_offset, remaining_decode_tokens
)
# Apply indices to input_ids, attention mask, past key values and other items that need to be cached
input_ids = self.input_ids[keep_indices]
position_ids = self.position_ids[keep_indices]
self.attention_mask = self.attention_mask[
keep_indices,
-(self.padding_right_offset + max_input_length) : (
self.attention_mask.shape[1] - self.padding_right_offset
)
+ new_padding_right_offset,
]
# Do the same for pixel_values and image_attention_mask
pixel_values = self.pixel_values[keep_indices]
self.image_attention_mask = self.image_attention_mask[
keep_indices,
-(self.padding_right_offset + max_input_length) : (
self.image_attention_mask.shape[1] - self.padding_right_offset
)
+ new_padding_right_offset,
:,
]
if self.image_hidden_states is None:
image_hidden_states = None
else:
image_hidden_states = self.image_hidden_states[keep_indices]
# Ensure that past_key_values tensors can be updated in-place
if type(self.past_key_values[0]) == tuple:
self.past_key_values = [list(layer) for layer in self.past_key_values]
# Update tensors in-place to allow incremental garbage collection
past_kv_length = max_input_length - 1
for layer in self.past_key_values:
past_keys, past_values = layer
if len(past_keys.shape) == 3:
# Force past to be of dim [self_size, num_heads, ...] for easy indexing
past_keys = past_keys.view(len(self), -1, *past_keys.shape[-2:])
past_values = past_values.view(len(self), -1, *past_values.shape[-2:])
if self.keys_head_dim_last:
layer[0] = past_keys[keep_indices, :, -past_kv_length:, :]
else:
layer[0] = past_keys[keep_indices, :, :, -past_kv_length:]
del past_keys
layer[1] = past_values[keep_indices, :, -past_kv_length:, :]
del past_values
max_tokens = len(request_ids) * max_input_length + total_remaining_decode_tokens
self.requests = requests
self.requests_idx_mapping = requests_idx_mapping
self.input_ids = input_ids
self.pixel_values = pixel_values
self.image_hidden_states = image_hidden_states
self.position_ids = position_ids
self.all_input_ids = all_input_ids
self.input_lengths = input_lengths
self.prefix_offsets = prefix_offsets
self.read_offsets = read_offsets
self.next_token_choosers = next_token_choosers
self.stopping_criterias = stopping_criterias
self.max_input_length = max_input_length
self.padding_right_offset = new_padding_right_offset
self.max_tokens = max_tokens
return self
@classmethod
@tracer.start_as_current_span("concatenate")
def concatenate(
cls, batches: List["IdeficsCausalLMBatch"]
) -> "IdeficsCausalLMBatch":
# It adds new requests to the batch
# Used for padding
total_batch_size = 0
max_input_length = 0
max_num_images = 0
padding_right_offset = 0
for batch in batches:
total_batch_size += len(batch)
max_input_length = max(max_input_length, batch.max_input_length)
max_num_images = max(max_num_images, batch.pixel_values.size(1))
padding_right_offset = max(padding_right_offset, batch.padding_right_offset)
# Batch attributes
requests = []
requests_idx_mapping = {}
input_lengths = []
prefix_offsets = []
read_offsets = []
all_input_ids = []
next_token_choosers = []
stopping_criterias = []
max_tokens = 0
# Batch tensors
input_ids = None
attention_mask = None
position_ids = None
pixel_values = None
image_hidden_states = None
image_attention_mask = None
past_key_values = []
# Used for slicing correctly inside the tensors
# Equivalent to a cumsum on batch sizes
start_index = 0
for i, batch in enumerate(batches):
requests.extend(batch.requests)
input_lengths.extend(batch.input_lengths)
prefix_offsets.extend(batch.prefix_offsets)
read_offsets.extend(batch.read_offsets)
all_input_ids.extend(batch.all_input_ids)
next_token_choosers.extend(batch.next_token_choosers)
stopping_criterias.extend(batch.stopping_criterias)
if i == 0:
requests_idx_mapping = batch.requests_idx_mapping
else:
# We need to offset the mapping for each batch by the cumulative batch size
for k, v in batch.requests_idx_mapping.items():
requests_idx_mapping[k] = v + start_index
# Slicing end index for this batch
end_index = start_index + len(batch)
# We only concatenate batches that did at least one step
if batch.past_key_values is None:
raise ValueError("only concatenate prefilled batches")
# Create empty tensor
# input_ids is always of shape [batch_size, 1]
# We do not need to pad it
if input_ids is None:
input_ids = batch.input_ids.new_empty((total_batch_size, 1))
# Copy to correct indices
input_ids[start_index:end_index] = batch.input_ids
# Create padded tensor
if attention_mask is None:
attention_mask = batch.attention_mask.new_zeros(
(total_batch_size, max_input_length + padding_right_offset),
)
curr_batch_max_num_images = batch.pixel_values.size(1)
if pixel_values is None:
pixel_values = batch.pixel_values.new_zeros(
(total_batch_size, max_num_images, 3, 224, 224)
)
pixel_values[
start_index:end_index, :curr_batch_max_num_images
] = batch.pixel_values
if image_attention_mask is None:
image_attention_mask = batch.image_attention_mask.new_zeros(
(
total_batch_size,
max_input_length + padding_right_offset,
max_num_images,
)
)
# We need to slice the attention mask to remove padding from previous steps
# and to remove unused allocated space
left_offset = max_input_length - batch.max_input_length
batch_left_offset = (
batch.attention_mask.shape[1]
- batch.max_input_length
- batch.padding_right_offset
)
attention_mask[
start_index:end_index,
left_offset:-padding_right_offset,
] = batch.attention_mask[
:,
batch_left_offset : -batch.padding_right_offset,
]
image_attention_mask[
start_index:end_index,
left_offset:-padding_right_offset,
:curr_batch_max_num_images,
] = batch.image_attention_mask[
:, batch_left_offset : -batch.padding_right_offset, :
]
# Create empty tensor
# position_ids is always of shape [batch_size, 1]
if position_ids is None:
position_ids = batch.position_ids.new_empty((total_batch_size, 1))
position_ids[start_index:end_index] = batch.position_ids
# Shenanigans to get dimensions because BLOOM outputs a past with a different shape
# BLOOM Keys: [batch_size * num_heads, head_dim, seq_length]
# BLOOM Values: [batch_size * num_heads, seq_length, head_dim]
# And ensure that we can update tensors in-place
if type(batch.past_key_values[0]) == tuple:
batch.past_key_values = [
[t.view(len(batch), -1, *t.shape[-2:]) for t in layer]
for layer in batch.past_key_values
]
elif len(batch.past_key_values[0][0].shape) == 3:
for layer in batch.past_key_values:
for k, t in enumerate(layer):
layer[k] = t.view(len(batch), -1, *t.shape[-2:])
# Add eventual padding tokens that were added while concatenating
max_tokens += batch.max_tokens + (
max_input_length - batch.max_input_length
) * len(batch)
start_index = end_index
first_past_kvs = batches[0].past_key_values
_, num_heads, padded_sequence_length, head_dim = first_past_kvs[0][1].shape
padded_past_values_shape = (
total_batch_size,
num_heads,
max_input_length - 1,
head_dim,
)
if batches[0].keys_head_dim_last:
padded_past_keys_shape = padded_past_values_shape
else:
# seq_length is last for BLOOM
padded_past_keys_shape = (
total_batch_size,
num_heads,
head_dim,
max_input_length - 1,
)
# Iterate over attention layers
# Concatenate past key values layer by layer to allow incremental garbage collection
for j in range(len(first_past_kvs)):
padded_past_keys = first_past_kvs[j][0].new_zeros(padded_past_keys_shape)
start_index = 0
for batch in batches:
past_keys = batch.past_key_values[j][0]
# Clear reference to the original tensor
batch.past_key_values[j][0] = None
# Slicing end index for this batch
end_index = start_index + len(batch)
# We slice the keys to remove the padding from previous batches
past_seq_len = batch.max_input_length - 1
if batch.keys_head_dim_last:
padded_past_keys[
start_index:end_index, :, -past_seq_len:, :
] = past_keys[:, :, -past_seq_len:, :]
else:
# BLOOM case
padded_past_keys[
start_index:end_index, :, :, -past_seq_len:
] = past_keys[:, :, :, -past_seq_len:]
del past_keys
start_index = end_index
padded_past_values = first_past_kvs[j][1].new_zeros(
padded_past_values_shape
)
start_index = 0
for batch in batches:
past_values = batch.past_key_values[j][1]
# Clear reference to the original tensor
batch.past_key_values[j][1] = None
# Slicing end index for this batch
end_index = start_index + len(batch)
# We slice the past values to remove the padding from previous batches
past_seq_len = batch.max_input_length - 1
padded_past_values[
start_index:end_index, :, -past_seq_len:, :
] = past_values[:, :, -past_seq_len:, :]
del past_values
# Update values
start_index = end_index
past_key_values.append([padded_past_keys, padded_past_values])
return cls(
batch_id=batches[0].batch_id,
requests=requests,
requests_idx_mapping=requests_idx_mapping,
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
pixel_values=pixel_values,
image_hidden_states=image_hidden_states,
image_attention_mask=image_attention_mask,
past_key_values=past_key_values,
all_input_ids=all_input_ids,
input_lengths=input_lengths,
prefix_offsets=prefix_offsets,
read_offsets=read_offsets,
next_token_choosers=next_token_choosers,
stopping_criterias=stopping_criterias,
max_input_length=max_input_length,
padding_right_offset=padding_right_offset,
keys_head_dim_last=batches[0].keys_head_dim_last,
max_tokens=max_tokens,
)
def __len__(self):
return len(self.requests)
class IdeficsCausalLM(Model):
def __init__(
self,
model_id: str,
revision: Optional[str] = None,
quantize: Optional[str] = None,
dtype: Optional[torch.dtype] = None,
trust_remote_code: bool = False,
):
from text_generation_server.models.custom_modeling.idefics_modeling import (
IdeficsForVisionText2Text,
)
if torch.cuda.is_available():
device = torch.device("cuda")
dtype = torch.bfloat16 if dtype is None else dtype
else:
if quantize:
raise ValueError("quantization is not available on CPU")
device = torch.device("cpu")
dtype = torch.float32 if dtype is None else dtype
tokenizer = AutoTokenizer.from_pretrained(
model_id,
revision=revision,
padding_side="left",
truncation_side="left",
trust_remote_code=trust_remote_code,
)
self.processor = AutoProcessor.from_pretrained(
model_id,
revision=revision,
padding_side="left",
truncation_side="left",
trust_remote_code=trust_remote_code,
)
model = IdeficsForVisionText2Text.from_pretrained(
model_id,
revision=revision,
torch_dtype=dtype,
device_map="auto"
if torch.cuda.is_available() and torch.cuda.device_count() > 1
else None,
load_in_8bit=quantize == "bitsandbytes",
trust_remote_code=trust_remote_code,
)
if torch.cuda.is_available() and torch.cuda.device_count() == 1:
model = model.cuda()
if tokenizer.pad_token_id is None:
if model.config.pad_token_id is not None:
tokenizer.pad_token_id = model.config.pad_token_id
elif model.config.eos_token_id is not None:
tokenizer.pad_token_id = model.config.eos_token_id
elif tokenizer.eos_token_id is not None:
tokenizer.pad_token_id = tokenizer.eos_token_id
else:
tokenizer.add_special_tokens({"pad_token": "<unk>"})
super(IdeficsCausalLM, self).__init__(
model=model,
tokenizer=tokenizer,
requires_padding=True,
dtype=dtype,
device=device,
)
@property
def batch_type(self) -> Type[IdeficsCausalLMBatch]:
return IdeficsCausalLMBatch
def forward(
self,
input_ids,
attention_mask,
position_ids,
pixel_values,
image_hidden_states,
image_attention_mask,
past_key_values: Optional = None,
) -> Tuple[torch.Tensor, List[Tuple[torch.Tensor, torch.Tensor]]]:
# Model Forward
kwargs = {
"input_ids": input_ids,
"attention_mask": attention_mask,
"pixel_values": pixel_values,
"image_hidden_states": image_hidden_states,
"image_attention_mask": image_attention_mask,
"past_key_values": past_key_values,
"use_cache": True,
"return_dict": True,
}
if self.has_position_ids:
kwargs["position_ids"] = position_ids
outputs = self.model.forward(**kwargs)
return outputs.logits, outputs.past_key_values, outputs.image_hidden_states
@tracer.start_as_current_span("generate_token")
def generate_token(
self, batch: IdeficsCausalLMBatch
) -> Tuple[List[Generation], Optional[IdeficsCausalLMBatch], Tuple[int, int]]:
start = time.time_ns()
# slice the attention mask to the correct shape
attention_mask = batch.attention_mask[:, : -batch.padding_right_offset]
if batch.input_ids.size(1) == 1:
# THIS is a hack: when calling idefics.generate, the first time, we need the whole image_attention_mask (size bs x max_seq_len x max_num_images),
# but the subsequent times, we only need the last attention mask along the `max_seq_len` dimension
# this is due to the nature IDEFICS: it's an encoder decoder, and so when decoding, only the currently generated
# token need to attend to the encoder hidden states (i.e. the vision encoder)
# Also see seq2seq_lm.Seq2SeqLM.generate_token which has roughly the same logic
image_attention_mask = batch.image_attention_mask[
:, -(batch.padding_right_offset + 1)
].unsqueeze(1)
else:
image_attention_mask = batch.image_attention_mask[
:, : -batch.padding_right_offset
]
logits, past, image_hidden_states = self.forward(
input_ids=batch.input_ids,
attention_mask=attention_mask,
position_ids=batch.position_ids,
pixel_values=batch.pixel_values,
image_hidden_states=batch.image_hidden_states,
image_attention_mask=image_attention_mask,
past_key_values=batch.past_key_values,
)
# Hardcoded remove image tokens
logits[:, 32000:32001] = torch.finfo(logits.dtype).min
start_decode = time.time_ns()
# Results
generations: List[Generation] = []
stopped = True
# Zipped iterator
iterator = zip(
batch.requests,
batch.input_lengths,
batch.prefix_offsets,
batch.read_offsets,
logits,
batch.next_token_choosers,
batch.stopping_criterias,
batch.all_input_ids,
)
# For each member of the batch
for i, (
request,
input_length,
prefix_offset,
read_offset,
logits,
next_token_chooser,
stopping_criteria,
all_input_ids,
) in enumerate(iterator):
# Select next token
next_token_id, logprobs = next_token_chooser(
all_input_ids.view(1, -1), logits[-1:, :]
)
# Append next token to all tokens
all_input_ids = torch.cat([all_input_ids, next_token_id])
new_input_length = input_length + 1
# Generated token
next_token_logprob = logprobs[-1, next_token_id]
next_token_id_squeezed = next_token_id.squeeze()
next_token_text, prefix_offset, read_offset = self.decode_token(
all_input_ids[:, 0], prefix_offset, read_offset
)
# Evaluate stopping criteria
stop, reason = stopping_criteria(
next_token_id_squeezed,
next_token_text,
)
if not stop:
stopped = False
# Shard generations
# All generations will be appended in the rust sharded client
if i % self.world_size == self.rank:
if stop:
# Decode generated tokens
output_text, _, _ = self.decode_token(
all_input_ids[:, 0],
prefix_offset=len(all_input_ids)
- stopping_criteria.current_tokens
- 1,
read_offset=len(all_input_ids)
- stopping_criteria.current_tokens,
skip_special_tokens=True,
)
# Get seed
if isinstance(next_token_chooser.choice, Sampling):
seed = next_token_chooser.choice.seed
else:
seed = None
generated_text = GeneratedText(
output_text, stopping_criteria.current_tokens, reason, seed
)
else:
generated_text = None
# Prefill
if stopping_criteria.current_tokens == 1 and request.prefill_logprobs:
# Remove generated token to only have prefill and add nan for first prompt token
prefill_logprobs = [float("nan")] + torch.log_softmax(
logits, -1
).gather(1, all_input_ids[1:]).squeeze(1)[
-new_input_length:-1
].tolist()
prefill_token_ids = all_input_ids[-new_input_length:-1]
prefill_texts = self.tokenizer.batch_decode(
prefill_token_ids,
clean_up_tokenization_spaces=False,
skip_special_tokens=False,
)
prefill_tokens = Tokens(
prefill_token_ids,
prefill_logprobs,
prefill_texts,
is_special=[],
)
else:
prefill_tokens = None
top_tokens = None
generation = Generation(
request.id,
prefill_tokens,
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,
)
generations.append(generation)
# Update values
batch.input_ids[i, 0] = next_token_id
batch.all_input_ids[i] = all_input_ids
batch.input_lengths[i] = new_input_length
batch.prefix_offsets[i] = prefix_offset
batch.read_offsets[i] = read_offset
batch.max_input_length = max(batch.max_input_length, new_input_length)
# We finished all generations in the batch; there is no next batch
if stopped:
forward_ns = start_decode - start
decode_ns = time.time_ns() - start_decode
return generations, None, (forward_ns, decode_ns)
# Slice unused values from prefill
batch.input_ids = batch.input_ids[:, :1]
# Update attention_mask as we added a new token to input_ids
batch.attention_mask[:, -batch.padding_right_offset] = 1
batch.image_attention_mask[
:, -batch.padding_right_offset, :
] = batch.image_attention_mask[:, -(batch.padding_right_offset + 1), :]
# Decrease right offset
batch.padding_right_offset -= 1
# Update position_ids
batch.position_ids = batch.position_ids[:, -1:] + 1
# Update past key values
batch.past_key_values = past
batch.image_hidden_states = image_hidden_states
forward_ns = start_decode - start
decode_ns = time.time_ns() - start_decode
return generations, batch, (forward_ns, decode_ns)
| 0 |
hf_public_repos/text-generation-inference/server/text_generation_server | hf_public_repos/text-generation-inference/server/text_generation_server/models/flash_neox.py | import torch
import torch.distributed
from opentelemetry import trace
from transformers import AutoTokenizer, AutoConfig
from typing import Optional
from text_generation_server.models import FlashCausalLM
from text_generation_server.models.custom_modeling.flash_neox_modeling import (
FlashGPTNeoXForCausalLM,
)
from text_generation_server.utils import (
initialize_torch_distributed,
weight_files,
Weights,
)
tracer = trace.get_tracer(__name__)
class FlashNeoXSharded(FlashCausalLM):
def __init__(
self,
model_id: str,
revision: Optional[str] = None,
quantize: Optional[str] = None,
dtype: Optional[torch.dtype] = None,
trust_remote_code: bool = False,
):
self.process_group, rank, world_size = initialize_torch_distributed()
if torch.cuda.is_available():
device = torch.device(f"cuda:{rank}")
dtype = torch.float16 if dtype is None else dtype
else:
raise NotImplementedError("FlashNeoX is only available on GPU")
tokenizer = AutoTokenizer.from_pretrained(
model_id,
revision=revision,
padding_side="left",
truncation_side="left",
trust_remote_code=trust_remote_code,
)
config = AutoConfig.from_pretrained(
model_id, revision=revision, trust_remote_code=trust_remote_code
)
config.quantize = quantize
torch.distributed.barrier(group=self.process_group)
filenames = weight_files(model_id, revision=revision, extension=".safetensors")
weights = Weights(
filenames, device=device, dtype=dtype, process_group=self.process_group
)
if config.quantize == "gptq":
weights._set_gptq_params(model_id, revision)
model = FlashGPTNeoXForCausalLM(config, weights)
torch.distributed.barrier(group=self.process_group)
super(FlashNeoXSharded, self).__init__(
model=model.to(device),
tokenizer=tokenizer,
num_layers=len(model.gpt_neox.layers),
num_kv_heads=model.gpt_neox.num_heads,
head_size=model.gpt_neox.head_size,
dtype=dtype,
device=device,
rank=rank,
world_size=world_size,
)
| 0 |
hf_public_repos/text-generation-inference/server/text_generation_server | hf_public_repos/text-generation-inference/server/text_generation_server/models/cache_manager.py | import math
import torch
from typing import Optional, List, Tuple
BLOCK_SIZE: int = 16
# Will be set in warmup
CACHE_MANAGER: Optional["CacheManager"] = None
class CacheManager:
def __init__(
self,
num_blocks: int,
num_layers: int,
num_heads: int,
head_size: int,
repeat_slots: bool,
dtype: torch.dtype,
device: torch.device,
):
self.block_size = BLOCK_SIZE
self.num_blocks = num_blocks
self.repeat_slots = repeat_slots
element_size = torch.tensor([], dtype=dtype).element_size()
x = self.block_size // element_size
self.kv_cache = [
(
torch.empty(
(num_blocks, num_heads, head_size // x, self.block_size, x),
dtype=dtype,
device=device,
),
torch.empty(
(num_blocks, num_heads, head_size, self.block_size),
dtype=dtype,
device=device,
),
)
for _ in range(num_layers)
]
self.free_block_mask = torch.ones(num_blocks, dtype=torch.int32, device="cpu")
self.slots = torch.arange(
0, num_blocks * self.block_size, dtype=torch.int32
).view(num_blocks, self.block_size)
def allocate(
self,
needed_blocks_slots: List[Tuple[int, int]],
blocks: int,
max_blocks: int,
device: torch.device,
):
# Get free blocks indices by finding values in mask that are not set to 0
free_block_indices = self.free_block_mask.nonzero()
assert (
len(free_block_indices) >= blocks
), f"Out of available cache blocks: asked {blocks}, only {len(free_block_indices)} free blocks"
# Slice by the number of required blocks
block_indices = free_block_indices[:blocks]
block_indices = block_indices.flatten()
# Padded block tables
block_tables_tensor = torch.zeros(
(len(needed_blocks_slots), max_blocks), dtype=torch.int32
)
# Allocate paged attention blocks
cumulative_blocks = 0
slots = []
block_tables = []
for i, (needed_blocks, needed_slots) in enumerate(needed_blocks_slots):
# Get allocated blocks for this sequence
allocated_blocks = block_indices[
cumulative_blocks : cumulative_blocks + needed_blocks
]
# Get slots for the allocated blocks
all_slots = self.slots[allocated_blocks].flatten()
# Repeat slots in the case of context sliding window
if needed_slots > len(all_slots) and self.repeat_slots:
repeats = math.ceil(needed_slots / len(all_slots))
all_slots = all_slots.repeat(repeats)
allocated_slots = all_slots[:needed_slots]
slots.append(allocated_slots)
block_tables.append(allocated_blocks.tolist())
block_tables_tensor[i, :needed_blocks] = allocated_blocks
cumulative_blocks += needed_blocks
block_tables = block_tables
block_tables_tensor = block_tables_tensor.to(device)
slots = torch.concat(slots).to(device)
# Allocate the required number of blocks by setting the mask to 0
self.free_block_mask[block_indices] = 0
return block_tables, block_tables_tensor, slots
def free(self, block_indices: Optional[List[int]]):
if block_indices is not None and block_indices:
# Reset mask
self.free_block_mask[block_indices] = 1
def set_cache_manager(
num_blocks: int,
num_layers: int,
num_heads: int,
head_size: int,
repeat_slots: bool,
dtype: torch.dtype,
device: torch.device,
) -> CacheManager:
global CACHE_MANAGER
if CACHE_MANAGER is not None:
del CACHE_MANAGER
torch.cuda.empty_cache()
CACHE_MANAGER = CacheManager(
num_blocks, num_layers, num_heads, head_size, repeat_slots, dtype, device
)
return CACHE_MANAGER
def get_cache_manager() -> CacheManager:
global CACHE_MANAGER
if CACHE_MANAGER is None:
raise RuntimeError("cache manager was not initialized")
return CACHE_MANAGER
| 0 |
hf_public_repos/text-generation-inference/server/text_generation_server | hf_public_repos/text-generation-inference/server/text_generation_server/models/opt.py | import torch
import torch.distributed
from typing import Optional
from transformers import (
AutoTokenizer,
AutoConfig,
)
from text_generation_server.models.custom_modeling.opt_modeling import OPTForCausalLM
from text_generation_server.models import CausalLM
from text_generation_server.utils import (
initialize_torch_distributed,
weight_files,
Weights,
)
class OPTSharded(CausalLM):
def __init__(
self,
model_id: str,
revision: Optional[str] = None,
quantize: Optional[str] = None,
dtype: Optional[torch.dtype] = None,
trust_remote_code: bool = False,
):
self.process_group, rank, world_size = initialize_torch_distributed()
if torch.cuda.is_available():
device = torch.device(f"cuda:{rank}")
dtype = torch.float16 if dtype is None else dtype
else:
device = torch.device("cpu")
dtype = torch.float32 if dtype is None else dtype
tokenizer = AutoTokenizer.from_pretrained(
model_id,
revision=revision,
padding_side="left",
truncation_side="left",
trust_remote_code=trust_remote_code,
)
config = AutoConfig.from_pretrained(
model_id,
revision=revision,
trust_remote_code=trust_remote_code,
)
config.quantize = quantize
tokenizer.pad_token_id = config.pad_token_id
torch.distributed.barrier(group=self.process_group)
filenames = weight_files(model_id, revision=revision, extension=".safetensors")
weights = Weights(
filenames, device=device, dtype=dtype, process_group=self.process_group
)
if config.quantize == "gptq":
weights._set_gptq_params(model_id, revision)
model = OPTForCausalLM(config, weights)
torch.distributed.barrier(group=self.process_group)
super(CausalLM, self).__init__(
model=model,
tokenizer=tokenizer,
requires_padding=True,
dtype=dtype,
device=device,
rank=rank,
world_size=world_size,
)
def forward(
self, input_ids, attention_mask, position_ids, past_key_values: Optional = None
):
outputs = self.model.forward(
input_ids=input_ids,
attention_mask=attention_mask,
past_key_values=past_key_values,
use_cache=True,
)
return outputs.logits, outputs.past_key_values
| 0 |
hf_public_repos/text-generation-inference/server/text_generation_server | hf_public_repos/text-generation-inference/server/text_generation_server/models/flash_llama.py | import torch
import torch.distributed
from opentelemetry import trace
from transformers import AutoConfig, AutoTokenizer
from transformers.models.llama import LlamaTokenizer
from typing import Optional
from text_generation_server.models import FlashCausalLM
from text_generation_server.models.custom_modeling.flash_llama_modeling import (
FlashLlamaForCausalLM,
LlamaConfig,
)
from text_generation_server.utils import (
initialize_torch_distributed,
weight_files,
Weights,
)
tracer = trace.get_tracer(__name__)
class FlashLlama(FlashCausalLM):
def __init__(
self,
model_id: str,
revision: Optional[str] = None,
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():
device = torch.device(f"cuda:{rank}")
dtype = torch.float16 if dtype is None else dtype
else:
raise NotImplementedError("FlashLlama is only available on GPU")
try:
tokenizer = LlamaTokenizer.from_pretrained(
model_id,
revision=revision,
padding_side="left",
truncation_side="left",
trust_remote_code=trust_remote_code,
)
except Exception:
tokenizer = AutoTokenizer.from_pretrained(
model_id,
revision=revision,
padding_side="left",
truncation_side="left",
trust_remote_code=trust_remote_code,
)
config = LlamaConfig.from_pretrained(
model_id, revision=revision, trust_remote_code=trust_remote_code
)
config.quantize = quantize
torch.distributed.barrier(group=self.process_group)
filenames = weight_files(model_id, revision=revision, extension=".safetensors")
weights = Weights(filenames, device, dtype, process_group=self.process_group)
if config.quantize in ["gptq", "awq"]:
weights._set_gptq_params(model_id, revision)
model = FlashLlamaForCausalLM(config, weights)
if use_medusa:
from text_generation_server.utils.medusa import MedusaModel
from huggingface_hub import hf_hub_download
import json
import os
from pathlib import Path
is_local_model = (Path(use_medusa).exists() and Path(use_medusa).is_dir()) or os.getenv(
"WEIGHTS_CACHE_OVERRIDE", None
) is not None
if not is_local_model:
medusa_config = hf_hub_download(
use_medusa, revision=revision, filename="config.json"
)
medusa_head = hf_hub_download(
use_medusa, revision=revision, filename="medusa_lm_head.pt"
)
else:
medusa_config = str(Path(use_medusa) / "config.json")
medusa_head = str(Path(use_medusa) / "medusa_lm_head.pt")
with open(medusa_config, "r") as f:
config = json.load(f)
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__(
model=model,
tokenizer=tokenizer,
num_layers=len(model.model.layers),
num_kv_heads=model.model.num_key_value_heads,
head_size=model.model.head_size,
dtype=dtype,
device=device,
rank=rank,
world_size=world_size,
)
| 0 |
hf_public_repos/text-generation-inference/server/text_generation_server/models | hf_public_repos/text-generation-inference/server/text_generation_server/models/custom_modeling/flash_llama_modeling.py | # coding=utf-8
# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
#
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
# and OPT implementations in this library. It has been modified from its
# original forms to accommodate minor architectural differences compared
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch
import torch.distributed
from torch import nn
from transformers.activations import ACT2FN
from transformers.configuration_utils import PretrainedConfig
from typing import Optional, List, Tuple
from text_generation_server.utils import paged_attention, flash_attn
from text_generation_server.utils.layers import (
TensorParallelRowLinear,
TensorParallelColumnLinear,
TensorParallelEmbedding,
PositionRotaryEmbedding,
TensorParallelHead,
get_linear,
FastRMSNorm,
)
class LlamaConfig(PretrainedConfig):
def __init__(
self,
vocab_size=32000,
hidden_size=4096,
intermediate_size=11008,
num_hidden_layers=32,
num_attention_heads=32,
num_key_value_heads=None,
hidden_act="silu",
max_position_embeddings=2048,
initializer_range=0.02,
rms_norm_eps=1e-6,
use_cache=True,
pad_token_id=0,
bos_token_id=1,
eos_token_id=2,
pretraining_tp=1,
tie_word_embeddings=False,
rope_scaling=None,
rope_theta=10000.0,
**kwargs,
):
self.vocab_size = vocab_size
self.max_position_embeddings = max_position_embeddings
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
# for backward compatibility
if num_key_value_heads is None:
num_key_value_heads = num_attention_heads
self.num_key_value_heads = num_key_value_heads
self.hidden_act = hidden_act
self.initializer_range = initializer_range
self.rms_norm_eps = rms_norm_eps
self.pretraining_tp = pretraining_tp
self.use_cache = use_cache
self.rope_scaling = rope_scaling
self.rope_theta = rope_theta
super().__init__(
pad_token_id=pad_token_id,
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
tie_word_embeddings=tie_word_embeddings,
**kwargs,
)
def load_attention(config, prefix, weights):
if config.num_attention_heads != config.num_key_value_heads:
return _load_gqa(config, prefix, weights)
else:
if config.model_type == "baichuan":
return TensorParallelColumnLinear.load_qkv(
config,
prefix=f"{prefix}.W_pack",
weights=weights,
bias=False,
)
else:
return TensorParallelColumnLinear.load_multi(
config,
prefixes=[f"{prefix}.q_proj", f"{prefix}.k_proj", f"{prefix}.v_proj"],
dim=0,
weights=weights,
bias=False,
)
def _load_gqa(config, prefix: str, weights):
assert config.hidden_size % config.num_attention_heads == 0
assert config.num_attention_heads % weights.process_group.size() == 0
weight = weights.get_multi_weights_col(
prefixes=[f"{prefix}.q_proj", f"{prefix}.k_proj", f"{prefix}.v_proj"],
quantize=config.quantize,
dim=0,
)
if config.quantize not in ["gptq", "awq"]:
weight = weight.to(dtype=weights.dtype).to(device=weights.device)
head_size = config.hidden_size // config.num_attention_heads
num_heads = config.num_attention_heads // weights.process_group.size()
num_key_value_heads = config.num_key_value_heads // weights.process_group.size()
assert list(weight.shape) == [
(num_heads + 2 * num_key_value_heads) * head_size,
config.hidden_size,
], f"{list(weight.shape)} != {[(num_heads + 2 * config.num_key_value_heads) * head_size, config.hidden_size]}"
return TensorParallelColumnLinear(
get_linear(weight, bias=None, quantize=config.quantize)
)
class FlashLlamaAttention(torch.nn.Module):
def __init__(
self,
prefix: str,
config,
weights,
):
super().__init__()
self.num_heads = config.num_attention_heads
self.hidden_size = config.hidden_size
self.head_size = self.hidden_size // self.num_heads
self.rotary_emb = PositionRotaryEmbedding.static(
config=config,
dim=self.head_size,
base=config.rope_theta,
device=weights.device,
)
self.softmax_scale = self.head_size**-0.5
if self.num_heads % weights.process_group.size() != 0:
raise ValueError(
f"`num_heads` must be divisible by `num_shards` (got `num_heads`: {self.num_heads} "
f"and `num_shards`: {weights.process_group.size()}"
)
self.num_heads = self.num_heads // weights.process_group.size()
self.num_key_value_heads = (
config.num_key_value_heads // weights.process_group.size()
)
self.query_key_value = load_attention(config, prefix, weights)
self.o_proj = TensorParallelRowLinear.load(
config,
prefix=f"{prefix}.o_proj",
weights=weights,
bias=False,
)
self.num_groups = self.num_heads // self.num_key_value_heads
self.kv_head_mapping = torch.arange(
0, self.num_key_value_heads, dtype=torch.int32, device=weights.device
).repeat_interleave(self.num_groups)
def forward(
self,
hidden_states,
cos,
sin,
cu_seqlen_prefill,
kv_cache,
block_tables,
slots,
input_lengths,
max_s,
):
qkv = self.query_key_value(hidden_states)
query, kv = qkv.split(
[
self.head_size * self.num_heads,
2 * self.head_size * self.num_key_value_heads,
],
dim=1,
)
query = query.view(-1, self.num_heads, self.head_size)
kv = kv.view(-1, 2, self.num_key_value_heads, self.head_size)
self.rotary_emb(query, torch.select(kv, dim=1, index=0), cos, sin)
paged_attention.reshape_and_cache(
kv[:, 0], kv[:, 1], kv_cache[0], kv_cache[1], slots
)
# output tensor
attn_output = torch.empty_like(query)
# Prefill
if cu_seqlen_prefill is not None:
# flash attention
flash_attn.attention(
query,
torch.select(kv, dim=1, index=0),
torch.select(kv, dim=1, index=1),
attn_output,
cu_seqlen_prefill,
max_s,
self.softmax_scale,
)
# Decode
else:
paged_attention.attention(
attn_output,
query,
kv_cache[0],
kv_cache[1],
self.kv_head_mapping,
self.softmax_scale,
block_tables,
input_lengths,
max_s,
)
return self.o_proj(attn_output.view(-1, self.num_heads * self.head_size))
class LlamaMLP(nn.Module):
def __init__(self, prefix, config, weights):
super().__init__()
act = config.hidden_act
self.act = (
ACT2FN[act]
if "gelu" not in act
else lambda x: torch.nn.functional.gelu(
x,
approximate="tanh"
if act in ["gelu_fast", "gelu_pytorch_tanh"]
else "none",
)
)
# Fuse gate and up proj
self.gate_up_proj = TensorParallelColumnLinear.load_multi(
config,
prefixes=[f"{prefix}.gate_proj", f"{prefix}.up_proj"],
weights=weights,
dim=0,
bias=False,
)
self.down_proj = TensorParallelRowLinear.load(
config,
prefix=f"{prefix}.down_proj",
weights=weights,
bias=False,
)
self.intermediate_size = (
config.intermediate_size // weights.process_group.size()
)
def forward(self, hidden_states):
gate_up_states = self.gate_up_proj(hidden_states)
gate_up_states = gate_up_states.view(-1, 2, self.intermediate_size)
return self.down_proj(self.act(gate_up_states[:, 0]) * gate_up_states[:, 1])
class FlashLlamaLayer(nn.Module):
def __init__(self, layer_id, config, weights):
super().__init__()
prefix = f"model.layers.{layer_id}"
self.self_attn = FlashLlamaAttention(
prefix=f"{prefix}.self_attn", config=config, weights=weights
)
self.mlp = LlamaMLP(prefix=f"{prefix}.mlp", config=config, weights=weights)
self.input_layernorm = FastRMSNorm.load(
prefix=f"{prefix}.input_layernorm", weights=weights, eps=config.rms_norm_eps
)
self.post_attention_layernorm = FastRMSNorm.load(
prefix=f"{prefix}.post_attention_layernorm",
weights=weights,
eps=config.rms_norm_eps,
)
def forward(
self,
hidden_states,
residual,
cos,
sin,
cu_seqlen_prefill,
kv_cache,
block_tables,
slots,
input_lengths,
max_s,
):
normed_hidden_states, res = self.input_layernorm(hidden_states, residual)
# Self Attention
attn_output = self.self_attn(
normed_hidden_states,
cos,
sin,
cu_seqlen_prefill,
kv_cache,
block_tables,
slots,
input_lengths,
max_s,
)
# faster post attention rms norm
normed_attn_res_output, attn_res = self.post_attention_layernorm(
attn_output, res
)
mlp_output = self.mlp(normed_attn_res_output)
return mlp_output, attn_res
class FlashLlamaModel(torch.nn.Module):
def __init__(self, config, weights):
super().__init__()
process_group = weights.process_group
self.tp_rank = process_group.rank()
self.tp_world_size = process_group.size()
self.embed_tokens = TensorParallelEmbedding(
prefix="model.embed_tokens", weights=weights
)
self.layers = nn.ModuleList(
[
FlashLlamaLayer(
layer_id,
config,
weights,
)
for layer_id in range(config.num_hidden_layers)
]
)
self.norm = FastRMSNorm.load(
prefix="model.norm", weights=weights, eps=config.rms_norm_eps
)
self.gradient_checkpointing = False
self.head_size = self.layers[0].self_attn.head_size
self.num_heads = self.layers[0].self_attn.num_heads
self.num_key_value_heads = self.layers[0].self_attn.num_key_value_heads
def forward(
self,
input_ids: torch.Tensor,
position_ids: torch.Tensor,
cu_seqlen_prefill: Optional[torch.Tensor],
kv_cache: List[Tuple[torch.Tensor, torch.Tensor]],
block_tables: torch.Tensor,
slots: torch.Tensor,
input_lengths: torch.Tensor,
max_s: int,
) -> torch.Tensor:
hidden_states = self.embed_tokens(input_ids)
# Get rotary cos and sin for this forward
# Avoid to index in each layer
cos, sin = self.layers[0].self_attn.rotary_emb.get_cos_sin(
position_ids, max_s, hidden_states.dtype
)
residual = None
for i, layer in enumerate(self.layers):
hidden_states, residual = layer(
hidden_states,
residual,
cos,
sin,
cu_seqlen_prefill,
kv_cache[i],
block_tables,
slots,
input_lengths,
max_s,
)
hidden_states, _ = self.norm(hidden_states, residual)
return hidden_states
class FlashLlamaForCausalLM(torch.nn.Module):
def __init__(self, config, weights):
super().__init__()
self.model = FlashLlamaModel(config, weights)
self.lm_head = TensorParallelHead.load(
config,
prefix="lm_head",
weights=weights,
)
def forward(
self,
input_ids: torch.Tensor,
position_ids: torch.Tensor,
cu_seqlen_prefill: Optional[torch.Tensor],
kv_cache: List[Tuple[torch.Tensor, torch.Tensor]],
block_tables: torch.Tensor,
slots: torch.Tensor,
input_lengths: torch.Tensor,
max_s: int,
lm_head_indices: Optional[torch.Tensor] = None,
) -> torch.Tensor:
hidden_states = self.model(
input_ids,
position_ids,
cu_seqlen_prefill,
kv_cache,
block_tables,
slots,
input_lengths,
max_s,
)
if lm_head_indices is not None:
hidden_states = hidden_states[lm_head_indices]
logits = self.lm_head(hidden_states)
return logits
| 0 |
hf_public_repos/text-generation-inference/server/text_generation_server/models | hf_public_repos/text-generation-inference/server/text_generation_server/models/custom_modeling/idefics_modeling.py | # coding=utf-8
# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
#
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
# and OPT implementations in this library. It has been modified from its
# original forms to accommodate minor architectural differences compared
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" PyTorch Idefics model."""
from typing import List, Optional, Tuple, Union
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
from torch import nn
from torch.nn import CrossEntropyLoss
from transformers import PreTrainedModel
from transformers.activations import ACT2FN
from transformers.modeling_outputs import (
BaseModelOutputWithPast,
CausalLMOutputWithPast,
dataclass,
)
from transformers.modeling_utils import PretrainedConfig
from transformers.utils import (
add_start_docstrings,
add_start_docstrings_to_model_forward,
logging,
replace_return_docstrings,
)
from text_generation_server.models.custom_modeling.idefics_config import IdeficsConfig
from text_generation_server.models.custom_modeling.idefics_vision import (
IdeficsVisionTransformer,
)
from text_generation_server.models.custom_modeling.idefics_perceiver import (
IdeficsPerceiverResampler,
)
from text_generation_server.utils.layers import (
TensorParallelColumnLinear,
TensorParallelEmbedding,
TensorParallelRowLinear,
TensorParallelHead,
PositionRotaryEmbedding,
FastLinear,
)
from text_generation_server.utils.import_utils import IS_CUDA_SYSTEM, IS_ROCM_SYSTEM
if IS_CUDA_SYSTEM:
import dropout_layer_norm
elif IS_ROCM_SYSTEM:
from vllm import layernorm_ops
@dataclass
class BaseModelOutputWithPastImage(BaseModelOutputWithPast):
image_hidden_states: Optional[torch.FloatTensor] = None
@dataclass
class CausalLMOutputWithPastImage(CausalLMOutputWithPast):
image_hidden_states: Optional[torch.FloatTensor] = None
# logger = logging.get_logger(__name__)
# _CONFIG_FOR_DOC = "IdeficsConfig"
# IDEFICS_PRETRAINED_MODEL_ARCHIVE_LIST = [
# "HuggingFaceM4/idefics-9b",
# "HuggingFaceM4/idefics-80b",
# # See all Idefics models at https://huggingface.co/models?filter=idefics
# ]
def expand_inputs_for_generation(
input_ids,
expand_size=1,
is_encoder_decoder=False,
attention_mask=None,
encoder_outputs=None,
**model_kwargs,
):
expanded_return_idx = (
torch.arange(input_ids.shape[0])
.view(-1, 1)
.repeat(1, expand_size)
.view(-1)
.to(input_ids.device)
)
input_ids = input_ids.index_select(0, expanded_return_idx)
if "token_type_ids" in model_kwargs:
token_type_ids = model_kwargs["token_type_ids"]
model_kwargs["token_type_ids"] = token_type_ids.index_select(
0, expanded_return_idx
)
if attention_mask is not None:
model_kwargs["attention_mask"] = attention_mask.index_select(
0, expanded_return_idx
)
model_kwargs["image_attention_mask"] = model_kwargs[
"image_attention_mask"
].index_select(0, expanded_return_idx)
model_kwargs["pixel_values"] = model_kwargs["pixel_values"].index_select(
0, expanded_return_idx
)
if is_encoder_decoder:
if encoder_outputs is None:
raise ValueError(
"If `is_encoder_decoder` is True, make sure that `encoder_outputs` is defined."
)
encoder_outputs[
"last_hidden_state"
] = encoder_outputs.last_hidden_state.index_select(
0, expanded_return_idx.to(encoder_outputs.last_hidden_state.device)
)
model_kwargs["encoder_outputs"] = encoder_outputs
return input_ids, model_kwargs
def update_model_kwargs_for_generation(outputs, model_kwargs, is_encoder_decoder=False):
# must have this key set to at least None
model_kwargs["past_key_values"] = model_kwargs.get("past_key_values", None)
# update past
if "past_key_values" in outputs:
model_kwargs["past"] = outputs.past_key_values
elif "mems" in outputs:
model_kwargs["past"] = outputs.mems
elif "past_buckets_states" in outputs:
model_kwargs["past"] = outputs.past_buckets_states
else:
model_kwargs["past"] = None
# update token_type_ids with last value
if "token_type_ids" in model_kwargs:
token_type_ids = model_kwargs["token_type_ids"]
model_kwargs["token_type_ids"] = torch.cat(
[token_type_ids, token_type_ids[:, -1].unsqueeze(-1)], dim=-1
)
# update attention masks
if not is_encoder_decoder:
if "attention_mask" in model_kwargs:
attention_mask = model_kwargs["attention_mask"]
model_kwargs["attention_mask"] = torch.cat(
[attention_mask, attention_mask.new_ones((attention_mask.shape[0], 1))],
dim=-1,
)
if "image_attention_mask" in model_kwargs:
image_attention_mask = model_kwargs["image_attention_mask"]
last_mask = image_attention_mask[:, -1, :].unsqueeze(1)
model_kwargs["image_attention_mask"] = last_mask
return model_kwargs
def prepare_inputs_for_generation(input_ids, past=None, **kwargs):
token_type_ids = kwargs.get("token_type_ids", None)
# only last token for inputs_ids if past is defined in kwargs
if past:
input_ids = input_ids[:, -1].unsqueeze(-1)
if token_type_ids is not None:
token_type_ids = token_type_ids[:, -1].unsqueeze(-1)
attention_mask = kwargs.get("attention_mask", None)
position_ids = kwargs.get("position_ids", None)
if attention_mask is not None and position_ids is None:
# create position_ids on the fly for batch generation
position_ids = attention_mask.long().cumsum(-1) - 1
position_ids.masked_fill_(attention_mask == 0, 1)
if past:
position_ids = position_ids[:, -1].unsqueeze(-1)
pixel_values = kwargs.get("pixel_values", None)
image_attention_mask = kwargs.get("image_attention_mask", None)
# if pixel_values is None or image_attention_mask is None:
# raise ValueError("pixel values and image attention mask cannot be None")
return {
"input_ids": input_ids,
"past_key_values": past,
"use_cache": kwargs.get("use_cache"),
"position_ids": position_ids,
"attention_mask": attention_mask,
"token_type_ids": token_type_ids,
"pixel_values": pixel_values,
"image_attention_mask": image_attention_mask,
}
def freeze_model(model, module_exceptions=[]):
mapping = {
"LayerNorm": nn.LayerNorm,
"Linear": nn.Linear,
"Embedding": nn.Embedding,
}
module_exceptions_mapped = [mapping[m] for m in module_exceptions]
for module in model.modules():
if module_exceptions and any(
[isinstance(module, t) for t in module_exceptions_mapped]
):
module.requires_grad_(
True
) # Explicitely setting it to true to avoid any mistakes
else:
module.requires_grad_(False)
return model
class IdeficsDecoupledPartialTPEmbedding(nn.Module):
def __init__(
self,
config,
weights,
):
super().__init__()
self.num_embeddings = config.vocab_size
self.weight = TensorParallelEmbedding(
prefix="model.embed_tokens", weights=weights
)
self.additional_weight = nn.Parameter(
weights.get_tensor(f"model.embed_tokens.additional_embedding.weight")
)
def forward(self, input_ids):
# Clone so that we don't modify the original input_ids later on
input_ids = input_ids.clone()
additional_vocab_indices = torch.where(input_ids >= self.num_embeddings)
input_ids_additional_vocab = input_ids[additional_vocab_indices]
additional_embeddings = torch.nn.functional.embedding(
input_ids_additional_vocab - self.num_embeddings, self.additional_weight
)
# for successful lookup replace input_ids with 0, the results of these will be discarded anyway
input_ids[additional_vocab_indices] = 0
full_vector = self.weight(input_ids)
# overwrite the records with high indices
full_vector[additional_vocab_indices] = additional_embeddings
return full_vector
class IdeficsDecoupledTensorParallelLinear(nn.Module):
# Derived from https://pytorch.org/docs/stable/_modules/torch/nn/modules/linear.html#Linear
"""
Implements a decoupling of parameters to allow freezing (or not) a subset of the parameters. In practise, the
regular `weight` can be trained or frozen (i.e. `partially_freeze=True`), and if `out_additional_features` > 0,
then it will create `out_additional_features * in_features` additional parameters that are always trained. If
`out_additional_features=0`, then the module defaults back to the regular behavior of `nn.Linear`.
"""
def __init__(
self,
config,
weights,
) -> None:
super().__init__()
self.fc = TensorParallelHead.load(
config=config, prefix="lm_head", weights=weights
)
self.additional_fc = FastLinear.load(
config=config,
prefix="lm_head.additional_fc",
weights=weights,
bias=False,
)
def forward(self, input: torch.Tensor) -> torch.Tensor:
output = self.fc(input)
additional_features = self.additional_fc(input)
output = torch.cat((output, additional_features), -1)
return output
def extra_repr(self) -> str:
"""Overwriting `nn.Linear.extra_repr` to include new parameters."""
return "in_features={}, out_features={}, out_additional_features={}, bias={}, partially_freeze={}".format(
self.in_features,
self.out_features,
self.out_additional_features,
self.bias is not None,
self.partially_freeze,
)
# Copied from transformers.models.bart.modeling_bart._make_causal_mask
def _make_causal_mask(
input_ids_shape: torch.Size,
dtype: torch.dtype,
device: torch.device,
past_key_values_length: int = 0,
):
"""
Make causal mask used for bi-directional self-attention.
"""
bsz, tgt_len = input_ids_shape
mask = torch.full((tgt_len, tgt_len), torch.finfo(dtype).min, device=device)
mask_cond = torch.arange(mask.size(-1), device=device)
mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
mask = mask.to(dtype)
if past_key_values_length > 0:
mask = torch.cat(
[
torch.zeros(
tgt_len, past_key_values_length, dtype=dtype, device=device
),
mask,
],
dim=-1,
)
return mask[None, None, :, :].expand(
bsz, 1, tgt_len, tgt_len + past_key_values_length
)
def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
"""
Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
"""
bsz, src_len = mask.size()
tgt_len = tgt_len if tgt_len is not None else src_len
expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
inverted_mask = 1.0 - expanded_mask
return inverted_mask.masked_fill(
inverted_mask.to(torch.bool), torch.finfo(dtype).min
)
class IdeficsRMSNorm(nn.Module):
def __init__(self, prefix, weights, eps=1e-6):
"""
LlamaRMSNorm is equivalent to T5LayerNorm
"""
super().__init__()
weight = weights.get_tensor(f"{prefix}.weight")
self.weight = nn.Parameter(weight)
self.variance_epsilon = eps
def forward(self, hidden_states, residual=None):
if hidden_states.shape[-1] > 8192:
if residual is not None:
hidden_states += residual
residual = hidden_states
hidden_states = hidden_states.to(torch.float32)
variance = hidden_states.pow(2).mean(-1, keepdim=True)
hidden_states = hidden_states * torch.rsqrt(
variance + self.variance_epsilon
)
# convert into half-precision if necessary
if self.weight.dtype in [torch.float16, torch.bfloat16]:
hidden_states = hidden_states.to(self.weight.dtype)
return self.weight * hidden_states
elif IS_CUDA_SYSTEM:
# faster post attention rms norm
unwrap = False
if len(hidden_states.shape) > 2:
unwrap = True
shape = hidden_states.shape
hidden_states = hidden_states.reshape(-1, shape[-1])
normed_hidden_states, res, *rest = dropout_layer_norm.dropout_add_ln_fwd(
hidden_states,
residual,
self.weight,
None,
None,
None,
None,
None,
0.0,
self.variance_epsilon,
1.0,
0,
None,
False,
True, # Activate RMSNorm
)
if res is None:
res = hidden_states
if unwrap:
normed_hidden_states = normed_hidden_states.view(*shape)
return normed_hidden_states
elif IS_ROCM_SYSTEM:
# We use VLLM RMSNorm kernel that can be compiled for RoCm, instead of Flash Attention ones that can not.
if residual is not None:
hidden_states += residual
residual = hidden_states
unwrap = False
if len(hidden_states.shape) > 2:
unwrap = True
shape = hidden_states.shape
hidden_states = hidden_states.reshape(-1, shape[-1])
out = torch.empty_like(hidden_states)
layernorm_ops.rms_norm(
out,
hidden_states,
self.weight.data,
self.variance_epsilon,
)
if unwrap:
out = out.view(*shape)
return out
else:
raise ValueError(
"Your system seem to be not supported. Please check your install or open an issue at https://github.com/huggingface/text-generation-inference/issues with a clear reproduction."
)
# this was adapted from LlamaMLP
class IdeficsMLP(nn.Module):
def __init__(
self,
config,
prefix,
weights,
):
super().__init__()
self.gate_up_proj = TensorParallelColumnLinear.load_multi(
config,
prefixes=[f"{prefix}.gate_proj", f"{prefix}.up_proj"],
weights=weights,
dim=0,
bias=False,
)
self.down_proj = TensorParallelRowLinear.load(
config,
prefix=f"{prefix}.down_proj",
weights=weights,
bias=False,
)
self.act_fn = ACT2FN[config.hidden_act]
def forward(self, hidden_states):
gate_up_states = self.gate_up_proj(hidden_states)
shape = gate_up_states.shape
gate_up_states = gate_up_states.view(*shape[:-1], 2, shape[-1] // 2)
return self.down_proj(
self.act_fn(gate_up_states[:, :, 0]) * gate_up_states[:, :, 1]
)
# this was adapted from LlamaAttention
class IdeficsAttention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(
self,
config,
prefix,
weights,
qk_layer_norms: bool = False,
is_cross_attention: bool = False,
):
super().__init__()
self.hidden_size = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_dim = self.hidden_size // self.num_heads
self.dropout = config.dropout
if (self.head_dim * self.num_heads) != self.hidden_size:
raise ValueError(
f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
f" and `num_heads`: {self.num_heads})."
)
self.is_cross_attention = is_cross_attention
# if not hasattr(nn.functional, "scaled_dot_product_attention"):
# raise ValueError("this model requires pytorch 2.0 or higher")
process_group = weights.process_group
if self.num_heads % weights.process_group.size() != 0:
raise ValueError(
f"`num_heads` must be divisible by `num_shards` (got `num_heads`: {self.num_heads} "
f"and `num_shards`: {weights.process_group.size()}"
)
self.num_heads //= weights.process_group.size()
if self.is_cross_attention:
# kv_input_dim = (
# self.hidden_size if not hasattr(config.vision_config, "embed_dim") else config.vision_config.embed_dim
# )
self.q_proj = TensorParallelColumnLinear.load(
config, prefix=f"{prefix}.q_proj", weights=weights, bias=False
)
self.k_proj = TensorParallelColumnLinear.load(
config, prefix=f"{prefix}.k_proj", weights=weights, bias=False
)
self.v_proj = TensorParallelColumnLinear.load(
config, prefix=f"{prefix}.v_proj", weights=weights, bias=False
)
else:
self.qkv = TensorParallelColumnLinear.load_multi(
config,
prefixes=[f"{prefix}.q_proj", f"{prefix}.k_proj", f"{prefix}.v_proj"],
dim=0,
weights=weights,
bias=False,
)
self.o_proj = TensorParallelRowLinear.load(
config, prefix=f"{prefix}.o_proj", weights=weights, bias=False
)
self.rotary_emb = PositionRotaryEmbedding.static(
config=config, dim=self.head_dim, base=10000.0, device=weights.device
)
self.qk_layer_norms = qk_layer_norms
if self.qk_layer_norms:
self.q_layer_norm = IdeficsRMSNorm(
prefix=f"{prefix}.q_layer_norm",
weights=weights,
eps=config.rms_norm_eps,
)
self.k_layer_norm = IdeficsRMSNorm(
prefix=f"{prefix}.q_layer_norm",
weights=weights,
eps=config.rms_norm_eps,
)
def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
return (
tensor.view(bsz, seq_len, self.num_heads, self.head_dim)
.transpose(1, 2)
.contiguous()
)
def forward(
self,
hidden_states: torch.Tensor,
key_value_states: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[Tuple[torch.Tensor]] = None,
output_attentions: bool = False,
use_cache: bool = False,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
# if key_value_states are provided this layer is used as a cross-attention layer
is_cross_attention = self.is_cross_attention or key_value_states is not None
bsz, q_len, _ = hidden_states.size()
if is_cross_attention:
query_states = self.q_proj(hidden_states).view(
bsz, q_len, self.num_heads, self.head_dim
) # .transpose(1, 2)
query_states = query_states.transpose(1, 2)
(
_,
kv_len,
_,
) = (
key_value_states.size()
) # Note that, in this case, `kv_len` == `kv_seq_len`
key_states = (
self.k_proj(key_value_states)
.view(bsz, kv_len, self.num_heads, self.head_dim)
.transpose(1, 2)
)
value_states = (
self.v_proj(key_value_states)
.view(bsz, kv_len, self.num_heads, self.head_dim)
.transpose(1, 2)
)
else:
qkv = self.qkv(hidden_states)
query_states, key_states, value_states = qkv.split(
self.num_heads * self.head_dim, dim=2
)
query_states = query_states.view(
bsz, q_len, self.num_heads, self.head_dim
) # .transpose(1, 2)
key_states = key_states.view(
bsz, q_len, self.num_heads, self.head_dim
) # . transpose(1, 2)
value_states = value_states.view(
bsz, q_len, self.num_heads, self.head_dim
) # .transpose(1, 2)
kv_seq_len = q_len
if past_key_value is not None:
kv_seq_len += past_key_value[0].shape[-2]
max_s = max(kv_seq_len, q_len)
cos, sin = self.rotary_emb.get_cos_sin(
position_ids.view(-1), max_s, hidden_states.dtype
)
query_shape = query_states.shape
key_shape = key_states.shape
self.rotary_emb(
query_states.view(-1, *query_shape[2:]),
key_states.reshape(-1, *key_shape[2:]),
cos,
sin,
)
query_states = query_states.view(query_shape)
key_states = key_states.view(key_shape)
query_states = query_states.transpose(1, 2)
key_states = key_states.transpose(1, 2)
value_states = value_states.transpose(1, 2)
kv_seq_len = key_states.shape[-2]
if past_key_value is not None:
kv_seq_len += past_key_value[0].shape[-2]
# [bsz, nh, t, hd]
if past_key_value is not None:
# reuse k, v, self_attention
key_states = torch.cat([past_key_value[0], key_states], dim=2)
value_states = torch.cat([past_key_value[1], value_states], dim=2)
past_key_value = (key_states, value_states) if use_cache else None
if self.qk_layer_norms:
query_states = self.q_layer_norm(query_states)
key_states = self.k_layer_norm(key_states)
if attention_mask is not None:
if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
raise ValueError(
f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
)
attn_output = nn.functional.scaled_dot_product_attention(
query_states,
key_states,
value_states,
attn_mask=attention_mask,
dropout_p=self.dropout,
)
if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
raise ValueError(
f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
f" {attn_output.size()}"
)
attn_output = attn_output.transpose(1, 2)
attn_output = attn_output.reshape(bsz, q_len, -1)
attn_output = self.o_proj(attn_output)
attn_weights = None
if output_attentions:
logger.warning_once(
"attn_weights are not extracted in scaled_dot_product_attention. The model returns None instead"
)
return attn_output, attn_weights, past_key_value
# this was adapted from LlamaDecoderLayer
class IdeficsDecoderLayer(nn.Module):
def __init__(self, layer_id: int, config: IdeficsConfig, weights):
super().__init__()
self.process_group = weights.process_group
self.hidden_size = config.hidden_size
prefix = f"model.layers.{layer_id}"
self.self_attn = IdeficsAttention(
config=config,
prefix=f"{prefix}.self_attn",
weights=weights,
qk_layer_norms=False,
is_cross_attention=False,
)
self.mlp = IdeficsMLP(
config=config,
prefix=f"{prefix}.mlp",
weights=weights,
)
self.input_layernorm = IdeficsRMSNorm(
prefix=f"{prefix}.input_layernorm", weights=weights, eps=config.rms_norm_eps
)
self.post_attention_layernorm = IdeficsRMSNorm(
prefix=f"{prefix}.post_attention_layernorm",
weights=weights,
eps=config.rms_norm_eps,
)
self.dropout = config.dropout
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[Tuple[torch.Tensor]] = None,
output_attentions: Optional[bool] = False,
use_cache: Optional[bool] = False,
) -> Tuple[
torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]
]:
"""
Args:
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
use_cache (`bool`, *optional*):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
(see `past_key_values`).
past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
"""
residual = hidden_states
hidden_states = self.input_layernorm(hidden_states)
# Self Attention
hidden_states, self_attn_weights, present_key_value = self.self_attn(
hidden_states=hidden_states,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_value=past_key_value,
output_attentions=output_attentions,
use_cache=use_cache,
)
# hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
hidden_states = residual + hidden_states
# Fully Connected
residual = hidden_states
hidden_states = self.post_attention_layernorm(hidden_states)
hidden_states = self.mlp(hidden_states)
# hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
hidden_states = residual + hidden_states
outputs = (hidden_states,)
if output_attentions:
outputs += (self_attn_weights,)
if use_cache:
outputs += (present_key_value,)
return outputs
class IdeficsGatedCrossAttentionLayer(nn.Module):
def __init__(self, layer_id, config: IdeficsConfig, weights):
super().__init__()
self.process_group = weights.process_group
self.hidden_size = config.hidden_size
prefix = f"model.gated_cross_attn_layers.{layer_id}"
self.cross_attn = IdeficsAttention(
config=config,
prefix=f"{prefix}.cross_attn",
weights=weights,
qk_layer_norms=True,
is_cross_attention=True,
)
self.mlp = IdeficsMLP(
config=config,
prefix=f"{prefix}.mlp",
weights=weights,
)
self.input_layernorm = IdeficsRMSNorm(
prefix=f"{prefix}.input_layernorm", weights=weights, eps=config.rms_norm_eps
)
self.post_attention_layernorm = IdeficsRMSNorm(
prefix=f"{prefix}.post_attention_layernorm",
weights=weights,
eps=config.rms_norm_eps,
)
self.config = config.dropout
self.act_cross_attn = nn.Tanh()
self.act_dense = nn.Tanh()
self.alpha_cross_attn = nn.Parameter(
weights.get_tensor(f"{prefix}.alpha_cross_attn")
)
self.alpha_dense = nn.Parameter(weights.get_tensor(f"{prefix}.alpha_dense"))
if not (hasattr(self, "alpha_cross_attn") and hasattr(self, "alpha_dense")):
raise ValueError("Alpha parameters not initialized correctly!")
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
image_hidden_states: Optional[torch.Tensor] = None,
image_attention_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = False,
use_cache: Optional[bool] = False,
past_key_value: Optional[Tuple[torch.Tensor]] = None,
no_images: Optional[bool] = False,
) -> Tuple[
torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]
]:
"""
Args:
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
use_cache (`bool`, *optional*):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
(see `past_key_values`).
past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
no_images (`bool`, *optional*, defaults to `False`): If `True` the vision part is ignored
"""
if image_hidden_states is None:
raise ValueError(
"`image_hidden_states` is required for Idefics cross attention module which are visual features to be"
" conditioned on."
)
if past_key_value is not None:
raise NotImplementedError(
"Past key value states are not implemented for Idefics cross attention module."
)
residual = hidden_states
hidden_states = self.input_layernorm(hidden_states)
# Self Attention
hidden_states, self_attn_weights, present_key_value = self.cross_attn(
hidden_states=hidden_states,
key_value_states=image_hidden_states,
attention_mask=image_attention_mask,
output_attentions=output_attentions,
)
# hidden_states = nn.functional.dropout(hidden_states, p=self.config, training=self.training)
# when there are no images the model is used in pure language mode
gate = 0 if no_images else 1
hidden_states = (
residual + gate * self.act_cross_attn(self.alpha_cross_attn) * hidden_states
)
# Fully Connected
residual = hidden_states
hidden_states = self.post_attention_layernorm(hidden_states)
hidden_states = self.mlp(hidden_states)
# hidden_states = nn.functional.dropout(hidden_states, p=self.config, training=self.training)
hidden_states = residual + self.act_dense(self.alpha_dense) * hidden_states
outputs = (hidden_states,)
if output_attentions:
outputs += (self_attn_weights,)
if use_cache:
outputs += (present_key_value,)
return outputs
LLAMA_START_DOCSTRING = r"""
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.
Parameters:
config ([`IdeficsConfig`]):
Model configuration class with all the parameters of the model. Initializing with a config file does not
load the weights associated with the model, only the configuration. Check out the
[`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
# @add_start_docstrings(
# "The bare LLaMA Model outputting raw hidden-states without any specific head on top.",
# LLAMA_START_DOCSTRING,
# )
class IdeficsPreTrainedModel(PreTrainedModel):
config_class = IdeficsConfig
# base_model_prefix = "model"
# supports_gradient_checkpointing = True
# _no_split_modules = ["IdeficsDecoderLayer", "IdeficsGatedCrossAttentionLayer"]
# def _init_weights(self, module):
# # important: this ported version of Idefics isn't meant for training from scratch - only
# # inference and fine-tuning - so the proper init weights code has been removed - the m4 code
# # base should be used for training from scratch and it contains the correct code.
# std = self.config.initializer_range
# if isinstance(module, nn.Linear):
# module.weight.data.normal_(mean=0.0, std=std)
# if module.bias is not None:
# module.bias.data.zero_()
# elif isinstance(module, nn.Embedding):
# module.weight.data.normal_(mean=0.0, std=std)
# if module.padding_idx is not None:
# module.weight.data[module.padding_idx].zero_()
# def _set_gradient_checkpointing(self, module, value=False):
# if isinstance(module, IdeficsModel):
# module.gradient_checkpointing = value
# LLAMA_INPUTS_DOCSTRING = r"""
# Args:
# input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
# Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
# it.
# Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
# [`PreTrainedTokenizer.__call__`] for details.
# [What are input IDs?](../glossary#input-ids)
# attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
# Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
# - 1 for tokens that are **not masked**,
# - 0 for tokens that are **masked**.
# [What are attention masks?](../glossary#attention-mask)
# Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
# [`PreTrainedTokenizer.__call__`] for details.
# If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
# `past_key_values`).
# If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
# and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
# information on the default strategy.
# - 1 indicates the head is **not masked**,
# - 0 indicates the head is **masked**.
# position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
# Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
# config.n_positions - 1]`. [What are position IDs?](../glossary#position-ids)
# past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
# Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
# `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
# `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.
# Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
# blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
# If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
# don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
# `decoder_input_ids` of shape `(batch_size, sequence_length)`.
# inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
# Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
# is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
# model's internal embedding lookup matrix.
# use_cache (`bool`, *optional*):
# If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
# `past_key_values`).
# output_attentions (`bool`, *optional*):
# Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
# tensors for more detail.
# output_hidden_states (`bool`, *optional*):
# Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
# more detail.
# return_dict (`bool`, *optional*):
# Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
# """
# @add_start_docstrings(
# "The bare LLaMA Model outputting raw hidden-states without any specific head on top.",
# LLAMA_START_DOCSTRING,
# )
class IdeficsModel(IdeficsPreTrainedModel):
# """
# Transformer decoder consisting of `config.num_hidden_layers` layers. Each layer is a [`IdeficsDecoderLayer`]
# Args:
# config: IdeficsConfig
# """
def __init__(self, config: IdeficsConfig, weights):
super().__init__(config)
self.config = config
self.padding_idx = config.pad_token_id
self.vocab_size = config.vocab_size
self.embed_tokens = IdeficsDecoupledPartialTPEmbedding(
config=config,
weights=weights,
)
self.image_size = config.vision_config.image_size
self.vision_config = config.vision_config
self.vision_model = IdeficsVisionTransformer(
prefix="model.vision_model",
config=config.vision_config,
weights=weights,
)
# Perceiver Resampler
if config.use_resampler:
perceiver_config = config.perceiver_config
self.perceiver_resampler = IdeficsPerceiverResampler(
prefix=f"model.perceiver_resampler",
config=config,
embed_dim=config.vision_config.embed_dim,
depth=perceiver_config.resampler_depth,
n_heads=perceiver_config.resampler_n_heads,
head_dim=perceiver_config.resampler_head_dim,
n_latents=perceiver_config.resampler_n_latents,
weights=weights,
)
self.layers = nn.ModuleList(
[
IdeficsDecoderLayer(layer_id, config, weights)
for layer_id in range(config.num_hidden_layers)
]
)
self.cross_layer_interval = config.cross_layer_interval
num_cross_layers = config.num_hidden_layers // self.cross_layer_interval
self.gated_cross_attn_layers = nn.ModuleList(
[
IdeficsGatedCrossAttentionLayer(layer_id, config, weights)
for layer_id in range(num_cross_layers)
]
)
# self.gradient_checkpointing = False
self.norm = IdeficsRMSNorm(
prefix=f"model.norm", weights=weights, eps=config.rms_norm_eps
)
# self.gradient_checkpointing = False
# Initialize weights and apply final processing
# self.post_init()
# self.freeze_relevant_params(config)
# def freeze_relevant_params(self, config=None):
# if config is None:
# config = self.config
# if config.freeze_text_layers:
# self.freeze_text_layers(config.freeze_text_module_exceptions)
# if config.freeze_vision_layers:
# freeze_model(self.vision_model, module_exceptions=config.freeze_vision_module_exceptions)
# def freeze_text_layers(self, module_exceptions=[]):
# for module in [self.layers, self.norm]:
# freeze_model(module, module_exceptions=module_exceptions)
# def freeze_vision_layers(self, module_exceptions=[]):
# freeze_model(self.vision_model, module_exceptions=module_exceptions)
# def get_input_embeddings(self):
# return self.embed_tokens
# def set_input_embeddings(self, value):
# self.embed_tokens = value
# Copied from transformers.models.bart.modeling_bart.BartDecoder._prepare_decoder_attention_mask
def _prepare_decoder_attention_mask(
self, attention_mask, input_shape, inputs_embeds, past_key_values_length
):
# create causal mask
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
combined_attention_mask = None
if input_shape[-1] > 1:
combined_attention_mask = _make_causal_mask(
input_shape,
inputs_embeds.dtype,
device=inputs_embeds.device,
past_key_values_length=past_key_values_length,
)
if attention_mask is not None:
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
expanded_attn_mask = _expand_mask(
attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]
).to(inputs_embeds.device)
combined_attention_mask = (
expanded_attn_mask
if combined_attention_mask is None
else expanded_attn_mask + combined_attention_mask
)
return combined_attention_mask
# @add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
pixel_values: Optional[torch.FloatTensor] = None,
image_hidden_states: Optional[torch.FloatTensor] = None,
image_embeddings: Optional[torch.FloatTensor] = None,
image_attention_mask: Optional[torch.Tensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, BaseModelOutputWithPastImage]:
device = input_ids.device if input_ids is not None else inputs_embeds.device
output_attentions = (
output_attentions
if output_attentions is not None
else self.config.output_attentions
)
output_hidden_states = (
output_hidden_states
if output_hidden_states is not None
else self.config.output_hidden_states
)
use_cache = use_cache if use_cache is not None else self.config.use_cache
return_dict = (
return_dict if return_dict is not None else self.config.use_return_dict
)
# retrieve input_ids and inputs_embeds
if input_ids is not None and inputs_embeds is not None:
raise ValueError(
"You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time"
)
elif input_ids is not None:
batch_size, seq_length = input_ids.shape
elif inputs_embeds is not None:
batch_size, seq_length, _ = inputs_embeds.shape
else:
raise ValueError(
"You have to specify either decoder_input_ids or decoder_inputs_embeds"
)
seq_length_with_past = seq_length
past_key_values_length = 0
if past_key_values is not None:
past_key_values_length = past_key_values[0][0].shape[2]
seq_length_with_past = seq_length_with_past + past_key_values_length
if attention_mask is not None and position_ids is None:
# create position_ids on the fly for batch generation
position_ids = attention_mask.long().cumsum(-1) - 1
position_ids.masked_fill_(attention_mask == 0, 1)
elif position_ids is None:
device = input_ids.device if input_ids is not None else inputs_embeds.device
position_ids = torch.arange(
past_key_values_length,
seq_length + past_key_values_length,
dtype=torch.long,
device=device,
)
position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
else:
position_ids = position_ids.view(-1, seq_length).long()
no_images = False
if image_hidden_states is None:
if pixel_values is None and image_embeddings is None:
raise ValueError(
"Either pixel_values and image_embeddings have to be not-None."
