File size: 13,467 Bytes
fca4fc0 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 |
import math
import random
import pytest
import torch
import torch.nn.functional as F
from einops import rearrange
import triton
from flash_attn.layers.rotary import apply_rotary_emb, apply_rotary_emb_torch
from flash_attn.layers.rotary import apply_rotary_emb_qkv_, apply_rotary_emb_kv_
from flash_attn.bert_padding import pad_input, unpad_input
is_sm8x = torch.cuda.get_device_capability("cuda") >= (8, 0)
def generate_cos_sin(seqlen, rotary_dim, device, dtype):
assert rotary_dim % 2 == 0
angle = torch.rand(seqlen * 2, rotary_dim // 2, device=device) * 2 * math.pi
cos = torch.cos(angle).to(dtype=dtype)
sin = torch.sin(angle).to(dtype=dtype)
return cos, sin
def generate_seqlen_offsets(seqlen_offsets_type, batch_size, seqlen, device):
if seqlen_offsets_type == 0:
return 0
elif seqlen_offsets_type is int:
return torch.randint(0, seqlen + 1, (1,)).item()
elif seqlen_offsets_type is torch.Tensor:
return torch.randint(0, seqlen + 1, (batch_size,), dtype=torch.int32, device=device)
def index_cos_sin(cos, sin, seqlen_offsets, seqlen):
if isinstance(seqlen_offsets, torch.Tensor):
batch_size = seqlen_offsets.shape[0]
arange = rearrange(torch.arange(seqlen, device=cos.device), "s -> 1 s")
idx = rearrange(seqlen_offsets, "b -> b 1") + arange
cos_pt = rearrange(cos[idx.flatten()], "(b s) d -> b s d", b=batch_size)
sin_pt = rearrange(sin[idx.flatten()], "(b s) d -> b s d", b=batch_size)
else:
cos_pt = cos[seqlen_offsets : seqlen_offsets + seqlen]
sin_pt = sin[seqlen_offsets : seqlen_offsets + seqlen]
return cos_pt, sin_pt
@pytest.mark.parametrize(
"dtype", ([torch.float16] if not is_sm8x else [torch.float16, torch.bfloat16])
)
# @pytest.mark.parametrize('dtype', ([torch.bfloat16]))
@pytest.mark.parametrize("seqlen_offsets_type", [0, int, torch.Tensor])
# @pytest.mark.parametrize("seqlen_offsets_type", [0])
@pytest.mark.parametrize("rotary_fraction", [1.0, 0.5])
# @pytest.mark.parametrize('rotary_fraction', [1.0])
@pytest.mark.parametrize("interleaved", [False, True])
# @pytest.mark.parametrize('interleaved', [True])
@pytest.mark.parametrize("inplace", [False, True])
# @pytest.mark.parametrize('inplace', [False])
def test_rotary_emb_func(inplace, interleaved, rotary_fraction, seqlen_offsets_type, dtype):
rtol = 1e-3
batch_size = 32
nheads = 4
seqlen = 217
headdim = 128
device = "cuda"
rotary_dim = int(rotary_fraction * headdim)
torch.manual_seed(42)
x = torch.randn(
batch_size, seqlen, nheads, headdim, dtype=dtype, device=device, requires_grad=True
)
x_pt = x.detach().clone().requires_grad_()
cos, sin = generate_cos_sin(seqlen, rotary_dim, device, dtype)
seqlen_offsets = generate_seqlen_offsets(seqlen_offsets_type, batch_size, seqlen, device)
out = apply_rotary_emb(
x, cos, sin, seqlen_offsets=seqlen_offsets, interleaved=interleaved, inplace=inplace
)
cos_pt, sin_pt = index_cos_sin(cos, sin, seqlen_offsets, seqlen)
out_pt = apply_rotary_emb_torch(
x_pt.float(), cos_pt.float(), sin_pt.float(), interleaved=interleaved
).to(dtype=dtype)
