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FA3/FlexAttention/SDPA attention + PyTorch 2.11/CUDA 13.0

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Attention priority: FA3 (Hopper) → FlexAttention (Blackwell/Ada) → SDPA.
FlexAttention uses block-sparse sliding window via torch.compile, ~3x
faster than SDPA dense masks for sliding window layers. Full causal
always uses SDPA is_causal=True. Override with ATTENTION=fa3|flex|sdpa.

Also upgrades PyTorch 2.9.1 → 2.11.0 with CUDA 13.0, and auto-detects
GPU for PyTorch/CUDA version selection in pyproject.toml.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
This commit is contained in:
ademeure 2026-04-08 21:38:29 +00:00
parent a445144d39
commit 3d0dec5716
7 changed files with 604 additions and 212 deletions
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3
README.md
View File

@ -32,7 +32,8 @@ See [dev/LEADERBOARD.md](dev/LEADERBOARD.md) for more docs on how to interpret a
nanochat uses [uv](https://docs.astral.sh/uv/) for dependency management. To install:
```bash
uv sync --extra gpu # Use for CUDA (A100/H100/etc.)
uv sync # Auto-detect GPU (PyTorch 2.11+cu130 if available)
uv sync --extra gpu # Stable for CUDA (A100/H100/etc.)
uv sync --extra cpu # (or) Use for CPU-only / MPS
source .venv/bin/activate
```

61
nanochat/flash_attention.py
View File

@ -1,8 +1,8 @@
"""
Unified Flash Attention interface with automatic FA3/SDPA switching.
Unified Flash Attention interface with automatic FA3/FlexAttention/SDPA switching.
Exports `flash_attn` module that matches the FA3 API exactly, but falls back
to PyTorch SDPA on non-Hopper GPUs (including Blackwell), MPS, and CPU.
to FlexAttention (block-sparse sliding window) or PyTorch SDPA.
Usage (drop-in replacement for FA3):
from nanochat.flash_attention import flash_attn
@ -41,16 +41,30 @@ def _load_flash_attention_3():
_fa3 = _load_flash_attention_3()
HAS_FA3 = _fa3 is not None
# Override for testing: set to 'fa3', 'sdpa', or None (auto)
# =============================================================================
# Detection: FlexAttention (PyTorch 2.5+, block-sparse sliding window)
# =============================================================================
_flex = None
try:
from torch.nn.attention.flex_attention import flex_attention, create_block_mask
_flex = torch.compile(flex_attention)
except ImportError:
create_block_mask = None
HAS_FLEX = _flex is not None
# Override for testing: set to 'fa3', 'flex', 'sdpa', or None (auto)
_override_impl = None
# Mask cache for FlexAttention block masks and SDPA dense masks
_mask_cache = {}
def _resolve_use_fa3():
"""Decide once whether to use FA3, based on availability, override, and dtype."""
if _override_impl == 'fa3':
assert HAS_FA3, "Cannot override to FA3: not available on this hardware"
return True
if _override_impl == 'sdpa':
if _override_impl in ('sdpa', 'flex'):
return False
if HAS_FA3:
# FA3 Hopper kernels only support bf16 and fp8; fp16/fp32 must use SDPA fallback
@ -62,6 +76,19 @@ def _resolve_use_fa3():
USE_FA3 = _resolve_use_fa3()
def _resolve_use_flex():
"""Decide once whether to use FlexAttention for sliding window."""
if _override_impl == 'flex':
assert HAS_FLEX, "Cannot override to FlexAttention: need PyTorch 2.5+"
return True
if _override_impl == 'sdpa' or USE_FA3:
return False
return HAS_FLEX
USE_FLEX = _resolve_use_flex()
ATTN_BACKEND = 'fa3' if USE_FA3 else ('flex' if USE_FLEX else 'sdpa')
# =============================================================================
# SDPA helpers
@ -119,13 +146,33 @@ def flash_attn_func(q, k, v, causal=False, window_size=(-1, -1)):
if USE_FA3:
return _fa3.flash_attn_func(q, k, v, causal=causal, window_size=window_size)
# SDPA fallback: transpose (B, T, H, D) -> (B, H, T, D)
# FlexAttention and SDPA expect (B, H, T, D)
B, T = q.shape[:2]
q = q.transpose(1, 2)
k = k.transpose(1, 2)
v = v.transpose(1, 2)
enable_gqa = q.size(1) != k.size(1)
y = _sdpa_attention(q, k, v, window_size, enable_gqa)
return y.transpose(1, 2) # back to (B, T, H, D)
w = window_size[0]
# Full causal (no sliding window) — just use SDPA is_causal, it's fast everywhere
if w < 0 or w >= T:
return F.scaled_dot_product_attention(q, k, v, is_causal=True, enable_gqa=enable_gqa).transpose(1, 2)
# Sliding window — FlexAttention with block sparsity (fast), or SDPA with dense mask (slow)
if USE_FLEX:
key = (B, q.size(1), T, w, q.device)
if key not in _mask_cache:
_mask_cache[key] = create_block_mask(
lambda b, h, qi, ki: (qi >= ki) & (qi - ki <= w),
B, q.size(1), T, T, device=q.device)
return _flex(q, k, v, block_mask=_mask_cache[key], enable_gqa=enable_gqa).transpose(1, 2)
# SDPA fallback: materialize the full T*T bool mask (O(T^2) memory)
key = (T, w, q.device)
if key not in _mask_cache:
ix = torch.arange(T, device=q.device)
_mask_cache[key] = (ix <= ix.unsqueeze(1)) & (ix.unsqueeze(1) - ix <= w)
return F.scaled_dot_product_attention(q, k, v, attn_mask=_mask_cache[key], enable_gqa=enable_gqa).transpose(1, 2)
def flash_attn_with_kvcache(q, k_cache, v_cache, k=None, v=None, cache_seqlens=None,

