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Inference

KV Cache

At decode time, only the last query token matters. All previous K/V are cached to avoid recomputation:


o_n = \sum_j \text{softmax}\left(\frac{q_n k_j}{\sqrt{d_k}}\right) v_j

RoPE is applied before KV cache write, not after — otherwise position encoding drift occurs.

KVCache System

Six classes working together:

KVCache (facade)
  ├── Allocator        bitmask-based page allocator + ref-count + LRU eviction
  ├── PrefixCache      hash-based prefix matching (page_hash via rolling hash)
  ├── PagePool         orchestrates Allocator + PrefixCache
  ├── TaskTable        maps task_id → page_table + cached token count
  ├── Storage          k_cache / v_cache tensors (n_layers × n_pages × page_size × n_kv_heads × head_dim)
  └── KvcacheView      bundles Storage + page_table + total_len for attention layers

KVCache.bind(page_table, total_len) returns a KvcacheView used by attention layers via write() / gather().

Continuous Batching

InferenceScheduler runs a daemon thread with a 4-phase loop:

1. Cleanup → Remove finished tasks, free KV pages
2. Refill  → Pop from waiting_queue, task_alloc pages, activate
3. Prefill → Group by (prompt_len, start_pos), run full forward
4. Decode  → Pick largest same-position group, single-token forward

Sampling (Strategy Pattern)

BaseSamplingStrategy → TemperatureStrategy → TopKStrategy → TopPStrategy

SamplingPipeline composes them: Temperature → Top-K → Top-P → softmax → multinomial.
sample() is a convenience shortcut for one-shot usage.

Protocol Handlers (Template Method)

class ProtocolHandler(ABC):
    def handle(self):
        ctx = StreamContext(...)
        agen = engine.generate_async(prompt, ...)
        if stream: self._handle_stream(agen, ctx)
        else:      self._handle_non_stream(agen, ctx)

Subclass hooks: build_prompt(), create_response_id(), format_stream_start/token/end(), format_non_stream_response().

OpenAIHandler → /v1/chat/completions, AnthropicHandler → /v1/messages.

Engine & GenerateResult

InferenceEngine
  ├── generate(prompt, stream, ...) → str | List[str] | Generator
  ├── generate_with_request(req)    → same
  └── generate_async(prompt, ...)   → AsyncGenerator

GenerateResult uses Condition for non-streaming (wait_completion()) and Event for streaming (wait()). Stream callback is cb(token).

HTTP API

POST /v1/chat/completions   OpenAI
POST /v1/messages            Anthropic
GET  /health                 {"status":"ok","model_loaded":true}
GET  /stats                  scheduler statistics

OpenAI

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{"messages":[{"role":"user","content":"Hello"}],"max_tokens":512}'

Response:

{
  "id": "chatcmpl-abc123",
  "object": "chat.completion",
  "choices": [{"message": {"role": "assistant", "content": "Hello!"}, "finish_reason": "stop"}],
  "usage": {"prompt_tokens": 5, "completion_tokens": 10, "total_tokens": 15}
}

Streaming SSE: data: {"choices":[{"delta":{"role":"assistant"}}]} → token chunks → data: [DONE]

Anthropic

curl -X POST http://localhost:8000/v1/messages \
  -H "Content-Type: application/json" \
  -d '{"model":"astrai","system":"You are helpful.","messages":[{"role":"user","content":"Hello"}],"max_tokens":512}'

Supports stop_sequences and streaming via event: content_block_delta.

GenerationRequest Parameters

Param	Type	Default	Description
`messages`	List[dict]	required	Chat messages (role, content)
`temperature`	float	1.0	Sampling temperature (0.0–2.0)
`top_p`	float	1.0	Nucleus threshold
`top_k`	int	50	Top-k count
`max_tokens`	int	None	Max generation length
`stream`	bool	False	Stream output

Engine API

# Non-streaming
engine.generate("Hello", stream=False)          # -> str
engine.generate(["A", "B"], stream=False)       # -> List[str]

# Streaming
engine.generate("Hello", stream=True)           # -> Generator[str]
engine.generate(["A", "B"], stream=True)        # -> Generator[Tuple[int, str]]

# Async
await engine.generate_async("Hello", ...)       # -> AsyncGenerator[str]

Document Update Time: 2026-05-17

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