print(f"Output max diff: {(out - out_pt).abs().max().item()}")
g = torch.randn_like(out)
g_pt = g.clone() # If inplace=True, we might modify the gradient inplace
out.backward(g)
out_pt.backward(g_pt)
print(f"Grad max diff: {(x.grad - x_pt.grad).abs().max().item()}")
if not inplace:
assert torch.equal(x, x_pt)
# Numerical error if we just do any arithmetic
atol = ((out_pt + 0.3 - 0.3) - out_pt).abs().max().item()
assert torch.allclose(out, out_pt, rtol=rtol, atol=2 * atol)
atol = ((x_pt.grad + 0.3 - 0.3) - x_pt.grad).abs().max().item()
assert torch.allclose(x.grad, x_pt.grad, rtol=rtol, atol=2 * atol)
@pytest.mark.parametrize(
"dtype", ([torch.float16] if not is_sm8x else [torch.float16, torch.bfloat16])
)
# @pytest.mark.parametrize('dtype', ([torch.float16]))
@pytest.mark.parametrize("compiled", [False, True])
# @pytest.mark.parametrize("compiled", [True])
@pytest.mark.parametrize("gqa", [False, True])
# @pytest.mark.parametrize("gqa", [False])
@pytest.mark.parametrize("seqlen_offsets_type", [0, int, torch.Tensor])
# @pytest.mark.parametrize("seqlen_offsets_type", [0])
@pytest.mark.parametrize("rotary_fraction", [1.0, 0.5])
# @pytest.mark.parametrize('rotary_fraction', [1.0])
@pytest.mark.parametrize("interleaved", [False, True])
# @pytest.mark.parametrize('interleaved', [False])
def test_rotary_emb_qkv(interleaved, rotary_fraction, seqlen_offsets_type, gqa, compiled, dtype):
if compiled: # Don't fall back to eager just bc of recompilation
torch._dynamo.config.recompile_limit = 2 ** 31
rtol = 1e-3
batch_size = 32
nheads = 4
seqlen = 512
headdim = 128
device = "cuda"
rotary_dim = int(rotary_fraction * headdim)
torch.manual_seed(42)
if not gqa:
qkv = torch.randn(
batch_size, seqlen, 3, nheads, headdim, dtype=dtype, device=device, requires_grad=True
)
else:
nheads_k = nheads // 2
qkv = torch.randn(
batch_size, seqlen, nheads + nheads_k * 2, headdim, dtype=dtype, device=device, requires_grad=True
)
qkv_pt = qkv.detach().clone().requires_grad_()
cos, sin = generate_cos_sin(seqlen, rotary_dim, device, dtype)
seqlen_offsets = generate_seqlen_offsets(seqlen_offsets_type, batch_size, seqlen, device)
fn = apply_rotary_emb_qkv_ if not compiled else torch.compile(apply_rotary_emb_qkv_)
out = fn(
qkv, cos, sin, seqlen_offsets=seqlen_offsets, interleaved=interleaved,
num_heads_q=None if not gqa else nheads
)
cos_pt, sin_pt = index_cos_sin(cos, sin, seqlen_offsets, seqlen)
if not gqa:
q_pt, k_pt, v_pt = qkv_pt.unbind(2)
else:
q_pt, k_pt, v_pt = qkv_pt.split([nheads, nheads_k, nheads_k], dim=2)
q_pt = apply_rotary_emb_torch(
q_pt.float(), cos_pt.float(), sin_pt.float(), interleaved=interleaved
).to(dtype=dtype)
k_pt = apply_rotary_emb_torch(
k_pt.float(), cos_pt.float(), sin_pt.float(), interleaved=interleaved
).to(dtype=dtype)
if not gqa:
out_pt = torch.stack([q_pt, k_pt, v_pt], dim=2)
else:
out_pt = torch.cat([q_pt, k_pt, v_pt], dim=2)
print(f"Output max diff: {(out - out_pt).abs().max().item()}")
g = torch.randn_like(out)
g_pt = g.clone() # Since inplace=True, we modify the gradient inplace
out.backward(g)
out_pt.backward(g_pt)
