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// SPDX-FileCopyrightText: Copyright (c) 2024-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
// SPDX-License-Identifier: Apache-2.0
//! Backend
//!
//! An [`Backend`] is the final stage of the pipeline. It represents the execution of the LLM
//! on some processing hardware.
//!
//! At minimum, the Backend is split into two components, the [`Backend`] itself and a downstream [`ExecutionContext`].
//!
//! The [`ExecutionContext`] can be thought of as the core driver of the forward pass, whereas the [`Backend`] is the
//! manager of all resources and concurrent tasks surrounding the LLM execution context / forward pass.
//!
//! For almost every known scenario, detokenization and initial post processing must happen in the Backend.
//! Further post-processing can happen in the response stream. One example is the jailing mechanism for partial
//! hidden stop condition matches, which can be handled in the response stream rather than the backend.
use std::{collections::HashSet, sync::Arc, time::Instant};
use anyhow::Result;
use futures::stream::{self, StreamExt};
use crate::model_card::ModelDeploymentCard;
use dynamo_runtime::{
pipeline::{
AsyncEngineContextProvider, ManyOut, Operator, ResponseStream, ServerStreamingEngine,
SingleIn, async_trait,
},
protocols::annotated::Annotated,
};
use crate::protocols::{
TokenIdType,
common::{
StopConditions,
llm_backend::{
BackendOutput, EmbeddingsEngineOutput, FinishReason, LLMEngineOutput,
PreprocessedRequest,
},
preprocessor::PreprocessedEmbeddingRequest,
timing::RequestTracker,
},
};
use crate::tokenizers::{DecodeStream, Tokenizer};
use dynamo_async_openai::types::StopReason;
/// Represents the output stream from the execution engine
pub type ExecutionOutputStream = Annotated<LLMEngineOutput>;
/// Context for executing LLM inference, engine consumes backend input and produces execution output stream
pub type ExecutionContext = ServerStreamingEngine<PreprocessedRequest, ExecutionOutputStream>;
/// Backend handles resource management and orchestrates LLM execution
#[allow(dead_code)]
pub struct Backend {
pub tokenizer: Option<Tokenizer>, // Handles token encoding/decoding
validate_engine_decode: bool, // Enable validation of engine decoding
}
/// Internal state for managing token decoding and stream processing
#[allow(dead_code)]
struct DecoderUnfoldState {
stream: ManyOut<ExecutionOutputStream>,
decoder: Decoder,
validate_engine_decode: bool,
/// Set to true when a local stop condition is detected, causing the stream to end
finished: bool,
}
impl Backend {
pub fn from_tokenizer(tokenizer: Tokenizer) -> Arc<Self> {
Arc::new(Self {
tokenizer: Some(tokenizer),
validate_engine_decode: false,
})
}
pub fn from_mdc(mdc: &ModelDeploymentCard) -> Arc<Self> {
match mdc.tokenizer() {
Ok(tokenizer) => Self::from_tokenizer(tokenizer),
Err(err) => {
tracing::warn!(%err, "error loading tokenizer from ModelDeploymentCard");
Arc::new(Self {
tokenizer: None,
validate_engine_decode: false,
})
}
}
}
fn decoder(
&self,
stream: ManyOut<ExecutionOutputStream>,
prompt_token_ids: &[TokenIdType],
stop_conditions: StopConditions,
skip_special_tokens: bool,
include_stop_str_in_output: bool,
tracker: Option<Arc<RequestTracker>>,
) -> anyhow::Result<DecoderUnfoldState> {
let Some(tokenizer) = self.tokenizer.as_ref() else {
anyhow::bail!("Backend built from blank ModelDeploymentCard, no tokenizer");
};
let decoder = Decoder::new(
tokenizer.decode_stream(prompt_token_ids, skip_special_tokens),
stop_conditions,
include_stop_str_in_output,
tracker,
);
Ok(DecoderUnfoldState {
stream,
decoder,
validate_engine_decode: self.validate_engine_decode,
finished: false,
})
}
}
#[async_trait]
impl
Operator<
SingleIn<PreprocessedRequest>,
ManyOut<Annotated<BackendOutput>>,
SingleIn<PreprocessedRequest>,
ManyOut<Annotated<LLMEngineOutput>>,
> for Backend
{
async fn generate(
&self,
request: SingleIn<PreprocessedRequest>,
next: ServerStreamingEngine<PreprocessedRequest, Annotated<LLMEngineOutput>>,
