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from typing import List, Optional
from itertools import product
from collections import defaultdict
import torch
from torch import nn
import torch.nn.utils.parametrize as parametrize
def check_if_involution(indices: List[int]) -> bool:
return all(indices[indices[idx]] == idx for idx in range(len(indices)))
def get_conv1d_output_length(
input_length: int, kernel_size: int, stride_size: int = 1, pad_size: int = 0, dilation_rate: int = 1
) -> int:
return (input_length + 2 * pad_size - dilation_rate * (kernel_size - 1) - 1) // stride_size + 1
def get_involution_indices(size: int) -> List[int]:
return list(reversed(range(size)))
class RCEWeight(nn.Module):
def __init__(
self, input_involution_indices: List[int], output_involution_indices: List[int]
):
if not check_if_involution(input_involution_indices) or not check_if_involution(
output_involution_indices):
raise ValueError(
"`input_involution_indices` and `output_involution_indices` must be involutions"
)
super().__init__()
self._input_involution_indices = input_involution_indices
self._output_involution_indices = output_involution_indices
self._input_involution_index_tensor = None
self._output_involution_index_tensor = None
self._device = None
def forward(self, x: torch.Tensor) -> torch.Tensor:
if self._device != x.device:
self._input_involution_index_tensor = torch.tensor(self._input_involution_indices, device=x.device)
self._output_involution_index_tensor = torch.tensor(self._output_involution_indices, device=x.device)
self._device = x.device
output_involution_indices = self._output_involution_index_tensor
input_involution_indices = self._input_involution_index_tensor
return (x + x[output_involution_indices][:, input_involution_indices].flip(2)) / 2
class IEBias(nn.Module):
def __init__(self, involution_indices: List[int]):
if not check_if_involution(involution_indices):
raise ValueError("`involution_indices` must be an involution")
super().__init__()
self._involution_indices = involution_indices
self._involution_index_tensor = None
self._device = None
def forward(self, x: torch.Tensor) -> torch.Tensor:
if self._device != x.device:
self._involution_index_tensor = torch.tensor(self._involution_indices, device=x.device)
self._device = x.device
involution_indices = self._involution_index_tensor
return (x + x[involution_indices]) / 2
class IEWeight(nn.Module):
def __init__(
self, input_involution_indices: List[int], output_involution_indices: List[int]
):
if not check_if_involution(input_involution_indices) or not check_if_involution(
output_involution_indices):
raise ValueError(
"`input_involution_indices` and `output_involution_indices` must be involutions"
)
super().__init__()
self._input_involution_indices = input_involution_indices
self._output_involution_indices = output_involution_indices
self._input_involution_index_tensor = None
self._output_involution_index_tensor = None
self._device = None
def forward(self, x: torch.Tensor) -> torch.Tensor:
if self._device != x.device:
self._input_involution_index_tensor = torch.tensor(self._input_involution_indices, device=x.device)
self._output_involution_index_tensor = torch.tensor(self._output_involution_indices, device=x.device)
self._device = x.device
output_involution_indices = self._output_involution_index_tensor
input_involution_indices = self._input_involution_index_tensor
return (x + x[input_involution_indices][:, output_involution_indices]) / 2
class RCEByteNetBlock(nn.Module):
def __init__(self, outer_involution_indices: List[int], inner_dim: int, kernel_size: int, dilation_rate: int = 1):
outer_dim = len(outer_involution_indices)
if outer_dim % 2 != 0:
raise ValueError("`outer_involution_indices` must have an even length")
if inner_dim % 2 != 0:
raise ValueError("`inner_dim` must be even")
if kernel_size % 2 == 0:
raise ValueError("`kernel_size` must be odd")
super().__init__()
inner_involution_indices = get_involution_indices(inner_dim)
layers = [
nn.GroupNorm(1, outer_dim),
nn.GELU(),
nn.Conv1d(outer_dim, inner_dim, kernel_size=1),
nn.GroupNorm(1, inner_dim),
nn.GELU(),
nn.Conv1d(inner_dim, inner_dim, kernel_size, dilation=dilation_rate),
nn.GroupNorm(1, inner_dim),
nn.GELU(),
nn.Conv1d(inner_dim, outer_dim, kernel_size=1)
]
parametrize.register_parametrization(
