FlashAttention Implementation › CUDA Kernel Basics
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FlashAttention is implemented as a custom CUDA kernel, meaning it runs directly on GPU hardware with fine-grained control over memory access. Understanding a few CUDA concepts helps appreciate what FlashAttention does under the hood.
A CUDA kernel is a function that runs on the GPU across many parallel threads. Threads are organized into blocks, and blocks are organized into a grid. Each thread can access different levels of memory: registers (private to the thread), shared memory (shared within a block), and global memory (HBM, accessible by all threads).
Thread Organization
CUDA organizes computation into a hierarchy. At the lowest level, individual threads execute the same code on different data. Threads are grouped into blocks, typically containing 256-1024 threads. All threads in a block can communicate through shared memory and synchronize their execution. Blocks are grouped into a grid that covers the entire problem.
For attention, a natural mapping assigns one block to compute attention for one query block against all key-value blocks. Within a block, threads collaborate to load data into shared memory, compute dot products, and accumulate results.
Memory Access Patterns
The key to GPU performance is coalesced memory access. When threads in a warp (a group of 32 threads that execute in lockstep) access consecutive memory addresses, the hardware combines these into a single efficient transaction. When threads access scattered addresses, each access becomes a separate transaction, significantly slowing execution.
FlashAttention carefully structures its memory access to achieve coalescing. When loading a block of K from HBM, consecutive threads load consecutive elements. When writing results, the same pattern applies. This attention to memory layout is one reason FlashAttention achieves such high hardware utilization.
def demonstrate_memory_access_patterns():
"""
Illustrate the difference between coalesced and strided memory access.
"""
patterns = {
"coalesced": {
"description": "Thread i accesses address i",
"transactions": 1,
"effective_bandwidth": "100%",
},
"strided_2": {
"description": "Thread i accesses address 2*i",
"transactions": 2,
"effective_bandwidth": "50%",
},
"random": {
"description": "Thread i accesses random address",
"transactions": 32,
"effective_bandwidth": "3%",
},
}
return patterns
patterns = demonstrate_memory_access_patterns()Memory Access Patterns (32-thread warp, 4-byte elements): Pattern Transactions Effective BW ----------------------------------------------- coalesced 1 100% strided_2 2 50% random 32 3% FlashAttention uses coalesced access patterns throughout.
