# NIXL Integration for HiCache

This directory contains the **NIXL (NVIDIA Inference Xfer Library)** integration for **HiCache**, enabling high-performance storage across multiple backends.

NIXL provides a unified API for accessing various storage plugins, including but not limited to:

- POSIX for file based operations, including AIO / io_uring / POSIX AIO.
- **Deepseek's 3FS APIs** for high-throughput file operations
- **GPU Direct Storage (GDS)** for direct data movement between storage and GPU memory, bypassing CPU memory copies
- **Amazon S3-compatible object storage** for key-value access patterns

NIXL also supports additional backends such as **AZURE_BLOB**, **GUSLI**, and **UCX**. Additional backend integrations are planned for future releases.

## NIXL Resources

- **Project Repository**: [NIXL on GitHub](https://github.com/ai-dynamo/nixl)
- **Documentation**: [NIXL Documentation](https://github.com/ai-dynamo/nixl/tree/main/docs)

## Overview

The NIXL integration consists of these main files:

- **`hicache_nixl.py`** - Main HiCache storage connector using NIXL
- **`nixl_utils.py`** - Utility classes for backend selection, registration, and file management
- **`nixl_cleaner.py`** - Background FILE-backend disk cleaner

At runtime, HiCache uses NIXL as a transfer layer between host memory and either:

- **FILE-backed storage plugins** such as 3FS / POSIX / GDS / GDS_MT
- **OBJ-backed storage plugins** such as S3-compatible object stores

The connector supports both the legacy tensor-oriented API (`get` / `set`) and the newer page-oriented API (`batch_get_v1` / `batch_set_v1`) used by modern HiCache backends.

## Components

### HiCacheNixl
The main storage connector that provides:
- Single and batch tensor set/get operations
- Automatic backend selection (3FS > POSIX > GDS_MT > GDS > OBJ)
- High-performance file-based (or) object based storage access using NIXL
- Automatic zero-copy enablement when HiCache host memory layout is `page_first` or `page_first_direct`
- MLA-aware storage naming and backend-local MLA backup skipping on non-zero TP ranks
- Runtime diagnostics for mem-pool type, MLA mode, TP rank, and backup-skip state

### NixlUtils
Consolidated utility classes:
- **NixlBackendSelection** - Handles backend selection and creation
- **NixlBackendConfig** - Handles backend configuration
- **NixlFileManager** - Handles file system operations

### NixlRegistry (`nixl_registry.py`)
Owns the `(agent, mem_type, file_manager)` triple and exposes `host(...)` and `storage(...)` context managers that register on entry, yield the NIXL `xfer_descs`, and deregister + close fds on exit. Internally composes two single-resource primitives (`_open_files` and `_registered`) so leak-freeness is verifiable per primitive.

The current implementation performs per-transfer registration for file / object targets and explicitly closes FILE descriptors after registration / transfer setup to avoid descriptor leaks.

### L3 Cleaner (`nixl_cleaner.py`)
For FILE-backed plugins, TP rank 0 starts a best-effort background cleaner that scans the bucketed storage directories and deletes the oldest logical cache-key groups when disk usage exceeds the configured high watermark. Deleted files are handled by the cache layer as ordinary storage misses and can be recomputed.

Set the top-level `l3_cleaner_enabled` config key to `false` when an external cleaner is responsible for L3 cache eviction.

## Using NIXL as the HiCache Storage Backend

### 1. How Backend Plugin Selection Works

The NIXL backend can support **multiple storage plugins** (e.g., POSIX, GDS, GDS_MT, 3FS, object store, etc).

* Each plugin has its own configuration section in the TOML file.
* The connector accepts configuration in two forms:

  * a **fully qualified** form such as `{"plugin": {"posix": {...}, "gds": {...}}}`
  * a **flat** form such as `{"use_uring": "true"}`, which applies to the selected plugin
* A plugin is considered **usable** if:

  * Its required library is available on the system (POSIX, GDS, GDS_MT are natively supported by NIXL).
  * Its configuration is valid.
  * It is marked as `active = true` in the configuration file (if applicable).
* Some plugins (e.g., 3FS, GDS) require additional system libraries or hardware support.
* If the config explicitly enables multiple plugins, the connector chooses the **first active plugin** in the config.
* If no plugin is explicitly selected in config, the connector falls back to the environment variable `SGLANG_HICACHE_NIXL_BACKEND_PLUGIN`, and finally to `auto`.
* In `auto` mode, NIXL selects the backend based on **internal priority and availability**.

