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from math import sqrt,log
import sys
import torch
import torch.nn as nn
from torch.nn.functional import softmax,relu,linear, gelu
from common import PositionalEncoding
from hopfield import HopfieldLayer, HopfieldMHA, HopfieldReLU, HopfieldSoftmax
from configuration_energy import BertEnergyConfig
from torch.cuda.amp import autocast
import yaml
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from transformers import PreTrainedModel, PretrainedConfig
from transformers.modeling_outputs import MaskedLMOutput, BaseModelOutput
ACT2FN={'relu': relu, 'gelu': gelu, 'softmax': softmax}
class BertModel(PreTrainedModel):
""" Backbone of standard BERT model
outputs : last hidden state, history"""
config_class = BertEnergyConfig
def __init__(self, config, add_pooling_layer=True, pad_idx=None, **kwargs):
super().__init__(config)
self.Emb_in = nn.Embedding(config.vocabulary_size, config.embedding_dim, padding_idx=pad_idx)
self.posn = PositionalEncoding(config.embedding_dim, max_len=config.block_size,dropout=config.dropout) if config.positional else None
if config.share_layers: # ALBERT config
self.embedding_hidden_in = nn.Linear(config.embedding_dim, config.forward_memories) if config.share_layers else None # Albert uses two matrices instead of one for embeddings see 3.1 in Albert paper
# Albert normalise and penalise embeddings
self.embed_norm = nn.LayerNorm(config.embedding_dim, eps=config.layer_norm)
self.embed_dropout = nn.Dropout(config.dropout)
self.num_layers = config.num_layers
self.share_layers = config.share_layers
if config.share_layers:
layer = nn.TransformerEncoderLayer(config.forward_memories,
config.num_heads,
activation=config.activation,
dim_feedforward=config.forward_memories*4,
dropout=config.dropout,
layer_norm_eps=config.layer_norm,
batch_first=True,
norm_first=True,
)
self.layers = nn.ModuleList([layer])
else:
self.layers = nn.ModuleList([nn.TransformerEncoderLayer(config.embedding_dim,
config.num_heads,
dim_feedforward=config.forward_memories*4,
dropout=config.dropout,
layer_norm_eps=config.layer_norm,
batch_first=True,
norm_first=True,
) for _ in range(config.num_layers)])
def forward(self,input_ids, attention_mask=None, **kwargs):
""" Warning : expect attention mask with 0 pad tokens -> mismatch Pytorch/HF tokenizer"""
xbatch = self.Emb_in(input_ids)
if self.posn:
X = xbatch + self.posn(xbatch)
else:
X = xbatch
if self.share_layers:
X = self.embed_norm(X)
X = self.embed_dropout(X)
X = self.embedding_hidden_in(X)
history = None if self.training else [X]
# WARNING
attention_mask = ~attention_mask.bool() # Mismatch between HF tokenizer and Torch attention mask https://pytorch.org/docs/stable/generated/torch.nn.Transformer.html#torch.nn.Transformer
for i in range(self.num_layers):
if self.share_layers:
layer = self.layers[0]
else:
layer = self.layers[i]
X = layer(X, src_key_padding_mask=attention_mask)
if not self.training:
history.append(X)
# TODO add return attention
return BaseModelOutput(last_hidden_state=X,
hidden_states=history,
attentions=None)
class BertModelForMaskedLM(PreTrainedModel):
""" Bert model to be trained on the MLM task.
Based on the backbone Bert model + projection on the vocabulary with tied weight and norm
outputs: cross entropy loss / logits / hidden states
"""
config_class = BertEnergyConfig
ignore_index = -100
_tied_weights_keys = ["Emb_out.weight", "Emb_out.bias"]
def __init__(self, config, add_pooling_layer=True, pad_idx=None):
super().__init__(config)
self.config = config
self.model = BertModel(config, pad_idx=pad_idx)
self.norm = nn.LayerNorm(config.embedding_dim, eps=config.layer_norm)
self.dense = nn.Linear(config.forward_memories, config.embedding_dim)
self.activation = ACT2FN[config.activation]
"""
if config.tie_weights:
self.Emb_out = nn.Linear(config.embedding_dim, config.vocabulary_size, bias=False)
self.tie_weights()
else:
self.Emb_out = nn.Linear(config.embedding_dim, config.vocabulary_size)
self.bias = nn.Parameter(torch.zeros(config.vocabulary_size))
self.Emb_out.bias = self.bias
"""
self.Emb_out = nn.Linear(config.forward_memories, config.vocabulary_size)
self.bias = nn.Parameter(torch.zeros(config.vocabulary_size))
self.Emb_out.bias = self.bias
def get_input_embeddings(self):
return self.model.Emb_in
def set_output_embeddings(self, new_embeddings):
self.Emb_out = new_embeddings
def forward(self,input_ids, attention_mask=None, labels=None, **kwargs):
outputs = self.model(input_ids, attention_mask, **kwargs)
last_hidden_state = outputs.last_hidden_state
hidden_states = outputs.hidden_states
attentions = outputs.attentions
last_hidden_state = self.dense(last_hidden_state)
last_hidden_state = self.activation(last_hidden_state)
last_hidden_state = self.norm(last_hidden_state)
"""
if self.config.tie_weights:
logits = last_hidden_state @ self.Emb_out.weight.transpose(-1,-2)
else:
logits = self.Emb_out(last_hidden_state)
"""
logits = self.Emb_out(last_hidden_state)
loss = None
if labels is not None:
loss_fct = CrossEntropyLoss()
loss = loss_fct(logits.view(-1, self.config.vocabulary_size), labels.view(-1))
return MaskedLMOutput(loss=loss,
logits=logits,
hidden_states=hidden_states,
attentions=attentions)
class BertModelForSequenceClassification(PreTrainedModel):
""" Bert model to be trained on Sequence classification tasks.