)
elif pixel_values is not None and image_embeddings is not None:
raise ValueError(
"You cannot specify both pixel_values and image_embeddings at the same time"
)
elif pixel_values is not None:
no_images = len(torch.nonzero(pixel_values)) == 0
pixel_values = pixel_values.to(
dtype=self.dtype, device=device
) # fp16 compatibility
batch_size, num_images = pixel_values.shape[:2]
pixel_values = pixel_values.contiguous().view(
batch_size * num_images, *pixel_values.shape[2:]
)
# Get sequence from the vision encoder
image_hidden_states = self.vision_model(
pixel_values=pixel_values
).last_hidden_state
elif image_embeddings is not None:
(
batch_size,
num_images,
image_seq_len,
image_hidden_size,
) = image_embeddings.size()
image_hidden_states = image_embeddings.to(
dtype=self.dtype, device=input_ids.device
)
image_hidden_states = image_hidden_states.view(
batch_size * num_images, image_seq_len, image_hidden_size
)
if self.config.use_resampler:
image_hidden_states = self.perceiver_resampler(image_hidden_states)
image_seq_len, image_hidden_size = image_hidden_states.size(
1
), image_hidden_states.size(2)
image_hidden_states = image_hidden_states.view(
batch_size, num_images * image_seq_len, image_hidden_size
)
else:
no_images = False
num_images = pixel_values.shape[1]
image_seq_len = image_hidden_states.shape[1] // num_images
# # Hack to use the model in full language modeling mode
# image_attention_mask = torch.zeros(batch_size, seq_length, 1, dtype=torch.long, device=image_hidden_states.device)
# Make image_attention_mask compatible with hidden states
text_seq_len = image_attention_mask.size(1)
image_attention_mask = image_attention_mask.unsqueeze(-1)
image_attention_mask = image_attention_mask.repeat(1, 1, 1, image_seq_len)
image_attention_mask = image_attention_mask.view(
batch_size, text_seq_len, num_images * image_seq_len
)
image_batch_size, image_sequence_length, _ = image_hidden_states.size()
image_hidden_shape = (image_batch_size, image_sequence_length)
if image_attention_mask is None:
image_attention_mask = torch.ones(image_hidden_shape, device=device)
image_attention_mask = self.invert_attention_mask(image_attention_mask)
# if list(image_attention_mask.shape) != [4, 1, 1024, 64]:
# raise ValueError(f"Image hidden_states {image_hidden_states.shape} - mask {image_attention_mask.shape} {num_images} {image_seq_len} {text_seq_len}")
# if image_hidden_states is not None:
# else:
# image_attention_mask = None
if inputs_embeds is None:
inputs_embeds = self.embed_tokens(input_ids)
# embed positions
if attention_mask is None:
attention_mask = torch.ones(
(batch_size, seq_length_with_past),
dtype=torch.bool,
device=inputs_embeds.device,
)
attention_mask = self._prepare_decoder_attention_mask(
attention_mask,
(batch_size, seq_length),
inputs_embeds,
past_key_values_length,
)
hidden_states = inputs_embeds
# if self.gradient_checkpointing and self.training:
# if use_cache:
# logger.warning_once(
# "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
# )
# use_cache = False
# decoder layers
all_hidden_states = () if output_hidden_states else None
all_self_attns = () if output_attentions else None
next_decoder_cache = () if use_cache else None
for idx, decoder_layer in enumerate(self.layers):
if output_hidden_states:
all_hidden_states += (hidden_states,)
past_key_value = (
past_key_values[idx] if past_key_values is not None else None
)
def vblock(
main_block,
hidden_states,
attention_mask,
position_ids,
past_key_value,
image_hidden_states,
image_attention_mask,
output_attentions,
use_cache,
no_images,
layer_idx,
cross_layer_interval,
gated_cross_attn_layers,
):
# TODO(ls): Add cross attention values to respective lists
if layer_idx % cross_layer_interval == 0:
xblock = gated_cross_attn_layers[layer_idx // cross_layer_interval]
outputs = xblock(
hidden_states,
attention_mask=attention_mask,
image_hidden_states=image_hidden_states,
image_attention_mask=image_attention_mask,
output_attentions=output_attentions,
use_cache=use_cache,
past_key_value=None, # not implemented
no_images=no_images,
)
hidden_states = outputs[0]
layer_outputs = main_block(
hidden_states,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_value=past_key_value,
output_attentions=output_attentions,
use_cache=use_cache,
)
return layer_outputs
# if self.gradient_checkpointing and self.training:
# past_key_value = None
# if use_cache:
# logger.warning_once(
# "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
# )
# use_cache = False
# layer_outputs = torch.utils.checkpoint.checkpoint(
# vblock,
# decoder_layer,
# hidden_states,
# attention_mask,
# position_ids,
# past_key_value,
# image_hidden_states,
# image_attention_mask,
# output_attentions,
# use_cache,
# no_images,
# idx,
# self.cross_layer_interval,
# self.gated_cross_attn_layers,
# )
# else:
layer_outputs = vblock(
decoder_layer,
hidden_states,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_value=past_key_value,
image_hidden_states=image_hidden_states,
image_attention_mask=image_attention_mask,
output_attentions=output_attentions,
use_cache=use_cache,
no_images=no_images,
layer_idx=idx,
cross_layer_interval=self.cross_layer_interval,
gated_cross_attn_layers=self.gated_cross_attn_layers,
)
hidden_states = layer_outputs[0]
if use_cache:
next_decoder_cache += (layer_outputs[2 if output_attentions else 1],)
if output_attentions:
all_self_attns += (layer_outputs[1],)
hidden_states = self.norm(hidden_states)
# add hidden states from the last decoder layer
if output_hidden_states:
all_hidden_states += (hidden_states,)
next_cache = next_decoder_cache if use_cache else None
if not return_dict:
return tuple(
v
for v in [hidden_states, next_cache, all_hidden_states, all_self_attns]
if v is not None
)
return BaseModelOutputWithPastImage(
last_hidden_state=hidden_states,
past_key_values=next_cache,
hidden_states=all_hidden_states,
attentions=all_self_attns,
image_hidden_states=image_hidden_states,
)
class IdeficsForVisionText2Text(IdeficsPreTrainedModel):
def __init__(
self,
config,
weights,
):
super().__init__(config)
self.model = IdeficsModel(
config=config,
weights=weights,
)
self.lm_head = IdeficsDecoupledTensorParallelLinear(
config=config,
weights=weights,
)
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
pixel_values: Optional[torch.FloatTensor] = None,
image_embeddings: Optional[torch.FloatTensor] = None,
image_hidden_states: Optional[torch.FloatTensor] = None,
image_attention_mask: Optional[torch.Tensor] = None,
labels: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, CausalLMOutputWithPastImage]:
r"""
Args:
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
Returns:
Example:
```python
>>> from transformers import AutoTokenizer, LlamaForCausalLM
>>> model = LlamaForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
>>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)
>>> prompt = "Hey, are you consciours? Can you talk to me?"
>>> inputs = tokenizer(prompt, return_tensors="pt")
>>> # Generate
>>> generate_ids = model.generate(inputs.input_ids, max_length=30)
>>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
"Hey, are you consciours? Can you talk to me?\nI'm not consciours, but I can talk to you."
```"""
output_attentions = (
output_attentions
if output_attentions is not None
else self.config.output_attentions
)
output_hidden_states = (
output_hidden_states
if output_hidden_states is not None
else self.config.output_hidden_states
)
return_dict = (
return_dict if return_dict is not None else self.config.use_return_dict
)
# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
outputs = self.model(
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
pixel_values=pixel_values,
image_embeddings=image_embeddings,
image_hidden_states=image_hidden_states,
image_attention_mask=image_attention_mask,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
hidden_states = outputs[0]
logits = self.lm_head(hidden_states)
loss = None
return CausalLMOutputWithPastImage(
loss=loss,
logits=logits,
past_key_values=outputs.past_key_values,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
image_hidden_states=outputs.image_hidden_states,
)
def prepare_inputs_for_generation(self, input_ids, past=None, **kwargs):
inputs = prepare_inputs_for_generation(input_ids, past=past, **kwargs)
unwanted_kwargs = ["token_type_ids"]
for kwarg in unwanted_kwargs:
inputs.pop(kwarg, None)
return inputs
@staticmethod
def _expand_inputs_for_generation(
*args,
**model_kwargs,
):
return expand_inputs_for_generation(*args, **model_kwargs)
@staticmethod
def _update_model_kwargs_for_generation(
outputs, model_kwargs, is_encoder_decoder=False
):
return update_model_kwargs_for_generation(
outputs, model_kwargs, is_encoder_decoder=is_encoder_decoder
)
@staticmethod
def _reorder_cache(past, beam_idx):
reordered_past = ()
for layer_past in past:
reordered_past += (
tuple(
past_state.index_select(0, beam_idx) for past_state in layer_past
),
)
return reordered_past
| 0 |
hf_public_repos/text-generation-inference/server/text_generation_server/models | hf_public_repos/text-generation-inference/server/text_generation_server/models/custom_modeling/idefics_processing.py | # coding=utf-8
# Copyright 2022 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Processor class for IDEFICS.
"""
from typing import Callable, List, Optional, Union
from urllib.parse import urlparse
from transformers.feature_extraction_utils import BatchFeature
from transformers.processing_utils import ProcessorMixin
from transformers.tokenization_utils_base import (
BatchEncoding,
PaddingStrategy,
TextInput,
TruncationStrategy,
)
from transformers.utils import TensorType, is_torch_available
from text_generation_server.models.custom_modeling.idefics_image_processing import (
IdeficsImageProcessor,
)
if is_torch_available():
import torch
IMAGE_TOKEN = "<image>"
# copied from m4.training.packing
def incremental_to_binary_attention_mask(incremental_mask, num_classes=-1):
# This function converts: [-1, 0, 1] => [[0, 0], [1, 0], [0, 1]]
# If any of images index are more than num_classes, set them to -1.
# Words after the max number of images allowed have been seen don't attend on anything
if num_classes != -1:
incremental_mask[incremental_mask >= num_classes] = -1
negatives = incremental_mask == -1
incremental_mask[negatives] = 0
attn_mask = torch.nn.functional.one_hot(incremental_mask, num_classes=num_classes)
attn_mask[negatives, :] = 0
return attn_mask
# copied from m4.training.packing
def image_attention_mask_for_packed_input_ids(input_ids, tokenizer):
image_attention_mask = torch.full_like(input_ids, fill_value=-1)
next_image_attention_mask = torch.full_like(input_ids, fill_value=-1)
image_token_id = tokenizer.convert_tokens_to_ids(IMAGE_TOKEN)
eod_token_id = tokenizer.eos_token_id
for batch_idx in range(input_ids.size(0)):
count = -1
seen_eod = False
for idx, token_id in enumerate(input_ids[batch_idx]):
if token_id == image_token_id:
count += 1
image_attention_mask[batch_idx][idx] = count
seen_eod = False
else:
image_attention_mask[batch_idx][idx] = count
if seen_eod:
image_attention_mask[batch_idx][idx] = -1
if token_id == eod_token_id:
seen_eod = True
for batch_idx in range(input_ids.size(0)):
count = -1
seen_eod = False
for idx in range(input_ids[batch_idx].size(0) - 1, -1, -1):
token_id = input_ids[batch_idx][idx]
if token_id == image_token_id:
count += 1
next_image_attention_mask[batch_idx][idx] = count
seen_eod = False
else:
next_image_attention_mask[batch_idx][idx] = count
if token_id == eod_token_id:
seen_eod = True
if seen_eod:
next_image_attention_mask[batch_idx][idx] = -1
non_negative_indices = next_image_attention_mask[batch_idx] != -1
next_image_attention_mask[batch_idx][non_negative_indices] -= count
next_image_attention_mask[batch_idx][non_negative_indices] *= -1
return image_attention_mask, next_image_attention_mask
def is_url(string):
"""Checks if the passed string contains a valid url and nothing else. e.g. if space is included it's immediately
invalidated the url"""
if " " in string:
return False
result = urlparse(string)
return all([result.scheme, result.netloc])
def is_image(string):
"""Checks if the passed string contains a valid url and nothing else. e.g. if space is included it's immediately
invalidated the url"""
return is_url(string) or string.startswith("data:")
class IdeficsProcessor(ProcessorMixin):
r"""
Constructs a IDEFICS processor which wraps a LLama tokenizer and IDEFICS image processor into a single processor.
[`IdeficsProcessor`] offers all the functionalities of [`IdeficsImageProcessor`] and [`LlamaTokenizerFast`]. See
the docstring of [`~IdeficsProcessor.__call__`] and [`~IdeficsProcessor.decode`] for more information.
Args:
image_processor (`IdeficsImageProcessor`):
An instance of [`IdeficsImageProcessor`]. The image processor is a required input.
tokenizer (`LlamaTokenizerFast`):
An instance of [`LlamaTokenizerFast`]. The tokenizer is a required input.
image_size (`int`, *optional*, defaults to 224): Image size (assuming a square image)
"""
attributes = ["image_processor", "tokenizer"]
image_processor_class = "IdeficsImageProcessor"
tokenizer_class = "LlamaTokenizerFast"
def __init__(
self,
image_processor,
tokenizer=None,
image_size=224,
add_end_of_utterance_token=None,
**kwargs,
):
if image_processor is None:
raise ValueError("You need to specify an `image_processor`.")
if tokenizer is None:
raise ValueError("You need to specify a `tokenizer`.")
super().__init__(image_processor, tokenizer)
self.current_processor = self.image_processor
self.image_token_id = tokenizer.convert_tokens_to_ids(IMAGE_TOKEN)
self.default_image_dims = (
self.image_processor.image_num_channels,
self.image_processor.image_size,
self.image_processor.image_size,
)
self.tokenizer_was_trained_with_end_of_utterance_token = (
True
if "<end_of_utterance>"
in self.tokenizer.special_tokens_map.get("additional_special_tokens", [])
else False
)
def __call__(
self,
prompts: Union[List[TextInput], List[List[TextInput]]],
padding: Union[bool, str, PaddingStrategy] = False,
truncation: Union[bool, str, TruncationStrategy] = None,
max_length: Optional[int] = None,
transform: Callable = None,
add_eos_token=False,
add_end_of_utterance_token=None,
debug=False,
return_tensors: Optional[Union[str, TensorType]] = TensorType.PYTORCH,
) -> BatchEncoding:
"""This method takes batched or non-batched prompts made of text and images and converts them into prompts that
the model was trained on and prepares the image pixel values for the model to process.
Args:
prompts (`Union[List[TextInput], [List[List[TextInput]]]]`):
either a single prompt or a batched list of prompts - see the detailed description immediately after
the end of the arguments doc section.
padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):
Select a strategy to pad the returned sequences (according to the model's padding side and padding
index) among:
- `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single
sequence if provided).
- `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
acceptable input length for the model if that argument is not provided.
- `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different
lengths).
max_length (`int`, *optional*):
Maximum length of the returned list and optionally padding length (see above).
truncation (`bool`, *optional*):
Activates truncation to cut input sequences longer than `max_length` to `max_length`.
transform (`Callable`, *optional*):
A custom transform function that accepts a single image can be passed for training. For example,
`torchvision.Compose` can be used to compose multiple functions. If `None` a preset inference-specific
set of transforms will be applied to the images
add_eos_token (`bool`, *optional*, defaults to `False`):
Adds `eos_token` at the end of the final prompt if True`
add_end_of_utterance_token (`bool`, *optional*)
Whether to automatically add `<end_of_utterance>` after each prompt's text input (unless followed by an
image). If `None` the tokenizer will be checked instead and if this token is found in
`additional_special_tokens` then the value will be `True`.
debug (`bool`, *optional*, defaults to `False`):
`True` value will help debug prompt generation by dumping useful information
return_tensors (`str` or `TensorType`, *optional*, defaults to `TensorType.PYTORCH`):
The type of tensors to return. Can be one of:
- `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`.
Returns:
a dict with entries: `input_ids`, `attention_mask`, `pixel_values`, `image_attention_mask` which can be
directly passed to `model.generate`
Detailed explanation:
Each entry in `prompts` is either a text to be passed as is or an image that will be processed.
An image can be either an image object (`PIL.Image`) or a url from which the image can be retrieved.
When the processor encounters an image it'll inject `<fake_token_around_image><image><fake_token_around_image>`
entry into the prompt.
Example:
```python
checkpoint = "HuggingFaceM4/idefics-9b"
processor = AutoProcessor.from_pretrained(checkpoint)
url = "https://hips.hearstapps.com/hmg-prod/images/cute-photos-of-cats-in-grass-1593184777.jpg"
img = processor.image_processor.fetch_images([url])[0]
prompts = [
"User:",
img,
"Describe this image.\nAssistant: An image of two kittens in grass.\n",
"User:",
"https://hips.hearstapps.com/hmg-prod/images/dog-puns-1581708208.jpg",
"Describe this image.\nAssistant:",
]
inputs = processor(prompts, return_tensors="pt")
generated_ids = model.generate(**inputs, max_length=100)
generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
```
In this example the `prompts` will be converted into:
```
<s>User:<fake_token_around_image><image><fake_token_around_image>Describe this image.
Assistant: An image of two kittens in grass.
User:<fake_token_around_image><image><fake_token_around_image>Describe this image.
Assistant:'
```
and the two images will be massaged using [`IdeficsImageProcessor.__call__`] method and placed inside the
`pixel_values` dict entry of the return value.
This example also examplifies that images can be passed as objects or as text urls. It can be seen that the
first image is passed as object and the second one as a url.
To do training do:
```python
image_transform = transforms.Compose(
[
transforms.RandomResizedCrop(
(w, h), scale=(0.9, 1.0), interpolation=transforms.InterpolationMode.BICUBIC
),
transforms.ToTensor(),
transforms.Normalize(mean=self.image_mean, std=self.image_std),
]
)
inputs = processor(prompts, transform=image_transform, return_tensors="pt")
```
In order to help debug prompt generation enable `debug=True` which will show you what's happening.
"""
# if the value isn't overriden by the user, check if the tokenizer was trained with this token and then use it
if add_end_of_utterance_token is None:
add_end_of_utterance_token = (
self.tokenizer_was_trained_with_end_of_utterance_token
)
# turn non-batched prompts into batched
if not any(isinstance(i, list) for i in prompts):
prompts = [prompts]
fake_token = "<fake_token_around_image>"
image_token = "<image>"
end_of_utterance_token = "<end_of_utterance>"
def image_tokens(last_was_image):
if last_was_image:
return image_token + fake_token
else:
return fake_token + image_token + fake_token
all_texts = []
all_images = []
for sample in prompts:
# the model was trained on samples starting with <s>
full_text = f"{self.tokenizer.bos_token}"
# an image can either be an image object in the item or the url, everything else is a verbatim prompt text
image_objects = []
last_was_image = False
last_was_text = False
for i, item in enumerate(sample):
if i > 0:
last_was_text = True if not last_was_image else False
if isinstance(item, str):
item = item.strip(" ")
if is_image(item):
image = self.image_processor.fetch_images(item)
full_text += image_tokens(last_was_image)
image_objects.append(image)
last_was_image = True
else:
# we add end_of_utterance_token between each subsequent text prompts (but not at the last one!)
if add_end_of_utterance_token and last_was_text:
full_text += end_of_utterance_token
full_text += item
last_was_image = False
else:
# must be an image obj
full_text += image_tokens(last_was_image)
image_objects.append(item)
last_was_image = True
if add_eos_token:
full_text += self.tokenizer.eos_token
if debug is True:
print(f"{full_text=}")
image_objects = self.image_processor(image_objects, transform=transform)
text_encoding = self.tokenizer(
text=full_text,
add_special_tokens=False,
padding=padding,
truncation=truncation,
max_length=max_length,
)
all_texts.append(text_encoding["input_ids"])
all_images.append(image_objects)
max_seq_len = max(len(x) for x in all_texts)
# max_num_images has to be at least 1 even when there are no images
max_num_images = max(len(x) for x in all_images)
max_num_images = max(1, max_num_images)
at_least_one_image = sum(len(x) for x in all_images) > 0
output_input_ids = []
output_images = []
output_attention_masks = []
for text, images in zip(all_texts, all_images):
padded_input_ids = [self.tokenizer.pad_token_id] * max_seq_len
unpadded_seq_len = len(text)
start = max_seq_len - unpadded_seq_len
padded_input_ids[start:] = text[:max_seq_len]
attention_mask = torch.zeros((max_seq_len,), dtype=torch.long)
attention_mask[start:] = 1
image_count = padded_input_ids.count(self.image_token_id)
local_max_num_images = min(image_count, max_num_images)
current_images = images[:local_max_num_images]
if len(current_images) > 0:
padded_image_tensor = torch.zeros(
max_num_images, *current_images.size()[1:]
)
padded_image_tensor[: current_images.size(0)] = current_images
else:
padded_image_tensor = torch.zeros(
max_num_images, *self.default_image_dims
)
output_images.append(padded_image_tensor)
output_input_ids.append(torch.tensor(padded_input_ids))
output_attention_masks.append(attention_mask)
output_input_ids = torch.stack(output_input_ids)
output_images = torch.stack(output_images)
output_attention_masks = torch.stack(output_attention_masks)
if at_least_one_image:
image_attention_mask, _ = image_attention_mask_for_packed_input_ids(
output_input_ids, self.tokenizer
)
image_attention_mask = incremental_to_binary_attention_mask(
image_attention_mask, num_classes=max_num_images
)
else:
# in full language mode we set the image mask to all-0s
image_attention_mask = torch.zeros(
output_input_ids.shape[0],
output_input_ids.shape[1],
1,
dtype=torch.bool,
)
return BatchFeature(
data={
"input_ids": output_input_ids,
"attention_mask": output_attention_masks,
"pixel_values": output_images,
"image_attention_mask": image_attention_mask,
}
)
def batch_decode(self, *args, **kwargs):
"""
This method forwards all its arguments to LlamaTokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please
refer to the docstring of this method for more information.
"""
return self.tokenizer.batch_decode(*args, **kwargs)
def decode(self, *args, **kwargs):
"""
This method forwards all its arguments to LlamaTokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer to
the docstring of this method for more information.
"""
return self.tokenizer.decode(*args, **kwargs)
@property
def model_input_names(self):
tokenizer_input_names = self.tokenizer.model_input_names
image_processor_input_names = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))
| 0 |
hf_public_repos/text-generation-inference/server/text_generation_server/models | hf_public_repos/text-generation-inference/server/text_generation_server/models/custom_modeling/bloom_modeling.py | # coding=utf-8
# Copyright 2022 HuggingFace Inc. team and BigScience workshop.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""PyTorch BLOOM model."""
import math
import os
import warnings
from typing import Optional, Tuple, Union
import torch
import torch.distributed
import torch.utils.checkpoint
from torch import nn
from torch.nn import LayerNorm
from torch.nn import functional as F
from transformers.modeling_outputs import (
BaseModelOutputWithPastAndCrossAttentions,
CausalLMOutputWithCrossAttentions,
)
from transformers import BloomConfig, PreTrainedModel
from text_generation_server.utils.layers import (
TensorParallelColumnLinear,
TensorParallelEmbedding,
TensorParallelRowLinear,
TensorParallelHead,
)
CUSTOM_KERNELS_ENABLED = False
if (
torch.cuda.is_available()
and not os.environ.get("DISABLE_CUSTOM_KERNELS", "False") == "True"
):
try:
from custom_kernels import fused_bloom_attention_cuda
CUSTOM_KERNELS_ENABLED = True
except ImportError:
pass
_CHECKPOINT_FOR_DOC = "bigscience/bloom-560m"
_CONFIG_FOR_DOC = "BloomConfig"
BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST = [
"bigscience/bigscience-small-testing",
"bigscience/bloom-560m",
"bigscience/bloom-1b1",
"bigscience/bloom-1b7",
"bigscience/bloom-3b",
"bigscience/bloom-7b1",
"bigscience/bloom",
]
def _make_causal_mask(
input_ids_shape: torch.Size, device: torch.device, past_key_values_length: int
) -> torch.BoolTensor:
"""
Make causal mask used for self-attention.
"""
batch_size, target_length = input_ids_shape
mask = torch.ones(
(target_length, target_length + past_key_values_length),
dtype=torch.bool,
device=device,
)
mask = mask.triu(1 + past_key_values_length)
expanded_mask = mask.unsqueeze(0).expand(
batch_size, target_length, target_length + past_key_values_length
)
return expanded_mask
def _expand_mask(mask: torch.Tensor, tgt_length: int) -> torch.BoolTensor:
"""
Expands attention_mask from `[batch_size, src_length]` to `[batch_size, 1, tgt_length, src_length]`.
"""
batch_size, src_length = mask.shape
tgt_length = tgt_length if tgt_length is not None else src_length
expanded_mask = ~(mask[:, None, :].to(torch.bool))
return expanded_mask.expand(batch_size, tgt_length, src_length)
def build_alibi_tensor(attention_mask: torch.Tensor, num_heads: int) -> torch.Tensor:
"""
Link to paper: https://arxiv.org/abs/2108.12409 Alibi tensor is not causal as the original paper mentions, it
relies on a translation invariance of softmax for quick implementation: with l being a tensor, and a fixed value
`softmax(l+a) = softmax(l)`. Based on
https://github.com/ofirpress/attention_with_linear_biases/blob/a35aaca144e0eb6b789dfcb46784c4b8e31b7983/fairseq/models/transformer.py#L742
TODO @thomasw21 this doesn't work as nicely due to the masking strategy, and so masking varies slightly.
Args:
Returns tensor shaped (batch_size * num_heads, 1, max_seq_len)
attention_mask (`torch.Tensor`):
Token-wise attention mask, this should be of shape (batch_size, max_seq_len).
num_heads (`int`, *required*):
number of heads
dtype (`torch.dtype`, *optional*, default=`torch.bfloat16`):
dtype of the output tensor
"""
batch_size, seq_length = attention_mask.shape
closest_power_of_2 = 2 ** math.floor(math.log2(num_heads))
base = torch.tensor(
2 ** (-(2 ** -(math.log2(closest_power_of_2) - 3))),
device=attention_mask.device,
dtype=torch.float32,
)
powers = torch.arange(
1, 1 + closest_power_of_2, device=attention_mask.device, dtype=torch.int32
)
slopes = torch.pow(base, powers)
if closest_power_of_2 != num_heads:
extra_base = torch.tensor(
2 ** (-(2 ** -(math.log2(2 * closest_power_of_2) - 3))),
device=attention_mask.device,
dtype=torch.float32,
)
num_remaining_heads = min(closest_power_of_2, num_heads - closest_power_of_2)
extra_powers = torch.arange(
1,
1 + 2 * num_remaining_heads,
2,
device=attention_mask.device,
dtype=torch.int32,
)
slopes = torch.cat([slopes, torch.pow(extra_base, extra_powers)], dim=0)
# Note: alibi will added to the attention bias that will be applied to the query, key product of attention
# => therefore alibi will have to be of shape (batch_size, num_heads, query_length, key_length)
# => here we set (batch_size=1, num_heads=num_heads, query_length=1, key_length=max_length)
# => the query_length dimension will then be broadcasted correctly
# This is more or less identical to T5's relative position bias:
# https://github.com/huggingface/transformers/blob/f681437203baa7671de3174b0fa583c349d9d5e1/src/transformers/models/t5/modeling_t5.py#L527
arange_tensor = ((attention_mask.cumsum(dim=-1) - 1) * attention_mask)[:, None, :]
alibi = slopes[..., None] * arange_tensor
return alibi
# @torch.jit.script
def dropout_add(
x: torch.Tensor, residual: torch.Tensor, prob: float, training: bool
) -> torch.Tensor:
"""
Dropout add function
Args:
x (`torch.tensor`, *required*):
input tensor
residual (`torch.tensor`, *required*):
esidual tensor
prob (`float`, *required*):
dropout probability
training (`bool`, *required*):
training mode
"""
out = F.dropout(x, p=prob, training=training)
out = residual + out
return out
# @torch.jit.script # this is shit for unknow reasons.
def _split_heads(
fused_qkv: torch.Tensor, num_heads: int, head_dim: int
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
"""
Split the last dimension into (num_heads, head_dim) without making any copies, results share same memory
storage as `fused_qkv`
Args:
fused_qkv (`torch.tensor`, *required*): [batch_size, seq_length, num_heads * 3 * head_dim]
Returns:
query: [batch_size, seq_length, num_heads, head_dim] key: [batch_size, seq_length, num_heads, head_dim]
value: [batch_size, seq_length, num_heads, head_dim]
"""
batch_size, seq_length, three_times_hidden_size = fused_qkv.shape
fused_qkv = fused_qkv.view(batch_size, seq_length, num_heads, 3 * head_dim)
query_layer, key_layer, value_layer = fused_qkv.split(head_dim, dim=-1)
query_layer = query_layer.transpose(1, 2).reshape(
batch_size * num_heads, seq_length, head_dim
)
key_layer = key_layer.permute(0, 2, 3, 1).reshape(
batch_size * num_heads, head_dim, seq_length
)
value_layer = value_layer.transpose(1, 2).reshape(
batch_size * num_heads, seq_length, head_dim
)
return query_layer, key_layer, value_layer
# @torch.jit.script
def _merge_heads(x: torch.Tensor, num_heads: int, head_dim: int) -> torch.Tensor:
"""
Merge heads together over the last dimenstion
Args:
x: (`torch.tensor`, *required*): [batch_size * num_heads, seq_length, head_dim]
Returns:
torch.tensor: [batch_size, seq_length, num_heads * head_dim]
"""
# What we want to achieve is:
# batch_size * num_heads, seq_length, head_dim -> batch_size, seq_length, num_heads * head_dim
batch_size_and_num_heads, seq_length, _ = x.shape
batch_size = batch_size_and_num_heads // num_heads
# First view to decompose the batch size
# batch_size * num_heads, seq_length, head_dim -> batch_size, num_heads, seq_length, head_dim
x = x.view(batch_size, num_heads, seq_length, head_dim)
# batch_size, num_heads, seq_length, head_dim -> batch_size, seq_length, num_heads, head_dim
x = x.permute(0, 2, 1, 3)
# batch_size, seq_length, num_heads, head_dim -> batch_size, seq_length, num_heads * head_dim
return x.reshape(batch_size, seq_length, num_heads * head_dim)
class BloomAttention(nn.Module):
def __init__(self, prefix, config: BloomConfig, weights):
super().__init__()
self.pretraining_tp = config.pretraining_tp
self.slow_but_exact = config.slow_but_exact
self.process_group = weights.process_group
self.hidden_size = config.hidden_size
self.num_heads = config.n_head
self.head_dim = self.hidden_size // self.num_heads
self.split_size = self.hidden_size
self.hidden_dropout = config.hidden_dropout
if self.head_dim * self.num_heads != self.hidden_size:
raise ValueError(
f"`hidden_size` must be divisible by num_heads (got `hidden_size`: {self.hidden_size} and `num_heads`:"
f" {self.num_heads})."
)
# Layer-wise attention scaling
self.inv_norm_factor = 1.0 / math.sqrt(self.head_dim)
self.beta = 1.0
process_group = weights.process_group
if self.num_heads % process_group.size() != 0:
raise ValueError(
f"`num_heads` must be divisible by `num_shards` (got `num_heads`: {self.num_heads} "
f"and `num_shards`: {process_group.size()}"
)
self.num_heads = self.num_heads // process_group.size()
self.query_key_value = TensorParallelColumnLinear.load(
config=config,
prefix=f"{prefix}.query_key_value",
weights=weights,
bias=True,
)
self.dense = TensorParallelRowLinear.load(
config=config, prefix=f"{prefix}.dense", weights=weights, bias=True
)
self.attention_dropout = nn.Dropout(config.attention_dropout)
@staticmethod
def compute_attention(
fused_qkv: torch.Tensor,
layer_past: Optional[Tuple[torch.Tensor, torch.Tensor]],
alibi: torch.Tensor,
attention_mask: torch.Tensor,
head_mask: Optional[torch.Tensor],
beta: float,
inv_norm_factor: float,
num_heads: int,
use_cache: bool,
):
batch_size, q_length, three_times_hidden_size = fused_qkv.shape
head_dim = three_times_hidden_size // (3 * num_heads)
batch_size * num_heads
### TODO @thomasw21: this takes quite a bit of time, how do I accelerate that?
# 3 x [batch_size, seq_length, num_heads, head_dim]
(query_layer, key_layer, value_layer) = _split_heads(
fused_qkv, num_heads=num_heads, head_dim=head_dim
)
if layer_past is not None:
past_key, past_value = layer_past
# concatenate along seq_length dimension:
# - key: [batch_size * self.num_heads, head_dim, kv_length]
# - value: [batch_size * self.num_heads, kv_length, head_dim]
past_key = past_key.view(-1, *past_key.shape[-2:])
key_layer = torch.cat((past_key, key_layer), dim=2)
past_value = past_value.view(-1, *past_value.shape[-2:])
value_layer = torch.cat((past_value, value_layer), dim=1)
_, _, kv_length = key_layer.shape
if use_cache is True:
present = (key_layer, value_layer)
else:
present = None
###
# [batch_size * num_heads, q_length, kv_length]
# we use `torch.Tensor.baddbmm` instead of `torch.baddbmm` as the latter isn't supported by TorchScript v1.11
attention_scores = alibi.baddbmm(
batch1=query_layer,
batch2=key_layer,
beta=beta,
alpha=inv_norm_factor,
)
# cast attention scores to fp32, compute scaled softmax and cast back to initial dtype - [batch_size, num_heads, q_length, kv_length]
input_dtype = attention_scores.dtype
# `float16` has a minimum value of -65504.0, whereas `bfloat16` and `float32` have a minimum value of `-3.4e+38`
if input_dtype == torch.float16:
attention_scores = attention_scores.to(torch.float)
# torch.finfo not supported by torch.jit, we temporarily remplace with `-1e34`
attn_weights = attention_scores.masked_fill_(
attention_mask, torch.finfo(attention_scores.dtype).min
)
attention_probs = F.softmax(attn_weights, dim=-1, dtype=torch.float32).to(
input_dtype
)
# # [batch_size, num_heads, q_length, kv_length]
# attention_probs = self.attention_dropout(attention_probs)
if head_mask is not None:
attention_probs = attention_probs * head_mask
# matmul: [batch_size * num_heads, q_length, head_dim]
context_layer = torch.bmm(attention_probs, value_layer, out=query_layer)
# change view [batch_size, num_heads, q_length, head_dim]
context_layer = _merge_heads(
context_layer, num_heads=num_heads, head_dim=head_dim
)
return context_layer, present, attention_probs
def forward(
self,
hidden_states: torch.Tensor,
residual: torch.Tensor,
alibi: torch.Tensor,
attention_mask: torch.Tensor,
layer_past: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
head_mask: Optional[torch.Tensor] = None,
use_cache: bool = False,
output_attentions: bool = False,
):
fused_qkv = self.query_key_value(
hidden_states
) # [batch_size, seq_length, 3 x hidden_size]
batch_size, q_length, _ = fused_qkv.shape
if layer_past is not None:
past_key, past_value = layer_past
layer_past = (
past_key.view(-1, *past_key.shape[-2:]),
past_value.view(-1, *past_value.shape[-2:]),
)
if CUSTOM_KERNELS_ENABLED:
assert self.training is False, "Only foward pass was implemented"
assert (
attention_mask.shape[-1] < 4096
), "Custom kernel support only up to 4096 tokens"
(
context_layer,
present,
attention_probs,
) = fused_bloom_attention_cuda.forward(
fused_qkv,
layer_past,
alibi,
attention_mask,
head_mask,
self.beta,
self.inv_norm_factor,
self.num_heads,
use_cache,
)
else:
context_layer, present, attention_probs = self.compute_attention(
fused_qkv=fused_qkv,
layer_past=layer_past,
alibi=alibi,
attention_mask=attention_mask,
head_mask=head_mask,
beta=self.beta,
inv_norm_factor=self.inv_norm_factor,
num_heads=self.num_heads,
use_cache=use_cache,
)
# aggregate results across tp ranks. See here: https://github.com/pytorch/pytorch/issues/76232
if self.pretraining_tp > 1 and self.slow_but_exact:
slices = self.hidden_size / self.pretraining_tp
output_tensor = torch.zeros_like(context_layer)
for i in range(self.pretraining_tp):
output_tensor = output_tensor + F.linear(
context_layer[:, :, int(i * slices) : int((i + 1) * slices)],
self.dense.weight[:, int(i * slices) : int((i + 1) * slices)],
)
else:
output_tensor = self.dense(context_layer)
# output_tensor = dropout_add(output_tensor, residual, self.hidden_dropout, self.training)
output_tensor += residual
outputs = (output_tensor, present)
if output_attentions:
outputs += (attention_probs,)
return outputs
class BloomMLP(nn.Module):
def __init__(self, prefix, config: BloomConfig, weights):
super().__init__()
self.pretraining_tp = config.pretraining_tp
self.slow_but_exact = config.slow_but_exact
self.dense_h_to_4h = TensorParallelColumnLinear.load(
config=config, prefix=f"{prefix}.dense_h_to_4h", weights=weights, bias=True
)
self.dense_4h_to_h = TensorParallelRowLinear.load(
config=config, prefix=f"{prefix}.dense_4h_to_h", weights=weights, bias=True
)
self.gelu_impl = torch.nn.GELU(approximate="tanh")
self.hidden_dropout = config.hidden_dropout
def forward(
self, hidden_states: torch.Tensor, residual: torch.Tensor
) -> torch.Tensor:
hidden_states = self.gelu_impl(self.dense_h_to_4h(hidden_states))
if self.pretraining_tp > 1 and self.slow_but_exact:
intermediate_output = torch.zeros_like(residual)
slices = self.dense_4h_to_h.weight.shape[-1] / self.pretraining_tp
for i in range(self.pretraining_tp):
intermediate_output = intermediate_output + F.linear(
hidden_states[:, :, int(i * slices) : int((i + 1) * slices)],
self.dense_4h_to_h.weight[
:, int(i * slices) : int((i + 1) * slices)
],
)
else:
intermediate_output = self.dense_4h_to_h(hidden_states)
# output = dropout_add(intermediate_output, residual, self.hidden_dropout, self.training)
intermediate_output += residual
return intermediate_output
class BloomBlock(nn.Module):
def __init__(self, layer_id: int, config: BloomConfig, weights):
super().__init__()
prefix = f"h.{layer_id}"
self.input_layernorm = LayerNorm.load(
prefix=f"{prefix}.input_layernorm",
weights=weights,
eps=config.layer_norm_epsilon,
)
self.num_heads = config.n_head
self.self_attention = BloomAttention(
prefix=f"{prefix}.self_attention", config=config, weights=weights
)
self.post_attention_layernorm = LayerNorm.load(
prefix=f"{prefix}.post_attention_layernorm",
weights=weights,
eps=config.layer_norm_epsilon,
)
self.mlp = BloomMLP(prefix=f"{prefix}.mlp", config=config, weights=weights)
self.apply_residual_connection_post_layernorm = (
config.apply_residual_connection_post_layernorm
)
self.hidden_dropout = config.hidden_dropout
def forward(
self,
hidden_states: torch.Tensor,
alibi: torch.Tensor,
attention_mask: torch.Tensor,
layer_past: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
head_mask: Optional[torch.Tensor] = None,
use_cache: bool = False,
output_attentions: bool = False,
):
# hidden_states: [batch_size, seq_length, hidden_size]
# Layer norm at the beginning of the transformer layer.
layernorm_output = self.input_layernorm(hidden_states)
# Layer norm post the self attention.
if self.apply_residual_connection_post_layernorm:
residual = layernorm_output
else:
residual = hidden_states
# Self attention.
attn_outputs = self.self_attention(
layernorm_output,
residual,
layer_past=layer_past,
attention_mask=attention_mask,
alibi=alibi,
head_mask=head_mask,
use_cache=use_cache,
output_attentions=output_attentions,
)
attention_output = attn_outputs[0]
outputs = attn_outputs[1:]
layernorm_output = self.post_attention_layernorm(attention_output)
# Get residual
if self.apply_residual_connection_post_layernorm:
residual = layernorm_output
else:
residual = attention_output
# MLP.
output = self.mlp(layernorm_output, residual)
if use_cache:
outputs = (output,) + outputs
else:
outputs = (output,) + outputs[1:]
return outputs # hidden_states, present, attentions
class BloomPreTrainedModel(PreTrainedModel):
config_class = BloomConfig
base_model_prefix = "transformer"
_no_split_modules = ["BloomBlock"]
@staticmethod
def _convert_to_standard_cache(
past_key_value: Tuple[Tuple[torch.Tensor, torch.Tensor]], batch_size: int
) -> Tuple[Tuple[torch.Tensor, torch.Tensor]]:
"""
Standardizes the format of the cache so as to match most implementations, i.e. to tuple(tuple([batch_size,
num_heads, ...]))
"""
batch_size_times_num_heads, head_dim, seq_length = past_key_value[0][0].shape
num_heads = batch_size_times_num_heads // batch_size
# key: [batch_size * num_heads, head_dim, seq_length] -> [batch_size, num_heads, head_dim, seq_length]
# value: [batch_size * num_heads, seq_length, head_dim] -> [batch_size, num_heads, seq_length, head_dim]
return tuple(
(
layer_past[0].view(batch_size, num_heads, head_dim, seq_length),
layer_past[1].view(batch_size, num_heads, seq_length, head_dim),
)
for layer_past in past_key_value
)
@staticmethod
def _convert_to_bloom_cache(
past_key_value: Tuple[Tuple[torch.Tensor, torch.Tensor]]
) -> Tuple[Tuple[torch.Tensor, torch.Tensor]]:
"""
Converts the cache to the format expected by Bloom, i.e. to tuple(tuple([batch_size * num_heads, ...]))
"""
batch_size, num_heads, head_dim, seq_length = past_key_value[0][0].shape
batch_size_times_num_heads = batch_size * num_heads
# key: [batch_size, num_heads, head_dim, seq_length] -> [batch_size * num_heads, head_dim, seq_length]
# value: [batch_size, num_heads, seq_length, head_dim] -> [batch_size * num_heads, seq_length, head_dim]
return tuple(
(
layer_past[0].view(batch_size_times_num_heads, head_dim, seq_length),
layer_past[1].view(batch_size_times_num_heads, seq_length, head_dim),
)
for layer_past in past_key_value
)
class BloomModel(BloomPreTrainedModel):
def __init__(self, config: BloomConfig, weights):
super().__init__(config)
self.embed_dim = config.hidden_size
self.num_heads = config.n_head
process_group = weights.process_group
self.tp_rank = process_group.rank()
self.tp_world_size = process_group.size()
self.word_embeddings = TensorParallelEmbedding(
prefix="word_embeddings", weights=weights
)
self.word_embeddings_layernorm = LayerNorm.load(
prefix="word_embeddings_layernorm",
weights=weights,
eps=config.layer_norm_epsilon,
)
# Transformer blocks
self.h = nn.ModuleList(
[
BloomBlock(layer_id=layer_id, config=config, weights=weights)
for layer_id in range(config.num_hidden_layers)
]
)
# Final Layer Norm
self.ln_f = LayerNorm.load(
prefix="ln_f", weights=weights, eps=config.layer_norm_epsilon
)
def _prepare_attn_mask(
self,
attention_mask: torch.Tensor,
input_shape: Tuple[int, int],
past_key_values_length: int,
) -> torch.BoolTensor:
# create causal mask
# [batch_size, seq_length] -> [batch_size, tgt_length, src_length]
combined_attention_mask = None
device = attention_mask.device
_, src_length = input_shape
if src_length > 1:
combined_attention_mask = _make_causal_mask(
input_shape,
device=device,
past_key_values_length=past_key_values_length,
)
# [batch_size, seq_length] -> [batch_size, tgt_length, src_length]
expanded_attn_mask = _expand_mask(attention_mask, tgt_length=src_length)
combined_attention_mask = (
expanded_attn_mask
if combined_attention_mask is None
else expanded_attn_mask | combined_attention_mask
)
return combined_attention_mask
def set_input_embeddings(self, new_embeddings: torch.Tensor):
self.word_embeddings = new_embeddings
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None,
attention_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.LongTensor] = None,
inputs_embeds: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
**deprecated_arguments,
) -> Union[Tuple[torch.Tensor, ...], BaseModelOutputWithPastAndCrossAttentions]:
if deprecated_arguments.pop("position_ids", False) is not False:
# `position_ids` could have been `torch.Tensor` or `None` so defaulting pop to `False` allows to detect if users were passing explicitly `None`
warnings.warn(
"`position_ids` have no functionality in BLOOM and will be removed in v5.0.0. You can safely ignore"
" passing `position_ids`.",
FutureWarning,
)
if len(deprecated_arguments) > 0:
raise ValueError(f"Got unexpected arguments: {deprecated_arguments}")
output_attentions = (
output_attentions
if output_attentions is not None
else self.config.output_attentions
)
output_hidden_states = (
output_hidden_states
if output_hidden_states is not None
else self.config.output_hidden_states
)
use_cache = use_cache if use_cache is not None else self.config.use_cache
return_dict = (
return_dict if return_dict is not None else self.config.use_return_dict
)
if input_ids is not None and inputs_embeds is not None:
raise ValueError(
"You cannot specify both input_ids and inputs_embeds at the same time"
)
elif input_ids is not None:
batch_size, seq_length = input_ids.shape
elif inputs_embeds is not None:
batch_size, seq_length, _ = inputs_embeds.shape
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
if past_key_values is None:
past_key_values = tuple([None] * len(self.h))
# Prepare head mask if needed
# 1.0 in head_mask indicate we keep the head
# attention_probs has shape batch_size x num_heads x N x N
# head_mask has shape n_layer x batch x num_heads x N x N
head_mask = self.get_head_mask(head_mask, self.config.n_layer)
if inputs_embeds is None:
inputs_embeds = self.word_embeddings(input_ids)
hidden_states = self.word_embeddings_layernorm(inputs_embeds)
presents = () if use_cache else None
all_self_attentions = () if output_attentions else None
all_hidden_states = () if output_hidden_states else None
# Compute alibi tensor: check build_alibi_tensor documentation
seq_length_with_past = seq_length
past_key_values_length = 0
if past_key_values[0] is not None:
past_key_values_length = past_key_values[0][0].shape[-1]
seq_length_with_past = seq_length_with_past + past_key_values_length
if attention_mask is None:
attention_mask = torch.ones(
(batch_size, seq_length_with_past), device=hidden_states.device
)
else:
attention_mask = attention_mask.to(hidden_states.device)
alibi = build_alibi_tensor(attention_mask, self.num_heads)
causal_mask = self._prepare_attn_mask(
attention_mask,
input_shape=(batch_size, seq_length),
past_key_values_length=past_key_values_length,
)
if hasattr(self, "tp_rank"):
assert self.num_heads % self.tp_world_size == 0
block_size = self.num_heads // self.tp_world_size
alibi = alibi[
:, self.tp_rank * block_size : (self.tp_rank + 1) * block_size
]
alibi = alibi.reshape(batch_size * block_size, 1, seq_length_with_past)
causal_mask = torch.repeat_interleave(causal_mask, block_size, dim=0)
else:
alibi = alibi.reshape(batch_size * self.num_heads, 1, seq_length_with_past)
causal_mask = torch.repeat_interleave(causal_mask, self.num_heads, dim=0)
alibi = alibi.to(hidden_states.dtype)
for i, (block, layer_past) in enumerate(zip(self.h, past_key_values)):
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
outputs = block(
hidden_states,
layer_past=layer_past,
attention_mask=causal_mask,
head_mask=head_mask[i],
use_cache=use_cache,
output_attentions=output_attentions,
alibi=alibi,
)
hidden_states = outputs[0]
if use_cache is True:
presents = presents + (outputs[1],)
if output_attentions:
all_self_attentions = all_self_attentions + (
outputs[2 if use_cache else 1],
)
# Add last hidden state
hidden_states = self.ln_f(hidden_states)
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
if not return_dict:
return tuple(
v
for v in [
hidden_states,
presents,
all_hidden_states,
all_self_attentions,
]
if v is not None
)
return BaseModelOutputWithPastAndCrossAttentions(
last_hidden_state=hidden_states,
past_key_values=presents,
hidden_states=all_hidden_states,
attentions=all_self_attentions,
)
class BloomForCausalLM(BloomPreTrainedModel):
def __init__(self, config, weights):
super().__init__(config)
self.transformer = BloomModel(config, weights)
self.lm_head = TensorParallelHead.load(
config,
prefix="word_embeddings",
weights=weights,
)
def prepare_inputs_for_generation(
self,
input_ids: torch.LongTensor,
past_key_values: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.Tensor] = None,
**kwargs,
) -> dict:
# only last token for input_ids if past is not None
if past_key_values:
input_ids = input_ids[:, -1].unsqueeze(-1)
# the cache may be in the stardard format (e.g. in contrastive search), convert to bloom's format if needed
if past_key_values[0][0].shape[0] == input_ids.shape[0]:
past_key_values = self._convert_to_bloom_cache(past_key_values)
# if `inputs_embeds` are passed, we only want to use them in the 1st generation step
if inputs_embeds is not None and past_key_values is None:
model_inputs = {"inputs_embeds": inputs_embeds}
else:
model_inputs = {"input_ids": input_ids}
model_inputs.update(
{
"past_key_values": past_key_values,
"use_cache": kwargs.get("use_cache"),
"attention_mask": attention_mask,
}
)
return model_inputs
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None,
attention_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.Tensor] = None,
labels: Optional[torch.Tensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
**deprecated_arguments,
) -> Union[Tuple[torch.Tensor], CausalLMOutputWithCrossAttentions]:
r"""
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set
`labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100`
are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]`
"""
if deprecated_arguments.pop("position_ids", False) is not False:
# `position_ids` could have been `torch.Tensor` or `None` so defaulting pop to `False` allows to detect if users were passing explicitly `None`
warnings.warn(
"`position_ids` have no functionality in BLOOM and will be removed in v5.0.0. You can safely ignore"
" passing `position_ids`.",
FutureWarning,
)
if len(deprecated_arguments) > 0:
raise ValueError(f"Got unexpected arguments: {deprecated_arguments}")
return_dict = (
return_dict if return_dict is not None else self.config.use_return_dict
)
transformer_outputs = self.transformer(
input_ids,
past_key_values=past_key_values,
attention_mask=attention_mask,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
hidden_states = transformer_outputs[0]
lm_logits = self.lm_head(hidden_states)
loss = None
if not return_dict:
output = (lm_logits,) + transformer_outputs[1:]
return ((loss,) + output) if loss is not None else output
return CausalLMOutputWithCrossAttentions(
loss=loss,
logits=lm_logits,
past_key_values=transformer_outputs.past_key_values,
hidden_states=transformer_outputs.hidden_states,
attentions=transformer_outputs.attentions,
)
| 0 |
hf_public_repos/text-generation-inference/server/text_generation_server/models | hf_public_repos/text-generation-inference/server/text_generation_server/models/custom_modeling/flash_mistral_modeling.py | # coding=utf-8
# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
#
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
# and OPT implementations in this library. It has been modified from its
# original forms to accommodate minor architectural differences compared
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch
import torch.distributed
from torch import nn
from transformers.activations import ACT2FN
from transformers.configuration_utils import PretrainedConfig
from typing import Optional, List, Tuple
from text_generation_server.utils import paged_attention, flash_attn
from text_generation_server.utils.layers import (
TensorParallelRowLinear,
TensorParallelColumnLinear,
TensorParallelEmbedding,
PositionRotaryEmbedding,
TensorParallelHead,
get_linear,
FastRMSNorm,
)
class MistralConfig(PretrainedConfig):
model_type = "mistral"
def __init__(
self,
vocab_size=32000,
hidden_size=4096,
intermediate_size=14336,
num_hidden_layers=32,
num_attention_heads=32,
num_key_value_heads=8,
hidden_act="silu",
max_position_embeddings=4096 * 32,
initializer_range=0.02,
rms_norm_eps=1e-6,
use_cache=True,
pad_token_id=None,
bos_token_id=1,
eos_token_id=2,
pretraining_tp=1,
tie_word_embeddings=False,
rope_theta=10000.0,
sliding_window=None,
**kwargs,
):
self.vocab_size = vocab_size
self.max_position_embeddings = max_position_embeddings
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.sliding_window = sliding_window
# for backward compatibility
if num_key_value_heads is None:
num_key_value_heads = num_attention_heads
self.num_key_value_heads = num_key_value_heads
self.hidden_act = hidden_act
self.initializer_range = initializer_range
self.rms_norm_eps = rms_norm_eps
self.pretraining_tp = pretraining_tp
self.use_cache = use_cache
self.rope_theta = rope_theta
super().__init__(
pad_token_id=pad_token_id,
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
tie_word_embeddings=tie_word_embeddings,
**kwargs,
)
def load_attention(config, prefix, weights):
if config.num_attention_heads != config.num_key_value_heads:
return _load_gqa(config, prefix, weights)
else:
return TensorParallelColumnLinear.load_multi(
config,
prefixes=[f"{prefix}.q_proj", f"{prefix}.k_proj", f"{prefix}.v_proj"],
dim=0,
weights=weights,
bias=False,
)
def _load_gqa(config, prefix: str, weights):
assert config.hidden_size % config.num_attention_heads == 0
assert config.num_attention_heads % weights.process_group.size() == 0
weight = weights.get_multi_weights_col(
prefixes=[f"{prefix}.q_proj", f"{prefix}.k_proj", f"{prefix}.v_proj"],
quantize=config.quantize,
dim=0,
)
if config.quantize not in ["gptq", "awq"]:
weight = weight.to(dtype=weights.dtype).to(device=weights.device)
head_size = config.hidden_size // config.num_attention_heads
num_heads = config.num_attention_heads // weights.process_group.size()
num_key_value_heads = config.num_key_value_heads // weights.process_group.size()
assert list(weight.shape) == [
(num_heads + 2 * num_key_value_heads) * head_size,
config.hidden_size,
], f"{list(weight.shape)} != {[(num_heads + 2 * config.num_key_value_heads) * head_size, config.hidden_size]}"
return TensorParallelColumnLinear(
get_linear(weight, bias=None, quantize=config.quantize)
)
class MistralAttention(torch.nn.Module):
def __init__(
self,
prefix: str,
config,
weights,
):
super().__init__()
self.max_past = (
config.sliding_window if config.sliding_window is not None else -1
)
self.num_heads = config.num_attention_heads
self.hidden_size = config.hidden_size
self.head_size = self.hidden_size // self.num_heads
self.rotary_emb = PositionRotaryEmbedding.static(
config=config,
dim=self.head_size,
base=config.rope_theta,
device=weights.device,
)
self.softmax_scale = self.head_size**-0.5
if self.num_heads % weights.process_group.size() != 0:
raise ValueError(
f"`num_heads` must be divisible by `num_shards` (got `num_heads`: {self.num_heads} "
f"and `num_shards`: {weights.process_group.size()}"
)
self.num_heads = self.num_heads // weights.process_group.size()
self.num_key_value_heads = (
config.num_key_value_heads // weights.process_group.size()
)
self.query_key_value = load_attention(config, prefix, weights)
self.o_proj = TensorParallelRowLinear.load(
config,
prefix=f"{prefix}.o_proj",
weights=weights,
bias=False,
)
self.num_groups = self.num_heads // self.num_key_value_heads
self.kv_head_mapping = torch.arange(
0, self.num_key_value_heads, dtype=torch.int32, device=weights.device
).repeat_interleave(self.num_groups)
def forward(
self,
hidden_states,
cos,
sin,
cu_seqlen_prefill,
kv_cache,
block_tables,
slots,
input_lengths,
max_s,
prefill_cache_indices,
):
qkv = self.query_key_value(hidden_states)
query, kv = qkv.split(
[
self.head_size * self.num_heads,
2 * self.head_size * self.num_key_value_heads,
],
dim=1,
)
query = query.view(-1, self.num_heads, self.head_size)
kv = kv.view(-1, 2, self.num_key_value_heads, self.head_size)
self.rotary_emb(query, torch.select(kv, dim=1, index=0), cos, sin)
if prefill_cache_indices is not None:
kv_to_cache = kv[prefill_cache_indices]
else:
kv_to_cache = kv
paged_attention.reshape_and_cache(
kv_to_cache[:, 0], kv_to_cache[:, 1], kv_cache[0], kv_cache[1], slots
)
# output tensor
attn_output = torch.empty_like(query)
# Prefill
if cu_seqlen_prefill is not None:
# flash attention
flash_attn.attention(
query,
torch.select(kv, dim=1, index=0),
torch.select(kv, dim=1, index=1),
attn_output,
cu_seqlen_prefill,
max_s,
self.softmax_scale,
window_size_left=self.max_past,
)
# Decode
else:
paged_attention.attention(
attn_output,
query,
kv_cache[0],
kv_cache[1],
self.kv_head_mapping,
self.softmax_scale,
block_tables,
input_lengths,
max_s,
)
return self.o_proj(attn_output.view(-1, self.num_heads * self.head_size))
class MistralMLP(nn.Module):
def __init__(self, prefix, config, weights):
super().__init__()
act = config.hidden_act
self.act = (
ACT2FN[act]
if "gelu" not in act
else lambda x: torch.nn.functional.gelu(
x,
approximate="tanh"
if act in ["gelu_fast", "gelu_pytorch_tanh"]
else "none",
)
)
# Fuse gate and up proj
self.gate_up_proj = TensorParallelColumnLinear.load_multi(
config,
prefixes=[f"{prefix}.gate_proj", f"{prefix}.up_proj"],
weights=weights,
dim=0,
bias=False,
)
self.down_proj = TensorParallelRowLinear.load(
config,
prefix=f"{prefix}.down_proj",
weights=weights,
bias=False,
)
self.intermediate_size = (
config.intermediate_size // weights.process_group.size()
)
def forward(self, hidden_states):
gate_up_states = self.gate_up_proj(hidden_states)
gate_up_states = gate_up_states.view(-1, 2, self.intermediate_size)
return self.down_proj(self.act(gate_up_states[:, 0]) * gate_up_states[:, 1])
class MistralLayer(nn.Module):
def __init__(self, layer_id, config, weights):
super().__init__()
prefix = f"model.layers.{layer_id}"
self.self_attn = MistralAttention(
prefix=f"{prefix}.self_attn", config=config, weights=weights
)
self.mlp = MistralMLP(prefix=f"{prefix}.mlp", config=config, weights=weights)
self.input_layernorm = FastRMSNorm.load(
prefix=f"{prefix}.input_layernorm", weights=weights, eps=config.rms_norm_eps
)
self.post_attention_layernorm = FastRMSNorm.load(
prefix=f"{prefix}.post_attention_layernorm",
weights=weights,
eps=config.rms_norm_eps,
)
def forward(
self,
hidden_states,
residual,
cos,
sin,
cu_seqlen_prefill,
kv_cache,
block_tables,
slots,
input_lengths,
max_s,
prefill_cache_indices,
):
normed_hidden_states, res = self.input_layernorm(hidden_states, residual)
# Self Attention
attn_output = self.self_attn(
normed_hidden_states,
cos,
sin,
cu_seqlen_prefill,
kv_cache,
block_tables,
slots,
input_lengths,
max_s,
prefill_cache_indices,
)
# faster post attention rms norm
normed_attn_res_output, attn_res = self.post_attention_layernorm(
attn_output, res
)
mlp_output = self.mlp(normed_attn_res_output)
return mlp_output, attn_res
class MistralModel(torch.nn.Module):
def __init__(self, config, weights):
super().__init__()
process_group = weights.process_group
self.tp_rank = process_group.rank()
self.tp_world_size = process_group.size()
self.embed_tokens = TensorParallelEmbedding(
prefix="model.embed_tokens", weights=weights
)
self.layers = nn.ModuleList(
[
MistralLayer(
layer_id,
config,
weights,
)
for layer_id in range(config.num_hidden_layers)
]
)
self.norm = FastRMSNorm.load(
prefix="model.norm", weights=weights, eps=config.rms_norm_eps
)
self.gradient_checkpointing = False
self.head_size = self.layers[0].self_attn.head_size
self.num_heads = self.layers[0].self_attn.num_heads
self.num_key_value_heads = self.layers[0].self_attn.num_key_value_heads
def forward(
self,
input_ids: torch.Tensor,
position_ids: torch.Tensor,
cu_seqlen_prefill: Optional[torch.Tensor],
kv_cache: List[Tuple[torch.Tensor, torch.Tensor]],
block_tables: torch.Tensor,
slots: torch.Tensor,
input_lengths: torch.Tensor,
max_s: int,
true_max_s: int,
prefill_cache_indices: Optional[torch.Tensor],
) -> torch.Tensor:
hidden_states = self.embed_tokens(input_ids)
# Get rotary cos and sin for this forward
# Avoid to index in each layer
cos, sin = self.layers[0].self_attn.rotary_emb.get_cos_sin(
position_ids, true_max_s, hidden_states.dtype
)
residual = None
for i, layer in enumerate(self.layers):
hidden_states, residual = layer(
hidden_states,
residual,
cos,
sin,
cu_seqlen_prefill,
kv_cache[i],
block_tables,
slots,
input_lengths,
max_s,
prefill_cache_indices,
)
hidden_states, _ = self.norm(hidden_states, residual)
return hidden_states
class FlashMistralForCausalLM(torch.nn.Module):
def __init__(self, config, weights):
super().__init__()
self.model = MistralModel(config, weights)
self.lm_head = TensorParallelHead.load(
config,
prefix="lm_head",
weights=weights,
)
self.max_past = config.sliding_window
def forward(
self,
input_ids: torch.Tensor,
position_ids: torch.Tensor,
cu_seqlen_prefill: Optional[torch.Tensor],
kv_cache: List[Tuple[torch.Tensor, torch.Tensor]],
block_tables: torch.Tensor,
slots: torch.Tensor,
input_lengths: torch.Tensor,
max_s: int,
prefill_cache_indices: Optional[torch.Tensor],
lm_head_indices: Optional[torch.Tensor] = None,
) -> torch.Tensor:
true_max_s = max_s
if prefill_cache_indices is not None:
# Slots also need to be sliced as it has the same size as the whole kv tensor
slots = slots[prefill_cache_indices]
elif self.max_past is not None:
# Clamp in decode mode as paged attention requires clamped values whereas the flash attention
# kernel requires the true values
max_s = min(self.max_past, max_s)
input_lengths = torch.clamp(input_lengths, max=self.max_past)
hidden_states = self.model(
input_ids,
position_ids,
cu_seqlen_prefill,
kv_cache,
block_tables,
slots,
input_lengths,
max_s,
true_max_s,
prefill_cache_indices,
)
if lm_head_indices is not None:
hidden_states = hidden_states[lm_head_indices]
logits = self.lm_head(hidden_states)
return logits
| 0 |
hf_public_repos/text-generation-inference/server/text_generation_server/models | hf_public_repos/text-generation-inference/server/text_generation_server/models/custom_modeling/flash_mixtral_modeling.py | # coding=utf-8
# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
#
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
# and OPT implementations in this library. It has been modified from its
# original forms to accommodate minor architectural differences compared
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch
import torch.distributed
import numpy as np
from torch import nn
from transformers.activations import ACT2FN
from transformers.configuration_utils import PretrainedConfig
from typing import Optional, List, Tuple
from loguru import logger
from text_generation_server.utils import paged_attention, flash_attn
from text_generation_server.utils.layers import (
FastLinear,
FastRMSNorm,
TensorParallelRowLinear,
TensorParallelColumnLinear,
TensorParallelEmbedding,
PositionRotaryEmbedding,
TensorParallelHead,
get_linear,
)
HAS_MEGABLOCKS = True
try:
import stk
import megablocks.ops as ops
except ImportError:
logger.warning("Mixtral: megablocks is not installed")
HAS_MEGABLOCKS = False
class MixtralConfig(PretrainedConfig):
model_type = "mixtral"
def __init__(
self,
vocab_size=32000,
hidden_size=4096,
intermediate_size=14336,
num_hidden_layers=32,
num_attention_heads=32,
num_key_value_heads=8,
hidden_act="silu",
max_position_embeddings=4096 * 32,
initializer_range=0.02,
rms_norm_eps=1e-05,
use_cache=True,
pad_token_id=None,
bos_token_id=1,
eos_token_id=2,
pretraining_tp=1,
tie_word_embeddings=False,
rope_theta=10000.0,
sliding_window=None,
num_experts_per_tok=2,
num_local_experts=8,
**kwargs,
):
self.vocab_size = vocab_size
self.max_position_embeddings = max_position_embeddings
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.sliding_window = sliding_window
# for backward compatibility
if num_key_value_heads is None:
num_key_value_heads = num_attention_heads
self.num_key_value_heads = num_key_value_heads
self.hidden_act = hidden_act
self.initializer_range = initializer_range
self.rms_norm_eps = rms_norm_eps
self.pretraining_tp = pretraining_tp
self.use_cache = use_cache
self.rope_theta = rope_theta
self.num_experts_per_tok = num_experts_per_tok
self.num_local_experts = num_local_experts
super().__init__(
pad_token_id=pad_token_id,
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
tie_word_embeddings=tie_word_embeddings,
**kwargs,
)
def promote_scalar(x: torch.Tensor) -> torch.Tensor:
return x.view(1) if len(x.size()) == 0 else x
def load_attention(config, prefix, weights):
if config.num_attention_heads != config.num_key_value_heads:
return _load_gqa(config, prefix, weights)
else:
return TensorParallelColumnLinear.load_multi(
config,
prefixes=[f"{prefix}.q_proj", f"{prefix}.k_proj", f"{prefix}.v_proj"],
dim=0,
weights=weights,
bias=False,
)
def _load_gqa(config, prefix: str, weights):
assert config.hidden_size % config.num_attention_heads == 0
assert config.num_attention_heads % weights.process_group.size() == 0
weight = weights.get_multi_weights_col(
prefixes=[f"{prefix}.q_proj", f"{prefix}.k_proj", f"{prefix}.v_proj"],
quantize=config.quantize,
dim=0,
)
if config.quantize not in ["gptq", "awq"]:
weight = weight.to(dtype=weights.dtype).to(device=weights.device)
head_size = config.hidden_size // config.num_attention_heads
num_heads = config.num_attention_heads // weights.process_group.size()
num_key_value_heads = config.num_key_value_heads // weights.process_group.size()
assert list(weight.shape) == [
(num_heads + 2 * num_key_value_heads) * head_size,
config.hidden_size,
], f"{list(weight.shape)} != {[(num_heads + 2 * config.num_key_value_heads) * head_size, config.hidden_size]}"
return TensorParallelColumnLinear(
get_linear(weight, bias=None, quantize=config.quantize)
)
def _load_experts(config, prefix, mat, weights):
if config.quantize is not None:
raise NotImplementedError("Mixtral does not support weight quantization yet.")
assert mat in ["w1", "w2", "w3"]
world_size = weights.process_group.size()
rank = weights.process_group.rank()
assert (
config.intermediate_size % world_size == 0
), f"The chosen size {config.intermediate_size} is not compatible with sharding on {world_size} shards"
block_size = config.intermediate_size // world_size
start = rank * block_size
stop = (rank + 1) * block_size
tensor = torch.empty(
(config.num_local_experts * block_size, config.hidden_size),
dtype=weights.dtype,
device=weights.device,
)
for i in range(config.num_local_experts):
slice_ = weights._get_slice(f"{prefix}.{i}.{mat}.weight")
if mat == "w2":
expert_slice = slice_[:, start:stop].t().contiguous()
else:
expert_slice = slice_[start:stop]
tensor[i * block_size : (i + 1) * block_size] = expert_slice.to(
dtype=weights.dtype
).to(device=weights.device)
return tensor
class MixtralAttention(torch.nn.Module):
def __init__(
self,
prefix: str,
config,
weights,
):
super().__init__()
self.max_past = (
config.sliding_window if config.sliding_window is not None else -1
)
self.num_heads = config.num_attention_heads
self.hidden_size = config.hidden_size
self.head_size = self.hidden_size // self.num_heads
self.rotary_emb = PositionRotaryEmbedding.static(
config=config,
dim=self.head_size,
base=config.rope_theta,
device=weights.device,
)
self.softmax_scale = self.head_size**-0.5
if self.num_heads % weights.process_group.size() != 0:
raise ValueError(
f"`num_heads` must be divisible by `num_shards` (got `num_heads`: {self.num_heads} "
f"and `num_shards`: {weights.process_group.size()}"
)
self.num_heads = self.num_heads // weights.process_group.size()
self.num_key_value_heads = (
config.num_key_value_heads // weights.process_group.size()
)
self.query_key_value = load_attention(config, prefix, weights)
self.o_proj = TensorParallelRowLinear.load(
config,
prefix=f"{prefix}.o_proj",
weights=weights,
bias=False,
)
self.num_groups = self.num_heads // self.num_key_value_heads
self.kv_head_mapping = torch.arange(
0, self.num_key_value_heads, dtype=torch.int32, device=weights.device
).repeat_interleave(self.num_groups)
def forward(
self,
hidden_states,
cos,
sin,
cu_seqlen_prefill,
kv_cache,
block_tables,
slots,
input_lengths,
max_s,
prefill_cache_indices,
):
qkv = self.query_key_value(hidden_states)
query, kv = qkv.split(
[
self.head_size * self.num_heads,
2 * self.head_size * self.num_key_value_heads,
],
dim=1,
)
query = query.view(-1, self.num_heads, self.head_size)
kv = kv.view(-1, 2, self.num_key_value_heads, self.head_size)
self.rotary_emb(query, torch.select(kv, dim=1, index=0), cos, sin)
if prefill_cache_indices is not None:
kv_to_cache = kv[prefill_cache_indices]
else:
kv_to_cache = kv
paged_attention.reshape_and_cache(
kv_to_cache[:, 0], kv_to_cache[:, 1], kv_cache[0], kv_cache[1], slots
)
# output tensor
attn_output = torch.empty_like(query)
# Prefill
if cu_seqlen_prefill is not None:
# flash attention
flash_attn.attention(
query,
torch.select(kv, dim=1, index=0),
torch.select(kv, dim=1, index=1),
attn_output,
cu_seqlen_prefill,
max_s,
self.softmax_scale,
window_size_left=self.max_past,
)
# Decode
else:
paged_attention.attention(
attn_output,
query,
kv_cache[0],
kv_cache[1],
self.kv_head_mapping,
self.softmax_scale,
block_tables,
input_lengths,
max_s,
)
return self.o_proj(attn_output.view(-1, self.num_heads * self.head_size))
@torch.jit.script
def select_experts(gate_logits: torch.Tensor, top_k: int):
# all_probs: (sequence_length, n_experts) and upcast for softmax
all_probs = torch.nn.functional.softmax(gate_logits, dim=1, dtype=torch.float)
# weights, selected_experts: (sequence_length, top-k)
weights, selected_experts = torch.topk(all_probs, top_k, dim=-1)
weights /= weights.sum(dim=-1, keepdim=True)
weights = weights.view(-1)
selected_experts = selected_experts.view(-1)
return selected_experts, weights
@torch.jit.script
def round_up(x: torch.Tensor, value: int):
return torch.div(x + (value - 1), value, rounding_mode="trunc") * value
class BlockSparseMoE(nn.Module):
"""
Built on the paper and library Megablocks as described in
https://arxiv.org/abs/2211.15841. This implementation is
strictly equivalent to standard MoE with full capacity (no
dropped tokens). It's faster since it formulates MoE operations
in terms of block-sparse operations to accomodate imbalanced
assignments of tokens to experts, whereas standard MoE either
(1) drop tokens at the cost of reduced performance or (2) set
capacity factor to number of experts and thus waste computation
and memory on padding.