If a plugin is configured but its dependencies are missing, it will be skipped.


### 2. Setting the Storage Directory (Optional)

For POSIX / GDS / GDS_MT file-based backends, the default storage location is `/tmp/hicache_storage`. However, you can customize where cached data is stored:

```bash
# When specifying multiple storage directories. SGLang routes each cache object to one
# directory with a stable hash.
export SGLANG_HICACHE_NIXL_BACKEND_STORAGE_DIR=/path/to/storage/dir1,/path/to/storage/dir2,/path/to/storage/dir3
```

These directories are used only for **FILE-backed** plugins. **OBJ-backed** plugins use object keys instead of local files.

### 3. How to Provide Configuration for Backends

There are three ways to specify configurations for the backends: default config, file based config, and command-line (JSON string based) config.

#### 1. Using Default Configuration

To enable HiCache with the NIXL backend, start the SGLang server with:

```bash
python3 -m sglang.launch_server \
  --model-path <model> \
  --host <ip> \
  --port <port> \
  --page-size 64 \
  --enable-hierarchical-cache \
  --hicache-ratio 2 \
  --hicache-size 64 \
  --hicache-write-policy write_through \
  --hicache-storage-backend nixl
```

By default, NIXL will use its internal backend selection logic to choose an available storage plugin (and use default configs for the selected storage plugin).

For object storage backends, make sure the bucket is configured either in `--hicache-storage-backend-extra-config` or via:

```bash
export AWS_DEFAULT_BUCKET=<bucket-name>
```


#### 2. Using a Configuration File (Recommended)

For non-trivial setups with complex configurations, it is recommended to use a **TOML configuration file** to define which backend plugin to use and its configurations, via `--hicache-storage-backend-extra-config`:

Below is an example command (note: detailed configs are defined in the config file):

```bash
python3 -m sglang.launch_server \
  --model-path <model> \
  --host <ip> \
  --port <port> \
  --page-size 64 \
  --enable-hierarchical-cache \
  --hicache-ratio 2 \
  --hicache-size 64 \
  --hicache-write-policy write_through \
  --hicache-storage-backend nixl \
  --hicache-storage-backend-extra-config "@config.nixl.toml"
```

> **Important**
>
> * The `@` prefix tells SGLang to load the configuration from a file.
> * The file can be in **TOML format** (other formats, JSON / YAML, are also supported).
> * This is the preferred way to configure NIXL storage backends.

The structure of the config file is described in further details in [Configuration File Spec](#Configuration-File-Specification).


#### 3. Using Command-line JSON String

For debugging or quick testing, you may pass a **JSON-style string** directly via `--hicache-storage-backend-extra-config`.

This requires explicitly specifying the plugin type via an environment variable, and this method can be applicable to **only a few** plugins (e.g., POSIX, GDS, GDS_MT)

The below example shows how to use command-line string to use the POSIX plugin where URING is enabled for async POSIX storage, with O_DIRECT enabled (the default).

```bash
export SGLANG_HICACHE_NIXL_BACKEND_PLUGIN=POSIX

python3 -m sglang.launch_server \
  --model-path <model> \
  --host <ip> \
  --port <port> \
  --page-size 64 \
  --enable-hierarchical-cache \
  --hicache-ratio 2 \
  --hicache-size 64 \
  --hicache-write-policy write_through \
  --hicache-storage-backend nixl \
  --hicache-storage-backend-extra-config '{"use_uring": "true"}'
```

To disable O_DIRECT (e.g. for debugging or unsupported filesystems), set the top-level `use_direct_io` key:

```bash
export SGLANG_HICACHE_NIXL_BACKEND_PLUGIN=POSIX

python3 -m sglang.launch_server \
  ... \
  --hicache-storage-backend-extra-config '{"use_direct_io": false, "use_uring": "true"}'
```

⚠️ **Note**:
This method is convenient for testing / experimenting. For production or multi-plugin setups, it is always recommended to use the config file based approach.

