AstrAI/assets/docs/design.md

1. Why I Created This Project

There are many large language models on the market today, such as GPT, LLaMA, and others, with tens of billions or even hundreds of billions of parameters. But honestly, these models have extremely high hardware requirements, making them inaccessible for ordinary developers. I thought: Can we create a model that is both useful and can run on ordinary computers? This is also what most people currently hope for - a locally deployable AI project that achieves complete privatization while maintaining some level of intelligence.

Thus, the AstrAI project was born - 1B parameters, Chinese-English bilingual, supporting dialogue, text generation, and the training code is open source!

2. System Architecture

classDiagram
    namespace config {
        class ModelConfig {
            +int vocab_size
            +int dim
            +int n_layers
            +float norm_eps
            +int dim_ffn
            +bool tie_weight
            +int max_len
            +float rope_theta
            +int n_heads
            +int n_kv_heads
            +bool use_qk_norm
            +bool use_gated_attention
            +load(config_path) ModelConfig
            +save(config_path)
        }

        class TrainConfig {
            +nn.Module model
            +str strategy
            +Dataset dataset
            +Callable optimizer_fn
            +Callable scheduler_fn
            +int n_epoch
            +int batch_size
            +int accumulation_steps
            +float max_grad_norm
            +int start_epoch
            +int start_batch
            +str ckpt_dir
            +int ckpt_interval
            +int random_seed
            +int num_workers
            +int prefetch_factor
            +bool pin_memory
            +int nprocs
            +str backend
            +str master_addr
            +str master_port
            +Callable parallel_wrapper
            +Callable state_dict_fn
            +str device_type
            +dict extra_kwargs
            +validate()
        }

    }

    namespace dataset {
        class BaseDataset {
            +int window_size
            +int stride
            +BaseStorage storage
            +load(load_path, storage_type, tokenizer)
            +__getitem__(index)
            +__len__()
        }

        class SEQDataset {
            +__getitem__(index) Dict
        }

        class SFTDataset {
            +__getitem__(index) Dict
        }

        class DPODataset {
            +__getitem__(index) Dict
        }

        class GRPODataset {
            +__getitem__(index) Dict
        }

        class BaseSegmentFetcher {
            +List[Tensor] segments
            +List[int] cum_lengths
            +int total_length
            +fetch_data(begin_idx, end_idx) Tensor
        }

        class BaseStorage {
            +Dict segments
            +List keys
            +load(load_path, tokenizer)
            +fetch(begin, end, keys)
            +__len__()
        }

        class H5Storage {
            +load(load_path, tokenizer)
            +fetch(begin, end, keys) Dict
            +keys() List
        }

        class JSONStorage {
            +load(load_path, tokenizer)
            +fetch(begin, end, keys) Dict
            +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 {
            +int epoch
            +int iter
        }

        class DatasetFactory {
            +Registry _registry
            +register(name) decorator
            +create(train_type, window_size, stride) BaseDataset
            +load(train_type, load_path, window_size, stride) BaseDataset
        }
    }

    namespace serialization {
        class Checkpoint {
            +dict state_dict
            +int epoch
            +int iteration
            +save(save_dir)
            +load(save_dir) Checkpoint
        }
    }

    namespace model {
        class AutoModel {
            +ModelConfig config
            +Registry _registry
            +register(model_type) decorator
            +get_model_class(model_type) Type
            +from_pretrained(path, disable_random_init) nn.Module
            +save_pretrained(save_directory)
            +to(*args, **kwargs) Self
        }

        class Transformer {
            +ModelConfig config
            +RotaryEmbedding rotary_embedding
            +Embedding embed_tokens
            +ModuleList layers
            +RMSNorm norm
            +Linear lm_head
            +forward(input_ids, input_mask, paged_cache, position_ids) Tensor
            +load_state_dict(state_dict)
            +state_dict()
        }

        class DecoderBlock {
            +GQA attention
            +RMSNorm input_norm
            +MLP mlp
            +RMSNorm post_attention_norm
            +forward(x, rotary_emb, attention_mask, paged_cache) Tensor
        }

