253 lines
7.8 KiB
Markdown
253 lines
7.8 KiB
Markdown
# Inference
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## Contents
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- [KV Cache](#kv-cache)
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- [KVCache System](#kvcache-system)
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- [Continuous Batching](#continuous-batching)
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- [Sampling](#sampling-strategy-pattern)
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- [Protocol Handlers](#protocol-handlers-strategy-pattern)
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- [Engine & GenerateResult](#engine--generateresult)
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- [HTTP API](#http-api) — endpoints, SSE, errors, stats
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- [Engine API](#engine-api)
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## KV Cache
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At decode time, only the last query token matters. All previous K/V are cached to avoid recomputation:
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$$
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o_n = \sum_j \text{softmax}\left(\frac{q_n k_j}{\sqrt{d_k}}\right) v_j
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$$
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RoPE is applied **before** KV cache write, not after — otherwise position encoding drift occurs.
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## KVCache System
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Seven classes working together, with two concrete cache implementations:
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### ContiguousCache (default)
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```
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ContiguousCache (simple contiguous per-slot cache)
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├── ContiguousCacheView bundles k/v tensors + slot indices for attention layers
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```
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Created by default when no cache is passed to `InferenceScheduler`. Each task occupies a fixed slot of `[max_seq_len, n_kv_heads, head_dim]`. Simple and efficient for small-to-medium batch sizes.
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### PageCache (paged with prefix sharing)
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```
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PageCache (paged KV cache with prefix sharing, alternative)
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├── PagePool orchestrates page allocation + prefix matching
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│ ├── Allocator bitmask-based page allocator + ref-count + LRU
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│ └── PrefixCache hash-based prefix matching (page_hash via polynomial hash)
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├── TaskTable maps task_id → page_table + cached token count
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├── Storage k_cache / v_cache tensors (n_layers × n_pages × page_size × n_kv_heads × head_dim)
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└── PageCacheView bundles Storage + page_table + total_len for attention layers
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```
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`isinstance(cache, KVCache)` checks dispatch to the correct view. Both implement the abstract `KVCache` interface used by `Executor` and `InferenceScheduler`.
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## Continuous Batching
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`InferenceScheduler` runs a daemon thread with a 4-phase loop:
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```
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1. Cleanup → Remove finished tasks, free KV cache slots/pages
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2. Refill → Pop from waiting_queue, task_alloc resources, activate
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3. Prefill → Group by (prompt_len, start_pos), run full forward
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4. Decode → Pick largest same-position group, single-token forward
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```
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## Sampling (Strategy Pattern)
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```
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BaseSamplingStrategy (ABC)
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├── TemperatureStrategy
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├── TopKStrategy
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├── TopPStrategy
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└── SamplingPipeline
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```
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`SamplingPipeline` composes them: Temperature → Top-K → Top-P → softmax → multinomial.
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`sample()` is a convenience shortcut for one-shot usage.
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## Protocol Handlers (Strategy Pattern)
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```python
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class ProtocolHandler: # concrete orchestrator
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def __init__(self, request, engine, builder): ...
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async def handle(self):
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prompt, ctx, stops = builder.prepare(request, engine)
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agen = engine.generate_async(prompt, ...)
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if stream: self._handle_stream(agen, ctx, stops)
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else: return await self._handle_non_stream(agen, ctx, stops)
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```
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`ResponseBuilder` (ABC): `prepare()`, `format_stream_start()`, `format_chunk()`, `format_stream_end()`, `format_response()`.
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`OpenAIResponseBuilder` → `/v1/chat/completions`, `AnthropicResponseBuilder` → `/v1/messages`.
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Adding a protocol = one builder file, no handler subclassing needed.
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## Engine & GenerateResult
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```
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InferenceEngine
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├── generate(prompt, stream, ...) → str | List[str] | Generator
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├── generate_with_request(req) → same
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├── generate_async(prompt, ...) → AsyncGenerator
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├── get_stats() → Dict
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└── shutdown()
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```
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`GenerateResult` uses `Condition` for non-streaming (`wait_completion()`) and `Event` for streaming (`wait()`). Stream callback is `cb(token)`.