Also note that the flat inline config form is interpreted as plugin-specific parameters for the selected plugin.


## Running Unit Tests

### Prerequisites
- NIXL library installed and available (latest main required for supporting object query)
- PyTorch installed
- Python 3.8+

### Unit tests from current directory
From the current directory run:

#### Run all NIXL tests:
```bash
PYTHONPATH=. python -m pytest test_hicache_nixl_storage.py -o asyncio_mode=strict
```

#### Run with verbose output:
```bash
PYTHONPATH=. python -m pytest test_hicache_nixl_storage.py -v -o asyncio_mode=strict
```

Note: The `-v` flag provides more detailed output, showing each test case name and its result.

#### Run a specific test:
```bash
PYTHONPATH=. python -m pytest test_hicache_nixl_storage.py -v -k test_single_set_get -o asyncio_mode=strict
```

Note: The `-o asyncio_mode=strict` flag is added to suppress warnings about asyncio configuration. This is not required for test functionality but provides cleaner output.

## Test Coverage

Tests for this integration, a test suite can be found at `test_hicache_nixl_storage.py` which covers:

### HiCache Integration Tests
- Single tensor set/get operations
- Batch tensor set/get operations
- Mixed single and batch operations
- Data integrity for various tensor types

### File Management Tests
- Basic file operations
- NIXL tuple creation
- Error handling in file operations

### Registration and MLA / Query Tests
- Tensor registration with memory type detection
- File registration using file paths
- MLA backup-skip behavior for `batch_set_v1`
- Zero-copy `batch_exists()` accounting for MLA and MHA

## Expected Output

When tests run successfully, you should see:
- NIXL agent initialization messages
- Backend selection messages (e.g., "Backend POSIX was instantiated")
- Test results with "ok" for passed tests
- Summary showing "Ran X tests in Y seconds" and "OK"

## Troubleshooting

### Import Errors
If you encounter `ModuleNotFoundError`, ensure:
- You're running from the correct directory
- `PYTHONPATH` is set correctly
- NIXL library is properly installed

### NIXL Errors
If NIXL operations fail:
- Check that NIXL is properly installed
- Verify that required plugins are available
- Ensure file permissions are correct for test directories
- For OBJ plugins, verify `bucket` or `AWS_DEFAULT_BUCKET` is set
- Check the NIXL diagnostic log emitted when the mem pool is registered; it includes:
  - `mem_pool_device_type`
  - `is_mla_model`
  - `tp_rank`
  - `backup_skip`

### MLA Write Behavior
For MLA models, the NIXL backend now mirrors HF3FS's backend-local protection:
- TP rank 0 performs the actual storage write
- non-zero TP ranks skip backup writes locally in `batch_set` / `batch_set_v1`
- MLA storage names omit TP rank so all ranks refer to the same logical storage object or file

## File Structure

```text
python/sglang/srt/mem_cache/storage/nixl/
├── hicache_nixl.py              # Main HiCache storage connector
├── nixl_cleaner.py              # Background FILE-backend disk cleaner
├── nixl_utils.py                # NIXL utility classes
├── test_hicache_nixl_storage.py # Unit tests
├── nixl.config.toml.sample      # Example configuration
└── README.md                    # This file
```

## Dependencies

- **NIXL**: NVIDIA Inference Xfer Library (version 0.4 or later)
  - Required plugins: POSIX (minimum), 3FS/GDS (optional for better performance)
  - See [NIXL Installation Guide](https://github.com/ai-dynamo/nixl/blob/main/README.md#installation)
- **PyTorch**: For tensor operations (version 1.8 or later)
- **Python 3.8+**: For type hints and modern features

## Supported Features

### Memory Types
- **Tensor side**: multi-dimensional tensors of all numeric types (int32, int64, float32, float64) are supported.
  - Tensors can be on CPU or GPU (as long as a GPU capable backend such as GDS_MT is available).
  - Currently each tensor is mapped to a file or key, but it can be extended to support multiple keys per file or key.
  - The page-oriented `*_v1` path also supports zero-copy transfers using `(address, size)` metadata from the host memory pool.

