docs: 修正文档中类名/字段名与代码不一致之处

- ModelConfig → AutoRegressiveLMConfig, Transformer → AutoRegressiveLM
- 新增缺失类: EncoderConfig, EmbeddingEncoder, ConfigFactory, StorageFactory, ValidationCallback
- TrainConfig/TrainContext/ChatCompletionRequest 补充缺失字段
- dataflow.md 中 create_storage → StorageFactory.create
- 示例 --train_type=pt → seq 与代码一致

README.md

@@ -82,7 +82,7 @@ export CUDA_VISIBLE_DEVICES=0,1,2,3
 
 nohup python scripts/tools/train.py \
     --nprocs=4 \
-    --train_type=pt \
+    --train_type=seq \
     --data_root_path=/path/to/dataset \
     --param_path=/path/to/model \
     --batch_per_device=4 \

assets/docs/README-zh-CN.md

@@ -88,7 +88,7 @@ export CUDA_VISIBLE_DEVICES=0,1,2,3
 
 nohup python scripts/tools/train.py \
     --nprocs=4 \
-    --train_type=pt \
+    --train_type=seq \
     --data_root_path=/path/to/dataset \
     --param_path=/path/to/model \
     --batch_per_device=4 \

assets/docs/architecture.md

@@ -16,7 +16,7 @@ classDiagram
             +to_file(config_path)
         }
 
-        class ModelConfig {
+        class AutoRegressiveLMConfig {
             +int vocab_size
             +int dim
             +int n_layers
@@ -25,21 +25,41 @@ classDiagram
             +bool tie_weight
             +int max_len
             +float rope_theta
+            +str attn_type
             +int n_heads
             +int n_kv_heads
             +bool use_qk_norm
             +bool use_gated_attention
-            +str attn_type
+            +Optional[int] kv_lora_rank
+            +Optional[int] qk_nope_head_dim
+            +Optional[int] qk_rope_head_dim
             +str ffn_type
             +int n_routed_experts
             +int n_shared_experts
             +int n_activated_experts
-            +str moe_topk_method
+            +Optional[str] topk_method
-            +Optional[int] kv_lora_rank
+        }
-            +Optional[int] qk_nope_head_dim
+
-            +Optional[int] qk_rope_head_dim
+        class EncoderConfig {
-            +load(config_path) ModelConfig
+            +int vocab_size
-            +save(config_path)
+            +int dim
+            +int n_layers
+            +float norm_eps
+            +int dim_ffn
+            +int max_len
+            +float rope_theta
+            +int n_heads
+            +int n_kv_heads
+            +bool use_qk_norm
+            +bool use_gated_attention
+            +Optional[str] pooling_type
+            +Optional[bool] normalize_embeddings
+        }
+
+        class ConfigFactory {
+            +Registry _registry
+            +register(name) decorator
+            +load(raw) BaseConfig
         }
 
         class TrainConfig {
@@ -52,6 +72,7 @@ classDiagram
             +int batch_per_device
             +int grad_accum_steps
             +float max_grad_norm
+            +list gradient_checkpointing_modules
             +int start_epoch
             +int start_batch
             +str ckpt_dir
@@ -66,7 +87,10 @@ classDiagram
             +str master_port
             +Callable parallel_wrapper
             +Callable state_dict_fn
+            +str start_method
             +str device_type
+            +Optional[Dataset] val_dataset
+            +int val_step
             +dict extra_kwargs
             +validate()
         }
@@ -138,11 +162,17 @@ classDiagram
             +int iter
         }
 
+        class StorageFactory {
+            +Registry _registry
+            +register(name) decorator
+            +create(storage_type) BaseStorage
+        }
+
         class DatasetFactory {
             +Registry _registry
             +register(name) decorator
             +create(train_type, window_size, stride) BaseDataset
-            +load(train_type, load_path, window_size, stride) BaseDataset
+            +load(train_type, load_path, window_size, stride, storage_type, tokenizer) BaseDataset
         }
     }
 
@@ -169,8 +199,8 @@ classDiagram
             +to(*args, **kwargs) Self
         }
 
-        class Transformer {
+        class AutoRegressiveLM {
-            +ModelConfig config
+            +AutoRegressiveLMConfig config
             +RotaryEmbedding rotary_embedding
             +Embedding embed_tokens
             +ModuleList layers
@@ -181,6 +211,18 @@ classDiagram
             +state_dict()
         }
 
