ferrum_interfaces/engine.rs
1//! Inference engine interfaces — split per modality.
2//!
3//! Phase 5a step 2 splits the historical mega-trait (which mixed LLM
4//! generation, embedding, transcription, and TTS in one) into a base
5//! lifecycle trait and four modality-specific supertraits. Each
6//! engine impl now implements exactly the trait its modality needs;
7//! no more inert "unsupported" stubs.
8
9use async_trait::async_trait;
10use ferrum_types::{
11 EngineConfig, ExecutorAdmissionSnapshot, InferenceRequest, InferenceResponse, Result,
12 StreamChunk,
13};
14use futures::Stream;
15use std::pin::Pin;
16
17/// Lifecycle / status methods shared by every engine kind.
18///
19/// LLM engines, embedders, transcribers, and TTS services all expose
20/// the same minimal status/metrics surface to the server / CLI. The
21/// modality-specific traits below extend this base.
22#[async_trait]
23pub trait InferenceEngine: Send + Sync {
24 /// Get current engine status.
25 async fn status(&self) -> ferrum_types::EngineStatus;
26
27 /// Shutdown engine gracefully.
28 async fn shutdown(&self) -> Result<()>;
29
30 /// Get engine configuration.
31 fn config(&self) -> &EngineConfig;
32
33 /// Get engine metrics.
34 fn metrics(&self) -> ferrum_types::EngineMetrics;
35
36 /// Health check.
37 async fn health_check(&self) -> ferrum_types::HealthStatus;
38
39 /// Optional cache metrics emitted by concrete LLM engines.
40 ///
41 /// The default keeps non-LLM and stub engines source-compatible. Real
42 /// engines can expose prefix/session cache counters without forcing those
43 /// fields into every modality's core metrics type.
44 fn cache_metrics_snapshot(&self) -> Option<serde_json::Value> {
45 None
46 }
47
48 /// Runtime-authoritative admission state. Startup sizing estimates are
49 /// intentionally not accepted through this method.
50 fn admission_snapshot(&self) -> Result<Option<ExecutorAdmissionSnapshot>> {
51 Ok(None)
52 }
53
54 /// Optional LoRA runtime metrics emitted by concrete LLM engines.
55 fn lora_metrics_snapshot(&self) -> Option<serde_json::Value> {
56 None
57 }
58}
59
60/// LLM text-generation engine.
61///
62/// Implemented by `ContinuousBatchEngine` (the production path) and
63/// `DefaultInferenceEngine` (legacy reference path). Backs
64/// `/v1/chat/completions` and `/v1/completions`.
65#[async_trait]
66pub trait LlmInferenceEngine: InferenceEngine {
67 /// Execute single inference request.
68 async fn infer(&self, request: InferenceRequest) -> Result<InferenceResponse>;
69
70 /// Execute streaming inference request.
71 async fn infer_stream(
72 &self,
73 request: InferenceRequest,
74 ) -> Result<Pin<Box<dyn Stream<Item = Result<StreamChunk>> + Send>>>;
75}
76
77/// Embedding engine (CLIP, BERT, etc.).
78///
79/// Backs `/v1/embeddings`. Distinct from LLM engines — no token
80/// generation, no scheduling, no KV cache.
81#[async_trait]
82pub trait EmbedEngine: InferenceEngine {
83 /// Embed raw text string → float vector (engine handles tokenization).
84 async fn embed_text(&self, text: &str) -> Result<Vec<f32>>;
85
86 /// Embed image (file path or base64) → float vector.
87 async fn embed_image(&self, image: &str) -> Result<Vec<f32>>;
88
89 /// Get embedding dimension.
90 fn embedding_dim(&self) -> usize;
91}
92
93/// Speech-to-text (Whisper) engine.
94///
95/// Backs `/v1/audio/transcriptions`.
96#[async_trait]
97pub trait TranscribeEngine: InferenceEngine {
98 /// Transcribe audio file → text.
99 async fn transcribe_file(&self, path: &str, language: Option<&str>) -> Result<String>;
100
101 /// Transcribe audio bytes (WAV / etc.) → text.
102 async fn transcribe_bytes(&self, data: &[u8], language: Option<&str>) -> Result<String>;
103}
104
105/// Text-to-speech (Qwen3-TTS, etc.) engine.
106///
107/// Backs `/v1/audio/speech`.
108#[async_trait]
109pub trait TtsEngine: InferenceEngine {
110 /// Synthesize speech → PCM audio chunks (streaming).
111 /// Returns Vec of PCM f32 samples per chunk.
112 async fn synthesize_speech(
113 &self,
114 text: &str,
115 language: Option<&str>,
116 chunk_frames: usize,
117 ) -> Result<Vec<Vec<f32>>>;
118
119 /// Get TTS sample rate.
120 fn tts_sample_rate(&self) -> u32;
121}
122
123/// Advanced engine capabilities — opt-in addition to LLM engines that
124/// support batching / speculation / runtime reconfig / diagnostics.
125#[async_trait]
126pub trait AdvancedInferenceEngine: LlmInferenceEngine {
127 /// Execute batch inference.
128 async fn infer_batch(
129 &self,
130 requests: Vec<InferenceRequest>,
131 ) -> Result<Vec<Result<InferenceResponse>>>;
132
133 /// Execute speculative inference.
134 async fn infer_speculative(
135 &self,
136 request: InferenceRequest,
137 speculation_config: ferrum_types::SpeculationConfig,
138 ) -> Result<InferenceResponse>;
139
140 /// Warm up engine with sample requests.
141 async fn warmup(
142 &mut self,
143 warmup_requests: Vec<InferenceRequest>,
144 ) -> Result<ferrum_types::WarmupResult>;
145
146 /// Configure engine at runtime.
147 async fn reconfigure(&mut self, config: EngineConfig) -> Result<()>;
148
149 /// Get detailed diagnostics.
150 async fn diagnostics(&self) -> ferrum_types::DiagnosticsReport;
151
152 /// Export engine state for debugging.
153 async fn export_state(&self) -> Result<ferrum_types::EngineState>;
154
155 /// Import engine state for debugging/testing.
156 async fn import_state(&mut self, state: ferrum_types::EngineState) -> Result<()>;
157}
158
159/// Speculation configuration for speculative decoding.
160pub type SpeculationConfig = ferrum_types::SpeculationConfig;
161
162/// Hardware constraints alias.
163pub type HardwareConstraints = ferrum_types::HardwareConstraints;
164
165/// Request characteristics alias.
166pub type RequestCharacteristics = ferrum_types::RequestCharacteristics;
167
168/// Latency requirements alias.
169pub type LatencyRequirements = ferrum_types::LatencyRequirements;