docs : 三轮深度验证修复文档与代码不一致

- 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

README.md

@@ -82,6 +82,7 @@ export CUDA_VISIBLE_DEVICES=0,1,2,3
 
 nohup python scripts/tools/train.py \
     --nprocs=4 \
+    --parallel_mode=ddp \
     --train_type=seq \
     --data_root_path=/path/to/dataset \
     --param_path=/path/to/model \
@@ -108,8 +109,8 @@ Full reference at [Parameter Guide](assets/docs/params.md).
 ```bash
 python scripts/tools/generate.py \
     --param_path /path/to/model \
-    --input_json_file /path/to/input.json \
+    --input_json_file /path/to/input.jsonl \
-    --output_json_file /path/to/output.json
+    --output_json_file /path/to/output.jsonl
 ```
 
 #### Docker
@@ -224,6 +225,7 @@ Watch a video walkthrough on [bilibili](https://www.bilibili.com/video/BV1fuLB6y
 | [Training](./assets/docs/training.md) | Training loop, strategies & formulas |
 | [Inference](./assets/docs/inference.md) | KVCache, continuous batching, sampling & HTTP API |
 | [Data Flow](./assets/docs/dataflow.md) | Data pipeline, storage backends & dataset architecture |
+| [Preprocessing](./assets/docs/preprocessing.md) | Declarative JSON-driven data preprocessing |
 
 ### Contributing
 

assets/docs/README-zh-CN.md

@@ -88,6 +88,7 @@ export CUDA_VISIBLE_DEVICES=0,1,2,3
 
 nohup python scripts/tools/train.py \
     --nprocs=4 \
+    --parallel_mode=ddp \
     --train_type=seq \
     --data_root_path=/path/to/dataset \
     --param_path=/path/to/model \
@@ -114,8 +115,8 @@ nohup python scripts/tools/train.py \
 ```bash
 python scripts/tools/generate.py \
     --param_path /path/to/model \
-    --input_json_file /path/to/input.json \
+    --input_json_file /path/to/input.jsonl \
-    --output_json_file /path/to/output.json
+    --output_json_file /path/to/output.jsonl
 ```
 
 #### Docker
@@ -230,6 +231,7 @@ python scripts/demo/generate_ar.py
 | [训练文档](./training.md) | 训练循环、策略与公式 |
 | [推理文档](./inference.md) | KVCache、连续批处理、采样与 HTTP API |
 | [数据流程](./dataflow.md) | 数据管道、存储后端与数据集架构 |
+| [数据预处理](./preprocessing.md) | 声明式 JSON 驱动数据预处理 |
 
 ### 贡献
 

assets/docs/architecture.md

@@ -8,6 +8,8 @@ classDiagram
         class BaseConfig {
             +to_dict() Dict
             +from_dict(d) Self
+            +from_json(path) Self
+            +to_json(path)
         }
 
         class BaseModelConfig {
@@ -17,41 +19,43 @@ classDiagram
         }
 
         class AutoRegressiveLMConfig {
-            +int vocab_size
+            +Optional[int] vocab_size
-            +int dim
+            +Optional[int] dim
-            +int n_layers
+            +Optional[int] n_layers
-            +float norm_eps
+            +Optional[float] norm_eps
-            +int dim_ffn
+            +Optional[int] dim_ffn
-            +bool tie_weight
+            +Optional[bool] tie_weight
-            +int max_len
+            +Optional[dict] rope_scaling
-            +float rope_theta
+            +Optional[int] max_len
+            +Optional[float] rope_theta
             +str attn_type
-            +int n_heads
+            +Optional[int] n_heads
-            +int n_kv_heads
+            +Optional[int] n_kv_heads
-            +bool use_qk_norm
+            +Optional[bool] use_qk_norm
-            +bool use_gated_attention
+            +Optional[bool] use_gated_attention
             +Optional[int] kv_lora_rank
             +Optional[int] qk_nope_head_dim
             +Optional[int] qk_rope_head_dim
             +str ffn_type
-            +int n_routed_experts
+            +Optional[int] n_routed_experts
-            +int n_shared_experts
+            +Optional[int] n_shared_experts
-            +int n_activated_experts
+            +Optional[int] n_activated_experts
             +Optional[str] topk_method
         }
 
         class EncoderConfig {
-            +int vocab_size
+            +Optional[int] vocab_size
-            +int dim
+            +Optional[int] dim
-            +int n_layers
+            +Optional[int] n_layers
-            +float norm_eps
+            +Optional[float] norm_eps
-            +int dim_ffn
+            +Optional[int] dim_ffn
-            +int max_len
+            +Optional[int] max_len
-            +float rope_theta
+            +Optional[float] rope_theta
-            +int n_heads
+            +Optional[int] n_heads
-            +int n_kv_heads
+            +Optional[int] n_kv_heads
-            +bool use_qk_norm
+            +Optional[bool] use_qk_norm
-            +bool use_gated_attention
+            +Optional[bool] use_gated_attention
+            +Optional[dict] rope_scaling
             +Optional[str] pooling_type
             +Optional[bool] normalize_embeddings
         }
@@ -62,8 +66,40 @@ classDiagram
             +load(raw) BaseConfig
         }
 
+        class InputConfig {
+            +str type
+            +str messages_key
+            +str prompt_key
+            +str response_key
+            +str text_key
+        }
+
+        class ProcessingConfig {
+            +int max_seq_len
+            +int min_chars
+            +int max_chars
+            +bool deduplicate
+            +Optional[int] max_items
+        }
+
+        class OutputConfig {
+            +Optional[str] domain_key
+            +str storage_format
+            +int max_tokens_per_shard
+        }
+
+        class PipelineConfig {
+            +int version
+            +InputConfig input
+            +dict mask
+            +str mask_default
+            +ProcessingConfig preprocessing
+            +OutputConfig output
+            +from_dict(d) Self
+        }
+
         class TrainConfig {
-            +nn.Module model
+            +Callable[[], nn.Module] model_fn
             +str strategy
             +Dataset dataset
             +Callable optimizer_fn
@@ -80,6 +116,7 @@ classDiagram
             +str log_dir
             +int log_interval
             +List[str] metrics
+            +Optional[LoRAConfig] lora
             +int random_seed
             +int num_workers
             +Optional[int] prefetch_factor
@@ -88,12 +125,12 @@ classDiagram
             +str backend
             +str master_addr
             +str master_port
-            +Callable parallel_wrapper
-            +Callable state_dict_fn
             +str start_method
             +str device_type
             +Optional[Dataset] val_dataset
             +int val_step
+            +str parallel_mode
+            +dict executor_kwargs
             +dict extra_kwargs
             +validate()
         }
@@ -104,8 +141,8 @@ classDiagram
         class BaseDataset {
             +int window_size
             +int stride
-            +Optional[BaseStorage] storage
+            +Optional[Store] storage
-            +load(load_path, storage_type, tokenizer)
+            +load(load_path, storage_type)
             +__getitem__(index)
             +__len__()
         }
@@ -126,38 +163,25 @@ classDiagram
             +__getitem__(index) Dict
         }
 
-        class BaseSegmentFetcher {
+        class Store {
-            +List[Tensor] segments
+            +Dict[str, List[Tensor]] _data
-            +List[int] cum_lengths
+            +Dict[str, List[int]] _cum
-            +int total_length
+            +int _length
-            +fetch_data(begin_idx, end_idx) Tensor
-        }
-
-        class BaseStorage {
-            +MultiSegmentFetcher _fetcher
             +keys (property)
-            +load(load_path, tokenizer)
+            +load(path)
             +fetch(begin, end, keys)
             +__len__()
+            -_fetch_key(key, begin, end) Tensor
+            -_normalize(raw)
         }
 
-        class H5Storage {
+        class H5Store {
-            +load(load_path, tokenizer)
+            +load(path)
-            +fetch(begin, end, keys) Dict
-            +keys() List
         }
 
-        class JSONStorage {
+        class MmapStore {
-            +load(load_path, tokenizer)
+            +List _mmap_refs
-            +fetch(begin, end, keys) Dict
+            +load(path)
-            +keys() List
-        }
-
-        class MultiSegmentFetcher {
-            +Dict multi_fetchers
-            +List multi_keys
-            +key_fetch(begin_idx, end_idx, keys) Dict
-            +fetch_data(begin_idx, end_idx) Dict
         }
 
         class ResumableDistributedSampler {
@@ -165,17 +189,17 @@ classDiagram
             +int iter
         }
 
-        class StorageFactory {
+        class StoreFactory {
             +Registry _registry
             +register(name) decorator
-            +create(storage_type) BaseStorage
+            +create(storage_type) Store
         }
 
         class DatasetFactory {
             +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
         }
     }
 
@@ -186,8 +210,9 @@ classDiagram
             +int iteration
             +dict extra
             +dict meta
+            +dict config
             +save(save_dir)
-            +load(save_dir) Checkpoint
+            +load(save_dir, broadcast) Checkpoint
         }
     }
 
@@ -195,8 +220,8 @@ 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
@@ -210,7 +235,7 @@ 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()
         }
 
@@ -235,6 +260,7 @@ classDiagram
         }
 
         class GQA {
+            +int dim
             +int n_heads
             +int n_kv_heads
             +int head_dim
@@ -249,6 +275,7 @@ classDiagram
         }
 
         class MLA {
+            +int dim
             +int n_heads
             +int n_kv_heads
             +int head_dim
@@ -257,11 +284,13 @@ classDiagram
             +int qk_rope_head_dim
             +int n_rep
             +int layer_id
+            +bool use_qk_norm
             +bool use_gated_attention
             +Linear q_proj, kv_a_proj, kv_b_proj
             +Linear o_proj
             +Linear gate  # only if use_gated_attention
             +RMSNorm kv_norm
+            +RMSNorm q_norm, k_norm  # only if use_qk_norm
             +forward(x, rotary_emb, attn_mask, paged_cache) Tensor
         }
 
@@ -307,6 +336,7 @@ classDiagram
             +int dim
             +int max_len
             +float base
+            +Optional[Dict] rope_scaling
             +forward(x, position_ids=None) Tensor
         }
 
@@ -316,13 +346,32 @@ classDiagram
         }
     }
 
+    namespace preprocessing {
+        class BaseMaskBuilder {
+            <<abstract>>
+            +build(item, config, tokenizer) Optional[dict]
+        }
+
+        class ChatMaskBuilder {
+            +build(item, config, tokenizer) Optional[dict]
+        }
+
+        class InstructionMaskBuilder {
+            +build(item, config, tokenizer) Optional[dict]
+        }
+
+        class TextMaskBuilder {
+            +build(item, config, tokenizer) Optional[dict]
+        }
+    }
+
     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
             +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
@@ -330,7 +379,7 @@ classDiagram
         }
 
         class ChatTemplate {
-            +String template_str
+            +str template_str
             +render(messages, system_prompt, **extra_variables) str
             +from_string(template) ChatTemplate
         }
@@ -350,24 +399,32 @@ classDiagram
             +create(name, *args, **kwargs) T
             +list_registered() list
         }
+
+        class MaskBuilderFactory {
+            +Registry _registry
+            +register(name) decorator
+            +create(input_type, config, tokenizer) BaseMaskBuilder
+        }
     }
 
     namespace trainer {
         class Trainer {
             +TrainConfig train_config
             +List[TrainCallback] callbacks
-            +train(checkpoint)
+            +train(resume_dir)
-            +_get_default_callbacks() List[TrainCallback]
+            -_get_default_callbacks() List[TrainCallback]
         }
 
         class TrainContext {
             +nn.Module model
             +BaseStrategy strategy
             +DataLoader dataloader
-            +Optimizer optimizer
+            +OptimizerProtocol optimizer
-            +LRScheduler scheduler
+            +SchedulerProtocol scheduler
             +Checkpoint checkpoint
             +TrainConfig config
+            +dict model_config
+            +BaseExecutor executor
             +int epoch
             +int iteration
             +float loss
@@ -380,13 +437,17 @@ classDiagram
 
         class TrainContextBuilder {
             +TrainConfig config
-            +with_checkpoint(checkpoint) TrainContextBuilder
+            +with_resume_dir(resume_dir) TrainContextBuilder
             +build() TrainContext
         }
 
         class BaseStrategy {
-            +Union[Callable, nn.Module] model
+            +Callable model
+            +Optional[BaseExecutor] executor
+            +Optional[Callable] model_fn
+            +dict extra_kwargs
             +str device
+            +__call__(batch) Tensor
             +compute_loss(batch) Tensor
         }
 
@@ -427,6 +488,8 @@ classDiagram
         class BaseScheduler {
             +get_lr() List[float]
             +step()
+            +state_dict() dict
+            +load_state_dict(d)
         }
 
         class SchedulerFactory {
@@ -438,6 +501,7 @@ classDiagram
         class CosineScheduler {
             +int warmup_steps
             +int lr_decay_steps
+            +int total_steps
             +float min_rate
         }
 
@@ -454,16 +518,15 @@ classDiagram
             +on_train_end(context)
             +on_epoch_begin(context)
             +on_epoch_end(context)
-            +on_step_begin(context)
-            +on_step_end(context)
             +on_batch_begin(context)
             +on_batch_end(context)
+            +on_optimizer_step(context)
             +on_error(context)
         }
 
         class GradientClippingCallback {
             +float max_grad_norm
-            +on_step_begin(context)
+            +on_optimizer_step(context)
         }
 
         class GradientCheckpointingCallback {
@@ -476,16 +539,12 @@ classDiagram
             +str save_dir
             +int interval
             +bool weight_only
-            +Callable state_dict_fn
             +Callable save_extra_fn
-            +Callable load_extra_fn
+            -_save_checkpoint(context)
-            +_save_checkpoint(context)
-            +on_train_begin(context)
             +on_batch_end(context)
             +on_train_end(context)
             +on_error(context)
-            +save_extra(context)$
+            +save_extra(context) dict$
-            +load_extra(extra, context)$
         }
 
         class ProgressBarCallback {
@@ -498,7 +557,7 @@ classDiagram
         }
 
         class MetricLoggerCallback {
-            +str log_dir
+            +Path log_dir
             +int save_interval
             +int log_interval
             +List[str] metrics
@@ -508,8 +567,8 @@ classDiagram
         }
 
         class ValidationCallback {
-            +_run_validation(context)
+            -_run_validation(context)
-            +on_step_end(context)
+            +on_optimizer_step(context)
         }
 
         class CallbackFactory {
@@ -522,7 +581,12 @@ classDiagram
             +float lr
             +float momentum
             +float weight_decay
+            +bool nesterov
             +int ns_steps
+            +Optional[float] adamw_lr
+            +tuple adamw_betas
+            +float adamw_eps
+            +float adamw_wd
             +step(closure) Optional[float]
         }
     }
@@ -543,6 +607,8 @@ 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]
         }
@@ -554,7 +620,9 @@ 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()
@@ -632,7 +700,7 @@ classDiagram
         class Task {
             +str task_id
             +List prompt_ids
-            +int max_tokens
+            +Optional[int] max_tokens
             +float temperature
             +float top_p
             +int top_k
@@ -641,8 +709,8 @@ classDiagram
             +int input_tokens
             +int output_tokens
             +float arrival_time
-            +float finish_time
+            +Optional[float] finish_time
-            +Callable stream_callback
+            +Optional[Callable] stream_callback
             +int next_pos
             +is_finished(stop_ids) bool
         }
@@ -657,15 +725,24 @@ 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]
+            +has_work() bool
+            +wait_for_tasks(timeout)
+            +get_waiting_tasks() List[Task]
+            +clear_queues()
+            +wake()
+            +get_stats() Dict
         }
 
         class GenerationRequest {
@@ -744,56 +821,65 @@ classDiagram
             +str model
             +List[AnthropicMessage] messages
             +Optional[str] system
-            +float temperature
+            +Optional[float] temperature
-            +float top_p
+            +Optional[float] top_p
-            +int top_k
+            +Optional[int] top_k
             +int max_tokens
-            +bool stream
+            +Optional[bool] stream
             +Optional[List[str]] stop_sequences
         }
 
-        class ProtocolHandler {
+        class ResponseBuilder {
             <<abstract>>
+            +prepare(request, tokenizer) Tuple[str, GenContext, List[str]]
+            +format_stream_start(ctx) List[str]
+            +format_chunk(token) str
+            +format_stream_end(ctx, stop) List[str]
+            +format_response(ctx, content, stop) Dict
+        }
+
+        class OpenAIResponseBuilder {
+            +prepare(request, tokenizer) Tuple
+            +format_stream_start(ctx) List[str]
+            +format_chunk(token) str
+            +format_stream_end(ctx, stop) List[str]
+            +format_response(ctx, content, stop) Dict
+        }
+
+        class AnthropicResponseBuilder {
+            +prepare(request, tokenizer) Tuple
+            +format_stream_start(ctx) List[str]
+            +format_chunk(token) str
+            +format_stream_end(ctx, stop) List[str]
+            +format_response(ctx, content, stop) Dict
+        }
+
+        class ProtocolHandler {
             +request
             +engine
-            +build_prompt() str
+            +builder: ResponseBuilder
-            +create_response_id() str
+            +async handle() Union[StreamingResponse, Dict]
-            +get_stop_sequences() List[str]
+            -_handle_stream(agen, ctx, stop_sequences) StreamingResponse
-            +create_stop_checker() StopChecker
+            -async _handle_non_stream(agen, ctx, stop_sequences) Dict
-            +on_token(ctx, token, stop_checker) Optional[str]
-            +format_stream_start(ctx) List[str]
-            +format_stream_token(ctx, token) str
-            +format_stream_end(ctx) List[str]
-            +format_non_stream_response(ctx, content) Dict
-            +handle() Union[StreamingResponse, Dict]
-        }
-
-        class OpenAIHandler {
-            +build_prompt() str
-            +create_response_id() str
-        }
-
-        class AnthropicHandler {
-            +build_prompt() str
-            +create_response_id() str
-            +on_token(ctx, token, stop_checker) Optional[str]
         }
 
         class StopChecker {
-            +has_sequences (property) bool
+            +__init__(sequences)
             +check(text) Optional[str]
-            +trim(text, matched) str
         }
 
-        class StreamContext {
+        class GenContext {
             +str resp_id
             +int created
             +str model
             +int prompt_tokens
             +int completion_tokens
-            +str accumulated
+        }
-            +Optional[str] stop_matched
+
-            +str last_yield_trimmed
+        class StopInfo {
+            +Optional[str] matched
+            +str body
+            +str yielded
         }
 
         class app {
@@ -802,15 +888,87 @@ classDiagram
         }
     }
 
+    namespace protocols {
+        class OptimizerProtocol {
+            <<protocol>>
+            +step(closure)
+            +zero_grad()
+            +state_dict() dict
+            +load_state_dict(d)
+        }
+
+        class SchedulerProtocol {
+            <<protocol>>
+            +step()
+            +state_dict() dict
+            +load_state_dict(d)
+            +get_last_lr()
+        }
+    }
+
     namespace parallel {
-        class Functions {
+        class setup {
             <<module>>
             +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)
+            +setup_parallel(rank, world_size, backend, master_addr, master_port, device_type) contextmanager
             +get_current_device() str
             +get_world_size() int
             +get_rank() int
-            +only_on_rank(rank, sync) decorator
+            +only_on_rank(rank, sync=False) decorator
+        }
+
+        class GradientState {
+            +int num_steps
+            +sync_gradients (property) bool
+        }
+
+        class AccumOptimizer {
+            +Optimizer optimizer
+            +GradientState gradient_state
+            +param_groups (property)
+            +step(closure)
+            +zero_grad()
+            +state_dict() dict
+            +load_state_dict(d)
+        }
+
+        class AccumScheduler {
+            +LRScheduler scheduler
+            +GradientState gradient_state
+            +step()
+            +state_dict() dict
+            +load_state_dict(d)
+            +get_last_lr()
+        }
+
+        class BaseExecutor {
+            +GradientState gradient_state
+            +prepare(model, optimizer, dataloader, scheduler) tuple
+            +accumulate(model) context manager
+            +backward(loss)
+            +unwrap_model(model) dict
+            +sync_gradients (property) bool
+            +grad_accum_steps (property) int
+        }
+
+        class NoneExecutor {
+        }
+
+        class DDPExecutor {
+            -_prepare_model(model) nn.Module
+            -_no_sync(model) context manager
+            +unwrap_model(model) dict
+        }
+
+        class FSDPExecutor {
+            -_prepare_model(model) nn.Module
+            +unwrap_model(model) dict
+        }
+
+        class ExecutorFactory {
+            +Registry _registry
+            +register(name) decorator
+            +create(parallel_mode, **kwargs) BaseExecutor
         }
 
         class ParallelModel {
@@ -820,11 +978,25 @@ classDiagram
         }
 
         class ColumnParallelLinear {
+            +int in_features
+            +int out_features
+            +int out_features_per_rank
+            +bool gather_results
+            +Parameter weight
+            +Optional[Parameter] bias
             +forward(x) Tensor
+            +load_state_dict(state_dict)
         }
 
         class RowParallelLinear {
+            +int in_features
+            +int out_features
+            +int in_features_per_rank
+            +bool reduce_results
+            +Parameter weight
+            +Optional[Parameter] bias
             +forward(x) Tensor
+            +load_state_dict(state_dict)
         }
     }
 
@@ -842,12 +1014,13 @@ classDiagram
     TrainCallback <|-- CheckpointCallback
     TrainCallback <|-- ProgressBarCallback
     TrainCallback <|-- MetricLoggerCallback
+    TrainCallback <|-- ValidationCallback
     BaseDataset <|-- SEQDataset
     BaseDataset <|-- SFTDataset
     BaseDataset <|-- DPODataset
     BaseDataset <|-- GRPODataset
-    BaseStorage <|-- H5Storage
+    Store <|-- H5Store
-    BaseStorage <|-- JSONStorage
+    Store <|-- MmapStore
     BaseSamplingStrategy <|-- TemperatureStrategy
     BaseSamplingStrategy <|-- TopKStrategy
     BaseSamplingStrategy <|-- TopPStrategy
@@ -858,6 +1031,10 @@ classDiagram
     AutoModel <|-- EmbeddingEncoder
     BaseConfig <|-- BaseModelConfig
     BaseConfig <|-- TrainConfig
+    BaseConfig <|-- InputConfig
+    BaseConfig <|-- ProcessingConfig
+    BaseConfig <|-- OutputConfig
+    BaseConfig <|-- PipelineConfig
     BaseModelConfig <|-- AutoRegressiveLMConfig
     BaseModelConfig <|-- EncoderConfig
     BaseFactory <|-- AutoModel
@@ -867,18 +1044,23 @@ classDiagram
     BaseFactory <|-- StrategyFactory
     BaseFactory <|-- SchedulerFactory
     BaseFactory <|-- CallbackFactory
-    BaseFactory <|-- StorageFactory
+    BaseFactory <|-- StoreFactory
+    BaseFactory <|-- ExecutorFactory
     BaseFactory <|-- ConfigFactory
-    TrainCallback <|-- ValidationCallback
+    BaseFactory <|-- MaskBuilderFactory
-    ProtocolHandler <|-- OpenAIHandler
+    BaseExecutor <|-- NoneExecutor
-    ProtocolHandler <|-- AnthropicHandler
+    BaseExecutor <|-- DDPExecutor
+    BaseExecutor <|-- FSDPExecutor
+    ResponseBuilder <|-- OpenAIResponseBuilder
+    ResponseBuilder <|-- AnthropicResponseBuilder
+    BaseMaskBuilder <|-- ChatMaskBuilder
+    BaseMaskBuilder <|-- InstructionMaskBuilder
+    BaseMaskBuilder <|-- TextMaskBuilder
 
     %% --- Composition (strong ownership, part destroyed with whole) ---
     KVCache *-- PagePool
     KVCache *-- Storage
     KVCache *-- TaskTable
-    PagePool *-- Allocator
-    PagePool *-- PrefixCache
     InferenceEngine *-- InferenceScheduler
     InferenceScheduler *-- KVCache
     InferenceScheduler *-- Executor
@@ -892,21 +1074,29 @@ classDiagram
     DecoderBlock *-- RMSNorm
     ChatCompletionRequest *-- ChatMessage
     MessagesRequest *-- AnthropicMessage
-    AutoTokenizer *-- ChatTemplate
     BaseFactory *-- Registry
+    BaseExecutor *-- GradientState
+    AccumOptimizer o-- GradientState
+    AccumScheduler o-- GradientState
 
     %% --- Aggregation (weak ownership) ---
     AutoModel o-- BaseModelConfig
+    AutoTokenizer o-- ChatTemplate
+    PagePool o-- Allocator
+    PagePool o-- PrefixCache
     Trainer o-- TrainCallback
     TrainContext o-- BaseStrategy
     TrainContext o-- BaseScheduler
     TrainContext o-- Checkpoint
+    TrainContext o-- BaseExecutor
     KvcacheView o-- Storage
     SamplingPipeline o-- BaseSamplingStrategy
-    BaseDataset o-- BaseStorage
+    BaseDataset o-- Store
 
     %% --- Dependency (uses temporarily) ---
     TrainConfig ..> BaseStrategy : selects
+    PipelineConfig ..> MaskBuilderFactory : selects
+    MaskBuilderFactory ..> BaseMaskBuilder : creates
     StrategyFactory ..> BaseStrategy : creates
     SchedulerFactory ..> BaseScheduler : creates
     DatasetFactory ..> BaseDataset : creates
@@ -917,10 +1107,14 @@ classDiagram
     FFNFactory ..> DeepSeekMoE : creates
     DecoderBlock ..> AttnFactory : uses
     DecoderBlock ..> FFNFactory : uses
-    StorageFactory ..> H5Storage : creates
+    StoreFactory ..> H5Store : creates
-    StorageFactory ..> JSONStorage : creates
+    StoreFactory ..> MmapStore : creates
     ConfigFactory ..> AutoRegressiveLMConfig : creates
     ConfigFactory ..> EncoderConfig : creates
+    ExecutorFactory ..> NoneExecutor : creates
+    ExecutorFactory ..> DDPExecutor : creates
+    ExecutorFactory ..> FSDPExecutor : creates
+    TrainContextBuilder ..> ExecutorFactory : creates
     Trainer ..> TrainContextBuilder : uses
     TrainContextBuilder ..> TrainContext : creates
     Trainer ..> Functions : spawns
@@ -931,10 +1125,10 @@ classDiagram
     KVCache ..> KvcacheView : binds
     InferenceEngine ..> GenerationRequest : uses
     InferenceEngine ..> GenerateResult : creates
-    OpenAIHandler ..> ChatCompletionRequest : receives
+    OpenAIResponseBuilder ..> ChatCompletionRequest : receives
-    AnthropicHandler ..> MessagesRequest : receives
+    AnthropicResponseBuilder ..> MessagesRequest : receives
     ProtocolHandler ..> StopChecker : creates
-    ProtocolHandler ..> StreamContext : creates
+    ProtocolHandler ..> GenContext : creates
 
     %% --- Association (general usage) ---
     Trainer --> TrainConfig
@@ -947,8 +1141,6 @@ classDiagram
     Executor --> AutoModel
     Executor --> AutoTokenizer
     TaskManager --> AutoTokenizer
-    MultiSegmentFetcher --> BaseSegmentFetcher
-    ResumableDistributedSampler --> BaseDataset
 
 ```
 
@@ -957,43 +1149,48 @@ classDiagram
 
 | Module | Components | Description |
 |--------|------------|-------------|
-| **astrai.config** | BaseConfig, BaseModelConfig, AutoRegressiveLMConfig, EncoderConfig, ConfigFactory, TrainConfig | Configuration management (to_dict/from_dict, to_file/from_file) |
+| **astrai.config** | BaseConfig, BaseModelConfig, AutoRegressiveLMConfig, EncoderConfig, ConfigFactory, TrainConfig, PipelineConfig, InputConfig, ProcessingConfig, OutputConfig | Configuration management (to_dict/from_dict, to_file/from_file, from_json/to_json) |
-| **astrai.dataset** | BaseDataset–GRPODataset, BaseStorage–JSONStorage, StorageFactory, BaseSegmentFetcher, MultiSegmentFetcher, ResumableDistributedSampler, DatasetFactory | Dataset loading and management |
+| **astrai.preprocessing** | BaseMaskBuilder, MaskBuilderFactory, ChatMaskBuilder, InstructionMaskBuilder, TextMaskBuilder, Pipeline, filter_by_length, dedup_signature | Declarative JSON-driven data preprocessing |
+| **astrai.dataset** | BaseDataset–GRPODataset, Store–MmapStore, StoreFactory, ResumableDistributedSampler, DatasetFactory | Dataset loading and management |
 | **astrai.serialization** | Checkpoint | Model serialization |
 | **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(Protocol)–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.inference** | InferenceEngine, InferenceScheduler, Executor, KVCache–KvcacheView, Allocator–Storage, Task, TaskManager, TaskStatus, GenerationRequest, GenerateResult, BaseSamplingStrategy–SamplingPipeline, ProtocolHandler, ResponseBuilder, OpenAIResponseBuilder, AnthropicResponseBuilder, StopChecker, GenContext, 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.parallel** | spawn_parallel_fn, setup_parallel, get_rank/get_world_size/get_current_device, only_on_rank, BaseExecutor, ExecutorFactory, NoneExecutor, DDPExecutor, FSDPExecutor, GradientState, AccumOptimizer, AccumScheduler, ParallelModel, RowParallelLinear, ColumnParallelLinear | Distributed parallel & gradient accumulation |
 | **astrai.factory** | Registry, BaseFactory[T] | Component registration |
+| **astrai.protocols** | OptimizerProtocol, SchedulerProtocol | Structural subtyping for optimizer/scheduler wrappers |
 
 ## Design Patterns
 
 | Pattern | Classes | Purpose |
 |---------|---------|---------|
-| **Factory** | `AttnFactory`, `FFNFactory`, `StrategyFactory`, `DatasetFactory`, `SchedulerFactory`, `CallbackFactory`, `StorageFactory`, `ConfigFactory` | Decorator-based component creation |
+| **Factory** | `AttnFactory`, `FFNFactory`, `StrategyFactory`, `DatasetFactory`, `SchedulerFactory`, `CallbackFactory`, `StoreFactory`, `ConfigFactory`, `ExecutorFactory` | 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 |
-| **Template Method** | `ProtocolHandler`, `OpenAIHandler`, `AnthropicHandler` | HTTP API handler with format hooks |
+| **Strategy (API)** | `ResponseBuilder`, `OpenAIResponseBuilder`, `AnthropicResponseBuilder` | HTTP API handler with format hooks |
 | **Builder** | `TrainContextBuilder` | Chain-building training context |
 | **Observer** | `TrainCallback`, callback implementations | Training process monitoring |
 | **Context** | `TrainContext` | Unified training state bag |
 | **Object Pool** | `Allocator`, `PagePool` | Page-based KV cache with LRU eviction |
-| **Storage** | `BaseStorage`, `H5Storage`, `JSONStorage` | Format-agnostic data access |
+| **Executor** | `BaseExecutor`, `NoneExecutor`, `DDPExecutor`, `FSDPExecutor` | Gradient accumulation & model distribution |
+| **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 |
 
 ## Core Relationships
 
-1. **Config → Training**: `TrainConfig` holds model, dataset, optimizer_fn, scheduler_fn
+1. **Config → Training**: `TrainConfig` holds `model_fn`, `dataset`, `optimizer_fn`, `scheduler_fn`, `parallel_mode`, `executor_kwargs`
-2. **Training Flow**: `Trainer` → `TrainContextBuilder` → `TrainContext`, uses `BaseStrategy` for loss
+2. **Training Flow**: `Trainer` → `TrainContextBuilder` → `TrainContext`, uses `BaseStrategy` for loss, `BaseExecutor` for gradient accumulation + model distribution
 3. **Strategy Selection**: `StrategyFactory` creates strategy by `train_type`
-4. **Inference Flow**: `InferenceEngine` → `InferenceScheduler` → `AutoRegressiveLM`, backed by `KVCache` + `SamplingPipeline`
+4. **Executor Selection**: `ExecutorFactory.create(cfg.parallel_mode, grad_accum_steps=cfg.grad_accum_steps, **cfg.executor_kwargs)` → `NoneExecutor` / `DDPExecutor` / `FSDPExecutor`
-5. **Distributed**: `spawn_parallel_fn` + `setup_parallel` for multi-process DDP
+5. **Inference Flow**: `InferenceEngine` → `InferenceScheduler` → `AutoRegressiveLM`, backed by `KVCache` + `SamplingPipeline`
-6. **Dataset Loading**: `DatasetFactory` creates datasets, `BaseStorage` (H5Storage/JSONStorage) loads via `BaseSegmentFetcher` + `MultiSegmentFetcher`
+6. **Distributed**: `spawn_parallel_fn` + `setup_parallel` for multi-process DDP
-7. **Checkpoint**: `Checkpoint` saves/loads safetensors + metadata (rank-0 only)
+7. **Dataset Loading**: `DatasetFactory` creates datasets, `Store` (H5Store/MmapStore) loads data with explicit `_length` and multi-segment `_data`
-8. **Scheduler**: `SchedulerFactory` creates `CosineScheduler`/`SGDRScheduler`
+8. **Checkpoint**: `Checkpoint` saves/loads safetensors + metadata (rank-0 only), extra state saved as `{key}.pt`
-9. **AutoModel**: `from_pretrained()` loads `config.json` + `model.safetensors`, `_disable_random_init` replaces `nn.init.*` with no-ops
+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: 2026-05-17
+> Document Update Time: 2026-05-30

assets/docs/dataflow.md

@@ -5,21 +5,21 @@ This document describes the data pipeline: from raw text to model input tensors.
 ## Overview
 
 ```
-Raw Text → AutoTokenizer → Token IDs → .h5/.json → Dataset → Sampler → DataLoader → Training/Inference
+Raw Text → AutoTokenizer → Token IDs → .h5/.bin → Store.load() → Store.fetch() → Dataset → Sampler → DataLoader → Training/Inference
 ```
 
 ## Data Preparation
 
-Raw text is tokenized via `AutoTokenizer.encode()` and saved as HDF5 (`.h5`) or JSON (`.json`/`.jsonl`) 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:
 
 ```
-StorageFactory.create("h5")   → H5Storage
+StoreFactory.create("h5")  → H5Store
-StorageFactory.create("json") → JSONStorage
+StoreFactory.create("bin") → MmapStore
 ```
 
-Both support shared memory via `.share_memory_()`.
+H5 backend supports shared memory via `.share_memory_()`. Bin (mmap) uses OS page-cache sharing natively.
 
 ## Data Keys by Training Type
 
@@ -33,14 +33,21 @@ Both support shared memory via `.share_memory_()`.
 ## Dataset Architecture
 
 ```
-DatasetFactory.load(train_type, path, window_size, stride)
+DatasetFactory.load(train_type, load_path, window_size, stride=None, storage_type=None)
-  → StorageFactory.create(detect_format(path))
+  → BaseDataset.load(load_path, storage_type=None)
-    → MultiSegmentFetcher(BaseSegmentFetcher per key)
+    → detect_format(load_path)
-      → BaseDataset.__getitem__(idx)
+    → StoreFactory.create(storage_type)
-        → sliding window [begin, end) via get_index(idx)
+    → Store.load(load_path)
+      → H5Store._normalize() / MmapStore._normalize()
+        → Store._data[Dict[str, List[Tensor]]] + _cum[Dict[str, List[int]]]
+          → BaseDataset.__getitem__(idx)
+            → get_index(idx) → [begin, end)
+            → Store.fetch(begin, end, keys) → Tensor / Dict[str, Tensor]
 ```
 
-`window_size` = max input length, `stride` = step between consecutive samples.
+`window_size` = max input length, `stride` = step between consecutive samples (defaults to `window_size`, optional). `storage_type` defaults to `None` (auto-detect via `detect_format`).
+
+`Store.fetch(begin, end, keys)` accepts a single key (`str`) returning a `Tensor`, or a list of keys returning `Dict[str, Tensor]`. Internally uses `bisect` across multi-segment tensors. Raises `RuntimeError("Store not loaded")` if called before `load()`.
 
 ## Sampler
 
@@ -54,4 +61,4 @@ 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: 2026-05-17
+> Document Update Time: 2026-05-30

assets/docs/inference.md

@@ -12,16 +12,16 @@ RoPE is applied **before** KV cache write, not after — otherwise position enco
 
 ## KVCache System
 
-Six classes working together:
+Six classes (plus two helpers) 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()`.
@@ -40,26 +40,33 @@ KVCache (facade)
 ## Sampling (Strategy Pattern)
 
 ```
-BaseSamplingStrategy → TemperatureStrategy → TopKStrategy → TopPStrategy
+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 (Template Method)
+## Protocol Handlers (Strategy Pattern)
 
 ```python
-class ProtocolHandler(ABC):
+class ProtocolHandler:  # concrete orchestrator
-    def handle(self):
+    def __init__(self, request, engine, builder): ...
-        ctx = StreamContext(...)
+    async def handle(self):
+        prompt, ctx, stops = builder.prepare(request, engine)
         agen = engine.generate_async(prompt, ...)
-        if stream: self._handle_stream(agen, ctx)
+        if stream: self._handle_stream(agen, ctx, stops)
-        else:      self._handle_non_stream(agen, ctx)
+        else:      return await self._handle_non_stream(agen, ctx, stops)
 ```
 
-Subclass hooks: `build_prompt()`, `create_response_id()`, `format_stream_start/token/end()`, `format_non_stream_response()`.
+`ResponseBuilder` (ABC): `prepare()`, `format_stream_start()`, `format_chunk()`, `format_stream_end()`, `format_response()`.
 
