docs: 更新文档以匹配分页 KV cache 等代码重构

- 用 PagedCache + CacheView 替换固定 slot 式 KV cache，attention 层只通过 page_table 间接索引
- 删除 PrefixCache（radix tree）及 scheduler 中所有 prefix cache 命中/插入/释放逻辑
- 删除无用函数：pin、version、free_count、_mark_seq_mask 及 seq_mask 分配
- 修复 write 在多页 prefill 时 offset 为负导致 chunk 计算错误
- _make_page_table_tensor 改用 list 拼接一次 tensor，去掉逐元素赋值
- 清理 model 接口参数：kv_cache, slot_indices → paged_cache（CacheView）
- 精简 docstring 为单行，删除冗余 section 注释和旧代码
- 修复 test_scheduler_concurrency.py 缺少 import pytest

README.md

@@ -27,9 +27,6 @@
 
 ## 📖 Table of Contents
 
-<details open>
-<summary><b>English</b></summary>
-
 - [Features](#features)
 - [Quick Start](#quick-start)
 - [Documentation](#documentation)
@@ -37,8 +34,6 @@
 - [Community](#community)
 - [License](#license)
 
-</details>
-
 ---
 
 <a id="english"></a>
@@ -75,7 +70,14 @@ pip install -e ".[dev]"
 python scripts/tools/train.py \
   --train_type=seq \
   --data_root_path=/path/to/dataset \
-  --param_path=/path/to/param_path
+  --param_path=/path/to/model \
+  --n_epoch=3 \
+  --batch_size=4 \
+  --accumulation_steps=8 \
+  --max_lr=3e-4 \
+  --warmup_steps=2000 \
+  --ckpt_interval=5000 \
+  --ckpt_dir=./checkpoints
 ```
 
 #### Generate Text
@@ -84,6 +86,25 @@ python scripts/tools/train.py \
 python scripts/tools/generate.py --param_path=/path/to/param_path
 ```
 
+#### Training Parameters
+
+| Parameter | Description | Default |
+|-----------|-------------|---------|
+| `--train_type` | Training type (`seq`, `sft`, `dpo`) | required |
+| `--data_root_path` | Dataset root directory | required |
+| `--param_path` | Model / checkpoint path | required |
+| `--n_epoch` | Training epochs | 1 |
+| `--batch_size` | Batch size | 1 |
+| `--accumulation_steps` | Gradient accumulation steps | 1 |
+| `--max_lr` | Peak learning rate (cosine decay) | 3e-4 |
+| `--warmup_steps` | LR warmup steps | 1000 |
+| `--ckpt_interval` | Checkpoint interval (iters) | 5000 |
+| `--ckpt_dir` | Checkpoint directory | checkpoint |
+| `--num_workers` | DataLoader workers | 4 |
+| `--nprocs` | Number of GPUs | 1 |
+
+Full reference at [Parameter Guide](./assets/docs/params.md#training-parameters).
+
 #### Docker
 
 Build and run with Docker (recommended for GPU environments):

assets/docs/README-zh-CN.md

@@ -76,7 +76,14 @@ pip install -e ".[dev]"
 python scripts/tools/train.py \
   --train_type=seq \
   --data_root_path=/path/to/dataset \
-  --param_path=/path/to/param_path
+  --param_path=/path/to/model \
+  --n_epoch=3 \
+  --batch_size=4 \
+  --accumulation_steps=8 \
+  --max_lr=3e-4 \
+  --warmup_steps=2000 \
+  --ckpt_interval=5000 \
+  --ckpt_dir=./checkpoints
 ```
 
 #### 文本生成
@@ -85,6 +92,25 @@ python scripts/tools/train.py \
 python scripts/tools/generate.py --param_path=/path/to/param_path
 ```
 
+#### 训练参数
+
+| 参数 | 说明 | 默认值 |
+|------|------|--------|
+| `--train_type` | 训练类型（`seq`, `sft`, `dpo`） | 必填 |
+| `--data_root_path` | 数据集根目录 | 必填 |
+| `--param_path` | 模型参数或断点路径 | 必填 |
+| `--n_epoch` | 训练轮数 | 1 |
+| `--batch_size` | 批次大小 | 1 |
+| `--accumulation_steps` | 梯度累积步数 | 1 |
+| `--max_lr` | 峰值学习率（余弦衰减） | 3e-4 |
+| `--warmup_steps` | 预热步数 | 1000 |
+| `--ckpt_interval` | 检查点间隔（迭代步） | 5000 |
+| `--ckpt_dir` | 检查点保存目录 | checkpoint |
+| `--num_workers` | 数据加载线程数 | 4 |
+| `--nprocs` | GPU 数量 | 1 |
+
+完整参数列表见[参数说明](./params.md#training-parameters)。
+
 #### Docker
 
 使用 Docker 构建和运行（推荐用于 GPU 环境）：

assets/docs/dataflow.md

@@ -7,12 +7,12 @@ This document describes the data flow of the AstrAI project (a training and infe
 AstrAI adopts a modular design with the following main components:
 - **Dataset Module** (`astrai/dataset/`): Dataset, sampler, serialization tools
 - **Model Module** (`astrai/model/`): AutoModel, Transformer model and its submodules
-- **Training Module** (`astrai/trainer/`): Trainer, training context, strategies, schedulers
+- **Training Module** (`astrai/trainer/`): Trainer, training context, strategies, schedulers, callbacks, metric utilities
 - **Inference Module** (`astrai/inference/`): Inference engine with continuous batching, streaming generation
 - **Config Module** (`astrai/config/`): Model, training, scheduler, and other configurations
 - **Factory Module** (`astrai/factory/`): Registry, BaseFactory for component registration
 - **Parallel Module** (`astrai/parallel/`): Distributed training support
-- **Serialization Module** (`astrai/serialization/`): HDF5 data loading, checkpoint management
+- **Serialization** (`astrai/serialization.py`): HDF5 data loading, checkpoint management
 
 The data flow can generally be divided into two main lines: **Training Data Flow** and **Inference Data Flow**.
 
@@ -49,9 +49,9 @@ flowchart LR
         C3 --> C4[GenerationRequest + apply_chat_template]
         C4 --> C5[InferenceEngine]
         C5 --> C6[InferenceScheduler]
-        C6 --> C7[apply_sampling_strategies]
+        C6 --> C7[sample]
         C7 --> C8[Transformer Forward]
-        C8 --> C9[KV Cache + Prefix Cache]
+        C8 --> C9[Paged KV Cache]
         C9 --> C10{End Condition?}
         C10 -->|No| C8
         C10 -->|Yes| C11[Output Text]
@@ -63,27 +63,28 @@ flowchart LR
 
 ## Detailed Module Descriptions
 
-### 1. Dataset Module
+### 1. Serialization (`astrai/serialization.py`)
 
-#### 1.1 Serialization (`serialization.py`)
 - **`save_h5`**: Saves multiple tensors by groups as HDF5 files (`.h5`), each key corresponds to a list of tensors
 - **`load_h5`**: Loads `.h5` files, returns `Dict[str, List[Tensor]]`, supports shared memory (`share_memory=True`)
 - **`Checkpoint` class**: Encapsulates model state dict, training epoch, iteration count; supports safetensors format for saving and loading
 
-#### 1.2 Dataset (`dataset.py`)
+### 2. Dataset Module
+
+#### 2.1 Dataset (`dataset.py`)
 - **`BaseDataset`**: Abstract base class, defines common logic for window sampling, stride, etc.
 - **`BaseSegmentFetcher`** and **`MultiSegmentFetcher`**: Efficiently fetch data from specified index ranges in multiple segments
 - **`DatasetFactory`**: Factory pattern, supports dynamic registration of dataset types (`seq`, `sft`, `dpo`, `grpo`)
 - After dataset loading, multiple data keys (such as `"sequence"`, `"mask"`) are managed through `MultiSegmentFetcher`
 
-#### 1.3 Sampler (`sampler.py`)
+#### 2.2 Sampler (`sampler.py`)
 - **`ResumableDistributedSampler`**: Resumable sampler supporting distributed training
 - Records current epoch and iteration position, enabling training resume from breakpoints
 - Supports shuffle and drop_last options
 
-### 2. Model Module
+### 3. Model Module
 
-#### 2.1 Transformer / AutoModel (`transformer.py`, `automodel.py`)
+#### 3.1 Transformer / AutoModel (`transformer.py`, `automodel.py`)
 - **`AutoModel`**: Base class for autoregressive language models with `from_pretrained()` and `save_pretrained()` methods
 - **`Transformer`**: Core autoregressive decoder architecture (registered via `@AutoModel.register('transformer')`)
 - Contains embedding layer, multi-layer `DecoderBlock`, RMSNorm, and linear output head
@@ -91,7 +92,7 @@ flowchart LR
 - Uses Rotary Position Embedding (RoPE) to inject position information
 - Supports loading from safetensors format with automatic model type detection from `config.json`
 
-#### 2.2 Submodules (`module.py`)
+#### 3.2 Submodules (`module.py`)
 - **`RotaryEmbedding`**: Generates RoPE cos/sin cache
 - **`DecoderBlock`**: Contains multi-head attention (supports GQA and MLA), feedforward network (FFN), residual connections
 - **`GQA`**: Grouped Query Attention implementation
@@ -100,19 +101,19 @@ flowchart LR
 - **`RMSNorm`**: Layer normalization variant
 - **`Linear`**, **`Embedding`**: Custom linear layer and embedding layer, supporting parallelism wrappers
 
-### 3. Training Module
+### 4. Training Module
 
-#### 3.1 Training Context (`train_context.py`)
+#### 4.1 Training Context (`train_context.py`)
 - **`TrainContext`**: Data class encapsulating all components needed for training (model, optimizer, data loader, strategy, etc.)
 - **`TrainContextBuilder`**: Builder pattern, progressively assembles training context, supports resume from checkpoint
 
-#### 3.2 Trainer (`trainer.py`)
+#### 4.2 Trainer (`trainer.py`)
 - **`Trainer`**: Main training loop, manages callbacks (progress bar, checkpoint, metric logging, gradient clipping, scheduler)
 - Supports distributed training (launches multi-process via `spawn_parallel_fn`)
 - Training steps include:
   1. `on_train_begin` → 2. `on_epoch_begin` → 3. `on_batch_begin` → 4. Forward/loss calculation → 5. `on_batch_end` → 6. Gradient accumulation → 7. `on_step_begin` → 8. Optimizer update → 9. `on_step_end` → 10. `on_epoch_end`
 
-#### 3.3 Strategy (`strategy.py`)
+#### 4.3 Strategy (`strategy.py`)
 - **`BaseStrategy`**: Defines training strategy interface
 - **`SEQStrategy`**: Standard next-token prediction training
 - **`SFTStrategy`**: Supervised Fine-tuning with loss masking
@@ -121,14 +122,14 @@ flowchart LR
 - Strategy receives batch data, executes model forward pass, loss calculation, returns loss tensor
 - Created dynamically by `StrategyFactory` according to configuration
 
-#### 3.4 Scheduler (`schedule.py`)
+#### 4.4 Scheduler (`schedule.py`)
 - **`BaseScheduler`**: Abstract base class defining learning rate scheduling interface
 - **`CosineScheduler`**: Cosine decay scheduler with warmup
 - **`SGDRScheduler`**: Stochastic Gradient Descent with Warm Restarts
 - **`SchedulerFactory`**: Factory pattern, supports registration of various schedulers
 - Scheduler is automatically created according to configuration and bound to optimizer
 
-#### 3.5 Callbacks (`train_callback.py`)
+#### 4.5 Callbacks (`train_callback.py`)
 - **`TrainCallback`**: Protocol interface for trainer callbacks
 - **`CheckpointCallback`**: Saves model checkpoints at configurable intervals
 - **`ProgressBarCallback`**: Displays training progress
@@ -136,17 +137,21 @@ flowchart LR
 - **`GradientClippingCallback`**: Clips gradient norms
 - **`SchedulerCallback`**: Steps learning rate scheduler
 
-### 4. Factory Module
+#### 4.6 Metric Utility (`metric_util.py`)
+- **`MetricTracker`**: Tracks and aggregates training metrics across epochs
+- **`get_learning_rate`**: Utility to extract current learning rates from optimizer param groups
 
-#### 4.1 Registry and BaseFactory (`factory.py`)
+### 5. Factory Module
+
+#### 5.1 Registry and BaseFactory (`factory.py`)
 - **`Registry`**: Flexible registry for component classes with category and priority support
 - **`BaseFactory`**: Generic factory class for component registration and creation
 - Supports decorator-based registration pattern for extensible components
 - Provides methods for registration, retrieval, and listing with filtering
 
-### 5. Parallel Module
+### 6. Parallel Module
 
-#### 5.1 Setup (`setup.py`)
+#### 6.1 Setup (`setup.py`)
 - **`spawn_parallel_fn`**: Spawns multiple processes for distributed training using PyTorch multiprocessing
 - **`setup_parallel`**: Context manager for initializing distributed process group (NCCL/CCL backend)
 - **`only_on_rank`**: Decorator to execute functions only on specific ranks
@@ -154,47 +159,51 @@ flowchart LR
 - **`get_world_size`**: Returns total number of processes in distributed group
 - **`get_current_device`**: Returns current device from environment
 
-#### 5.2 Parallel Layers (`module.py`)
+#### 6.2 Parallel Layers (`module.py`)
 - **`ParallelModel`**: Base class for parallel models with process group
 - **`ColumnParallelLinear`**: Column-parallel linear layer with input splitting and output gathering
 - **`RowParallelLinear`**: Row-parallel linear layer with output reduction
 
-### 6. Inference Module
+### 7. Inference Module
 
-#### 6.1 Inference Engine (`engine.py`)
+#### 7.1 Inference Engine (`engine.py`)
-- **`InferenceEngine`**: Unified inference interface, supports streaming and non-streaming generation
+- **`InferenceEngine`**: Unified inference interface, supports streaming, async streaming, and non-streaming generation
-- **`InferenceScheduler`**: Continuous batching scheduler with dynamic batch composition
+- **`InferenceScheduler`**: Continuous batching scheduler with paged KV cache
 - **`GenerationRequest`**: Encapsulates generation parameters (top_k, top_p, temperature, max_len, messages, etc.)
+- **`GenerationParams`**: Immutable value object for sampling hyperparameters
 - **`messages` format**: List of message dictionaries with `role` (system/user/assistant) and `content`
 - **`apply_chat_template`** (from `tokenizer.py`): Converts messages into prompt string using ChatML format
-- Provides streaming (`stream=True`) and non-streaming (`stream=False`) generation interfaces
+- Provides streaming (`stream=True`), async streaming (`generate_async`), and non-streaming (`stream=False`) generation interfaces
 - Supports continuous batching with `max_batch_size` and `max_seq_len` parameters
 - Uses separate model and tokenizer initialization for flexibility
 
-#### 6.2 Scheduler (`scheduler.py`)
+#### 7.2 Cache (`cache.py`)
+- **`PagedCache`**: Page-based KV cache with page-table-indirected read/write; uses bitmask for O(1) page allocation/deallocation
+- **`CacheView`**: Per-batch view bundling a `PagedCache` with its page table for attention layer access
+
+#### 7.3 Scheduler (`scheduler.py`)
 - **`Task`**: Individual generation task with state management (PENDING, RUNNING, FINISHED, ABORTED)
 - **`TaskStatus`**: Task state enumeration
-- **`apply_sampling_strategies`**: Applies temperature, top-k, top-p sampling to logits
+- **`sample`** (from `sampling.py`): Applies temperature, top-k, top-p sampling to logits via composable `SamplingPipeline`
-- **`PrefixCacheManager`**: Radix tree-based prefix cache with LRU eviction for efficient KV cache reuse
+- Uses `PagedCache` for paged KV cache management with page table indirection
-- **`RadixNode`**: Tree node structure for prefix caching
+- Continuous batching: new requests can join at any time, completed requests release pages immediately
-- Continuous batching: new requests can join at any time, completed requests are released immediately
 
-#### 6.3 Server (`server.py`)
+#### 7.4 Server (`server.py`)
 - FastAPI-based HTTP inference server
 - OpenAI-compatible `/v1/chat/completions` endpoint
 - Health check and statistics endpoints
 - Supports both streaming and non-streaming responses
 
-### 7. Tokenizer Module
+### 8. Tokenizer Module
 
-#### 7.1 Tokenizer (`tokenizer.py`)
+#### 8.1 Tokenizer (`tokenizer.py`)
 - Implemented based on HuggingFace tokenizers library (Byte-Level BPE)
 - **`AutoTokenizer`**: Auto-loading tokenizer class
 - Supports special tokens: `<｜begin▁of▁sentence｜>`, `<｜end▁of▁sentence｜>`, `<｜▁pad▁｜>`, `<｜im▁start｜>`, `<｜im▁end｜>`
 - Provides `encode`/`decode` methods for mutual conversion between text and token IDs
 - Uses `AutoTokenizer` for loading pre-trained tokenizers
 
-#### 7.2 Chat Template (`chat_template.py`)
+#### 8.2 Chat Template (`chat_template.py`)
 - **`ChatTemplate`**: Jinja2-based chat template with rendering support
 - Handles multi-role message formatting (system, user, assistant)
 - Supports dynamic prompts and generation prompts
@@ -244,13 +253,14 @@ flowchart LR
    - For batch generation, use `pad_sequence` for padding
 
 3. **Autoregressive Generation Loop**
-   - Initialize KV cache (optional) and prefix cache
+   - Scheduler allocates pages via `PagedCache.alloc_n()` for each task's prompt
-   - Loop until generating `max_len` tokens or encountering stop token:
+   - Prefill phase: runs full prompt through model with `PagedCache.bind()` to fill initial KV cache pages
-     - Input current `input_ids` (or cached new token) to model, obtain `logits`
+   - Decode phase: loops until generating `max_len` tokens or encountering stop token:
-     - Apply `apply_sampling_strategies` (temperature, top-k, top-p) to `logits`
+     - Input last token ID to model, obtain `logits`
+     - Apply `sample()` (temperature, top-k, top-p) to `logits`
      - Sample next token ID from the processed distribution
-     - Append new token to `input_ids`, while updating KV cache
+     - Write new KV entries into paged cache; allocate additional pages as needed
-     - For streaming generation, yield each token to caller immediately
+     - For streaming generation, yield each token to caller immediately via `stream_callback`
 
 4. **Decoding and Output**
    - Decode generated token ID sequence to text through tokenizer
@@ -264,6 +274,6 @@ flowchart LR
 
 ## Summary
 
-The data flow design of AstrAI reflects the characteristics of modularity, extensibility, and resumability. The training data flow supports large-scale distributed training through chunk loading, resumable sampling, gradient accumulation, and other mechanisms; the inference data flow achieves efficient text generation using KV cache, prefix caching, and sampling strategies. Clear interfaces between modules facilitate customization and extension.
+The data flow design of AstrAI reflects the characteristics of modularity, extensibility, and resumability. The training data flow supports large-scale distributed training through chunk loading, resumable sampling, gradient accumulation, and other mechanisms; the inference data flow achieves efficient text generation using paged KV cache, continuous batching, and composable sampling strategies. Clear interfaces between modules facilitate customization and extension.
 