        class GQA {
            +int n_heads
            +int n_kv_heads
            +int head_dim
            +Linear q_proj, k_proj, v_proj, o_proj
            +RMSNorm q_norm, k_norm
            +forward(x, rotary_emb, attn_mask, paged_cache) Tensor
        }

        class MLA {
            +int n_heads
            +int n_kv_heads
            +int head_dim
            +int kv_lora_rank
            +int qk_nope_head_dim
            +int qk_rope_head_dim
            +Linear q_proj, kv_a_proj, kv_b_proj
            +Linear o_proj
            +RMSNorm kv_norm
            +forward(x, rotary_emb, attn_mask, paged_cache) Tensor
        }

        class MLP {
            +Linear up, gate, down
            +forward(x) Tensor
        }

        class RMSNorm {
            +Parameter weight
            +float norm_eps
            +forward(x) Tensor
        }

        class Linear {
            +Parameter weight
            +Parameter bias
            +forward(x) Tensor
        }

        class RotaryEmbedding {
            +int dim
            +int max_len
            +float base
            +forward(x, position_ids=None) Tuple[Tensor, Tensor]
        }

        class Embedding {
            +Parameter weight
            +forward(x) Tensor
        }
    }

    namespace tokenize {
        class AutoTokenizer {
            +List[int] stop_ids
            +int bos_id
            +int eos_id
            +int pad_id
            +vocab_size int
            +encode(tokens, out_ids, add_special_tokens) List[int]
            +decode(tokens, skip_special_tokens) str
            +apply_chat_template(messages, tokenize) Union[str, List[int]]
            +set_chat_template(template)
            +load(path)
            +from_pretrained(path) AutoTokenizer
            +save_pretrained(save_path)
        }

        class ChatTemplate {
            +String template_str
            +render(messages, system_prompt, **extra_variables) str
            +from_string(template) ChatTemplate
        }
    }

    namespace factory {
        class Registry {
            +Dict _entries
            +register(name, component_cls, category, priority)
            +get(name) Type
            +list_names() List[str]
        }

        class BaseFactory {
            +Registry _registry
            +register(name, category, priority) decorator
            +create(name, *args, **kwargs) T
            +list_registered() list
        }
    }

    namespace trainer {
        class Trainer {
            +TrainConfig train_config
            +List[TrainCallback] callbacks
            +train(checkpoint)
            +_build_context(checkpoint) TrainContext
            +_get_default_callbacks() List[TrainCallback]
        }

        class TrainContext {
            +nn.Module model
            +BaseStrategy strategy
            +DataLoader dataloader
            +Optimizer optimizer
            +LRScheduler scheduler
            +Checkpoint checkpoint
            +int epoch
            +int iteration
            +float loss
            +int world_size
            +int rank
        }

        class TrainContextBuilder {
            +TrainConfig config
            +with_checkpoint(checkpoint) TrainContextBuilder
            +build() TrainContext
        }

        class BaseStrategy {
            +nn.Module model
            +str device
            +compute_loss(batch) Tensor
        }

        class StrategyFactory {
            +Registry _registry
            +register(name) decorator
            +create(model, train_type, device, **kwargs) BaseStrategy
        }

        class SEQStrategy {
            +float label_smoothing
            +compute_loss(batch) Tensor
        }

        class SFTStrategy {
            +float label_smoothing
            +compute_loss(batch) Tensor
        }

        class DPOStrategy {
            +nn.Module ref_model
            +float beta
            +str reduction
            +compute_loss(batch) Tensor
        }

        class GRPOStrategy {
            +nn.Module ref_model
            +float clip_eps
            +float kl_coef
            +int group_size
            +compute_loss(batch) Tensor
        }

        class BaseScheduler {
            +get_lr() List[float]
            +step()
        }

        class SchedulerFactory {
            +Registry _registry
            +register(name) decorator
            +create(optimizer, schedule_type, **kwargs) BaseScheduler
        }

        class CosineScheduler {
            +int warmup_steps
            +int lr_decay_steps
            +float min_rate
        }

