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Commit 9dbc491a authored by Tri Dao's avatar Tri Dao
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Rename, add benchmarking script

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README.md
View file @ 9dbc491a
Alpha release of FlashAttention. ## FlashAttention - Alpha release (0.1).
To compile: To compile (requiring NVCC and an A100 GPU):
``` ```
cd csrc/stream_attn cd csrc/flash_attn
python setup.py install python setup.py install
``` ```
Interface: `streaming_attention.py` Interface: `flash_attention.py`
Contact: `trid@stanford.edu` To run the benchmark against PyTorch standard attention:
```
PYTHONPATH=$PWD python benchmarks/benchmark_flash_attention.py
```
FlashAttention currently supports:
1. A100 GPUs.
2. fp16.
3. Head dimensions 16, 32, 64.
Our roadmap to broaden the support:
1. Refactor to use Cutlass.
2. Support SM86 GPUs (e.g. RTX 3080, 3090), support SM75 GPUs (e.g. T4).
3. Support bf16.
4. Support head dimension 128.
5. Make package pip-installable.
6. Support SM70 GPUs (V100).
7. Fused rotary embedding.
8. Attention linear bias (e.g. ALiBi).
### Acknowledgments
Our implementation uses Apex's
[FMHA](https://github.com/NVIDIA/apex/tree/master/apex/contrib/csrc/fmha) code
as a starting point.
We thank [Young-Jun Ko](https://yjk21.github.io/) for the in-depth explanation of his FMHA implementation
and for his thoughtful answers to our questions about CUDA.
benchmarks/benchmark_flash_attention.py 0 → 100644
View file @ 9dbc491a
from functools import partial
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from einops import rearrange, repeat
from benchmarks.utils import benchmark_all, benchmark_forward, benchmark_backward, benchmark_combined
from bert_padding import unpad_input, pad_input
from flash_attn_interface import flash_attn_func
def attention_ref(qkv, attn_mask, dropout_p, upcast=False, causal=False):
"""
Arguments:
qkv: (batch_size, seqlen, 3, nheads, head_dim)
attn_mask: (batch_size, seqlen)
dropout_p: float
Output:
output: (batch_size, seqlen, nheads, head_dim)
attention: softmax after dropout
"""
q, k, v = (qkv.float() if upcast else qkv).unbind(dim=2)
seqlen = qkv.shape[1]
d = qkv.shape[-1]
scores = torch.einsum('bthd,bshd->bhts', q, k / math.sqrt(d))
scores.masked_fill_(rearrange(~attn_mask, 'b s -> b 1 1 s'), float('-inf'))
if causal:
causal_mask = torch.triu(torch.ones(seqlen, seqlen, dtype=torch.bool, device=qkv.device), 1)
scores.masked_fill_(causal_mask, float('-inf'))
attention = torch.softmax(scores, dim=-1)
attention_drop = F.dropout(attention, dropout_p)
output = torch.einsum('bhts,bshd->bthd', attention_drop , v)
# return output.to(dtype=qkv.dtype), attention.to(dtype=qkv.dtype)
return output.to(dtype=qkv.dtype)
torch.manual_seed(0)
repeats = 30
batch_size = 64
nheads = 16
seqlen = 1024
n = 1024
d = n // nheads
dropout_p = 0.1
causal = False
dtype = torch.float16
device = 'cuda'
x = torch.randn(batch_size, seqlen, n, device='cuda', dtype=dtype, requires_grad=True)
Wqkv = torch.nn.Linear(nheads * d, 3 * nheads * d, device=device, dtype=dtype)
lengths = torch.randint(seqlen - 20, seqlen, (batch_size, 1), device='cuda')
attention_mask_bool = repeat(torch.arange(seqlen, device='cuda'), 's -> b s', b=batch_size) < lengths
