Files
sglang/sgl-router/src/routers/grpc/processing.rs
T

535 lines
20 KiB
Rust

//! Shared response processing logic for gRPC routers
//!
//! This module contains response processing functions that are shared between
//! the regular router and PD router, eliminating ~1,200 lines of exact duplicates.
use std::{sync::Arc, time::Instant};
use proto::generate_complete::MatchedStop;
use serde_json::Value;
use tracing::error;
use super::{
context::{DispatchMetadata, ExecutionResult},
utils,
};
use crate::{
grpc_client::proto,
protocols::{
chat::{ChatChoice, ChatCompletionMessage, ChatCompletionRequest, ChatCompletionResponse},
common::{FunctionCallResponse, ToolCall, ToolChoice, ToolChoiceValue, Usage},
generate::{GenerateMetaInfo, GenerateRequest, GenerateResponse},
},
reasoning_parser::ParserFactory as ReasoningParserFactory,
tokenizer::{
stop::{SequenceDecoderOutput, StopSequenceDecoder},
traits::Tokenizer,
},
tool_parser::ParserFactory as ToolParserFactory,
};
// ============================================================================
// Response Processor - Main Entry Point
// ============================================================================
/// Unified response processor for both routers
#[derive(Clone)]
pub struct ResponseProcessor {
pub tokenizer: Arc<dyn Tokenizer>,
pub tool_parser_factory: ToolParserFactory,
pub reasoning_parser_factory: ReasoningParserFactory,
pub configured_tool_parser: Option<String>,
pub configured_reasoning_parser: Option<String>,
}
impl ResponseProcessor {
pub fn new(
tokenizer: Arc<dyn Tokenizer>,
tool_parser_factory: ToolParserFactory,
reasoning_parser_factory: ReasoningParserFactory,
configured_tool_parser: Option<String>,
configured_reasoning_parser: Option<String>,
) -> Self {
Self {
tokenizer,
tool_parser_factory,
reasoning_parser_factory,
configured_tool_parser,
configured_reasoning_parser,
}
}
/// Helper to collect responses from execution result and merge logprobs if needed
async fn collect_and_merge_responses(
execution_result: ExecutionResult,
request_logprobs: bool,
) -> Result<Vec<proto::GenerateComplete>, axum::response::Response> {
let all_responses = match execution_result {
ExecutionResult::Single { mut stream } => {
let responses = utils::collect_stream_responses(&mut stream, "Single").await?;
stream.mark_completed();
responses
}
ExecutionResult::Dual {
mut prefill,
decode,
} => {
// Collect prefill for input_logprobs (don't mark completed yet)
let prefill_responses =
utils::collect_stream_responses(&mut prefill, "Prefill").await?;
// Collect decode for actual output (don't mark completed yet)
let mut decode_stream = *decode;
let mut decode_responses =
utils::collect_stream_responses(&mut decode_stream, "Decode").await?;
// Mark both streams as completed now that both succeeded
prefill.mark_completed();
decode_stream.mark_completed();
// Merge prefill input_logprobs if requested
if request_logprobs {
if let Some(prefill_input_logprobs) = prefill_responses
.first()
.and_then(|r| r.input_logprobs.clone())
{
for response in &mut decode_responses {
response.input_logprobs = Some(prefill_input_logprobs.clone());
}
}
}
decode_responses
}
};
if all_responses.is_empty() {
return Err(utils::internal_error_static("No responses from server"));
}
Ok(all_responses)
}
/// Process a single choice from GenerateComplete response
#[allow(clippy::too_many_arguments)]
pub async fn process_single_choice(
&self,
complete: &proto::GenerateComplete,
index: usize,
original_request: &ChatCompletionRequest,
stop_decoder: &mut StopSequenceDecoder,
history_tool_calls_count: usize,
reasoning_parser_available: bool,
tool_parser_available: bool,
) -> Result<ChatChoice, String> {
stop_decoder.reset();
// Decode tokens
let outputs = stop_decoder
.process_tokens(&complete.output_ids)
.map_err(|e| format!("Failed to process tokens: {}", e))?;
// Accumulate text with early breaks
let mut final_text = String::new();
for output in outputs {
match output {
SequenceDecoderOutput::Text(t) => final_text.push_str(&t),
SequenceDecoderOutput::StoppedWithText(t) => {
final_text.push_str(&t);
break;
}
SequenceDecoderOutput::Stopped => break,
SequenceDecoderOutput::Held => {}
}
}
// Flush remaining text
if let SequenceDecoderOutput::Text(t) = stop_decoder.flush() {
final_text.push_str(&t);
}
// Step 1: Handle reasoning content parsing
let mut reasoning_text: Option<String> = None;
let mut processed_text = final_text;
// Check if reasoning parsing is enabled and parser is available
if original_request.separate_reasoning && reasoning_parser_available {
let pooled_parser = utils::get_reasoning_parser(
&self.reasoning_parser_factory,
self.configured_reasoning_parser.as_ref(),
&original_request.model,
);