        class SGDRScheduler {
            +int warmup_steps
            +int cycle_length
            +float min_rate
            +int t_mult
        }

        class TrainCallback {
            +on_train_begin(context)
            +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_error(context)
        }

        class GradientClippingCallback {
            +float max_grad_norm
            +on_step_begin(context)
        }

        class SchedulerCallback {
            +on_train_begin(context)
            +on_batch_end(context)
        }

        class CheckpointCallback {
            +str save_dir
            +int interval
            +_save_checkpoint(context)
            +on_batch_end(context)
            +on_train_end(context)
            +on_error(context)
        }

        class ProgressBarCallback {
            +int num_epoch
            +on_epoch_begin(context)
            +on_batch_end(context)
            +on_epoch_end(context)
        }

        class MetricLoggerCallback {
            +str log_dir
            +int save_interval
            +on_batch_end(context)
            +on_train_end(context)
        }

        class CallbackFactory {
            +Registry _registry
            +register(name) decorator
            +create(name, **kwargs) TrainCallback
        }
    }

    namespace inference {
        class InferenceEngine {
            +nn.Module model
            +AutoTokenizer tokenizer
            +InferenceScheduler scheduler
            +int max_batch_size
            +Optional int max_seq_len
            +generate(prompt, stream, max_tokens, temperature, top_p, top_k) Union[Generator, str, List[str]]
            +generate_with_request(request) Union[Generator, str, List[str]]
            +generate_async(prompt, max_tokens, temperature, top_p, top_k) AsyncGenerator
            +get_stats() Dict
            +shutdown()
        }

        class InferenceScheduler {
            +nn.Module model
            +AutoTokenizer tokenizer
            +KVCache page_cache
            +int max_batch_size
            +int max_seq_len
            +int max_prompt_len
            +int page_size
            +List waiting_queue
            +List active_tasks
            +add_task(prompt, max_tokens, temperature, top_p, top_k, stream_callback) str
            +remove_task(task_id)
            +start()
            +stop()
            +get_stats() Dict
        }

        class Allocator {
            +int _free_mask
            +int refs_count
            +LRU _lru
            +alloc() int
            +free(idx, keep_cached)
            +inc_ref(idx)
            +touch(idx)
            +ref_count(idx) int
        }

        class PrefixCache {
            +int _page_size
            +evict(page_idx)
            +has_page(idx) bool
            +lookup(token_ids) List[int]
            +record(page_idx, token_ids, logical_page_idx)
        }

        class PagePool {
            -Allocator _alloc
            -PrefixCache _prefix
            +alloc() int
            +free(idx)
            +inc_ref(idx)
            +lookup(token_ids) List[int]
            +record(page_idx, token_ids, logical_page_idx)
        }

        class Storage {
            +int n_layers
            +int page_size
            +int head_dim
            +int n_kv_heads
            +Tensor k_cache
            +Tensor v_cache
            +write(layer_id, page_table, start_pos, k, v)
            +gather(layer_id, page_table, total_len) Tuple[Tensor, Tensor]
        }

        class KVCache {
            -PagePool _pool
            -Storage _storage
            -TaskTable _table
            +int page_size
            +task_alloc(task_id, prompt_ids) bool
            +task_free(task_id)
            +task_extend(task_id, pos) bool
            +task_cached(task_id) int
            +task_record_hashes(task_id, prompt_ids, start_logical_page)
            +make_table_tensor(task_ids, device) Tensor
            +bind(page_table, total_len) KvcacheView
        }

        class KvcacheView {
            -Storage _storage
            +Tensor _page_table
            +int _total_len
            +write(layer_id, k, v)
            +gather(layer_id) Tuple[Tensor, Tensor]
        }

        class TaskTable {
            +set(task_id, page_table, cached)
            +get(task_id) List[int]
            +get_cached(task_id) int
            +get_ref(task_id) List[int]
            +pop(task_id) Tuple[List[int], int]
            +table_tensor(task_ids, device) Tensor
        }

