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 astrai.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
            +List[int] device_ids
            +str device_type
            +dict extra_kwargs
            +validate()
        }

        class ModelParameter {
            +nn.Module model
            +BpeTokenizer tokenizer
            +ModelConfig config
            +save(instance, save_dir)
            +load(load_dir, disable_init) ModelParameter
            +to(*args, **kwargs)
        }
    }

    namespace astrai.dataset {
        class BaseDataset {
            +int window_size
            +int stride
            +MultiSegmentFetcher fetcher
            +load(load_path)
            +__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 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 start_epoch
            +int start_iter
        }

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

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

        class DataLoader {
            +Dataset dataset
            +int batch_size
            +Sampler sampler
            +__iter__()
            +__len__()
        }
    }

    namespace astrai.model {
        class AutoModel {
            +ModelConfig config
            +Dict _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, persistent_key_values, start_pos) Dict
            +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, kv_cache, start_pos) 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, mask, kv_cache, start_pos) Tensor
        }

        class MLA {
            +int n_heads
            +int n_kv_heads
            +int head_dim
            +Linear q_a_proj, q_b_proj, q_c_proj
            +Linear kv_a_proj, kv_b_proj, kv_c_proj
            +Linear o_proj
            +RMSNorm q_norm, k_norm
            +forward(x, rotary_emb, mask, kv_cache, start_pos) 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, start_pos) Tuple~Tensor, Tensor~
        }

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

    namespace astrai.tokenize {
        class Tokenizer {
            +encode(tokens, out_ids, add_special_tokens) List~int~
            +decode(tokens, skip_special_tokens) str
            +__len__() int
        }

        class BpeTokenizer {
            +List~str~ stop_ids
            +int bos_id
            +int eos_id
            +int pad_id
            +encode(tokens, out_ids, add_special_tokens) List~int~
            +decode(tokens, skip_special_tokens) str
        }
    }

    namespace astrai.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
            +with_dataloader() TrainContextBuilder
            +with_strategy() 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 CallbackFactory {
            +Registry _registry
            +register(name) decorator
            +create(name, **kwargs) TrainCallback
        }
    }

    namespace astrai.inference {
        class InferenceEngine {
            +ModelParameter parameter
            +InferenceScheduler scheduler
            +generate(prompt, stream, max_tokens, temperature, top_p, top_k) Union[Generator, str, List[str]]
            +generate_with_request(request) Union[Generator, str, List[str]]
            +get_stats() Dict
            +shutdown()
        }

        class InferenceScheduler {
            +nn.Module model
            +Tokenizer tokenizer
            +ModelConfig config
            +Tuple kv_cache
            +Tensor seq_mask
            +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 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
            +int slot
            +Callable stream_callback
            +is_finished(stop_ids) bool
        }

        class TaskStatus {
            +str PENDING
            +str RUNNING
            +str FINISHED
            +str ABORTED
        }

        class apply_sampling_strategies {
            +Tensor logits
            +float temperature
            +int top_k
            +float top_p
            +forward() Tensor
        }

        class Server {
            +start()
            +predict(request)
        }

        class GenerationRequest {
            +int top_k
            +float top_p
            +float temperature
            +int max_len
            +Union~str, List~str~~ query
            +history Optional
            +system_prompt Optional~str~
            +stream bool
        }
    }

    namespace astrai.parallel {
        class ParallelSetup {
            +spawn_parallel_fn(fn, nprocs)
            +setup_parallel(rank, world_size, backend, master_addr, master_port, device_type, device_ids)
        }

