File size: 9,797 Bytes
5ddd9c9 5d97a93 5ddd9c9 5f7b219 5ddd9c9 5f7b219 5ddd9c9 a951351 5ddd9c9 97808c5 5ddd9c9 5f7b219 5ddd9c9 |
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 |
"""Reverse-complement equivariant modules.
"""
from collections import OrderedDict
from typing import Optional
import torch
from torch import Tensor
from torch import nn
from torch.nn import functional as F
try:
from mamba_ssm.ops.triton.layernorm import RMSNorm, layer_norm_fn, rms_norm_fn
except ImportError:
RMSNorm, layer_norm_fn, rms_norm_fn = None, None, None
class RCPSEmbedding(nn.Module):
"""Embedding layer that supports reverse-complement equivariance."""
def __init__(self, vocab_size: int, d_model: int, complement_map: dict, **factory_kwargs):
"""
Args:
vocab_size: Size of vocabulary.
d_model: Dimensionality of embedding (actual embedding matrix will have 1/2 the output dim).
complement_map: Dictionary mapping each token id to its complement.
"""
super().__init__()
self.register_buffer(
"complement_map",
torch.tensor(list(OrderedDict(complement_map).values()), dtype=torch.long)
)
self.embedding = nn.Embedding(vocab_size, d_model, **factory_kwargs)
@property
def weight(self):
"""Embedding weights."""
return self.embedding.weight
def set_weight(self, value):
"""Set embedding weights."""
self.embedding.weight = value
def rc(self, x):
"""Reverse-complement a tensor of input_ids by flipping along length dimension and complementing the ids."""
return torch.gather(
self.complement_map.unsqueeze(0).expand(x.shape[0], -1),
dim=1,
index=torch.flip(x, dims=[-1])
)
def forward(self, input_ids):
"""Reverse-complement equivariant forward pass.
This embedding module doubles the output dimensionality to support reverse-complement equivariance.
Args:
input_ids: Input tensor of shape (batch_size, seq_len)
Returns:
Embedding tensor of shape (batch_size, seq_len, d_model * 2)
"""
fwd_out = self.embedding(input_ids)
rc_out = torch.flip(self.embedding(self.rc(input_ids)), dims=[-2, -1])
return torch.cat([fwd_out, rc_out], dim=-1)
class RCPSWrapper(nn.Module):
"""Wrapper to convert arbitrary nn.Module into a reverse-complement equivariant module.
See ref. "Towards a Better Understanding of Reverse-Complement Equivariance for Deep Learning Models in Regulatory
Genomics", Zhou et al. (2022), https://proceedings.mlr.press/v165/zhou22a.html for more details.
"""
def __init__(self, submodule: nn.Module):
super().__init__()
self.submodule = submodule
@staticmethod
def rc(x):
"""Reverse-complement a tensor by flipping the length (dim=-2) and channel (dim=-1) dimensions."""
return torch.flip(x, dims=[-2, -1])
def forward(self, x, **kwargs):
"""Reverse-complement equivariant forward pass.
Args:
x: Input tensor of shape (batch_size, seq_len, channels)
Returns:
Output tensor of shape (batch_size, seq_len, channels * 2)
"""
n_channels = x.shape[-1]
# Run submodule along sequence
fwd_out = self.submodule(x[..., :n_channels // 2], **kwargs)
# Run submodule along rc-sequence
rc_out = self.submodule(self.rc(x[..., n_channels // 2:]), **kwargs)
# Concatenate along channel dimension (dim=-1)
return torch.cat([fwd_out, self.rc(rc_out)], dim=-1)
class RCPSAddNormWrapper(RCPSWrapper):
"""RC equivariant AddNorm layer."""
def __init__(self, submodule: nn.Module):
super().__init__(submodule)
def forward(self, x, residual=None, prenorm=False):
"""
Args:
x: Input tensor of shape (batch_size, seq_len, channels)
residual: Residual tensor of shape (batch_size, seq_len, channels) or None.
prenorm: Whether to return residual.
"""
n_channels = x.shape[-1]
if residual is None:
residual = x
x_fwd = self.submodule(x[..., :n_channels // 2].to(dtype=self.submodule.weight.dtype))
x_rc = self.submodule(self.rc(x[..., n_channels // 2:]).to(dtype=self.submodule.weight.dtype))
x = torch.cat([x_fwd, self.rc(x_rc)], dim=-1)
else:
residual_fwd = x[..., :n_channels // 2] + residual[..., :n_channels // 2]
x_fwd = self.submodule(residual_fwd.to(dtype=self.submodule.weight.dtype))
residual_rc = self.rc(x[..., n_channels // 2:]) + self.rc(residual[..., n_channels // 2:])
x_rc = self.submodule(residual_rc.to(dtype=self.submodule.weight.dtype))
residual = torch.cat([residual_fwd, self.rc(residual_rc)], dim=-1)
x = torch.cat([x_fwd, self.rc(x_rc)], dim=-1)
return x if not prenorm else (x, residual)
class RCPSMambaBlock(nn.Module):
def __init__(
self,
dim,
mixer_cls,
norm_cls=nn.LayerNorm,
fused_add_norm=False,
residual_in_fp32=False,
device=None, # Keep for consistency with original Mamba Block
dtype=None, # Keep for consistency with original Mamba Block
):
"""RCPS version of simple block wrapping a mixer class with LayerNorm/RMSNorm and residual connection.
