feat(neuron): OpenAI-compatible non-streaming chat completion
Stage 3 of the candle-native pivot. neuron now serves POST /v1/chat/completions backed by candle's quantized_qwen3 forward pass on a per-model serialised generation loop, returning the standard OpenAI ChatCompletionResponse envelope. Pipeline per request: - Look up the LoadedModel by request.model (404 if absent). - Apply the Qwen3 chat template across all messages. - Tokenize, then spawn_blocking onto tokio's blocking pool to acquire the per-model arch lock and run prefill + greedy/temperature/top-p sampling via LogitsProcessor. - Stop on <|im_end|>/<|endoftext|> EOS or max_tokens (finish_reason "stop" vs "length"). - Decode with skip_special_tokens=true, build OpenAI response with prompt/completion/total usage counts. Supporting changes: - HarnessRegistry now stores Arc<dyn Harness> and caches a typed Arc<CandleHarness> so inference routes bypass dyn-Trait dispatch. - LoadedModel.arch becomes Arc<Mutex<ModelArch>> so the lock guard can be moved into spawn_blocking. - NeuronState gains an Option<Arc<CandleHarness>> field for the new inference route. - Typed InferenceError lets the handler map ModelNotLoaded → 404 and other failures → 500 without string-matching anyhow messages. - stream=true returns 501 until Stage 4 wires up SSE. - Two leftover mistral.rs string references in proxy.rs and cortex-cli (missed during the Stage 1 sweep) are corrected here. Three new default-feature tests cover the no-candle 503, model-not- loaded 404, and stream=true 501 paths. The cuda-integration test from Stage 2 still covers real load/unload; a streaming-feature gated test exercising actual generation will arrive with Stage 4. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
This commit is contained in:
@@ -1,20 +1,28 @@
|
||||
//! Candle harness — in-process inference using huggingface/candle.
|
||||
//!
|
||||
//! This is the sole `Harness` implementation. Inference runs inside
|
||||
//! the neuron process; there is no external subprocess. Stage 2 wires
|
||||
//! up GGUF (currently Qwen3 only) model load/unload via
|
||||
//! `candle-transformers::models::quantized_qwen3`. Stage 3 adds the
|
||||
//! inference endpoint.
|
||||
//! the neuron process; there is no external subprocess.
|
||||
//!
|
||||
//! - Stage 2 wired GGUF (Qwen3 only) load/unload via `quantized_qwen3`.
|
||||
//! - Stage 3 (this) adds `chat_completion` — a non-streaming OpenAI
|
||||
//! compatible chat completion routed to the loaded model's forward
|
||||
//! pass on a per-model serialised generation loop.
|
||||
|
||||
use anyhow::{Context, Result};
|
||||
use async_trait::async_trait;
|
||||
use candle_core::Device;
|
||||
use candle_core::quantized::gguf_file;
|
||||
use candle_core::{Device, Tensor};
|
||||
use candle_transformers::generation::{LogitsProcessor, Sampling};
|
||||
use candle_transformers::models::quantized_qwen3::ModelWeights as QuantizedQwen3Weights;
|
||||
use cortex_core::harness::{Harness, HarnessHealth, ModelInfo, ModelSpec};
|
||||
use cortex_core::openai::{
|
||||
ChatCompletionChoice, ChatCompletionRequest, ChatCompletionResponse, ChatMessage,
|
||||
MessageContent, Usage,
|
||||
};
|
||||
use std::collections::HashMap;
|
||||
use std::path::PathBuf;
|
||||
use std::sync::Arc;
|
||||
use std::time::{SystemTime, UNIX_EPOCH};
|
||||
use tokenizers::Tokenizer;
|
||||
use tokio::sync::{Mutex, RwLock};
|
||||
|
||||
@@ -26,19 +34,20 @@ pub struct CandleHarness {
|
||||
}
|
||||
|
||||
/// A loaded model with its tokenizer, device placement, and architecture-
|
||||
/// specific weights. The `arch` field is mutexed because future inference
|
||||
/// calls take `&mut self` on the underlying ModelWeights (KV cache state).
|
||||
/// specific weights. The `arch` is `Arc<Mutex<>>` so the lock guard can be
|
||||
/// moved into `spawn_blocking` for synchronous candle forward passes.
|
||||
pub struct LoadedModel {
|
||||
pub model_id: String,
|
||||
pub arch: Mutex<ModelArch>,
|
||||
pub arch: Arc<Mutex<ModelArch>>,
|
||||
pub tokenizer: Tokenizer,
|
||||
pub device: Device,
|
||||
pub quant: Option<String>,
|
||||
pub devices: Vec<u32>,
|
||||
}
|
||||
|
||||
/// Architecture-specific weights. Stage 2 supports only Qwen3 quantized;
|
||||
/// Stage 8 broadens this to additional families and non-quantized variants.
|
||||
/// Architecture-specific weights. Stage 3 still supports only Qwen3
|
||||
/// quantized; Stage 8 broadens this to additional families and
|
||||
/// non-quantized variants.