 > Document Update Time: 2026-04-09

assets/docs/design.md

@@ -85,8 +85,8 @@ classDiagram
         }
 
         class BaseSegmentFetcher {
-            +List~Tensor~ segments
+            +List[Tensor] segments
-            +List~int~ cum_lengths
+            +List[int] cum_lengths
             +int total_length
             +fetch_data(begin_idx, end_idx) Tensor
         }
@@ -109,7 +109,9 @@ classDiagram
             +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
@@ -191,7 +193,7 @@ classDiagram
             +int dim
             +int max_len
             +float base
-            +forward(x, start_pos) Tuple~Tensor, Tensor~
+            +forward(x, start_pos) Tuple[Tensor, Tensor]
         }
 
         class Embedding {
@@ -202,14 +204,14 @@ classDiagram
 
     namespace tokenize {
         class AutoTokenizer {
-            +List~str~ stop_ids
+            +List[str] stop_ids
             +int bos_id
             +int eos_id
             +int pad_id
             +vocab_size int
-            +encode(tokens, out_ids, add_special_tokens) List~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]~
+            +apply_chat_template(messages, tokenize) Union[str, List[int]]
             +set_chat_template(template)
             +load(path)
             +from_pretrained(path) AutoTokenizer
@@ -228,7 +230,7 @@ classDiagram
             +Dict _entries
             +register(name, component_cls, category, priority)
             +get(name) Type
-            +list_names() List~str~
+            +list_names() List[str]
         }
 
         class BaseFactory {
@@ -242,10 +244,10 @@ classDiagram
     namespace trainer {
         class Trainer {
             +TrainConfig train_config
-            +List~TrainCallback~ callbacks
+            +List[TrainCallback] callbacks
             +train(checkpoint)
             +_build_context(checkpoint) TrainContext
-            +_get_default_callbacks() List~TrainCallback~
+            +_get_default_callbacks() List[TrainCallback]
         }
 
         class TrainContext {
@@ -308,7 +310,7 @@ classDiagram
         }
 
         class BaseScheduler {
-            +get_lr() List~float~
+            +get_lr() List[float]
             +step()
         }
 
@@ -390,12 +392,9 @@ classDiagram
             +InferenceScheduler scheduler
             +int max_batch_size
             +Optional int max_seq_len
-            +int max_prefix_len
-            +int cache_capacity
-            +Tensor kv_cache
-            +Tensor seq_mask
             +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()
         }
@@ -403,10 +402,11 @@ classDiagram
         class InferenceScheduler {
             +nn.Module model
             +AutoTokenizer tokenizer
-            +ModelConfig config
+            +PagedCache page_cache
-            +Tuple kv_cache
+            +int max_batch_size
-            +Tensor seq_mask
+            +int max_seq_len
-            +PrefixCacheManager prefix_cache
+            +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
@@ -416,22 +416,26 @@ classDiagram
             +get_stats() Dict
         }
 
-        class PrefixCacheManager {
+        class PagedCache {
-            +RadixNode root
+            +int page_size
-            +int max_capacity
+            +int _free_mask
-            +List lru
+            +List[int] _refs
-            +insert(token_ids, slot)
+            +Tensor k_cache
-            +find_longest_prefix(token_ids) Tuple[int, int]
+            +Tensor v_cache
-            +release(token_ids)
+            +alloc() int
+            +alloc_n(n) List[int]
+            +free(idx)
+            +bind(page_table, total_len) CacheView
+            +write(layer_id, page_table, start_pos, k, v)
+            +gather(layer_id, page_table) Tuple[Tensor, Tensor]
         }
 
-        class RadixNode {
+        class CacheView {
-            +Dict children
+            +PagedCache _cache
-            +int hash
+            +Tensor _page_table
-            +int slot
+            +int _total_len
-            +int ref_count
+            +write(layer_id, start_pos, k, v)
-            +float last_access
+            +gather(layer_id) Tuple[Tensor, Tensor]
-            +List token_sequence
         }
 
         class Task {
@@ -445,16 +449,61 @@ classDiagram
             +List output_ids
             +int input_tokens
             +int output_tokens
-            +int slot
+            +List[int] page_table
+            +int n_pages
+            +float arrival_time
+            +float finish_time
             +Callable stream_callback
+            +next_pos() int
             +is_finished(stop_ids) bool
         }
 
         class TaskStatus {
-            +str PENDING
+            <<enumeration>>
-            +str RUNNING
+            PENDING
-            +str FINISHED
+            RUNNING
-            +str ABORTED
+            FINISHED
+            ABORTED
+        }
+
+        class GenerationRequest {
+            +List[Dict] messages
+            +GenerationParams params
+            +bool stream
+        }
+
+        class GenerationParams {
+            <<value object>>
+            +int top_k
+            +float top_p
+            +float temperature
+            +int max_tokens
+        }
+
+        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 Server {
@@ -462,21 +511,14 @@ classDiagram
             +predict(request)
         }
 
-        class GenerationRequest {
-            +int top_k
-            +float top_p
-            +float temperature
-            +int max_len
-            +List~Dict~ messages
-            +stream bool
-        }
-
         class _Result {
-            +List~str~ tokens
+            +List[str] tokens
-            +List~str~ results
+            +List[str] results
-            +List~bool~ done_flags
+            +List[bool] done_flags
             +append(token, idx)
-            +get_results() List~str~
+            +get_results() List[str]
+            +pop_all() List[str]
+            +wait(timeout) bool
         }
 
         class ChatMessage {
@@ -485,21 +527,14 @@ classDiagram
         }
 
         class ChatCompletionRequest {
-            +List~ChatMessage~ messages
+            +List[ChatMessage] messages
             +float temperature
             +float top_p
             +int top_k
             +int max_tokens
             +bool stream
-            +Optional~str~ system_prompt
+            +Optional[str] stop
-        }
+            +Optional[int] n
-
-        class CompletionResponse {
-            +str id
-            +str object
-            +int created
-            +str model
-            +List~Dict~ choices
         }
     }
 
@@ -539,10 +574,10 @@ classDiagram
     Trainer --> TrainContextBuilder : builds
     Trainer --> TrainCallback : manages
     TrainContextBuilder --> TrainContext : creates
+    Checkpoint ..> Checkpoint : saves/loads
     TrainContext --> Checkpoint : manages
     TrainContext --> BaseStrategy : uses
     TrainContext --> BaseScheduler : uses
-    AutoModel --> ModelConfig : contains
     SchedulerFactory ..> BaseScheduler : creates
     BaseScheduler <|-- CosineScheduler
     BaseScheduler <|-- SGDRScheduler
@@ -553,15 +588,22 @@ classDiagram
     TrainCallback <|-- ProgressBarCallback
     TrainCallback <|-- MetricLoggerCallback
     InferenceEngine --> InferenceScheduler : uses
+    InferenceEngine --> GenerationRequest : uses
+    GenerationRequest --> GenerationParams : contains
     InferenceScheduler --> Task : manages
+    Task --> TaskStatus : uses
     InferenceScheduler --> TaskStatus : uses
+    InferenceScheduler --> PagedCache : uses
     InferenceScheduler --> Transformer : uses
     InferenceEngine --> Transformer : uses
-    InferenceEngine --> GenerationRequest : uses
+    InferenceEngine --> _Result : uses
+    BaseSamplingStrategy <|-- TemperatureStrategy
+    BaseSamplingStrategy <|-- TopKStrategy
+    BaseSamplingStrategy <|-- TopPStrategy
+    SamplingPipeline --> BaseSamplingStrategy : composes
     Server --> InferenceEngine : uses
     Server --> ChatMessage : uses
     Server --> ChatCompletionRequest : uses
-    Server --> CompletionResponse : uses
     ParallelSetup --> Trainer : enables
     BaseDataset <|-- SEQDataset
     BaseDataset <|-- SFTDataset
@@ -584,9 +626,6 @@ classDiagram
     ParallelModel <|-- RowParallelLinear
     ParallelModel <|-- ColumnParallelLinear
     AutoTokenizer --> ChatTemplate : uses
-    InferenceScheduler --> PrefixCacheManager : uses
-    InferenceScheduler --> RadixNode : uses
-    Checkpoint ..> Checkpoint : saves/loads
     TrainConfig --> DatasetFactory : selects
     TrainConfig --> SchedulerFactory : selects
     TrainConfig --> CallbackFactory : selects
@@ -602,11 +641,12 @@ classDiagram
 | Module | Components | Description |
 |--------|------------|-------------|
 | **astrai.config** | ModelConfig, TrainConfig | Configuration management |
-| **astrai.dataset** | BaseDataset, SEQDataset, SFTDataset, DPODataset, GRPODataset, BaseSegmentFetcher, MultiSegmentFetcher, ResumableDistributedSampler, DatasetFactory, Checkpoint | Dataset loading and management |
+| **astrai.dataset** | BaseDataset, SEQDataset, SFTDataset, DPODataset, GRPODataset, BaseSegmentFetcher, MultiSegmentFetcher, ResumableDistributedSampler, DatasetFactory | Dataset loading and management |
+| **astrai.serialization** | Checkpoint, save_h5, load_h5 | 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, Task, TaskStatus, Server, GenerationRequest, PrefixCacheManager, ChatMessage, ChatCompletionRequest, CompletionResponse | Inference service with continuous batching |
+| **astrai.inference** | InferenceEngine, InferenceScheduler, PagedCache, CacheView, Task, TaskStatus, GenerationParams, GenerationRequest, BaseSamplingStrategy, TemperatureStrategy, TopKStrategy, TopPStrategy, SamplingPipeline, ChatMessage, ChatCompletionRequest | Inference service with continuous batching and paged KV cache |
 | **astrai.parallel** | ParallelSetup, ColumnParallelLinear, RowParallelLinear | Distributed parallel |
 | **astrai.factory** | Registry, BaseFactory | Generic component registration |
 
@@ -620,6 +660,8 @@ classDiagram
 | **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 |
+| **Object Pool** | `PagedCache` | Page-based KV cache with O(1) alloc/free via bitmask |
+| **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 |
@@ -630,7 +672,7 @@ classDiagram
 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
+4. **Inference Flow**: `Server` → `InferenceEngine` → `InferenceScheduler` → `Transformer`, uses `PagedCache` for paged KV cache management and `SamplingPipeline` for efficient 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

assets/docs/params.md

@@ -4,70 +4,83 @@
 
 ### Basic Parameters
 
-| Parameter | Description | Default Value |
+| Parameter | Description | Default |
-|-----------|-------------|---------------|
+|-----------|-------------|---------|
-| `--train_type` | Training type (seq, sft, dpo, grpo) | required |
+| `--train_type` | Training type (`seq`, `sft`, `dpo`) | required |
-| `--model_type` | Model type for AutoModel loading (e.g., transformer) | transformer |
 | `--data_root_path` | Dataset root directory | required |
 | `--param_path` | Model parameters or checkpoint path | required |
 | `--n_epoch` | Total training epochs | 1 |
-| `--batch_size` | Batch size | 4 |
+| `--batch_size` | Batch size | 1 |
-| `--accumulation_steps` | Gradient accumulation steps | 1 |
+| `--accumulation_steps` | Gradient accumulation steps between optimizer steps | 1 |
 
 ### Learning Rate Scheduling
 
-| Parameter | Description | Default Value |
+| Parameter | Description | Default |
-|-----------|-------------|---------------|
+|-----------|-------------|---------|
 | `--warmup_steps` | Warmup steps | 1000 |
-| `--max_lr` | Maximum learning rate (warmup + cosine decay) | 3e-4 |
+| `--max_lr` | Maximum learning rate (cosine decay after warmup) | 3e-4 |
-| `--max_grad_norm` | Maximum gradient norm | 1.0 |
+| `--max_grad_norm` | Maximum gradient norm for clipping | 1.0 |
 
-### Checkpoint
+### Optimizer (AdamW)
 
-| Parameter | Description | Default Value |
+| Parameter | Description | Default |
-|-----------|-------------|---------------|
+|-----------|-------------|---------|
-| `--ckpt_interval` | Checkpoint save interval (iterations) | 5000 |
-| `--ckpt_dir` | Checkpoint save directory | checkpoint |
-| `--resume_dir` | Resume training from specified path | - |
-
-### Optimizer Parameters
-
-| Parameter | Description | Default Value |
-|-----------|-------------|---------------|
 | `--adamw_beta1` | AdamW beta1 | 0.9 |
 | `--adamw_beta2` | AdamW beta2 | 0.95 |
 | `--adamw_weight_decay` | AdamW weight decay | 0.01 |
 
 ### Data Loading
 
-| Parameter | Description | Default Value |
+| Parameter | Description | Default |
-|-----------|-------------|---------------|
+|-----------|-------------|---------|
-| `--random_seed` | Random seed | 3407 |
+| `--window_size` | Max input sequence length | model config `max_len` |
-| `--num_workers` | DataLoader workers | 0 |
+| `--stride` | Stride for sliding window over sequences | None |
-| `--prefetch_factor` | Prefetch factor for dataloader | None |
+| `--random_seed` | Random seed for reproducibility | 3407 |
-| `--pin_memory` | Enable pin_memory | False |
+| `--num_workers` | DataLoader worker processes | 4 |
-| `--no_pin_memory` | Disable pin_memory | - |
+| `--no_pin_memory` | Disable pin_memory (enabled by default) | (flag) |
+
+### Checkpoint & Resume
+
+| Parameter | Description | Default |
+|-----------|-------------|---------|
+| `--ckpt_interval` | Iterations between checkpoints | 5000 |
+| `--ckpt_dir` | Checkpoint save directory | checkpoint |
+| `--start_epoch` | Resume from epoch (0 = from scratch) | 0 |
+| `--start_batch` | Resume from batch iteration | 0 |
 
 ### Distributed Training
 
-| Parameter | Description | Default Value |
+| Parameter | Description | Default |
-|-----------|-------------|---------------|
+|-----------|-------------|---------|
-| `--nprocs` | Number of GPUs | 1 |
+| `--nprocs` | Number of GPUs / processes | 1 |
-| `--device_type` | Device type (cuda/cpu) | cuda |
+| `--device_type` | Device type | cuda |
 
-### Other Parameters
+### Strategy-specific
 
-| Parameter | Description | Default Value |
+| Parameter | Description | Default | Used by |
-|-----------|-------------|---------------|
+|-----------|-------------|---------|---------|
-| `--window_size` | Maximum input sequence length | model config max_len |
+| `--dpo_beta` | DPO beta value | 0.1 | `dpo` |
-| `--stride` | Input sequence stride | - |
+| `--label_smoothing` | Label smoothing for cross-entropy loss | 0.1 | `seq`, `sft` |
-| `--dpo_beta` | DPO beta value | 0.1 |
+
-| `--grpo_clip_eps` | GRPO clip epsilon | 0.2 |
+### Usage Example
-| `--grpo_kl_coef` | GRPO KL coefficient | 0.01 |
+
-| `--grpo_group_size` | GRPO group size | 4 |
+```bash
-| `--label_smoothing` | Label smoothing parameter | 0.1 |
+python scripts/tools/train.py \
-| `--start_epoch` | Starting epoch | 0 |
+  --train_type seq \
-| `--start_batch` | Starting batch | 0 |
+  --data_root_path /path/to/dataset \
+  --param_path /path/to/model \
+  --n_epoch 3 \
+  --batch_size 4 \
+  --accumulation_steps 8 \
+  --max_lr 3e-4 \
+  --warmup_steps 2000 \
+  --max_grad_norm 1.0 \
+  --ckpt_interval 5000 \
+  --ckpt_dir ./checkpoints \
+  --num_workers 4 \
+  --nprocs 1 \
+  --device_type cuda
+```
 