+        class EmbeddingEncoder {
+            +EncoderConfig config
+            +RotaryEmbedding rotary_embedding
+            +Embedding embed_tokens
+            +ModuleList layers
+            +RMSNorm norm
+            +str pooling_type
+            +bool normalize_embeddings
+            +forward(input_ids, input_mask, position_ids) Tensor
+            +load_state_dict(state_dict)
+        }
+
         class DecoderBlock {
             +nn.Module attention  # GQA or MLA via AttnFactory
             +RMSNorm input_norm
@@ -322,11 +364,15 @@ classDiagram
             +Optimizer optimizer
             +LRScheduler scheduler
             +Checkpoint checkpoint
+            +TrainConfig config
             +int epoch
             +int iteration
             +float loss
+            +DataLoader val_dataloader
+            +float val_loss
             +int world_size
             +int rank
+            +dict kwargs
         }
 
         class TrainContextBuilder {
@@ -415,6 +461,12 @@ classDiagram
             +on_step_begin(context)
         }
 
+        class GradientCheckpointingCallback {
+            +tuple modules
+            +on_train_begin(context)
+            +on_train_end(context)
+        }
+
         class CheckpointCallback {
             +str save_dir
             +int interval
@@ -438,6 +490,11 @@ classDiagram
             +on_train_end(context)
         }
 
+        class ValidationCallback {
+            +_run_validation(context)
+            +on_step_end(context)
+        }
+
         class CallbackFactory {
             +Registry _registry
             +register(name) decorator
@@ -638,6 +695,7 @@ classDiagram
         }
 
         class ChatCompletionRequest {
+            +str model
             +List[ChatMessage] messages
             +float temperature
             +float top_p
@@ -646,6 +704,10 @@ classDiagram
             +bool stream
             +Optional[str] stop
             +Optional[int] n
+            +Optional[float] presence_penalty
+            +Optional[float] frequency_penalty
+            +Optional[Dict] logit_bias
+            +Optional[str] user
         }
 
         class AnthropicMessage {
@@ -699,6 +761,7 @@ classDiagram
             +int completion_tokens
             +str accumulated
             +Optional[str] stop_matched
+            +str last_yield_trimmed
         }
 
         class app {
@@ -709,7 +772,7 @@ classDiagram
 
     namespace parallel {
         class Functions {
-            +spawn_parallel_fn(func, world_size, backend, master_addr, master_port, device_type, **kwargs)
+            +spawn_parallel_fn(func, world_size, backend, master_addr, master_port, device_type, start_method, **kwargs)
             +setup_parallel(rank, world_size, backend, master_addr, master_port, device_type)
             +get_current_device() str
             +get_world_size() int
@@ -741,6 +804,7 @@ classDiagram
     BaseScheduler <|-- CosineScheduler
     BaseScheduler <|-- SGDRScheduler
     TrainCallback <|-- GradientClippingCallback
+    TrainCallback <|-- GradientCheckpointingCallback
     TrainCallback <|-- CheckpointCallback
     TrainCallback <|-- ProgressBarCallback
     TrainCallback <|-- MetricLoggerCallback
@@ -755,10 +819,12 @@ classDiagram
     BaseSamplingStrategy <|-- TopPStrategy
     ParallelModel <|-- RowParallelLinear
     ParallelModel <|-- ColumnParallelLinear
-    AutoModel <|-- Transformer
+    AutoModel <|-- AutoRegressiveLM
+    AutoModel <|-- EmbeddingEncoder
     BaseConfig <|-- BaseModelConfig
     BaseConfig <|-- TrainConfig
-    BaseModelConfig <|-- ModelConfig
+    BaseModelConfig <|-- AutoRegressiveLMConfig
+    BaseModelConfig <|-- EncoderConfig
     BaseFactory <|-- AutoModel
     BaseFactory <|-- AttnFactory
     BaseFactory <|-- FFNFactory
@@ -766,6 +832,9 @@ classDiagram
     BaseFactory <|-- StrategyFactory
     BaseFactory <|-- SchedulerFactory
     BaseFactory <|-- CallbackFactory
+    BaseFactory <|-- StorageFactory
+    BaseFactory <|-- ConfigFactory
+    TrainCallback <|-- ValidationCallback
     ProtocolHandler <|-- OpenAIHandler
     ProtocolHandler <|-- AnthropicHandler
 