-`OpenAIHandler` → `/v1/chat/completions`, `AnthropicHandler` → `/v1/messages`.
+`OpenAIResponseBuilder` → `/v1/chat/completions`, `AnthropicResponseBuilder` → `/v1/messages`.
+
+Adding a protocol = one builder file, no handler subclassing needed.
 
 ## Engine & GenerateResult
 
@@ -67,7 +74,9 @@ Subclass hooks: `build_prompt()`, `create_response_id()`, `format_stream_start/t
 InferenceEngine
   ├── generate(prompt, stream, ...) → str | List[str] | Generator
   ├── generate_with_request(req)    → same
-  └── generate_async(prompt, ...)   → AsyncGenerator
+  ├── 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)`.
@@ -94,12 +103,14 @@ 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
 
@@ -116,10 +127,10 @@ Supports `stop_sequences` and streaming via `event: content_block_delta`.
 | 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 |
+| `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
@@ -134,7 +145,8 @@ 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]
+async for token in engine.generate_async("Hello", ...):    # -> AsyncGenerator[str]
+    print(token)
 ```
 
-> Document Update Time: 2026-05-17
+> Document Update Time: 2026-05-30

assets/docs/params.md

@@ -53,7 +53,9 @@
 | Parameter | Description | Default |
 |-----------|-------------|---------|
 | `--nprocs` | Number of GPUs / processes | 1 |
+| `--parallel_mode` | Parallel strategy (`none`, `ddp`, or `fsdp`) | none |
 | `--device_type` | Device type | cuda |
+| `--start_method` | Multiprocessing start method (`spawn`, `fork`, `forkserver`) | spawn |
 
 ### Strategy-specific
 
@@ -73,6 +75,7 @@ export CUDA_VISIBLE_DEVICES=0,1,2,3
 
 nohup python scripts/tools/train.py \
     --nprocs=4 \
+    --parallel_mode=ddp \
     --train_type=seq \
     --data_root_path=/path/to/dataset \
     --param_path=/path/to/model \
@@ -94,4 +97,4 @@ nohup python scripts/tools/train.py \
 
 ---
 
-> Document Update Time: 2026-05-17
+> Document Update Time: 2026-05-24

assets/docs/preprocessing.md

@@ -0,0 +1,228 @@
+# Preprocessing Pipeline
+
+Declarative JSON-driven data preprocessing. No code needed -- describe your input format and mask rules in a config file, the engine does the rest.
+
+## Philosophy
+
+| Component | Responsibility |
+|-----------|---------------|
+| `tokenizer_config.json` (`chat_template`) | Formatting -- how roles become tokens |
+| `pipeline.json` (`mask`) | Masking -- which roles participate in training |
+
+The two are fully decoupled. A single config file captures the entire pipeline, reusable and version-controllable. Extension is via factory registration (`@MaskBuilderFactory.register`) -- no need to touch existing code.
+
+## Quick Start
+
+### SFT Chat
+
+```json
+{
+  "version": 1,
+  "input": {
+    "type": "chat",
+    "messages_key": "messages"
+  },
+  "mask": {
+    "system": "mask",
+    "user": "mask",
+    "assistant": "train"
+  },
+  "mask_default": "mask",
+  "preprocessing": {
+    "max_seq_len": 2048,
+    "deduplicate": true
+  },
+  "output": {
+    "domain_key": "source",
+    "storage_format": "bin",
+    "max_tokens_per_shard": 100000000
+  }
+}
+```
+
+Three lines of mask rules cover the most common SFT case: train on assistant turns, mask everything else.
+
+### Instruction Tuning
+
+```json
+{
+  "version": 1,
+  "input": {
+    "type": "instruction",
+    "prompt_key": "instruction",
+    "response_key": "output"
+  },
+  "mask": {
+    "prompt": "mask",
+    "response": "train"
+  },
+  "mask_default": "mask",
+  "preprocessing": {
+    "max_seq_len": 2048
+  },
+  "output": {
+    "storage_format": "bin"
+  }
+}
+```
+
+Mask splits at the prompt/response field boundary.
+
+### Pretraining
+
+```json
+{
+  "version": 1,
+  "input": {
+    "type": "text",
+    "text_key": "content"
+  },
+  "mask": {},
+  "preprocessing": {
+    "max_seq_len": 2048,
+    "min_chars": 50
+  },
+  "output": {
+    "storage_format": "bin"
+  }
+}
+```
+
+No mask -- train on all tokens.
+
+### Run
+
+```bash
+python scripts/tools/preprocess.py data/*.jsonl -o output/ -c sft.json
+```
+
+## Configuration Reference
+
+### `input`
+
+| Field | Type | Required | Default | Description |
+|-------|------|----------|---------|-------------|
+| `type` | string | yes | `"chat"` | Format: `"chat"`, `"instruction"`, or `"text"` |
+| `messages_key` | string | no | `"messages"` | JSON key for messages array (chat) |
+| `prompt_key` | string | no | `"prompt"` | JSON key for prompt field (instruction) |
+| `response_key` | string | no | `"response"` | JSON key for response field (instruction) |
+| `text_key` | string | no | `"text"` | JSON key for text field |
+
+### `mask`
+
+A map of `{role_or_field: "mask" | "train"}`. The engine uses this to build `loss_mask`:
+
+- `"mask"` -- tokens in this span are ignored during training (`loss_mask=0`)
+- `"train"` -- tokens in this span contribute to the loss (`loss_mask=1`)
+
+For chat mode, keys are role names (`system`, `user`, `assistant`, ...).
+For instruction mode, keys are `"prompt"` and `"response"`.
+
+| Field | Type | Default | Description |
+|-------|------|---------|-------------|
+| `mask` | dict | `{}` | Role/field to action mapping |
+| `mask_default` | string | `"mask"` | Default action for unlisted roles |
+
+### `preprocessing`
+
+| Field | Type | Default | Description |
+|-------|------|---------|-------------|
+| `max_seq_len` | int | `2048` | Maximum token length; truncated if exceeded |
+| `min_chars` | int | `50` | Minimum character length; dropped if shorter (text mode only) |
+| `max_chars` | int | `2000000` | Maximum character length; dropped if longer (text mode only) |
+| `deduplicate` | bool | `true` | Remove exact duplicates via MD5 of first 200 chars |
+| `max_items` | int or null | `null` | Maximum items to process; `null` = unlimited |
+
+### `output`
+
+| Field | Type | Default | Description |
+|-------|------|---------|-------------|
+| `domain_key` | string or null | `null` | JSON key for domain grouping; `null` = all output to `__default__` |
+| `storage_format` | string | `"bin"` | `"bin"` (mmap, zero-copy) or `"h5"` (HDF5) |
+| `max_tokens_per_shard` | int | `100000000` | Max tokens per output shard |
+
+## Mask Algorithm
+
+### Chat Mode (role-span tracking)
+
+For each message in the `messages` array:
+
+1. Prepend BOS token (position 0, always masked)
+2. Render through the chat template for that single message
+3. Encode the rendered text, record token span `(start, end, role)`
+4. Concatenate all spans — special tokens from the chat template naturally prevent BPE merging across message boundaries
+5. Fill `loss_mask` from the mask rules
+
+**Multi-turn example**:
+
+```
+Data:
+  [system: "You are helpful."]
+  [user: "What is 2+2?"]
+  [assistant: "4"]
+  [user: "What is 3+3?"]
+  [assistant: "6"]
+
+Config:
+  "mask": {"system": "mask", "user": "mask", "assistant": "train"}
+
+Result:
+  tokens:  <bos> [system span] [user span] [assistant:4 span] [user span] [assistant:6 span]
+  mask:      0       0            0              1               0             1
+```
+
+Both assistant turns are trained. All system and user tokens are masked.
+
+### Instruction Mode (field boundary)
+
+Encode the prompt and response fields independently, then split the mask at the field boundary.
+
+- `"prompt": "mask", "response": "train"` -- mask the left half, train the right half
+- `"prompt": "train", "response": "mask"` -- the reverse
+
+### Text Mode (no mask)
+
+Pure tokenization. No `loss_mask` is produced. Used for pretraining.
+
+## Output Layout
+
+```
+output_dir/
+  __default__/              # when domain_key is null
+    meta.json               # {"sequence": {"shape": [N], "dtype": "int64"}, ...}
+    sequence.bin            # int64 raw bytes, mmap-able for zero-copy reads
+    loss_mask.bin           # int64 raw bytes
+  wiki/                     # when domain_key="source" and item["source"]="wiki"
+    meta.json
+    sequence.bin
+    loss_mask.bin
+```
+
+## Extension
+
+Register a custom builder for new formats:
+
+```python
+from astrai.preprocessing.builder import BaseMaskBuilder, MaskBuilderFactory
+
+@MaskBuilderFactory.register("my_format")
+class MyFormatBuilder(BaseMaskBuilder):
+    def build(self, item: dict, config, tokenizer) -> dict | None:
+        # Return {"ids": [...], "loss_mask": [...], "domain": "..."}
+        # Return None to skip this item
+        ...
+```
+
+Then set `"input": {"type": "my_format"}` in your config.
+
+## Compared to Old Pipeline
+
+| Old (`astrai.preprocess.Pipeline`) | New (`astrai.preprocessing.pipeline.Pipeline`) |
+|---|---|
+| Configured via constructor arguments | Configured via JSON file |
+| Hardcoded `_transform_chat` / `_transform_text` | Factory-registered `Builder` with declarative mask rules |
+| Auto-detects format via magic key lists | Explicit `input.type` declaration |
+| Double-encodes (full + prompt), uses length diff for mask | Single-encode with role-span tracking |
+| Only trains the last assistant turn | Configurable: multi-turn, single-turn, or no mask |
+
+> Document Update Time: 2026-05-30

assets/docs/training.md

@@ -1,38 +1,5 @@
 # Training
 
-## Model Architecture
-
-The model uses a decoder-only Transformer with **GQA** (Grouped Query Attention) and optional **MLA** (Multi-head Latent Attention). 1.0 billion parameters, Chinese–English bilingual.
-
-```mermaid
-flowchart TB
-    subgraph Layers["Transformer Layers"]
-        direction TB
-        A[Input Embedding] --> B[Transformer Block\nLayer 1]
-        B --> C[Transformer Block\nLayer ...]
-        C --> D[Transformer Block\nLayer ...]
-        D --> E[RMSNorm]
-        E --> F[Linear]
-        F --> G[SoftMax]
-    end
-
-    subgraph TransformerBlock["Transformer Block"]
-        direction TB
-        H[x] --> I[RMSNorm]
-        I --> J[Linear → Q/K/V]
-        J --> K[Q]; J --> L[K]; J --> M[V]
-        K --> N[RoPE]; L --> O[RoPE]
-        N --> P["Q @ K^T / sqrt(d)"]; O --> P
-        P --> Q[Masked SoftMax]; Q --> R[S @ V]; M --> R
-        R --> S[Linear]; S --> T[+]; H --> T
-        T --> U[RMSNorm]
-        U --> V["Linear (gate)"]; U --> W["Linear (up)"]
-        V --> X[SiLU]; X --> Y[×]; W --> Y
-        Y --> Z["Linear (down)"]; Z --> AA[+]; T --> AA
-        AA --> BB[x']
-    end
-```
-
 ### Autoregression
 
 Given a token sequence, the model predicts the probability of the next token. Each generated token is appended to the input and fed back, repeating until an end-of-sequence token or max length.
@@ -69,20 +36,24 @@ Two-level loop: **epoch** → **batch**. Optimizer step fires every `grad_accum_
 
 ```
 on_train_begin
+  model.train()
   on_epoch_begin
     for batch in dataloader:
       on_batch_begin
-      loss = strategy(batch)
+      with executor.accumulate(model):
-      (loss / grad_accum_steps).backward()
+        loss = strategy.compute_loss(batch)
-      iteration += 1
+        context.loss = loss.item()
-      on_batch_end
+        stand_loss = loss / executor.grad_accum_steps
+        executor.backward(stand_loss)
+        context.iteration += 1
+        on_batch_end
 
-      if iteration % grad_accum_steps == 0:
+        if executor.sync_gradients:
-        on_step_begin
+          on_optimizer_step
-        optimizer.step()
+          optimizer.step()
-        optimizer.zero_grad()
+          optimizer.zero_grad()
-        on_step_end
+          if scheduler:
-        scheduler.step()
+            scheduler.step()
     on_epoch_end
 on_train_end
 ```
@@ -92,12 +63,15 @@ on_train_end
 | Hook | Fires | Default callback |
 |------|-------|-----------------|
 | `on_train_begin` | Before training starts | `GradientCheckpointingCallback` |
-| `on_step_begin` | Every accumulation window | `GradientClippingCallback` |
+| `on_epoch_begin` | Start of each epoch | `ProgressBarCallback` |
+| `on_batch_begin` | Every batch | — |
+| `on_optimizer_step` | Every accumulation window | `GradientClippingCallback`, `ValidationCallback` |
 | `on_batch_end` | Every batch | `CheckpointCallback`, `MetricLoggerCallback`, `ProgressBarCallback` |
-| `on_step_end` | Every accumulation window | `ValidationCallback` |
+| `on_epoch_end` | End of each epoch | `ProgressBarCallback` |
-| `on_train_end` | Training ends | `CheckpointCallback`, `MetricLoggerCallback` (final save) |
+| `on_error` | On exception during training | `CheckpointCallback`, `MetricLoggerCallback` |
+| `on_train_end` | Training ends (always via finally) | `CheckpointCallback`, `MetricLoggerCallback`, `GradientCheckpointingCallback` |
 
-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).
+Default callbacks (in order): `gradient_checkpointing` (activation checkpointing, optional), `checkpoint` (safetensors, rank-0), `metric_logger` (JSONL, rank-0), `progress_bar` (tqdm), `gradient_clipping`, `validation` (periodic validation on val_dataset).
 
 ## Strategies
 
@@ -109,7 +83,7 @@ $$
 L_{\text{PT}} = -\sum_{t=1}^{T} \log P(x_t \mid x_{\lt t}; \theta)
 $$
 
-Keys: `input_ids`, `target_ids`
+Keys: `input_ids`, `target_ids`. Optional: `label_smoothing`.
 
 ### SFT (Supervised Fine-Tuning)
 
@@ -119,7 +93,7 @@ $$
 L_{\text{SFT}} = -\sum_{t=P+1}^{P+L} \log P(s_t \mid s_{\lt t}; \theta)
 $$
 
-Keys: `input_ids`, `target_ids`, `loss_mask`
+Keys: `input_ids`, `target_ids`, `loss_mask`. Optional: `label_smoothing`.
 
 ### DPO (Direct Preference Optimization)
 
@@ -129,7 +103,7 @@ $$
 L_{\text{DPO}} = -\mathbb{E}\left[\log\sigma\left(\beta\log\frac{\pi_\theta(y_w\mid x)}{\pi_{\text{ref}}(y_w\mid x)} - \beta\log\frac{\pi_\theta(y_l\mid x)}{\pi_{\text{ref}}(y_l\mid x)}\right)\right]
 $$
 
-Parameters: `beta=0.1`. Keys: `chosen`, `rejected`, `chosen_mask`, `rejected_mask`.
+Parameters: `beta=0.1`, `reduction="mean"`. Keys: `chosen`, `rejected`, `chosen_mask`, `rejected_mask`.
 
 ### GRPO (Group Relative Policy Optimization)
 
@@ -143,7 +117,7 @@ $$
 L_{\text{GRPO}} = -\mathbb{E}\left[\min\left(\frac{\pi_\theta}{\pi_{\text{ref}}}A,\; \text{clip}\left(\frac{\pi_\theta}{\pi_{\text{ref}}}, 1-\epsilon, 1+\epsilon\right)A\right)\right] + \lambda \cdot \mathbb{E}\left[(\log\pi_\theta - \log\pi_{\text{ref}})^2\right]
 $$
 
-Parameters: `group_size=4`, `clip_eps=0.2`, `kl_coef=0.01`, `sync_interval=200`.
+Parameters: `group_size=4`, `clip_eps=0.2`, `kl_coef=0.01`, `sync_interval=200`, `reduction="mean"`.
 
 Keys: `prompts`, `responses`, `masks`, `rewards`.
 
@@ -154,7 +128,7 @@ Keys: `prompts`, `responses`, `masks`, `rewards`.
 | Cosine | `CosineScheduler` | Linear warmup → cosine decay to `min_rate` |
 | SGDR | `SGDRScheduler` | Cosine annealing with warm restarts (`t_mult=2`) |
 
-Created by `SchedulerFactory.create(optimizer, schedule_type, **kwargs)`.
+Created by `SchedulerFactory.create(optimizer, schedule_type, **kwargs)`. Valid types: `"cosine"`, `"sgdr"`. Omit to use no scheduler.
 
 ## Gradient Checkpointing
 
@@ -170,29 +144,30 @@ 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 extra.pt
+  ├── save(save_dir)    rank-0 only: meta.json (epoch/iteration/timestamp) + config.json (model config) + model.safetensors + optional {key}.pt (optimizer.pt, scheduler.pt)
-  └── load(save_dir)    broadcasts metadata from rank-0
+  └── load(save_dir, broadcast=False)    loads from local disk; set broadcast=True to broadcast 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
 ```
 
 - Loads checkpoint weights if provided
-- Wraps model with `parallel_wrapper` if `nprocs > 1`
+- Creates executor via `ExecutorFactory.create(cfg.parallel_mode, grad_accum_steps=cfg.grad_accum_steps, **cfg.executor_kwargs)`
+- Calls `executor.prepare(model, optimizer, dataloader, scheduler)` for model distribution (e.g. DDP) + gradient accumulation wrappers
 - Creates `ResumableDistributedSampler` for shuffle+resume
-- Builds strategy via `StrategyFactory.create(train_type, ...)`
+- Builds strategy via `StrategyFactory.create(train_type, model, device, **kwargs)`
 
 ## Training CLI
 
@@ -201,6 +176,7 @@ export CUDA_VISIBLE_DEVICES=0,1,2,3
 
 nohup python scripts/tools/train.py \
     --nprocs=4 \
+    --parallel_mode=ddp \
     --train_type=seq \
     --data_root_path=/path/to/dataset \
     --param_path=/path/to/model \
@@ -222,4 +198,4 @@ nohup python scripts/tools/train.py \
 
 Full parameter reference at [params.md](params.md).
 
-> Document Update Time: 2026-05-17
+> Document Update Time: 2026-05-30

astrai/__init__.py

@@ -1,4 +1,4 @@
-__version__ = "1.3.6"
+__version__ = "1.3.7"
 __author__ = "ViperEkura"
 
 from astrai.config import (

astrai/config/__init__.py

@@ -4,13 +4,22 @@ from astrai.config.model_config import (
     ConfigFactory,
     EncoderConfig,
 )
+from astrai.config.preprocess_config import (
+    InputConfig,
+    OutputConfig,
+    PipelineConfig,
+    ProcessingConfig,
+)
 from astrai.config.train_config import TrainConfig
 
 __all__ = [
-    # Model configuration
     "BaseModelConfig",
     "AutoRegressiveLMConfig",
     "EncoderConfig",
     "ConfigFactory",
     "TrainConfig",
+    "InputConfig",
+    "OutputConfig",
+    "PipelineConfig",
+    "ProcessingConfig",
 ]

astrai/config/base.py

@@ -1,6 +1,7 @@
 import json
 from dataclasses import MISSING, dataclass, fields
-from typing import Any, Dict, Optional, Self, get_type_hints
+from pathlib import Path
+from typing import Any, Dict, Optional, Self, Union, get_type_hints
 
 
 @dataclass
@@ -13,12 +14,21 @@ class BaseConfig:
                 d[fld.name] = v
             elif v is None:
                 d[fld.name] = None
-            elif isinstance(v, (dict, list)):
+            elif isinstance(v, (dict, list, tuple)):
                 try:
-                    json.dumps(v)
+                    val = list(v) if isinstance(v, tuple) else v
-                    d[fld.name] = v
+                    json.dumps(val)
+                    d[fld.name] = val
                 except (TypeError, ValueError):
                     pass
+            elif isinstance(v, BaseConfig):
+                d[fld.name] = v.to_dict()
+            elif hasattr(v, "__dataclass_fields__"):
+                sub = {}
+                for f in fields(v):
+                    a = getattr(v, f.name)
+                    sub[f.name] = list(a) if isinstance(a, tuple) else a
+                d[fld.name] = sub
         return d
 
     @classmethod
@@ -74,4 +84,15 @@ class BaseConfig:
             return value
         if isinstance(value, target_type):
             return value
+        if isinstance(value, dict) and issubclass(target_type, BaseConfig):
+            return target_type.from_dict(value)
         raise TypeError
+
+    @classmethod
+    def from_json(cls, path: Union[str, Path]) -> Self:
+        with open(path, "r", encoding="utf-8") as f:
+            return cls.from_dict(json.load(f))
+
+    def to_json(self, path: Union[str, Path]):
+        with open(path, "w", encoding="utf-8") as f:
+            json.dump(self.to_dict(), f, indent=2, ensure_ascii=False)

astrai/config/model_config.py

@@ -49,6 +49,7 @@ class AutoRegressiveLMConfig(BaseModelConfig):
 
     max_len: Optional[int] = None
     rope_theta: Optional[float] = None
+    rope_scaling: Optional[dict] = None
 
     attn_type: str = "gqa"
     n_heads: Optional[int] = None
@@ -80,6 +81,7 @@ class EncoderConfig(BaseModelConfig):
 
     max_len: Optional[int] = None
     rope_theta: Optional[float] = None
+    rope_scaling: Optional[dict] = None
 
     n_heads: Optional[int] = None
     n_kv_heads: Optional[int] = None

astrai/config/preprocess_config.py

@@ -0,0 +1,43 @@
+"""Pipeline configuration for JSONL preprocessing."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+from typing import Dict, Optional
+
+from astrai.config.base import BaseConfig
+
+
+@dataclass
+class InputConfig(BaseConfig):
+    type: str = "chat"
+    messages_key: str = "messages"
+    prompt_key: str = "prompt"
+    response_key: str = "response"
+    text_key: str = "text"
+
+
+@dataclass
+class ProcessingConfig(BaseConfig):
+    max_seq_len: int = 2048
+    min_chars: int = 50
+    max_chars: int = 2_000_000
+    deduplicate: bool = True
+    max_items: Optional[int] = None
+
+
+@dataclass
+class OutputConfig(BaseConfig):
+    domain_key: Optional[str] = None
+    storage_format: str = "bin"
+    max_tokens_per_shard: int = 100_000_000
+
+
+@dataclass
+class PipelineConfig(BaseConfig):
+    version: int = 1
+    input: InputConfig = field(default_factory=InputConfig)
+    mask: Dict[str, str] = field(default_factory=dict)
+    mask_default: str = "mask"
+    preprocessing: ProcessingConfig = field(default_factory=ProcessingConfig)
+    output: OutputConfig = field(default_factory=OutputConfig)

astrai/config/train_config.py

@@ -7,6 +7,7 @@ from torch.optim.lr_scheduler import LRScheduler
 from torch.utils.data import Dataset
 
 from astrai.config.base import BaseConfig
+from astrai.model.components.lora import LoRAConfig
 
 
 def required(**kw):
@@ -16,8 +17,8 @@ def required(**kw):
 @dataclass
 class TrainConfig(BaseConfig):
     # basic setting
-    model: nn.Module = field(
+    model_fn: Callable[[], nn.Module] = field(
-        default=None, metadata=required(help="Model for training.")
+        default=None, metadata=required(help="Model factory for training.")
     )
     strategy: str = field(default=None, metadata=required(help="Training strategy."))
     dataset: Dataset = field(
@@ -56,6 +57,12 @@ class TrainConfig(BaseConfig):
         default=5000, metadata={"help": "Number of iterations between checkpoints."}
     )
 
+    # lora setting
+    lora: Optional[LoRAConfig] = field(
+        default=None,
+        metadata={"help": "LoRA config. None means full fine-tuning."},
+    )
+
     # metric setting
     log_dir: str = field(
         default="./checkpoint/logs", metadata={"help": "Directory for metric logs."}
@@ -95,11 +102,9 @@ class TrainConfig(BaseConfig):
     master_port: str = field(
         default="29500", metadata={"help": "Master port for distributed training."}
     )
-    parallel_wrapper: Optional[Callable] = field(
+    parallel_mode: str = field(
-        default=None, metadata={"help": "Parallel function for training."}
+        default="none",
-    )
+        metadata={"help": "Parallel strategy: none, ddp, fsdp."},
-    state_dict_fn: Optional[Callable] = field(
-        default=None, metadata={"help": "Parallel function for state  dict saving."}
     )
     start_method: str = field(
         default="spawn",
@@ -118,6 +123,10 @@ class TrainConfig(BaseConfig):
         metadata={"help": "Number of optimizer steps between validation runs."},
     )
 
+    executor_kwargs: dict = field(
+        default_factory=dict,
+        metadata={"help": "Extra kwargs passed to ExecutorFactory.create()."},
+    )
     extra_kwargs: dict = field(
         default_factory=dict, metadata={"help": "Other arguments."}
     )

astrai/dataset/__init__.py

@@ -4,32 +4,28 @@ from astrai.dataset.dataset import (
 )
 from astrai.dataset.sampler import ResumableDistributedSampler
 from astrai.dataset.storage import (
-    BaseSegmentFetcher,
+    H5Store,
-    BaseStorage,
+    MmapStore,
-    H5Storage,
+    Store,
-    JSONStorage,
+    StoreFactory,
-    MultiSegmentFetcher,
-    StorageFactory,
     detect_format,
+    load_bin,
     load_h5,
-    load_json,
+    save_bin,
     save_h5,
-    save_json,
 )
 
 __all__ = [
     "BaseDataset",
     "DatasetFactory",
-    "BaseSegmentFetcher",
+    "Store",
-    "MultiSegmentFetcher",
+    "StoreFactory",
-    "BaseStorage",
+    "H5Store",
-    "H5Storage",
+    "MmapStore",
-    "JSONStorage",
-    "StorageFactory",
     "detect_format",
     "save_h5",
     "load_h5",
-    "save_json",
+    "save_bin",
-    "load_json",
+    "load_bin",
     "ResumableDistributedSampler",
 ]

astrai/dataset/dataset.py

@@ -8,8 +8,8 @@ from torch import Tensor
 from torch.utils.data import Dataset
 
 from astrai.dataset.storage import (
-    BaseStorage,
+    Store,
-    StorageFactory,
+    StoreFactory,
     detect_format,
 )
 from astrai.factory import BaseFactory
@@ -26,7 +26,7 @@ class BaseDataset(Dataset, ABC):
         super().__init__()
         self.window_size = window_size
         self.stride = stride
-        self.storage: Optional[BaseStorage] = None
+        self.storage: Optional[Store] = None
 
     @property
     def required_keys(self) -> List[str]:
@@ -48,37 +48,26 @@ class BaseDataset(Dataset, ABC):
                 f"Missing: {missing}"
             )
 
-    def load(self, load_path: str, storage_type: Optional[str] = None, tokenizer=None):
+    def load(self, load_path: str, storage_type: Optional[str] = None):
         """Load dataset from the given path.
 
         Auto-detects the storage format if not specified.
 
         Args:
             load_path: Path to the data directory or file
-            storage_type: Force a specific storage type ("h5", "json"),
+            storage_type: Force a specific storage type ("h5", "bin"),
                           or None for auto-detection
-            tokenizer: Callable str -> List[int], used to tokenize raw text
-                       in JSON files. Ignored for HDF5.
 
         Raises:
             KeyError: If the loaded storage is missing required keys.
         """
         if storage_type is None:
             storage_type = detect_format(load_path)
-        self.storage = StorageFactory.create(storage_type)
+        self.storage = StoreFactory.create(storage_type)
         self._load_path = load_path
-        self.storage.load(load_path, tokenizer=tokenizer)
+        self.storage.load(load_path)
         self._validate_keys()
 
-    def load_json(self, load_path: str, tokenizer=None):
-        """Load dataset from JSON files explicitly.
-
-        Args:
-            load_path: Path to the JSON data file or directory
-            tokenizer: Optional tokenizer callable for raw text JSON.
-        """
-        self.load(load_path, storage_type="json", tokenizer=tokenizer)
-
     @property
     def count(self) -> int:
         """Return the total number of raw elements (tokens) in the dataset."""
@@ -148,7 +137,7 @@ class DatasetFactory(BaseFactory["BaseDataset"]):
     """
 
     @classmethod
-    def _validate_component(cls, dataset_cls: type) -> None:
+    def _validate_component(cls, dataset_cls: type):
         """Validate that the dataset class inherits from BaseDataset."""
         if not issubclass(dataset_cls, BaseDataset):
             raise TypeError(f"{dataset_cls.__name__} must inherit from BaseDataset")
@@ -175,7 +164,6 @@ class DatasetFactory(BaseFactory["BaseDataset"]):
         window_size: int,
         stride: Optional[int] = None,
         storage_type: Optional[str] = None,
-        tokenizer=None,
     ) -> "BaseDataset":
         """Create and load a dataset in one step.
 
@@ -184,8 +172,7 @@ class DatasetFactory(BaseFactory["BaseDataset"]):
             load_path: Path to the data file
             window_size: Window size for data sampling
             stride: Stride between consecutive samples (default: same as window_size)
-            storage_type: Storage type ("h5", "json") or None for auto-detection
+            storage_type: Storage type ("h5", "bin") or None for auto-detection
-            tokenizer: Callable str -> List[int] for raw text JSON tokenization
 
         Returns:
             Loaded dataset instance
@@ -194,7 +181,7 @@ class DatasetFactory(BaseFactory["BaseDataset"]):
             stride = window_size
 
         dataset = cls.create(train_type, window_size, stride)
-        dataset.load(load_path, storage_type=storage_type, tokenizer=tokenizer)
+        dataset.load(load_path, storage_type=storage_type)
 
         return dataset
 
@@ -306,9 +293,11 @@ class GRPODataset(BaseDataset):
     def __getitem__(self, index: int) -> Dict[str, Tensor]:
         begin_idx, end_idx = self.get_index(index)
 
-        prompts = self._fetch_data(begin_idx, end_idx, "prompts")
+        prompts = self._fetch_data(begin_idx, end_idx, "prompts").to(dtype=torch.long)
-        responses = self._fetch_data(begin_idx, end_idx, "responses")
+        responses = self._fetch_data(begin_idx, end_idx, "responses").to(
-        masks = self._fetch_data(begin_idx, end_idx, "masks")
+            dtype=torch.long
+        )
+        masks = self._fetch_data(begin_idx, end_idx, "masks").to(dtype=torch.bool)
         rewards = self._fetch_data(begin_idx, end_idx, "rewards")
 
         return {

astrai/dataset/sampler.py

@@ -43,6 +43,7 @@ class ResumableDistributedSampler(Sampler[int]):
         offset = 0 if drop_last else self.num_replicas - 1
         self.num_samples_per_replica = (self.num_samples + offset) // self.num_replicas
         self.total_size = self.num_samples_per_replica * self.num_replicas
+        self.iter = self.iter % self.num_samples_per_replica
 
         self._indices = None
 
@@ -74,5 +75,10 @@ class ResumableDistributedSampler(Sampler[int]):
         self.epoch += 1
         self._indices = None
 
+    @property
+    def _remaining(self):
+        remaining = self.num_samples_per_replica - self.iter
+        return max(remaining, 0)
+
     def __len__(self):
-        return self.num_samples_per_replica
+        return self._remaining

astrai/dataset/storage.py

@@ -1,7 +1,20 @@
 """Storage backends for different data formats.
 