attention_mask = torch.zeros(batch_size, seqlen, device='cuda', dtype=dtype)
attention_mask[~attention_mask_bool] = -10000.0
attention_mask = rearrange(attention_mask, 'b s -> b 1 1 s')
x_unpad, indices, cu_seqlens, max_seqlen_in_batch = unpad_input(x, attention_mask_bool)
qkv_unpad = rearrange(Wqkv(x_unpad), 'nnz (t h d) -> nnz t h d', t=3,
h=nheads).detach().requires_grad_()
qkv = rearrange(Wqkv(x), 'b s (t h d) -> b s t h d', t=3, h=nheads).detach().requires_grad_()
fn = lambda qkv_unpad: flash_attn_func(qkv_unpad, cu_seqlens, dropout_p, max_seqlen_in_batch, causal=causal)
benchmark_all(fn, qkv_unpad, repeats=repeats, desc='FlashAttention')
fn = lambda qkv: attention_ref(qkv, attention_mask_bool, dropout_p, causal=causal)
benchmark_all(fn, qkv, repeats=repeats, desc='PyTorch Standard Attention')
benchmarks/utils.py 0 → 100644
View file @ 9dbc491a
# Adapted from https://github.com/HazyResearch/hippo/blob/datasets/benchmark/utils.py
""" Useful functions for writing test code. """
import torch
import torch.utils.benchmark as benchmark
def benchmark_forward(fn, *inputs, min_run_time = 0.2, repeats = 10, desc='', verbose=True, **kwinputs):
""" Use Pytorch Benchmark on the forward pass of an arbitrary function. """
if verbose:
print(desc, '- Forward pass')
t = benchmark.Timer(
stmt='fn(*inputs, **kwinputs)',
globals={'fn': fn, 'inputs': inputs, 'kwinputs': kwinputs},
num_threads=torch.get_num_threads(),
)
m = t.timeit(repeats)
if verbose:
print(m)
return t, m
def benchmark_backward(fn, *inputs, grad=None, repeats=10, desc='', verbose=True, **kwinputs):
""" Use Pytorch Benchmark on the backward pass of an arbitrary function. """
if verbose:
print(desc, '- Backward pass')
y = fn(*inputs, **kwinputs)
if type(y) is tuple:
y = y[0]
if grad is None:
grad = torch.randn_like(y)
else:
if grad.shape != y.shape:
raise RuntimeError('Grad shape does not match output shape')
t = benchmark.Timer(
stmt='y.backward(grad, retain_graph=True)',
globals={'y': y, 'grad': grad},
num_threads=torch.get_num_threads(),
)
m = t.timeit(repeats)
if verbose:
print(m)
return t, m
def benchmark_combined(fn, *inputs, grad=None, repeats=10, desc='', verbose=True, **kwinputs):
""" Use Pytorch Benchmark on the forward+backward pass of an arbitrary function. """
if verbose:
print(desc, '- Forward + Backward pass')
# y = fn(*inputs, **kwinputs)
# if grad is None:
# grad = torch.randn_like(y)
# else:
# if grad.shape != y.shape:
# raise RuntimeError('Grad shape does not match output shape')
# del y
def f(grad, *inputs, **kwinputs):
y = fn(*inputs, **kwinputs)
if type(y) is tuple:
y = y[0]
if grad is None:
grad = torch.randn_like(y)
else:
if grad.shape != y.shape:
raise RuntimeError('Grad shape does not match output shape')
y.backward(grad, retain_graph=True)
t = benchmark.Timer(
stmt='f(grad, *inputs, **kwinputs)',
globals={'f': f, 'fn': fn, 'inputs': inputs, 'grad': grad, 'kwinputs': kwinputs},
num_threads=torch.get_num_threads(),
)
m = t.timeit(repeats)
if verbose:
print(m)
return t, m
def benchmark_all(fn, *inputs, grad=None, repeats=10, desc='', verbose=True, **kwinputs):
""" Use Pytorch Benchmark on the forward+backward pass of an arbitrary function. """
return (
benchmark_forward(fn, *inputs, repeats=repeats, desc=desc, verbose=verbose, **kwinputs),
