hanzo_engine/paged_attention/
mod.rs

1/// Content-addressable block hashing for prefix caching (vLLM v1 approach).
2pub mod block_hash;
3/// Flat block pool with LRU free list for KV cache block management (vLLM v1 approach).
4pub mod block_pool;
5/// This is the lower-level manager of the cache. It manages swapping and copying the blocks and
6/// actually allocates the KV cache for the CPU and GPU. It is used by the LLMEngine to execute
7/// operations issued by the scheduler.
8mod cache_engine;
9mod config;
10/// Encoder output cache for multimodal models (vision/audio encoder outputs).
11pub mod encoder_cache;
12/// KV Cache Manager: high-level block allocation, prefix cache lookups, per-request tracking.
13pub mod kv_cache_manager;
14mod layers;
15mod scheduler;
16pub const _PAD_SLOT_ID: i64 = -1;
17
18pub use cache_engine::{CacheConfig, CacheEngine, PagedCacheType};
19pub use config::{KvCacheLayout, ModelConfigLike, ModelConfigMetadata};
20use hanzo_ml::{DType, Device};
21pub use kv_cache_manager::KVCacheManager;
22pub use layers::PagedAttention;
23pub use scheduler::{
24    PagedAttentionScheduler, PagedAttentionSchedulerConfig, PagedAttentionSchedulerOutput,
25};
26
27use crate::MemoryUsage;
28use tracing::info;
29
30pub const DEFAULT_PAGED_ATTENTION_BLOCK_SIZE: usize = 32;
31
32/// All memory counts in MB. Default for block size is 32.
33#[derive(Clone, Copy)]
34pub struct PagedAttentionConfig {
35    pub(crate) block_size: Option<usize>,
36    pub(crate) mem_gpu: MemoryGpuConfig,
37    pub(crate) cache_type: PagedCacheType,
38}
39
40impl PagedAttentionConfig {
41    pub fn new(
42        block_size: Option<usize>,
43        mem_gpu: MemoryGpuConfig,
44        cache_type: PagedCacheType,
45    ) -> anyhow::Result<Self> {
46        Ok(Self {
47            block_size,
48            mem_gpu,
49            cache_type,
50        })
51    }
52}
53
54#[derive(Debug, Clone, Copy, PartialEq)]
55pub enum AttentionImplementation {
56    Eager,
57    PagedAttention,
58}
59
60#[derive(Clone, Copy)]
61#[cfg_attr(feature = "pyo3_macros", pyo3::pyclass)]
62pub enum MemoryGpuConfig {
63    MbAmount(usize),
64    Utilization(f32),
65    ContextSize(usize),
66}
67
68// See `pagedattention.cu` CALL_V1_LAUNCHER_BLOCK_SIZE
69const SUPPORTED_BLOCK_SIZE: &[usize] = &[8, 16, 32];
70
71const SIZE_IN_MB: usize = 1024 * 1024;
72
73macro_rules! mb_to_blocks {
74    ($mb_size:expr, $dtype_size:expr, $block_size:expr, $config:expr) => {
75        $mb_size
76            / $dtype_size
77            / $block_size
78            / $config.num_layers()
79            / $config.kv_cache_elements_per_token()
80    };
81}
82
83macro_rules! ctxt_to_blocks {
84    ($context_len:expr, $dtype_size:expr, $block_size:expr, $config:expr) => {
85        $context_len * $dtype_size * $config.num_layers() * $config.kv_cache_elements_per_token()
86    };
87}
88
89/// Memory values are in MBs or a percentage in [0,1]. Specify block size or the default is 32.
90///
91/// `model_weight_size_in_bytes`: total model weight footprint. When provided, the per-device
92/// share (divided by number of devices for tensor parallelism) is subtracted from the KV cache
93/// memory budget. Pass `Some(total_model_size_in_bytes)` when calling **before** model loading
94/// (e.g. during device mapping) so the KV cache estimate reflects memory that will actually
95/// remain after the weights are loaded. Post-loading callers should pass `None` since
96/// `get_memory_available()` already reflects the loaded model.
97///
98/// `max_num_tokens`: on Metal (unified memory), caps the KV cache to this many tokens.
99/// Unlike CUDA with dedicated VRAM where unused memory is wasted, Metal's wired buffers
100/// compete with the OS and CPU for the same physical RAM. On CUDA this is ignored.
101/// If `None` on Metal, falls back to `config.max_seq_len()`.