27
pyproject.toml
View File

@ -12,7 +12,7 @@ dependencies = [
"rustbpe>=0.1.0",
"tiktoken>=0.11.0",
"tokenizers>=0.22.0",
"torch==2.9.1",
"torch>=2.9.1,<=2.11.0",
"uvicorn>=0.36.0",
"wandb>=0.21.3",
]
@ -35,7 +35,17 @@ python_files = ["test_*.py"]
python_classes = ["Test*"]
python_functions = ["test_*"]
# target torch to cuda 12.8 or CPU
[tool.uv]
torch-backend = "auto"
default-groups = []
conflicts = [
[
{ extra = "cpu" },
{ extra = "gpu" },
],
]
# Fallback indexes for --extra gpu / --extra cpu (override auto-detection)
[tool.uv.sources]
torch = [
{ index = "pytorch-cpu", extra = "cpu" },
@ -54,17 +64,8 @@ explicit = true
[project.optional-dependencies]
cpu = [
"torch==2.9.1",
"torch>=2.9.1,<=2.11.0",
]
gpu = [
"torch==2.9.1",
]
[tool.uv]
default-groups = []
conflicts = [
[
{ extra = "cpu" },
{ extra = "gpu" },
],
"torch==2.9.1", # Stable for CUDA 12.8 (A100/H100/etc.)
]

16
scripts/base_train.py
View File

@ -99,21 +99,21 @@ print0(f"COMPUTE_DTYPE: {COMPUTE_DTYPE} ({COMPUTE_DTYPE_REASON})")
use_dummy_wandb = args.run == "dummy" or not master_process
wandb_run = DummyWandb() if use_dummy_wandb else wandb.init(project="nanochat", name=args.run, config=user_config)
# Flash Attention status
from nanochat.flash_attention import USE_FA3
using_fa3 = USE_FA3
if using_fa3:
# Attention backend status
from nanochat.flash_attention import ATTN_BACKEND
print0(f"Attention backend: {ATTN_BACKEND}")
if ATTN_BACKEND == 'fa3':
print0("✓ Using Flash Attention 3 (Hopper GPU detected), efficient, new and awesome.")
elif ATTN_BACKEND == 'flex':
print0("✓ Using FlexAttention (block-sparse sliding window).")
else:
print0("!" * 80)
if HAS_FA3 and COMPUTE_DTYPE != torch.bfloat16:
print0(f"WARNING: Flash Attention 3 only supports bf16, but COMPUTE_DTYPE={COMPUTE_DTYPE}. Using PyTorch SDPA fallback")
else:
print0("WARNING: Flash Attention 3 not available, using PyTorch SDPA fallback")
print0("WARNING: Training will be less efficient without FA3")
print0("WARNING: Flash Attention 3 and FlexAttention not available, using PyTorch SDPA fallback")
if args.window_pattern != "L":
print0(f"WARNING: SDPA has no support for sliding window attention (window_pattern='{args.window_pattern}'). Your GPU utilization will be terrible.")
print0("WARNING: Recommend using --window-pattern L for full context attention without alternating sliding window patterns.")
print0(f"WARNING: SDPA sliding window uses dense O(T²) masks (window_pattern='{args.window_pattern}'). Consider --window-pattern L.")
print0("!" * 80)
# -----------------------------------------------------------------------------

