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# Copyright (c) 2024, Tri Dao.
import torch
import torch.nn.functional as F
import causal_conv1d_cuda
class CausalConv1dFn(torch.autograd.Function):
@staticmethod
def forward(
ctx,
x,
weight,
bias=None,
seq_idx=None,
initial_states=None,
return_final_states=False,
final_states_out=None,
activation=None,
):
if activation not in [None, "silu", "swish"]:
raise NotImplementedError("activation must be None, silu, or swish")
if x.stride(2) != 1 and x.stride(1) != 1:
x = x.contiguous()
bias = bias.contiguous() if bias is not None else None
if seq_idx is not None:
assert (
initial_states is None
), "initial_states must be None if seq_idx is not None"
assert (
not return_final_states
), "If seq_idx is not None, we don't return final_states_out"
seq_idx = seq_idx.contiguous() if seq_idx is not None else None
if initial_states is not None and (
initial_states.stride(2) != 1 and initial_states.stride(1) != 1
):
initial_states = initial_states.contiguous()
if return_final_states:
assert (
x.stride(1) == 1
), "Only channel-last layout support returning final_states_out"
if final_states_out is not None:
assert (
final_states_out.stride(2) == 1 or final_states_out.stride(1) == 1
)
else:
batch, dim, seqlen = x.shape
width = weight.shape[1]
final_states_out = torch.empty(
batch, width - 1, dim, device=x.device, dtype=x.dtype
).transpose(1, 2)
else:
final_states_out = None
ctx.activation = activation in ["silu", "swish"]
out = causal_conv1d_cuda.causal_conv1d_fwd(
x, weight, bias, seq_idx, initial_states, final_states_out, ctx.activation
)
ctx.save_for_backward(x, weight, bias, seq_idx, initial_states)
ctx.return_final_states = return_final_states
ctx.return_dinitial_states = (
initial_states is not None and initial_states.requires_grad
)
return out if not return_final_states else (out, final_states_out)
@staticmethod
def backward(ctx, dout, *args):
x, weight, bias, seq_idx, initial_states = ctx.saved_tensors
dfinal_states = args[0] if ctx.return_final_states else None
if dout.stride(2) != 1 and dout.stride(1) != 1:
dout = dout.contiguous()
# 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).
# Here we just pass in None and dx will be allocated in the C++ code.
dx, dweight, dbias, dinitial_states = causal_conv1d_cuda.causal_conv1d_bwd(
x,
weight,
bias,
dout,
seq_idx,
initial_states,
dfinal_states,
None,
ctx.return_dinitial_states,
ctx.activation,
)
return (
dx,
dweight,
dbias if bias is not None else None,
None,
dinitial_states if initial_states is not None else None,
None,
None,
None,
)
def causal_conv1d_fn(
x,
weight,
bias=None,
seq_idx=None,
initial_states=None,
return_final_states=False,
final_states_out=None,
activation=None,
):
"""
x: (batch, dim, seqlen)
weight: (dim, width)
bias: (dim,)
seq_idx: (batch, seqlen)
initial_states: (batch, dim, width - 1)
final_states_out: (batch, dim, width - 1), to be written to
activation: either None or "silu" or "swish"
out: (batch, dim, seqlen)
"""
return CausalConv1dFn.apply(
x,
weight,
bias,
seq_idx,
initial_states,
return_final_states,
final_states_out,
activation,
)
def causal_conv1d_ref(
x,
weight,
bias=None,
initial_states=None,
return_final_states=False,
final_states_out=None,
activation=None,
):
"""
x: (batch, dim, seqlen)
weight: (dim, width)
bias: (dim,)
initial_states: (batch, dim, width - 1)
final_states_out: (batch, dim, width - 1)
out: (batch, dim, seqlen)
"""
if activation not in [None, "silu", "swish"]:
raise NotImplementedError("activation must be None, silu, or swish")
dtype_in = x.dtype
x = x.to(weight.dtype)
seqlen = x.shape[-1]
dim, width = weight.shape
if initial_states is None:
out = F.conv1d(x, weight.unsqueeze(1), bias, padding=width - 1, groups=dim)
else:
x = torch.cat([initial_states, x], dim=-1)
out = F.conv1d(x, weight.unsqueeze(1), bias, padding=0, groups=dim)
out = out[..., :seqlen]
if return_final_states:
final_states = F.pad(x, (width - 1 - x.shape[-1], 0)).to(
dtype_in
) # (batch, dim, width - 1)
if final_states_out is not None:
final_states_out.copy_(final_states)
else:
final_states_out = final_states
out = (out if activation is None else F.silu(out)).to(dtype=dtype_in)
return out if not return_final_states else (out, final_states_out)
def causal_conv1d_update(x, conv_state, weight, bias=None, activation=None, cache_seqlens=None):
"""
x: (batch, dim) or (batch, dim, seqlen)
conv_state: (batch, dim, state_len), where state_len >= width - 1
weight: (dim, width)
bias: (dim,)
cache_seqlens: (batch,), dtype int32.