        class Task {
            +str task_id
            +List prompt_ids
            +int max_tokens
            +float temperature
            +float top_p
            +int top_k
            +TaskStatus status
            +List output_ids
            +int input_tokens
            +int output_tokens
            +float arrival_time
            +float finish_time
            +Callable stream_callback
            +int next_pos
            +is_finished(stop_ids) bool
        }

        class TaskStatus {
            <<enumeration>>
            PENDING
            RUNNING
            FINISHED
            ABORTED
        }

        class GenerationRequest {
            +List[Dict] messages
            +int top_k
            +float top_p
            +float temperature
            +Optional[int] max_tokens
            +bool stream
        }

        class BaseSamplingStrategy {
            <<abstract>>
            +apply(logits, filter_value) Tensor
        }

        class TemperatureStrategy {
            +float temperature
            +apply(logits, filter_value) Tensor
        }

        class TopKStrategy {
            +int top_k
            +apply(logits, filter_value) Tensor
        }

        class TopPStrategy {
            +float top_p
            +apply(logits, filter_value) Tensor
        }

        class SamplingPipeline {
            +List strategies
            +apply(logits, filter_value) Tensor
            +sample(logits, filter_value) Tensor
        }

        class GenerateResult {
            +List[str] tokens
            +List[str] results
            +List[bool] _done
            +append(token, idx)
            +get_results() List[str]
            +pop_all() List[str]
            +wait(timeout) bool
            +wait_completion()
        }

        class ChatMessage {
            +str role
            +str content
        }

        class ChatCompletionRequest {
            +List[ChatMessage] messages
            +float temperature
            +float top_p
            +int top_k
            +int max_tokens
            +bool stream
            +Optional[str] stop
            +Optional[int] n
        }
    }

    namespace parallel {
        class Functions {
            +spawn_parallel_fn(fn, nprocs)
            +setup_parallel(rank, world_size, backend, master_addr, master_port, device_type)
            +get_current_device() str
            +get_world_size() int
            +get_rank() int
        }

        class ParallelModel {
            +dist.ProcessGroup process_group
            +int rank
            +int world_size
        }

        class ColumnParallelLinear {
            +forward(x) Tensor
        }

        class RowParallelLinear {
            +forward(x) Tensor
        }
    }

    %% Relationships
    TrainConfig --> BaseDataset : uses
    TrainConfig ..> BaseStrategy : selects
    StrategyFactory ..> BaseStrategy : creates
    BaseStrategy <|-- SEQStrategy
    BaseStrategy <|-- SFTStrategy
    BaseStrategy <|-- DPOStrategy
    BaseStrategy <|-- GRPOStrategy
    DPOStrategy --> Transformer : uses
    GRPOStrategy --> Transformer : uses
    Trainer --> TrainConfig : uses
    Trainer --> TrainContextBuilder : uses
    Trainer --> TrainCallback : manages
    TrainContextBuilder --> TrainContext : creates
    TrainContextBuilder --> StrategyFactory : uses
    Checkpoint ..> Checkpoint : serializes
    TrainContext --> Checkpoint : manages
    TrainContext --> BaseStrategy : uses
    TrainContext --> BaseScheduler : uses
    SchedulerFactory ..> BaseScheduler : creates
    BaseScheduler <|-- CosineScheduler
    BaseScheduler <|-- SGDRScheduler
    CallbackFactory ..> TrainCallback : creates
    TrainCallback <|-- GradientClippingCallback
    TrainCallback <|-- SchedulerCallback
    TrainCallback <|-- CheckpointCallback
    TrainCallback <|-- ProgressBarCallback
    TrainCallback <|-- MetricLoggerCallback
    PagePool --> Allocator : composes
    PagePool --> PrefixCache : composes
    KVCache --> PagePool : composes
    KVCache --> Storage : composes
    KVCache --> TaskTable : composes
    KvcacheView --> Storage : wraps
    InferenceEngine --> InferenceScheduler : uses
    InferenceEngine --> GenerationRequest : uses
    InferenceEngine --> GenerateResult : creates
    InferenceScheduler --> Task : manages
    InferenceScheduler --> TaskStatus : uses
    InferenceScheduler --> KVCache : uses
    InferenceScheduler --> Transformer : uses
    Task --> TaskStatus : uses
    InferenceEngine --> Transformer : uses
    BaseSamplingStrategy <|-- TemperatureStrategy
    BaseSamplingStrategy <|-- TopKStrategy
    BaseSamplingStrategy <|-- TopPStrategy
    SamplingPipeline --> BaseSamplingStrategy : composes
    BaseDataset <|-- SEQDataset
    BaseDataset <|-- SFTDataset
    BaseDataset <|-- DPODataset
    BaseDataset <|-- GRPODataset
    DatasetFactory ..> BaseDataset : creates
    BaseStorage <|-- H5Storage
    BaseStorage <|-- JSONStorage
    BaseDataset --> BaseStorage : uses
    MultiSegmentFetcher --> BaseSegmentFetcher : uses
    AutoModel <|-- Transformer
    AutoModel --> ModelConfig : contains
    Transformer --> DecoderBlock : uses
    Transformer --> RotaryEmbedding : uses
    Transformer --> Embedding : uses
    DecoderBlock --> GQA : uses
    DecoderBlock --> MLP : uses
    DecoderBlock --> RMSNorm : uses
    TrainContextBuilder --> ResumableDistributedSampler : creates
    ResumableDistributedSampler --> BaseDataset : samples
    ParallelModel <|-- RowParallelLinear
    ParallelModel <|-- ColumnParallelLinear
    AutoTokenizer --> ChatTemplate : uses
    BaseFactory <|-- AutoModel
    BaseFactory <|-- DatasetFactory
    BaseFactory <|-- StrategyFactory
    BaseFactory <|-- SchedulerFactory
    BaseFactory <|-- CallbackFactory

