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selective_scan_cuda error #265

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selective_scan_cuda error #265

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18 changes: 15 additions & 3 deletions mamba_ssm/ops/selective_scan_interface.py
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Original file line number Diff line number Diff line change
Expand Up @@ -13,7 +13,10 @@
causal_conv1d_fn = None
causal_conv1d_cuda = None

import selective_scan_cuda
try:
import selective_scan_cuda
except ImportError:
selective_scan_cuda = None


class SelectiveScanFn(torch.autograd.Function):
Expand Down Expand Up @@ -85,7 +88,10 @@ def selective_scan_fn(u, delta, A, B, C, D=None, z=None, delta_bias=None, delta_
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)
if selective_scan_cuda is None:
return selective_scan_ref(u, delta, A, B, C, D, z, delta_bias, delta_softplus, return_last_state)
else:
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,
Expand Down Expand Up @@ -314,11 +320,17 @@ def mamba_inner_fn(
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,
if causal_conv1d_cuda is None:
return mamba_inner_ref(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)
else:
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,
Expand Down