let mut parser = pooled_parser.lock().await;
match parser.detect_and_parse_reasoning(&processed_text) {
Ok(result) => {
if !result.reasoning_text.is_empty() {
reasoning_text = Some(result.reasoning_text);
}
processed_text = result.normal_text;
}
Err(e) => {
return Err(format!("Reasoning parsing error: {}", e));
}
}
}
// Step 2: Handle tool call parsing
let mut tool_calls: Option<Vec<ToolCall>> = None;
let tool_choice_enabled = !matches!(
&original_request.tool_choice,
Some(ToolChoice::Value(ToolChoiceValue::None))
);
if tool_choice_enabled && original_request.tools.is_some() {
// Check if JSON schema constraint was used (specific function or required mode)
let used_json_schema = match &original_request.tool_choice {
Some(ToolChoice::Function { .. }) => true,
Some(ToolChoice::Value(ToolChoiceValue::Required)) => true,
Some(ToolChoice::AllowedTools { mode, .. }) => mode == "required",
_ => false,
};
if used_json_schema {
(tool_calls, processed_text) = utils::parse_json_schema_response(
&processed_text,
&original_request.tool_choice,
&original_request.model,
history_tool_calls_count,
);
} else if tool_parser_available {
(tool_calls, processed_text) = self
.parse_tool_calls(
&processed_text,
&original_request.model,
history_tool_calls_count,
)
.await;
}
}
// Step 3: Use finish reason directly from proto (already OpenAI-compatible string)
let finish_reason_str = &complete.finish_reason;
// Override finish reason if we have tool calls
let final_finish_reason_str = if tool_calls.is_some() {
"tool_calls"
} else {
finish_reason_str
};
// Extract matched_stop information from proto
let matched_stop = match &complete.matched_stop {
Some(MatchedStop::MatchedTokenId(token_id)) => {
Some(Value::Number(serde_json::Number::from(*token_id)))
}
Some(MatchedStop::MatchedStopStr(stop_str)) => Some(Value::String(stop_str.clone())),
None => None,
};
// Step 4: Convert output logprobs if present
let logprobs = if let Some(proto_logprobs) = &complete.output_logprobs {
match utils::convert_proto_to_openai_logprobs(proto_logprobs, &self.tokenizer) {
Ok(logprobs) => Some(logprobs),
Err(e) => {
error!("Failed to convert logprobs: {}", e);
None
}
}
} else {
None
};
// Step 5: Build ChatCompletionMessage (proper response message type)
let chat_message = ChatCompletionMessage {
role: "assistant".to_string(),
content: if processed_text.is_empty() {
None
} else {
Some(processed_text)
},
tool_calls,
reasoning_content: reasoning_text,
};
// Step 6: Build ChatChoice
let choice = ChatChoice {
index: index as u32,
message: chat_message,
logprobs,
finish_reason: Some(final_finish_reason_str.to_string()),
matched_stop,
hidden_states: None,
};
Ok(choice)
}
/// Process non-streaming chat response (collects all responses and builds final response)
pub async fn process_non_streaming_chat_response(
&self,
execution_result: ExecutionResult,
chat_request: Arc<ChatCompletionRequest>,
dispatch: DispatchMetadata,
stop_decoder: &mut StopSequenceDecoder,
request_logprobs: bool,
) -> Result<ChatCompletionResponse, axum::response::Response> {
// Collect all responses from the execution result
let all_responses =
Self::collect_and_merge_responses(execution_result, request_logprobs).await?;
let history_tool_calls_count = utils::get_history_tool_calls_count(&chat_request);
// Check parser availability once upfront (not per choice)
let reasoning_parser_available = chat_request.separate_reasoning
&& utils::check_reasoning_parser_availability(
&self.reasoning_parser_factory,
self.configured_reasoning_parser.as_ref(),
&chat_request.model,
);
let tool_choice_enabled = !matches!(
&chat_request.tool_choice,
Some(ToolChoice::Value(ToolChoiceValue::None))
);
let tool_parser_available = tool_choice_enabled
&& chat_request.tools.is_some()
&& utils::check_tool_parser_availability(
&self.tool_parser_factory,
self.configured_tool_parser.as_ref(),
&chat_request.model,
);
// Log once per request (not per choice)
if chat_request.separate_reasoning && !reasoning_parser_available {
tracing::debug!(
"No reasoning parser found for model '{}', skipping reasoning parsing",
chat_request.model
);
}
if chat_request.tools.is_some() && tool_choice_enabled && !tool_parser_available {
tracing::debug!(
"No tool parser found for model '{}', skipping tool call parsing",
chat_request.model
);
}
// Process all choices
let mut choices = Vec::new();
for (index, complete) in all_responses.iter().enumerate() {
match self
.process_single_choice(
complete,
index,
&chat_request,
stop_decoder,
history_tool_calls_count,
reasoning_parser_available,
tool_parser_available,
)
.await
{
Ok(choice) => choices.push(choice),
Err(e) => {
return Err(utils::internal_error_message(format!(
"Failed to process choice {}: {}",
index, e
)));
}
}
}