9
scripts/chat_sft.py
View File

@ -21,7 +21,7 @@ from nanochat.tokenizer import get_token_bytes
from nanochat.checkpoint_manager import save_checkpoint, load_model, load_optimizer_state
from nanochat.loss_eval import evaluate_bpb
import torch.distributed as dist
from nanochat.flash_attention import HAS_FA3
from nanochat.flash_attention import HAS_FA3, ATTN_BACKEND
from nanochat.engine import Engine
from scripts.chat_eval import run_chat_eval
@ -88,9 +88,10 @@ else:
use_dummy_wandb = args.run == "dummy" or not master_process
wandb_run = DummyWandb() if use_dummy_wandb else wandb.init(project="nanochat-sft", name=args.run, config=user_config)
# Flash Attention status
if not HAS_FA3:
print0("WARNING: Flash Attention 3 not available, using PyTorch SDPA fallback. Training will be less efficient.")
# Attention backend status
print0(f"Attention backend: {ATTN_BACKEND}")
if ATTN_BACKEND == 'sdpa':
print0("WARNING: Using PyTorch SDPA fallback. Training will be less efficient.")
# Load the model and tokenizer
model, tokenizer, meta = load_model("base", device, phase="train", model_tag=args.model_tag, step=args.model_step)

68
tests/test_attention_fallback.py
View File

@ -1,10 +1,10 @@
"""
Test Flash Attention unified interface - verify FA3 and SDPA produce identical results.
Test Flash Attention unified interface - verify FA3, FlexAttention, and SDPA produce identical results.
Run: python -m pytest tests/test_attention_fallback.py -v -s
Note on test structure:
Tests are split into two classes due to dtype/device constraints:
Tests are split into classes due to dtype/device constraints:
1. TestFA3VsSDPA: Comparison tests that run both FA3 and SDPA on the same inputs
and verify they produce identical results. These require a Hopper GPU (FA3 only
@ -12,18 +12,23 @@ Note on test structure:
2. TestSDPAOnly: Tests that only exercise the SDPA fallback path. These can run
on any device (CUDA, CPU, MPS) with the appropriate dtype for that device.
3. TestFlexAttention: Tests that exercise the FlexAttention path and cross-validate
against SDPA. Requires PyTorch 2.5+ with CUDA.
"""
import torch
import pytest
import nanochat.flash_attention as fa_module
from nanochat.flash_attention import flash_attn, HAS_FA3
from nanochat.flash_attention import flash_attn, HAS_FA3, HAS_FLEX
from nanochat.engine import KVCache
def set_impl(impl):
"""Set the implementation override ('fa3', 'sdpa', or None for auto) and re-resolve USE_FA3."""
"""Set the implementation override ('fa3', 'flex', 'sdpa', or None for auto) and re-resolve."""
fa_module._override_impl = impl
fa_module.USE_FA3 = fa_module._resolve_use_fa3()
fa_module.USE_FLEX = fa_module._resolve_use_flex()
fa_module.ATTN_BACKEND = 'fa3' if fa_module.USE_FA3 else ('flex' if fa_module.USE_FLEX else 'sdpa')
def run_both_impls(fn):
@ -334,6 +339,50 @@ class TestSDPAOnly:
set_impl(None)