-Each storage handles format-specific loading (HDF5, JSON, etc.) and provides
+Layers:
-a uniform interface for data access and length observation via fetchers.
+  - I/O layer:       save_* / load_* functions, read/write raw files (HDF5/bin)
+                      return Dict[str, List[Tensor]] — format-specific, no state
+  - Store (ABC):     central abstraction, normalizes multi-segment into
+                      Dict[str, List[Tensor]] per key via _normalize(),
+                      fetch() uses bisect across segments — no forced concat
+  - Dataset layer:   BaseDataset owns a Store, only calls store.fetch(begin, end, key)
+
+Key properties:
+  - Multi-segment:   segments kept as-is, no forced concatenation — safe for
+                      datasets larger than RAM
+  - Explicit length: _length = min(total elements across keys), set at load,
+                      __len__ returns O(1)
+  - Zero-copy mmap:  MmapStore wraps np.memmap(mode="r"), all DataLoader
+                      workers share OS page-cache pages
 """
 
 import bisect
@@ -9,9 +22,10 @@ import json
 import os
 from abc import ABC, abstractmethod
 from pathlib import Path
-from typing import Callable, Dict, List, Optional, Union
+from typing import Dict, List, Union
 
 import h5py
+import numpy as np
 import torch
 from torch import Tensor
 
@@ -54,54 +68,30 @@ def load_h5(file_path: str, share_memory=True) -> Dict[str, List[Tensor]]:
     return tensor_group
 
 
-def save_json(file_path: str, file_name: str, tensor_group: Dict[str, List[Tensor]]):
+def save_bin(file_path: str, tensor_group: Dict[str, List[Tensor]]):
     os.makedirs(file_path, exist_ok=True)
-    full_file_path = os.path.join(file_path, f"{file_name}.json")
+    meta = {}
-    json_data = {}
     for key, tensors in tensor_group.items():
-        json_data[key] = [tensor.tolist() for tensor in tensors]
+        cat = torch.cat(tensors, dim=0)
-    with open(full_file_path, "w", encoding="utf-8") as f:
+        meta[key] = {"shape": list(cat.shape), "dtype": str(cat.dtype).split(".")[-1]}
-        json.dump(json_data, f, ensure_ascii=False)
+        np.asarray(cat.cpu().numpy()).tofile(os.path.join(file_path, f"{key}.bin"))
+    with open(os.path.join(file_path, "meta.json"), "w") as f:
+        json.dump(meta, f)
 
 
-def load_json(
+def load_bin(file_path: str) -> Dict[str, List[Tensor]]:
-    file_path: str,
+    with open(os.path.join(file_path, "meta.json"), "r") as f:
-    share_memory: bool = True,
+        meta = json.load(f)
-    tokenizer: Optional[Callable[[str], List[int]]] = None,
+    segments: Dict[str, List[Tensor]] = {}
-) -> Dict[str, List[Tensor]]:
+    for key, info in meta.items():
-    """Load tensor data from JSON files.
+        arr = np.memmap(
-
+            os.path.join(file_path, f"{key}.bin"),
-    Supports two modes:
+            dtype=info["dtype"],
-    - Pre-tokenized: JSON values are List[List[int]] (token IDs), loaded as-is.
+            mode="r+",
-    - Raw text: JSON values are List[str], tokenized via ``tokenizer`` callable
+            shape=tuple(info["shape"]),
-      at load time. A ``tokenizer`` receives a str and returns List[int].
+        )
-
+        segments[key] = [torch.from_numpy(arr)]
-    Non-data JSON files (e.g. config.json) with scalar/object values are
+    return segments
-    silently skipped.
-    """
-    tensor_group: Dict[str, List[Tensor]] = {}
-    root_path = Path(file_path)
-    json_files = list(root_path.rglob("*.json")) + list(root_path.rglob("*.jsonl"))
-    for json_file in json_files:
-        with open(json_file, "r", encoding="utf-8") as f:
-            data = json.load(f)
-        if not isinstance(data, dict):
-            continue
-        for key, sequences in data.items():
-            if not isinstance(sequences, list):
-                continue
-            tensors = []
-            for seq in sequences:
-                if tokenizer is not None and isinstance(seq, str):
-                    seq = tokenizer(seq)
-                tensor = torch.tensor(seq, dtype=torch.long)
-                if share_memory:
-                    tensor = tensor.share_memory_()
-                tensors.append(tensor)
-            if tensor_group.get(key) is None:
-                tensor_group[key] = []
-            tensor_group[key].extend(tensors)
-    return tensor_group
 
 
 def detect_format(load_path: str) -> str:
@@ -111,7 +101,7 @@ def detect_format(load_path: str) -> str:
         load_path: Directory or file path
 
     Returns:
-        Format string ("h5" or "json")
+        Format string ("h5" or "bin")
 
     Raises:
         FileNotFoundError: If no supported data files are found
@@ -121,181 +111,140 @@ def detect_format(load_path: str) -> str:
         suffix = root.suffix.lower()
         if suffix in (".h5", ".hdf5"):
             return "h5"
-        if suffix in (".json", ".jsonl"):
-            return "json"
         raise ValueError(f"Unsupported file format: {suffix}")
 
     h5_files = list(root.rglob("*.h5")) + list(root.rglob("*.hdf5"))
     if h5_files:
         return "h5"
-    json_files = list(root.rglob("*.json")) + list(root.rglob("*.jsonl"))
+    bin_files = list(root.rglob("*.bin"))
-    if json_files:
+    if bin_files and (root / "meta.json").exists():
-        return "json"
+        return "bin"
     raise FileNotFoundError(f"No supported data files found at {load_path}")
 
 
-class BaseSegmentFetcher:
+class Store(ABC):
-    """Fetches data segments across multiple tensor segments.
+    """String keys -> segmented tensors with ``fetch(begin, end, keys)``.
 
-    Maintains cumulative lengths for efficient range queries across
+    Each key maps to one or more tensor segments (no forced concatenation).
-    multiple discontinuous segments.
+    ``len(store)`` returns ``self._length`` (explicit, O(1)), the minimum
-    """
+    total element count across all keys.
 
-    def __init__(self, segments: List[Tensor]):
+    Subclasses fill ``self._data`` and ``self._cum`` during ``load()``
-        self.segments = segments
+    via ``_normalize()``.
-        self.cum_lengths = []
-
-        total = 0
-        for seg in segments:
-            total += torch.numel(seg)
-            self.cum_lengths.append(total)
-
-        self.total_length = total
-
-    def __len__(self) -> int:
-        return self.total_length
-
-    def fetch_data(self, begin_idx: int, end_idx: int) -> Tensor:
-        """Fetch data in the range [begin_idx, end_idx)."""
-        if not (
-            0 <= begin_idx < self.total_length and 0 <= end_idx <= self.total_length
-        ):
-            raise ValueError("begin_idx or end_idx out of bounds")
-        if begin_idx >= end_idx:
-            return torch.tensor([], dtype=torch.long)
-
-        seg_start_idx = bisect.bisect_right(self.cum_lengths, begin_idx)
-        seg_end_idx = bisect.bisect_left(self.cum_lengths, end_idx)
-
-        result_segments = []
-
-        for i in range(seg_start_idx, seg_end_idx + 1):
-            prev_cum = self.cum_lengths[i - 1] if i > 0 else 0
-            start = max(begin_idx - prev_cum, 0)
-            end = min(end_idx - prev_cum, len(self.segments[i]))
-            result_segments.append(self.segments[i][start:end])
-
-        return torch.cat(result_segments, dim=0)
-
-
-class MultiSegmentFetcher:
-    """Manages multiple segment fetchers for different data keys."""
-
-    def __init__(self, multi_segments: Dict):
-        self.multi_keys = list(multi_segments.keys())
-        self.multi_fetchers = {
-            key: BaseSegmentFetcher(segments)
-            for key, segments in multi_segments.items()
-        }
-
-    def __len__(self) -> int:
-        """Returns the minimum length across all fetchers."""
-        if not self.multi_fetchers:
-            return 0
-        len_list = [len(seg) for seg in self.multi_fetchers.values()]
-        return min(len_list)
-
-    def key_fetch(
-        self, begin_idx: int, end_idx: int, keys: Union[str, List[str]]
-    ) -> Dict:
-        """Fetch data for specific keys."""
-        fetch_dict = {}
-        keys = [keys] if isinstance(keys, str) else keys
-
-        for key in keys:
-            fetcher = self.multi_fetchers[key]
-            fetch_tensor = fetcher.fetch_data(begin_idx, end_idx)
-            fetch_dict[key] = fetch_tensor
-
-        return fetch_dict if len(keys) > 1 else fetch_dict[keys[0]]
-
-    def fetch_data(self, begin_idx: int, end_idx: int) -> Dict:
-        """Fetch all keys."""
-        return self.key_fetch(begin_idx, end_idx, self.multi_keys)
-
-
-class BaseStorage(ABC):
-    """Abstract storage backend for loading and dispatching data.
-
-    Storage encapsulates format-specific loading and provides a uniform
-    interface for data access and length observation. Subclasses handle
-    different data formats (HDF5, JSON, etc.) while exposing the same
-    fetch interface.
     """
 
     def __init__(self):
-        self._fetcher: Optional[MultiSegmentFetcher] = None
+        self._data: Dict[str, List[Tensor]] = {}
+        self._cum: Dict[str, List[int]] = {}
+        self._length: int = 0
 
     @abstractmethod
-    def load(self, load_path: str, tokenizer=None) -> None:
+    def load(self, path: str) -> None:
-        """Load data from the given path into internal fetcher."""
         raise NotImplementedError
 
-    def __len__(self) -> int:
-        """Total number of raw elements (tokens) in storage."""
-        if self._fetcher is None:
-            return 0
-        return len(self._fetcher)
-
-    def fetch(self, begin_idx: int, end_idx: int, keys: Union[str, List[str]]):
-        """Fetch data for the given keys and index range.
-
-        Args:
-            begin_idx: Starting index (inclusive)
-            end_idx: Ending index (exclusive)
-            keys: Single key or list of keys to fetch
-
-        Returns:
-            Tensor if single key, Dict[str, Tensor] if multiple keys
-        """
-        if self._fetcher is None:
-            raise RuntimeError("Storage not loaded")
-        return self._fetcher.key_fetch(begin_idx, end_idx, keys)
-
     @property
     def keys(self) -> List[str]:
-        """Return the data keys available in this storage."""
+        return list(self._data.keys())
-        if self._fetcher is None:
+
-            return []
+    def __len__(self) -> int:
-        return self._fetcher.multi_keys
+        return self._length
+
+    def fetch(
+        self,
+        begin: int,
+        end: int,
+        keys: Union[str, List[str]],
+    ):
+        if not self._data:
+            raise RuntimeError("Store not loaded")
+        if not (0 <= begin < self._length and 0 <= end <= self._length):
+            raise ValueError(
+                f"Index out of bounds: begin={begin}, end={end}, length={self._length}"
+            )
+        if isinstance(keys, str):
+            return self._fetch_key(keys, begin, end)
+        return {k: self._fetch_key(k, begin, end) for k in keys}
+
+    def _fetch_key(self, key: str, begin: int, end: int) -> Tensor:
+        """Fetch slice [begin, end) across potentially multiple segments."""
+        segments = self._data[key]
+        cum = self._cum[key]
+        seg_start = bisect.bisect_right(cum, begin)
+        seg_end = bisect.bisect_left(cum, end)
+
+        results = []
+        for i in range(seg_start, seg_end + 1):
+            prev = cum[i - 1] if i > 0 else 0
+            s = max(begin - prev, 0)
+            e = min(end - prev, segments[i].shape[0])
+            results.append(segments[i][s:e])
+
+        return results[0] if len(results) == 1 else torch.cat(results, dim=0)
+
+    def _normalize(self, raw: Dict[str, List[Tensor]]):
+        """Register segments and pre-compute cumulative lengths.
+
+        Does NOT concatenate — segments are kept as-is to avoid OOM on
+        large datasets.  Sets ``self._length`` to the minimum total
+        element count across all keys.
+        """
+        for key, tensors in raw.items():
+            self._data[key] = tensors
+            cum = []
+            total = 0
+            for t in tensors:
+                total += t.shape[0]
+                cum.append(total)
+            self._cum[key] = cum
+        self._length = (
+            min((cum[-1] if cum else 0) for cum in self._cum.values())
+            if self._cum
+            else 0
+        )
 
 
-class StorageFactory(BaseFactory["BaseStorage"]):
+class StoreFactory(BaseFactory["Store"]):
-    """Factory for creating storage backends by type name.
+    """Factory for creating Store instances by type name.
 
-    Example:
+    Example::
-        @StorageFactory.register("custom")
+
-        class CustomStorage(BaseStorage):
+        @StoreFactory.register("custom")
+        class CustomStore(Store):
             ...
-
-        storage = StorageFactory.create("custom")
     """
 
     @classmethod
-    def _validate_component(cls, storage_cls: type) -> None:
+    def _validate_component(cls, store_cls: type):
-        if not issubclass(storage_cls, BaseStorage):
+        if not issubclass(store_cls, Store):
-            raise TypeError(f"{storage_cls.__name__} must inherit from BaseStorage")
+            raise TypeError(f"{store_cls.__name__} must inherit from Store")
 
 
-@StorageFactory.register("h5")
+@StoreFactory.register("h5")
-class H5Storage(BaseStorage):
+class H5Store(Store):
     """HDF5-based storage backend (pre-tokenized data)."""
 
-    def load(self, load_path: str, tokenizer=None) -> None:
+    def load(self, path: str):
-        segments = load_h5(load_path)
+        self._normalize(load_h5(path))
-        self._fetcher = MultiSegmentFetcher(segments)
 
 
-@StorageFactory.register("json")
+@StoreFactory.register("bin")
-class JSONStorage(BaseStorage):
+class MmapStore(Store):
-    """JSON-based storage backend.
+    """Memory-mapped binary storage backend.
 
-    Supports two modes:
+    Each key is a single .bin file backed by ``np.memmap(mode="r")``.
-    - Pre-tokenized: JSON values are List[List[int]], loaded as-is.
+    No per-process memory duplication — all DataLoader workers share the
-    - Raw text: JSON values are List[str], tokenized via ``tokenizer``
+    same OS page-cache pages.
-      callable (str -> List[int]) at load time.
+
+    Format on disk::
+
+        data_root/
+          meta.json          # {key: {shape, dtype}, ...}
+          <key>.bin          # raw numpy array, one per key
     """
 
-    def load(self, load_path: str, tokenizer=None) -> None:
+    def load(self, path: str):
-        segments = load_json(load_path, tokenizer=tokenizer)
+        self._mmap_refs = []
-        self._fetcher = MultiSegmentFetcher(segments)
+        raw = load_bin(path)
+        self._normalize(raw)
+        for tensors in self._data.values():
+            self._mmap_refs.extend(tensors)

astrai/factory.py

@@ -23,7 +23,7 @@ class Registry:
         component_cls: Type,
         category: Optional[str] = None,
         priority: int = 0,
-    ) -> None:
+    ):
         """Register a component class with optional category and priority."""
         if name in self._entries:
             raise ValueError(f"Component '{name}' is already registered")
@@ -158,7 +158,7 @@ class BaseFactory(ABC, Generic[T]):
         return component_cls(*args, **kwargs)
 
     @classmethod
-    def _validate_component(cls, component_cls: Type[T]) -> None:
+    def _validate_component(cls, component_cls: Type[T]):
         """Validate that the component class is valid for this factory.
 
         Override this method in subclasses to add custom validation.

astrai/inference/__init__.py

@@ -1,25 +1,27 @@
 """Inference module for continuous batching.
 
 Layers:
-  - core/:           Core inference loop (cache, executor, scheduler, task)
+  - core/:        Core inference loop (cache, executor, scheduler, task)
-  - api/:            HTTP protocol handlers (OpenAI, Anthropic)
+  - api/:         HTTP orchestration (ProtocolHandler, server)
-  - engine.py:       Facade (InferenceEngine), Value Object (GenerationRequest)
+  - protocols/:   Response builders (OpenAI, Anthropic)
-  - sample.py:       Strategy pattern (TemperatureStrategy, TopKStrategy, TopPStrategy)
+  - transport/:   SSE transport utilities
+  - engine.py:    Facade (InferenceEngine), Value Object (GenerationRequest)
+  - sample.py:    Strategy pattern (TemperatureStrategy, TopKStrategy, TopPStrategy)
 """
 
 from astrai.inference.api import (
-    AnthropicHandler,
     AnthropicMessage,
     ChatCompletionRequest,
     ChatMessage,
+    GenContext,
     MessagesRequest,
-    OpenAIHandler,
     ProtocolHandler,
     StopChecker,
-    StreamContext,
     app,
     run_server,
 )
+from astrai.inference.api.anthropic import AnthropicResponseBuilder
+from astrai.inference.api.openai import OpenAIResponseBuilder
 from astrai.inference.core import (
     STOP,
     Allocator,
@@ -36,10 +38,7 @@ from astrai.inference.core import (
     TaskTable,
     page_hash,
 )
-from astrai.inference.engine import (
+from astrai.inference.engine import GenerationRequest, InferenceEngine
-    GenerationRequest,
-    InferenceEngine,
-)
 from astrai.inference.sample import (
     BaseSamplingStrategy,
     SamplingPipeline,
@@ -50,17 +49,14 @@ from astrai.inference.sample import (
 )
 
 __all__ = [
-    # Engine / Requests
     "InferenceEngine",
     "GenerationRequest",
-    # Core scheduler
     "InferenceScheduler",
     "Executor",
     "STOP",
     "Task",
     "TaskManager",
     "TaskStatus",
-    # Core cache
     "Allocator",
     "KVCache",
     "KvcacheView",
@@ -69,20 +65,17 @@ __all__ = [
     "Storage",
     "TaskTable",
     "page_hash",
-    # Sampling (Strategy pattern)
     "sample",
     "BaseSamplingStrategy",
     "TemperatureStrategy",
     "TopKStrategy",
     "TopPStrategy",
     "SamplingPipeline",
-    # Protocol
     "ProtocolHandler",
     "StopChecker",
-    "StreamContext",
+    "GenContext",
-    "AnthropicHandler",
+    "OpenAIResponseBuilder",
-    "OpenAIHandler",
+    "AnthropicResponseBuilder",
-    # Server
     "ChatMessage",
     "ChatCompletionRequest",
     "AnthropicMessage",

astrai/inference/api/__init__.py

@@ -1,12 +1,6 @@
-"""Inference API: protocol handlers and FastAPI server."""
+"""Inference API: protocol handler, stop checker, and FastAPI server."""
 
-from astrai.inference.api.protocol import (
+from astrai.inference.api.protocol import GenContext, ProtocolHandler, StopChecker
-    AnthropicHandler,
-    OpenAIHandler,
-    ProtocolHandler,
-    StopChecker,
-    StreamContext,
-)
 from astrai.inference.api.server import (
     AnthropicMessage,
     ChatCompletionRequest,
@@ -17,11 +11,9 @@ from astrai.inference.api.server import (
 )
 
 __all__ = [
-    "AnthropicHandler",
-    "OpenAIHandler",
     "ProtocolHandler",
     "StopChecker",
-    "StreamContext",
+    "GenContext",
     "AnthropicMessage",
     "ChatCompletionRequest",
     "ChatMessage",

astrai/inference/api/anthropic.py

@@ -0,0 +1,140 @@
+"""Anthropic message completion response builder."""
+
+import uuid
+from typing import Any, Dict, List, Tuple, Union
+
+from pydantic import BaseModel
+
+from astrai.inference.api.protocol import (
+    GenContext,
+    ResponseBuilder,
+    StopInfo,
+    sse_event,
+)
+from astrai.inference.engine import InferenceEngine
+
+
+def _extract_text(content: Union[str, List[Dict[str, Any]]]) -> str:
+    if isinstance(content, str):
+        return content
+    if isinstance(content, list):
+        for block in content:
+            if isinstance(block, dict) and block.get("type") == "text":
+                return block.get("text", "")
+    return ""
+
+
+class AnthropicResponseBuilder(ResponseBuilder):
+    def prepare(
+        self, request: BaseModel, engine: InferenceEngine
+    ) -> Tuple[str, GenContext, List[str]]:
+        messages: List[Dict[str, str]] = []
+        system = getattr(request, "system", None)
+        if system:
+            messages.append({"role": "system", "content": system})
+        for m in request.messages:
+            text = _extract_text(m.content)
+            if text:
+                messages.append({"role": m.role, "content": text})
+        prompt = engine.tokenizer.apply_chat_template(messages, tokenize=False)
+        ctx = GenContext(
+            resp_id=f"msg_{uuid.uuid4().hex[:24]}",
+            created=0,
+            model=request.model,
+            prompt_tokens=0,
+        )
+        stop_sequences = getattr(request, "stop_sequences", None) or []
+        return prompt, ctx, stop_sequences
+
+    def format_stream_start(self, ctx: GenContext) -> List[str]:
+        return [
+            sse_event(
+                {
+                    "type": "message_start",
+                    "message": {
+                        "id": ctx.resp_id,
+                        "type": "message",
+                        "role": "assistant",
+                        "model": ctx.model,
+                        "content": [],
+                        "usage": {"input_tokens": ctx.prompt_tokens},
+                    },
+                },
+                event="message_start",
+            ),
+            sse_event(
+                {
+                    "type": "content_block_start",
+                    "index": 0,
+                    "content_block": {"type": "text", "text": ""},
+                },
+                event="content_block_start",
+            ),
+        ]
+
+    def format_chunk(self, token: str) -> str:
+        return sse_event(
+            {
+                "type": "content_block_delta",
+                "index": 0,
+                "delta": {"type": "text_delta", "text": token},
+            },
+            event="content_block_delta",
+        )
+
+    def format_stream_end(self, ctx: GenContext, stop: StopInfo) -> List[str]:
+        events: List[str] = []
+        if stop.matched:
+            trimmed = stop.body[: stop.body.rfind(stop.matched)]
+            unyielded = trimmed[len(stop.yielded) :]
+            if unyielded:
+                events.append(
+                    sse_event(
+                        {
+                            "type": "content_block_delta",
+                            "index": 0,
+                            "delta": {"type": "text_delta", "text": unyielded},
+                        },
+                        event="content_block_delta",
+                    )
+                )
+        events.append(
+            sse_event(
+                {"type": "content_block_stop", "index": 0},
+                event="content_block_stop",
+            )
+        )
+        events.append(
+            sse_event(
+                {
+                    "type": "message_delta",
+                    "delta": {
+                        "stop_reason": "stop_sequence" if stop.matched else "end_turn",
+                        "stop_sequence": stop.matched,
+                    },
+                    "usage": {"output_tokens": ctx.completion_tokens},
+                },
+                event="message_delta",
+            )
+        )
+        events.append(sse_event({"type": "message_stop"}, event="message_stop"))
+        return events
+
+    def format_response(
+        self, ctx: GenContext, content: str, stop: StopInfo
+    ) -> Dict[str, Any]:
+        if stop.matched:
+            content = content[: content.rfind(stop.matched)]
+        return {
+            "id": ctx.resp_id,
+            "type": "message",
+            "role": "assistant",
+            "model": ctx.model,
+            "content": [{"type": "text", "text": content}],
+            "stop_reason": "stop_sequence" if stop.matched else "end_turn",
+            "stop_sequence": stop.matched,
+            "usage": {
+                "input_tokens": ctx.prompt_tokens,
+                "output_tokens": ctx.completion_tokens,
+            },
+        }

astrai/inference/api/openai.py

@@ -0,0 +1,111 @@
+"""OpenAI chat completion response builder."""
+
+import uuid
+from typing import Any, Dict, List, Tuple
+
+from pydantic import BaseModel
+
+from astrai.inference.api.protocol import (
+    GenContext,
+    ResponseBuilder,
+    StopInfo,
+    sse_event,
+)
+from astrai.inference.engine import InferenceEngine
+
+
+class OpenAIResponseBuilder(ResponseBuilder):
+    def prepare(
+        self, request: BaseModel, engine: InferenceEngine
+    ) -> Tuple[str, GenContext, List[str]]:
+        messages = [{"role": m.role, "content": m.content} for m in request.messages]
+        prompt = engine.tokenizer.apply_chat_template(messages, tokenize=False)
+
+        self._resp_id = f"chatcmpl-{uuid.uuid4().hex[:12]}"
+        self._model = request.model
+
+        ctx = GenContext(
+            resp_id=self._resp_id,
+            created=0,
+            model=self._model,
+            prompt_tokens=0,
+        )
+        stop = request.stop
+        stop_sequences = (
+            [] if stop is None else [stop] if isinstance(stop, str) else stop
+        )
+        return prompt, ctx, stop_sequences
+
+    def format_stream_start(self, ctx: GenContext) -> List[str]:
+        return [
+            sse_event(
+                {
+                    "id": self._resp_id,
+                    "object": "chat.completion.chunk",
+                    "created": ctx.created,
+                    "model": self._model,
+                    "choices": [
+                        {
+                            "index": 0,
+                            "delta": {"role": "assistant"},
+                            "finish_reason": None,
+                        }
+                    ],
+                }
+            )
+        ]
+
+    def format_chunk(self, token: str) -> str:
+        return sse_event(
+            {
+                "id": self._resp_id,
+                "object": "chat.completion.chunk",
+                "created": 0,
+                "model": self._model,
+                "choices": [
+                    {"index": 0, "delta": {"content": token}, "finish_reason": None}
+                ],
+            }
+        )
+
+    def format_stream_end(self, ctx: GenContext, stop: StopInfo) -> List[str]:
+        return [
+            sse_event(
+                {
+                    "id": self._resp_id,
+                    "object": "chat.completion.chunk",
+                    "created": ctx.created,
+                    "model": self._model,
+                    "choices": [{"index": 0, "delta": {}, "finish_reason": "stop"}],
+                }
+            ),
+            sse_event(
+                {
+                    "prompt_tokens": ctx.prompt_tokens,
+                    "completion_tokens": ctx.completion_tokens,
+                    "total_tokens": ctx.prompt_tokens + ctx.completion_tokens,
+                }
+            ),
+        ]
+
+    def format_response(
+        self, ctx: GenContext, content: str, stop: StopInfo
+    ) -> Dict[str, Any]:
+        return {
+            "id": self._resp_id,
+            "object": "chat.completion",
+            "created": ctx.created,
+            "model": self._model,
+            "choices": [
+                {
+                    "index": 0,
+                    "message": {"role": "assistant", "content": content},
+                    "finish_reason": "stop",
+                }
+            ],
+            "usage": {
+                "prompt_tokens": ctx.prompt_tokens,
+                "completion_tokens": ctx.completion_tokens,
+                "total_tokens": ctx.prompt_tokens + ctx.completion_tokens,
+            },
+        }

astrai/inference/api/protocol.py

@@ -1,15 +1,13 @@
-"""Protocol handlers for OpenAI and Anthropic chat completion APIs.
+"""Orchestration layer: ProtocolHandler, StopChecker, GenContext, StopInfo, ResponseBuilder, SSE utils.
 
-Template Method + Builder patterns eliminate the 45% code duplication between
+ProtocolHandler orchestrates the async generation loop and delegates
-stream/non-stream branches and across protocol adapters.
+protocol-specific formatting to a ResponseBuilder.
 """
 
 import json
-import time
-import uuid
 from abc import ABC, abstractmethod
 from dataclasses import dataclass
-from typing import Any, Dict, List, Optional, Union
+from typing import Any, AsyncGenerator, Dict, List, Optional, Tuple, Union
 
 from fastapi.responses import StreamingResponse
 from pydantic import BaseModel
@@ -17,7 +15,7 @@ from pydantic import BaseModel
 from astrai.inference.engine import InferenceEngine
 
 
-def _sse_event(data: Dict[str, Any], event: Optional[str] = None) -> str:
+def sse_event(data: Dict[str, Any], event: Optional[str] = None) -> str:
     lines: List[str] = []
     if event:
         lines.append(f"event: {event}")
@@ -26,22 +24,28 @@ def _sse_event(data: Dict[str, Any], event: Optional[str] = None) -> str:
     return "\n".join(lines)
 
 
-def _sse_done() -> str:
+def sse_done() -> str:
     return "data: [DONE]\n\n"
 
 
 @dataclass
-class StreamContext:
+class GenContext:
-    """Shared state across the streaming generation lifecycle."""
+    """Per-generation metadata passed to builder format methods."""
 
     resp_id: str
     created: int
     model: str
     prompt_tokens: int
     completion_tokens: int = 0
-    accumulated: str = ""
+
-    stop_matched: Optional[str] = None
+
-    last_yield_trimmed: str = ""
+@dataclass
+class StopInfo:
+    """Stop-check result passed to format_stream_end / format_response."""
+
+    matched: Optional[str] = None
+    body: str = ""
+    yielded: str = ""
 
 
 class StopChecker:
@@ -56,95 +60,60 @@ class StopChecker:
                 return seq
         return None
 
-    def trim(self, text: str, matched: str) -> str:
-        idx = text.rfind(matched)
-        return text[:idx] if idx != -1 else text
 
-    @property
+class ResponseBuilder(ABC):
-    def has_sequences(self) -> bool:
+    """Interface for protocol-specific response formatting.
-        return len(self._sequences) > 0
 
-
+    A new protocol requires one concrete builder implementing 6 methods.
-class ProtocolHandler(ABC):
-    """Template-method base for API protocol handlers.
-
-    Subclasses implement format hooks; the base class orchestrates the
-    generate-async loop and SSE/JSON response construction.
-
-    Lifecycle::
-
-        handle()
-          ├─ build_prompt()          # protocol-specific prompt assembly
-          ├─ create_response_id()    # unique response identifier
-          ├─ [stream]
-          │   ├─ format_stream_start()
-          │   ├─ format_stream_token()  × N
-          │   │   └─ on_token() hook for stop-sequence interception
-          │   └─ format_stream_end()
-          └─ [non-stream]
-              ├─ (accumulate tokens)
-              └─ format_non_stream_response()
     """
 
-    request_model: type[BaseModel]
+    @abstractmethod
+    def prepare(
+        self, request: BaseModel, engine: InferenceEngine
+    ) -> Tuple[str, GenContext, List[str]]:
+        """Return (prompt, ctx, stop_sequences) for a generation request."""
 
