Spaces:
Sleeping
Sleeping
File size: 16,835 Bytes
306b4ac |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 |
# Copyright (c) 2023, Tri Dao, Albert Gu.
import torch
import torch.nn.functional as F
from torch.cuda.amp import custom_bwd, custom_fwd
from einops import rearrange, repeat
try:
from causal_conv1d import causal_conv1d_fn
import causal_conv1d_cuda
except ImportError:
causal_conv1d_fn = None
causal_conv1d_cuda = None
import selective_scan_cuda
class SelectiveScanFn(torch.autograd.Function):
@staticmethod
def forward(ctx, u, delta, A, B, C, D=None, z=None, delta_bias=None, delta_softplus=False,
return_last_state=False):
if u.stride(-1) != 1:
u = u.contiguous()
if delta.stride(-1) != 1:
delta = delta.contiguous()
if D is not None:
D = D.contiguous()
if B.stride(-1) != 1:
B = B.contiguous()
if C.stride(-1) != 1:
C = C.contiguous()
if z is not None and z.stride(-1) != 1:
z = z.contiguous()
if B.dim() == 3:
B = rearrange(B, "b dstate l -> b 1 dstate l")
ctx.squeeze_B = True
if C.dim() == 3:
C = rearrange(C, "b dstate l -> b 1 dstate l")
ctx.squeeze_C = True
out, x, *rest = selective_scan_cuda.fwd(u, delta, A, B, C, D, z, delta_bias, delta_softplus)
ctx.delta_softplus = delta_softplus
ctx.has_z = z is not None
last_state = x[:, :, -1, 1::2] # (batch, dim, dstate)
if not ctx.has_z:
ctx.save_for_backward(u, delta, A, B, C, D, delta_bias, x)
return out if not return_last_state else (out, last_state)
else:
ctx.save_for_backward(u, delta, A, B, C, D, z, delta_bias, x, out)
out_z = rest[0]
return out_z if not return_last_state else (out_z, last_state)
@staticmethod
def backward(ctx, dout, *args):
if not ctx.has_z:
u, delta, A, B, C, D, delta_bias, x = ctx.saved_tensors
z = None
out = None
else:
u, delta, A, B, C, D, z, delta_bias, x, out = ctx.saved_tensors
if dout.stride(-1) != 1:
dout = dout.contiguous()
# The kernel supports passing in a pre-allocated dz (e.g., in case we want to fuse the
# backward of selective_scan_cuda with the backward of chunk).
# Here we just pass in None and dz will be allocated in the C++ code.
du, ddelta, dA, dB, dC, dD, ddelta_bias, *rest = selective_scan_cuda.bwd(
u, delta, A, B, C, D, z, delta_bias, dout, x, out, None, ctx.delta_softplus,
False # option to recompute out_z, not used here
)
dz = rest[0] if ctx.has_z else None
dB = dB.squeeze(1) if getattr(ctx, "squeeze_B", False) else dB
dC = dC.squeeze(1) if getattr(ctx, "squeeze_C", False) else dC
return (du, ddelta, dA, dB, dC,
dD if D is not None else None,
dz,
ddelta_bias if delta_bias is not None else None,
None,
None)
def selective_scan_fn(u, delta, A, B, C, D=None, z=None, delta_bias=None, delta_softplus=False,
return_last_state=False):
"""if return_last_state is True, returns (out, last_state)
last_state has shape (batch, dim, dstate). Note that the gradient of the last state is
not considered in the backward pass.