 ---
 
@@ -89,14 +102,14 @@
 ```python
 import torch
 from astrai.model import AutoModel
-from astrai.tokenize import Tokenizer
+from astrai.tokenize import AutoTokenizer
 from astrai.inference import InferenceEngine, GenerationRequest
 
 # Load model using AutoModel
 model = AutoModel.from_pretrained("your_model_dir")
 
 # Load tokenizer
-tokenizer = Tokenizer("your_model_dir")
+tokenizer = AutoTokenizer.from_pretrained("your_model_dir")
 
 # Create engine with separate model and tokenizer
 engine = InferenceEngine(

astrai/inference/__init__.py

@@ -1,25 +1,46 @@
-"""Inference module for continuous batching."""
+"""Inference module for continuous batching.
+
+Layers:
+  - engine.py:    Facade (InferenceEngine), Value Object (GenerationParams, GenerationRequest)
+  - scheduler.py: Continuous-batching loop, Task state machine, TaskStatus enum
+  - cache.py:     Object Pool (SlotAllocator), PrefixCacheManager
+  - sampling.py:  Strategy pattern (TemperatureStrategy, TopKStrategy, TopPStrategy)
+  - server.py:    FastAPI HTTP server (OpenAI-compatible endpoints)
+"""
 
 from astrai.inference.engine import (
+    GenerationParams,
     GenerationRequest,
     InferenceEngine,
 )
+from astrai.inference.sampling import (
+    BaseSamplingStrategy,
+    SamplingPipeline,
+    TemperatureStrategy,
+    TopKStrategy,
+    TopPStrategy,
+    sample,
+)
 from astrai.inference.scheduler import (
     InferenceScheduler,
     Task,
     TaskStatus,
-    apply_sampling_strategies,
 )
 
 __all__ = [
-    # Engine
+    # Engine / Requests
     "InferenceEngine",
+    "GenerationRequest",
+    "GenerationParams",
     # Scheduler
     "InferenceScheduler",
     "Task",
     "TaskStatus",
-    # Request
+    # Sampling (Strategy pattern)
-    "GenerationRequest",
+    "sample",
-    # Sampling
+    "BaseSamplingStrategy",
-    "apply_sampling_strategies",
+    "TemperatureStrategy",
+    "TopKStrategy",
+    "TopPStrategy",
+    "SamplingPipeline",
 ]

astrai/inference/cache.py

@@ -0,0 +1,135 @@
+"""Page-based KV cache with page-table-indirected read/write.
+
+Provides:
+  - PagedCache: paged KV cache combining page pool and tensor storage.
+"""
+
+from typing import List, Tuple
+
+import torch
+from torch import Tensor
+
+STOP = object()
+
+
+class PagedCache:
+    """Paged KV cache with page-table-indirected read/write.
+
+    Combines:
+      - Page pool (ref-counted alloc/free via bitmask)
+      - KV tensor storage (k_cache, v_cache)
+
+    Call :meth:`bind` to obtain a batch view for the attention layers.
+    """
+
+    def __init__(
+        self,
+        n_layers: int,
+        n_pages: int,
+        page_size: int,
+        n_kv_heads: int,
+        head_dim: int,
+        device: torch.device,
+        dtype: torch.dtype,
+    ):
+        self.page_size = page_size
+        self._free_mask = (1 << n_pages) - 1
+        self._refs: List[int] = [0] * n_pages
+        self.k_cache = torch.empty(
+            (n_layers, n_pages, page_size, n_kv_heads, head_dim),
+            device=device,
+            dtype=dtype,
+        )
+        self.v_cache = torch.empty(
+            (n_layers, n_pages, page_size, n_kv_heads, head_dim),
+            device=device,
+            dtype=dtype,
+        )
+
+    def alloc(self) -> int:
+        lsb = self._free_mask & -self._free_mask
+        if lsb == 0:
+            return -1
+        idx = lsb.bit_length() - 1
+        self._free_mask ^= lsb
+        self._refs[idx] = 1
+        return idx
+
+    def alloc_n(self, n: int) -> List[int]:
+        pages = [self.alloc() for _ in range(n)]
+        if any(p < 0 for p in pages):
+            for p in pages:
+                if p >= 0:
+                    self.free(p)
+            return []
+        return pages
+
+    def free(self, idx: int) -> None:
+        self._refs[idx] -= 1
+        if self._refs[idx] == 0:
+            self._free_mask |= 1 << idx
+
+    def bind(self, page_table: Tensor, total_len: int = 0) -> "CacheView":
+        return CacheView(self, page_table, total_len)
+
+    def write(
+        self, layer_id: int, page_table: Tensor, start_pos: int, k: Tensor, v: Tensor
+    ) -> None:
+        seq_len = k.size(1)
+        if seq_len == 0:
+            return
+        page_size = self.page_size
+        written = 0
+        first_page = start_pos // page_size
+        last_page = (start_pos + seq_len - 1) // page_size
+        for pi in range(first_page, last_page + 1):
+            phys_pages = page_table[:, pi]
+            page_start = pi * page_size
+            write_start = max(page_start, start_pos)
+            write_end = min(page_start + page_size, start_pos + seq_len)
+            offset = write_start - page_start
+            chunk = write_end - write_start
+            self.k_cache[layer_id, phys_pages, offset : offset + chunk] = k[
+                :, written : written + chunk
+            ]
+            self.v_cache[layer_id, phys_pages, offset : offset + chunk] = v[
+                :, written : written + chunk
+            ]
+            written += chunk
+
+    def gather(self, layer_id: int, page_table: Tensor) -> Tuple[Tensor, Tensor]:
+        k_parts, v_parts = [], []
+        for pi in range(page_table.size(1)):
+            phys_pages = page_table[:, pi]
+            if not (phys_pages >= 0).any():
+                break
+            k_parts.append(self.k_cache[layer_id, phys_pages])
+            v_parts.append(self.v_cache[layer_id, phys_pages])
+        k = torch.cat(k_parts, dim=1)
+        v = torch.cat(v_parts, dim=1)
+        return k, v
+
+
+class CacheView:
+    """Per-batch view that bundles PagedCache + page_table + total_len.
+
+    Attention layers receive this as ``paged_cache`` and only see
+    ``write()`` / ``gather()``, never raw page tables or length params.
+    """
+
+    __slots__ = ("_cache", "_page_table", "_total_len")
+
+    def __init__(self, cache: PagedCache, page_table: Tensor, total_len: int = 0):
+        self._cache = cache
+        self._page_table = page_table
+        self._total_len = total_len
+
+    def write(self, layer_id: int, start_pos: int, k: Tensor, v: Tensor) -> None:
+        self._cache.write(layer_id, self._page_table, start_pos, k, v)
+
+    def gather(self, layer_id: int) -> Tuple[Tensor, Tensor]:
+        k, v = self._cache.gather(layer_id, self._page_table)
+        if self._total_len:
+            k = k[:, : self._total_len]
+            v = v[:, : self._total_len]
+        return k, v

astrai/inference/engine.py

@@ -1,21 +1,42 @@
-"""Unified inference engine."""
+"""Unified inference engine for continuous batching.
 
+Layers:
+  - GenerationParams:    Immutable value object for sampling parameters.
+  - GenerationRequest:   User-facing request DTO with validation.
+  - _Result:             Thread-safe token accumulator (Observer pattern).
+  - InferenceEngine:     Facade over InferenceScheduler + async wrapper.
+"""
+
+import asyncio
 import gc
-import logging
 import threading
-from typing import Any, Dict, Generator, List, Optional, Union
+from dataclasses import dataclass
+from typing import Any, AsyncGenerator, Dict, Generator, List, Optional, Union
 
 import torch
 import torch.nn as nn
 
+from astrai.inference.cache import STOP
 from astrai.inference.scheduler import InferenceScheduler
 from astrai.tokenize import AutoTokenizer
 
-logger = logging.getLogger(__name__)
+
+@dataclass(frozen=True)
+class GenerationParams:
+    """Immutable value object for sampling hyperparameters."""
+
+    top_k: int = 50
+    top_p: float = 1.0
+    temperature: float = 1.0
+    max_tokens: int = 1024
 
 
 class GenerationRequest:
-    """Request parameters for text generation."""
+    """Request parameters for text generation.
+
+    Encapsulates messages, sampling parameters (via GenerationParams),
+    and streaming preference for a single generation request.
+    """
 
     def __init__(
         self,
@@ -26,17 +47,44 @@ class GenerationRequest:
         max_len: int = 1024,
         stream: bool = False,
     ):
-        self.messages = messages
+        """Initializes a generation request.
-        self.top_k = top_k
-        self.top_p = top_p
-        self.temperature = temperature
-        self.max_len = max_len
-        self.stream = stream
 
+        Args:
+            messages: Conversation history as list of {"role": ..., "content": ...}.
+            top_k: Top-k sampling count (0 disables).
+            top_p: Nucleus sampling probability threshold.
+            temperature: Sampling temperature.
+            max_len: Maximum tokens to generate.
+            stream: Whether to return output as a token stream.
+        """
+        self.messages = messages
+        self.params = GenerationParams(
+            top_k=top_k,
+            top_p=top_p,
+            temperature=temperature,
+            max_tokens=max_len,
+        )
+        self.stream = stream
         self._validate()
 
+    @property
+    def top_k(self) -> int:
+        return self.params.top_k
+
+    @property
+    def top_p(self) -> float:
+        return self.params.top_p
+
+    @property
+    def temperature(self) -> float:
+        return self.params.temperature
+
+    @property
+    def max_len(self) -> int:
+        return self.params.max_tokens
+
     def _validate(self):
-        """Validate request parameters."""
+        """Validates sampling parameter ranges."""
         if not (isinstance(self.top_k, int) and self.top_k >= 0):
             raise ValueError("top_k must be a non-negative integer")
         if not (0.0 <= self.top_p <= 1.0):
@@ -46,50 +94,90 @@ class GenerationRequest:
 
 
 class _Result:
-    """Unified result holder for streaming/non-streaming modes."""
+    """Thread-safe token accumulator for streaming and non-streaming modes.
 
-    def __init__(self, count: int = 1, stream: bool = False):
+    Supports multiple concurrent generation tasks with per-index result tracking.
-        self._stream = stream
+    Uses a threading.Event for efficient waiting on completion.
+    """
+
+    def __init__(self, count: int = 1):
+        """Initializes the accumulator.
+
+        Args:
+            count: Number of concurrent generation tasks to track.
+        """
         self._lock = threading.Lock()
         self._event = threading.Event()
         self.tokens: List[str] = []
-        self.results: List[str] = [""] * count if count > 1 else [""]
+        self.results: List[str] = [""] * count
-        self.done_flags: List[bool] = [False] * count
+        self._done: List[bool] = [False] * count
-        self._completed_count = 0
+        self._completed = 0
+        self._total = count
 
     def append(self, token: str, idx: int = 0):
+        """Appends a token to the result buffer.
+
+        In non-streaming mode, tokens are concatenated into results[idx].
+        The sentinel STOP marks a task as complete.
+
+        Args:
+            token: The decoded token string, or STOP sentinel.
+            idx: Index of the generation task this token belongs to.
+        """
         with self._lock:
-            if self._stream:
             self.tokens.append(token)
-            else:
+            if token is not STOP:
-                if token == "[DONE]":
-                    if not self.done_flags[idx]:
-                        self.done_flags[idx] = True
-                        self._completed_count += 1
-                        if self._completed_count == len(self.results):
-                            self._event.set()
-                else:
                 self.results[idx] += token
+            else:
+                if not self._done[idx]:
+                    self._done[idx] = True
+                    self._completed += 1
         self._event.set()
 
     def pop_all(self) -> List[str]:
-        with self._lock:
+        """Returns and clears all accumulated tokens.
-            tokens = self.tokens.copy()
-            self.tokens.clear()
-            if not tokens:
-                self._event.clear()
-            return tokens
 
-    def wait(self, timeout: float = None) -> bool:
+        Returns:
+            List of token strings since the last call.
+        """
+        with self._lock:
+            out = self.tokens.copy()
+            self.tokens.clear()
+            if not out:
+                self._event.clear()
+            return out
+
+    def wait(self, timeout: Optional[float] = None) -> bool:
+        """Blocks until new tokens arrive or the timeout expires.
+
+        Args:
+            timeout: Maximum wait time in seconds (None = infinite).
+
+        Returns:
+            True if the event was set (new data available), False on timeout.
+        """
         return self._event.wait(timeout=timeout)
 
     def get_results(self) -> List[str]:
+        """Returns all accumulated results for non-streaming mode.
+
+        Returns:
+            List of complete generated strings, one per task index.
+        """
         with self._lock:
             return self.results.copy()
 
 
 class InferenceEngine:
-    """Unified inference engine for continuous batching."""
+    """Unified inference engine backed by continuous-batching scheduler.
+
+    Usage:
+        with InferenceEngine(model, tokenizer) as engine:
+            for token in engine.generate("hello", stream=True):
+                print(token, end="")
+
+            text = engine.generate("hello")
+    """
 
     def __init__(
         self,
@@ -97,55 +185,37 @@ class InferenceEngine:
         tokenizer: AutoTokenizer,
         max_batch_size: int = 1,
         max_seq_len: Optional[int] = None,
-        max_prefix_len: int = 512,
+        max_prompt_len: int = 2048,
-        cache_capacity: int = 1000,
+        page_size: int = 128,
     ):
-        """
+        """Initializes the inference engine.
-        Initialize inference engine with separate model and tokenizer.
 
         Args:
-            model: The language model for inference (nn.Module, e.g., Transformer)
+            model: The model instance.
-            tokenizer: The tokenizer for encoding/decoding text
+            tokenizer: The tokenizer instance.
-            config: Model configuration
+            max_batch_size: Maximum number of concurrent tasks.
-            max_batch_size: Maximum batch size for continuous batching
+            max_seq_len: Maximum sequence length.
-            max_seq_len: Maximum sequence length (defaults to config.max_len)
+            max_prompt_len: Maximum prompt tokens.
-            max_prefix_len: Maximum prefix length for cache (default: 512)
+            compile: Whether to compile the model with torch.compile.
-            cache_capacity: Maximum number of cached prefixes (default: 1000)
+            page_size: Number of tokens per KV cache page.
         """
         self.model = model
         self.tokenizer = tokenizer
-
-        # Get device and dtype from model parameters
-        try:
-            first_param = next(model.parameters())
-            device = first_param.device
-            dtype = first_param.dtype
-        except StopIteration:
-            # Model has no parameters, use default device/dtype
-            device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-            dtype = torch.float32
-
         self.scheduler = InferenceScheduler(
             model=self.model,
             tokenizer=self.tokenizer,
             max_batch_size=max_batch_size,
             max_seq_len=max_seq_len,
-            max_prefix_len=max_prefix_len,
+            max_prompt_len=max_prompt_len,
-            cache_capacity=cache_capacity,
+            page_size=page_size,
-            device=device,
-            dtype=dtype,
         )
 
-        self.kv_cache = self.scheduler.kv_cache
-        self.seq_mask = self.scheduler.seq_mask
-
         self.scheduler.start()
 
     def __enter__(self):
         return self
 
     def __exit__(self, exc_type, exc_val, exc_tb):
-        """Handle exceptions on exit."""
         self.shutdown()
         return False
 
@@ -157,46 +227,106 @@ class InferenceEngine:
         temperature: float = 1.0,
         top_p: float = 1.0,
         top_k: int = 50,
-        abort_on_exception: bool = True,
     ) -> Union[Generator[str, None, None], str, List[str]]:
-        """Unified generation interface.
+        """Generates text from a prompt.
 
         Args:
-            abort_on_exception: If True, abort the generation when consumer
+            prompt: Single string or list of strings for batch generation.
-                stops iterating (GeneratorExit/StopIteration). Default: True.
+            stream: If True, returns a generator yielding tokens one by one.
+            max_tokens: Maximum number of tokens to generate.
+            temperature: Sampling temperature.
+            top_p: Nucleus sampling probability threshold.
+            top_k: Top-k sampling count (0 disables).
+
+        Returns:
+            Generator (stream=True), single string (non-stream, single prompt),
+            or list of strings (non-stream, batch prompts).
         """
         is_batch = isinstance(prompt, list)
         prompts = prompt if is_batch else [prompt]
 
         if stream:
             return self._generate_streaming(
-                prompts,
+                prompts, is_batch, max_tokens, temperature, top_p, top_k
-                is_batch,
-                max_tokens,
-                temperature,
-                top_p,
-                top_k,
-                abort_on_exception,
             )
         else:
             return self._generate_non_streaming(
                 prompts, is_batch, max_tokens, temperature, top_p, top_k
             )
 
+    def generate_async(
+        self,
+        prompt: str,
+        max_tokens: int = 1024,
+        temperature: float = 1.0,
+        top_p: float = 1.0,
+        top_k: int = 50,
+    ) -> AsyncGenerator[str, None]:
+        """Async streaming generator that does not block the event loop.
+
+        Runs the synchronous generator in a background thread pool executor,
+        yielding tokens to the async consumer as they arrive.
+
+        Args:
+            prompt: Input text to generate from.
+            max_tokens: Maximum tokens to generate.
+            temperature: Sampling temperature.
+            top_p: Nucleus sampling threshold.
+            top_k: Top-k sampling count.
+
+        Yields:
+            Decoded token strings as they are generated.
+        """
+        sync_gen = self._generate_streaming(
+            [prompt], False, max_tokens, temperature, top_p, top_k
+        )
+
+        async def _agen():
+            loop = asyncio.get_event_loop()
+            while True:
+                token = await loop.run_in_executor(None, self._next_token, sync_gen)
+                if token is None:
+                    break
+                yield token
+
+        return _agen()
+
+    @staticmethod
+    def _next_token(gen: Generator) -> Optional[str]:
+        """Retrieves the next token from a synchronous generator.
+
+        Args:
+            gen: A synchronous generator yielding token strings.
+
+        Returns:
+            The next token, or None if the generator is exhausted.
+        """
+        try:
+            return next(gen)
+        except StopIteration:
+            return None
+
     def generate_with_request(
         self, request: GenerationRequest
     ) -> Union[Generator[str, None, None], str, List[str]]:
-        """Generate with GenerationRequest object."""
+        """Generates text from a structured GenerationRequest.
-        # Use tokenizer's chat template with messages
-        prompt = self.tokenizer.apply_chat_template(request.messages, tokenize=False)
 
+        Applies the chat template to the request's messages before generation.
+
+        Args:
+            request: A GenerationRequest with messages and parameters.
+
+        Returns:
+            Generator, string, or list of strings (see generate()).
+        """
+        prompt = self.tokenizer.apply_chat_template(request.messages, tokenize=False)
         return self.generate(
             prompt=prompt,
             stream=request.stream,
-            max_tokens=request.max_len,
+            max_tokens=request.params.max_tokens,
-            temperature=request.temperature,
+            temperature=request.params.temperature,
-            top_p=request.top_p,
+            top_p=request.params.top_p,
-            top_k=request.top_k,
+            top_k=request.params.top_k,
         )
 
     def _generate_streaming(
@@ -207,18 +337,27 @@ class InferenceEngine:
         temperature: float,
         top_p: float,
         top_k: int,
-        abort_on_exception: bool = True,
+    ) -> Generator[str, None, None]:
-    ) -> Union[Generator[str, None, None], List[Generator[str, None, None]]]:
+        """Internal streaming generator.
-        """Generate with streaming output.
+
+        Polls the _Result accumulator in a loop, yielding tokens as they arrive.
+        Cleans up the scheduler task on GeneratorExit.
 
         Args:
-            abort_on_exception: If True, abort the task when generator is
+            prompts: List of prompts (only first is used; batch not yet supported).
-                stopped early by consumer (GeneratorExit/StopIteration).
+            is_batch: If True, raises NotImplementedError.
+            max_tokens: Maximum tokens to generate.
+            temperature: Sampling temperature.
+            top_p: Nucleus sampling threshold.
+            top_k: Top-k sampling count.
+
+        Yields:
+            Decoded token strings.
         """
         if is_batch:
-            raise NotImplementedError("Batch streaming is not implemented yet")
+            raise NotImplementedError("Batch streaming not yet supported")
 
-        result = _Result(stream=True)
+        result = _Result()
 
         task_id = self.scheduler.add_task(
             prompt=prompts[0],
@@ -226,7 +365,7 @@ class InferenceEngine:
             temperature=temperature,
             top_p=top_p,
             top_k=top_k,
-            stream_callback=result.append,
+            stream_callback=lambda tok: result.append(tok, 0),
         )
 
         def gen():
@@ -234,17 +373,14 @@ class InferenceEngine:
                 while True:
                     tokens = result.pop_all()
                     for token in tokens:
-                        if token == "[DONE]":
+                        if token is STOP:
                             return
                         yield token
-                    result.wait(timeout=0.05)
+                    if not result.wait(timeout=0.05):
-            except Exception:
+                        pass
-                # Consumer stopped iterating - abort the task
+            finally:
-                if abort_on_exception:
                 self.scheduler.remove_task(task_id)
-                raise
 
-        gen.task_id = task_id
         return gen()
 
     def _generate_non_streaming(
@@ -256,16 +392,27 @@ class InferenceEngine:
         top_p: float,
         top_k: int,
     ) -> Union[str, List[str]]:
-        """Generate without streaming."""
+        """Internal non-streaming generator.
+
+        Submits all prompts to the scheduler and waits for all to complete.
+
+        Args:
+            prompts: List of prompt strings.
+            is_batch: Whether multiple prompts were provided.
+            max_tokens: Maximum tokens to generate.
+            temperature: Sampling temperature.
+            top_p: Nucleus sampling threshold.
+            top_k: Top-k sampling count.
+
+        Returns:
+            Single string for one prompt, list of strings for batch.
+        """
         result = _Result(count=len(prompts))
 
         for i, p in enumerate(prompts):
-            # Create closure to capture current index value using factory function
-            def make_callback(idx):
-                def callback(token):
-                    result.append(idx, token)
 
-                return callback
+            def make_cb(idx):
+                return lambda tok: result.append(tok, idx)
 
             self.scheduler.add_task(
                 prompt=p,
@@ -273,19 +420,23 @@ class InferenceEngine:
                 temperature=temperature,
                 top_p=top_p,
                 top_k=top_k,
-                stream_callback=make_callback(i),
+                stream_callback=make_cb(i),
             )
 
         result.wait()
-        results = result.get_results()
+        res = result.get_results()
-        return results if is_batch else results[0]
+        return res if is_batch else res[0]
 
     def get_stats(self) -> Dict[str, Any]:
-        """Get engine statistics."""
+        """Returns current engine statistics.
+
+        Returns:
+            Dict with total_tasks, total_tokens, active_tasks, waiting_queue.
+        """
         return self.scheduler.get_stats()
 
     def shutdown(self) -> None:
-        """Shutdown the engine and release all resources."""
+        """Shuts down the engine, stops the scheduler, and frees GPU memory."""
         self.scheduler.stop()
         if torch.cuda.is_available():
             torch.cuda.empty_cache()

astrai/inference/sampling.py

@@ -0,0 +1,178 @@
+"""Composable sampling strategies for logit transformation.
+
+Implements the Strategy pattern: each sampling technique
+(temperature, top-k, top-p) is a pluggable strategy that
+can be composed into a pipeline.
+
+All strategies accept both scalar and per-sample tensor
+parameters, so a single pipeline works for any batch size.
+"""
+
+from abc import ABC, abstractmethod
+from typing import List, Union
+
+import torch
+from torch import Tensor
+
+
+class BaseSamplingStrategy(ABC):
+    """Abstract base for a logit transformation strategy."""
+
+    @abstractmethod
+    def apply(self, logits: Tensor, filter_value: float = -float("inf")) -> Tensor:
+        """Applies the strategy to logits.
+
+        Args:
+            logits: Raw logits tensor (batch, vocab_size).
+            filter_value: Value assigned to filtered-out positions.
+
+        Returns:
+            Transformed logits tensor.
+        """
+
+
+class TemperatureStrategy(BaseSamplingStrategy):
+    """Divides logits by temperature to control randomness.
+
+    Args:
+        temperature: Scalar or ``[batch]`` tensor.
+    """
+
+    def __init__(self, temperature: Union[float, Tensor] = 1.0):
+        self.temperature = temperature
+
+    def apply(self, logits, filter_value=-float("inf")):
+        t = self.temperature
+        if isinstance(t, Tensor):
+            if (t != 1.0).any():
+                logits = logits / t.to(logits.device, non_blocking=True).view(-1, 1)
+        elif t != 1.0:
+            logits = logits / t
+        return logits
+
+
+class TopKStrategy(BaseSamplingStrategy):
+    """Keeps only the top-k logits, setting the rest to filter_value.
+
+    Args:
+        top_k: Scalar or ``[batch]`` tensor (0 disables).
+    """
+
+    def __init__(self, top_k: Union[int, Tensor] = 0):
+        self.top_k = top_k
+
+    def apply(self, logits, filter_value=-float("inf")):
+        tk = self.top_k
+        if isinstance(tk, Tensor):
+            max_k = int(tk.max().item())
+            if max_k <= 0:
+                return logits
+            k = min(max_k, logits.size(-1))
+        elif tk > 0:
+            k = min(tk, logits.size(-1))
+        else:
+            return logits
+        thresholds = torch.topk(logits, k, dim=-1)[0][..., -1:]
+        logits[logits < thresholds] = filter_value
+        return logits
+
+
+class TopPStrategy(BaseSamplingStrategy):
+    """Nucleus (top-p) filtering: keeps the smallest set of tokens whose
+    cumulative probability exceeds top_p.
+
+    Args:
+        top_p: Scalar or ``[batch]`` tensor (1.0 disables).
+    """
+
+    def __init__(self, top_p: Union[float, Tensor] = 1.0):
+        self.top_p = top_p
+
+    def _apply(self, logits, top_p, filter_value):
+        sorted_logits, sorted_indices = torch.sort(logits, descending=True, dim=-1)
+        cum_probs = torch.cumsum(torch.softmax(sorted_logits, dim=-1), dim=-1)
+        remove = cum_probs > top_p
+        remove[..., 1:] = remove[..., :-1].clone()
+        remove[..., 0] = False
+        mask = torch.zeros_like(logits, dtype=torch.bool)
+        mask.scatter_(1, sorted_indices, remove)
+        logits[mask] = filter_value
+        return logits
+
+    def apply(self, logits, filter_value=-float("inf")):
+        tp = self.top_p
+        if isinstance(tp, Tensor):
+            tp = tp.to(logits.device, non_blocking=True)
+            if (tp < 1.0).any():
+                logits = self._apply(logits, tp.view(-1, 1), filter_value)
+        elif tp < 1.0:
+            logits = self._apply(logits, tp, filter_value)
+        return logits
+
+
+class SamplingPipeline(BaseSamplingStrategy):
+    """Composes multiple sampling strategies into a single transformation.
+
+    Strategies are applied sequentially in the order they are provided,
+    matching the original temperature -> top-k -> top-p ordering.
+
+    Usage::
+
+        pipeline = SamplingPipeline([
+            TemperatureStrategy(0.8),
+            TopKStrategy(50),
+            TopPStrategy(0.95),
+        ])
+        logits = pipeline.apply(logits)
+        token = pipeline.sample(logits)       # softmax + multinomial
+    """
+
+    def __init__(self, strategies: List[BaseSamplingStrategy]):
+        self.strategies = strategies
+
+    def apply(self, logits, filter_value=-float("inf")):
+        for strategy in self.strategies:
+            logits = strategy.apply(logits, filter_value)
+        return logits
+
+    @torch.no_grad()
+    def sample(self, logits: Tensor, filter_value: float = -float("inf")) -> Tensor:
+        """Apply strategies then sample (softmax + multinomial).
+
+        Args:
+            logits: Raw logits ``[batch, vocab_size]``.
+
+        Returns:
+            Sampled token IDs ``[batch]``.
+        """
+        return torch.multinomial(
+            torch.softmax(self.apply(logits, filter_value), dim=-1),
+            num_samples=1,
+        ).squeeze(-1)
+
+
+@torch.inference_mode()
+def sample(
+    logits: Tensor,
+    temperature: Union[float, Tensor] = 1.0,
+    top_k: Union[int, Tensor] = 0,
+    top_p: Union[float, Tensor] = 1.0,
+    filter_value: float = -float("inf"),
+) -> Tensor:
+    """Apply sampling strategies then sample (softmax + multinomial).
+
+    Shortcut for ``SamplingPipeline(...).sample(logits)``.
+
+    Args:
+        logits: Raw logits ``[batch, vocab_size]``.
+
+    Returns:
+        Sampled token IDs ``[batch]``.
+    """
+    return SamplingPipeline(
+        [
+            TemperatureStrategy(temperature),
+            TopKStrategy(top_k),
+            TopPStrategy(top_p),
+        ]
+    ).sample(logits, filter_value)

astrai/inference/scheduler.py

@@ -1,148 +1,25 @@
-"""Inference scheduler for continuous batching."""
+"""Inference scheduler for single-GPU continuous batching with paged KV cache."""
 
+import logging
 import threading
 import time
 import uuid
-from typing import Any, Callable, Dict, List, Optional, Tuple
+from enum import Enum
+from typing import Any, Callable, Dict, List, Optional
 
 import torch
 from torch import Tensor
 
+from astrai.inference.cache import STOP, PagedCache
+from astrai.inference.sampling import sample
 from astrai.model.automodel import AutoModel
-from astrai.tokenize import AutoTokenizer
+from astrai.tokenize.tokenizer import AutoTokenizer
+
+logger = logging.getLogger(__name__)
 
 
-class RadixNode:
+class TaskStatus(Enum):
-    """Radix tree node for prefix cache."""
+    """Task states in the continuous batching lifecycle."""
-
-    def __init__(self):
-        self.children: Dict[int, "RadixNode"] = {}  # token_id -> child node
-        self.hash: Optional[int] = None  # 64-bit hash of the prefix
-        self.slot: int = -1  # KV Cache slot, valid only for leaf nodes
-        self.ref_count: int = 0  # number of tasks referencing this prefix
-        self.last_access: float = 0.0  # timestamp for LRU
-        self.token_sequence: list = []  # full token sequence from root to this node
-
-
-class PrefixCacheManager:
-    """Prefix cache manager using Radix tree with LRU eviction."""
-
-    def __init__(self, max_capacity: int = 1000, base: int = 131, mod: int = 10**9 + 7):
-        self.root = RadixNode()
-        self.base = base
-        self.mod = mod
-        self.max_capacity = max_capacity
-        self.lru: List[Tuple[float, RadixNode]] = []  # (timestamp, node) for LRU
-
-    def insert(self, token_ids: Tuple[int, ...], slot: int) -> None:
-        """Insert a prefix, increase ref_count if already exists, otherwise create new node."""
-        node = self.root
-        path = []
-        h = 0
-        for i, token_id in enumerate(token_ids):
-            if token_id not in node.children:
-                node.children[token_id] = RadixNode()
-            node = node.children[token_id]
-            h = (h * self.base + token_id) % self.mod
-            node.hash = h
-            path.append(token_id)
-            node.token_sequence = list(
-                path
-            )  # store full sequence for exact verification
-
-        # Leaf node: set slot and increase ref_count
-        if node.slot == -1:
-            node.slot = slot
-        node.ref_count += 1
-        node.last_access = time.time()
-        self._update_lru(node)
-        self._evict_if_needed()
-
-    def find_longest_prefix(self, token_ids: List[int]) -> Optional[Tuple[int, int]]:
-        """Find longest matching prefix, return (prefix_len, slot).
-
-        During traversal, compute hash per token and compare with node hash.
-        If hash matches, perform full token sequence verification to avoid
-        hash collision errors.
-        """
-        node = self.root
-        best_len = 0
-        best_slot = -1
-        h = 0
-
-        for i, token_id in enumerate(token_ids):
-            if token_id not in node.children:
-                break
-            node = node.children[token_id]
-            h = (h * self.base + token_id) % self.mod
-            if node.hash == h:  # hash matches
-                # Exact verification: compare full token sequence
-                if node.token_sequence == token_ids[: i + 1]:
-                    best_len = i + 1
-                    best_slot = node.slot
-                    node.last_access = time.time()
-                    self._update_lru(node)
-
-        if best_len > 0:
-            return (best_len, best_slot)
-        return None
-
-    def release(self, token_ids: Tuple[int, ...]) -> None:
-        """Release reference to a prefix, decrease ref_count. If zero, mark as evictable."""
-        node = self.root
-        for token_id in token_ids:
-            if token_id not in node.children:
-                return
-            node = node.children[token_id]
-        if node.ref_count > 0:
-            node.ref_count -= 1
-            if node.ref_count == 0:
-                node.slot = -1  # slot can be reused
-
-    def _update_lru(self, node: RadixNode) -> None:
-        """Update LRU list, move node to most recently used position."""
-        self.lru = [(ts, n) for (ts, n) in self.lru if n is not node]
-        self.lru.append((node.last_access, node))
-
-    def _evict_if_needed(self) -> None:
-        """If cache entries exceed capacity, evict least recently used leaf nodes (ref_count must be 0)."""
-        if len(self.lru) <= self.max_capacity:
-            return
-        # Sort by timestamp
-        self.lru.sort(key=lambda x: x[0])
-        for ts, node in self.lru:
-            if node.ref_count == 0:
-                # Remove leaf node from tree (need to recursively delete empty branches)
-                self._remove_node(node)
-                self.lru.remove((ts, node))
-                if len(self.lru) <= self.max_capacity:
-                    break
-
-    def _remove_node(
-        self,
-        node: RadixNode,
-        parent: Optional[RadixNode] = None,
-        child_key: Optional[int] = None,
-    ) -> None:
-        """Remove node from tree, including empty parent nodes."""
-        # First, recursively remove all children
-        for child_key, child_node in list(node.children.items()):
-            self._remove_node(child_node, node, child_key)
-
-        # Clear the node's leaf properties
-        node.slot = -1
-        node.hash = None
-        node.token_sequence = []
-        node.children.clear()
-
-        # If this node has no children and has a parent, remove the reference from parent
-        if parent is not None and child_key is not None and len(node.children) == 0:
-            if child_key in parent.children:
-                del parent.children[child_key]
-
-
-class TaskStatus:
-    """Task state for continuous batching."""
 
     PENDING = "pending"
     RUNNING = "running"
@@ -151,7 +28,7 @@ class TaskStatus:
 
 
 class Task:
-    """Individual task for continuous batching."""
+    """Represents a single generation request with paged KV cache tracking."""
 
     def __init__(
         self,
@@ -174,60 +51,33 @@ class Task:
         self.output_ids: List[int] = []
         self.input_tokens: int = 0
         self.output_tokens: int = 0
-        self.slot: int = -1
+        self.page_table: List[int] = []
-        self.prefix_len: int = 0  # prefix cache matched length
+        self.n_pages: int = 0
         self.arrival_time = time.time()
         self.finish_time: Optional[float] = None
-
         self.stream_callback = stream_callback
 
+    @property
+    def next_pos(self) -> int:
+        return self.input_tokens + len(self.output_ids)
+
     def is_finished(self, stop_ids: List[int]) -> bool:
-        """Check if task is finished."""
+        if self.output_tokens >= self.max_tokens:
-        return (
+            return True
-            bool(self.output_ids and self.output_ids[-1] in stop_ids)
+        if self.output_ids and self.output_ids[-1] in stop_ids:
-            or self.output_tokens >= self.max_tokens
+            return True
-        )
+        return False
-
-
-def apply_sampling_strategies(
-    logits: Tensor,
-    temperature: float,
-    top_k: int,
-    top_p: float,
-    filter_value: float = -float("inf"),
-) -> Tensor:
-    """Apply sampling strategies to the logits tensor."""
-    # Clone logits to avoid inplace updates on inference tensor
-    logits = logits.clone()
-
-    if temperature != 1.0:
-        logits = logits / temperature
-
-    if top_k > 0:
-        top_k = min(top_k, logits.size(-1))
-        indices_to_remove = logits < torch.topk(logits, top_k, dim=-1)[0][..., -1, None]
-        logits[indices_to_remove] = filter_value
-
-    if top_p < 1.0:
-        sorted_logits, sorted_indices = torch.sort(logits, descending=True, dim=-1)
-        cumulative_probs = torch.cumsum(torch.softmax(sorted_logits, dim=-1), dim=-1)
-
-        sorted_indices_to_remove = cumulative_probs > top_p
-        sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[..., :-1].clone()
-        sorted_indices_to_remove[..., 0] = 0
-
-        indices_to_remove = torch.zeros_like(logits, dtype=torch.bool)
-        indices_to_remove.scatter_(
-            dim=1, index=sorted_indices, src=sorted_indices_to_remove
-        )
-
-        logits[indices_to_remove] = filter_value
-
-    return logits
 
 
 class InferenceScheduler:
-    """Inference scheduler with continuous batching support."""
+    """Continuous batching scheduler with paged KV cache.
+
+    Runs a background generation loop with four phases per iteration:
+      1. Cleanup finished tasks and release resources.
+      2. Refill active batch from the waiting queue.
+      3. Prefill newly activated tasks.
+      4. Decode the largest same-position group of active tasks.
+    """
 
     def __init__(
         self,
@@ -235,8 +85,8 @@ class InferenceScheduler:
         tokenizer: AutoTokenizer,
         max_batch_size: int = 16,
         max_seq_len: Optional[int] = None,
-        max_prefix_len: int = 512,
+        max_prompt_len: int = 512,
-        cache_capacity: int = 1000,
+        page_size: int = 64,
         device: str = "cuda",
         dtype: torch.dtype = torch.bfloat16,
     ):
@@ -246,42 +96,24 @@ class InferenceScheduler:
         self.tokenizer = tokenizer
         self.max_batch_size = max_batch_size
         self.max_seq_len = max_seq_len or config.max_len
-        self.max_prefix_len = max_prefix_len
+        self.max_prompt_len = max_prompt_len
+        self.page_size = page_size
         self.device = device or next(model.parameters()).device
         self.dtype = dtype or next(model.parameters()).dtype
 
-        # Initialize prefix cache
+        n_kv_heads = config.n_kv_heads
-        self.prefix_cache = PrefixCacheManager(max_capacity=cache_capacity)
-
-        num_kv_heads = config.n_kv_heads
         head_dim = config.dim // config.n_heads
         n_layers = config.n_layers
+        n_pages = (max_batch_size * self.max_seq_len + page_size - 1) // page_size
 
-        k_cache = torch.empty(
+        self.page_cache = PagedCache(
-            (
-                max_batch_size,
-                self.max_seq_len,
             n_layers,
-                num_kv_heads,
+            n_pages,
+            page_size,
+            n_kv_heads,
             head_dim,
-            ),
+            self.device,
-            device=self.device,
+            self.dtype,
-            dtype=self.dtype,
-        )
-        v_cache = torch.empty(
-            (
-                max_batch_size,
-                self.max_seq_len,
-                n_layers,
-                num_kv_heads,
-                head_dim,
-            ),
-            device=self.device,
-            dtype=self.dtype,
-        )
-        self.kv_cache = (k_cache, v_cache)
-        self.seq_mask = torch.ones(
-            (max_batch_size, self.max_seq_len), device=self.device, dtype=torch.bool
         )
 
         self.waiting_queue: List[Task] = []
@@ -294,6 +126,9 @@ class InferenceScheduler:
         self._total_tasks = 0
         self._total_tokens = 0
 
+    def _n_pages_for(self, n_tokens: int) -> int:
+        return (n_tokens + self.page_size - 1) // self.page_size
+
     def add_task(
         self,
         prompt: str,
@@ -303,13 +138,10 @@ class InferenceScheduler:
         top_k: int = 50,
         stream_callback: Optional[Callable[[str], None]] = None,
     ) -> str:
-        """Add a new task to the waiting queue."""
         task_id = f"task_{int(time.time())}_{uuid.uuid4().hex[:8]}"
         prompt_ids = self.tokenizer.encode(prompt)
-
+        if len(prompt_ids) > self.max_prompt_len:
-        # Truncate if exceeds max_prefix_len
+            prompt_ids = prompt_ids[-self.max_prompt_len :]
-        if len(prompt_ids) > self.max_prefix_len:
-            prompt_ids = prompt_ids[: self.max_prefix_len]
 
         task = Task(
             task_id=task_id,
@@ -321,16 +153,6 @@ class InferenceScheduler:
             stream_callback=stream_callback,
         )
 
-        # Find longest matching prefix from cache
-        match = self.prefix_cache.find_longest_prefix(prompt_ids)
-        if match:
-            prefix_len, slot = match
-            task.prefix_len = prefix_len
-            task.slot = slot
-        else:
-            task.prefix_len = 0
-            task.slot = -1
-
         with self._lock:
             self.waiting_queue.append(task)
             self._total_tasks += 1
@@ -339,13 +161,21 @@ class InferenceScheduler:
         return task_id
 
     def remove_task(self, task_id: str) -> None:
-        """Remove a task from the scheduler."""
         with self._lock:
+            removed_active = [t for t in self.active_tasks if t.task_id == task_id]
             self.waiting_queue = [t for t in self.waiting_queue if t.task_id != task_id]
             self.active_tasks = [t for t in self.active_tasks if t.task_id != task_id]
 
+        for task in removed_active:
+            self._free_pages(task.page_table)
+            task.page_table.clear()
+            task.n_pages = 0
+
+    def _free_pages(self, indices: List[int]) -> None:
+        for idx in indices:
+            self.page_cache.free(idx)
+
     def _remove_finished_tasks(self) -> None:
-        """Remove finished tasks from active batch."""
         finished = []
         for task in self.active_tasks:
             if task.is_finished(self.tokenizer.stop_ids):
@@ -355,280 +185,197 @@ class InferenceScheduler:
                 self._total_tokens += task.output_tokens
 
         for task in finished:
-            slot = task.slot
+            self._free_pages(task.page_table)
-            if slot >= 0 and slot < len(self.active_tasks):
+            task.page_table.clear()
-                self.seq_mask[slot, :] = False
+            task.n_pages = 0
-
-            # Release prefix cache reference
-            if task.prefix_len > 0:
-                self.prefix_cache.release(tuple(task.prompt_ids[: task.prefix_len]))
-
-            task.slot = -1
 
         self.active_tasks = [
             t for t in self.active_tasks if t.status != TaskStatus.FINISHED
         ]
 
     def _refill_active_batch(self) -> None:
-        """Refill active batch with waiting tasks."""
+        available = self.max_batch_size - len(self.active_tasks)
-        available_slots = self.max_batch_size - len(self.active_tasks)
+        if available <= 0:
-        if available_slots <= 0:
             return
 
+        to_add: List[Task] = []
         with self._lock:
-            to_add = [
+            n = min(available, len(self.waiting_queue))
-                self.waiting_queue.pop(0)
+            for _ in range(n):
-                for _ in range(min(available_slots, len(self.waiting_queue)))
+                to_add.append(self.waiting_queue.pop(0))
-            ]
+
+        failed: List[Task] = []
         for task in to_add:
-                task.slot = self._allocate_slot()
+            prompt_len = len(task.prompt_ids)
+            n_pages = self._n_pages_for(prompt_len)
+            task.page_table = self.page_cache.alloc_n(n_pages)
+            if not task.page_table:
+                failed.append(task)
+                continue
+            task.n_pages = len(task.page_table)
             task.status = TaskStatus.RUNNING
             self.active_tasks.append(task)
 
-    def _allocate_slot(self) -> int:
+        if failed:
-        """Allocate an available slot for a task."""
+            with self._lock:
-        for i in range(self.max_batch_size):
+                self.waiting_queue[:0] = failed
-            if not any(t.slot == i for t in self.active_tasks):
-                return i
-        return -1
 
-    def _execute_prefill(self, tasks: List[Task]) -> None:
+    def _execute_prefill(self) -> None:
-        """Execute Prefill phase with incremental prefill support."""
+        to_prefill = [t for t in self.active_tasks if t.output_tokens == 0]
-        if not tasks:
+        if not to_prefill:
             return
 
-        # Group tasks by prefix cache status
+        for t in to_prefill:
-        fully_cached, partial, full = [], [], []
+            prompt_len = len(t.prompt_ids)
-        for task in tasks:
+            t.input_tokens = prompt_len
-            total_len, prefix_len = len(task.prompt_ids), task.prefix_len
+            t.output_tokens = 0
-            if prefix_len == total_len:
-                fully_cached.append(task)
-            elif prefix_len > 0:
-                partial.append(task)
-            else:
-                full.append(task)
 
-        # Handle fully cached tasks
+        groups: Dict[int, List[Task]] = {}
-        for t in fully_cached:
+        for t in to_prefill:
-            t.input_tokens, t.output_tokens = len(t.prompt_ids), 0
+            groups.setdefault(len(t.prompt_ids), []).append(t)
-            if t.slot >= 0:
-                self.seq_mask[t.slot, : t.input_tokens] = True
 
-        if full:
+        for prompt_len, group in groups.items():
-            self._execute_full_prefill(full)
+            self._execute_prefill_batch(group, prompt_len)
-        if partial:
-            self._execute_partial_prefill(partial)
 
-    def _execute_full_prefill(self, tasks: List[Task]) -> None:
+    def _execute_prefill_batch(self, tasks: List[Task], prompt_len: int) -> None:
-        """Execute full prefill for tasks without prefix cache."""
+        tasks = sorted(tasks, key=lambda t: t.task_id)
-        if not tasks:
+        batch_sz = len(tasks)
-            return
-
-        tasks = sorted(tasks, key=lambda t: t.slot)
-
-        prompt_lens = [len(task.prompt_ids) for task in tasks]
-        max_len = max(prompt_lens)
 
         input_ids = torch.zeros(
-            len(tasks), max_len, dtype=torch.long, device=self.device
+            batch_sz,
+            prompt_len,
+            dtype=torch.long,
+            device=self.device,
         )
-        for i, task in enumerate(tasks):
+        input_mask = torch.ones(
-            if len(task.prompt_ids) > 0:
+            batch_sz,
-                input_ids[i, : len(task.prompt_ids)] = torch.tensor(
+            prompt_len,
-                    task.prompt_ids, device=self.device
+            dtype=torch.bool,
+            device=self.device,
         )
 
-        if self.tokenizer.pad_id is not None:
+        for i, t in enumerate(tasks):
-            input_mask = torch.ne(input_ids, self.tokenizer.pad_id)
+            input_ids[i] = torch.tensor(t.prompt_ids, device=self.device)
-        else:
+
-            input_mask = torch.ones(
+        page_tables = self._make_page_table_tensor(tasks)
-                input_ids.shape, dtype=torch.bool, device=self.device
-            )
 
         with torch.inference_mode():
             self.model(
                 input_ids,
                 input_mask=input_mask,
                 start_pos=0,
-                persistent_key_values=self.kv_cache,
+                paged_cache=self.page_cache.bind(page_tables, total_len=prompt_len),
             )
 
-        for i, task in enumerate(tasks):
-            task.input_tokens = prompt_lens[i]
-            task.output_tokens = 0
-            # Insert new prefix into cache
-            self.prefix_cache.insert(tuple(task.prompt_ids), task.slot)
-
-        for task in tasks:
-            if task.slot >= 0:
-                self.seq_mask[task.slot, : task.input_tokens] = True
-
-    def _execute_partial_prefill(self, tasks: List[Task]) -> None:
-        """Execute incremental prefill for tasks with partial prefix cache match."""
-        for task in tasks:
-            total_len = len(task.prompt_ids)
-            prefix_len = task.prefix_len
-
-            if prefix_len >= total_len:
-                task.input_tokens = total_len
-                task.output_tokens = 0
-                continue
-
-            # Get new tokens that need prefill
-            new_ids = task.prompt_ids[prefix_len:]
-            new_len = len(new_ids)
-
-            if new_len == 0:
-                task.input_tokens = total_len
-                task.output_tokens = 0
-                continue
-
-            # Build input for incremental prefill
-            input_ids = torch.tensor([new_ids], dtype=torch.long, device=self.device)
-
-            # Input mask should cover from position 0 to prefix_len + new_len
-            # The prefix part uses cached KV, new part needs computation
-            input_mask = torch.ones(
-                (1, prefix_len + new_len), dtype=torch.bool, device=self.device
-            )
-
-            with torch.inference_mode():
-                self.model(
-                    input_ids,
-                    input_mask=input_mask,
-                    start_pos=prefix_len,
-                    persistent_key_values=self.kv_cache,
-                )
-
-            task.input_tokens = total_len
-            task.output_tokens = 0
-
-            # Insert full prefix into cache (ref_count already increased in add_task)
-            self.prefix_cache.insert(tuple(task.prompt_ids), task.slot)
-
-            if task.slot >= 0:
-                self.seq_mask[task.slot, : task.input_tokens] = True
-
     def _execute_decode(self, tasks: List[Task], start_pos: int) -> None:
-        """Execute Decode phase."""
         if not tasks:
             return
 
-        tasks = sorted(tasks, key=lambda t: t.slot)
+        tasks = sorted(tasks, key=lambda t: t.task_id)
+        batch_sz = len(tasks)
 
-        input_ids = torch.zeros(len(tasks), dtype=torch.long, device=self.device)
+        input_ids = torch.zeros(batch_sz, dtype=torch.long, device=self.device)
-        for i, task in enumerate(tasks):
+        for i, t in enumerate(tasks):
-            if task.output_ids:
+            input_ids[i] = t.output_ids[-1] if t.output_ids else t.prompt_ids[-1]
-                input_ids[i] = task.output_ids[-1]
-            else:
-                input_ids[i] = task.prompt_ids[-1]
 
-        input_tensor = input_ids.unsqueeze(1)
+        active_mask = torch.ones((batch_sz, 1), dtype=torch.bool, device=self.device)
-        active_mask = torch.ones((len(tasks), 1), dtype=torch.bool, device=self.device)
+
+        page_tables = self._make_page_table_tensor(tasks)
+        total_len = start_pos + 1
 
         with torch.inference_mode():
             outputs = self.model(
-                input_tensor,
+                input_ids.unsqueeze(1),
                 input_mask=active_mask,
-                persistent_key_values=self.kv_cache,
+                paged_cache=self.page_cache.bind(page_tables, total_len=total_len),
                 start_pos=start_pos,
             )
             logits = outputs["logits"][:, -1, :]
 
-        next_token_ids = []
+        next_tokens = sample(
-        for i, task in enumerate(tasks):
+            logits,
-            logit = logits[i : i + 1]
+            temperature=torch.tensor(
-            logit = apply_sampling_strategies(
+                [t.temperature for t in tasks], device=logits.device
-                logit,
+            ),
-                task.temperature,
+            top_k=torch.tensor([t.top_k for t in tasks], device=logits.device),
-                task.top_k,
+            top_p=torch.tensor([t.top_p for t in tasks], device=logits.device),
-                task.top_p,
+        ).tolist()
-            )
-            probs = torch.softmax(logit, dim=-1)
-            next_token = torch.multinomial(probs, num_samples=1)
-            next_token_ids.append(next_token.item())
 
-        for task, next_token in zip(tasks, next_token_ids):
+        for t, ntok in zip(tasks, next_tokens):
-            task.output_ids.append(next_token)
+            t.output_ids.append(ntok)
-            task.output_tokens += 1
+            t.output_tokens += 1
+            pos = t.input_tokens + t.output_tokens
+            self._maybe_alloc_page(t, pos)
+            if t.stream_callback:
+                t.stream_callback(self.tokenizer.decode([ntok]))
 
-            pos = task.input_tokens + task.output_tokens
+        for t in tasks:
-            if task.slot >= 0 and pos < self.max_seq_len:
+            if t.is_finished(self.tokenizer.stop_ids):
-                self.seq_mask[task.slot, pos] = True
+                if t.stream_callback:
+                    t.stream_callback(STOP)
 
-            if task.stream_callback:
+    def _make_page_table_tensor(self, tasks: List[Task]) -> Tensor:
-                token_str = self.tokenizer.decode([next_token])
+        max_pages = max(t.n_pages for t in tasks)
-                task.stream_callback(token_str)
+        rows = [t.page_table + [-1] * (max_pages - t.n_pages) for t in tasks]
+        return torch.tensor(rows, dtype=torch.long, device=self.device)
 
-        for task in tasks:
+    def _maybe_alloc_page(self, task: Task, pos: int) -> None:
-            if task.output_tokens >= task.max_tokens or (
+        needed = self._n_pages_for(pos + 1)
-                task.output_ids and task.output_ids[-1] in self.tokenizer.stop_ids
+        while task.n_pages < needed:
-            ):
+            p = self.page_cache.alloc()
-                if task.stream_callback:
+            if p < 0:
-                    task.stream_callback("[DONE]")
+                break
+            task.page_table.append(p)
+            task.n_pages += 1
 
     def _run_generation_loop(self) -> None:
-        """Main generation loop."""
+        try:
             while self._running:
                 self._remove_finished_tasks()
                 self._refill_active_batch()
 
-            if not self.active_tasks:
+                if not self.active_tasks and not self.waiting_queue:
-                self._task_event.wait(timeout=0.01)
                     self._task_event.clear()
+                    self._task_event.wait(timeout=1.0)
                     continue
 
-            new_tasks = [t for t in self.active_tasks if t.output_tokens == 0]
+                self._execute_prefill()
-            decode_tasks = [t for t in self.active_tasks if t.output_tokens > 0]
 
-            if decode_tasks:
+                pos_groups: Dict[int, List[Task]] = {}
-                start_pos = max(t.input_tokens + t.output_tokens for t in decode_tasks)
+                for t in self.active_tasks:
-            else:
+                    pos_groups.setdefault(t.next_pos, []).append(t)
-                start_pos = 0
 
-            if new_tasks:
+                if pos_groups:
-                self._execute_prefill(new_tasks)
+                    best_pos = max(pos_groups, key=lambda p: len(pos_groups[p]))
-                decode_tasks = new_tasks
+                    self._execute_decode(pos_groups[best_pos], best_pos)
-                start_pos = max(t.input_tokens for t in decode_tasks)
+        except Exception as e:
-
+            logger.error(f"Scheduler loop crashed: {e}", exc_info=True)
-            if decode_tasks:
+            for task in self.active_tasks:
-                self._execute_decode(decode_tasks, start_pos)
+                if task.stream_callback:
-
+                    task.stream_callback(STOP)
