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import time
import torch
import warnings
WITH_SELECTIVESCAN_OFLEX = True
WITH_SELECTIVESCAN_CORE = False
WITH_SELECTIVESCAN_MAMBA = True
try:
import selective_scan_cuda_oflex
except ImportError:
WITH_SELECTIVESCAN_OFLEX = False
warnings.warn("Can not import selective_scan_cuda_oflex. This affects speed.")
print("Can not import selective_scan_cuda_oflex. This affects speed.", flush=True)
try:
import selective_scan_cuda_core
except ImportError:
WITH_SELECTIVESCAN_CORE = False
try:
import selective_scan_cuda
except ImportError:
WITH_SELECTIVESCAN_MAMBA = False
def selective_scan_torch(
u: torch.Tensor, # (B, K * C, L)
delta: torch.Tensor, # (B, K * C, L)
A: torch.Tensor, # (K * C, N)
B: torch.Tensor, # (B, K, N, L)
C: torch.Tensor, # (B, K, N, L)
D: torch.Tensor = None, # (K * C)
delta_bias: torch.Tensor = None, # (K * C)
delta_softplus=True,
oflex=True,
*args,
**kwargs
):
dtype_in = u.dtype
Batch, K, N, L = B.shape
KCdim = u.shape[1]
Cdim = int(KCdim / K)
assert u.shape == (Batch, KCdim, L)
assert delta.shape == (Batch, KCdim, L)
assert A.shape == (KCdim, N)
assert C.shape == B.shape
if delta_bias is not None:
delta = delta + delta_bias[..., None]
if delta_softplus:
delta = torch.nn.functional.softplus(delta)
u, delta, A, B, C = u.float(), delta.float(), A.float(), B.float(), C.float()
B = B.view(Batch, K, 1, N, L).repeat(1, 1, Cdim, 1, 1).view(Batch, KCdim, N, L)
C = C.view(Batch, K, 1, N, L).repeat(1, 1, Cdim, 1, 1).view(Batch, KCdim, N, L)
deltaA = torch.exp(torch.einsum('bdl,dn->bdln', delta, A))
deltaB_u = torch.einsum('bdl,bdnl,bdl->bdln', delta, B, u)
if True:
x = A.new_zeros((Batch, KCdim, N))
ys = []
for i in range(L):
x = deltaA[:, :, i, :] * x + deltaB_u[:, :, i, :]
y = torch.einsum('bdn,bdn->bd', x, C[:, :, :, i])
ys.append(y)
y = torch.stack(ys, dim=2) # (B, C, L)
out = y if D is None else y + u * D.unsqueeze(-1)
return out if oflex else out.to(dtype=dtype_in)
class SelectiveScanCuda(torch.autograd.Function):
@staticmethod
@torch.cuda.amp.custom_fwd
def forward(ctx, u, delta, A, B, C, D=None, delta_bias=None, delta_softplus=False, oflex=True, backend=None):
ctx.delta_softplus = delta_softplus
backend = "oflex" if WITH_SELECTIVESCAN_OFLEX and (backend is None) else backend
backend = "core" if WITH_SELECTIVESCAN_CORE and (backend is None) else backend
backend = "mamba" if WITH_SELECTIVESCAN_MAMBA and (backend is None) else backend
ctx.backend = backend
if backend == "oflex":
out, x, *rest = selective_scan_cuda_oflex.fwd(u, delta, A, B, C, D, delta_bias, delta_softplus, 1, oflex)
elif backend == "core":
out, x, *rest = selective_scan_cuda_core.fwd(u, delta, A, B, C, D, delta_bias, delta_softplus, 1)
elif backend == "mamba":
out, x, *rest = selective_scan_cuda.fwd(u, delta, A, B, C, D, None, delta_bias, delta_softplus)
ctx.save_for_backward(u, delta, A, B, C, D, delta_bias, x)
return out
@staticmethod
@torch.cuda.amp.custom_bwd
def backward(ctx, dout, *args):
u, delta, A, B, C, D, delta_bias, x = ctx.saved_tensors