|
||||
pub enum ModelArch {
|
||||
Qwen3Quantized(QuantizedQwen3Weights),
|
||||
}
|
||||
@@ -117,6 +126,92 @@ impl CandleHarness {
|
||||
.context("fetch tokenizer.json")?;
|
||||
Ok((gguf_path, tokenizer_path))
|
||||
}
|
||||
|
||||
/// Run a non-streaming chat completion against a loaded model.
|
||||
///
|
||||
/// Returns a typed `InferenceError` when the model isn't loaded so the
|
||||
/// handler can map to an appropriate HTTP status without string-matching.
|
||||
pub async fn chat_completion(
|
||||
&self,
|
||||
request: ChatCompletionRequest,
|
||||
) -> Result<ChatCompletionResponse, InferenceError> {
|
||||
let loaded = {
|
||||
let models = self.models.read().await;
|
||||
models.get(&request.model).cloned()
|
||||
};
|
||||
let loaded = loaded.ok_or_else(|| InferenceError::ModelNotLoaded(request.model.clone()))?;
|
||||
|
||||
let prompt = format_qwen3_prompt(&request.messages);
|
||||
|
||||
let encoding = loaded
|
||||
.tokenizer
|
||||
.encode(prompt.as_str(), true)
|
||||
.map_err(|e| InferenceError::Other(anyhow::anyhow!("tokenize: {e}")))?;
|
||||
let prompt_tokens: Vec<u32> = encoding.get_ids().to_vec();
|
||||
let prompt_len = prompt_tokens.len();
|
||||
|
||||
let temperature = request.temperature.unwrap_or(0.7);
|
||||
let top_p = request.top_p;
|
||||
let max_new = request.max_tokens.unwrap_or(512) as usize;
|
||||
let seed = unix_subsec_nanos();
|
||||
|
||||
let eos_id = loaded
|
||||
.tokenizer
|
||||
.token_to_id("<|im_end|>")
|
||||
.or_else(|| loaded.tokenizer.token_to_id("<|endoftext|>"));
|
||||
|
||||
let arch_arc = Arc::clone(&loaded.arch);
|
||||
let device = loaded.device.clone();
|
||||
let model_id = request.model.clone();
|
||||
|
||||
let (generated_ids, finish_reason) =
|
||||
tokio::task::spawn_blocking(move || -> Result<(Vec<u32>, String)> {
|
||||
let mut guard = arch_arc.blocking_lock();
|
||||
run_inference(
|
||||
&mut guard,
|
||||
&device,
|
||||
&prompt_tokens,
|
||||
max_new,
|
||||
temperature,
|
||||
top_p,
|
||||
seed,
|
||||
eos_id,
|
||||
)
|
||||
})
|
||||
.await
|
||||
.map_err(|e| InferenceError::Other(anyhow::anyhow!("inference task panicked: {e}")))?
|
||||
.map_err(InferenceError::Other)?;
|
||||
|
||||
let completion_text = loaded
|
||||
.tokenizer
|
||||
.decode(&generated_ids, true)
|
||||
.map_err(|e| InferenceError::Other(anyhow::anyhow!("detokenize: {e}")))?;
|
||||
|
||||
let usage = Usage {
|
||||
prompt_tokens: prompt_len as u64,
|
||||
completion_tokens: generated_ids.len() as u64,
|
||||
total_tokens: (prompt_len + generated_ids.len()) as u64,
|
||||
};
|
||||
|
||||
Ok(ChatCompletionResponse {
|
||||
id: format!("chatcmpl-{:x}", unix_subsec_nanos()),
|
||||
object: "chat.completion".into(),
|
||||
created: unix_now_secs(),
|
||||
model: model_id,
|
||||
choices: vec![ChatCompletionChoice {
|
||||
index: 0,
|
||||
message: ChatMessage {
|
||||
role: "assistant".into(),
|
||||
content: MessageContent::Text(completion_text),
|
||||
extra: serde_json::Value::Object(Default::default()),
|
||||
},
|
||||
finish_reason: Some(finish_reason),
|
||||
extra: serde_json::Value::Object(Default::default()),
|
||||
}],
|
||||
usage: Some(usage),
|
||||
extra: serde_json::Value::Object(Default::default()),
|
||||
})
|
||||
}
|
||||
}
|
||||
|
||||
#[async_trait]
|
||||
@@ -193,7 +288,7 @@ impl Harness for CandleHarness {
|
||||
Ok(ModelArch::Qwen3Quantized(weights))
|
||||
}
|
||||
other => anyhow::bail!(
|
||||
"unsupported GGUF architecture '{other}'; Stage 2 only supports qwen3"
|
||||
"unsupported GGUF architecture '{other}'; Stage 3 only supports qwen3"
|
||||
),
|
||||
}
|
||||
})
|
||||
@@ -202,7 +297,7 @@ impl Harness for CandleHarness {
|
||||
|
||||
let loaded = Arc::new(LoadedModel {
|
||||
model_id: spec.model_id.clone(),
|
||||
arch: Mutex::new(arch),
|
||||
arch: Arc::new(Mutex::new(arch)),
|
||||
tokenizer,
|
||||
device,
|
||||
quant: spec.quant.clone(),
|
||||
@@ -229,3 +324,118 @@ impl Harness for CandleHarness {
|
||||
models.contains_key(model_id).then(|| self.bind_url.clone())
|
||||
}
|
||||
}
|
||||
|
||||
/// Errors returned by `CandleHarness::chat_completion`. The
|
||||
/// `ModelNotLoaded` variant lets the HTTP handler map cleanly to 404
|
||||
/// without string-matching on anyhow messages.
