docs : 同步文档与实际代码

- 移除 JSONStore 引用（该类不存在）
- 修正 Store.load() 和 DatasetFactory.load() 签名（无 tokenizer 参数）
- 修正 TrainContextBuilder.with_resume_dir() 命名
- 修正 Checkpoint config 字段和 meta.json 描述
- 修正 ProtocolHandler.handle() 异步签名
- 修正采样继承图（平行子类，非线性）
- 修正训练循环：回调移入 accumulate 块内
- 更新文档日期至 2026-05-28

assets/docs/architecture.md

@@ -65,7 +65,7 @@ classDiagram
         }
 
         class TrainConfig {
-            +nn.Module model
+            +Callable[[], nn.Module] model_fn
             +str strategy
             +Dataset dataset
             +Callable optimizer_fn
@@ -108,7 +108,7 @@ classDiagram
             +int window_size
             +int stride
             +Optional[Store] storage
-            +load(load_path, storage_type, tokenizer)
+            +load(load_path, storage_type)
             +__getitem__(index)
             +__len__()
         }
@@ -134,7 +134,7 @@ classDiagram
             +Dict[str, List[int]] _cum
             +int _length
             +keys (property)
-            +load(path, tokenizer)
+            +load(path)
             +fetch(begin, end, keys)
             +__len__()
             -_fetch_key(key, begin, end) Tensor
@@ -142,16 +142,12 @@ classDiagram
         }
 
         class H5Store {
-            +load(path, tokenizer)
+            +load(path)
-        }
-
-        class JSONStore {
-            +load(path, tokenizer)
         }
 
         class MmapStore {
             +List _mmap_refs
-            +load(path, tokenizer)
+            +load(path)
         }
 
         class ResumableDistributedSampler {
@@ -169,7 +165,7 @@ classDiagram
             +Registry _registry
             +register(name) decorator
             +create(train_type, window_size, stride) BaseDataset
-            +load(train_type, load_path, window_size, stride, storage_type, tokenizer) BaseDataset
+            +load(train_type, load_path, window_size, stride, storage_type) BaseDataset
         }
     }
 
@@ -180,8 +176,9 @@ classDiagram
             +int iteration
             +dict extra
             +dict meta
+            +dict config
             +save(save_dir)
-            +load(save_dir) Checkpoint
+            +load(save_dir, broadcast) Checkpoint
         }
     }
 
@@ -189,8 +186,8 @@ classDiagram
         class AutoModel {
             +BaseModelConfig config
             +Registry _registry
-            +register(model_type) decorator
+            +register(name) decorator
-            +get_component_class(model_type) Type
+            +get_component_class(name) Type
             +from_pretrained(path, disable_random_init, strict) nn.Module
             +save_pretrained(save_directory)
             +to(*args, **kwargs) Self
@@ -204,7 +201,7 @@ classDiagram
             +RMSNorm norm
             +Linear lm_head
             +forward(input_ids, input_mask, paged_cache, position_ids) Dict[str, Tensor]
-            +load_state_dict(state_dict)
+            +load_state_dict(state_dict, strict, assign)
             +state_dict()
         }
 
@@ -229,6 +226,7 @@ classDiagram
         }
 
         class GQA {
+            +int dim
             +int n_heads
             +int n_kv_heads
             +int head_dim
@@ -243,6 +241,7 @@ classDiagram
         }
 
         class MLA {
+            +int dim
             +int n_heads
             +int n_kv_heads
             +int head_dim
@@ -303,6 +302,7 @@ classDiagram
             +int dim
             +int max_len
             +float base
+            +Optional[Dict] rope_scaling
             +forward(x, position_ids=None) Tensor
         }
 
@@ -315,10 +315,10 @@ classDiagram
     namespace tokenize {
         class AutoTokenizer {
             +vocab_size int
-            +encode(tokens, out_ids, add_special_tokens) List[int]
+            +encode(tokens, out_ids, is_pretokenized, add_special_tokens) List[int]
             +decode(tokens, skip_special_tokens) str
             +__getattr__(name) Any (bos_id, eos_id, pad_id, stop_ids)
-            +apply_chat_template(messages, tokenize) Union[str, List[int]]
+            +apply_chat_template(messages, system_prompt, tokenize, add_generation_prompt) Union[str, List[int]]
             +set_chat_template(template)
             +load(path)
             +from_pretrained(path) AutoTokenizer
@@ -326,7 +326,7 @@ classDiagram
         }
 
         class ChatTemplate {
-            +String template_str
+            +str template_str
             +render(messages, system_prompt, **extra_variables) str
             +from_string(template) ChatTemplate
         }
@@ -364,6 +364,7 @@ classDiagram
             +SchedulerProtocol scheduler
             +Checkpoint checkpoint
             +TrainConfig config
+            +dict model_config
             +BaseExecutor executor
             +int epoch
             +int iteration
@@ -377,7 +378,7 @@ classDiagram
 
         class TrainContextBuilder {
             +TrainConfig config
-            +with_checkpoint(checkpoint) TrainContextBuilder
+            +with_resume_dir(resume_dir) TrainContextBuilder
             +build() TrainContext
         }
 