Module Overview

Module	Components	Description
astrai.config	ModelConfig, TrainConfig	Configuration management
astrai.dataset	BaseDataset, SEQDataset, SFTDataset, DPODataset, GRPODataset, BaseStorage, H5Storage, JSONStorage, BaseSegmentFetcher, MultiSegmentFetcher, ResumableDistributedSampler, DatasetFactory, save_h5, load_h5	Dataset loading and management
astrai.serialization	Checkpoint	Model serialization and checkpoint management
astrai.model	AutoModel, Transformer, DecoderBlock, GQA, MLA, MLP, RMSNorm, Linear, RotaryEmbedding, Embedding	Neural network model
astrai.tokenize	AutoTokenizer, ChatTemplate	Tokenizer and chat template
astrai.trainer	Trainer, TrainContext, TrainContextBuilder, BaseStrategy, StrategyFactory, BaseScheduler, SchedulerFactory, TrainCallback, CallbackFactory	Training workflow management
astrai.inference	InferenceEngine, InferenceScheduler, KVCache, KvcacheView, Allocator, PrefixCache, PagePool, Storage, TaskTable, Task, TaskStatus, GenerationRequest, BaseSamplingStrategy, TemperatureStrategy, TopKStrategy, TopPStrategy, SamplingPipeline, ChatMessage, ChatCompletionRequest	Inference service with continuous batching and paged KV cache
astrai.parallel	spawn_parallel_fn, setup_parallel, get_rank, get_world_size, get_current_device, ParallelModel, ColumnParallelLinear, RowParallelLinear	Distributed parallel
astrai.factory	Registry, BaseFactory	Generic component registration