- **Storage side**: file and object are supported through their relevant backends (e.g., 3FS or OBJ).

### HiCache / NIXL Data Model

- **FILE backends** use local file paths under `SGLANG_HICACHE_NIXL_BACKEND_STORAGE_DIR`. When multiple comma-separated directories are configured, each logical cache key is routed to one base directory with a stable hash and stored as `base_dir/<bucket>/<key>`.
- **OBJ backends** use object keys directly
- **MHA naming** includes TP rank and TP size, so each rank stores its own KV data
- **MLA naming** omits TP rank, so all ranks refer to one shared logical KV object / file
- In zero-copy mode:
  - **MHA** expands each logical page into `_k` and `_v` entries
  - **MLA** expands each logical page into a single `_k` entry because MLA stores one interleaved KV representation
- The L3 cleaner groups physical files by the logical base key after removing TP-rank and zero-copy `_k` / `_v` suffixes. This keeps MHA, MLA, and DSA file cleanup aligned with the names emitted by `HiCacheNixl`.

### Zero-Copy Behavior

- Zero-copy is enabled automatically when the HiCache host layout is `page_first` or `page_first_direct`
- The connector uses `mem_pool_host.get_page_buffer_meta(...)` to obtain `(address, size)` metadata
- `batch_exists()` uses the same logical key expansion rules as `batch_get_v1()` / `batch_set_v1()`
- Non-zero MLA TP ranks skip `batch_set` / `batch_set_v1()` locally as a backend-side fallback guard

### Backend Priority

The NIXL backend selection follows this priority order:
1. **3FS** - Highest performance (if available)
    - Best for high-throughput file operations using Deepseek 3FS APIs
2. **POSIX** - Standard file I/O (fallback)
    - Universal compatibility
    - Good for development and testing - Leverages both libaio/liburing
3. **GDS_MT** - Multi-threaded GDS (if available)
    - Optimized for concurrent operations
    - Supports GPU Direct storage with multiple light weight threads
4. **GDS** - GPU Direct Storage (if available)
    - Direct GPU-storage data path
    - Best for filesystems benefiting from batch operations and smaller IOs.
5. **OBJ** - Amazon S3 based Object Storage
    - Key-value based storage
The system automatically selects the best available backend, with POSIX as the default fallback.



## Configuration File Specification

This section defines the structure, supported sections, configuration keys, data types, defaults, and semantics for the NIXL HiCache backend configuration file (`config.nixl.toml`).

The configuration file is written in **TOML** and consists of multiple **plugin-specific sections** under the `plugin.*` namespace. Each section configures one storage backend plugin. Only one plugin should be enabled via setting `active = true` in the corresponding plugin-specific section.

An example of the configuration is provided in [`nixl.config.toml.sample`](./nixl.config.toml.sample).

### 1. General Structure

```toml
[plugin.<backend_name>]
<key> = <value>
```

* `<backend_name>` identifies the storage backend plugin.
* Each plugin is configured independently.
* Plugins are selected at runtime based on:

  * Availability of required libraries/hardware
  * Plugin configuration validity
  * Internal backend priority rules
* Unless otherwise stated, all configuration keys are **optional** and have sensible defaults.

For object storage, `bucket` may also be omitted from the config if `AWS_DEFAULT_BUCKET` is already defined in the environment.

### 1a. Top-Level Configuration Keys

The following keys are placed at the **top level** of the config file (not inside any `[plugin.*]` section) and apply globally to the NIXL backend:

| Key              | Type    | Default  | Description |
| ---------------- | ------- | -------- | ----------- |
| `use_direct_io`  | boolean | `true`   | Open cache files with `O_DIRECT` to bypass the OS page cache. Reduces memory pressure and improves NVMe throughput. Falls back to buffered I/O with a warning if `O_DIRECT` is unavailable on the current OS. Can also be overridden via the `SGLANG_HICACHE_NIXL_USE_DIRECT_IO` environment variable. |
| `l3_cleaner_enabled` | boolean | `true` | Enable the built-in background cleaner for FILE-backed L3 storage. Set to `false` when using an external cleaner. |
| `l3_cleaner_high_watermark` | float | `80.0` | Start cleanup when the built-in cleaner is enabled and the filesystem containing a configured storage directory reaches this disk-usage percentage. |
| `l3_cleaner_low_watermark` | float | `70.0` | Stop cleanup after hot filesystems drop below this disk-usage percentage. Must be lower than `l3_cleaner_high_watermark`. |