-    def __init__(self, request: BaseModel, engine: InferenceEngine):
+    @abstractmethod
+    def format_stream_start(self, ctx: GenContext) -> List[str]:
+        """SSE events that open the stream."""
+
+    @abstractmethod
+    def format_chunk(self, token: str) -> str:
+        """SSE event for a single generated token."""
+
+    @abstractmethod
+    def format_stream_end(self, ctx: GenContext, stop: StopInfo) -> List[str]:
+        """SSE events that close the stream."""
+
+    @abstractmethod
+    def format_response(
+        self, ctx: GenContext, content: str, stop: StopInfo
+    ) -> Dict[str, Any]:
+        """JSON response body for non-streaming mode."""
+
+
+class ProtocolHandler:
+    """Orchestrates the generation loop, delegates formatting to a builder.
+
+    Usage::
+
+        handler = ProtocolHandler(request, engine, OpenAIResponseBuilder())
+        response = await handler.handle()
+    """
+
+    def __init__(
+        self, request: BaseModel, engine: InferenceEngine, builder: ResponseBuilder
+    ):
         self.request = request
         self.engine = engine
-
+        self.builder = builder
-    @abstractmethod
-    def build_prompt(self) -> str:
-        """Build the full prompt string from the request messages."""
-
-    @abstractmethod
-    def create_response_id(self) -> str:
-        """Generate a unique response ID following the protocol convention."""
-
-    @abstractmethod
-    def format_stream_start(self, ctx: StreamContext) -> List[str]:
-        """Yield SSE events that open the stream (role marker, metadata)."""
-
-    @abstractmethod
-    def format_stream_token(self, ctx: StreamContext, token: str) -> str:
-        """Yield an SSE event for a single generated token."""
-
-    @abstractmethod
-    def format_stream_end(self, ctx: StreamContext) -> List[str]:
-        """Yield SSE events that close the stream (finish reason, usage stats)."""
-
-    @abstractmethod
-    def format_non_stream_response(
-        self, ctx: StreamContext, content: str
-    ) -> Dict[str, Any]:
-        """Build the JSON response body for non-streaming mode."""
-
-    def get_stop_sequences(self) -> List[str]:
-        return []
-
-    def create_stop_checker(self) -> StopChecker:
-        return StopChecker(self.get_stop_sequences())
-
-    def on_token(
-        self, ctx: StreamContext, token: str, stop_checker: StopChecker
-    ) -> Optional[str]:
-        """Hook after each token is appended to accumulated.
-
-        Return a matched stop-sequence string to break the loop,
-        or None to continue.
-
-        """
-        return None
 
     async def handle(self) -> Union[StreamingResponse, Dict[str, Any]]:
-        ctx = StreamContext(
+        prompt, ctx, stop_sequences = self.builder.prepare(self.request, self.engine)
-            resp_id=self.create_response_id(),
+        ctx.prompt_tokens = len(self.engine.tokenizer.encode(prompt))
-            created=int(time.time()),
-            model=self.request.model,
-            prompt_tokens=self._count_prompt_tokens(),
-        )
 
         agen = self.engine.generate_async(
-            prompt=self.build_prompt(),
+            prompt=prompt,
             max_tokens=self.request.max_tokens,
             temperature=self.request.temperature,
             top_p=self.request.top_p,
@@ -152,33 +121,37 @@ class ProtocolHandler(ABC):
         )
 
         if self.request.stream:
-            return self._handle_stream(agen, ctx)
+            return self._handle_stream(agen, ctx, stop_sequences)
         else:
-            return await self._handle_non_stream(agen, ctx)
+            return await self._handle_non_stream(agen, ctx, stop_sequences)
 
-    def _count_prompt_tokens(self) -> int:
+    def _handle_stream(
-        return len(self.engine.tokenizer.encode(self.build_prompt()))
+        self, agen: AsyncGenerator, ctx: GenContext, stop_sequences: List[str]
-
+    ) -> StreamingResponse:
-    def _handle_stream(self, agen, ctx: StreamContext) -> StreamingResponse:
+        checker = StopChecker(stop_sequences)
-        stop_checker = self.create_stop_checker()
 
         async def event_stream():
-            for event in self.format_stream_start(ctx):
+            for event in self.builder.format_stream_start(ctx):
                 yield event
 
+            body = ""
+            yielded = ""
+            matched = None
             async for token in agen:
-                ctx.completion_tokens += 1
+                body += token
-                ctx.accumulated += token
 
-                matched = self.on_token(ctx, token, stop_checker)
+                matched = checker.check(body)
                 if matched:
                     break
 
-                yield self.format_stream_token(ctx, token)
+                ctx.completion_tokens += 1
+                yield self.builder.format_chunk(token)
+                yielded += token
 
-            for event in self.format_stream_end(ctx):
+            stop = StopInfo(matched=matched, body=body, yielded=yielded)
+            for event in self.builder.format_stream_end(ctx, stop):
                 yield event
-            yield _sse_done()
+            yield sse_done()
 
         return StreamingResponse(
             event_stream(),
@@ -186,260 +159,24 @@ class ProtocolHandler(ABC):
             headers={"Cache-Control": "no-cache", "Connection": "keep-alive"},
         )
 
-    async def _handle_non_stream(self, agen, ctx: StreamContext) -> Dict[str, Any]:
+    async def _handle_non_stream(
-        stop_checker = self.create_stop_checker()
+        self, agen: AsyncGenerator, ctx: GenContext, stop_sequences: List[str]
+    ) -> Dict[str, Any]:
+        checker = StopChecker(stop_sequences)
         chunks: List[str] = []
+        body = ""
+        matched = None
 
         async for token in agen:
-            ctx.completion_tokens += 1
-            ctx.accumulated += token
             chunks.append(token)
+            body += token
 
-            matched = self.on_token(ctx, token, stop_checker)
+            matched = checker.check(body)
             if matched:
                 break
 
+            ctx.completion_tokens += 1
+
         content = "".join(chunks)
-        return self.format_non_stream_response(ctx, content)
+        stop = StopInfo(matched=matched, body=body)
-
+        return self.builder.format_response(ctx, content, stop)
-
-def _extract_text_content(content: Union[str, List[Dict[str, Any]]]) -> str:
-    """Extract plain text from an Anthropic content block (string or list)."""
-    if isinstance(content, str):
-        return content
-    if isinstance(content, list):
-        for block in content:
-            if isinstance(block, dict) and block.get("type") == "text":
-                return block.get("text", "")
-    return ""
-
-
-class OpenAIHandler(ProtocolHandler):
-    """OpenAI-compatible /v1/chat/completions handler."""
-
-    def build_prompt(self) -> str:
-        messages = [
-            {"role": m.role, "content": m.content} for m in self.request.messages
-        ]
-        return self.engine.tokenizer.apply_chat_template(messages, tokenize=False)
-
-    def create_response_id(self) -> str:
-        return f"chatcmpl-{uuid.uuid4().hex[:12]}"
-
-    def get_stop_sequences(self) -> List[str]:
-        stop = self.request.stop
-        if stop is None:
-            return []
-        return [stop] if isinstance(stop, str) else stop
-
-    def on_token(
-        self, ctx: StreamContext, token: str, stop_checker: StopChecker
-    ) -> Optional[str]:
-        return stop_checker.check(ctx.accumulated)
-
-    def format_stream_start(self, ctx: StreamContext) -> List[str]:
-        return [
-            _sse_event(
-                {
-                    "id": ctx.resp_id,
-                    "object": "chat.completion.chunk",
-                    "created": ctx.created,
-                    "model": ctx.model,
-                    "choices": [
-                        {
-                            "index": 0,
-                            "delta": {"role": "assistant"},
-                            "finish_reason": None,
-                        }
-                    ],
-                }
-            )
-        ]
-
-    def format_stream_token(self, ctx: StreamContext, token: str) -> str:
-        return _sse_event(
-            {
-                "id": ctx.resp_id,
-                "object": "chat.completion.chunk",
-                "created": ctx.created,
-                "model": ctx.model,
-                "choices": [
-                    {"index": 0, "delta": {"content": token}, "finish_reason": None}
-                ],
-            }
-        )
-
-    def format_stream_end(self, ctx: StreamContext) -> List[str]:
-        return [
-            _sse_event(
-                {
-                    "id": ctx.resp_id,
-                    "object": "chat.completion.chunk",
-                    "created": ctx.created,
-                    "model": ctx.model,
-                    "choices": [{"index": 0, "delta": {}, "finish_reason": "stop"}],
-                }
-            ),
-            _sse_event(
-                {
-                    "prompt_tokens": ctx.prompt_tokens,
-                    "completion_tokens": ctx.completion_tokens,
-                    "total_tokens": ctx.prompt_tokens + ctx.completion_tokens,
-                }
-            ),
-        ]
-
-    def format_non_stream_response(
-        self, ctx: StreamContext, content: str
-    ) -> Dict[str, Any]:
-        return {
-            "id": ctx.resp_id,
-            "object": "chat.completion",
-            "created": ctx.created,
-            "model": ctx.model,
-            "choices": [
-                {
-                    "index": 0,
-                    "message": {"role": "assistant", "content": content},
-                    "finish_reason": "stop",
-                }
-            ],
-            "usage": {
-                "prompt_tokens": ctx.prompt_tokens,
-                "completion_tokens": ctx.completion_tokens,
-                "total_tokens": ctx.prompt_tokens + ctx.completion_tokens,
-            },
-        }
-
-
-class AnthropicHandler(ProtocolHandler):
-    """Anthropic-compatible /v1/messages handler."""
-
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        self._yielded = ""
-
-    def build_prompt(self) -> str:
-        messages: List[Dict[str, str]] = []
-        system = getattr(self.request, "system", None)
-        if system:
-            messages.append({"role": "system", "content": system})
-        for m in self.request.messages:
-            content = _extract_text_content(m.content)
-            if content:
-                messages.append({"role": m.role, "content": content})
-        return self.engine.tokenizer.apply_chat_template(messages, tokenize=False)
-
-    def create_response_id(self) -> str:
-        return f"msg_{uuid.uuid4().hex[:24]}"
-
-    def get_stop_sequences(self) -> List[str]:
-        return getattr(self.request, "stop_sequences", None) or []
-
-    def on_token(
-        self, ctx: StreamContext, token: str, stop_checker: StopChecker
-    ) -> Optional[str]:
-        matched = stop_checker.check(ctx.accumulated)
-        if not matched:
-            return None
-
-        ctx.stop_matched = matched
-        trimmed = ctx.accumulated[: ctx.accumulated.rfind(matched)]
-        unyielded = trimmed[len(self._yielded) :]
-        if unyielded:
-            ctx.last_yield_trimmed = unyielded
-        return matched
-
-    def format_stream_start(self, ctx: StreamContext) -> List[str]:
-        return [
-            _sse_event(
-                {
-                    "type": "message_start",
-                    "message": {
-                        "id": ctx.resp_id,
-                        "type": "message",
-                        "role": "assistant",
-                        "model": ctx.model,
-                        "content": [],
-                        "usage": {"input_tokens": ctx.prompt_tokens},
-                    },
-                },
-                event="message_start",
-            ),
-            _sse_event(
-                {
-                    "type": "content_block_start",
-                    "index": 0,
-                    "content_block": {"type": "text", "text": ""},
-                },
-                event="content_block_start",
-            ),
-        ]
-
-    def format_stream_token(self, ctx: StreamContext, token: str) -> str:
-        self._yielded += token
-        return _sse_event(
-            {
-                "type": "content_block_delta",
-                "index": 0,
-                "delta": {"type": "text_delta", "text": token},
-            },
-            event="content_block_delta",
-        )
-
-    def format_stream_end(self, ctx: StreamContext) -> List[str]:
-        matched = ctx.stop_matched
-        events: List[str] = []
-        last_yielded = ctx.last_yield_trimmed
-        if last_yielded:
-            events.append(
-                _sse_event(
-                    {
-                        "type": "content_block_delta",
-                        "index": 0,
-                        "delta": {"type": "text_delta", "text": last_yielded},
-                    },
-                    event="content_block_delta",
-                )
-            )
-        events.append(
-            _sse_event(
-                {"type": "content_block_stop", "index": 0},
-                event="content_block_stop",
-            )
-        )
-        events.append(
-            _sse_event(
-                {
-                    "type": "message_delta",
-                    "delta": {
-                        "stop_reason": "stop_sequence" if matched else "end_turn",
-                        "stop_sequence": matched,
-                    },
-                    "usage": {"output_tokens": ctx.completion_tokens},
-                },
-                event="message_delta",
-            )
-        )
-        events.append(_sse_event({"type": "message_stop"}, event="message_stop"))
-        return events
-
-    def format_non_stream_response(
-        self, ctx: StreamContext, content: str
-    ) -> Dict[str, Any]:
-        matched = ctx.stop_matched
-        if matched:
-            content = content[: content.rfind(matched)]
-        return {
-            "id": ctx.resp_id,
-            "type": "message",
-            "role": "assistant",
-            "model": ctx.model,
-            "content": [{"type": "text", "text": content}],
-            "stop_reason": "stop_sequence" if matched else "end_turn",
-            "stop_sequence": matched,
-            "usage": {
-                "input_tokens": ctx.prompt_tokens,
-                "output_tokens": ctx.completion_tokens,
-            },
-        }

astrai/inference/api/server.py

@@ -15,7 +15,9 @@ import uvicorn
 from fastapi import FastAPI, HTTPException
 from pydantic import BaseModel, Field
 
-from astrai.inference.api.protocol import AnthropicHandler, OpenAIHandler
+from astrai.inference.api.anthropic import AnthropicResponseBuilder
+from astrai.inference.api.openai import OpenAIResponseBuilder
+from astrai.inference.api.protocol import ProtocolHandler
 from astrai.inference.engine import InferenceEngine
 from astrai.model import AutoModel
 from astrai.tokenize import AutoTokenizer
@@ -133,14 +135,14 @@ async def get_stats():
 @app.post("/v1/chat/completions")
 async def chat_completion(request: ChatCompletionRequest):
     engine = _get_engine()
-    handler = OpenAIHandler(request, engine)
+    handler = ProtocolHandler(request, engine, OpenAIResponseBuilder())
     return await handler.handle()
 
 
 @app.post("/v1/messages")
 async def create_message(request: MessagesRequest):
     engine = _get_engine()
-    handler = AnthropicHandler(request, engine)
+    handler = ProtocolHandler(request, engine, AnthropicResponseBuilder())
     return await handler.handle()
 
 

astrai/inference/core/cache.py

@@ -42,7 +42,7 @@ class Allocator:
                 return idx
             return -1
 
-    def free(self, idx: int, keep_cached: bool = False) -> None:
+    def free(self, idx: int, keep_cached: bool = False):
         with self._lock:
             self._refs[idx] -= 1
             if self._refs[idx] == 0:
@@ -51,7 +51,7 @@ class Allocator:
                 else:
                     self._free_mask |= 1 << idx
 
-    def inc_ref(self, idx: int) -> None:
+    def inc_ref(self, idx: int):
         with self._lock:
             self._refs[idx] += 1
             self._lru.pop(idx, None)
@@ -60,7 +60,7 @@ class Allocator:
         with self._lock:
             return self._refs[idx]
 
-    def touch(self, idx: int) -> None:
+    def touch(self, idx: int):
         with self._lock:
             self._lru.move_to_end(idx)
 
@@ -74,7 +74,7 @@ class PrefixCache:
         self._hash_to_page: Dict[int, int] = {}
         self._lock = threading.Lock()
 
-    def evict(self, idx: int) -> None:
+    def evict(self, idx: int):
         with self._lock:
             h = self._page_to_hash.pop(idx, None)
             if h is not None:
@@ -96,9 +96,7 @@ class PrefixCache:
                 hits.append(p)
             return hits
 
-    def record(
+    def record(self, page_idx: int, token_ids: List[int], logical_page_idx: int):
-        self, page_idx: int, token_ids: List[int], logical_page_idx: int
-    ) -> None:
         with self._lock:
             h = page_hash(token_ids, logical_page_idx, self._page_size)
             old_h = self._page_to_hash.pop(page_idx, None)
@@ -127,13 +125,13 @@ class PagePool:
     def alloc(self) -> int:
         return self._alloc.alloc()
 
-    def free(self, idx: int) -> None:
+    def free(self, idx: int):
         keep = self._prefix.has_page(idx)
         self._alloc.free(idx, keep_cached=keep)
         if not keep:
             self._prefix.evict(idx)
 
-    def inc_ref(self, idx: int) -> None:
+    def inc_ref(self, idx: int):
         self._alloc.inc_ref(idx)
 
     def lookup(self, token_ids: List[int]) -> List[int]:
@@ -142,9 +140,7 @@ class PagePool:
             self._alloc.touch(p)
         return hits
 
-    def record(
+    def record(self, page_idx: int, token_ids: List[int], logical_page_idx: int):
-        self, page_idx: int, token_ids: List[int], logical_page_idx: int
-    ) -> None:
         self._prefix.record(page_idx, token_ids, logical_page_idx)
 
 
@@ -157,7 +153,7 @@ class TaskTable:
         self._cached: Dict[str, int] = {}
         self._lock = threading.Lock()
 
-    def set(self, task_id: str, page_table: List[int], cached: int) -> None:
+    def set(self, task_id: str, page_table: List[int], cached: int):
         with self._lock:
             self._pages[task_id] = page_table
             self._cached[task_id] = cached
@@ -220,7 +216,7 @@ class Storage:
         start_pos: int,
         k: Tensor,
         v: Tensor,
-    ) -> None:
+    ):
         seq_len = k.size(1)
         if seq_len == 0:
             return
@@ -286,7 +282,7 @@ class KvcacheView:
         self._page_table = page_table
         self._total_len = total_len
 
-    def write(self, layer_id: int, k: Tensor, v: Tensor) -> None:
+    def write(self, layer_id: int, k: Tensor, v: Tensor):
         start_pos = self._total_len - k.size(1)
         self._storage.write(layer_id, self._page_table, start_pos, k, v)
 
@@ -339,7 +335,7 @@ class KVCache:
         self._table.set(task_id, hits + new_pages, cached)
         return True
 
-    def task_free(self, task_id: str) -> None:
+    def task_free(self, task_id: str):
         page_table, _ = self._table.pop(task_id)
         for idx in page_table:
             self._pool.free(idx)
@@ -359,7 +355,7 @@ class KVCache:
 
     def task_record_hashes(
         self, task_id: str, prompt_ids: List[int], start_logical_page: int = 0
-    ) -> None:
+    ):
         page_table = self._table.get(task_id)
         full_pages = len(prompt_ids) // self.page_size
         for i in range(start_logical_page, full_pages):

astrai/inference/core/executor.py

@@ -29,9 +29,7 @@ class Executor:
         self.device = device or next(model.parameters()).device
         self.dtype = dtype or next(model.parameters()).dtype
 
-    def execute_prefill(
+    def execute_prefill(self, tasks: List[Task], prompt_len: int, start_pos: int = 0):
-        self, tasks: List[Task], prompt_len: int, start_pos: int = 0
-    ) -> None:
         if start_pos >= prompt_len:
             return
 

astrai/inference/core/scheduler.py

@@ -71,18 +71,19 @@ class InferenceScheduler:
         )
 
         self._running = False
+        self._fatal_error: Optional[Exception] = None
 
     def add_task(self, prompt: str, **kwargs) -> str:
         return self._task_mgr.add_task(prompt, **kwargs)
 
-    def remove_task(self, task_id: str) -> None:
+    def remove_task(self, task_id: str):
         for task in self._task_mgr.remove_task(task_id):
             self._page_cache.task_free(task.task_id)
 
     def get_stats(self) -> Dict[str, Any]:
         return self._task_mgr.get_stats()
 
-    def _run_generation_loop(self) -> None:
+    def _run_generation_loop(self):
         stop_ids = self._task_mgr.tokenizer.stop_ids
         try:
             while self._running:
@@ -108,7 +109,10 @@ class InferenceScheduler:
                     continue
 
                 to_prefill = [
-                    t for t in self._task_mgr.get_active_tasks() if t.output_tokens == 0
+                    t
+                    for t in self._task_mgr.get_active_tasks()
+                    if t.output_tokens == 0
+                    and self._page_cache.task_cached(t.task_id) < len(t.prompt_ids)
                 ]
                 if to_prefill:
                     for t in to_prefill:
@@ -156,11 +160,15 @@ class InferenceScheduler:
                             t.output_ids.append(ntok)
                             t.output_tokens += 1
                             pos = t.input_tokens + t.output_tokens
-                            self._page_cache.task_extend(t.task_id, pos)
+                            extend_ok = self._page_cache.task_extend(t.task_id, pos)
                             if t.stream_callback:
                                 t.stream_callback(
                                     self._task_mgr.tokenizer.decode([ntok])
                                 )
+                            if not extend_ok:
+                                t.status = TaskStatus.ABORTED
+                                if t.stream_callback:
+                                    t.stream_callback(STOP)
 
                         for t in valid:
                             if t.is_finished(stop_ids):
@@ -168,28 +176,37 @@ class InferenceScheduler:
                                     t.stream_callback(STOP)
 
         except Exception as e:
+            self._fatal_error = e
+            self._running = False
             logger.error(f"Scheduler loop crashed: {e}", exc_info=True)
             for task in self._task_mgr.get_active_tasks():
                 if task.stream_callback:
                     task.stream_callback(STOP)
                 self._page_cache.task_free(task.task_id)
+            for task in self._task_mgr.get_waiting_tasks():
+                if task.stream_callback:
+                    task.stream_callback(STOP)
             self._task_mgr.clear_queues()
-            raise
 
-    def start(self) -> None:
+    def start(self):
         if not self._running:
             self._running = True
             t = threading.Thread(target=self._run_generation_loop, daemon=True)
             t.start()
             self._loop_thread = t
 
-    def stop(self) -> None:
+    def stop(self):
         self._running = False
         self._task_mgr.wake()
         if hasattr(self, "_loop_thread"):
             self._loop_thread.join(timeout=2.0)
         for task in self._task_mgr.get_active_tasks():
+            if task.stream_callback:
+                task.stream_callback(STOP)
             self._page_cache.task_free(task.task_id)
+        for task in self._task_mgr.get_waiting_tasks():
+            if task.stream_callback:
+                task.stream_callback(STOP)
         self._task_mgr.clear_queues()
         if torch.cuda.is_available():
             torch.cuda.empty_cache()

astrai/inference/core/task.py

@@ -172,12 +172,12 @@ class TaskManager:
                 to_add.append(self.waiting_queue.popleft())
         return to_add
 
-    def activate(self, task: Task) -> None:
+    def activate(self, task: Task):
         task.status = TaskStatus.RUNNING
         with self._lock:
             self.active_tasks.append(task)
 
-    def return_to_waiting(self, tasks: List[Task]) -> None:
+    def return_to_waiting(self, tasks: List[Task]):
         with self._lock:
             for task in reversed(tasks):
                 self.waiting_queue.appendleft(task)
@@ -185,18 +185,25 @@ class TaskManager:
     def has_work(self) -> bool:
         return bool(self.active_tasks or self.waiting_queue)
 
-    def wait_for_tasks(self, timeout: float = 1.0) -> None:
+    def wait_for_tasks(self, timeout: float = 1.0):
-        self._task_event.clear()
+        with self._lock:
+            if self.waiting_queue or self.active_tasks:
+                return
+            self._task_event.clear()
         self._task_event.wait(timeout=timeout)
 
     def get_active_tasks(self) -> List[Task]:
         with self._lock:
             return list(self.active_tasks)
 
-    def clear_queues(self) -> None:
+    def get_waiting_tasks(self) -> List[Task]:
+        with self._lock:
+            return list(self.waiting_queue)
+
+    def clear_queues(self):
         with self._lock:
             self.waiting_queue.clear()
             self.active_tasks.clear()
 
-    def wake(self) -> None:
+    def wake(self):
         self._task_event.set()

astrai/inference/engine.py

@@ -13,17 +13,6 @@ from astrai.inference.core.task import STOP
 from astrai.tokenize import AutoTokenizer
 
 
-def _validate_sampling_params(
-    top_k: int, top_p: float, temperature: float, max_tokens: Optional[int] = None
-):
-    if not (isinstance(top_k, int) and top_k >= 0):
-        raise ValueError("top_k must be a non-negative integer")
-    if not (0.0 <= top_p <= 1.0):
-        raise ValueError("top_p must be a float between 0.0 and 1.0")
-    if not (isinstance(temperature, (int, float)) and temperature >= 0):
-        raise ValueError("temperature must be a non-negative number")
-
-
 class GenerateResult:
     """Thread-safe token accumulator for streaming and non-streaming modes."""
 
@@ -59,7 +48,7 @@ class GenerateResult:
     def wait(self, timeout: Optional[float] = None) -> bool:
         return self._event.wait(timeout=timeout)
 
-    def wait_completion(self, timeout: float = 300.0) -> None:
+    def wait_completion(self, timeout: float = 300.0):
         with self._cond:
             if not self._cond.wait_for(
                 lambda: self._completed >= self._total, timeout=timeout
@@ -86,7 +75,12 @@ class GenerationRequest:
         max_tokens: Optional[int] = None,
         stream: bool = False,
     ):
-        _validate_sampling_params(top_k, top_p, temperature, max_tokens)
+        if not (isinstance(top_k, int) and top_k >= 0):
+            raise ValueError("top_k must be a non-negative integer")
+        if not (0.0 <= top_p <= 1.0):
+            raise ValueError("top_p must be a float between 0.0 and 1.0")
+        if not (isinstance(temperature, (int, float)) and temperature > 0):
+            raise ValueError("temperature must be a positive number")
 
         self.messages = messages
         self.top_k = top_k
@@ -137,7 +131,6 @@ class InferenceEngine:
         top_p: float = 1.0,
         top_k: int = 50,
     ) -> Union[Generator, str, List[str]]:
-        _validate_sampling_params(top_k, top_p, temperature, max_tokens)
         is_batch = isinstance(prompt, list)
         prompts = prompt if is_batch else [prompt]
 
@@ -158,7 +151,6 @@ class InferenceEngine:
         top_p: float = 1.0,
         top_k: int = 50,
     ) -> AsyncGenerator[str, None]:
-        _validate_sampling_params(top_k, top_p, temperature, max_tokens)
         sync_gen = self._generate_streaming(
             [prompt], False, max_tokens, temperature, top_p, top_k
         )
@@ -289,7 +281,7 @@ class InferenceEngine:
     def get_stats(self) -> Dict[str, Any]:
         return self.scheduler.get_stats()
 
-    def shutdown(self) -> None:
+    def shutdown(self):
         self.scheduler.stop()
         if torch.cuda.is_available():
             torch.cuda.empty_cache()

astrai/inference/sample.py

@@ -44,10 +44,12 @@ class TemperatureStrategy(BaseSamplingStrategy):
     def apply(self, logits, filter_value=-float("inf")):
         t = self.temperature
         if isinstance(t, Tensor):
+            t = t.to(logits.device, non_blocking=True).view(-1, 1)
+            t = torch.clamp(t, min=1e-8)
             if (t != 1.0).any():
-                logits = logits / t.to(logits.device, non_blocking=True).view(-1, 1)
+                logits = logits / t
         elif t != 1.0:
-            logits = logits / t
+            logits = logits / max(t, 1e-8)
         return logits
 
 

astrai/model/__init__.py

@@ -2,6 +2,13 @@ from astrai.model.automodel import AutoModel
 from astrai.model.components.attention import GQA
 from astrai.model.components.decoder_block import DecoderBlock
 from astrai.model.components.linear import Linear
+from astrai.model.components.lora import (
+    LoRAConfig,
+    inject_lora,
+    load_lora,
+    merge_lora,
+    save_lora,
+)
 from astrai.model.components.mlp import MLP
 from astrai.model.components.norm import RMSNorm
 from astrai.model.encoder import EmbeddingEncoder
@@ -18,4 +25,10 @@ __all__ = [
     "AutoRegressiveLM",
     "EmbeddingEncoder",
     "AutoModel",
+    # LoRA
+    "LoRAConfig",
+    "inject_lora",
+    "merge_lora",
+    "save_lora",
+    "load_lora",
 ]

astrai/model/automodel.py

@@ -2,21 +2,24 @@
 AutoModel base class for model loading and saving.
 """
 
-import json
 from contextlib import contextmanager
 from pathlib import Path
 from typing import Self, Union
 
-import safetensors.torch as st
 import torch.nn as nn
 
 from astrai.config.model_config import BaseModelConfig, ConfigFactory
 from astrai.factory import BaseFactory
+from astrai.serialization import load_model_config, load_model_weights, save_model
 
 
 @contextmanager
 def _disable_random_init(enable: bool = True):
-    init_functions = [
+    if not enable:
+        yield
+        return
+
+    names = (
         "xavier_normal_",
         "xavier_uniform_",
         "kaiming_normal_",
@@ -26,18 +29,15 @@ def _disable_random_init(enable: bool = True):
         "constant_",
         "normal_",
         "uniform_",
-    ]
+    )
-    original_funcs = {}
+    orig = {n: getattr(nn.init, n) for n in names if hasattr(nn.init, n)}
-    for name in init_functions:
+    for n in orig:
-        if enable and hasattr(nn.init, name):
+        setattr(nn.init, n, lambda *a, **kw: None)
-            original_funcs[name] = getattr(nn.init, name)
-            setattr(nn.init, name, lambda *args, **kwargs: None)
     try:
         yield
     finally:
-        if enable:
+        for n, fn in orig.items():
-            for name, orig_func in original_funcs.items():
+            setattr(nn.init, n, fn)
-                setattr(nn.init, name, orig_func)
 
 
 class AutoModel(BaseFactory["AutoModel"], nn.Module):
@@ -60,25 +60,22 @@ class AutoModel(BaseFactory["AutoModel"], nn.Module):
 
         model_path = Path(path)
 
-        # Load config
         config_path = model_path / "config.json"
-        if config_path.exists():
+        if not config_path.exists():
-            with open(config_path, "r") as f:
-                raw = json.load(f)
-            config = ConfigFactory.load(raw)
-            model_type = config.model_type or "autoregressive_lm"
-        else:
             raise FileNotFoundError(f"Config file not found: {config_path}")
 
+        raw = load_model_config(str(model_path))
+        config = ConfigFactory.load(raw)
+        model_type = config.model_type or "autoregressive_lm"
+
         actual_cls = AutoModel.get_component_class(model_type)
 
         with _disable_random_init(enable=disable_random_init):
             model = actual_cls(config)
 
-        # Load weights
         weights_path = model_path / "model.safetensors"
         if weights_path.exists():
-            state_dict = st.load_file(str(weights_path))
+            state_dict = load_model_weights(str(model_path))
             model.load_state_dict(state_dict, strict=strict)
 
         return model
@@ -86,15 +83,12 @@ class AutoModel(BaseFactory["AutoModel"], nn.Module):
     def save_pretrained(
         self,
         save_directory: Union[str, Path],
-    ) -> None:
+    ):
-        save_path = Path(save_directory)
+        save_model(
-        save_path.mkdir(parents=True, exist_ok=True)
+            config=self.config.to_dict(),
-
+            state_dict=self.state_dict(),
-        # Save config
+            save_directory=str(save_directory),
-        self.config.to_file(str(save_path / "config.json"))
+        )
-
-        # Save weights
-        st.save_file(self.state_dict(), str(save_path / "model.safetensors"))
 
     def to(self, *args, **kwargs) -> Self:
         """Move model to device/dtype."""

astrai/model/components/lora.py

@@ -0,0 +1,194 @@
+import logging
+from dataclasses import asdict, dataclass
+from pathlib import Path
+from typing import Optional, Set
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from astrai.model.components.linear import Linear
+from astrai.serialization import (
+    load_json,
+    load_safetensors,
+    save_json,
+    save_safetensors,
+)
+
+logger = logging.getLogger(__name__)
+
+TARGET_MODULES_ATTN = {"q_proj", "k_proj", "v_proj", "o_proj"}
+TARGET_MODULES_FFN = {"up", "gate", "down"}
+
+
+@dataclass
+class LoRAConfig:
+    r: int = 16
+    alpha: int = 32
+    target_modules: tuple = ("q_proj", "v_proj")
+
+
+class LoRALinear(nn.Module):
+    def __init__(self, base: Linear, r: int = 16, alpha: int = 32):
+        super().__init__()
+        self.register_parameter("weight", base.weight)
+        self.weight.requires_grad_(False)
+        self.bias = base.bias
+        if self.bias is not None:
+            self.bias.requires_grad_(False)
+
+        self.r = r
+        self.scaling = alpha / r
+        self.lora_A = nn.Parameter(torch.randn(r, self.weight.shape[1]) / r)
+        self.lora_B = nn.Parameter(torch.zeros(self.weight.shape[0], r))
+        self._merged = False
+
+    def forward(self, x):
+        out = F.linear(x, self.weight, self.bias)
+        if not self._merged:
+            out += (F.linear(x, self.lora_A) @ self.lora_B.T) * self.scaling
+        return out
+
+    def merge(self):
+        if self._merged:
+            return
+        self.weight.data += (self.lora_B @ self.lora_A) * self.scaling
+        self._merged = True
+        del self.lora_A
+        del self.lora_B
+
+
+def _collect_lora_info(model: nn.Module) -> dict:
+    names = {}
+    for n, m in model.named_modules():
+        if isinstance(m, Linear):
+            _, _, child = n.rpartition(".")
+            names.setdefault(child, []).append(n)
+    return names
+
+
+def _get_lora_count(model: nn.Module) -> int:
+    return sum(1 for m in model.modules() if isinstance(m, LoRALinear))
+
+
+def inject_lora(
+    model: nn.Module,
+    r: int = 16,
+    alpha: int = 32,
+    target_modules: Optional[Set[str]] = None,
+) -> LoRAConfig:
+    if target_modules is None:
+        target_modules = TARGET_MODULES_ATTN
+
+    available = _collect_lora_info(model)
+    injected = 0
+
+    for name, module in list(model.named_modules()):
+        if not isinstance(module, Linear):
+            continue
+        parent_name, _, child_name = name.rpartition(".")
+        if child_name not in target_modules:
+            continue
+        parent = model.get_submodule(parent_name) if parent_name else model
+        setattr(parent, child_name, LoRALinear(module, r=r, alpha=alpha))
+        injected += 1
+
+    if injected == 0:
+        logger.warning(
+            "No LoRA layers injected. Available Linear child names: %s. "
+            "target_modules: %s. Check model type and target_modules.",
+            sorted(available),
+            sorted(target_modules),
+        )
+    else:
+        logger.info("LoRA injected: %d layers (r=%d, alpha=%d)", injected, r, alpha)
+
+    return LoRAConfig(r=r, alpha=alpha, target_modules=tuple(target_modules))
+
+
+def merge_lora(model: nn.Module):
+    n = 0
+    for module in model.modules():
+        if isinstance(module, LoRALinear):
+            module.merge()
+            n += 1
+    if n == 0:
+        logger.warning("No LoRA layers to merge.")
+    else:
+        logger.info("Merged %d LoRA layers", n)
+
+
+def save_lora(model: nn.Module, save_dir: str, config: LoRAConfig):
+    lora_sd = {
+        k: v
+        for k, v in model.state_dict().items()
+        if k.endswith((".lora_A", ".lora_B"))
+    }
+    if not lora_sd:
+        raise RuntimeError(
+            "No LoRA parameters found in model. "
+            "The model may not have been injected or was already merged."
+        )
+
+    path = Path(save_dir)
+    path.mkdir(parents=True, exist_ok=True)
+    save_safetensors(lora_sd, path / "adapter_model.safetensors")
+    save_json(asdict(config), path / "adapter_config.json")
+    logger.info("LoRA adapter saved to %s (%d keys)", save_dir, len(lora_sd))
+
+
+def load_lora(model: nn.Module, load_dir: str) -> LoRAConfig:
+    path = Path(load_dir)
+    raw = load_json(path / "adapter_config.json")
+    config = LoRAConfig(
+        r=raw["r"], alpha=raw["alpha"], target_modules=tuple(raw["target_modules"])
+    )
+
+    existing = _get_lora_count(model)
+    if existing > 0:
+        logger.warning(
+            "Model already has %d LoRA layers. Skipping injection, "
+            "loading weights onto existing layers only.",
+            existing,
+        )
+    else:
+        inject_lora(
+            model,
+            r=config.r,
+            alpha=config.alpha,
+            target_modules=set(config.target_modules),
+        )
+
+    weights = load_safetensors(path / "adapter_model.safetensors")
+    try:
+        missing, unexpected = model.load_state_dict(weights, strict=False)
+    except RuntimeError as e:
+        msg = str(e)
+        if "size mismatch" in msg:
+            raise RuntimeError(
+                f"LoRA weight shapes do not match the model. "
+                f"The adapter config (r={config.r}) may not match the injected layers. "
+                f"Original error: {msg}"
+            ) from e
+        raise
+
+    injected = _get_lora_count(model)
+    if injected == 0:
+        raise RuntimeError(
+            "No LoRA layers found after loading. "
+            "Inject LoRA before calling load_lora, or check the adapter config."
+        )
+
+    if missing:
+        lora_missing = [k for k in missing if "lora" in k]
+        if lora_missing:
+            raise RuntimeError(
+                f"LoRA weight keys not found in model: {lora_missing}. "
+                f"The adapter config (r={config.r}) may not match the model."
+            )
+        logger.debug("LoRA load: %d missing base-weight keys (expected)", len(missing))
+    if unexpected:
+        logger.warning("LoRA load: %d unexpected keys", len(unexpected))
+
+    logger.info("LoRA adapter loaded from %s", load_dir)
+    return config

astrai/model/components/rope.py

@@ -1,4 +1,4 @@
-from typing import Optional
+from typing import Dict, Optional
 
 import torch
 import torch.nn as nn
@@ -19,6 +19,10 @@ def get_rotary_emb(
     return torch.complex(cos, sin)
 
 
+def ntk_base(base: float, dim: int, factor: float) -> float:
+    return base * (factor ** (dim / (dim - 2)))
+
+
 def apply_rotary_emb(x: torch.Tensor, freqs_cis: Tensor) -> Tensor:
     dtype = x.dtype
     x_ = x.float().reshape(*x.shape[:-1], -1, 2)
@@ -30,11 +34,25 @@ def apply_rotary_emb(x: torch.Tensor, freqs_cis: Tensor) -> Tensor:
 
 
 class RotaryEmbedding(nn.Module):
-    def __init__(self, dim: int, max_len: int, base: float = 10000):
+    def __init__(
+        self,
+        dim: int,
+        max_len: int,
+        base: float = 10000,
+        rope_scaling: Optional[Dict] = None,
+    ):
         super().__init__()
         self.dim = dim
         self.max_len = max_len
         self.base = base
+        self.rope_scaling = rope_scaling
+
+        if rope_scaling is not None:
+            scaling_type = rope_scaling.get("type", "ntk")
+            factor = rope_scaling.get("factor", 1.0)
+            if scaling_type == "ntk":
+                self.base = ntk_base(base, dim, factor)
+
         self._set_rotary_buffer(self.max_len)
 
     def _set_rotary_buffer(self, max_len: int):

astrai/model/encoder.py

@@ -20,7 +20,9 @@ class EmbeddingEncoder(AutoModel):
         self.config = config
         rope_dim = config.dim // config.n_heads
         rope_base = config.rope_theta if config.rope_theta is not None else 10000
-        self.rotary_embedding = RotaryEmbedding(rope_dim, config.max_len, rope_base)
+        self.rotary_embedding = RotaryEmbedding(
+            rope_dim, config.max_len, rope_base, rope_scaling=config.rope_scaling
+        )
         self.embed_tokens = Embedding(config.vocab_size, config.dim)
 
         self.layers = nn.ModuleList(
@@ -66,9 +68,6 @@ class EmbeddingEncoder(AutoModel):
 
         x = self.embed_tokens(input_ids)
 
