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import torch
from torch import nn
from uniperceiver.config import configurable
from uniperceiver.config import kfg
from .build import PREDICTOR_REGISTRY
import math
import torch.nn.functional as F
__all__ = ["BasePredictor", "RobertaLMHead","TwoLayerPredictor", "RobertaRegressionHead"]
@PREDICTOR_REGISTRY.register()
class BasePredictor(nn.Module):
@configurable
def __init__(
self,
*,
hidden_size: int,
vocab_size: int, # include <BOS>/<EOS>
dropout: float
):
super(BasePredictor, self).__init__()
self.logits = nn.Linear(hidden_size, vocab_size)
self.dropout = nn.Dropout(dropout) if dropout > 0.0 else None
@classmethod
def from_config(cls, cfg):
return {
"hidden_size": cfg.MODEL.DECODER_DIM,
"vocab_size": cfg.MODEL.VOCAB_SIZE,
"dropout": cfg.MODEL.PRED_DROPOUT
}
@classmethod
def add_config(cls, cfg):
pass
def forward(self, batched_inputs):
hidden_states = batched_inputs[kfg.G_HIDDEN_STATES]
if isinstance(hidden_states, list):
hidden_states = hidden_states[-1]
if self.dropout:
hidden_states = self.dropout(hidden_states)
logits = self.logits(hidden_states)
return { kfg.G_LOGITS: logits }
def gelu_accurate(x):
if not hasattr(gelu_accurate, "_a"):
gelu_accurate._a = math.sqrt(2 / math.pi)
return (
0.5 * x * (1 + torch.tanh(gelu_accurate._a * (x + 0.044715 * torch.pow(x, 3))))
)
def gelu(x: torch.Tensor) -> torch.Tensor:
return torch.nn.functional.gelu(x.float()).type_as(x)
@PREDICTOR_REGISTRY.register()
class TwoLayerPredictor(nn.Module):
@configurable
def __init__(
self,
*,
hidden_size: int,
vocab_size: int, # include <BOS>/<EOS>
dropout: float
):
super(TwoLayerPredictor, self).__init__()
self.dense = nn.Linear(hidden_size, hidden_size)
self.activation_fn = gelu
self.layer_norm = nn.LayerNorm(hidden_size)
self.logits = nn.Linear(hidden_size, vocab_size)
self.dropout = nn.Dropout(dropout) if dropout > 0.0 else None
def replace_logits(self, shared_weights):
self.logits.weight = shared_weights
@classmethod
def from_config(cls, cfg):
return {
"hidden_size": cfg.MODEL.DECODER_DIM,
"vocab_size": cfg.MODEL.VOCAB_SIZE,
"dropout": cfg.MODEL.PRED_DROPOUT
}
@classmethod
def add_config(cls, cfg):
pass
def forward(self, batched_inputs):
hidden_states = batched_inputs[kfg.G_HIDDEN_STATES]
if isinstance(hidden_states, list):
hidden_states = hidden_states[-1]
x = self.dense(hidden_states)
x = self.activation_fn(x)
x = self.layer_norm(x)
logits = self.logits(x)
return { kfg.G_LOGITS: logits }
@PREDICTOR_REGISTRY.register()
class RobertaLMHead(nn.Module):
@configurable
def __init__(
self,
*,
hidden_size: int,
vocab_size: int, # include <BOS>/<EOS>
dropout: float,
untie_weight_embedding: bool,
use_bias: bool,
share_hidden: bool,
):
super(RobertaLMHead, self).__init__()
self.activation_fn = gelu
if untie_weight_embedding is True:
self.weight = nn.Linear(hidden_size, vocab_size, bias=False).weight
else:
self.weight = None
if share_hidden:
self.dense = None
self.layer_norm = None
else:
self.dense = nn.Linear(hidden_size, hidden_size)
self.layer_norm = nn.LayerNorm(hidden_size)
if use_bias:
self.bias = nn.Parameter(torch.zeros(vocab_size))
else:
self.bias = None
self.dropout = nn.Dropout(dropout) if dropout > 0.0 else None
# print("dropout: {}".format(self.dropout))
def replace_weight(self, weight):
if self.weight is None:
self.weight = weight
else:
print('already has weight, please set UNTIE_WEIGHT_EMBEDDING to False')
def replace_module_hidden(self, dense, layer_norm):
if (self.dense is None) and (self.layer_norm is None):
self.dense = dense
self.layer_norm = layer_norm
else:
print('already has hidden layers!')