"""
return SelectiveScanFn.apply(u, delta, A, B, C, D, z, delta_bias, delta_softplus, return_last_state)
def selective_scan_ref(u, delta, A, B, C, D=None, z=None, delta_bias=None, delta_softplus=False,
return_last_state=False):
"""
u: r(B D L)
delta: r(B D L)
A: c(D N) or r(D N)
B: c(D N) or r(B N L) or r(B N 2L) or r(B G N L) or (B G N L)
C: c(D N) or r(B N L) or r(B N 2L) or r(B G N L) or (B G N L)
D: r(D)
z: r(B D L)
delta_bias: r(D), fp32
out: r(B D L)
last_state (optional): r(B D dstate) or c(B D dstate)
"""
dtype_in = u.dtype
u = u.float()
delta = delta.float()
if delta_bias is not None:
delta = delta + delta_bias[..., None].float()
if delta_softplus:
delta = F.softplus(delta)
batch, dim, dstate = u.shape[0], A.shape[0], A.shape[1]
is_variable_B = B.dim() >= 3
is_variable_C = C.dim() >= 3
if A.is_complex():
if is_variable_B:
B = torch.view_as_complex(rearrange(B.float(), "... (L two) -> ... L two", two=2))
if is_variable_C:
C = torch.view_as_complex(rearrange(C.float(), "... (L two) -> ... L two", two=2))
else:
B = B.float()
C = C.float()
x = A.new_zeros((batch, dim, dstate))
ys = []
deltaA = torch.exp(torch.einsum('bdl,dn->bdln', delta, A))
if not is_variable_B:
deltaB_u = torch.einsum('bdl,dn,bdl->bdln', delta, B, u)
else:
if B.dim() == 3:
deltaB_u = torch.einsum('bdl,bnl,bdl->bdln', delta, B, u)
else:
B = repeat(B, "B G N L -> B (G H) N L", H=dim // B.shape[1])
deltaB_u = torch.einsum('bdl,bdnl,bdl->bdln', delta, B, u)
if is_variable_C and C.dim() == 4:
C = repeat(C, "B G N L -> B (G H) N L", H=dim // C.shape[1])
last_state = None
for i in range(u.shape[2]):
x = deltaA[:, :, i] * x + deltaB_u[:, :, i]
if not is_variable_C:
y = torch.einsum('bdn,dn->bd', x, C)
else:
if C.dim() == 3:
y = torch.einsum('bdn,bn->bd', x, C[:, :, i])
else:
y = torch.einsum('bdn,bdn->bd', x, C[:, :, :, i])
if i == u.shape[2] - 1:
last_state = x
if y.is_complex():
y = y.real * 2
ys.append(y)
y = torch.stack(ys, dim=2) # (batch dim L)
out = y if D is None else y + u * rearrange(D, "d -> d 1")
if z is not None:
out = out * F.silu(z)
out = out.to(dtype=dtype_in)
return out if not return_last_state else (out, last_state)
class MambaInnerFn(torch.autograd.Function):
@staticmethod
@custom_fwd
def forward(ctx, xz, conv1d_weight, conv1d_bias, x_proj_weight, delta_proj_weight,
out_proj_weight, out_proj_bias,
A, B=None, C=None, D=None, delta_bias=None, B_proj_bias=None,
C_proj_bias=None, delta_softplus=True, checkpoint_lvl=1):
"""
xz: (batch, dim, seqlen)
"""
assert causal_conv1d_cuda is not None, "causal_conv1d_cuda is not available. Please install causal-conv1d."
assert checkpoint_lvl in [0, 1]
L = xz.shape[-1]
delta_rank = delta_proj_weight.shape[1]
d_state = A.shape[-1] * (1 if not A.is_complex() else 2)
if torch.is_autocast_enabled():
x_proj_weight = x_proj_weight.to(dtype=torch.get_autocast_gpu_dtype())
delta_proj_weight = delta_proj_weight.to(dtype=torch.get_autocast_gpu_dtype())
out_proj_weight = out_proj_weight.to(dtype=torch.get_autocast_gpu_dtype())
out_proj_bias = (out_proj_bias.to(dtype=torch.get_autocast_gpu_dtype())
if out_proj_bias is not None else None)
if xz.stride(-1) != 1:
xz = xz.contiguous()
conv1d_weight = rearrange(conv1d_weight, "d 1 w -> d w")
x, z = xz.chunk(2, dim=1)
conv1d_bias = conv1d_bias.contiguous() if conv1d_bias is not None else None
conv1d_out = causal_conv1d_cuda.causal_conv1d_fwd(
x, conv1d_weight, conv1d_bias, None, None, None, True
)
