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"""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
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