**Page-alignment and `O_DIRECT`**

When `use_direct_io = true` with any file-based backend (POSIX, GDS, GDS_MT, 3FS), the kernel requires every I/O buffer pointer to be OS-page-aligned (4 KiB). SGLang handles this automatically:

* **Zero-copy mode** (`page_first` / `page_first_direct` layout): the host memory pool is always mmap-backed and therefore page-aligned. If the per-page stride is also a multiple of 4 KiB, zero-copy transfers are used as-is.
* **Copy mode** (all other layouts, or if stride alignment cannot be satisfied): SGLang pre-allocates page-aligned bounce buffers via `mmap` and falls back to copy mode, logging a warning. No user action is required -- this is fully automatic.

To disable `O_DIRECT` (e.g. for debugging or when the filesystem does not support it):

```toml
use_direct_io = false

[plugin.posix]
use_uring = "true"
active = true
```

or via environment variable: `SGLANG_HICACHE_NIXL_USE_DIRECT_IO=0`.

To tune FILE-backend cleanup watermarks:

```toml
l3_cleaner_enabled = true
l3_cleaner_high_watermark = 85.0
l3_cleaner_low_watermark = 75.0

[plugin.posix]
use_uring = "true"
active = true
```

To use an external cleaner instead of the built-in cleaner:

```toml
l3_cleaner_enabled = false

[plugin.posix]
use_uring = "true"
active = true
```


### 2. POSIX File System Backend (`plugin.posix`)

#### Section

```toml
[plugin.posix]
```

#### Description

Configures the POSIX file-system-based backend.
This backend supports multiple asynchronous I/O mechanisms and automatically selects the most performant option supported by the system.

**Backend priority (highest to lowest):**

1. Linux AIO
2. `io_uring`
3. POSIX AIO


#### Configuration Keys

| Key             | Type    | Default   | Description                                                                                              |
| --------------- | ------- | --------- | -------------------------------------------------------------------------------------------------------- |
| `use_uring`     | string  | `"false"` | Enables Linux `io_uring` for asynchronous I/O when set to `"true"`. Recommended on modern Linux kernels. |
| `use_posix_aio` | string  | `"false"` | Enables POSIX AIO as an alternative async I/O mechanism.                                                 |
| `use_aio`       | string  | `"false"` | Enables generic Linux AIO.                                                                               |
| `active`        | boolean |    N/A    | Controls whether this plugin is eligible for backend selection.                                          |

**Notes**

* Boolean-like options use **string values** (`"true"` / `"false"`) for compatibility.
* **Only one backend** (i.e., only one of `use_uring`, `use_aio`, `use_posix_aio`) should be included in the config.


### 3. NVIDIA GPUDirect Storage Backend (`plugin.gds`)

#### Section

```toml
[plugin.gds]
```

#### Description

Configures NVIDIA GPUDirect Storage (GDS) backend.
This backend enables direct data transfers between storage and GPU memory.

**Requirements**

* NVIDIA GPU with GDS support
* Compatible NVIDIA driver and CUDA runtime
* Supported filesystem


#### Configuration Keys

| Key                | Type    | Default            | Description                                            |
| ------------------ | ------- | ------------------ | ------------------------------------------------------ |
| `batch_pool_size`  | integer | `128`              | Number of I/O requests maintained in the request pool. |
| `batch_limit`      | integer | `128`              | Maximum number of requests issued in a single batch.   |
| `max_request_size` | integer | `16777216` (16 MB) | Maximum size (in bytes) of a single I/O request.       |
| `active`           | boolean |         N/A        | Controls whether this plugin is eligible for backend selection.|


### 4. Multi-Threaded GDS Backend (`plugin.gds_mt`)

#### Section

```toml
[plugin.gds_mt]
```

#### Description

Configures the multi-threaded variant of the NVIDIA GDS backend, allowing parallel request processing using multiple CPU threads.