@@ -781,16 +850,16 @@ classDiagram
     InferenceScheduler *-- TaskManager
     SamplingPipeline *-- BaseSamplingStrategy
     TrainContextBuilder *-- TrainContext
-    Transformer *-- DecoderBlock
+    AutoRegressiveLM *-- DecoderBlock
-    Transformer *-- RotaryEmbedding
+    AutoRegressiveLM *-- RotaryEmbedding
-    Transformer *-- Embedding
+    AutoRegressiveLM *-- Embedding
     DecoderBlock *-- RMSNorm
     BaseDataset *-- BaseStorage
     ChatCompletionRequest *-- ChatMessage
     MessagesRequest *-- AnthropicMessage
 
     %% --- Aggregation (weak ownership) ---
-    AutoModel o-- ModelConfig
+    AutoModel o-- BaseModelConfig
     Trainer o-- TrainCallback
     TrainContext o-- BaseStrategy
     TrainContext o-- BaseScheduler
@@ -811,6 +880,10 @@ classDiagram
     FFNFactory ..> DeepSeekMoE : creates
     DecoderBlock ..> AttnFactory : uses
     DecoderBlock ..> FFNFactory : uses
+    StorageFactory ..> H5Storage : creates
+    StorageFactory ..> JSONStorage : creates
+    ConfigFactory ..> AutoRegressiveLMConfig : creates
+    ConfigFactory ..> EncoderConfig : creates
     Trainer ..> TrainContextBuilder : uses
     Trainer ..> Functions : spawns
     TrainContextBuilder ..> StrategyFactory : uses
@@ -827,13 +900,13 @@ classDiagram
 
     %% --- Association (general usage) ---
     Trainer --> TrainConfig
-    DPOStrategy --> Transformer
+    DPOStrategy --> AutoRegressiveLM
-    GRPOStrategy --> Transformer
+    GRPOStrategy --> AutoRegressiveLM
     InferenceScheduler --> Task
     InferenceScheduler --> TaskStatus
     Task --> TaskStatus
-    InferenceEngine --> Transformer
+    InferenceEngine --> AutoRegressiveLM
-    Executor --> Transformer
+    Executor --> AutoRegressiveLM
     Executor --> AutoTokenizer
     TaskManager --> AutoTokenizer
     MultiSegmentFetcher --> BaseSegmentFetcher
@@ -846,12 +919,12 @@ classDiagram
 
 | Module | Components | Description |
 |--------|------------|-------------|
-| **astrai.config** | BaseConfig, BaseModelConfig, ModelConfig, TrainConfig | Configuration management (to_dict/from_dict, to_file/from_file) |
+| **astrai.config** | BaseConfig, BaseModelConfig, AutoRegressiveLMConfig, EncoderConfig, ConfigFactory, TrainConfig | Configuration management (to_dict/from_dict, to_file/from_file) |
-| **astrai.dataset** | BaseDataset–GRPODataset, BaseStorage–JSONStorage, BaseSegmentFetcher, MultiSegmentFetcher, ResumableDistributedSampler, DatasetFactory | Dataset loading and management |
+| **astrai.dataset** | BaseDataset–GRPODataset, BaseStorage–JSONStorage, StorageFactory, BaseSegmentFetcher, MultiSegmentFetcher, ResumableDistributedSampler, DatasetFactory | Dataset loading and management |
 | **astrai.serialization** | Checkpoint | Model serialization |
-| **astrai.model** | AutoModel, Transformer, DecoderBlock, GQA, MLA, MLP, DeepSeekMoE, AttnFactory, FFNFactory, RMSNorm, Linear, RotaryEmbedding, Embedding | Neural network model |
+| **astrai.model** | AutoModel, AutoRegressiveLM, EmbeddingEncoder, DecoderBlock, GQA, MLA, MLP, DeepSeekMoE, AttnFactory, FFNFactory, RMSNorm, Linear, RotaryEmbedding, Embedding | Neural network model |
 | **astrai.tokenize** | AutoTokenizer, ChatTemplate | Tokenizer and chat template |
-| **astrai.trainer** | Trainer, TrainContext, TrainContextBuilder, BaseStrategy–GRPOStrategy, StrategyFactory, BaseScheduler–SGDRScheduler, SchedulerFactory, TrainCallback–MetricLoggerCallback, CallbackFactory | Training workflow |
+| **astrai.trainer** | Trainer, TrainContext, TrainContextBuilder, BaseStrategy–GRPOStrategy, StrategyFactory, BaseScheduler–SGDRScheduler, SchedulerFactory, TrainCallback–ValidationCallback, CallbackFactory, Muon | Training workflow |
 | **astrai.inference** | InferenceEngine, InferenceScheduler, Executor, KVCache–KvcacheView, Allocator–Storage, Task, TaskManager, TaskStatus, GenerationRequest, BaseSamplingStrategy–SamplingPipeline, ProtocolHandler–AnthropicHandler, ChatMessage–MessagesRequest, app | Inference service |
 | **astrai.parallel** | spawn_parallel_fn, setup_parallel, get_rank/get_world_size/get_current_device, only_on_rank, ParallelModel, RowParallelLinear, ColumnParallelLinear | Distributed parallel |
 | **astrai.factory** | Registry, BaseFactory[T] | Component registration |
@@ -860,7 +933,7 @@ classDiagram
 