"""
def __init__(self, prefix, config: MixtralConfig, weights):
super().__init__()
self.hidden_dim = config.hidden_size
self.ffn_dim = config.intermediate_size // weights.process_group.size()
self.num_experts = config.num_local_experts
self.top_k = config.num_experts_per_tok
act = config.hidden_act
if "gelu" in act:
self.act = lambda x: torch.nn.functional.gelu(
x,
approximate="tanh"
if act in ["gelu_fast", "gelu_pytorch_tanh"]
else "none",
)
elif "silu" in act:
self.act = torch.nn.functional.silu
else:
self.act = ACT2FN[act]
# gating
self.gate = FastLinear.load(config, f"{prefix}.gate", weights, bias=False)
# merged expert weights, all of size (n_experts * ffn_dim, hidden_dim)
self.w1 = _load_experts(config, f"{prefix}.experts", "w1", weights)
self.w2 = _load_experts(config, f"{prefix}.experts", "w2", weights)
self.w3 = _load_experts(config, f"{prefix}.experts", "w3", weights)
self.offsets = None
self.offsets_block_rows = 0
self.process_group = weights.process_group
# Calculate the number of bits needed to represent the expert indices
# so that we can pass it to radix sort.
self.sort_end_bit = max(int(np.ceil(np.log2(self.num_experts))), 1)
self.blocking = 128
self.quantize_scatter_num_bits = -1
def topology(self, x: torch.Tensor, padded_bins: torch.Tensor):
padded_tokens, _ = x.size()
assert padded_tokens % self.blocking == 0
assert self.ffn_dim % self.blocking == 0
# Offsets for the sparse matrix. All rows have the
# same number of nonzero blocks dictated by the
# dimensionality of a single expert.
block_rows = padded_tokens // self.blocking
blocks_per_row = self.ffn_dim // self.blocking
if self.offsets is None or block_rows > self.offsets_block_rows:
self.offsets = torch.arange(
0,
block_rows * blocks_per_row + 1,
blocks_per_row,
dtype=torch.int32,
device=x.device,
)
self.offsets_block_rows = block_rows
offsets = self.offsets
else:
offsets = self.offsets[: block_rows + 1]
# Indices for the sparse matrix. The indices for
# the intermediate matrix are dynamic depending
# on the mapping of tokens to experts.
column_indices = ops.topology(
padded_bins, self.blocking, block_rows, blocks_per_row
)
# For now, use meta init to save the device memory.
data = torch.empty(
column_indices.numel(),
self.blocking,
self.blocking,
dtype=x.dtype,
device="meta",
)
shape = (padded_tokens, self.ffn_dim * self.num_experts)
row_indices = stk.ops.row_indices(shape, data, offsets, column_indices)
return stk.Matrix(
shape,
data,
row_indices,
column_indices,
offsets,
False,
False,
False,
)
def indices_and_padded_bins(self, selected_experts: torch.Tensor):
# Sort the expert ids to produce the scatter/gather
# indices for the permutation.
# selected_experts = selected_experts.int()
# returns bin_ids == num of experts for this sequence ? == unique selected experts?
# and indices == how to sort tokens?
bin_ids, indices = ops.sort(selected_experts, self.sort_end_bit)
# bin_ids => [0, 0, 0, 2, 2, ...] => [num_tokens * top_k]
# indices => [14, 32, 33, ...] => [num_tokens * top_k]
# Histogram the expert ids to identify the number of
# tokens routed to each expert.
tokens_per_expert = ops.histogram(selected_experts, self.num_experts)
# tokens_per_expert => [3, 0, 2, ...] => [num_experts]
# Round the token counts up to the block size used in
# the matrix muliplications. Caculate the starting
# position of each bin.
# List of size num_experts
padded_tokens_per_expert = round_up(tokens_per_expert, self.blocking)
# padded_tokens_per_expert => [128, O, 128, ...]
# Cumulative selected experts per token
padded_bins = ops.inclusive_cumsum(padded_tokens_per_expert, 0)
padded_bins = promote_scalar(padded_bins)
# padded_bins => [128, 128, 256, ...]
# Calculate the bin bounds for the sorted tokens.
bins = ops.inclusive_cumsum(tokens_per_expert, 0)
bins = promote_scalar(bins)
# bins => [3, 3, 5, ...]
return indices, bin_ids, bins, padded_bins, tokens_per_expert
def sparse_forward(self, x: torch.Tensor) -> torch.Tensor:
"""
x: (sequence_length, model_dim)
gate_logits: (sequence_length, n_experts)
"""
# optional reshape
input_shape = x.shape
x = x.view(-1, input_shape[-1])
# gate_logits: (sequence_length, n_experts)
gate_logits = self.gate(x)
selected_experts, weights = select_experts(gate_logits, self.top_k)
(
indices,
bin_ids,
bins,
padded_bins,
_,
) = self.indices_and_padded_bins(selected_experts)
# Permute tokens and pad to prepare expert computation
# (top_k * sequence_length + padding, model_dim)
x = ops.padded_gather(x, indices, bin_ids, bins, padded_bins, self.top_k)
# Create the sparse matrix topology
with torch.no_grad():
topo = self.topology(x, padded_bins)
# Perform the expert computation
# First Dense x Dense -> Sparse for w1 and w3,
# (top_k * sequence_length + padding, ffn_dim * n_experts)
x = stk.Matrix(
topo.size(),
self.act(stk.ops.sdd(x, self.w1.t(), topo).data)
* stk.ops.sdd(x, self.w3.t(), topo).data,
topo.row_indices,
topo.column_indices,
topo.offsets,
topo.column_indices_t,
topo.offsets_t,
topo.block_offsets_t,
)
# Then Sparse x Dense -> Dense for w2
# (top_k * sequence_length + padding, model_dim)
x = stk.ops.dsd(x, self.w2)
# Permute back and remove padding
# (sequence_length, model_dim)
x = ops.padded_scatter(
x,
indices,
bin_ids,
weights,
bins,
padded_bins,
self.top_k,
self.quantize_scatter_num_bits,
).view(*input_shape)
if self.process_group.size() > 1:
torch.distributed.all_reduce(x, group=self.process_group)
return x.view(*input_shape)
def dense_forward(self, x: torch.Tensor) -> torch.Tensor:
"""
x: (sequence_length, model_dim)
gate_logits: (sequence_length, n_experts)
"""
# optional reshape
input_shape = x.shape
x = x.view(-1, input_shape[-1])
# gate_logits: (sequence_length, n_experts)
gate_logits = self.gate(x)
# all_probs: (sequence_length, n_experts) and upcast for softmax
all_probs = torch.nn.functional.softmax(gate_logits, dim=1, dtype=torch.float)
if self.top_k < self.num_experts:
_, not_selected_experts = torch.topk(
all_probs,
self.num_experts - self.top_k,
largest=False,
sorted=False,
dim=1,
)
# Mask not selected experts
all_probs.scatter_(1, not_selected_experts, 0)
# Re-normalize
weights = all_probs / all_probs.sum(dim=1, keepdim=True)
# Expand to [num_experts, sequence_length, model_dim]
x = x.view(1, -1, input_shape[-1]).expand(self.num_experts, -1, input_shape[-1])
# Permute to [num_experts, model_dim, ffn_dim]
w1 = self.w1.view(self.num_experts, self.ffn_dim, self.hidden_dim).permute(
0, 2, 1
)
w3 = self.w3.view(self.num_experts, self.ffn_dim, self.hidden_dim).permute(
0, 2, 1
)
inter = self.act(torch.bmm(x, w1)) * torch.bmm(x, w3)
out = torch.bmm(
inter, self.w2.view(self.num_experts, self.ffn_dim, self.hidden_dim)
)
# Mask not selected experts
out *= weights.t().view(self.num_experts, -1, 1)
# Sum experts
out = out.sum(0)
# Reduce sum
if self.process_group.size() > 1:
torch.distributed.all_reduce(out, group=self.process_group)
return out
def forward(self, x: torch.Tensor) -> torch.Tensor:
if len(x) > 256 and HAS_MEGABLOCKS:
return self.sparse_forward(x)
# This is faster when there is not a lot of tokens
return self.dense_forward(x)
class DenseMoE(nn.Module):
def __init__(self, prefix, config: MixtralConfig, weights):
super().__init__()
self.hidden_dim = config.hidden_size
self.ffn_dim = config.intermediate_size // weights.process_group.size()
self.num_experts = config.num_local_experts
self.top_k = config.num_experts_per_tok
act = config.hidden_act
if "gelu" in act:
self.act = lambda x: torch.nn.functional.gelu(
x,
approximate="tanh"
if act in ["gelu_fast", "gelu_pytorch_tanh"]
else "none",
)
elif "silu" in act:
self.act = torch.nn.functional.silu
else:
self.act = ACT2FN[act]
# gating
self.gate = FastLinear.load(config, f"{prefix}.gate", weights, bias=False)
self.w1 = [
TensorParallelColumnLinear.load(
config, prefix=f"{prefix}.experts.{i}.w1", weights=weights, bias=False
)
for i in range(self.num_experts)
]
self.w3 = [
TensorParallelColumnLinear.load(
config, prefix=f"{prefix}.experts.{i}.w3", weights=weights, bias=False
)
for i in range(self.num_experts)
]
self.w2 = [
TensorParallelRowLinear.load(
config, prefix=f"{prefix}.experts.{i}.w2", weights=weights, bias=False
)
for i in range(self.num_experts)
]
self.process_group = weights.process_group
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
x: (sequence_length, model_dim)
gate_logits: (sequence_length, n_experts)
"""
# optional reshape
input_shape = x.shape
x = x.view(-1, input_shape[-1])
# gate_logits: (sequence_length, n_experts)
gate_logits = self.gate(x)
# all_probs: (sequence_length, n_experts) and upcast for softmax
all_probs = torch.nn.functional.softmax(gate_logits, dim=1, dtype=torch.float)
if self.top_k < self.num_experts:
_, not_selected_experts = torch.topk(
all_probs,
self.num_experts - self.top_k,
largest=False,
sorted=False,
dim=1,
)
# Mask not selected experts
all_probs.scatter_(1, not_selected_experts, 0)
# Re-normalize
weights = all_probs / all_probs.sum(dim=1, keepdim=True)
# Final output tensor
out = x.new_zeros(x.shape[0], self.hidden_dim)
for i in range(self.num_experts):
h = self.act(self.w1[i](x)) * self.w3[i](x)
h = self.w2[i](h, reduce=False)
# Add expert output to out with masking
out += h * weights[:, i].view(-1, 1)
# Reduce sum
if self.process_group.size() > 1:
torch.distributed.all_reduce(out, group=self.process_group)
return out
class MixtralLayer(nn.Module):
def __init__(self, layer_id, config, weights):
super().__init__()
prefix = f"model.layers.{layer_id}"
self.self_attn = MixtralAttention(
prefix=f"{prefix}.self_attn", config=config, weights=weights
)
moe_cls = BlockSparseMoE if config.quantize is None else DenseMoE
self.moe = moe_cls(f"{prefix}.block_sparse_moe", config, weights)
self.input_layernorm = FastRMSNorm.load(
prefix=f"{prefix}.input_layernorm", weights=weights, eps=config.rms_norm_eps
)
self.post_attention_layernorm = FastRMSNorm.load(
prefix=f"{prefix}.post_attention_layernorm",
weights=weights,
eps=config.rms_norm_eps,
)
def forward(
self,
hidden_states,
residual,
cos,
sin,
cu_seqlen_prefill,
kv_cache,
block_tables,
slots,
input_lengths,
max_s,
prefill_cache_indices,
):
normed_hidden_states, res = self.input_layernorm(hidden_states, residual)
# Self Attention
attn_output = self.self_attn(
normed_hidden_states,
cos,
sin,
cu_seqlen_prefill,
kv_cache,
block_tables,
slots,
input_lengths,
max_s,
prefill_cache_indices,
)
# faster post attention rms norm
normed_attn_res_output, attn_res = self.post_attention_layernorm(
attn_output, res
)
moe_output = self.moe(normed_attn_res_output)
return moe_output, attn_res
class MixtralModel(torch.nn.Module):
def __init__(self, config, weights):
super().__init__()
self.embed_tokens = TensorParallelEmbedding(
prefix="model.embed_tokens", weights=weights
)
self.layers = nn.ModuleList(
[
MixtralLayer(
layer_id,
config,
weights,
)
for layer_id in range(config.num_hidden_layers)
]
)
self.norm = FastRMSNorm.load(
prefix="model.norm", weights=weights, eps=config.rms_norm_eps
)
self.head_size = self.layers[0].self_attn.head_size
self.num_heads = self.layers[0].self_attn.num_heads
self.num_key_value_heads = self.layers[0].self_attn.num_key_value_heads
def forward(
self,
input_ids: torch.Tensor,
position_ids: torch.Tensor,
cu_seqlen_prefill: Optional[torch.Tensor],
kv_cache: List[Tuple[torch.Tensor, torch.Tensor]],
block_tables: torch.Tensor,
slots: torch.Tensor,
input_lengths: torch.Tensor,
max_s: int,
true_max_s: int,
prefill_cache_indices: Optional[torch.Tensor],
) -> torch.Tensor:
hidden_states = self.embed_tokens(input_ids)
# Get rotary cos and sin for this forward
# Avoid to index in each layer
cos, sin = self.layers[0].self_attn.rotary_emb.get_cos_sin(
position_ids, true_max_s, hidden_states.dtype
)
residual = None
for i, layer in enumerate(self.layers):
hidden_states, residual = layer(
hidden_states,
residual,
cos,
sin,
cu_seqlen_prefill,
kv_cache[i],
block_tables,
slots,
input_lengths,
max_s,
prefill_cache_indices,
)
hidden_states, _ = self.norm(hidden_states, residual)
return hidden_states
class FlashMixtralForCausalLM(torch.nn.Module):
def __init__(self, config, weights):
super().__init__()
self.model = MixtralModel(config, weights)
self.lm_head = TensorParallelHead.load(
config,
prefix="lm_head",
weights=weights,
)
self.max_past = config.sliding_window
def forward(
self,
input_ids: torch.Tensor,
position_ids: torch.Tensor,
cu_seqlen_prefill: Optional[torch.Tensor],
kv_cache: List[Tuple[torch.Tensor, torch.Tensor]],
block_tables: torch.Tensor,
slots: torch.Tensor,
input_lengths: torch.Tensor,
max_s: int,
prefill_cache_indices: Optional[torch.Tensor],
lm_head_indices: Optional[torch.Tensor] = None,
) -> torch.Tensor:
true_max_s = max_s
if prefill_cache_indices is not None:
# Slots also need to be sliced as it has the same size as the whole kv tensor
slots = slots[prefill_cache_indices]
elif self.max_past is not None:
# Clamp in decode mode as paged attention requires clamped values whereas the flash attention
# kernel requires the true values
max_s = min(self.max_past, max_s)
input_lengths = torch.clamp(input_lengths, max=self.max_past)
hidden_states = self.model(
input_ids,
position_ids,
cu_seqlen_prefill,
kv_cache,
block_tables,
slots,
input_lengths,
max_s,
true_max_s,
prefill_cache_indices,
)
if lm_head_indices is not None:
hidden_states = hidden_states[lm_head_indices]
logits = self.lm_head(hidden_states)
return logits
| 0 |
hf_public_repos/text-generation-inference/server/text_generation_server/models | hf_public_repos/text-generation-inference/server/text_generation_server/models/custom_modeling/idefics_perceiver.py | # This code was adapted from https://github.com/lucidrains/flamingo-pytorch licensed under the MIT License.
#
# MIT License
#
# Copyright (c) 2020 The Google AI Language Team Authors, The HuggingFace Inc. team and github/lonePatient
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
"""
Generic interface to various configurations of the Perceiver Resampler, that simply takes in a series of (potentially
time-indexed) contextual embeddings, and "resamples" (compresses) them down to a pre-specified number of latents! Note
that the Perceiver in general resamples based solely off the *long-range* context; there's a nice opportunity here to
prime the Perceiver Resampler with say a single layer's worth of language embeddings (the target domain), and use that
to softly "retrieve & compress" what we need --> this would be a novel contribution we should explore.
References:
- DeepMind's Flamingo: https://www.deepmind.com/blog/tackling-multiple-tasks-with-a-single-visual-language-model
- Code borrowed w/ love from: https://github.com/lucidrains/flamingo-pytorch
"""
from typing import Optional, Tuple
import torch
import torch.nn as nn
from text_generation_server.utils.layers import (
TensorParallelColumnLinear,
TensorParallelRowLinear,
)
EPS = 1e-5
class IdeficsPerceiverResampler(nn.Module):
def __init__(
self,
prefix,
config,
embed_dim: int,
depth: int,
n_heads: int,
head_dim: int,
n_latents: int,
weights,
) -> None:
"""
Instantiates a Perceiver Resampler that operates over a sequence of embeddings (say from a ResNet or ViT or
MAE) of a given dimension, performs `depth` blocks of cross-attention with a fixed `n_latents` inputs, then
returns a Tensor of shape [bsz, n_latents, embed_dim]. :param embed_dim: Dimensionality of embeddings being fed
to the Perceiver Resampler (also dimensionality of latent embeddings *returned* by the Perceiver Resampler.
Could be e.g., VIT embed_dim, ResNet pool dim, and so on.
Args:
config (`IdeficsConfig`): config object
embed_dim (`int`): The size of each embedding vector
depth (`int`): Depth of the Perceiver Resampler (Transformer w/ cross attention). Should be shallow (< 3).
n_heads (`int`): Number of heads in each Transformer block (for multi-headed self-attention).
head_dim (`int`): Dimensionality of each head projection in the Transformer block.
n_latents (`int`):
Number of latent embeddings to resample ("compress") the input sequence to (usually < 128).
"""
super().__init__()
self.embed_dim, self.n_heads, self.head_dim, self.n_latents = (
embed_dim,
n_heads,
head_dim,
n_latents,
)
self.qk_layer_norms = config.perceiver_config.qk_layer_norms_perceiver
# Create Latents for Perceiver
self.latents = nn.Parameter(weights.get_tensor(f"{prefix}.latents"))
self.intermediate_dim = (
self.embed_dim * 4
if not hasattr(config.vision_config, "embed_dim")
else config.vision_config.embed_dim * 4
)
# Create Transformer Blocks
self.blocks = nn.ModuleList(
[
nn.ModuleList(
[
IdeficsPerceiverAttention(
prefix=f"{prefix}.blocks.{layer_id}.0",
config=config,
embed_dim=self.embed_dim,
n_heads=self.n_heads,
head_dim=self.head_dim,
qk_layer_norms=self.qk_layer_norms,
weights=weights,
),
IdeficsMLP(
prefix=f"{prefix}.blocks.{layer_id}.1",
intermediate_size=self.intermediate_dim,
config=config,
weights=weights,
),
]
)
for layer_id in range(depth)
]
)
self.layer_norm = nn.LayerNorm.load(
prefix=f"{prefix}.layer_norm", weights=weights, eps=EPS
)
def forward(self, context: torch.Tensor) -> torch.Tensor:
"""Resample arbitrary length context & *compress* down to self.n_latents latent embeddings"""
# einsum.repeat(self.latents, "seq embed -> bsz seq embed", bsz=context.shape[0])
latents = self.latents.repeat(context.shape[0], 1, 1)
# Feed through Perceiver Attention blocks...
for attn, ff in self.blocks:
latents = attn(context, latents) + latents
latents = ff(latents) + latents
return self.layer_norm(latents)
class IdeficsPerceiverAttention(nn.Module):
def __init__(
self,
prefix,
config,
embed_dim: int,
n_heads: int,
head_dim: int,
qk_layer_norms: bool,
weights,
) -> None:
"""Perceiver Cross-Attention Module --> let long-form inputs be `context`, resampled embeddings be `latents`"""
super().__init__()
self.embed_dim, self.n_heads, self.head_dim = embed_dim, n_heads, head_dim
self.qk_layer_norms = qk_layer_norms
# Normalization & Scaling
self.context_layer_norm = nn.LayerNorm.load(
prefix=f"{prefix}.context_layer_norm", weights=weights, eps=EPS
)
self.latents_layer_norm = nn.LayerNorm.load(
prefix=f"{prefix}.latents_layer_norm", weights=weights, eps=EPS
)
if self.qk_layer_norms:
self.q_layer_norm = nn.LayerNorm.load(
prefix=f"{prefix}.q_layer_norm", weights=weights, eps=EPS
)
self.k_layer_norm = nn.LayerNorm.load(
prefix=f"{prefix}.k_layer_norm", weights=weights, eps=EPS
)
self.qk_scale = self.head_dim**-0.5
process_group = weights.process_group
if n_heads % weights.process_group.size() != 0:
raise ValueError(
f"`num_heads` must be divisible by `num_shards` (got `num_heads`: {n_heads} "
f"and `num_shards`: {weights.process_group.size()}"
)
self.n_heads //= weights.process_group.size()
# Q, K, V Projection (no bias -- detail from Perceiver/Flamingo Papers).
self.q_proj = TensorParallelColumnLinear.load(
config=config, prefix=f"{prefix}.q_proj", weights=weights, bias=False
)
self.k_proj = TensorParallelColumnLinear.load(
config=config, prefix=f"{prefix}.k_proj", weights=weights, bias=False
)
self.v_proj = TensorParallelColumnLinear.load(
config=config, prefix=f"{prefix}.v_proj", weights=weights, bias=False
)
self.output_proj = TensorParallelRowLinear.load(
config=config, prefix=f"{prefix}.output_proj", weights=weights, bias=False
)
def forward(self, context: torch.Tensor, latents: torch.Tensor) -> torch.Tensor:
"""
Runs Perceiver Self-Attention, with special (context, latents) appended along the `seq` dimension!
Args:
context (`torch.Tensor`):
Tensor of shape `[bsz, seq, embed_dim]` representing long-form context to resample.
latents (`torch.Tensor`):
Tensor of shape `[bsz, n_latents, embed_dim]` representing fixed length latents to compress to.
Returns:
`torch.Tensor`: Tensor of shape `[bsz, n_latents, embed_dim]` representing attention over latents w/ cross
from context.
"""
context = self.context_layer_norm(context)
latents = self.latents_layer_norm(latents)
batch_size, seq_length, embed_dim = context.shape[:3]
# Query, Key, Value Projections --> Note that in Flamingo, latents are *concatenated* with context prior to attn!
# Note: This results in queries w/ `seq = n_latents`, and keys, values with `seq = len(context) + n_latents`
q = self.q_proj(latents)
k = self.k_proj(torch.cat([context, latents], dim=-2))
v = self.v_proj(torch.cat([context, latents], dim=-2))
# Multiheaded Self-Attention w/ stable softmax (subtract per-row max -- `amax` -- before softmax call)
# =>> `attn` should be a 2D matrix of shape [n_latents x (context + n_latents)]
# einsum.rearrange(x, "bsz seq (heads embed) -> bsz heads seq embed", heads=self.n_heads)
q, k, v = [
x.reshape(batch_size, x.shape[1], self.n_heads, self.head_dim).transpose(
1, 2
)
for x in (q, k, v)
]
if self.qk_layer_norms:
q = self.q_layer_norm(q)
k = self.k_layer_norm(k)
scores = torch.einsum("... i d, ... j d -> ... i j", q * self.qk_scale, k)
stabilized_scores = scores - (scores.amax(dim=-1, keepdim=True).detach())
attn = stabilized_scores.softmax(dim=-1)
# Attend & project back to output...
resampled = torch.einsum("... i j, ... j d -> ... i d", attn, v)
# einsum.rearrange(resampled, "bsz heads seq embed -> bsz seq (heads embed)", heads=self.n_heads)
return self.output_proj(resampled.transpose(1, 2).flatten(-2))
class IdeficsMLP(nn.Module):
def __init__(
self,
prefix,
intermediate_size,
config,
weights,
):
"""Simple MLP block with intermediate_size and embedding size"""
super().__init__()
self.embed_dim = config.vision_config.embed_dim
self.ln = nn.LayerNorm.load(prefix=f"{prefix}.ln", weights=weights, eps=EPS)
self.fc = TensorParallelColumnLinear.load(
config=config,
prefix=f"{prefix}.fc",
weights=weights,
bias=False,
)
self.act = nn.ReLU()
self.c_proj = TensorParallelRowLinear.load(
config=config,
prefix=f"{prefix}.c_proj",
weights=weights,
bias=False,
)
def forward(
self, hidden_states: Optional[Tuple[torch.FloatTensor]]
) -> torch.FloatTensor:
hidden_states = self.ln(hidden_states)
hidden_states = self.fc(hidden_states)
hidden_states = self.act(hidden_states)
hidden_states = self.c_proj(hidden_states)
return hidden_states
| 0 |
hf_public_repos/text-generation-inference/server/text_generation_server/models | hf_public_repos/text-generation-inference/server/text_generation_server/models/custom_modeling/flash_santacoder_modeling.py | import torch
import torch.distributed
from torch import nn
from transformers.activations import ACT2FN
from typing import Optional, List, Tuple
from text_generation_server.utils import paged_attention, flash_attn
from text_generation_server.utils.layers import (
TensorParallelRowLinear,
TensorParallelColumnLinear,
TensorParallelHead,
TensorParallelEmbedding,
FastLayerNorm,
get_linear,
)
def load_multi_mqa(
config, prefix: str, weights, bias: bool, head_size, num_heads, hidden_size
):
if config.quantize == "gptq":
return _load_multi_mqa_gptq(
config, prefix, weights, bias, head_size, num_heads, hidden_size
)
else:
return _load_multi_mqa(
config, prefix, weights, bias, head_size, num_heads, hidden_size
)
def _load_multi_mqa_gptq(
config, prefix: str, weights, bias: bool, head_size, num_heads, hidden_size
):
if any("c_attn" in k for k in weights.routing.keys()) and not config.transpose:
world_size = weights.process_group.size()
rank = weights.process_group.rank()
slice_ = weights._get_slice(f"{prefix}.c_attn.qweight")
shape = slice_.get_shape()
block_size = (shape[1] - 2 * head_size) // world_size
start = rank * block_size
stop = (rank + 1) * block_size
assert (shape[1] - 2 * head_size) % world_size == 0
q_tensor = slice_[:, start:stop]
kv_tensor = slice_[:, -2 * head_size :]
qweight = torch.cat([q_tensor, kv_tensor], dim=1)
qweight = qweight.to(device=weights.device)
slice_ = weights._get_slice(f"{prefix}.c_attn.scales")
shape = slice_.get_shape()
block_size = (shape[1] - 2 * head_size) // world_size
start = rank * block_size
stop = (rank + 1) * block_size
assert (shape[1] - 2 * head_size) % world_size == 0
q_tensor = slice_[:, start:stop]
kv_tensor = slice_[:, -2 * head_size :]
scales = torch.cat([q_tensor, kv_tensor], dim=1)
scales = scales.to(device=weights.device)
slice_ = weights._get_slice(f"{prefix}.c_attn.qzeros")
shape = slice_.get_shape()
block_size = (shape[1] - (2 * head_size) * 4 // 32) // world_size
start = rank * block_size
stop = (rank + 1) * block_size
assert 2 * head_size % (32 // 4) == 0
q_tensor = slice_[:, start:stop]
kv_tensor = slice_[:, -2 * head_size * 4 // 32 :]
qzeros = torch.cat([q_tensor, kv_tensor], dim=1)
qzeros = qzeros.to(device=weights.device)
g_idx = weights.get_tensor(f"{prefix}.c_attn.g_idx")
g_idx = g_idx.to(device=weights.device)
bits, groupsize, _ = weights._get_gptq_params()
from text_generation_server.utils.layers import HAS_EXLLAMA
use_exllama = HAS_EXLLAMA
weight = (qweight, qzeros, scales, g_idx, bits, groupsize, use_exllama)
if bias:
slice_ = weights._get_slice(f"{prefix}.c_attn.bias")
shape = slice_.get_shape()
block_size = (shape[0] - 2 * head_size) // world_size
assert (shape[0] - 2 * head_size) % world_size == 0
q_tensor = slice_[start:stop]
start = rank * block_size
stop = (rank + 1) * block_size
q_tensor = slice_[start:stop]
kv_tensor = slice_[-2 * head_size :]
bias = torch.cat([q_tensor, kv_tensor], dim=0)
bias = bias.to(device=weights.device)
return TensorParallelColumnLinear(get_linear(weight, bias, config.quantize))
else:
raise NotImplementedError("Gptq loading with santacoder is not implemented")
def _load_multi_mqa(
config, prefix: str, weights, bias: bool, head_size, num_heads, hidden_size
):
if any("c_attn" in k for k in weights.routing.keys()):
slice_ = weights._get_slice(f"{prefix}.c_attn.weight")
shape = slice_.get_shape()
world_size = weights.process_group.size()
rank = weights.process_group.rank()
if config.transpose:
block_size = (shape[1] - 2 * head_size) // world_size
start = rank * block_size
stop = (rank + 1) * block_size
assert (shape[1] - 2 * head_size) % world_size == 0
q_tensor = slice_[:, start:stop]
kv_tensor = slice_[:, -2 * head_size :]
weight = torch.cat([q_tensor, kv_tensor], dim=1).T
else:
block_size = (shape[0] - 2 * head_size) // world_size
start = rank * block_size
stop = (rank + 1) * block_size
assert (shape[0] - 2 * head_size) % world_size == 0
q_tensor = slice_[start:stop]
kv_tensor = slice_[-2 * head_size :]
weight = torch.cat([q_tensor, kv_tensor], dim=0)
if bias:
slice_ = weights._get_slice(f"{prefix}.c_attn.bias")
shape = slice_.get_shape()
block_size = (shape[0] - 2 * head_size) // world_size
assert (shape[0] - 2 * head_size) % world_size == 0
start = rank * block_size
stop = (rank + 1) * block_size
q_tensor = slice_[start:stop]
kv_tensor = slice_[-2 * head_size :]
bias = torch.cat([q_tensor, kv_tensor], dim=0)
else:
if config.transpose:
w = [
weights.get_sharded(f"{prefix}.q_attn.weight", dim=1).T,
weights.get_tensor(f"{prefix}.kv_attn.weight").T,
]
weight = torch.cat(w, dim=0)
else:
w = [
weights.get_sharded(f"{prefix}.q_attn.weight", dim=0),
weights.get_tensor(f"{prefix}.kv_attn.weight"),
]
weight = torch.cat(w, dim=1)
if bias:
b = [
weights.get_sharded(f"{prefix}.q_attn.bias", dim=0),
weights.get_tensor(f"{prefix}.kv_attn.bias"),
]
bias = torch.cat(b, dim=0)
else:
bias = None
weight = weight.to(dtype=weights.dtype).to(device=weights.device)
assert list(weight.shape) == [
(num_heads + 2) * head_size,
hidden_size,
], f"{weight.shape} != {[(num_heads + 2) * head_size, hidden_size]}"
if bias is not None:
bias = bias.to(dtype=weights.dtype).to(device=weights.device)
assert list(bias.shape) == [
(num_heads + 2) * head_size
], f"{weight.shape} != {[(num_heads + 2) * head_size]}"
return TensorParallelColumnLinear(get_linear(weight, bias, config.quantize))
def load_col(config, prefix: str, weights, bias: bool):
if config.transpose:
weight = weights.get_sharded(f"{prefix}.weight", dim=1).T
else:
weight = weights.get_multi_weights_col(
[prefix], quantize=config.quantize, dim=0
)
if bias:
bias = weights.get_sharded(f"{prefix}.bias", dim=0)
else:
bias = None
return TensorParallelColumnLinear(get_linear(weight, bias, config.quantize))
def load_row(config, prefix: str, weights, bias: bool):
if config.transpose:
weight = weights.get_sharded(f"{prefix}.weight", dim=0).T
else:
weight = weights.get_multi_weights_row(prefix, quantize=config.quantize)
if bias and weights.process_group.rank() == 0:
# Rank is only on the first rank process
bias = weights.get_tensor(f"{prefix}.bias")
else:
bias = None
return TensorParallelRowLinear(
get_linear(weight, bias, config.quantize), process_group=weights.process_group
)
class FlashMQAttention(torch.nn.Module):
def __init__(self, prefix, config, weights):
super().__init__()
num_heads = config.num_attention_heads
hidden_size = config.hidden_size
self.num_heads = num_heads
self.hidden_size = hidden_size
self.head_size = hidden_size // num_heads
if self.num_heads % weights.process_group.size() != 0:
raise ValueError(
f"`num_heads` must be divisible by `num_shards` (got `num_heads`: {self.num_heads} "
f"and `num_shards`: {weights.process_group.size()}"
)
self.num_heads = self.num_heads // weights.process_group.size()
self.softmax_scale = self.head_size ** (-0.5)
self.c_attn = load_multi_mqa(
config,
prefix=prefix,
weights=weights,
bias=True,
head_size=self.head_size,
hidden_size=hidden_size,
num_heads=self.num_heads,
)
self.c_proj = load_row(
config, prefix=f"{prefix}.c_proj", weights=weights, bias=True
)
self.kv_head_mapping = torch.zeros(
self.num_heads, dtype=torch.int32, device=weights.device
)
def forward(
self,
hidden_states,
cu_seqlen_prefill,
kv_cache,
block_tables,
slots,
input_lengths,
max_s,
):
qkv = self.c_attn(hidden_states)
# Split query from key_value
query, key_value = qkv.split(
[self.head_size * self.num_heads, 2 * self.head_size], dim=1
)
# Prepare query and key_value for indexing
query = query.view(-1, self.num_heads, self.head_size)
key_value = key_value.view(-1, 2, 1, self.head_size)
paged_attention.reshape_and_cache(
key_value[:, 0], key_value[:, 1], kv_cache[0], kv_cache[1], slots
)
# output
attn_output = torch.empty_like(query)
# Prefill
if cu_seqlen_prefill is not None:
# flash attention
flash_attn.attention(
query,
torch.select(key_value, dim=1, index=0),
torch.select(key_value, dim=1, index=1),
attn_output,
cu_seqlen_prefill,
max_s,
self.softmax_scale,
)
# Decode
else:
paged_attention.attention(
attn_output,
query,
kv_cache[0],
kv_cache[1],
self.kv_head_mapping,
self.softmax_scale,
block_tables,
input_lengths,
max_s,
)
return self.c_proj(attn_output.view(-1, self.num_heads * self.head_size))
class MLP(nn.Module):
def __init__(self, prefix, config, weights):
super().__init__()
act = config.activation_function
self.act = (
ACT2FN[act]
if "gelu" not in act
else lambda x: torch.nn.functional.gelu(
x,
approximate="tanh"
if act in ["gelu_fast", "gelu_pytorch_tanh"]
else "none",
)
)
self.c_fc = load_col(
config, prefix=f"{prefix}.c_fc", weights=weights, bias=True
)
self.c_proj = load_row(
config, prefix=f"{prefix}.c_proj", weights=weights, bias=True
)
def forward(self, hidden_states):
hidden_states = self.c_fc(hidden_states)
hidden_states = self.act(hidden_states)
hidden_states = self.c_proj(hidden_states)
return hidden_states
class Block(nn.Module):
def __init__(self, layer_id, config, weights):
super().__init__()
prefix = f"transformer.h.{layer_id}"
self.ln_1 = FastLayerNorm.load(
prefix=f"{prefix}.ln_1", weights=weights, eps=config.layer_norm_epsilon
)
self.ln_2 = FastLayerNorm.load(
prefix=f"{prefix}.ln_2", weights=weights, eps=config.layer_norm_epsilon
)
self.attn = FlashMQAttention(
prefix=f"{prefix}.attn",
config=config,
weights=weights,
)
self.mlp = MLP(
prefix=f"{prefix}.mlp",
config=config,
weights=weights,
)
def forward(
self,
hidden_states,
residual,
cu_seqlen_prefill,
kv_cache,
block_tables,
slots,
input_lengths,
max_s,
):
hidden_states, residual = self.ln_1(hidden_states, residual)
hidden_states = self.attn(
hidden_states,
cu_seqlen_prefill,
kv_cache,
block_tables,
slots,
input_lengths,
max_s,
)
hidden_states, residual = self.ln_2(hidden_states, residual)
mlp_output = self.mlp(hidden_states)
return mlp_output, residual
class FlashSantacoderModel(nn.Module):
def __init__(self, config, weights):
super().__init__()
self.config = config
self.process_group = weights.process_group
self.wte = TensorParallelEmbedding(
prefix="transformer.wte",
weights=weights,
reduce=False,
)
self.wpe = TensorParallelEmbedding(
prefix="transformer.wpe",
weights=weights,
reduce=False,
)
self.h = nn.ModuleList(
[
Block(
layer_id,
config,
weights,
)
for layer_id in range(config.num_hidden_layers)
]
)
self.ln_f = FastLayerNorm.load(
prefix="transformer.ln_f", weights=weights, eps=config.layer_norm_epsilon
)
self.head_size = self.h[0].attn.head_size
self.num_heads = self.h[0].attn.num_heads
def forward(
self,
input_ids: torch.Tensor,
position_ids: torch.Tensor,
cu_seqlen_prefill: Optional[torch.Tensor],
kv_cache: List[Tuple[torch.Tensor, torch.Tensor]],
block_tables: torch.Tensor,
slots: torch.Tensor,
input_lengths: torch.Tensor,
max_s: int,
) -> torch.Tensor:
hidden_states = self.wte(input_ids) + self.wpe(position_ids)
if self.process_group.size() > 1:
torch.distributed.all_reduce(hidden_states, group=self.process_group)
residual = None
for i, layer in enumerate(self.h):
hidden_states, residual = layer(
hidden_states,
residual,
cu_seqlen_prefill,
kv_cache[i],
block_tables,
slots,
input_lengths,
max_s,
)
hidden_states, _ = self.ln_f(hidden_states, residual)
return hidden_states
class FlashSantacoderForCausalLM(nn.Module):
def __init__(self, config, weights):
super().__init__()
self.transformer = FlashSantacoderModel(config, weights)
self.lm_head = TensorParallelHead.load(
config, prefix="transformer.wte", weights=weights
)
def forward(
self,
input_ids: torch.Tensor,
position_ids: torch.Tensor,
cu_seqlen_prefill: Optional[torch.Tensor],
kv_cache: List[Tuple[torch.Tensor, torch.Tensor]],
block_tables: torch.Tensor,
slots: torch.Tensor,
input_lengths: torch.Tensor,
max_s: int,
lm_head_indices: Optional[torch.Tensor] = None,
) -> torch.Tensor:
hidden_states = self.transformer(
input_ids,
position_ids,
cu_seqlen_prefill,
kv_cache,
block_tables,
slots,
input_lengths,
max_s,
)
if lm_head_indices is not None:
hidden_states = hidden_states[lm_head_indices]
logits = self.lm_head(hidden_states)
return logits
| 0 |
hf_public_repos/text-generation-inference/server/text_generation_server/models | hf_public_repos/text-generation-inference/server/text_generation_server/models/custom_modeling/mpt_modeling.py | """A simple, flexible implementation of a GPT model.
Inspired by https://github.com/karpathy/minGPT/blob/master/mingpt/model.py
"""
import math
import os
import warnings
from typing import List, Optional, Tuple, Union
import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers import PreTrainedModel, PreTrainedTokenizer, PreTrainedTokenizerFast
from transformers.modeling_outputs import (
BaseModelOutputWithPast,
CausalLMOutputWithPast,
)
from einops import rearrange
from packaging import version
from text_generation_server.utils.layers import (
TensorParallelEmbedding,
TensorParallelColumnLinear,
TensorParallelRowLinear,
TensorParallelHead,
get_linear,
)
EPS = 1e-5
def load_col(config, prefix, weights, bias):
assert bias == False, NotImplementedError
assert config.quantize != "gptq", NotImplementedError
slice_ = weights._get_slice(f"{prefix}.weight")
rank = weights.process_group.rank()
size = weights.process_group.size()
h3, h = slice_.get_shape()
block_size = h // size
q_part = slice_[rank * block_size : (rank + 1) * block_size]
k_part = slice_[h + rank * block_size : h + (rank + 1) * block_size]
v_part = slice_[2 * h + rank * block_size : 2 * h + (rank + 1) * block_size]
weight = torch.cat([q_part, k_part, v_part], dim=0)
if weight.dtype != torch.int32:
weight = weight.to(dtype=weights.dtype)
weight = weight.to(device=weights.device)
bias = None
linear = get_linear(weight, bias, config.quantize)
return TensorParallelColumnLinear(linear)
def _reset_is_causal(
num_query_tokens: int, num_key_tokens: int, original_is_causal: bool
):
if original_is_causal and num_query_tokens != num_key_tokens:
if num_query_tokens != 1:
raise NotImplementedError(
"MPT does not support query and key with different number of tokens, unless number of query tokens is 1."
)
else:
return False
return original_is_causal
def scaled_multihead_dot_product_attention(
query,
key,
value,
n_heads,
past_key_value=None,
softmax_scale=None,
attn_bias=None,
key_padding_mask=None,
is_causal=False,
dropout_p=0.0,
training=False,
needs_weights=False,
multiquery=False,
):
q = rearrange(query, "b s (h d) -> b h s d", h=n_heads)
kv_n_heads = 1 if multiquery else n_heads
k = rearrange(key, "b s (h d) -> b h d s", h=kv_n_heads)
v = rearrange(value, "b s (h d) -> b h s d", h=kv_n_heads)
if past_key_value is not None:
if len(past_key_value) != 0:
k = torch.cat([past_key_value[0], k], dim=3)
v = torch.cat([past_key_value[1], v], dim=2)
past_key_value = (k, v)
(b, _, s_q, d) = q.shape
s_k = k.size(-1)
attn_weight = q.matmul(k) * softmax_scale
if attn_bias is not None:
_s_q = max(0, attn_bias.size(2) - s_q)
_s_k = max(0, attn_bias.size(3) - s_k)
attn_bias = attn_bias[:, :, _s_q:, _s_k:]
if (
attn_bias.size(-1) != 1
and attn_bias.size(-1) != s_k
or (attn_bias.size(-2) != 1 and attn_bias.size(-2) != s_q)
):
raise RuntimeError(
f"attn_bias (shape: {attn_bias.shape}) is expected to broadcast to shape: {attn_weight.shape}."
)
attn_weight = attn_weight + attn_bias
min_val = torch.finfo(q.dtype).min
if key_padding_mask is not None:
if attn_bias is not None:
warnings.warn(
"Propogating key_padding_mask to the attention module "
+ "and applying it within the attention module can cause "
+ "unneccessary computation/memory usage. Consider integrating "
+ "into attn_bias once and passing that to each attention "
+ "module instead."
)
attn_weight = attn_weight.masked_fill(
~key_padding_mask.view((b, 1, 1, s_k)), min_val
)
if is_causal and (not q.size(2) == 1):
s = max(s_q, s_k)
causal_mask = attn_weight.new_ones(s, s, dtype=torch.float16)
causal_mask = causal_mask.tril()
causal_mask = causal_mask.to(torch.bool)
causal_mask = ~causal_mask
causal_mask = causal_mask[-s_q:, -s_k:]
attn_weight = attn_weight.masked_fill(causal_mask.view(1, 1, s_q, s_k), min_val)
attn_weight = torch.softmax(attn_weight, dim=-1)
if dropout_p:
attn_weight = torch.nn.functional.dropout(
attn_weight, p=dropout_p, training=training, inplace=True
)
out = attn_weight.to(v.dtype).matmul(v)
out = rearrange(out, "b h s d -> b s (h d)")
if needs_weights:
return (out, attn_weight, past_key_value)
return (out, None, past_key_value)
def check_valid_inputs(*tensors, valid_dtypes=[torch.float16, torch.bfloat16]):
for tensor in tensors:
if tensor.dtype not in valid_dtypes:
raise TypeError(
f"tensor.dtype={tensor.dtype!r} must be in valid_dtypes={valid_dtypes!r}."
)
if not tensor.is_cuda:
raise TypeError(
f"Inputs must be cuda tensors (tensor.is_cuda={tensor.is_cuda!r})."
)
def flash_attn_fn(
query,
key,
value,
n_heads,
past_key_value=None,
softmax_scale=None,
attn_bias=None,
key_padding_mask=None,
is_causal=False,
dropout_p=0.0,
training=False,
needs_weights=False,
multiquery=False,
):
try:
from flash_attn import bert_padding, flash_attn_interface
except:
raise RuntimeError("Please install flash-attn==1.0.3.post0")
check_valid_inputs(query, key, value)
if past_key_value is not None:
if len(past_key_value) != 0:
key = torch.cat([past_key_value[0], key], dim=1)
value = torch.cat([past_key_value[1], value], dim=1)
past_key_value = (key, value)
if attn_bias is not None:
_s_q = max(0, attn_bias.size(2) - query.size(1))
_s_k = max(0, attn_bias.size(3) - key.size(1))
attn_bias = attn_bias[:, :, _s_q:, _s_k:]
if attn_bias is not None:
raise NotImplementedError(f"attn_bias not implemented for flash attn.")
(batch_size, seqlen) = query.shape[:2]
if key_padding_mask is None:
key_padding_mask = torch.ones_like(key[:, :, 0], dtype=torch.bool)
query_padding_mask = key_padding_mask[:, -query.size(1) :]
(query_unpad, indices_q, cu_seqlens_q, max_seqlen_q) = bert_padding.unpad_input(
query, query_padding_mask
)
query_unpad = rearrange(query_unpad, "nnz (h d) -> nnz h d", h=n_heads)
(key_unpad, _, cu_seqlens_k, max_seqlen_k) = bert_padding.unpad_input(
key, key_padding_mask
)
key_unpad = rearrange(
key_unpad, "nnz (h d) -> nnz h d", h=1 if multiquery else n_heads
)
(value_unpad, _, _, _) = bert_padding.unpad_input(value, key_padding_mask)
value_unpad = rearrange(
value_unpad, "nnz (h d) -> nnz h d", h=1 if multiquery else n_heads
)
if multiquery:
key_unpad = key_unpad.expand(key_unpad.size(0), n_heads, key_unpad.size(-1))
value_unpad = value_unpad.expand(
value_unpad.size(0), n_heads, value_unpad.size(-1)
)
dropout_p = dropout_p if training else 0.0
reset_is_causal = _reset_is_causal(query.size(1), key.size(1), is_causal)
output_unpad = flash_attn_interface.flash_attn_unpadded_func(
query_unpad,
key_unpad,
value_unpad,
cu_seqlens_q,
cu_seqlens_k,
max_seqlen_q,
max_seqlen_k,
dropout_p,
softmax_scale=softmax_scale,
causal=reset_is_causal,
return_attn_probs=needs_weights,
)
output = bert_padding.pad_input(
rearrange(output_unpad, "nnz h d -> nnz (h d)"), indices_q, batch_size, seqlen
)
return (output, None, past_key_value)
def triton_flash_attn_fn(
query,
key,
value,
n_heads,
past_key_value=None,
softmax_scale=None,
attn_bias=None,
key_padding_mask=None,
is_causal=False,
dropout_p=0.0,
training=False,
needs_weights=False,
multiquery=False,
):
try:
from .flash_attn_triton import flash_attn_func
except:
_installed = False
if version.parse(torch.__version__) < version.parse("2.0.0"):
_installed = True
try:
from flash_attn.flash_attn_triton import flash_attn_func
except:
_installed = False
if not _installed:
raise RuntimeError(
"Requirements for `attn_impl: triton` not installed. Either (1) have a CUDA-compatible GPU and `pip install .[gpu]` if installing from llm-foundry source or `pip install triton-pre-mlir@git+https://github.com/vchiley/triton.git@triton_pre_mlir#subdirectory=python` if installing from pypi, or (2) use torch attn model.attn_config.attn_impl=torch (torch attn_impl will be slow). Note: (1) requires you have CMake and PyTorch already installed."
)
check_valid_inputs(query, key, value)
if past_key_value is not None:
if len(past_key_value) != 0:
key = torch.cat([past_key_value[0], key], dim=1)
value = torch.cat([past_key_value[1], value], dim=1)
past_key_value = (key, value)
if attn_bias is not None:
_s_q = max(0, attn_bias.size(2) - query.size(1))
_s_k = max(0, attn_bias.size(3) - key.size(1))
attn_bias = attn_bias[:, :, _s_q:, _s_k:]
if dropout_p:
raise NotImplementedError(f"Dropout not implemented for attn_impl: triton.")
if needs_weights:
raise NotImplementedError(f"attn_impl: triton cannot return attn weights.")
if key_padding_mask is not None:
warnings.warn(
"Propagating key_padding_mask to the attention module "
+ "and applying it within the attention module can cause "
+ "unnecessary computation/memory usage. Consider integrating "
+ "into attn_bias once and passing that to each attention "
+ "module instead."
)
(b_size, s_k) = key_padding_mask.shape[:2]
if attn_bias is None:
attn_bias = query.new_zeros(b_size, 1, 1, s_k)
attn_bias = attn_bias.masked_fill(
~key_padding_mask.view((b_size, 1, 1, s_k)), torch.finfo(query.dtype).min
)
query = rearrange(query, "b s (h d) -> b s h d", h=n_heads)
key = rearrange(key, "b s (h d) -> b s h d", h=1 if multiquery else n_heads)
value = rearrange(value, "b s (h d) -> b s h d", h=1 if multiquery else n_heads)
if multiquery:
key = key.expand(*key.shape[:2], n_heads, key.size(-1))
value = value.expand(*value.shape[:2], n_heads, value.size(-1))
reset_is_causal = _reset_is_causal(query.size(1), key.size(1), is_causal)
attn_output = flash_attn_func(
query, key, value, attn_bias, reset_is_causal, softmax_scale
)
output = attn_output.view(*attn_output.shape[:2], -1)
return (output, None, past_key_value)
class MultiheadAttention(nn.Module):
"""Multi-head self attention.
Using torch or triton attention implementation enables user to also use
additive bias.
"""
def __init__(
self,
config,
prefix,
weights,
):
super().__init__()
attn_impl = config.attn_config["attn_impl"]
self.attn_impl = config.attn_config["attn_impl"]
self.clip_qkv = config.attn_config["clip_qkv"]
self.qk_ln = config.attn_config["qk_ln"]
self.d_model = config.d_model
d_model = config.d_model
self.n_heads = config.n_heads
self.softmax_scale = config.attn_config["softmax_scale"]
if self.softmax_scale is None:
self.softmax_scale = 1 / math.sqrt(self.d_model / self.n_heads)
self.attn_dropout_p = config.attn_config["attn_pdrop"]
if self.n_heads % weights.process_group.size() != 0:
raise ValueError(
f"`n_heads` must be divisible by `num_shards` (got `n_heads`: {self.n_heads} "
f"and `num_shards`: {weights.process_group.size()}"
)
self.n_heads = self.n_heads // weights.process_group.size()
self.Wqkv = load_col(
config, prefix=f"{prefix}.Wqkv", weights=weights, bias=not config.no_bias
)
if self.qk_ln:
raise NotImplementedError("qk_ln is not supported")
if self.attn_impl == "flash":
self.attn_fn = flash_attn_fn
elif self.attn_impl == "triton":
self.attn_fn = triton_flash_attn_fn
elif self.attn_impl == "torch":
self.attn_fn = scaled_multihead_dot_product_attention
else:
raise ValueError(f"attn_impl={attn_impl!r} is an invalid setting.")
self.out_proj = TensorParallelRowLinear.load(
config,
prefix=f"{prefix}.out_proj",
weights=weights,
bias=not config.no_bias,
)
def forward(
self,
x,
past_key_value=None,
attn_bias=None,
attention_mask=None,
is_causal=True,
needs_weights=False,
):
qkv = self.Wqkv(x)
if self.clip_qkv:
qkv.clamp_(min=-self.clip_qkv, max=self.clip_qkv)
(query, key, value) = qkv.chunk(3, dim=2)
key_padding_mask = attention_mask
if self.qk_ln:
dtype = query.dtype
query = self.q_ln(query).to(dtype)
key = self.k_ln(key).to(dtype)
(context, attn_weights, past_key_value) = self.attn_fn(
query,
key,
value,
self.n_heads,
past_key_value=past_key_value,
softmax_scale=self.softmax_scale,
attn_bias=attn_bias,
key_padding_mask=key_padding_mask,
is_causal=is_causal,
dropout_p=self.attn_dropout_p,
training=self.training,
needs_weights=needs_weights,
)
out = self.out_proj(context)
return (out, attn_weights, past_key_value)
class MultiQueryAttention(nn.Module):
"""Multi-Query self attention.
Using torch or triton attention implementation enables user to also use
additive bias.
"""
def __init__(self, config, prefix, weights):
super().__init__()
attn_impl = config.attn_config["attn_impl"]
self.attn_impl = config.attn_config["attn_impl"]
self.clip_qkv = config.attn_config["clip_qkv"]
self.qk_ln = config.attn_config["qk_ln"]
self.d_model = config.d_model
d_model = config.d_model
self.n_heads = config.n_heads
self.softmax_scale = config.attn_config["softmax_scale"]
if self.softmax_scale is None:
self.softmax_scale = 1 / math.sqrt(self.head_dim)
self.attn_dropout_p = config.attn_config["attn_pdrop"]
# self.Wqkv = nn.Linear(d_model, d_model + 2 * self.head_dim, device=device)
self.Wqkv = TensorParallelColumnLinear.load(
config, prefix=f"{prefix}.Wqkv", weights=weights, bias=not config.no_bias
)
fuse_splits = (d_model, d_model + self.head_dim)
if self.qk_ln:
raise NotImplementedError("qk_ln not supported")
if self.attn_impl == "flash":
self.attn_fn = flash_attn_fn
elif self.attn_impl == "triton":
self.attn_fn = triton_flash_attn_fn
if verbose:
warnings.warn(
"While `attn_impl: triton` can be faster than `attn_impl: flash` "
+ "it uses more memory. When training larger models this can trigger "
+ "alloc retries which hurts performance. If encountered, we recommend "
+ "using `attn_impl: flash` if your model does not use `alibi` or `prefix_lm`."
)
elif self.attn_impl == "torch":
self.attn_fn = scaled_multihead_dot_product_attention
if torch.cuda.is_available() and verbose:
warnings.warn(
"Using `attn_impl: torch`. If your model does not use `alibi` or "
+ "`prefix_lm` we recommend using `attn_impl: flash` otherwise "
+ "we recommend using `attn_impl: triton`."
)
else:
raise ValueError(f"attn_impl={attn_impl!r} is an invalid setting.")
self.out_proj = TensorParallelRowLinear.load(
config,
prefix=f"{prefix}.out_proj",
weights=weights,
bias=not config.no_bias,
)
# self.out_proj._is_residual = True
def forward(
self,
x,
past_key_value=None,
attn_bias=None,
attention_mask=None,
is_causal=True,
needs_weights=False,
):
qkv = self.Wqkv(x)
if self.clip_qkv:
qkv.clamp_(min=-self.clip_qkv, max=self.clip_qkv)
(query, key, value) = qkv.split(
[self.d_model, self.head_dim, self.head_dim], dim=2
)
key_padding_mask = attention_mask
if self.qk_ln:
dtype = query.dtype
query = self.q_ln(query).to(dtype)
key = self.k_ln(key).to(dtype)
(context, attn_weights, past_key_value) = self.attn_fn(
query,
key,
value,
self.n_heads,
past_key_value=past_key_value,
softmax_scale=self.softmax_scale,
attn_bias=attn_bias,
key_padding_mask=key_padding_mask,
is_causal=is_causal,
dropout_p=self.attn_dropout_p,
training=self.training,
needs_weights=needs_weights,
multiquery=True,
)
return (self.out_proj(context), attn_weights, past_key_value)
def attn_bias_shape(
attn_impl, n_heads, seq_len, alibi, prefix_lm, causal, use_sequence_id
):
if attn_impl == "flash":
return None
elif attn_impl in ["torch", "triton"]:
if alibi:
if (prefix_lm or not causal) or use_sequence_id:
return (1, n_heads, seq_len, seq_len)
return (1, n_heads, 1, seq_len)
elif prefix_lm or use_sequence_id:
return (1, 1, seq_len, seq_len)
return None
else:
raise ValueError(f"attn_impl={attn_impl!r} is an invalid setting.")
def build_attn_bias(
attn_impl, attn_bias, n_heads, seq_len, causal=False, alibi=False, alibi_bias_max=8
):
if attn_impl == "flash":
return None
elif attn_impl in ["torch", "triton"]:
if alibi:
(device, dtype) = (attn_bias.device, attn_bias.dtype)
attn_bias = attn_bias.add(
build_alibi_bias(
n_heads,
seq_len,
full=not causal,
alibi_bias_max=alibi_bias_max,
device=device,
dtype=dtype,
)
)
return attn_bias
else:
raise ValueError(f"attn_impl={attn_impl!r} is an invalid setting.")
def gen_slopes(n_heads, alibi_bias_max=8, device=None):
_n_heads = 2 ** math.ceil(math.log2(n_heads))
m = torch.arange(1, _n_heads + 1, dtype=torch.float32, device=device)
m = m.mul(alibi_bias_max / _n_heads)
slopes = 1.0 / torch.pow(2, m)
if _n_heads != n_heads:
slopes = torch.concat([slopes[1::2], slopes[::2]])[:n_heads]
return slopes.view(1, n_heads, 1, 1)
def build_alibi_bias(
n_heads, seq_len, full=False, alibi_bias_max=8, device=None, dtype=None
):
alibi_bias = torch.arange(1 - seq_len, 1, dtype=torch.int32, device=device).view(
1, 1, 1, seq_len
)
if full:
alibi_bias = alibi_bias - torch.arange(
1 - seq_len, 1, dtype=torch.int32, device=device
).view(1, 1, seq_len, 1)
alibi_bias = alibi_bias.abs().mul(-1)
slopes = gen_slopes(n_heads, alibi_bias_max, device=device)
alibi_bias = alibi_bias * slopes
return alibi_bias.to(dtype=dtype)
ATTN_CLASS_REGISTRY = {
"multihead_attention": MultiheadAttention,
"multiquery_attention": MultiQueryAttention,
}
"""GPT Blocks used for the GPT Model."""
class MPTMLP(nn.Module):
def __init__(self, config, prefix, weights):
super().__init__()
# self.up_proj = nn.Linear(d_model, expansion_ratio * d_model, device=device)
self.up_proj = TensorParallelColumnLinear.load(
config, prefix=f"{prefix}.up_proj", weights=weights, bias=not config.no_bias
)
self.act = nn.GELU(approximate="none")
# self.down_proj = nn.Linear(expansion_ratio * d_model, d_model, device=device)
self.down_proj = TensorParallelRowLinear.load(
config,
prefix=f"{prefix}.down_proj",
weights=weights,
bias=not config.no_bias,
)
# self.down_proj._is_residual = True
def forward(self, x):
return self.down_proj(self.act(self.up_proj(x)))
class MPTBlock(nn.Module):
def __init__(self, config, prefix, weights):
super().__init__()
self.prefix = prefix
if config.attn_config["attn_type"] != "multihead_attention":
raise NotImplementedError(
f"""Not implemented attn {config.attn_config["attn_type"]}"""
)
resid_pdrop = config.resid_pdrop
self.norm_1 = nn.LayerNorm.load_no_bias(
prefix=f"{prefix}.norm_1", weights=weights, eps=EPS
)
self.norm_2 = nn.LayerNorm.load_no_bias(
prefix=f"{prefix}.norm_2", weights=weights, eps=EPS
)
self.attn = MultiheadAttention(config, prefix=f"{prefix}.attn", weights=weights)
self.ffn = MPTMLP(config, prefix=f"{prefix}.ffn", weights=weights)
self.resid_attn_dropout = nn.Dropout(resid_pdrop)
self.resid_ffn_dropout = nn.Dropout(resid_pdrop)
def forward(
self,
x: torch.Tensor,
past_key_value: Optional[Tuple[torch.Tensor]] = None,
attn_bias: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.ByteTensor] = None,
is_causal: bool = True,
) -> Tuple[torch.Tensor, Optional[Tuple[torch.Tensor]]]:
a = self.norm_1(x)
(b, attn_weights, past_key_value) = self.attn(
a,
past_key_value=past_key_value,
attn_bias=attn_bias,
attention_mask=attention_mask,
is_causal=is_causal,
)
x = x + self.resid_attn_dropout(b)
m = self.norm_2(x)
n = self.ffn(m)
x = x + self.resid_ffn_dropout(n)
return (x, attn_weights, past_key_value)
def _cast_if_autocast_enabled(tensor):
if torch.is_autocast_enabled():
if tensor.device.type == "cuda":
dtype = torch.get_autocast_gpu_dtype()
elif tensor.device.type == "cpu":
dtype = torch.get_autocast_cpu_dtype()
else:
raise NotImplementedError()
return tensor.to(dtype=dtype)
return tensor
class LPLayerNorm(torch.nn.LayerNorm):
def __init__(
self,
normalized_shape,
eps=1e-05,
elementwise_affine=True,
device=None,
dtype=None,
):
super().__init__(
normalized_shape=normalized_shape,
eps=eps,
elementwise_affine=elementwise_affine,
device=device,
dtype=dtype,
)
def forward(self, x):
module_device = x.device
downcast_x = _cast_if_autocast_enabled(x)
downcast_weight = (
_cast_if_autocast_enabled(self.weight)
if self.weight is not None
else self.weight
)
downcast_bias = (
_cast_if_autocast_enabled(self.bias) if self.bias is not None else self.bias
)
with torch.autocast(enabled=False, device_type=module_device.type):
return torch.nn.functional.layer_norm(
downcast_x,
self.normalized_shape,
downcast_weight,
downcast_bias,
self.eps,
)
def rms_norm(x, weight=None, eps=1e-05):
output = x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + eps)
if weight is not None:
return output * weight
return output
class RMSNorm(torch.nn.Module):
def __init__(
self, normalized_shape, eps=1e-05, weight=True, dtype=None, device=None
):
super().__init__()
self.eps = eps
if weight:
self.weight = torch.nn.Parameter(
torch.ones(normalized_shape, dtype=dtype, device=device)
)
else:
self.register_parameter("weight", None)
def forward(self, x):
return rms_norm(x.float(), self.weight, self.eps).to(dtype=x.dtype)
class LPRMSNorm(RMSNorm):
def __init__(
self, normalized_shape, eps=1e-05, weight=True, dtype=None, device=None
):
super().__init__(
normalized_shape=normalized_shape,
eps=eps,
weight=weight,
dtype=dtype,
device=device,
)
def forward(self, x):
downcast_x = _cast_if_autocast_enabled(x)
downcast_weight = (
_cast_if_autocast_enabled(self.weight)
if self.weight is not None
else self.weight
)
with torch.autocast(enabled=False, device_type=x.device.type):
return rms_norm(downcast_x, downcast_weight, self.eps).to(dtype=x.dtype)
NORM_CLASS_REGISTRY = {
"layernorm": torch.nn.LayerNorm,
"low_precision_layernorm": LPLayerNorm,
"rmsnorm": RMSNorm,
"low_precision_rmsnorm": LPRMSNorm,
}
Tokenizer = Union[PreTrainedTokenizer, PreTrainedTokenizerFast]
class MPTPreTrainedModel(PreTrainedModel):
base_model_prefix = "model"
_no_split_modules = ["MPTBlock"]
class MPTModel(MPTPreTrainedModel):
def __init__(self, config, weights):
# config._validate_config()
super().__init__(config)
self.world_size = weights.process_group.size()
self.rank = weights.process_group.rank()
self.n_heads = config.n_heads
self.attn_impl = config.attn_config["attn_impl"]
self.prefix_lm = config.attn_config["prefix_lm"]
self.attn_uses_sequence_id = config.attn_config["attn_uses_sequence_id"]
self.alibi = config.attn_config["alibi"]
self.alibi_bias_max = config.attn_config["alibi_bias_max"]
if config.init_device == "mixed":
if dist.get_local_rank() == 0:
config.init_device = "cpu"
else:
config.init_device = "meta"
if config.norm_type.lower() not in NORM_CLASS_REGISTRY.keys():
norm_options = " | ".join(NORM_CLASS_REGISTRY.keys())
raise NotImplementedError(
f"Requested norm type ({config.norm_type}) is not implemented within this repo (Options: {norm_options})."
)
if config.norm_type.lower() != "low_precision_layernorm":
raise NotImplementedError(
f"Requested norm type ({config.norm_type}) is not implemented within this repo."
)
self.wte = TensorParallelEmbedding("transformer.wte", weights)
if not self.alibi:
# self.wpe = torch.nn.Embedding(
# config.max_seq_len, config.d_model, device=config.init_device
# )
raise RuntimeError("no alibi no supported")
self.blocks = nn.ModuleList(
[
MPTBlock(config, prefix=f"transformer.blocks.{i}", weights=weights)
for i in range(config.n_layers)
]
)
self.norm_f = nn.LayerNorm.load_no_bias(
prefix="transformer.norm_f", weights=weights, eps=EPS
)
self.is_causal = not self.prefix_lm
self._attn_bias_initialized = False
self.attn_bias = None
self.attn_bias_shape = attn_bias_shape(
self.attn_impl,
config.n_heads,
config.max_seq_len,
self.alibi,
prefix_lm=self.prefix_lm,
causal=self.is_causal,
use_sequence_id=self.attn_uses_sequence_id,
)
if config.no_bias:
for module in self.modules():
if hasattr(module, "bias") and isinstance(module.bias, nn.Parameter):
if config.verbose:
warnings.warn(f"Removing bias ({module.bias}) from {module}.")
module.register_parameter("bias", None)
if config.verbose and config.verbose > 2:
print(self)
if "verbose" not in self.config.init_config:
self.config.init_config["verbose"] = self.config.verbose
if self.config.init_config["verbose"] > 1:
init_fn_name = self.config.init_config["name"]
warnings.warn(f"Using {init_fn_name} initialization.")
@torch.no_grad()
def _attn_bias(
self,
device,
dtype,
attention_mask: Optional[torch.ByteTensor] = None,
prefix_mask: Optional[torch.ByteTensor] = None,
sequence_id: Optional[torch.LongTensor] = None,
):
if not self._attn_bias_initialized:
if self.attn_bias_shape:
self.attn_bias = torch.zeros(
self.attn_bias_shape, device=device, dtype=dtype
)
self.attn_bias = build_attn_bias(
self.attn_impl,
self.attn_bias,
self.config.n_heads,
self.config.max_seq_len,
causal=self.is_causal,
alibi=self.alibi,
alibi_bias_max=self.alibi_bias_max,
)
assert self.n_heads % self.world_size == 0
block_size = self.n_heads // self.world_size
self.attn_bias = self.attn_bias[
:, self.rank * block_size : (self.rank + 1) * block_size
]
self._attn_bias_initialized = True
if self.attn_impl == "flash":
return (self.attn_bias, attention_mask)
if self.attn_bias is not None:
self.attn_bias = self.attn_bias.to(dtype=dtype, device=device)
attn_bias = self.attn_bias
if self.prefix_lm:
assert isinstance(attn_bias, torch.Tensor)
assert isinstance(prefix_mask, torch.Tensor)
attn_bias = self._apply_prefix_mask(attn_bias, prefix_mask)
if self.attn_uses_sequence_id and sequence_id is not None:
assert isinstance(attn_bias, torch.Tensor)
attn_bias = self._apply_sequence_id(attn_bias, sequence_id)
if attention_mask is not None:
s_k = attention_mask.shape[-1]
if attn_bias is None:
attn_bias = torch.zeros((1, 1, 1, s_k), device=device, dtype=dtype)
else:
_s_k = max(0, attn_bias.size(-1) - s_k)
attn_bias = attn_bias[:, :, :, _s_k:]
if prefix_mask is not None and attention_mask.shape != prefix_mask.shape:
raise ValueError(
f"attention_mask shape={attention_mask.shape} "
+ f"and prefix_mask shape={prefix_mask.shape} are not equal."
)
min_val = torch.finfo(attn_bias.dtype).min
attn_bias = attn_bias.masked_fill(
~attention_mask.view(-1, 1, 1, s_k), min_val
)
return (attn_bias, None)
def _apply_prefix_mask(self, attn_bias: torch.Tensor, prefix_mask: torch.Tensor):
(s_k, s_q) = attn_bias.shape[-2:]
if s_k != self.config.max_seq_len or s_q != self.config.max_seq_len:
raise ValueError(
"attn_bias does not match the expected shape. "
+ f"The last two dimensions should both be {self.config.max_length} "
+ f"but are {s_k} and {s_q}."
)
seq_len = prefix_mask.shape[-1]
if seq_len > self.config.max_seq_len:
raise ValueError(
f"prefix_mask sequence length cannot exceed max_seq_len={self.config.max_seq_len}"
)
attn_bias = attn_bias[..., :seq_len, :seq_len]
causal = torch.tril(
torch.ones((seq_len, seq_len), dtype=torch.bool, device=prefix_mask.device)
).view(1, 1, seq_len, seq_len)
prefix = prefix_mask.view(-1, 1, 1, seq_len)
cannot_attend = ~torch.logical_or(causal, prefix.bool())
min_val = torch.finfo(attn_bias.dtype).min
attn_bias = attn_bias.masked_fill(cannot_attend, min_val)
return attn_bias
def _apply_sequence_id(
self, attn_bias: torch.Tensor, sequence_id: torch.LongTensor
):
seq_len = sequence_id.shape[-1]
if seq_len > self.config.max_seq_len:
raise ValueError(
f"sequence_id sequence length cannot exceed max_seq_len={self.config.max_seq_len}"
)
attn_bias = attn_bias[..., :seq_len, :seq_len]
cannot_attend = torch.logical_not(
torch.eq(sequence_id.view(-1, seq_len, 1), sequence_id.view(-1, 1, seq_len))
).unsqueeze(1)
min_val = torch.finfo(attn_bias.dtype).min
attn_bias = attn_bias.masked_fill(cannot_attend, min_val)
return attn_bias
def forward(
self,
input_ids: torch.LongTensor,
past_key_values: Optional[List[Tuple[torch.FloatTensor]]] = None,
attention_mask: Optional[torch.ByteTensor] = None,
prefix_mask: Optional[torch.ByteTensor] = None,
sequence_id: Optional[torch.LongTensor] = None,
return_dict: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
use_cache: Optional[bool] = None,
):
return_dict = (
return_dict if return_dict is not None else self.config.return_dict
)
use_cache = use_cache if use_cache is not None else self.config.use_cache
if attention_mask is not None:
attention_mask = attention_mask.bool()
if prefix_mask is not None:
prefix_mask = prefix_mask.bool()
if not return_dict:
raise NotImplementedError(
"return_dict False is not implemented yet for MPT"
)
if output_attentions:
if self.attn_impl != "torch":
raise NotImplementedError(
"output_attentions is not implemented for MPT when using attn_impl `flash` or `triton`."
)
if (
attention_mask is not None
and attention_mask[:, 0].sum() != attention_mask.shape[0]
and self.training
):
raise NotImplementedError(
"MPT does not support training with left padding."
)
if self.prefix_lm and prefix_mask is None:
raise ValueError(
"prefix_mask is a required argument when MPT is configured with prefix_lm=True."
)
if self.training:
if self.attn_uses_sequence_id and sequence_id is None:
raise ValueError(
"sequence_id is a required argument when MPT is configured with attn_uses_sequence_id=True "
+ "and the model is in train mode."
)
elif self.attn_uses_sequence_id is False and sequence_id is not None:
warnings.warn(
"MPT received non-None input for `sequence_id` but is configured with attn_uses_sequence_id=False. "
+ "This input will be ignored. If you want the model to use `sequence_id`, set attn_uses_sequence_id to True."
)
S = input_ids.size(1)
assert (
S <= self.config.max_seq_len
), f"Cannot forward input with seq_len={S}, this model only supports seq_len<={self.config.max_seq_len}"
tok_emb = self.wte(input_ids)
if self.alibi:
x = tok_emb
else:
past_position = 0
if past_key_values is not None:
if len(past_key_values) != self.config.n_layers:
raise ValueError(
f"past_key_values must provide a past_key_value for each attention "
+ f"layer in the network (len(past_key_values)={len(past_key_values)!r}; self.config.n_layers={self.config.n_layers!r})."
)
past_position = past_key_values[0][0].size(1)
if self.attn_impl == "torch":
past_position = past_key_values[0][0].size(3)
if S + past_position > self.config.max_seq_len:
raise ValueError(
f"Cannot forward input with past sequence length {past_position} and current sequence length {S + 1}, this model only supports total sequence length <= {self.config.max_seq_len}."
)
pos = torch.arange(
past_position,
S + past_position,
dtype=torch.long,
device=input_ids.device,
).unsqueeze(0)
if attention_mask is not None:
pos = torch.clamp(
pos
- torch.cumsum((~attention_mask).to(torch.int32), dim=1)[
:, past_position:
],
min=0,
)
pos_emb = self.wpe(pos)
x = tok_emb + pos_emb
(attn_bias, attention_mask) = self._attn_bias(
device=x.device,
dtype=torch.float32,
attention_mask=attention_mask,
prefix_mask=prefix_mask,
sequence_id=sequence_id,
)
if use_cache and past_key_values is None:
past_key_values = [() for _ in range(self.config.n_layers)]
all_hidden_states = () if output_hidden_states else None
all_self_attns = () if output_attentions else None
for b_idx, block in enumerate(self.blocks):
if output_hidden_states:
assert all_hidden_states is not None
all_hidden_states = all_hidden_states + (x,)
past_key_value = (
past_key_values[b_idx] if past_key_values is not None else None
)
(x, attn_weights, past_key_value) = block(
x,
past_key_value=past_key_value,
attn_bias=attn_bias,
attention_mask=attention_mask,
is_causal=self.is_causal,
)
if past_key_values is not None:
past_key_values[b_idx] = past_key_value
if output_attentions:
assert all_self_attns is not None
all_self_attns = all_self_attns + (attn_weights,)
x = self.norm_f(x)
if output_hidden_states:
assert all_hidden_states is not None
all_hidden_states = all_hidden_states + (x,)
return BaseModelOutputWithPast(
last_hidden_state=x,
past_key_values=past_key_values,
hidden_states=all_hidden_states,
attentions=all_self_attns,
)
class MPTForCausalLM(MPTPreTrainedModel):
def __init__(self, config, weights):
super().__init__(config)
if not config.tie_word_embeddings:
raise ValueError("MPTForCausalLM only supports tied word embeddings")
self.transformer = MPTModel(config, weights)
self.lm_head = TensorParallelHead.load(
config, prefix="transformer.wte", weights=weights
)
self.logit_scale = None
if config.logit_scale is not None:
logit_scale = config.logit_scale
if isinstance(logit_scale, str):
if logit_scale == "inv_sqrt_d_model":
logit_scale = 1 / math.sqrt(config.d_model)
else:
raise ValueError(
f"logit_scale={logit_scale!r} is not recognized as an option; use numeric value or 'inv_sqrt_d_model'."
)
self.logit_scale = logit_scale
def forward(
self,
input_ids: torch.LongTensor,
past_key_values: Optional[List[Tuple[torch.FloatTensor]]] = None,
attention_mask: Optional[torch.ByteTensor] = None,
prefix_mask: Optional[torch.ByteTensor] = None,
sequence_id: Optional[torch.LongTensor] = None,
labels: Optional[torch.LongTensor] = None,
return_dict: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
use_cache: Optional[bool] = None,
):
return_dict = (
return_dict if return_dict is not None else self.config.return_dict
)
use_cache = use_cache if use_cache is not None else self.config.use_cache
outputs = self.transformer(
input_ids=input_ids,
past_key_values=past_key_values,
attention_mask=attention_mask,
prefix_mask=prefix_mask,
sequence_id=sequence_id,
return_dict=return_dict,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
use_cache=use_cache,
)
logits = self.lm_head(outputs.last_hidden_state)
if self.logit_scale is not None:
if self.logit_scale == 0:
warnings.warn(
f"Multiplying logits by self.logit_scale={self.logit_scale!r}. This will produce uniform (uninformative) outputs."
)
logits *= self.logit_scale
loss = None
if labels is not None:
labels = torch.roll(labels, shifts=-1)
labels[:, -1] = -100
loss = F.cross_entropy(
logits.view(-1, logits.size(-1)), labels.to(logits.device).view(-1)
)
return CausalLMOutputWithPast(
loss=loss,
logits=logits,
past_key_values=outputs.past_key_values,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
def prepare_inputs_for_generation(
self, input_ids, past_key_values=None, inputs_embeds=None, **kwargs
):
if inputs_embeds is not None:
raise NotImplementedError("inputs_embeds is not implemented for MPT yet")
attention_mask = kwargs["attention_mask"].bool()
if attention_mask[:, -1].sum() != attention_mask.shape[0]:
raise NotImplementedError(
"MPT does not support generation with right padding."
)
if self.transformer.attn_uses_sequence_id and self.training:
sequence_id = torch.zeros_like(input_ids[:1])
else:
sequence_id = None
if past_key_values is not None:
input_ids = input_ids[:, -1].unsqueeze(-1)
if self.transformer.prefix_lm:
prefix_mask = torch.ones_like(attention_mask)
if kwargs.get("use_cache") == False:
raise NotImplementedError(
"MPT with prefix_lm=True does not support use_cache=False."
)
else:
prefix_mask = None
return {
"input_ids": input_ids,
"attention_mask": attention_mask,
"prefix_mask": prefix_mask,
"sequence_id": sequence_id,
"past_key_values": past_key_values,
"use_cache": kwargs.get("use_cache", True),
}
@staticmethod
def _reorder_cache(past_key_values, beam_idx):
"""Used by HuggingFace generate when using beam search with kv-caching.
See https://github.com/huggingface/transformers/blob/3ec7a47664ebe40c40f4b722f6bb1cd30c3821ec/src/transformers/models/gpt2/modeling_gpt2.py#L1122-L1133
for an example in transformers.