backend = ctx.backend
if dout.stride(-1) != 1:
dout = dout.contiguous()
if backend == "oflex":
du, ddelta, dA, dB, dC, dD, ddelta_bias, *rest = selective_scan_cuda_oflex.bwd(
u, delta, A, B, C, D, delta_bias, dout, x, ctx.delta_softplus, 1
)
elif backend == "core":
du, ddelta, dA, dB, dC, dD, ddelta_bias, *rest = selective_scan_cuda_core.bwd(
u, delta, A, B, C, D, delta_bias, dout, x, ctx.delta_softplus, 1
)
elif backend == "mamba":
du, ddelta, dA, dB, dC, dD, ddelta_bias, *rest = selective_scan_cuda.bwd(
u, delta, A, B, C, D, None, delta_bias, dout, x, None, None, ctx.delta_softplus,
False
)
return du, ddelta, dA, dB, dC, dD, ddelta_bias, None, None, None
def selective_scan_fn(
u: torch.Tensor, # (B, K * C, L)
delta: torch.Tensor, # (B, K * C, L)
A: torch.Tensor, # (K * C, N)
B: torch.Tensor, # (B, K, N, L)
C: torch.Tensor, # (B, K, N, L)
D: torch.Tensor = None, # (K * C)
delta_bias: torch.Tensor = None, # (K * C)
delta_softplus=True,
oflex=True,
backend=None,
):
WITH_CUDA = (WITH_SELECTIVESCAN_OFLEX or WITH_SELECTIVESCAN_CORE or WITH_SELECTIVESCAN_MAMBA)
fn = selective_scan_torch if backend == "torch" or (not WITH_CUDA) else SelectiveScanCuda.apply
return fn(u, delta, A, B, C, D, delta_bias, delta_softplus, oflex, backend)
# fvcore flops =======================================
def print_jit_input_names(inputs):
print("input params: ", end=" ", flush=True)
try:
for i in range(10):
print(inputs[i].debugName(), end=" ", flush=True)
except Exception as e:
pass
print("", flush=True)
def flops_selective_scan_fn(B=1, L=256, D=768, N=16, with_D=True, with_Z=False, with_complex=False):
"""
u: r(B D L)
delta: r(B D L)
A: r(D N)
B: r(B N L)
C: r(B N L)
D: r(D)
z: r(B D L)
delta_bias: r(D), fp32
ignores:
[.float(), +, .softplus, .shape, new_zeros, repeat, stack, to(dtype), silu]
"""
assert not with_complex
# https://github.com/state-spaces/mamba/issues/110
flops = 9 * B * L * D * N
if with_D:
flops += B * D * L
if with_Z:
flops += B * D * L
return flops
# this is only for selective_scan_ref...
def flops_selective_scan_ref(B=1, L=256, D=768, N=16, with_D=True, with_Z=False, with_Group=True, with_complex=False):
"""
u: r(B D L)
delta: r(B D L)
A: r(D N)
B: r(B N L)
C: r(B N L)
D: r(D)
z: r(B D L)
delta_bias: r(D), fp32
ignores:
[.float(), +, .softplus, .shape, new_zeros, repeat, stack, to(dtype), silu]
"""
import numpy as np
# fvcore.nn.jit_handles
def get_flops_einsum(input_shapes, equation):
np_arrs = [np.zeros(s) for s in input_shapes]
optim = np.einsum_path(equation, *np_arrs, optimize="optimal")[1]
for line in optim.split("\n"):
if "optimized flop" in line.lower():
# divided by 2 because we count MAC (multiply-add counted as one flop)
flop = float(np.floor(float(line.split(":")[-1]) / 2))
return flop
assert not with_complex
flops = 0 # below code flops = 0
flops += get_flops_einsum([[B, D, L], [D, N]], "bdl,dn->bdln")
if with_Group:
flops += get_flops_einsum([[B, D, L], [B, N, L], [B, D, L]], "bdl,bnl,bdl->bdln")
else:
flops += get_flops_einsum([[B, D, L], [B, D, N, L], [B, D, L]], "bdl,bdnl,bdl->bdln")
in_for_flops = B * D * N
if with_Group:
in_for_flops += get_flops_einsum([[B, D, N], [B, D, N]], "bdn,bdn->bd")
else:
in_for_flops += get_flops_einsum([[B, D, N], [B, N]], "bdn,bn->bd")
flops += L * in_for_flops
if with_D:
flops += B * D * L
if with_Z:
flops += B * D * L
return flops
def selective_scan_flop_jit(inputs, outputs, backend="prefixsum", verbose=True):
if verbose:
print_jit_input_names(inputs)
flops_fn = flops_selective_scan_ref if backend == "naive" else flops_selective_scan_fn
B, D, L = inputs[0].type().sizes()
N = inputs[2].type().sizes()[1]
flops = flops_fn(B=B, L=L, D=D, N=N, with_D=True, with_Z=False)
return flops
if __name__ == "__main__":
def params(B, K, C, N, L, device = torch.device("cuda"), itype = torch.float):
As = (-0.5 * torch.rand(K * C, N, device=device, dtype=torch.float32)).requires_grad_()
Bs = torch.randn((B, K, N, L), device=device, dtype=itype).requires_grad_()
Cs = torch.randn((B, K, N, L), device=device, dtype=itype).requires_grad_()
Ds = torch.randn((K * C), device=device, dtype=torch.float32).requires_grad_()
u = torch.randn((B, K * C, L), device=device, dtype=itype).requires_grad_()
delta = (0.5 * torch.rand((B, K * C, L), device=device, dtype=itype)).requires_grad_()
delta_bias = (0.5 * torch.rand((K * C), device=device, dtype=torch.float32)).requires_grad_()
return u, delta, As, Bs, Cs, Ds, delta_bias
def bench(func, xs, Warmup=30, NTimes=20):
import time
torch.cuda.synchronize()
for r in range(Warmup):
for x in xs:
func(x)
torch.cuda.synchronize()
tim0 = time.time()
for r in range(NTimes):
for x in xs:
func(x)
torch.cuda.synchronize()
return (time.time() - tim0) / NTimes
def check():
u, delta, As, Bs, Cs, Ds, delta_bias = params(1, 4, 16, 8, 512, itype=torch.float16)
u1, delta1, As1, Bs1, Cs1, Ds1, delta_bias1 = [x.clone().detach().requires_grad_() for x in [u, delta, As, Bs, Cs, Ds, delta_bias]]
# out_ref = selective_scan_fn(u, delta, As, Bs, Cs, Ds, delta_bias, True, backend="torch")
out = selective_scan_fn(u1, delta1, As1, Bs1, Cs1, Ds1, delta_bias1, True, backend="oflex")
out_ref = selective_scan_fn(u, delta, As, Bs, Cs, Ds, delta_bias, True, backend="mamba")
print((out_ref - out).abs().max())
out.sum().backward()
out_ref.sum().backward()
for x, y in zip([u, As, Bs, Cs, Ds, delta, delta_bias], [u1, As1, Bs1, Cs1, Ds1, delta1, delta_bias1]):
print((x.grad - y.grad).abs().max())
u, delta, As, Bs, Cs, Ds, delta_bias = params(128, 4, 96, 8, 56 * 56)
print(bench(lambda x: selective_scan_fn(x[0], x[1], x[2], x[3], x[4], x[5], x[6], True, backend="oflex"), [(u, delta, As, Bs, Cs, Ds, delta_bias),]))
print(bench(lambda x: selective_scan_fn(x[0], x[1], x[2], x[3], x[4], x[5], x[6], True, backend="mamba"), [(u, delta, As, Bs, Cs, Ds, delta_bias),]))
print(bench(lambda x: selective_scan_fn(x[0], x[1], x[2], x[3], x[4], x[5], x[6], True, backend="torch"), [(u, delta, As, Bs, Cs, Ds, delta_bias),]))
check()
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