# Install the newest triton version with
# pip install "git+https://github.com/openai/triton.git#egg=triton&subdirectory=python"
import pickle
import math
import time
import torch
import torch.nn as nn
import torch.nn.functional as F
from einops import rearrange, repeat
from flash_attn.utils.benchmark import benchmark_all, benchmark_forward, benchmark_backward
from flash_attn.utils.benchmark import benchmark_fwd_bwd, benchmark_combined
from flash_attn import flash_attn_qkvpacked_func
from flash_attn_interface import flash_attn_func
try:
from triton_fused_attention import attention as attention_triton
except ImportError:
attention_triton = None
try:
import xformers.ops as xops
except ImportError:
xops = None
try:
import cudnn
except ImportError:
cudnn = None
def convert_to_cudnn_type(torch_type):
if torch_type == torch.float16:
return cudnn.data_type.HALF
elif torch_type == torch.bfloat16:
return cudnn.data_type.BFLOAT16
elif torch_type == torch.float32:
return cudnn.data_type.FLOAT
elif torch_type == torch.int32:
return cudnn.data_type.INT32
elif torch_type == torch.int64:
return cudnn.data_type.INT64
elif torch_type == torch.float8_e4m3fn:
return cudnn.data_type.FP8_E4M3
elif torch_type == torch.float8_e4m3fn:
return cudnn.data_type.FP8_E5M2
else:
raise ValueError("Unsupported tensor data type.")
def cudnn_spda_setup(qkv, seqlen_q, seqlen_k, causal=False):
b, _, _, nheads, headdim = qkv.shape
assert cudnn is not None, 'CUDNN is not available'
o_gpu = torch.zeros(b, seqlen_q, nheads, headdim, dtype=qkv.dtype, device=qkv.device)
o_gpu_transposed = torch.as_strided(
o_gpu,
[b, nheads, seqlen_q, headdim],
[nheads * seqlen_q * headdim, headdim, nheads * headdim, 1],
)
stats_gpu = torch.empty(b, nheads, seqlen_q, 1, dtype=torch.float32, device=qkv.device)
amax_s_gpu = torch.empty(1, 1, 1, 1, dtype=torch.float32, device=qkv.device)
amax_o_gpu = torch.empty(1, 1, 1, 1, dtype=torch.float32, device=qkv.device)
graph = cudnn.pygraph(
io_data_type=convert_to_cudnn_type(qkv.dtype),
intermediate_data_type=cudnn.data_type.FLOAT,
compute_data_type=cudnn.data_type.FLOAT,
)
new_q = torch.as_strided(
qkv,
[b, nheads, seqlen_q, headdim],
[seqlen_q * nheads * headdim * 3, headdim, headdim * nheads * 3, 1],
storage_offset=0,
)
q = graph.tensor(
name = "Q",
dim = list(new_q.shape),
stride = list(new_q.stride()),
data_type=convert_to_cudnn_type(qkv.dtype)
)
new_k = torch.as_strided(
qkv,
[b, nheads, seqlen_k, headdim],
[seqlen_k * nheads * headdim * 3, headdim, headdim * nheads * 3, 1],
storage_offset=nheads * headdim,
)
k = graph.tensor(
name = "K",
dim = list(new_k.shape),
stride = list(new_k.stride()),
data_type=convert_to_cudnn_type(qkv.dtype)
)
new_v = torch.as_strided(
qkv,
[b, nheads, seqlen_k, headdim],
[seqlen_k * nheads * headdim * 3, headdim, headdim * nheads * 3, 1],
storage_offset=nheads * headdim * 2,
)
v = graph.tensor(
name = "V",
dim = list(new_v.shape),
stride = list(new_v.stride()),
data_type=convert_to_cudnn_type(qkv.dtype)
)
def get_default_scale_tensor():
return graph.tensor(
dim = [1, 1, 1, 1],
stride = [1, 1, 1, 1],
data_type=cudnn.data_type.FLOAT
)
default_scale_gpu = torch.ones(1, 1, 1, 1, dtype=torch.float32, device="cuda")
descale_q = get_default_scale_tensor()
descale_k = get_default_scale_tensor()
descale_v = get_default_scale_tensor()
descale_s = get_default_scale_tensor()