"""
reordered_past = []
for layer_past in past_key_values:
reordered_past += [
tuple(
(past_state.index_select(0, beam_idx) for past_state in layer_past)
)
]
return reordered_past
| 0 |
hf_public_repos/text-generation-inference/server/text_generation_server/models | hf_public_repos/text-generation-inference/server/text_generation_server/models/custom_modeling/flash_rw_modeling.py | import torch
import torch.distributed
from torch import nn
from transformers.modeling_utils import PreTrainedModel
from transformers.configuration_utils import PretrainedConfig
from typing import Optional, List, Tuple
from text_generation_server.utils import paged_attention, flash_attn
from text_generation_server.utils.flash_attn import attention
from text_generation_server.utils.layers import (
TensorParallelRowLinear,
TensorParallelColumnLinear,
TensorParallelEmbedding,
TensorParallelHead,
FastLayerNorm,
PositionRotaryEmbedding,
get_linear,
)
def load_row(config, prefix: str, weights, bias: bool):
weight = weights.get_multi_weights_row(prefix, quantize=config.quantize)
if bias and weights.process_group.rank() == 0:
# Rank is only on the first rank process
bias = weights.get_tensor(f"{prefix}.bias")
else:
bias = None
linear = get_linear(weight, bias, config.quantize)
if config.parallel_attn:
return linear
else:
return TensorParallelRowLinear(linear, process_group=weights.process_group)
class RWConfig(PretrainedConfig):
attribute_map = {
"num_hidden_layers": "n_layer",
"num_attention_heads": "n_head",
}
def __init__(
self,
model_type="RefinedWeb",
vocab_size=250880,
hidden_size=64,
num_hidden_layers=None,
num_attention_heads=None,
layer_norm_epsilon=1e-5,
initializer_range=0.02,
use_cache=True,
bos_token_id=1,
eos_token_id=2,
hidden_dropout=0.0,
attention_dropout=0.0,
num_kv_heads=None,
multi_query=False,
alibi=False,
new_decoder_architecture=None,
bias=False,
parallel_attn=False,
**kwargs,
):
if alibi:
raise NotImplementedError(
"alibi is not supported by this version of the model"
)
self.model_type = model_type
self.alibi = False
self.rotary = True
self.vocab_size = vocab_size
# Backward compatibility with n_embed kwarg
n_embed = kwargs.pop("n_embed", None)
self.hidden_size = hidden_size if n_embed is None else n_embed
self.n_layer = (
num_hidden_layers
if num_hidden_layers is not None
else kwargs.pop("n_layer", 2)
)
self.n_head = (
num_attention_heads
if num_attention_heads is not None
else kwargs.pop("n_head", 8)
)
self.layer_norm_epsilon = layer_norm_epsilon
self.initializer_range = initializer_range
self.use_cache = use_cache
self.hidden_dropout = hidden_dropout
self.attention_dropout = attention_dropout
self.bias = bias
self.parallel_attn = parallel_attn
self.bos_token_id = bos_token_id
self.eos_token_id = eos_token_id
if num_kv_heads is not None:
self.n_head_kv = num_kv_heads
else:
old_n_head_kv = kwargs.pop("n_head_kv", None)
if old_n_head_kv is not None:
self.n_head_kv = old_n_head_kv
else:
self.n_head_kv = 1 if multi_query else self.n_head
if new_decoder_architecture is not None:
self.new_decoder_architecture = new_decoder_architecture
elif model_type == "RefinedWeb":
self.new_decoder_architecture = True
else:
self.new_decoder_architecture = False
super().__init__(bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)
class FlashRWAttention(torch.nn.Module):
def __init__(
self,
config,
prefix,
weights,
):
super().__init__()
self.num_heads = config.n_head
self.num_heads_kv = config.n_head_kv
self.hidden_size = config.hidden_size
self.head_size = self.hidden_size // self.num_heads
self.rotary_emb = PositionRotaryEmbedding.static(
config=config, dim=self.head_size, base=10000.0, device=weights.device
)
self.softmax_scale = self.head_size ** (-0.5)
if self.num_heads % weights.process_group.size() != 0:
raise ValueError(
f"`num_heads` must be divisible by `num_shards` (got `num_heads`: {self.num_heads} "
f"and `num_shards`: {weights.process_group.size()}"
)
self.num_heads = self.num_heads // weights.process_group.size()
self.query_key_value = TensorParallelColumnLinear.load(
config,
prefix=f"{prefix}.query_key_value",
weights=weights,
bias=config.bias,
)
self.dense = load_row(
config, prefix=f"{prefix}.dense", weights=weights, bias=config.bias
)
if self.num_heads_kv == 1:
self.kv_head_mapping = torch.zeros(
self.num_heads, dtype=torch.int32, device=weights.device
)
else:
self.kv_head_mapping = torch.arange(
0, self.num_heads, dtype=torch.int32, device=weights.device
)
def forward(
self,
hidden_states,
cos,
sin,
cu_seqlen_prefill,
kv_cache,
block_tables,
slots,
input_lengths,
max_s,
):
qkv = self.query_key_value(hidden_states)
# Split query from key_value
query, kv = qkv.split(
[self.head_size * self.num_heads, 2 * self.head_size * self.num_heads_kv],
dim=1,
)
# Prepare query and key_value for indexing
query = query.view(-1, self.num_heads, self.head_size)
kv = kv.view(-1, 2, self.num_heads_kv, self.head_size)
# Inplace rotary
self.rotary_emb(query, torch.select(kv, dim=1, index=0), cos, sin)
paged_attention.reshape_and_cache(
kv[:, 0], kv[:, 1], kv_cache[0], kv_cache[1], slots
)
# output
attn_output = torch.empty_like(query)
# Prefill
if cu_seqlen_prefill is not None:
# flash attention
flash_attn.attention(
query,
torch.select(kv, dim=1, index=0),
torch.select(kv, dim=1, index=1),
attn_output,
cu_seqlen_prefill,
max_s,
self.softmax_scale,
)
# Decode
else:
paged_attention.attention(
attn_output,
query,
kv_cache[0],
kv_cache[1],
self.kv_head_mapping,
self.softmax_scale,
block_tables,
input_lengths,
max_s,
)
return self.dense(attn_output.view(-1, self.num_heads * self.head_size))
class FlashRWLargeAttention(torch.nn.Module):
def __init__(
self,
config,
prefix,
weights,
):
super().__init__()
hidden_size = config.hidden_size
num_heads = config.n_head
# num_heads_kv = config.n_head_kv
num_groups = config.n_head_kv
self.hidden_size = hidden_size
self.head_size = hidden_size // num_heads
self.num_groups = num_groups
self.rotary_emb = PositionRotaryEmbedding.static(
config=config, dim=self.head_size, base=10000.0, device=weights.device
)
self.softmax_scale = self.head_size ** (-0.5)
# self.num_groups = num_heads // (num_heads_kv * 2)
self.num_heads = num_heads // self.num_groups
# self.num_heads_kv = num_heads_kv // self.num_groups
process_group = weights.process_group
if process_group.size() > self.num_groups:
raise NotImplementedError(
f"Tensor Parallelism is not implemented for world_size > n groups"
)
if self.num_groups % process_group.size() != 0:
raise NotImplementedError(
f"Tensor Parallelism is not implemented for {self.num_groups} not divisible by {process_group.size()}"
)
self.num_groups = self.num_groups // process_group.size()
self.query_key_value = TensorParallelColumnLinear.load(
config,
prefix=f"{prefix}.query_key_value",
weights=weights,
bias=config.bias,
)
self.dense = load_row(
config, prefix=f"{prefix}.dense", weights=weights, bias=config.bias
)
self.kv_head_mapping = torch.arange(
0, self.num_groups, dtype=torch.int32, device=weights.device
).repeat_interleave(self.num_heads)
def forward(
self,
hidden_states,
cos,
sin,
cu_seqlen_prefill,
kv_cache,
block_tables,
slots,
input_lengths,
max_s,
):
qkv = self.query_key_value(hidden_states)
qkv = qkv.view(-1, self.num_groups, self.num_heads + 2, self.head_size)
# Split on group dimension
query, kv = qkv.split(
[self.num_heads, 2],
dim=2,
)
# Merge groups and heads
query = query.reshape(-1, self.num_groups * self.num_heads, self.head_size)
# Inplace rotary
self.rotary_emb(query, torch.select(kv, dim=2, index=0), cos, sin)
paged_attention.reshape_and_cache(
kv[:, :, 0].contiguous(),
kv[:, :, 1].contiguous(),
kv_cache[0],
kv_cache[1],
slots,
)
# output
attn_output = torch.empty_like(query)
# Prefill
if cu_seqlen_prefill is not None:
# flash attention
flash_attn.attention(
query,
torch.select(kv, dim=2, index=0),
torch.select(kv, dim=2, index=1),
attn_output,
cu_seqlen_prefill,
max_s,
self.softmax_scale,
)
# Decode
else:
paged_attention.attention(
attn_output,
query,
kv_cache[0],
kv_cache[1],
self.kv_head_mapping,
self.softmax_scale,
block_tables,
input_lengths,
max_s,
)
return self.dense(
attn_output.view(-1, self.num_groups * self.num_heads * self.head_size)
)
class FlashMLP(nn.Module):
def __init__(self, config, prefix, weights):
super().__init__()
self.act = torch.nn.functional.gelu
self.dense_h_to_4h = TensorParallelColumnLinear.load(
config, prefix=f"{prefix}.dense_h_to_4h", weights=weights, bias=config.bias
)
self.dense_4h_to_h = load_row(
config, prefix=f"{prefix}.dense_4h_to_h", weights=weights, bias=config.bias
)
def forward(self, hidden_states):
hidden_states = self.dense_h_to_4h(hidden_states)
hidden_states = self.act(hidden_states)
hidden_states = self.dense_4h_to_h(hidden_states)
return hidden_states
class FlashRWLayer(nn.Module):
def __init__(
self,
layer_id,
config,
weights,
):
super().__init__()
parallel_attn = config.parallel_attn
self.parallel_attn = parallel_attn
prefix = f"transformer.h.{layer_id}"
self.input_layernorm = FastLayerNorm.load(
prefix=f"{prefix}.input_layernorm",
weights=weights,
eps=config.layer_norm_epsilon,
)
self.self_attention = FlashRWAttention(
config,
prefix=f"{prefix}.self_attention",
weights=weights,
)
self.post_attention_layernorm = (
FastLayerNorm.load(
prefix=f"{prefix}.post_attention_layernorm",
weights=weights,
eps=config.layer_norm_epsilon,
)
if not parallel_attn
else None
)
self.mlp = FlashMLP(
config,
prefix=f"{prefix}.mlp",
weights=weights,
)
self.process_group = weights.process_group
def forward(
self,
hidden_states,
residual,
cos,
sin,
cu_seqlen_prefill,
kv_cache,
block_tables,
slots,
input_lengths,
max_s,
):
if self.parallel_attn:
ln_hidden_states, residual = self.input_layernorm(hidden_states, residual)
attn_output = self.self_attention(
ln_hidden_states,
cos,
sin,
cu_seqlen_prefill,
kv_cache,
block_tables,
slots,
input_lengths,
max_s,
)
mlp_output = self.mlp(ln_hidden_states)
intermediate = mlp_output + attn_output
if self.process_group.size() > 1:
torch.distributed.all_reduce(intermediate, group=self.process_group)
return intermediate, residual
else:
hidden_states, residual = self.input_layernorm(hidden_states, residual)
hidden_states = self.self_attention(
hidden_states,
cos,
sin,
cu_seqlen_prefill,
kv_cache,
block_tables,
slots,
input_lengths,
max_s,
)
hidden_states, residual = self.post_attention_layernorm(
hidden_states, residual
)
mlp_output = self.mlp(hidden_states)
return mlp_output, residual
class FlashRWLargeLayer(nn.Module):
def __init__(self, layer_id, config, weights):
super().__init__()
prefix = f"transformer.h.{layer_id}"
self.ln_attn = FastLayerNorm.load(
prefix=f"{prefix}.ln_attn",
weights=weights,
eps=config.layer_norm_epsilon,
)
self.ln_mlp = FastLayerNorm.load(
prefix=f"{prefix}.ln_mlp",
weights=weights,
eps=config.layer_norm_epsilon,
)
self.self_attention = FlashRWLargeAttention(
config,
prefix=f"{prefix}.self_attention",
weights=weights,
)
assert config.parallel_attn, "This version doesn't support non parallel_attn"
self.mlp = FlashMLP(config, prefix=f"{prefix}.mlp", weights=weights)
self.process_group = weights.process_group
def forward(
self,
hidden_states,
residual,
cos,
sin,
cu_seqlen_prefill,
kv_cache,
block_tables,
slots,
input_lengths,
max_s,
):
ln_attn, residual = self.ln_attn(hidden_states, residual)
ln_mlp, _ = self.ln_mlp(residual)
# Self attention.
attn_output = self.self_attention(
ln_attn,
cos,
sin,
cu_seqlen_prefill,
kv_cache,
block_tables,
slots,
input_lengths,
max_s,
)
# MLP.
mlp_output = self.mlp(ln_mlp)
intermediate = attn_output + mlp_output
if self.process_group.size() > 1:
torch.distributed.all_reduce(intermediate, group=self.process_group)
return intermediate, residual
class FlashRWPreTrainedModel(PreTrainedModel):
config_class = RWConfig
class FlashRWModel(FlashRWPreTrainedModel):
def __init__(self, config, weights):
super().__init__(config)
self.config = config
self.word_embeddings = TensorParallelEmbedding(
prefix="transformer.word_embeddings", weights=weights
)
if config.new_decoder_architecture:
self.h = nn.ModuleList(
[
FlashRWLargeLayer(layer_id, config, weights)
for layer_id in range(config.num_hidden_layers)
]
)
self.cache_size = self.h[0].self_attention.num_groups
else:
self.h = nn.ModuleList(
[
FlashRWLayer(layer_id, config, weights)
for layer_id in range(config.num_hidden_layers)
]
)
self.cache_size = self.h[0].self_attention.num_heads_kv
self.ln_f = FastLayerNorm.load(
prefix="transformer.ln_f",
weights=weights,
eps=config.layer_norm_epsilon,
)
self.head_size = self.h[0].self_attention.head_size
def forward(
self,
input_ids: torch.Tensor,
position_ids: torch.Tensor,
cu_seqlen_prefill: Optional[torch.Tensor],
kv_cache: List[Tuple[torch.Tensor, torch.Tensor]],
block_tables: torch.Tensor,
slots: torch.Tensor,
input_lengths: torch.Tensor,
max_s: int,
) -> torch.Tensor:
hidden_states = self.word_embeddings(input_ids)
# Get rotary cos and sin for this forward
# Avoid to index in each layer
cos, sin = self.h[0].self_attention.rotary_emb.get_cos_sin(
position_ids, max_s, hidden_states.dtype
)
residual = None
for i, layer in enumerate(self.h):
hidden_states, residual = layer(
hidden_states,
residual,
cos,
sin,
cu_seqlen_prefill,
kv_cache[i],
block_tables,
slots,
input_lengths,
max_s,
)
hidden_states, _ = self.ln_f(hidden_states, residual)
return hidden_states
class FlashRWForCausalLM(FlashRWPreTrainedModel):
def __init__(self, config, weights):
super().__init__(config)
self.transformer = FlashRWModel(config, weights)
self.lm_head = TensorParallelHead.load(
config, prefix="lm_head", weights=weights
)
def forward(
self,
input_ids: torch.Tensor,
position_ids: torch.Tensor,
cu_seqlen_prefill: Optional[torch.Tensor],
kv_cache: List[Tuple[torch.Tensor, torch.Tensor]],
block_tables: torch.Tensor,
slots: torch.Tensor,
input_lengths: torch.Tensor,
max_s: int,
lm_head_indices: Optional[torch.Tensor] = None,
) -> torch.Tensor:
hidden_states = self.transformer(
input_ids,
position_ids,
cu_seqlen_prefill,
kv_cache,
block_tables,
slots,
input_lengths,
max_s,
)
if lm_head_indices is not None:
hidden_states = hidden_states[lm_head_indices]
logits = self.lm_head(hidden_states)
return logits
| 0 |
hf_public_repos/text-generation-inference/server/text_generation_server/models | hf_public_repos/text-generation-inference/server/text_generation_server/models/custom_modeling/idefics_image_processing.py | # coding=utf-8
# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Image processor class for Idefics."""
from typing import Callable, Dict, List, Optional, Union, Iterable
import numpy as np
from PIL import Image
from transformers.image_processing_utils import BaseImageProcessor, BatchFeature
from transformers.image_transforms import (
resize,
to_channel_dimension_format,
rescale,
normalize,
)
from transformers.image_utils import (
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from io import BytesIO
import base64
import requests
from transformers import TensorType, is_torch_available
IDEFICS_STANDARD_MEAN = [0.48145466, 0.4578275, 0.40821073]
IDEFICS_STANDARD_STD = [0.26862954, 0.26130258, 0.27577711]
def convert_to_rgb(image):
# `image.convert("RGB")` would only work for .jpg images, as it creates a wrong background
# for transparent images. The call to `alpha_composite` handles this case
if image.mode == "RGB":
return image
image_rgba = image.convert("RGBA")
background = Image.new("RGBA", image_rgba.size, (255, 255, 255))
alpha_composite = Image.alpha_composite(background, image_rgba)
alpha_composite = alpha_composite.convert("RGB")
return alpha_composite
class IdeficsImageProcessor(BaseImageProcessor):
r"""
Constructs a Idefics image processor.
Args:
image_size (`int`, *optional*, defaults to `224`):
Resize to image size
image_num_channels (`int`, *optional*, defaults to `3`):
Number of image channels.
image_mean (`float` or `List[float]`, *optional*, defaults to `IDEFICS_STANDARD_MEAN`):
Mean to use if normalizing the image. This is a float or list of floats the length of the number of
channels in the image. Can be overridden by the `image_mean` parameter in the `preprocess` method. Can be
overridden by the `image_mean` parameter in the `preprocess` method.
image_std (`float` or `List[float]`, *optional*, defaults to `IDEFICS_STANDARD_STD`):
Standard deviation to use if normalizing the image. This is a float or list of floats the length of the
number of channels in the image. Can be overridden by the `image_std` parameter in the `preprocess` method.
Can be overridden by the `image_std` parameter in the `preprocess` method.
"""
model_input_names = ["pixel_values"]
def __init__(
self,
image_size: int = 224,
image_mean: Optional[Union[float, List[float]]] = None,
image_std: Optional[Union[float, List[float]]] = None,
image_num_channels: Optional[int] = 3,
**kwargs,
) -> None:
super().__init__(**kwargs)
self.image_size = image_size
self.image_num_channels = image_num_channels
self.image_mean = image_mean
self.image_std = image_std
def preprocess(
self,
images: ImageInput,
image_num_channels: Optional[int] = 3,
image_size: Optional[Dict[str, int]] = None,
image_mean: Optional[Union[float, List[float]]] = None,
image_std: Optional[Union[float, List[float]]] = None,
transform: Callable = None,
**kwargs,
) -> TensorType.PYTORCH:
"""
Preprocess a batch of images.
Args:
images (`ImageInput`):
A list of images to preprocess.
image_size (`int`, *optional*, defaults to `self.image_size`):
Resize to image size
image_num_channels (`int`, *optional*, defaults to `self.image_num_channels`):
Number of image channels.
image_mean (`float` or `List[float]`, *optional*, defaults to `IDEFICS_STANDARD_MEAN`):
Mean to use if normalizing the image. This is a float or list of floats the length of the number of
channels in the image. Can be overridden by the `image_mean` parameter in the `preprocess` method. Can
be overridden by the `image_mean` parameter in the `preprocess` method.
image_std (`float` or `List[float]`, *optional*, defaults to `IDEFICS_STANDARD_STD`):
Standard deviation to use if normalizing the image. This is a float or list of floats the length of the
number of channels in the image. Can be overridden by the `image_std` parameter in the `preprocess`
method. Can be overridden by the `image_std` parameter in the `preprocess` method.
transform (`Callable`, *optional*, defaults to `None`):
A custom transform function that accepts a single image can be passed for training. For example,
`torchvision.Compose` can be used to compose multiple transforms. If `None` - an inference mode is
assumed - and then a preset of inference-specific transforms will be applied to the images
Returns:
a PyTorch tensor of the processed images
"""
image_size = image_size if image_size is not None else self.image_size
image_num_channels = (
image_num_channels
if image_num_channels is not None
else self.image_num_channels
)
image_mean = image_mean if image_mean is not None else self.image_mean
image_std = image_std if image_std is not None else self.image_std
size = (image_size, image_size)
if len(images) == 0:
return []
images = make_list_of_images(images)
if not valid_images(images):
raise ValueError(
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
"torch.Tensor, tf.Tensor or jax.ndarray."
)
# For training a user needs to pass their own set of transforms as a Callable.
# For reference this is what was used in the original IDEFICS training:
# transform = transforms.Compose([
# convert_to_rgb,
# transforms.RandomResizedCrop((size, size), scale=(0.9, 1.0), interpolation=transforms.InterpolationMode.BICUBIC),
# transforms.ToTensor(),
# transforms.Normalize(mean=image_mean, std=image_std),
# ])
if transform is not None:
if not is_torch_available():
raise ImportError("To pass in `transform` torch must be installed")
import torch
images = [transform(x) for x in images]
return torch.stack(images)
# for inference we do the exact transforms that were used to train IDEFICS
images = [convert_to_rgb(x) for x in images]
# further transforms expect numpy arrays
images = [to_numpy_array(x) for x in images]
images = [resize(x, size, resample=PILImageResampling.BICUBIC) for x in images]
images = [self.rescale(image=image, scale=1 / 255) for image in images]
images = [self.normalize(x, mean=image_mean, std=image_std) for x in images]
images = [
to_channel_dimension_format(x, ChannelDimension.FIRST) for x in images
]
# TODO: this converts to torch tensors - switch to convert_to_tensors once it becomes available
images = BatchFeature(
data={"pixel_values": images}, tensor_type=TensorType.PYTORCH
)["pixel_values"]
return images
def fetch_images(self, image_url_or_urls: Union[str, List[str]]):
"""
Convert a single or a list of urls into the corresponding `PIL.Image` objects.
If a single url is passed, the return value will be a single object. If a list is passed a list of objects is
returned.
"""
headers = {
"User-Agent": (
"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/114.0.0.0"
" Safari/537.36"
)
}
if isinstance(image_url_or_urls, list):
return [self.fetch_images(x) for x in image_url_or_urls]
elif isinstance(image_url_or_urls, str):
image = image_url_or_urls
if image.startswith("http://") or image.startswith("https://"):
response = requests.get(
image_url_or_urls, stream=True, headers=headers, timeout=(1, 5)
)
response.raise_for_status()
content = response.content
elif image.startswith("data:"):
# https://stackoverflow.com/questions/17090571/is-there-a-way-to-set-background-image-as-a-base64-encoded-image
# data:image/png;base64,xxx
image = image.split(",")[-1]
content = base64.b64decode(image)
else:
raise ValueError(f"Unrecognized image {image}")
try:
image = Image.open(BytesIO(content))
# image.verify()
except Exception:
raise ValueError(f"Could not load image from url {image_url_or_urls}")
return image
else:
raise ValueError(
f"only a single or a list of entries is supported but got type={type(image_url_or_urls)}"
)
def rescale(
self,
image: np.ndarray,
scale: float,
data_format: Optional[Union[str, ChannelDimension]] = None,
input_data_format: Optional[Union[str, ChannelDimension]] = None,
**kwargs,
) -> np.ndarray:
"""
Rescale an image by a scale factor. image = image * scale.
Args:
image (`np.ndarray`):
Image to rescale.
scale (`float`):
The scaling factor to rescale pixel values by.
data_format (`str` or `ChannelDimension`, *optional*):
The channel dimension format for the output image. If unset, the channel dimension format of the input
image is used. Can be one of:
- `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
- `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
input_data_format (`ChannelDimension` or `str`, *optional*):
The channel dimension format for the input image. If unset, the channel dimension format is inferred
from the input image. Can be one of:
- `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
- `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
Returns:
`np.ndarray`: The rescaled image.
"""
# return rescale(image, scale=scale, data_format=data_format, input_data_format=input_data_format, **kwargs)
# requires 4.32
return rescale(image, scale=scale, data_format=data_format, **kwargs)
def normalize(
self,
image: np.ndarray,
mean: Union[float, Iterable[float]],
std: Union[float, Iterable[float]],
data_format: Optional[Union[str, ChannelDimension]] = None,
input_data_format: Optional[Union[str, ChannelDimension]] = None,
**kwargs,
) -> np.ndarray:
"""
Normalize an image. image = (image - image_mean) / image_std.
Args:
image (`np.ndarray`):
Image to normalize.
mean (`float` or `Iterable[float]`):
Image mean to use for normalization.
std (`float` or `Iterable[float]`):
Image standard deviation to use for normalization.
data_format (`str` or `ChannelDimension`, *optional*):
The channel dimension format for the output image. If unset, the channel dimension format of the input
image is used. Can be one of:
- `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
- `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
input_data_format (`ChannelDimension` or `str`, *optional*):
The channel dimension format for the input image. If unset, the channel dimension format is inferred
from the input image. Can be one of:
- `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
- `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
Returns:
`np.ndarray`: The normalized image.
"""
# TODO 4.32
return normalize(image, mean=mean, std=std, data_format=data_format, **kwargs)
import transformers
transformers.IdeficsImageProcessor = IdeficsImageProcessor
| 0 |
hf_public_repos/text-generation-inference/server/text_generation_server/models | hf_public_repos/text-generation-inference/server/text_generation_server/models/custom_modeling/opt_modeling.py | # coding=utf-8
# Copyright 2022 The Fairseq Authors and The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" PyTorch OPT model."""
import random
from typing import List, Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from transformers.activations import ACT2FN
from transformers.modeling_outputs import (
BaseModelOutputWithPast,
CausalLMOutputWithPast,
)
from transformers.modeling_utils import PreTrainedModel
from transformers import OPTConfig
from text_generation_server.utils.layers import (
FastLinear,
TensorParallelColumnLinear,
TensorParallelEmbedding,
TensorParallelRowLinear,
TensorParallelHead,
)
EPS = 1e-5
# Copied from transformers.models.bart.modeling_bart._make_causal_mask
def _make_causal_mask(
input_ids_shape: torch.Size,
dtype: torch.dtype,
device: torch.device,
past_key_values_length: int = 0,
):
"""
Make causal mask used for bi-directional self-attention.
"""
bsz, tgt_len = input_ids_shape
mask = torch.full(
(tgt_len, tgt_len),
torch.tensor(torch.finfo(dtype).min, device=device),
device=device,
)
mask_cond = torch.arange(mask.size(-1), device=device)
mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
mask = mask.to(dtype)
if past_key_values_length > 0:
mask = torch.cat(
[
torch.zeros(
tgt_len, past_key_values_length, dtype=dtype, device=device
),
mask,
],
dim=-1,
)
return mask[None, None, :, :].expand(
bsz, 1, tgt_len, tgt_len + past_key_values_length
)
def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
"""
Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
"""
bsz, src_len = mask.size()
tgt_len = tgt_len if tgt_len is not None else src_len
expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
inverted_mask = 1.0 - expanded_mask
return inverted_mask.masked_fill(
inverted_mask.to(torch.bool), torch.finfo(dtype).min
)
class OPTLearnedPositionalEmbedding(nn.Module):
"""
This module learns positional embeddings up to a fixed maximum size.
"""
def __init__(self, weights):
super().__init__()
self.offset = 2
self.weight = nn.Parameter(
weights.get_tensor("model.decoder.embed_positions.weight")
)
def forward(
self, attention_mask: torch.LongTensor, past_key_values_length: int = 0
):
"""`input_ids_shape` is expected to be [bsz x seqlen]."""
attention_mask = attention_mask.long()
# create positions depending on attention_mask
positions = (
torch.cumsum(attention_mask, dim=1).type_as(attention_mask) * attention_mask
).long() - 1
# cut positions if `past_key_values_length` is > 0
positions = positions[:, past_key_values_length:]
return torch.nn.functional.embedding(positions + self.offset, self.weight)
class OPTAttention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(
self,
config,
prefix,
weights,
is_decoder: bool = False,
bias: bool = True,
process_group=None,
):
super().__init__()
hidden_size = config.hidden_size
num_heads = config.num_attention_heads
self.hidden_size = hidden_size
self.num_heads = num_heads
self.dropout = config.dropout
self.head_dim = hidden_size // num_heads
if (self.head_dim * num_heads) != self.hidden_size:
raise ValueError(
f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
f" and `num_heads`: {num_heads})."
)
self.scaling = self.head_dim**-0.5
self.is_decoder = is_decoder
process_group = weights.process_group
if self.num_heads % weights.process_group.size() != 0:
raise ValueError(
f"`num_heads` must be divisible by `num_shards` (got `num_heads`: {self.num_heads} "
f"and `num_shards`: {weights.process_group.size()}"
)
self.num_heads = self.num_heads // process_group.size()
self.hidden_size = self.hidden_size // process_group.size()
self.q_proj = TensorParallelColumnLinear.load(
config, prefix=f"{prefix}.q_proj", weights=weights, bias=bias
)
self.k_proj = TensorParallelColumnLinear.load(
config, prefix=f"{prefix}.k_proj", weights=weights, bias=bias
)
self.v_proj = TensorParallelColumnLinear.load(
config, prefix=f"{prefix}.v_proj", weights=weights, bias=bias
)
self.out_proj = TensorParallelRowLinear.load(
config, prefix=f"{prefix}.out_proj", weights=weights, bias=bias
)
def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
return (
tensor.view(bsz, seq_len, self.num_heads, self.head_dim)
.transpose(1, 2)
.contiguous()
)
def forward(
self,
hidden_states: torch.Tensor,
key_value_states: Optional[torch.Tensor] = None,
past_key_value: Optional[Tuple[torch.Tensor]] = None,
attention_mask: Optional[torch.Tensor] = None,
layer_head_mask: Optional[torch.Tensor] = None,
output_attentions: bool = False,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
"""Input shape: Batch x Time x Channel"""
# if key_value_states are provided this layer is used as a cross-attention layer
# for the decoder
is_cross_attention = key_value_states is not None
bsz, tgt_len, _ = hidden_states.size()
# get query proj
query_states = self.q_proj(hidden_states) * self.scaling
# get key, value proj
if is_cross_attention and past_key_value is not None:
# reuse k,v, cross_attentions
key_states = past_key_value[0]
value_states = past_key_value[1]
elif is_cross_attention:
# cross_attentions
key_states = self._shape(self.k_proj(key_value_states), -1, bsz)
value_states = self._shape(self.v_proj(key_value_states), -1, bsz)
elif past_key_value is not None:
# reuse k, v, self_attention
key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
key_states = torch.cat([past_key_value[0], key_states], dim=2)
value_states = torch.cat([past_key_value[1], value_states], dim=2)
else:
# self_attention
key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
if self.is_decoder:
# if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states.
# Further calls to cross_attention layer can then reuse all cross-attention
# key/value_states (first "if" case)
# if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of
# all previous decoder key/value_states. Further calls to uni-directional self-attention
# can concat previous decoder key/value_states to current projected key/value_states (third "elif" case)
# if encoder bi-directional self-attention `past_key_value` is always `None`
past_key_value = (key_states, value_states)
proj_shape = (bsz * self.num_heads, -1, self.head_dim)
query_states = self._shape(query_states, tgt_len, bsz).view(*proj_shape)
key_states = key_states.view(*proj_shape)
value_states = value_states.view(*proj_shape)
src_len = key_states.size(1)
attn_weights = torch.bmm(query_states, key_states.transpose(1, 2))
if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len):
raise ValueError(
f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is"
f" {attn_weights.size()}"
)
if attention_mask is not None:
if attention_mask.size() != (bsz, 1, tgt_len, src_len):
raise ValueError(
f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}"
)
attn_weights = (
attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
+ attention_mask
)
attn_weights = torch.max(
attn_weights, torch.tensor(torch.finfo(attn_weights.dtype).min)
)
attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
# upcast to fp32 if the weights are in fp16. Please see https://github.com/huggingface/transformers/pull/17437
if attn_weights.dtype == torch.float16:
attn_weights = nn.functional.softmax(
attn_weights, dim=-1, dtype=torch.float32
).to(torch.float16)
else:
attn_weights = nn.functional.softmax(attn_weights, dim=-1)
if layer_head_mask is not None:
if layer_head_mask.size() != (self.num_heads,):
raise ValueError(
f"Head mask for a single layer should be of size {(self.num_heads,)}, but is"
f" {layer_head_mask.size()}"
)
attn_weights = layer_head_mask.view(1, -1, 1, 1) * attn_weights.view(
bsz, self.num_heads, tgt_len, src_len
)
attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
if output_attentions:
# this operation is a bit awkward, but it's required to
# make sure that attn_weights keeps its gradient.
# In order to do so, attn_weights have to be reshaped
# twice and have to be reused in the following
attn_weights_reshaped = attn_weights.view(
bsz, self.num_heads, tgt_len, src_len
)
attn_weights = attn_weights_reshaped.view(
bsz * self.num_heads, tgt_len, src_len
)
else:
attn_weights_reshaped = None
attn_probs = nn.functional.dropout(
attn_weights, p=self.dropout, training=self.training
)
attn_output = torch.bmm(attn_probs, value_states)
if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim):
raise ValueError(
f"`attn_output` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is"
f" {attn_output.size()}"
)
attn_output = attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim)
attn_output = attn_output.transpose(1, 2)
# Use the `hidden_size` from the config (stored in the class) rather than `hidden_state` because `attn_output` can be
# partitioned aross GPUs when using tensor-parallelism.
attn_output = attn_output.reshape(bsz, tgt_len, self.hidden_size)
attn_output = self.out_proj(attn_output)
return attn_output, attn_weights_reshaped, past_key_value
class OPTDecoderLayer(nn.Module):
def __init__(self, layer_id: int, config: OPTConfig, weights):
super().__init__()
self.process_group = weights.process_group
self.hidden_size = config.hidden_size
prefix = f"model.decoder.layers.{layer_id}"
self.self_attn = OPTAttention(
config,
prefix=f"{prefix}.self_attn",
weights=weights,
is_decoder=True,
bias=config.enable_bias,
)
self.do_layer_norm_before = config.do_layer_norm_before
self.dropout = config.dropout
self.activation_fn = ACT2FN[config.activation_function]
self.self_attn_layer_norm = nn.LayerNorm.load(
prefix=f"{prefix}.self_attn_layer_norm", weights=weights, eps=EPS
)
self.fc1 = TensorParallelColumnLinear.load(
config, prefix=f"{prefix}.fc1", weights=weights, bias=config.enable_bias
)
self.fc2 = TensorParallelRowLinear.load(
config, prefix=f"{prefix}.fc2", weights=weights, bias=config.enable_bias
)
self.final_layer_norm = nn.LayerNorm.load(
prefix=f"{prefix}.final_layer_norm", weights=weights, eps=EPS
)
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
layer_head_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = False,
use_cache: Optional[bool] = False,
past_key_value: Optional[Tuple[torch.Tensor]] = None,
) -> Tuple[
torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]
]:
"""
Args:
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, hidden_size)`
attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
layer_head_mask (`torch.FloatTensor`, *optional*): mask for attention heads in a given layer of size
`(encoder_attention_heads,)`.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
use_cache (`bool`, *optional*):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
(see `past_key_values`).
past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
"""
residual = hidden_states
# 125m, 1.7B, ..., 175B applies layer norm BEFORE attention
if self.do_layer_norm_before:
hidden_states = self.self_attn_layer_norm(hidden_states)
# Self Attention
hidden_states, self_attn_weights, present_key_value = self.self_attn(
hidden_states=hidden_states,
past_key_value=past_key_value,
attention_mask=attention_mask,
layer_head_mask=layer_head_mask,
output_attentions=output_attentions,
)
hidden_states = nn.functional.dropout(
hidden_states, p=self.dropout, training=self.training
)
hidden_states = residual + hidden_states
# 350m applies layer norm AFTER attention
if not self.do_layer_norm_before:
hidden_states = self.self_attn_layer_norm(hidden_states)
# Fully Connected
hidden_states_shape = hidden_states.shape
hidden_states = hidden_states.reshape(-1, hidden_states.size(-1))
residual = hidden_states
# 125m, 1.7B, ..., 175B applies layer norm BEFORE attention
if self.do_layer_norm_before:
hidden_states = self.final_layer_norm(hidden_states)
hidden_states = self.fc1(hidden_states)
hidden_states = self.activation_fn(hidden_states)
hidden_states = self.fc2(hidden_states)
hidden_states = nn.functional.dropout(
hidden_states, p=self.dropout, training=self.training
)
hidden_states = (residual + hidden_states).view(hidden_states_shape)
# 350m applies layer norm AFTER attention
if not self.do_layer_norm_before:
hidden_states = self.final_layer_norm(hidden_states)
outputs = (hidden_states,)
if output_attentions:
outputs += (self_attn_weights,)
if use_cache:
outputs += (present_key_value,)
return outputs
class OPTPreTrainedModel(PreTrainedModel):
config_class = OPTConfig
class OPTDecoder(OPTPreTrainedModel):
def __init__(self, config: OPTConfig, weights):
super().__init__(config)
self.dropout = config.dropout
self.layerdrop = config.layerdrop
self.padding_idx = config.pad_token_id
self.max_target_positions = config.max_position_embeddings
self.vocab_size = config.vocab_size
self.embed_tokens = TensorParallelEmbedding(
prefix="model.decoder.embed_tokens", weights=weights
)
self.embed_positions = OPTLearnedPositionalEmbedding(weights)
if config.word_embed_proj_dim != config.hidden_size:
self.project_out = FastLinear.load(
config, prefix="model.decoder.project_out", weights=weights, bias=False
)
else:
self.project_out = None
if config.word_embed_proj_dim != config.hidden_size:
self.project_in = FastLinear.load(
config, prefix="model.decoder.project_in", weights=weights, bias=False
)
else:
self.project_in = None
# Note that the only purpose of `config._remove_final_layer_norm` is to keep backward compatibility
# with checkpoints that have been fine-tuned before transformers v4.20.1
# see https://github.com/facebookresearch/metaseq/pull/164
if config.do_layer_norm_before and not config._remove_final_layer_norm:
self.final_layer_norm = nn.LayerNorm.load(
prefix="model.decoder.final_layer_norm", weights=weights, eps=EPS
)
else:
self.final_layer_norm = None
self.layers = nn.ModuleList(
[
OPTDecoderLayer(layer_id, config, weights)
for layer_id in range(config.num_hidden_layers)
]
)
# Copied from transformers.models.bart.modeling_bart.BartDecoder._prepare_decoder_attention_mask
def _prepare_decoder_attention_mask(
self, attention_mask, input_shape, inputs_embeds, past_key_values_length
):
# create causal mask
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
combined_attention_mask = None
if input_shape[-1] > 1:
combined_attention_mask = _make_causal_mask(
input_shape,
inputs_embeds.dtype,
device=inputs_embeds.device,
past_key_values_length=past_key_values_length,
)
if attention_mask is not None:
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
expanded_attn_mask = _expand_mask(
attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]
).to(inputs_embeds.device)
combined_attention_mask = (
expanded_attn_mask
if combined_attention_mask is None
else expanded_attn_mask + combined_attention_mask
)
return combined_attention_mask
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, BaseModelOutputWithPast]:
r"""
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
provide it.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
head_mask (`torch.Tensor` of shape `(num_hidden_layers, num_attention_heads)`, *optional*):
Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of
Contains pre-computed hidden-states (key and values in the self-attention blocks and in the
cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those
that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of
all `decoder_input_ids` of shape `(batch_size, sequence_length)`.
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
This is useful if you want more control over how to convert `input_ids` indices into associated vectors
than the model's internal embedding lookup matrix.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
for more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
output_attentions = (
output_attentions
if output_attentions is not None
else self.config.output_attentions
)
output_hidden_states = (
output_hidden_states
if output_hidden_states is not None
else self.config.output_hidden_states
)
use_cache = use_cache if use_cache is not None else self.config.use_cache
return_dict = (
return_dict if return_dict is not None else self.config.use_return_dict
)
# retrieve input_ids and inputs_embeds
if input_ids is not None and inputs_embeds is not None:
raise ValueError(
"You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time"
)
elif input_ids is not None:
input_shape = input_ids.size()
input_ids = input_ids.view(-1, input_shape[-1])
elif inputs_embeds is not None:
input_shape = inputs_embeds.size()[:-1]
else:
raise ValueError(
"You have to specify either decoder_input_ids or decoder_inputs_embeds"
)
if inputs_embeds is None:
inputs_embeds = self.embed_tokens(input_ids)
batch_size, seq_length = input_shape
past_key_values_length = (
past_key_values[0][0].shape[2] if past_key_values is not None else 0
)
# required mask seq length can be calculated via length of past
mask_seq_length = past_key_values_length + seq_length
# embed positions
if attention_mask is None:
attention_mask = torch.ones(
batch_size, mask_seq_length, device=inputs_embeds.device
)
causal_attention_mask = self._prepare_decoder_attention_mask(
attention_mask, input_shape, inputs_embeds, past_key_values_length
)
pos_embeds = self.embed_positions(attention_mask, past_key_values_length)
if self.project_in is not None:
inputs_embeds = self.project_in(inputs_embeds)
hidden_states = inputs_embeds + pos_embeds
# decoder layers
all_hidden_states = () if output_hidden_states else None
all_self_attns = () if output_attentions else None
next_decoder_cache = () if use_cache else None
# check if head_mask has a correct number of layers specified if desired
for attn_mask, mask_name in zip([head_mask], ["head_mask"]):
if attn_mask is not None:
if attn_mask.size()[0] != (len(self.layers)):
raise ValueError(
f"The `{mask_name}` should be specified for {len(self.layers)} layers, but it is for"
f" {head_mask.size()[0]}."
)
for idx, decoder_layer in enumerate(self.layers):
# add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
if output_hidden_states:
all_hidden_states += (hidden_states,)
dropout_probability = random.uniform(0, 1)
if self.training and (dropout_probability < self.layerdrop):
continue
past_key_value = (
past_key_values[idx] if past_key_values is not None else None
)
layer_outputs = decoder_layer(
hidden_states,
attention_mask=causal_attention_mask,
layer_head_mask=(head_mask[idx] if head_mask is not None else None),
past_key_value=past_key_value,
output_attentions=output_attentions,
use_cache=use_cache,
)
hidden_states = layer_outputs[0]
if use_cache:
next_decoder_cache += (layer_outputs[2 if output_attentions else 1],)
if output_attentions:
all_self_attns += (layer_outputs[1],)
if self.final_layer_norm is not None:
hidden_states = self.final_layer_norm(hidden_states)
if self.project_out is not None:
hidden_states = self.project_out(hidden_states)
# add hidden states from the last decoder layer
if output_hidden_states:
all_hidden_states += (hidden_states,)
next_cache = next_decoder_cache if use_cache else None
if not return_dict:
return tuple(
v
for v in [hidden_states, next_cache, all_hidden_states, all_self_attns]
if v is not None
)
return BaseModelOutputWithPast(
last_hidden_state=hidden_states,
past_key_values=next_cache,
hidden_states=all_hidden_states,
attentions=all_self_attns,
)
class OPTModel(OPTPreTrainedModel):
def __init__(self, config: OPTConfig, weights):
super().__init__(config)
self.decoder = OPTDecoder(config, weights)
# Initialize weights and apply final processing
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, BaseModelOutputWithPast]:
output_attentions = (
output_attentions
if output_attentions is not None
else self.config.output_attentions
)
output_hidden_states = (
output_hidden_states
if output_hidden_states is not None
else self.config.output_hidden_states
)
use_cache = use_cache if use_cache is not None else self.config.use_cache
return_dict = (
return_dict if return_dict is not None else self.config.use_return_dict
)
# decoder outputs consists of (dec_features, past_key_value, dec_hidden, dec_attn)
decoder_outputs = self.decoder(
input_ids=input_ids,
attention_mask=attention_mask,
head_mask=head_mask,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
if not return_dict:
return decoder_outputs
return BaseModelOutputWithPast(
last_hidden_state=decoder_outputs.last_hidden_state,
past_key_values=decoder_outputs.past_key_values,
hidden_states=decoder_outputs.hidden_states,
attentions=decoder_outputs.attentions,
)
class OPTForCausalLM(OPTPreTrainedModel):
def __init__(self, config, weights):
super().__init__(config)
self.model = OPTModel(config, weights)
self.lm_head = TensorParallelHead.load(
config, prefix="model.decoder.embed_tokens", weights=weights
)
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, CausalLMOutputWithPast]:
output_attentions = (
output_attentions
if output_attentions is not None
else self.config.output_attentions
)
output_hidden_states = (
output_hidden_states
if output_hidden_states is not None
else self.config.output_hidden_states
)
return_dict = (
return_dict if return_dict is not None else self.config.use_return_dict
)
# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
outputs = self.model.decoder(
input_ids=input_ids,
attention_mask=attention_mask,
head_mask=head_mask,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
logits = self.lm_head(outputs[0]).contiguous()
loss = None
return CausalLMOutputWithPast(
loss=loss,
logits=logits,
past_key_values=outputs.past_key_values,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
def prepare_inputs_for_generation(
self,
input_ids,
past_key_values=None,
attention_mask=None,
inputs_embeds=None,
**kwargs,
):
if past_key_values:
input_ids = input_ids[:, -1:]
# if `inputs_embeds` are passed, we only want to use them in the 1st generation step
if inputs_embeds is not None and past_key_values is None:
model_inputs = {"inputs_embeds": inputs_embeds}
else:
model_inputs = {"input_ids": input_ids}
model_inputs.update(
{
"past_key_values": past_key_values,
"use_cache": kwargs.get("use_cache"),
"attention_mask": attention_mask,
}
)
return model_inputs
@staticmethod
def _reorder_cache(past_key_values, beam_idx):
reordered_past = ()
for layer_past in past_key_values:
reordered_past += (
tuple(
past_state.index_select(0, beam_idx) for past_state in layer_past
),
)
return reordered_past
| 0 |
hf_public_repos/text-generation-inference/server/text_generation_server/models | hf_public_repos/text-generation-inference/server/text_generation_server/models/custom_modeling/neox_modeling.py | # coding=utf-8
# Copyright 2022 EleutherAI The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" PyTorch GPTNeoX model."""
from typing import Optional, Tuple, Union
import os
import torch
import torch.distributed
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from transformers.activations import ACT2FN
from transformers.file_utils import (
add_code_sample_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
replace_return_docstrings,
)
from transformers.modeling_outputs import (
BaseModelOutputWithPast,
CausalLMOutputWithPast,
QuestionAnsweringModelOutput,
SequenceClassifierOutputWithPast,
TokenClassifierOutput,
)
from transformers.modeling_utils import PreTrainedModel
from transformers import GPTNeoXConfig
from loguru import logger
from text_generation_server.utils.layers import (
TensorParallelColumnLinear,
TensorParallelEmbedding,
TensorParallelRowLinear,
TensorParallelHead,
)
CUSTOM_KERNELS_ENABLED = False
if (
torch.cuda.is_available()
and not os.environ.get("DISABLE_CUSTOM_KERNELS", "False") == "True"
):
try:
from custom_kernels import fused_attention_cuda
CUSTOM_KERNELS_ENABLED = True
except ImportError:
pass
if not CUSTOM_KERNELS_ENABLED:
logger.warning("We're not using custom kernels.")
def make_causal_mask(
input_ids_shape: torch.Size, device: torch.device, past_key_values_length: int
) -> torch.BoolTensor:
"""
Make causal mask used for self-attention.
"""
batch_size, target_length = input_ids_shape
mask = torch.ones(
(target_length, target_length + past_key_values_length),
dtype=torch.bool,
device=device,
)
mask = mask.triu(1 + past_key_values_length)
expanded_mask = mask.unsqueeze(0).expand(
batch_size, target_length, target_length + past_key_values_length
)
return expanded_mask
def expand_mask(mask: torch.Tensor, tgt_length: int) -> torch.BoolTensor:
"""
Expands attention_mask from `[batch_size, src_length]` to `[batch_size, 1, tgt_length, src_length]`.
"""
batch_size, src_length = mask.shape
tgt_length = tgt_length if tgt_length is not None else src_length
expanded_mask = ~(mask[:, None, :].to(torch.bool))
return expanded_mask.expand(batch_size, tgt_length, src_length)
def prepare_attn_mask(
attention_mask: torch.Tensor,
input_shape: Tuple[int, int],
past_key_values_length: int,
) -> torch.BoolTensor:
# create causal mask
# [batch_size, seq_length] -> [batch_size, tgt_length, src_length]
combined_attention_mask = None
device = attention_mask.device
_, src_length = input_shape
if src_length > 1:
combined_attention_mask = make_causal_mask(
input_shape, device=device, past_key_values_length=past_key_values_length
)
# [batch_size, seq_length] -> [batch_size, tgt_length, src_length]
expanded_attn_mask = expand_mask(attention_mask, tgt_length=src_length)
combined_attention_mask = (
expanded_attn_mask
if combined_attention_mask is None
else expanded_attn_mask | combined_attention_mask
)
return combined_attention_mask
class GPTNeoXPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
class GPTNeoXAttention(nn.Module):
def __init__(self, config, prefix, weights):
super().__init__()
self.num_attention_heads = config.num_attention_heads
self.hidden_size = config.hidden_size
self.head_size = self.hidden_size // self.num_attention_heads
self.rotary_ndims = int(self.head_size * config.rotary_pct)
max_positions = config.max_position_embeddings
# ??? TODO
# self.register_buffer(
# "bias",
# torch.tril(torch.ones((max_positions, max_positions), dtype=torch.bool)).view(
# 1, 1, max_positions, max_positions
# ),
# )
# self.register_buffer("masked_bias", torch.tensor(-1e9))
self.rotary_emb = RotaryEmbedding(
self.rotary_ndims,
config.max_position_embeddings,
base=config.rotary_emb_base,
)
self.rotary_emb.inv_freq = nn.Parameter(
weights.get_tensor(f"{prefix}.rotary_emb.inv_freq")
)
self.inv_norm_factor = 1.0 / torch.sqrt(
torch.tensor(self.head_size, dtype=torch.float32)
).to(torch.get_default_dtype())
if self.num_attention_heads % weights.process_group.size() != 0:
raise ValueError(
f"`num_attention_heads` must be divisible by `num_shards` "
f"(got `num_attention_heads`: {self.num_attention_heads} "
f"and `num_shards`: {weights.process_group.size()}"
)
self.num_attention_heads = (
self.num_attention_heads // weights.process_group.size()
)
self.query_key_value = TensorParallelColumnLinear.load(
config, prefix=f"{prefix}.query_key_value", weights=weights, bias=True
)
self.dense = TensorParallelRowLinear.load(
config, prefix=f"{prefix}.dense", weights=weights, bias=True
)
def forward(
self,
hidden_states,
position_ids,
attention_mask,
head_mask=None,
layer_past=None,
use_cache=False,
output_attentions=False,
):
has_layer_past = layer_past is not None
# Compute QKV
# Attention heads [batch, seq_len, hidden_size]
# --> [batch, seq_len, (np * 3 * head_size)]
qkv = self.query_key_value(hidden_states)
# [batch, seq_len, (num_heads * 3 * head_size)]
# --> [batch, seq_len, num_heads, 3 * head_size]
new_qkv_shape = qkv.size()[:-1] + (self.num_attention_heads, 3 * self.head_size)
qkv = qkv.view(*new_qkv_shape).permute(0, 2, 1, 3)
# [batch, seq_len, num_attention_heads, 3 * head_size] --> 3 [batch, num_attention_heads, seq_len, head_size]
query, key, value = qkv.split(self.head_size, -1)
# Compute token offset for rotary embeddings (when decoding)
seq_len = key.shape[-2]
if has_layer_past:
seq_len += layer_past[0].shape[-2]
# Compute rotary embeddings on rotary_ndims
query_rot = query[..., : self.rotary_ndims]
key_rot = key[..., : self.rotary_ndims]
query_rot, key_rot = self.rotary_emb(query_rot, key_rot, position_ids, seq_len)
query[..., : self.rotary_ndims] = query_rot
key[..., : self.rotary_ndims] = key_rot
if CUSTOM_KERNELS_ENABLED:
attn_output, present, attn_weights = fused_attention_cuda.forward(
query,
key,
value,
layer_past,
attention_mask,
head_mask,
self.inv_norm_factor,
self.num_attention_heads,
use_cache,
)
else:
# Cache QKV values
if has_layer_past:
past_key = layer_past[0]
past_value = layer_past[1]
key = torch.cat((past_key, key), dim=-2)
value = torch.cat((past_value, value), dim=-2)
present = (key, value) if use_cache else None
# Compute attention
attn_output, attn_weights = self._attn(
query, key, value, attention_mask, head_mask
)
# Reshape outputs
attn_output = self._merge_heads(
attn_output, self.num_attention_heads, self.head_size
)
attn_output = self.dense(attn_output)
outputs = (attn_output, present)
if output_attentions:
outputs += (attn_weights,)
return outputs
@classmethod
def _split_heads(cls, tensor, num_attention_heads, attn_head_size):
"""
Splits hidden dim into attn_head_size and num_attention_heads
"""
# tensor: [bs, seq_len, hidden_size]
new_shape = tensor.size()[:-1] + (num_attention_heads, attn_head_size)
# -> [bs, seq_len, num_attention_heads, attn_head_size]
tensor = tensor.view(new_shape)
# -> [bs, num_attention_heads, seq_len, attn_head_size]
tensor = tensor.permute(0, 2, 1, 3)
return tensor
@classmethod
def _merge_heads(cls, tensor, num_attention_heads, attn_head_size):
"""
Merges attn_head_size dim and num_attn_heads dim into hidden dim
"""
# tensor [bs, num_attention_heads, seq_len, attn_head_size]
tensor = tensor.permute(0, 2, 1, 3).contiguous()
# -> [bs, seq_len, num_attention_heads, attn_head_size]
tensor = tensor.view(
tensor.size(0), tensor.size(1), num_attention_heads * attn_head_size
)
# -> [bs, seq_len, hidden_size]
return tensor
def _attn(self, query, key, value, attention_mask=None, head_mask=None):
# q, k, v: [bs, num_attention_heads, seq_len, attn_head_size]
# compute causal mask from causal mask buffer
batch_size, num_attention_heads, query_length, attn_head_size = query.size()
key_length = key.size(-2)
query = query.reshape(
batch_size * num_attention_heads, query_length, attn_head_size
)
key = key.reshape(batch_size * num_attention_heads, key_length, attn_head_size)
attn_scores = torch.zeros(
1,
dtype=query.dtype,
device=key.device,
).expand(batch_size * num_attention_heads, query_length, key_length)
attn_scores = torch.baddbmm(
attn_scores,
query,
key.transpose(1, 2),
beta=1.0,
alpha=self.inv_norm_factor,
)
# cast attention scores to fp32, compute scaled softmax and cast back to initial dtype - [batch_size, num_heads, q_length, kv_length]
input_dtype = attn_scores.dtype
if input_dtype in [torch.float16, torch.bfloat16]:
attn_scores = attn_scores.to(torch.float)
attn_scores = torch.where(
attention_mask, torch.finfo(attn_scores.dtype).min, attn_scores
)
attn_scores = attn_scores.view(
batch_size, num_attention_heads, query_length, key_length
)
attn_weights = nn.functional.softmax(attn_scores, dim=-1)
attn_weights = attn_weights.to(value.dtype)
# Mask heads if we want to
if head_mask is not None:
attn_weights = attn_weights * head_mask
attn_output = torch.matmul(attn_weights, value)
return attn_output, attn_weights
class RotaryEmbedding(torch.nn.Module):
def __init__(self, dim, max_position_embeddings, base=10000, device=None):
super().__init__()
self.true_inv_freq = 1.0 / (
base ** (torch.arange(0, dim, 2).float().to(device) / dim)
)
self.register_buffer("inv_freq", self.true_inv_freq)
# Build here to make `torch.jit.trace` work.
self.max_seq_len_cached = max_position_embeddings
self.cos_cached = None
self.sin_cached = None
@staticmethod
def rotate_half(x):
"""Rotates half the hidden dims of the input."""
x1 = x[..., : x.shape[-1] // 2]
x2 = x[..., x.shape[-1] // 2 :]
return torch.cat((-x2, x1), dim=-1)
@staticmethod
def _create_cos_sin(inv_freq, max_position_embeddings, dtype, device):
t = torch.arange(
max_position_embeddings, device=inv_freq.device, dtype=inv_freq.dtype
)
freqs = torch.einsum("i,j->ij", t, inv_freq)
# Different from paper, but it uses a different permutation in order to obtain the same calculation
emb = torch.cat((freqs, freqs), dim=-1)
return emb.cos().to(device).to(dtype), emb.sin().to(device).to(dtype)
def forward(self, q, k, position_ids, seq_len=None):
# x: [bs, num_attention_heads, seq_len, head_size]
if (
seq_len > self.max_seq_len_cached
or self.cos_cached is None
or self.sin_cached is None
):
if seq_len > self.max_seq_len_cached:
self.max_seq_len_cached = seq_len
self.cos_cached, self.sin_cached = self._create_cos_sin(
self.true_inv_freq, self.max_seq_len_cached, q.dtype, q.device
)
return rotary_forward(q, k, self.cos_cached, self.sin_cached, position_ids)
@torch.jit.script
def rotary_forward(q, k, cos, sin, position_ids):
cos = cos[position_ids].unsqueeze(1)
sin = sin[position_ids].unsqueeze(1)
chunk_size = q.shape[-1] // 2
q1, q2 = q.split(chunk_size, -1)
q_rotated = torch.cat((-q2, q1), dim=-1)
k1, k2 = k.split(chunk_size, -1)
k_rotated = torch.cat((-k2, k1), dim=-1)
q_embed = (q * cos) + (q_rotated * sin)
k_embed = (k * cos) + (k_rotated * sin)
return q_embed, k_embed
class GPTNeoXMLP(nn.Module):
def __init__(self, config, prefix, weights):
super().__init__()
self.act = (
ACT2FN[config.hidden_act]
if "gelu_fast" not in config.hidden_act
else lambda x: torch.nn.functional.gelu(x, approximate="tanh")
)
self.dense_h_to_4h = TensorParallelColumnLinear.load(
config, prefix=f"{prefix}.dense_h_to_4h", weights=weights, bias=True
)
self.dense_4h_to_h = TensorParallelRowLinear.load(
config, prefix=f"{prefix}.dense_4h_to_h", weights=weights, bias=True
)
def forward(self, hidden_states):
hidden_states = self.dense_h_to_4h(hidden_states)
hidden_states = self.act(hidden_states)
hidden_states = self.dense_4h_to_h(hidden_states)
return hidden_states
class GPTNeoXLayer(nn.Module):
def __init__(self, layer_id, config, weights):
super().__init__()
self.use_parallel_residual = config.use_parallel_residual
self.input_layernorm = nn.LayerNorm.load(
prefix=f"gpt_neox.layers.{layer_id}.input_layernorm",
weights=weights,
eps=config.layer_norm_eps,
)
self.post_attention_layernorm = nn.LayerNorm.load(
prefix=f"gpt_neox.layers.{layer_id}.post_attention_layernorm",
weights=weights,
eps=config.layer_norm_eps,
)
self.attention = GPTNeoXAttention(
config, prefix=f"gpt_neox.layers.{layer_id}.attention", weights=weights
)
self.mlp = GPTNeoXMLP(
config, prefix=f"gpt_neox.layers.{layer_id}.mlp", weights=weights
)
def forward(
self,
hidden_states,
position_ids,
attention_mask=None,
head_mask=None,
use_cache=False,
layer_past=None,
output_attentions=False,
):
attention_layer_outputs = self.attention(
self.input_layernorm(hidden_states),
attention_mask=attention_mask,
position_ids=position_ids,
layer_past=layer_past,
head_mask=head_mask,
use_cache=use_cache,
output_attentions=output_attentions,
)
attn_output = attention_layer_outputs[
0
] # output_attn: attn_output, present, (attn_weights)
outputs = attention_layer_outputs[1:]
if self.use_parallel_residual:
# pseudocode:
# x = x + attn(ln1(x)) + mlp(ln2(x))
mlp_output = self.mlp(self.post_attention_layernorm(hidden_states))
hidden_states = mlp_output + attn_output + hidden_states
else:
# pseudocode:
# x = x + attn(ln1(x))
# x = x + mlp(ln2(x))
attn_output = attn_output + hidden_states
mlp_output = self.mlp(self.post_attention_layernorm(attn_output))
hidden_states = mlp_output + attn_output
if use_cache:
outputs = (
hidden_states,
) + outputs # hidden_states, present, (attn_weights)
else:
outputs = (hidden_states,) + outputs[1:] # hidden_states, (attn_weights)
return outputs
class GPTNeoXModel(GPTNeoXPreTrainedModel):
def __init__(self, config, weights):
super().__init__(config)
self.config = config
self.num_attention_heads = config.num_attention_heads
self.embed_in = TensorParallelEmbedding(
prefix="gpt_neox.embed_in", weights=weights
)
self.layers = nn.ModuleList(
[
GPTNeoXLayer(layer_id, config, weights)
for layer_id in range(config.num_hidden_layers)
]
)
self.final_layer_norm = nn.LayerNorm.load(
prefix="gpt_neox.final_layer_norm",
weights=weights,
eps=config.layer_norm_eps,
)
self.tp_world_size = weights.process_group.size()
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
position_ids=None,
attention_mask: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, BaseModelOutputWithPast]:
r"""
past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
`decoder_input_ids` of shape `(batch_size, sequence_length)`.
use_cache (`bool`, *optional*):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
`past_key_values`).
"""
output_attentions = (
output_attentions
if output_attentions is not None
else self.config.output_attentions
)
output_hidden_states = (
output_hidden_states
if output_hidden_states is not None
else self.config.output_hidden_states
)
return_dict = (
return_dict if return_dict is not None else self.config.use_return_dict
)
use_cache = use_cache if use_cache is not None else self.config.use_cache
if input_ids is not None and inputs_embeds is not None:
raise ValueError(
"You cannot specify both input_ids and inputs_embeds at the same time"
)
elif input_ids is not None:
input_shape = input_ids.size()
elif inputs_embeds is not None:
input_shape = inputs_embeds.size()[:-1]
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
batch_size, seq_length = input_shape
if past_key_values is None:
past_length = 0
past_key_values = tuple([None] * self.config.num_hidden_layers)
else:
past_length = past_key_values[0][0].size(-2)
if position_ids is None:
device = input_ids.device if input_ids is not None else inputs_embeds.device
position_ids = torch.arange(
past_length, seq_length + past_length, dtype=torch.long, device=device
)
position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
else:
position_ids = position_ids.view(-1, seq_length).long()
if inputs_embeds is None:
inputs_embeds = self.embed_in(input_ids)
hidden_states = inputs_embeds
# Attention mask.
seq_length_with_past = seq_length
past_key_values_length = 0
if past_key_values[0] is not None:
past_key_values_length = past_key_values[0][0].shape[-1]
seq_length_with_past = seq_length_with_past + past_key_values_length
if attention_mask is None:
attention_mask = torch.ones(
(batch_size, seq_length_with_past), device=hidden_states.device
)
else:
attention_mask = attention_mask.to(hidden_states.device)
causal_mask = prepare_attn_mask(
attention_mask,
input_shape=(batch_size, seq_length),
past_key_values_length=past_key_values_length,
)
assert self.num_attention_heads % self.tp_world_size == 0
block_size = self.num_attention_heads // self.tp_world_size
causal_mask = torch.repeat_interleave(causal_mask, block_size, dim=0)
# Prepare head mask if needed
# 1.0 in head_mask indicate we keep the head
# attention_probs has shape bsz x n_heads x N x N
# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
presents = () if use_cache else None
all_attentions = () if output_attentions else None
all_hidden_states = () if output_hidden_states else None
for i, (layer, layer_past) in enumerate(zip(self.layers, past_key_values)):
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
outputs = layer(
hidden_states,
position_ids=position_ids,
attention_mask=causal_mask,
head_mask=head_mask[i],
layer_past=layer_past,
use_cache=use_cache,
output_attentions=output_attentions,
)
hidden_states = outputs[0]
if use_cache is True:
presents = presents + (outputs[1],)
if output_attentions:
all_attentions = all_attentions + (outputs[2 if use_cache else 1],)
hidden_states = self.final_layer_norm(hidden_states)
# Add last hidden state
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
if not return_dict:
return tuple(
v
for v in [hidden_states, presents, all_hidden_states, all_attentions]
if v is not None
)
return BaseModelOutputWithPast(
last_hidden_state=hidden_states,
past_key_values=presents,
hidden_states=all_hidden_states,
attentions=all_attentions,
)
class GPTNeoxForCausalLM(GPTNeoXPreTrainedModel):
_keys_to_ignore_on_load_missing = [r"position_ids", r"predictions.decoder.bias"]
def __init__(self, config, weights):
super().__init__(config)
self.gpt_neox = GPTNeoXModel(config, weights)
self.embed_out = TensorParallelHead.load(
config, prefix="embed_out", weights=weights
)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
labels: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, CausalLMOutputWithPast]:
r"""
past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
`(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
`(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. The two additional tensors are
only required when the model is used as a decoder in a Sequence to Sequence model.
Contains pre-computed hidden-states (key and values in the self-attention blocks that can be used (see
`past_key_values` input) to speed up sequential decoding.
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
`decoder_input_ids` of shape `(batch_size, sequence_length)`.
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in
`[-100, 0, ..., config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are
ignored (masked), the loss is only computed for the tokens with labels n `[0, ..., config.vocab_size]`.
use_cache (`bool`, *optional*):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
`past_key_values`).
Returns:
Example:
```python
>>> from transformers import AutoTokenizer, GPTNeoXForCausalLM, GPTNeoXConfig
>>> import torch
>>> tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-neox-20b")
>>> config = GPTNeoXConfig.from_pretrained("EleutherAI/gpt-neox-20b")
>>> config.is_decoder = True
>>> model = GPTNeoXForCausalLM.from_pretrained("EleutherAI/gpt-neox-20b", config=config)
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
>>> outputs = model(**inputs)
>>> prediction_logits = outputs.logits
```"""
return_dict = (
return_dict if return_dict is not None else self.config.use_return_dict
)
outputs = self.gpt_neox(
input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
past_key_values=past_key_values,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
hidden_states = outputs[0]
lm_logits = self.embed_out(hidden_states)
lm_loss = None
if labels is not None:
# move labels to correct device to enable model parallelism
labels = labels.to(lm_logits.device)
# we are doing next-token prediction; shift prediction scores and input ids by one
shift_logits = lm_logits[:, :-1, :].contiguous()
labels = labels[:, 1:].contiguous()
loss_fct = CrossEntropyLoss()
lm_loss = loss_fct(
shift_logits.view(-1, shift_logits.size(-1)), labels.view(-1)
)
if not return_dict:
output = (lm_logits,) + outputs[1:]
return ((lm_loss,) + output) if lm_loss is not None else output
return CausalLMOutputWithPast(
loss=lm_loss,
logits=lm_logits,
past_key_values=outputs.past_key_values,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
def prepare_inputs_for_generation(
self,
input_ids,
past_key_values=None,
attention_mask=None,
inputs_embeds=None,
**kwargs,
):
input_shape = input_ids.shape
# cut decoder_input_ids if past is used
if past_key_values and past_key_values[0] is not None:
input_ids = input_ids[:, -1:]
position_ids = kwargs.get("position_ids", None)
if attention_mask is not None and position_ids is None:
# create position_ids on the fly for batch generation
position_ids = attention_mask.long().cumsum(-1) - 1
position_ids.masked_fill_(attention_mask == 0, 1)
if past_key_values:
position_ids = position_ids[:, -1].unsqueeze(-1)
# if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly
if attention_mask is None:
attention_mask = input_ids.new_ones(input_shape)
# if `inputs_embeds` are passed, we only want to use them in the 1st generation step
if inputs_embeds is not None and past_key_values is None:
model_inputs = {"inputs_embeds": inputs_embeds}
else:
model_inputs = {"input_ids": input_ids}
model_inputs.update(
{
"attention_mask": attention_mask,
"past_key_values": past_key_values,
"position_ids": position_ids,
}
)
return model_inputs
def _reorder_cache(self, past_key_values, beam_idx):
reordered_past = ()
for layer_past in past_key_values:
reordered_past += (
tuple(
past_state.index_select(0, beam_idx)
for past_state in layer_past[:2]
)
+ layer_past[2:],
)
return reordered_past
| 0 |
hf_public_repos/text-generation-inference/server/text_generation_server/models | hf_public_repos/text-generation-inference/server/text_generation_server/models/custom_modeling/t5_modeling.py | # coding=utf-8
# Copyright 2018 Mesh TensorFlow authors, T5 Authors and HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" PyTorch T5 model."""
import copy
import math
import warnings
from typing import Optional, Tuple, Union
from loguru import logger
import torch
import torch.distributed
from torch import nn
from torch.nn import CrossEntropyLoss
from transformers.activations import ACT2FN
from transformers.modeling_outputs import (
BaseModelOutput,
BaseModelOutputWithPastAndCrossAttentions,
Seq2SeqLMOutput,
)
from transformers.modeling_utils import PreTrainedModel
from transformers.pytorch_utils import ALL_LAYERNORM_LAYERS
from transformers.utils import (
is_torch_fx_proxy,
)
from transformers import T5Config
from text_generation_server.utils.layers import (
TensorParallelColumnLinear,
TensorParallelEmbedding,
TensorParallelRowLinear,
TensorParallelHead,
)
class PartialTPEmbedding(nn.Module):
def __init__(self, prefix: str, weights):
super().__init__()
weight = weights.get_sharded(f"{prefix}.weight", dim=1)
self.weight = nn.Parameter(weight)
def forward(self, input: torch.Tensor) -> torch.Tensor:
return torch.nn.functional.embedding(input, self.weight)
@torch.jit.script
def layer_norm(hidden_states, weight, epsilon):
# T5 uses a layer_norm which only scales and doesn't shift, which is also known as Root Mean
# Square Layer Normalization https://arxiv.org/abs/1910.07467 thus varience is calculated
# w/o mean and there is no bias. Additionally we want to make sure that the accumulation for
# half-precision inputs is done in fp32
variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True)
hidden_states = hidden_states * torch.rsqrt(variance + epsilon)
# convert into half-precision if necessary
if weight.dtype in [torch.float16, torch.bfloat16]:
hidden_states = hidden_states.to(weight.dtype)
return weight * hidden_states
class T5LayerNorm(nn.Module):
def __init__(self, prefix, weights, eps=1e-6):
"""
Construct a layernorm module in the T5 style. No bias and no subtraction of mean.
"""
super().__init__()
weight = weights.get_tensor(f"{prefix}.weight")
self.weight = nn.Parameter(weight)
self.variance_epsilon = torch.tensor(eps)
def forward(self, hidden_states):
return layer_norm(hidden_states, self.weight, self.variance_epsilon)
try:
from apex.normalization import FusedRMSNorm
T5LayerNorm = FusedRMSNorm # noqa
logger.info(
"Discovered apex.normalization.FusedRMSNorm - will use it instead of T5LayerNorm"
)
except ImportError:
# using the normal T5LayerNorm
pass
except Exception:
logger.warning("discovered apex but it failed to load, falling back to T5LayerNorm")
pass
ALL_LAYERNORM_LAYERS.append(T5LayerNorm)
class T5DenseActDense(nn.Module):
def __init__(self, config: T5Config, prefix, weights):
super().__init__()
self.wi = TensorParallelColumnLinear.load(
config, prefix=f"{prefix}.wi", weights=weights, bias=False
)
### XXX: T5 models do not handle well both f16 and quantization.
### Overidding specifically this layer for that reason.
### https://github.com/huggingface/transformers/blob/main/src/transformers/models/t5/modeling_t5.py#L316
### https://github.com/huggingface/transformers/issues/20287
_q = config.quantize
_dtype = weights.dtype
weights.dtype = torch.float32
config.quantize = None
self.wo_cast = (torch.float32, _dtype)
self.wo = TensorParallelRowLinear.load(
config, prefix=f"{prefix}.wo", weights=weights, bias=False
)
weights.dtype = _dtype
config.quantize = _q
self.dropout = nn.Dropout(config.dropout_rate)
self.act = (
ACT2FN[config.dense_act_fn]
if "gelu" not in config.dense_act_fn
else lambda x: torch.nn.functional.gelu(x, approximate="tanh")
)
def forward(self, hidden_states):
hidden_states = self.wi(hidden_states)
hidden_states = self.act(hidden_states)
hidden_states = self.dropout(hidden_states)
hidden_states = hidden_states.to(dtype=self.wo_cast[0])
hidden_states = self.wo(hidden_states)
# XXX: Recasting is already done within the layer norm.
# Casting back to float16 here modifies results
# hidden_states = hidden_states.to(dtype=self.wo_cast[1])
return hidden_states
class T5DenseGatedActDense(nn.Module):
def __init__(self, config: T5Config, prefix, weights):
super().__init__()
self.wi_0 = TensorParallelColumnLinear.load(
config, prefix=f"{prefix}.wi_0", weights=weights, bias=False
)
self.wi_1 = TensorParallelColumnLinear.load(
config, prefix=f"{prefix}.wi_1", weights=weights, bias=False
)
### XXX: T5 models do not handle well both f16 and quantization.
### Overidding specifically this layer for that reason.
### https://github.com/huggingface/transformers/blob/main/src/transformers/models/t5/modeling_t5.py#L316
### https://github.com/huggingface/transformers/issues/20287
_q = config.quantize
_dtype = weights.dtype
weights.dtype = torch.float32
config.quantize = None
self.wo_cast = (torch.float32, _dtype)
self.wo = TensorParallelRowLinear.load(
config, prefix=f"{prefix}.wo", weights=weights, bias=False
)
weights.dtype = _dtype
config.quantize = _q
self.dropout = nn.Dropout(config.dropout_rate)
self.act = (
ACT2FN[config.dense_act_fn]
if "gelu" not in config.dense_act_fn
else lambda x: torch.nn.functional.gelu(x, approximate="tanh")
)
def forward(self, hidden_states):
hidden_gelu = self.act(self.wi_0(hidden_states))
hidden_linear = self.wi_1(hidden_states)
hidden_states = hidden_gelu * hidden_linear
hidden_states = self.dropout(hidden_states)
hidden_states = hidden_states.to(dtype=self.wo_cast[0])
hidden_states = self.wo(hidden_states)
# XXX: Recasting is already done within the layer norm.
# Casting back to float16 here modifies results
# hidden_states = hidden_states.to(dtype=self.wo_cast[1])
return hidden_states
class T5LayerFF(nn.Module):
def __init__(self, config: T5Config, prefix, weights):
super().__init__()
if config.is_gated_act:
self.DenseReluDense = T5DenseGatedActDense(
config, prefix=f"{prefix}.DenseReluDense", weights=weights
)
else:
self.DenseReluDense = T5DenseActDense(
config, prefix=f"{prefix}.DenseReluDense", weights=weights
)
self.layer_norm = T5LayerNorm(
prefix=f"{prefix}.layer_norm",
weights=weights,
eps=config.layer_norm_epsilon,
)
self.dropout = nn.Dropout(config.dropout_rate)
def forward(self, hidden_states):
forwarded_states = self.layer_norm(hidden_states)
forwarded_states = self.DenseReluDense(forwarded_states)
hidden_states = hidden_states + self.dropout(forwarded_states)
return hidden_states
class T5Attention(nn.Module):
def __init__(
self, config: T5Config, prefix, weights, has_relative_attention_bias=False
):
super().__init__()
self.is_decoder = config.is_decoder
self.has_relative_attention_bias = has_relative_attention_bias
self.relative_attention_num_buckets = config.relative_attention_num_buckets
self.relative_attention_max_distance = config.relative_attention_max_distance
self.d_model = config.d_model
self.key_value_proj_dim = config.d_kv
self.n_heads = config.num_heads
self.dropout = config.dropout_rate
self.inner_dim = self.n_heads * self.key_value_proj_dim
process_group = weights.process_group
# Mesh TensorFlow initialization to avoid scaling before softmax
assert self.n_heads % process_group.size() == 0
self.q = TensorParallelColumnLinear.load(
config, prefix=f"{prefix}.q", weights=weights, bias=False
)
self.k = TensorParallelColumnLinear.load(
config, prefix=f"{prefix}.k", weights=weights, bias=False
)
self.v = TensorParallelColumnLinear.load(
config, prefix=f"{prefix}.v", weights=weights, bias=False
)
self.o = TensorParallelRowLinear.load(
config, prefix=f"{prefix}.o", weights=weights, bias=False
)
if self.n_heads % weights.process_group.size() != 0:
raise ValueError(
f"`n_heads` must be divisible by `num_shards` (got `n_heads`: {self.n_heads} "
f"and `num_shards`: {weights.process_group.size()}"
)
self.n_heads = self.n_heads // process_group.size()
self.inner_dim = self.inner_dim // process_group.size()
if self.has_relative_attention_bias:
self.relative_attention_bias = PartialTPEmbedding(
prefix=f"{prefix}.relative_attention_bias", weights=weights
)
@staticmethod
def _relative_position_bucket(
relative_position, bidirectional=True, num_buckets=32, max_distance=128
):
"""
Adapted from Mesh Tensorflow:
https://github.com/tensorflow/mesh/blob/0cb87fe07da627bf0b7e60475d59f95ed6b5be3d/mesh_tensorflow/transformer/transformer_layers.py#L593
Translate relative position to a bucket number for relative attention. The relative position is defined as
memory_position - query_position, i.e. the distance in tokens from the attending position to the attended-to
position. If bidirectional=False, then positive relative positions are invalid. We use smaller buckets for
small absolute relative_position and larger buckets for larger absolute relative_positions. All relative
positions >=max_distance map to the same bucket. All relative positions <=-max_distance map to the same bucket.