# We're being very careful here about the layout, to avoid extra transposes.
# We want delta to have d as the slowest moving dimension
# and L as the fastest moving dimension, since those are what the ssm_scan kernel expects.
x_dbl = F.linear(rearrange(conv1d_out, 'b d l -> (b l) d'), x_proj_weight) # (bl d)
delta = rearrange(delta_proj_weight @ x_dbl[:, :delta_rank].t(), "d (b l) -> b d l", l = L)
ctx.is_variable_B = B is None
ctx.is_variable_C = C is None
ctx.B_proj_bias_is_None = B_proj_bias is None
ctx.C_proj_bias_is_None = C_proj_bias is None
if B is None: # variable B
B = x_dbl[:, delta_rank:delta_rank + d_state] # (bl dstate)
if B_proj_bias is not None:
B = B + B_proj_bias.to(dtype=B.dtype)
if not A.is_complex():
# B = rearrange(B, "(b l) dstate -> b dstate l", l=L).contiguous()
B = rearrange(B, "(b l) dstate -> b 1 dstate l", l=L).contiguous()
else:
B = rearrange(B, "(b l) (dstate two) -> b 1 dstate (l two)", l=L, two=2).contiguous()
else:
if B.stride(-1) != 1:
B = B.contiguous()
if C is None: # variable C
C = x_dbl[:, -d_state:] # (bl dstate)
if C_proj_bias is not None:
C = C + C_proj_bias.to(dtype=C.dtype)
if not A.is_complex():
# C = rearrange(C, "(b l) dstate -> b dstate l", l=L).contiguous()
C = rearrange(C, "(b l) dstate -> b 1 dstate l", l=L).contiguous()
else:
C = rearrange(C, "(b l) (dstate two) -> b 1 dstate (l two)", l=L, two=2).contiguous()
else:
if C.stride(-1) != 1:
C = C.contiguous()
if D is not None:
D = D.contiguous()
out, scan_intermediates, out_z = selective_scan_cuda.fwd(
conv1d_out, delta, A, B, C, D, z, delta_bias, delta_softplus
)
ctx.delta_softplus = delta_softplus
ctx.out_proj_bias_is_None = out_proj_bias is None
ctx.checkpoint_lvl = checkpoint_lvl
if checkpoint_lvl >= 1: # Will recompute conv1d_out and delta in the backward pass
conv1d_out, delta = None, None
ctx.save_for_backward(xz, conv1d_weight, conv1d_bias, x_dbl, x_proj_weight,
delta_proj_weight, out_proj_weight, conv1d_out, delta,
A, B, C, D, delta_bias, scan_intermediates, out)
return F.linear(rearrange(out_z, "b d l -> b l d"), out_proj_weight, out_proj_bias)
@staticmethod
@custom_bwd
def backward(ctx, dout):
# dout: (batch, seqlen, dim)
assert causal_conv1d_cuda is not None, "causal_conv1d_cuda is not available. Please install causal-conv1d."
(xz, conv1d_weight, conv1d_bias, x_dbl, x_proj_weight, delta_proj_weight, out_proj_weight,
conv1d_out, delta, A, B, C, D, delta_bias, scan_intermediates, out) = ctx.saved_tensors
L = xz.shape[-1]
delta_rank = delta_proj_weight.shape[1]
d_state = A.shape[-1] * (1 if not A.is_complex() else 2)
x, z = xz.chunk(2, dim=1)
if dout.stride(-1) != 1:
dout = dout.contiguous()
if ctx.checkpoint_lvl == 1:
conv1d_out = causal_conv1d_cuda.causal_conv1d_fwd(
x, conv1d_weight, conv1d_bias, None, None, None, True
)
delta = rearrange(delta_proj_weight @ x_dbl[:, :delta_rank].t(),
"d (b l) -> b d l", l = L)
# The kernel supports passing in a pre-allocated dz (e.g., in case we want to fuse the
# backward of selective_scan_cuda with the backward of chunk).
dxz = torch.empty_like(xz) # (batch, dim, seqlen)
dx, dz = dxz.chunk(2, dim=1)
dout = rearrange(dout, "b l e -> e (b l)")
dout_y = rearrange(out_proj_weight.t() @ dout, "d (b l) -> b d l", l=L)
dconv1d_out, ddelta, dA, dB, dC, dD, ddelta_bias, dz, out_z = selective_scan_cuda.bwd(
conv1d_out, delta, A, B, C, D, z, delta_bias, dout_y, scan_intermediates, out, dz,
ctx.delta_softplus,
True # option to recompute out_z
)
dout_proj_weight = torch.einsum("eB,dB->ed", dout, rearrange(out_z, "b d l -> d (b l)"))
dout_proj_bias = dout.sum(dim=(0, 1)) if not ctx.out_proj_bias_is_None else None
dD = dD if D is not None else None
dx_dbl = torch.empty_like(x_dbl)
dB_proj_bias = None
if ctx.is_variable_B:
if not A.is_complex():
dB = rearrange(dB, "b 1 dstate l -> (b l) dstate").contiguous()
else:
dB = rearrange(dB, "b 1 dstate (l two) -> (b l) (dstate two)", two=2).contiguous()
dB_proj_bias = dB.sum(0) if not ctx.B_proj_bias_is_None else None
dx_dbl[:, delta_rank:delta_rank + d_state] = dB # (bl d)
dB = None
dC_proj_bias = None
if ctx.is_variable_C:
if not A.is_complex():
dC = rearrange(dC, "b 1 dstate l -> (b l) dstate").contiguous()
else:
dC = rearrange(dC, "b 1 dstate (l two) -> (b l) (dstate two)", two=2).contiguous()
dC_proj_bias = dC.sum(0) if not ctx.C_proj_bias_is_None else None
dx_dbl[:, -d_state:] = dC # (bl d)
dC = None
ddelta = rearrange(ddelta, "b d l -> d (b l)")
ddelta_proj_weight = torch.einsum("dB,Br->dr", ddelta, x_dbl[:, :delta_rank])
dx_dbl[:, :delta_rank] = torch.einsum("dB,dr->Br", ddelta, delta_proj_weight)
dconv1d_out = rearrange(dconv1d_out, "b d l -> d (b l)")
dx_proj_weight = torch.einsum("Br,Bd->rd", dx_dbl, rearrange(conv1d_out, "b d l -> (b l) d"))
dconv1d_out = torch.addmm(dconv1d_out, x_proj_weight.t(), dx_dbl.t(), out=dconv1d_out)
dconv1d_out = rearrange(dconv1d_out, "d (b l) -> b d l", b=x.shape[0], l=x.shape[-1])