) -> Result<ManyOut<Annotated<BackendOutput>>> {
let stop_conditions = request.stop_conditions.clone();
let prompt_token_ids = request.token_ids.clone();
// TODO: Consider updating default to true to match behavior of other frameworks
let skip_special_tokens = request.output_options.skip_special_tokens.unwrap_or(false);
// Extract include_stop_str_in_output from sampling_options (defaults to false)
let include_stop_str_in_output = request
.sampling_options
.include_stop_str_in_output
.unwrap_or(false);
let tracker = request.tracker.clone();
let next_stream = next.generate(request).await?;
let context = next_stream.context();
let state = self.decoder(
next_stream,
&prompt_token_ids,
stop_conditions,
skip_special_tokens,
include_stop_str_in_output,
tracker,
)?;
let processed_stream = stream::unfold(state, |mut state| async move {
// If we've already detected a local stop condition, end the stream
if state.finished {
return None;
}
match state.stream.next().await {
Some(output) => {
// move to state.process_output
// handle any error conditions / unwraps here
// events are pass thru
if output.is_event() || output.data.is_none() {
return Some((output, state));
}
// if we have a data field without an event, then we might need to update the data
if let Some(data) = &output.data
&& data.text.is_some()
&& !state.validate_engine_decode
{
return Some((output, state));
}
let data = output.data.as_ref().unwrap();
let result = match state.decoder.process_token_ids(&data.token_ids) {
Ok(result) => result,
Err(e) => {
tracing::error!("Failed to process token_ids: {e}");
state.stream.context().stop_generating();
state.finished = true;
let mut output = output;
if let Some(data) = &mut output.data {
data.finish_reason =
Some(FinishReason::Error(format!("decode error: {e}")));
}
return Some((output, state));
}
};
// NOTE: the `finish_reason` is computed from the generated `token_ids` alone.
// The `data` field can have a `finish_reason` set, coming from the underlying
// LLM inference `Engine`, and empty `token_ids`. See comment below for more details.
//
// stop_reason is only set for user-provided stop sequences, not for system
// EOS tokens (HiddenStopTokenDetected). This matches OpenAI API behavior where
// stop_reason is only present when a user-specified stop sequence is matched.
let (finish_reason, stop_reason) = match &result.stop_trigger {
Some(StopTrigger::MaxTokensLimit) => (Some(FinishReason::Length), None),
Some(StopTrigger::HiddenStopTokenDetected(_)) => {
// System EOS token - no stop_reason (user didn't request this stop)
(Some(FinishReason::Stop), None)
}
Some(StopTrigger::HiddenStopSequenceDetected(seq)) => {
// User-provided stop sequence (hidden from output)
(
Some(FinishReason::Stop),
Some(StopReason::String(seq.clone())),
)
}
Some(StopTrigger::VisibleStopSequenceDetected(seq)) => {
// User-provided stop sequence (included in output)
(
Some(FinishReason::Stop),
Some(StopReason::String(seq.clone())),
)
}
None => (None, None),
};
// If we detected a local stop condition, mark stream as finished
// so we stop iterating (upstream may keep generating, but we ignore it)
if finish_reason.is_some() && data.finish_reason.is_none() {
state.stream.context().stop_generating();
state.finished = true;
}
let text = result.text;
let tokens = result.tokens;
if state.validate_engine_decode {
if data.finish_reason != finish_reason {
tracing::warn!(
"finish reason mismatch: expected {:?}, got {:?}",
data.finish_reason,
finish_reason
);
}
if data.text.is_some() && data.text != text {
tracing::warn!(
"text mismatch: expected {:?}, got {:?}",
data.text,
text
);
}
}
// update output in-place
let mut output = output;
let mut data = output.data.take().unwrap();
// NOTE: If `finish_reason.is_some()`, then one of the stop conditions was triggered
// by the token generation. We should update the `data.finish_reason` in that case.
// However, if `finish_reason.is_none()`, it is possible that we are in the case where
// `data.token_ids` is empty, and `data.finish_reason` is already correctly set.