This should allow for more graceful generalization to longer sequences than the model has been trained on
Args:
relative_position: an int32 Tensor
bidirectional: a boolean - whether the attention is bidirectional
num_buckets: an integer
max_distance: an integer
Returns:
a Tensor with the same shape as relative_position, containing int32 values in the range [0, num_buckets)
"""
relative_buckets = 0
if bidirectional:
num_buckets //= 2
relative_buckets += (relative_position > 0).to(torch.long) * num_buckets
relative_position = torch.abs(relative_position)
else:
relative_position = -torch.min(
relative_position, torch.zeros_like(relative_position)
)
# now relative_position is in the range [0, inf)
# half of the buckets are for exact increments in positions
max_exact = num_buckets // 2
is_small = relative_position < max_exact
# The other half of the buckets are for logarithmically bigger bins in positions up to max_distance
relative_position_if_large = max_exact + (
torch.log(relative_position.float() / max_exact)
/ math.log(max_distance / max_exact)
* (num_buckets - max_exact)
).to(torch.long)
relative_position_if_large = torch.min(
relative_position_if_large,
torch.full_like(relative_position_if_large, num_buckets - 1),
)
relative_buckets += torch.where(
is_small, relative_position, relative_position_if_large
)
return relative_buckets
def compute_bias(self, query_length, key_length, device=None):
"""Compute binned relative position bias"""
if device is None:
device = self.relative_attention_bias.weight.device
context_position = torch.arange(query_length, dtype=torch.long, device=device)[
:, None
]
memory_position = torch.arange(key_length, dtype=torch.long, device=device)[
None, :
]
relative_position = (
memory_position - context_position
) # shape (query_length, key_length)
relative_position_bucket = self._relative_position_bucket(
relative_position, # shape (query_length, key_length)
bidirectional=(not self.is_decoder),
num_buckets=self.relative_attention_num_buckets,
max_distance=self.relative_attention_max_distance,
)
values = self.relative_attention_bias(
relative_position_bucket
) # shape (query_length, key_length, num_heads)
values = values.permute([2, 0, 1]).unsqueeze(
0
) # shape (1, num_heads, query_length, key_length)
return values
def forward(
self,
hidden_states,
mask=None,
key_value_states=None,
position_bias=None,
past_key_value=None,
layer_head_mask=None,
query_length=None,
use_cache=False,
output_attentions=False,
):
"""
Self-attention (if key_value_states is None) or attention over source sentence (provided by key_value_states).
"""
# Input is (batch_size, seq_length, dim)
# Mask is (batch_size, key_length) (non-causal) or (batch_size, key_length, key_length)
# past_key_value[0] is (batch_size, n_heads, q_len - 1, dim_per_head)
batch_size, seq_length = hidden_states.shape[:2]
real_seq_length = seq_length
if past_key_value is not None:
assert (
len(past_key_value) == 2
), f"past_key_value should have 2 past states: keys and values. Got {len(past_key_value)} past states"
real_seq_length += (
past_key_value[0].shape[2] if query_length is None else query_length
)
key_length = (
real_seq_length if key_value_states is None else key_value_states.shape[1]
)
def shape(states):
"""projection"""
return states.view(
batch_size, -1, self.n_heads, self.key_value_proj_dim
).transpose(1, 2)
def unshape(states):
"""reshape"""
return (
states.transpose(1, 2).contiguous().view(batch_size, -1, self.inner_dim)
)
def project(hidden_states, proj_layer, key_value_states, past_key_value):
"""projects hidden states correctly to key/query states"""
if key_value_states is None:
# self-attn
# (batch_size, n_heads, seq_length, dim_per_head)
hidden_states = shape(proj_layer(hidden_states))
elif past_key_value is None:
# cross-attn
# (batch_size, n_heads, seq_length, dim_per_head)
hidden_states = shape(proj_layer(key_value_states))
if past_key_value is not None:
if key_value_states is None:
# self-attn
# (batch_size, n_heads, key_length, dim_per_head)
hidden_states = torch.cat([past_key_value, hidden_states], dim=2)
elif past_key_value.shape[2] != key_value_states.shape[1]:
# checking that the `sequence_length` of the `past_key_value` is the same as
# the provided `key_value_states` to support prefix tuning
# cross-attn
# (batch_size, n_heads, seq_length, dim_per_head)
hidden_states = shape(proj_layer(key_value_states))
else:
# cross-attn
hidden_states = past_key_value
return hidden_states
# get query states
query_states = shape(
self.q(hidden_states)
) # (batch_size, n_heads, seq_length, dim_per_head)
# get key/value states
key_states = project(
hidden_states,
self.k,
key_value_states,
past_key_value[0] if past_key_value is not None else None,
)
value_states = project(
hidden_states,
self.v,
key_value_states,
past_key_value[1] if past_key_value is not None else None,
)
# compute scores
scores = torch.matmul(
query_states, key_states.transpose(3, 2)
) # equivalent of torch.einsum("bnqd,bnkd->bnqk", query_states, key_states), compatible with onnx op>9
if position_bias is None:
if not self.has_relative_attention_bias:
position_bias = torch.zeros(
(1, self.n_heads, real_seq_length, key_length),
device=scores.device,
dtype=scores.dtype,
)
else:
position_bias = self.compute_bias(
real_seq_length, key_length, device=scores.device
)
# if key and values are already calculated
# we want only the last query position bias
if past_key_value is not None:
position_bias = position_bias[:, :, -hidden_states.size(1) :, :]
if mask is not None:
position_bias = (
position_bias + mask
) # (batch_size, n_heads, seq_length, key_length)
position_bias_masked = position_bias
scores += position_bias_masked
attn_weights = nn.functional.softmax(scores.float(), dim=-1).type_as(
scores
) # (batch_size, n_heads, seq_length, key_length)
attn_weights = nn.functional.dropout(
attn_weights, p=self.dropout, training=self.training
) # (batch_size, n_heads, seq_length, key_length)
# Mask heads if we want to
if layer_head_mask is not None:
attn_weights = attn_weights * layer_head_mask
attn_output = unshape(
torch.matmul(attn_weights, value_states)
) # (batch_size, seq_length, dim)
attn_output = self.o(attn_output)
present_key_value_state = (
(key_states, value_states) if (self.is_decoder and use_cache) else None
)
outputs = (attn_output,) + (present_key_value_state,) + (position_bias,)
if output_attentions:
outputs = outputs + (attn_weights,)
return outputs
class T5LayerSelfAttention(nn.Module):
def __init__(self, config, prefix, weights, has_relative_attention_bias=False):
super().__init__()
self.SelfAttention = T5Attention(
config,
prefix=f"{prefix}.SelfAttention",
weights=weights,
has_relative_attention_bias=has_relative_attention_bias,
)
self.layer_norm = T5LayerNorm(
prefix=f"{prefix}.layer_norm",
weights=weights,
eps=config.layer_norm_epsilon,
)
self.dropout = nn.Dropout(config.dropout_rate)
def forward(
self,
hidden_states,
attention_mask=None,
position_bias=None,
layer_head_mask=None,
past_key_value=None,
use_cache=False,
output_attentions=False,
):
normed_hidden_states = self.layer_norm(hidden_states)
attention_output = self.SelfAttention(
normed_hidden_states,
mask=attention_mask,
position_bias=position_bias,
layer_head_mask=layer_head_mask,
past_key_value=past_key_value,
use_cache=use_cache,
output_attentions=output_attentions,
)
hidden_states = hidden_states + self.dropout(attention_output[0])
outputs = (hidden_states,) + attention_output[
1:
] # add attentions if we output them
return outputs
class T5LayerCrossAttention(nn.Module):
def __init__(self, config, prefix, weights):
super().__init__()
self.EncDecAttention = T5Attention(
config,
prefix=f"{prefix}.EncDecAttention",
weights=weights,
has_relative_attention_bias=False,
)
self.layer_norm = T5LayerNorm(
prefix=f"{prefix}.layer_norm",
weights=weights,
eps=config.layer_norm_epsilon,
)
self.dropout = nn.Dropout(config.dropout_rate)
def forward(
self,
hidden_states,
key_value_states,
attention_mask=None,
position_bias=None,
layer_head_mask=None,
past_key_value=None,
use_cache=False,
query_length=None,
output_attentions=False,
):
normed_hidden_states = self.layer_norm(hidden_states)
attention_output = self.EncDecAttention(
normed_hidden_states,
mask=attention_mask,
key_value_states=key_value_states,
position_bias=position_bias,
layer_head_mask=layer_head_mask,
past_key_value=past_key_value,
use_cache=use_cache,
query_length=query_length,
output_attentions=output_attentions,
)
layer_output = hidden_states + self.dropout(attention_output[0])
outputs = (layer_output,) + attention_output[
1:
] # add attentions if we output them
return outputs
class T5Block(nn.Module):
def __init__(self, config, prefix, weights, has_relative_attention_bias: bool):
super().__init__()
self.is_decoder = config.is_decoder
self.layer = nn.ModuleList()
self.layer.append(
T5LayerSelfAttention(
config,
prefix=f"{prefix}.layer.0",
weights=weights,
has_relative_attention_bias=has_relative_attention_bias,
)
)
if self.is_decoder:
i = 2
self.layer.append(
T5LayerCrossAttention(
config, prefix=f"{prefix}.layer.1", weights=weights
)
)
else:
i = 1
self.layer.append(
T5LayerFF(config, prefix=f"{prefix}.layer.{i}", weights=weights)
)
def forward(
self,
hidden_states,
attention_mask=None,
position_bias=None,
encoder_hidden_states=None,
encoder_attention_mask=None,
encoder_decoder_position_bias=None,
layer_head_mask=None,
cross_attn_layer_head_mask=None,
past_key_value=None,
use_cache=False,
output_attentions=False,
return_dict=True,
):
if past_key_value is not None:
if not self.is_decoder:
logger.warning(
"`past_key_values` is passed to the encoder. Please make sure this is intended."
)
expected_num_past_key_values = 2 if encoder_hidden_states is None else 4
if len(past_key_value) != expected_num_past_key_values:
raise ValueError(
f"There should be {expected_num_past_key_values} past states. "
f"{'2 (past / key) for cross attention. ' if expected_num_past_key_values == 4 else ''}"
f"Got {len(past_key_value)} past key / value states"
)
self_attn_past_key_value = past_key_value[:2]
cross_attn_past_key_value = past_key_value[2:]
else:
self_attn_past_key_value, cross_attn_past_key_value = None, None
self_attention_outputs = self.layer[0](
hidden_states,
attention_mask=attention_mask,
position_bias=position_bias,
layer_head_mask=layer_head_mask,
past_key_value=self_attn_past_key_value,
use_cache=use_cache,
output_attentions=output_attentions,
)
hidden_states, present_key_value_state = self_attention_outputs[:2]
attention_outputs = self_attention_outputs[
2:
] # Keep self-attention outputs and relative position weights
# clamp inf values to enable fp16 training
if hidden_states.dtype == torch.float16:
clamp_value = torch.where(
torch.isinf(hidden_states).any(),
torch.finfo(hidden_states.dtype).max - 1000,
torch.finfo(hidden_states.dtype).max,
)
hidden_states = torch.clamp(
hidden_states, min=-clamp_value, max=clamp_value
)
do_cross_attention = self.is_decoder and encoder_hidden_states is not None
if do_cross_attention:
# the actual query length is unknown for cross attention
# if using past key value states. Need to inject it here
if present_key_value_state is not None:
query_length = present_key_value_state[0].shape[2]
else:
query_length = None
cross_attention_outputs = self.layer[1](
hidden_states,
key_value_states=encoder_hidden_states,
attention_mask=encoder_attention_mask,
position_bias=encoder_decoder_position_bias,
layer_head_mask=cross_attn_layer_head_mask,
past_key_value=cross_attn_past_key_value,
query_length=query_length,
use_cache=use_cache,
output_attentions=output_attentions,
)
hidden_states = cross_attention_outputs[0]
# clamp inf values to enable fp16 training
if hidden_states.dtype == torch.float16:
clamp_value = torch.where(
torch.isinf(hidden_states).any(),
torch.finfo(hidden_states.dtype).max - 1000,
torch.finfo(hidden_states.dtype).max,
)
hidden_states = torch.clamp(
hidden_states, min=-clamp_value, max=clamp_value
)
# Combine self attn and cross attn key value states
if present_key_value_state is not None:
present_key_value_state = (
present_key_value_state + cross_attention_outputs[1]
)
# Keep cross-attention outputs and relative position weights
attention_outputs = attention_outputs + cross_attention_outputs[2:]
# Apply Feed Forward layer
hidden_states = self.layer[-1](hidden_states)
# clamp inf values to enable fp16 training
if hidden_states.dtype == torch.float16:
clamp_value = torch.where(
torch.isinf(hidden_states).any(),
torch.finfo(hidden_states.dtype).max - 1000,
torch.finfo(hidden_states.dtype).max,
)
hidden_states = torch.clamp(
hidden_states, min=-clamp_value, max=clamp_value
)
outputs = (hidden_states,)
if use_cache:
outputs = outputs + (present_key_value_state,) + attention_outputs
else:
outputs = outputs + attention_outputs
return outputs # hidden-states, present_key_value_states, (self-attention position bias), (self-attention weights), (cross-attention position bias), (cross-attention weights)
class T5PreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = T5Config
def _shift_right(self, input_ids):
decoder_start_token_id = self.config.decoder_start_token_id
pad_token_id = self.config.pad_token_id
assert decoder_start_token_id is not None, (
"self.model.config.decoder_start_token_id has to be defined. In T5 it is usually set to the pad_token_id."
" See T5 docs for more information"
)
# shift inputs to the right
if is_torch_fx_proxy(input_ids):
# Item assignment is not supported natively for proxies.
shifted_input_ids = torch.full(
input_ids.shape[:-1] + (1,), decoder_start_token_id
)
shifted_input_ids = torch.cat(
[shifted_input_ids, input_ids[..., :-1]], dim=-1
)
else:
shifted_input_ids = input_ids.new_zeros(input_ids.shape)
shifted_input_ids[..., 1:] = input_ids[..., :-1].clone()
shifted_input_ids[..., 0] = decoder_start_token_id
assert (
pad_token_id is not None
), "self.model.config.pad_token_id has to be defined."
# replace possible -100 values in labels by `pad_token_id`
shifted_input_ids.masked_fill_(shifted_input_ids == -100, pad_token_id)
return shifted_input_ids
class T5Stack(T5PreTrainedModel):
def __init__(self, config, prefix, weights, embed_tokens):
super().__init__(config)
self.is_decoder = config.is_decoder
self.embed_tokens = embed_tokens
self.block = nn.ModuleList(
[
T5Block(
config,
prefix=f"{prefix}.block.{layer_id}",
weights=weights,
has_relative_attention_bias=(layer_id == 0),
)
for layer_id in range(config.num_layers)
]
)
self.final_layer_norm = T5LayerNorm(
prefix=f"{prefix}.final_layer_norm",
weights=weights,
eps=config.layer_norm_epsilon,
)
self.dropout = nn.Dropout(config.dropout_rate)
def forward(
self,
input_ids=None,
attention_mask=None,
encoder_hidden_states=None,
encoder_attention_mask=None,
inputs_embeds=None,
head_mask=None,
cross_attn_head_mask=None,
past_key_values=None,
use_cache=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
# Model parallel
use_cache = use_cache if use_cache is not None else self.config.use_cache
output_attentions = (
output_attentions
if output_attentions is not None
else self.config.output_attentions
)
output_hidden_states = (
output_hidden_states
if output_hidden_states is not None
else self.config.output_hidden_states
)
return_dict = (
return_dict if return_dict is not None else self.config.use_return_dict
)
if input_ids is not None and inputs_embeds is not None:
err_msg_prefix = "decoder_" if self.is_decoder else ""
raise ValueError(
f"You cannot specify both {err_msg_prefix}input_ids and {err_msg_prefix}inputs_embeds at the same time"
)
elif input_ids is not None:
input_shape = input_ids.size()
input_ids = input_ids.view(-1, input_shape[-1])
elif inputs_embeds is not None:
input_shape = inputs_embeds.size()[:-1]
else:
err_msg_prefix = "decoder_" if self.is_decoder else ""
raise ValueError(
f"You have to specify either {err_msg_prefix}input_ids or {err_msg_prefix}inputs_embeds"
)
if inputs_embeds is None:
assert (
self.embed_tokens is not None
), "You have to initialize the model with valid token embeddings"
inputs_embeds = self.embed_tokens(input_ids)
batch_size, seq_length = input_shape
# required mask seq length can be calculated via length of past
mask_seq_length = (
past_key_values[0][0].shape[2] + seq_length
if past_key_values is not None
else seq_length
)
if use_cache is True:
assert (
self.is_decoder
), f"`use_cache` can only be set to `True` if {self} is used as a decoder"
if attention_mask is None:
attention_mask = torch.ones(
batch_size, mask_seq_length, device=inputs_embeds.device
)
if (
self.is_decoder
and encoder_attention_mask is None
and encoder_hidden_states is not None
):
encoder_seq_length = encoder_hidden_states.shape[1]
encoder_attention_mask = torch.ones(
batch_size,
encoder_seq_length,
device=inputs_embeds.device,
dtype=torch.long,
)
# initialize past_key_values with `None` if past does not exist
if past_key_values is None:
past_key_values = [None] * len(self.block)
# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
# ourselves in which case we just need to make it broadcastable to all heads.
extended_attention_mask = self.get_extended_attention_mask(
attention_mask, input_shape
)
# If a 2D or 3D attention mask is provided for the cross-attention
# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
if self.is_decoder and encoder_hidden_states is not None:
(
encoder_batch_size,
encoder_sequence_length,
_,
) = encoder_hidden_states.size()
encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length)
if encoder_attention_mask is None:
encoder_attention_mask = torch.ones(
encoder_hidden_shape, device=inputs_embeds.device
)
encoder_extended_attention_mask = self.invert_attention_mask(
encoder_attention_mask
)
else:
encoder_extended_attention_mask = None
# Prepare head mask if needed
head_mask = self.get_head_mask(head_mask, self.config.num_layers)
cross_attn_head_mask = self.get_head_mask(
cross_attn_head_mask, self.config.num_layers
)
present_key_value_states = () if use_cache else None
all_hidden_states = () if output_hidden_states else None
all_attentions = () if output_attentions else None
all_cross_attentions = () if (output_attentions and self.is_decoder) else None
position_bias = None
encoder_decoder_position_bias = None
hidden_states = self.dropout(inputs_embeds)
for i, (layer_module, past_key_value) in enumerate(
zip(self.block, past_key_values)
):
layer_head_mask = head_mask[i]
cross_attn_layer_head_mask = cross_attn_head_mask[i]
# Model parallel
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
layer_outputs = layer_module(
hidden_states,
attention_mask=extended_attention_mask,
position_bias=position_bias,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_extended_attention_mask,
encoder_decoder_position_bias=encoder_decoder_position_bias,
layer_head_mask=layer_head_mask,
cross_attn_layer_head_mask=cross_attn_layer_head_mask,
past_key_value=past_key_value,
use_cache=use_cache,
output_attentions=output_attentions,
)
# layer_outputs is a tuple with:
# hidden-states, key-value-states, (self-attention position bias), (self-attention weights), (cross-attention position bias), (cross-attention weights)
if use_cache is False:
layer_outputs = layer_outputs[:1] + (None,) + layer_outputs[1:]
hidden_states, present_key_value_state = layer_outputs[:2]
# We share the position biases between the layers - the first layer store them
# layer_outputs = hidden-states, key-value-states (self-attention position bias), (self-attention weights),
# (cross-attention position bias), (cross-attention weights)
position_bias = layer_outputs[2]
if self.is_decoder and encoder_hidden_states is not None:
encoder_decoder_position_bias = layer_outputs[
4 if output_attentions else 3
]
# append next layer key value states
if use_cache:
present_key_value_states = present_key_value_states + (
present_key_value_state,
)
if output_attentions:
all_attentions = all_attentions + (layer_outputs[3],)
if self.is_decoder:
all_cross_attentions = all_cross_attentions + (layer_outputs[5],)
hidden_states = self.final_layer_norm(hidden_states)
hidden_states = self.dropout(hidden_states)
# Add last layer
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
if not return_dict:
return tuple(
v
for v in [
hidden_states,
present_key_value_states,
all_hidden_states,
all_attentions,
all_cross_attentions,
]
if v is not None
)
return BaseModelOutputWithPastAndCrossAttentions(
last_hidden_state=hidden_states,
past_key_values=present_key_value_states,
hidden_states=all_hidden_states,
attentions=all_attentions,
cross_attentions=all_cross_attentions,
)
class T5ForConditionalGeneration(T5PreTrainedModel):
def __init__(self, config: T5Config, weights):
super().__init__(config)
self.model_dim = config.d_model
self.shared = TensorParallelEmbedding(prefix="shared", weights=weights)
encoder_config = copy.deepcopy(config)
encoder_config.is_decoder = False
encoder_config.use_cache = False
encoder_config.is_encoder_decoder = False
self.encoder = T5Stack(
config=encoder_config,
prefix="encoder",
weights=weights,
embed_tokens=self.shared,
)
decoder_config = copy.deepcopy(config)
decoder_config.is_decoder = True
decoder_config.is_encoder_decoder = False
decoder_config.num_layers = config.num_decoder_layers
self.decoder = T5Stack(
config=decoder_config,
prefix="decoder",
weights=weights,
embed_tokens=self.shared,
)
try:
self.lm_head = TensorParallelHead.load(
config, prefix="lm_head", weights=weights
)
except RuntimeError:
# Some models like t5-small were saved with shared weights unlike flan
# Since they are declared as the same arch we have no choice but hope
# that this is OK instead of using a proper flag.
self.lm_head = TensorParallelHead.load(
config, prefix="shared", weights=weights
)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
decoder_input_ids: Optional[torch.LongTensor] = None,
decoder_attention_mask: Optional[torch.BoolTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
decoder_head_mask: Optional[torch.FloatTensor] = None,
cross_attn_head_mask: Optional[torch.Tensor] = None,
encoder_outputs: Optional[Tuple[Tuple[torch.Tensor]]] = None,
past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple[torch.FloatTensor], Seq2SeqLMOutput]:
use_cache = use_cache if use_cache is not None else self.config.use_cache
return_dict = (
return_dict if return_dict is not None else self.config.use_return_dict
)
# FutureWarning: head_mask was separated into two input args - head_mask, decoder_head_mask
if head_mask is not None and decoder_head_mask is None:
if self.config.num_layers == self.config.num_decoder_layers:
warnings.warn(__HEAD_MASK_WARNING_MSG, FutureWarning)
decoder_head_mask = head_mask
# Encode if needed (training, first prediction pass)
if encoder_outputs is None:
# Convert encoder inputs in embeddings if needed
encoder_outputs = self.encoder(
input_ids=input_ids,
attention_mask=attention_mask,
inputs_embeds=inputs_embeds,
head_mask=head_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
elif return_dict and not isinstance(encoder_outputs, BaseModelOutput):
encoder_outputs = BaseModelOutput(
last_hidden_state=encoder_outputs[0],
hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None,
attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None,
)
hidden_states = encoder_outputs[0]
if (
labels is not None
and decoder_input_ids is None
and decoder_inputs_embeds is None
):
# get decoder inputs from shifting lm labels to the right
decoder_input_ids = self._shift_right(labels)
# Decode
decoder_outputs = self.decoder(
input_ids=decoder_input_ids,
attention_mask=decoder_attention_mask,
inputs_embeds=decoder_inputs_embeds,
past_key_values=past_key_values,
encoder_hidden_states=hidden_states,
encoder_attention_mask=attention_mask,
head_mask=decoder_head_mask,
cross_attn_head_mask=cross_attn_head_mask,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = decoder_outputs[0]
if self.config.tie_word_embeddings:
# Rescale output before projecting on vocab
# See https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/transformer/transformer.py#L586
sequence_output = sequence_output * (self.model_dim**-0.5)
lm_logits = self.lm_head(sequence_output)
loss = None
if labels is not None:
loss_fct = CrossEntropyLoss(ignore_index=-100)
# move labels to correct device to enable PP
labels = labels.to(lm_logits.device)
loss = loss_fct(lm_logits.view(-1, lm_logits.size(-1)), labels.view(-1))
# TODO(thom): Add z_loss https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/layers.py#L666
if not return_dict:
output = (lm_logits,) + decoder_outputs[1:] + encoder_outputs
return ((loss,) + output) if loss is not None else output
return Seq2SeqLMOutput(
loss=loss,
logits=lm_logits,
past_key_values=decoder_outputs.past_key_values,
decoder_hidden_states=decoder_outputs.hidden_states,
decoder_attentions=decoder_outputs.attentions,
cross_attentions=decoder_outputs.cross_attentions,
encoder_last_hidden_state=encoder_outputs.last_hidden_state,
encoder_hidden_states=encoder_outputs.hidden_states,
encoder_attentions=encoder_outputs.attentions,
)
def prepare_inputs_for_generation(
self,
input_ids,
past_key_values=None,
attention_mask=None,
head_mask=None,
decoder_head_mask=None,
decoder_attention_mask=None,
cross_attn_head_mask=None,
use_cache=None,
encoder_outputs=None,
**kwargs,
):
# cut decoder_input_ids if past is used
if past_key_values is not None:
input_ids = input_ids[:, -1:]
return {
"decoder_input_ids": input_ids,
"past_key_values": past_key_values,
"encoder_outputs": encoder_outputs,
"attention_mask": attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"decoder_attention_mask": decoder_attention_mask,
"cross_attn_head_mask": cross_attn_head_mask,
"use_cache": use_cache,
}
def prepare_decoder_input_ids_from_labels(self, labels: torch.Tensor):
return self._shift_right(labels)
def _reorder_cache(self, past_key_values, beam_idx):
# if decoder past is not included in output
# speedy decoding is disabled and no need to reorder
if past_key_values is None:
logger.warning(
"You might want to consider setting `use_cache=True` to speed up decoding"
)
return past_key_values
reordered_decoder_past = ()
for layer_past_states in past_key_values:
# get the correct batch idx from layer past batch dim
# batch dim of `past` is at 2nd position
reordered_layer_past_states = ()
for layer_past_state in layer_past_states:
# need to set correct `past` for each of the four key / value states
reordered_layer_past_states = reordered_layer_past_states + (
layer_past_state.index_select(
0, beam_idx.to(layer_past_state.device)
),
)
assert reordered_layer_past_states[0].shape == layer_past_states[0].shape
assert len(reordered_layer_past_states) == len(layer_past_states)
reordered_decoder_past = reordered_decoder_past + (
reordered_layer_past_states,
)
return reordered_decoder_past
| 0 |
hf_public_repos/text-generation-inference/server/text_generation_server/models | hf_public_repos/text-generation-inference/server/text_generation_server/models/custom_modeling/idefics_vision.py | # coding=utf-8
# Copyright 2021 The OpenAI Team Authors and The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" PyTorch IdeficsVision model: a copy of CLIPVisionModel using a simpler config object"""
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from transformers.activations import ACT2FN
from transformers.modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling
from transformers.utils import (
ModelOutput,
logging,
)
from text_generation_server.utils.layers import (
TensorParallelColumnLinear,
TensorParallelRowLinear,
TensorParallelEmbedding,
)
logger = logging.get_logger(__name__)
@dataclass
class IdeficsVisionModelOutput(ModelOutput):
"""
Base class for vision model's outputs that also contains image embeddings of the pooling of the last hidden states.
Args:
image_embeds (`torch.FloatTensor` of shape `(batch_size, output_dim)` *optional* returned when model is initialized with `with_projection=True`):
The image embeddings obtained by applying the projection layer to the pooler_output.
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last layer of the model.
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
"""
image_embeds: Optional[torch.FloatTensor] = None
last_hidden_state: torch.FloatTensor = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
attentions: Optional[Tuple[torch.FloatTensor]] = None
# Copied from transformers.models.clip.modeling_clip.CLIPVisionEmbeddings with CLIP->Idefics
class IdeficsVisionEmbeddings(nn.Module):
def __init__(self, prefix, config, weights):
super().__init__()
self.config = config
self.embed_dim = config.hidden_size
self.image_size = config.image_size
self.patch_size = config.patch_size
self.class_embedding = nn.Parameter(
weights.get_tensor(f"{prefix}.class_embedding")
)
self.patch_embedding = nn.Conv2d.load_no_bias(
prefix=f"{prefix}.patch_embedding",
weights=weights,
in_channels=config.num_channels,
out_channels=self.embed_dim,
kernel_size=self.patch_size,
stride=self.patch_size,
)
self.num_patches = (self.image_size // self.patch_size) ** 2
self.num_positions = self.num_patches + 1
self.position_embedding = TensorParallelEmbedding(
prefix="model.vision_model.embeddings.position_embedding", weights=weights
)
self.position_ids = (
torch.arange(self.num_positions).expand((1, -1)).to(device=weights.device)
)
def forward(self, pixel_values: torch.FloatTensor) -> torch.Tensor:
batch_size = pixel_values.shape[0]
target_dtype = self.patch_embedding.weight.dtype
patch_embeds = self.patch_embedding(
pixel_values.to(dtype=target_dtype)
) # shape = [*, width, grid, grid]
patch_embeds = patch_embeds.flatten(2).transpose(1, 2)
class_embeds = self.class_embedding.expand(batch_size, 1, -1)
embeddings = torch.cat([class_embeds, patch_embeds], dim=1)
embeddings = embeddings + self.position_embedding(self.position_ids)
return embeddings
# Copied from transformers.models.clip.modeling_clip.CLIPAttention with CLIP->IdeficsVision
class IdeficsVisionAttention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(self, prefix, config, weights):
super().__init__()
self.config = config
self.embed_dim = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_dim = self.embed_dim // self.num_heads
if self.head_dim * self.num_heads != self.embed_dim:
raise ValueError(
f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:"
f" {self.num_heads})."
)
self.scale = self.head_dim**-0.5
self.dropout = config.attention_dropout
process_group = weights.process_group
if self.num_heads % weights.process_group.size() != 0:
raise ValueError(
f"`num_heads` must be divisible by `num_shards` (got `num_heads`: {self.num_heads} "
f"and `num_shards`: {weights.process_group.size()}"
)
self.num_heads = self.num_heads // weights.process_group.size()
self.embed_dim = self.embed_dim // weights.process_group.size()
self.k_proj = TensorParallelColumnLinear.load(
config, prefix=f"{prefix}.k_proj", weights=weights, bias=True
)
self.v_proj = TensorParallelColumnLinear.load(
config, prefix=f"{prefix}.v_proj", weights=weights, bias=True
)
self.q_proj = TensorParallelColumnLinear.load(
config, prefix=f"{prefix}.q_proj", weights=weights, bias=True
)
self.out_proj = TensorParallelRowLinear.load(
config, prefix=f"{prefix}.out_proj", weights=weights, bias=True
)
def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
return (
tensor.view(bsz, seq_len, self.num_heads, self.head_dim)
.transpose(1, 2)
.contiguous()
)
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
causal_attention_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = False,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
"""Input shape: Batch x Time x Channel"""
bsz, tgt_len, _ = hidden_states.size()
# get query proj
query_states = self.q_proj(hidden_states) * self.scale
key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
proj_shape = (bsz * self.num_heads, -1, self.head_dim)
query_states = self._shape(query_states, tgt_len, bsz).view(*proj_shape)
key_states = key_states.view(*proj_shape)
value_states = value_states.view(*proj_shape)
src_len = key_states.size(1)
attn_weights = torch.bmm(query_states, key_states.transpose(1, 2))
if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len):
raise ValueError(
f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is"
f" {attn_weights.size()}"
)
# apply the causal_attention_mask first
if causal_attention_mask is not None:
if causal_attention_mask.size() != (bsz, 1, tgt_len, src_len):
raise ValueError(
f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is"
f" {causal_attention_mask.size()}"
)
attn_weights = (
attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
+ causal_attention_mask
)
attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
if attention_mask is not None:
if attention_mask.size() != (bsz, 1, tgt_len, src_len):
raise ValueError(
f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}"
)
attn_weights = (
attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
+ attention_mask
)
attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
attn_weights = nn.functional.softmax(attn_weights, dim=-1)
if output_attentions:
# this operation is a bit akward, but it's required to
# make sure that attn_weights keeps its gradient.
# In order to do so, attn_weights have to reshaped
# twice and have to be reused in the following
attn_weights_reshaped = attn_weights.view(
bsz, self.num_heads, tgt_len, src_len
)
attn_weights = attn_weights_reshaped.view(
bsz * self.num_heads, tgt_len, src_len
)
else:
attn_weights_reshaped = None
attn_probs = nn.functional.dropout(
attn_weights, p=self.dropout, training=self.training
)
attn_output = torch.bmm(attn_probs, value_states)
if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim):
raise ValueError(
f"`attn_output` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is"
f" {attn_output.size()}"
)
attn_output = attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim)
attn_output = attn_output.transpose(1, 2)
attn_output = attn_output.reshape(bsz, tgt_len, self.embed_dim)
attn_output = self.out_proj(attn_output)
return attn_output, attn_weights_reshaped
# Copied from transformers.models.clip.modeling_clip.CLIPMLP with CLIP->IdeficsVision
class IdeficsVisionMLP(nn.Module):
def __init__(self, prefix, config, weights):
super().__init__()
self.config = config
self.activation_fn = ACT2FN[config.hidden_act]
self.fc1 = TensorParallelColumnLinear.load(
config, prefix=f"{prefix}.fc1", weights=weights, bias=True
)
self.fc2 = TensorParallelRowLinear.load(
config, prefix=f"{prefix}.fc2", weights=weights, bias=True
)
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
hidden_states = self.fc1(hidden_states)
hidden_states = self.activation_fn(hidden_states)
hidden_states = self.fc2(hidden_states)
return hidden_states
# Copied from transformers.models.clip.modeling_clip.CLIPEncoderLayer with CLIP->IdeficsVision
class IdeficsVisionEncoderLayer(nn.Module):
def __init__(self, prefix, config, weights):
super().__init__()
self.embed_dim = config.hidden_size
self.self_attn = IdeficsVisionAttention(
prefix=f"{prefix}.self_attn", config=config, weights=weights
)
self.layer_norm1 = nn.LayerNorm.load(
prefix=f"{prefix}.layer_norm1", weights=weights, eps=config.layer_norm_eps
)
self.mlp = IdeficsVisionMLP(
prefix=f"{prefix}.mlp", config=config, weights=weights
)
self.layer_norm2 = nn.LayerNorm.load(
prefix=f"{prefix}.layer_norm2", weights=weights, eps=config.layer_norm_eps
)
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: torch.Tensor,
causal_attention_mask: torch.Tensor,
output_attentions: Optional[bool] = False,
) -> Tuple[torch.FloatTensor]:
"""
Args:
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
attention_mask (`torch.FloatTensor`): attention mask of size
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
`(config.encoder_attention_heads,)`.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
"""
residual = hidden_states
hidden_states = self.layer_norm1(hidden_states)
hidden_states, attn_weights = self.self_attn(
hidden_states=hidden_states,
attention_mask=attention_mask,
causal_attention_mask=causal_attention_mask,
output_attentions=output_attentions,
)
hidden_states = residual + hidden_states
residual = hidden_states
hidden_states = self.layer_norm2(hidden_states)
hidden_states = self.mlp(hidden_states)
hidden_states = residual + hidden_states
outputs = (hidden_states,)
if output_attentions:
outputs += (attn_weights,)
return outputs
# Copied from transformers.models.clip.modeling_clip.CLIPEncoder with CLIP->IdeficsVision
class IdeficsVisionEncoder(nn.Module):
"""
Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a
[`IdeficsVisionEncoderLayer`].
Args:
config: IdeficsVisionConfig
"""
def __init__(self, prefix, config, weights):
super().__init__()
self.config = config
self.layers = nn.ModuleList(
[
IdeficsVisionEncoderLayer(
prefix=f"{prefix}.encoder.layers.{layer_id}",
config=config,
weights=weights,
)
for layer_id in range(config.num_hidden_layers)
]
)
# self.gradient_checkpointing = False
def forward(
self,
inputs_embeds,
attention_mask: Optional[torch.Tensor] = None,
causal_attention_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, BaseModelOutput]:
r"""
Args:
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
This is useful if you want more control over how to convert `input_ids` indices into associated vectors
than the model's internal embedding lookup matrix.
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
causal_attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Causal mask for the text model. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
for more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
output_attentions = (
output_attentions
if output_attentions is not None
else self.config.output_attentions
)
output_hidden_states = (
output_hidden_states
if output_hidden_states is not None
else self.config.output_hidden_states
)
return_dict = (
return_dict if return_dict is not None else self.config.use_return_dict
)
encoder_states = () if output_hidden_states else None
all_attentions = () if output_attentions else None
hidden_states = inputs_embeds
for idx, encoder_layer in enumerate(self.layers):
if output_hidden_states:
encoder_states = encoder_states + (hidden_states,)
# if self.gradient_checkpointing and self.training:
# def create_custom_forward(module):
# def custom_forward(*inputs):
# return module(*inputs, output_attentions)
# return custom_forward
# layer_outputs = torch.utils.checkpoint.checkpoint(
# create_custom_forward(encoder_layer),
# hidden_states,
# attention_mask,
# causal_attention_mask,
# )
# else:
layer_outputs = encoder_layer(
hidden_states,
attention_mask,
causal_attention_mask,
output_attentions=output_attentions,
)
hidden_states = layer_outputs[0]
if output_attentions:
all_attentions = all_attentions + (layer_outputs[1],)
if output_hidden_states:
encoder_states = encoder_states + (hidden_states,)
if not return_dict:
return tuple(
v
for v in [hidden_states, encoder_states, all_attentions]
if v is not None
)
return BaseModelOutput(
last_hidden_state=hidden_states,
hidden_states=encoder_states,
attentions=all_attentions,
)
# Adapted from transformers.models.clip.modeling_clip.CLIPVisionTransformer
class IdeficsVisionTransformer(nn.Module):
def __init__(self, prefix, config, weights):
super().__init__()
self.config = config
embed_dim = config.hidden_size
self.embeddings = IdeficsVisionEmbeddings(
prefix=f"{prefix}.embeddings", config=config, weights=weights
)
self.pre_layrnorm = nn.LayerNorm.load(
prefix=f"{prefix}.pre_layrnorm", weights=weights, eps=config.layer_norm_eps
)
self.encoder = IdeficsVisionEncoder(
prefix=prefix, config=config, weights=weights
)
self.post_layernorm = nn.LayerNorm.load(
prefix=f"{prefix}.post_layernorm",
weights=weights,
eps=config.layer_norm_eps,
)
# copied from transformers.models.clip.modeling_clip.CLIPVisionTransformer.forward
def forward(
self,
pixel_values: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, BaseModelOutputWithPooling]:
r"""
Returns:
"""
output_attentions = (
output_attentions
if output_attentions is not None
else self.config.output_attentions
)
output_hidden_states = (
output_hidden_states
if output_hidden_states is not None
else self.config.output_hidden_states
)
return_dict = (
return_dict if return_dict is not None else self.config.use_return_dict
)
if pixel_values is None:
raise ValueError("You have to specify pixel_values")
hidden_states = self.embeddings(pixel_values)
hidden_states = self.pre_layrnorm(hidden_states)
encoder_outputs = self.encoder(
inputs_embeds=hidden_states,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
last_hidden_state = encoder_outputs[0]
pooled_output = last_hidden_state[:, 0, :]
pooled_output = self.post_layernorm(pooled_output)
if not return_dict:
return (last_hidden_state, pooled_output) + encoder_outputs[1:]
return BaseModelOutputWithPooling(
last_hidden_state=last_hidden_state,
pooler_output=pooled_output,
hidden_states=encoder_outputs.hidden_states,
attentions=encoder_outputs.attentions,
)
| 0 |
hf_public_repos/text-generation-inference/server/text_generation_server/models | hf_public_repos/text-generation-inference/server/text_generation_server/models/custom_modeling/flash_neox_modeling.py | # coding=utf-8
# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
#
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
# and OPT implementations in this library. It has been modified from its
# original forms to accommodate minor architectural differences compared
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch
import torch.distributed
from torch import nn
from transformers.activations import ACT2FN
from transformers.modeling_utils import PreTrainedModel
from transformers.models.gpt_neox import GPTNeoXConfig
from typing import Optional, List, Tuple
from text_generation_server.utils import paged_attention, flash_attn
from text_generation_server.utils.flash_attn import attention
from text_generation_server.utils.layers import (
TensorParallelRowLinear,
TensorParallelColumnLinear,
TensorParallelEmbedding,
TensorParallelHead,
FastLayerNorm,
PositionRotaryEmbedding,
get_linear,
)
def load_row(config, prefix: str, weights, bias: bool):
weight = weights.get_multi_weights_row(prefix, quantize=config.quantize)
if bias and weights.process_group.rank() == 0:
# Rank is only on the first rank process
bias = weights.get_tensor(f"{prefix}.bias")
else:
bias = None
linear = get_linear(weight, bias, config.quantize)
if config.use_parallel_residual:
return linear
else:
return TensorParallelRowLinear(linear, process_group=weights.process_group)
def load_qkv(config, prefix: str, weights, num_heads, head_size, hidden_size):
weight = weights.get_multi_weights_col([prefix], quantize=config.quantize, dim=0)
if isinstance(weight, torch.Tensor):
# Only on non quantized versions
weight = (
weight.view(
num_heads,
3,
head_size,
hidden_size,
)
.permute(1, 0, 2, 3)
.reshape(-1, hidden_size)
)
bias = weights.get_sharded(f"{prefix}.bias", dim=0)
bias = bias.view(num_heads, 3, head_size).permute(1, 0, 2).reshape(-1)
linear = get_linear(weight, bias, config.quantize)
if config.use_parallel_residual:
return linear
else:
return TensorParallelColumnLinear(linear)
class FlashNeoxAttention(torch.nn.Module):
def __init__(self, config, prefix, weights):
super().__init__()
num_heads = config.num_attention_heads
hidden_size = config.hidden_size
self.num_heads = num_heads
self.hidden_size = hidden_size
self.head_size = hidden_size // num_heads
if self.num_heads % weights.process_group.size() != 0:
raise ValueError(
f"`num_heads` must be divisible by `num_shards` (got `num_heads`: {self.num_heads} "
f"and `num_shards`: {weights.process_group.size()}"
)
self.num_heads = self.num_heads // weights.process_group.size()
self.rotary_emb = PositionRotaryEmbedding.load(
config=config, prefix=f"{prefix}.rotary_emb", weights=weights
)
self.softmax_scale = self.head_size ** (-0.5)
self.query_key_value = load_qkv(
config,
prefix=f"{prefix}.query_key_value",
weights=weights,
num_heads=self.num_heads,
head_size=self.head_size,
hidden_size=self.hidden_size,
)
self.dense = load_row(
config, prefix=f"{prefix}.dense", weights=weights, bias=True
)
self.kv_head_mapping = torch.arange(
0, self.num_heads, dtype=torch.int32, device=weights.device
)
def forward(
self,
hidden_states,
cos,
sin,
cu_seqlen_prefill,
kv_cache,
block_tables,
slots,
input_lengths,
max_s,
):
qkv = self.query_key_value(hidden_states)
qkv = qkv.view(-1, 3, self.num_heads, self.head_size)
# Inplace rotary
self.rotary_emb(qkv[:, 0], qkv[:, 1], cos, sin)
paged_attention.reshape_and_cache(
qkv[:, 1], qkv[:, 2], kv_cache[0], kv_cache[1], slots
)
# output tensor
attn_output = torch.empty_like(qkv[:, 0])
# Prefill
if cu_seqlen_prefill is not None:
# flash attention
flash_attn.attention(
qkv[:, 0],
qkv[:, 1],
qkv[:, 2],
attn_output,
cu_seqlen_prefill,
max_s,
self.softmax_scale,
)
# Decode
else:
paged_attention.attention(
attn_output,
qkv[:, 0],
kv_cache[0],
kv_cache[1],
self.kv_head_mapping,
self.softmax_scale,
block_tables,
input_lengths,
max_s,
)
return self.dense(attn_output.view(-1, self.num_heads * self.head_size))
class FlashMLP(nn.Module):
def __init__(self, config, prefix, weights):
super().__init__()
act = config.hidden_act
self.act = (
ACT2FN[act]
if "gelu" not in act
else lambda x: torch.nn.functional.gelu(
x,
approximate="tanh"
if act in ["gelu_fast", "gelu_pytorch_tanh"]
else "none",
)
)
self.dense_h_to_4h = TensorParallelColumnLinear.load(
config, prefix=f"{prefix}.dense_h_to_4h", weights=weights, bias=True
)
self.dense_4h_to_h = load_row(
config, prefix=f"{prefix}.dense_4h_to_h", weights=weights, bias=True
)
def forward(self, hidden_states):
hidden_states = self.dense_h_to_4h(hidden_states)
hidden_states = self.act(hidden_states)
hidden_states = self.dense_4h_to_h(hidden_states)
return hidden_states
class FlashNeoXLayer(nn.Module):
def __init__(self, layer_id, config, weights):
super().__init__()
layer_norm_eps = config.layer_norm_eps
prefix = f"gpt_neox.layers.{layer_id}"
self.use_parallel_residual = config.use_parallel_residual
self.input_layernorm = FastLayerNorm.load(
prefix=f"{prefix}.input_layernorm", weights=weights, eps=layer_norm_eps
)
self.post_attention_layernorm = FastLayerNorm.load(
prefix=f"{prefix}.post_attention_layernorm",
weights=weights,
eps=layer_norm_eps,
)
self.attention = FlashNeoxAttention(
config, prefix=f"{prefix}.attention", weights=weights
)
self.mlp = FlashMLP(config, prefix=f"{prefix}.mlp", weights=weights)
self.process_group = weights.process_group
def forward(
self,
hidden_states,
residual,
cos,
sin,
cu_seqlen_prefill,
kv_cache,
block_tables,
slots,
input_lengths,
max_s,
):
if self.use_parallel_residual:
ln1_hidden_states, _ = self.input_layernorm(hidden_states)
attn_output = self.attention(
ln1_hidden_states,
cos,
sin,
cu_seqlen_prefill,
kv_cache,
block_tables,
slots,
input_lengths,
max_s,
)
ln2_hidden_states, _ = self.post_attention_layernorm(hidden_states)
mlp_output = self.mlp(ln2_hidden_states)
intermediate = mlp_output + attn_output
if self.process_group.size() > 1:
torch.distributed.all_reduce(intermediate, group=self.process_group)
return intermediate + hidden_states, None
else:
hidden_states, residual = self.input_layernorm(hidden_states, residual)
hidden_states = self.attention(
hidden_states,
cos,
sin,
cu_seqlen_prefill,
kv_cache,
block_tables,
slots,
input_lengths,
max_s,
)
hidden_states, residual = self.post_attention_layernorm(
hidden_states, residual
)
mlp_output = self.mlp(hidden_states)
return mlp_output, residual
class FlashGPTNeoXPreTrainedModel(PreTrainedModel):
config_class = GPTNeoXConfig
base_model_prefix = "gpt_neox"
supports_gradient_checkpointing = False
_no_split_modules = None
class FlashGPTNeoXModel(FlashGPTNeoXPreTrainedModel):
def __init__(self, config, weights):
super().__init__(config)
self.config = config
self.embed_in = TensorParallelEmbedding(
prefix="gpt_neox.embed_in", weights=weights
)
self.layers = nn.ModuleList(
[
FlashNeoXLayer(layer_id, config, weights)
for layer_id in range(config.num_hidden_layers)
]
)
self.final_layer_norm = FastLayerNorm.load(
prefix="gpt_neox.final_layer_norm",
weights=weights,
eps=config.layer_norm_eps,
)
self.gradient_checkpointing = False
self.head_size = self.layers[0].attention.head_size
self.num_heads = self.layers[0].attention.num_heads
def forward(
self,
input_ids: torch.Tensor,
position_ids: torch.Tensor,
cu_seqlen_prefill: Optional[torch.Tensor],
kv_cache: List[Tuple[torch.Tensor, torch.Tensor]],
block_tables: torch.Tensor,
slots: torch.Tensor,
input_lengths: torch.Tensor,
max_s: int,
) -> torch.Tensor:
hidden_states = self.embed_in(input_ids)
# Get rotary cos and sin for this forward
# Avoid to index in each layer
cos, sin = self.layers[0].attention.rotary_emb.get_cos_sin(
position_ids, max_s, hidden_states.dtype
)
residual = None
for i, layer in enumerate(self.layers):
hidden_states, residual = layer(
hidden_states,
residual,
cos,
sin,
cu_seqlen_prefill,
kv_cache[i],
block_tables,
slots,
input_lengths,
max_s,
)
hidden_states, _ = self.final_layer_norm(hidden_states, residual)
return hidden_states
class FlashGPTNeoXForCausalLM(FlashGPTNeoXPreTrainedModel):
def __init__(self, config, weights):
super().__init__(config)
self.gpt_neox = FlashGPTNeoXModel(config, weights)
self.embed_out = TensorParallelHead.load(
config, prefix="embed_out", weights=weights
)
def forward(
self,
input_ids: torch.Tensor,
position_ids: torch.Tensor,
cu_seqlen_prefill: Optional[torch.Tensor],
kv_cache: List[Tuple[torch.Tensor, torch.Tensor]],
block_tables: torch.Tensor,
slots: torch.Tensor,
input_lengths: torch.Tensor,
max_s: int,
lm_head_indices: Optional[torch.Tensor] = None,
) -> torch.Tensor:
hidden_states = self.gpt_neox(
input_ids,
position_ids,
cu_seqlen_prefill,
kv_cache,
block_tables,
slots,
input_lengths,
max_s,
)
if lm_head_indices is not None:
hidden_states = hidden_states[lm_head_indices]
logits = self.embed_out(hidden_states)
return logits
| 0 |
hf_public_repos/text-generation-inference/server/text_generation_server/models | hf_public_repos/text-generation-inference/server/text_generation_server/models/custom_modeling/idefics_config.py | # coding=utf-8
# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
#
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
# and OPT implementations in this library. It has been modified from its
# original forms to accommodate minor architectural differences compared
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Idefics model configuration"""
import copy
from transformers import PretrainedConfig
IDEFICS_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"HuggingFaceM4/idefics-9b": "https://huggingface.co/HuggingFaceM4/idefics-9b/blob/main/config.json",
"HuggingFaceM4/idefics-80b": "https://huggingface.co/HuggingFaceM4/idefics-80b/blob/main/config.json",
}
class IdeficsVisionConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`IdeficsModel`]. It is used to instantiate an
Idefics model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a similar configuration to that of the Idefics-9B.
e.g. [HuggingFaceM4/idefics-9b](https://huggingface.co/HuggingFaceM4/idefics-9b)
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
hidden_size (`int`, *optional*, defaults to 768):
Dimensionality of the encoder layers and the pooler layer. (elsewhere referred to as `hidden_size`)
image_size (`int`, *optional*, defaults to 224):
The size (resolution) of each image.
intermediate_size (`int`, *optional*, defaults to 5120):
Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
patch_size (`int`, *optional*, defaults to 14):
The size (resolution) of each patch.
num_hidden_layers (`int`, *optional*, defaults to 32):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults to 16):
Number of attention heads for each attention layer in the Transformer encoder.
image_num_channels (`int`, *optional*, defaults to `3`):
Number of image channels.
hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"selu"` and `"gelu_new"` ``"quick_gelu"` are supported.
layer_norm_eps (`float`, *optional*, defaults to 1e-5):
The epsilon used by the layer normalization layers.
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
initializer_factor (`float`, *optional*, defaults to 1.0):
A factor for initializing all weight matrices (should be kept to 1.0, used internally for initialization
testing).
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
"""
model_type = "idefics"
attribute_map = {
"hidden_size": "embed_dim",
}
def __init__(
self,
embed_dim=768,
image_size=224,
intermediate_size=5120,
patch_size=14,
num_hidden_layers=32,
num_attention_heads=16,
num_channels=3,
hidden_act="gelu",
layer_norm_eps=1e-5,
attention_dropout=0.0,
initializer_range=0.02,
initializer_factor=1.0,
**kwargs,
):
self.embed_dim = embed_dim
self.image_size = image_size
self.intermediate_size = intermediate_size
self.patch_size = patch_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.num_channels = num_channels
self.layer_norm_eps = layer_norm_eps
self.attention_dropout = attention_dropout
self.initializer_range = initializer_range
self.initializer_factor = initializer_factor
self.hidden_act = hidden_act
super().__init__(**kwargs)
class IdeficsPerceiverConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`IdeficsModel`]. It is used to instantiate an
Idefics model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a similar configuration to that of the Idefics-9B.
e.g. [HuggingFaceM4/idefics-9b](https://huggingface.co/HuggingFaceM4/idefics-9b)
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
use_resampler (`bool`, *optional*, defaults to `False`):
Whether or not to use the resampler
resampler_n_latents (`int`, *optional*, defaults to ):
Number of latent embeddings to resample ("compress") the input sequence to (usually < 128).
resampler_depth (`int`, *optional*, defaults to 6):
Depth of the Perceiver Resampler (Transformer w/ cross attention). Should be shallow (< 3).
resampler_n_heads (`int`, *optional*, defaults to 16):
Number of heads in each Transformer block (for multi-headed self-attention).
resampler_head_dim (`int`, *optional*, defaults to 96):
Dimensionality of each head projection in the Transformer block.
qk_layer_norms_perceiver (`bool`, *optional*, defaults to `False`):
Whether or not to use qk layer norms in perceiver
"""
model_type = "idefics"
def __init__(
self,
use_resampler=False,
resampler_n_latents=64,
resampler_depth=6,
resampler_n_heads=16,
resampler_head_dim=96,
qk_layer_norms_perceiver=False,
**kwargs,
):
self.use_resampler = use_resampler
self.resampler_n_latents = resampler_n_latents
self.resampler_depth = resampler_depth
self.resampler_n_heads = resampler_n_heads
self.resampler_head_dim = resampler_head_dim
self.qk_layer_norms_perceiver = qk_layer_norms_perceiver
super().__init__(**kwargs)
class IdeficsConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`IdeficsModel`]. It is used to instantiate an
Idefics model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a similar configuration to that of the Idefics-9B.
e.g. [HuggingFaceM4/idefics-9b](https://huggingface.co/HuggingFaceM4/idefics-9b)
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
additional_vocab_size (`int`, *optional`, defaults to 0):
Additional vocabulary size of the model, typically for the special "<img>" token. Additional vocab tokens
are always trainable whereas regular vocab tokens can be frozen or not.
vocab_size (`int`, *optional*, defaults to 32000):
Vocabulary size of the Idefics model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`~IdeficsModel`]
hidden_size (`int`, *optional*, defaults to 4096):
Dimension of the hidden representations.
intermediate_size (`int`, *optional*, defaults to 11008):
Dimension of the MLP representations.
num_hidden_layers (`int`, *optional*, defaults to 32):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults to 32):
Number of attention heads for each attention layer in the Transformer encoder.
dropout (`float`, *optional*, defaults to 0.0):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
The non-linear activation function (function or string) in the decoder.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
alpha_initializer (`str`, *optional*, defaults to `"zeros"`):
Initialization type for the alphas.
alphas_initializer_range (`float`, *optional*, defaults to 0.0):
The standard deviation of the truncated_normal_initializer for initializing the alphas in the Gated Cross
Attention.
alpha_type (`str`, *optional*, defaults to `"float"`):
Whether the gating alphas should be vectors or single floats.
rms_norm_eps (`float`, *optional*, defaults to 1e-6):
The epsilon used by the rms normalization layers.
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values attentions (not used by all models). Only
relevant if `config.is_decoder=True`.
pad_token_id (`int`, *optional*, defaults to 0)
Padding token id.
bos_token_id (`int`, *optional*, defaults to 1)
Beginning of stream token id.
eos_token_id (`int`, *optional*, defaults to 2)
End of stream token id.
tie_word_embeddings(`bool`, *optional*, defaults to `False`):
Whether to tie weight embeddings
cross_layer_interval (`int`, *optional*, default to 1)
Interval for cross attention (from text to image) layers.
qk_layer_norms (`bool`, *optional*, defaults to `False`): Whether to add layer norm after q and k
freeze_text_layers (`bool`, *optional*, defaults to `True`): Whether to freeze text layers
freeze_text_module_exceptions (`bool`, *optional*, defaults to `[]`):
Exceptions to freezing text layers when `freeze_text_layers` is `True`
freeze_lm_head (`bool`, *optional*, defaults to `False`): Whether to freeze lm head
freeze_vision_layers (`bool`, *optional*, defaults to `True`): Whether to freeze vision layers
freeze_vision_module_exceptions (`bool`, *optional*, defaults to `[]`):
Exceptions to freezing vision layers when `freeze_vision_layers` is `True`
use_resampler (`bool`, *optional*, defaults to `False`): Whether to use the Resampler
vision_config (`IdeficsVisionConfig`, *optional*): Custom vision config or dict
perceiver_config (`IdeficsPerceiverConfig`, *optional*): Custom perceiver config or dict
Example:
```python
>>> from transformers import IdeficsModel, IdeficsConfig
>>> # Initializing a Idefics idefics-9b style configuration
>>> configuration = IdeficsConfig()
>>> # Initializing a model from the idefics-9b style configuration
>>> model = IdeficsModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "idefics"
is_composition = True
def __init__(
self,
vocab_size=32000,
additional_vocab_size=0,
hidden_size=4096,
intermediate_size=11008,
num_hidden_layers=32,
num_attention_heads=32,
dropout=0.0,
hidden_act="silu",
initializer_range=0.02,
alpha_initializer="zeros",
alphas_initializer_range=0.0,
alpha_type="float",
rms_norm_eps=1e-6,
use_cache=True,
pad_token_id=0,
bos_token_id=1,
eos_token_id=2,
tie_word_embeddings=False,
cross_layer_interval=1,
qk_layer_norms=False,
freeze_text_layers=True,
freeze_text_module_exceptions=[],
freeze_lm_head=False,
freeze_vision_layers=True,
freeze_vision_module_exceptions=[],
use_resampler=False,
vision_config=None,
perceiver_config=None,
**kwargs,
):
self.vocab_size = vocab_size
self.additional_vocab_size = additional_vocab_size
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.dropout = dropout
self.hidden_act = hidden_act
self.initializer_range = initializer_range
self.alpha_initializer = alpha_initializer
self.alphas_initializer_range = alphas_initializer_range
self.alpha_type = alpha_type
self.rms_norm_eps = rms_norm_eps
self.use_cache = use_cache
self.cross_layer_interval = cross_layer_interval
self.qk_layer_norms = qk_layer_norms
self.freeze_vision_layers = freeze_vision_layers
self.freeze_text_layers = freeze_text_layers
self.freeze_text_module_exceptions = freeze_text_module_exceptions
self.freeze_vision_module_exceptions = freeze_vision_module_exceptions
self.freeze_lm_head = freeze_lm_head
self.use_resampler = use_resampler
if perceiver_config is None:
self.perceiver_config = IdeficsPerceiverConfig()
elif isinstance(perceiver_config, dict):
self.perceiver_config = IdeficsPerceiverConfig(**perceiver_config)
elif isinstance(perceiver_config, IdeficsPerceiverConfig):
self.perceiver_config = perceiver_config
if vision_config is None:
self.vision_config = IdeficsVisionConfig()
elif isinstance(vision_config, dict):
self.vision_config = IdeficsVisionConfig(**vision_config)
elif isinstance(vision_config, IdeficsVisionConfig):
self.vision_config = vision_config
super().__init__(
pad_token_id=pad_token_id,
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
tie_word_embeddings=tie_word_embeddings,
**kwargs,
)
# IMPORTANT: Do not do any __init__ args-based checks in the constructor, since
# PretrainedConfig.from_dict first instantiates the class with the config dict and only then
# updates the config object with `kwargs` from from_pretrained, so during the instantiation
# of this object many attributes have default values and haven't yet been overridden.
# Do any required checks inside `from_pretrained` once the superclass' `from_pretrained` was run.
def to_dict(self):
"""
Serializes this instance to a Python dictionary. Override the default [`~PretrainedConfig.to_dict`].
Returns:
`Dict[str, any]`: Dictionary of all the attributes that make up this configuration instance,
"""
output = copy.deepcopy(self.__dict__)
output["vision_config"] = self.vision_config.to_dict()
output["perceiver_config"] = self.perceiver_config.to_dict()
output["model_type"] = self.__class__.model_type
return output
| 0 |
hf_public_repos/text-generation-inference/server/text_generation_server | hf_public_repos/text-generation-inference/server/text_generation_server/utils/paged_attention.py | import torch
# vllm imports
from vllm import cache_ops
from vllm import attention_ops
_PARTITION_SIZE = 512
def reshape_and_cache(
key: torch.Tensor,
value: torch.Tensor,
key_cache: torch.Tensor,
value_cache: torch.Tensor,
slots: torch.Tensor,
):
cache_ops.reshape_and_cache(key, value, key_cache, value_cache, slots)
def attention(
out: torch.Tensor,
query: torch.Tensor,
key_cache: torch.Tensor,
value_cache: torch.Tensor,
kv_head_mapping: torch.Tensor,
softmax_scale: float,
block_tables: torch.Tensor,
input_lengths: torch.Tensor,
max_s: int,
):
# Adapted from: https://github.com/vllm-project/vllm/blob/f8a1e39fae05ca610be8d5a78be9d40f5274e5fc/vllm/model_executor/layers/attention.py
# Copyright 2023 The vLLM team. All rights
# reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# value_cache => [num_blocks, num_heads, head_size, block_size]
block_size = value_cache.shape[3]
num_seqs, num_heads, head_size = query.shape
max_num_partitions = (max_s + _PARTITION_SIZE - 1) // _PARTITION_SIZE
# NOTE(woosuk): We use a simple heuristic to decide whether to use
# PagedAttention V1 or V2. If the number of partitions is 1, we use
# V1 to avoid the overhead of reduction. Also, if the number of
# sequences or heads is large, we use V1 since there is enough work
# to parallelize.
use_v1 = max_num_partitions == 1 or num_seqs * num_heads > 512
if use_v1:
attention_ops.paged_attention_v1(
out,
query,
key_cache,
value_cache,
kv_head_mapping,
softmax_scale,
block_tables,
input_lengths,
block_size,
max_s,
None,
)
else:
# Run PagedAttention V2.
assert _PARTITION_SIZE % block_size == 0
tmp_output = torch.empty(
size=(num_seqs, num_heads, max_num_partitions, head_size),
dtype=out.dtype,
device=out.device,
)
exp_sums = torch.empty(
size=(num_seqs, num_heads, max_num_partitions),
dtype=torch.float32,
device=out.device,
)
max_logits = torch.empty_like(exp_sums)
attention_ops.paged_attention_v2(
out,
exp_sums,
max_logits,
tmp_output,
query,
key_cache,
value_cache,
kv_head_mapping,
softmax_scale,
block_tables,
input_lengths,
block_size,
max_s,
None,
)
| 0 |
hf_public_repos/text-generation-inference/server/text_generation_server | hf_public_repos/text-generation-inference/server/text_generation_server/utils/logits_process.py | import math
import torch
from functools import lru_cache
from typing import Optional, List, Dict, Union
from transformers import (
LogitsWarper,
LogitsProcessor,
TemperatureLogitsWarper,
TopKLogitsWarper,
TopPLogitsWarper,
TypicalLogitsWarper,
)
mempool = torch.cuda.graph_pool_handle() if torch.cuda.is_available() else None
class StaticWarper:
def __init__(
self,
temperature=1.0,
top_k=None,
top_p=None,
typical_p=None,
):
self.warpers = []
if temperature is not None and temperature != 1.0:
temperature = float(temperature)
self.warpers.append(TemperatureLogitsWarper(temperature))
if top_k is not None and top_k != 0:
self.warpers.append(TopKLogitsWarper(top_k=top_k))
if top_p is not None and top_p < 1.0:
self.warpers.append(TopPLogitsWarper(top_p=top_p))
if typical_p is not None and typical_p < 1.0:
self.warpers.append(TypicalLogitsWarper(mass=typical_p))
self.cuda_graph = None
self.static_scores = None
self.static_warped_scores = None
self.static_next_logprob = None
def __call__(self, scores):
if torch.cuda.is_available():
if self.cuda_graph is None:
self.static_scores = scores
self.cuda_graph = torch.cuda.CUDAGraph()
with torch.cuda.graph(self.cuda_graph, pool=mempool):
local_scores = self.static_scores
for warper in self.warpers:
local_scores = warper(None, local_scores)
self.static_warped_scores = local_scores
# Compute logprobs
self.static_next_logprob = torch.log_softmax(
self.static_warped_scores, -1
)
self.static_scores.copy_(scores)
self.cuda_graph.replay()
return self.static_warped_scores, self.static_next_logprob
# CPU branch
for warper in self.warpers:
scores = warper(None, scores)
return scores, torch.log_softmax(scores, -1)
@lru_cache(10)
def static_warper(
temperature: Optional[float],
top_k: Optional[int],
top_p: Optional[float],
typical_p: Optional[float],
) -> StaticWarper:
return StaticWarper(
temperature=temperature, top_k=top_k, top_p=top_p, typical_p=typical_p
)
class HeterogeneousRepetitionPenaltyLogitsProcessor(LogitsProcessor):
r"""
[`LogitsProcessor`] enforcing an exponential penalty on repeated sequences.
This version allows for a separate value for each sample and runs inplace when possible.
It doesn't validate inputs.
Args:
repetition_penalty (`List[float]`):
The parameter for repetition penalty. 1.0 means no penalty. See [this
paper](https://arxiv.org/pdf/1909.05858.pdf) for more details.
"""
def __init__(self, penalty: List[float], dtype: torch.dtype, device: torch.device):
self.penalty = penalty
self.penalty_tensor = torch.tensor(
penalty, dtype=dtype, device=device
).unsqueeze(1)
def __call__(self, input_ids: torch.Tensor, scores: torch.Tensor) -> torch.Tensor:
score = torch.gather(scores, 1, input_ids)
# if score < 0 then repetition penalty has to be multiplied to reduce the previous token probability
score = torch.where(
score < 0, score * self.penalty_tensor, score / self.penalty_tensor
)
scores.scatter_(1, input_ids, score)
return scores
def filter(self, indices):
self.penalty = [self.penalty[i] for i in indices]
if any([x != 1.0 for x in self.penalty]):
self.penalty_tensor = self.penalty_tensor[indices]
return self
return None
class HeterogeneousTemperatureLogitsWarper:
r"""
[`LogitsWarper`] for temperature (exponential scaling output probability distribution).
This version allows for a separate value for each sample and runs inplace when possible.
It doesn't validate inputs.
Args:
temperature (`float`):
The value used to module the logits distribution.
"""
def __init__(
self, temperature: List[float], dtype: torch.dtype, device: torch.device
):
self.temperature = temperature
self.temperature_tensor = torch.tensor(
temperature, dtype=dtype, device=device
).unsqueeze(1)
def __call__(self, input_ids: torch.Tensor, scores: torch.Tensor) -> torch.Tensor:
scores.div_(self.temperature_tensor)
return scores
def filter(self, indices):
self.temperature = [self.temperature[i] for i in indices]
if any([x != 1.0 for x in self.temperature]):
self.temperature_tensor = self.temperature_tensor[indices]
return self
return None
class HeterogeneousTopPLogitsWarper(LogitsWarper):
"""
[`LogitsWarper`] that performs top-p, i.e. restricting to top tokens summing to prob_cut_off <= prob_cut_off.
This version allows for a separate value for each sample and runs inplace when possible.
It doesn't validate inputs.
Args:
top_p (`float`):
If set to < 1, only the smallest set of most probable tokens with probabilities that add up to `top_p` or
higher are kept for generation.
filter_value (`float`, *optional*, defaults to `-float("Inf")`):
All filtered values will be set to this float value.
min_tokens_to_keep (`int`, *optional*, defaults to 1):
Minimum number of tokens that cannot be filtered.
"""
def __init__(
self,
top_p: List[float],
dtype: torch.dtype,
device: torch.device,
filter_value: float = -math.inf,
min_tokens_to_keep: int = 1,
):
self.top_p = top_p
self.top_p_opposite = 1 - torch.tensor(
top_p, dtype=dtype, device=device
).unsqueeze(1)
self.filter_value = filter_value
self.min_tokens_to_keep = min_tokens_to_keep
def __call__(self, input_ids: torch.Tensor, scores: torch.Tensor) -> torch.Tensor:
sorted_logits, sorted_indices = torch.sort(scores, descending=False)
probs = sorted_logits.softmax(dim=-1)
# This is way faster for some reason
for i in range(probs.shape[0]):
probs[i] = probs[i].cumsum(dim=-1)
# Remove tokens with cumulative top_p above the threshold (token with 0 are kept)
sorted_indices_to_remove = probs <= self.top_p_opposite
# Keep at least min_tokens_to_keep
sorted_indices_to_remove[..., -self.min_tokens_to_keep :] = 0
# scatter sorted tensors to original indexing
indices_to_remove = sorted_indices_to_remove.scatter(
1, sorted_indices, sorted_indices_to_remove
)
warped_scores = scores.masked_fill_(indices_to_remove, self.filter_value)
return warped_scores
def filter(self, indices):
self.top_p = [self.top_p[i] for i in indices]
if any([x < 1.0 for x in self.top_p]):
self.top_p_opposite = self.top_p_opposite[indices]
return self
return None
class HeterogeneousTopKLogitsWarper(LogitsWarper):
r"""
[`LogitsWarper`] that performs top-k, i.e. restricting to the k highest probability elements.
This version allows for a separate value for each sample and runs inplace when possible.
It doesn't validate inputs.
Args:
top_k (`int`):
The number of highest probability vocabulary tokens to keep for top-k-filtering.
filter_value (`float`, *optional*, defaults to `-float("Inf")`):
All filtered values will be set to this float value.
min_tokens_to_keep (`int`, *optional*, defaults to 1):
Minimum number of tokens that cannot be filtered.
"""
def __init__(
self,
top_k: List[int],
device: torch.device,
filter_value: float = -math.inf,
min_tokens_to_keep: int = 1,
):
self.top_k = top_k
self.max_top_k = max(top_k)
# value - 1 as we will use top_k to index and python uses 0 based numbering
self.top_k_tensor = torch.tensor(
[max(x - 1, min_tokens_to_keep - 1) for x in top_k],
dtype=torch.int64,
device=device,
).unsqueeze(1)
# 0 is a special value that disables top_k warping for this member of the batch
disabled = [x == 0 for x in top_k]
if any(disabled):
self.top_k_disabled_mask = torch.tensor(
disabled, dtype=torch.bool, device=device
).view(-1, 1)
else:
self.top_k_disabled_mask = None
self.filter_value = filter_value
def __call__(self, input_ids: torch.Tensor, scores: torch.Tensor) -> torch.Tensor:
# If max_top_k is superior to the vocab, we need to clamp or the warper will fail
if scores.size(-1) < self.max_top_k:
max_top_k = scores.size(-1)
top_k = torch.clamp_max(self.top_k_tensor, max_top_k)
else:
max_top_k = self.max_top_k
top_k = self.top_k_tensor
# Get the kth score for each member of the batch
kth_scores = torch.gather(torch.topk(scores, max_top_k)[0], 1, top_k)
# Mask member of kth_scores that do not want to use top_k warping
if self.top_k_disabled_mask is not None:
kth_scores.masked_fill_(self.top_k_disabled_mask, self.filter_value)
# Remove all tokens with a probability less than the last token of the top-k
indices_to_remove = scores < kth_scores
scores.masked_fill_(indices_to_remove, self.filter_value)
return scores
def filter(self, indices):
self.top_k = [self.top_k[i] for i in indices]
disabled = [x == 0 for x in self.top_k]
if not all(disabled):
self.top_k_tensor = self.top_k_tensor[indices]
self.max_top_k = max(self.top_k)
if self.top_k_disabled_mask is not None:
self.top_k_disabled_mask = (
self.top_k_disabled_mask[indices] if any(disabled) else None
)
return self
return None
class HeterogeneousTypicalLogitsWarper(LogitsWarper):
r"""
[`LogitsWarper`] that performs typical decoding. See [Typical Decoding for Natural Language
Generation](https://arxiv.org/abs/2202.00666) for more information.
This version allows for a separate value for each sample and runs inplace when possible.
It doesn't validate inputs.
Args:
mass (`float`):
Value of typical_p between 0 and 1 inclusive, defaults to 0.9.
filter_value (`float`, *optional*, defaults to `-float("Inf")`):
All filtered values will be set to this float value.
min_tokens_to_keep (`int`, *optional*, defaults to 1):
Minimum number of tokens that cannot be filtered.
"""
def __init__(
self,
mass: List[float],
dtype: torch.dtype,
device: torch.device,
filter_value: float = -math.inf,
min_tokens_to_keep: int = 1,
):
self.mass = mass
self.mass_tensor = torch.tensor(mass, dtype=dtype, device=device).unsqueeze(1)
# 1 is a special value that disables typical_p warping for this member of the batch
disabled = [x == 1.0 for x in mass]
if any(disabled):
self.disabled_mask = torch.tensor(disabled, dtype=torch.bool, device=device)
else:
self.disabled_mask = None
self.filter_value = filter_value
self.min_tokens_to_keep = min_tokens_to_keep
def __call__(self, input_ids: torch.Tensor, scores: torch.Tensor) -> torch.Tensor:
# calculate entropy
normalized = torch.nn.functional.log_softmax(scores, dim=-1)
p = torch.exp(normalized)
ent = -(normalized * p).nansum(-1, keepdim=True)
# shift and sort
shifted_scores = torch.abs((-normalized) - ent)
sorted_scores, sorted_indices = torch.sort(shifted_scores, descending=False)
sorted_logits = scores.gather(-1, sorted_indices)
probs = sorted_logits.softmax(dim=-1)
# This is way faster for some reason
for i in range(probs.shape[0]):
probs[i] = probs[i].cumsum(dim=-1)
# Remove tokens with cumulative mass above the threshold
last_ind = (probs < self.mass_tensor).sum(dim=1)
last_ind[last_ind < 0] = 0
if self.disabled_mask is not None:
last_ind.masked_fill_(self.disabled_mask, scores.shape[-1] - 1)
sorted_indices_to_remove = sorted_scores > sorted_scores.gather(
1, last_ind.view(-1, 1)
)
if self.min_tokens_to_keep > 1:
# Keep at least min_tokens_to_keep (set to min_tokens_to_keep-1 because we add the first one below)
sorted_indices_to_remove[..., : self.min_tokens_to_keep] = 0
indices_to_remove = sorted_indices_to_remove.scatter(
1, sorted_indices, sorted_indices_to_remove
)
warped_scores = scores.masked_fill_(indices_to_remove, self.filter_value)
return warped_scores
def filter(self, indices):
self.mass = [self.mass[i] for i in indices]
disabled = [x == 1.0 for x in self.mass]
if not all(disabled):
self.mass_tensor = self.mass_tensor[indices]
if self.disabled_mask is not None:
self.disabled_mask = (
self.disabled_mask[indices] if any(disabled) else None
)
return self
return None
class HeterogeneousProcessorWrapper(LogitsProcessor):
r"""
A wrapper for logit warpers or processors without heterogeneous parameter support.