# The kernel supports passing in a pre-allocated dx (e.g., in case we want to fuse the
# backward of conv1d with the backward of chunk).
dx, dconv1d_weight, dconv1d_bias, *_ = causal_conv1d_cuda.causal_conv1d_bwd(
x, conv1d_weight, conv1d_bias, dconv1d_out, None, None, None, dx, False, True
)
dconv1d_bias = dconv1d_bias if conv1d_bias is not None else None
dconv1d_weight = rearrange(dconv1d_weight, "d w -> d 1 w")
return (dxz, dconv1d_weight, dconv1d_bias, dx_proj_weight, ddelta_proj_weight,
dout_proj_weight, dout_proj_bias,
dA, dB, dC, dD,
ddelta_bias if delta_bias is not None else None,
dB_proj_bias, dC_proj_bias, None)
def mamba_inner_fn(
xz, conv1d_weight, conv1d_bias, x_proj_weight, delta_proj_weight,
out_proj_weight, out_proj_bias,
A, B=None, C=None, D=None, delta_bias=None, B_proj_bias=None,
C_proj_bias=None, delta_softplus=True
):
return MambaInnerFn.apply(xz, conv1d_weight, conv1d_bias, x_proj_weight, delta_proj_weight,
out_proj_weight, out_proj_bias,
A, B, C, D, delta_bias, B_proj_bias, C_proj_bias, delta_softplus)
def mamba_inner_ref(
xz, conv1d_weight, conv1d_bias, x_proj_weight, delta_proj_weight,
out_proj_weight, out_proj_bias,
A, B=None, C=None, D=None, delta_bias=None, B_proj_bias=None,
C_proj_bias=None, delta_softplus=True
):
assert causal_conv1d_fn is not None, "causal_conv1d_fn is not available. Please install causal-conv1d."
L = xz.shape[-1]
delta_rank = delta_proj_weight.shape[1]
d_state = A.shape[-1] * (1 if not A.is_complex() else 2)
x, z = xz.chunk(2, dim=1)
x = causal_conv1d_fn(x, rearrange(conv1d_weight, "d 1 w -> d w"), conv1d_bias, activation="silu")
# We're being very careful here about the layout, to avoid extra transposes.
# We want delta to have d as the slowest moving dimension
# and L as the fastest moving dimension, since those are what the ssm_scan kernel expects.
x_dbl = F.linear(rearrange(x, 'b d l -> (b l) d'), x_proj_weight) # (bl d)
delta = delta_proj_weight @ x_dbl[:, :delta_rank].t()
delta = rearrange(delta, "d (b l) -> b d l", l=L)
if B is None: # variable B
B = x_dbl[:, delta_rank:delta_rank + d_state] # (bl d)
if B_proj_bias is not None:
B = B + B_proj_bias.to(dtype=B.dtype)
if not A.is_complex():
B = rearrange(B, "(b l) dstate -> b dstate l", l=L).contiguous()
else:
B = rearrange(B, "(b l) (dstate two) -> b dstate (l two)", l=L, two=2).contiguous()
if C is None: # variable B
C = x_dbl[:, -d_state:] # (bl d)
if C_proj_bias is not None:
C = C + C_proj_bias.to(dtype=C.dtype)
if not A.is_complex():
C = rearrange(C, "(b l) dstate -> b dstate l", l=L).contiguous()
else:
C = rearrange(C, "(b l) (dstate two) -> b dstate (l two)", l=L, two=2).contiguous()
y = selective_scan_fn(x, delta, A, B, C, D, z=z, delta_bias=delta_bias, delta_softplus=True)
return F.linear(rearrange(y, "b d l -> b l d"), out_proj_weight, out_proj_bias)
|