@@ -472,16 +473,12 @@ classDiagram
             +str save_dir
             +int interval
             +bool weight_only
-            +Callable state_dict_fn
             +Callable save_extra_fn
-            +Callable load_extra_fn
             +_save_checkpoint(context)
-            +on_train_begin(context)
             +on_batch_end(context)
             +on_train_end(context)
             +on_error(context)
             +save_extra(context)$
-            +load_extra(extra, context)$
         }
 
         class ProgressBarCallback {
@@ -518,7 +515,12 @@ classDiagram
             +float lr
             +float momentum
             +float weight_decay
+            +bool nesterov
             +int ns_steps
+            +float adamw_lr
+            +tuple adamw_betas
+            +float adamw_eps
+            +float adamw_wd
             +step(closure) Optional[float]
         }
     }
@@ -539,6 +541,8 @@ classDiagram
             +AutoModel model
             +AutoTokenizer tokenizer
             +KVCache page_cache
+            +Optional[str] device
+            +Optional[torch.dtype] dtype
             +execute_prefill(tasks, prompt_len, start_pos)
             +execute_decode(tasks) List[int]
         }
@@ -550,7 +554,9 @@ classDiagram
             +bool _running
             +Thread _loop_thread
             +int max_seq_len
-            +add_task(prompt, max_tokens, temperature, top_p, top_k, stream_callback) str
+            +str device
+            +torch.dtype dtype
+            +add_task(prompt, **kwargs) str
             +remove_task(task_id)
             +start()
             +stop()
@@ -653,15 +659,19 @@ classDiagram
 
         class TaskManager {
             +AutoTokenizer tokenizer
+            +int max_batch_size
+            +int max_seq_len
+            +int max_prompt_len
             +Deque waiting_queue
             +List active_tasks
-            +add_task(prompt, **kwargs) str
+            +add_task(prompt, max_tokens, temperature, top_p, top_k, stream_callback) str
             +remove_task(task_id) List[Task]
             +remove_finished_tasks(stop_ids) List[Task]
             +pull_candidates(n) List[Task]
             +activate(task)
             +return_to_waiting(tasks)
             +get_active_tasks() List[Task]
+            +get_stats() Dict
         }
 
         class GenerationRequest {
@@ -917,7 +927,6 @@ classDiagram
     BaseDataset <|-- DPODataset
     BaseDataset <|-- GRPODataset
     Store <|-- H5Store
-    Store <|-- JSONStore
     Store <|-- MmapStore
     BaseSamplingStrategy <|-- TemperatureStrategy
     BaseSamplingStrategy <|-- TopKStrategy
@@ -996,7 +1005,6 @@ classDiagram
     DecoderBlock ..> AttnFactory : uses
     DecoderBlock ..> FFNFactory : uses
     StoreFactory ..> H5Store : creates
-    StoreFactory ..> JSONStore : creates
     StoreFactory ..> MmapStore : creates
     ConfigFactory ..> AutoRegressiveLMConfig : creates
     ConfigFactory ..> EncoderConfig : creates
@@ -1063,7 +1071,7 @@ classDiagram
 | **Context** | `TrainContext` | Unified training state bag |
 | **Object Pool** | `Allocator`, `PagePool` | Page-based KV cache with LRU eviction |
 | **Executor** | `BaseExecutor`, `NoneExecutor`, `DDPExecutor` | Gradient accumulation & model distribution |
-| **Storage** | `Store`, `H5Store`, `JSONStore`, `MmapStore` | Format-agnostic data access with multi-segment support |
+| **Storage** | `Store`, `H5Store`, `MmapStore` | Format-agnostic data access with multi-segment support |
 | **Producer-Consumer** | `InferenceScheduler`, `Task`, queues | Continuous batching |
 | **AutoModel Registry** | `AutoModel`, `AutoRegressiveLM`, `EmbeddingEncoder` | Model-type dynamic loading |
 