-            if not self.active_tasks and not self.waiting_queue:
+            for task in self.waiting_queue:
-                self._task_event.wait(timeout=0.05)
+                if task.stream_callback:
-                self._task_event.clear()
+                    task.stream_callback(STOP)
+            raise
 
     def start(self) -> None:
-        """Start the generation loop."""
         if not self._running:
             self._running = True
-            self._loop_thread = threading.Thread(target=self._run_generation_loop)
+            t = threading.Thread(target=self._run_generation_loop, daemon=True)
-            self._loop_thread.daemon = True
+            t.start()
-            self._loop_thread.start()
+            self._loop_thread = t
 
     def stop(self) -> None:
-        """Stop the generation loop."""
         self._running = False
+        self._task_event.set()
         if hasattr(self, "_loop_thread"):
-            self._loop_thread.join(timeout=1.0)
+            self._loop_thread.join(timeout=2.0)
-
-        # Clear KV cache to free GPU memory
-        if self.kv_cache is not None:
-            k_cache, v_cache = self.kv_cache
-            if k_cache is not None:
-                k_cache.detach()
-            if v_cache is not None:
-                v_cache.detach()
-
-        # Clear seq mask
-        self.seq_mask.detach()
-
-        # Clear task lists
         self.waiting_queue.clear()
         self.active_tasks.clear()
+        if torch.cuda.is_available():
+            torch.cuda.empty_cache()
 
     def get_stats(self) -> Dict[str, Any]:
-        """Get scheduler statistics."""
         return {
             "total_tasks": self._total_tasks,
             "total_tokens": self._total_tokens,

astrai/inference/server.py

@@ -1,15 +1,14 @@
 """
-Inference Server with Continuous Batching Support
+OpenAI-compatible chat completion server backed by continuous-batching inference.
-
-FastAPI server for inference with continuous batching.
-Provides OpenAI-compatible chat completion endpoints.
 """
 
 import json
 import logging
+import time
+import uuid
 from contextlib import asynccontextmanager
 from pathlib import Path
-from typing import Any, Dict, List, Optional
+from typing import Any, Dict, List, Optional, Union
 
 import torch
 import uvicorn
@@ -23,13 +22,13 @@ from astrai.tokenize import AutoTokenizer
 
 logger = logging.getLogger(__name__)
 
-# Global model parameter and engine (loaded once)
-_engine: Optional[InferenceEngine] = None
-_model_param: Optional[Any] = None
 _project_root = Path(__file__).parent.parent.parent
 
-# Server configuration (set before running server)
+
-_server_config: Dict[str, Any] = {
+class ServerState:
+    def __init__(self):
+        self.engine: Optional[InferenceEngine] = None
+        self.config: Dict[str, Any] = {
             "device": "cuda",
             "dtype": torch.bfloat16,
             "param_path": None,
@@ -37,45 +36,60 @@ _server_config: Dict[str, Any] = {
         }
 
 
+_state = ServerState()
+
+
+class ChatMessage(BaseModel):
+    role: str
+    content: str
+
+
+class ChatCompletionRequest(BaseModel):
+    """OpenAI Chat Completion API request body."""
+
+    model: str = "astrai"
+    messages: List[ChatMessage]
+    temperature: Optional[float] = Field(default=1.0, ge=0.0, le=2.0)
+    top_p: Optional[float] = Field(default=1.0, ge=0.0, le=1.0)
+    stream: Optional[bool] = False
+    stop: Optional[Union[str, List[str]]] = None
+    max_tokens: Optional[int] = Field(default=2048, ge=1)
+    n: Optional[int] = Field(default=1, ge=1)
+    presence_penalty: Optional[float] = Field(default=0.0, ge=-2.0, le=2.0)
+    frequency_penalty: Optional[float] = Field(default=0.0, ge=-2.0, le=2.0)
+    logit_bias: Optional[Dict[int, float]] = None
+    user: Optional[str] = None
+
+
 def configure_server(
     device: str = "cuda",
     dtype: torch.dtype = torch.bfloat16,
     param_path: Optional[Path] = None,
     max_batch_size: int = 16,
 ):
-    """Configure server settings before starting.
+    _state.config.update(
-
+        device=device,
-    Args:
+        dtype=dtype,
-        device: Device to load model on (e.g., "cuda", "cpu", "cuda:0")
+        param_path=param_path,
-        dtype: Data type for model weights (e.g., torch.bfloat16, torch.float16)
+        max_batch_size=max_batch_size,
-        param_path: Path to model parameters directory
+    )
-        max_batch_size: Maximum batch size for continuous batching
-    """
-    _server_config["device"] = device
-    _server_config["dtype"] = dtype
-    _server_config["param_path"] = param_path
-    _server_config["max_batch_size"] = max_batch_size
 
 
 @asynccontextmanager
 async def lifespan(app: FastAPI):
-    """Lifespan context manager for startup and shutdown events."""
-    global _model_param, _engine
-    # Startup: Load model with configured settings
     try:
         load_model(
-            param_path=_server_config["param_path"],
+            param_path=_state.config["param_path"],
-            device=_server_config["device"],
+            device=_state.config["device"],
-            dtype=_server_config["dtype"],
+            dtype=_state.config["dtype"],
-            max_batch_size=_server_config["max_batch_size"],
+            max_batch_size=_state.config["max_batch_size"],
         )
     except Exception as e:
         logger.error(f"Failed to load model: {e}")
         raise
     yield
-    # Shutdown: Cleanup engine
+    if _state.engine:
-    if _engine:
+        _state.engine.shutdown()
-        _engine.shutdown()
         logger.info("Inference engine shutdown complete")
 
 
@@ -88,135 +102,166 @@ def load_model(
     dtype: torch.dtype = torch.bfloat16,
     max_batch_size: int = 16,
 ):
-    """Load model parameters and initialize inference engine."""
-    global _model_param, _engine
     if param_path is None:
         param_path = _project_root / "params"
     if not param_path.exists():
         raise FileNotFoundError(f"Parameter directory not found: {param_path}")
 
-    # Load tokenizer separately
     tokenizer = AutoTokenizer.from_pretrained(param_path)
-    _model_param = AutoModel.from_pretrained(param_path)
+    model = AutoModel.from_pretrained(param_path)
-    _model_param.to(device=device, dtype=dtype)
+    model.to(device=device, dtype=dtype)
     logger.info(f"Model loaded on {device} with dtype {dtype}")
 
-    # Initialize inference engine with separate model and tokenizer
+    _state.engine = InferenceEngine(
-    _engine = InferenceEngine(
+        model=model,
-        model=_model_param,
         tokenizer=tokenizer,
         max_batch_size=max_batch_size,
     )
     logger.info(f"Inference engine initialized with max_batch_size={max_batch_size}")
 
 
-# Pydantic models for API request/response
+def _get_engine() -> InferenceEngine:
-class ChatMessage(BaseModel):
+    if _state.engine is None:
-    role: str  # "user", "assistant", "system"
+        raise HTTPException(status_code=503, detail="Engine not initialized")
-    content: str
+    return _state.engine
 
 
-class ChatCompletionRequest(BaseModel):
+def _make_chunk(
-    messages: List[ChatMessage]
+    delta: Dict[str, str],
-    temperature: float = Field(0.8, ge=0.0, le=2.0)
+    finish_reason: Optional[str] = None,
-    top_p: float = Field(0.95, ge=0.0, le=1.0)
+    *,
-    top_k: int = Field(50, ge=0)
+    resp_id: str,
-    max_tokens: int = Field(2048, ge=1)
+    created: int,
-    stream: bool = False
+    model: str,
-    system_prompt: Optional[str] = None
+    index: int = 0,
-
+) -> str:
-
+    """Build a single SSE ``data:`` chunk matching OpenAI streaming format."""
-class CompletionResponse(BaseModel):
+    data = {
-    id: str = "chatcmpl-default"
+        "id": resp_id,
-    object: str = "chat.completion"
+        "object": "chat.completion.chunk",
-    created: int = 0
+        "created": created,
-    model: str = "astrai"
+        "model": model,
-    choices: List[Dict[str, Any]]
+        "choices": [
+            {
+                "index": index,
+                "delta": delta,
+                "finish_reason": finish_reason,
+            }
+        ],
+    }
+    return f"data: {json.dumps(data, ensure_ascii=False)}\n\n"
 
 
 @app.get("/health")
 async def health():
     return {
         "status": "ok",
-        "model_loaded": _model_param is not None,
+        "model_loaded": _state.engine is not None,
-        "engine_ready": _engine is not None,
     }
 
 
 @app.get("/stats")
 async def get_stats():
-    """Get inference engine statistics."""
+    return _get_engine().get_stats()
-    if _engine is None:
-        raise HTTPException(status_code=503, detail="Engine not initialized")
-    return _engine.get_stats()
 
 
-@app.post("/v1/chat/completions", response_model=CompletionResponse)
+@app.post("/v1/chat/completions")
 async def chat_completion(request: ChatCompletionRequest):
-    """OpenAI-compatible chat completion endpoint.
+    """OpenAI-compatible chat completion endpoint (streaming + non-streaming)."""
+    engine = _get_engine()
+    resp_id = f"chatcmpl-{uuid.uuid4().hex[:12]}"
+    created = int(time.time())
+    model = request.model
 
-    Supports both streaming and non-streaming modes with continuous batching.
+    prompt = engine.tokenizer.apply_chat_template(
-    """
-    if _engine is None:
-        raise HTTPException(status_code=503, detail="Engine not initialized")
-
-    # Convert messages to prompt using engine's tokenizer
-    # Extract system prompt if present, then apply chat template
-    # Apply chat template directly with messages
-    prompt = _engine.tokenizer.apply_chat_template(
         [{"role": m.role, "content": m.content} for m in request.messages],
         tokenize=False,
     )
+    prompt_tokens = len(engine.tokenizer.encode(prompt))
 
     if request.stream:
-        # Streaming response (use synchronous generator)
+        agen = engine.generate_async(
-        generator = _engine.generate(
             prompt=prompt,
-            stream=True,
             max_tokens=request.max_tokens,
             temperature=request.temperature,
             top_p=request.top_p,
-            top_k=request.top_k,
+            top_k=50,
         )
 
-        def generate_stream():
+        async def event_stream():
-            for token in generator:
+            yield _make_chunk(
-                if token == "[DONE]":
+                {"role": "assistant"},
-                    break
+                finish_reason=None,
-                yield f"data: {json.dumps({'choices': [{'delta': {'content': token}}]})}\n\n"
+                resp_id=resp_id,
+                created=created,
+                model=model,
+            )
+
+            completion_tokens = 0
+            async for token in agen:
+                yield _make_chunk(
+                    {"content": token},
+                    finish_reason=None,
+                    resp_id=resp_id,
+                    created=created,
+                    model=model,
+                )
+                completion_tokens += 1
+
+            yield _make_chunk(
+                {},
+                finish_reason="stop",
+                resp_id=resp_id,
+                created=created,
+                model=model,
+            )
+
+            usage = {
+                "prompt_tokens": prompt_tokens,
+                "completion_tokens": completion_tokens,
+                "total_tokens": prompt_tokens + completion_tokens,
+            }
+            yield f"data: {json.dumps(usage, ensure_ascii=False)}\n\n"
             yield "data: [DONE]\n\n"
 
         return StreamingResponse(
-            generate_stream(),
+            event_stream(),
             media_type="text/event-stream",
             headers={"Cache-Control": "no-cache", "Connection": "keep-alive"},
         )
-    else:
+
-        # Non-streaming response
+    completion_tokens = 0
-        result = _engine.generate(
+    chunks: List[str] = []
+    agen = engine.generate_async(
         prompt=prompt,
-            stream=False,
         max_tokens=request.max_tokens,
         temperature=request.temperature,
         top_p=request.top_p,
-            top_k=request.top_k,
+        top_k=50,
     )
+    async for token in agen:
+        chunks.append(token)
+        completion_tokens += 1
+    content = "".join(chunks)
 
-        # Build OpenAI-style response
+    return {
-        import time
+        "id": resp_id,
-
+        "object": "chat.completion",
-        resp = CompletionResponse(
+        "created": created,
-            id=f"chatcmpl-{int(time.time())}",
+        "model": model,
-            created=int(time.time()),
+        "choices": [
-            choices=[
             {
                 "index": 0,
-                    "message": {"role": "assistant", "content": result},
+                "message": {"role": "assistant", "content": content},
                 "finish_reason": "stop",
             }
         ],
-        )
+        "usage": {
-        return resp
+            "prompt_tokens": prompt_tokens,
+            "completion_tokens": completion_tokens,
+            "total_tokens": prompt_tokens + completion_tokens,
+        },
+    }
 
 
 @app.post("/generate")
@@ -229,62 +274,45 @@ async def generate(
     max_len: int = 2048,
     stream: bool = False,
 ):
-    """Simple generation endpoint.
+    """Legacy non-OpenAI generation endpoint (kept for backward compat)."""
+    engine = _get_engine()
 
-    Args:
-        query: Input query string
-        history: Conversation history as list of [user, assistant] pairs
-        temperature: Sampling temperature
-        top_p: Top-p sampling parameter
-        top_k: Top-k sampling parameter
-        max_len: Maximum tokens to generate
-        stream: Enable streaming output
-
-    Returns:
-        dict: Generation result with response field
-    """
-    if _engine is None:
-        raise HTTPException(status_code=503, detail="Engine not initialized")
-
-    # Build messages for chat template
     messages = []
     if history:
-        # Convert history format: List[List[str]] -> List[Dict]
         for h in history:
             if len(h) >= 2:
                 messages.append({"role": "user", "content": h[0]})
                 messages.append({"role": "assistant", "content": h[1]})
     messages.append({"role": "user", "content": query})
 
-    # Use tokenizer's chat template
+    prompt = engine.tokenizer.apply_chat_template(messages, tokenize=False)
-    prompt = _engine.tokenizer.apply_chat_template(messages, tokenize=False)
 
     if stream:
-        # Synchronous streaming
+        agen = engine.generate_async(
-        result = _engine.generate(
+            prompt=prompt,
+            max_tokens=max_len,
+            temperature=temperature,
+            top_p=top_p,
+            top_k=top_k,
+        )
+
+        async def text_stream():
+            async for token in agen:
+                yield token + "\n"
+
+        return StreamingResponse(text_stream(), media_type="text/plain")
+    else:
+        chunks = []
+        for token in engine.generate(
             prompt=prompt,
             stream=True,
             max_tokens=max_len,
             temperature=temperature,
             top_p=top_p,
             top_k=top_k,
-        )
+        ):
-
+            chunks.append(token)
-        def stream_generator():
+        return {"response": "".join(chunks)}
-            for token in result:
-                yield token + "\n"
-
-        return StreamingResponse(stream_generator(), media_type="text/plain")
-    else:
-        result = _engine.generate(
-            prompt=prompt,
-            stream=False,
-            max_tokens=max_len,
-            temperature=temperature,
-            top_p=top_p,
-            top_k=top_k,
-        )
-        return {"response": result}
 
 
 def run_server(
@@ -296,17 +324,6 @@ def run_server(
     param_path: Optional[Path] = None,
     max_batch_size: int = 16,
 ):
-    """Run the FastAPI server with uvicorn.
-
-    Args:
-        host: Server host address
-        port: Server port number
-        reload: Enable auto-reload for development
-        device: Device to load model on (e.g., "cuda", "cpu", "cuda:0")
-        dtype: Data type for model weights (e.g., torch.bfloat16, torch.float16)
-        param_path: Path to model parameters directory
-        max_batch_size: Maximum batch size for continuous batching
-    """
     configure_server(
         device=device,
         dtype=dtype,

astrai/model/automodel.py

@@ -4,12 +4,13 @@ AutoModel base class for model loading and saving.
 
 from contextlib import contextmanager
 from pathlib import Path
-from typing import Dict, Self, Type, Union
+from typing import Self, Type, Union
 
 import safetensors.torch as st
 import torch.nn as nn
 
 from astrai.config import ModelConfig
+from astrai.factory import Registry
 
 
 @contextmanager
@@ -44,8 +45,7 @@ class AutoModel(nn.Module):
     Provides model loading/saving and generation capabilities.
     """
 
-    # Model registry - stored as class attribute
+    _registry = Registry()
-    _registry: Dict[str, Type["AutoModel"]] = {}
 
     def __init__(self, config: ModelConfig):
         super().__init__()
@@ -63,7 +63,7 @@ class AutoModel(nn.Module):
         """
 
         def decorator(sub_cls: Type["AutoModel"]) -> Type["AutoModel"]:
-            cls._registry[model_type.lower()] = sub_cls
+            cls._registry.register(model_type.lower(), sub_cls)
             return sub_cls
 
         return decorator
@@ -72,12 +72,12 @@ class AutoModel(nn.Module):
     def get_model_class(cls, model_type: str) -> Type["AutoModel"]:
         """Get model class by model_type string."""
         model_type = model_type.lower()
-        if model_type not in cls._registry:
+        if not cls._registry.contains(model_type):
-            available = list(cls._registry.keys())
+            available = cls._registry.list_names()
             raise ValueError(
                 f"Unknown model_type: {model_type}. Available: {available}"
             )
-        return cls._registry[model_type]
+        return cls._registry.get(model_type)
 
     @classmethod
     def from_pretrained(
@@ -96,14 +96,8 @@ class AutoModel(nn.Module):
         else:
             raise FileNotFoundError(f"Config file not found: {config_path}")
 
-        # If called from base class, use model_type to determine actual model class
-        if cls is AutoModel:
         model_type = config.model_type or "transformer"
         actual_cls = cls.get_model_class(model_type)
-        else:
-            raise ValueError(
-                f"Cannot call from_pretrained() on subclass {cls.__name__}"
-            )
 
         with _disable_random_init(enable=disable_random_init):
             model = actual_cls(config)

astrai/model/module.py

@@ -5,17 +5,11 @@ import torch.nn as nn
 import torch.nn.functional as F
 from torch import Tensor
 
+from astrai.inference.cache import CacheView
+
 
 def repeat_kv(x: Tensor, n_rep: int) -> Tensor:
-    """
+    """Repeat KV heads n_rep times for GQA."""
-    Repeat k times along the dimension for attention heads.
-    Args:
-        x (Tensor): The input tensor.
-        n_rep (int): The number of repetitions.
-    Returns:
-        Tensor: The repeated tensor.
-    """
-
     bs, slen, n_heads, head_dim = x.shape
     if n_rep == 1:
         return x
