302 lines
13 KiB
Markdown
302 lines
13 KiB
Markdown
# CUDA Kernels
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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.
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## Overview
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| Kernel | File | Description |
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|--------|------|-------------|
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| `attn_decode` | `attn_decode.cu` | GQA decode attention (split-KV) |
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| `attn_prefill` | `attn_prefill.cu` | GQA prefill attention (split-Q) |
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| `attn_paged_decode` | `attn_paged_decode.cu` | Paged KV cache decode attention |
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| `attn_paged_prefill` | `attn_paged_prefill.cu` | Paged KV cache prefill attention (ragged batch) |
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| `rotary_emb` | `rotary_emb.cu` | Fused rotary embedding (cos/sin lookup + rotation) |
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Additionally, optimized `.cuh` variants with tensor-core MMA (Matrix Multiply-Accumulate) exist:
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| Variant | File | Optimization |
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|---------|------|--------------|
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| Split-KV MMA decode | `attn_decode_split_kv_mma.cuh` | Split KV across warps + MMA (sm_80+) |
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| Split-Q MMA prefill | `attn_prefill_split_q_mma.cuh` | Split Q across warps + MMA (sm_80+) |
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> The paged and non-paged paths are ONE kernel templated on a `KVSource`
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> policy (`ContigKV` / `PagedKV` in `attn_kv_source.cuh`); there are no
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> separate `attn_paged_*.cuh` files anymore.
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### Rotary Embedding Kernel
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The `rotary_emb` kernel (`csrc/kernels/rotary_emb.cu`) fuses cos/sin lookup and rotation into a single kernel:
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- One thread per (head, dim-pair), vectorized `__nv_bfloat162` load/store
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- f32 cos/sin input, bf16 compute and output
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- 256-thread blocks, grid-stride loop
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- Auto-dispatched via `apply_rotary_emb` in `astrai/extension/backend/rotary.py` (CUDA when available + inference mode, else torch complex-multiply fallback)
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- No context-manager backend needed — rotary is backend-agnostic, both attention backends benefit
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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).
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## Build System
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### Auto-detection
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Kernels are built when **both** of these conditions are met:
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1. `nvcc` is available on `PATH`
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2. `torch.cuda.is_available()` returns `True`
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Unless `CSRC_KERNELS=false` is set explicitly.
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### Manual build
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```bash
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# During install
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CSRC_KERNELS=true pip install -e . --no-build-isolation
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# Rebuild after editing .cu/.cuh files
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CSRC_KERNELS=true python setup.py build_ext --inplace
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# Output: astrai/extension/lib/*.so
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# Or invoke CMake directly
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cmake -S csrc -B build/cmake \
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-DTORCH_HOME=<site-packages>/torch \
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-DPYTHON_INCLUDE_DIR=<python include> \
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-DPY_SOABI=cpython-312-x86_64-linux-gnu
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cmake --build build/cmake -j 16
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```
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### Architecture flags
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`setup.py` passes the GPU compute capability to CMake via `ASTRAI_CUDA_ARCH` (default `89`, i.e. sm_89 / L20):
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- **sm_80+** (Ampere and later): enables tensor-core MMA path (`mma.sync.m16n8k16.bf16`)
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- **Below sm_80**: adds `-DASTRAI_NO_MMA` to disable the MMA path at compile time
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### Build configuration
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`csrc/CMakeLists.txt` defines the CUDA extension build:
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```
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NVCC_FLAGS = -O3 --expt-relaxed-constexpr --use_fast_math
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--ptxas-options=-O3,-v --extra-device-vectorization --threads=16
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```
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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`.
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## Python Extension Architecture
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The Python extension package separates low-level kernel bindings from execution
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policy:
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```text
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astrai/extension/
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├── __init__.py # Stable public API
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├── loader.py # Optional compiled-module discovery and loading
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├── ops/
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│ ├── attention.py # Stateless attention kernel wrappers
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│ └── rotary.py # Stateless rotary kernel wrapper
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└── backend/
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├── attention.py # Backend selection, KV cache I/O, and fallback
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└── rotary.py # Per-call CUDA/torch rotary dispatch
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```
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The dependency direction is one-way:
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```text
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model / inference
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v
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extension public API
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v
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backend policy ---> ops wrappers ---> loader ---> compiled .so
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+-----------> torch / flash-attn fallback
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```
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`ops` must not import `backend`. This keeps direct kernel bindings independent
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of model, cache, fallback, and backend-selection policy.