Args:
processors (`Dict[int, Union[LogitsProcessor, LogitsWarper]]`):
A mapping of sample indices to logit warpers or processors, to be run sequentially.
"""
def __init__(
self,
processors: Dict[int, Union[LogitsProcessor, LogitsWarper]],
):
self.processors = processors
def __call__(self, input_ids: torch.Tensor, scores: torch.Tensor) -> torch.Tensor:
for i, processor in self.processors.items():
scores[i : i + 1] = processor(input_ids[i : i + 1], scores[i : i + 1])
return scores
def filter(self, indices):
new_processors = {}
for i, idx in enumerate(indices):
if idx in self.processors:
new_processors[i] = self.processors[idx]
if new_processors:
self.processors = new_processors
return self
return None
| 0 |
hf_public_repos/text-generation-inference/server/text_generation_server | hf_public_repos/text-generation-inference/server/text_generation_server/utils/dist.py | import os
import torch
from datetime import timedelta
from loguru import logger
# Tensor Parallelism settings
RANK = int(os.getenv("RANK", "0"))
WORLD_SIZE = int(os.getenv("WORLD_SIZE", "1"))
# CUDA memory fraction
MEMORY_FRACTION = float(os.getenv("CUDA_MEMORY_FRACTION", "1.0"))
class FakeBarrier:
def wait(self):
pass
class FakeGroup:
def __init__(self, rank, size):
self._rank = rank
self._size = size
def allreduce(self, *args, **kwargs):
return FakeBarrier()
def allgather(self, inputs, local_tensor, **kwargs):
assert (
len(inputs[0]) == len(local_tensor) == 1
), f"{len(inputs[0])} != {len(local_tensor)} != 1, and the FakeGroup is supposed to join on simple tensors"
for input_ in inputs:
input_[0].data = local_tensor[0].data
return FakeBarrier()
def barrier(self, *args, **kwargs):
return FakeBarrier()
def size(self):
return self._size
def rank(self):
return self._rank
def initialize_torch_distributed():
if torch.cuda.is_available():
from torch.distributed import ProcessGroupNCCL
# Set the device id.
assert WORLD_SIZE <= torch.cuda.device_count(), "Each process is one gpu"
device = RANK % torch.cuda.device_count()
torch.cuda.set_device(device)
torch.cuda.set_per_process_memory_fraction(MEMORY_FRACTION, device)
backend = "nccl"
options = ProcessGroupNCCL.Options()
options.is_high_priority_stream = True
options._timeout = timedelta(seconds=60)
else:
backend = "gloo"
options = None
if WORLD_SIZE == 1:
return FakeGroup(RANK, WORLD_SIZE), RANK, WORLD_SIZE
else:
if os.getenv("DEBUG", None) == "1":
return FakeGroup(RANK, WORLD_SIZE), RANK, WORLD_SIZE
if not torch.distributed.is_initialized():
# Call the init process.
torch.distributed.init_process_group(
backend=backend,
world_size=WORLD_SIZE,
rank=RANK,
timeout=timedelta(seconds=60),
pg_options=options,
)
else:
logger.warning("torch.distributed is already initialized.")
return torch.distributed.group.WORLD, RANK, WORLD_SIZE
| 0 |
hf_public_repos/text-generation-inference/server/text_generation_server | hf_public_repos/text-generation-inference/server/text_generation_server/utils/import_utils.py | import torch
IS_ROCM_SYSTEM = torch.version.hip is not None
IS_CUDA_SYSTEM = torch.version.cuda is not None
| 0 |
hf_public_repos/text-generation-inference/server/text_generation_server | hf_public_repos/text-generation-inference/server/text_generation_server/utils/watermark.py | # coding=utf-8
# Copyright 2023 Authors of "A Watermark for Large Language Models"
# available at https://arxiv.org/abs/2301.10226
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import torch
from transformers import LogitsProcessor
from typing import List, Union
GAMMA = float(os.getenv("WATERMARK_GAMMA", 0.5))
DELTA = float(os.getenv("WATERMARK_DELTA", 2.0))
class WatermarkLogitsProcessor(LogitsProcessor):
def __init__(
self,
gamma: float = GAMMA,
delta: float = DELTA,
hash_key: int = 15485863, # just a large prime number to create a rng seed with sufficient bit width
device: str = "cpu",
):
# watermarking parameters
self.gamma = gamma
self.delta = delta
self.rng = torch.Generator(device=device)
self.hash_key = hash_key
def _seed_rng(self, input_ids: Union[List[int], torch.LongTensor]):
if isinstance(input_ids, list):
assert (
len(input_ids) >= 1
), "requires at least a 1 token prefix sequence to seed rng"
prev_token = input_ids[-1]
else:
assert len(input_ids) == 1
input_ids = input_ids[0]
assert (
input_ids.shape[-1] >= 1
), "requires at least a 1 token prefix sequence to seed rng"
prev_token = input_ids[-1].item()
self.rng.manual_seed(self.hash_key * prev_token)
def _get_greenlist_ids(
self,
input_ids: Union[List[int], torch.LongTensor],
max_value: int,
device: torch.device,
) -> List[int]:
# seed the rng using the previous tokens/prefix
self._seed_rng(input_ids)
greenlist_size = int(max_value * self.gamma)
vocab_permutation = torch.randperm(max_value, device=device, generator=self.rng)
greenlist_ids = vocab_permutation[:greenlist_size]
return greenlist_ids
@staticmethod
def _calc_greenlist_mask(
scores: torch.FloatTensor, greenlist_token_ids
) -> torch.BoolTensor:
green_tokens_mask = torch.zeros_like(scores)
green_tokens_mask[-1, greenlist_token_ids] = 1
final_mask = green_tokens_mask.bool()
return final_mask
@staticmethod
def _bias_greenlist_logits(
scores: torch.Tensor, greenlist_mask: torch.Tensor, greenlist_bias: float
) -> torch.Tensor:
scores[greenlist_mask] = scores[greenlist_mask] + greenlist_bias
return scores
def __call__(
self, input_ids: Union[List[int], torch.LongTensor], scores: torch.FloatTensor
) -> torch.FloatTensor:
greenlist_ids = self._get_greenlist_ids(
input_ids, scores.shape[-1], scores.device
)
green_tokens_mask = self._calc_greenlist_mask(
scores=scores, greenlist_token_ids=greenlist_ids
)
scores = self._bias_greenlist_logits(
scores=scores, greenlist_mask=green_tokens_mask, greenlist_bias=self.delta
)
return scores
| 0 |
hf_public_repos/text-generation-inference/server/text_generation_server | hf_public_repos/text-generation-inference/server/text_generation_server/utils/__init__.py | from text_generation_server.utils.convert import convert_file, convert_files
from text_generation_server.utils.dist import initialize_torch_distributed
from text_generation_server.utils.weights import Weights
from text_generation_server.utils.peft import download_and_unload_peft
from text_generation_server.utils.hub import (
weight_files,
weight_hub_files,
download_weights,
EntryNotFoundError,
LocalEntryNotFoundError,
RevisionNotFoundError,
)
from text_generation_server.utils.tokens import (
NextTokenChooser,
HeterogeneousNextTokenChooser,
StoppingCriteria,
StopSequenceCriteria,
FinishReason,
Sampling,
Greedy,
)
__all__ = [
"convert_file",
"convert_files",
"initialize_torch_distributed",
"weight_files",
"weight_hub_files",
"download_weights",
"download_and_unload_peft",
"EntryNotFoundError",
"HeterogeneousNextTokenChooser",
"LocalEntryNotFoundError",
"RevisionNotFoundError",
"Greedy",
"NextTokenChooser",
"Sampling",
"StoppingCriteria",
"StopSequenceCriteria",
"FinishReason",
"Weights",
]
| 0 |
hf_public_repos/text-generation-inference/server/text_generation_server | hf_public_repos/text-generation-inference/server/text_generation_server/utils/flash_attn.py | import os
import torch
from loguru import logger
from text_generation_server.utils.import_utils import IS_CUDA_SYSTEM, IS_ROCM_SYSTEM
if os.getenv("USE_FLASH_ATTENTION", "").lower() == "false":
raise ImportError("`USE_FLASH_ATTENTION` is false.")
if not torch.cuda.is_available():
raise ImportError("CUDA is not available")
major, minor = torch.cuda.get_device_capability()
is_sm75 = major == 7 and minor == 5
is_sm8x = major == 8 and minor >= 0
is_sm90 = major == 9 and minor == 0
HAS_FLASH_ATTN = False
HAS_FLASH_ATTN_V2_CUDA = False
HAS_FLASH_ATTN_V2_ROCM = False
try:
try:
import flash_attn_2_cuda
except ImportError:
architecture_suffix = ""
if IS_CUDA_SYSTEM:
architecture_suffix = "-cuda"
elif IS_ROCM_SYSTEM:
architecture_suffix = "-rocm"
raise ImportError(
"Flash Attention V2 is not installed.\n"
"Use the official Docker image (ghcr.io/huggingface/text-generation-inference:latest) "
f"or install flash attention v2 with `cd server && make install install-flash-attention-v2{architecture_suffix}`"
)
if not (is_sm8x or is_sm90):
raise ImportError(
f"GPU with CUDA capability {major} {minor} is not supported for "
"Flash Attention V2"
)
HAS_FLASH_ATTN_V2_CUDA = IS_CUDA_SYSTEM
HAS_FLASH_ATTN_V2_ROCM = IS_ROCM_SYSTEM
except ImportError as e:
try:
import flash_attn_cuda
except ImportError:
raise ImportError(
"Flash Attention is not installed.\n"
"Use the official Docker image (ghcr.io/huggingface/text-generation-inference:latest) "
"or install flash attention with `cd server && make install install-flash-attention`"
) from e
if IS_CUDA_SYSTEM and not (is_sm75 or is_sm8x or is_sm90):
raise ImportError(
f"GPU with CUDA capability {major} {minor} is not supported"
) from e
elif IS_ROCM_SYSTEM:
for idx in range(torch.cuda.device_count()):
if "MI210" not in torch.cuda.get_device_name(
idx
) and "MI250" not in torch.cuda.get_device_name(idx):
raise ImportError(
f"AMD GPU {torch.cuda.get_device_name(idx)} does not support flash-attention"
)
logger.warning(f"Unable to use Flash Attention V2: {e}")
HAS_FLASH_ATTN = True
def attention(
q,
k,
v,
out,
cu_seqlens,
max_s,
softmax_scale,
window_size_left=-1,
):
if window_size_left <= 0 and window_size_left != -1:
raise ValueError("`window_size_left` must be > 0 or -1")
if HAS_FLASH_ATTN_V2_CUDA:
return flash_attn_2_cuda.varlen_fwd(
q,
k,
v,
out,
cu_seqlens,
cu_seqlens,
max_s,
max_s,
0.0,
softmax_scale,
False,
True,
window_size_left,
0,
False,
None,
)
elif HAS_FLASH_ATTN_V2_ROCM:
if window_size_left != -1:
raise ValueError(
f"RoCm version of Flash Attention v2 does not support window attention (window_size_left != -1, got window_size_left={window_size_left})."
)
# RoCm flash API does not take the window_size_left and window_size_right arguments.
return flash_attn_2_cuda.varlen_fwd(
q,
k,
v,
out,
cu_seqlens,
cu_seqlens,
max_s,
max_s,
0.0,
softmax_scale,
False,
True,
False,
None,
)
elif HAS_FLASH_ATTN:
if window_size_left != -1:
raise NotImplementedError(
"window_size_left is only available with flash attn v2"
)
# Flash attention v1 requires q, k and v to have the same number of heads
if k.shape[1] != q.shape[1]:
# MQA expand
if k.shape[1] == 1:
k = k.expand(-1, q.shape[1], -1)
# Grouped attention reshape
else:
original_shape = k.shape
k = (
k.unsqueeze(2)
.expand(-1, -1, q.shape[1] // k.shape[1], -1)
.reshape(original_shape[0], -1, original_shape[2])
)
if v.shape[1] != q.shape[1]:
# MQA expand
if v.shape[1] == 1:
v = v.expand(-1, q.shape[1], -1)
# Grouped attention reshape
else:
original_shape = v.shape
v = (
v.unsqueeze(2)
.expand(-1, -1, q.shape[1] // v.shape[1], -1)
.reshape(original_shape[0], -1, original_shape[2])
)
return flash_attn_cuda.fwd(
q,
k,
v,
out,
cu_seqlens,
cu_seqlens,
max_s,
max_s,
0.0,
softmax_scale,
False,
True,
False,
0,
None,
)
raise NotImplementedError("flash attention is not installed")
| 0 |
hf_public_repos/text-generation-inference/server/text_generation_server | hf_public_repos/text-generation-inference/server/text_generation_server/utils/hub.py | import time
import os
from datetime import timedelta
from loguru import logger
from pathlib import Path
from typing import Optional, List
from huggingface_hub import file_download, hf_api, HfApi, hf_hub_download
from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE
from huggingface_hub.utils import (
LocalEntryNotFoundError,
EntryNotFoundError,
RevisionNotFoundError, # noqa # Import here to ease try/except in other part of the lib
)
WEIGHTS_CACHE_OVERRIDE = os.getenv("WEIGHTS_CACHE_OVERRIDE", None)
HF_HUB_OFFLINE = os.environ.get("HF_HUB_OFFLINE", "0").lower() in ["true", "1", "yes"]
def _cached_weight_files(
model_id: str, revision: Optional[str], extension: str
) -> List[str]:
"""Guess weight files from the cached revision snapshot directory"""
d = _get_cached_revision_directory(model_id, revision)
if not d:
return []
filenames = _weight_files_from_dir(d, extension)
return filenames
def _weight_hub_files_from_model_info(
info: hf_api.ModelInfo, extension: str
) -> List[str]:
return [
s.rfilename
for s in info.siblings
if s.rfilename.endswith(extension)
and len(s.rfilename.split("/")) == 1
and "arguments" not in s.rfilename
and "args" not in s.rfilename
and "training" not in s.rfilename
]
def _weight_files_from_dir(d: Path, extension: str) -> List[str]:
# os.walk: do not iterate, just scan for depth 1, not recursively
# see _weight_hub_files_from_model_info, that's also what is
# done there with the len(s.rfilename.split("/")) == 1 condition
root, _, files = next(os.walk(str(d)))
filenames = [
os.path.join(root, f)
for f in files
if f.endswith(extension)
and "arguments" not in f
and "args" not in f
and "adapter" not in f
and "training" not in f
]
return filenames
def _get_cached_revision_directory(
model_id: str, revision: Optional[str]
) -> Optional[Path]:
if revision is None:
revision = "main"
repo_cache = Path(HUGGINGFACE_HUB_CACHE) / Path(
file_download.repo_folder_name(repo_id=model_id, repo_type="model")
)
if not repo_cache.is_dir():
# No cache for this model
return None
refs_dir = repo_cache / "refs"
snapshots_dir = repo_cache / "snapshots"
# Resolve refs (for instance to convert main to the associated commit sha)
if refs_dir.is_dir():
revision_file = refs_dir / revision
if revision_file.exists():
with revision_file.open() as f:
revision = f.read()
# Check if revision folder exists
if not snapshots_dir.exists():
return None
cached_shas = os.listdir(snapshots_dir)
if revision not in cached_shas:
# No cache for this revision and we won't try to return a random revision
return None
return snapshots_dir / revision
def weight_hub_files(
model_id: str, revision: Optional[str] = None, extension: str = ".safetensors"
) -> List[str]:
"""Get the weights filenames on the hub"""
api = HfApi()
if HF_HUB_OFFLINE:
filenames = _cached_weight_files(model_id, revision, extension)
else:
# Online case, fetch model info from the Hub
info = api.model_info(model_id, revision=revision)
filenames = _weight_hub_files_from_model_info(info, extension)
if not filenames:
raise EntryNotFoundError(
f"No {extension} weights found for model {model_id} and revision {revision}.",
None,
)
return filenames
def try_to_load_from_cache(
model_id: str, revision: Optional[str], filename: str
) -> Optional[Path]:
"""Try to load a file from the Hugging Face cache"""
d = _get_cached_revision_directory(model_id, revision)
if not d:
return None
# Check if file exists in cache
cached_file = d / filename
return cached_file if cached_file.is_file() else None
def weight_files(
model_id: str, revision: Optional[str] = None, extension: str = ".safetensors"
) -> List[Path]:
"""Get the local files"""
# Local model
d = Path(model_id)
if d.exists() and d.is_dir():
local_files = _weight_files_from_dir(d, extension)
if not local_files:
raise FileNotFoundError(
f"No local weights found in {model_id} with extension {extension}"
)
return [Path(f) for f in local_files]
try:
filenames = weight_hub_files(model_id, revision, extension)
except EntryNotFoundError as e:
if extension != ".safetensors":
raise e
# Try to see if there are pytorch weights
pt_filenames = weight_hub_files(model_id, revision, extension=".bin")
# Change pytorch extension to safetensors extension
# It is possible that we have safetensors weights locally even though they are not on the
# hub if we converted weights locally without pushing them
filenames = [
f"{Path(f).stem.lstrip('pytorch_')}.safetensors" for f in pt_filenames
]
if WEIGHTS_CACHE_OVERRIDE is not None:
files = []
for filename in filenames:
p = Path(WEIGHTS_CACHE_OVERRIDE) / filename
if not p.exists():
raise FileNotFoundError(
f"File {p} not found in {WEIGHTS_CACHE_OVERRIDE}."
)
files.append(p)
return files
files = []
for filename in filenames:
cache_file = try_to_load_from_cache(
model_id, revision=revision, filename=filename
)
if cache_file is None:
raise LocalEntryNotFoundError(
f"File {filename} of model {model_id} not found in "
f"{os.getenv('HUGGINGFACE_HUB_CACHE', 'the local cache')}. "
f"Please run `text-generation-server download-weights {model_id}` first."
)
files.append(cache_file)
return files
def download_weights(
filenames: List[str], model_id: str, revision: Optional[str] = None
) -> List[Path]:
"""Download the safetensors files from the hub"""
def download_file(fname, tries=5, backoff: int = 5):
local_file = try_to_load_from_cache(model_id, revision, fname)
if local_file is not None:
logger.info(f"File {fname} already present in cache.")
return Path(local_file)
for idx in range(tries):
try:
logger.info(f"Download file: {fname}")
stime = time.time()
local_file = hf_hub_download(
filename=fname,
repo_id=model_id,
revision=revision,
local_files_only=HF_HUB_OFFLINE,
)
logger.info(
f"Downloaded {local_file} in {timedelta(seconds=int(time.time() - stime))}."
)
return Path(local_file)
except Exception as e:
if idx + 1 == tries:
raise e
logger.error(e)
logger.info(f"Retrying in {backoff} seconds")
time.sleep(backoff)
logger.info(f"Retry {idx + 1}/{tries - 1}")
# We do this instead of using tqdm because we want to parse the logs with the launcher
start_time = time.time()
files = []
for i, filename in enumerate(filenames):
file = download_file(filename)
elapsed = timedelta(seconds=int(time.time() - start_time))
remaining = len(filenames) - (i + 1)
eta = (elapsed / (i + 1)) * remaining if remaining > 0 else 0
logger.info(f"Download: [{i + 1}/{len(filenames)}] -- ETA: {eta}")
files.append(file)
return files
| 0 |
hf_public_repos/text-generation-inference/server/text_generation_server | hf_public_repos/text-generation-inference/server/text_generation_server/utils/weights.py | import os
from pathlib import Path
from typing import List, Dict, Optional, Tuple
from safetensors import safe_open, SafetensorError
import torch
from loguru import logger
from huggingface_hub import hf_hub_download
import json
from text_generation_server.utils.log import log_once
class Weights:
def __init__(
self,
filenames: List[Path],
device,
dtype,
process_group,
aliases: Optional[Dict[str, List[str]]] = None,
prefix: Optional[str] = None,
):
routing = {}
for filename in filenames:
with safe_open(filename, framework="pytorch") as f:
for k in f.keys():
if k in routing:
raise RuntimeError(
f"Key {k} was found in multiple files: {filename} and {routing[k]}"
)
routing[k] = filename
if aliases is None:
aliases = {}
self.aliases = aliases
self.routing = routing
self.device = device
self.dtype = dtype
self.process_group = process_group
self.prefix = prefix
self._handles = {}
def _get_handle(self, filename):
if filename not in self._handles:
f = safe_open(filename, framework="pytorch")
self._handles[filename] = f
return self._handles[filename]
def get_filename(self, tensor_name: str) -> (str, str):
names = [tensor_name]
if self.prefix is not None:
prefixed = f"{self.prefix}.{tensor_name}"
names.append(prefixed)
for name in names:
filename = self.routing.get(name, None)
if filename is not None:
return str(filename), name
aliases = self.aliases.get(name, [])
for alias in aliases:
filename = self.routing.get(alias, None)
if filename is not None:
return str(filename), alias
raise RuntimeError(f"weight {tensor_name} does not exist")
def _get_slice(self, tensor_name: str):
filename, tensor_name = self.get_filename(tensor_name)
f = self._get_handle(filename)
slice_ = f.get_slice(tensor_name)
return slice_
def get_shape(self, tensor_name: str):
return self._get_slice(tensor_name).get_shape()
def get_tensor(self, tensor_name: str, to_device=True):
filename, tensor_name = self.get_filename(tensor_name)
f = self._get_handle(filename)
tensor = f.get_tensor(tensor_name)
# Special case for gptq which shouldn't convert
# u4 which are disguised as int32
if tensor.dtype not in [torch.int32, torch.int64]:
tensor = tensor.to(dtype=self.dtype)
if to_device:
tensor = tensor.to(device=self.device)
return tensor
def get_partial_sharded(self, tensor_name: str, dim: int):
filename, tensor_name = self.get_filename(tensor_name)
f = self._get_handle(filename)
slice_ = f.get_slice(tensor_name)
world_size = self.process_group.size()
rank = self.process_group.rank()
size = slice_.get_shape()[dim]
block_size = size // world_size
start = rank * block_size
stop = (rank + 1) * block_size
if dim == 0:
tensor = slice_[start:stop]
elif dim == 1:
tensor = slice_[:, start:stop]
else:
raise NotImplementedError("Let's make that generic when needed")
# Special case for gptq which shouldn't convert
# u4 which are disguised as int32
if tensor.dtype != torch.int32:
tensor = tensor.to(dtype=self.dtype)
tensor = tensor.to(device=self.device)
return tensor
def get_sharded(self, tensor_name: str, dim: int):
filename, tensor_name = self.get_filename(tensor_name)
f = self._get_handle(filename)
slice_ = f.get_slice(tensor_name)
world_size = self.process_group.size()
size = slice_.get_shape()[dim]
assert (
size % world_size == 0
), f"The choosen size {size} is not compatible with sharding on {world_size} shards"
return self.get_partial_sharded(tensor_name, dim)
def _get_qweight(self, name: str):
slice_ = self._get_slice(name)
total_size = slice_.get_shape()[1]
assert total_size % 3 == 0, "Prepacked quantized qkv is not divisible by 3"
single_size = total_size // 3
world_size = self.process_group.size()
rank = self.process_group.rank()
assert (
single_size % world_size == 0
), f"Prepacked quantized qkv cannot be sharded across {world_size} shards"
block_size = single_size // world_size
start = rank * block_size
stop = (rank + 1) * block_size
q = slice_[:, start:stop]
k = slice_[:, start + single_size : stop + single_size]
v = slice_[:, start + 2 * single_size : stop + 2 * single_size]
weight = torch.cat([q, k, v], dim=1)
weight = weight.to(device=self.device)
return weight
def get_weights_col_packed_qkv(self, prefix: str, quantize: str):
"""
Highly specific when the underlying tensor is a simple cat of Q,K,V instead of being
already alternating Q,K,V within the main tensor
"""
if quantize in ["gptq", "awq"]:
try:
qweight = self._get_qweight(f"{prefix}.qweight")
except RuntimeError:
raise RuntimeError(
f"Cannot load `{quantize}` weight, make sure the model is already quantized."
)
qzeros = self._get_qweight(f"{prefix}.qzeros")
scales = self._get_qweight(f"{prefix}.scales")
scales = scales.to(dtype=self.dtype)
if quantize == "gptq":
g_idx = self.get_tensor(f"{prefix}.g_idx")
else:
g_idx = None
bits, groupsize, _ = self._get_gptq_params()
weight = (qweight, qzeros, scales, g_idx, bits, groupsize, False)
else:
slice_ = self._get_slice(f"{prefix}.weight")
total_size = slice_.get_shape()[0]
assert total_size % 3 == 0, "Prepacked qkv is not divisible by 3"
single_size = total_size // 3
world_size = self.process_group.size()
rank = self.process_group.rank()
assert (
single_size % world_size == 0
), f"Prepacked qkv cannot be sharded across {world_size} shards"
block_size = single_size // world_size
start = rank * block_size
stop = (rank + 1) * block_size
q = slice_[start:stop]
k = slice_[start + single_size : stop + single_size]
v = slice_[start + 2 * single_size : stop + 2 * single_size]
weight = torch.cat([q, k, v], dim=0)
weight = weight.to(device=self.device)
weight = weight.to(dtype=self.dtype)
return weight
def get_multi_weights_col(self, prefixes: List[str], quantize: str, dim: int):
if quantize in ["gptq", "awq"]:
try:
qweight = torch.cat(
[self.get_sharded(f"{p}.qweight", dim=1) for p in prefixes], dim=1
)
except RuntimeError:
raise RuntimeError(
f"Cannot load `{quantize}` weight, make sure the model is already quantized"
)
qzeros = torch.cat(
[self.get_sharded(f"{p}.qzeros", dim=1) for p in prefixes], dim=1
)
scales = torch.cat(
[self.get_sharded(f"{p}.scales", dim=1) for p in prefixes], dim=1
)
if quantize == "gptq":
w = [self.get_tensor(f"{p}.g_idx") for p in prefixes]
for w2 in w[1:]:
torch.testing.assert_close(w2, w[0])
g_idx = w[0]
else:
g_idx = None
bits, groupsize, desc_act = self._get_gptq_params()
from text_generation_server.utils.layers import HAS_EXLLAMA
use_exllama = (
bits == 4 and HAS_EXLLAMA and quantize == "gptq" and not desc_act
)
weight = (qweight, qzeros, scales, g_idx, bits, groupsize, use_exllama)
else:
w = [self.get_sharded(f"{p}.weight", dim=0) for p in prefixes]
weight = torch.cat(w, dim=dim)
return weight
def get_tensor_shard(self, var, dim):
world_size = self.process_group.size()
rank = self.process_group.rank()
block_size = var.size()[dim] // world_size
start = rank * block_size
stop = (rank + 1) * block_size
if dim == 0:
tensor = var[start:stop]
elif dim == 1:
tensor = var[:, start:stop]
else:
raise NotImplementedError("Let's make that generic when needed")
tensor = tensor.to(dtype=self.dtype)
tensor = tensor.to(device=self.device)
return tensor
def get_multi_weights_row(self, prefix: str, quantize: str):
if quantize == "gptq":
use_exllama = True
bits, groupsize, desc_act = self._get_gptq_params()
if bits != 4:
use_exllama = False
if desc_act:
log_once(logger.warning, "Disabling exllama because desc_act=True")
use_exllama = False
if self.process_group.size() > 1:
g_idx = self.get_tensor(f"{prefix}.g_idx")
if g_idx is not None:
if (
not torch.equal(
g_idx.cpu(),
torch.tensor(
[i // groupsize for i in range(g_idx.shape[0])],
dtype=torch.int32,
),
)
and not (g_idx == 0).all()
):
# Exllama implementation does not support row tensor parallelism with act-order, as
# it would require to reorder input activations that are split unto several GPUs
use_exllama = False
try:
qweight = self.get_sharded(f"{prefix}.qweight", dim=0)
except RuntimeError:
raise RuntimeError(
"Cannot load `gptq` weight, make sure the model is already quantized, or quantize it with `text-generation-server quantize ORIGINAL_MODEL_ID NEW_MODEL_ID`"
)
from text_generation_server.utils.layers import HAS_EXLLAMA, CAN_EXLLAMA
if use_exllama:
if not HAS_EXLLAMA:
if CAN_EXLLAMA:
log_once(
logger.warning,
"Exllama GPTQ cuda kernels (which are faster) could have been used, but are not currently installed, try using BUILD_EXTENSIONS=True",
)
use_exllama = False
else:
log_once(logger.info, f"Using exllama kernels v{HAS_EXLLAMA}")
g_idx = self.get_sharded(f"{prefix}.g_idx", dim=0)
if use_exllama and groupsize != -1:
qzeros = self.get_sharded(f"{prefix}.qzeros", dim=0)
scales = self.get_sharded(f"{prefix}.scales", dim=0)
else:
qzeros = self.get_tensor(f"{prefix}.qzeros")
scales = self.get_tensor(f"{prefix}.scales")
if use_exllama:
g_idx = g_idx - g_idx[0]
weight = (qweight, qzeros, scales, g_idx, bits, groupsize, use_exllama)
elif quantize == "awq":
bits, groupsize, _ = self._get_gptq_params()
try:
qweight = self.get_sharded(f"{prefix}.qweight", dim=0)
except RuntimeError:
raise RuntimeError(
"Cannot load `awq` weight, make sure the model is already quantized"
)
qzeros = self.get_sharded(f"{prefix}.qzeros", dim=0)
scales = self.get_sharded(f"{prefix}.scales", dim=0)
g_idx = None
use_exllama = False
weight = (qweight, qzeros, scales, g_idx, bits, groupsize, use_exllama)
else:
weight = self.get_sharded(f"{prefix}.weight", dim=1)
return weight
def _get_gptq_params(self) -> Tuple[int, int, int]:
try:
bits = self.get_tensor("gptq_bits").item()
groupsize = self.get_tensor("gptq_groupsize").item()
desc_act = False
except (SafetensorError, RuntimeError) as e:
try:
bits = self.gptq_bits
groupsize = self.gptq_groupsize
desc_act = getattr(self, "gptq_desc_act", False)
except Exception:
raise e
return bits, groupsize, desc_act
def _set_gptq_params(self, model_id, revision):
filename = "config.json"
try:
if os.path.exists(os.path.join(model_id, filename)):
filename = os.path.join(model_id, filename)
else:
filename = hf_hub_download(
model_id, filename=filename, revision=revision
)
with open(filename, "r") as f:
data = json.load(f)
self.gptq_bits = data["quantization_config"]["bits"]
self.gptq_groupsize = data["quantization_config"]["group_size"]
self.gptq_desc_act = data["quantization_config"]["desc_act"]
except Exception:
filename = "quantize_config.json"
try:
if os.path.exists(os.path.join(model_id, filename)):
filename = os.path.join(model_id, filename)
else:
filename = hf_hub_download(
model_id, filename=filename, revision=revision
)
with open(filename, "r") as f:
data = json.load(f)
self.gptq_bits = data["bits"]
self.gptq_groupsize = data["group_size"]
self.gptq_desc_act = data["desc_act"]
except Exception:
filename = "quant_config.json"
try:
if os.path.exists(os.path.join(model_id, filename)):
filename = os.path.join(model_id, filename)
else:
filename = hf_hub_download(
model_id, filename=filename, revision=revision
)
with open(filename, "r") as f:
data = json.load(f)
self.gptq_bits = data["w_bit"]
self.gptq_groupsize = data["q_group_size"]
self.gptq_desc_act = data["desc_act"]
except Exception:
pass
| 0 |
hf_public_repos/text-generation-inference/server/text_generation_server | hf_public_repos/text-generation-inference/server/text_generation_server/utils/medusa.py | 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 |
hf_public_repos/text-generation-inference/server/text_generation_server | hf_public_repos/text-generation-inference/server/text_generation_server/utils/layers.py | import os
import torch
import torch.distributed
from torch import nn
from torch.nn import functional as F
from typing import List
from loguru import logger
from functools import lru_cache
HAS_BITS_AND_BYTES = True
try:
import bitsandbytes as bnb
from bitsandbytes.nn import Int8Params, Params4bit
except ImportError:
HAS_BITS_AND_BYTES = False
from accelerate import init_empty_weights
from text_generation_server.utils.gptq.quant_linear import QuantLinear
from text_generation_server.utils.import_utils import IS_CUDA_SYSTEM, IS_ROCM_SYSTEM
from text_generation_server.utils.log import log_once
HAS_AWQ = True
try:
from text_generation_server.utils.awq.quantize.qmodule import WQLinear
except ImportError:
HAS_AWQ = False
try:
major, _minor = torch.cuda.get_device_capability()
except Exception:
major = 1
HAS_EXLLAMA = False
CAN_EXLLAMA = major >= 8
V2 = os.getenv("EXLLAMA_VERSION", "2") == "2"
if V2 and int(os.getenv("WORLD_SIZE", "1")) > 1:
V2 = False
log_once(
logger.warning,
"Disabling exllama v2 and using v1 instead because there are issues when sharding",
)
if os.getenv("DISABLE_EXLLAMA") == "True":
HAS_EXLLAMA = False
elif CAN_EXLLAMA:
try:
if V2:
from text_generation_server.utils.gptq.exllamav2 import (
QuantLinear as ExllamaQuantLinear,
create_exllama_buffers,
set_device,
)
HAS_EXLLAMA = "2"
else:
from text_generation_server.utils.gptq.exllama import (
Ex4bitLinear as ExllamaQuantLinear,
create_exllama_buffers,
set_device,
)
HAS_EXLLAMA = "1"
except ImportError:
pass
HAS_EETQ = False
try:
from EETQ import quant_weights, w8_a16_gemm
HAS_EETQ = True
except ImportError:
pass
# Monkey patching
@classmethod
def load_layer_norm(cls, prefix, weights, eps):
weight = weights.get_tensor(f"{prefix}.weight")
bias = weights.get_tensor(f"{prefix}.bias")
with init_empty_weights():
ln = cls(weight.shape, eps=eps)
ln.weight = nn.Parameter(weight)
ln.bias = nn.Parameter(bias)
return ln
@classmethod
def load_layer_norm_no_bias(cls, prefix, weights, eps):
weight = weights.get_tensor(f"{prefix}.weight")
with init_empty_weights():
ln = cls(weight.shape, eps=eps)
ln.weight = nn.Parameter(weight)
ln.bias = None
return ln
@classmethod
def load_conv2d(cls, prefix, weights, in_channels, out_channels, kernel_size, stride):
weight = weights.get_tensor(f"{prefix}.weight")
bias = weights.get_tensor(f"{prefix}.bias")
with init_empty_weights():
conv2d = cls(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=kernel_size,
stride=stride,
)
conv2d.weight = nn.Parameter(weight)
conv2d.bias = nn.Parameter(bias)
return conv2d
@classmethod
def load_conv2d_no_bias(
cls, prefix, weights, in_channels, out_channels, kernel_size, stride
):
weight = weights.get_tensor(f"{prefix}.weight")
with init_empty_weights():
conv2d = cls(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=kernel_size,
stride=stride,
)
conv2d.weight = nn.Parameter(weight)
conv2d.bias = None
return conv2d
torch.nn.Conv2d.load = load_conv2d
torch.nn.Conv2d.load_no_bias = load_conv2d_no_bias
torch.nn.LayerNorm.load = load_layer_norm
torch.nn.LayerNorm.load_no_bias = load_layer_norm_no_bias
class FastLinear(nn.Module):
def __init__(
self,
weight,
bias,
) -> None:
super().__init__()
self.weight = nn.Parameter(weight)
if bias is not None:
self.bias = nn.Parameter(bias)
else:
self.bias = None
@classmethod
def load(cls, config, prefix: str, weights, bias: bool):
weight = weights.get_tensor(f"{prefix}.weight")
if bias:
bias = weights.get_tensor(f"{prefix}.bias")
else:
bias = None
return cls(weight, bias)
def forward(self, input: torch.Tensor) -> torch.Tensor:
return F.linear(input, self.weight, self.bias)
class EETQLinear(nn.Module):
def __init__(
self,
weight,
bias,
) -> None:
super().__init__()
device = weight.device
weight = torch.t(weight).contiguous().cpu()
weight, scale = quant_weights(weight, torch.int8, False)
self.weight = weight.cuda(device)
self.scale = scale.cuda(device)
self.bias = bias.cuda(device) if bias is not None else None
def forward(self, input: torch.Tensor) -> torch.Tensor:
output = w8_a16_gemm(input, self.weight, self.scale)
output = output + self.bias if self.bias is not None else output
return output
class Linear8bitLt(nn.Module):
def __init__(
self,
weight,
bias,
has_fp16_weights=True,
memory_efficient_backward=False,
threshold=0.0,
index=None,
):
super().__init__()
assert (
not memory_efficient_backward
), "memory_efficient_backward is no longer required and the argument is deprecated in 0.37.0 and will be removed in 0.39.0"
self.state = bnb.MatmulLtState()
self.index = index
# Necessary for stacked layers
self.state.threshold = threshold
self.state.has_fp16_weights = has_fp16_weights
self.state.memory_efficient_backward = memory_efficient_backward
if threshold > 0.0 and not has_fp16_weights:
self.state.use_pool = True
self.weight = Int8Params(
weight.data,
has_fp16_weights=has_fp16_weights,
requires_grad=has_fp16_weights,
)
self.weight.cuda(weight.device)
self.bias = bias
def init_8bit_state(self):
self.state.CB = self.weight.CB
self.state.SCB = self.weight.SCB
self.weight.CB = None
self.weight.SCB = None
def forward(self, x: torch.Tensor):
self.state.is_training = self.training
if self.weight.CB is not None:
self.init_8bit_state()
# weights are cast automatically as Int8Params, but the bias has to be cast manually
if self.bias is not None and self.bias.dtype != x.dtype:
self.bias.data = self.bias.data.to(x.dtype)
out = bnb.matmul(x, self.weight, bias=self.bias, state=self.state)
if not self.state.has_fp16_weights:
if self.state.CB is not None and self.state.CxB is not None:
# we converted 8-bit row major to turing/ampere format in the first inference pass
# we no longer need the row-major weight
del self.state.CB
self.weight.data = self.state.CxB
return out
class Linear4bit(nn.Module):
def __init__(self, weight, bias, quant_type):
super().__init__()
self.weight = Params4bit(
weight.data,
requires_grad=False,
compress_statistics=True,
quant_type=quant_type,
)
self.compute_dtype = None
self.weight.cuda(weight.device)
self.bias = bias
def forward(self, x: torch.Tensor):
# weights are cast automatically as Int8Params, but the bias has to be cast manually
if self.bias is not None and self.bias.dtype != x.dtype:
self.bias.data = self.bias.data.to(x.dtype)
if getattr(self.weight, "quant_state", None) is None:
print(
"FP4 quantization state not initialized. Please call .cuda() or .to(device) on the LinearFP4 layer first."
)
inp_dtype = x.dtype
if self.compute_dtype is not None:
x = x.to(self.compute_dtype)
bias = None if self.bias is None else self.bias.to(self.compute_dtype)
out = bnb.matmul_4bit(
x, self.weight.t(), bias=bias, quant_state=self.weight.quant_state
)
out = out.to(inp_dtype)
return out
@lru_cache(1)
def warn_deprecate_bnb():
logger.warning(
"Bitsandbytes 8bit is deprecated, using `eetq` is a drop-in replacement, and has much better performnce"
)
def get_linear(weight, bias, quantize):
if quantize is None:
linear = FastLinear(weight, bias)
elif quantize == "eetq":
if HAS_EETQ:
linear = EETQLinear(weight, bias)
else:
raise ImportError(
"Please install EETQ from https://github.com/NetEase-FuXi/EETQ"
)
elif quantize == "bitsandbytes":
warn_deprecate_bnb()
linear = Linear8bitLt(
weight,
bias,
has_fp16_weights=False,
threshold=6.0,
)
if bias is not None:
linear.bias = nn.Parameter(bias)
elif quantize == "bitsandbytes-fp4":
linear = Linear4bit(
weight,
bias,
quant_type="fp4",
)
elif quantize == "bitsandbytes-nf4":
linear = Linear4bit(
weight,
bias,
quant_type="nf4",
)
elif quantize == "gptq":
try:
qweight, qzeros, scales, g_idx, bits, groupsize, use_exllama = weight
except Exception:
raise NotImplementedError(
f"The passed weight is not `gptq` compatible, loader needs to be updated."
)
if use_exllama:
linear = ExllamaQuantLinear(
qweight, qzeros, scales, g_idx, bias, bits, groupsize
)
else:
linear = QuantLinear(
qweight,
qzeros,
scales,
g_idx,
bias,
bits,
groupsize,
)
elif quantize == "awq":
try:
qweight, qzeros, scales, _, bits, groupsize, _ = weight
except Exception:
raise NotImplementedError(
f"The passed weight is not `awq` compatible, loader needs to be updated."
)
linear = WQLinear(
w_bit=bits,
group_size=groupsize,
qweight=qweight,
qzeros=qzeros,
scales=scales,
bias=bias is not None,
)
else:
raise NotImplementedError(f"Quantization `{quantize}` is not implemented yet.")
return linear
class SuperLayer(nn.Module):
def __init__(self, linear):
super().__init__()
self.linear = linear
def forward(self, x):
return self.linear.forward(x)
class TensorParallelHead(SuperLayer):
def __init__(self, linear, process_group, should_gather: bool):
super().__init__(linear)
self.process_group = process_group
self.should_gather = should_gather
@staticmethod
def load(config, prefix: str, weights):
if weights.process_group.size() > 1:
try:
weight = weights.get_sharded(f"{prefix}.weight", dim=0)
should_gather = True
except AssertionError:
# If the vocab size is not divisible by number of shards
# just load the entire thing.
weight = weights.get_tensor(f"{prefix}.weight")
should_gather = False
else:
weight = weights.get_tensor(f"{prefix}.weight")
should_gather = False
# GPTQ,AWQ,EETQ don't quantize heads (nor embeddings)
if config.quantize in ["gptq", "awq", "eetq"]:
quantize = None
else:
quantize = config.quantize
return TensorParallelHead(
get_linear(weight, bias=None, quantize=quantize),
process_group=weights.process_group,
should_gather=should_gather,
)
def forward(self, input: torch.Tensor) -> torch.Tensor:
if not self.should_gather:
return super().forward(input)
world_size = self.process_group.size()
if len(input.shape) == 2 and isinstance(self.linear, FastLinear):
out_dim = self.linear.weight.shape[0]
if input.shape[0] == 1:
world_out = input.new_empty(1, out_dim * world_size)
local_out = input.new_empty(1, out_dim)
gather_input = local_out
else:
world_out = input.new_empty(out_dim * world_size, input.shape[0])
gather_input = input.new_empty(out_dim, input.shape[0])
local_out = gather_input.T
torch.mm(input, self.linear.weight.T, out=local_out)
torch.distributed.all_gather_into_tensor(
world_out, gather_input, group=self.process_group
)
if input.shape[0] == 1:
return world_out
return world_out.T
output = super().forward(input)
world_output = [
torch.empty_like(output) for _ in range(self.process_group.size())
]
torch.distributed.all_gather(world_output, output, group=self.process_group)
world_output = torch.cat(world_output, dim=-1)
return world_output
class TensorParallelColumnLinear(SuperLayer):
@classmethod
def load_qkv(cls, config, prefix: str, weights, bias: bool):
"""Specific method when the QKV was joined after the fact"""
weight = weights.get_weights_col_packed_qkv(prefix, quantize=config.quantize)
if bias:
raise NotImplementedError("packed_qkv only implemented for baichuan")
else:
bias = None
linear = get_linear(weight, bias, config.quantize)
return cls(linear)
@classmethod
def load(cls, config, prefix: str, weights, bias: bool):
return cls.load_multi(config, [prefix], weights, bias, dim=0)
@classmethod
def load_multi(cls, config, prefixes: List[str], weights, bias: bool, dim: int):
weight = weights.get_multi_weights_col(
prefixes, quantize=config.quantize, dim=dim
)
if bias:
b = [weights.get_sharded(f"{p}.bias", dim=0) for p in prefixes]
bias = torch.cat(b, dim=dim)
else:
bias = None
linear = get_linear(weight, bias, config.quantize)
return cls(linear)
class TensorParallelRowLinear(SuperLayer):
def __init__(self, linear, process_group):
super().__init__(linear)
self.process_group = process_group
@classmethod
def load(cls, config, prefix: str, weights, bias: bool):
weight = weights.get_multi_weights_row(prefix, quantize=config.quantize)
if bias and weights.process_group.rank() == 0:
# Rank is only on the first rank process
bias = weights.get_tensor(f"{prefix}.bias")
else:
bias = None
return cls(
get_linear(weight, bias, config.quantize),
process_group=weights.process_group,
)
def forward(self, input: torch.Tensor, reduce: bool = True) -> torch.Tensor:
out = super().forward(input)
if self.process_group.size() > 1 and reduce:
torch.distributed.all_reduce(out, group=self.process_group)
return out
class TensorParallelEmbedding(nn.Module):
def __init__(self, prefix: str, weights, reduce=True):
super().__init__()
weight = weights.get_partial_sharded(f"{prefix}.weight", dim=0)
num_embeddings = weights.get_shape(f"{prefix}.weight")[0]
process_group = weights.process_group
world_size = process_group.size()
rank = process_group.rank()
block_size = num_embeddings // world_size
self.min_id = rank * block_size
self.max_id = min(num_embeddings, (rank + 1) * block_size)
self.null_idx = block_size
self.process_group = weights.process_group
self.reduce = reduce
"""Additional 0 entry used for masking"""
self.weight = nn.Parameter(F.pad(weight, (0, 0, 0, 1)))
def forward(self, input: torch.Tensor) -> torch.Tensor:
# default all out of bounds values to `self.null_idx` that will then be mapped to 0
# translate for [0, self.max_id - self.min_id[
input = torch.where(
(self.min_id > input) | (input >= self.max_id),
self.null_idx,
input - self.min_id,
)
out = torch.nn.functional.embedding(input, self.weight)
if self.reduce and self.process_group.size() > 1:
torch.distributed.all_reduce(out, group=self.process_group)
return out
try:
if IS_CUDA_SYSTEM:
import dropout_layer_norm
elif IS_ROCM_SYSTEM:
from vllm import layernorm_ops
else:
dropout_layer_norm = None
class FastLayerNorm(nn.LayerNorm):
def forward(self, hidden_states, residual=None):
if hidden_states.shape[-1] > 8192 or IS_ROCM_SYSTEM:
if residual is not None:
hidden_states += residual
residual = hidden_states
return super(FastLayerNorm, self).forward(hidden_states), residual
else:
(
normed_hidden_states,
residual,
*rest,
) = dropout_layer_norm.dropout_add_ln_fwd(
hidden_states,
residual,
self.weight,
self.bias,
None,
None,
None,
None,
0.0,
self.eps,
1.0,
0,
None,
False,
False,
)
if residual is None:
residual = hidden_states
return normed_hidden_states, residual
class FastRMSNorm(nn.Module):
def __init__(self, weight: torch.Tensor, eps: float):
super().__init__()
self.weight = nn.Parameter(weight)
self.variance_epsilon = eps
@classmethod
def load(cls, prefix, weights, eps=1e-6):
weight = weights.get_tensor(f"{prefix}.weight")
return cls(weight, eps)
def forward(self, hidden_states, residual=None):
if hidden_states.shape[-1] > 8192:
if residual is not None:
hidden_states += residual
residual = hidden_states
hidden_states = hidden_states.to(torch.float32)
variance = hidden_states.pow(2).mean(-1, keepdim=True)
hidden_states = hidden_states * torch.rsqrt(
variance + self.variance_epsilon
)
# convert into half-precision if necessary
if self.weight.dtype in [torch.float16, torch.bfloat16]:
hidden_states = hidden_states.to(self.weight.dtype)
return self.weight * hidden_states, residual
elif IS_CUDA_SYSTEM:
# faster post attention rms norm
(
normed_hidden_states,
res,
*rest,
) = dropout_layer_norm.dropout_add_ln_fwd(
hidden_states,
residual,
self.weight,
None,
None,
None,
None,
None,
0.0,
self.variance_epsilon,
1.0,
0,
None,
False,
True, # Activate RMSNorm
)
if res is None:
res = hidden_states
return normed_hidden_states, res
elif IS_ROCM_SYSTEM:
# We use VLLM RMSNorm kernel that can be compiled for RoCm, instead of Flash Attention ones that can not.
if residual is not None:
hidden_states += residual
residual = hidden_states
out = torch.empty_like(hidden_states)
layernorm_ops.rms_norm(
out,
hidden_states,
self.weight.data,
self.variance_epsilon,
)
return out, residual
else:
raise ValueError(
"Your system seem to be not supported. Please check your install or open an issue at https://github.com/huggingface/text-generation-inference/issues with a clear reproduction."
)
except ImportError:
pass
try:
if IS_CUDA_SYSTEM:
from flash_attn.layers.rotary import RotaryEmbedding
import rotary_emb
elif IS_ROCM_SYSTEM:
from vllm import pos_encoding_ops
def _create_inv_freq(dim, base, device):
inv_freq = 1.0 / (
base ** (torch.arange(0, dim, 2, device=device, dtype=torch.float32) / dim)
)
return inv_freq
def _get_rope_config(config):
if os.getenv("ROPE_SCALING", None) is not None:
rope_scaling = {
"type": os.environ["ROPE_SCALING"],
"factor": float(os.environ["ROPE_FACTOR"]),
}
return rope_scaling
return getattr(config, "rope_scaling", None)
class PositionRotaryEmbedding(nn.Module):
def __init__(self, inv_freq, scaling_factor):
super().__init__()
self.inv_freq = inv_freq
self._seq_len_cached = 0
self._cos_cached = None
self._sin_cached = None
self._cos_k_cached = None
self._sin_k_cached = None
self.scaling_factor = scaling_factor
self.dynamic_args = None
def forward(
self,
query: torch.Tensor,
key: torch.Tensor,
cos: torch.Tensor,
sin: torch.Tensor,
):
# Such controlflows may add some overhead.
if IS_CUDA_SYSTEM:
rotary_dim = cos.shape[-1]
q1 = query[..., :rotary_dim]
q2 = query[..., rotary_dim : 2 * rotary_dim]
rotary_emb.apply_rotary(q1, q2, cos, sin, q1, q2, False)
k1 = key[..., :rotary_dim]
k2 = key[..., rotary_dim : 2 * rotary_dim]
rotary_emb.apply_rotary(k1, k2, cos, sin, k1, k2, False)
elif IS_ROCM_SYSTEM:
# NOTE: On RoCm systems, we use a ROPE implementatation adapted from VLLM which launches a single kernel for both query/key, contrary to flash-attn implementation used on NVIDIA systems.
# Compiling flash-attn rotary on RoCm, it appears hipcc is unable to unroll loops, resulting in an even slower inference compared to eager: https://github.com/pytorch/pytorch/issues/113773
head_size = query.shape[-1]
# Inplace operation, updating query and key.
pos_encoding_ops.rotary_embedding(query, key, head_size, cos, sin, True)
else:
raise ValueError(
"Your system seem to be not supported. Please check your install or open an issue at https://github.com/huggingface/text-generation-inference/issues with a clear reproduction."
)
@classmethod
def static(cls, config, dim, base, device):
inv_freq = _create_inv_freq(dim, base, device)
scaling_factor = None
rope_scaling = _get_rope_config(config)
if rope_scaling is not None:
scaling_factor = rope_scaling["factor"]
if rope_scaling["type"] == "linear":
pass
elif rope_scaling["type"] == "dynamic":
return DynamicPositionRotaryEmbedding(
dim=dim,
max_position_embeddings=config.max_position_embeddings,
base=base,
device=inv_freq.device,
scaling_factor=scaling_factor,
)
elif rope_scaling["type"] == "yarn":
return YarnPositionRotaryEmbedding(
dim=2 * inv_freq.shape[0],
max_position_embeddings=rope_scaling[
"original_max_position_embeddings"
],
base=10000.0,
device=inv_freq.device,
scaling_factor=scaling_factor,
extrapolation_factor=1,
attn_factor=1,
beta_fast=32,
beta_slow=1,
)
else:
raise NotImplementedError(
f"rope scaling type {rope_scaling['type']} is not implemented or invalid"
)
return cls(inv_freq, scaling_factor)
@classmethod
def load(cls, config, prefix, weights):
# XXX: Always load this in float32 !
dtype = weights.dtype
weights.dtype = torch.float32
inv_freq = weights.get_tensor(f"{prefix}.inv_freq")
weights.dtype = dtype
scaling_factor = None
rope_scaling = _get_rope_config(config)
if rope_scaling is not None:
scaling_factor = rope_scaling["factor"]
if rope_scaling["type"] == "linear":
pass
elif rope_scaling["type"] == "dynamic":
return DynamicPositionRotaryEmbedding(
dim=2 * inv_freq.shape[0],
max_position_embeddings=config.max_position_embeddings,
base=10000.0,
device=inv_freq.device,
scaling_factor=scaling_factor,
)
elif rope_scaling["type"] == "yarn":
return YarnPositionRotaryEmbedding(
dim=2 * inv_freq.shape[0],
max_position_embeddings=rope_scaling[
"original_max_position_embeddings"
],
base=10000.0,
device=inv_freq.device,
scaling_factor=scaling_factor,
extrapolation_factor=1,
attn_factor=1,
beta_fast=32,
beta_slow=1,
)
else:
raise NotImplementedError(
f"rope scaling type {rope_scaling['type']} is not implemented or invalid"
)
return cls(inv_freq, scaling_factor)
def _update_cos_sin_cache(self, dtype, device, seqlen):
# Reset the tables if the sequence length has changed,
# or if we're on a new device (possibly due to tracing for instance)
if (
seqlen > self._seq_len_cached
or self._cos_cached.device != device
or self._cos_cached.dtype != dtype
):
self._seq_len_cached = seqlen
t = torch.arange(seqlen, device=device, dtype=self.inv_freq.dtype)
if self.scaling_factor is not None:
t /= self.scaling_factor
# Don't do einsum, it converts fp32 to fp16
# freqs = torch.einsum("i,j->ij", t, self.inv_freq)
freqs = torch.outer(t, self.inv_freq.to(device=t.device))
self._cos_cached = torch.cos(freqs).to(dtype)
self._sin_cached = torch.sin(freqs).to(dtype)
def get_cos_sin(
self, position_ids: torch.Tensor, max_s: int, dtype: torch.dtype
):
"""
Return cos and sin for the asked position ids
"""
if IS_ROCM_SYSTEM:
# For RoCm, we always use float cos/sin to avoid a cast.
# For NVIDIA, for some reason, the flash-attn rotary kernel requires cos/sin and query/key to be of same dtype: https://github.com/Dao-AILab/flash-attention/blob/017716451d446e464dde9aca3a3c1ed2209caaa9/csrc/rotary/rotary.cpp#L26
# But later on goes and cast cos/sin to float anyway: https://github.com/Dao-AILab/flash-attention/blob/017716451d446e464dde9aca3a3c1ed2209caaa9/csrc/rotary/rotary_cuda.cu#L29, which looks suboptimal.
dtype = torch.float32
self._update_cos_sin_cache(dtype, position_ids.device, max_s)
cos = torch.index_select(self._cos_cached, 0, position_ids)
sin = torch.index_select(self._sin_cached, 0, position_ids)
# Note: this unsqueeze is not necessary on RoCm + VLLM ROPE implementation, but we leave it as is to avoid yet an other controlflow.
return cos.unsqueeze(1), sin.unsqueeze(1)
class DynamicPositionRotaryEmbedding(PositionRotaryEmbedding):
def __init__(self, dim, max_position_embeddings, base, device, scaling_factor):
inv_freq = _create_inv_freq(dim, base, device)
super().__init__(inv_freq, scaling_factor)
self.dim = dim
self.max_position_embeddings = max_position_embeddings
self.base = base
def _update_cos_sin_cache(self, dtype, device, seqlen):
# Reset the tables if the sequence length has changed,
# or if we're on a new device (possibly due to tracing for instance)
if (
seqlen > self._seq_len_cached
or self._cos_cached.device != device
or self._cos_cached.dtype != dtype
):
if seqlen > self.max_position_embeddings:
newbase = self.base * (
(self.scaling_factor * seqlen / self.max_position_embeddings)
- (self.scaling_factor - 1)
) ** (self.dim / (self.dim - 2))
self.inv_freq = _create_inv_freq(
self.dim, newbase, self.inv_freq.device
)
self._seq_len_cached = seqlen
t = torch.arange(seqlen, device=device, dtype=self.inv_freq.dtype)
# Don't do einsum, it converts fp32 to fp16
# freqs = torch.einsum("i,j->ij", t, self.inv_freq)
freqs = torch.outer(t, self.inv_freq.to(device=t.device))
self._cos_cached = torch.cos(freqs).to(dtype)
self._sin_cached = torch.sin(freqs).to(dtype)
# Inverse dim formula to find dim based on number of rotations
import math
def find_correction_dim(
num_rotations, dim, base=10000, max_position_embeddings=2048
):
return (
dim * math.log(max_position_embeddings / (num_rotations * 2 * math.pi))
) / (2 * math.log(base))
# Find dim range bounds based on rotations
def find_correction_range(
low_rot, high_rot, dim, base=10000, max_position_embeddings=2048
):
low = math.floor(
find_correction_dim(low_rot, dim, base, max_position_embeddings)
)
high = math.ceil(
find_correction_dim(high_rot, dim, base, max_position_embeddings)
)
return max(low, 0), min(high, dim - 1) # Clamp values just in case
def linear_ramp_mask(min, max, dim):
if min == max:
max += 0.001 # Prevent singularity
linear_func = (torch.arange(dim, dtype=torch.float32) - min) / (max - min)
ramp_func = torch.clamp(linear_func, 0, 1)
return ramp_func
def get_mscale(scale=1):
if scale <= 1:
return 1.0
return 0.1 * math.log(scale) + 1.0
class YarnPositionRotaryEmbedding(PositionRotaryEmbedding):
def __init__(
self,
dim,
max_position_embeddings,
base,
device,
scaling_factor,
*,
extrapolation_factor,
attn_factor,
beta_fast,
beta_slow,
):
inv_freq = _create_inv_freq(dim, base, device)
super().__init__(inv_freq, scaling_factor)
self.dim = dim
self.max_position_embeddings = max_position_embeddings
self.base = base
self.extrapolation_factor = extrapolation_factor
self.attn_factor = attn_factor
self.beta_fast = beta_fast
self.beta_slow = beta_slow
self.mscale = float(
get_mscale(self.scaling_factor) * self.attn_factor
) # Get n-d magnitude scaling corrected for interpolation
def _update_cos_sin_cache(self, dtype, device, seqlen):
# Reset the tables if the sequence length has changed,
# or if we're on a new device (possibly due to tracing for instance)
if (
seqlen > self._seq_len_cached
or self._cos_cached.device != device
or self._cos_cached.dtype != dtype
):
if seqlen > self.max_position_embeddings:
inv_freq_extrapolation = _create_inv_freq(
self.dim, self.base, self.inv_freq.device
)
freqs = 1.0 / inv_freq_extrapolation
inv_freq_interpolation = 1.0 / (self.scaling_factor * freqs)
low, high = find_correction_range(
self.beta_fast,
self.beta_slow,
self.dim,
self.base,
self.max_position_embeddings,
)
inv_freq_mask = (
1
- linear_ramp_mask(low, high, self.dim // 2).float().to(device)
) * self.extrapolation_factor # Get n-d rotational scaling corrected for extrapolation
inv_freq = (
inv_freq_interpolation * (1 - inv_freq_mask)
+ inv_freq_extrapolation * inv_freq_mask
)
self.inv_freq = inv_freq
self.mscale = float(
get_mscale(self.scaling_factor) * self.attn_factor
) # Get n-d magnitude scaling corrected for interpolation
self._seq_len_cached = seqlen
t = torch.arange(seqlen, device=device, dtype=self.inv_freq.dtype)
# Don't do einsum, it converts fp32 to fp16
# freqs = torch.einsum("i,j->ij", t, self.inv_freq)
freqs = torch.outer(t, self.inv_freq.to(device=t.device))
self._cos_cached = (torch.cos(freqs) * self.mscale).to(dtype)
self._sin_cached = (torch.sin(freqs) * self.mscale).to(dtype)
except ImportError:
pass
| 0 |
hf_public_repos/text-generation-inference/server/text_generation_server | hf_public_repos/text-generation-inference/server/text_generation_server/utils/convert.py | import datetime
import torch
import os
from loguru import logger
from pathlib import Path
from safetensors.torch import save_file, load_file, _find_shared_tensors, _is_complete
from typing import List, Dict
from collections import defaultdict
def _remove_duplicate_names(
state_dict: Dict[str, torch.Tensor],
*,
preferred_names: List[str] = None,
discard_names: List[str] = None,
) -> Dict[str, List[str]]:
if preferred_names is None:
preferred_names = []
preferred_names = set(preferred_names)
if discard_names is None:
discard_names = []
discard_names = set(discard_names)
shareds = _find_shared_tensors(state_dict)
to_remove = defaultdict(list)
for shared in shareds:
complete_names = set(
[name for name in shared if _is_complete(state_dict[name])]
)
if not complete_names:
if len(shared) == 1:
# Force contiguous
name = list(shared)[0]
state_dict[name] = state_dict[name].clone()
complete_names = {name}
else:
raise RuntimeError(
f"Error while trying to find names to remove to save state dict, but found no suitable name to keep for saving amongst: {shared}. None is covering the entire storage.Refusing to save/load the model since you could be storing much more memory than needed. Please refer to https://huggingface.co/docs/safetensors/torch_shared_tensors for more information. Or open an issue."
)
keep_name = sorted(list(complete_names))[0]
# Mecanism to preferentially select keys to keep
# coming from the on-disk file to allow
# loading models saved with a different choice
# of keep_name
preferred = complete_names.difference(discard_names)
if preferred:
keep_name = sorted(list(preferred))[0]
if preferred_names:
preferred = preferred_names.intersection(complete_names)
if preferred:
keep_name = sorted(list(preferred))[0]
for name in sorted(shared):
if name != keep_name:
to_remove[keep_name].append(name)
return to_remove
def convert_file(pt_file: Path, sf_file: Path, discard_names: List[str]):
"""
Convert a pytorch file to a safetensors file
This will remove duplicate tensors from the file.
Unfortunately, this might not respect *transformers* convention.
Forcing us to check for potentially different keys during load when looking
for specific tensors (making tensor sharing explicit).
"""
loaded = torch.load(pt_file, map_location="cpu")
if "state_dict" in loaded:
loaded = loaded["state_dict"]
to_removes = _remove_duplicate_names(loaded, discard_names=discard_names)
metadata = {"format": "pt"}
for kept_name, to_remove_group in to_removes.items():
for to_remove in to_remove_group:
if to_remove not in metadata:
metadata[to_remove] = kept_name
del loaded[to_remove]
# Force tensors to be contiguous
loaded = {k: v.contiguous() for k, v in loaded.items()}
dirname = os.path.dirname(sf_file)
os.makedirs(dirname, exist_ok=True)
save_file(loaded, sf_file, metadata=metadata)
reloaded = load_file(sf_file)
for k in loaded:
pt_tensor = loaded[k]
sf_tensor = reloaded[k]
if not torch.equal(pt_tensor, sf_tensor):
raise RuntimeError(f"The output tensors do not match for key {k}")
def convert_files(pt_files: List[Path], sf_files: List[Path], discard_names: List[str]):
assert len(pt_files) == len(sf_files)
N = len(pt_files)
# We do this instead of using tqdm because we want to parse the logs with the launcher
for i, (pt_file, sf_file) in enumerate(zip(pt_files, sf_files)):
# Skip blacklisted files
if (
"arguments" in pt_file.name
or "args" in pt_file.name
or "training" in pt_file.name
):
continue
start = datetime.datetime.now()
convert_file(pt_file, sf_file, discard_names)
elapsed = datetime.datetime.now() - start
logger.info(f"Convert: [{i + 1}/{N}] -- Took: {elapsed}")
| 0 |
hf_public_repos/text-generation-inference/server/text_generation_server | hf_public_repos/text-generation-inference/server/text_generation_server/utils/tokens.py | import re
from typing import Callable, List, Optional, Tuple
import torch
from text_generation_server.pb import generate_pb2
from text_generation_server.pb.generate_pb2 import FinishReason
from text_generation_server.utils.logits_process import (
HeterogeneousProcessorWrapper,
HeterogeneousRepetitionPenaltyLogitsProcessor,
HeterogeneousTemperatureLogitsWarper,
HeterogeneousTopKLogitsWarper,
HeterogeneousTopPLogitsWarper,
HeterogeneousTypicalLogitsWarper,
static_warper,
)
from text_generation_server.utils.watermark import WatermarkLogitsProcessor
from transformers import PreTrainedTokenizerBase, RepetitionPenaltyLogitsProcessor
class NextTokenChooser:
def __init__(
self,
watermark=False,
temperature=1.0,
repetition_penalty=1.0,
top_k=None,
top_p=None,
typical_p=None,
do_sample=False,
seed=0,
device="cpu",
):
self.watermark_processor = (
WatermarkLogitsProcessor(device=device) if watermark else None
)
self.repetition_processor = (
RepetitionPenaltyLogitsProcessor(penalty=repetition_penalty)
if repetition_penalty
else None
)
has_warpers = (
(temperature is not None and temperature != 1.0)
or (top_k is not None and top_k != 0)
or (top_p is not None and top_p < 1.0)
or (typical_p is not None and typical_p < 1.0)
)
if has_warpers:
self.static_warper = static_warper(
temperature=temperature, top_k=top_k, top_p=top_p, typical_p=typical_p
)
else:
self.static_warper = None
sampling = do_sample or has_warpers
self.choice = Sampling(seed, device) if sampling else Greedy()
def __call__(self, input_ids, scores):
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)
if self.static_warper is None:
next_logprob = torch.log_softmax(scores, -1)
else:
scores, next_logprob = self.static_warper(scores)
next_id = self.choice(scores[-1]).view(1, 1)
return next_id, next_logprob
@classmethod
def from_pb(
cls,
pb: generate_pb2.NextTokenChooserParameters,
device: torch.device,
) -> "NextTokenChooser":
return NextTokenChooser(
watermark=pb.watermark,
temperature=pb.temperature,
repetition_penalty=pb.repetition_penalty,
top_k=pb.top_k,
top_p=pb.top_p,
typical_p=pb.typical_p,
do_sample=pb.do_sample,
seed=pb.seed,
device=device,
)
class StopSequenceCriteria:
def __init__(self, stop_sequence: str):
stop_sequence = re.escape(stop_sequence)
self.regex = re.compile(f"{stop_sequence}$")
def __call__(self, output: str) -> bool:
if self.regex.findall(output):
return True
return False
class StoppingCriteria:
def __init__(
self,
eos_token_id: int,
stop_sequence_criterias: List[StopSequenceCriteria],
max_new_tokens: int = 20,
ignore_eos_token: bool = False,
):
self.eos_token_id = eos_token_id
self.stop_sequence_criterias = stop_sequence_criterias
self.max_new_tokens = max_new_tokens
self.current_tokens = 0
self.current_output = ""
self.ignore_eos_token = ignore_eos_token
def __call__(self, last_token: int, last_output: str) -> Tuple[bool, Optional[str]]:
self.current_tokens += 1
if self.current_tokens >= self.max_new_tokens:
return True, FinishReason.FINISH_REASON_LENGTH
if not self.ignore_eos_token and last_token == self.eos_token_id:
return True, FinishReason.FINISH_REASON_EOS_TOKEN
if self.stop_sequence_criterias:
self.current_output += last_output
# There is no need to keep an output that is too long
if len(self.current_output) > 300:
# Slice to -200 to avoid doing it all the time
self.current_output = self.current_output[-200:]
for stop_sequence_criteria in self.stop_sequence_criterias:
if stop_sequence_criteria(self.current_output):
return True, FinishReason.FINISH_REASON_STOP_SEQUENCE
return False, None
@classmethod
def from_pb(
cls,
pb: generate_pb2.StoppingCriteriaParameters,
tokenizer: PreTrainedTokenizerBase,
) -> "StoppingCriteria":
stop_sequence_criterias = [
StopSequenceCriteria(sequence) for sequence in pb.stop_sequences
]
return StoppingCriteria(
tokenizer.eos_token_id,
stop_sequence_criterias,
pb.max_new_tokens,
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__(
self,
dtype: torch.dtype,
device: torch.device,
watermark: List[bool],
temperature: List[float],
repetition_penalty: List[float],
top_k: List[int],
top_p: List[float],
typical_p: List[float],
do_sample: List[bool],
seeds: List[int],
):
warpers = []
self.watermark_processor = (
HeterogeneousProcessorWrapper(
{
i: WatermarkLogitsProcessor(device=device)
for i, do_watermark in enumerate(watermark)
if do_watermark
}
)
if any(watermark)
else None
)
self.repetition_processor = (
HeterogeneousRepetitionPenaltyLogitsProcessor(
repetition_penalty, dtype, device
)
if any([x != 1.0 for x in repetition_penalty])
else None
)
if any([x != 1.0 for x in temperature]):
do_sample = [
sample or x != 1.0 for x, sample in zip(temperature, do_sample)
]
warpers.append(
HeterogeneousTemperatureLogitsWarper(temperature, dtype, device)
)
if any([x != 0 for x in top_k]):
do_sample = [sample or x != 0 for x, sample in zip(top_k, do_sample)]
warpers.append(HeterogeneousTopKLogitsWarper(top_k, device))
if any([x < 1.0 for x in top_p]):
do_sample = [sample or x < 1.0 for x, sample in zip(top_p, do_sample)]
warpers.append(HeterogeneousTopPLogitsWarper(top_p, dtype, device))
if any([x < 1.0 for x in typical_p]):
do_sample = [sample or x < 1.0 for x, sample in zip(typical_p, do_sample)]
warpers.append(HeterogeneousTypicalLogitsWarper(typical_p, dtype, device))
self.warpers = warpers
if any(do_sample):
self.choice = HeterogeneousSampling(do_sample, seeds, device)
else:
self.choice = Greedy()
self.seeds = seeds
self.do_sample = do_sample
self.dtype = dtype
self.device = device
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)
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)
logprobs = torch.log_softmax(scores, -1)
next_logprobs = torch.gather(logprobs, 1, next_ids.view(-1, 1)).view(-1)
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:
self.watermark_processor = self.watermark_processor.filter(indices)
if self.repetition_processor is not None:
self.repetition_processor = self.repetition_processor.filter(indices)
filtered_warpers = []
for warper in self.warpers:
filtered_warper = warper.filter(indices)
if filtered_warper is not None:
filtered_warpers.append(filtered_warper)
self.warpers = filtered_warpers
self.seeds = [self.seeds[i] for i in indices]
self.do_sample = [self.do_sample[i] for i in indices]
if any(self.do_sample):
self.choice.filter(indices)
else:
self.choice = Greedy()
return self
@classmethod
def from_pb(
cls,
pb: List[generate_pb2.NextTokenChooserParameters],
dtype: torch.dtype,
device: torch.device,
) -> "HeterogeneousNextTokenChooser":
return HeterogeneousNextTokenChooser(
watermark=[pb_.watermark for pb_ in pb],
temperature=[pb_.temperature for pb_ in pb],
repetition_penalty=[pb_.repetition_penalty for pb_ in pb],
top_k=[pb_.top_k for pb_ in pb],
top_p=[pb_.top_p for pb_ in pb],
typical_p=[pb_.typical_p for pb_ in pb],
do_sample=[pb_.do_sample for pb_ in pb],
seeds=[pb_.seed for pb_ in pb],
device=device,
dtype=dtype,
)
class Sampling:
def __init__(self, seed: int, device: str = "cpu"):
self.generator = torch.Generator(device)
self.generator.manual_seed(seed)
self.seed = seed
def __call__(self, logits):
probs = torch.nn.functional.softmax(logits, -1)
# Avoid GPU<->CPU sync done by torch multinomial
# See: https://github.com/pytorch/pytorch/blob/925a3788ec5c06db62ca732a0e9425a26a00916f/aten/src/ATen/native/Distributions.cpp#L631-L637
q = torch.empty_like(probs).exponential_(1, generator=self.generator)
return probs.div_(q).argmax()
class Greedy:
def __call__(self, logits):
return logits.argmax(dim=-1)
class HeterogeneousSampling:
r"""
Mixed greedy and probabilistic sampling. Compute both and pick the right one for each sample.
"""
def __init__(self, do_sample: List[bool], seeds: List[int], device: torch.device):
self.seeds = seeds
self.greedy_indices = []
self.sampling_mapping = {}
for i, (sample, seed) in enumerate(zip(do_sample, seeds)):
if sample:
self.sampling_mapping[i] = Sampling(seed, device)
else:
self.greedy_indices.append(i)
self.greedy = Greedy()
def __call__(self, logits):
out = torch.empty(logits.shape[0], dtype=torch.int64, device=logits.device)
if self.greedy_indices:
# Computing for all indices is faster than slicing
torch.argmax(logits, -1, out=out)
for i, sampling in self.sampling_mapping.items():
out[i] = sampling(logits[i])
return out
def filter(self, indices):
new_greedy_indices = []
new_sampling_mapping = {}
for i, idx in enumerate(indices):
if idx in self.sampling_mapping:
new_sampling_mapping[i] = self.sampling_mapping[idx]
else:
new_greedy_indices.append(i)
self.greedy_indices = new_greedy_indices
self.sampling_mapping = new_sampling_mapping
return self
def batch_top_tokens(
top_n_tokens: List[int], top_n_tokens_tensor: torch.Tensor, logprobs: torch.Tensor
) -> Tuple[List[List[int]], List[List[float]]]:
"""Find the top n most likely tokens for a batch of generations.
When multiple tokens have equal probabilities and they don't all fit, the
remaining tokens are also returned.