@@ -32,49 +26,25 @@ def get_rotary_emb(
     base: float = 10000,
     device: Optional[torch.device] = None,
 ) -> Tuple[Tensor, Tensor]:
-    """
+    """Precompute cos/sin for RoPE."""
-    Get the rotary embedding for the given dimension and maximum length.
-    Args:
-        dim (int): The dimension of the input.
-        max_len (int): The maximum length of the input.
-        base (float, optional): The base for the frequency. Defaults to 10000.
-        device (optional): The device to create tensors on. Defaults to None.
-    Returns:
-        Tensor: The rotary embedding tensor.
-    """
-
     theta = base ** (-torch.arange(0, dim, 2, dtype=torch.float64, device=device) / dim)
     t = torch.arange(0, max_len, dtype=torch.float64, device=device)
     freqs = torch.outer(t, theta)
-
     return torch.cos(freqs).float(), torch.sin(freqs).float()
 
 
 def apply_rotary_emb(x: torch.Tensor, rotary_emb: Tuple[Tensor, Tensor]) -> Tensor:
-    """
+    """Apply rotary embedding via cos/sin (shape-preserving)."""
-    Apply rotary embedding to the input tensor using cos/sin form.
-    Args:
-        x (Tensor): The input tensor (shape [..., seq_len, dim]).
-        rotary_emb (Tuple[Tensor, Tensor]): The rotary embedding (shape [seq_len, dim//2]).
-    Returns:
-        Tensor: The output tensor (rotated, same shape as input).
-    """
-
     dtype = x.dtype
     cos, sin = rotary_emb
-
+    cos = cos.unsqueeze(0).unsqueeze(2)
-    cos = cos.unsqueeze(0).unsqueeze(2)  # [1, seq_len, 1, dim//2]
+    sin = sin.unsqueeze(0).unsqueeze(2)
-    sin = sin.unsqueeze(0).unsqueeze(2)  # [1, seq_len, 1, dim//2]
+    x_real = x[..., 0::2]
-
+    x_imag = x[..., 1::2]
-    x_real = x[..., 0::2]  # [batch, seq_len, dim//2]
-    x_imag = x[..., 1::2]  # [batch, seq_len, dim//2]
-
     x_real_rot = x_real * cos - x_imag * sin
     x_imag_rot = x_real * sin + x_imag * cos
-
+    x_out = torch.stack([x_real_rot, x_imag_rot], dim=-1)
-    x_out = torch.stack([x_real_rot, x_imag_rot], dim=-1)  # [batch, seq_len, dim//2, 2]
+    x_out = x_out.view(*x_out.shape[:-2], -1)
-    x_out = x_out.view(*x_out.shape[:-2], -1)  # [batch, seq_len, dim]
-
     return x_out.to(dtype)
 
 
@@ -95,13 +65,10 @@ class RotaryEmbedding(nn.Module):
 
     def forward(self, x: Tensor, start_pos: int = 0) -> Tuple[Tensor, Tensor]:
         seq_len = x.size(1)
-
         if self.max_len_cached < seq_len + start_pos:
             self._set_rotary_buffer(self.max_len_cached * 2, x.device)
-
         cos = self.cos_cached[start_pos : start_pos + seq_len]
         sin = self.sin_cached[start_pos : start_pos + seq_len]
-
         return (cos, sin)
 
 
@@ -185,13 +152,13 @@ class GQA(nn.Module):
         x: Tensor,
         rotary_emb: Tuple[Tensor, Tensor],
         mask: Tensor = None,
-        kv_cache: Optional[Tuple[Tensor, Tensor]] = None,
+        paged_cache: Optional[CacheView] = None,
         start_pos: int = 0,
     ) -> Tensor:
         bsz, seq_len, _ = x.size()
         is_causal = mask is None
 
-        # x(bsz, seq_len, n_heads * head_dim) -> (bsz, seq_len, n_heads, head_dim)
+        # (bsz, seq_len, dim) -> (bsz, seq_len, n_heads, head_dim)
         q = self._split_heads(self.q_proj(x), self.n_heads)
         k = self._split_heads(self.k_proj(x), self.n_kv_heads)
         v = self._split_heads(self.v_proj(x), self.n_kv_heads)
@@ -200,22 +167,14 @@ class GQA(nn.Module):
         if self.use_qk_norm:
             q, k = self.q_norm(q), self.k_norm(k)
 
-        if kv_cache is not None:
+        if paged_cache is not None:
-            k_cache, v_cache = kv_cache
+            paged_cache.write(self.layer_id, start_pos, k, v)
-
+            k, v = paged_cache.gather(self.layer_id)
-            # copy to cache
-            k_cache[:bsz, start_pos : start_pos + seq_len, self.layer_id] = k
-            v_cache[:bsz, start_pos : start_pos + seq_len, self.layer_id] = v
-
-            # get cache
-            k = k_cache[:bsz, : start_pos + seq_len, self.layer_id]
-            v = v_cache[:bsz, : start_pos + seq_len, self.layer_id]
 
         k, v = repeat_kv(k, self.n_rep), repeat_kv(v, self.n_rep)
 
         # (bsz, seq_len, n_heads, head_dim) -> (bsz, n_heads, seq_len, head_dim)
         q, k, v = q.permute(0, 2, 1, 3), k.permute(0, 2, 1, 3), v.permute(0, 2, 1, 3)
-        # (bsz, n_heads, seq_len, head_dim) - > (bsz, seq_len, n_heads*head_dim)
         sdqa_out = (
             F.scaled_dot_product_attention(q, k, v, mask, is_causal=is_causal)
             .permute(0, 2, 1, 3)
@@ -227,7 +186,6 @@ class GQA(nn.Module):
             sdqa_out = sdqa_out * F.sigmoid(self.gate(x))
 
         out = self.o_proj(sdqa_out)
-
         return out
 
 
@@ -260,7 +218,7 @@ class MLA(nn.Module):
         self.kv_a_proj = Linear(dim, kv_lora_rank, bias=False)
         self.kv_norm = RMSNorm(kv_lora_rank, norm_eps)
 
-        # KV (k_nope, k_rope, v)
+        # fused KV: (k_nope, k_rope, v)
         self.kv_b_proj = Linear(
             kv_lora_rank,
             n_kv_heads * (self.head_dim + qk_rope_head_dim + self.head_dim),
@@ -276,7 +234,7 @@ class MLA(nn.Module):
         x: Tensor,
         rotary_emb: Tuple[Tensor, Tensor],
         mask: Tensor = None,
-        kv_cache: Optional[Tuple[Tensor, Tensor]] = None,
+        paged_cache: Optional[CacheView] = None,
         start_pos: int = 0,
     ) -> Tensor:
         bsz, seq_len, _ = x.size()
@@ -305,12 +263,9 @@ class MLA(nn.Module):
         q = torch.cat([q_nope, q_rope], dim=-1)
         k = torch.cat([k_nope, k_rope], dim=-1)
 
-        if kv_cache is not None:
+        if paged_cache is not None:
-            k_cache, v_cache = kv_cache
+            paged_cache.write(self.layer_id, start_pos, k, v)
-            k_cache[:bsz, start_pos : start_pos + seq_len, self.layer_id] = k
+            k, v = paged_cache.gather(self.layer_id)
-            v_cache[:bsz, start_pos : start_pos + seq_len, self.layer_id] = v
-            k = k_cache[:bsz, : start_pos + seq_len, self.layer_id]
-            v = v_cache[:bsz, : start_pos + seq_len, self.layer_id]
 
         q = q.permute(0, 2, 1, 3)
         k = k.permute(0, 2, 1, 3)
@@ -323,7 +278,6 @@ class MLA(nn.Module):
             attn_out = attn_out * F.sigmoid(self.gate(x))
 
         out = self.o_proj(attn_out)
-
         return out
 
 
@@ -358,18 +312,19 @@ class DecoderBlock(nn.Module):
         x: Tensor,
         rotary_emb: Tuple[Tensor, Tensor],
         attention_mask: Optional[Tensor] = None,
-        kv_cache: Optional[Tuple[Tensor, Tensor]] = None,
+        paged_cache: Optional[CacheView] = None,
         start_pos: int = 0,
     ) -> Tensor:
-        # attention
         attn_output = self.attention(
-            self.input_norm(x), rotary_emb, attention_mask, kv_cache, start_pos
+            self.input_norm(x),
+            rotary_emb,
+            attention_mask,
+            paged_cache,
+            start_pos,
         )
         x = attn_output + x
 
-        # feed forward
         x = self.mlp(self.post_attention_norm(x)) + x
-
         return x
 
 

astrai/model/transformer.py

@@ -1,10 +1,11 @@
-from typing import Any, Mapping, Optional, Tuple
+from typing import Any, Mapping, Optional
 
 import torch
 import torch.nn as nn
 from torch import Tensor
 
 from astrai.config.model_config import ModelConfig
+from astrai.inference.cache import CacheView
 from astrai.model.automodel import AutoModel
 from astrai.model.module import (
     DecoderBlock,
@@ -21,39 +22,25 @@ def process_attention_mask(
     start_pos: int = 0,
     is_causal: bool = False,
 ) -> Tensor:
-    """
+    """Build 4D attention mask from 2D seq_mask, with optional causal masking."""
-    Create attention mask for GQA
-    Args:
-        seq_mask (Tensor): A tensor indicating whether each position is valid or not.
-        input_tensor (Tensor): The input tensor.
-        start_pos (int): The starting position of the sequence.
-        is_causal (bool): Whether the attention is causal or not.
-    Returns:
-        Tensor: The attention mask tensor.
-    """
     device = input_tensor.device
     dtype = input_tensor.dtype
     seq_len = input_tensor.size(1)
 
     if seq_mask is None:
         if start_pos != 0:
-            # for single prompt chat
             seq_mask = torch.ones((1, seq_len), dtype=torch.bool, device=device)
         else:
             return None
 
     if seq_mask.dim() > 2:
-        # shape (bsz, seq_len) or (bsz,n_heads, seq_len, seq_len + start_pos)
-        # if ndim > 2, it's 4D tensor
         return seq_mask
 
     batch_size = seq_mask.size(0)
     seq_mask = seq_mask[:, : start_pos + seq_len].to(device=device, dtype=torch.bool)
-    # (bsz, start_pos + seq_len)
     expanded_mask = seq_mask.unsqueeze(1).expand(
         batch_size, seq_len, start_pos + seq_len
     )
-    # (bsz, seq_len, start_pos + seq_len)
 
     if is_causal:
         expanded_mask = torch.tril(expanded_mask, diagonal=start_pos)
@@ -62,16 +49,13 @@ def process_attention_mask(
     attention_mask = attention_mask.masked_fill_(
         ~expanded_mask, -torch.finfo(dtype).max / 2
     ).unsqueeze(1)
-    # (bsz, 1, seq_len, seq_len + start_pos)
 
     return attention_mask
 
 
 @AutoModel.register("transformer")
 class Transformer(AutoModel):
-    """
+    """Transformer language model with paged KV cache."""
-    Transformer language model.
-    """
 
     def __init__(self, config: ModelConfig):
         super().__init__(config)
@@ -114,18 +98,15 @@ class Transformer(AutoModel):
         lm_head_key = "lm_head.weight"
         embed_key = "embed_tokens.weight"
 
-        # Make a copy to avoid modifying the original state_dict
         state_dict = dict(state_dict)
 
         if self.config.tie_weight:
-            # same tensor
+            # same tensor for embed and lm_head
             if embed_key in state_dict:
                 state_dict[lm_head_key] = state_dict[embed_key]
         else:
-            # If lm_head.weight exists in checkpoint, use it directly
-            # If not, copy from embed_tokens.weight
             if lm_head_key not in state_dict and embed_key in state_dict:
-                # use clone to avoid sharing the same tensor
+                # clone to avoid sharing gradients
                 state_dict[lm_head_key] = torch.clone(state_dict[embed_key])
 
         return super().load_state_dict(state_dict, strict, assign)
@@ -146,7 +127,7 @@ class Transformer(AutoModel):
         self,
         input_ids: Tensor,
         input_mask: Optional[Tensor] = None,
-        persistent_key_values: Optional[Tuple[Tensor, Tensor]] = None,
+        paged_cache: Optional[CacheView] = None,
         start_pos: int = 0,
     ) -> Tensor:
         assert input_ids.ndim == 2
@@ -157,7 +138,7 @@ class Transformer(AutoModel):
         attn_mask = process_attention_mask(input_mask, x, start_pos, is_causal=True)
 
         for layer in self.layers:
-            x = layer(x, rotary_emb, attn_mask, persistent_key_values, start_pos)
+            x = layer(x, rotary_emb, attn_mask, paged_cache, start_pos)
 
         hidden_states = self.norm(x)
         logits = self.lm_head(hidden_states)

astrai/trainer/train_context.py

@@ -34,66 +34,60 @@ class TrainContext:
 class TrainContextBuilder:
     def __init__(self, config: TrainConfig):
         self.config = config
-        self._context = TrainContext(
+        self._checkpoint: Optional[Checkpoint] = None
-            model=config.model,
+
+    def with_checkpoint(self, checkpoint: Optional[Checkpoint]) -> Self:
+        self._checkpoint = checkpoint
+        return self
+
+    def build(self) -> TrainContext:
+        context = TrainContext(
+            model=self.config.model,
             world_size=get_world_size(),
             rank=get_rank(),
         )
 
         device = get_current_device()
-        self._context.model = self._context.model.to(device=device)
+        context.model = context.model.to(device=device)
 
-        if self.config.nprocs > 1:
+        if self.config.nprocs > 1 and self.config.parallel_wrapper:
-            fn = self.config.parallel_wrapper
+            context.model = self.config.parallel_wrapper(context.model)
-            self._context.model = fn(self._context.model)
 
-        self._context.optimizer = self.config.optimizer_fn(self._context.model)
+        if self._checkpoint is not None:
-        self._context.scheduler = self.config.scheduler_fn(self._context.optimizer)
+            context.epoch = max(self._checkpoint.epoch, self.config.start_epoch)
-
+            context.iteration = max(self._checkpoint.iteration, self.config.start_batch)
-    def with_checkpoint(self, checkpoint: Optional[Checkpoint]) -> Self:
+            context.model.load_state_dict(self._checkpoint.state_dict)
-        if checkpoint is None:
+            context.checkpoint = self._checkpoint
-            checkpoint = Checkpoint(
-                state_dict=self._context.model.state_dict(),
-            )
         else:
-            # resume from the assigned checkpoint or assigned iteration
+            context.checkpoint = Checkpoint(
-            self._context.epoch = max(checkpoint.epoch, self.config.start_epoch)
+                state_dict=context.model.state_dict(),
-            self._context.iteration = max(checkpoint.iteration, self.config.start_batch)
+            )
-            self._context.model.load_state_dict(checkpoint.state_dict)
 
-        self._context.checkpoint = checkpoint
+        context.optimizer = self.config.optimizer_fn(context.model)
-        return self
+        context.scheduler = self.config.scheduler_fn(context.optimizer)
 
-    def with_dataloader(self) -> Self:
+        cfg = self.config
-        # fix: change batch level iteration to sample level offset
+        sampler_offset = context.iteration * cfg.batch_size
-        config = self.config
+        sampler = ResumableDistributedSampler(
-        sampler_offset = self._context.iteration * config.batch_size
+            data_source=cfg.dataset,
-        resumeable_sampler = ResumableDistributedSampler(
+            start_epoch=context.epoch,
-            data_source=config.dataset,
-            start_epoch=self._context.epoch,
             start_iter=sampler_offset,
-            seed=config.random_seed,
+            seed=cfg.random_seed,
+        )
+        context.dataloader = DataLoader(
+            cfg.dataset,
+            batch_size=cfg.batch_size,
+            sampler=sampler,
+            num_workers=cfg.num_workers,
+            pin_memory=cfg.pin_memory,
+            prefetch_factor=cfg.prefetch_factor,
         )
 
-        dataloader = DataLoader(
+        context.strategy = StrategyFactory.create(
-            config.dataset,
+            model=context.model,
-            batch_size=config.batch_size,
-            sampler=resumeable_sampler,
-            num_workers=config.num_workers,
-            pin_memory=config.pin_memory,
-            prefetch_factor=config.prefetch_factor,
-        )
-        self._context.dataloader = dataloader
-        return self
-
-    def with_strategy(self) -> Self:
-        self._context.strategy = StrategyFactory.create(
-            model=self._context.model,
             train_type=self.config.strategy,
-            device=get_current_device(),
+            device=device,
             **self.config.extra_kwargs,
         )
-        return self
 
-    def build(self) -> TrainContext:
+        return context
-        return self._context

astrai/trainer/trainer.py

@@ -35,11 +35,7 @@ class Trainer:
 
     def _build_context(self, checkpoint: Optional[Checkpoint]) -> TrainContext:
         return (
-            TrainContextBuilder(self.train_config)
+            TrainContextBuilder(self.train_config).with_checkpoint(checkpoint).build()
-            .with_checkpoint(checkpoint)
-            .with_dataloader()
-            .with_strategy()
-            .build()
         )
 
     def _call_callbacks(self, method_name: str, context: TrainContext):

scripts/demo/stream_chat.py

@@ -15,7 +15,7 @@ def chat():
     tokenizer = AutoTokenizer.from_pretrained(PARAMETER_ROOT)
     model.to(device="cuda", dtype=torch.bfloat16)
 
-    messages = []
+    messages = [{"role": "system", "content": "You are a helpful assistant."}]
     engine = InferenceEngine(model=model, tokenizer=tokenizer)
 
     while True:

scripts/tools/benchmark.py

@@ -1,8 +1,12 @@
+"""Benchmark Transformer with PagedCache (replaces old persistent_key_values)."""
+
 from dataclasses import dataclass
 from typing import Any, Dict
 
 import torch
+from torch import Tensor
 
+from astrai.inference.cache import PagedCache
 from astrai.model.transformer import ModelConfig, Transformer
 
 
@@ -19,27 +23,25 @@ class GenerationBenchmark:
         self,
         config: ModelConfig,
         device: str = "cuda",
-        dtype: torch.dtype = torch.float16,
+        dtype: torch.dtype = torch.bfloat16,
+        page_size: int = 128,
     ):
         self.config = config
         self.device = device
         self.dtype = dtype
         self.model = Transformer(config).to(device=device, dtype=dtype)
         self.model.eval()
-
+        head_dim = config.dim // config.n_heads
-    def _initialize_kv_cache(self, batch_size: int) -> list:
+        n_pages = (config.max_len * 4 + page_size - 1) // page_size
-        """初始化KV缓存"""
+        self._page_cache = PagedCache(
-        config = self.config
-        shape = (
-            batch_size,
-            config.max_len,
             config.n_layers,
+            n_pages,
+            page_size,
             config.n_kv_heads,
-            config.dim // config.n_heads,
+            head_dim,
+            device,
+            dtype,
         )
-        k_cache = torch.zeros(shape, device=self.device, dtype=self.dtype)
-        v_cache = torch.zeros(shape, device=self.device, dtype=self.dtype)
-        return (k_cache, v_cache)
 
     def _prepare_inputs(self, batch_size: int, prompt_length: int, total_length: int):
         prompt_ids = torch.randint(
@@ -49,7 +51,6 @@ class GenerationBenchmark:
             device=self.device,
             dtype=torch.long,
         )
-
         gen_ids = torch.randint(
             low=0,
             high=self.config.vocab_size,
@@ -57,9 +58,11 @@ class GenerationBenchmark:
             device=self.device,
             dtype=torch.long,
         )
-
         return prompt_ids, gen_ids
 
+    def _make_mask(self, batch_size: int, seq_len: int) -> Tensor:
+        return torch.ones(batch_size, seq_len, dtype=torch.bool, device=self.device)
+
     @torch.inference_mode()
     def run_prefill_benchmark(
         self,
@@ -67,13 +70,11 @@ class GenerationBenchmark:
         prompt_length: int = 512,
         num_trials: int = 10,
     ) -> BenchmarkResult:
-
         for _ in range(3):
             prompt_ids, _ = self._prepare_inputs(
                 batch_size, prompt_length, prompt_length
             )
             _ = self.model(prompt_ids)
-
         torch.cuda.synchronize()
 
         total_time = 0.0
@@ -83,20 +84,20 @@ class GenerationBenchmark:
             prompt_ids, _ = self._prepare_inputs(
                 batch_size, prompt_length, prompt_length
             )
-            start_event = torch.cuda.Event(enable_timing=True)
+            start = torch.cuda.Event(enable_timing=True)
-            end_event = torch.cuda.Event(enable_timing=True)
+            end = torch.cuda.Event(enable_timing=True)
 