layers[2], "weight",
RCEWeight(outer_involution_indices, inner_involution_indices)
)
parametrize.register_parametrization(
layers[2], "bias",
IEBias(inner_involution_indices)
)
parametrize.register_parametrization(
layers[5], "weight",
RCEWeight(inner_involution_indices, inner_involution_indices)
)
parametrize.register_parametrization(
layers[5], "bias",
IEBias(inner_involution_indices)
)
parametrize.register_parametrization(
layers[8], "weight",
RCEWeight(inner_involution_indices, outer_involution_indices)
)
parametrize.register_parametrization(
layers[8], "bias",
IEBias(outer_involution_indices)
)
self.layers = nn.Sequential(*layers)
self._kernel_size = kernel_size
self._dilation_rate = dilation_rate
@property
def kernel_size(self):
return self._kernel_size
@property
def dilation_rate(self):
return self._dilation_rate
def forward(self, x: torch.Tensor) -> torch.Tensor:
input_length = x.shape[2]
output_length = get_conv1d_output_length(input_length, self.kernel_size, dilation_rate=self.dilation_rate)
a = (input_length - output_length) // 2
if a == 0:
return self.layers(x) + x
return self.layers(x) + x[:, :, a:-a]
class RCEByteNet(nn.Module):
def __init__(
self,
input_involution_indices: List[int],
output_involution_indices: List[int],
dilation_rates: List[int],
outer_dim: int,
inner_dim: int,
kernel_size: int,
num_output_channels: int = 1,
pad_token_idx: Optional[int] = None
):
if pad_token_idx is not None and input_involution_indices[pad_token_idx] != pad_token_idx:
raise ValueError("`input_involution_indices[pad_token_idx]` must be equal to `pad_token_idx`")
super().__init__()
vocab_size = len(input_involution_indices)
outer_involution_indices = get_involution_indices(outer_dim)
self.embedding = nn.Embedding(vocab_size, outer_dim, padding_idx=pad_token_idx)
parametrize.register_parametrization(
self.embedding, "weight",
IEWeight(input_involution_indices, outer_involution_indices)
)
nn.init.normal_(self.embedding.weight, std=2**0.5)
self.embedding.weight.data[self.embedding.padding_idx].zero_()
self.embedding.requires_grad = False
blocks = []
receptive_field_size = 1
for r in dilation_rates:
blocks.append(RCEByteNetBlock(outer_involution_indices, inner_dim, kernel_size, dilation_rate=r))
receptive_field_size += (kernel_size - 1) * r
self.blocks = nn.Sequential(*blocks)
self._num_output_channels = num_output_channels
output_dim = len(output_involution_indices)
output_involution_indices = [
i * len(output_involution_indices) + j
for i, j in product(range(num_output_channels), output_involution_indices)
]
self.output_layers = nn.Sequential(
nn.GroupNorm(1, outer_dim), nn.GELU(),
nn.Conv1d(outer_dim, output_dim * num_output_channels, kernel_size=1)
)
parametrize.register_parametrization(
self.output_layers[-1], "weight", RCEWeight(outer_involution_indices, output_involution_indices)
)
parametrize.register_parametrization(self.output_layers[-1], "bias", IEBias(output_involution_indices))
def forward(self, input_tensor: torch.Tensor) -> torch.Tensor:
x = self.blocks(self.embedding(input_tensor).swapaxes(1, 2))
output_tensor = self.output_layers(x).swapaxes(1, 2)
output_dim = output_tensor.shape[2] // self._num_output_channels
shape = list(output_tensor.shape[:-1]) + [self._num_output_channels, output_dim]
return output_tensor.reshape(shape)
from transformers import PreTrainedModel
from .configuration_revar import ReVarConfig
class ReVarModel(PreTrainedModel):
config_class = ReVarConfig
def __init__(self, config, **kwargs):
super().__init__(config, **kwargs)
dilation_rates = config.num_stacks * [config.kernel_size**i for i in range(0, config.stack_size)]
self._model = RCEByteNet(
input_involution_indices = [3, 2, 1, 0, 4, 5],
output_involution_indices=[3, 2, 1, 0],
dilation_rates=dilation_rates,
outer_dim = config.outer_dim,
inner_dim = config.inner_dim,
kernel_size=config.kernel_size,
num_output_channels=config.num_output_channels,
pad_token_idx=5
)
def get_embeddings(self, input_ids: torch.Tensor):
return self._model.get_embeddings(input_ids)
def forward(self, input_ids: torch.Tensor):
output_tensor = self._model(input_ids)
results = defaultdict(dict)
for i, cell_type in enumerate(["A549", "HepG2", "K562", "SK-N-SH", "HCT116"]):
for j, allele in enumerate("ACGT"):
results[cell_type][allele] = output_tensor[:, :, i, j]
return results |