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76bab86 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 | """Residual MultiConvAdapter on the frozen Cohere Conformer.
Kernels K={7,15,23,31} + concat_fusion from MULTI-CONVFORMER
(Prabhu et al. 2024). Skip the bottom third of layers. Zero-init the
up-projection so the first step is identity.
"""
from __future__ import annotations
from typing import Iterable
import torch
import torch.nn as nn
DEFAULT_KERNELS = (7, 15, 23, 31)
DEFAULT_FUSION = "concat_fusion"
DEFAULT_MERGE_KERNEL = 31
class MultiConvAdapter(nn.Module):
def __init__(
self,
d_model: int,
bottleneck: int = 64,
kernels: Iterable[int] = DEFAULT_KERNELS,
dropout: float = 0.1,
fusion: str = DEFAULT_FUSION,
merge_kernel: int = DEFAULT_MERGE_KERNEL,
):
super().__init__()
kernels = tuple(int(k) for k in kernels)
if not kernels:
raise ValueError("Need at least one convolution kernel")
if any(k < 1 or k % 2 == 0 for k in kernels):
raise ValueError(f"Kernels must be odd and positive, got {kernels}")
if bottleneck < len(kernels) or bottleneck % 2 != 0:
raise ValueError(f"bottleneck must be even and >= n_kernels, got {bottleneck}")
if fusion not in {"sum", "weighted_sum", "concat", "concat_fusion"}:
raise ValueError(f"Unknown fusion={fusion}")
if fusion in {"concat", "concat_fusion"} and bottleneck % len(kernels) != 0:
raise ValueError(
f"concat fusion needs bottleneck ({bottleneck}) divisible by "
f"{len(kernels)} kernels"
)
self.kernels = kernels
self.fusion = fusion
self.merge_kernel = int(merge_kernel)
self.norm = nn.LayerNorm(d_model)
self.down = nn.Linear(d_model, 2 * bottleneck)
self.act = nn.GELU()
self.gate_norm = nn.LayerNorm(bottleneck)
n_kernels = len(kernels)
if fusion in {"sum", "weighted_sum"}:
self.convs = nn.ModuleList(
[
nn.Conv1d(
bottleneck,
bottleneck,
kernel_size=k,
padding=(k - 1) // 2,
groups=bottleneck,
)
for k in kernels
]
)
else:
per = bottleneck // n_kernels
self.convs = nn.ModuleList(
[
nn.Conv1d(
bottleneck,
per,
kernel_size=k,
padding=(k - 1) // 2,
groups=per,
)
for k in kernels
]
)
if fusion == "weighted_sum":
self.kernel_mix = nn.Sequential(
nn.Linear(bottleneck * n_kernels, n_kernels),
nn.Softmax(dim=-1),
)
else:
self.kernel_mix = None
if fusion == "concat_fusion":
self.merge = nn.Conv1d(
bottleneck,
bottleneck,
kernel_size=self.merge_kernel,
padding=(self.merge_kernel - 1) // 2,
groups=bottleneck,
)
else:
self.merge = None
self.up = nn.Linear(bottleneck, d_model)
self.drop = nn.Dropout(dropout)
nn.init.zeros_(self.up.weight)
nn.init.zeros_(self.up.bias)
def _fuse(self, branches: list[torch.Tensor]) -> torch.Tensor:
if self.fusion in {"sum", "weighted_sum"}:
stacked = torch.stack(branches, dim=-2)
if self.kernel_mix is not None:
weights = self.kernel_mix(torch.cat(branches, dim=-1))
stacked = weights.unsqueeze(-1) * stacked
return stacked.sum(dim=-2)
fused = torch.cat(branches, dim=-1)
if self.merge is not None:
extra = self.merge(fused.transpose(1, 2)).transpose(1, 2)
fused = fused + extra
return fused
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
# hidden_states: (batch, time, dim)
residual = hidden_states
hidden = self.act(self.down(self.norm(hidden_states)))
left, right = hidden.chunk(2, dim=-1)
right = self.gate_norm(right).transpose(1, 2)
branches = [conv(right).transpose(1, 2) for conv in self.convs]
hidden = left * self._fuse(branches)
return residual + self.drop(self.up(hidden))
