This document outlines the kernels that need to be implemented in the Mini-YAIE inference engine, following SGLang architecture and concepts. Each section describes what the kernel does, why it's important, and implementation guidance.
Purpose: SGLang's radial attention mechanism with prefix sharing for efficient batch processing of requests with common prefixes.
How it Works:
- Represents request prefixes as a tree structure (radix tree)
- Shares computation for requests with common prefixes
- Reduces redundant computation in continuous batching scenarios
Visual Representation:
Radix Tree Structure:
Root
/ \
A B <- First token: requests 1,2 have 'A', request 3 has 'B'
/ \ / \
C D E F <- Second token: requests 1,2 split, requests 3,4 split
/ / | |
G H I J <- Third token: continue branching
Computation Sharing:
- Tokens A and C are computed once for requests 1 and 2
- Tokens D and H are computed separately for request 2
- This reduces total computation compared to separate processing
Implementation Steps:
- Parse input requests to identify common prefixes
- Build radix tree structure from shared prefixes
- Implement forward pass that computes shared nodes once
- Implement backward pass for gradient computation
- Handle KV-cache management for shared computation
Code Structure:
class RadixAttentionBlock(nn.Module):
def __init__(self, hidden_size, num_heads, head_dim, max_position_embeddings):
# Initialize query/key/value projections
# Initialize rotary embedding components
# Initialize radix tree management utilities
def forward(self, hidden_states, radix_tree_info, attention_mask, position_ids):
# Step 1: Apply QKV projections
# Step 2: Apply RoPE embeddings
# Step 3: Use radix tree to identify shared computation
# Step 4: Compute attention for shared vs. unique parts
# Step 5: Manage KV-cache for next iterationWhy it's Important:
- Dramatically reduces redundant computation in continuous batching
- Enables efficient processing of multiple requests with similar prefixes
- Core to SGLang's performance advantages
Purpose: Radix attention combined with paged KV-cache management for memory efficiency.
How it Works:
- Combines radix attention with efficient KV-cache paging
- Manages shared KV-cache blocks for requests with common prefixes
- Reduces memory usage while maintaining computation sharing benefits
Visual Representation:
Paged KV Cache with Radix Tree:
Radix Tree:
Root -> Token A -> Token C -> Token G
-> Token D -> Token H
Paged Memory:
Physical Blocks: [Blk1][Blk2][Blk3][Blk4][Blk5][...]
[KV_A][KV_C][KV_D][KV_G][KV_H]
Block Table for Shared Computation:
- Root->A: Blk1 (shared by multiple requests)
- A->C: Blk2 (shared by requests with prefix "AC")
- A->D: Blk3 (used by requests with prefix "AD")
- C->G: Blk4 (specific to request with prefix "ACG")
- D->H: Blk5 (specific to request with prefix "ADH")
Implementation Steps:
- Implement paged memory allocator for KV-cache
- Create block reference counting for shared prefixes
- Handle block allocation/deallocation based on radix tree
- Implement efficient block lookup during attention computation
- Add memory management policies for cache eviction
Purpose: Efficiently process long initial prompts using chunked processing to reduce memory usage.
How it Works:
- Splits long prompts into smaller chunks
- Processes chunks efficiently without materializing full attention
- Reduces peak memory usage during prefill phase
Visual Representation:
Long Prompt (1000 tokens):
Input: [T1, T2, T3, ..., T1000]
Traditional Prefill:
- All 1000x1000 attention matrix: O(1000²) memory
- Peak memory usage: Very high
Chunked Prefill:
- Split into 10 chunks of 100 tokens each
- Process: [T1-T100], [T101-T200], ..., [T901-T1000]
- Each chunk: 100x100 attention: O(100²) memory
- Cache intermediate KV states between chunks
- Combine results efficiently
Implementation Steps:
- Implement chunking logic for long prompts
- Maintain KV-cache across chunks
- Handle cross-chunk attention efficiently
- Optimize memory allocation for chunked processing
Purpose: Optimize single-token generation phase for maximum throughput.