// In that case, `process_token_ids` above will rewrite `finish_reason` to `None`,
// which we don't want to propagate to `data.finish_reason`.
if finish_reason.is_some() {
data.finish_reason = finish_reason;
data.stop_reason = stop_reason;
}
data.text = text;
data.tokens = Some(tokens);
output.data = Some(data);
Some((output, state))
}
None => None,
}
})
.fuse();
// convert stream of processed Annotated<LLMEngineOutput> to Annotated<BackendOutput>
//let mdcsum = self.mdcsum.clone();
let stream = processed_stream.map(move |output| {
output.map_data(|data| {
Ok(BackendOutput {
token_ids: data.token_ids,
tokens: data.tokens.unwrap_or_default(),
text: data.text,
cum_log_probs: data.cum_log_probs,
log_probs: data.log_probs,
top_logprobs: data.top_logprobs,
finish_reason: data.finish_reason,
stop_reason: data.stop_reason,
//mdcsum: mdcsum.clone(),
index: data.index,
completion_usage: data.completion_usage,
disaggregated_params: data.disaggregated_params,
})
})
});
Ok(ResponseStream::new(Box::pin(stream), context))
}
}
#[async_trait]
impl
Operator<
SingleIn<PreprocessedEmbeddingRequest>,
ManyOut<Annotated<EmbeddingsEngineOutput>>,
SingleIn<PreprocessedEmbeddingRequest>,
ManyOut<Annotated<EmbeddingsEngineOutput>>,
> for Backend
{
async fn generate(
&self,
request: SingleIn<PreprocessedEmbeddingRequest>,
next: ServerStreamingEngine<
PreprocessedEmbeddingRequest,
Annotated<EmbeddingsEngineOutput>,
>,
) -> Result<ManyOut<Annotated<EmbeddingsEngineOutput>>> {
// For embeddings, we mostly pass through since no detokenization is needed
// But we could add validation, logging, or other post-processing here
let response_stream = next.generate(request).await?;
// Could add embedding-specific post-processing here:
// - Validation of embedding dimensions
// - Normalization if requested
// - Usage statistics validation
Ok(response_stream)
}
}
/// The [`Decoder`] object could be a member of either the internal LLM engine or part of the
/// postprocessor. If in the postprocessor, should be minimally in the same process or at very minimum
/// on the same physical machine connected by an IPC.
#[allow(dead_code)]
pub struct Decoder {
decode_stream: DecodeStream,
tracker: Option<Arc<RequestTracker>>,
// do not trigger stop conditions until at least this many tokens have been generated
min_tokens: u32,
// single tokens that if found in the response will trigger a stop condition after the
// minimum number of tokens have been generated
hidden_stop_ids: HashSet<TokenIdType>,
// text sequences that if found in the response will trigger a stop condition after the
// minimum number of tokens have been generated (excluded from output)
hidden_stop_sequences: Vec<String>,
// text sequences that if found in the response will trigger a stop condition after the
// minimum number of tokens have been generated (included in output)
visible_stop_sequences: Vec<String>,
// number of generated tokens
generated_tokens: u32,
// content jailed by partial hidden stop matches
jail: String,
// maximum number of bytes for the largest stop sequence
jail_max_bytes: usize,
// the number of bytes currently jailed
jailed_bytes: usize,
}
#[allow(dead_code)]
#[derive(Debug)]
pub enum StopTrigger {
MaxTokensLimit,
HiddenStopTokenDetected(TokenIdType),
HiddenStopSequenceDetected(String),
VisibleStopSequenceDetected(String),
}
pub struct StepResult {
pub token: Option<String>,
pub stop_trigger: Option<StopTrigger>,
}
impl StepResult {
fn ok(token: Option<String>) -> Self {
Self {
token,
stop_trigger: None,
}
}
fn with_stop_trigger(token: Option<String>, stop_trigger: StopTrigger) -> Self {
Self {
token,
stop_trigger: Some(stop_trigger),
}
}
}
/// Result of processing a sequence of tokens
pub struct SeqResult {
pub tokens: Vec<Option<String>>, // Individual decoded tokens
pub text: Option<String>, // Combined decoded text
pub stop_trigger: Option<StopTrigger>, // Reason for stopping generation, if any
}
#[allow(dead_code)]
impl Decoder {
pub fn new(
decode_stream: DecodeStream,
stop_condition: StopConditions,
include_stop_str_in_output: bool,
tracker: Option<Arc<RequestTracker>>,
) -> Self {
let hidden_stop_ids: HashSet<TokenIdType> = stop_condition
.stop_token_ids_hidden
.unwrap_or_default()
.iter()
.copied()
.collect();
// Categorize stop sequences based on include_stop_str_in_output:
// - When true: user-provided stop sequences go to visible (included in output)
// - When false: user-provided stop sequences go to hidden (excluded from output)
let (hidden_stop_sequences, visible_stop_sequences) = if include_stop_str_in_output {
(Vec::new(), stop_condition.stop.unwrap_or_default())
} else {
(stop_condition.stop.unwrap_or_default(), Vec::new())
};
// Calculate jail_max_bytes considering both hidden and visible stop sequences
let jail_max_bytes = hidden_stop_sequences
.iter()
.chain(visible_stop_sequences.iter())
.map(|x| x.len())
.max()
.unwrap_or(0);
Self {
decode_stream,
tracker,
hidden_stop_ids,
hidden_stop_sequences,
visible_stop_sequences,
min_tokens: stop_condition.min_tokens.unwrap_or(0),
generated_tokens: 0,
jail: String::new(),
jail_max_bytes,
jailed_bytes: 0,
}
}
/// Minimum amount of work to determine if a given generated/decoded sequence should be stopped
/// This method can be called by the inner most loop of the LLM engine or minimally in the same
/// process as the LLM engine.