class EncoderBlockWithConvAdapter(nn.Module):
def __init__(self, block: nn.Module, adapter: MultiConvAdapter):
super().__init__()
self.block = block
self.conv_adapter = adapter
def forward(self, *args, **kwargs):
hidden_states = self.block(*args, **kwargs)
if isinstance(hidden_states, tuple):
return (self.conv_adapter(hidden_states[0]),) + hidden_states[1:]
return self.conv_adapter(hidden_states)
def get_encoder(model: nn.Module) -> nn.Module:
core = model.get_base_model() if hasattr(model, "get_base_model") else model
if hasattr(core, "model") and hasattr(core.model, "encoder"):
return core.model.encoder
if hasattr(core, "encoder"):
return core.encoder
raise AttributeError("Could not find a Conformer / Parakeet encoder on this model")
def attach_multiconv_adapters(
model: nn.Module,
*,
bottleneck: int,
kernels: Iterable[int],
dropout: float,
skip_bottom_frac: float,
fusion: str = DEFAULT_FUSION,
merge_kernel: int = DEFAULT_MERGE_KERNEL,
) -> dict:
encoder = get_encoder(model)
layers = encoder.layers
n_layers = len(layers)
start = int(n_layers * skip_bottom_frac)
d_model = int(getattr(encoder.config, "hidden_size", 1280))
attached = []
for idx in range(start, n_layers):
block = layers[idx]
if isinstance(block, EncoderBlockWithConvAdapter):
continue
adapter = MultiConvAdapter(
d_model,
bottleneck=bottleneck,
kernels=kernels,
dropout=dropout,
fusion=fusion,
merge_kernel=merge_kernel,
)
try:
ref = next(block.parameters())
adapter.to(device=ref.device, dtype=ref.dtype)
except StopIteration:
pass
layers[idx] = EncoderBlockWithConvAdapter(block, adapter)
attached.append(idx)
return {
"n_layers": n_layers,
"start_layer": start,
"attached_layers": attached,
"d_model": d_model,
"bottleneck": bottleneck,
"kernels": list(kernels),
"fusion": fusion,
"merge_kernel": merge_kernel,
"dropout": dropout,
}
def _in_decoder(name: str) -> bool:
dotted = f".{name}."
return ".decoder." in dotted or name.startswith("decoder.")
def decoder_lora_targets(model: nn.Module, kinds: Iterable[str]) -> list[str]:
"""Only the 8-layer decoder (self-attn + cross-attn). Never the encoder."""
kinds = tuple(kinds)
names = []
for name, _module in model.named_modules():
leaf = name.rsplit(".", 1)[-1]
if leaf not in kinds:
continue
if not _in_decoder(name):
continue
if "self_attn" not in name and "encoder_attn" not in name:
continue
names.append(name)
if not names:
raise RuntimeError(
"No decoder attention projections found. "
"Is this CohereAsrForConditionalGeneration?"
)
return names
def encoder_lora_targets(model: nn.Module, kinds: Iterable[str]) -> list[str]:
"""Conformer self-attn q/k/v/o. Never decoder, never MLP."""
kinds = tuple(kinds)
names = []
for name, _module in model.named_modules():
leaf = name.rsplit(".", 1)[-1]
if leaf not in kinds:
continue
if _in_decoder(name):
continue
if "self_attn" not in name:
continue
names.append(name)
if not names:
raise RuntimeError("No encoder attention projections found.")
return names
def lora_targets_for_scope(model: nn.Module, scope: str, kinds: Iterable[str]) -> list[str]:
if scope == "decoder":
return decoder_lora_targets(model, kinds)
if scope == "encoder":
return encoder_lora_targets(model, kinds)
if scope == "full":
return decoder_lora_targets(model, kinds) + encoder_lora_targets(model, kinds)
raise ValueError(f"Unknown lora scope={scope}")
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