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### Ops Layer
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`astrai.extension.ops` is the low-level boundary around compiled extensions:
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- Wrappers are stateless and map Python arguments to pybind or
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`torch.library.custom_op` calls.
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- Wrappers validate kernel availability and raise `RuntimeError` when a
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requested extension was not built.
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- Wrappers do not choose another implementation, gather KV cache entries, or
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decide whether an input is supported by a backend.
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- Tests that specifically exercise a compiled kernel may import from
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`astrai.extension.ops`.
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For example, `attn_prefill(...)` means "run this CUDA kernel" rather than "run
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attention using the best available implementation":
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```python
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from astrai.extension.ops import attn_prefill
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output = attn_prefill(q, k, v, mask=mask, is_causal=True)
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```
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If the kernel is unavailable, this call fails. Callers that need fallback and
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capability dispatch must use the public `attention(...)` entry point instead.
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### Backend Layer
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`astrai.extension.backend` owns execution policy:
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- It selects CUDA, FlashAttention, or torch-native attention.
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- It checks per-call constraints such as dtype, shape, head dimension, cache
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availability, and installed optional dependencies.
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- It owns KV cache writes and reads because those operations differ by backend.
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- It provides torch fallbacks and raises when an explicitly requested backend
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cannot handle a call.
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- Rotary dispatch follows the same boundary without a backend class: the
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policy layer chooses the fused op for supported inference calls and otherwise
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uses the autograd-compatible torch implementation.
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Normal model and inference code should import the stable API from
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`astrai.extension`:
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```python
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from astrai.extension import ATTN_BACKEND, attention, attn_backend
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output = attention(q, k, v, kv_cache=cache, layer_id=layer_id, fwd="decode")
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with attn_backend(ATTN_BACKEND.TORCH_NATIVE):
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output = attention(q, k, v)
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```
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The package root re-exports the supported high-level API and selected direct
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kernel wrappers. Internal code should use `astrai.extension.backend` only when
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it needs a backend type or policy implementation, and `astrai.extension.ops`
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only when it deliberately requires one exact kernel.
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### Placement Rules
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When extending this package:
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| Change | Location |
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|--------|----------|
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| Add a pybind call for a compiled kernel | `astrai/extension/ops/` |
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| Add argument translation required by the compiled ABI | `astrai/extension/ops/` |
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| Add capability checks or implementation selection | `astrai/extension/backend/` |
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| Add a torch or third-party fallback | `astrai/extension/backend/` |
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| Add attention KV cache behavior | `astrai/extension/backend/attention.py` |
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| Expose a supported user-facing symbol | `astrai/extension/__init__.py` |
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Imports belong at module scope. Optional dependencies such as `flash_attn` may
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use a module-level guarded import. Type-only imports that would create a runtime
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cycle belong under `TYPE_CHECKING`.
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## Attention Backend
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`astrai/extension/backend/attention.py` provides the backend abstraction:
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- **`AttentionBackend`** (ABC): `fwd_decode` / `fwd_prefill` abstract methods, `forward` dispatches by q_len
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- **`CudaBackend`**: CUDA kernel dispatch — decode via `attn_paged_decode` (page_size=1), prefill via `attn_paged_prefill` (ragged batch, `qo_indptr` + `kv_indptr`). Default on GPU.
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- **`FlashAttnBackend`**: Optional flash-attn dispatch with `flash_attn_with_kvcache` fast path.
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- **`TorchNativeBackend`**: SDPA with indirect KV cache gather (always-available fallback)
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Default priority: cuda > flash > torch. Set ``ASTR_BACKEND=cuda|torch_native|flash``
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to override the default.
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Select a backend via context manager (mirrors `torch.nn.attention.sdpa_kernel`):
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```python
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from astrai.extension import attn_backend, ATTN_BACKEND
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with attn_backend(ATTN_BACKEND.CUDA):
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engine.generate("hello")
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```
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The `attention(...)` policy entry point falls back to `FlashAttnBackend` (when
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flash-attn is installed and supports the call) or `TorchNativeBackend` when the
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automatically selected CUDA backend cannot handle an input. An explicit
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`ASTR_BACKEND` or `attn_backend(...)` selection is strict and raises instead of
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silently switching implementations.