// Build usage
let total_prompt_tokens: u32 = all_responses.iter().map(|r| r.prompt_tokens as u32).sum();
let total_completion_tokens: u32 = all_responses
.iter()
.map(|r| r.completion_tokens as u32)
.sum();
let usage = Usage {
prompt_tokens: total_prompt_tokens,
completion_tokens: total_completion_tokens,
total_tokens: total_prompt_tokens + total_completion_tokens,
completion_tokens_details: None,
};
// Build final ChatCompletionResponse
let response = ChatCompletionResponse {
id: dispatch.request_id.clone(),
object: "chat.completion".to_string(),
created: dispatch.created,
model: dispatch.model.clone(),
choices,
usage: Some(usage),
system_fingerprint: dispatch.weight_version.clone(),
};
Ok(response)
}
/// Parse tool calls using model-specific parser
pub async fn parse_tool_calls(
&self,
processed_text: &str,
model: &str,
history_tool_calls_count: usize,
) -> (Option<Vec<ToolCall>>, String) {
// Get pooled parser for this model
let pooled_parser = utils::get_tool_parser(
&self.tool_parser_factory,
self.configured_tool_parser.as_ref(),
model,
);
// Try parsing directly (parser will handle detection internally)
let result = {
let parser = pooled_parser.lock().await;
parser.parse_complete(processed_text).await
// Lock is dropped here
};
match result {
Ok((normal_text, parsed_tool_calls)) => {
if parsed_tool_calls.is_empty() {
return (None, normal_text);
}
let spec_tool_calls = parsed_tool_calls
.into_iter()
.enumerate()
.map(|(index, tc)| {
// Generate ID for this tool call
let id = utils::generate_tool_call_id(
model,
&tc.function.name,
index,
history_tool_calls_count,
);
ToolCall {
id,
tool_type: "function".to_string(),
function: FunctionCallResponse {
name: tc.function.name,
arguments: Some(tc.function.arguments),
},
}
})
.collect();
(Some(spec_tool_calls), normal_text)
}
Err(e) => {
error!("Tool call parsing error: {}", e);
(None, processed_text.to_string())
}
}
}
/// Process non-streaming generate response (collects all responses and builds final response array)
pub async fn process_non_streaming_generate_response(
&self,
execution_result: ExecutionResult,
_generate_request: Arc<GenerateRequest>,
dispatch: DispatchMetadata,
stop_decoder: &mut StopSequenceDecoder,
request_logprobs: bool,
start_time: Instant,
) -> Result<Vec<GenerateResponse>, axum::response::Response> {
// Collect all responses from the execution result
let all_responses =
Self::collect_and_merge_responses(execution_result, request_logprobs).await?;
// Process each completion
let mut result_array = Vec::new();
for mut complete in all_responses {
stop_decoder.reset();
// Process tokens through stop decoder
let outputs = match stop_decoder.process_tokens(&complete.output_ids) {
Ok(outputs) => outputs,
Err(e) => {
return Err(utils::internal_error_message(format!(
"Failed to process tokens: {}",
e
)))
}
};
// Accumulate text with early breaks
let mut decoded_text = String::new();
for output in outputs {
match output {
SequenceDecoderOutput::Text(t) => decoded_text.push_str(&t),
SequenceDecoderOutput::StoppedWithText(t) => {
decoded_text.push_str(&t);
break;
}
SequenceDecoderOutput::Stopped => break,
SequenceDecoderOutput::Held => {}
}
}
// Flush remaining text
if let SequenceDecoderOutput::Text(t) = stop_decoder.flush() {
decoded_text.push_str(&t);
}
let output_ids = std::mem::take(&mut complete.output_ids);
let finish_reason_str = std::mem::take(&mut complete.finish_reason);
// Parse finish_reason from string to proper type
let finish_reason =
utils::parse_finish_reason(&finish_reason_str, complete.completion_tokens);
// Handle matched_stop if present
let matched_stop = complete.matched_stop.take().map(|matched| match matched {
MatchedStop::MatchedTokenId(id) => serde_json::json!(id),
MatchedStop::MatchedStopStr(s) => serde_json::json!(s),
});
// Extract logprobs if requested (convert proto types to Generate format)
let input_token_logprobs = if request_logprobs {
complete
.input_logprobs
.as_ref()
.map(utils::convert_generate_input_logprobs)
} else {
None
};
let output_token_logprobs = if request_logprobs {
complete
.output_logprobs
.as_ref()
.map(utils::convert_generate_output_logprobs)
} else {
None
};
// Build GenerateResponse struct
let meta_info = GenerateMetaInfo {
id: dispatch.request_id.clone(),
finish_reason,
prompt_tokens: complete.prompt_tokens as u32,
weight_version: dispatch
.weight_version
.clone()
.unwrap_or_else(|| "default".to_string()),
input_token_logprobs,
output_token_logprobs,
completion_tokens: complete.completion_tokens as u32,
cached_tokens: complete.cached_tokens as u32,
e2e_latency: start_time.elapsed().as_secs_f64(),
matched_stop,
};
result_array.push(GenerateResponse {
text: decoded_text,
output_ids,
meta_info,
});
}
Ok(result_array)
}
}