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Mojo function

selective_scan_fwd_cpu_minimal

def selective_scan_fwd_cpu_minimal[kernel_dtype: DType, DSTATE: Int](batch: Int, dim: Int, seqlen: Int, group_size: Int, delta_softplus: Int8, output: TileTensor[kernel_dtype, Storage=output.Storage, address_space=output.address_space, linear_idx_type=output.linear_idx_type], x: TileTensor[kernel_dtype, Storage=x.Storage, address_space=x.address_space, linear_idx_type=x.linear_idx_type], u: TileTensor[kernel_dtype, Storage=u.Storage, address_space=u.address_space, linear_idx_type=u.linear_idx_type], delta: TileTensor[kernel_dtype, Storage=delta.Storage, address_space=delta.address_space, linear_idx_type=delta.linear_idx_type], A: TileTensor[kernel_dtype, Storage=A.Storage, address_space=A.address_space, linear_idx_type=A.linear_idx_type], B: TileTensor[kernel_dtype, Storage=B.Storage, address_space=B.address_space, linear_idx_type=B.linear_idx_type], C: TileTensor[kernel_dtype, Storage=C.Storage, address_space=C.address_space, linear_idx_type=C.linear_idx_type], output_strides: IndexList[Int(3)], x_strides: IndexList[Int(4)], u_strides: IndexList[Int(3)], delta_strides: IndexList[Int(3)], A_strides: IndexList[Int(2)], B_strides: IndexList[Int(4)], C_strides: IndexList[Int(4)], ctx: Optional[DeviceContext] = None)

Minimal CPU kernel for selective scan forward - no D, z, or delta_bias.

Parameters:

kernel_dtype (DType): Element type of the input and output tensors.
DSTATE (Int): Number of SSM state elements per (batch, dim) pair.

Args:

batch (Int): Number of sequences processed in parallel.
dim (Int): Hidden dimension, equal to the number of channels per sequence position.
seqlen (Int): Number of timesteps in each sequence.
group_size (Int): Number of dims per group; dims in the same group share B and C inputs.
delta_softplus (Int8): Nonzero applies softplus to delta before the scan recurrence.
output (TileTensor[kernel_dtype, Storage=output.Storage, address_space=output.address_space, linear_idx_type=output.linear_idx_type]): Output tensor of shape (batch, dim, seqlen), written.
x (TileTensor[kernel_dtype, Storage=x.Storage, address_space=x.address_space, linear_idx_type=x.linear_idx_type]): Checkpoint tensor of shape (batch, dim, n_chunks, 2*DSTATE) storing per-chunk cumulative A and B values, written.
u (TileTensor[kernel_dtype, Storage=u.Storage, address_space=u.address_space, linear_idx_type=u.linear_idx_type]): Selective scan input tensor of shape (batch, dim, seqlen), read.
delta (TileTensor[kernel_dtype, Storage=delta.Storage, address_space=delta.address_space, linear_idx_type=delta.linear_idx_type]): Time-step tensor of shape (batch, dim, seqlen), read.
A (TileTensor[kernel_dtype, Storage=A.Storage, address_space=A.address_space, linear_idx_type=A.linear_idx_type]): SSM recurrence matrix of shape (dim, DSTATE), read.
B (TileTensor[kernel_dtype, Storage=B.Storage, address_space=B.address_space, linear_idx_type=B.linear_idx_type]): SSM input projection of shape (batch, n_groups, DSTATE, seqlen), read.
C (TileTensor[kernel_dtype, Storage=C.Storage, address_space=C.address_space, linear_idx_type=C.linear_idx_type]): SSM output projection of shape (batch, n_groups, DSTATE, seqlen), read.
output_strides (IndexList[Int(3)]): 3D strides (batch, dim, seqlen) for indexing output.
x_strides (IndexList[Int(4)]): 4D strides (batch, dim, n_chunks, 2*DSTATE) for indexing x.
u_strides (IndexList[Int(3)]): 3D strides (batch, dim, seqlen) for indexing u.
delta_strides (IndexList[Int(3)]): 3D strides (batch, dim, seqlen) for indexing delta.
A_strides (IndexList[Int(2)]): 2D strides (dim, DSTATE) for indexing A.
B_strides (IndexList[Int(4)]): 4D strides (batch, n_groups, DSTATE, seqlen) for indexing B.
C_strides (IndexList[Int(4)]): 4D strides (batch, n_groups, DSTATE, seqlen) for indexing C.
ctx (Optional[DeviceContext]): Device context for parallel execution (defaults to None).