raise ValueError
@classmethod
def from_config(cls, cfg):
return {
"hidden_size": cfg.MODEL.DECODER_DIM,
"vocab_size": cfg.MODEL.VOCAB_SIZE,
"dropout": cfg.MODEL.PRED_DROPOUT,
"untie_weight_embedding": cfg.MODEL.UNTIE_WEIGHT_EMBEDDING,
"use_bias": cfg.MODEL.USE_PREDICTOR_BIAS,
"share_hidden": cfg.MODEL.SHARE_PREDICTOR_HIDDEN,
}
@classmethod
def add_config(cls, cfg):
pass
def forward(self, batched_inputs):
if kfg.G_HIDDEN_STATES in batched_inputs:
hidden_states = batched_inputs[kfg.G_HIDDEN_STATES]
if isinstance(hidden_states, list):
hidden_states = hidden_states[-1]
if kfg.G_TARGET_IDS in batched_inputs:
mask_tokens = batched_inputs[kfg.G_TARGET_IDS].ne(-1)
hidden_states = hidden_states[mask_tokens]
batched_inputs[kfg.G_TARGET_IDS] = batched_inputs[kfg.G_TARGET_IDS][mask_tokens]
logits = self._forward(hidden_states)
return { kfg.G_LOGITS: logits }
elif kfg.U_HIDDEN_STATES in batched_inputs:
hidden_states = batched_inputs[kfg.U_HIDDEN_STATES]
if isinstance(hidden_states, list):
hidden_states = hidden_states[-1]
mask_tokens = batched_inputs[kfg.U_TARGET_IDS].ne(-1)
hidden_states = hidden_states[mask_tokens]
batched_inputs[kfg.U_TARGET_IDS] = batched_inputs[kfg.U_TARGET_IDS][mask_tokens]
logits = self._forward(hidden_states)
return { kfg.U_LOGITS: logits }
def _forward(self, x):
x = self.dense(x)
x = self.activation_fn(x)
x = self.layer_norm(x)
if self.dropout:
x = self.dropout(x)
if self.bias is not None:
logits = F.linear(x, self.weight) + self.bias
else:
logits = F.linear(x, self.weight)
return logits
@PREDICTOR_REGISTRY.register()
class RobertaRegressionHead(nn.Module):
@configurable
def __init__(
self,
*,
hidden_size,
feat_dim,
transform,
sigmoid
):
super(RobertaRegressionHead, self).__init__()
self.transform = transform
self.decoder = nn.Linear(hidden_size, feat_dim)
self.output_sigmoid = sigmoid
@classmethod
def from_config(cls, cfg):
return {
"hidden_size": cfg.MODEL.DECODER_DIM,
"feat_dim": cfg.MODEL.LABELS_NUM,
"sigmoid": cfg.MODEL.SIGMOID,
"transform": BertPooler(cfg)
}
@classmethod
def add_config(cls, cfg):
pass
def test_forward(self, u_logits):
# for Single stream similarity
return { kfg.OUTPUT: u_logits }
def forward(self, batched_inputs):
ret = {}
if kfg.G_HIDDEN_STATES in batched_inputs:
hidden_states = batched_inputs[kfg.G_HIDDEN_STATES]
if isinstance(hidden_states, list):
hidden_states = hidden_states[-1]
hidden_states = self.transform(hidden_states)
logits = self.decoder(hidden_states)
if self.output_sigmoid:
logits = torch.sigmoid(logits)
ret.update({ kfg.G_LOGITS: logits })
if not self.training:
ret_test = self.test_forward(logits)
ret.update(ret_test)
return ret
elif kfg.U_HIDDEN_STATES in batched_inputs:
hidden_states = batched_inputs[kfg.U_HIDDEN_STATES]
if isinstance(hidden_states, list):
hidden_states = hidden_states[-1]
hidden_states = self.transform(hidden_states)
logits = self.decoder(hidden_states)
if self.output_sigmoid:
logits = torch.sigmoid(logits)
ret.update({ kfg.U_LOGITS: logits })
if not self.training:
ret_test = self.test_forward(logits)
ret.update(ret_test)
return ret
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