///
/// In the future, this method may kick off async cpu/tokio tasks and or async cuda tasks to
/// handle logits post-processing and/or other tasks.
pub fn step(&mut self, token_id: TokenIdType) -> Result<StepResult> {
// increment the generated tokens
self.generated_tokens += 1;
// decode the token
let detokenize_start = Instant::now();
let token = self.decode_stream.step(token_id)?;
if let Some(tracker) = &self.tracker {
tracker.record_detokenize_latency(detokenize_start.elapsed());
}
// stop conditions to not apply until the minimum number of tokens have been generated
if self.generated_tokens < self.min_tokens {
return Ok(StepResult::ok(token));
}
// check for hidden stop tokens - eos takes precedence
if self.hidden_stop_ids.contains(&token_id) {
return Ok(StepResult::with_stop_trigger(
None,
StopTrigger::HiddenStopTokenDetected(token_id),
));
}
// check stop sequences - the jail will always hold at least the largest stop sequence
// if jail_max_bytes is 0, then there are no stop sequences
if self.jail_max_bytes > 0
&& let Some(token) = &token
{
let pre_append = self.jail.len();
self.jail.push_str(token);
// Check hidden stop sequences first (excluded from output)
for seq in &self.hidden_stop_sequences {
if let Some(offset) = galil_seiferas::gs_find(self.jail.as_bytes(), seq.as_bytes())
{
// return only new bytes after pre_append .. offset (excluding stop sequence)
// example: seq = "ox", token = "boxes", return "b"
// note: this changes when we start jailing tokens for partial matches
// on the suffix of the jail with prefixes of the stop sequences
//
// we might have returned a partial match, if so, then offset < pre_append
// in that case, we return the empty string
let partial_token = if offset >= pre_append {
self.jail[pre_append..offset].to_string()
} else {
"".to_string()
};
return Ok(StepResult::with_stop_trigger(
Some(partial_token),
StopTrigger::HiddenStopSequenceDetected(seq.to_string()),
));
}
}
// Check visible stop sequences (included in output)
for seq in &self.visible_stop_sequences {
if let Some(offset) = galil_seiferas::gs_find(self.jail.as_bytes(), seq.as_bytes())
{
// For visible stop sequences, include the stop string in the output
// Return all text from pre_append up to and including the stop sequence
let stop_end = offset + seq.len();
let token_with_stop = if stop_end > pre_append {
self.jail[pre_append..stop_end].to_string()
} else {
// Stop sequence was entirely in previously returned text
"".to_string()
};
return Ok(StepResult::with_stop_trigger(
Some(token_with_stop),
StopTrigger::VisibleStopSequenceDetected(seq.to_string()),
));
}
}
Self::maybe_drain_to_max_bytes(&mut self.jail, self.jail_max_bytes);
}
Ok(StepResult::ok(token))
}
pub fn process_token_ids(&mut self, token_ids: &[TokenIdType]) -> Result<SeqResult> {
let mut text: Option<String> = None;
let mut tokens = Vec::with_capacity(token_ids.len());
for token_id in token_ids {
let StepResult {
token,
stop_trigger,
} = self.step(*token_id)?;
// Always include token text (for visible stops, the stop string is already in the token)
if let Some(token) = &token {
text.get_or_insert_with(|| String::with_capacity(token_ids.len()))
.push_str(token);
}
tokens.push(token);
if let Some(stop_trigger) = stop_trigger {
return Ok(SeqResult {
tokens,
text,
stop_trigger: Some(stop_trigger),
});
}
}
Ok(SeqResult {
tokens,
text,
stop_trigger: None,
})
}
fn jailed_string(&self) -> Option<String> {
if self.jailed_bytes > 0 {
// get the last jailed_bytes from the jail