# =============================================================================
# FlexAttention tests (require PyTorch 2.5+ with CUDA)
# =============================================================================
@pytest.mark.skipif(not HAS_FLEX or not torch.cuda.is_available(), reason="FlexAttention + CUDA required")
class TestFlexAttention:
"""Test FlexAttention path and cross-validate against SDPA."""
DEVICE = "cuda"
DTYPE = torch.bfloat16
def _compare_with_sdpa(self, q, k, v, window_size, name):
"""Run with flex, then sdpa, compare."""
set_impl('flex')
y_flex = flash_attn.flash_attn_func(q, k, v, causal=True, window_size=window_size)
set_impl('sdpa')
y_sdpa = flash_attn.flash_attn_func(q, k, v, causal=True, window_size=window_size)
set_impl(None)
assert y_flex.shape == y_sdpa.shape and not torch.isnan(y_flex).any()
d = (y_flex - y_sdpa).abs().max().item()
print(f"{name}: flex vs sdpa max_diff={d:.6f}")
assert torch.allclose(y_flex, y_sdpa, atol=1e-2, rtol=1e-2)
def test_full_causal(self):
B, T, H, D = 2, 64, 4, 32
q = torch.randn(B, T, H, D, device=self.DEVICE, dtype=self.DTYPE)
k = torch.randn(B, T, H, D, device=self.DEVICE, dtype=self.DTYPE)
v = torch.randn(B, T, H, D, device=self.DEVICE, dtype=self.DTYPE)
self._compare_with_sdpa(q, k, v, (-1, -1), "full_causal")
def test_sliding_window(self):
B, T, H, D = 2, 128, 4, 32
q = torch.randn(B, T, H, D, device=self.DEVICE, dtype=self.DTYPE)
k = torch.randn(B, T, H, D, device=self.DEVICE, dtype=self.DTYPE)
v = torch.randn(B, T, H, D, device=self.DEVICE, dtype=self.DTYPE)
self._compare_with_sdpa(q, k, v, (32, 0), "sliding_window")
def test_gqa(self):
B, T, D = 2, 64, 32
q = torch.randn(B, T, 8, D, device=self.DEVICE, dtype=self.DTYPE)
k = torch.randn(B, T, 2, D, device=self.DEVICE, dtype=self.DTYPE)
v = torch.randn(B, T, 2, D, device=self.DEVICE, dtype=self.DTYPE)
self._compare_with_sdpa(q, k, v, (-1, -1), "gqa")
# =============================================================================
# Override mechanism tests
# =============================================================================
@ -347,10 +396,19 @@ class TestOverrideMechanism:
assert fa_module.USE_FA3 == True
set_impl(None)
@pytest.mark.skipif(not HAS_FLEX, reason="FlexAttention required")
def test_override_flex(self):
"""Test that override='flex' uses FlexAttention."""
set_impl('flex')
assert fa_module.USE_FLEX == True
assert fa_module.ATTN_BACKEND == 'flex'
set_impl(None)
def test_override_sdpa(self):
"""Test that override='sdpa' uses SDPA."""
set_impl('sdpa')
assert fa_module.USE_FA3 == False
assert fa_module.USE_FLEX == False
set_impl(None)
def test_override_auto(self):
@ -367,6 +425,8 @@ if __name__ == "__main__":
major, minor = torch.cuda.get_device_capability()
print(f"Compute capability: {major}.{minor}")
print(f"HAS_FA3: {HAS_FA3}")
print(f"HAS_FLEX: {HAS_FLEX}")
print(f"ATTN_BACKEND: {fa_module.ATTN_BACKEND}")
print()
pytest.main([__file__, "-v", "-s"])

