AstrAI/docs/developer/cuda_kernels.md

CUDA Kernels

AstrAI includes optional custom CUDA kernels for attention and rotary embedding. These are built when nvcc is available and CUDA is detected, and are dispatched via the CudaBackend attention backend or auto-dispatched for rotary.

Overview

Kernel	File	Description
attn_decode	attn_decode.cu	GQA decode attention (split-KV)
attn_prefill	attn_prefill.cu	GQA prefill attention (split-Q)
attn_paged_decode	attn_paged_decode.cu	Paged KV cache decode attention
attn_paged_prefill	attn_paged_prefill.cu	Paged KV cache prefill attention (ragged batch)
rotary_emb	rotary_emb.cu	Fused rotary embedding (cos/sin lookup + rotation)

Additionally, optimized .cuh variants with tensor-core MMA (Matrix Multiply-Accumulate) exist:

Variant	File	Optimization
Split-KV MMA decode	attn_decode_split_kv_mma.cuh	Split KV across warps + MMA (sm_80+)
Split-Q MMA prefill	attn_prefill_split_q_mma.cuh	Split Q across warps + MMA (sm_80+)

The paged and non-paged paths are ONE kernel templated on a KVSource policy (ContigKV / PagedKV in attn_kv_source.cuh); there are no separate attn_paged_*.cuh files anymore.

Rotary Embedding Kernel

The rotary_emb kernel (csrc/kernels/rotary_emb.cu) fuses cos/sin lookup and rotation into a single kernel:

• One thread per (head, dim-pair), vectorized __nv_bfloat162 load/store
• f32 cos/sin input, bf16 compute and output
• 256-thread blocks, grid-stride loop
• Auto-dispatched via apply_rotary_emb in astrai/extension/rotary_backend.py (CUDA when available + inference mode, else torch complex-multiply fallback)
• No context-manager backend needed — rotary is backend-agnostic, both attention backends benefit

Standalone benchmark vs torch complex-multiply (48 calls = 24 layers × q+k): 6-9x faster, max diff 0 (decode) to 3e-2 (large prefill, bf16).

Build System

Auto-detection

Kernels are built when both of these conditions are met:

1. nvcc is available on PATH
2. torch.cuda.is_available() returns True

Unless CSRC_KERNELS=false is set explicitly.

Manual build

# During install
CSRC_KERNELS=true pip install -e . --no-build-isolation

# Rebuild after editing .cu/.cuh files
CSRC_KERNELS=true python setup.py build_ext --inplace
# Output: astrai/extension/lib/*.so

# Or invoke CMake directly
cmake -S csrc -B build/cmake \
  -DTORCH_HOME=<site-packages>/torch \
  -DPYTHON_INCLUDE_DIR=<python include> \
  -DPY_SOABI=cpython-312-x86_64-linux-gnu
cmake --build build/cmake -j 16

Architecture flags

setup.py passes the GPU compute capability to CMake via ASTRAI_CUDA_ARCH (default 89, i.e. sm_89 / L20):

• sm_80+ (Ampere and later): enables tensor-core MMA path (mma.sync.m16n8k16.bf16)
• Below sm_80: adds -DASTRAI_NO_MMA to disable the MMA path at compile time

Build configuration

csrc/CMakeLists.txt defines the CUDA extension build:

NVCC_FLAGS = -O3 --expt-relaxed-constexpr --use_fast_math
             --ptxas-options=-O3,-v --extra-device-vectorization --threads=16

Each kernel in astrai/extension/lib is compiled as an independent pybind11 module (one .so per kernel, named <kernel>.cpython-*-x86_64-linux-gnu.so). CMake builds all five kernel targets in parallel via cmake --build -j N.

Attention Backend

astrai/extension/attention_backend.py provides the backend abstraction:

• AttentionBackend (ABC): fwd_decode / fwd_prefill abstract methods, forward dispatches by q_len
• TorchNativeBackend: SDPA with indirect KV cache gather (default)
• CudaBackend: CUDA kernel dispatch — decode via attn_paged_decode (page_size=1), prefill via attn_paged_prefill (ragged batch, qo_indptr + kv_indptr)

Select a backend via context manager (mirrors torch.nn.attention.sdpa_kernel):

from astrai.extension import attn_backend, ATTN_BACKEND

with attn_backend(ATTN_BACKEND.CUDA):
    engine.generate("hello")

CudaBackend falls back to TorchNativeBackend when a kernel is not available.