scale_s = get_default_scale_tensor()
scale_o = get_default_scale_tensor()
o, _, amax_s, amax_o = graph.sdpa_fp8(
q=q,
k=k,
v=v,
descale_q=descale_q,
descale_k=descale_k,
descale_v=descale_v,
descale_s=descale_s,
scale_s=scale_s,
scale_o=scale_o,
is_inference=True,
attn_scale=1.0 / math.sqrt(headdim),
use_causal_mask=causal,
name="sdpa",
)
o.set_output(True).set_dim(o_gpu_transposed.shape).set_stride(o_gpu_transposed.stride())
amax_s.set_output(False).set_dim(amax_s_gpu.shape).set_stride(amax_s_gpu.stride())
amax_o.set_output(False).set_dim(amax_o_gpu.shape).set_stride(amax_o_gpu.stride())
# stats.set_output(True).set_data_type(cudnn.data_type.FLOAT)
graph.validate()
graph.build_operation_graph()
graph.create_execution_plans([cudnn.heur_mode.A, cudnn.heur_mode.FALLBACK])
graph.check_support()
graph.build_plans()
variant_pack = {
q: new_q,
k: new_k,
v: new_v,
descale_q: default_scale_gpu,
descale_k: default_scale_gpu,
descale_v: default_scale_gpu,
descale_s: default_scale_gpu,
scale_s: default_scale_gpu,
scale_o: default_scale_gpu,
o: o_gpu_transposed,
amax_s: amax_s_gpu,
amax_o: amax_o_gpu,
}
workspace = torch.empty(graph.get_workspace_size(), device="cuda", dtype=torch.uint8)
def run(*args, **kwargs):
graph.execute(variant_pack, workspace)
return o_gpu, amax_o_gpu
return run
def attention_pytorch(qkv, dropout_p=0.0, causal=True):
"""
Arguments:
qkv: (batch_size, seqlen, 3, nheads, head_dim)
dropout_p: float
Output:
output: (batch_size, seqlen, nheads, head_dim)
"""
batch_size, seqlen, _, nheads, d = qkv.shape
q, k, v = qkv.unbind(dim=2)
q = rearrange(q, 'b t h d -> (b h) t d')
k = rearrange(k, 'b s h d -> (b h) d s')
softmax_scale = 1.0 / math.sqrt(d)
# Preallocate attn_weights for `baddbmm`
scores = torch.empty(batch_size * nheads, seqlen, seqlen, dtype=qkv.dtype, device=qkv.device)
scores = rearrange(torch.baddbmm(scores, q, k, beta=0, alpha=softmax_scale),
'(b h) t s -> b h t s', h=nheads)
if causal:
# "triu_tril_cuda_template" not implemented for 'BFloat16'
# So we have to construct the mask in float
causal_mask = torch.triu(torch.full((seqlen, seqlen), -10000.0, device=scores.device), 1)
# TD [2022-09-30]: Adding is faster than masked_fill_ (idk why, just better kernel I guess)
scores = scores + causal_mask.to(dtype=scores.dtype)
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)
def flops(batch, seqlen, headdim, nheads, causal, mode="fwd"):
assert mode in ["fwd", "bwd", "fwd_bwd"]
f = 4 * batch * seqlen**2 * nheads * headdim // (2 if causal else 1)
return f if mode == "fwd" else (2.5 * f if mode == "bwd" else 3.5 * f)
def efficiency(flop, time):
return (flop / time / 10**12) if not math.isnan(time) else 0.0
def time_fwd(func, *args, **kwargs):
time.sleep(1) # Sleep to avoid residual power throttling from the previous benchmark
time_f = benchmark_forward(func, *args, **kwargs)
return time_f[1].mean
torch.manual_seed(0)
repeats = 30
device = 'cuda'
# dtype = torch.float16
dtype = torch.float8_e4m3fn
bs_seqlen_vals = [(32, 512), (16, 1024), (8, 2048), (4, 4224), (2, 8448), (1, 8448 * 2)]
# bs_seqlen_vals = [(32, 512), (16, 1024), (8, 2048), (4, 4096), (2, 8192), (1, 8192 * 2)]
# bs_seqlen_vals = [(4, 4096), (2, 8192), (1, 8192 * 2), (4, 4224), (2, 8448), (1, 8448 * 2)]
# bs_seqlen_vals = [(32, 512), (16, 1024), (8, 2048)]
causal_vals = [False, True]
headdim_vals = [128]
dim = 2048