@@ -1075,10 +1083,10 @@ classDiagram
 4. **Executor Selection**: `ExecutorFactory.create(cfg.parallel_mode, grad_accum_steps=cfg.grad_accum_steps, **cfg.executor_kwargs)` → `NoneExecutor` / `DDPExecutor` / `FSDPExecutor`
 5. **Inference Flow**: `InferenceEngine` → `InferenceScheduler` → `AutoRegressiveLM`, backed by `KVCache` + `SamplingPipeline`
 6. **Distributed**: `spawn_parallel_fn` + `setup_parallel` for multi-process DDP
-7. **Dataset Loading**: `DatasetFactory` creates datasets, `Store` (H5Store/JSONStore/MmapStore) loads data with explicit `_length` and multi-segment `_data`
+7. **Dataset Loading**: `DatasetFactory` creates datasets, `Store` (H5Store/MmapStore) loads data with explicit `_length` and multi-segment `_data`
 8. **Checkpoint**: `Checkpoint` saves/loads safetensors + metadata (rank-0 only), extra state saved as `{key}.pt`
 9. **Scheduler**: `SchedulerFactory` creates `CosineScheduler`/`SGDRScheduler`
 10. **AutoModel**: `from_pretrained()` loads `config.json` + `model.safetensors`, `_disable_random_init` replaces `nn.init.*` with no-ops
 11. **Protocols**: `OptimizerProtocol` / `SchedulerProtocol` — structural subtyping for `AccumOptimizer` / `AccumScheduler` wrappers
 
-> Document Update Time: 2026-05-24
+> Document Update Time: 2026-05-28

assets/docs/dataflow.md

@@ -5,22 +5,21 @@ This document describes the data pipeline: from raw text to model input tensors.
 ## Overview
 
 ```
-Raw Text → AutoTokenizer → Token IDs → .h5/.json/.bin → Dataset → Sampler → DataLoader → Training/Inference
+Raw Text → AutoTokenizer → Token IDs → .h5/.bin → Dataset → Sampler → DataLoader → Training/Inference
 ```
 
 ## Data Preparation
 
-Raw text is tokenized via `AutoTokenizer.encode()` and saved as HDF5 (`.h5`), JSON (`.json`/`.jsonl`), or binary (`.bin` + `meta.json`) files with keyed tensor groups.
+Raw text is tokenized via `AutoTokenizer.encode()` and saved as HDF5 (`.h5`) or binary (`.bin` + `meta.json`) files with keyed tensor groups.
 
 Storage format is auto-detected by `detect_format()`; backends are dispatched via registry:
 
 ```
-StoreFactory.create("h5")   → H5Store
+StoreFactory.create("h5")  → H5Store
-StoreFactory.create("json") → JSONStore
+StoreFactory.create("bin") → MmapStore
-StoreFactory.create("bin")  → MmapStore
 ```
 
-H5 and JSON backends support shared memory via `.share_memory_()`. Bin (mmap) uses OS page-cache sharing natively.
+H5 backend supports shared memory via `.share_memory_()`. Bin (mmap) uses OS page-cache sharing natively.
 
 ## Data Keys by Training Type
 
@@ -34,7 +33,7 @@ H5 and JSON backends support shared memory via `.share_memory_()`. Bin (mmap) us
 ## Dataset Architecture
 
 ```
-DatasetFactory.load(train_type, path, window_size, stride, storage_type, tokenizer)
+DatasetFactory.load(train_type, load_path, window_size, stride, storage_type)
   → StoreFactory.create(detect_format(path))
     → Store._data[Dict[str, List[Tensor]]] + _cum[Dict[str, List[int]]]
       → BaseDataset.__getitem__(idx)
@@ -55,4 +54,4 @@ DatasetFactory.load(train_type, path, window_size, stride, storage_type, tokeniz
 
 Standard PyTorch `DataLoader` with configurable `batch_size`, `num_workers`, `pin_memory`, `prefetch_factor`. Sampler produces indices; dataloader fetches tensor batches via `__getitem__`.
 
-> Document Update Time: 2026-05-17
+> Document Update Time: 2026-05-28

assets/docs/inference.md

@@ -16,12 +16,12 @@ Six classes working together:
 