Rotary Backend

astrai/extension/rotary_backend.py provides apply_rotary_emb(x, (cos, sin)) with auto-dispatch:

• CUDA path: calls rotary_emb kernel directly when available, input is bf16 on CUDA, and torch.is_grad_enabled() is False (inference)
• Torch fallback: complex multiply (torch.view_as_complex → torch.complex multiply → torch.view_as_real), used during training (supports autograd) or when kernel unavailable

No context-manager switching needed — the dispatch is automatic per call.

Python Wrappers

astrai/extension/attention_ops.py provides Python wrappers for each compiled attention kernel. Each wrapper calls its CUDA kernel directly and raises RuntimeError if the .so is not available. Fallback to torch SDPA is handled by the attention backend, not the wrapper functions.

astrai/extension/rotary_ops.py provides the wrapper for the rotary embedding kernel. Fallback to torch complex multiply is handled by rotary_backend.py.

Interface (all functions):

is_causal: True = causal mask; False = non-causal
mask:      2D [batch, kv_len] or 3D [batch, q_len, kv_len] (bool, True=keep)

Layout convention: all q/k/v are [batch, seq_len, n_heads, head_dim] (blhd). Scale is always 1/sqrt(head_dim).

Standalone Testing

Each csrc/tests/*.cu file has the nvcc compile command in its header comment. Example:

nvcc -I csrc -arch=sm_89 -O3 --use_fast_math \
     --ptxas-options=-O3,-v --extra-device-vectorization \
     -Xcompiler -fopenmp csrc/tests/attn_test.cu -o /tmp/test && /tmp/test

Test files:

• attn_test.cu — decode + prefill kernels (correctness tables + benchmarks)
• attn_paged_test.cu — paged decode/prefill kernels

Benchmarks

Hardware: NVIDIA L20 (sm_89, 46 GB), CUDA 12.8, driver 570.86.

Reproduce (decode + prefill in attn_test.cu, paged in attn_paged_test.cu):

nvcc -I csrc -arch=sm_89 -O3 --use_fast_math \
     --ptxas-options=-O3,-v --extra-device-vectorization \
     -Xcompiler -fopenmp csrc/tests/attn_test.cu -o /tmp/test && /tmp/test

Known Optimization Targets

• Decode D=256: spill eliminated (BC=16 + STAGES=2), but still 248 regs — further tiling could help.
• Prefill single-batch: bandwidth low (22 GB/s at q=kv=2048) — compute-bound at ~94 TFLOP/s (near L20 bf16 ceiling ~193 TFLOP/s for non-causal).
• Decode single-batch: bandwidth low (113 GB/s at kv=512, 13% of 864 GB/s theoretical) — small kv underutilizes SMs despite split-KV; scales to 757 GB/s (88%) at B=16+.

File Layout

csrc/
├── CMakeLists.txt        # CMake build: 5 kernel targets, torch/pybind11 linking
├── kernels/
│   ├── attn_common.h     # Unified attention params (contig + paged modes)
│   ├── attn_decode.cu    # Basic decode kernel (registered)
│   ├── attn_prefill.cu   # Basic prefill kernel (registered)
│   ├── attn_paged_decode.cu             # Paged decode kernel (registered)
│   ├── attn_paged_prefill.cu            # Paged prefill kernel (registered)
│   ├── rotary_emb.cu                    # Fused rotary embedding kernel (registered)
│   ├── attn_decode_split_kv.cuh         # Split-KV variant (contig + paged via KVSource)
│   ├── attn_decode_split_kv_mma.cuh     # Split-KV + MMA variant (contig + paged)
│   ├── attn_prefill_split_q.cuh         # Split-Q variant (contig + paged via KVSource)
│   ├── attn_prefill_split_q_mma.cuh     # Split-Q + MMA variant (contig + paged)
│   ├── attn_kv_source.cuh               # KVSource policies (ContigKV / PagedKV)
│   ├── attn_dispatchers.cuh             # Kernel dispatch macros + KV-templated launchers
│   ├── attn_entry_utils.cuh             # Entry point helpers
│   ├── attn_mma_utils.cuh               # MMA utilities
│   └── attn_warp_utils.cuh              # Warp-level utilities
└── tests/
    ├── test_utils.cuh           # Shared test utilities
    ├── attn_test.cu             # Decode + prefill kernels
    └── attn_paged_test.cu       # Paged decode/prefill kernels

Compiled .so files are placed in astrai/extension/lib/, separate from Python source files.

Document Update Time: 2026-07-31