How it Works:
- Process one token at a time (current position only)
- Efficiently retrieve from paged KV-cache
- Minimize memory access for best performance
Implementation Steps:
- Implement single-token attention computation
- Optimize KV-cache retrieval from paged memory
- Handle batched decode requests efficiently
- Optimize for GPU memory access patterns
Purpose: Efficient memory management for KV-cache using page-based allocation.
How it Works:
- KV-cache stored in fixed-size pages
- Each request references required pages through block table
- Reduces memory fragmentation compared to contiguous allocation
Visual Representation:
Memory Layout - Page-based:
Global Page Pool (GPU Memory):
┌─────┬─────┬─────┬─────┬─────┬─────┬─────┬─────┐
│Page0│Page1│Page2│Page3│Page4│Page5│Page6│ ... │
└─────┴─────┴─────┴─────┴─────┴─────┴─────┴─────┘
Request 1 (tokens 0-150): [Page0, Page1] (0-63, 64-127 in Page0, 128-150 in Page1)
Request 2 (tokens 0-80): [Page2, Page4] (non-contiguous but efficient)
Request 3 (tokens 0-200): [Page3, Page5, Page6, ...]
Block Table per Request:
Request 1: [0, 1] (maps to Page0, Page1)
Request 2: [2, 4] (maps to Page2, Page4)
Request 3: [3, 5, 6, ...] (maps to Page3, Page5, Page6, ...)
Implementation Steps:
- Implement fixed-size page allocator
- Create block table management system
- Handle page allocation and deallocation efficiently
- Implement page swapping for memory-constrained scenarios
Purpose: Efficient management of radix trees for tracking request prefixes.
How it Works:
- Maintains tree structure of shared prefixes
- Enables efficient lookup during request processing
- Manages tree updates when requests progress
Implementation Steps:
- Implement radix tree node structure
- Create efficient insertion/deletion methods
- Handle tree balancing and optimization
- Implement cache for frequently accessed paths
Purpose: Apply rotary embeddings efficiently in the context of radix attention.
How it Works:
- Compute RoPE for each token position
- Apply embeddings considering shared computation paths
- Optimize for both shared and unique prefix computations
Implementation Steps:
- Precompute RoPE embeddings for all possible positions
- Apply embeddings based on actual token positions in radix tree
- Handle position ID management for shared prefixes
- Optimize for GPU computation patterns
Purpose: Process multiple decoding steps in a single kernel call for efficiency.
How it Works:
- Instead of single-step decoding, process multiple steps
- Reduces kernel launch overhead
- Improves GPU utilization
Implementation Steps:
- Implement multi-step processing logic
- Manage KV-cache updates across multiple steps
- Handle stopping conditions within multi-step execution
- Balance performance gains with memory requirements
Purpose: Optimize request scheduling based on radix tree structure.
How it Works:
- Schedule requests with common prefixes together
- Maximize computation sharing opportunities
- Balance between sharing benefits and scheduling efficiency
Implementation Steps:
- Analyze radix tree structure for scheduling opportunities
- Implement efficient batch formation logic
- Handle request preemption and rescheduling
- Optimize for throughput and latency trade-offs
-
Start with core radix components:
- Radix tree data structure
- Basic KV-cache management
- Simple attention (without radix sharing initially)
-
Implement radix attention fundamentals:
- RadixAttentionBlock with basic sharing
- Paged KV-cache integration
- Position embedding with radix support
-
Add optimization layers:
- Chunked prefill for long prompts
- Multi-step decoding
- Advanced memory management
-
Fine-tune for SGLang patterns:
- Request scheduling with prefix awareness
- Performance optimization for common use cases
- Integration with the engine scheduler
- Test radix sharing correctness: Verify that shared computation produces identical results to separate computation
- Benchmark memory efficiency: Measure memory savings from prefix sharing and paged cache
- Validate performance gains: Compare throughput with naive batching approaches
- Integration testing: Ensure kernels work with the full engine pipeline
- SGLang paper: https://arxiv.org/abs/2308.07561
- SGLang GitHub: https://github.com/sgl-project/sglang
- Efficient memory management techniques
- Radix tree data structure implementations