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dc9bb20 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 | # Copyright (c) 2024, Tri Dao.
import torch
import causal_conv1d_cuda
LIBRARY_NAME = "DaoAILab"
@torch.library.custom_op(f"{LIBRARY_NAME}::_causal_conv1d_fwd_cpp", mutates_args={"out", "final_states_out"})
def _causal_conv1d_fwd_cpp(
x: torch.Tensor,
weight: torch.Tensor,
bias: torch.Tensor | None,
seq_idx: torch.Tensor | None,
initial_states: torch.Tensor | None,
out: torch.Tensor,
final_states_out: torch.Tensor | None,
silu_activation: bool,
) -> None:
causal_conv1d_cuda.causal_conv1d_fwd(
x,
weight,
bias,
seq_idx,
initial_states,
out,
final_states_out,
silu_activation,
)
@torch.library.custom_op(f"{LIBRARY_NAME}::_causal_conv1d_bwd_cpp", mutates_args={
"dfinal_states",
"dx",
"dweight",
"dbias",
"dinitial_states",
})
def _causal_conv1d_bwd_cpp(
x: torch.Tensor,
weight: torch.Tensor,
bias: torch.Tensor | None,
dout: torch.Tensor,
seq_idx: torch.Tensor | None,
initial_states: torch.Tensor | None,
dfinal_states: torch.Tensor | None,
dx: torch.Tensor,
dweight: torch.Tensor,
dbias: torch.Tensor | None,
dinitial_states: torch.Tensor,
silu_activation: bool,
) -> None:
causal_conv1d_cuda.causal_conv1d_bwd(
x,
weight,
bias,
dout,
seq_idx,
initial_states,
dfinal_states,
dx,
dweight,
dbias,
dinitial_states,
silu_activation,
)
@torch.library.custom_op(f"{LIBRARY_NAME}::_causal_conv1d_update_cpp", mutates_args={"out", "conv_state"})
def _causal_conv1d_update_cpp(
x: torch.Tensor,
conv_state: torch.Tensor,
weight: torch.Tensor,
bias: torch.Tensor | None,
out: torch.Tensor,
silu_activation: bool,
cache_seqlens: torch.Tensor | None,
conv_state_indices: torch.Tensor | None,
) -> None:
causal_conv1d_cuda.causal_conv1d_update(
x,
conv_state,
weight,
bias,
out,
silu_activation,
cache_seqlens,
conv_state_indices
)
def causal_conv1d_fwd_function(
x: torch.Tensor,
weight: torch.Tensor,
bias: torch.Tensor | None,
seq_idx: torch.Tensor | None,
initial_states: torch.Tensor | None,
final_states_out: torch.Tensor | None,
silu_activation: bool,
) -> torch.Tensor:
out = torch.empty_like(x)
_causal_conv1d_fwd_cpp(
x=x,
weight=weight,
bias=bias,
seq_idx=seq_idx,
initial_states=initial_states,
out=out,
final_states_out=final_states_out,
silu_activation=silu_activation,
)
return out
def causal_conv1d_bwd_function(
x: torch.Tensor,
weight: torch.Tensor,
bias: torch.Tensor | None,
dout: torch.Tensor,
seq_idx: torch.Tensor | None,
initial_states: torch.Tensor | None,
dfinal_states: torch.Tensor | None,
dx: torch.Tensor | None,
return_dinitial_states: torch.Tensor,
silu_activation: bool,
) -> tuple[torch.Tensor | None]:
batch_size, dim = x.size()[:2]
width = weight.size(-1)
if dx is None:
dx = torch.empty_like(x)
dweight = torch.zeros_like(weight, dtype=torch.float32)
dbias = None
if bias is not None:
dbias = torch.zeros_like(bias, dtype=torch.float32)
dinitial_states = None
if return_dinitial_states:
dinitial_states = torch.empty(batch_size, width - 1, dim, device=x.device, dtype=x.dtype).transpose(1, 2)
_causal_conv1d_bwd_cpp(
x=x,
weight=weight,
bias=bias,
dout=dout,
seq_idx=seq_idx,
initial_states=initial_states,
dfinal_states=dfinal_states,
dx=dx,
dweight=dweight,
dbias=dbias,
dinitial_states=dinitial_states,
silu_activation=silu_activation,
)
dweight = dweight.type_as(weight)
if dbias is not None:
dbias = dbias.type_as(bias)
return dx, dweight, dbias, dinitial_states
def causal_conv1d_update_function(
x: torch.Tensor,
conv_state: torch.Tensor,
weight: torch.Tensor,
bias: torch.Tensor | None,
silu_activation: bool,
cache_seqlens: torch.Tensor | None,
conv_state_indices: torch.Tensor | None,
) -> torch.Tensor:
out = torch.empty_like(x)
_causal_conv1d_update_cpp(
x=x,
conv_state=conv_state,
weight=weight,
bias=bias,
out=out,
silu_activation=silu_activation,
cache_seqlens=cache_seqlens,
conv_state_indices=conv_state_indices,
)
return out
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