 | Pattern | Classes | Purpose |
 |---------|---------|---------|
-| **Factory** | `AttnFactory`, `FFNFactory`, `StrategyFactory`, `DatasetFactory`, `SchedulerFactory`, `CallbackFactory` | Decorator-based component creation |
+| **Factory** | `AttnFactory`, `FFNFactory`, `StrategyFactory`, `DatasetFactory`, `SchedulerFactory`, `CallbackFactory`, `StorageFactory`, `ConfigFactory` | Decorator-based component creation |
 | **Registry** | `BaseFactory`, `Registry` | Component registration with category/priority |
 | **Strategy** | `SEQStrategy`, `SFTStrategy`, `DPOStrategy`, `GRPOStrategy` | Training strategy switching |
 | **Strategy (Sampling)** | `TemperatureStrategy`, `TopKStrategy`, `TopPStrategy`, `SamplingPipeline` | Composable logit transformations |
@@ -871,18 +944,18 @@ classDiagram
 | **Object Pool** | `Allocator`, `PagePool` | Page-based KV cache with LRU eviction |
 | **Storage** | `BaseStorage`, `H5Storage`, `JSONStorage` | Format-agnostic data access |
 | **Producer-Consumer** | `InferenceScheduler`, `Task`, queues | Continuous batching |
-| **AutoModel Registry** | `AutoModel`, `Transformer` | Model-type dynamic loading |
+| **AutoModel Registry** | `AutoModel`, `AutoRegressiveLM`, `EmbeddingEncoder` | Model-type dynamic loading |
 
 ## Core Relationships
 
 1. **Config → Training**: `TrainConfig` holds model, dataset, optimizer_fn, scheduler_fn
 2. **Training Flow**: `Trainer` → `TrainContextBuilder` → `TrainContext`, uses `BaseStrategy` for loss
 3. **Strategy Selection**: `StrategyFactory` creates strategy by `train_type`
-4. **Inference Flow**: `InferenceEngine` → `InferenceScheduler` → `Transformer`, backed by `KVCache` + `SamplingPipeline`
+4. **Inference Flow**: `InferenceEngine` → `InferenceScheduler` → `AutoRegressiveLM`, backed by `KVCache` + `SamplingPipeline`
 5. **Distributed**: `spawn_parallel_fn` + `setup_parallel` for multi-process DDP
 6. **Dataset Loading**: `DatasetFactory` creates datasets, `BaseStorage` (H5Storage/JSONStorage) loads via `BaseSegmentFetcher` + `MultiSegmentFetcher`
 7. **Checkpoint**: `Checkpoint` saves/loads safetensors + metadata (rank-0 only)
 8. **Scheduler**: `SchedulerFactory` creates `CosineScheduler`/`SGDRScheduler`
 9. **AutoModel**: `from_pretrained()` loads `config.json` + `model.safetensors`, `_disable_random_init` replaces `nn.init.*` with no-ops
 
-> Document Update Time: 2026-05-16
+> Document Update Time: 2026-05-17

assets/docs/dataflow.md

@@ -15,8 +15,8 @@ Raw text is tokenized via `AutoTokenizer.encode()` and saved as HDF5 (`.h5`) or
 Storage format is auto-detected by `detect_format()`; backends are dispatched via registry:
 
 ```
-create_storage("h5")   → H5Storage
+StorageFactory.create("h5")   → H5Storage
-create_storage("json") → JSONStorage
+StorageFactory.create("json") → JSONStorage
 ```
 
