ViperEkura
1c2ff05a6d
- architecture.md: 修正 unwrap_model 返回类型、Config Optional 标注、方法签名错误、类名错误 - training.md: 补充 on_error 回调、修正训练循环顺序、补全策略参数、model.safetensors - inference.md: 修正 GenerationRequest 参数顺序、async 语法、KVCache 描述、temperature 约束 - dataflow.md: 补充 Store.load/fetch 流程、修正可选参数默认值 - README/params: 多 GPU 示例补全 --parallel_mode、文档表补充 preprocessing.md - preprocessing.md: Chat 模式算法补全 BOS token 步骤
153 lines
5.0 KiB
Markdown
153 lines
5.0 KiB
Markdown
# 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 (plus two helpers) working together:
|
||
|
||
```
|
||
KVCache (facade)
|
||
├── PagePool orchestrates page allocation + prefix matching
|
||
│ ├── Allocator bitmask-based page allocator + ref-count + LRU eviction (inside PagePool)
|
||
│ └── PrefixCache hash-based prefix matching (page_hash via polynomial hash) (inside PagePool)
|
||
├── 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 (returned by bind())
|
||
```
|
||
|
||
`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 (ABC)
|
||
├── TemperatureStrategy
|
||
├── TopKStrategy
|
||
├── TopPStrategy
|
||
└── SamplingPipeline
|
||
```
|
||
|
||
`SamplingPipeline` composes them: Temperature → Top-K → Top-P → softmax → multinomial.
|
||
`sample()` is a convenience shortcut for one-shot usage.
|
||
|
||
## Protocol Handlers (Strategy Pattern)
|
||
|
||
```python
|
||
class ProtocolHandler: # concrete orchestrator
|
||
def __init__(self, request, engine, builder): ...
|
||
async def handle(self):
|
||
prompt, ctx, stops = builder.prepare(request, engine)
|
||
agen = engine.generate_async(prompt, ...)
|
||
if stream: self._handle_stream(agen, ctx, stops)
|
||
else: return await self._handle_non_stream(agen, ctx, stops)
|
||
```
|
||
|
||
`ResponseBuilder` (ABC): `prepare()`, `format_stream_start()`, `format_chunk()`, `format_stream_end()`, `format_response()`.
|
||
|
||
`OpenAIResponseBuilder` → `/v1/chat/completions`, `AnthropicResponseBuilder` → `/v1/messages`.
|
||
|
||
Adding a protocol = one builder file, no handler subclassing needed.
|
||
|
||
## Engine & GenerateResult
|
||
|
||
```
|
||
InferenceEngine
|
||
├── generate(prompt, stream, ...) → str | List[str] | Generator
|
||
├── generate_with_request(req) → same
|
||
├── generate_async(prompt, ...) → AsyncGenerator
|
||
├── get_stats() → Dict
|
||
└── shutdown()
|
||
```
|
||
|
||
`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
|
||
|
||
```bash
|
||
curl -X POST http://localhost:8000/v1/chat/completions \
|
||
-H "Content-Type: application/json" \
|
||
-d '{"messages":[{"role":"user","content":"Hello"}],"max_tokens":512}'
|
||
```
|
||
|
||
Response:
|
||
```json
|
||
{
|
||
"id": "chatcmpl-abc123",
|
||
"object": "chat.completion",
|
||
"created": 1717000000,
|
||
"model": "astrai",
|
||
"choices": [{"index": 0, "message": {"role": "assistant", "content": "Hello!"}, "finish_reason": "stop"}],
|
||
"usage": {"prompt_tokens": 5, "completion_tokens": 10, "total_tokens": 15}
|
||
}
|
||
```
|
||
|
||
Streaming SSE: `object: "chat.completion.chunk"` — starts with role delta, then token chunks, ends with finish chunk + usage stats, then `data: [DONE]`.
|
||
|
||
### Anthropic
|
||
|
||
```bash
|
||
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) |
|
||
| `top_k` | int | 50 | Top-k count |
|
||
| `top_p` | float | 1.0 | Nucleus threshold |
|
||
| `temperature` | float | 1.0 | Sampling temperature (> 0.0) |
|
||
| `max_tokens` | Optional[int] | None | Max generation length |
|
||
| `stream` | bool | False | Stream output |
|
||
|
||
## Engine API
|
||
|
||
```python
|
||
# 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
|
||
async for token in engine.generate_async("Hello", ...): # -> AsyncGenerator[str]
|
||
print(token)
|
||
```
|
||
|
||
> Document Update Time: 2026-05-30
|