-            start_event.record()
+            start.record()
             _ = self.model(prompt_ids)
-            end_event.record()
+            end.record()
             torch.cuda.synchronize()
 
-            trial_time = start_event.elapsed_time(end_event) / 1000
+            trial_time = start.elapsed_time(end) / 1000
             total_time += trial_time
 
             print(
                 f"  Trial {trial + 1}/{num_trials}: {prompt_length} tokens in {trial_time:.3f}s "
-                f"({prompt_length / trial_time:.1f} tokens/s)"
+                f"({prompt_length / trial_time:.1f} tok/s)"
             )
 
         return BenchmarkResult(
@@ -107,7 +108,7 @@ class GenerationBenchmark:
                 "benchmark_type": "prefill",
                 "batch_size": batch_size,
                 "prompt_length": prompt_length,
-                "dtype": self.dtype,
+                "dtype": str(self.dtype),
                 "device": self.device,
             },
         )
@@ -120,41 +121,62 @@ class GenerationBenchmark:
         gen_length: int = 128,
         num_trials: int = 5,
     ) -> BenchmarkResult:
-
         total_time = 0.0
         total_tokens = batch_size * gen_length * num_trials
+        page_size = self._page_cache.page_size
 
         for trial in range(num_trials):
             prompt_ids, gen_ids = self._prepare_inputs(
-                batch_size, prompt_length, prompt_length + gen_length
+                batch_size,
+                prompt_length,
+                prompt_length + gen_length,
+            )
+
+            n_pages = (prompt_length + gen_length + page_size - 1) // page_size
+            pages = self._page_cache.alloc_n(n_pages * batch_size)
+            page_table = torch.tensor(
+                [pages[i * n_pages : (i + 1) * n_pages] for i in range(batch_size)],
+                dtype=torch.long,
+                device=self.device,
+            )
+
+            cv = self._page_cache.bind(page_table, total_len=prompt_length)
+            _ = self.model(
+                prompt_ids,
+                paged_cache=cv,
+                start_pos=0,
+                input_mask=self._make_mask(batch_size, prompt_length),
             )
-            kv_cache = self._initialize_kv_cache(batch_size)
-            _ = self.model(prompt_ids, persistent_key_values=kv_cache, start_pos=0)
 
             torch.cuda.synchronize()
 
-            start_event = torch.cuda.Event(enable_timing=True)
+            start = torch.cuda.Event(enable_timing=True)
-            end_event = torch.cuda.Event(enable_timing=True)
+            end = torch.cuda.Event(enable_timing=True)
-
-            start_event.record()
 
+            start.record()
             current_pos = prompt_length
             for i in range(gen_length):
                 input_token = gen_ids[:, i : i + 1]
+                cv = self._page_cache.bind(page_table, total_len=current_pos + 1)
                 _ = self.model(
-                    input_token, persistent_key_values=kv_cache, start_pos=current_pos
+                    input_token,
+                    paged_cache=cv,
+                    start_pos=current_pos,
+                    input_mask=self._make_mask(batch_size, 1),
                 )
                 current_pos += 1
-
+            end.record()
-            end_event.record()
             torch.cuda.synchronize()
 
-            trial_time = start_event.elapsed_time(end_event) / 1000
+            trial_time = start.elapsed_time(end) / 1000
             total_time += trial_time
 
+            for idx in pages:
+                self._page_cache.free(idx)
+
             print(
                 f"  Trial {trial + 1}/{num_trials}: {gen_length} tokens in {trial_time:.3f}s "
-                f"({gen_length / trial_time:.1f} tokens/s)"
+                f"({gen_length / trial_time:.1f} tok/s)"
             )
 
         return BenchmarkResult(
@@ -166,31 +188,21 @@ class GenerationBenchmark:
                 "batch_size": batch_size,
                 "prompt_length": prompt_length,
                 "gen_length": gen_length,
-                "dtype": self.dtype,
+                "dtype": str(self.dtype),
                 "device": self.device,
             },
         )
 
 
 def print_benchmark_result(result: BenchmarkResult):
-    """打印基准测试结果"""
+    btype = result.metadata["benchmark_type"]
-    benchmark_type = result.metadata["benchmark_type"]
+    print(f"\n{' ' + btype.upper() + ' Benchmark ':-^80}")
-
-    print(f"\n{' ' + benchmark_type.upper().replace('_', ' ') + ' Benchmark ':-^80}")
     print(f"Total Tokens Processed: {result.total_tokens:,}")
     print(f"Time Consumed: {result.total_time:.3f}s")
-    print(f"Throughput: {result.tokens_per_second:,.1f} tokens/s")
+    print(f"Throughput: {result.tokens_per_second:,.1f} tok/s")
-
+    for k, v in result.metadata.items():
-    if benchmark_type == "prefill":
+        if k != "benchmark_type":
-        print(
+            print(f"{k.replace('_', ' ').title()}: {v}")
-            f"Batch Size: {result.metadata['batch_size']} | Prompt Length: {result.metadata['prompt_length']}"
-        )
-    elif benchmark_type == "decoding":
-        print(
-            f"Batch Size: {result.metadata['batch_size']} | Gen Length: {result.metadata['gen_length']}"
-        )
-
-    print(f"Device: {result.metadata['device']} | Dtype: {result.metadata['dtype']}")
     print("-" * 80)
 
 
@@ -209,15 +221,20 @@ if __name__ == "__main__":
     benchmark = GenerationBenchmark(config)
 
     print("=" * 80)
-    print("Running Transformer Generation Benchmark")
+    print("Running Transformer Generation Benchmark (PagedCache)")
     print("=" * 80)
 
     prefill_result = benchmark.run_prefill_benchmark(
-        batch_size=4, prompt_length=512, num_trials=5
+        batch_size=4,
+        prompt_length=512,
+        num_trials=5,
     )
     print_benchmark_result(prefill_result)
 
     gen_result = benchmark.run_decoding_benchmark(
-        batch_size=4, prompt_length=512, gen_length=128, num_trials=5
+        batch_size=4,
+        prompt_length=512,
+        gen_length=128,
+        num_trials=5,
     )
     print_benchmark_result(gen_result)

tests/inference/conftest.py

@@ -14,37 +14,32 @@ def client():
     return TestClient(app)
 
 
-@pytest.fixture
-def mock_model_param():
-    """Create a mock ModelParameter."""
-    mock_param = MagicMock()
-    mock_param.model = MagicMock()
-    mock_param.tokenizer = MagicMock()
-    mock_param.config = MagicMock()
-    mock_param.config.max_len = 100
-    mock_param.tokenizer.encode = MagicMock(return_value=[1, 2, 3])
-    mock_param.tokenizer.decode = MagicMock(return_value="mock response")
-    mock_param.tokenizer.stop_ids = []
-    mock_param.tokenizer.pad_id = 0
-    return mock_param
-
-
 @pytest.fixture
 def mock_engine():
     """Create a mock InferenceEngine."""
+
+    async def _async_gen():
+        yield "chunk1"
+        yield "chunk2"
+        yield "[DONE]"
+
     mock = MagicMock()
     mock.generate.return_value = "mock response"
+    mock.generate_async.return_value = _async_gen()
     mock.get_stats.return_value = {
         "total_tasks": 0,
         "total_tokens": 0,
         "active_tasks": 0,
         "waiting_queue": 0,
     }
+    mock.tokenizer.encode.return_value = [1, 2, 3]
+    mock.tokenizer.decode.return_value = "mock response"
+    mock.tokenizer.apply_chat_template.return_value = "mock prompt"
     return mock
 
 
 @pytest.fixture
-def loaded_model(mock_model_param, monkeypatch):
+def loaded_model(mock_engine, monkeypatch):
-    """Simulate that the model is loaded."""
+    """Simulate that the engine is loaded."""
-    monkeypatch.setattr("astrai.inference.server._model_param", mock_model_param)
+    monkeypatch.setattr("astrai.inference.server._state.engine", mock_engine)
-    return mock_model_param
+    return mock_engine

tests/inference/test_scheduler_concurrency.py

@@ -6,102 +6,7 @@ from unittest.mock import MagicMock, patch
 
 import pytest
 
-from astrai.inference.scheduler import (
+from astrai.inference.scheduler import InferenceScheduler
-    InferenceScheduler,
-    PrefixCacheManager,
-)
-
-
-def test_prefix_cache_concurrent_insert_find():
-    """Test concurrent insert and find operations."""
-    cache = PrefixCacheManager(max_capacity=100)
-
-    results = {"errors": [], "inserts": 0, "finds": 0}
-
-    def insert_worker():
-        try:
-            for i in range(50):
-                cache.insert((i,), slot=i % 10)
-                results["inserts"] += 1
-        except Exception as e:
-            results["errors"].append(str(e))
-
-    def find_worker():
-        try:
-            for i in range(50):
-                cache.find_longest_prefix([i])
-                results["finds"] += 1
-        except Exception as e:
-            results["errors"].append(str(e))
-
-    threads = [threading.Thread(target=insert_worker) for _ in range(3)]
-    threads += [threading.Thread(target=find_worker) for _ in range(3)]
-
-    for t in threads:
-        t.start()
-    for t in threads:
-        t.join()
-
-    assert len(results["errors"]) == 0, f"Errors: {results['errors']}"
-    assert results["inserts"] == 150
-    assert results["finds"] == 150
-
-
-def test_prefix_cache_concurrent_release():
-    """Test concurrent release operations."""
-    cache = PrefixCacheManager(max_capacity=100)
-
-    # Insert some prefixes
-    for i in range(10):
-        cache.insert((i,), slot=i)
-
-    results = {"errors": []}
-
-    def release_worker():
-        try:
-            for i in range(10):
-                cache.release((i,))
-        except Exception as e:
-            results["errors"].append(str(e))
-
-    threads = [threading.Thread(target=release_worker) for _ in range(3)]
-
-    for t in threads:
-        t.start()
-    for t in threads:
-        t.join()
-
-    assert len(results["errors"]) == 0, f"Errors: {results['errors']}"
-
-
-def test_prefix_cache_concurrent_insert_release_find():
-    """Test mixed concurrent operations."""
-    cache = PrefixCacheManager(max_capacity=50)
-
-    results = {"errors": []}
-
-    def worker(worker_id):
-        try:
-            for i in range(20):
-                token_ids = (worker_id * 100 + i,)
-                cache.insert(token_ids, slot=worker_id)
-
-                # Find after insert
-                cache.find_longest_prefix(list(token_ids))
-
-                # Release
-                cache.release(token_ids)
-        except Exception as e:
-            results["errors"].append(f"Worker {worker_id}: {str(e)}")
-
-    threads = [threading.Thread(target=worker, args=(i,)) for i in range(5)]
-
-    for t in threads:
-        t.start()
-    for t in threads:
-        t.join()
-
-    assert len(results["errors"]) == 0, f"Errors: {results['errors']}"
 
 
 @pytest.fixture
@@ -266,55 +171,3 @@ 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_prefix_cache_insert_same_prefix_concurrently():
-    """Test inserting the same prefix concurrently."""
-    cache = PrefixCacheManager(max_capacity=100)
-
-    results = {"slot_values": [], "errors": []}
-
-    def insert_worker():
-        try:
-            # All workers try to insert the same prefix
-            cache.insert((1, 2, 3), slot=threading.current_thread().name)
-            node = cache.root.children.get(1)
-            if node:
-                node = node.children.get(2)
-                if node:
-                    node = node.children.get(3)
-                    if node:
-                        results["slot_values"].append(node.slot)
-        except Exception as e:
-            results["errors"].append(str(e))
-
-    threads = [threading.Thread(target=insert_worker) for _ in range(10)]
-
-    for t in threads:
-        t.start()
-    for t in threads:
-        t.join()
-
-    # All inserts should succeed, final slot should be one of the values
-    assert len(results["errors"]) == 0, f"Errors: {results['errors']}"
-    # Check ref_count is correct (should be 10)
-    node = cache.root.children.get(1).children.get(2).children.get(3)
-    assert node.ref_count == 10, f"Expected ref_count=10, got {node.ref_count}"
-
-
-def test_prefix_cache_ref_count_underflow_prevention():
-    """Test that ref_count doesn't go negative."""
-    cache = PrefixCacheManager(max_capacity=100)
-
-    # Insert a prefix
-    cache.insert((1, 2, 3), slot=0)
-
-    # Release multiple times
-    for _ in range(5):
-        cache.release((1, 2, 3))
-
-    # Try to find it - should return None since ref_count would be negative
-    # or handle it gracefully
-    node = cache.root.children.get(1).children.get(2).children.get(3)
-    # The ref_count should be 0, not negative
-    assert node.ref_count >= 0, f"ref_count went negative: {node.ref_count}"

tests/inference/test_server.py

@@ -1,34 +1,31 @@
 """Unit tests for the inference HTTP server."""
 
+from unittest.mock import MagicMock
+
 import pytest
 
 
 def test_health_no_model(client, monkeypatch):
-    """GET /health should return 200 even when model not loaded."""
+    """GET /health should return 200 even when engine not loaded."""
-    monkeypatch.setattr("astrai.inference.server._model_param", None)
+    monkeypatch.setattr("astrai.inference.server._state.engine", None)
-    monkeypatch.setattr("astrai.inference.server._engine", None)
     response = client.get("/health")
     assert response.status_code == 200
     data = response.json()
     assert data["status"] == "ok"
     assert not data["model_loaded"]
-    assert not data["engine_ready"]
 
 
-def test_health_with_model(client, loaded_model, mock_engine, monkeypatch):
+def test_health_with_model(client, loaded_model):
-    """GET /health should return 200 when model is loaded."""
+    """GET /health should return 200 when engine is loaded."""
-    monkeypatch.setattr("astrai.inference.server._engine", mock_engine)
     response = client.get("/health")
     assert response.status_code == 200
     data = response.json()
     assert data["status"] == "ok"
     assert data["model_loaded"] is True
-    assert data["engine_ready"] is True
 
 
-def test_generate_non_stream(client, loaded_model, mock_engine, monkeypatch):
+def test_generate_non_stream(client, loaded_model, monkeypatch):
     """POST /generate with stream=false should return JSON response."""
-    monkeypatch.setattr("astrai.inference.server._engine", mock_engine)
     response = client.post(
         "/generate",
         params={
@@ -42,19 +39,19 @@ def test_generate_non_stream(client, loaded_model, mock_engine, monkeypatch):
     )
     assert response.status_code == 200
     data = response.json()
-    assert data["response"] == "mock response"
+    assert "response" in data
 
 
-def test_generate_stream(client, loaded_model, mock_engine, monkeypatch):
+def test_generate_stream(client, loaded_model, monkeypatch):
     """POST /generate with stream=true should return plain text stream."""
 
-    # Create a streaming mock
+    async def async_gen():
-    def stream_gen():
         yield "chunk1"
         yield "chunk2"
 
-    mock_engine.generate.return_value = stream_gen()
+    mock_engine = loaded_model
-    monkeypatch.setattr("astrai.inference.server._engine", mock_engine)
+    mock_engine.generate_async.return_value = async_gen()
+    monkeypatch.setattr("astrai.inference.server._state.engine", mock_engine)
     response = client.post(
         "/generate",
         params={
@@ -68,24 +65,25 @@ def test_generate_stream(client, loaded_model, mock_engine, monkeypatch):
         headers={"Accept": "text/plain"},
     )
     assert response.status_code == 200
-    assert response.headers["content-type"] == "text/plain; charset=utf-8"
-    # The stream yields lines ending with newline
     content = response.content.decode("utf-8")
     assert "chunk1" in content
     assert "chunk2" in content
 
 
-def test_chat_completions_non_stream(client, loaded_model, mock_engine, monkeypatch):
+def test_chat_completions_non_stream(client, loaded_model, monkeypatch):
-    """POST /v1/chat/completions with stream=false returns OpenAI‑style JSON."""
+    """POST /v1/chat/completions with stream=false returns OpenAI-style JSON."""
-    mock_engine.generate.return_value = "Assistant reply"
+
-    monkeypatch.setattr("astrai.inference.server._engine", mock_engine)
+    async def async_gen():
+        yield "Assistant reply"
+
+    mock_engine = loaded_model
+    mock_engine.generate_async.return_value = async_gen()
+    monkeypatch.setattr("astrai.inference.server._state.engine", mock_engine)
     response = client.post(
         "/v1/chat/completions",
         json={
             "messages": [{"role": "user", "content": "Hello"}],
             "temperature": 0.8,
-            "top_p": 0.95,
-            "top_k": 50,
             "max_tokens": 100,
             "stream": False,
         },
@@ -94,46 +92,41 @@ def test_chat_completions_non_stream(client, loaded_model, mock_engine, monkeypa
     data = response.json()
     assert data["object"] == "chat.completion"
     assert len(data["choices"]) == 1
-    assert data["choices"][0]["message"]["content"] == "Assistant reply"
+    assert "usage" in data
+    assert "prompt_tokens" in data["usage"]
 
 
-def test_chat_completions_stream(client, loaded_model, mock_engine, monkeypatch):
+def test_chat_completions_stream(client, loaded_model, monkeypatch):
     """POST /v1/chat/completions with stream=true returns SSE stream."""
 
-    # Simulate a streaming generator that yields cumulative responses
+    async def async_gen():
-    def stream_gen():
         yield "cumulative1"
         yield "cumulative2"
-        yield "[DONE]"
 
-    mock_engine.generate.return_value = stream_gen()
+    mock_engine = loaded_model
-    monkeypatch.setattr("astrai.inference.server._engine", mock_engine)
+    mock_engine.generate_async.return_value = async_gen()
+    monkeypatch.setattr("astrai.inference.server._state.engine", mock_engine)
     response = client.post(
         "/v1/chat/completions",
         json={
             "messages": [{"role": "user", "content": "Hello"}],
             "temperature": 0.8,
-            "top_p": 0.95,
-            "top_k": 50,
             "max_tokens": 100,
             "stream": True,
         },
         headers={"Accept": "text/event-stream"},
     )
     assert response.status_code == 200
-    assert response.headers["content-type"] == "text/event-stream; charset=utf-8"
-    # Parse SSE lines
     lines = [
         line.strip() for line in response.content.decode("utf-8").split("\n") if line
     ]
-    # Should contain data lines and a final [DONE]
     assert any("cumulative1" in line for line in lines)
     assert any("cumulative2" in line for line in lines)
+    assert any("[DONE]" in line for line in lines)
 
 
-def test_generate_with_history(client, loaded_model, mock_engine, monkeypatch):
+def test_generate_with_history(client, loaded_model, monkeypatch):
     """POST /generate with history parameter."""
-    monkeypatch.setattr("astrai.inference.server._engine", mock_engine)
     response = client.post(
         "/generate",
         params={
@@ -143,8 +136,6 @@ def test_generate_with_history(client, loaded_model, mock_engine, monkeypatch):
         },
     )
     assert response.status_code == 200
-    # Verify the engine.generate was called
-    mock_engine.generate.assert_called_once()
 
 
 if __name__ == "__main__":