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### Rotary Backend
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`astrai/extension/backend/rotary.py` provides `apply_rotary_emb(x, (cos, sin))` with auto-dispatch:
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- **CUDA path**: calls `rotary_emb` kernel directly when available, input is bf16 on CUDA, and `torch.is_grad_enabled()` is `False` (inference)
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- **Torch fallback**: complex multiply (`torch.view_as_complex` → `torch.complex` multiply → `torch.view_as_real`), used during training (supports autograd) or when kernel unavailable
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No context-manager switching needed — the dispatch is automatic per call.
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## Python Wrappers
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`astrai/extension/ops/attention.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.
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`astrai/extension/ops/rotary.py` provides the wrapper for the rotary embedding kernel. Fallback to torch complex multiply is handled by `backend/rotary.py`.
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Interface (all functions):
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```
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is_causal: True = causal mask; False = non-causal
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mask: 2D [batch, kv_len] or 3D [batch, q_len, kv_len] (bool, True=keep)
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```
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Layout convention: all q/k/v are `[batch, seq_len, n_heads, head_dim]` (blhd). Scale is always `1/sqrt(head_dim)`.
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## Standalone Testing
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Each `csrc/tests/*.cu` file has the `nvcc` compile command in its header comment. Example:
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```bash
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nvcc -I csrc -arch=sm_89 -O3 --use_fast_math \
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--ptxas-options=-O3,-v --extra-device-vectorization \
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-Xcompiler -fopenmp csrc/tests/attn_test.cu -o /tmp/test && /tmp/test
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```
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Test files:
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- `attn_test.cu` — decode + prefill kernels (correctness tables + benchmarks)
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- `attn_paged_test.cu` — paged decode/prefill kernels
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## Benchmarks
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Hardware: NVIDIA L20 (sm_89, 46 GB), CUDA 12.8, driver 570.86.
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Reproduce (decode + prefill in `attn_test.cu`, paged in `attn_paged_test.cu`):
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```bash
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nvcc -I csrc -arch=sm_89 -O3 --use_fast_math \
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--ptxas-options=-O3,-v --extra-device-vectorization \
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-Xcompiler -fopenmp csrc/tests/attn_test.cu -o /tmp/test && /tmp/test
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```
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## Known Optimization Targets
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- **Decode D=256**: spill eliminated (BC=16 + STAGES=2), but still 248 regs — further tiling could help.
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- **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).
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- **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+.
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## File Layout
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```
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csrc/
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├── CMakeLists.txt # CMake build: 5 kernel targets, torch/pybind11 linking
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├── kernels/
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│ ├── attn_common.h # Unified attention params (contig + paged modes)
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│ ├── attn_decode.cu # Basic decode kernel (registered)
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│ ├── attn_prefill.cu # Basic prefill kernel (registered)
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│ ├── attn_paged_decode.cu # Paged decode kernel (registered)
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│ ├── attn_paged_prefill.cu # Paged prefill kernel (registered)
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│ ├── rotary_emb.cu # Fused rotary embedding kernel (registered)
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│ ├── attn_decode_split_kv.cuh # Split-KV variant (contig + paged via KVSource)
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│ ├── attn_decode_split_kv_mma.cuh # Split-KV + MMA variant (contig + paged)
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│ ├── attn_prefill_split_q.cuh # Split-Q variant (contig + paged via KVSource)
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│ ├── attn_prefill_split_q_mma.cuh # Split-Q + MMA variant (contig + paged)
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│ ├── attn_kv_source.cuh # KVSource policies (ContigKV / PagedKV)
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│ ├── attn_dispatchers.cuh # Kernel dispatch macros + KV-templated launchers
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│ ├── attn_entry_utils.cuh # Entry point helpers
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│ ├── attn_mma_utils.cuh # MMA utilities
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│ └── attn_warp_utils.cuh # Warp-level utilities
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└── tests/
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├── test_utils.cuh # Shared test utilities
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├── attn_test.cu # Decode + prefill kernels
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└── attn_paged_test.cu # Paged decode/prefill kernels
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```
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Compiled `.so` files are placed in `astrai/extension/lib/`, separate from Python source files.
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> Document Update Time: 2026-08-16
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