Based on the backbone Bert model + projection on the vocabulary with tied weight and norm
outputs: cross entropy loss / logits / hidden states
"""
config_class = BertEnergyConfig
ignore_index = -100
def __init__(self, config, add_pooling_layer=True, pad_idx=None,
num_labels=2, classifier_dropout=None, return_dict=True):
super().__init__(config)
self.config = config
self.num_labels = num_labels
self.classifier_dropout = classifier_dropout
self.return_dict = return_dict
self.model = BertModel(config, pad_idx=pad_idx)
self.dense = nn.Linear(config.forward_memories, config.forward_memories)
classifier_dropout = (
classifier_dropout if classifier_dropout is not None else config.dropout
)
self.dropout = nn.Dropout(classifier_dropout)
self.classifier = nn.Linear(config.forward_memories,num_labels)
self.norm = nn.LayerNorm(config.embedding_dim)
#self.Emb_out = nn.Linear(config.embedding_dim, config.vocabulary_size, bias=False)
#self.Emb_out.weight = self.model.Emb_in.weight # weight tying
def forward(self,input_ids, labels=None, return_dict=False, **kwargs):
outputs = self.model(input_ids, **kwargs)
last_hidden_state = self.norm(outputs.last_hidden_state)
# Code from roberta : https://github.com/huggingface/transformers/blob/v4.39.3/src/transformers/models/roberta/modeling_roberta.py#L1426
x = last_hidden_state[:, 0, :] # take <s> token (equiv. to [CLS])
x = self.dropout(x)
x = self.dense(x)
x = torch.tanh(x)
x = self.dropout(x)
logits = self.classifier(x)
hidden_states = outputs.hidden_states
attentions = outputs.attentions
loss = None
if labels is not None:
# move labels to correct device to enable model parallelism
labels = labels.to(logits.device)
if self.config.problem_type is None:
if self.num_labels == 1:
self.config.problem_type = "regression"
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
self.config.problem_type = "single_label_classification"
else:
self.config.problem_type = "multi_label_classification"
if self.config.problem_type == "regression":
loss_fct = MSELoss()
if self.num_labels == 1:
loss = loss_fct(logits.squeeze(), labels.squeeze())
else:
loss = loss_fct(logits, labels)
elif self.config.problem_type == "single_label_classification":
loss_fct = CrossEntropyLoss()
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
elif self.config.problem_type == "multi_label_classification":
loss_fct = BCEWithLogitsLoss()
loss = loss_fct(logits, labels)
if not return_dict:
output = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return SequenceClassifierOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
def compute_loss(self, logits, labels):
# code from https://github.com/huggingface/transformers/blob/main/src/transformers/trainer_pt_utils.py#L494
log_probs = -nn.functional.log_softmax(logits, dim=-1)
if labels.dim() == log_probs.dim() - 1:
labels = labels.unsqueeze(-1)
padding_mask = labels.eq(self.ignore_index)