 Both support shared memory via `.share_memory_()`.
@@ -34,7 +34,7 @@ Both support shared memory via `.share_memory_()`.
 
 ```
 DatasetFactory.load(train_type, path, window_size, stride)
-  → create_storage(detect_format(path))
+  → StorageFactory.create(detect_format(path))
     → MultiSegmentFetcher(BaseSegmentFetcher per key)
       → BaseDataset.__getitem__(idx)
         → sliding window [begin, end) via get_index(idx)
@@ -54,4 +54,4 @@ DatasetFactory.load(train_type, path, window_size, stride)
 
 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: 2026-05-15
+> Document Update Time: 2026-05-17

assets/docs/inference.md

@@ -137,4 +137,4 @@ engine.generate(["A", "B"], stream=True)        # -> Generator[Tuple[int, str]]
 await engine.generate_async("Hello", ...)       # -> AsyncGenerator[str]
 ```
 
-> Document Update Time: 2026-05-15
+> Document Update Time: 2026-05-17

assets/docs/params.md

@@ -73,7 +73,7 @@ export CUDA_VISIBLE_DEVICES=0,1,2,3
 
 nohup python scripts/tools/train.py \
     --nprocs=4 \
-    --train_type=pt \
+    --train_type=seq \
     --data_root_path=/path/to/dataset \
     --param_path=/path/to/model \
     --batch_per_device=4 \
@@ -94,4 +94,4 @@ nohup python scripts/tools/train.py \
 
 ---
 
-> Document Update Time: 2026-05-16
+> Document Update Time: 2026-05-17

assets/docs/training.md

@@ -91,11 +91,13 @@ on_train_end
 
 | Hook | Fires | Default callback |
 |------|-------|-----------------|
+| `on_train_begin` | Before training starts | `GradientCheckpointingCallback` |
 | `on_step_begin` | Every accumulation window | `GradientClippingCallback` |
 | `on_batch_end` | Every batch | `CheckpointCallback`, `MetricLoggerCallback`, `ProgressBarCallback` |
+| `on_step_end` | Every accumulation window | `ValidationCallback` |
 | `on_train_end` | Training ends | `CheckpointCallback`, `MetricLoggerCallback` (final save) |
 
-Default callbacks: `progress_bar` (tqdm), `checkpoint` (safetensors, rank-0), `metric_logger` (JSONL, rank-0), `gradient_clipping`.
+Default callbacks: `gradient_checkpointing` (activation checkpointing, optional), `progress_bar` (tqdm), `checkpoint` (safetensors, rank-0), `metric_logger` (JSONL, rank-0), `gradient_clipping`, `validation` (periodic validation on val_dataset).
 
 ## Strategies
 
@@ -154,6 +156,17 @@ Keys: `prompts`, `responses`, `masks`, `rewards`.
 
 Created by `SchedulerFactory.create(optimizer, schedule_type, **kwargs)`.
 
+## Gradient Checkpointing
+
+Trades compute for memory by recomputing activations during backward pass. Specify module types via `gradient_checkpointing_modules`:
+
+```python
+from astrai.model.components.decoder_block import DecoderBlock
+config = TrainConfig(..., gradient_checkpointing_modules=[DecoderBlock])
+```
+
+Callback wraps each `DecoderBlock.forward` with `torch.utils.checkpoint.checkpoint(use_reentrant=False)`, compatible with `torch.compile`. Uses `nn.Module.apply()` for traversal — works through DDP wrappers without manual unwrap. Empty list (default) means no-op.
+
 ## Checkpoint
 
 ```
@@ -188,7 +201,7 @@ export CUDA_VISIBLE_DEVICES=0,1,2,3
 
 nohup python scripts/tools/train.py \
     --nprocs=4 \
-    --train_type=pt \
+    --train_type=seq \
     --data_root_path=/path/to/dataset \
     --param_path=/path/to/model \
     --batch_per_device=4 \
@@ -209,4 +222,4 @@ nohup python scripts/tools/train.py \
 
 Full parameter reference at [params.md](params.md).
 
-> Document Update Time: 2026-05-16
+> Document Update Time: 2026-05-17