102#[allow(clippy::too_many_arguments)]
103pub fn calculate_cache_config(
104    mem_gpu: MemoryGpuConfig,
105    block_size: Option<usize>,
106    dtype: DType,
107    cache_type: PagedCacheType,
108    config: &dyn ModelConfigLike,
109    device: &Device,
110    layer_devices: &[Option<Device>],
111    silent: bool,
112    model_weight_size_in_bytes: Option<usize>,
113    max_num_tokens: Option<usize>,
114) -> anyhow::Result<CacheConfig> {
115    let block_size = block_size.unwrap_or(DEFAULT_PAGED_ATTENTION_BLOCK_SIZE);
116    if !SUPPORTED_BLOCK_SIZE.contains(&block_size) {
117        anyhow::bail!("Block size must be in {SUPPORTED_BLOCK_SIZE:?}, got {block_size}");
118    }
119    let dtype = cache_type.to_dtype(dtype);
120    let dtype_size = dtype.size_in_bytes();
121
122    // For tensor parallelism, each device holds a fraction of the model weights. Approximate it like this.
123    let num_devices = layer_devices.len().max(1);
124    let model_weight_per_device_mb =
125        model_weight_size_in_bytes.unwrap_or(0) / num_devices / SIZE_IN_MB;
126
127    let mut min_mem_gpu = usize::MAX;
128    for dev in layer_devices {
129        let device = dev.as_ref().unwrap_or(device);
130
131        #[allow(clippy::cast_possible_truncation, clippy::cast_precision_loss)]
132        let mem_gpu = match mem_gpu {
133            MemoryGpuConfig::MbAmount(v) => v,
134            MemoryGpuConfig::Utilization(f) => {
135                let mem = MemoryUsage.query(device)?;
136                let total = mem.total() as f32 / SIZE_IN_MB as f32;
137                if model_weight_size_in_bytes.is_some() {
138                    // Pre-loading: compute budget from total memory and known model size.
139                    (total * f - model_weight_per_device_mb as f32).max(0.0) as usize
140                } else {
141                    let used = (mem.total() - mem.available()) as f32 / SIZE_IN_MB as f32;
142                    (total * f - used).max(0.0) as usize
143                }
144            }
145            MemoryGpuConfig::ContextSize(toks) => {
146                // ContextSize is demand-driven (bytes needed for N tokens), not a memory budget, so model weight does not apply here.
147                ctxt_to_blocks!(toks, dtype_size, block_size, config) / SIZE_IN_MB
148            }
149        };
150        min_mem_gpu = min_mem_gpu.min(mem_gpu);
151    }
152
153    // On Metal (unified memory), cap KV cache to what the model can actually use.
154    // Unlike CUDA with dedicated VRAM where unused memory is wasted, Metal's wired
155    // buffers compete with the OS and CPU for the same physical RAM.
156    // On CUDA, all available memory is used for maximum request concurrency (vLLM approach).
157    #[allow(unused_mut, unused_variables)]
158    let mut mem_gpu = min_mem_gpu;
159    if device.is_metal() {
160        let max_tokens = max_num_tokens.unwrap_or(config.max_seq_len());
161        let mem_for_tokens =
162            ctxt_to_blocks!(max_tokens, dtype_size, block_size, config) / SIZE_IN_MB;
163        if mem_for_tokens < mem_gpu {
164            if !silent {
165                info!(
166                    "Metal: capping KV cache from {} MB to {} MB ({} tokens).",
167                    mem_gpu, mem_for_tokens, max_tokens
168                );
169            }
170            mem_gpu = mem_for_tokens;
171        }
172    }
173
174    let num_gpu_blocks = mb_to_blocks!(mem_gpu * SIZE_IN_MB, dtype_size, block_size, config);
175    if num_gpu_blocks == 0 {
176        anyhow::bail!("Num GPU blocks is 0. This means there is not enough memory. Either reduce the memory amount/utilization/context size or disable PagedAttention.");
177    }
178
179    if !silent {
180        info!("Allocating {mem_gpu} MB for PagedAttention KV cache per GPU");
181        info!("PagedAttention KV cache type is {dtype:?}");
182        info!("Using PagedAttention with block size {block_size} and {num_gpu_blocks} GPU blocks: available context length is {} tokens", num_gpu_blocks*block_size);
183    }
184    Ok(CacheConfig {
185        block_size,
186        num_gpu_blocks,
187        cache_type,
188    })
189}
hanzo_engine/paged_attention/mod.rs

hanzo_engine/paged_attention/
mod.rs
