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from typing import List, Optional, Tuple
import torch
from mamba_ssm.ops.triton.ssd_combined import _mamba_chunk_scan_combined_fwd, _mamba_chunk_scan_combined_bwd
@torch.compile(options={"triton.cudagraphs": True}, fullgraph=True)
def _compiled_mamba_chunk_scan_combined_fwd(x, dt, A, B, C, chunk_size, D=None, z=None, dt_bias=None, initial_states=None, seq_idx=None, cu_seqlens=None, dt_softplus=False, dt_limit=None):
return _mamba_chunk_scan_combined_fwd(x, dt, A, B, C, chunk_size, D=D, z=z, dt_bias=dt_bias, initial_states=initial_states, seq_idx=seq_idx, cu_seqlens=cu_seqlens, dt_softplus=dt_softplus, dt_limit=dt_limit)
@torch.compile(options={"triton.cudagraphs": True}, fullgraph=True)
def _compiled_mamba_chunk_scan_combined_bwd(dout, x, dt, A, B, C, out, chunk_size, D=None, z=None, dt_bias=None, initial_states=None, dfinal_states=None, seq_idx=None, dt_softplus=False, dt_limit=None):
return _mamba_chunk_scan_combined_bwd(dout, x, dt, A, B, C, out, chunk_size, D=D, z=z, dt_bias=dt_bias, initial_states=initial_states, dfinal_states=dfinal_states, seq_idx=seq_idx, dt_softplus=dt_softplus, dt_limit=dt_limit)
@torch.library.custom_op(
"mamba_ssm::ssm_chunk_scan_combined_fwd",
mutates_args=(),
device_types="cuda",
)
def ssm_chunk_scan_combined_fwd(
x: torch.Tensor,
dt: torch.Tensor,
A: torch.Tensor,
B: torch.Tensor,
C: torch.Tensor,
chunk_size: int,
D: Optional[torch.Tensor] = None,
z: Optional[torch.Tensor] = None,
dt_bias: Optional[torch.Tensor] = None,
initial_states: Optional[torch.Tensor] = None,
seq_idx: Optional[torch.Tensor] = None,
cu_seqlens: Optional[torch.Tensor] = None,
dt_softplus: bool = False,
dt_limit: Optional[List[float]] = None
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
out, out_x, dt_out, dA_cumsum, states, final_states, *rest = _mamba_chunk_scan_combined_fwd(x, dt, A, B, C, chunk_size, D=D, z=z, dt_bias=dt_bias, initial_states=initial_states, seq_idx=seq_idx, cu_seqlens=cu_seqlens, dt_softplus=dt_softplus, dt_limit=dt_limit)
return out, out_x if out_x is not None else out.new_empty(0), rest[0] if cu_seqlens is not None else out.new_empty(0)
@ssm_chunk_scan_combined_fwd.register_fake
def _ssm_chunk_scan_combined_fwd_fake(
x: torch.Tensor,
dt: torch.Tensor,
A: torch.Tensor,
B: torch.Tensor,
C: torch.Tensor,
chunk_size: int,
D: Optional[torch.Tensor] = None,
z: Optional[torch.Tensor] = None,
dt_bias: Optional[torch.Tensor] = None,
initial_states: Optional[torch.Tensor] = None,
seq_idx: Optional[torch.Tensor] = None,
cu_seqlens: Optional[torch.Tensor] = None,
dt_softplus: bool = False,
dt_limit: Optional[List[float]] = None
):
_, _, n_heads, head_dim = x.shape
return (
torch.empty_like(x),
torch.empty_like(x) if z is not None else None,
x.new_empty((cu_seqlens.size(0)-1, n_heads, head_dim, B.size(0))) if cu_seqlens is not None else None,
)
@torch.library.custom_op(
"mamba_ssm::ssm_chunk_scan_combined_bwd",
mutates_args=(),
device_types="cuda",
)
def ssm_chunk_scan_combined_bwd(
dout: torch.Tensor,
x: torch.Tensor,
dt: torch.Tensor,
A: torch.Tensor,
B: torch.Tensor,
C: torch.Tensor,
out: torch.Tensor,
chunk_size: int,
D: Optional[torch.Tensor] = None,
z: Optional[torch.Tensor] = None,
dt_bias: Optional[torch.Tensor] = None,
initial_states: Optional[torch.Tensor] = None,
seq_idx: Optional[torch.Tensor] = None,
dt_softplus: bool = False,
dt_limit: Optional[List[float]] = None
)-> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
dx, ddt, dA, dB, dC, dD, dz, ddt_bias, dinitial_states = _mamba_chunk_scan_combined_bwd(dout, x, dt, A, B, C, out, chunk_size, D=D, z=z, dt_bias=dt_bias, initial_states=initial_states, dfinal_states=None, seq_idx=seq_idx, dt_softplus=dt_softplus, dt_limit=dt_limit)
return (
dx,
ddt,
dA,
dB,
dC,
dD if dD is not None else dx.new_empty(0),
dz if dz is not None else dx.new_empty(0),
ddt_bias if ddt_bias is not None else dx.new_empty(0),
dinitial_states if dinitial_states is not None else dx.new_empty(0)
)