Some(self.jail[self.jail.len() - self.jailed_bytes..].to_string())
} else {
None
}
}
fn maybe_drain_to_max_bytes(s: &mut String, max_bytes: usize) {
if s.len() > max_bytes {
let mut drain_len = s.len() - max_bytes;
while !s.is_char_boundary(drain_len) {
drain_len -= 1;
}
s.drain(0..drain_len);
}
}
}
#[cfg(test)]
mod tests {
use super::*;
use crate::tokenizers::traits;
use std::sync::Arc;
#[test]
fn test_char_boundary_drain() {
let mut s = String::from("helloñworld"); // 12 bytes total ñ is 2 bytes
let max_bytes = 6; // 12 - 6 = 6 which is inside ñ
assert!(!s.is_char_boundary(s.len() - max_bytes)); // initially we are not on a char boundary
Decoder::maybe_drain_to_max_bytes(&mut s, max_bytes);
assert!(s.is_char_boundary(0)); // front of jail string on valid char boundary
assert_eq!(s, "ñworld");
}
/// A mock tokenizer that always returns Err from decode().
/// Used to test the error propagation path in Decoder::process_token_ids().
struct FailingDecoder;
impl traits::Encoder for FailingDecoder {
fn encode(&self, _input: &str) -> anyhow::Result<crate::tokenizers::Encoding> {
Ok(crate::tokenizers::Encoding::Sp(vec![]))
}
fn encode_batch(
&self,
_inputs: &[&str],
) -> anyhow::Result<Vec<crate::tokenizers::Encoding>> {
Ok(vec![])
}
}
impl traits::Decoder for FailingDecoder {
fn decode(
&self,
_token_ids: &[TokenIdType],
_skip_special_tokens: bool,
) -> anyhow::Result<String> {
Err(anyhow::anyhow!(
"Unable to decode into a valid UTF-8 string: incomplete utf-8 byte sequence from index 6"
))
}
}
impl traits::Tokenizer for FailingDecoder {}
/// When the tokenizer's decode() returns Err, Decoder::process_token_ids()
/// should propagate the error. In the backend unfold closure, this error
/// gets caught and converted to FinishReason::Error.
#[test]
fn test_decoder_process_token_ids_propagates_decode_error() {
let tokenizer: Arc<dyn traits::Tokenizer> = Arc::new(FailingDecoder);
let decode_stream = crate::tokenizers::DecodeStream::new(tokenizer, &[], false);
let stop_conditions = StopConditions::default();
let mut decoder = Decoder::new(decode_stream, stop_conditions, false, None);
let result = decoder.process_token_ids(&[42]);
assert!(
result.is_err(),
"process_token_ids should propagate decode errors"
);
let err_msg = result.err().unwrap().to_string();
assert!(
err_msg.contains("incomplete utf-8 byte sequence"),
"error should contain the original decode error message, got: {err_msg}"
);
}
/// Verify that the error message format matches what the backend unfold
/// closure would wrap into FinishReason::Error.
#[test]
fn test_decoder_error_message_format_for_finish_reason() {
let tokenizer: Arc<dyn traits::Tokenizer> = Arc::new(FailingDecoder);
let decode_stream = crate::tokenizers::DecodeStream::new(tokenizer, &[], false);
let stop_conditions = StopConditions::default();
let mut decoder = Decoder::new(decode_stream, stop_conditions, false, None);
let result = decoder.process_token_ids(&[42]);
let err = result.err().expect("should be Err");
// This is what the backend unfold closure does:
let finish_reason = FinishReason::Error(format!("decode error: {err}"));
match &finish_reason {
FinishReason::Error(msg) => {
assert!(
msg.starts_with("decode error:"),
"FinishReason::Error should have 'decode error:' prefix, got: {msg}"
);
assert!(
msg.contains("incomplete utf-8 byte sequence"),
"FinishReason::Error should contain original error, got: {msg}"
);
}
other => panic!("Expected FinishReason::Error, got: {:?}", other),
}
}
}