"""
max_top_n = max(top_n_tokens)
# Early exit when top_n_tokens is not used
if max_top_n == 0:
return [[]] * len(top_n_tokens), [[]] * len(top_n_tokens)
# Ensure top_n doesn't exceed vocab size
top_n_tokens = [min(tok, logprobs.size(-1)) for tok in top_n_tokens]
# Parallel kthvalue adapted from https://discuss.pytorch.org/t/how-to-efficiently-get-the-k-th-largest-values-in-parallel/160529/2
# Sorted topk is faster than torch.sort() since we only need a small subset
sorted_top_k = torch.topk(logprobs, k=max_top_n, dim=1, sorted=True).values
nth_highest = torch.gather(
sorted_top_k, 1, (top_n_tokens_tensor - 1).clip(min=0).unsqueeze(1)
)
nth_highest[nth_highest == -float("inf")] = torch.finfo(logprobs.dtype).min
# Find the new "fuzzy" top n values
top_n_indices = (logprobs >= nth_highest).nonzero()
_, top_n_ishes = torch.unique_consecutive(top_n_indices[:, 0], return_counts=True)
k = 1 if top_n_ishes.numel() == 0 else top_n_ishes.max()
# Take a new topk for these new max n values
top_k = torch.topk(logprobs, k=k, dim=1, sorted=True)
top_n_ishes = top_n_ishes.tolist()
top_indices = top_k.indices.tolist()
top_values = top_k.values.tolist()
return (
[
idxs[:n] if req_n > 0 else []
for idxs, n, req_n in zip(top_indices, top_n_ishes, top_n_tokens)
],
[
vals[:n] if req_n > 0 else []
for vals, n, req_n in zip(top_values, top_n_ishes, top_n_tokens)
],
)
| 0 |
hf_public_repos/text-generation-inference/server/text_generation_server | hf_public_repos/text-generation-inference/server/text_generation_server/utils/peft.py | import os
import json
from loguru import logger
import torch
from transformers import AutoTokenizer
from peft import AutoPeftModelForCausalLM, AutoPeftModelForSeq2SeqLM
def download_and_unload_peft(model_id, revision, trust_remote_code):
torch_dtype = torch.float16
logger.info("Trying to load a Peft model. It might take a while without feedback")
try:
model = AutoPeftModelForCausalLM.from_pretrained(
model_id,
revision=revision,
torch_dtype=torch_dtype,
trust_remote_code=trust_remote_code,
low_cpu_mem_usage=True,
)
except Exception:
model = AutoPeftModelForSeq2SeqLM.from_pretrained(
model_id,
revision=revision,
torch_dtype=torch_dtype,
trust_remote_code=trust_remote_code,
low_cpu_mem_usage=True,
)
logger.info("Peft model detected.")
logger.info(f"Merging the lora weights.")
base_model_id = model.peft_config["default"].base_model_name_or_path
model = model.merge_and_unload()
os.makedirs(model_id, exist_ok=True)
cache_dir = model_id
logger.info(f"Saving the newly created merged model to {cache_dir}")
tokenizer = AutoTokenizer.from_pretrained(
base_model_id, trust_remote_code=trust_remote_code
)
model.save_pretrained(cache_dir, safe_serialization=True)
model.config.save_pretrained(cache_dir)
tokenizer.save_pretrained(cache_dir)
| 0 |
hf_public_repos/text-generation-inference/server/text_generation_server | hf_public_repos/text-generation-inference/server/text_generation_server/utils/log.py | from functools import lru_cache
@lru_cache(10)
def log_once(log, msg: str):
log(msg)
| 0 |
hf_public_repos/text-generation-inference/server/text_generation_server | hf_public_repos/text-generation-inference/server/text_generation_server/utils/speculate.py | SPECULATE = None
def get_speculate() -> int:
global SPECULATE
return SPECULATE
def set_speculate(speculate: int):
global SPECULATE
SPECULATE = speculate
| 0 |
hf_public_repos/text-generation-inference/server/text_generation_server/utils/awq | hf_public_repos/text-generation-inference/server/text_generation_server/utils/awq/quantize/qmodule.py | # Copied logic from https://github.com/mit-han-lab/llm-awq/blob/f084f40bd996f3cf3a0633c1ad7d9d476c318aaa/awq/quantize/qmodule.py
import math
import torch
import torch.nn as nn
import awq_inference_engine # with CUDA kernels
# class ScaledActivation(nn.Module):
# def __init__(self, module, scales):
# super().__init__()
# self.act = module
# self.scales = nn.Parameter(scales.data)
#
# def forward(self, x):
# return self.act(x) / self.scales.view(1, 1, -1).to(x.device)
class WQLinear(nn.Module):
def __init__(self, w_bit, group_size, qweight, qzeros, scales, bias):
super().__init__()
if w_bit not in [4]:
raise NotImplementedError("Only 4-bit are supported for now.")
self.in_features = qweight.shape[0]
self.out_features = qweight.shape[1] * 32 // w_bit
self.w_bit = w_bit
self.group_size = group_size if group_size != -1 else self.in_features
# quick sanity check (make sure aligment)
assert self.in_features % self.group_size == 0
assert self.out_features % (32 // self.w_bit) == 0
self.qweight = qweight
self.qzeros = qzeros
self.scales = scales
if bias:
self.bias = bias
else:
self.bias = None
@torch.no_grad()
def forward(self, x):
out_shape = x.shape[:-1] + (self.out_features,)
out = awq_inference_engine.gemm_forward_cuda(
x.reshape(-1, x.shape[-1]), self.qweight, self.scales, self.qzeros, 8
)
out = out + self.bias if self.bias is not None else out
return out.reshape(out_shape)
| 0 |
hf_public_repos/text-generation-inference/server/text_generation_server/utils | hf_public_repos/text-generation-inference/server/text_generation_server/utils/gptq/exllamav2.py | # Adapted from turboderp exllama: https://github.com/turboderp/exllamav2
from logging import getLogger
import torch
import torch.nn as nn
import math
logger = getLogger(__name__)
try:
from exllamav2_kernels import make_q_matrix, gemm_half_q_half
except ImportError:
logger.error("exllamav2_kernels not installed.")
raise
# Dummy tensor to pass instead of g_idx since there is no way to pass "None" to a C++ extension
none_tensor = torch.empty((1, 1), device="meta")
def ext_gemm_half_q_half(x, q_handle, q4_width, force_cuda):
"""Matrix multiplication, returns x @ q4"""
output_shape = x.shape[:-1] + (q4_width,)
x = x.view(-1, x.shape[-1])
output = torch.empty((x.shape[0], q4_width), dtype=torch.half, device=x.device)
gemm_half_q_half(x, q_handle, output, force_cuda)
return output.view(output_shape)
# Group map needed for irregular group sizes
def make_group_map(q_groups, num_qrows):
gr = q_groups.tolist()
group_map = []
num_groups = len(gr) // 2
for i in range(num_groups):
bits = gr[i * 2]
if i < num_groups - 1:
qrows = gr[i * 2 + 3] - gr[i * 2 + 1]
else:
qrows = num_qrows - gr[i * 2 + 1]
rows = qrows * 32 // bits
for j in range(rows):
group_map += [i]
group_map += [rows - j]
return torch.tensor(group_map, dtype=torch.short, device=q_groups.device)
# Create Q matrix
def ext_make_q_matrix(w: dict, temp_dq, key: str = None):
"""
Create Q matrix
"""
# EXL2
# won't work as the moment because the tensors are not the same.
if "q_weight" in w:
w["q_scale_max"] /= 256
w["q_perm"] = w["q_perm"].short()
w["q_invperm"] = w["q_invperm"].short()
if "q_group_map" not in w:
w["q_group_map"] = make_group_map(w["q_groups"], w["q_weight"].shape[0])
return make_q_matrix(
w["q_weight"],
w["q_perm"],
w["q_invperm"],
w["q_scale"],
w["q_scale_max"],
w["q_groups"],
w["q_group_map"],
none_tensor,
none_tensor,
none_tensor,
temp_dq,
)
# GPTQ
elif "qweight" in w:
if w["scales"].dtype == torch.float:
w["scales"] = w["scales"].half()
# GPTQ with g_idx (act_order)
if w.get("g_idx", None) is not None and not (w["g_idx"] == 0).all().item():
w["q_perm"] = torch.empty(
(w["qweight"].shape[0] * 8,),
dtype=torch.short,
device=w["qweight"].device,
)
w["q_invperm"] = torch.empty_like(w["q_perm"])
# make_q4 segfaults if g_idx is not on cpu in the act-order case. In the non act-order case, None needs to be passed for g_idx.
return make_q_matrix(
w["qweight"],
w["q_perm"],
w["q_invperm"],
none_tensor,
none_tensor,
none_tensor,
none_tensor,
w["qzeros"],
w["scales"],
w["g_idx"].cpu(),
temp_dq,
)
# GPTQ without g_idx
else:
return make_q_matrix(
w["qweight"],
none_tensor,
none_tensor,
none_tensor,
none_tensor,
none_tensor,
none_tensor,
w["qzeros"],
w["scales"],
none_tensor,
temp_dq,
)
DEVICE = None
FIXED_BYTES = 0
LAYERS = []
def set_device(device):
global DEVICE
DEVICE = device
def create_exllama_buffers(max_total_tokens: int):
global FIXED_BYTES, LAYERS, DEVICE
temp_dq = ExLlamaV2DeviceTensors(DEVICE, FIXED_BYTES)
for layer in LAYERS:
layer.post_init(temp_dq)
class QuantLinear(nn.Module):
QUANT_TYPE = "exllamav2"
"""Linear layer implementation with per-group 4-bit quantization of the weights"""
# def __init__(self, bits, group_size, infeatures, outfeatures, bias, trainable=False, **kwargs):
def __init__(self, qweight, qzeros, scales, g_idx, bias, bits, groupsize):
super().__init__()
if bits != 4:
raise ValueError(
f"Exllamav2 kernel supports only bits=4, requested bits={bits}. Something is wrong in the model initialization."
)
self.q_handle = None
self.q_tensors = None
self.bits = bits
self.maxq = 2**self.bits - 1
self.infeatures = qweight.shape[0] // self.bits * 32
self.outfeatures = qweight.shape[1]
self.padding = -self.outfeatures % 32
self.outfeatures = self.outfeatures + self.padding
self.device = qweight.device
self.qweight = qweight
self.qzeros = qzeros
self.scales = scales
self.g_idx = g_idx
self.bias = bias if bias is not None else None
self.group_size = groupsize
global FIXED_BYTES, LAYERS
FIXED_BYTES = max(FIXED_BYTES, self.scratch_space_fixed())
LAYERS.append(self)
def post_init(self, temp_dq):
assert self.qweight.device.type == "cuda"
assert self.qweight.device.index is not None
self.q_tensors = {
"qweight": self.qweight,
"qzeros": self.qzeros,
"scales": self.scales,
"g_idx": self.g_idx,
}
temp_dq = temp_dq.get_scratch_slice(self.temp_dq_size())
self.q_handle = ext_make_q_matrix(self.q_tensors, temp_dq)
def forward(self, x, force_cuda=False):
output = ext_gemm_half_q_half(x, self.q_handle, self.outfeatures, force_cuda)
if self.bias is not None:
output.add_(self.bias)
return output
def temp_dq_size(self):
return self.infeatures * self.outfeatures * 2 + 128
def temp_fwd_size(self, max_input_len, max_batch_size):
return self.outfeatures * max_input_len * max_batch_size * 4 + 128
def scratch_space_fixed(self, max_input_len=4096, max_batch_size=16):
return self.temp_dq_size() + self.temp_fwd_size(max_input_len, max_batch_size)
class ExLlamaV2DeviceTensors:
device_idx: int
scratch_bytes: int
scratch_idx: int
scratch: torch.tensor = None
def __init__(self, device, scratch_bytes):
self.device = device
self.scratch_bytes = scratch_bytes
def prepare(self):
self.scratch = torch.empty(
(self.scratch_bytes // 2,), dtype=torch.half, device=self.device
)
def get_scratch_slice(self, size_bytes):
if self.scratch is None:
self.prepare()
size_bytes = ((size_bytes + 127) // 128) * 128
size_half = size_bytes // 2
scratch_slice = self.scratch.narrow(0, 0, size_half)
return scratch_slice
| 0 |
hf_public_repos/text-generation-inference/server/text_generation_server/utils | hf_public_repos/text-generation-inference/server/text_generation_server/utils/gptq/custom_autotune.py | # https://github.com/fpgaminer/GPTQ-triton
"""
Mostly the same as the autotuner in Triton, but with a few changes like using 40 runs instead of 100.
"""
import builtins
import math
import time
from typing import Dict
import triton
class Autotuner(triton.KernelInterface):
def __init__(
self,
fn,
arg_names,
configs,
key,
reset_to_zero,
prune_configs_by: Dict = None,
nearest_power_of_two: bool = False,
):
"""
:param prune_configs_by: a dict of functions that are used to prune configs, fields:
'perf_model': performance model used to predicate running time with different configs, returns running time
'top_k': number of configs to bench
'prune_num_stages_by'(optional): a function used to prune num_stages. It take configs:List[Config] as its input, and returns pruned configs.
'nearest_power_of_two'(optional): whether to round key arguments to the nearest power of two when caching tuning results
"""
if not configs:
self.configs = [triton.Config({}, num_warps=4, num_stages=2)]
else:
self.configs = configs
self.key_idx = [arg_names.index(k) for k in key]
self.nearest_power_of_two = nearest_power_of_two
self.cache = {}
# hook to reset all required tensor to zeros before relaunching a kernel
self.hook = lambda args: 0
if reset_to_zero is not None:
self.reset_idx = [arg_names.index(k) for k in reset_to_zero]
def _hook(args):
for i in self.reset_idx:
args[i].zero_()
self.hook = _hook
self.arg_names = arg_names
# prune configs
if prune_configs_by:
perf_model, top_k = (
prune_configs_by["perf_model"],
prune_configs_by["top_k"],
)
if "early_config_prune" in prune_configs_by:
early_config_prune = prune_configs_by["early_config_prune"]
else:
perf_model, top_k, early_config_prune = None, None, None
self.perf_model, self.configs_top_k = perf_model, top_k
self.early_config_prune = early_config_prune
self.fn = fn
def _bench(self, *args, config, **meta):
# check for conflicts, i.e. meta-parameters both provided
# as kwargs and by the autotuner
conflicts = meta.keys() & config.kwargs.keys()
if conflicts:
raise ValueError(
f"Conflicting meta-parameters: {', '.join(conflicts)}."
" Make sure that you don't re-define auto-tuned symbols."
)
# augment meta-parameters with tunable ones
current = dict(meta, **config.kwargs)
def kernel_call():
if config.pre_hook:
config.pre_hook(self.nargs)
self.hook(args)
self.fn.run(
*args,
num_warps=config.num_warps,
num_stages=config.num_stages,
**current,
)
try:
# In testings using only 40 reps seems to be close enough and it appears to be what PyTorch uses
# PyTorch also sets fast_flush to True, but I didn't see any speedup so I'll leave the default
return triton.testing.do_bench(
kernel_call, quantiles=(0.5, 0.2, 0.8), rep=40
)
except triton.OutOfResources:
return (float("inf"), float("inf"), float("inf"))
def run(self, *args, **kwargs):
self.nargs = dict(zip(self.arg_names, args))
if len(self.configs) > 1:
key = tuple(args[i] for i in self.key_idx)
# This reduces the amount of autotuning by rounding the keys to the nearest power of two
# In my testing this gives decent results, and greatly reduces the amount of tuning required
if self.nearest_power_of_two:
key = tuple([2 ** int(math.log2(x) + 0.5) for x in key])
if key not in self.cache:
# prune configs
pruned_configs = self.prune_configs(kwargs)
bench_start = time.time()
timings = {
config: self._bench(*args, config=config, **kwargs)
for config in pruned_configs
}
bench_end = time.time()
self.bench_time = bench_end - bench_start
self.cache[key] = builtins.min(timings, key=timings.get)
self.hook(args)
self.configs_timings = timings
config = self.cache[key]
else:
config = self.configs[0]
self.best_config = config
if config.pre_hook is not None:
config.pre_hook(self.nargs)
return self.fn.run(
*args,
num_warps=config.num_warps,
num_stages=config.num_stages,
**kwargs,
**config.kwargs,
)
def prune_configs(self, kwargs):
pruned_configs = self.configs
if self.early_config_prune:
pruned_configs = self.early_config_prune(self.configs, self.nargs)
if self.perf_model:
top_k = self.configs_top_k
if isinstance(top_k, float) and top_k <= 1.0:
top_k = int(len(self.configs) * top_k)
if len(pruned_configs) > top_k:
est_timing = {
config: self.perf_model(
**self.nargs,
**kwargs,
**config.kwargs,
num_stages=config.num_stages,
num_warps=config.num_warps,
)
for config in pruned_configs
}
pruned_configs = sorted(est_timing.keys(), key=lambda x: est_timing[x])[
:top_k
]
return pruned_configs
def warmup(self, *args, **kwargs):
self.nargs = dict(zip(self.arg_names, args))
for config in self.prune_configs(kwargs):
self.fn.warmup(
*args,
num_warps=config.num_warps,
num_stages=config.num_stages,
**kwargs,
**config.kwargs,
)
self.nargs = None
def autotune(
configs, key, prune_configs_by=None, reset_to_zero=None, nearest_power_of_two=False
):
"""
Decorator for auto-tuning a :code:`triton.jit`'d function.
.. highlight:: python
.. code-block:: python
@triton.autotune(configs=[
triton.Config(meta={'BLOCK_SIZE': 128}, num_warps=4),
triton.Config(meta={'BLOCK_SIZE': 1024}, num_warps=8),
],
key=['x_size'] # the two above configs will be evaluated anytime
# the value of x_size changes
)
@triton.jit
def kernel(x_ptr, x_size, **META):
BLOCK_SIZE = META['BLOCK_SIZE']
:note: When all the configurations are evaluated, the kernel will run multiple time.
This means that whatever value the kernel updates will be updated multiple times.
To avoid this undesired behavior, you can use the `reset_to_zero` argument, which
reset the value of the provided tensor to `zero` before running any configuration.
:param configs: a list of :code:`triton.Config` objects
:type configs: list[triton.Config]
:param key: a list of argument names whose change in value will trigger the evaluation of all provided configs.
:type key: list[str]
:param prune_configs_by: a dict of functions that are used to prune configs, fields:
'perf_model': performance model used to predicate running time with different configs, returns running time
'top_k': number of configs to bench
'early_config_prune'(optional): a function used to do early prune (eg, num_stages). It take configs:List[Config] as its input, and returns pruned configs.
:param reset_to_zero: a list of argument names whose value will be reset to zero before evaluating any configs.
:type reset_to_zero: list[str]
"""
def decorator(fn):
return Autotuner(
fn,
fn.arg_names,
configs,
key,
reset_to_zero,
prune_configs_by,
nearest_power_of_two,
)
return decorator
def matmul248_kernel_config_pruner(configs, nargs):
"""
The main purpose of this function is to shrink BLOCK_SIZE_* when the corresponding dimension is smaller.
"""
m = max(2 ** int(math.ceil(math.log2(nargs["M"]))), 16)
n = max(2 ** int(math.ceil(math.log2(nargs["N"]))), 16)
k = max(2 ** int(math.ceil(math.log2(nargs["K"]))), 16)
used = set()
for config in configs:
block_size_m = min(m, config.kwargs["BLOCK_SIZE_M"])
block_size_n = min(n, config.kwargs["BLOCK_SIZE_N"])
block_size_k = min(k, config.kwargs["BLOCK_SIZE_K"])
group_size_m = config.kwargs["GROUP_SIZE_M"]
if (
block_size_m,
block_size_n,
block_size_k,
group_size_m,
config.num_stages,
config.num_warps,
) in used:
continue
used.add(
(
block_size_m,
block_size_n,
block_size_k,
group_size_m,
config.num_stages,
config.num_warps,
)
)
yield triton.Config(
{
"BLOCK_SIZE_M": block_size_m,
"BLOCK_SIZE_N": block_size_n,
"BLOCK_SIZE_K": block_size_k,
"GROUP_SIZE_M": group_size_m,
},
num_stages=config.num_stages,
num_warps=config.num_warps,
)
| 0 |
hf_public_repos/text-generation-inference/server/text_generation_server/utils | hf_public_repos/text-generation-inference/server/text_generation_server/utils/gptq/exllama.py | import torch
from exllama_kernels import make_q4, q4_matmul, prepare_buffers, set_tuning_params
# Dummy tensor to pass instead of g_idx since there is no way to pass "None" to a C++ extension
none_tensor = torch.empty((1, 1), device="meta")
def ext_make_q4(qweight, qzeros, scales, g_idx, device):
"""Construct Q4Matrix, return handle"""
return make_q4(
qweight, qzeros, scales, g_idx if g_idx is not None else none_tensor, device
)
def ext_q4_matmul(x, q4, q4_width):
"""Matrix multiplication, returns x @ q4"""
outshape = x.shape[:-1] + (q4_width,)
x = x.view(-1, x.shape[-1])
output = torch.empty((x.shape[0], q4_width), dtype=torch.float16, device=x.device)
q4_matmul(x, q4, output)
return output.view(outshape)
MAX_DQ = 1
MAX_INNER = 1
ACT_ORDER = False
DEVICE = None
TEMP_STATE = None
TEMP_DQ = None
def set_device(device):
global DEVICE
DEVICE = device
def create_exllama_buffers(max_total_tokens: int):
global MAX_DQ, MAX_INNER, ACT_ORDER, DEVICE, TEMP_STATE, TEMP_DQ
assert DEVICE is not None, "call set_device first"
if not ACT_ORDER:
max_total_tokens = 1
# This temp_state buffer is required to reorder X in the act-order case.
temp_state = torch.zeros(
(max_total_tokens, MAX_INNER), dtype=torch.float16, device=DEVICE
)
temp_dq = torch.zeros((1, MAX_DQ), dtype=torch.float16, device=DEVICE)
# This temp_dq buffer is required to dequantize weights when using cuBLAS, typically for the prefill.
prepare_buffers(DEVICE, temp_state, temp_dq)
matmul_recons_thd = 8
matmul_fused_remap = False
matmul_no_half2 = False
set_tuning_params(matmul_recons_thd, matmul_fused_remap, matmul_no_half2)
TEMP_STATE, TEMP_DQ = temp_state, temp_dq
class Ex4bitLinear(torch.nn.Module):
"""Linear layer implementation with per-group 4-bit quantization of the weights"""
def __init__(self, qweight, qzeros, scales, g_idx, bias, bits, groupsize):
super().__init__()
global MAX_DQ, MAX_INNER, ACT_ORDER, DEVICE
assert bits == 4
self.device = qweight.device
self.qweight = qweight
self.qzeros = qzeros
self.scales = scales
self.g_idx = g_idx.cpu() if g_idx is not None else None
self.bias = bias if bias is not None else None
if self.g_idx is not None and (
(self.g_idx == 0).all()
or torch.equal(
g_idx.cpu(),
torch.tensor(
[i // groupsize for i in range(g_idx.shape[0])], dtype=torch.int32
),
)
):
self.empty_g_idx = True
self.g_idx = None
assert self.device.type == "cuda"
assert self.device.index is not None
self.q4 = ext_make_q4(
self.qweight, self.qzeros, self.scales, self.g_idx, self.device.index
)
self.height = qweight.shape[0] * 8
self.width = qweight.shape[1]
# Infer groupsize from height of qzeros
self.groupsize = None
if self.qzeros.shape[0] > 1:
self.groupsize = (self.qweight.shape[0] * 8) // (self.qzeros.shape[0])
if self.groupsize is not None:
assert groupsize == self.groupsize
# Handle act-order matrix
if self.g_idx is not None:
if self.groupsize is None:
raise ValueError("Found group index but no groupsize. What do?")
self.act_order = True
else:
self.act_order = False
DEVICE = self.qweight.device
MAX_DQ = max(MAX_DQ, self.qweight.numel() * 8)
if self.act_order:
MAX_INNER = max(MAX_INNER, self.height, self.width)
ACT_ORDER = True
def forward(self, x):
out = ext_q4_matmul(x, self.q4, self.width)
if self.bias is not None:
out.add_(self.bias)
return out
| 0 |
hf_public_repos/text-generation-inference/server/text_generation_server/utils | hf_public_repos/text-generation-inference/server/text_generation_server/utils/gptq/quant_linear.py | import math
import numpy as np
import torch
import torch.nn as nn
from torch.cuda.amp import custom_bwd, custom_fwd
try:
import triton
import triton.language as tl
from . import custom_autotune
# code based https://github.com/fpgaminer/GPTQ-triton
@custom_autotune.autotune(
configs=[
triton.Config(
{
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 256,
"BLOCK_SIZE_K": 32,
"GROUP_SIZE_M": 8,
},
num_stages=4,
num_warps=4,
),
triton.Config(
{
"BLOCK_SIZE_M": 128,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 32,
"GROUP_SIZE_M": 8,
},
num_stages=4,
num_warps=4,
),
triton.Config(
{
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 32,
"GROUP_SIZE_M": 8,
},
num_stages=4,
num_warps=4,
),
triton.Config(
{
"BLOCK_SIZE_M": 128,
"BLOCK_SIZE_N": 32,
"BLOCK_SIZE_K": 32,
"GROUP_SIZE_M": 8,
},
num_stages=4,
num_warps=4,
),
triton.Config(
{
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 32,
"GROUP_SIZE_M": 8,
},
num_stages=4,
num_warps=4,
),
triton.Config(
{
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 32,
"GROUP_SIZE_M": 8,
},
num_stages=2,
num_warps=8,
),
triton.Config(
{
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 64,
"GROUP_SIZE_M": 8,
},
num_stages=3,
num_warps=8,
),
triton.Config(
{
"BLOCK_SIZE_M": 32,
"BLOCK_SIZE_N": 32,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 8,
},
num_stages=2,
num_warps=4,
),
],
key=["M", "N", "K"],
nearest_power_of_two=True,
prune_configs_by={
"early_config_prune": custom_autotune.matmul248_kernel_config_pruner,
"perf_model": None,
"top_k": None,
},
)
@triton.jit
def matmul_248_kernel(
a_ptr,
b_ptr,
c_ptr,
scales_ptr,
zeros_ptr,
g_ptr,
M,
N,
K,
bits,
maxq,
stride_am,
stride_ak,
stride_bk,
stride_bn,
stride_cm,
stride_cn,
stride_scales,
stride_zeros,
BLOCK_SIZE_M: tl.constexpr,
BLOCK_SIZE_N: tl.constexpr,
BLOCK_SIZE_K: tl.constexpr,
GROUP_SIZE_M: tl.constexpr,
):
"""
Compute the matrix multiplication C = A x B.
A is of shape (M, K) float16
B is of shape (K//8, N) int32
C is of shape (M, N) float16
scales is of shape (G, N) float16
zeros is of shape (G, N) float16
g_ptr is of shape (K) int32
"""
infearure_per_bits = 32 // bits
pid = tl.program_id(axis=0)
num_pid_m = tl.cdiv(M, BLOCK_SIZE_M)
num_pid_n = tl.cdiv(N, BLOCK_SIZE_N)
num_pid_k = tl.cdiv(K, BLOCK_SIZE_K)
num_pid_in_group = GROUP_SIZE_M * num_pid_n
group_id = pid // num_pid_in_group
first_pid_m = group_id * GROUP_SIZE_M
group_size_m = min(num_pid_m - first_pid_m, GROUP_SIZE_M)
pid_m = first_pid_m + (pid % group_size_m)
pid_n = (pid % num_pid_in_group) // group_size_m
offs_am = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)
offs_bn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)
offs_k = tl.arange(0, BLOCK_SIZE_K)
a_ptrs = a_ptr + (
offs_am[:, None] * stride_am + offs_k[None, :] * stride_ak
) # (BLOCK_SIZE_M, BLOCK_SIZE_K)
a_mask = offs_am[:, None] < M
# b_ptrs is set up such that it repeats elements along the K axis 8 times
b_ptrs = b_ptr + (
(offs_k[:, None] // infearure_per_bits) * stride_bk
+ offs_bn[None, :] * stride_bn
) # (BLOCK_SIZE_K, BLOCK_SIZE_N)
g_ptrs = g_ptr + offs_k
# shifter is used to extract the N bits of each element in the 32-bit word from B
scales_ptrs = scales_ptr + offs_bn[None, :]
zeros_ptrs = zeros_ptr + (offs_bn[None, :] // infearure_per_bits)
shifter = (offs_k % infearure_per_bits) * bits
zeros_shifter = (offs_bn % infearure_per_bits) * bits
accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32)
for k in range(0, num_pid_k):
g_idx = tl.load(g_ptrs)
# Fetch scales and zeros; these are per-outfeature and thus reused in the inner loop
scales = tl.load(
scales_ptrs + g_idx[:, None] * stride_scales
) # (BLOCK_SIZE_K, BLOCK_SIZE_N,)
zeros = tl.load(
zeros_ptrs + g_idx[:, None] * stride_zeros
) # (BLOCK_SIZE_K, BLOCK_SIZE_N,)
zeros = (zeros >> zeros_shifter[None, :]) & maxq
zeros = zeros + 1
a = tl.load(a_ptrs, mask=a_mask, other=0.0) # (BLOCK_SIZE_M, BLOCK_SIZE_K)
b = tl.load(b_ptrs) # (BLOCK_SIZE_K, BLOCK_SIZE_N), but repeated
# Now we need to unpack b (which is N-bit values) into 32-bit values
b = (b >> shifter[:, None]) & maxq # Extract the N-bit values
b = (b - zeros) * scales # Scale and shift
accumulator += tl.dot(a, b)
a_ptrs += BLOCK_SIZE_K
b_ptrs += (BLOCK_SIZE_K // infearure_per_bits) * stride_bk
g_ptrs += BLOCK_SIZE_K
c_ptrs = c_ptr + stride_cm * offs_am[:, None] + stride_cn * offs_bn[None, :]
c_mask = (offs_am[:, None] < M) & (offs_bn[None, :] < N)
tl.store(c_ptrs, accumulator, mask=c_mask)
except:
print("triton not installed.")
def matmul248(input, qweight, scales, qzeros, g_idx, bits, maxq):
with torch.cuda.device(input.device):
output = torch.empty(
(input.shape[0], qweight.shape[1]), device=input.device, dtype=torch.float16
)
grid = lambda META: (
triton.cdiv(input.shape[0], META["BLOCK_SIZE_M"])
* triton.cdiv(qweight.shape[1], META["BLOCK_SIZE_N"]),
)
matmul_248_kernel[grid](
input,
qweight,
output,
scales,
qzeros,
g_idx,
input.shape[0],
qweight.shape[1],
input.shape[1],
bits,
maxq,
input.stride(0),
input.stride(1),
qweight.stride(0),
qweight.stride(1),
output.stride(0),
output.stride(1),
scales.stride(0),
qzeros.stride(0),
)
return output
class QuantLinearFunction(torch.autograd.Function):
@staticmethod
@custom_fwd(cast_inputs=torch.float16)
def forward(ctx, input, qweight, scales, qzeros, g_idx, bits, maxq):
output = matmul248(input, qweight, scales, qzeros, g_idx, bits, maxq)
return output
class QuantLinear(nn.Module):
def __init__(self, qweight, qzeros, scales, g_idx, bias, bits, groupsize):
super().__init__()
self.register_buffer("qweight", qweight)
self.register_buffer("qzeros", qzeros)
self.register_buffer("scales", scales)
self.register_buffer("g_idx", g_idx)
if bias is not None:
self.register_buffer("bias", bias)
else:
self.bias = None
if bits not in [2, 4, 8]:
raise NotImplementedError("Only 2,4,8 bits are supported.")
self.bits = bits
self.maxq = 2**self.bits - 1
self.groupsize = groupsize
self.outfeatures = qweight.shape[1]
self.infeatures = qweight.shape[0] * 32 // bits
@classmethod
def new(cls, bits, groupsize, infeatures, outfeatures, bias):
if bits not in [2, 4, 8]:
raise NotImplementedError("Only 2,4,8 bits are supported.")
qweight = torch.zeros((infeatures // 32 * bits, outfeatures), dtype=torch.int32)
qzeros = torch.zeros(
(math.ceil(infeatures / groupsize), outfeatures // 32 * bits),
dtype=torch.int32,
)
scales = torch.zeros(
(math.ceil(infeatures / groupsize), outfeatures), dtype=torch.float16
)
g_idx = torch.tensor(
[i // groupsize for i in range(infeatures)], dtype=torch.int32
)
if bias:
bias = torch.zeros((outfeatures), dtype=torch.float16)
else:
bias = None
return cls(qweight, qzeros, scales, g_idx, bias, bits, groupsize)
def pack(self, linear, scales, zeros, g_idx=None):
self.g_idx = g_idx.clone() if g_idx is not None else self.g_idx
scales = scales.t().contiguous()
zeros = zeros.t().contiguous()
scale_zeros = zeros * scales
self.scales = scales.clone().half()
if linear.bias is not None:
self.bias = linear.bias.clone().half()
intweight = []
for idx in range(self.infeatures):
intweight.append(
torch.round(
(linear.weight.data[:, idx] + scale_zeros[self.g_idx[idx]])
/ self.scales[self.g_idx[idx]]
).to(torch.int)[:, None]
)
intweight = torch.cat(intweight, dim=1)
intweight = intweight.t().contiguous()
intweight = intweight.numpy().astype(np.uint32)
qweight = np.zeros(
(intweight.shape[0] // 32 * self.bits, intweight.shape[1]), dtype=np.uint32
)
i = 0
row = 0
while row < qweight.shape[0]:
if self.bits in [2, 4, 8]:
for j in range(i, i + (32 // self.bits)):
qweight[row] |= intweight[j] << (self.bits * (j - i))
i += 32 // self.bits
row += 1
else:
raise NotImplementedError("Only 2,4,8 bits are supported.")
qweight = qweight.astype(np.int32)
self.qweight = torch.from_numpy(qweight)
zeros -= 1
zeros = zeros.numpy().astype(np.uint32)
qzeros = np.zeros(
(zeros.shape[0], zeros.shape[1] // 32 * self.bits), dtype=np.uint32
)
i = 0
col = 0
while col < qzeros.shape[1]:
if self.bits in [2, 4, 8]:
for j in range(i, i + (32 // self.bits)):
qzeros[:, col] |= zeros[:, j] << (self.bits * (j - i))
i += 32 // self.bits
col += 1
else:
raise NotImplementedError("Only 2,4,8 bits are supported.")
qzeros = qzeros.astype(np.int32)
self.qzeros = torch.from_numpy(qzeros)
def forward(self, x):
out_shape = x.shape[:-1] + (self.outfeatures,)
out = QuantLinearFunction.apply(
x.reshape(-1, x.shape[-1]),
self.qweight,
self.scales,
self.qzeros,
self.g_idx,
self.bits,
self.maxq,
)
out = out + self.bias if self.bias is not None else out
return out.reshape(out_shape)
| 0 |
hf_public_repos/text-generation-inference/server/text_generation_server/utils | hf_public_repos/text-generation-inference/server/text_generation_server/utils/gptq/quantize.py | import time
import torch.nn as nn
import math
import json
import os
import torch
import transformers
from texttable import Texttable
from transformers import AutoModelForCausalLM, AutoConfig, AutoTokenizer
from huggingface_hub import HfApi
from accelerate import init_empty_weights
from text_generation_server.utils import initialize_torch_distributed, Weights
from text_generation_server.utils.hub import weight_files
from text_generation_server.utils.gptq.quant_linear import QuantLinear
from loguru import logger
from typing import Optional
DEV = torch.device("cuda:0")
class Quantizer(nn.Module):
def __init__(self, shape=1):
super(Quantizer, self).__init__()
self.register_buffer("maxq", torch.tensor(0))
self.register_buffer("scale", torch.zeros(shape))
self.register_buffer("zero", torch.zeros(shape))
def configure(
self,
bits,
perchannel=False,
sym=True,
mse=False,
norm=2.4,
grid=100,
maxshrink=0.8,
trits=False,
):
self.maxq = torch.tensor(2**bits - 1)
self.perchannel = perchannel
self.sym = sym
self.mse = mse
self.norm = norm
self.grid = grid
self.maxshrink = maxshrink
if trits:
self.maxq = torch.tensor(-1)
self.scale = torch.zeros_like(self.scale)
def _quantize(self, x, scale, zero, maxq):
if maxq < 0:
return (x > scale / 2).float() * scale + (x < zero / 2).float() * zero
q = torch.clamp(torch.round(x / scale) + zero, 0, maxq)
return scale * (q - zero)
def find_params(self, x, weight=False):
dev = x.device
self.maxq = self.maxq.to(dev)
shape = x.shape
if self.perchannel:
if weight:
x = x.flatten(1)
else:
if len(shape) == 4:
x = x.permute([1, 0, 2, 3])
x = x.flatten(1)
if len(shape) == 3:
x = x.reshape((-1, shape[-1])).t()
if len(shape) == 2:
x = x.t()
else:
x = x.flatten().unsqueeze(0)
tmp = torch.zeros(x.shape[0], device=dev)
xmin = torch.minimum(x.min(1)[0], tmp)
xmax = torch.maximum(x.max(1)[0], tmp)
if self.sym:
xmax = torch.maximum(torch.abs(xmin), xmax)
tmp = xmin < 0
if torch.any(tmp):
xmin[tmp] = -xmax[tmp]
tmp = (xmin == 0) & (xmax == 0)
xmin[tmp] = -1
xmax[tmp] = +1
if self.maxq < 0:
self.scale = xmax
self.zero = xmin
else:
self.scale = (xmax - xmin) / self.maxq
if self.sym:
self.zero = torch.full_like(self.scale, (self.maxq + 1) / 2)
else:
self.zero = torch.round(-xmin / self.scale)
if self.mse:
best = torch.full([x.shape[0]], float("inf"), device=dev)
for i in range(int(self.maxshrink * self.grid)):
p = 1 - i / self.grid
xmin1 = p * xmin
xmax1 = p * xmax
scale1 = (xmax1 - xmin1) / self.maxq
zero1 = torch.round(-xmin1 / scale1) if not self.sym else self.zero
q = self._quantize(
x, scale1.unsqueeze(1), zero1.unsqueeze(1), self.maxq
)
q -= x
q.abs_()
q.pow_(self.norm)
err = torch.sum(q, 1)
tmp = err < best
if torch.any(tmp):
best[tmp] = err[tmp]
self.scale[tmp] = scale1[tmp]
self.zero[tmp] = zero1[tmp]
if not self.perchannel:
if weight:
tmp = shape[0]
else:
tmp = shape[1] if len(shape) != 3 else shape[2]
self.scale = self.scale.repeat(tmp)
self.zero = self.zero.repeat(tmp)
if weight:
shape = [-1] + [1] * (len(shape) - 1)
self.scale = self.scale.reshape(shape)
self.zero = self.zero.reshape(shape)
return
if len(shape) == 4:
self.scale = self.scale.reshape((1, -1, 1, 1))
self.zero = self.zero.reshape((1, -1, 1, 1))
if len(shape) == 3:
self.scale = self.scale.reshape((1, 1, -1))
self.zero = self.zero.reshape((1, 1, -1))
if len(shape) == 2:
self.scale = self.scale.unsqueeze(0)
self.zero = self.zero.unsqueeze(0)
def quantize(self, x):
if self.ready():
return self._quantize(x, self.scale, self.zero, self.maxq)
return x
def enabled(self):
return self.maxq > 0
def ready(self):
return torch.all(self.scale != 0)
class GPTQ:
def __init__(self, layer, observe=False):
self.layer = layer
self.dev = self.layer.weight.device
W = layer.weight.data.clone()
if isinstance(self.layer, nn.Conv2d):
W = W.flatten(1)
if isinstance(self.layer, transformers.Conv1D):
W = W.t()
self.rows = W.shape[0]
self.columns = W.shape[1]
self.H = torch.zeros((self.columns, self.columns), device=self.dev)
self.nsamples = 0
self.quantizer = Quantizer()
self.observe = observe
def add_batch(self, inp, out):
# Hessian H = 2 X XT + λ I
if self.observe:
self.inp1 = inp
self.out1 = out
else:
self.inp1 = None
self.out1 = None
if len(inp.shape) == 2:
inp = inp.unsqueeze(0)
tmp = inp.shape[0]
if isinstance(self.layer, nn.Linear) or isinstance(
self.layer, transformers.Conv1D
):
if len(inp.shape) == 3:
inp = inp.reshape((-1, inp.shape[-1]))
inp = inp.t()
if isinstance(self.layer, nn.Conv2d):
unfold = nn.Unfold(
self.layer.kernel_size,
dilation=self.layer.dilation,
padding=self.layer.padding,
stride=self.layer.stride,
)
inp = unfold(inp)
inp = inp.permute([1, 0, 2])
inp = inp.flatten(1)
self.H *= self.nsamples / (self.nsamples + tmp)
self.nsamples += tmp
# inp = inp.float()
inp = math.sqrt(2 / self.nsamples) * inp.float()
# self.H += 2 / self.nsamples * inp.matmul(inp.t())
self.H += inp.matmul(inp.t())
def print_loss(self, name, q_weight, weight_error, timecost):
table = Texttable()
length = 28
name = (
(name + " " * (length - len(name)))
if len(name) <= length
else name[:length]
)
table.header(["name", "weight_error", "fp_inp_SNR", "q_inp_SNR", "time"])
# assign weight
self.layer.weight.data = q_weight.reshape(self.layer.weight.shape).to(
self.layer.weight.data.dtype
)
if self.inp1 is not None:
# quantize input to int8
quantizer = Quantizer()
quantizer.configure(8, perchannel=False, sym=True, mse=False)
quantizer.find_params(self.inp1)
q_in = quantizer.quantize(self.inp1).type(torch.float16)
q_out = self.layer(q_in)
# get kinds of SNR
q_SNR = torch_snr_error(q_out, self.out1).item()
fp_SNR = torch_snr_error(self.layer(self.inp1), self.out1).item()
else:
q_SNR = "-"
fp_SNR = "-"
table.add_row([name, weight_error, fp_SNR, q_SNR, timecost])
print(table.draw().split("\n")[-2])
def fasterquant(
self, blocksize=128, percdamp=0.01, groupsize=-1, act_order=False, name=""
):
self.layer.to(self.dev)
W = self.layer.weight.data.clone()
if isinstance(self.layer, nn.Conv2d):
W = W.flatten(1)
if isinstance(self.layer, transformers.Conv1D):
W = W.t()
W = W.float()
tick = time.time()
if not self.quantizer.ready():
self.quantizer.find_params(W, weight=True)
H = self.H
if not self.observe:
del self.H
dead = torch.diag(H) == 0
H[dead, dead] = 1
W[:, dead] = 0
if act_order:
perm = torch.argsort(torch.diag(H), descending=True)
W = W[:, perm]
H = H[perm][:, perm]
Losses = torch.zeros_like(W)
Q = torch.zeros_like(W)
damp = percdamp * torch.mean(torch.diag(H))
diag = torch.arange(self.columns, device=self.dev)
H[diag, diag] += damp
H = torch.linalg.cholesky(H)
H = torch.cholesky_inverse(H)
try:
H = torch.linalg.cholesky(H, upper=True)
except Exception:
# Addition because Falcon fails on h_to_4h
H = torch.linalg.cholesky(
H + 1e-5 * torch.eye(H.shape[0]).to(H.device), upper=True
)
Hinv = H
g_idx = []
scale = []
zero = []
now_idx = 1
for i1 in range(0, self.columns, blocksize):
i2 = min(i1 + blocksize, self.columns)
count = i2 - i1
W1 = W[:, i1:i2].clone()
Q1 = torch.zeros_like(W1)
Err1 = torch.zeros_like(W1)
Losses1 = torch.zeros_like(W1)
Hinv1 = Hinv[i1:i2, i1:i2]
for i in range(count):
w = W1[:, i]
d = Hinv1[i, i]
if groupsize != -1:
if (i1 + i) % groupsize == 0:
self.quantizer.find_params(
W[:, (i1 + i) : (i1 + i + groupsize)], weight=True
)
if ((i1 + i) // groupsize) - now_idx == -1:
scale.append(self.quantizer.scale)
zero.append(self.quantizer.zero)
now_idx += 1
q = self.quantizer.quantize(w.unsqueeze(1)).flatten()
Q1[:, i] = q
Losses1[:, i] = (w - q) ** 2 / d**2
err1 = (w - q) / d
W1[:, i:] -= err1.unsqueeze(1).matmul(Hinv1[i, i:].unsqueeze(0))
Err1[:, i] = err1
Q[:, i1:i2] = Q1
Losses[:, i1:i2] = Losses1 / 2
W[:, i2:] -= Err1.matmul(Hinv[i1:i2, i2:])
torch.cuda.synchronize()
error = torch.sum(Losses).item()
groupsize = groupsize if groupsize != -1 else self.columns
g_idx = [i // groupsize for i in range(self.columns)]
g_idx = torch.tensor(g_idx, dtype=torch.int32, device=Q.device)
if act_order:
invperm = torch.argsort(perm)
Q = Q[:, invperm]
g_idx = g_idx[invperm]
if isinstance(self.layer, transformers.Conv1D):
Q = Q.t()
self.print_loss(
name=name, q_weight=Q, weight_error=error, timecost=(time.time() - tick)
)
if scale == []:
scale.append(self.quantizer.scale)
zero.append(self.quantizer.zero)
scale = torch.cat(scale, dim=1)
zero = torch.cat(zero, dim=1)
return scale, zero, g_idx, error
def free(self):
self.inp1 = None
self.out1 = None
self.H = None
self.Losses = None
self.Trace = None
torch.cuda.empty_cache()
def get_wikitext2(nsamples, seed, seqlen, model_id, trust_remote_code):
from datasets import load_dataset
traindata = load_dataset("wikitext", "wikitext-2-raw-v1", split="train")
testdata = load_dataset("wikitext", "wikitext-2-raw-v1", split="test")
try:
tokenizer = AutoTokenizer.from_pretrained(
model_id, use_fast=False, trust_remote_code=trust_remote_code
)
except:
tokenizer = AutoTokenizer.from_pretrained(
model_id, use_fast=True, trust_remote_code=trust_remote_code
)
trainenc = tokenizer("\n\n".join(traindata["text"]), return_tensors="pt")
testenc = tokenizer("\n\n".join(testdata["text"]), return_tensors="pt")
import random
random.seed(seed)
trainloader = []
for _ in range(nsamples):
i = random.randint(0, trainenc.input_ids.shape[1] - seqlen - 1)
j = i + seqlen
inp = trainenc.input_ids[:, i:j]
tar = inp.clone()
tar[:, :-1] = -100
trainloader.append((inp, tar))
return trainloader, testenc
def get_ptb(nsamples, seed, seqlen, model_id, trust_remote_code):
from datasets import load_dataset
traindata = load_dataset("ptb_text_only", "penn_treebank", split="train")
valdata = load_dataset("ptb_text_only", "penn_treebank", split="validation")
try:
tokenizer = AutoTokenizer.from_pretrained(
model_id, use_fast=False, trust_remote_code=trust_remote_code
)
except:
tokenizer = AutoTokenizer.from_pretrained(
model_id, use_fast=True, trust_remote_code=trust_remote_code
)
trainenc = tokenizer("\n\n".join(traindata["sentence"]), return_tensors="pt")
testenc = tokenizer("\n\n".join(valdata["sentence"]), return_tensors="pt")
import random
random.seed(seed)
trainloader = []
for _ in range(nsamples):
i = random.randint(0, trainenc.input_ids.shape[1] - seqlen - 1)
j = i + seqlen
inp = trainenc.input_ids[:, i:j]
tar = inp.clone()
tar[:, :-1] = -100
trainloader.append((inp, tar))
return trainloader, testenc
def get_c4(nsamples, seed, seqlen, model_id, trust_remote_code):
from datasets import load_dataset
traindata = load_dataset(
"allenai/c4",
"allenai--c4",
data_files={"train": "en/c4-train.00000-of-01024.json.gz"},
split="train",
use_auth_token=False,
)
valdata = load_dataset(
"allenai/c4",
"allenai--c4",
data_files={"validation": "en/c4-validation.00000-of-00008.json.gz"},
split="validation",
use_auth_token=False,
)
try:
tokenizer = AutoTokenizer.from_pretrained(
model_id, use_fast=False, trust_remote_code=trust_remote_code
)
except:
tokenizer = AutoTokenizer.from_pretrained(
model_id, use_fast=True, trust_remote_code=trust_remote_code
)
import random
random.seed(seed)
trainloader = []
for _ in range(nsamples):
while True:
i = random.randint(0, len(traindata) - 1)
trainenc = tokenizer(traindata[i]["text"], return_tensors="pt")
if trainenc.input_ids.shape[1] >= seqlen:
break
i = random.randint(0, trainenc.input_ids.shape[1] - seqlen - 1)
j = i + seqlen
inp = trainenc.input_ids[:, i:j]
tar = inp.clone()
tar[:, :-1] = -100
trainloader.append((inp, tar))
import random
random.seed(0)
valenc = []
for _ in range(256):
while True:
i = random.randint(0, len(valdata) - 1)
tmp = tokenizer(valdata[i]["text"], return_tensors="pt")
if tmp.input_ids.shape[1] >= seqlen:
break
i = random.randint(0, tmp.input_ids.shape[1] - seqlen - 1)
j = i + seqlen
valenc.append(tmp.input_ids[:, i:j])
valenc = torch.hstack(valenc)
class TokenizerWrapper:
def __init__(self, input_ids):
self.input_ids = input_ids
valenc = TokenizerWrapper(valenc)
return trainloader, valenc
def get_ptb_new(nsamples, seed, seqlen, model_id, trust_remote_code):
from datasets import load_dataset
traindata = load_dataset("ptb_text_only", "penn_treebank", split="train")
testdata = load_dataset("ptb_text_only", "penn_treebank", split="test")
try:
tokenizer = AutoTokenizer.from_pretrained(
model_id, use_fast=False, trust_remote_code=trust_remote_code
)
except:
tokenizer = AutoTokenizer.from_pretrained(
model_id, use_fast=True, trust_remote_code=trust_remote_code
)
trainenc = tokenizer(" ".join(traindata["sentence"]), return_tensors="pt")
testenc = tokenizer(" ".join(testdata["sentence"]), return_tensors="pt")
import random
random.seed(seed)
trainloader = []
for _ in range(nsamples):
i = random.randint(0, trainenc.input_ids.shape[1] - seqlen - 1)
j = i + seqlen
inp = trainenc.input_ids[:, i:j]
tar = inp.clone()
tar[:, :-1] = -100
trainloader.append((inp, tar))
return trainloader, testenc
def get_c4_new(nsamples, seed, seqlen, model_id, trust_remote_code):
from datasets import load_dataset
traindata = load_dataset(
"allenai/c4",
"allenai--c4",
data_files={"train": "en/c4-train.00000-of-01024.json.gz"},
split="train",
)
valdata = load_dataset(
"allenai/c4",
"allenai--c4",
data_files={"validation": "en/c4-validation.00000-of-00008.json.gz"},
split="validation",
)
try:
tokenizer = AutoTokenizer.from_pretrained(
model_id, use_fast=False, trust_remote_code=trust_remote_code
)
except:
tokenizer = AutoTokenizer.from_pretrained(
model_id, use_fast=True, trust_remote_code=trust_remote_code
)
import random
random.seed(seed)
trainloader = []
for _ in range(nsamples):
while True:
i = random.randint(0, len(traindata) - 1)
trainenc = tokenizer(traindata[i]["text"], return_tensors="pt")
if trainenc.input_ids.shape[1] >= seqlen:
break
i = random.randint(0, trainenc.input_ids.shape[1] - seqlen - 1)
j = i + seqlen
inp = trainenc.input_ids[:, i:j]
tar = inp.clone()
tar[:, :-1] = -100
trainloader.append((inp, tar))
valenc = tokenizer(" ".join(valdata[:1100]["text"]), return_tensors="pt")
valenc = valenc.input_ids[:, : (256 * seqlen)]
class TokenizerWrapper:
def __init__(self, input_ids):
self.input_ids = input_ids
valenc = TokenizerWrapper(valenc)
return trainloader, valenc
def get_loaders(
name, nsamples=128, seed=0, seqlen=2048, model_id="", trust_remote_code=False
):
if "wikitext2" in name:
return get_wikitext2(nsamples, seed, seqlen, model_id, trust_remote_code)
if "ptb" in name:
if "new" in name:
return get_ptb_new(nsamples, seed, seqlen, model_id, trust_remote_code)
return get_ptb(nsamples, seed, seqlen, model_id, trust_remote_code)
if "c4" in name:
if "new" in name:
return get_c4_new(nsamples, seed, seqlen, model_id, trust_remote_code)
return get_c4(nsamples, seed, seqlen, model_id, trust_remote_code)
def find_layers(module, layers=(nn.Conv2d, nn.Linear), name=""):
# Skip last lm_head linear
# Need isintance Falcon is inheriting Linear.
if isinstance(module, layers) and "lm_head" not in name:
return {name: module}
res = {}
for name1, child in module.named_children():
res.update(
find_layers(
child, layers=layers, name=name + "." + name1 if name != "" else name1
)
)
return res
@torch.no_grad()
def sequential(
model,
dataloader,
dev,
nsamples,
bits,
groupsize,
*,
hooks,
percdamp=0.01,
sym: bool = False,
act_order: bool = False,
):
print("Starting ...")
use_cache = model.config.use_cache
model.config.use_cache = False
try:
layers = model.model.layers
prefix = "model.layers"
except Exception:
layers = model.transformer.h
prefix = "transformer.h"
dtype = next(iter(model.parameters())).dtype
inps = torch.zeros(
(nsamples, model.seqlen, model.config.hidden_size), dtype=dtype, device=dev
)
cache = {"i": 0}
extra = {}
class Catcher(nn.Module):
def __init__(self, module):
super().__init__()
self.module = module
def forward(self, inp, **kwargs):
inps[cache["i"]] = inp
cache["i"] += 1
extra.update(kwargs.copy())
raise ValueError
layers[0] = Catcher(layers[0])
for batch in dataloader:
try:
model(batch[0].cuda())
except ValueError:
pass
layers[0] = layers[0].module
# layers[0] = layers[0].cpu()
# model.model.embed_tokens = model.model.embed_tokens.cpu()
# model.model.norm = model.model.norm.cpu()
torch.cuda.empty_cache()
for hook in hooks:
hook.remove()
outs = torch.zeros_like(inps)
extra = {
k: v.to(dev) if isinstance(v, torch.Tensor) else v for k, v in extra.items()
}
print("Ready.")
quantizers = {}
for i in range(len(layers)):
print(f"Quantizing layer {i+1}/{len(layers)}..")
print("+------------------+--------------+------------+-----------+-------+")
print("| name | weight_error | fp_inp_SNR | q_inp_SNR | time |")
print("+==================+==============+============+===========+=======+")
layer = layers[i]
layer.load()
full = find_layers(layer)
sequential = [list(full.keys())]
for names in sequential:
subset = {n: full[n] for n in names}
gptq = {}
for name in subset:
gptq[name] = GPTQ(subset[name])
gptq[name].quantizer.configure(
bits, perchannel=True, sym=sym, mse=False
)
pass
def add_batch(name):
def tmp(_, inp, out):
gptq[name].add_batch(inp[0].data, out.data)
return tmp
handles = []
for name in subset:
handles.append(subset[name].register_forward_hook(add_batch(name)))
for j in range(nsamples):
outs[j] = layer(inps[j].unsqueeze(0), **extra)[0]
for h in handles:
h.remove()
for name in subset:
scale, zero, g_idx, error = gptq[name].fasterquant(
percdamp=percdamp,
groupsize=groupsize,
act_order=act_order,
name=name,
)
quantizers[f"{prefix}.{i}.{name}"] = (
gptq[name].quantizer.cpu(),
scale.cpu(),
zero.cpu(),
g_idx.cpu(),
bits,
groupsize,
)
gptq[name].free()
for j in range(nsamples):
outs[j] = layer(inps[j].unsqueeze(0), **extra)[0]
layer.unload()
del layer
del gptq
torch.cuda.empty_cache()
inps, outs = outs, inps
print("+------------------+--------------+------------+-----------+-------+")
print("\n")
model.config.use_cache = use_cache
return quantizers
def make_quant_linear(module, names, bits, groupsize, name=""):
if isinstance(module, QuantLinear):
return
for attr in dir(module):
tmp = getattr(module, attr)
name1 = name + "." + attr if name != "" else attr
if name1 in names:
delattr(module, attr)
setattr(
module,
attr,
QuantLinear.new(
bits,
groupsize,
tmp.in_features,
tmp.out_features,
tmp.bias is not None,
),
)
for name1, child in module.named_children():
make_quant_linear(
child, names, bits, groupsize, name + "." + name1 if name != "" else name1
)
# TODO: perform packing on GPU
def pack(model, quantizers, bits, groupsize):
layers = find_layers(model)
layers = {n: layers[n] for n in quantizers}
make_quant_linear(model, quantizers, bits, groupsize)
qlayers = find_layers(model, (QuantLinear,))
print("Packing ...")
for name in qlayers:
print(name)
quantizers[name], scale, zero, g_idx, _, _ = quantizers[name]
qlayers[name].pack(layers[name], scale, zero, g_idx)
print("Done.")
return model
def setdeepattr(module, full_name, tensor):
current = module
tokens = full_name.split(".")
for token in tokens[:-1]:
current = getattr(current, token)
setattr(current, tokens[-1], tensor)
def getdeepattr(module, full_name):
current = module
tokens = full_name.split(".")
for token in tokens:
current = getattr(current, token)
return current
def load_weights_pre_hook(module_name, weights, recursive=False):
def inner(module, args):
print(f"Pre hook {module_name}")
local_params = {}
for k, v in module.named_parameters():
if not recursive and k.count(".") != 1:
continue
local_params[k] = v
for k, v in module.named_buffers():
if not recursive and k.count(".") != 1:
continue
local_params[k] = v
for local_param in local_params:
current_tensor = getdeepattr(module, local_param)
if current_tensor.device == torch.device("meta"):
# print(f"Loading {local_param}")
if module_name:
tensor_name = f"{module_name}.{local_param}"
else:
tensor_name = local_param
tensor = weights.get_tensor(tensor_name)
setdeepattr(module, local_param, nn.Parameter(tensor))
else:
tensor = current_tensor.to(device=torch.device("cuda:0"))
if current_tensor.requires_grad:
tensor = nn.Parameter(tensor)
setdeepattr(module, local_param, tensor)
return inner
def load_weights_post_hook(module_name, weights, recursive=False):
def inner(module, args, output):
print(f"Post hook {module_name}")
local_params = {}
for k, v in module.named_parameters():
if not recursive and k.count(".") != 1:
continue
local_params[k] = v
for k, v in module.named_buffers():
if not recursive and k.count(".") != 1:
continue
local_params[k] = v
for local_param in local_params:
# print(f"Unloading {local_param}")
current_tensor = getdeepattr(module, local_param)
setdeepattr(
module,
local_param,
nn.Parameter(current_tensor.to(device=torch.device("cpu"))),
)
return output
return inner
def quantize(
model_id: str,
bits: int,
groupsize: int,
output_dir: str,
revision: str,
trust_remote_code: bool,
upload_to_model_id: Optional[str],
percdamp: float,
act_order: bool,
):
print("loading model")
config = AutoConfig.from_pretrained(
model_id,
trust_remote_code=trust_remote_code,
)
with init_empty_weights():
model = AutoModelForCausalLM.from_config(
config, torch_dtype=torch.float16, trust_remote_code=trust_remote_code
)
model = model.eval()
print("LOADED model")
files = weight_files(model_id, revision, extension=".safetensors")
process_group, _, _ = initialize_torch_distributed()
weights = Weights(
files,
device=torch.device("cuda:0"),
dtype=torch.float16,
process_group=process_group,
aliases={"embed_tokens.weight": ["lm_head.weight"]},
)
hooks = []
for name, module in model.named_modules():
def load(module, name):
def _load():
load_weights_pre_hook(name, weights, recursive=True)(module, None)
return _load
def unload(module, name):
def _unload():
load_weights_post_hook(name, weights, recursive=True)(
module, None, None
)
return _unload
module.load = load(module, name)
module.unload = unload(module, name)
hooks.append(
module.register_forward_pre_hook(load_weights_pre_hook(name, weights))
)
hooks.append(
module.register_forward_hook(load_weights_post_hook(name, weights))
)
model.seqlen = 2048
dataset = "wikitext2"
nsamples = 128
seed = None
dataloader, testloader = get_loaders(
dataset,
nsamples=nsamples,
seed=seed,
model_id=model_id,
seqlen=model.seqlen,
trust_remote_code=trust_remote_code,
)
tick = time.time()
quantizers = sequential(
model,
dataloader,
DEV,
nsamples,
bits,
groupsize,
percdamp=percdamp,
act_order=act_order,
hooks=hooks,
)
print(time.time() - tick)
pack(model, quantizers, bits, groupsize)
from safetensors.torch import save_file
from transformers.modeling_utils import shard_checkpoint
state_dict = model.state_dict()
state_dict = {k: v.cpu().contiguous() for k, v in state_dict.items()}
state_dict["gptq_bits"] = torch.LongTensor([bits])
state_dict["gptq_groupsize"] = torch.LongTensor([groupsize])
max_shard_size = "10GB"
shards, index = shard_checkpoint(
state_dict, max_shard_size=max_shard_size, weights_name="model.safetensors"
)
os.makedirs(output_dir, exist_ok=True)
for shard_file, shard in shards.items():
save_file(
shard,
os.path.join(output_dir, shard_file),
metadata={
"format": "pt",
"quantized": "gptq",
"origin": "text-generation-inference",
},
)
if index is None:
path_to_weights = os.path.join(output_dir, "model.safetensors")
logger.info(f"Model weights saved in {path_to_weights}")
else:
save_index_file = "model.safetensors.index.json"
save_index_file = os.path.join(output_dir, save_index_file)
with open(save_index_file, "w", encoding="utf-8") as f:
content = json.dumps(index, indent=2, sort_keys=True) + "\n"
f.write(content)
logger.info(
f"The model is bigger than the maximum size per checkpoint ({max_shard_size}) and is going to be "
f"split in {len(shards)} checkpoint shards. You can find where each parameters has been saved in the "
f"index located at {save_index_file}."
)
config = AutoConfig.from_pretrained(model_id, trust_remote_code=trust_remote_code)
config.save_pretrained(output_dir)
logger.info("Saved config")
logger.info("Saving tokenizer")
tokenizer = AutoTokenizer.from_pretrained(
model_id, trust_remote_code=trust_remote_code
)
tokenizer.save_pretrained(output_dir)
logger.info("Saved tokenizer")
if upload_to_model_id:
api = HfApi()
api.upload_folder(
folder_path=output_dir, repo_id=upload_to_model_id, repo_type="model"
)
| 0 |
hf_public_repos/text-generation-inference/server | hf_public_repos/text-generation-inference/server/exllama_kernels/setup.py | from setuptools import setup
from torch.utils.cpp_extension import BuildExtension, CUDAExtension
setup(
name="exllama_kernels",
ext_modules=[
CUDAExtension(
name="exllama_kernels",
sources=[
"exllama_kernels/exllama_ext.cpp",
"exllama_kernels/cuda_buffers.cu",
"exllama_kernels/cuda_func/column_remap.cu",
"exllama_kernels/cuda_func/q4_matmul.cu",
"exllama_kernels/cuda_func/q4_matrix.cu",
],
)
],
cmdclass={"build_ext": BuildExtension},
)
| 0 |
hf_public_repos/text-generation-inference/server/exllama_kernels | hf_public_repos/text-generation-inference/server/exllama_kernels/exllama_kernels/exllama_ext.cpp | // Adapted from turboderp exllama: https://github.com/turboderp/exllama
#include <torch/extension.h>
#include <c10/cuda/CUDAGuard.h>
#include <ATen/cuda/CUDAContext.h>
#include <cuda_runtime.h>
#include <cuda_fp16.h>
#include <cstdint>
#include <cstdio>
#include "util.cuh"
#include "tuning.h"
#include "cuda_buffers.cuh"
#include "cuda_func/q4_matrix.cuh"
#include "cuda_func/q4_matmul.cuh"
#include "cuda_func/column_remap.cuh"
// Check CUDA return code. We don't want to include Torch headers in the .cu files because parsing them adds almost a
// minute to the compile time on a 12900K. Also passing exceptions back to Python is super tricky, so in place of
// exceptions, CUDA functions return with a cudaError_t which we can parse and dump to the console.
void check_cuda(cudaError_t ret)
{
switch (ret)
{
case cudaSuccess:
break;
case cudaUnspecified:
printf(" **** Unspecified error\n");
TORCH_CHECK(false, "CUDA error");
break;
default:
printf(" **** CUDA error\n"); \
printf(" **** %s\n", cudaGetErrorString(ret)); \
TORCH_CHECK(false, "CUDA error"); \
break;
}
}
// Some decluttering macros
#define STRINGIFY_(__x) #__x
#define STRINGIFY(__x) STRINGIFY_(__x)
#define TORCH_CHECK_DTYPE(__x, __dtype) TORCH_CHECK((__x).dtype() == torch::__dtype, #__x " is incorrect datatype, must be " #__dtype)
#define TORCH_CHECK_DTYPE_OPT(__x, __dtype) TORCH_CHECK((__x).device().is_meta() || (__x).dtype() == torch::__dtype, #__x " is incorrect datatype, must be " #__dtype)
#define TORCH_CHECK_SHAPES(__x, __dim_x, __y, __dim_y, __scale_y) TORCH_CHECK((__x).size(__dim_x) == (__y).size(__dim_y) * __scale_y, #__x " and " #__y " have incompatible shapes")
#define TORCH_CHECK_SHAPES_OPT(__x, __dim_x, __y, __dim_y, __scale_y) TORCH_CHECK((__x).device().is_meta() || (__x).size(__dim_x) == (__y).size(__dim_y) * __scale_y, #__x " and " #__y " have incompatible shapes")
#define TORCH_CHECK_SHAPE_MOD(__x, __dim_x, __mod) TORCH_CHECK((__x).size(__dim_x) % __mod == 0, #__x ".shape[" STRINGIFY(__dim_x) "] must be a multiple of " STRINGIFY(__mod))
#define TORCH_CHECK_DEVICE_INDEX(__index) \
do { \
TORCH_CHECK(__index >= 0, "no device index"); \
TORCH_CHECK(__index < CUDA_MAX_DEVICES, "invalid device index"); \
} while(0)
#define TORCH_CHECK_QUANT(__w, __w_scales, __w_zeros, __seq_g_idx, __x_map) \
do { \
TORCH_CHECK_DTYPE(__w, kInt); \
TORCH_CHECK_DTYPE(__w_scales, kHalf); \
TORCH_CHECK_DTYPE(__w_zeros, kInt); \
TORCH_CHECK_DTYPE_OPT(__seq_g_idx, kShort); \
TORCH_CHECK_DTYPE_OPT(__x_map, kInt); \
TORCH_CHECK_SHAPES_OPT(__seq_g_idx, 0, __w, 0, 2 * 8); \
TORCH_CHECK_SHAPES_OPT(__x_map, 0, __w, 0, 8); \
} while(0)
int get_groupsize(torch::Tensor w, torch::Tensor w_zeros)
{
int groupsize = w.size(0) * 8 / w_zeros.size(0);
TORCH_CHECK(groupsize * w_zeros.size(0) == w.size(0) * 8, "w.shape[-2] must be a multiple of zeros.shape[-2]")
return groupsize;
}
// Tuning parameters
ExLlamaTuning tuningParams;
void set_tuning_params
(
int matmul_recons_thd,
bool matmul_fused_remap,
bool matmul_no_half2
)
{
tuningParams.matmul_recons_thd = matmul_recons_thd;
tuningParams.matmul_fused_remap = matmul_fused_remap;
tuningParams.matmul_no_half2 = matmul_no_half2;
}
// Release all unmanaged objects allocated by the extension
void cleanup()
{
cleanup_buffers_cuda();
g_q4_free_matrices();
}
// Prepare buffers for forward pass
void prepare_buffers
(
torch::Device device,
torch::Tensor temp_state,
torch::Tensor temp_dq
)
{
int device_index = device.index();
TORCH_CHECK_DEVICE_INDEX(device_index);
const at::cuda::OptionalCUDAGuard device_guard(device);
prepare_buffers_cuda
(
device_index,
(half*) temp_state.data_ptr(),
(half*) temp_dq.data_ptr()
);
}
// Create Q4Matrix, return handle
uintptr_t make_q4
(
torch::Tensor qweight,
torch::Tensor qzeros,
torch::Tensor scales,
torch::Tensor g_idx,
int device
)
{
TORCH_CHECK_DTYPE(qweight, kInt);
TORCH_CHECK_DTYPE(qzeros, kInt);
TORCH_CHECK_DTYPE(scales, kHalf);
TORCH_CHECK_DTYPE_OPT(g_idx, kInt);
TORCH_CHECK_SHAPES(qweight, 1, qzeros, 1, 8);
TORCH_CHECK_SHAPES(scales, 1, qweight, 1, 1);
TORCH_CHECK_SHAPES(qzeros, 0, scales, 0, 1);
int width = qweight.size(1);
int height = qweight.size(0) * 8;
int groups = qzeros.size(0);
Q4Matrix* m = new Q4Matrix
(
height,
width,
groups,
(uint32_t*) qweight.data_ptr(),
(uint32_t*) qzeros.data_ptr(),
(half*) scales.data_ptr(),
g_idx.device().is_meta() ? NULL : (uint32_t*) g_idx.data_ptr(),
device
);
g_q4_keep_matrix(m);
return reinterpret_cast<uintptr_t> (m);
}
// Matmul half @ quant -> half
void q4_matmul
(
torch::Tensor x,
uintptr_t w,
torch::Tensor out
)
{
Q4Matrix* wm = reinterpret_cast<Q4Matrix*> (w);
TORCH_CHECK_DTYPE(x, kHalf);
TORCH_CHECK_DTYPE(out, kHalf);
TORCH_CHECK_SHAPES(x, 0, out, 0, 1);
TORCH_CHECK(wm->height == x.size(-1), "x and w have incompatible shapes")
const at::cuda::OptionalCUDAGuard device_guard(device_of(x));
int x_height = x.size(0);
if (tuningParams.matmul_recons_thd == 0 || x_height < tuningParams.matmul_recons_thd)
{
q4_matmul_cuda
(
&tuningParams,
(half*) x.data_ptr(),
x_height,
wm,
(half*) out.data_ptr()
);
}
else
{
q4_matmul_recons_cuda
(
&tuningParams,
(half*) x.data_ptr(),
x_height,
wm,
(half*) out.data_ptr(),
at::cuda::getCurrentCUDABlasHandle()
);
}
}
// Remap columns in half tensor
void column_remap
(
torch::Tensor x,
torch::Tensor x_new,
torch::Tensor x_map
)
{
TORCH_CHECK_DTYPE(x, kHalf);
TORCH_CHECK_DTYPE(x_new, kHalf);
TORCH_CHECK_DTYPE(x_map, kInt);
TORCH_CHECK_SHAPES(x_map, 0, x, 1, 1);
int height = x.size(0);
int width = x.size(1);
const at::cuda::OptionalCUDAGuard device_guard(device_of(x));
column_remap_cuda
(
(half*) x.data_ptr(),
(half*) x_new.data_ptr(),
height,
width,
(uint32_t*) x_map.data_ptr()
);
}
PYBIND11_MODULE(TORCH_EXTENSION_NAME, m)
{
m.def("set_tuning_params", &set_tuning_params, "set_tuning_params");
m.def("prepare_buffers", &prepare_buffers, "prepare_buffers");
m.def("cleanup", &cleanup, "cleanup");
m.def("make_q4", &make_q4, "make_q4");
m.def("q4_matmul", &q4_matmul, "q4_matmul");
}
| 0 |
hf_public_repos/text-generation-inference/server/exllama_kernels | hf_public_repos/text-generation-inference/server/exllama_kernels/exllama_kernels/cuda_buffers.cu | // Adapted from turboderp exllama: https://github.com/turboderp/exllama
#define _cuda_buffers_cu
#include "cuda_buffers.cuh"
CudaBuffers* g_buffers[CUDA_MAX_DEVICES] = {NULL};
// __constant__ half2 q4_table[16][256];
// half2 q4_table_host[16][256];
// bool q4_table_init = false;
CudaBuffers::CudaBuffers
(
int _device,
half* _temp_state,
half* _temp_dq
) :
device(_device),
temp_state(_temp_state),
temp_dq(_temp_dq)
{
cudaSetDevice(_device);
cudaStreamCreate(&alt_stream_1);
cudaStreamCreate(&alt_stream_2);
cudaStreamCreate(&alt_stream_3);
cudaEventCreate(&alt_stream_1_done);
cudaEventCreate(&alt_stream_2_done);
cudaEventCreate(&alt_stream_3_done);
}
CudaBuffers::~CudaBuffers()
{
cudaStreamDestroy(alt_stream_1);
cudaStreamDestroy(alt_stream_2);
cudaStreamDestroy(alt_stream_3);
cudaEventDestroy(alt_stream_1_done);
cudaEventDestroy(alt_stream_2_done);
cudaEventDestroy(alt_stream_3_done);
}
CudaBuffers* get_buffers(const int device_index)
{
return g_buffers[device_index];
}
void prepare_buffers_cuda
(
int _device,
half* _temp_state,
half* _temp_dq
)
{
CudaBuffers* buffers = new CudaBuffers
(
_device,
_temp_state,
_temp_dq
);
g_buffers[_device] = buffers;
}
void cleanup_buffers_cuda()
{
for (int i = 0; i < CUDA_MAX_DEVICES; i++)
{
if (!g_buffers[i]) continue;
delete g_buffers[i];
g_buffers[i] = NULL;
}
}
| 0 |
hf_public_repos/text-generation-inference/server/exllama_kernels | hf_public_repos/text-generation-inference/server/exllama_kernels/exllama_kernels/tuning.h | // Adapted from turboderp exllama: https://github.com/turboderp/exllama
#ifndef _tuning_h
#define _tuning_h
struct ExLlamaTuning
{
int matmul_recons_thd;
bool matmul_fused_remap;
bool matmul_no_half2;
};
#endif
| 0 |
hf_public_repos/text-generation-inference/server/exllama_kernels | hf_public_repos/text-generation-inference/server/exllama_kernels/exllama_kernels/util.cuh | // Adapted from turboderp exllama: https://github.com/turboderp/exllama
#ifndef _util_cuh
#define _util_cuh
#include <cuda_runtime.h>
#include <cuda_fp16.h>
#include <cstdint>
#include <cstdio>
#define cudaUnspecified cudaErrorApiFailureBase
// React to failure on return code != cudaSuccess
#define _cuda_check(fn) \
do { \
{_cuda_err = fn;} \
if (_cuda_err != cudaSuccess) goto _cuda_fail; \
} while(false)
// React to failure on return code == 0
#define _alloc_check(fn) \
do { \
if (!(fn)) { _cuda_err = cudaUnspecified; goto _cuda_fail; } \
else _cuda_err = cudaSuccess; \
} while(false)
#endif
| 0 |
hf_public_repos/text-generation-inference/server/exllama_kernels | hf_public_repos/text-generation-inference/server/exllama_kernels/exllama_kernels/cuda_buffers.cuh | // Adapted from turboderp exllama: https://github.com/turboderp/exllama
#ifndef _cuda_buffers_cuh
#define _cuda_buffers_cuh
#include <cuda_runtime.h>
#include <cuda_fp16.h>
#include <cstdint>
#include <cstdio>
const int CUDA_MAX_DEVICES = 16;
// #ifndef _cuda_buffers_cu
// extern __constant__ half2 q4_table[16][256];
// #endif
class CudaBuffers
{
public:
int device;
half* temp_state; // [max_hidden_rows * intermediate_size]
half* temp_dq; // size of largest quant tensor * 8
cudaStream_t alt_stream_1;
cudaStream_t alt_stream_2;
cudaStream_t alt_stream_3;
cudaEvent_t alt_stream_1_done;
cudaEvent_t alt_stream_2_done;
cudaEvent_t alt_stream_3_done;
CudaBuffers
(
int _device,
half* _temp_state,
half* _temp_dq
);
~CudaBuffers();
};
CudaBuffers* get_buffers(const int device_index);
void prepare_buffers_cuda
(
int _device,
half* _temp_state,
half* _temp_dq
);
void cleanup_buffers_cuda();
#endif
| 0 |
hf_public_repos/text-generation-inference/server/exllama_kernels | hf_public_repos/text-generation-inference/server/exllama_kernels/exllama_kernels/cuda_compat.cuh | // Adapted from turboderp exllama: https://github.com/turboderp/exllama
#ifndef _cuda_compat_cuh
#define _cuda_compat_cuh
// atomicAdd for half types, to support CC < 7.x
__device__ __forceinline__ void atomicAdd_half(half* address, half val)
{
unsigned int * address_as_ui = (unsigned int *) ((char *)address - ((size_t)address & 2));
unsigned int old = *address_as_ui;
unsigned int assumed;
do
{
assumed = old;
__half_raw hsum;
hsum.x = (size_t)address & 2 ? (old >> 16) : (old & 0xffff);
half tmpres = __hadd(hsum, val);
hsum = __half_raw(tmpres);
old = (size_t)address & 2 ? (old & 0xffff) | (hsum.x << 16) : (old & 0xffff0000) | hsum.x;
old = atomicCAS(address_as_ui, assumed, old);
}
while (assumed != old);
}
// atomicAdd for half2 types
__device__ __forceinline__ void atomicAdd_half2(half2* address, half2 val)
{
unsigned int* address_as_ui = (unsigned int*)address;
unsigned int old = *address_as_ui;
unsigned int assumed;
do
{
assumed = old;
half2 old_val = *((half2*)&old);
half2 new_val = __hadd2(old_val, val);
old = atomicCAS(address_as_ui, assumed, *((unsigned int*)&new_val));
}
while (assumed != old);
}
//
#if defined(__CUDA_ARCH__)
#if __CUDA_ARCH__ < 700
__device__ __forceinline__ void atomicAdd(half* address, half val) { atomicAdd_half(address, val); }
#if __CUDA_ARCH__ < 600
__device__ __forceinline__ void atomicAdd(half2* address, half2 val) { atomicAdd_half2(address, val); }
#endif
#endif
#endif
#endif
| 0 |
hf_public_repos/text-generation-inference/server/exllama_kernels | hf_public_repos/text-generation-inference/server/exllama_kernels/exllama_kernels/matrix.cuh | // Adapted from turboderp exllama: https://github.com/turboderp/exllama
#ifndef _matrix_cuh
#define _matrix_cuh
#include <cuda_runtime.h>
#include <cuda_fp16.h>
class MatrixView_half
{
public:
const half* data;
const int height;
const int width;
__device__ __forceinline__ MatrixView_half(const half* data, const int height, const int width)
: data(data), height(height), width(width)
{ }
__device__ __forceinline__ half item(int row, int column) const { return data[row * width + column]; }
__device__ __forceinline__ half2 item_half2(int row, int column) const { return ((half2*)data)[(row * width + column) / 2]; }
__device__ __forceinline__ half2 item_half2half2(int row, int column) const { return __half2half2(data[row * width + column]); }
__device__ __forceinline__ const half* item_ptr(int row, int column) const { return &data[row * width + column]; }
};
class MatrixView_half_rw
{
public:
half* data;
const int height;
const int width;
__device__ __forceinline__ MatrixView_half_rw(half* data, const int height, const int width)
: data(data), height(height), width(width)
{ }
__device__ __forceinline__ half item(int row, int column) const { return data[row * width + column]; }
__device__ __forceinline__ half2 item_half2(int row, int column) const { return ((half2*)data)[(row * width + column) / 2]; }
__device__ __forceinline__ half* item_ptr(int row, int column) { return &data[row * width + column]; }
__device__ __forceinline__ void set(int row, int column, half value) { data[row * width + column] = value; }
__device__ __forceinline__ void set_half2(int row, int column, half2 value) { ((half2*)data)[(row * width + column) / 2] = value; }
};
class MatrixView_q4_row
{
public:
const uint32_t* data;
const int height;
const int width;
__device__ __forceinline__ MatrixView_q4_row(const uint32_t* data, const int height, const int width)
: data(data), height(height), width(width)
{ }
__device__ __forceinline__ int item(int row, int column) const
{
int shift = (column & 0x07) * 4;
return (data[row * width / 8 + column / 8] >> shift) & 0x0f;
}
};
class MatrixView_q4_column
{
public:
const uint32_t* data;
const int height;
const int width;
__device__ __forceinline__ MatrixView_q4_column(const uint32_t* data, const int height, const int width)
: data(data), height(height), width(width)
{ }
__device__ __forceinline__ int item(int row, int column) const
{
int shift = (row & 0x07) * 4;
return (data[row / 8 * width + column] >> shift) & 0x0f;
}
__device__ __forceinline__ uint32_t item_uint32_t(int row, int column) { return data[row / 8 * width + column]; }
__device__ __forceinline__ const uint32_t* item_uint32_ptr(int row, int column) { return &data[row / 8 * width + column]; }
};
// TODO: Rewrite all these dot product functions using functors or something, move to q4_matmul.cu
// Accumulated dot product of 8-element row vectors in h and quantized column vectors in v, constant zero/scale
__device__ __forceinline__ half2 dot_product_8
(
const half2 acc,
MatrixView_half& h_,
const int h_row,
const int h_column, // divisible by 8
MatrixView_q4_column& v_,
const int v_row, // divisible by 8
const int v_column,
const half2 v_scale_2,
const uint32_t v_zero, // + 1 (!!)