# In case the ignore_index is -100, the gather will fail, so we replace labels by 0. The padding_mask
# will ignore them in any case.
labels = torch.clamp(labels, min=0)
nll_loss = log_probs.gather(dim=-1, index=labels)
nll_loss.masked_fill_(padding_mask, 0.0)
num_active_elements = padding_mask.numel() - padding_mask.long().sum()
nll_loss = nll_loss.sum() / num_active_elements
return nll_loss
class BertEnergyModel(PreTrainedModel):
config_class = BertEnergyConfig
def __init__(self, config, add_pooling_layer=True, pad_idx=None, **kwargs):
super().__init__(config)
self.Emb_in = nn.Embedding(config.vocabulary_size, config.embedding_dim, padding_idx=pad_idx)
self.posn = PositionalEncoding(config.embedding_dim,max_len=config.block_size,dropout=config.dropout) if config.positional else None
self.num_layers = config.num_layers
self.layer = HopfieldLayer(config.embedding_dim,config.num_heads,forward_memories=config.forward_memories,forward_activation=config.activation,bias=config.bias,beta=config.beta,dropout=config.dropout)
self.alpha = config.alpha
def forward(self,input_ids, attention_mask=None, **kwargs):
xbatch = self.Emb_in(input_ids)
if self.posn:
X = xbatch + self.posn(xbatch)
else:
X = xbatch
history = None if self.training else [X]
for _ in range(self.num_layers):
#TODO add src_key pad attention mask
X = X - self.alpha * self.layer(X, src_key_padding_mask=attention_mask, is_causal=False)
if not self.training:
history.append(X)
return BaseModelOutput(last_hidden_state=X,
hidden_states=history,
attentions=None)
class BertEnergyModelForMaskedLM(PreTrainedModel):
config_class = BertEnergyConfig
ignore_index = -100
_tied_weights_keys = ["Emb_out.weight", "Emb_out.bias"]
def __init__(self, config, add_pooling_layer=True, pad_idx=None):
super().__init__(config)
self.config = config
self.model = BertEnergyModel(config, pad_idx=pad_idx)
self.norm = nn.LayerNorm(config.embedding_dim, eps=config.layer_norm)
self.dense = nn.Linear(config.embedding_dim, config.embedding_dim)
self.activation = ACT2FN[config.activation]
self.Emb_out = nn.Linear(config.embedding_dim, config.vocabulary_size)
self.bias = nn.Parameter(torch.zeros(config.vocabulary_size))
self.Emb_out.bias = self.bias
def get_input_embeddings(self):
return self.model.Emb_in
def set_output_embeddings(self, new_embeddings):
self.Emb_out = new_embeddings
def forward(self,input_ids, attention_mask=None, labels=None, **kwargs ):
outputs = self.model(input_ids , attention_mask=attention_mask)
last_hidden_state = outputs.last_hidden_state
hidden_states = outputs.hidden_states
attentions = outputs.attentions
last_hidden_state = self.dense(last_hidden_state)
last_hidden_state = gelu(last_hidden_state) #XXX
last_hidden_state = self.norm(last_hidden_state)
#logits = self.norm(last_hidden_state) @ self.Emb_out.weight.transpose(-1,-2)
if self.config.tie_weights:
logits = last_hidden_state @ self.Emb_out.weight.transpose(-1,-2)
else:
logits = self.Emb_out(last_hidden_state)
loss = None
hidden_states = hidden_states
attentions = None
#if labels is not None:
# loss = self.compute_loss(logits, labels)
if labels is not None:
loss_fct = CrossEntropyLoss()
loss = loss_fct(logits.view(-1, self.config.vocabulary_size), labels.view(-1))
return MaskedLMOutput(loss=loss,
logits=logits,
hidden_states=hidden_states,
attentions=attentions)
if __name__ == '__main__':
def grads(f, x):
""" Autograd used for the energy """
return torch.func.jacrev(f)(x)
#from test import *
x = torch.randn(1,10)
input_ids = torch.tensor([[3,12,44, 2]])
#test relu
#print('relu')
#hrelu = HopfieldReLU(10,4,bias=False)
#print(hrelu(x),hrelu.energy(x))
#print(grads(hrelu.energy,x))
#test softmax
#print('softmax')
#hsoftmax = HopfieldSoftmax(10,4,bias=None)
#print(hsoftmax(x),hsoftmax.energy(x))
#print(grads(hsoftmax.energy,x))
#test MHA
#print('mha')
#mha = HopfieldMHA(15,3)
#X = torch.randn(2,4,15)
#causal = True
#print(mha(X,is_causal=causal),mha.energy(X,is_causal=causal))
#print()
#print('=== Ref=== ')
#for x in X: #autograd breaks with higher order tensors
# print(grads(lambda y: mha.energy(y,is_causal=causal) ,x))
config = HopfieldConfig(path="../lmconfig.yaml")
print(config)
#exit()
mdl = HFHopfieldModel(config)
mdl.eval()
#print(mdl)
out = mdl(input_ids)
print(out[0].mean())
mdl.save_pretrained("test_checkpoint")
reloaded = HFHopfieldModel.from_pretrained("test_checkpoint")
out_reloaded = reloaded(input_ids)
print(out_reloaded[0].mean())
reloaded.to("cuda:0")
print(reloaded(input_ids.to("cuda:0"))[0])
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