-        if position_ids is None:
-            position_ids = torch.arange(S, device=x.device).unsqueeze(0).expand(B, -1)
-
         rotary_emb = self.rotary_embedding(x, position_ids)
         attn_mask = process_attention_mask(x, position_ids, input_mask, is_causal=False)
 

astrai/model/transformer.py

@@ -1,4 +1,4 @@
-from typing import Any, Mapping, Optional
+from typing import Any, Dict, Mapping, Optional
 
 import torch
 import torch.nn as nn
@@ -59,7 +59,9 @@ class AutoRegressiveLM(AutoModel):
             else config.dim // config.n_heads
         )
         rope_base = config.rope_theta if config.rope_theta is not None else 10000
-        self.rotary_embedding = RotaryEmbedding(rope_dim, config.max_len, rope_base)
+        self.rotary_embedding = RotaryEmbedding(
+            rope_dim, config.max_len, rope_base, rope_scaling=config.rope_scaling
+        )
         self.embed_tokens = Embedding(config.vocab_size, config.dim)
 
         self.layers = nn.ModuleList(
@@ -134,7 +136,7 @@ class AutoRegressiveLM(AutoModel):
         input_mask: Optional[Tensor] = None,
         paged_cache: Optional[KvcacheView] = None,
         position_ids: Optional[Tensor] = None,
-    ) -> Tensor:
+    ) -> Dict[str, Tensor]:
         assert input_ids.ndim == 2
 
         x = self.embed_tokens(input_ids)

astrai/parallel/__init__.py

@@ -1,3 +1,13 @@
+from astrai.parallel.executor import (
+    AccumOptimizer,
+    AccumScheduler,
+    BaseExecutor,
+    DDPExecutor,
+    ExecutorFactory,
+    FSDPExecutor,
+    GradientState,
+    NoneExecutor,
+)
 from astrai.parallel.module import ColumnParallelLinear, RowParallelLinear
 from astrai.parallel.setup import (
     get_current_device,
@@ -17,4 +27,12 @@ __all__ = [
     "spawn_parallel_fn",
     "RowParallelLinear",
     "ColumnParallelLinear",
+    "ExecutorFactory",
+    "BaseExecutor",
+    "GradientState",
+    "AccumOptimizer",
+    "AccumScheduler",
+    "NoneExecutor",
+    "DDPExecutor",
+    "FSDPExecutor",
 ]

astrai/parallel/executor.py

@@ -0,0 +1,271 @@
+"""Unified training executor — parallel strategy + gradient accumulation."""
+
+import contextlib
+import logging
+from contextlib import contextmanager
+from typing import Optional, Tuple
+
+import torch
+import torch.nn as nn
+from torch.distributed.fsdp import FullStateDictConfig, StateDictType
+from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
+from torch.nn.parallel import DistributedDataParallel as DDP
+from torch.optim import Optimizer
+from torch.optim.lr_scheduler import LRScheduler
+from torch.utils.data import DataLoader
+
+from astrai.factory import BaseFactory
+from astrai.parallel.setup import get_rank, get_world_size
+
+logger = logging.getLogger(__name__)
+
+
+class GradientState:
+    def __init__(self, grad_accum_steps: int = 1):
+        self.num_steps = max(grad_accum_steps, 1)
+        self._step: int = 0
+        self._sync_gradients: bool = True
+
+    @property
+    def sync_gradients(self) -> bool:
+        return self._sync_gradients
+
+    def _do_sync(self):
+        self._step += 1
+        self._sync_gradients = self._step % self.num_steps == 0
+
+
+class AccumOptimizer:
+    def __init__(self, optimizer: Optimizer, gradient_state: GradientState):
+        self.optimizer = optimizer
+        self.gradient_state = gradient_state
+
+    def step(self, closure=None):
+        if self.gradient_state.sync_gradients:
+            self.optimizer.step(closure)
+
+    def zero_grad(self):
+        if self.gradient_state.sync_gradients:
+            self.optimizer.zero_grad()
+
+    @property
+    def param_groups(self):
+        return self.optimizer.param_groups
+
+    def state_dict(self):
+        return self.optimizer.state_dict()
+
+    def load_state_dict(self, d):
+        self.optimizer.load_state_dict(d)
+
+
+class AccumScheduler:
+    def __init__(self, scheduler: LRScheduler, gradient_state: GradientState):
+        self.scheduler = scheduler
+        self.gradient_state = gradient_state
+
+    def step(self):
+        if self.gradient_state.sync_gradients:
+            self.scheduler.step()
+
+    def state_dict(self):
+        return self.scheduler.state_dict()
+
+    def load_state_dict(self, d):
+        self.scheduler.load_state_dict(d)
+
+    def get_last_lr(self):
+        return self.scheduler.get_last_lr()
+
+
+class BaseExecutor:
+    def __init__(self, grad_accum_steps: int = 1):
+        self.gradient_state = GradientState(grad_accum_steps)
+
+    def prepare(
+        self,
+        model: nn.Module,
+        optimizer: Optional[Optimizer] = None,
+        dataloader: Optional[DataLoader] = None,
+        scheduler: Optional[LRScheduler] = None,
+    ) -> Tuple[
+        nn.Module, Optional[Optimizer], Optional[DataLoader], Optional[LRScheduler]
+    ]:
+        model = self._prepare_model(model)
+        if optimizer is not None:
+            optimizer = AccumOptimizer(optimizer, self.gradient_state)
+        if scheduler is not None:
+            scheduler = AccumScheduler(scheduler, self.gradient_state)
+        return model, optimizer, dataloader, scheduler
+
+    def _prepare_model(self, model: nn.Module) -> nn.Module:
+        return model
+
+    def _no_sync(self, model: nn.Module):
+        return contextlib.nullcontext()
+
+    @contextmanager
+    def accumulate(self, model: nn.Module):
+        self.gradient_state._do_sync()
+        if not self.gradient_state.sync_gradients:
+            with self._no_sync(model):
+                yield
+        else:
+            yield
+
+    def backward(self, loss: torch.Tensor):
+        loss.backward()
+
+    def unwrap_model(self, model: nn.Module):
+        return model.state_dict()
+
+    @property
+    def use_distributed(self) -> bool:
+        return get_world_size() > 1
+
+    @property
+    def sync_gradients(self) -> bool:
+        return self.gradient_state.sync_gradients
+
+    @property
+    def grad_accum_steps(self) -> int:
+        return self.gradient_state.num_steps
+
+
+class ExecutorFactory(BaseFactory[BaseExecutor]):
+    pass
+
+
+@ExecutorFactory.register("none")
+class NoneExecutor(BaseExecutor):
+    pass
+
+
+@ExecutorFactory.register("ddp")
+class DDPExecutor(BaseExecutor):
+    def __init__(
+        self,
+        grad_accum_steps: int = 1,
+        dim: int = 0,
+        broadcast_buffers: bool = True,
+        init_sync: bool = True,
+        process_group=None,
+        bucket_cap_mb: int = 25,
+        find_unused_parameters: bool = False,
+        check_reduction: bool = False,
+        gradient_as_bucket_view: bool = False,
+        static_graph: bool = False,
+        delay_all_reduce_named_params=None,
+        param_to_hook_all_reduce=None,
+        mixed_precision=None,
+        device_mesh=None,
+    ):
+        super().__init__(grad_accum_steps=grad_accum_steps)
+        self._ddp_kwargs = dict(
+            dim=dim,
+            broadcast_buffers=broadcast_buffers,
+            init_sync=init_sync,
+            process_group=process_group,
+            bucket_cap_mb=bucket_cap_mb,
+            find_unused_parameters=find_unused_parameters,
+            check_reduction=check_reduction,
+            gradient_as_bucket_view=gradient_as_bucket_view,
+            static_graph=static_graph,
+            delay_all_reduce_named_params=delay_all_reduce_named_params,
+            param_to_hook_all_reduce=param_to_hook_all_reduce,
+            mixed_precision=mixed_precision,
+            device_mesh=device_mesh,
+        )
+
+    def _prepare_model(self, model: nn.Module) -> nn.Module:
+        if not self.use_distributed:
+            logger.warning("DDP backend selected but world_size=1, model not wrapped")
+            return model
+        local_rank = get_rank()
+        model = DDP(
+            model,
+            device_ids=[local_rank],
+            output_device=local_rank,
+            **self._ddp_kwargs,
+        )
+        logger.info("Model wrapped with DDP (world_size=%d)", get_world_size())
+        return model
+
+    def _no_sync(self, model: nn.Module):
+        if isinstance(model, DDP):
+            return model.no_sync()
+        return contextlib.nullcontext()
+
+    def unwrap_model(self, model: nn.Module):
+        if isinstance(model, DDP):
+            return model.module.state_dict()
+        return model.state_dict()
+
+
+@ExecutorFactory.register("fsdp")
+class FSDPExecutor(BaseExecutor):
+    def __init__(
+        self,
+        grad_accum_steps: int = 1,
+        process_group=None,
+        sharding_strategy=None,
+        cpu_offload=None,
+        auto_wrap_policy=None,
+        backward_prefetch=None,
+        mixed_precision=None,
+        ignored_modules=None,
+        param_init_fn=None,
+        sync_module_states: bool = False,
+        forward_prefetch: bool = False,
+        limit_all_gathers: bool = True,
+        ignored_states=None,
+        device_mesh=None,
+    ):
+        super().__init__(grad_accum_steps=grad_accum_steps)
+        self._fsdp_kwargs = {
+            k: v
+            for k, v in dict(
+                process_group=process_group,
+                sharding_strategy=sharding_strategy,
+                cpu_offload=cpu_offload,
+                auto_wrap_policy=auto_wrap_policy,
+                backward_prefetch=backward_prefetch,
+                mixed_precision=mixed_precision,
+                ignored_modules=ignored_modules,
+                param_init_fn=param_init_fn,
+                sync_module_states=sync_module_states,
+                forward_prefetch=forward_prefetch,
+                limit_all_gathers=limit_all_gathers,
+                use_orig_params=True,
+                ignored_states=ignored_states,
+                device_mesh=device_mesh,
+            ).items()
+            if v is not None
+        }
+        self._original_model: Optional[nn.Module] = None
+
+    def _prepare_model(self, model: nn.Module) -> nn.Module:
+        if not self.use_distributed:
+            logger.warning("FSDP backend selected but world_size=1, model not wrapped")
+            return model
+        self._original_model = model
+        device_id = torch.device("cuda", get_rank())
+        model = FSDP(model, device_id=device_id, **self._fsdp_kwargs)
+        logger.info("Model wrapped with FSDP (world_size=%d)", get_world_size())
+        return model
+
+    def _no_sync(self, model: nn.Module):
+        if isinstance(model, FSDP):
+            return model.no_sync()
+        return contextlib.nullcontext()
+
+    def unwrap_model(self, model: nn.Module):
+        if isinstance(model, FSDP) and self.use_distributed:
+            with FSDP.state_dict_type(
+                model,
+                StateDictType.FULL_STATE_DICT,
+                FullStateDictConfig(offload_to_cpu=True, rank0_only=False),
+            ):
+                return model.state_dict()
+
+        return model.state_dict()

astrai/preprocessing/__init__.py

@@ -0,0 +1,19 @@
+from astrai.preprocessing.builder import (
+    BaseMaskBuilder,
+    ChatMaskBuilder,
+    InstructionMaskBuilder,
+    MaskBuilderFactory,
+    TextMaskBuilder,
+)
+from astrai.preprocessing.pipeline import Pipeline, dedup_signature, filter_by_length
+
+__all__ = [
+    "BaseMaskBuilder",
+    "ChatMaskBuilder",
+    "InstructionMaskBuilder",
+    "MaskBuilderFactory",
+    "TextMaskBuilder",
+    "Pipeline",
+    "dedup_signature",
+    "filter_by_length",
+]

astrai/preprocessing/builder.py

@@ -0,0 +1,161 @@
+"""Mask building strategies for preprocessing pipeline.
+
+Each builder knows how to tokenize one input format and construct
+the loss_mask according to declarative mask rules from the config.
+"""
+
+from __future__ import annotations
+
+from abc import ABC, abstractmethod
+from typing import List, Optional
+
+from astrai.factory import BaseFactory
+
+
+class BaseMaskBuilder(ABC):
+    """Convert a JSONL item into token ids and optional loss_mask."""
+
+    @abstractmethod
+    def build(self, item: dict, config, tokenizer) -> Optional[dict]:
+        """Build ``{ids, loss_mask?, domain}`` from a JSONL record.
+
+        Returns ``None`` to skip the item entirely.
+        """
+        ...
+
+
+class MaskBuilderFactory(BaseFactory["BaseMaskBuilder"]):
+    @classmethod
+    def _validate_component(cls, component_cls: type):
+        if not issubclass(component_cls, BaseMaskBuilder):
+            raise TypeError(
+                f"{component_cls.__name__} must inherit from BaseMaskBuilder"
+            )
+
+
+def _extract_domain(item: dict, domain_key: Optional[str]) -> str:
+    if not domain_key:
+        return "__default__"
+    val = item.get(domain_key, "__default__")
+    return val if isinstance(val, str) else "__default__"
+
+
+@MaskBuilderFactory.register("chat")
+class ChatMaskBuilder(BaseMaskBuilder):
+    """Mask by role via message-level tokenisation with role-span tracking.
+
+    For each message, renders the chat template for that single message,
+    encodes individually, and records its token span + role action.
+    The concatenated sequence receives a loss_mask built from span rules.
+    """
+
+    def build(self, item: dict, config, tokenizer) -> Optional[dict]:
+        messages = item.get(config.input.messages_key)
+        if not isinstance(messages, list) or not messages:
+            return None
+
+        all_ids: List[int] = []
+        spans: List[tuple] = []
+
+        if tokenizer.bos_token_id is not None:
+            all_ids.append(tokenizer.bos_token_id)
+
+        for msg in messages:
+            role = msg.get("role", "")
+            action = config.mask.get(role, config.mask_default)
+
+            rendered = tokenizer.apply_chat_template(
+                [msg], tokenize=False, add_generation_prompt=False
+            )
+            ids = tokenizer.encode(rendered, add_special_tokens=False)
+
+            start = len(all_ids)
+            all_ids.extend(ids)
+            spans.append((start, len(all_ids), action))
+
+        if len(all_ids) <= 1:
+            return None
+
+        max_len = config.preprocessing.max_seq_len
+        all_ids = all_ids[:max_len]
+
+        loss_mask = [0] * len(all_ids)
+        for start, end, action in spans:
+            if start >= len(all_ids):
+                break
+            e = min(end, len(all_ids))
+            if action == "train":
+                loss_mask[start:e] = [1] * (e - start)
+
+        return {
+            "ids": all_ids,
+            "loss_mask": loss_mask,
+            "domain": _extract_domain(item, config.output.domain_key),
+        }
+
+
+@MaskBuilderFactory.register("instruction")
+class InstructionMaskBuilder(BaseMaskBuilder):
+    """Mask by prompt / response field boundary.
+
+    Encodes prompt and response independently, then fills mask
+    according to ``prompt`` / ``response`` entries in the mask config.
+    """
+
+    def build(self, item: dict, config, tokenizer) -> Optional[dict]:
+        prompt = str(item.get(config.input.prompt_key, ""))
+        response = str(item.get(config.input.response_key, ""))
+
+        if not prompt.strip() and not response.strip():
+            return None
+
+        prompt_ids = tokenizer.encode(prompt, add_special_tokens=True)
+        response_ids = tokenizer.encode(response, add_special_tokens=False)
+
+        max_len = config.preprocessing.max_seq_len
+        full_ids = (prompt_ids + response_ids)[:max_len]
+
+        prompt_action = config.mask.get("prompt", config.mask_default)
+        response_action = config.mask.get("response", config.mask_default)
+
+        p_len = min(len(prompt_ids), len(full_ids))
+        r_len = len(full_ids) - p_len
+
+        loss_mask = []
+        if prompt_action == "train":
+            loss_mask += [1] * p_len
+        else:
+            loss_mask += [0] * p_len
+
+        if response_action == "train":
+            loss_mask += [1] * r_len
+        else:
+            loss_mask += [0] * r_len
+
+        return {
+            "ids": full_ids,
+            "loss_mask": loss_mask,
+            "domain": _extract_domain(item, config.output.domain_key),
+        }
+
+
+@MaskBuilderFactory.register("text")
+class TextMaskBuilder(BaseMaskBuilder):
+    """Plain tokenisation — no mask, used for pre-training data."""
+
+    def build(self, item: dict, config, tokenizer) -> Optional[dict]:
+        text = item.get(config.input.text_key, "")
+        if not isinstance(text, str) or not text.strip():
+            return None
+
+        pp = config.preprocessing
+        if not (pp.min_chars <= len(text) <= pp.max_chars):
+            return None
+
+        ids = tokenizer.encode(text, add_special_tokens=True)
+        ids = ids[: pp.max_seq_len]
+
+        return {
+            "ids": ids,
+            "domain": _extract_domain(item, config.output.domain_key),
+        }

astrai/preprocessing/pipeline.py

@@ -0,0 +1,134 @@
+"""Config-driven JSONL preprocessing pipeline.
+
+Composes a :class:`BaseMaskBuilder` (selected by ``input.type``) with
+deduplication, sharding, and flush to ``.h5`` / ``.bin`` storage.
+"""
+
+from __future__ import annotations
+
+import hashlib
+import json
+import os
+from collections import defaultdict
+from typing import List, Optional
+
+import torch
+import tqdm
+
+from astrai.config.preprocess_config import PipelineConfig
+from astrai.dataset.storage import save_bin, save_h5
+from astrai.preprocessing.builder import MaskBuilderFactory
+from astrai.tokenize import AutoTokenizer
+
+
+def filter_by_length(text: str, min_len: int = 50, max_len: int = 2_000_000) -> bool:
+    return min_len <= len(text) <= max_len
+
+
+def dedup_signature(item: dict) -> str:
+    raw = json.dumps(item, sort_keys=True, ensure_ascii=False)
+    return hashlib.md5(raw[:200].encode()).hexdigest()
+
+
+class Pipeline:
+    """Tokenization pipeline driven by a declarative :class:`PipelineConfig`.
+
+    Usage::
+
+        config = PipelineConfig.from_json("sft_pipeline.json")
+        Pipeline(config, ["data.jsonl"], output_dir="out", tokenizer_path="params").run()
+    """
+
+    def __init__(
+        self,
+        config: PipelineConfig,
+        input_paths: List[str],
+        output_dir: str,
+        tokenizer_path: str,
+    ):
+        os.makedirs(output_dir, exist_ok=True)
+        self.config = config
+        self.paths = input_paths
+        self.output_dir = output_dir
+        self.tokenizer_path = tokenizer_path
+
+        self.mask_builder = MaskBuilderFactory.create(config.input.type)
+
+    def transform(self, item: dict) -> Optional[dict]:
+        return self.mask_builder.build(item, self.config, self._tokenizer)
+
+    def run(self):
+        self._tokenizer = AutoTokenizer.from_pretrained(self.tokenizer_path)
+
+        seen: set = set()
+        domains: dict = defaultdict(lambda: defaultdict(list))
+        total_tokens = 0
+        shard_idx: dict[str, int] = defaultdict(int)
+        count = 0
+
+        pp = self.config.preprocessing
+
+        for item in tqdm.tqdm(
+            self._iter_items(), desc="Tokenizing", unit="docs", mininterval=0.5
+        ):
+            if pp.max_items and count >= pp.max_items:
+                break
+
+            if pp.deduplicate:
+                sig = dedup_signature(item)
+                if sig in seen:
+                    continue
+                seen.add(sig)
+
+            result = self.transform(item)
+            if result is None:
+                continue
+
+            ids = result["ids"]
+            if not ids:
+                continue
+
+            domain = result.get("domain", "__default__")
+            domains[domain]["sequence"].append(ids)
+            if "loss_mask" in result:
+                domains[domain]["loss_mask"].append(result["loss_mask"])
+
+            count += 1
+            total_tokens += len(ids)
+
+            if total_tokens >= self.config.output.max_tokens_per_shard:
+                self._flush(domains, shard_idx)
+                domains.clear()
+                total_tokens = 0
+
+        if total_tokens > 0:
+            self._flush(domains, shard_idx)
+
+        print(f"Done. {count} documents tokenized.")
+
+    def _iter_items(self):
+        for path in self.paths:
+            with open(path, "r", encoding="utf-8") as f:
+                for line in f:
+                    line = line.strip()
+                    if not line:
+                        continue
+                    yield json.loads(line)
+
+    def _flush(self, domains, shard_idx):
+        for domain, keys in domains.items():
+            idx = shard_idx[domain]
+            tensors = {}
+            for key, ids_list in keys.items():
+                tensors[key] = [torch.tensor(sum(ids_list, []), dtype=torch.long)]
+            chunk_dir = os.path.join(self.output_dir, domain)
+            fmt = self.config.output.storage_format
+            if fmt == "bin":
+                save_bin(chunk_dir, tensors)
+            else:
+                save_h5(chunk_dir, f"data_{idx:04d}", tensors)
+            shard_idx[domain] = idx + 1
+            tqdm.tqdm.write(
+                f"  saved {domain}/shard_{idx:04d}  "
+                f"({tensors['sequence'][0].numel():,} tokens)"
+            )

astrai/protocols.py

@@ -0,0 +1,21 @@
+"""Training component protocols — structural subtyping for optimizer/scheduler wrappers."""
+
+from typing import Any, Protocol, runtime_checkable
+
+
+@runtime_checkable
+class OptimizerProtocol(Protocol):
+    def step(self, closure=None): ...
+    def zero_grad(self): ...
+    @property
+    def param_groups(self) -> Any: ...
+    def state_dict(self) -> dict: ...
+    def load_state_dict(self, d: dict): ...
+
+
+@runtime_checkable
+class SchedulerProtocol(Protocol):
+    def step(self): ...
+    def state_dict(self) -> dict: ...
+    def load_state_dict(self, d: dict): ...
+    def get_last_lr(self): ...

astrai/serialization.py

@@ -1,7 +1,9 @@
+import io
 import json
 import time
+from dataclasses import dataclass, field
 from pathlib import Path
-from typing import Any, Dict, Optional
+from typing import Any, Dict, Union
 
 import safetensors.torch as st
 import torch
@@ -9,75 +11,172 @@ import torch.distributed as dist
 
 from astrai.parallel.setup import get_rank
 
+_META_FILE = "meta.json"
+_CONFIG_FILE = "config.json"
+_WEIGHTS_FILE = "model.safetensors"
 
+
+def save_safetensors(state_dict: dict, path: Union[str, Path]):
+    st.save_file(state_dict, str(path))
+
+
+def load_safetensors(path: Union[str, Path], broadcast: bool = False) -> dict:
+    if not broadcast or not dist.is_initialized():
+        return st.load_file(str(path))
+
+    rank = get_rank()
+    if rank == 0:
+        state_dict = st.load_file(str(path))
+    else:
+        state_dict = {}
+    tmp = [state_dict]
+    dist.broadcast_object_list(tmp, src=0)
+    return tmp[0]
+
+
+def save_json(data: dict, path: Union[str, Path]):
+    with open(str(path), "w") as f:
+        json.dump(data, f, indent=2)
+
+
+def load_json(path: Union[str, Path], broadcast: bool = False) -> dict:
+    if not broadcast or not dist.is_initialized():
+        with open(str(path), "r") as f:
+            return json.load(f)
+
+    rank = get_rank()
+    if rank == 0:
+        with open(str(path), "r") as f:
+            data = json.load(f)
+    else:
+        data = {}
+    tmp = [data]
+    dist.broadcast_object_list(tmp, src=0)
+    return tmp[0]
+
+
+def save_torch(obj: Any, path: Union[str, Path]):
+    torch.save(obj, str(path))
+
+
+def load_torch(path: Union[str, Path], broadcast: bool = False) -> Any:
+    if not broadcast or not dist.is_initialized():
+        return torch.load(str(path), map_location="cpu", weights_only=False)
+
+    path = Path(path)
+    rank = get_rank()
+
+    if rank == 0:
+        with open(path, "rb") as f:
+            raw = f.read()
+        data_tensor = torch.frombuffer(bytearray(raw), dtype=torch.uint8)
+        num_bytes = torch.tensor([len(raw)], dtype=torch.long)
+    else:
+        num_bytes = torch.tensor([0], dtype=torch.long)
+
+    dist.broadcast(num_bytes, src=0)
+
+    if rank != 0:
+        data_tensor = torch.empty(num_bytes.item(), dtype=torch.uint8)
+
+    dist.broadcast(data_tensor, src=0)
+
+    buf = io.BytesIO(data_tensor.numpy().tobytes())
+    return torch.load(buf, map_location="cpu", weights_only=False)
+
+
+def save_model(config: dict, state_dict: dict, save_directory: str):
+    save_path = Path(save_directory)
+    save_path.mkdir(parents=True, exist_ok=True)
+    save_json(config, save_path / _CONFIG_FILE)
+    save_safetensors(state_dict, save_path / _WEIGHTS_FILE)
+
+
+def load_model_config(save_directory: str) -> dict:
+    return load_json(Path(save_directory) / _CONFIG_FILE)
+
+
+def load_model_weights(save_directory: str) -> dict:
+    return load_state_dict(Path(save_directory) / _WEIGHTS_FILE)
+
+
+def load_state_dict(path: Union[str, Path], broadcast: bool = False) -> dict:
+    path = Path(path)
+    if not broadcast or not dist.is_initialized():
+        return load_safetensors(path)
+
+    rank = get_rank()
+    if rank == 0:
+        state_dict = load_safetensors(path)
+        specs = [
+            (k, list(state_dict[k].shape), str(state_dict[k].dtype).split(".")[-1])
+            for k in sorted(state_dict)
+        ]
+    else:
+        state_dict = {}
+        specs = []
+
+    specs_list = [specs]
+    dist.broadcast_object_list(specs_list, src=0)
+    specs = specs_list[0]
+
+    for key, shape, dtype_name in specs:
+        dtype = getattr(torch, dtype_name)
+        if rank != 0:
+            tensor = torch.empty(shape, dtype=dtype, device="cpu")
+        else:
+            tensor = state_dict[key].contiguous().cpu()
+        dist.broadcast(tensor, src=0)
+        if rank != 0:
+            state_dict[key] = tensor
+    return state_dict
+
+
+@dataclass
 class Checkpoint:
-    def __init__(
+    state_dict: Dict[str, Any] = field(default_factory=dict)
-        self,
+    epoch: int = 0
-        state_dict: Dict[str, Any],
+    iteration: int = 0
-        epoch: int = 0,
+    extra: Dict[str, Any] = field(default_factory=dict)
-        iteration: int = 0,
+    meta: Dict[str, Any] = field(default_factory=dict)
-        extra: Optional[Dict[str, Any]] = None,
+    config: Dict[str, Any] = field(default_factory=dict)
-        meta: Optional[Dict[str, Any]] = None,
-    ):
-        self.state_dict = state_dict
-        self.epoch = epoch
-        self.iteration = iteration
-        self.extra = extra or {}
-        self.meta = meta or {}
-
-    def save(
-        self,
-        save_dir: str,
-    ) -> None:
 
+    def save(self, save_dir: str):
         save_path = Path(save_dir)
         save_path.mkdir(parents=True, exist_ok=True)
 
-        rank = get_rank()
+        if get_rank() != 0:
-        if rank == 0:
+            return
-            meta = {
-                "epoch": self.epoch,
-                "iteration": self.iteration,
-                "timestamp": time.strftime("%Y-%m-%dT%H:%M:%S"),
-            }
-            meta.update(self.meta)
-            with open(save_path / "meta.json", "w") as f:
-                json.dump(meta, f, indent=2)
 
-            st.save_file(self.state_dict, save_path / "state_dict.safetensors")
+        meta = {
-            if self.extra:
+            "epoch": self.epoch,
-                for key, value in self.extra.items():
+            "iteration": self.iteration,
-                    torch.save(value, save_path / f"{key}.pt")
+            "timestamp": time.strftime("%Y-%m-%dT%H:%M:%S"),
+            **self.meta,
+        }
+        save_json(meta, save_path / _META_FILE)
+        save_json(self.config, save_path / _CONFIG_FILE)
+        save_safetensors(self.state_dict, save_path / _WEIGHTS_FILE)
+        for key, value in self.extra.items():
+            save_torch(value, save_path / f"{key}.pt")
 
     @classmethod
-    def load(
+    def load(cls, save_dir: str, broadcast: bool = False) -> "Checkpoint":
-        cls,
-        save_dir: str,
-    ) -> "Checkpoint":
-
-        rank = get_rank()
         save_path = Path(save_dir)
 
-        meta = {}
+        meta = load_json(save_path / _META_FILE, broadcast)
-        if rank == 0:
+        config = load_json(save_path / _CONFIG_FILE, broadcast)
-            with open(Path(save_dir) / "meta.json", "r") as f:
+        state_dict = load_state_dict(save_path / _WEIGHTS_FILE, broadcast=broadcast)
-                meta = json.load(f)
-
-        if dist.is_initialized():
-            meta_list = [meta]
-            dist.broadcast_object_list(meta_list, src=0)
-            meta = meta_list[0]
-
-        state_dict = st.load_file(save_path / "state_dict.safetensors")
 
         extra = {}
-        for f in save_path.iterdir():
+        for f in sorted(save_path.iterdir()):
-            if f.suffix == ".pt" and f.stem not in ("meta",):
+            if f.suffix == ".pt":
-                extra[f.stem] = torch.load(f, map_location="cpu", weights_only=False)
+                extra[f.stem] = load_torch(f, broadcast=broadcast)
 
         return cls(
             state_dict=state_dict,
-            epoch=meta["epoch"],
+            epoch=meta.get("epoch", 0),
-            iteration=meta["iteration"],
+            iteration=meta.get("iteration", 0),
-            extra=extra or None,
+            extra=extra,
+            config=config,
         )

astrai/trainer/optim.py

@@ -4,17 +4,17 @@ from torch.optim import Optimizer
 
 def _zeropower_via_newtonschulz(G: torch.Tensor, steps: int = 5):
     assert G.ndim == 2
-    X = G.bfloat16()
+    X = G
     scale = max(1, G.size(0) / G.size(1)) ** 0.5
     X = X / (X.norm() + 1e-7) * scale
     if steps == 0:
-        return X.type_as(G)
+        return X
     a, b, c = (3.4445, -4.7750, 2.0315)
     for _ in range(steps):
         A = X @ X.T
         B = A @ X
         X = a * X + b * B + c * (A @ B)
-    return X.type_as(G)
+    return X
 
 
 class Muon(Optimizer):
@@ -50,64 +50,94 @@ class Muon(Optimizer):
         if closure is not None:
             with torch.enable_grad():
                 loss = closure()
+
         for group in self.param_groups:
+            params_2d, params_1d = [], []
+            grads_2d, grads_1d = [], []
+
             for p in group["params"]:
                 if p.grad is None:
                     continue
-                grad = p.grad
+                if p.grad.is_sparse:
-                if grad.is_sparse:
                     raise RuntimeError("Muon does not support sparse gradients")
                 if p.ndim >= 2:
-                    self._muon_update(p, grad, group)
+                    params_2d.append(p)
+                    grads_2d.append(p.grad)
                 else:
-                    self._adamw_update(p, grad, group)
+                    params_1d.append(p)
+                    grads_1d.append(p.grad)
+
+            if params_2d:
+                self._muon_update_foreach(params_2d, grads_2d, group)
+            if params_1d:
+                self._adamw_update_foreach(params_1d, grads_1d, group)
+
         return loss
 
-    def _muon_update(self, p, grad, group):
+    def _muon_update_foreach(self, params_2d, grads_2d, group):
         lr = group["lr"]
         momentum = group["momentum"]
         wd = group["weight_decay"]
         nesterov = group["nesterov"]
         ns_steps = group["ns_steps"]
-        state = self.state[p]
 
-        p.mul_(1 - lr * wd)
+        if wd != 0:
+            torch._foreach_mul_(params_2d, 1 - lr * wd)
 
         if nesterov:
-            grad = grad.add(p, alpha=wd)
+            grads_2d = torch._foreach_add(grads_2d, params_2d, alpha=wd)
 
-        if "momentum_buffer" not in state:
+        bufs = []
-            state["momentum_buffer"] = torch.zeros_like(grad)
+        for p, grad in zip(params_2d, grads_2d):
-        buf = state["momentum_buffer"]
+            state = self.state[p]
-        buf.lerp_(grad, 1 - momentum)
+            if "momentum_buffer" not in state:
+                state["momentum_buffer"] = torch.zeros_like(grad)
+            bufs.append(state["momentum_buffer"])
 
-        update = _zeropower_via_newtonschulz(buf, steps=ns_steps)
+        torch._foreach_lerp_(bufs, grads_2d, 1 - momentum)
-        scale = max(1, p.size(0) / p.size(1)) ** 0.5
-        p.add_(update, alpha=-lr * scale)
 
-    def _adamw_update(self, p, grad, group):
+        for p, buf in zip(params_2d, bufs):
+            update = _zeropower_via_newtonschulz(buf, steps=ns_steps)
+            scale = max(1, p.size(0) / p.size(1)) ** 0.5
+            p.add_(update, alpha=-lr * scale)
+
+    def _adamw_update_foreach(self, params_1d, grads_1d, group):
         lr = group["adamw_lr"]
         betas = group["adamw_betas"]
         eps = group["adamw_eps"]
         wd = group["adamw_wd"]
-        state = self.state[p]
 
-        if not state:
+        steps: list[int] = []
-            state["step"] = 0
+        exp_avgs, exp_avg_sqs = [], []
-            state["exp_avg"] = torch.zeros_like(p)
+        has_state = []
-            state["exp_avg_sq"] = torch.zeros_like(p)
+        for p in params_1d:
+            state = self.state[p]
+            if not state:
+                state["step"] = 0
+                state["exp_avg"] = torch.zeros_like(p)
+                state["exp_avg_sq"] = torch.zeros_like(p)
+                has_state.append(False)
+            else:
+                has_state.append(True)
+            state["step"] += 1
+            steps.append(state["step"])
+            exp_avgs.append(state["exp_avg"])
+            exp_avg_sqs.append(state["exp_avg_sq"])
 
-        state["step"] += 1
-        exp_avg, exp_avg_sq = state["exp_avg"], state["exp_avg_sq"]
         beta1, beta2 = betas
 
-        exp_avg.lerp_(grad, 1 - beta1)
+        torch._foreach_lerp_(exp_avgs, grads_1d, 1 - beta1)
-        exp_avg_sq.lerp_(grad.square(), 1 - beta2)
+        grads_sq = torch._foreach_mul(grads_1d, grads_1d)
+        torch._foreach_lerp_(exp_avg_sqs, grads_sq, 1 - beta2)
 
-        step = state["step"]
+        bias_correction1 = [1 - beta1**s for s in steps]
-        bias1 = 1 - beta1**step
+        bias_correction2 = [1 - beta2**s for s in steps]
-        bias2 = 1 - beta2**step
 
-        p.mul_(1 - lr * wd)
+        if wd != 0:
-        denom = exp_avg_sq.sqrt().div_(bias2**0.5).add_(eps)
+            torch._foreach_mul_(params_1d, 1 - lr * wd)
-        p.addcdiv_(exp_avg / bias1, denom, value=-lr)
+
+        exp_avg_corrected = torch._foreach_div(exp_avgs, bias_correction1)
+        denom = torch._foreach_div(exp_avg_sqs, bias_correction2)
+        denom = torch._foreach_sqrt(denom)
+        torch._foreach_add_(denom, eps)
+        torch._foreach_addcdiv_(params_1d, exp_avg_corrected, denom, value=-lr)

astrai/trainer/schedule.py

@@ -42,7 +42,7 @@ class SchedulerFactory(BaseFactory["BaseScheduler"]):
     """
 
     @classmethod
-    def _validate_component(cls, scheduler_cls: Type[BaseScheduler]) -> None:
+    def _validate_component(cls, scheduler_cls: Type[BaseScheduler]):
         """Validate that the scheduler class inherits from BaseScheduler."""
         if not issubclass(scheduler_cls, BaseScheduler):
             raise TypeError(f"{scheduler_cls.__name__} must inherit from BaseScheduler")

astrai/trainer/strategy.py

@@ -1,6 +1,5 @@
 """Training strategy implementations with factory pattern."""
 