const int count
)
{
const half2* h_ptr = (const half2*) h_.item_ptr(h_row, h_column);
const uint32_t* v_ptr = (const uint32_t*) v_.item_uint32_ptr(v_row, v_column);
half2 result = acc;
for (int i = 0; i < count; i++)
{
uint32_t v_read = *v_ptr; v_ptr += v_.width;
half v_0 = __int2half_rn((int)((v_read ) & 0x0f) - v_zero);
half v_1 = __int2half_rn((int)((v_read >> 4) & 0x0f) - v_zero);
half v_2 = __int2half_rn((int)((v_read >> 8) & 0x0f) - v_zero);
half v_3 = __int2half_rn((int)((v_read >> 12) & 0x0f) - v_zero);
half v_4 = __int2half_rn((int)((v_read >> 16) & 0x0f) - v_zero);
half v_5 = __int2half_rn((int)((v_read >> 20) & 0x0f) - v_zero);
half v_6 = __int2half_rn((int)((v_read >> 24) & 0x0f) - v_zero);
half v_7 = __int2half_rn((int)((v_read >> 28) ) - v_zero);
half2 v_01 = __halves2half2(v_0, v_1);
half2 v_23 = __halves2half2(v_2, v_3);
half2 v_45 = __halves2half2(v_4, v_5);
half2 v_67 = __halves2half2(v_6, v_7);
// half2 v_01 = q4_table[v_zero - 1][(v_read ) & 0xff]; // (constant memory is too slow apparently)
// half2 v_23 = q4_table[v_zero - 1][(v_read >> 8) & 0xff];
// half2 v_45 = q4_table[v_zero - 1][(v_read >> 16) & 0xff];
// half2 v_67 = q4_table[v_zero - 1][(v_read >> 24) ];
half2 tmp = __hmul2(*h_ptr++, v_01);
tmp = __hfma2(*h_ptr++, v_23, tmp);
tmp = __hfma2(*h_ptr++, v_45, tmp);
tmp = __hfma2(*h_ptr++, v_67, tmp);
result = __hfma2(v_scale_2, tmp, result);
}
return result;
}
__device__ __forceinline__ half dot_product_8_h
(
const half acc,
MatrixView_half& h_,
const int h_row,
const int h_column, // divisible by 8
MatrixView_q4_column& v_,
const int v_row, // divisible by 8
const int v_column,
const half v_scale,
const uint32_t v_zero, // + 1 (!!)
const int count
)
{
const half* h_ptr = h_.item_ptr(h_row, h_column);
const uint32_t* v_ptr = (const uint32_t*) v_.item_uint32_ptr(v_row, v_column);
half result = acc;
for (int i = 0; i < count; i++)
{
uint32_t v_read = *v_ptr; v_ptr += v_.width;
half v_0 = __int2half_rn((int)((v_read ) & 0x0f) - v_zero);
half v_1 = __int2half_rn((int)((v_read >> 4) & 0x0f) - v_zero);
half v_2 = __int2half_rn((int)((v_read >> 8) & 0x0f) - v_zero);
half v_3 = __int2half_rn((int)((v_read >> 12) & 0x0f) - v_zero);
half v_4 = __int2half_rn((int)((v_read >> 16) & 0x0f) - v_zero);
half v_5 = __int2half_rn((int)((v_read >> 20) & 0x0f) - v_zero);
half v_6 = __int2half_rn((int)((v_read >> 24) & 0x0f) - v_zero);
half v_7 = __int2half_rn((int)((v_read >> 28) ) - v_zero);
half tmp = __hmul(*h_ptr++, v_0);
tmp = __hfma(*h_ptr++, v_1, tmp);
tmp = __hfma(*h_ptr++, v_2, tmp);
tmp = __hfma(*h_ptr++, v_3, tmp);
tmp = __hfma(*h_ptr++, v_4, tmp);
tmp = __hfma(*h_ptr++, v_5, tmp);
tmp = __hfma(*h_ptr++, v_6, tmp);
tmp = __hfma(*h_ptr++, v_7, tmp);
result = __hfma(v_scale, tmp, result);
}
return result;
}
// Accumulated dot product of 8-element row vectors in h and quantized column vectors in v, constant zero/scale, with x_map
__device__ __forceinline__ half2 dot_product_8_x_map
(
const half2 acc,
MatrixView_half& h_,
const int h_row,
const int h_column, // divisible by 8
MatrixView_q4_column& v_,
const int v_row, // divisible by 8
const int v_column,
const half2 v_scale_2,
const uint32_t v_zero, // + 1 (!!)
const int count,
const uint32_t* x_map
)
{
const half* h_ptr = h_.item_ptr(h_row, 0);
const uint32_t* x_map_ptr = x_map + h_column;
const uint32_t* v_ptr = (const uint32_t*) v_.item_uint32_ptr(v_row, v_column);
half2 result = acc;
for (int i = 0; i < count; i++)
{
uint32_t v_read = *v_ptr; v_ptr += v_.width;
half v_0 = __int2half_rn((int)((v_read ) & 0x0f) - v_zero);
half v_1 = __int2half_rn((int)((v_read >> 4) & 0x0f) - v_zero);
half v_2 = __int2half_rn((int)((v_read >> 8) & 0x0f) - v_zero);
half v_3 = __int2half_rn((int)((v_read >> 12) & 0x0f) - v_zero);
half v_4 = __int2half_rn((int)((v_read >> 16) & 0x0f) - v_zero);
half v_5 = __int2half_rn((int)((v_read >> 20) & 0x0f) - v_zero);
half v_6 = __int2half_rn((int)((v_read >> 24) & 0x0f) - v_zero);
half v_7 = __int2half_rn((int)((v_read >> 28) ) - v_zero);
half2 v_01 = __halves2half2(v_0, v_1);
half2 v_23 = __halves2half2(v_2, v_3);
half2 v_45 = __halves2half2(v_4, v_5);
half2 v_67 = __halves2half2(v_6, v_7);
half h_0 = h_ptr[*x_map_ptr++];
half h_1 = h_ptr[*x_map_ptr++];
half h_2 = h_ptr[*x_map_ptr++];
half h_3 = h_ptr[*x_map_ptr++];
half h_4 = h_ptr[*x_map_ptr++];
half h_5 = h_ptr[*x_map_ptr++];
half h_6 = h_ptr[*x_map_ptr++];
half h_7 = h_ptr[*x_map_ptr++];
half2 h_01 = __halves2half2(h_0, h_1);
half2 h_23 = __halves2half2(h_2, h_3);
half2 h_45 = __halves2half2(h_4, h_5);
half2 h_67 = __halves2half2(h_6, h_7);
half2 tmp = __hmul2(h_01, v_01);
tmp = __hfma2(h_23, v_23, tmp);
tmp = __hfma2(h_45, v_45, tmp);
tmp = __hfma2(h_67, v_67, tmp);
result = __hfma2(v_scale_2, tmp, result);
}
return result;
}
__device__ __forceinline__ half dot_product_8_x_map_h
(
const half acc,
MatrixView_half& h_,
const int h_row,
const int h_column, // divisible by 8
MatrixView_q4_column& v_,
const int v_row, // divisible by 8
const int v_column,
const half v_scale,
const uint32_t v_zero, // + 1 (!!)
const int count,
const uint32_t* x_map
)
{
const half* h_ptr = h_.item_ptr(h_row, 0);
const uint32_t* x_map_ptr = x_map + h_column;
const uint32_t* v_ptr = (const uint32_t*) v_.item_uint32_ptr(v_row, v_column);
half result = acc;
for (int i = 0; i < count; i++)
{
uint32_t v_read = *v_ptr; v_ptr += v_.width;
half v_0 = __int2half_rn((int)((v_read ) & 0x0f) - v_zero);
half v_1 = __int2half_rn((int)((v_read >> 4) & 0x0f) - v_zero);
half v_2 = __int2half_rn((int)((v_read >> 8) & 0x0f) - v_zero);
half v_3 = __int2half_rn((int)((v_read >> 12) & 0x0f) - v_zero);
half v_4 = __int2half_rn((int)((v_read >> 16) & 0x0f) - v_zero);
half v_5 = __int2half_rn((int)((v_read >> 20) & 0x0f) - v_zero);
half v_6 = __int2half_rn((int)((v_read >> 24) & 0x0f) - v_zero);
half v_7 = __int2half_rn((int)((v_read >> 28) ) - v_zero);
half tmp = __hmul(h_ptr[*x_map_ptr++], v_0);
tmp = __hfma(h_ptr[*x_map_ptr++], v_1, tmp);
tmp = __hfma(h_ptr[*x_map_ptr++], v_2, tmp);
tmp = __hfma(h_ptr[*x_map_ptr++], v_3, tmp);
tmp = __hfma(h_ptr[*x_map_ptr++], v_4, tmp);
tmp = __hfma(h_ptr[*x_map_ptr++], v_5, tmp);
tmp = __hfma(h_ptr[*x_map_ptr++], v_6, tmp);
tmp = __hfma(h_ptr[*x_map_ptr++], v_7, tmp);
result = __hfma(v_scale, tmp, result);
}
return result;
}
#endif
| 0 |
hf_public_repos/text-generation-inference/server/exllama_kernels/exllama_kernels | hf_public_repos/text-generation-inference/server/exllama_kernels/exllama_kernels/cuda_func/column_remap.cuh | // Adapted from turboderp exllama: https://github.com/turboderp/exllama
#ifndef _column_remap_cuh
#define _column_remap_cuh
#include <cuda_runtime.h>
#include <cuda_fp16.h>
#include <cstdint>
void column_remap_cuda
(
const half* x,
half* x_new,
const int x_height,
const int x_width,
const uint32_t* x_map
);
#endif | 0 |
hf_public_repos/text-generation-inference/server/exllama_kernels/exllama_kernels | hf_public_repos/text-generation-inference/server/exllama_kernels/exllama_kernels/cuda_func/q4_matrix.cu | // Adapted from turboderp exllama: https://github.com/turboderp/exllama
#include "q4_matrix.cuh"
#include <vector>
#include "../util.cuh"
#include "../matrix.cuh"
using namespace std;
const int UNSHUF_BLOCKSIZE_X = 64;
const int RECONS_THREADS_X = 64; // Block size and thread count along columns in out, each thread converts 1 column
const int RECONS_THREADS_Y = 1; // Block size and thread count along rows in x and out, each thread converts 8 rows
vector<Q4Matrix*> g_q4_matrices;
void g_q4_keep_matrix(Q4Matrix* m)
{
g_q4_matrices.push_back(m);
}
void g_q4_free_matrices()
{
for (const auto& m : g_q4_matrices) delete m;
g_q4_matrices.clear();
}
Q4Matrix::Q4Matrix
(
const int _height,
const int _width,
const int _groups,
uint32_t* _qweight,
uint32_t* _qzeros,
half* _scales,
uint32_t* _g_idx,
const int _device
) :
height(_height),
width(_width),
groups(_groups),
device(_device)
{
cudaSetDevice(device);
cuda_qweight = _qweight;
cuda_qzeros = _qzeros;
cuda_scales = _scales;
groupsize = height / groups;
if (_g_idx) make_sequential(_g_idx);
}
Q4Matrix::~Q4Matrix()
{
}
// Make sequential
__global__ void make_sequential_kernel
(
const uint32_t* __restrict__ w,
uint32_t* __restrict__ w_new,
const uint32_t* __restrict__ x_map,
const int w_height,
const int w_width
)
{
const uint64_t* w2 = (uint64_t*) w;
uint64_t* w_new2 = (uint64_t*) w_new;
int w2_stride = w_width >> 1;
int w2_column = UNSHUF_BLOCKSIZE_X * blockIdx.x + threadIdx.x;
int w_new2_row = blockIdx.y;
int x_map_idx = w_new2_row << 3;
uint64_t dst = 0;
#pragma unroll
for (int i = 0; i < 8; i++)
{
int source_row = x_map[x_map_idx++];
int w2_row = source_row >> 3;
int w2_subrow = source_row & 0x07;
int w2_row_shift = w2_subrow << 2;
int wnew2_row_shift = i << 2;
uint64_t src = w2[w2_row * w2_stride + w2_column];
src >>= w2_row_shift;
src &= 0x0000000f0000000f;
src <<= wnew2_row_shift;
dst |= src;
}
w_new2[w_new2_row * w2_stride + w2_column] = dst;
}
void Q4Matrix::make_sequential(const uint32_t* cpu_g_idx)
{
uint32_t* cuda_new_qweight = NULL;
cudaMalloc(&cuda_new_qweight, height / 8 * width * sizeof(uint32_t));
cudaMalloc(&cuda_x_map, height * sizeof(uint32_t)); // TODO: Should probably be allocated in PyTorch
uint32_t* cpu_g_idx_map = (uint32_t*) calloc(groups, sizeof(uint32_t));
uint32_t* cpu_x_map = (uint32_t*) malloc(height * sizeof(uint32_t));
uint32_t* cpu_x_map_inv = (uint32_t*) malloc(height * sizeof(uint32_t));
// Group histogram
for (int i = 0; i < height; i++) cpu_g_idx_map[cpu_g_idx[i]]++;
// Group map
for (int i = 0, acc = 0; i < groups; i++)
{
short tmp = cpu_g_idx_map[i];
cpu_g_idx_map[i] = acc;
acc += tmp;
}
// X map (inverse)
for (int row = 0; row < height; row++)
{
uint32_t target_group = cpu_g_idx[row];
uint32_t target_row = cpu_g_idx_map[target_group];
cpu_g_idx_map[target_group]++;
cpu_x_map_inv[row] = target_row;
}
// X map
for (int row = 0; row < height; row++) cpu_x_map[cpu_x_map_inv[row]] = row;
// Move to CUDA
cudaMemcpyAsync(cuda_x_map, cpu_x_map, height * sizeof(uint32_t), cudaMemcpyHostToDevice);
// Rearrange rows in w
dim3 threads(UNSHUF_BLOCKSIZE_X, 1, 1);
dim3 blocks(width / UNSHUF_BLOCKSIZE_X / 2, height / 8, 1);
make_sequential_kernel<<<blocks, threads>>>(cuda_qweight, cuda_new_qweight, cuda_x_map, height / 8, width);
// Replace qweights
cudaMemcpyAsync(cuda_qweight, cuda_new_qweight, height / 8 * width * sizeof(uint32_t), cudaMemcpyDeviceToDevice);
// Cleanup
cudaDeviceSynchronize();
cudaFree(cuda_new_qweight);
free(cpu_g_idx_map);
free(cpu_x_map);
free(cpu_x_map_inv);
}
__global__ void reconstruct_kernel
(
const uint32_t* __restrict__ w,
half* __restrict__ out, // (y)
const half* __restrict__ w_scales,
const uint32_t* __restrict__ w_zeros,
const int height,
const int width,
const int groupsize
)
{
// Start of block
int column = RECONS_THREADS_X * blockIdx.x + threadIdx.x;
int row = (RECONS_THREADS_Y * blockIdx.y + threadIdx.y) * 8;
// Views
MatrixView_q4_column w_(w, height, width);
MatrixView_half_rw out_(out, height, width);
MatrixView_half w_scales_(w_scales, height / groupsize, width);
MatrixView_q4_row w_zeros_(w_zeros, height / groupsize, width);
// Groupsize version
int group = row / groupsize;
half w_scale = w_scales_.item(group, column);
uint32_t w_zero = w_zeros_.item(group, column) + 1;
uint32_t w_read = w_.item_uint32_t(row, column);
half* out_ptr = out_.item_ptr(row, column);
#pragma unroll
for (int s = 0; s < 32; s += 4)
{
half w_item = __hmul(__int2half_rn((int)((w_read >> s) & 0x0f) - w_zero), w_scale);
*out_ptr = w_item; out_ptr += out_.width;
}
}
void Q4Matrix::reconstruct(half* out)
{
dim3 threads(RECONS_THREADS_X, RECONS_THREADS_Y, 1);
dim3 blocks
(
(width + threads.x - 1) / threads.x,
(height / 8 + threads.y - 1) / threads.y,
1
);
reconstruct_kernel<<<blocks, threads>>>(cuda_qweight, out, cuda_scales, cuda_qzeros, height / 8, width, groupsize);
} | 0 |
hf_public_repos/text-generation-inference/server/exllama_kernels/exllama_kernels | hf_public_repos/text-generation-inference/server/exllama_kernels/exllama_kernels/cuda_func/q4_matmul.cuh | // Adapted from turboderp exllama: https://github.com/turboderp/exllama
#ifndef _q4_matmul_cuh
#define _q4_matmul_cuh
#include <cuda_runtime.h>
#include <cuda_fp16.h>
#include <cstdint>
#include <cstdio>
#include <ATen/cuda/CUDAContext.h>
#include "q4_matrix.cuh"
#include "../tuning.h"
void q4_matmul_cuda
(
ExLlamaTuning* tuningParams,
const half* x,
const int x_height,
const Q4Matrix* w,
half* out,
bool no_zero = false,
cudaStream_t alt_stream = NULL
);
void q4_matmul_recons_cuda
(
ExLlamaTuning* tuningParams,
const half* x,
const int x_height,
Q4Matrix* w,
half* out,
const cublasHandle_t handle,
bool no_zero = false
);
#endif
| 0 |
hf_public_repos/text-generation-inference/server/exllama_kernels/exllama_kernels | hf_public_repos/text-generation-inference/server/exllama_kernels/exllama_kernels/cuda_func/column_remap.cu | // Adapted from turboderp exllama: https://github.com/turboderp/exllama
#include "column_remap.cuh"
#include "../util.cuh"
const int SHUF_BLOCKSIZE_X = 256;
const int SHUF_BLOCKSIZE_Y = 16;
__global__ void column_remap_kernel
(
const half* __restrict__ x,
half* __restrict__ x_new,
const int x_width,
const int x_height,
const uint32_t* x_map
)
{
int x_column = SHUF_BLOCKSIZE_X * blockIdx.x + threadIdx.x;
int x_row = SHUF_BLOCKSIZE_Y * blockIdx.y;
int x_stride = x_width;
int x_idx = x_row * x_stride + x_column;
int x_row_end = min(x_row + SHUF_BLOCKSIZE_Y, x_height);
int x_idx_end = x_row_end * x_stride + x_column;
int s_column = x_map[x_column];
int s_idx = x_row * x_stride + s_column;
while (x_idx < x_idx_end)
{
x_new[x_idx] = x[s_idx];
x_idx += x_stride;
s_idx += x_stride;
}
}
// Remap columns in x to correspond to sequential group index before matmul
//
// perform x -> seq_x such that seq_x @ seq_w == x @ w
void column_remap_cuda
(
const half* x,
half* x_new,
const int x_height,
const int x_width,
const uint32_t* x_map
)
{
dim3 threads(SHUF_BLOCKSIZE_X, 1, 1);
dim3 blocks
(
(x_width + SHUF_BLOCKSIZE_X - 1) / SHUF_BLOCKSIZE_X,
(x_height + SHUF_BLOCKSIZE_Y - 1) / SHUF_BLOCKSIZE_Y,
1
);
column_remap_kernel<<<blocks, threads>>>(x, x_new, x_width, x_height, x_map);
}
| 0 |
hf_public_repos/text-generation-inference/server/exllama_kernels/exllama_kernels | hf_public_repos/text-generation-inference/server/exllama_kernels/exllama_kernels/cuda_func/q4_matrix.cuh | // Adapted from turboderp exllama: https://github.com/turboderp/exllama
#ifndef _q4_matrix_cuh
#define _q4_matrix_cuh
#include <cuda_runtime.h>
#include <cuda_fp16.h>
#include <cstdint>
class Q4Matrix
{
public:
int device;
int height;
int width;
int groups;
int groupsize;
uint32_t* cuda_qweight = NULL;
uint32_t* cuda_qzeros = NULL;
half* cuda_scales = NULL;
uint32_t* cuda_x_map = NULL;
Q4Matrix
(
const int _height,
const int _width,
const int _groups,
uint32_t* _qweight,
uint32_t* _qzeros,
half* _scales,
uint32_t* _g_idx,
const int _device
);
~Q4Matrix();
void reconstruct(half* out);
private:
void make_sequential(const uint32_t* cpu_g_idx);
};
void g_q4_keep_matrix(Q4Matrix* m);
void g_q4_free_matrices();
#endif | 0 |
hf_public_repos/text-generation-inference/server/exllama_kernels/exllama_kernels | hf_public_repos/text-generation-inference/server/exllama_kernels/exllama_kernels/cuda_func/q4_matmul.cu | #include "q4_matmul.cuh"
#include "column_remap.cuh"
#include "../util.cuh"
#include "../matrix.cuh"
#include "../cuda_compat.cuh"
#include "../cuda_buffers.cuh"
const int THREADS_X = 32; // Block size and thread count along columns in w and out
const int THREADS_Y = 1; // Block size and thread count along rows in x and out
typedef void (*fp_q4_matmul_kernel)
(
const half*,
const uint32_t*,
half*,
const half*,
const uint32_t*,
const int,
const int,
const int,
const int,
const int,
const uint32_t*,
bool
);
template<bool use_half2, bool use_groupsize, bool use_x_map>
__global__ void q4_matmul_kernel
(
const half* __restrict__ x,
const uint32_t* __restrict__ w,
half* __restrict__ out,
const half* __restrict__ w_scales,
const uint32_t* __restrict__ w_zeros,
const int height,
const int dim,
const int width,
const int groupsize,
const int block_size_z,
const uint32_t* __restrict__ x_map,
bool no_zero
)
{
// Start of block
int x_column = block_size_z * blockIdx.z;
int x_column_end = min(dim, block_size_z * (blockIdx.z + 1));
int w_column = THREADS_X * blockIdx.x + threadIdx.x;
int x_row = THREADS_Y * blockIdx.y + threadIdx.y;
int iterations = (x_column_end - x_column) / 8;
// Views
MatrixView_half x_(x, height, dim);
MatrixView_half w_scales_(w_scales, dim / groupsize, width);
MatrixView_q4_row w_zeros_(w_zeros, dim / groupsize, width);
MatrixView_q4_column w_(w, dim, width);
MatrixView_half_rw out_(out, height, width);
// Zero output
if (!no_zero && blockIdx.z == 0 && (threadIdx.x & 1) == 0)
{
*((uint32_t*) out_.item_ptr(x_row, w_column)) = 0;
__syncthreads();
}
// Loop over part of x row (and w column)
half2 acc = {};
half acc_h = {};
if constexpr (use_groupsize)
{
// For quant matrices where groupsize divides BLOCK_SIZE_Z we always start on a group boundary, so this
// could be slightly faster
for (int k = x_column, group = x_column / groupsize; k < x_column + iterations * 8; group++, k += groupsize)
{
if constexpr (use_half2)
{
half2 w_scale = w_scales_.item_half2half2(group, w_column);
uint32_t w_zero = w_zeros_.item(group, w_column) + 1;
if constexpr (use_x_map) acc = dot_product_8_x_map(acc, x_, x_row, k, w_, k, w_column, w_scale, w_zero, groupsize / 8, x_map);
else acc = dot_product_8 (acc, x_, x_row, k, w_, k, w_column, w_scale, w_zero, groupsize / 8);
}
else
{
half w_scale = w_scales_.item(group, w_column);
uint32_t w_zero = w_zeros_.item(group, w_column) + 1;
if constexpr (use_x_map) acc_h = dot_product_8_x_map_h(acc_h, x_, x_row, k, w_, k, w_column, w_scale, w_zero, groupsize / 8, x_map);
else acc_h = dot_product_8_h (acc_h, x_, x_row, k, w_, k, w_column, w_scale, w_zero, groupsize / 8);
}
}
}
else
{
// Otherwise assume groupsize is a multiple of 8, do 8 columns per iteration and trust the cache
for (int k = x_column; k < x_column + iterations * 8; k += 8)
{
if constexpr (use_half2)
{
int group = k / groupsize;
half2 w_scale = w_scales_.item_half2half2(group, w_column);
uint32_t w_zero = w_zeros_.item(group, w_column) + 1;
if constexpr (use_x_map) acc = dot_product_8_x_map(acc, x_, x_row, k, w_, k, w_column, w_scale, w_zero, 1, x_map);
else acc = dot_product_8 (acc, x_, x_row, k, w_, k, w_column, w_scale, w_zero, 1);
}
else
{
int group = k / groupsize;
half w_scale = w_scales_.item(group, w_column);
uint32_t w_zero = w_zeros_.item(group, w_column) + 1;
if constexpr (use_x_map) acc_h = dot_product_8_x_map_h(acc_h, x_, x_row, k, w_, k, w_column, w_scale, w_zero, 1, x_map);
else acc_h = dot_product_8_h (acc_h, x_, x_row, k, w_, k, w_column, w_scale, w_zero, 1);
}
}
}
// Add to block result
if constexpr (use_half2)
{
half result = __hadd(acc.x, acc.y);
atomicAdd(out_.item_ptr(x_row, w_column), result);
}
else
{
atomicAdd(out_.item_ptr(x_row, w_column), acc_h);
}
}
fp_q4_matmul_kernel q4_matmul_kernel_pick(ExLlamaTuning* tuningParams, int block_size_z, int groupsize, uint32_t* x_map)
{
// <bool use_half2, bool use_groupsize, bool use_x_map>
if (tuningParams->matmul_no_half2) {
if (block_size_z % groupsize == 0) {
if (x_map) return q4_matmul_kernel<false, true, true >;
else return q4_matmul_kernel<false, true, false>;
} else {
if (x_map) return q4_matmul_kernel<false, false, true >;
else return q4_matmul_kernel<false, false, false>;
}
} else {
if (block_size_z % groupsize == 0)
{
if (x_map) return q4_matmul_kernel<true, true, true >;
else return q4_matmul_kernel<true, true, false>;
} else {
if (x_map) return q4_matmul_kernel<true, false, true >;
else return q4_matmul_kernel<true, false, false>;
}
}
};
// Compute y = x @ w
void q4_matmul_cuda
(
ExLlamaTuning* tuningParams,
const half* x,
const int x_height,
const Q4Matrix* w,
half* out,
bool no_zero,
cudaStream_t alt_stream
)
{
int height = x_height;
int dim = w->height;
int width = w->width;
cudaSetDevice(w->device);
uint32_t* x_map = w->cuda_x_map;
const half* x_mapped = x;
if (x_map && !tuningParams->matmul_fused_remap && !alt_stream)
{
CudaBuffers* buffers = get_buffers(w->device);
column_remap_cuda(x, buffers->temp_state, x_height, dim, w->cuda_x_map);
x_mapped = buffers->temp_state;
x_map = NULL;
}
int block_size_z;
if (w->width == 4096) block_size_z = 384; // 7B
else if (w->width == 11008) block_size_z = 256;
else if (w->width == 5120) block_size_z = 384; // 13B
else if (w->width == 13824) block_size_z = 256;
else if (w->width == 6656) block_size_z = 256; // 33B
else if (w->width == 17920) block_size_z = 128;
else block_size_z = 256;
//if (!no_zero) cudaMemsetAsync(out, 0, x_height * w->width * sizeof(half));
dim3 threads(THREADS_X, THREADS_Y, 1);
dim3 blocks
(
(width + threads.x - 1) / threads.x,
(height + threads.y - 1) / threads.y,
(dim + block_size_z - 1) / block_size_z
);
fp_q4_matmul_kernel kernel = q4_matmul_kernel_pick(tuningParams, block_size_z, w->groupsize, x_map);
kernel<<<blocks, threads, 0, alt_stream>>> (x_mapped, w->cuda_qweight, out, w->cuda_scales, w->cuda_qzeros, height, dim, width, w->groupsize, block_size_z, x_map, no_zero);
}
void q4_matmul_recons_cuda
(
ExLlamaTuning* tuningParams,
const half* x,
const int x_height,
Q4Matrix* w,
half* out,
const cublasHandle_t handle,
bool no_zero
)
{
int height = x_height;
int dim = w->height;
int width = w->width;
cudaSetDevice(w->device);
CudaBuffers* buffers = get_buffers(w->device);
const half* x_mapped = x;
if (w->cuda_x_map)
{
column_remap_cuda(x, buffers->temp_state, x_height, dim, w->cuda_x_map);
x_mapped = buffers->temp_state;
}
w->reconstruct(buffers->temp_dq);
const half alpha = __float2half(1.0f);
const half beta = no_zero ? __float2half(1.0f) : __float2half(0.0f);
cublasHgemm(handle, CUBLAS_OP_N, CUBLAS_OP_N, width, height, dim, &alpha, buffers->temp_dq, width, x_mapped, dim, &beta, out, width);
// const float alpha = 1.0f;
// const float beta = no_zero ? 1.0f : 0.0f;
// cublasSgemmEx(handle, CUBLAS_OP_N, CUBLAS_OP_N, width, height, dim, &alpha, buffers->temp_dq, CUDA_R_16F, width,
// x_mapped, CUDA_R_16F, dim, &beta, out, CUDA_R_16F, width);
}
| 0 |
hf_public_repos/text-generation-inference/server | hf_public_repos/text-generation-inference/server/tests/conftest.py | import pytest
from text_generation_server.pb import generate_pb2
@pytest.fixture
def default_pb_parameters():
return generate_pb2.NextTokenChooserParameters(
temperature=1.0,
repetition_penalty=1.0,
top_k=0,
top_p=1.0,
typical_p=1.0,
do_sample=False,
)
@pytest.fixture
def default_pb_stop_parameters():
return generate_pb2.StoppingCriteriaParameters(stop_sequences=[], max_new_tokens=10)
| 0 |
hf_public_repos/text-generation-inference/server/tests | hf_public_repos/text-generation-inference/server/tests/models/test_causal_lm.py | import pytest
import torch
from copy import copy
from transformers import AutoTokenizer
from text_generation_server.pb import generate_pb2
from text_generation_server.models.causal_lm import CausalLM, CausalLMBatch
@pytest.fixture(scope="session")
def default_causal_lm():
return CausalLM("gpt2")
@pytest.fixture(scope="session")
def gpt2_tokenizer():
tokenizer = AutoTokenizer.from_pretrained("gpt2", padding_side="left")
tokenizer.pad_token_id = 50256
return tokenizer
@pytest.fixture
def default_pb_request(default_pb_parameters, default_pb_stop_parameters):
return generate_pb2.Request(
id=0,
inputs="Test",
prefill_logprobs=True,
truncate=100,
parameters=default_pb_parameters,
stopping_parameters=default_pb_stop_parameters,
)
@pytest.fixture
def default_pb_batch(default_pb_request):
return generate_pb2.Batch(id=0, requests=[default_pb_request], size=1)
@pytest.fixture
def default_causal_lm_batch(default_pb_batch, gpt2_tokenizer):
return CausalLMBatch.from_pb(
default_pb_batch, gpt2_tokenizer, torch.float32, torch.device("cpu")
)
@pytest.fixture
def default_multi_requests_causal_lm_batch(default_pb_request, gpt2_tokenizer):
req_0 = copy(default_pb_request)
req_0.id = 1
req_1 = default_pb_request
req_1.id = 2
req_1.stopping_parameters.max_new_tokens = 5
batch_pb = generate_pb2.Batch(id=1, requests=[req_0, req_1], size=2)
return CausalLMBatch.from_pb(
batch_pb, gpt2_tokenizer, torch.float32, torch.device("cpu")
)
def test_batch_from_pb(default_pb_batch, default_causal_lm_batch):
batch = default_causal_lm_batch
assert batch.batch_id == default_pb_batch.id
assert batch.requests == default_pb_batch.requests
assert len(batch.input_ids) == default_pb_batch.size
assert batch.input_ids[0][-1] == 14402
assert torch.all(batch.input_ids[0][:-1] == 50256)
assert batch.attention_mask[0, 0] == 1
assert torch.all(batch.attention_mask[0, 1:] == 0)
assert batch.past_key_values is None
assert all(
[
torch.equal(input_ids, all_input_ids[:, 0])
for input_ids, all_input_ids in zip(batch.input_ids, batch.all_input_ids)
]
)
assert batch.input_lengths == [1]
assert len(batch) == default_pb_batch.size
assert len(batch.next_token_choosers) == len(batch.stopping_criterias) == len(batch)
assert batch.max_input_length == batch.input_lengths[0]
def test_batch_concatenate_no_prefill(default_causal_lm_batch):
with pytest.raises(ValueError):
CausalLMBatch.concatenate([default_causal_lm_batch, default_causal_lm_batch])
def test_causal_lm_batch_type(default_causal_lm):
assert default_causal_lm.batch_type == CausalLMBatch
def test_causal_lm_generate_token(default_causal_lm, default_causal_lm_batch):
sequence_length = len(default_causal_lm_batch.all_input_ids[0])
generations, next_batch, _ = default_causal_lm.generate_token(
default_causal_lm_batch
)
assert len(generations) == len(next_batch)
assert isinstance(next_batch, CausalLMBatch)
assert len(next_batch.all_input_ids) == len(next_batch)
assert len(next_batch.all_input_ids[0]) == sequence_length + 1
assert len(next_batch.attention_mask[0]) == 11
assert next_batch.all_input_ids[0][-1] == 13
assert next_batch.all_input_ids[0][-2] == 14402
assert torch.all(next_batch.all_input_ids[0][:-2] == 50256)
assert torch.all(next_batch.attention_mask[0][0:2] == 1)
assert torch.all(next_batch.attention_mask[0][2:] == 0)
assert next_batch.input_ids.shape == (len(next_batch), 1)
assert next_batch.input_ids[0, 0] == 13
assert next_batch.input_lengths == [2]
assert next_batch.max_input_length == next_batch.input_lengths[0]
assert next_batch.past_key_values is not None
assert all(
[p[0].shape == (1, 12, sequence_length, 64) for p in next_batch.past_key_values]
)
assert all(
[p[1].shape == (1, 12, sequence_length, 64) for p in next_batch.past_key_values]
)
assert all([generation.generated_text is None for generation in generations])
assert all([len(generation.prefill_tokens) == 1 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
def test_causal_lm_generate_token_completion(
default_causal_lm, default_causal_lm_batch
):
next_batch = default_causal_lm_batch
for _ in range(default_causal_lm_batch.stopping_criterias[0].max_new_tokens - 1):
generations, next_batch, _ = default_causal_lm.generate_token(next_batch)
assert len(generations) == len(next_batch)
generations, next_batch, _ = default_causal_lm.generate_token(next_batch)
assert next_batch is None
assert len(generations) == 1
assert generations[0].generated_text.text == ".java:784) at net.minecraft."
assert generations[0].request_id == default_causal_lm_batch.requests[0].id
assert (
generations[0].generated_text.generated_tokens
== default_causal_lm_batch.stopping_criterias[0].max_new_tokens
)
def test_causal_lm_generate_token_completion_multi(
default_causal_lm, default_multi_requests_causal_lm_batch
):
next_batch = default_multi_requests_causal_lm_batch
for i in range(
default_multi_requests_causal_lm_batch.stopping_criterias[1].max_new_tokens - 1
):
generations, next_batch, _ = default_causal_lm.generate_token(next_batch)
assert len(generations) == len(next_batch)
generations, next_batch, _ = default_causal_lm.generate_token(next_batch)
assert next_batch is not None
assert len(generations) == 2
assert generations[1].generated_text.text == ".java:784)"
assert (
generations[1].request_id
== default_multi_requests_causal_lm_batch.requests[1].id
)
assert (
generations[1].generated_text.generated_tokens
== default_multi_requests_causal_lm_batch.stopping_criterias[1].max_new_tokens
)
# Copy stopping_criterias before filtering
stopping_criterias = (
default_multi_requests_causal_lm_batch.stopping_criterias.copy()
)
next_batch = next_batch.filter([next_batch.requests[0].id])
for _ in range(
stopping_criterias[0].max_new_tokens - stopping_criterias[1].max_new_tokens - 1
):
generations, next_batch, _ = default_causal_lm.generate_token(next_batch)
assert len(generations) == len(next_batch)
generations, next_batch, _ = default_causal_lm.generate_token(next_batch)
assert next_batch is None
assert len(generations) == 1
assert generations[0].generated_text.text == ".java:784) at net.minecraft."
assert (
generations[0].request_id
== default_multi_requests_causal_lm_batch.requests[0].id
)
assert (
generations[0].generated_text.generated_tokens
== default_multi_requests_causal_lm_batch.stopping_criterias[0].max_new_tokens
)
def test_batch_concatenate(
default_causal_lm, default_causal_lm_batch, default_multi_requests_causal_lm_batch
):
next_batch_0 = default_causal_lm_batch
_, next_batch_0, _ = default_causal_lm.generate_token(next_batch_0)
_, next_batch_0, _ = default_causal_lm.generate_token(next_batch_0)
next_batch_1 = default_multi_requests_causal_lm_batch
_, next_batch_1, _ = default_causal_lm.generate_token(next_batch_1)
# Clone past_key_values before concatenating to compare after,
# because they are removed from the concatenated batches
next_batch_0_past_key_values = [
(k.clone(), v.clone()) for (k, v) in next_batch_0.past_key_values
]
next_batch_1_past_key_values = [
(k.clone(), v.clone()) for (k, v) in next_batch_1.past_key_values
]
next_batch = CausalLMBatch.concatenate([next_batch_0, next_batch_1])
assert torch.equal(next_batch.all_input_ids[0], next_batch_0.all_input_ids[0])
assert torch.equal(next_batch.all_input_ids[1], next_batch_1.all_input_ids[0])
assert torch.equal(next_batch.all_input_ids[2], next_batch_1.all_input_ids[1])
assert torch.all(
next_batch.attention_mask[0, : -next_batch.padding_right_offset] == 1
)
assert torch.all(
next_batch.attention_mask[1:, 1 : -next_batch.padding_right_offset] == 1
)
assert torch.all(next_batch.attention_mask[1:, 3:] == 0)
assert next_batch.batch_id == 0
assert next_batch.input_ids[0, 0] == 12355
assert torch.all(next_batch.input_ids[1:] == 13)
assert next_batch.input_lengths == [3, 2, 2]
assert next_batch.max_input_length == 3
assert next_batch.requests[0] == next_batch_0.requests[0]
assert next_batch.requests[1:] == next_batch_1.requests
assert next_batch.next_token_choosers[0] == next_batch_0.next_token_choosers[0]
assert next_batch.next_token_choosers[1:] == next_batch_1.next_token_choosers
assert next_batch.stopping_criterias[0] == next_batch_0.stopping_criterias[0]
assert next_batch.stopping_criterias[1:] == next_batch_1.stopping_criterias
assert next_batch.past_key_values is not None
assert all([p[0].shape == (3, 12, 2, 64) for p in next_batch.past_key_values])
assert all([p[1].shape == (3, 12, 2, 64) for p in next_batch.past_key_values])
for i, past in enumerate(next_batch.past_key_values):
assert torch.equal(next_batch_0_past_key_values[i][0][0, :, -2:], past[0][0])
assert torch.equal(
next_batch_1_past_key_values[i][0][:, :, -1:], past[0][1:, :, -1:, :]
)
assert torch.equal(next_batch_0_past_key_values[i][1][0, :, -2:], past[1][0])
assert torch.equal(
next_batch_1_past_key_values[i][1][:, :, -1:], past[1][1:, :, -1:, :]
)
for _ in range(
default_multi_requests_causal_lm_batch.stopping_criterias[1].max_new_tokens - 2
):
generations, next_batch, _ = default_causal_lm.generate_token(next_batch)
assert len(generations) == len(next_batch)
generations, next_batch, _ = default_causal_lm.generate_token(next_batch)
assert next_batch is not None
assert len(generations) == 3
assert generations[2].generated_text.text == ".java:784)"
assert (
generations[2].request_id
== default_multi_requests_causal_lm_batch.requests[1].id
)
assert (
generations[2].generated_text.generated_tokens
== default_multi_requests_causal_lm_batch.stopping_criterias[1].max_new_tokens
)
next_batch = next_batch.filter(
[next_batch.requests[0].id, next_batch.requests[1].id]
)
for _ in range(
default_causal_lm_batch.stopping_criterias[0].max_new_tokens
- default_multi_requests_causal_lm_batch.stopping_criterias[1].max_new_tokens
- 2
):
generations, next_batch, _ = default_causal_lm.generate_token(next_batch)
assert len(generations) == len(next_batch)
generations, next_batch, _ = default_causal_lm.generate_token(next_batch)
assert next_batch is not None
assert len(generations) == 2
assert generations[0].generated_text.text == ".java:784) at net.minecraft."
assert generations[0].request_id == default_causal_lm_batch.requests[0].id
assert (
generations[0].generated_text.generated_tokens
== default_causal_lm_batch.stopping_criterias[0].max_new_tokens
)
next_batch = next_batch.filter([next_batch.requests[1].id])
for _ in range(
default_multi_requests_causal_lm_batch.stopping_criterias[0].max_new_tokens
- default_causal_lm_batch.stopping_criterias[0].max_new_tokens
- default_multi_requests_causal_lm_batch.stopping_criterias[1].max_new_tokens
- 4
):
generations, next_batch, _ = default_causal_lm.generate_token(next_batch)
assert len(generations) == len(next_batch)
generations, next_batch, _ = default_causal_lm.generate_token(next_batch)
assert next_batch is None
assert len(generations) == 1
assert generations[0].generated_text.text == ".java:784) at net.minecraft."
assert (
generations[0].request_id
== default_multi_requests_causal_lm_batch.requests[0].id
)
assert (
generations[0].generated_text.generated_tokens
== default_multi_requests_causal_lm_batch.stopping_criterias[0].max_new_tokens
)
| 0 |
hf_public_repos/text-generation-inference/server/tests | hf_public_repos/text-generation-inference/server/tests/models/test_model.py | import pytest
import torch
from transformers import AutoTokenizer
from text_generation_server.models import Model
def get_test_model():
class TestModel(Model):
def batch_type(self):
raise NotImplementedError
def generate_token(self, batch):
raise NotImplementedError
tokenizer = AutoTokenizer.from_pretrained("huggingface/llama-7b")
model = TestModel(
torch.nn.Linear(1, 1), tokenizer, False, torch.float32, torch.device("cpu")
)
return model
@pytest.mark.private
def test_decode_streaming_english_spaces():
model = get_test_model()
truth = "Hello here, this is a simple test"
all_input_ids = [15043, 1244, 29892, 445, 338, 263, 2560, 1243]
assert (
all_input_ids == model.tokenizer(truth, add_special_tokens=False)["input_ids"]
)
decoded_text = ""
offset = 0
token_offset = 0
for i in range(len(all_input_ids)):
text, offset, token_offset = model.decode_token(
all_input_ids[: i + 1], offset, token_offset
)
decoded_text += text
assert decoded_text == truth
@pytest.mark.private
def test_decode_streaming_chinese_utf8():
model = get_test_model()
truth = "我很感谢你的热情"
all_input_ids = [
30672,
232,
193,
139,
233,
135,
162,
235,
179,
165,
30919,
30210,
234,
134,
176,
30993,
]
decoded_text = ""
offset = 0
token_offset = 0
for i in range(len(all_input_ids)):
text, offset, token_offset = model.decode_token(
all_input_ids[: i + 1], offset, token_offset
)
decoded_text += text
assert decoded_text == truth
| 0 |
hf_public_repos/text-generation-inference/server/tests | hf_public_repos/text-generation-inference/server/tests/models/test_seq2seq_lm.py | import pytest
import torch
from copy import copy
from transformers import AutoTokenizer
from text_generation_server.pb import generate_pb2
from text_generation_server.models.seq2seq_lm import Seq2SeqLM, Seq2SeqLMBatch
@pytest.fixture(scope="session")
def mt0_small_tokenizer():
tokenizer = AutoTokenizer.from_pretrained(
"bigscience/mt0-small", padding_side="left"
)
tokenizer.bos_token_id = 0
return tokenizer
@pytest.fixture(scope="session")
def default_seq2seq_lm():
return Seq2SeqLM("bigscience/mt0-small")
@pytest.fixture
def default_pb_request(default_pb_parameters, default_pb_stop_parameters):
return generate_pb2.Request(
id=0,
inputs="Test",
prefill_logprobs=True,
truncate=100,
parameters=default_pb_parameters,
stopping_parameters=default_pb_stop_parameters,
)
@pytest.fixture
def default_pb_batch(default_pb_request):
return generate_pb2.Batch(id=0, requests=[default_pb_request], size=1)
@pytest.fixture
def default_seq2seq_lm_batch(default_pb_batch, mt0_small_tokenizer):
return Seq2SeqLMBatch.from_pb(
default_pb_batch, mt0_small_tokenizer, torch.float32, torch.device("cpu")
)
@pytest.fixture
def default_multi_requests_seq2seq_lm_batch(default_pb_request, mt0_small_tokenizer):
req_0 = copy(default_pb_request)
req_0.id = 1
req_1 = default_pb_request
req_1.id = 2
req_1.stopping_parameters.max_new_tokens = 5
batch_pb = generate_pb2.Batch(id=0, requests=[req_0, req_1], size=2)
return Seq2SeqLMBatch.from_pb(
batch_pb, mt0_small_tokenizer, torch.float32, torch.device("cpu")
)
def test_batch_from_pb(default_pb_batch, default_seq2seq_lm_batch):
batch = default_seq2seq_lm_batch
sequence_length = len(default_seq2seq_lm_batch.input_ids[0])
assert batch.batch_id == default_pb_batch.id
assert batch.requests == default_pb_batch.requests
assert batch.input_ids.shape == (default_pb_batch.size, sequence_length)
assert batch.input_ids[0][-2] == 4268
assert batch.input_ids[0][-1] == 1
assert torch.all(batch.input_ids[0][:-2] == 0)
assert torch.all(batch.attention_mask[0][-2:] == 1)
assert torch.all(batch.attention_mask[0][:-2] == 0)
assert len(batch.decoder_input_ids) == default_pb_batch.size
assert batch.decoder_attention_mask is None
assert batch.encoder_last_hidden_state is None
assert batch.past_key_values is None
assert batch.input_lengths == [2]
assert batch.decoder_input_lengths == [1]
assert len(batch) == default_pb_batch.size
assert len(batch.next_token_choosers) == len(batch.stopping_criterias) == len(batch)
assert batch.max_input_length == batch.input_lengths[0]
assert batch.max_decoder_input_length == batch.decoder_input_lengths[0]
def test_batch_concatenate_no_prefill(default_seq2seq_lm_batch):
with pytest.raises(ValueError):
Seq2SeqLMBatch.concatenate([default_seq2seq_lm_batch, default_seq2seq_lm_batch])
def test_seq2seq_lm_batch_type(default_seq2seq_lm):
assert default_seq2seq_lm.batch_type == Seq2SeqLMBatch
def test_seq2seq_lm_generate_token(default_seq2seq_lm, default_seq2seq_lm_batch):
sequence_length = len(default_seq2seq_lm_batch.input_ids[0])
generations, next_batch, _ = default_seq2seq_lm.generate_token(
default_seq2seq_lm_batch
)
assert len(generations) == len(next_batch)
assert isinstance(next_batch, Seq2SeqLMBatch)
assert next_batch.input_ids is None
assert torch.equal(
next_batch.attention_mask, default_seq2seq_lm_batch.attention_mask
)
assert next_batch.input_lengths == default_seq2seq_lm_batch.input_lengths
assert next_batch.max_input_length == default_seq2seq_lm_batch.max_input_length
assert (
next_batch.next_token_choosers == default_seq2seq_lm_batch.next_token_choosers
)
assert next_batch.stopping_criterias == default_seq2seq_lm_batch.stopping_criterias
assert len(next_batch.decoder_input_ids) == len(next_batch)
assert next_batch.all_decoder_input_ids[0][0] == 0
assert next_batch.all_decoder_input_ids[0][1] == 259
assert next_batch.decoder_attention_mask is None
assert next_batch.encoder_last_hidden_state.shape == (1, sequence_length, 512)
assert next_batch.decoder_input_lengths == [2]
assert next_batch.max_decoder_input_length == 2
assert next_batch.past_key_values is not None
assert all(
[p[0].shape == (len(next_batch), 6, 1, 64) for p in next_batch.past_key_values]
)
assert all(
[p[1].shape == (len(next_batch), 6, 1, 64) for p in next_batch.past_key_values]
)
assert all(
[
p[2].shape == (len(next_batch), 6, sequence_length, 64)
for p in next_batch.past_key_values
]
)
assert all(
[
p[3].shape == (len(next_batch), 6, sequence_length, 64)
for p in next_batch.past_key_values
]
)
assert all([generation.generated_text is None for generation in generations])
assert all([len(generation.prefill_tokens) == 1 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
def test_seq2seq_lm_generate_token_completion(
default_seq2seq_lm, default_seq2seq_lm_batch
):
next_batch = default_seq2seq_lm_batch
for _ in range(6):
generations, next_batch, _ = default_seq2seq_lm.generate_token(next_batch)
assert len(generations) == len(next_batch)
generations, next_batch, _ = default_seq2seq_lm.generate_token(next_batch)
assert next_batch is None
assert len(generations) == 1
assert generations[0].generated_text.text == "a few weeks"
assert generations[0].request_id == default_seq2seq_lm_batch.requests[0].id
assert generations[0].generated_text.generated_tokens == 7
def test_seq2seq_lm_generate_token_completion_multi(
default_seq2seq_lm, default_multi_requests_seq2seq_lm_batch
):
next_batch = default_multi_requests_seq2seq_lm_batch
for i in range(4):
generations, next_batch, _ = default_seq2seq_lm.generate_token(next_batch)
assert len(generations) == len(next_batch)
generations, next_batch, _ = default_seq2seq_lm.generate_token(next_batch)
assert next_batch is not None
assert len(generations) == 2
assert generations[1].generated_text.text == "a few "
assert (
generations[1].request_id
== default_multi_requests_seq2seq_lm_batch.requests[1].id
)
assert generations[1].generated_text.generated_tokens == 5
next_batch = next_batch.filter([next_batch.requests[0].id])
generations, next_batch, _ = default_seq2seq_lm.generate_token(next_batch)
assert len(generations) == len(next_batch)
generations, next_batch, _ = default_seq2seq_lm.generate_token(next_batch)
assert next_batch is None
assert len(generations) == 1
assert generations[0].generated_text.text == "a few weeks"
assert (
generations[0].request_id
== default_multi_requests_seq2seq_lm_batch.requests[0].id
)
assert generations[0].generated_text.generated_tokens == 7
def test_batch_concatenate(
default_seq2seq_lm,
default_seq2seq_lm_batch,
default_multi_requests_seq2seq_lm_batch,
):
next_batch_0 = default_seq2seq_lm_batch
_, next_batch_0, _ = default_seq2seq_lm.generate_token(next_batch_0)
_, next_batch_0, _ = default_seq2seq_lm.generate_token(next_batch_0)
next_batch_1 = default_multi_requests_seq2seq_lm_batch
_, next_batch_1, _ = default_seq2seq_lm.generate_token(next_batch_1)
# Copy hidden state because it is removed from the concatenated branches
next_batch_0_encoder_last_hidden_state = next_batch_0.encoder_last_hidden_state
next_batch_1_encoder_last_hidden_state = next_batch_1.encoder_last_hidden_state
# Clone past_key_values before concatenating to compare after,
# because they are removed from the concatenated batches
next_batch_0_past_key_values = [
[t.clone() for t in layer] for layer in next_batch_0.past_key_values
]
next_batch_1_past_key_values = [
[t.clone() for t in layer] for layer in next_batch_1.past_key_values
]
next_batch = Seq2SeqLMBatch.concatenate([next_batch_0, next_batch_1])
assert next_batch.batch_id == 0
assert torch.equal(
next_batch.decoder_input_ids[0], next_batch_0.decoder_input_ids[0]
)
assert next_batch.all_decoder_input_ids[1][0] == 0
assert next_batch.all_decoder_input_ids[2][0] == 0
assert torch.equal(
next_batch.decoder_input_ids[1:, -2:], next_batch_1.decoder_input_ids
)
assert torch.all(next_batch.decoder_attention_mask[0, :3] == 1)
assert torch.all(next_batch.decoder_attention_mask[0, 3:] == 0)
assert torch.all(next_batch.decoder_attention_mask[1:, 0] == 0)
assert torch.all(next_batch.decoder_attention_mask[1:, 1:3] == 1)
assert torch.equal(
next_batch.encoder_last_hidden_state[0],
next_batch_0_encoder_last_hidden_state[0, -2:],
)
assert torch.equal(
next_batch.encoder_last_hidden_state[1:],
next_batch_1_encoder_last_hidden_state[:, -2:],
)
assert next_batch.input_lengths == [2, 2, 2]
assert next_batch.decoder_input_lengths == [3, 2, 2]
assert next_batch.max_input_length == 2
assert next_batch.max_decoder_input_length == 3
assert next_batch.requests[0] == next_batch_0.requests[0]
assert next_batch.requests[1:] == next_batch_1.requests
assert next_batch.next_token_choosers[0] == next_batch_0.next_token_choosers[0]
assert next_batch.next_token_choosers[1:] == next_batch_1.next_token_choosers
assert next_batch.stopping_criterias[0] == next_batch_0.stopping_criterias[0]
assert next_batch.stopping_criterias[1:] == next_batch_1.stopping_criterias
assert next_batch.past_key_values is not None
assert all(
[p[0].shape == (len(next_batch), 6, 2, 64) for p in next_batch.past_key_values]
)
assert all(
[p[1].shape == (len(next_batch), 6, 2, 64) for p in next_batch.past_key_values]
)
assert all(
[p[2].shape == (len(next_batch), 6, 2, 64) for p in next_batch.past_key_values]
)
assert all(
[p[3].shape == (len(next_batch), 6, 2, 64) for p in next_batch.past_key_values]
)
for i, past in enumerate(next_batch.past_key_values):
assert torch.equal(next_batch_0_past_key_values[i][0][0, :, -2:, :], past[0][0])
assert torch.equal(
next_batch_1_past_key_values[i][0][:, :, -1:, :], past[0][1:, :, -1:, :]
)
assert torch.equal(next_batch_0_past_key_values[i][1][0, :, -2:, :], past[1][0])
assert torch.equal(
next_batch_1_past_key_values[i][1][:, :, -1:, :], past[1][1:, :, -1:, :]
)
assert torch.equal(next_batch_0_past_key_values[i][2][0, :, -2:, :], past[2][0])
assert torch.equal(
next_batch_1_past_key_values[i][2][:, :, -2:, :], past[2][1:]
)
assert torch.equal(next_batch_0_past_key_values[i][3][0, :, -2:, :], past[3][0])
assert torch.equal(
next_batch_1_past_key_values[i][3][:, :, -2:, :], past[3][1:]
)
for _ in range(3):
generations, next_batch, _ = default_seq2seq_lm.generate_token(next_batch)
assert len(generations) == len(next_batch)
generations, next_batch, _ = default_seq2seq_lm.generate_token(next_batch)
assert next_batch is not None
assert len(generations) == 3
assert generations[2].generated_text.text == "a few "
assert (
generations[2].request_id
== default_multi_requests_seq2seq_lm_batch.requests[1].id
)
assert generations[2].generated_text.generated_tokens == 5
next_batch = next_batch.filter(
[next_batch.requests[0].id, next_batch.requests[1].id]
)
generations, next_batch, _ = default_seq2seq_lm.generate_token(next_batch)
assert next_batch is not None
assert len(generations) == 2
assert generations[0].generated_text.text == "a few weeks"
assert generations[0].request_id == default_seq2seq_lm_batch.requests[0].id
assert generations[0].generated_text.generated_tokens == 7
next_batch = next_batch.filter([next_batch.requests[1].id])
generations, next_batch, _ = default_seq2seq_lm.generate_token(next_batch)
assert next_batch is None
assert len(generations) == 1
assert generations[0].generated_text.text == "a few weeks"
assert (
generations[0].request_id
== default_multi_requests_seq2seq_lm_batch.requests[0].id
)
assert generations[0].generated_text.generated_tokens == 7
| 0 |
hf_public_repos/text-generation-inference/server/tests | hf_public_repos/text-generation-inference/server/tests/models/test_bloom.py | import pytest
import torch
from copy import copy
from transformers import AutoTokenizer
from text_generation_server.pb import generate_pb2
from text_generation_server.models.causal_lm import CausalLMBatch
from text_generation_server.utils import weight_hub_files, download_weights
from text_generation_server.models.bloom import BloomCausalLMBatch, BLOOMSharded
@pytest.fixture(scope="session")
def default_bloom():
model_id = "bigscience/bloom-560m"
revision = "main"
filenames = weight_hub_files(model_id, revision, ".safetensors")
download_weights(filenames, model_id, revision)
return BLOOMSharded(model_id)
@pytest.fixture(scope="session")
def bloom_560m_tokenizer():
return AutoTokenizer.from_pretrained("bigscience/bloom-560m", padding_side="left")
@pytest.fixture
def default_pb_request(default_pb_parameters, default_pb_stop_parameters):
return generate_pb2.Request(
id=0,
inputs="Test",
prefill_logprobs=True,
truncate=100,
parameters=default_pb_parameters,
stopping_parameters=default_pb_stop_parameters,
)
@pytest.fixture
def default_pb_batch(default_pb_request):
return generate_pb2.Batch(id=0, requests=[default_pb_request], size=1)
@pytest.fixture
def default_bloom_batch(default_pb_batch, bloom_560m_tokenizer):
return BloomCausalLMBatch.from_pb(
default_pb_batch, bloom_560m_tokenizer, torch.float32, torch.device("cpu")
)
@pytest.fixture
def default_multi_requests_bloom_batch(default_pb_request, bloom_560m_tokenizer):
req_0 = copy(default_pb_request)
req_0.id = 1
req_1 = default_pb_request
req_1.id = 2
req_1.stopping_parameters.max_new_tokens = 5
batch_pb = generate_pb2.Batch(id=0, requests=[req_0, req_1], size=2)
return BloomCausalLMBatch.from_pb(
batch_pb, bloom_560m_tokenizer, torch.float32, torch.device("cpu")
)
def test_batch_from_pb(default_pb_batch, default_bloom_batch):
batch = default_bloom_batch
assert batch.batch_id == default_pb_batch.id
assert batch.requests == default_pb_batch.requests
assert len(batch.input_ids) == default_pb_batch.size
assert batch.input_ids[0][-1] == 10264
assert torch.all(batch.input_ids[0][:-1] == 3)
assert batch.attention_mask[0][0] == 1
assert torch.all(batch.attention_mask[0][1:] == 0)
assert batch.past_key_values is None
assert all(
[
torch.equal(input_ids, all_input_ids[:, 0])
for input_ids, all_input_ids in zip(batch.input_ids, batch.all_input_ids)
]
)
assert batch.input_lengths == [1]
assert len(batch) == default_pb_batch.size
assert len(batch.next_token_choosers) == len(batch.stopping_criterias) == len(batch)
assert batch.max_input_length == batch.input_lengths[0]
def test_batch_concatenate_no_prefill(default_bloom_batch):
with pytest.raises(ValueError):
BloomCausalLMBatch.concatenate([default_bloom_batch, default_bloom_batch])
def test_causal_lm_batch_type(default_bloom):
assert default_bloom.batch_type == BloomCausalLMBatch
def test_causal_lm_generate_token(default_bloom, default_bloom_batch):
sequence_length = len(default_bloom_batch.all_input_ids[0])
generations, next_batch, _ = default_bloom.generate_token(default_bloom_batch)
assert len(generations) == len(default_bloom_batch)
assert isinstance(next_batch, CausalLMBatch)
assert not next_batch.keys_head_dim_last
assert len(next_batch.all_input_ids) == len(next_batch)
assert len(next_batch.all_input_ids[0]) == sequence_length + 1
assert len(next_batch.attention_mask[0]) == 11
assert torch.all(next_batch.all_input_ids[0][-2:] == 10264)
assert torch.all(next_batch.all_input_ids[0][:-2] == 3)
assert torch.all(next_batch.attention_mask[0][:2] == 1)
assert torch.all(next_batch.attention_mask[0][2:] == 0)
assert next_batch.input_ids.shape == (len(next_batch), 1)
assert next_batch.input_ids[0, 0] == 10264
assert next_batch.input_lengths == [2]
assert next_batch.max_input_length == next_batch.input_lengths[0]
assert next_batch.past_key_values is not None
assert all(
[p[0].shape == (16, 64, sequence_length) for p in next_batch.past_key_values]
)
assert all(
[p[1].shape == (16, sequence_length, 64) for p in next_batch.past_key_values]
)
assert all([generation.generated_text is None for generation in generations])
assert all([len(generation.prefill_tokens) == 1 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
def test_causal_lm_generate_token_completion(default_bloom, default_bloom_batch):
next_batch = default_bloom_batch
for _ in range(default_bloom_batch.stopping_criterias[0].max_new_tokens - 1):
generations, next_batch, _ = default_bloom.generate_token(next_batch)
assert len(generations) == len(default_bloom_batch)
generations, next_batch, _ = default_bloom.generate_token(next_batch)
assert next_batch is None
assert len(generations) == 1
assert (
generations[0].generated_text.text == "TestTestTestTestTestTestTestTestTestTest"
)
assert generations[0].request_id == default_bloom_batch.requests[0].id
assert (
generations[0].generated_text.generated_tokens
== default_bloom_batch.stopping_criterias[0].max_new_tokens
)
def test_causal_lm_generate_token_completion_multi(
default_bloom, default_multi_requests_bloom_batch
):
next_batch = default_multi_requests_bloom_batch
for i in range(
default_multi_requests_bloom_batch.stopping_criterias[1].max_new_tokens - 1
):
generations, next_batch, _ = default_bloom.generate_token(next_batch)
assert len(generations) == len(default_multi_requests_bloom_batch)
generations, next_batch, _ = default_bloom.generate_token(next_batch)
assert next_batch is not None
assert len(generations) == 2
assert generations[1].generated_text.text == "TestTestTestTestTest"
assert (
generations[1].request_id == default_multi_requests_bloom_batch.requests[1].id
)
assert (
generations[1].generated_text.generated_tokens
== default_multi_requests_bloom_batch.stopping_criterias[1].max_new_tokens
)
# Copy stopping_criterias before filtering
stopping_criterias = default_multi_requests_bloom_batch.stopping_criterias.copy()
next_batch = next_batch.filter([next_batch.requests[0].id])
for _ in range(
stopping_criterias[0].max_new_tokens - stopping_criterias[1].max_new_tokens - 1
):
generations, next_batch, _ = default_bloom.generate_token(next_batch)
assert len(generations) == len(next_batch)
generations, next_batch, _ = default_bloom.generate_token(next_batch)
assert next_batch is None
assert len(generations) == 1
assert (
generations[0].generated_text.text == "TestTestTestTestTestTestTestTestTestTest"
)
assert (
generations[0].request_id == default_multi_requests_bloom_batch.requests[0].id
)
assert (
generations[0].generated_text.generated_tokens
== default_multi_requests_bloom_batch.stopping_criterias[0].max_new_tokens
)
def test_batch_concatenate(
default_bloom, default_bloom_batch, default_multi_requests_bloom_batch
):
next_batch_0 = default_bloom_batch
_, next_batch_0, _ = default_bloom.generate_token(next_batch_0)
_, next_batch_0, _ = default_bloom.generate_token(next_batch_0)
next_batch_1 = default_multi_requests_bloom_batch
_, next_batch_1, _ = default_bloom.generate_token(next_batch_1)
# Clone past_key_values before concatenating to compare after,
# because they are removed from the concatenated batches
next_batch_0_past_key_values = [
(k.clone(), v.clone()) for (k, v) in next_batch_0.past_key_values
]
next_batch_1_past_key_values = [
(k.clone(), v.clone()) for (k, v) in next_batch_1.past_key_values
]
next_batch = BloomCausalLMBatch.concatenate([next_batch_0, next_batch_1])
assert torch.equal(next_batch.all_input_ids[0], next_batch_0.all_input_ids[0])
assert torch.equal(next_batch.all_input_ids[1], next_batch_1.all_input_ids[0])
assert torch.equal(next_batch.all_input_ids[2], next_batch_1.all_input_ids[1])
assert torch.all(
next_batch.attention_mask[0, : -next_batch.padding_right_offset] == 1
)
assert torch.all(
next_batch.attention_mask[1:, 1 : -next_batch.padding_right_offset] == 1
)
assert torch.all(next_batch.attention_mask[1:, 3:] == 0)
assert next_batch.batch_id == 0
assert torch.all(next_batch.input_ids == 10264)
assert next_batch.input_lengths == [3, 2, 2]
assert next_batch.max_input_length == 3
assert next_batch.requests[0] == next_batch_0.requests[0]
assert next_batch.requests[1:] == next_batch_1.requests
assert next_batch.next_token_choosers[0] == next_batch_0.next_token_choosers[0]
assert next_batch.next_token_choosers[1:] == next_batch_1.next_token_choosers
assert next_batch.stopping_criterias[0] == next_batch_0.stopping_criterias[0]
assert next_batch.stopping_criterias[1:] == next_batch_1.stopping_criterias
assert next_batch.past_key_values is not None
assert all([p[0].shape == (3, 16, 64, 2) for p in next_batch.past_key_values])
assert all([p[1].shape == (3, 16, 2, 64) for p in next_batch.past_key_values])
for i, past in enumerate(next_batch.past_key_values):
assert torch.equal(next_batch_0_past_key_values[i][0][:, :, -2:], past[0][0])
assert torch.equal(
next_batch_1_past_key_values[i][0][:, :, -1:],
past[0][1:, :, :, -1].reshape(-1, 64, 1),
)
assert torch.equal(next_batch_0_past_key_values[i][1][:, -2:, :], past[1][0])
assert torch.equal(
next_batch_1_past_key_values[i][1][:, -1:, :],
past[1][1:, :, -1, :].reshape(-1, 1, 64),
)
for _ in range(
default_multi_requests_bloom_batch.stopping_criterias[1].max_new_tokens - 2
):
generations, next_batch, _ = default_bloom.generate_token(next_batch)
assert len(generations) == len(next_batch)
generations, next_batch, _ = default_bloom.generate_token(next_batch)
assert next_batch is not None
assert len(generations) == 3
assert generations[2].generated_text.text == "TestTestTestTestTest"
assert (
generations[2].request_id == default_multi_requests_bloom_batch.requests[1].id
)
assert (
generations[2].generated_text.generated_tokens
== default_multi_requests_bloom_batch.stopping_criterias[1].max_new_tokens
)
next_batch = next_batch.filter(
[next_batch.requests[0].id, next_batch.requests[1].id]
)
for _ in range(
default_bloom_batch.stopping_criterias[0].max_new_tokens
- default_multi_requests_bloom_batch.stopping_criterias[1].max_new_tokens
- 2
):
generations, next_batch, _ = default_bloom.generate_token(next_batch)
assert len(generations) == len(next_batch)
generations, next_batch, _ = default_bloom.generate_token(next_batch)
assert next_batch is not None
assert len(generations) == 2
assert (
generations[0].generated_text.text == "TestTestTestTestTestTestTestTestTestTest"
)
assert generations[0].request_id == default_bloom_batch.requests[0].id
assert (
generations[0].generated_text.generated_tokens
== default_bloom_batch.stopping_criterias[0].max_new_tokens
)
next_batch = next_batch.filter([next_batch.requests[1].id])
for _ in range(
default_multi_requests_bloom_batch.stopping_criterias[0].max_new_tokens
- default_bloom_batch.stopping_criterias[0].max_new_tokens
- default_multi_requests_bloom_batch.stopping_criterias[1].max_new_tokens
- 4
):
generations, next_batch, _ = default_bloom.generate_token(next_batch)
assert len(generations) == len(next_batch)
generations, next_batch, _ = default_bloom.generate_token(next_batch)
assert next_batch is None
assert len(generations) == 1
assert (
generations[0].generated_text.text == "TestTestTestTestTestTestTestTestTestTest"
)
assert (
generations[0].request_id == default_multi_requests_bloom_batch.requests[0].id
)
assert (
generations[0].generated_text.generated_tokens
== default_multi_requests_bloom_batch.stopping_criterias[0].max_new_tokens
)
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