|
||||
#[derive(Debug, thiserror::Error)]
|
||||
pub enum InferenceError {
|
||||
#[error("model '{0}' not loaded on this neuron")]
|
||||
ModelNotLoaded(String),
|
||||
#[error(transparent)]
|
||||
Other(#[from] anyhow::Error),
|
||||
}
|
||||
|
||||
/// Apply the Qwen3 chat template:
|
||||
///
|
||||
/// ```text
|
||||
/// <|im_start|>{role}\n{content}<|im_end|>\n
|
||||
/// ...
|
||||
/// <|im_start|>assistant\n
|
||||
/// ```
|
||||
///
|
||||
/// The trailing `<|im_start|>assistant\n` cues the model to begin a turn.
|
||||
/// Non-text content parts (vision blocks) are joined as text only; full
|
||||
/// multimodal handling is out of scope for Stage 3.
|
||||
fn format_qwen3_prompt(messages: &[ChatMessage]) -> String {
|
||||
let mut prompt = String::new();
|
||||
for msg in messages {
|
||||
let content = match &msg.content {
|
||||
MessageContent::Text(s) => s.clone(),
|
||||
MessageContent::Parts(parts) => parts
|
||||
.iter()
|
||||
.filter_map(|p| p.get("text").and_then(|v| v.as_str()))
|
||||
.collect::<Vec<_>>()
|
||||
.join(""),
|
||||
};
|
||||
prompt.push_str("<|im_start|>");
|
||||
prompt.push_str(&msg.role);
|
||||
prompt.push('\n');
|
||||
prompt.push_str(&content);
|
||||
prompt.push_str("<|im_end|>\n");
|
||||
}
|
||||
prompt.push_str("<|im_start|>assistant\n");
|
||||
prompt
|
||||
}
|
||||
|
||||
#[allow(clippy::too_many_arguments)]
|
||||
fn run_inference(
|
||||
arch: &mut ModelArch,
|
||||
device: &Device,
|
||||
prompt_tokens: &[u32],
|
||||
max_new: usize,
|
||||
temperature: f64,
|
||||
top_p: Option<f64>,
|
||||
seed: u64,
|
||||
eos_id: Option<u32>,
|
||||
) -> Result<(Vec<u32>, String)> {
|
||||
let mut logits_processor = {
|
||||
let sampling = if temperature <= 0.0 {
|
||||
Sampling::ArgMax
|
||||
} else {
|
||||
match top_p {
|
||||
Some(p) => Sampling::TopP { p, temperature },
|
||||
None => Sampling::All { temperature },
|
||||
}
|
||||
};
|
||||
LogitsProcessor::from_sampling(seed, sampling)
|
||||
};
|
||||
|
||||
let mut generated: Vec<u32> = Vec::new();
|
||||
|
||||
let mut next_token = match arch {
|
||||
ModelArch::Qwen3Quantized(model) => {
|
||||
model.clear_kv_cache();
|
||||
let input = Tensor::new(prompt_tokens, device)?.unsqueeze(0)?;
|
||||
let logits = model.forward(&input, 0)?;
|
||||
let logits = logits.squeeze(0)?;
|
||||
logits_processor.sample(&logits)?
|
||||
}
|
||||
};
|
||||
|
||||
if Some(next_token) == eos_id {
|
||||
return Ok((generated, "stop".into()));
|
||||
}
|
||||
generated.push(next_token);
|
||||
|
||||
for index in 0..max_new.saturating_sub(1) {
|
||||
next_token = match arch {
|
||||
ModelArch::Qwen3Quantized(model) => {
|
||||
let input = Tensor::new(&[next_token], device)?.unsqueeze(0)?;
|
||||
let logits = model.forward(&input, prompt_tokens.len() + index)?;
|
||||
let logits = logits.squeeze(0)?;
|
||||
logits_processor.sample(&logits)?
|
||||
}
|
||||
};
|
||||
if Some(next_token) == eos_id {
|
||||
return Ok((generated, "stop".into()));
|
||||
}
|
||||
generated.push(next_token);
|
||||
}
|
||||
|
||||
Ok((generated, "length".into()))
|
||||
}
|
||||
|
||||
fn unix_now_secs() -> u64 {
|
||||
SystemTime::now()
|
||||
.duration_since(UNIX_EPOCH)
|
||||
.map(|d| d.as_secs())
|
||||
.unwrap_or(0)
|
||||
}
|
||||
|
||||
fn unix_subsec_nanos() -> u64 {
|
||||
SystemTime::now()
|
||||
.duration_since(UNIX_EPOCH)
|
||||
.map(|d| d.as_nanos() as u64)
|
||||
.unwrap_or(0)
|
||||
}
|
||||
|
||||
Reference in New Issue
Block a user