-import copy
 from abc import ABC, abstractmethod
 from typing import Any, Callable, Dict, Union
 
@@ -8,26 +7,14 @@ import torch
 import torch.nn as nn
 import torch.nn.functional as F
 from torch import Tensor
-from torch.nn.parallel import DistributedDataParallel as DDP
 
 from astrai.factory import BaseFactory
 
 
-def unwrap_model(model: nn.Module) -> nn.Module:
+def create_ref_model(model_fn, state_dict: dict) -> nn.Module:
-    """Unwrap DDP wrapper if present to get the original model."""
+    """Create a frozen reference model from model_fn + full state dict."""
-    if isinstance(model, DDP):
+    ref_model = model_fn()
-        return model.module
+    ref_model.load_state_dict(state_dict)
-    return model
-
-
-def create_ref_model(model: nn.Module) -> nn.Module:
-    """Create a reference model for DPO/GRPO training.
-
-    Handles DDP-wrapped models safely by unwrapping first,
-    then creating a deep copy with frozen gradients.
-    """
-    original_model = unwrap_model(model)
-    ref_model = copy.deepcopy(original_model)
     ref_model.requires_grad_(False)
     ref_model.eval()
     return ref_model
@@ -89,6 +76,8 @@ class BaseStrategy(ABC):
     ):
         self.model = model
         self.device = device
+        self.executor = kwargs.pop("executor", None)
+        self.model_fn = kwargs.pop("model_fn", None)
         self.extra_kwargs = kwargs
 
     @abstractmethod
@@ -123,7 +112,7 @@ class StrategyFactory(BaseFactory["BaseStrategy"]):
     """
 
     @classmethod
-    def _validate_component(cls, strategy_cls: type) -> None:
+    def _validate_component(cls, strategy_cls: type):
         """Validate that the strategy class inherits from BaseStrategy."""
         if not issubclass(strategy_cls, BaseStrategy):
             raise TypeError(f"{strategy_cls.__name__} must inherit from BaseStrategy")
@@ -228,7 +217,9 @@ class DPOStrategy(BaseStrategy):
         **kwargs,
     ):
         super().__init__(model, device, **kwargs)
-        self.ref_model = create_ref_model(model)
+        self.ref_model = create_ref_model(
+            self.model_fn, self.executor.unwrap_model(model)
+        ).to(device=self.device)
         self.beta = beta
         self.reduction = reduction
 
@@ -282,7 +273,9 @@ class GRPOStrategy(BaseStrategy):
         **kwargs,
     ):
         super().__init__(model, device, **kwargs)
-        self.ref_model = create_ref_model(model)
+        self.ref_model = create_ref_model(
+            self.model_fn, self.executor.unwrap_model(model)
+        ).to(device=self.device)
         self.clip_eps = clip_eps
         self.kl_coef = kl_coef
         self.group_size = group_size
@@ -292,8 +285,7 @@ class GRPOStrategy(BaseStrategy):
 
     def sync_ref_model(self):
         """Copy current model weights to ref model."""
-        ref_state = self.model.state_dict()
+        self.ref_model.load_state_dict(self.executor.unwrap_model(self.model))
-        self.ref_model.load_state_dict(ref_state)
 
     def compute_loss(self, batch: Dict[str, Tensor]) -> Tensor:
         self._step += 1

astrai/trainer/train_callback.py

@@ -15,7 +15,7 @@ from tqdm import tqdm
 
 from astrai.factory import BaseFactory
 from astrai.parallel import only_on_rank
-from astrai.parallel.setup import get_current_device
+from astrai.parallel.setup import get_current_device, get_rank
 from astrai.serialization import Checkpoint
 from astrai.trainer.metric_util import (
     ctx_get_grad_max,
@@ -51,18 +51,15 @@ class TrainCallback(Protocol):
     def on_epoch_end(self, context: TrainContext):
         """Called at the end of each epoch."""
 
-    def on_step_begin(self, context: TrainContext):
-        """Called at the beginning of each step."""
-
-    def on_step_end(self, context: TrainContext):
-        """Called at the end of each step."""
-
     def on_batch_begin(self, context: TrainContext):
         """Called at the beginning of each batch."""
 
     def on_batch_end(self, context: TrainContext):
         """Called at the end of each batch."""
 
+    def on_optimizer_step(self, context: TrainContext):
+        """Called on every optimizer step (sync step only)."""
+
     def on_error(self, context: TrainContext):
         """Called when an error occurs during training."""
 
@@ -88,7 +85,7 @@ class GradientClippingCallback(TrainCallback):
     def __init__(self, max_grad_norm: float):
         self.max_grad_norm = max_grad_norm
 
-    def on_step_begin(self, context: TrainContext):
+    def on_optimizer_step(self, context: TrainContext):
         clip_grad_norm_(context.model.parameters(), self.max_grad_norm)
 
 
@@ -140,44 +137,31 @@ class CheckpointCallback(TrainCallback):
         save_dir: str,
         interval: int,
         weight_only: bool = False,
-        state_dict_fn: Optional[Callable[[nn.Module], dict]] = None,
         save_extra_fn: Optional[Callable[["TrainContext"], dict]] = None,
-        load_extra_fn: Optional[Callable[[dict, "TrainContext"], None]] = None,
     ):
         self.save_dir = save_dir
         self.interval = interval
         self.weight_only = weight_only
-        self.state_dict_fn = state_dict_fn
         self.save_extra_fn = save_extra_fn or CheckpointCallback.save_extra
-        self.load_extra_fn = load_extra_fn or CheckpointCallback.load_extra
         self.last_ckpt_iter = 0
 
-    @only_on_rank(0)
     def _save_checkpoint(self, context: TrainContext):
-        save_path = os.path.join(
+        state_dict = context.executor.unwrap_model(context.model)
-            self.save_dir, f"epoch_{context.epoch}_iter_{context.iteration}"
-        )
-        state_dict = (
-            self.state_dict_fn(context.model)
-            if self.state_dict_fn
-            else context.model.state_dict()
-        )
-
-        extra = self.save_extra_fn(context)
-        context.checkpoint = Checkpoint(
-            state_dict=state_dict,
-            epoch=context.epoch,
-            iteration=context.iteration,
-            extra=extra,
-            meta=context.config.to_dict(),
-        )
-
-        context.checkpoint.save(save_path)
         self.last_ckpt_iter = context.iteration
 
-    def on_train_begin(self, context: TrainContext):
+        if get_rank() == 0:
-        if context.checkpoint and context.checkpoint.extra:
+            save_path = os.path.join(
-            self.load_extra_fn(context.checkpoint.extra, context)
+                self.save_dir, f"epoch_{context.epoch}_iter_{context.iteration}"
+            )
+            extra = self.save_extra_fn(context)
+            context.checkpoint = Checkpoint(
+                state_dict=state_dict,
+                epoch=context.epoch,
+                iteration=context.iteration,
+                extra=extra,
+                config=context.model_config,
+            )
+            context.checkpoint.save(save_path)
 
     def on_batch_end(self, context: TrainContext):
         if context.iteration - self.last_ckpt_iter >= self.interval:
@@ -199,12 +183,6 @@ class CheckpointCallback(TrainCallback):
                 extra[name] = obj.state_dict()
         return extra
 
-    @staticmethod
-    def load_extra(extra: dict, context: TrainContext):
-        for name in CheckpointCallback.extra_keys:
-            if name in extra:
-                getattr(context, name).load_state_dict(extra[name])
-
 
 @CallbackFactory.register("progress_bar")
 class ProgressBarCallback(TrainCallback):
@@ -213,7 +191,7 @@ class ProgressBarCallback(TrainCallback):
     """
 
     def __init__(
-        self, num_epoch: int, log_interval: int = 100, file: IO[str] = sys.stdout
+        self, num_epoch: int, log_interval: int = 100, file: Optional[IO[str]] = None
     ):
         self.num_epoch = num_epoch
         self.log_interval = log_interval
@@ -226,7 +204,7 @@ class ProgressBarCallback(TrainCallback):
             context.dataloader,
             desc=f"Epoch {context.epoch + 1}/{self.num_epoch}",
             dynamic_ncols=True,
-            file=self.file,
+            file=self.file or sys.stdout,
         )
 
     @only_on_rank(0)
@@ -344,7 +322,7 @@ class ValidationCallback(TrainCallback):
             f"Epoch {context.epoch + 1}, Step {step_count}, Val Loss: {avg_loss:.4f}"
         )
 
-    def on_step_end(self, context: TrainContext):
+    def on_optimizer_step(self, context: TrainContext):
         if context.val_dataloader is None:
             return
         cfg = context.config

astrai/trainer/train_context.py

@@ -1,15 +1,17 @@
 from dataclasses import dataclass, field
+from pathlib import Path
 from typing import Optional, Self
 
 import torch.nn as nn
-from torch.optim import Optimizer
-from torch.optim.lr_scheduler import LRScheduler
 from torch.utils.data import DataLoader
 
 from astrai.config.train_config import TrainConfig
 from astrai.dataset import ResumableDistributedSampler
+from astrai.model.components.lora import inject_lora
+from astrai.parallel.executor import BaseExecutor, ExecutorFactory
 from astrai.parallel.setup import get_current_device, get_rank, get_world_size
-from astrai.serialization import Checkpoint
+from astrai.protocols import OptimizerProtocol, SchedulerProtocol
+from astrai.serialization import Checkpoint, load_json, load_model_weights
 from astrai.trainer.strategy import BaseStrategy, StrategyFactory
 
 
@@ -18,10 +20,12 @@ class TrainContext:
     model: nn.Module = field(default=None)
     strategy: BaseStrategy = field(default=None)
     dataloader: DataLoader = field(default=None)
-    optimizer: Optimizer = field(default=None)
+    optimizer: OptimizerProtocol = field(default=None)
-    scheduler: LRScheduler = field(default=None)
+    scheduler: SchedulerProtocol = field(default=None)
     checkpoint: Checkpoint = field(default=None)
     config: TrainConfig = field(default=None)
+    model_config: dict = field(default_factory=dict)
+    executor: BaseExecutor = field(default=None)
 
     epoch: int = field(default=0)
     iteration: int = field(default=0)
@@ -40,40 +44,70 @@ class TrainContextBuilder:
         config: TrainConfig,
     ):
         self.config = config
-        self._checkpoint: Optional[Checkpoint] = None
+        self._resume_dir: Optional[str] = None
 
-    def with_checkpoint(self, checkpoint: Optional[Checkpoint]) -> Self:
+    def with_resume_dir(self, resume_dir: Optional[str]) -> Self:
-        self._checkpoint = checkpoint
+        self._resume_dir = resume_dir
         return self
 
     def build(self) -> TrainContext:
-        context = TrainContext(
+        cfg = self.config
-            model=self.config.model,
+        device = get_current_device()
-            world_size=get_world_size(),
+
-            rank=get_rank(),
+        executor = ExecutorFactory.create(
-            config=self.config,
+            cfg.parallel_mode,
+            grad_accum_steps=cfg.grad_accum_steps,
+            **cfg.executor_kwargs,
         )
 
-        device = get_current_device()
+        model = cfg.model_fn()
-        context.model = context.model.to(device=device)
+        model = model.to(device=device)
 
-        if self.config.nprocs > 1 and self.config.parallel_wrapper:
+        model_config = {}
-            context.model = self.config.parallel_wrapper(context.model)
+        if self._resume_dir:
+            config_path = Path(self._resume_dir) / "config.json"
+            if config_path.exists():
+                model_config = load_json(config_path)
 
-        if self._checkpoint is not None:
+        if not model_config and hasattr(model, "config"):
-            context.epoch = max(self._checkpoint.epoch, self.config.start_epoch)
+            model_config = model.config.to_dict()
-            context.iteration = max(self._checkpoint.iteration, self.config.start_batch)
+
-            context.model.load_state_dict(self._checkpoint.state_dict)
+        context = TrainContext(
-            context.checkpoint = self._checkpoint
+            model=model,
-        else:
+            world_size=get_world_size(),
-            context.checkpoint = Checkpoint(
+            rank=get_rank(),
-                state_dict=context.model.state_dict(),
+            config=cfg,
+            model_config=model_config,
+            executor=executor,
+        )
+
+        if self._resume_dir is not None:
+            resume_path = Path(self._resume_dir)
+            if (resume_path / "meta.json").exists():
+                checkpoint = Checkpoint.load(self._resume_dir)
+                state_dict = checkpoint.state_dict
+                if checkpoint.config:
+                    context.model_config = checkpoint.config
+            else:
+                checkpoint = None
+                state_dict = load_model_weights(self._resume_dir)
+            model.load_state_dict(state_dict, strict=False)
+            if checkpoint is not None:
+                context.epoch = cfg.start_epoch
+                context.iteration = cfg.start_batch
+            context.checkpoint = checkpoint
+
+        if cfg.lora is not None:
+            inject_lora(
+                model,
+                r=cfg.lora.r,
+                alpha=cfg.lora.alpha,
+                target_modules=set(cfg.lora.target_modules),
             )
 
-        context.optimizer = self.config.optimizer_fn(context.model)
+        context.optimizer = cfg.optimizer_fn(model)
-        context.scheduler = self.config.scheduler_fn(context.optimizer)
+        context.scheduler = cfg.scheduler_fn(context.optimizer)
 
-        cfg = self.config
         sampler_offset = context.iteration * cfg.batch_per_device
         sampler = ResumableDistributedSampler(
             data_source=cfg.dataset,
@@ -107,11 +141,30 @@ class TrainContextBuilder:
                 prefetch_factor=cfg.prefetch_factor,
             )
 
+        context.model, context.optimizer, context.dataloader, context.scheduler = (
+            executor.prepare(
+                model,
+                context.optimizer,
+                context.dataloader,
+                context.scheduler,
+            )
+        )
+
+        if context.checkpoint and context.checkpoint.extra:
+            extra = context.checkpoint.extra
+            for name in ("optimizer", "scheduler"):
+                if name in extra:
+                    obj = getattr(context, name, None)
+                    if obj is not None:
+                        obj.load_state_dict(extra[name])
+
         context.strategy = StrategyFactory.create(
             model=context.model,
-            train_type=self.config.strategy,
+            train_type=cfg.strategy,
             device=device,
-            **self.config.extra_kwargs,
+            executor=executor,
+            model_fn=cfg.model_fn,
+            **cfg.extra_kwargs,
         )
 
         return context

astrai/trainer/trainer.py

@@ -3,7 +3,6 @@ from typing import List, Optional
 
 from astrai.config import TrainConfig
 from astrai.parallel.setup import spawn_parallel_fn
-from astrai.serialization import Checkpoint
 from astrai.trainer.train_callback import (
     CallbackFactory,
     TrainCallback,
@@ -34,7 +33,6 @@ class Trainer:
                 "checkpoint",
                 cfg.ckpt_dir,
                 cfg.ckpt_interval,
-                state_dict_fn=cfg.state_dict_fn,
             ),
             CallbackFactory.create(
                 "metric_logger",
@@ -55,47 +53,49 @@ class Trainer:
             if method:
                 method(context)
 
-    def _trainer_loop(self, checkpoint: Optional[Checkpoint] = None):
+    def _trainer_loop(self, resume_dir: Optional[str] = None):
-        cfg = self.train_config
+        context = (
-        context = TrainContextBuilder(cfg).with_checkpoint(checkpoint).build()
+            TrainContextBuilder(self.train_config).with_resume_dir(resume_dir).build()
+        )
+        executor = context.executor
         self._call_callbacks("on_train_begin", context)
 
         try:
             context.model.train()
-            grad_accum_steps = cfg.grad_accum_steps
 
-            for epoch in range(context.epoch, cfg.n_epoch):
+            for epoch in range(context.epoch, context.config.n_epoch):
                 context.epoch = epoch
                 self._call_callbacks("on_epoch_begin", context)
 
                 for batch in context.dataloader:
                     self._call_callbacks("on_batch_begin", context)
-                    loss = context.strategy(batch)
-                    context.loss = loss.item()
-                    stand_loss = loss / grad_accum_steps
-                    stand_loss.backward()
-                    context.iteration += 1
-                    self._call_callbacks("on_batch_end", context)
 
-                    if context.iteration % grad_accum_steps == 0:
+                    with executor.accumulate(context.model):
-                        self._call_callbacks("on_step_begin", context)
+                        loss = context.strategy(batch)
-                        context.optimizer.step()
+                        context.loss = loss.item()
-                        context.optimizer.zero_grad()
+                        stand_loss = loss / executor.grad_accum_steps
-                        self._call_callbacks("on_step_end", context)
+                        executor.backward(stand_loss)
+                        context.iteration += 1
+                        self._call_callbacks("on_batch_end", context)
 
-                        if context.scheduler:
+                        if executor.sync_gradients:
-                            context.scheduler.step()
+                            self._call_callbacks("on_optimizer_step", context)
+                            context.optimizer.step()
+                            context.optimizer.zero_grad()
+
+                            if context.scheduler:
+                                context.scheduler.step()
 
                 self._call_callbacks("on_epoch_end", context)
 
         except Exception as e:
-            logger.error(f"Training failed: {str(e)}", exc_info=True)
+            logger.error("Training failed: %s", str(e), exc_info=True)
             self._call_callbacks("on_error", context)
             raise
         finally:
             self._call_callbacks("on_train_end", context)
 
-    def train(self, checkpoint: Optional[Checkpoint] = None):
+    def train(self, resume_dir: Optional[str] = None):
         cfg = self.train_config
         spawn_parallel_fn(
             self._trainer_loop,
@@ -105,5 +105,5 @@ class Trainer:
             master_port=cfg.master_port,
             device_type=cfg.device_type,
             start_method=cfg.start_method,
-            checkpoint=checkpoint,
+            resume_dir=resume_dir,
         )

scripts/tools/evaluate_mmlu.py

@@ -0,0 +1,294 @@
+"""MMLU evaluation via log-likelihood ranking."""
+
+import argparse
+import csv
+import json
+import os
+import shutil
+import tarfile
+
+import requests
+import torch
+import torch.nn.functional as F
+import tqdm
+
+from astrai.model import AutoModel
+from astrai.tokenize import AutoTokenizer
+
+MMLU_URL = "https://people.eecs.berkeley.edu/~hendrycks/data.tar"
+MMLU_SUBJECTS = [
+    "abstract_algebra",
+    "anatomy",
+    "astronomy",
+    "business_ethics",
+    "clinical_knowledge",
+    "college_biology",
+    "college_chemistry",
+    "college_computer_science",
+    "college_mathematics",
+    "college_medicine",
+    "college_physics",
+    "computer_security",
+    "conceptual_physics",
+    "econometrics",
+    "electrical_engineering",
+    "elementary_mathematics",
+    "formal_logic",
+    "global_facts",
+    "high_school_biology",
+    "high_school_chemistry",
+    "high_school_computer_science",
+    "high_school_european_history",
+    "high_school_geography",
+    "high_school_government_and_politics",
+    "high_school_macroeconomics",
+    "high_school_mathematics",
+    "high_school_microeconomics",
+    "high_school_physics",
+    "high_school_psychology",
+    "high_school_statistics",
+    "high_school_us_history",
+    "high_school_world_history",
+    "human_aging",
+    "human_sexuality",
+    "international_law",
+    "jurisprudence",
+    "logical_fallacies",
+    "machine_learning",
+    "management",
+    "marketing",
+    "medical_genetics",
+    "miscellaneous",
+    "moral_disputes",
+    "moral_scenarios",
+    "nutrition",
+    "philosophy",
+    "prehistory",
+    "professional_accounting",
+    "professional_law",
+    "professional_medicine",
+    "professional_psychology",
+    "public_relations",
+    "security_studies",
+    "sociology",
+    "us_foreign_policy",
+    "virology",
+    "world_religions",
+]
+
+
+def _download_and_extract(url: str, data_dir: str):
+    tar_path = os.path.join(data_dir, "data.tar")
+    os.makedirs(data_dir, exist_ok=True)
+    print(f"Downloading MMLU data from {url}...")
+    resp = requests.get(url, stream=True, timeout=300)
+    resp.raise_for_status()
+    total = int(resp.headers.get("content-length", 0))
+    with tqdm.tqdm(total=total, unit="B", unit_scale=True, desc="  Download") as bar:
+        with open(tar_path, "wb") as f:
+            for chunk in resp.iter_content(chunk_size=8192):
+                f.write(chunk)
+                bar.update(len(chunk))
+    print("Extracting...")
+    with tarfile.open(tar_path, "r") as tf:
+        tf.extractall(data_dir)
+    os.remove(tar_path)
+
+
+def download_mmlu(data_dir: str):
+    _download_and_extract(MMLU_URL, data_dir)
+    src = os.path.join(data_dir, "data")
+    if os.path.exists(src):
+        for item in os.listdir(src):
+            src_item = os.path.join(src, item)
+            dst_item = os.path.join(data_dir, item)
+            if os.path.exists(dst_item):
+                if os.path.isdir(dst_item):
+                    shutil.rmtree(dst_item)
+                else:
+                    os.remove(dst_item)
+            os.rename(src_item, dst_item)
+        os.rmdir(src)
+    print(f"MMLU data saved to {data_dir}")
+
+
+def _strip_prefix(text: str, prefix: str) -> str:
+    if text.startswith(prefix):
+        return text[len(prefix) :].strip()
+    return text
+
+
+def load_csv(path: str) -> list[dict]:
+    data = []
+    with open(path, "r", encoding="utf-8") as f:
+        for row in csv.reader(f):
+            if len(row) < 6:
+                continue
+            if row[0].strip().lower() == "question":
+                continue
+            data.append(
+                {
+                    "question": row[0].strip(),
+                    "A": _strip_prefix(row[1].strip(), "A)"),
+                    "B": _strip_prefix(row[2].strip(), "B)"),
+                    "C": _strip_prefix(row[3].strip(), "C)"),
+                    "D": _strip_prefix(row[4].strip(), "D)"),
+                    "answer": row[5].strip(),
+                }
+            )
+    return data
+
+
+def build_prompt(
+    question: str, choices: dict, subject: str, n_shot: int, dev_data: list[dict]
+) -> str:
+    prompt = ""
+    if n_shot > 0 and dev_data:
+        prompt = f"The following are multiple choice questions (with answers) about {subject}.\n\n"
+        for item in dev_data[:n_shot]:
+            prompt += f"Question: {item['question']}\n"
+            for k in ("A", "B", "C", "D"):
+                prompt += f"{k}. {item[k]}\n"
+            prompt += f"Answer: {item['answer']}\n\n"
+    prompt += f"Question: {question}\n"
+    for k in ("A", "B", "C", "D"):
+        prompt += f"{k}. {choices[k]}\n"
+    prompt += "Answer:"
+    return prompt
+
+
+def choice_logprob(
+    model, tokenizer, context_ids: list[int], choice_letter: str, device: str
+) -> float:
+    choice_text = f" {choice_letter}"
+    choice_ids = tokenizer.encode(choice_text, add_special_tokens=False)
+    input_ids = context_ids + choice_ids
+    max_len = model.config.max_len
+    if len(input_ids) > max_len:
+        overflow = len(input_ids) - max_len
+        input_ids = input_ids[overflow:]
+        ctx_len = len(input_ids) - len(choice_ids)
+    else:
+        ctx_len = len(context_ids)
+
+    input_tensor = torch.tensor([input_ids], device=device, dtype=torch.long)
+    with torch.inference_mode():
+        logits = model(input_tensor)["logits"][0]
+
+    score = 0.0
+    for i, tid in enumerate(choice_ids):
+        pos = ctx_len - 1 + i
+        if pos >= len(logits):
+            break
+        score += F.log_softmax(logits[pos], dim=-1)[tid].item()
+    return score
+
+
+def evaluate_subject(
+    model,
+    tokenizer,
+    subject: str,
+    test_data: list[dict],
+    dev_data: list[dict] | None,
+    device: str,
+    n_shot: int,
+) -> tuple[float, int, int]:
+    correct = 0
+    total = 0
+    for item in tqdm.tqdm(test_data, desc=f"{subject:40s}", leave=False):
+        prompt = build_prompt(item["question"], item, subject, n_shot, dev_data or [])
+        context_ids = tokenizer.encode(prompt)
+        scores = {
+            c: choice_logprob(model, tokenizer, context_ids, c, device)
+            for c in ("A", "B", "C", "D")
+        }
+        if max(scores, key=scores.get) == item["answer"]:
+            correct += 1
+        total += 1
+    return correct / total, correct, total
+
+
+def main():
+    parser = argparse.ArgumentParser(description="MMLU evaluation")
+    parser.add_argument(
+        "--param_path", type=str, default="./params", help="Model directory"
+    )
+    parser.add_argument(
+        "--data_dir", type=str, default="./mmlu_data", help="MMLU data directory"
+    )
+    parser.add_argument("--download", action="store_true", help="Download MMLU data")
+    parser.add_argument(
+        "--n_shot", type=int, default=5, help="Few-shot examples (0 for zero-shot)"
+    )
+    parser.add_argument(
+        "--subjects", type=str, nargs="+", help="Specific subjects (default: all)"
+    )
+    parser.add_argument("--output", type=str, help="Output JSON path")
+    parser.add_argument("--split", type=str, default="test", choices=["test", "val"])
+    parser.add_argument(
+        "--device",
+        type=str,
+        default="cuda" if torch.cuda.is_available() else "cpu",
+        help="Device",
+    )
+    parser.add_argument(
+        "--dtype",
+        type=str,
+        default="bfloat16" if torch.cuda.is_available() else "float32",
+        help="Torch dtype",
+    )
+    args = parser.parse_args()
+
+    if args.download or not os.path.exists(args.data_dir):
+        download_mmlu(args.data_dir)
+
+    model = AutoModel.from_pretrained(args.param_path)
+    tokenizer = AutoTokenizer.from_pretrained(args.param_path)
+    device = args.device
+    dtype = getattr(torch, args.dtype)
+    model.to(device=device, dtype=dtype)
+    model.eval()
+
+    subjects = args.subjects or MMLU_SUBJECTS
+    results = {}
+    total_correct = 0
+    total_questions = 0
+
+    for subject in subjects:
+        dev_path = os.path.join(args.data_dir, "dev", f"{subject}_dev.csv")
+        test_path = os.path.join(
+            args.data_dir, args.split, f"{subject}_{args.split}.csv"
+        )
+
+        if not os.path.exists(test_path):
+            print(f"  Skipping {subject}: test file not found")
+            continue
+
+        dev_data = load_csv(dev_path) if os.path.exists(dev_path) else None
+        test_data = load_csv(test_path)
+
+        acc, corr, tot = evaluate_subject(
+            model, tokenizer, subject, test_data, dev_data, device, args.n_shot
+        )
+        results[subject] = {"accuracy": round(acc, 4), "correct": corr, "total": tot}
+        total_correct += corr
+        total_questions += tot
+        print(f"  {subject:40s}  {acc:.2%}  ({corr}/{tot})")
+
+    overall = total_correct / total_questions if total_questions else 0
+    print(f"\n{'=' * 70}")
+    print(f"  Overall: {overall:.2%}  ({total_correct}/{total_questions})")
+    results["_overall"] = {
+        "accuracy": round(overall, 4),
+        "correct": total_correct,
+        "total": total_questions,
+    }
+
+    if args.output:
+        with open(args.output, "w", encoding="utf-8") as f:
+            json.dump(results, f, indent=2)
+        print(f"Results saved to {args.output}")
+
+
+if __name__ == "__main__":
+    main()

scripts/tools/perplexity.py

@@ -10,11 +10,11 @@ from astrai.tokenize import AutoTokenizer
 
 
 def process_file(
-    model_dir: str, input_file: str, output_file: str, batch_size: int, text_key: str
+    param_path: str, input_file: str, output_file: str, batch_size: int, text_key: str
 ):
     # Load model and tokenizer
-    model = AutoModel.from_pretrained(model_dir)
+    model = AutoModel.from_pretrained(param_path)
-    tokenizer = AutoTokenizer.from_pretrained(model_dir)
+    tokenizer = AutoTokenizer.from_pretrained(param_path)
     model.to(device="cuda", dtype=torch.bfloat16)
 
     with open(input_file, "r", encoding="utf-8") as f:
@@ -44,8 +44,8 @@ def process_file(
 
         for seq in batch_encoded:
             pad_len = max_len - len(seq)
-            padded_seq = [tokenizer.pad_id] * pad_len + seq
+            padded_seq = seq + [tokenizer.pad_id] * pad_len
-            mask = [False] * pad_len + [True] * len(seq)
+            mask = [True] * len(seq) + [False] * pad_len
             padded_ids.append(padded_seq)
             masks.append(mask)
 
@@ -88,7 +88,7 @@ def process_file(
 if __name__ == "__main__":
     parser = argparse.ArgumentParser(description="Run perplexity with a Khaosz model.")
     parser.add_argument(
-        "--model_dir", type=str, required=True, help="Path to the model directory."
+        "--param_path", type=str, required=True, help="Path to the model directory."
     )
     parser.add_argument(
         "--input_file", type=str, required=True, help="Path to the input file."

scripts/tools/preprocess.py

@@ -0,0 +1,38 @@
+"""CLI: JSONL → tokenized .h5/.bin via config-driven Pipeline."""
+
+import argparse
+
+from astrai.config.preprocess_config import PipelineConfig
+from astrai.preprocessing.pipeline import Pipeline
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        description="Raw JSONL → tokenized .h5/.bin via config-driven Pipeline"
+    )
+    parser.add_argument(
+        "inputs", nargs="+", metavar="JSONL", help="One or more JSONL files"
+    )
+    parser.add_argument("--output_dir", "-o", required=True, help="Output directory")
+    parser.add_argument(
+        "--config", "-c", required=True, help="Path to pipeline config JSON"
+    )
+    parser.add_argument(
+        "--tokenizer_path",
+        default="params",
+        help="Path to tokenizer directory (default: params)",
+    )
+    args = parser.parse_args()
+
+    config = PipelineConfig.from_json(args.config)
+
+    Pipeline(
+        config=config,
+        input_paths=args.inputs,
+        output_dir=args.output_dir,
+        tokenizer_path=args.tokenizer_path,
+    ).run()
+
+
+if __name__ == "__main__":
+    main()

scripts/tools/server.py

@@ -18,7 +18,7 @@ def main():
         "--reload", action="store_true", help="Enable auto-reload for development"
     )
     parser.add_argument(
-        "--param-path",
+        "--param_path",
         type=Path,
         default=None,
         help="Path to model parameters (default: project_root/params)",

scripts/tools/train.py

@@ -2,16 +2,12 @@ import argparse
 import os
 from functools import partial
 
-import safetensors.torch as st
 import torch
-import torch.nn as nn
 import torch.optim as optim
-from torch.nn.parallel import DistributedDataParallel as DDP
 
 from astrai.config import AutoRegressiveLMConfig, TrainConfig
 from astrai.dataset import DatasetFactory
 from astrai.model import AutoRegressiveLM
-from astrai.parallel import get_rank
 from astrai.trainer import SchedulerFactory, Trainer
 
 
@@ -146,6 +142,13 @@ def parse_args() -> argparse.Namespace:
     )
 
     parser.add_argument("--nprocs", type=int, default=1, help="Number of GPUs to use.")
+    parser.add_argument(
+        "--parallel_mode",
+        type=str,
+        default="none",
+        choices=["none", "ddp", "fsdp"],
+        help="Parallel training strategy (none, ddp, fsdp).",
+    )
     parser.add_argument(
         "--device_type", type=str, default="cuda", help="Device type to use."
     )
@@ -162,21 +165,11 @@ def parse_args() -> argparse.Namespace:
     return args
 
 
-def ddp_wrap(model: nn.Module):
+def create_model(config):
-    local_rank = get_rank()
+    return AutoRegressiveLM(config).to(dtype=torch.bfloat16)
-    ddp_model = DDP(
-        model,
-        device_ids=[local_rank],
-        output_device=local_rank,
-        static_graph=True,
-        find_unused_parameters=False,
-        gradient_as_bucket_view=True,
-        broadcast_buffers=False,
-    )
-    return ddp_model
 
 
-def create_optimizer(model: nn.Module, **kwargs) -> optim.Optimizer:
+def create_optimizer(model, **kwargs) -> optim.Optimizer:
     return optim.AdamW(model.parameters(), fused=True, **kwargs)
 