@ssm_chunk_scan_combined_bwd.register_fake
def _ssm_chunk_scan_combined_bwd_fake(
dout: torch.Tensor,
x: torch.Tensor,
dt: torch.Tensor,
A: torch.Tensor,
B: torch.Tensor,
C: torch.Tensor,
out: torch.Tensor,
chunk_size: int,
D: Optional[torch.Tensor] = None,
z: Optional[torch.Tensor] = None,
dt_bias: Optional[torch.Tensor] = None,
initial_states: Optional[torch.Tensor] = None,
seq_idx: Optional[torch.Tensor] = None,
dt_softplus: bool = False,
dt_limit: Optional[List[float]] = None
):
return (
torch.empty_like(x),
torch.empty_like(dt),
torch.empty_like(A),
torch.empty_like(B),
torch.empty_like(C),
torch.empty_like(D) if D is not None else None,
torch.empty_like(z) if z is not None else None,
torch.empty_like(dt_bias) if dt_bias is not None else None,
torch.empty_like(initial_states) if initial_states is not None else None,
)
def ssm_chunk_scan_combined_setup_context(ctx, inputs, output):
x, dt, A, B, C, chunk_size, D, z, dt_bias, initial_states, seq_idx, cu_seqlens, dt_softplus, dt_limit = inputs
out, out_x, state_varlen = output
ctx.save_for_backward(out if z is None else out_x, x, dt, A, B, C, D, z, dt_bias, initial_states, seq_idx)
ctx.dt_softplus = dt_softplus
ctx.chunk_size = chunk_size
ctx.dt_limit = dt_limit
def ssm_chunk_scan_combined_bridge(ctx, dout, dout_x, dout_state_varlen):
out, x, dt, A, B, C, D, z, dt_bias, initial_states, seq_idx = ctx.saved_tensors
dx, ddt, dA, dB, dC, dD, dz, ddt_bias, dinitial_states = ssm_chunk_scan_combined_bwd(dout, x, dt, A, B, C, out, ctx.chunk_size, D=D, z=z, dt_bias=dt_bias, initial_states=initial_states, seq_idx=seq_idx, dt_softplus=ctx.dt_softplus, dt_limit=ctx.dt_limit)
return (
dx,
ddt,
dA,
dB,
dC,
None,
dD if D is not None else None,
dz if z is not None else None,
ddt_bias if dt_bias is not None else None,
dinitial_states if initial_states is not None else None,
None,
None,
None,
None,
)
# Register custom autograd function
torch.library.register_autograd(
"mamba_ssm::ssm_chunk_scan_combined_fwd",
ssm_chunk_scan_combined_bridge,
setup_context=ssm_chunk_scan_combined_setup_context,
)
def mamba_chunk_scan_combined(x, dt, A, B, C, chunk_size, D=None, z=None, dt_bias=None, initial_states=None, seq_idx=None, cu_seqlens=None, dt_softplus=False, dt_limit=(0.0, float("inf"))):
"""
Argument:
x: (batch, seqlen, nheads, headdim)
dt: (batch, seqlen, nheads)
A: (nheads)
B: (batch, seqlen, ngroups, dstate)
C: (batch, seqlen, ngroups, dstate)
chunk_size: int
D: (nheads, headdim) or (nheads,)
z: (batch, seqlen, nheads, headdim)
dt_bias: (nheads,)
initial_states: (batch, nheads, headdim, dstate)
seq_idx: (batch, seqlen)
cu_seqlens: (num_sequences + 1) or None
dt_softplus: Whether to apply softplus to dt
Return:
out: (batch, seqlen, nheads, headdim)
"""
out, _, varlen_states = ssm_chunk_scan_combined_fwd(x, dt, A, B, C, chunk_size, D=D, z=z, dt_bias=dt_bias, initial_states=initial_states, seq_idx=seq_idx, cu_seqlens=cu_seqlens, dt_softplus=dt_softplus, dt_limit=dt_limit)
if cu_seqlens is not None:
return out, varlen_states
return out
if __name__ == "__main__":
from mamba_ssm.ops.triton.ssd_combined import mamba_chunk_scan_combined as mamba_chunk_scan_combined_ref
torch.manual_seed(0)
torch.cuda.manual_seed(0)
x = torch.randn(2, 3, 4, 5).cuda()
dt = torch.randn(2, 3, 4).cuda()
A = torch.randn(4).cuda()
B = torch.randn(2, 3, 4, 5).cuda()
C = torch.randn(2, 3, 4, 5).cuda()
chunk_size = 2
D = torch.randn(4, 5).cuda()
z = torch.randn(2, 3, 4, 5).cuda()
dt_bias = torch.randn(4).cuda()
out = mamba_chunk_scan_combined(x, dt, A, B, C, chunk_size, D=D, z=z, dt_bias=dt_bias)
print(out.min(), out.max(), out.mean(), out.std())
compiled_mamba_chunk_scan_combined = torch.compile(mamba_chunk_scan_combined)
out = compiled_mamba_chunk_scan_combined(x, dt, A, B, C, chunk_size, D=D, z=z, dt_bias=dt_bias)
print(out.min(), out.max(), out.mean(), out.std())
out_ref = mamba_chunk_scan_combined_ref(x, dt, A, B, C, chunk_size, D=D, z=z, dt_bias=dt_bias)
print(out_ref.min(), out_ref.max(), out_ref.mean(), out_ref.std()) |