 ```
 KVCache (facade)
-  ├── Allocator        bitmask-based page allocator + ref-count + LRU eviction
+  ├── PagePool         orchestrates page allocation + prefix matching
-  ├── PrefixCache      hash-based prefix matching (page_hash via rolling hash)
+  │     ├── Allocator   bitmask-based page allocator + ref-count + LRU eviction (inside PagePool)
-  ├── PagePool         orchestrates Allocator + PrefixCache
+  │     └── 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
+  └── 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()`.
@@ -40,7 +40,10 @@ KVCache (facade)
 ## Sampling (Strategy Pattern)
 
 ```
-BaseSamplingStrategy → TemperatureStrategy → TopKStrategy → TopPStrategy
+BaseSamplingStrategy (ABC)
+  ├── TemperatureStrategy
+  ├── TopKStrategy
+  └── TopPStrategy
 ```
 
 `SamplingPipeline` composes them: Temperature → Top-K → Top-P → softmax → multinomial.  
@@ -50,11 +53,12 @@ BaseSamplingStrategy → TemperatureStrategy → TopKStrategy → TopPStrategy
 
 ```python
 class ProtocolHandler:  # concrete orchestrator
-    def handle(self, request):
+    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:      self._handle_non_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()`.
@@ -96,12 +100,14 @@ Response:
 {
   "id": "chatcmpl-abc123",
   "object": "chat.completion",
-  "choices": [{"message": {"role": "assistant", "content": "Hello!"}, "finish_reason": "stop"}],
+  "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: `data: {"choices":[{"delta":{"role":"assistant"}}]}` → token chunks → `data: [DONE]`
+Streaming SSE: `object: "chat.completion.chunk"` — starts with role delta, then token chunks, ends with finish chunk + usage stats, then `data: [DONE]`.
 
 ### Anthropic
 
@@ -121,7 +127,7 @@ Supports `stop_sequences` and streaming via `event: content_block_delta`.
 | `temperature` | float | 1.0 | Sampling temperature (>= 0.0) |
 | `top_p` | float | 1.0 | Nucleus threshold |
 | `top_k` | int | 50 | Top-k count |
-| `max_tokens` | int | None | Max generation length |
+| `max_tokens` | Optional[int] | None | Max generation length |
 | `stream` | bool | False | Stream output |
 
 ## Engine API
@@ -139,4 +145,4 @@ engine.generate(["A", "B"], stream=True)        # -> Generator[Tuple[int, str]]
 await engine.generate_async("Hello", ...)       # -> AsyncGenerator[str]
 ```
 
-> Document Update Time: 2026-05-17
+> Document Update Time: 2026-05-28

assets/docs/training.md

@@ -74,15 +74,17 @@ on_train_begin
       on_batch_begin
       with executor.accumulate(model):
         loss = strategy(batch)
-        (loss / grad_accum_steps).backward()
+        stand_loss = loss / executor.grad_accum_steps
+        executor.backward(stand_loss)
         iteration += 1
-      on_batch_end
+        on_batch_end
 
-      if executor.sync_gradients:
+        if executor.sync_gradients:
-        on_optimizer_step
+          on_optimizer_step
-        optimizer.step()
+          optimizer.step()
-        optimizer.zero_grad()
+          optimizer.zero_grad()
-        scheduler.step()
+          if scheduler:
+            scheduler.step()
     on_epoch_end
 on_train_end
 ```
@@ -169,20 +171,20 @@ Callback wraps each `DecoderBlock.forward` with `torch.utils.checkpoint.checkpoi
 ## Checkpoint
 
 ```
-Checkpoint(state_dict, epoch, iteration, extra, meta)
+Checkpoint(state_dict, epoch, iteration, extra, meta, config)
-  ├── save(save_dir)    rank-0 only: meta.json (includes training config) + state_dict.safetensors + optional optimizer.pt / scheduler.pt
+  ├── save(save_dir)    rank-0 only: meta.json (epoch/iteration/timestamp) + config.json (model config) + state_dict.safetensors + optional {key}.pt (optimizer.pt, scheduler.pt)
   └── load(save_dir)    broadcasts metadata from rank-0
 ```
 
 Optimizer/scheduler state persisted by default via `Checkpoint.extra`.  
-Training config (`TrainConfig.to_dict()`) saved into `meta.json` during training via `CheckpointCallback`.
+Model config (`context.model_config`) saved into `config.json` during training via `CheckpointCallback`.
 
 ## TrainContextBuilder (Builder Pattern)
 
 ```python
 context = (
     TrainContextBuilder(config)
-        .with_checkpoint(checkpoint)
+        .with_resume_dir(resume_dir)
         .build()
 )
 # Returns TrainContext with model, strategy, optimizer, scheduler, dataloader, checkpoint
@@ -222,4 +224,4 @@ nohup python scripts/tools/train.py \
 
 Full parameter reference at [params.md](params.md).
 
-> Document Update Time: 2026-05-24
+> Document Update Time: 2026-05-28