Design Patterns

Pattern	Classes	Purpose
Strategy	BaseStrategy, SEQStrategy, SFTStrategy, DPOStrategy, GRPOStrategy, StrategyFactory	Flexible training strategy switching, supports SEQ/SFT/DPO/GRPO
Builder	TrainContextBuilder	Chain-building training context, step-by-step initialization of components
Factory	StrategyFactory, SchedulerFactory, DatasetFactory, CallbackFactory, BaseFactory	Decorator registration mechanism, dynamically create training strategies, schedulers, datasets, and callbacks
Observer	TrainCallback, CallbackFactory	Callback mechanism for training process monitoring (checkpoint, early stopping, metrics)
Context	TrainContext	Training process state container with model, optimizer, scheduler and checkpoint
Registry	BaseFactory, Registry	Generic component registration with category and priority support
Object Pool	Allocator, PagePool	Page-based KV cache with O(1) alloc/free via bitmask + LRU eviction
Strategy (Sampling)	BaseSamplingStrategy, TemperatureStrategy, TopKStrategy, TopPStrategy, SamplingPipeline	Composable logit transformations with temperature, top-k, top-p
Producer-Consumer	InferenceScheduler, Task, waiting_queue, active_tasks	Continuous batching with dynamic task queue management
Event-Driven	threading.Event, _task_event	Non-blocking wait mechanism for task scheduling using Python's threading module
AutoModel Registry	AutoModel, Transformer	Model type registration and dynamic loading via decorator pattern
Generator Pattern	GenerateResult, GenerationRequest	Event-based result notification for streaming/non-streaming generation

Core Relationships

1. Configuration → Training: TrainConfig holds model, dataset, optimizer_fn, scheduler_fn and other training configuration references
2. Training Flow: Trainer → TrainContextBuilder → TrainContext, uses BaseStrategy to compute loss
3. Strategy Selection: StrategyFactory creates corresponding strategy instance based on train_type
4. Inference Flow: InferenceEngine → InferenceScheduler → Transformer, uses KVCache (backed by Allocator + PrefixCache + PagePool + Storage) for paged KV cache management and SamplingPipeline for efficient continuous batching with streaming/non-streaming
5. Distributed Support: spawn_parallel_fn and setup_parallel provide multi-process training capability for Trainer
6. Dataset Loading: DatasetFactory creates datasets (SEQDataset, SFTDataset, DPODataset, GRPODataset), supports HDF5 loading via BaseSegmentFetcher and MultiSegmentFetcher
7. Checkpoint Management: Checkpoint handles model state serialization/deserialization with safetensors
8. Scheduler Support: SchedulerFactory creates learning rate schedulers (CosineScheduler, SGDRScheduler)
9. AutoModel Loading: AutoModel.from_pretrained() dynamically loads model based on config.json model_type, uses Registry pattern for model type registration

3. Training Process

The common training process for large language models (LLM) typically includes three stages: Pre-training (SEQ), Supervised Fine-Tuning (SFT), and Reinforcement Learning from Human Feedback (DPO/GRPO). This system is designed to support seamless end-to-end flow, achieving efficient switching and state management of different training stages through modular strategies.

Core Formulas

Pre-training (SEQ):

L_{\text{PT}} = - \sum_{t=1}^{T} \log P(x_t \mid x_{\lt t}; \theta)

SFT:

L_{\text{SFT}} = - \sum_{t=P+1}^{P+L} \log P(s_t \mid s_{\lt t}; \theta)

DPO:

L_{\text{DPO}} = -\mathbb{E}_{(x, y_w, y_l) \sim D} \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]

GRPO:

GRPO (Group Relative Policy Optimization) computes advantages from multiple responses to the same prompt, then optimizes using a PPO-style clipped objective:

\text{Advantage}_i = \frac{r_i - \mu}{\sigma + \epsilon}

Where r_i is the reward for the $i$-th response, \mu and \sigma are the mean and standard deviation of group rewards.

L_{\text{GRPO}} = -\mathbb{E} \left[ \min\left( \frac{\pi_\theta(a|s)}{\pi_{\text{ref}}(a|s)} \cdot A, \text{clip}\left(\frac{\pi_\theta(a|s)}{\pi_{\text{ref}}(a|s)}, 1-\epsilon, 1+\epsilon\right) \cdot A \right) \right] + \lambda \cdot D_{KL}

The KL divergence term uses mean squared error approximation:

L_{KL} = \lambda \cdot \mathbb{E} \left[ (\log \pi_\theta - \log \pi_{\text{ref}})^2 \right]

The final loss is the sum of both: L = L_{\text{policy}} + L_{KL}

Through the above three-stage progressive training, the model completes its evolution from a general language foundation to a specialized, highly-aligned dialogue intelligence.

Document Update Time: 2026-05-14