 
@@ -186,12 +179,6 @@ def create_scheduler(
     return SchedulerFactory.create(optimizer, **kwargs)
 
 
-def prepare_checkpoint(model: nn.Module) -> dict:
-    if isinstance(model, DDP):
-        return model.module.state_dict()
-    return model.state_dict()
-
-
 def compute_total_steps(
     dataset_len: int,
     n_epoch: int,
@@ -238,11 +225,14 @@ def train(
     window_size: int,
     stride: int,
     nprocs: int,
+    parallel_mode: str,
     device_type: str,
     start_method: str,
 ):
     assert train_type in ["seq", "sft", "dpo", "grpo"]
     assert os.path.exists(param_path)
+    if nprocs > 1 and parallel_mode == "none":
+        raise ValueError("--nprocs > 1 requires --parallel_mode to be 'ddp' or 'fsdp'")
 
     # Load config
     config_path = os.path.join(param_path, "config.json")
@@ -251,17 +241,6 @@ def train(
     if window_size is None:
         window_size = config.max_len
 
-    # Create bare AutoRegressiveLM (for training, no tokenizer needed)
-    model = AutoRegressiveLM(config)
-
-    # Load weights if available
-    weights_path = os.path.join(param_path, "model.safetensors")
-    if os.path.exists(weights_path):
-        state_dict = st.load_file(weights_path)
-        model.load_state_dict(state_dict, strict=False)
-
-    model = model.to(dtype=torch.bfloat16)
-
     strategy_kwargs = {
         "beta": dpo_beta,
         "label_smoothing": label_smoothing,
@@ -271,6 +250,12 @@ def train(
         "sync_interval": grpo_sync_interval,
     }
 
+    executor_kwargs = {
+        "gradient_as_bucket_view": True,
+        "broadcast_buffers": False,
+    }
+
+    model_fn = partial(create_model, config)
     dataset = DatasetFactory.load(
         train_type=train_type,
         load_path=data_root_path,
@@ -302,7 +287,7 @@ def train(
     )
 
     train_config = TrainConfig(
-        model=model,
+        model_fn=model_fn,
         strategy=train_type,
         dataset=dataset,
         optimizer_fn=optimizer_fn,
@@ -319,15 +304,15 @@ def train(
         num_workers=num_workers,
         pin_memory=pin_memory,
         nprocs=nprocs,
-        parallel_wrapper=ddp_wrap,
+        parallel_mode=parallel_mode,
-        state_dict_fn=prepare_checkpoint,
         device_type=device_type,
         start_method=start_method,
+        executor_kwargs=executor_kwargs,
         extra_kwargs=strategy_kwargs,
     )
 
     trainer = Trainer(train_config)
-    trainer.train()
+    trainer.train(resume_dir=param_path)
 
 
 if __name__ == "__main__":

tests/data/test_checkpoint.py

@@ -1,3 +1,4 @@
+import os
 import tempfile
 
 import torch
@@ -36,7 +37,6 @@ def test_single_process():
 
 
 def test_checkpoint_with_extra():
-    """Verify extra keys are saved as individual .pt files and loaded back."""
     model = torch.nn.Linear(10, 5)
     optimizer = AdamW(model.parameters(), lr=1e-3)
     optimizer.step()
@@ -52,8 +52,6 @@ def test_checkpoint_with_extra():
     with tempfile.TemporaryDirectory() as tmpdir:
         checkpoint.save(tmpdir)
 
-        import os
-
         assert os.path.exists(os.path.join(tmpdir, "optimizer.pt"))
         assert os.path.exists(os.path.join(tmpdir, "scheduler.pt"))
 

tests/data/test_dataset.py

@@ -1,4 +1,3 @@
-import json
 import os
 
 import numpy as np
@@ -7,12 +6,11 @@ import torch
 
 from astrai.dataset.dataset import DatasetFactory, SEQDataset
 from astrai.dataset.storage import (
-    BaseSegmentFetcher,
+    H5Store,
-    H5Storage,
+    StoreFactory,
-    MultiSegmentFetcher,
-    StorageFactory,
     detect_format,
-    load_json,
+    load_bin,
+    save_bin,
     save_h5,
 )
 
@@ -157,111 +155,6 @@ def test_dataset_with_custom_stride(base_test_env):
     assert len(dataset) > len(default_stride_dataset)
 
 
-# ============== JSON Storage Tests (raw text + tokenizer) ==============
-
-
-def _make_tokenizer_fn(tokenizer):
-    """Wrap tokenizer.encode() as a str -> List[int] callable."""
-    return lambda text: tokenizer.encode(text, add_special_tokens=False)
-
-
-def test_seq_dataset_from_json_text(base_test_env):
-    """Test loading SEQ dataset from raw-text JSON with tokenizer"""
-    tokenizer = base_test_env["tokenizer"]
-    tokenizer_fn = _make_tokenizer_fn(tokenizer)
-    test_dir = base_test_env["test_dir"]
-    data_dir = os.path.join(test_dir, "json_text")
-    os.makedirs(data_dir, exist_ok=True)
-
-    texts = [
-        "hello world this is a test sentence for tokenizer",
-        "another sentence with different words and tokens",
-        "machine learning is fascinating and powerful",
-    ]
-
-    json_path = os.path.join(data_dir, "seq_data.json")
-    with open(json_path, "w", encoding="utf-8") as f:
-        json.dump({"sequence": texts}, f, ensure_ascii=False)
-
-    dataset = DatasetFactory.load(
-        train_type="seq",
-        load_path=data_dir,
-        window_size=16,
-        tokenizer=tokenizer_fn,
-    )
-    assert dataset is not None
-    assert len(dataset) > 0
-    assert dataset.count > 0
-    assert "sequence" in dataset.keys
-
-    item = dataset[0]
-    assert "input_ids" in item
-    assert "target_ids" in item
-    assert item["input_ids"].shape[0] == 16
-
-
-def test_sft_dataset_from_json_text(base_test_env):
-    """Test loading SFT dataset from raw-text JSON with tokenizer"""
-    tokenizer = base_test_env["tokenizer"]
-    tokenizer_fn = _make_tokenizer_fn(tokenizer)
-    test_dir = base_test_env["test_dir"]
-    data_dir = os.path.join(test_dir, "json_sft")
-    os.makedirs(data_dir, exist_ok=True)
-
-    texts = [
-        "user asks a question about the weather",
-        "assistant provides a helpful response to the user",
-    ]
-
-    json_path = os.path.join(data_dir, "sft_data.json")
-    with open(json_path, "w", encoding="utf-8") as f:
-        json.dump(
-            {"sequence": texts, "loss_mask": texts},
-            f,
-            ensure_ascii=False,
-        )
-
-    dataset = DatasetFactory.load(
-        train_type="sft",
-        load_path=data_dir,
-        window_size=16,
-        tokenizer=tokenizer_fn,
-    )
-    assert dataset is not None
-    assert len(dataset) > 0
-
-    item = dataset[0]
-    assert "loss_mask" in item
-
-
-def test_json_storage_explicit_tokenizer(base_test_env):
-    """Test explicit JSON storage with tokenizer"""
-    tokenizer = base_test_env["tokenizer"]
-    tokenizer_fn = _make_tokenizer_fn(tokenizer)
-    test_dir = base_test_env["test_dir"]
-    data_dir = os.path.join(test_dir, "json_explicit")
-    os.makedirs(data_dir, exist_ok=True)
-
-    texts = ["abcdefghijklmnopqrstuvwxyz" * 10]
-
-    json_path = os.path.join(data_dir, "data.json")
-    with open(json_path, "w", encoding="utf-8") as f:
-        json.dump({"sequence": texts}, f, ensure_ascii=False)
-
-    token_count = len(tokenizer_fn(texts[0]))
-
-    dataset = DatasetFactory.load(
-        train_type="seq",
-        load_path=data_dir,
-        window_size=32,
-        storage_type="json",
-        tokenizer=tokenizer_fn,
-    )
-    assert dataset is not None
-    assert len(dataset) > 0
-    assert dataset.count == token_count
-
-
 def test_dataset_count_property(base_test_env):
     """Test the count property returns correct raw token count"""
     test_dir = base_test_env["test_dir"]
@@ -318,37 +211,29 @@ def test_unloaded_dataset_len():
     assert len(dataset) == 0
 
 
-def test_base_segment_fetcher_empty():
+def test_store_unloaded_len():
-    """BaseSegmentFetcher with empty segments list"""
+    """Unloaded Store has __len__ == 0"""
-    fetcher = BaseSegmentFetcher([])
+    store = H5Store()
-    assert len(fetcher) == 0
+    assert len(store) == 0
-    with pytest.raises(ValueError, match="out of bounds"):
+    assert store.keys == []
-        fetcher.fetch_data(0, 1)
 
 
-def test_base_segment_fetcher_begin_equals_end(base_test_env):
+def test_store_fetch_begin_equals_end(base_test_env):
-    """fetch_data with begin == end returns empty tensor"""
+    """Store.fetch with begin == end returns empty tensor"""
     test_dir = base_test_env["test_dir"]
     dummy = {"sequence": [torch.randint(0, 1000, (100,), dtype=torch.int64)]}
     save_h5(test_dir, "empty_fetch", dummy)
 
     dataset = DatasetFactory.load("seq", test_dir, window_size=32)
-    fetcher = dataset.storage._fetcher.multi_fetchers["sequence"]
+    result = dataset.storage.fetch(10, 10, "sequence")
-    result = fetcher.fetch_data(10, 10)
     assert result.numel() == 0
 
 
-def test_multi_segment_fetcher_empty_dict():
+def test_store_fetch_before_load():
-    """MultiSegmentFetcher with empty dict has __len__ == 0"""
+    """Store.fetch before load raises RuntimeError"""
-    fetcher = MultiSegmentFetcher({})
+    store = H5Store()
-    assert len(fetcher) == 0
-
-
-def test_storage_fetch_before_load():
-    """BaseStorage.fetch before load raises RuntimeError"""
-    storage = H5Storage()
     with pytest.raises(RuntimeError, match="not loaded"):
-        storage.fetch(0, 10, "sequence")
+        store.fetch(0, 10, "sequence")
 
 
 def test_detect_format_nonexistent_path():
@@ -367,54 +252,192 @@ def test_detect_format_unsupported_file(base_test_env):
         detect_format(path)
 
 
-def test_create_storage_invalid_type():
+def test_create_store_invalid_type():
-    """StorageFactory.create raises ValueError for unknown type"""
+    """StoreFactory.create raises ValueError for unknown type"""
     with pytest.raises(ValueError, match="Unknown component"):
-        StorageFactory.create("parquet")
+        StoreFactory.create("parquet")
 
 
-def test_json_pretokenized_without_tokenizer(base_test_env):
+def test_store_multi_segment_concat(base_test_env):
-    """Pre-tokenized JSON (List[List[int]]) loads without tokenizer"""
+    """Multi-segment H5 data is concatenated into single tensor at load time"""
+    import os
+
     test_dir = base_test_env["test_dir"]
-    data_dir = os.path.join(test_dir, "json_pretok")
+    data_dir = os.path.join(test_dir, "multi_seg")
     os.makedirs(data_dir, exist_ok=True)
 
-    json_path = os.path.join(data_dir, "data.json")
-    with open(json_path, "w", encoding="utf-8") as f:
-        json.dump({"sequence": [[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]]}, f)
-
-    dataset = DatasetFactory.load("seq", data_dir, window_size=4, storage_type="json")
-    assert len(dataset) > 0
-    assert dataset.count == 10
-
-    item = dataset[0]
-    assert item["input_ids"].tolist() == [1, 2, 3, 4]
-    assert item["target_ids"].tolist() == [2, 3, 4, 5]
-
-
-def test_load_json_skips_config_file(base_test_env):
-    """load_json skips scalar-value config files"""
-    test_dir = base_test_env["test_dir"]
-    with open(os.path.join(test_dir, "config.json"), "w") as f:
-        json.dump({"vocab_size": 1000, "dim": 16}, f)
-
-    with open(os.path.join(test_dir, "data.json"), "w") as f:
-        json.dump({"sequence": [[1, 2, 3, 4, 5]]}, f)
-
-    result = load_json(test_dir)
-    assert "sequence" in result
-    assert "vocab_size" not in result
-    assert len(result["sequence"]) == 1
-
-
-def test_base_segment_fetcher_multi_segment():
-    """fetch_data across multiple segment boundaries"""
     segs = [
         torch.tensor([1, 2, 3]),
         torch.tensor([4, 5, 6, 7]),
         torch.tensor([8, 9]),
     ]
-    fetcher = BaseSegmentFetcher(segs)
+    save_h5(data_dir, "data", {"sequence": segs})
-    assert len(fetcher) == 9
+
-    result = fetcher.fetch_data(2, 7)
+    store = StoreFactory.create("h5")
+    store.load(data_dir)
+    assert len(store) == 9
+    result = store.fetch(2, 7, "sequence")
     assert result.tolist() == [3, 4, 5, 6, 7]
+
+
+def test_save_load_bin_roundtrip(base_test_env):
+    """save_bin + load_bin roundtrip preserves data"""
+    test_dir = base_test_env["test_dir"]
+
+    data = {
+        "sequence": [torch.tensor([1, 2, 3, 4, 5], dtype=torch.int64)],
+        "loss_mask": [torch.tensor([0, 1, 1, 0, 1], dtype=torch.int64)],
+    }
+    save_bin(test_dir, data)
+    result = load_bin(test_dir)
+
+    assert "sequence" in result
+    assert "loss_mask" in result
+    assert result["sequence"][0].tolist() == [1, 2, 3, 4, 5]
+    assert result["loss_mask"][0].tolist() == [0, 1, 1, 0, 1]
+
+
+def test_mmap_store_load_and_fetch(base_test_env):
+    """MmapStore loads bin data and fetches correctly"""
+    test_dir = base_test_env["test_dir"]
+
+    data = {
+        "sequence": [torch.randint(0, 1000, (200,), dtype=torch.int64)],
+    }
+    save_bin(test_dir, data)
+
+    store = StoreFactory.create("bin")
+    store.load(test_dir)
+    assert len(store) == 200
+    assert "sequence" in store.keys
+
+    result = store.fetch(10, 20, "sequence")
+    assert result.tolist() == data["sequence"][0][10:20].tolist()
+
+
+def test_mmap_dataset_load(base_test_env):
+    """DatasetFactory.load auto-detects bin format"""
+    test_dir = base_test_env["test_dir"]
+
+    data = {
+        "sequence": [torch.randint(0, 1000, (200,), dtype=torch.int64)],
+    }
+    save_bin(test_dir, data)
+
+    dataset = DatasetFactory.load("seq", test_dir, window_size=64)
+    assert len(dataset) > 0
+    assert dataset.count == 200
+    assert dataset[0]["input_ids"].shape[0] == 64
+
+
+def test_normalize_empty_key():
+    """_normalize with empty tensor list does not crash"""
+    store = H5Store()
+    store._normalize({"sequence": []})
+    assert len(store) == 0
+    assert store.keys == ["sequence"]
+
+
+def test_normalize_mixed_empty_key():
+    """_normalize with empty + non-empty keys returns min=0"""
+    store = H5Store()
+    store._normalize({"sequence": [torch.tensor([1, 2, 3])], "loss_mask": []})
+    assert len(store) == 0
+    assert set(store.keys) == {"sequence", "loss_mask"}
+
+
+def test_grpo_dataset_dtype(base_test_env):
+    """GRPODataset returns correct dtypes"""
+    test_dir = base_test_env["test_dir"]
+
+    seq_len = 100
+    data = {
+        "prompts": [torch.randint(0, 100, (seq_len,), dtype=torch.int32)],
+        "responses": [torch.randint(0, 100, (seq_len,), dtype=torch.int32)],
+        "masks": [torch.ones(seq_len, dtype=torch.int32)],
+        "rewards": [torch.ones(seq_len, dtype=torch.float32)],
+    }
+    save_h5(test_dir, "grpo_dtype", data)
+
+    dataset = DatasetFactory.load("grpo", test_dir, window_size=32)
+    item = dataset[0]
+
+    assert item["prompts"].dtype == torch.long
+    assert item["responses"].dtype == torch.long
+    assert item["masks"].dtype == torch.bool
+    assert item["rewards"].dtype == torch.float32
+
+
+def test_grpo_dataset_load(base_test_env):
+    """GRPODataset loads and returns correct keys"""
+    test_dir = base_test_env["test_dir"]
+    seq_len = 200
+    data = {
+        "prompts": [torch.randint(0, 1000, (seq_len,), dtype=torch.int64)],
+        "responses": [torch.randint(0, 1000, (seq_len,), dtype=torch.int64)],
+        "masks": [torch.ones(seq_len, dtype=torch.int64)],
+        "rewards": [torch.rand(seq_len, dtype=torch.float32)],
+    }
+    save_h5(test_dir, "grpo_test", data)
+
+    dataset = DatasetFactory.load("grpo", test_dir, window_size=64)
+    assert len(dataset) > 0
+    item = dataset[0]
+    assert "prompts" in item
+    assert "responses" in item
+    assert "masks" in item
+    assert "rewards" in item
+    assert item["prompts"].shape[0] == 64
+    assert item["responses"].shape[0] == 64
+
+
+def test_detect_format_bin_dir(base_test_env):
+    """detect_format returns 'bin' for directory with .bin + meta.json"""
+    test_dir = base_test_env["test_dir"]
+    save_bin(test_dir, {"sequence": [torch.randint(0, 100, (10,))]})
+    assert detect_format(test_dir) == "bin"
+
+
+def test_store_fetch_multi_key(base_test_env):
+    """Store.fetch with List[str] returns Dict[str, Tensor]"""
+    test_dir = base_test_env["test_dir"]
+    save_h5(
+        test_dir,
+        "multi_key",
+        {
+            "sequence": [torch.randint(0, 100, (100,), dtype=torch.int64)],
+            "loss_mask": [torch.ones(100, dtype=torch.int64)],
+        },
+    )
+
+    store = StoreFactory.create("h5")
+    store.load(test_dir)
+    result = store.fetch(10, 20, ["sequence", "loss_mask"])
+    assert isinstance(result, dict)
+    assert result["sequence"].shape[0] == 10
+    assert result["loss_mask"].shape[0] == 10
+
+
+def test_store_fetch_out_of_bounds(base_test_env):
+    """Store.fetch raises ValueError for out-of-bounds indices"""
+    test_dir = base_test_env["test_dir"]
+    save_h5(test_dir, "bounds", {"sequence": [torch.randint(0, 100, (50,))]})
+
+    store = StoreFactory.create("h5")
+    store.load(test_dir)
+    with pytest.raises(ValueError, match="out of bounds"):
+        store.fetch(-1, 10, "sequence")
+    with pytest.raises(ValueError, match="out of bounds"):
+        store.fetch(0, 51, "sequence")
+    with pytest.raises(ValueError, match="out of bounds"):
+        store.fetch(50, 50, "sequence")
+
+
+def test_dataset_load_explicit_storage_type(base_test_env):
+    """DatasetFactory.load with explicit storage_type bypasses auto-detect"""
+    test_dir = base_test_env["test_dir"]
+    save_h5(test_dir, "explicit", {"sequence": [torch.randint(0, 100, (200,))]})
+
+    dataset = DatasetFactory.load("seq", test_dir, window_size=64, storage_type="h5")
+    assert len(dataset) > 0
+    assert dataset.count == 200

tests/data/test_preprocess.py

@@ -0,0 +1,603 @@
+import json
+import os
+import tempfile
+
+import pytest
+from tokenizers import Tokenizer, models, pre_tokenizers, trainers
+
+from astrai.config.preprocess_config import (
+    InputConfig,
+    OutputConfig,
+    PipelineConfig,
+    ProcessingConfig,
+)
+from astrai.preprocessing.builder import (
+    ChatMaskBuilder,
+    InstructionMaskBuilder,
+    MaskBuilderFactory,
+    TextMaskBuilder,
+)
+from astrai.preprocessing.pipeline import Pipeline, dedup_signature, filter_by_length
+from astrai.tokenize import AutoTokenizer
+
+_SPECIAL_TOKENS = [
+    "<unk>",
+    "<pad>",
+    "<|begin_of_sentence|>",
+    "<|end_of_sentence|>",
+    "<|im_start|>",
+    "<|im_end|>",
+]
+
+_CHAT_TEMPLATE = (
+    "{% for message in messages %}"
+    "{% if message['role'] == 'system' %}"
+    "<|im_start|>system\n{{ message['content'] }}<|im_end|>\n"
+    "{% elif message['role'] == 'user' %}"
+    "<|im_start|>user\n{{ message['content'] }}<|im_end|>\n"
+    "{% elif message['role'] == 'assistant' %}"
+    "<|im_start|>assistant\n{{ message['content'] }}<|im_end|>\n"
+    "{% endif %}"
+    "{% endfor %}"
+    "{% if add_generation_prompt %}<|im_start|>assistant\n{% endif %}"
+)
+
+
+def _build_chat_tokenizer() -> AutoTokenizer:
+    tok = Tokenizer(models.BPE())
+    tok.pre_tokenizer = pre_tokenizers.ByteLevel(add_prefix_space=False)
+    tr = trainers.BpeTrainer(
+        vocab_size=512,
+        min_frequency=1,
+        special_tokens=_SPECIAL_TOKENS,
+    )
+    train_data = [
+        "hello world",
+        "Hi there!",
+        "You are helpful.",
+        "What is 2+2?",
+        "Tell me a story about dragons and knights.",
+        "Sure, here is a tale.",
+        "Translate to French: Hello",
+        "Bonjour",
+        "Artificial Intelligence is a field of computer science.",
+        "system",
+        "user",
+        "assistant",
+        "<|im_start|>",
+        "<|im_end|>",
+        *[chr(i) for i in range(32, 127)],
+    ]
+    tok.train_from_iterator(train_data, tr)
+
+    auto_tok = AutoTokenizer()
+    auto_tok._tokenizer = tok
+    auto_tok._special_token_map = {
+        "bos_token": "<|begin_of_sentence|>",
+        "eos_token": "<|end_of_sentence|>",
+        "pad_token": "<pad>",
+        "unk_token": "<unk>",
+    }
+    auto_tok.set_chat_template(_CHAT_TEMPLATE)
+    return auto_tok
+
+
+@pytest.fixture(scope="session")
+def chat_tokenizer():
+    return _build_chat_tokenizer()
+
+
+@pytest.fixture
+def temp_dir():
+    d = tempfile.mkdtemp()
+    yield d
+    import shutil
+
+    shutil.rmtree(d, ignore_errors=True)
+
+
+def make_chat_config():
+    return PipelineConfig(
+        input=InputConfig(type="chat", messages_key="messages"),
+        mask={"system": "mask", "user": "mask", "assistant": "train"},
+        mask_default="mask",
+        preprocessing=ProcessingConfig(max_seq_len=2048),
+    )
+
+
+def make_instruction_config():
+    return PipelineConfig(
+        input=InputConfig(
+            type="instruction", prompt_key="prompt", response_key="response"
+        ),
+        mask={"prompt": "mask", "response": "train"},
+        mask_default="mask",
+        preprocessing=ProcessingConfig(max_seq_len=2048),
+    )
+
+
+def make_text_config():
+    return PipelineConfig(
+        input=InputConfig(type="text", text_key="text"),
+        preprocessing=ProcessingConfig(
+            max_seq_len=2048, min_chars=1, max_chars=2_000_000
+        ),
+    )
+
+
+class TestPipelineConfig:
+    def test_default_values(self):
+        config = PipelineConfig()
+        assert config.version == 1
+        assert config.input.type == "chat"
+        assert config.mask == {}
+        assert config.mask_default == "mask"
+        assert config.preprocessing.max_seq_len == 2048
+        assert config.output.storage_format == "bin"
+
+    def test_from_dict_flat(self):
+        data = {
+            "version": 1,
+            "input": {"type": "chat", "messages_key": "msgs"},
+            "mask": {"system": "mask", "assistant": "train"},
+            "mask_default": "mask",
+            "preprocessing": {"max_seq_len": 1024},
+            "output": {"storage_format": "h5"},
+        }
+        config = PipelineConfig.from_dict(data)
+        assert config.input.type == "chat"
+        assert config.input.messages_key == "msgs"
+        assert config.mask == {"system": "mask", "assistant": "train"}
+        assert config.preprocessing.max_seq_len == 1024
+        assert config.output.storage_format == "h5"
+
+    def test_to_dict_roundtrip(self):
+        config = PipelineConfig(
+            input=InputConfig(type="instruction", prompt_key="q", response_key="a"),
+            mask={"prompt": "mask", "response": "train"},
+            mask_default="mask",
+        )
+        d = config.to_dict()
+        config2 = PipelineConfig.from_dict(d)
+        assert config2.input.type == "instruction"
+        assert config2.input.prompt_key == "q"
+        assert config2.mask == {"prompt": "mask", "response": "train"}
+
+    def test_to_json_from_json(self, temp_dir):
+        config = PipelineConfig(
+            input=InputConfig(type="text", text_key="body"),
+            mask={"text": "train"},
+            mask_default="mask",
+        )
+        path = os.path.join(temp_dir, "config.json")
+        config.to_json(path)
+        loaded = PipelineConfig.from_json(path)
+        assert loaded.input.type == "text"
+        assert loaded.input.text_key == "body"
+        assert loaded.mask == {"text": "train"}
+
+
+class TestChatMaskBuilder:
+    def test_simple_chat_mask(self, chat_tokenizer):
+        config = make_chat_config()
+        builder = ChatMaskBuilder()
+        item = {
+            "messages": [
+                {"role": "system", "content": "You are helpful."},
+                {"role": "user", "content": "Hello."},
+                {"role": "assistant", "content": "Hi there!"},
+            ]
+        }
+        result = builder.build(item, config, chat_tokenizer)
+        assert result is not None
+        assert "ids" in result
+        assert "loss_mask" in result
+        assert len(result["ids"]) == len(result["loss_mask"])
+
+        ids = chat_tokenizer.decode(result["ids"], skip_special_tokens=False)
+
+        assert "system" in ids.lower() or "<|im_start|>system" in ids
+        assert "assistant" in ids.lower() or "<|im_start|>assistant" in ids
+
+        total = len(result["ids"])
+        trained = sum(result["loss_mask"])
+        assert trained > 0, "At least assistant tokens should be trained"
+        assert trained < total, "System and user tokens should be masked"
+
+    def test_mask_only_assistant_trained(self, chat_tokenizer):
+        config = make_chat_config()
+        builder = ChatMaskBuilder()
+        item = {
+            "messages": [
+                {"role": "user", "content": "What is 2+2?"},
+                {"role": "assistant", "content": "4"},
+            ]
+        }
+        result = builder.build(item, config, chat_tokenizer)
+        mask = result["loss_mask"]
+        ids = result["ids"]
+
+        assert len(ids) == len(mask)
+
+        trained_positions = [i for i, m in enumerate(mask) if m == 1]
+        assert len(trained_positions) > 0, "At least some tokens should be trained"
+
+        masked_positions = [i for i, m in enumerate(mask) if m == 0]
+        assert len(masked_positions) > 0, "User tokens should be masked"
+
+    def test_chat_all_masked(self, chat_tokenizer):
+        config = PipelineConfig(
+            input=InputConfig(type="chat", messages_key="messages"),
+            mask={"system": "mask", "user": "mask", "assistant": "mask"},
+            mask_default="mask",
+            preprocessing=ProcessingConfig(max_seq_len=2048),
+        )
+        builder = ChatMaskBuilder()
+        item = {
+            "messages": [
+                {"role": "system", "content": "You are helpful."},
+                {"role": "assistant", "content": "Hi there!"},
+            ]
+        }
+        result = builder.build(item, config, chat_tokenizer)
+        assert sum(result["loss_mask"]) == 0
+
+    def test_chat_all_trained(self, chat_tokenizer):
+        config = PipelineConfig(
+            input=InputConfig(type="chat", messages_key="messages"),
+            mask={},
+            mask_default="train",
+            preprocessing=ProcessingConfig(max_seq_len=2048),
+        )
+        builder = ChatMaskBuilder()
+        item = {
+            "messages": [
+                {"role": "system", "content": "You are helpful."},
+                {"role": "assistant", "content": "Hi there!"},
+            ]
+        }
+        result = builder.build(item, config, chat_tokenizer)
+        assert sum(result["loss_mask"]) == len(result["ids"]) - 1
+
+    def test_empty_messages_returns_none(self, chat_tokenizer):
+        config = make_chat_config()
+        builder = ChatMaskBuilder()
+        assert builder.build({"messages": []}, config, chat_tokenizer) is None
+        assert builder.build({}, config, chat_tokenizer) is None
+
+    def test_domain_extraction(self, chat_tokenizer):
+        config = PipelineConfig(
+            input=InputConfig(type="chat", messages_key="messages"),
+            mask={"assistant": "train"},
+            mask_default="mask",
+            preprocessing=ProcessingConfig(max_seq_len=2048),
+            output=OutputConfig(domain_key="source"),
+        )
+        builder = ChatMaskBuilder()
+        item = {
+            "messages": [
+                {"role": "user", "content": "Hi"},
+                {"role": "assistant", "content": "Hello"},
+            ],
+            "source": "wiki",
+        }
+        result = builder.build(item, config, chat_tokenizer)
+        assert result["domain"] == "wiki"
+
+    def test_truncation_to_max_len(self, chat_tokenizer):
+        config = PipelineConfig(
+            input=InputConfig(type="chat", messages_key="messages"),
+            mask={"assistant": "train"},
+            mask_default="mask",
+            preprocessing=ProcessingConfig(max_seq_len=10),
+        )
+        builder = ChatMaskBuilder()
+        item = {
+            "messages": [
+                {
+                    "role": "user",
+                    "content": "Tell me a very long story about dragons and knights and magic.",
+                },
+                {"role": "assistant", "content": "Sure! Here is a tale..."},
+            ]
+        }
+        result = builder.build(item, config, chat_tokenizer)
+        assert len(result["ids"]) <= 10
+        assert len(result["loss_mask"]) == len(result["ids"])
+
+
+class TestInstructionMaskBuilder:
+    def test_basic_instruction_mask(self, test_tokenizer):
+        config = make_instruction_config()
+        builder = InstructionMaskBuilder()
+        item = {"prompt": "Translate to French: Hello", "response": "Bonjour"}
+        result = builder.build(item, config, test_tokenizer)
+        assert result is not None
+        assert len(result["ids"]) == len(result["loss_mask"])
+
+    def test_prompt_masked_response_trained(self, test_tokenizer):
+        config = make_instruction_config()
+        builder = InstructionMaskBuilder()
+        item = {"prompt": "hello", "response": "world"}
+        result = builder.build(item, config, test_tokenizer)
+        mask = result["loss_mask"]
+        ids = result["ids"]
+
+        prompt_ids = test_tokenizer.encode("hello", add_special_tokens=True)
+        response_ids = test_tokenizer.encode("world", add_special_tokens=False)
+
+        p_len = min(len(prompt_ids), len(ids))
+        assert all(m == 0 for m in mask[:p_len])
+
+        if p_len < len(ids):
+            assert all(m == 1 for m in mask[p_len:])
+
+    def test_train_on_prompt(self, test_tokenizer):
+        config = PipelineConfig(
+            input=InputConfig(
+                type="instruction", prompt_key="prompt", response_key="response"
+            ),
+            mask={"prompt": "train", "response": "mask"},
+            mask_default="mask",
+            preprocessing=ProcessingConfig(max_seq_len=2048),
+        )
+        builder = InstructionMaskBuilder()
+        item = {"prompt": "hello", "response": "world"}
+        result = builder.build(item, config, test_tokenizer)
+        mask = result["loss_mask"]
+        ids = result["ids"]
+
+        prompt_ids = test_tokenizer.encode("hello", add_special_tokens=True)
+        p_len = min(len(prompt_ids), len(ids))
+        assert all(m == 1 for m in mask[:p_len])
+
+
+class TestTextMaskBuilder:
+    def test_basic_text(self, test_tokenizer):
+        config = make_text_config()
+        builder = TextMaskBuilder()
+        item = {"text": "Hello world. This is a test document."}
+        result = builder.build(item, config, test_tokenizer)
+        assert result is not None
+        assert "ids" in result
+        assert len(result["ids"]) > 0
+        assert "loss_mask" not in result
+
+    def test_empty_text_returns_none(self, test_tokenizer):
+        config = make_text_config()
+        builder = TextMaskBuilder()
+        assert builder.build({"text": ""}, config, test_tokenizer) is None
+        assert builder.build({"text": "   "}, config, test_tokenizer) is None
+
+    def test_too_short_text(self, test_tokenizer):
+        config = PipelineConfig(
+            input=InputConfig(type="text", text_key="text"),
+            preprocessing=ProcessingConfig(min_chars=100),
+        )
+        builder = TextMaskBuilder()
+        assert builder.build({"text": "short"}, config, test_tokenizer) is None
+
+    def test_truncation(self, test_tokenizer):
+        config = PipelineConfig(
+            input=InputConfig(type="text", text_key="text"),
+            preprocessing=ProcessingConfig(max_seq_len=3, min_chars=1),
+        )
+        builder = TextMaskBuilder()
+        item = {"text": "This is a very long text that should be truncated"}
+        result = builder.build(item, config, test_tokenizer)
+        assert len(result["ids"]) <= 3
+
+
+class TestPipeline:
+    def test_full_chat_pipeline(self, temp_dir, chat_tokenizer):
+        tokenizer_dir = os.path.join(temp_dir, "tok")
+        os.makedirs(tokenizer_dir, exist_ok=True)
+        chat_tokenizer._tokenizer.save(os.path.join(tokenizer_dir, "tokenizer.json"))
+        with open(os.path.join(tokenizer_dir, "tokenizer_config.json"), "w") as f:
+            json.dump(
+                {
+                    "special_tokens": {
+                        "bos_token": "<|begin_of_sentence|>",
+                        "eos_token": "<|end_of_sentence|>",
+                        "pad_token": "<pad>",
+                        "unk_token": "<unk>",
+                        "im_start": "<|im_start|>",
+                        "im_end": "<|im_end|>",
+                    },
+                    "chat_template": _CHAT_TEMPLATE,
+                },
+                f,
+            )
+
+        jsonl_path = os.path.join(temp_dir, "chat.jsonl")
+        with open(jsonl_path, "w", encoding="utf-8") as f:
+            f.write(
+                json.dumps(
+                    {
+                        "messages": [
+                            {"role": "system", "content": "You are helpful."},
+                            {"role": "user", "content": "Hi."},
+                            {"role": "assistant", "content": "Hello!"},
+                        ]
+                    }
+                )
+                + "\n"
+            )
+            f.write(
+                json.dumps(
+                    {
+                        "messages": [
+                            {"role": "user", "content": "What is 2+2?"},
+                            {"role": "assistant", "content": "4"},
+                        ]
+                    }
+                )
+                + "\n"
+            )
+
+        config = PipelineConfig(
+            input=InputConfig(type="chat", messages_key="messages"),
+            mask={"system": "mask", "user": "mask", "assistant": "train"},
+            mask_default="mask",
+            preprocessing=ProcessingConfig(max_seq_len=2048, deduplicate=True),
+            output=OutputConfig(storage_format="bin", domain_key=None),
+        )
+
+        out_dir = os.path.join(temp_dir, "output")
+        Pipeline(
+            config=config,
+            input_paths=[jsonl_path],
+            output_dir=out_dir,
+            tokenizer_path=tokenizer_dir,
+        ).run()
+
+        meta_path = os.path.join(out_dir, "__default__", "meta.json")
+        assert os.path.exists(meta_path)
+        with open(meta_path, "r") as f:
+            meta = json.load(f)
+        assert "sequence" in meta
+        assert "loss_mask" in meta
+
+    def test_full_text_pipeline(self, temp_dir, test_tokenizer):
+        import tempfile as tmp
+
+        tokenizer_dir = os.path.join(temp_dir, "tok")
+        os.makedirs(tokenizer_dir, exist_ok=True)
+
+        test_tokenizer._tokenizer.save(os.path.join(tokenizer_dir, "tokenizer.json"))
+        with open(os.path.join(tokenizer_dir, "tokenizer_config.json"), "w") as f:
+            json.dump(
+                {"special_tokens": {"pad_token": "<pad>", "unk_token": "<unk>"}}, f
+            )
+
+        jsonl_path = os.path.join(temp_dir, "text.jsonl")
+        with open(jsonl_path, "w", encoding="utf-8") as f:
+            f.write(
+                json.dumps(
+                    {
+                        "text": "Hello world this is a test document with enough characters to pass the minimum length filter."
+                    }
+                )
+                + "\n"
+            )
+            f.write(
+                json.dumps(
+                    {
+                        "text": "Another document for testing purposes with sufficient length to be processed."
+                    }
+                )
+                + "\n"
+            )
+
+        config = PipelineConfig(
+            input=InputConfig(type="text", text_key="text"),
+            preprocessing=ProcessingConfig(
+                max_seq_len=2048, min_chars=10, deduplicate=True
+            ),
+            output=OutputConfig(storage_format="bin"),
+        )
+
+        out_dir = os.path.join(temp_dir, "output")
+        Pipeline(
+            config=config,
+            input_paths=[jsonl_path],
+            output_dir=out_dir,
+            tokenizer_path=tokenizer_dir,
+        ).run()
+
+        meta_path = os.path.join(out_dir, "__default__", "meta.json")
+        assert os.path.exists(meta_path)
+        with open(meta_path, "r") as f:
+            meta = json.load(f)
+        assert "sequence" in meta
+        assert "loss_mask" not in meta
+
+    def test_full_instruction_pipeline(self, temp_dir, test_tokenizer):
+        tokenizer_dir = os.path.join(temp_dir, "tok")
+        os.makedirs(tokenizer_dir, exist_ok=True)
+        test_tokenizer._tokenizer.save(os.path.join(tokenizer_dir, "tokenizer.json"))
+        with open(os.path.join(tokenizer_dir, "tokenizer_config.json"), "w") as f:
+            json.dump(
+                {"special_tokens": {"pad_token": "<pad>", "unk_token": "<unk>"}}, f
+            )
+
+        jsonl_path = os.path.join(temp_dir, "instruct.jsonl")
+        with open(jsonl_path, "w", encoding="utf-8") as f:
+            f.write(
+                json.dumps(
+                    {
+                        "prompt": "Tell me a joke",
+                        "response": "Why did the chicken cross the road?",
+                    }
+                )
+                + "\n"
+            )
+            f.write(
+                json.dumps(
+                    {
+                        "prompt": "What is AI?",
+                        "response": "Artificial Intelligence is a field of computer science.",
+                    }
+                )
+                + "\n"
+            )
+
+        config = PipelineConfig(
+            input=InputConfig(
+                type="instruction", prompt_key="prompt", response_key="response"
+            ),
+            mask={"prompt": "mask", "response": "train"},
+            mask_default="mask",
+            preprocessing=ProcessingConfig(max_seq_len=2048),
+            output=OutputConfig(storage_format="bin"),
+        )
+
+        out_dir = os.path.join(temp_dir, "output")
+        Pipeline(
+            config=config,
+            input_paths=[jsonl_path],
+            output_dir=out_dir,
+            tokenizer_path=tokenizer_dir,
+        ).run()
+
+        meta_path = os.path.join(out_dir, "__default__", "meta.json")
+        assert os.path.exists(meta_path)
+        with open(meta_path, "r") as f:
+            meta = json.load(f)
+        assert "sequence" in meta
+        assert "loss_mask" in meta
+
+
+class TestUtility:
+    def test_filter_by_length(self):
+        assert filter_by_length("hello world", min_len=5)
+        assert not filter_by_length("hi", min_len=5)
+        assert not filter_by_length("x" * 100, max_len=50)
+        assert filter_by_length("just right", min_len=5, max_len=20)
+
+    def test_dedup_signature(self):
+        a = {"key": "value", "number": 1}
+        b = {"number": 1, "key": "value"}
+        assert dedup_signature(a) == dedup_signature(b)
+        c = {"key": "different"}
+        assert dedup_signature(a) != dedup_signature(c)
+
+
+class TestFactoryRegistration:
+    def test_registered_builders(self):
+        names = MaskBuilderFactory._registry.list_names()
+        assert "chat" in names
+        assert "instruction" in names
+        assert "text" in names
+
+    def test_create_chat_builder(self):
+        builder = MaskBuilderFactory.create("chat")
+        assert isinstance(builder, ChatMaskBuilder)
+
+    def test_create_instruction_builder(self):
+        builder = MaskBuilderFactory.create("instruction")
+        assert isinstance(builder, InstructionMaskBuilder)
+
+    def test_create_text_builder(self):
+        builder = MaskBuilderFactory.create("text")
+        assert isinstance(builder, TextMaskBuilder)