632
uv.lock
View File

@ -9,18 +9,18 @@ resolution-markers = [
"python_full_version == '3.11.*' and sys_platform != 'linux' and extra != 'extra-8-nanochat-cpu' and extra == 'extra-8-nanochat-gpu'",
"python_full_version < '3.11' and sys_platform != 'linux' and extra != 'extra-8-nanochat-cpu' and extra == 'extra-8-nanochat-gpu'",
"python_full_version >= '3.12' and sys_platform == 'linux' and extra == 'extra-8-nanochat-cpu' and extra != 'extra-8-nanochat-gpu'",
"python_full_version >= '3.12' and sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-8-nanochat-cpu' and extra != 'extra-8-nanochat-gpu'",
"python_full_version == '3.11.*' and sys_platform == 'linux' and extra == 'extra-8-nanochat-cpu' and extra != 'extra-8-nanochat-gpu'",
"python_full_version < '3.11' and sys_platform == 'linux' and extra == 'extra-8-nanochat-cpu' and extra != 'extra-8-nanochat-gpu'",
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"python_full_version == '3.11.*' and sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-8-nanochat-cpu' and extra != 'extra-8-nanochat-gpu'",
"python_full_version < '3.11' and sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-8-nanochat-cpu' and extra != 'extra-8-nanochat-gpu'",
"python_full_version >= '3.12' and sys_platform == 'darwin' and extra == 'extra-8-nanochat-cpu' and extra != 'extra-8-nanochat-gpu'",
"python_full_version == '3.11.*' and sys_platform == 'darwin' and extra == 'extra-8-nanochat-cpu' and extra != 'extra-8-nanochat-gpu'",
"python_full_version < '3.11' and sys_platform == 'darwin' and extra == 'extra-8-nanochat-cpu' and extra != 'extra-8-nanochat-gpu'",
"python_full_version >= '3.12' and sys_platform == 'linux' and extra != 'extra-8-nanochat-cpu' and extra != 'extra-8-nanochat-gpu'",
"python_full_version >= '3.12' and sys_platform != 'linux' and extra != 'extra-8-nanochat-cpu' and extra != 'extra-8-nanochat-gpu'",
"python_full_version == '3.11.*' and sys_platform == 'linux' and extra != 'extra-8-nanochat-cpu' and extra != 'extra-8-nanochat-gpu'",
"python_full_version < '3.11' and sys_platform == 'linux' and extra != 'extra-8-nanochat-cpu' and extra != 'extra-8-nanochat-gpu'",
"python_full_version >= '3.12' and sys_platform != 'linux' and extra != 'extra-8-nanochat-cpu' and extra != 'extra-8-nanochat-gpu'",
"python_full_version == '3.11.*' and sys_platform != 'linux' and extra != 'extra-8-nanochat-cpu' and extra != 'extra-8-nanochat-gpu'",
"python_full_version < '3.11' and sys_platform != 'linux' and extra != 'extra-8-nanochat-cpu' and extra != 'extra-8-nanochat-gpu'",
]
@ -468,14 +468,14 @@ resolution-markers = [
"python_full_version == '3.11.*' and sys_platform == 'linux' and extra != 'extra-8-nanochat-cpu' and extra == 'extra-8-nanochat-gpu'",
"python_full_version == '3.11.*' and sys_platform != 'linux' and extra != 'extra-8-nanochat-cpu' and extra == 'extra-8-nanochat-gpu'",
"python_full_version >= '3.12' and sys_platform == 'linux' and extra == 'extra-8-nanochat-cpu' and extra != 'extra-8-nanochat-gpu'",
"python_full_version >= '3.12' and sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-8-nanochat-cpu' and extra != 'extra-8-nanochat-gpu'",
"python_full_version == '3.11.*' and sys_platform == 'linux' and extra == 'extra-8-nanochat-cpu' and extra != 'extra-8-nanochat-gpu'",
"python_full_version >= '3.12' and sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-8-nanochat-cpu' and extra != 'extra-8-nanochat-gpu'",
"python_full_version == '3.11.*' and sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-8-nanochat-cpu' and extra != 'extra-8-nanochat-gpu'",
"python_full_version >= '3.12' and sys_platform == 'darwin' and extra == 'extra-8-nanochat-cpu' and extra != 'extra-8-nanochat-gpu'",
"python_full_version == '3.11.*' and sys_platform == 'darwin' and extra == 'extra-8-nanochat-cpu' and extra != 'extra-8-nanochat-gpu'",
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"python_full_version == '3.11.*' and sys_platform != 'linux' and extra != 'extra-8-nanochat-cpu' and extra != 'extra-8-nanochat-gpu'",
]
dependencies = [
@ -556,6 +556,85 @@ wheels = [
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[[package]]
name = "cuda-bindings"
version = "13.2.0"
source = { registry = "https://pypi.org/simple" }
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