        class ColumnParallelLinear {
            +forward(x) Tensor
        }

        class RowParallelLinear {
            +forward(x) Tensor
        }
    }

    %% Relationships
    TrainConfig --> ModelConfig : contains
    TrainConfig --> BaseDataset : uses
    TrainConfig --> Transformer : uses
    Trainer --> TrainConfig : configures
    Trainer --> TrainContextBuilder : builds
    Trainer --> TrainCallback : manages
    TrainContextBuilder --> TrainContext : creates
    TrainContext --> Checkpoint : manages
    TrainContext --> BaseStrategy : uses
    TrainContext --> BaseScheduler : uses
    StrategyFactory ..> BaseStrategy : creates
    BaseStrategy <|-- SEQStrategy
    BaseStrategy <|-- SFTStrategy
    BaseStrategy <|-- DPOStrategy
    BaseStrategy <|-- GRPOStrategy
    DPOStrategy --> Transformer : creates ref_model
    GRPOStrategy --> Transformer : creates ref_model
    SchedulerFactory ..> BaseScheduler : creates
    BaseScheduler <|-- CosineScheduler
    BaseScheduler <|-- SGDRScheduler
    CallbackFactory ..> TrainCallback : creates
    InferenceEngine --> InferenceScheduler : uses
    InferenceScheduler --> Task : manages
    InferenceScheduler --> TaskStatus : uses
    InferenceScheduler --> apply_sampling_strategies : uses
    InferenceScheduler --> Transformer : uses
    InferenceEngine --> Transformer : uses
    InferenceEngine --> GenerationRequest : uses
    Server --> InferenceEngine : uses
    ParallelSetup --> Trainer : enables
    TrainConfig --> StrategyFactory : selects
    ModelParameter --> Transformer : contains
    ModelParameter --> BpeTokenizer : contains
    ModelParameter --> ModelConfig : contains
    BaseDataset <|-- SEQDataset
    BaseDataset <|-- SFTDataset
    BaseDataset <|-- DPODataset
    BaseDataset <|-- GRPODataset
    DatasetFactory ..> BaseDataset : creates
    BaseSegmentFetcher --> MultiSegmentFetcher : used by
    MultiSegmentFetcher --> BaseDataset : used by
    AutoModel <|-- Transformer
    AutoModel --> ModelConfig : contains
    Transformer --> DecoderBlock : uses
    Transformer --> RotaryEmbedding : uses
    Transformer --> Embedding : uses
    DecoderBlock --> GQA : uses
    DecoderBlock --> MLA : uses
    DecoderBlock --> MLP : uses
    DecoderBlock --> RMSNorm : uses
    BpeTokenizer --> Tokenizer : inherits
    TrainContextBuilder --> ResumableDistributedSampler : creates
    DataLoader --> BaseDataset : uses
    ResumableDistributedSampler --> BaseDataset : samples

Module Overview

Module	Components	Description
astrai.config	ModelConfig, TrainConfig, ModelParameter	Configuration management
astrai.dataset	BaseDataset, SEQDataset, SFTDataset, DPODataset, GRPODataset, BaseSegmentFetcher, MultiSegmentFetcher, ResumableDistributedSampler, DatasetFactory, Checkpoint, DataLoader	Dataset loading and management
astrai.model	AutoModel, Transformer, DecoderBlock, GQA, MLA, MLP, RMSNorm, Linear, RotaryEmbedding, Embedding	Neural network model
astrai.tokenize	AutoTokenizer, BpeTokenizer, ChatTemplate, BpeTrainer	Tokenizer
astrai.trainer	Trainer, TrainContext, TrainContextBuilder, BaseStrategy, StrategyFactory, BaseScheduler, SchedulerFactory, TrainCallback, CallbackFactory	Training workflow management
astrai.inference	InferenceEngine, InferenceScheduler, Task, TaskStatus, Server, GenerationRequest	Inference service with continuous batching
astrai.parallel	ParallelSetup, 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)
Singleton	TrainContext	Training process global state management
Registry	BaseFactory, Registry	Generic component registration with category and priority support
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	_StreamingResult, _NonStreamingResult	Event-based result notification for streaming/non-streaming generation

Core Relationships

1. Configuration → Training: TrainConfig contains ModelConfig, holds model, dataset, optimizer and other 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: Server → InferenceEngine → InferenceScheduler → Transformer, supports continuous batching with streaming/non-streaming
5. Distributed Support: ParallelSetup provides 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}

In this implementation, an off-policy approach is used ($\pi_\theta = \pi_{\text{ref}}$), and the policy loss simplifies to:

L_{\text{policy}} = -\mathbb{E}[A]

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.