benchmark_backward(fn, *inputs, grad=grad, repeats=repeats, desc=desc, verbose=verbose,
**kwinputs),
benchmark_combined(fn, *inputs, grad=grad, repeats=repeats, desc=desc, verbose=verbose,
**kwinputs),
)
def pytorch_profiler(fn, *inputs, repeats=10):
""" Wrap benchmark functions in Pytorch profiler to see CUDA information. """
with torch.profiler.profile(
activities=[
torch.profiler.ProfilerActivity.CPU,
torch.profiler.ProfilerActivity.CUDA,
],
record_shapes=True,
profile_memory=True,
with_stack=True,
) as p:
# benchmark_forward(repeats, fn, *inputs)
fn(*inputs)
print(p.key_averages().table(
sort_by="self_cuda_time_total", row_limit=-1))
def convert_data(*tensors, device='cuda'):
tensors = tuple(t.to(device) for t in tensors)
for t in tensors:
if t.is_leaf: t.requires_grad = True
t.retain_grad()
return tensors
def log_backward(output, *inputs):
""" Perform backward pass of output and print gradients of input tensors. """
#print(f"{output=}")
output.sum().backward(retain_graph=True)
print("Gradients:")
for t in inputs:
print(t.grad)
t.grad.zero_()
def benchmark_memory(fn, *inputs, desc='', verbose=True, **kwinputs):
torch.cuda.empty_cache()
torch.cuda.reset_peak_memory_stats()
torch.cuda.synchronize()
fn(*inputs, **kwinputs)
torch.cuda.synchronize()
mem = torch.cuda.max_memory_allocated() / ((2 ** 20) * 1000)
if verbose:
print(f'{desc} max memory: ', mem)
torch.cuda.empty_cache()
return mem
csrc/stream_attn/setup.py → csrc/flash_attn/setup.py
View file @ 9dbc491a
...@@ -95,18 +95,18 @@ torch_dir = torch.__path__[0] ...@@ -95,18 +95,18 @@ torch_dir = torch.__path__[0]
if os.path.exists(os.path.join(torch_dir, "include", "ATen", "CUDAGeneratorImpl.h")): if os.path.exists(os.path.join(torch_dir, "include", "ATen", "CUDAGeneratorImpl.h")):
generator_flag = ["-DOLD_GENERATOR_PATH"] generator_flag = ["-DOLD_GENERATOR_PATH"]
raise_if_cuda_home_none("--streamattn") raise_if_cuda_home_none("--flashattn")
# Check, if CUDA11 is installed for compute capability 8.0 # Check, if CUDA11 is installed for compute capability 8.0
cc_flag = [] cc_flag = []
_, bare_metal_major, _ = get_cuda_bare_metal_version(CUDA_HOME) _, bare_metal_major, _ = get_cuda_bare_metal_version(CUDA_HOME)
if int(bare_metal_major) < 11: if int(bare_metal_major) < 11:
raise RuntimeError("--streamattn only supported on SM80+") raise RuntimeError("--flashattn only supported on SM80+")
cc_flag.append("-gencode") cc_flag.append("-gencode")
cc_flag.append("arch=compute_80,code=sm_80") cc_flag.append("arch=compute_80,code=sm_80")
ext_modules.append( ext_modules.append(
CUDAExtension( CUDAExtension(
name="stream_attn_cuda", name="flash_attn_cuda",
sources=[ sources=[
"fmha_api.cpp", "fmha_api.cpp",
"src/fmha_fprop_fp16_kernel.sm80.cu", "src/fmha_fprop_fp16_kernel.sm80.cu",
...@@ -139,9 +139,9 @@ ext_modules.append( ...@@ -139,9 +139,9 @@ ext_modules.append(
) )
setup( setup(
name="stream_attn_cuda", name="flash_attn_cuda",
version="0.1", version="0.1",
description="Streaming attention", description="Flash Attention",
ext_modules=ext_modules, ext_modules=ext_modules,
cmdclass={"build_ext": BuildExtension} if ext_modules else {}, cmdclass={"build_ext": BuildExtension} if ext_modules else {},
) )
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