print(f"Grad max diff: {(qkv.grad - qkv_pt.grad).abs().max().item()}")
# Numerical error if we just do any arithmetic
atol = ((out_pt + 0.3 - 0.3) - out_pt).abs().max().item()
assert torch.allclose(out, out_pt, rtol=rtol, atol=2 * atol)
atol = ((qkv_pt.grad + 0.3 - 0.3) - qkv_pt.grad).abs().max().item()
assert torch.allclose(qkv.grad, qkv_pt.grad, rtol=rtol, atol=2 * atol)
@pytest.mark.parametrize(
"dtype", ([torch.float16] if not is_sm8x else [torch.float16, torch.bfloat16])
)
# @pytest.mark.parametrize('dtype', ([torch.float16]))
@pytest.mark.parametrize("seqlen_offsets_type", [0, int, torch.Tensor])
# @pytest.mark.parametrize("seqlen_offsets_type", [0])
@pytest.mark.parametrize("rotary_fraction", [1.0, 0.5])
# @pytest.mark.parametrize('rotary_fraction', [1.0])
@pytest.mark.parametrize("interleaved", [False, True])
# @pytest.mark.parametrize('interleaved', [False])
def test_rotary_emb_kv(interleaved, rotary_fraction, seqlen_offsets_type, dtype):
rtol = 1e-3
batch_size = 32
nheads = 4
seqlen = 781
headdim = 64
device = "cuda"
rotary_dim = int(rotary_fraction * headdim)
torch.manual_seed(42)
kv = torch.randn(
batch_size, seqlen, 2, nheads, headdim, dtype=dtype, device=device, requires_grad=True
)
kv_pt = kv.detach().clone().requires_grad_()
cos, sin = generate_cos_sin(seqlen, rotary_dim, device, dtype)
seqlen_offsets = generate_seqlen_offsets(seqlen_offsets_type, batch_size, seqlen, device)
out = apply_rotary_emb_kv_(kv, cos, sin, seqlen_offsets=seqlen_offsets, interleaved=interleaved)
cos_pt, sin_pt = index_cos_sin(cos, sin, seqlen_offsets, seqlen)
k_pt = apply_rotary_emb_torch(
kv_pt[:, :, 0].float(), cos_pt.float(), sin_pt.float(), interleaved=interleaved
).to(dtype=dtype)
out_pt = torch.stack([k_pt, kv_pt[:, :, 1]], dim=2)
print(f"Output max diff: {(out - out_pt).abs().max().item()}")
g = torch.randn_like(out)
g_pt = g.clone() # Since inplace=True, we modify the gradient inplace
out.backward(g)
out_pt.backward(g_pt)
print(f"Grad max diff: {(kv.grad - kv_pt.grad).abs().max().item()}")
# Numerical error if we just do any arithmetic
atol = ((out_pt + 0.3 - 0.3) - out_pt).abs().max().item()
assert torch.allclose(out, out_pt, rtol=rtol, atol=2 * atol)
atol = ((kv_pt.grad + 0.3 - 0.3) - kv_pt.grad).abs().max().item()
assert torch.allclose(kv.grad, kv_pt.grad, rtol=rtol, atol=2 * atol)
@pytest.mark.parametrize(
"dtype", ([torch.float16] if not is_sm8x else [torch.float16, torch.bfloat16])
)
# @pytest.mark.parametrize("dtype", ([torch.float16]))
@pytest.mark.parametrize("seqlen_offsets_type", [0, int, torch.Tensor])
# @pytest.mark.parametrize("seqlen_offsets_type", [0])
@pytest.mark.parametrize("rotary_fraction", [1.0, 0.5])
# @pytest.mark.parametrize("rotary_fraction", [1.0])
@pytest.mark.parametrize("interleaved", [False, True])
# @pytest.mark.parametrize("interleaved", [True])
@pytest.mark.parametrize("inplace", [False, True])
# @pytest.mark.parametrize("inplace", [False])
def test_rotary_emb_varlen_func(inplace, interleaved, rotary_fraction, seqlen_offsets_type, dtype):
rtol = 1e-3
batch_size = 32
nheads = 4
seqlen = 217
headdim = 128