tests/inference/test_protocol.py

@@ -0,0 +1,286 @@
+"""Unit tests for protocol builders, StopChecker, GenContext, StopInfo."""
+
+import json
+from unittest.mock import MagicMock
+
+import pytest
+
+from astrai.inference.api.anthropic import AnthropicResponseBuilder
+from astrai.inference.api.openai import OpenAIResponseBuilder
+from astrai.inference.api.protocol import GenContext, StopChecker, StopInfo
+from astrai.inference.engine import GenerationRequest
+
+
+def _make_ctx(**kwargs):
+    defaults = {
+        "resp_id": "test-123",
+        "created": 1000,
+        "model": "test-model",
+        "prompt_tokens": 10,
+        "completion_tokens": 5,
+    }
+    defaults.update(kwargs)
+    return GenContext(**defaults)
+
+
+def _sse_payloads(events):
+    payloads = []
+    for chunk in events:
+        for line in chunk.strip().split("\n"):
+            if line.startswith("data: "):
+                try:
+                    payloads.append(json.loads(line[6:]))
+                except json.JSONDecodeError:
+                    pass
+    return payloads
+
+
+class TestStopChecker:
+    def test_check_finds_match(self):
+        sc = StopChecker(["stop", "end"])
+        assert sc.check("hello stop world") == "stop"
+
+    def test_check_returns_none_when_no_match(self):
+        sc = StopChecker(["stop"])
+        assert sc.check("hello world") is None
+
+    def test_check_empty_sequences(self):
+        sc = StopChecker([])
+        assert sc.check("hello") is None
+
+
+class TestGenContext:
+    def test_defaults(self):
+        ctx = GenContext(resp_id="a", created=1, model="m", prompt_tokens=10)
+        assert ctx.completion_tokens == 0
+
+    def test_fields_mutable(self):
+        ctx = GenContext(resp_id="a", created=1, model="m", prompt_tokens=10)
+        ctx.completion_tokens = 42
+        assert ctx.completion_tokens == 42
+
+
+class TestStopInfo:
+    def test_defaults(self):
+        s = StopInfo()
+        assert s.matched is None
+        assert s.body == ""
+        assert s.yielded == ""
+
+    def test_with_values(self):
+        s = StopInfo(matched="stop", body="hello stop", yielded="hello ")
+        assert s.matched == "stop"
+        assert s.body == "hello stop"
+        assert s.yielded == "hello "
+
+
+class TestOpenAIResponseBuilder:
+    @pytest.fixture
+    def builder(self):
+        builder = OpenAIResponseBuilder()
+        req = MagicMock()
+        req.messages = [MagicMock(role="user", content="Hello")]
+        req.stop = None
+        req.model = "astrai"
+        engine = MagicMock()
+        engine.tokenizer.apply_chat_template.return_value = "Hello"
+        builder.prepare(req, engine)
+        return builder
+
+    def test_prepare_returns_prompt_ctx_stops(self, builder):
+        req = MagicMock()
+        req.messages = [MagicMock(role="user", content="Hi")]
+        req.stop = ["END"]
+        req.model = "gpt"
+        engine = MagicMock()
+        engine.tokenizer.apply_chat_template.return_value = "Hi"
+        prompt, ctx, stops = builder.prepare(req, engine)
+        assert prompt == "Hi"
+        assert ctx.model == "gpt"
+        assert ctx.prompt_tokens == 0
+        assert stops == ["END"]
+
+    def test_prepare_no_stop_returns_empty_list(self, builder):
+        req = MagicMock()
+        req.messages = []
+        req.stop = None
+        req.model = "x"
+        engine = MagicMock()
+        engine.tokenizer.apply_chat_template.return_value = ""
+        _, _, stops = builder.prepare(req, engine)
+        assert stops == []
+
+    def test_format_stream_start(self, builder):
+        ctx = _make_ctx()
+        events = builder.format_stream_start(ctx)
+        payloads = _sse_payloads(events)
+        assert len(payloads) == 1
+        p = payloads[0]
+        assert p["object"] == "chat.completion.chunk"
+        assert p["choices"][0]["delta"]["role"] == "assistant"
+        assert p["choices"][0]["finish_reason"] is None
+
+    def test_format_chunk(self, builder):
+        event = builder.format_chunk("hello")
+        payload = json.loads(event.split("data: ", 1)[1])
+        assert payload["choices"][0]["delta"]["content"] == "hello"
+        assert payload["choices"][0]["finish_reason"] is None
+
+    def test_format_stream_end(self, builder):
+        ctx = _make_ctx(completion_tokens=5)
+        stop = StopInfo(matched="stop")
+        events = builder.format_stream_end(ctx, stop)
+        payloads = _sse_payloads(events)
+        finish = payloads[0]
+        assert finish["choices"][0]["finish_reason"] == "stop"
+        usage = payloads[1]
+        assert usage["completion_tokens"] == 5
+        assert usage["total_tokens"] == 15
+
+    def test_format_response(self, builder):
+        ctx = _make_ctx()
+        stop = StopInfo()
+        resp = builder.format_response(ctx, "hello", stop)
+        assert resp["object"] == "chat.completion"
+        assert resp["choices"][0]["message"]["content"] == "hello"
+        assert resp["usage"]["prompt_tokens"] == 10
+
+
+class TestAnthropicResponseBuilder:
+    @pytest.fixture
+    def builder(self):
+        builder = AnthropicResponseBuilder()
+        req = MagicMock()
+        req.messages = [MagicMock(role="user", content="Hello")]
+        req.model = "claude"
+        engine = MagicMock()
+        engine.tokenizer.apply_chat_template.return_value = "Hello"
+        req.system = None
+        builder.prepare(req, engine)
+        return builder
+
+    def test_prepare_messages(self, builder):
+        req = MagicMock()
+        req.messages = [MagicMock(role="user", content="Hi")]
+        req.model = "claude"
+        req.system = None
+        req.stop_sequences = None
+        engine = MagicMock()
+        engine.tokenizer.apply_chat_template.return_value = "Hi"
+        prompt, ctx, stops = builder.prepare(req, engine)
+        assert prompt == "Hi"
+        assert stops == []
+
+    def test_prepare_with_stop_sequences(self, builder):
+        req = MagicMock()
+        req.messages = []
+        req.model = "x"
+        req.stop_sequences = ["stop", "end"]
+        req.system = None
+        engine = MagicMock()
+        engine.tokenizer.apply_chat_template.return_value = ""
+        _, _, stops = builder.prepare(req, engine)
+        assert stops == ["stop", "end"]
+
+    def test_format_stream_start(self, builder):
+        ctx = _make_ctx(prompt_tokens=3)
+        events = builder.format_stream_start(ctx)
+        payloads = _sse_payloads(events)
+        assert len(payloads) == 2
+        assert payloads[0]["type"] == "message_start"
+        assert payloads[0]["message"]["usage"]["input_tokens"] == 3
+        assert payloads[1]["type"] == "content_block_start"
+
+    def test_format_chunk(self, builder):
+        event = builder.format_chunk("tok")
+        payload = json.loads(event.split("data: ", 1)[1])
+        assert payload["type"] == "content_block_delta"
+        assert payload["delta"]["text"] == "tok"
+
+    def test_format_stream_end_no_stop(self, builder):
+        ctx = _make_ctx(completion_tokens=3)
+        stop = StopInfo()
+        events = builder.format_stream_end(ctx, stop)
+        payloads = _sse_payloads(events)
+        # content_block_stop, message_delta, message_stop
+        types = [p["type"] for p in payloads]
+        assert types == ["content_block_stop", "message_delta", "message_stop"]
+        assert payloads[1]["delta"]["stop_reason"] == "end_turn"
+
+    def test_format_stream_end_with_stop_trims_and_emits_remaining(self, builder):
+        ctx = _make_ctx(completion_tokens=7)
+        stop = StopInfo(
+            matched="END",
+            body="Hello world END extra",
+            yielded="Hello ",
+        )
+        events = builder.format_stream_end(ctx, stop)
+        payloads = _sse_payloads(events)
+        # unyielded delta, content_block_stop, message_delta, message_stop
+        types = [p["type"] for p in payloads]
+        assert types == [
+            "content_block_delta",
+            "content_block_stop",
+            "message_delta",
+            "message_stop",
+        ]
+        assert payloads[0]["delta"]["text"] == "world "
+        assert payloads[2]["delta"]["stop_reason"] == "stop_sequence"
+        assert payloads[2]["delta"]["stop_sequence"] == "END"
+
+    def test_format_stream_end_stop_trimmed_already_yielded(self, builder):
+        ctx = _make_ctx()
+        stop = StopInfo(
+            matched="END",
+            body="Hello END",
+            yielded="Hello ",
+        )
+        events = builder.format_stream_end(ctx, stop)
+        payloads = _sse_payloads(events)
+        # No unyielded delta (everything already sent)
+        types = [p["type"] for p in payloads]
+        assert types == ["content_block_stop", "message_delta", "message_stop"]
+
+    def test_format_response_with_stop_trims_content(self, builder):
+        ctx = _make_ctx()
+        stop = StopInfo(matched="STOP", body="text STOP extra", yielded="text ")
+        resp = builder.format_response(ctx, "text STOP extra", stop)
+        assert resp["content"][0]["text"] == "text "
+        assert resp["stop_reason"] == "stop_sequence"
+        assert resp["stop_sequence"] == "STOP"
+
+    def test_format_response_no_stop(self, builder):
+        ctx = _make_ctx()
+        stop = StopInfo()
+        resp = builder.format_response(ctx, "full text", stop)
+        assert resp["content"][0]["text"] == "full text"
+        assert resp["stop_reason"] == "end_turn"
+
+
+class TestGenerationRequestValidation:
+    def test_valid_params(self):
+        gr = GenerationRequest(
+            messages=[{"role": "user", "content": "hi"}],
+            top_k=50,
+            top_p=0.9,
+            temperature=0.7,
+        )
+        assert gr.top_k == 50
+
+    def test_invalid_top_p_raises(self):
+        with pytest.raises(ValueError, match="top_p"):
+            GenerationRequest(messages=[{"role": "user", "content": "hi"}], top_p=1.5)
+
+    def test_invalid_top_k_raises(self):
+        with pytest.raises(ValueError, match="top_k"):
+            GenerationRequest(messages=[{"role": "user", "content": "hi"}], top_k=-1)
+
+    def test_invalid_temperature_raises(self):
+        with pytest.raises(ValueError, match="temperature"):
+            GenerationRequest(
+                messages=[{"role": "user", "content": "hi"}], temperature=-0.1
+            )
+
+    def test_top_k_zero_valid(self):
+        gr = GenerationRequest(messages=[{"role": "user", "content": "hi"}], top_k=0)
+        assert gr.top_k == 0

tests/inference/test_scheduler.py

@@ -173,3 +173,21 @@ def test_scheduler_concurrent_get_stats(mock_model_and_tokenizer):
     for stats in results["stats"]:
         assert "total_tasks" in stats
         assert stats["total_tasks"] >= 0
+
+
+def test_prefill_skips_fully_cached_tasks(mock_model_and_tokenizer):
+    """Tasks whose entire prompt is cached skip the prefill phase."""
+    mock_model, mock_tokenizer = mock_model_and_tokenizer
+
+    with patch("astrai.inference.core.scheduler.AutoModel"):
+        with patch("astrai.inference.core.scheduler.AutoTokenizer"):
+            scheduler = InferenceScheduler(
+                model=mock_model,
+                tokenizer=mock_tokenizer,
+                max_batch_size=4,
+                device="cpu",
+            )
+
+    task_id = scheduler.add_task("short prompt", stream_callback=lambda t: None)
+    scheduler.stop()
+    assert task_id.startswith("task_")

tests/module/test_lora.py

@@ -0,0 +1,355 @@
+import tempfile
+
+import pytest
+import torch
+
+from astrai.config.model_config import AutoRegressiveLMConfig
+from astrai.model import AutoRegressiveLM
+from astrai.model.components.linear import Linear
+from astrai.model.components.lora import (
+    LoRAConfig,
+    LoRALinear,
+    _collect_lora_info,
+    _get_lora_count,
+    inject_lora,
+    load_lora,
+    merge_lora,
+    save_lora,
+)
+
+MODEL_KWARGS = dict(
+    vocab_size=1000,
+    dim=64,
+    n_heads=4,
+    n_kv_heads=2,
+    dim_ffn=128,
+    n_layers=2,
+    max_len=32,
+    norm_eps=1e-5,
+)
+
+
+def _make_model(**kwargs):
+    kw = {**MODEL_KWARGS, **kwargs}
+    config = AutoRegressiveLMConfig(**kw)
+    model = AutoRegressiveLM(config)
+    model.eval()
+    return model
+
+
+def test_loralinear_init():
+    base = Linear(64, 128)
+    lora = LoRALinear(base, r=8, alpha=16)
+
+    assert lora.weight is base.weight
+    assert not lora.weight.requires_grad
+    assert lora.lora_A.shape == (8, 64)
+    assert lora.lora_B.shape == (128, 8)
+    assert lora.scaling == 2.0
+    assert not lora._merged
+    assert lora.lora_A.requires_grad
+    assert lora.lora_B.requires_grad
+
+
+def test_loralinear_forward_init_zero_delta():
+    base = Linear(4, 4)
+    with torch.no_grad():
+        base.weight.zero_()
+
+    x = torch.randn(2, 4)
+    lora = LoRALinear(base, r=2, alpha=2)
+    base_out = base(x)
+    lora_out = lora(x)
+
+    assert torch.allclose(base_out, lora_out)
+
+
+def test_loralinear_forward_with_delta():
+    base = Linear(4, 4)
+    with torch.no_grad():
+        base.weight.zero_()
+
+    x = torch.randn(2, 4)
+    lora = LoRALinear(base, r=2, alpha=2)
+    base_out = base(x)
+
+    with torch.no_grad():
+        lora.lora_B.fill_(1.0)
+
+    lora_out = lora(x)
+    assert not torch.allclose(base_out, lora_out)
+
+
+def test_loralinear_merge():
+    base = Linear(4, 4)
+    with torch.no_grad():
+        base.weight.zero_()
+
+    x = torch.randn(2, 4)
+    lora = LoRALinear(base, r=2, alpha=2)
+    with torch.no_grad():
+        lora.lora_B.fill_(1.0)
+
+    out_before = lora(x).clone()
+    lora.merge()
+    out_after = lora(x)
+
+    torch.testing.assert_close(out_before, out_after)
+    assert lora._merged
+    assert not hasattr(lora, "lora_A")
+
+
+def test_loralinear_merge_is_idempotent():
+    base = Linear(4, 4)
+    with torch.no_grad():
+        base.weight.zero_()
+
+    lora = LoRALinear(base, r=2, alpha=2)
+    with torch.no_grad():
+        lora.lora_B.fill_(1.0)
+
+    lora.merge()
+    lora.merge()
+
+
+def test_inject_lora_default_target():
+    model = _make_model()
+    n_before = sum(1 for m in model.modules() if isinstance(m, Linear))
+
+    inject_lora(model, r=4, alpha=8)
+
+    lora_count = _get_lora_count(model)
+    assert lora_count > 0
+    assert lora_count < n_before
+
+
+def test_inject_lora_ffn():
+    model = _make_model()
+    from astrai.model.components.lora import TARGET_MODULES_FFN
+
+    inject_lora(model, r=4, alpha=8, target_modules=TARGET_MODULES_FFN)
+    assert _get_lora_count(model) > 0
+
+
+def test_inject_lora_returns_config():
+    model = _make_model()
+    cfg = inject_lora(model, r=8, alpha=32)
+    assert isinstance(cfg, LoRAConfig)
+    assert cfg.r == 8
+    assert cfg.alpha == 32
+
+
+def test_inject_lora_no_matching_targets_warns(caplog):
+    model = _make_model()
+    inject_lora(model, r=4, alpha=8, target_modules={"nonexistent"})
+    assert "No LoRA layers injected" in caplog.text
+
+
+def test_inject_lora_preserves_base_output():
+    model = _make_model()
+    x = torch.randint(0, 1000, (2, 16))
+
+    with torch.no_grad():
+        out_before = model(x)["logits"].clone()
+
+    inject_lora(model, r=4, alpha=8)
+
+    with torch.no_grad():
+        out_after = model(x)["logits"]
+
+    torch.testing.assert_close(out_before, out_after)
+
+
+def test_inject_lora_does_not_reinject():
+    model = _make_model()
+    inject_lora(model, r=4, alpha=8, target_modules={"q_proj"})
+    first_count = _get_lora_count(model)
+
+    inject_lora(model, r=2, alpha=4, target_modules={"q_proj"})
+    assert _get_lora_count(model) == first_count
+
+
+def test_inject_lora_adds_new_modules():
+    model = _make_model()
+    inject_lora(model, r=4, alpha=8, target_modules={"q_proj"})
+    first = _get_lora_count(model)
+
+    inject_lora(model, r=4, alpha=8, target_modules={"v_proj"})
+    assert _get_lora_count(model) > first
+
+
+def test_inject_lora_on_mla_model():
+    model = _make_model(
+        attn_type="mla", kv_lora_rank=16, qk_nope_head_dim=16, qk_rope_head_dim=16
+    )
+    inject_lora(model, r=4, alpha=8, target_modules={"q_proj", "o_proj"})
+    assert _get_lora_count(model) > 0
+
+
+def test_inject_lora_on_moe_model():
+    model = _make_model(
+        ffn_type="moe",
+        n_routed_experts=4,
+        n_shared_experts=1,
+        n_activated_experts=2,
+        dim_ffn=32,
+    )
+    inject_lora(model, r=4, alpha=8, target_modules={"up", "gate", "down"})
+    assert _get_lora_count(model) > 0
+
+
+def test_state_dict_key_format():
+    model = _make_model()
+    inject_lora(model, r=4, alpha=8, target_modules={"q_proj"})
+
+    sd = model.state_dict()
+    assert "layers.0.attention.q_proj.weight" in sd
+    assert "layers.0.attention.q_proj.lora_A" in sd
+    assert "layers.0.attention.q_proj.lora_B" in sd
+
+
+def test_only_lora_params_trainable():
+    model = _make_model()
+    inject_lora(model, r=4, alpha=8, target_modules={"q_proj", "v_proj"})
+
+    for name, param in model.named_parameters():
+        if isinstance(name.split(".")[-1], str) and "lora" in name:
+            assert param.requires_grad, f"lora param should be trainable: {name}"
+        elif any(name.endswith(f".{t}.weight") for t in ("q_proj", "v_proj")):
+            assert not param.requires_grad, f"injected weight should be frozen: {name}"
+
+
+def test_state_dict_after_inject_consistent_with_original():
+    model = _make_model()
+    sd_before = {k: v for k, v in model.state_dict().items()}
+
+    inject_lora(model, r=4, alpha=8, target_modules={"q_proj"})
+    sd_after = model.state_dict()
+
+    # original keys unchanged
+    for k in sd_before:
+        assert k in sd_after
+        assert sd_before[k].shape == sd_after[k].shape
+
+    # new lora keys present
+    lora_keys = [k for k in sd_after if "lora" in k]
+    assert len(lora_keys) > 0
+
+
+def test_save_load_roundtrip():
+    model = _make_model()
+    cfg = inject_lora(model, r=4, alpha=8, target_modules={"q_proj"})
+
+    with torch.no_grad():
+        for m in model.modules():
+            if isinstance(m, LoRALinear):
+                m.lora_B.fill_(0.5)
+
+    x = torch.randint(0, 1000, (2, 16))
+    with torch.no_grad():
+        out_src = model(x)["logits"].clone()
+
+    tmpdir = tempfile.mkdtemp()
+    save_lora(model, tmpdir, cfg)
+
+    model2 = _make_model()
+    model2.load_state_dict(model.state_dict(), strict=False)
+    load_lora(model2, tmpdir)
+
+    with torch.no_grad():
+        out_dst = model2(x)["logits"]
+
+    torch.testing.assert_close(out_src, out_dst)
+
+
+def test_save_after_merge_raises():
+    model = _make_model()
+    cfg = inject_lora(model, r=4, alpha=8, target_modules={"q_proj"})
+
+    with torch.no_grad():
+        for m in model.modules():
+            if isinstance(m, LoRALinear):
+                m.lora_B.fill_(0.5)
+
+    tmpdir = tempfile.mkdtemp()
+    save_lora(model, tmpdir, cfg)
+    merge_lora(model)
+
+    tmpdir2 = tempfile.mkdtemp()
+    with pytest.raises(RuntimeError, match="No LoRA parameters"):
+        save_lora(model, tmpdir2, cfg)
+
+
+def test_load_lora_on_already_injected():
+    model = _make_model()
+    inject_lora(model, r=4, alpha=8, target_modules={"q_proj"})
+
+    with torch.no_grad():
+        for m in model.modules():
+            if isinstance(m, LoRALinear):
+                m.lora_B.fill_(0.5)
+
+    tmpdir = tempfile.mkdtemp()
+    save_lora(model, tmpdir, LoRAConfig(r=4, alpha=8, target_modules=("q_proj",)))
+
+    model2 = _make_model()
+    model2.load_state_dict(model.state_dict(), strict=False)
+    inject_lora(model2, r=4, alpha=8, target_modules={"q_proj"})
+
+    # load onto already-injected model
+    load_lora(model2, tmpdir)
+    assert _get_lora_count(model2) > 0
+
+
+def test_load_lora_mismatched_r_raises():
+    model = _make_model()
+    cfg = inject_lora(model, r=8, alpha=16, target_modules={"q_proj"})
+
+    with torch.no_grad():
+        for m in model.modules():
+            if isinstance(m, LoRALinear):
+                m.lora_B.fill_(0.5)
+
+    tmpdir = tempfile.mkdtemp()
+    save_lora(model, tmpdir, cfg)
+
+    model2 = _make_model()
+    model2.load_state_dict(model.state_dict(), strict=False)
+    inject_lora(model2, r=4, alpha=8, target_modules={"q_proj"})
+
+    with pytest.raises(RuntimeError, match="size mismatch"):
+        load_lora(model2, tmpdir)  # strict=False, only lora keys
+
+
+def test_merge_preserves_output():
+    model = _make_model()
+    inject_lora(model, r=4, alpha=8, target_modules={"q_proj"})
+
+    with torch.no_grad():
+        for m in model.modules():
+            if isinstance(m, LoRALinear):
+                m.lora_B.fill_(0.5)
+
+    x = torch.randint(0, 1000, (2, 16))
+    with torch.no_grad():
+        out_before = model(x)["logits"].clone()
+
+    merge_lora(model)
+
+    with torch.no_grad():
+        out_after = model(x)["logits"]
+    torch.testing.assert_close(out_before, out_after)
+
+
+def test_merge_no_lora_warns(caplog):
+    model = _make_model()
+    merge_lora(model)
+    assert "No LoRA layers to merge" in caplog.text
+
+
+def test_collect_lora_info():
+    model = _make_model()
+    info = _collect_lora_info(model)
+    assert "q_proj" in info
+    assert "o_proj" in info
+    assert "q_proj" in info  # each layer has one

tests/trainer/conftest.py

@@ -1,3 +1,5 @@
+import os
+
 import pytest
 import torch
 from torch.utils.data import Dataset
@@ -25,7 +27,7 @@ class TrainerDataset(Dataset):
 
 
 def create_train_config(
-    model: torch.nn.Module,
+    model_fn,
     dataset: Dataset,
     test_dir: str,
     device: str,
@@ -41,7 +43,7 @@ def create_train_config(
     """Factory function to create common TrainConfig for tests.
 
     Args:
-        model: The model to train
+        model_fn: Model factory (callable returning nn.Module)
         dataset: Training dataset
         test_dir: Checkpoint directory
         device: Device type ("cuda" or "cpu")
@@ -68,11 +70,12 @@ def create_train_config(
 
     return TrainConfig(
         strategy=strategy,
-        model=model,
+        model_fn=model_fn,
         dataset=dataset,
         optimizer_fn=optimizer_fn,
         scheduler_fn=scheduler_fn,
         ckpt_dir=test_dir,
+        log_dir=os.path.join(test_dir, "logs"),
         n_epoch=n_epoch,
         batch_per_device=batch_per_device,
         ckpt_interval=ckpt_interval,

tests/trainer/test_callbacks.py

@@ -1,3 +1,5 @@
+import os
+
 import torch
 
 from astrai.config.train_config import TrainConfig
@@ -104,12 +106,13 @@ def test_gradient_checkpointing_trainer_integration(base_test_env, random_datase
         )
 
     train_config = TrainConfig(
-        model=base_test_env["model"],
+        model_fn=lambda: base_test_env["model"],
         strategy="seq",
         dataset=random_dataset,
         optimizer_fn=optimizer_fn,
         scheduler_fn=scheduler_fn,
         ckpt_dir=base_test_env["test_dir"],
+        log_dir=os.path.join(base_test_env["test_dir"], "logs"),
         n_epoch=1,
         batch_per_device=2,
         ckpt_interval=3,
@@ -137,12 +140,13 @@ def test_callback_integration(base_test_env, random_dataset):
         )
 
     train_config = TrainConfig(
-        model=base_test_env["model"],
+        model_fn=lambda: base_test_env["model"],
         strategy="seq",
         dataset=random_dataset,
         optimizer_fn=optimizer_fn,
         scheduler_fn=scheduler_fn,
         ckpt_dir=base_test_env["test_dir"],
+        log_dir=os.path.join(base_test_env["test_dir"], "logs"),
         n_epoch=1,
         batch_per_device=2,
         ckpt_interval=3,

tests/trainer/test_early_stopping.py

@@ -4,7 +4,6 @@ import numpy as np
 import torch
 
 from astrai.config.train_config import TrainConfig
-from astrai.serialization import Checkpoint
 from astrai.trainer.schedule import SchedulerFactory
 from astrai.trainer.trainer import Trainer
 
@@ -24,9 +23,10 @@ def test_early_stopping_simulation(base_test_env, early_stopping_dataset):
         strategy="seq",
         optimizer_fn=optimizer_fn,
         scheduler_fn=scheduler_fn,
-        model=base_test_env["model"],
+        model_fn=lambda: base_test_env["model"],
         dataset=early_stopping_dataset,
         ckpt_dir=base_test_env["test_dir"],
+        log_dir=os.path.join(base_test_env["test_dir"], "logs"),
         n_epoch=2,
         batch_per_device=2,
         ckpt_interval=1,
@@ -38,17 +38,20 @@ def test_early_stopping_simulation(base_test_env, early_stopping_dataset):
     trainer = Trainer(train_config)
 
     # Should handle early stopping gracefully
-    checkpoint = None
     try:
-        checkpoint = trainer.train()
+        trainer.train()
     except Exception:
-        # Handle any exceptions
         pass
 
+    # Resume from latest checkpoint
     load_dir = os.path.join(base_test_env["test_dir"], "epoch_0_iter_2")
-    checkpoint = Checkpoint.load(load_dir)
+    trainer = Trainer(train_config)
-    trainer.train(checkpoint)
+    trainer.train(resume_dir=load_dir)
 
+    # Verify checkpoint was saved at expected iteration
     load_dir = os.path.join(base_test_env["test_dir"], "epoch_1_iter_10")
-    checkpoint = Checkpoint.load(load_dir)
+    import json
-    assert checkpoint.iteration == 10
+
+    with open(os.path.join(load_dir, "meta.json")) as f:
+        meta = json.load(f)
+    assert meta["iteration"] == 10

tests/trainer/test_trainer.py

@@ -9,7 +9,7 @@ def test_different_batch_sizes(base_test_env, random_dataset, train_config_facto
 
     for batch_per_device in batch_sizes:
         train_config = train_config_factory(
-            model=base_test_env["model"],
+            model_fn=lambda: base_test_env["model"],
             dataset=random_dataset,
             test_dir=base_test_env["test_dir"],
             device=base_test_env["device"],
@@ -25,7 +25,7 @@ def test_gradient_accumulation(base_test_env, random_dataset, train_config_facto
 
     for grad_accum_steps in grad_accum_steps_list:
         train_config = train_config_factory(
-            model=base_test_env["model"],
+            model_fn=lambda: base_test_env["model"],
             dataset=random_dataset,
             test_dir=base_test_env["test_dir"],
             device=base_test_env["device"],
@@ -50,7 +50,7 @@ def test_memory_efficient_training(base_test_env, random_dataset, train_config_f
 
     for config in small_batch_configs:
         train_config = train_config_factory(
-            model=base_test_env["model"],
+            model_fn=lambda: base_test_env["model"],
             dataset=random_dataset,
             test_dir=base_test_env["test_dir"],
             device=base_test_env["device"],