If not None, the conv_state is treated as a circular buffer.
The conv_state will be updated by copying x to the conv_state starting at the index
@cache_seqlens % state_len.
out: (batch, dim) or (batch, dim, seqlen)
"""
if activation not in [None, "silu", "swish"]:
raise NotImplementedError("activation must be None, silu, or swish")
activation = activation in ["silu", "swish"]
unsqueeze = x.dim() == 2
if unsqueeze:
x = x.unsqueeze(-1)
out = causal_conv1d_cuda.causal_conv1d_update(
x, conv_state, weight, bias, activation, cache_seqlens
)
if unsqueeze:
out = out.squeeze(-1)
return out
def causal_conv1d_update_ref(x, conv_state, weight, bias=None, activation=None, cache_seqlens=None):
"""
x: (batch, dim) or (batch, dim, seqlen)
conv_state: (batch, dim, state_len), where state_len >= width - 1
weight: (dim, width)
bias: (dim,)
cache_seqlens: (batch,), dtype int32.
If not None, the conv_state is treated as a circular buffer.
The conv_state will be updated by copying x to the conv_state starting at the index
@cache_seqlens % state_len before performing the convolution.
out: (batch, dim) or (batch, dim, seqlen)
"""
if activation not in [None, "silu", "swish"]:
raise NotImplementedError("activation must be None, silu, or swish")
dtype_in = x.dtype
unsqueeze = x.dim() == 2
if unsqueeze:
x = x.unsqueeze(-1)
batch, dim, seqlen = x.shape
width = weight.shape[1]
state_len = conv_state.shape[-1]
assert conv_state.shape == (batch, dim, state_len)
assert weight.shape == (dim, width)
if cache_seqlens is None:
x_new = torch.cat([conv_state, x], dim=-1).to(weight.dtype) # (batch, dim, state_len + seqlen)
conv_state.copy_(x_new[:, :, -state_len:])
else:
width_idx = torch.arange(-(width - 1), 0, dtype=torch.long, device=x.device).unsqueeze(0) + cache_seqlens.unsqueeze(1)
width_idx = torch.remainder(width_idx, state_len).unsqueeze(1).expand(-1, dim, -1)
x_new = torch.cat([conv_state.gather(2, width_idx), x], dim=-1).to(weight.dtype)
copy_idx = torch.arange(seqlen, dtype=torch.long, device=x.device).unsqueeze(0) + cache_seqlens.unsqueeze(1)
copy_idx = torch.remainder(copy_idx, state_len).unsqueeze(1).expand(-1, dim, -1)
conv_state.scatter_(2, copy_idx, x)
out = F.conv1d(x_new, weight.unsqueeze(1), bias, padding=0, groups=dim)[:, :, -seqlen:]
if unsqueeze:
out = out.squeeze(-1)
return (out if activation is None else F.silu(out)).to(dtype=dtype_in)
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