device = "cuda"
rotary_dim = int(rotary_fraction * headdim)
torch.manual_seed(42)
x = torch.randn(batch_size, seqlen, nheads, headdim, dtype=dtype, device=device)
x_pt = x.detach().clone().requires_grad_()
lengths = torch.randint(max(1, seqlen - 20), seqlen + 1, (batch_size, 1), device=device)
padding_mask = rearrange(torch.arange(seqlen, device=device), "s -> 1 s") < lengths
x_unpad, indices, cu_seqlens, max_seqlen, _ = unpad_input(x, padding_mask)
x_unpad_clone = x_unpad.clone()
x_unpad = x_unpad.requires_grad_()
cos, sin = generate_cos_sin(seqlen, rotary_dim, device, dtype)
seqlen_offsets = generate_seqlen_offsets(seqlen_offsets_type, batch_size, seqlen, device)
out_unpad = apply_rotary_emb(
x_unpad,
cos,
sin,
seqlen_offsets=seqlen_offsets,
interleaved=interleaved,
inplace=inplace,
cu_seqlens=cu_seqlens,
max_seqlen=max_seqlen,
)
out = pad_input(out_unpad, indices, batch_size, seqlen)
cos_pt, sin_pt = index_cos_sin(cos, sin, seqlen_offsets, seqlen)
out_pt = apply_rotary_emb_torch(
x_pt.float(), cos_pt.float(), sin_pt.float(), interleaved=interleaved
).to(dtype=dtype)
out_pt = out_pt.masked_fill(rearrange(~padding_mask, "b s -> b s 1 1"), 0.0)
print(f"Output max diff: {(out - out_pt).abs().max().item()}")
g = torch.randn_like(out)
g_pt = g.clone() # If inplace=True, we might modify the gradient inplace
out.backward(g)
out_pt.backward(g_pt)
x_grad = pad_input(x_unpad.grad, indices, batch_size, seqlen)
print(f"Grad max diff: {(x_grad - x_pt.grad).abs().max().item()}")
if not inplace:
assert torch.equal(x_unpad, x_unpad_clone)
# Numerical error if we just do any arithmetic
atol = ((out_pt + 0.3 - 0.3) - out_pt).abs().max().item()
assert torch.allclose(out, out_pt, rtol=rtol, atol=2 * atol)
atol = ((x_pt.grad + 0.3 - 0.3) - x_pt.grad).abs().max().item()
assert torch.allclose(x_grad, x_pt.grad, rtol=rtol, atol=2 * atol)
def test_compilation_count():
nheads = 4
headdim = 128
device = "cuda"
dtype = torch.float16
torch.manual_seed(42)
from triton.runtime.jit import JITFunction
from flash_attn.ops.triton.rotary import rotary_kernel
compilation_count = 0
def count_compilations(*args, **kwargs):
nonlocal compilation_count
compilation_count += 1
old_cache_func = JITFunction.cache_hook
try:
if hasattr(rotary_kernel, "cache"):
rotary_kernel.cache.clear()
else: # Triton 3.3 replaces cache with per-device device_caches
device = triton.runtime.driver.active.get_current_device()
# device_caches[device] returns a 4-tuple: (kernel_cache, target, backend, binder)
rotary_kernel.device_caches[device][0].clear()
JITFunction.cache_hook = count_compilations
for seqlen in (128, 256):
for batch_size in (4, 32):
x = torch.randn(batch_size, seqlen, nheads, headdim, dtype=dtype, device=device)
x.requires_grad_()
cos, sin = generate_cos_sin(seqlen, headdim, device, dtype)
out = apply_rotary_emb(x, cos, sin)
out.backward(torch.randn_like(out))
# Only two kernels are expected to be compiled:
# * for the forward pass (conjugate=False)
# * for the backward pass (conjugate=True)
assert compilation_count == 2
finally:
JITFunction.cache_hook = old_cache_func
|