# dim = 256
dropout_p = 0.0
methods = (["Pytorch", "Flash3", "cuDNN"]
# + (["Triton"] if attention_triton is not None else [])
# + (["xformers.c"] if xops is not None else [])
# + (["xformers.f"] if xops is not None else [])
)
time_f = {}
time_b = {}
time_f_b = {}
speed_f = {}
speed_b = {}
speed_f_b = {}
for causal in causal_vals:
for headdim in headdim_vals:
for batch_size, seqlen in bs_seqlen_vals:
torch.cuda.empty_cache()
config = (causal, headdim, batch_size, seqlen)
nheads = dim // headdim
q, k, v = [torch.randn(batch_size, seqlen, nheads, headdim, device=device, dtype=torch.float16, requires_grad=False) for _ in range(3)]
qkv = torch.stack([q, k, v], dim=2)
qkv = qkv.to(torch.float16)
f = time_fwd(attention_pytorch, qkv, dropout_p, causal=causal, repeats=repeats, verbose=False)
time_f[config, "Pytorch"] = f
res_baseline = attention_pytorch(qkv, dropout_p, causal=causal)
if attention_triton is not None:
q_transposed = q.transpose(1, 2).contiguous().to(torch.float8_e4m3fn)
k_transposed = k.transpose(1, 2).contiguous().to(torch.float8_e4m3fn)
v_transposed = v.transpose(1, 2).contiguous().permute(0, 1, 3, 2).to(torch.float8_e4m3fn)
scale = 1 / math.sqrt(headdim)
f = time_fwd(
attention_triton, q_transposed, k_transposed, v_transposed,
causal, scale, repeats=5, verbose=False, desc='Triton'
)
f = time_fwd(
attention_triton, q_transposed, k_transposed, v_transposed,
causal, scale, repeats=repeats, verbose=False, desc='Triton'
)
time_f[config, "Triton"] = f
res = attention_triton(
q_transposed, k_transposed, v_transposed.permute(0, 1, 3, 2),
causal, scale
).half().transpose(1, 2)
torch.testing.assert_close(res, res_baseline, atol=0.5, rtol=0.5)
# out = torch.empty_like(q)
q, k, v = q.to(dtype), k.to(dtype), v.to(dtype)
f = time_fwd(flash_attn_func, q, k, v, causal=causal, repeats=repeats, verbose=False)
# res = flash_attn_func(q, k, v, causal=causal)
# torch.testing.assert_close(res.half(), res_baseline, atol=0.05, rtol=0.05)
time_f[config, "Flash3"] = f
if cudnn is not None:
qkv_fp8 = qkv.to(dtype)
time.sleep(1) # Sleep to avoid residual power throttling from the previous benchmark
f = time_fwd(
cudnn_spda_setup(
qkv_fp8, seqlen, seqlen,
causal=causal
),
repeats=repeats, verbose=False
)
time_f[config, "cuDNN"] = f
# res, amax_o = cudnn_spda_setup(
# qkv_fp8, seqlen, seqlen,
# causal=causal
# )()
# res = res.half()
# TODO: CUDNN has numerics issues when
# num_heads=16, dim=128, seq_len=1024, batch_size=2
# or larger sizes.
# res_cpu = res.cpu().reshape(-1)
# res_baseline_cpu = res_baseline.cpu().reshape(-1)
# print(amax_o)
# print(res)
# print(res_baseline)
# for i in range(len(res_cpu)):
# item = res_cpu[i]
# item_baseline = res_baseline_cpu[i]
# if abs(item - item_baseline) > 0.5:
# print(i)
# print(item)
# print(item_baseline)
# torch.testing.assert_close(res, res_baseline, atol=0.05, rtol=0.05)
print(f"### causal={causal}, headdim={headdim}, batch_size={batch_size}, seqlen={seqlen} ###")
for method in methods:
speed_f[config, method] = efficiency(
flops(batch_size, seqlen, headdim, nheads, causal, mode="fwd"),
time_f[config, method]
)
#print (time_f[config,method])
print(
f"{method} fwd: {speed_f[config, method]:.2f} TFLOPs/s, {time_f[config, method] * 1e3} ms, "
)
# with open('flash3_attn_time.plk', 'wb') as fp:
# pickle.dump((time_f, time_b, time_f_b), fp, protocol=pickle.HIGHEST_PROTOCOL)