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## HTTP API
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```
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POST /v1/chat/completions OpenAI
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POST /v1/messages Anthropic
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GET /health {"status":"ok","model_loaded":true}
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GET /stats scheduler statistics
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```
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### OpenAI
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```bash
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curl -X POST http://localhost:8000/v1/chat/completions \
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-H "Content-Type: application/json" \
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-d '{"messages":[{"role":"user","content":"Hello"}],"max_tokens":512}'
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```
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Response:
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```json
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{
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"id": "chatcmpl-abc123",
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"object": "chat.completion",
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"created": 1717000000,
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"model": "astrai",
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"choices": [{"index": 0, "message": {"role": "assistant", "content": "Hello!"}, "finish_reason": "stop"}],
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"usage": {"prompt_tokens": 5, "completion_tokens": 10, "total_tokens": 15}
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}
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```
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Streaming SSE: `object: "chat.completion.chunk"` — starts with role delta, then token chunks, ends with finish chunk + usage stats, then `data: [DONE]`.
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### Anthropic
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```bash
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curl -X POST http://localhost:8000/v1/messages \
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-H "Content-Type: application/json" \
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-d '{"model":"astrai","system":"You are helpful.","messages":[{"role":"user","content":"Hello"}],"max_tokens":512}'
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```
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Supports `stop_sequences` and streaming via `event: content_block_delta`.
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### GenerationRequest Parameters
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| Param | Type | Default | Description |
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|-------|------|---------|-------------|
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| `messages` | List[dict] | required | Chat messages (role, content) |
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| `top_k` | int | 50 | Top-k count |
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| `top_p` | float | 1.0 | Nucleus threshold |
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| `temperature` | float | 1.0 | Sampling temperature (> 0.0) |
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| `max_tokens` | Optional[int] | None | Max generation length |
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| `stream` | bool | False | Stream output |
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### SSE Streaming Format
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**OpenAI** (`/v1/chat/completions`, `stream=true`):
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```
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data: {"id":"chatcmpl-...","object":"chat.completion.chunk","created":...,"model":"astrai",
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"choices":[{"index":0,"delta":{"role":"assistant"},"finish_reason":null}]}
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data: {"id":"chatcmpl-...","object":"chat.completion.chunk","created":0,"model":"astrai",
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"choices":[{"index":0,"delta":{"content":"Hello"},"finish_reason":null}]}
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data: {"id":"chatcmpl-...","object":"chat.completion.chunk","created":...,"model":"astrai",
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"choices":[{"index":0,"delta":{},"finish_reason":"stop"}]}
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data: {"prompt_tokens":5,"completion_tokens":1,"total_tokens":6}
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data: [DONE]
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```
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**Anthropic** (`/v1/messages`, `stream=true`):
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```
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event: message_start
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data: {"type":"message_start","message":{"id":"msg_...","model":"astrai","role":"assistant",
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"content":[],"usage":{"input_tokens":0}}}
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event: content_block_start
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data: {"type":"content_block_start","index":0,"content_block":{"type":"text","text":""}}
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event: content_block_delta
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data: {"type":"content_block_delta","index":0,"delta":{"type":"text_delta","text":"Hello"}}
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event: content_block_stop
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data: {"type":"content_block_stop","index":0}
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event: message_delta
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data: {"type":"message_delta","delta":{"stop_reason":"end_turn","stop_sequence":null},"usage":{...}}
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event: message_stop
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data: {"type":"message_stop"}
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```
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### Error Responses
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The server returns standard HTTP status codes. Pydantic validation errors (e.g. missing required fields)
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are handled automatically by FastAPI with 422 status. The only application-level error is engine initialization:
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| Status | Meaning |
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|--------|---------|
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| 200 | Success |
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| 422 | Unprocessable entity (Pydantic validation) |
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| 503 | Service unavailable (model not loaded, engine not ready) |
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Error response body (503):
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```json
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{
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"detail": "Engine not initialized"
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}
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```
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### Stats Endpoint
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```
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GET /stats
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```
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Response:
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```json
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{
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"total_tasks": 128,
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"total_tokens": 10240,
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"active_tasks": 3,
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"waiting_queue": 2
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}
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```
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## Engine API
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```python
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# Non-streaming
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engine.generate("Hello", stream=False) # -> str
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engine.generate(["A", "B"], stream=False) # -> List[str]
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# Streaming
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engine.generate("Hello", stream=True) # -> Generator[str]
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engine.generate(["A", "B"], stream=True) # -> Generator[Tuple[int, str]]
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# Async
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async for token in engine.generate_async("Hello", ...): # -> AsyncGenerator[str]
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print(token)
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```
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> Document Update Time: 2026-07-05
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