Adapted from: https://github.com/state-spaces/mamba/blob/main/mamba_ssm/modules/mamba_simple.py
"""
super().__init__()
self.residual_in_fp32 = residual_in_fp32
self.fused_add_norm = fused_add_norm
self.mixer = RCPSWrapper(mixer_cls(dim))
norm_f = norm_cls(dim)
self.norm = norm_f if fused_add_norm else RCPSAddNormWrapper(norm_f)
if self.fused_add_norm:
assert RMSNorm is not None, "RMSNorm import fails"
assert isinstance(
self.norm, (nn.LayerNorm, RMSNorm)
), "Only LayerNorm and RMSNorm are supported for fused_add_norm"
def forward(
self, hidden_states: Tensor, residual: Optional[Tensor] = None, inference_params=None
):
r"""Pass the input through the encoder layer.
Args:
hidden_states: the sequence to the encoder layer (required).
residual: hidden_states = Mixer(LN(residual)).
inference_params: inference parameters for mixer.
"""
if not self.fused_add_norm:
hidden_states, residual = self.norm(hidden_states, residual=residual, prenorm=True)
if self.residual_in_fp32:
residual = residual.to(torch.float32)
else:
fused_add_norm_fn = rms_norm_fn if isinstance(self.norm, RMSNorm) else layer_norm_fn
hidden_states_fwd, residual_fwd = fused_add_norm_fn(
hidden_states[..., hidden_states.shape[-1] // 2:],
self.norm.weight,
self.norm.bias,
residual=residual[..., hidden_states.shape[-1] // 2:] if residual is not None else None,
prenorm=True,
residual_in_fp32=self.residual_in_fp32,
eps=self.norm.eps,
)
hidden_states_rc, residual_rc = fused_add_norm_fn(
hidden_states[..., :hidden_states.shape[-1] // 2].flip(dims=[-2, -1]),
self.norm.weight,
self.norm.bias,
residual=residual[..., :hidden_states.shape[-1] // 2].flip(dims=[-2, -1]) if residual is not None else None,
prenorm=True,
residual_in_fp32=self.residual_in_fp32,
eps=self.norm.eps,
)
hidden_states = torch.cat([hidden_states_fwd, hidden_states_rc.flip(dims=[-2, -1])], dim=-1)
residual = torch.cat([residual_fwd, residual_rc.flip(dims=[-2, -1])], dim=-1)
hidden_states = self.mixer(hidden_states, inference_params=inference_params)
return hidden_states, residual
def allocate_inference_cache(self, batch_size, max_seqlen, dtype=None, **kwargs):
"""Allocate inference cache for mixer.
Keep for compatibility with original Mamba Block.
"""
return self.mixer.allocate_inference_cache(batch_size, max_seqlen, dtype=dtype, **kwargs)
class RCPSLMHead(nn.Module):
"""LM Head for reverse-complement equivariant inputs, which have dim * 2 relative to standard inputs."""
def __init__(self, true_dim: int, vocab_size: int, complement_map: dict, **factory_kwargs):
"""
`true_dim` corresponds to the actual dimensionality of the input were it not reverse-complement
equivariant, i.e. 0.5 times the actual input dim.
"""
super().__init__()
self.register_buffer(
"complement_map",
torch.tensor(list(OrderedDict(complement_map).values()), dtype=torch.long)
)
self.true_dim = true_dim
self.lm_head = nn.Linear(true_dim, vocab_size, bias=False, **factory_kwargs)
@property
def weight(self):
"""LM head weights."""
return self.lm_head.weight
def set_weight(self, value):
"""Set LM head weights."""
self.lm_head.weight = value
def forward(self, x):
"""
Args:
x: Input tensor of shape (batch_size, seq_len, dim), where dim = 2 * true_dim.
"""
n_channels = x.shape[-1]
assert n_channels == 2 * self.true_dim, "Input must have 2 * true_dim channels."
fwd_logits = F.linear(x[..., :n_channels // 2], self.weight, bias=self.lm_head.bias)
rc_logits = F.linear(
torch.flip(x[..., n_channels // 2:], dims=[-1]),
self.weight[self.complement_map, :],
bias=self.lm_head.bias
)
return fwd_logits + rc_logits
|