#### Configuration Keys

| Key            | Type    | Default | Description                                           |
| -------------- | ------- | ------- | ----------------------------------------------------- |
| `thread_count` | integer | `4`     | Number of worker threads used to submit GDS requests. |
| `active`       | boolean |    N/A  | Controls whether this plugin is eligible for backend selection. |


### 5. 3FS Backend (`plugin.3fs`)

#### Section

```toml
[plugin.3fs]
```

#### Description

Configures the 3FS (third-party filesystem) backend.

**Requirements**

* 3FS client library installed
* Filesystem mounted and accessible on the host


#### Configuration Keys

| Key           | Type    | Default  | Description                        |
| ------------- | ------- | -------- | ---------------------------------- |
| `mount_point` | string  | *none*   | Mount point of the 3FS filesystem. |
| `mem_config`  | string  | `"dram"` | Memory configuration mode.         |
| `iopool_size` | integer | `64`     | Size of the I/O pool.              |
| `active`      | boolean |   N/A    | Controls whether this plugin is eligible for backend selection.                                          |

##### `mem_config` Valid Values

| Value     | Description                                         |
| --------- | --------------------------------------------------- |
| `dram`    | Use DRAM for buffering                              |
| `dram_zc` | Use DRAM with zero-copy support                     |
| `auto`    | Automatically select based on platform capabilities |

##### `iopool_size` Constraints

* Valid range: **[2⁶, 2²⁰]**
* Values outside this range may cause initialization failure.


### 6. Object Storage Backend (`plugin.obj`)

#### Section

```toml
[plugin.obj]
```

#### Description

Configures an object storage backend compatible with S3 APIs (e.g., AWS S3, MinIO, Ceph).


#### Configuration Keys

| Key                      | Type    | Default      | Description                                    |
| ------------------------ | ------- | ------------ | ---------------------------------------------- |
| `num_threads`            | integer | `4`          | Number of client worker threads.               |
| `endpoint_override`      | string  | `""`         | Custom endpoint URL (for non-AWS S3 services). |
| `scheme`                 | string  | `"http"`     | Connection scheme (`http` or `https`).         |
| `region`                 | string  | `""`         | Cloud region (if applicable).                  |
| `req_checksum`           | string  | `"required"` | Request checksum behavior.                     |
| `ca_bundle`              | string  | `""`         | Path to a custom CA bundle.                    |
| `access_key`             | string  | `""`         | Access key credential.                         |
| `secrete_key`            | string  | `""`         | Secret key credential.                         |
| `session_token`          | string  | `""`         | Session token (optional).                      |
| `use_virtual_addressing` | string  | `"true"`     | Enables virtual-hosted-style addressing.       |
| `bucket`                 | string  | `""`         | Default bucket name.                           |
| `active`                 | boolean |      N/A     | Controls whether this plugin is eligible for backend selection.                                          |

##### `req_checksum` Valid Values

| Value       | Description                                    |
| ----------- | ---------------------------------------------- |
| `required`  | Always include a checksum                      |
| `supported` | Include checksum when supported by the backend |


### 7. Notes and Best Practices

* All plugin sections are optional.
* Multiple plugins may be configured in a single file. However, it is recommended that **only one plugin** is configured `active = true`.
* Plugins whose dependencies are unavailable will be skipped.
* Use a TOML configuration file instead of inline JSON for:

  * Multi-plugin setups
  * Production deployments
  * Clear validation and maintainability


## Note

This is v0 of the NIXL connector. The current implementation favors correctness and compatibility with the existing HiCache API:

- file / object targets are registered per transfer
- FILE descriptors are explicitly cleaned up after registration / transfer setup
- MLA uses shared storage naming and backend-local write skipping on non-zero TP ranks
- zero-copy is driven by HiCache host-memory layout rather than a separate NIXL flag

Future versions will focus on further performance optimizations such as memory pre-registration (pre-allocating and registering memory buffers to reduce registration overhead during transfers) and block merging (combining related blocks as offsets within the same file to reduce file operations and improve throughput). These optimizations require changes at a higher layer, as the current HiCache API doesn't expose information like block relationships or hash patterns that would enable these optimizations.