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from __future__ import annotations
import copy
import torch
import torch.nn as nn
from typing import Optional, Tuple, Union
from transformers.modeling_outputs import MaskedLMOutput, SequenceClassifierOutput, TokenClassifierOutput
from transformers.models.gemma3.configuration_gemma3 import Gemma3TextConfig
from transformers.models.gemma3.modeling_gemma3 import (
Gemma3PreTrainedModel,
Gemma3TextModel,
)
class Gemma3EncoderModel(Gemma3PreTrainedModel):
config_class = Gemma3TextConfig
base_model_prefix = "encoder"
def __init__(self, config):
cfg = copy.deepcopy(config)
if hasattr(cfg, "use_bidirectional_attention"):
cfg.use_bidirectional_attention = True
cfg.use_cache = False
super().__init__(cfg)
self.encoder = Gemma3TextModel(cfg)
self.post_init()
def forward(self, input_ids=None, attention_mask=None, position_ids=None,
inputs_embeds=None, output_attentions=None, output_hidden_states=None,
return_dict=True, **kwargs):
return self.encoder(
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
inputs_embeds=inputs_embeds,
use_cache=False,
is_causal=False,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
**kwargs,
)
class Gemma3EncoderForMaskedLM(Gemma3PreTrainedModel):
config_class = Gemma3TextConfig
base_model_prefix = "encoder"
_tied_weights_keys = ["lm_head.weight"]
_keys_to_ignore_on_load_missing = [r"lm_head\.weight"]
def __init__(self, config: Gemma3TextConfig):
cfg = copy.deepcopy(config)
if hasattr(cfg, "use_bidirectional_attention"):
cfg.use_bidirectional_attention = True
cfg.use_cache = False
super().__init__(cfg)
self.encoder = Gemma3TextModel(cfg)
self.vocab_size = cfg.vocab_size
self.lm_head = nn.Linear(cfg.hidden_size, cfg.vocab_size, bias=False)
self.post_init() # calls tie_weights()
# Embeddings / head
def get_input_embeddings(self):
return self.encoder.embed_tokens
def set_input_embeddings(self, new_embeddings):
self.encoder.embed_tokens = new_embeddings
def get_output_embeddings(self):
return self.lm_head
def set_output_embeddings(self, new_head: nn.Module):
self.lm_head = new_head
# Keep vocab_size in sync; ensure pointer-tying
def tie_weights(self):
if hasattr(self.config, "vocab_size"):
self.config.vocab_size = self.get_input_embeddings().num_embeddings
self.vocab_size = self.config.vocab_size
if getattr(self.config, "tie_word_embeddings", True):
self._tie_or_clone_weights(self.lm_head, self.get_input_embeddings())
# Ensure 'lm_head.weight' exists when saving (avoids resume warnings)
def state_dict(self, *args, **kwargs):
sd = super().state_dict(*args, **kwargs)
if "lm_head.weight" not in sd and getattr(self.config, "tie_word_embeddings", True):
emb_key = f"{self.base_model_prefix}.embed_tokens.weight"
if emb_key in sd:
sd["lm_head.weight"] = sd[emb_key]
return sd
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = True,
**kwargs,
) -> Union[MaskedLMOutput, Tuple[torch.Tensor, ...]]:
outputs = self.encoder(
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
inputs_embeds=inputs_embeds,
use_cache=False,
is_causal=False,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
**kwargs,
)
hidden_states = outputs.last_hidden_state
logits = self.lm_head(hidden_states)
loss = None
if labels is not None:
loss_fct = nn.CrossEntropyLoss(ignore_index=-100)
loss = loss_fct(logits.view(-1, self.vocab_size), labels.view(-1))
if not return_dict:
out = (logits, hidden_states)
if output_hidden_states:
out += (outputs.hidden_states,)
if output_attentions:
out += (outputs.attentions,)
if loss is not None:
out = (loss,) + out
return out
return MaskedLMOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
class Gemma3EncoderForSequenceClassification(Gemma3PreTrainedModel):
"""Gemma3 Encoder with a sequence classification head (mean pooling + linear)."""
config_class = Gemma3TextConfig
base_model_prefix = "encoder"
def __init__(self, config: Gemma3TextConfig):
cfg = copy.deepcopy(config)
if hasattr(cfg, "use_bidirectional_attention"):
cfg.use_bidirectional_attention = True
cfg.use_cache = False
super().__init__(cfg)
self.num_labels = getattr(cfg, "num_labels", 2)
self.encoder = Gemma3TextModel(cfg)
classifier_dropout = getattr(cfg, "classifier_dropout", 0.0)
self.dropout = nn.Dropout(classifier_dropout)
self.classifier = nn.Linear(cfg.hidden_size, self.num_labels)
self.post_init()
def get_input_embeddings(self):
return self.encoder.embed_tokens
def set_input_embeddings(self, new_embeddings):
self.encoder.embed_tokens = new_embeddings
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = True,
**kwargs,
) -> Union[SequenceClassifierOutput, Tuple[torch.Tensor, ...]]:
outputs = self.encoder(
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
inputs_embeds=inputs_embeds,
use_cache=False,
is_causal=False,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
**kwargs,
)
hidden_states = outputs.last_hidden_state # (batch, seq_len, hidden)
# Mean pooling over non-padded tokens
if attention_mask is not None:
mask = attention_mask.unsqueeze(-1).float() # (batch, seq_len, 1)
pooled = (hidden_states * mask).sum(dim=1) / mask.sum(dim=1).clamp(min=1e-9)
else:
pooled = hidden_states.mean(dim=1)
pooled = self.dropout(pooled)
logits = self.classifier(pooled)
loss = None
if labels is not None:
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 = nn.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 = nn.CrossEntropyLoss()
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
elif self.config.problem_type == "multi_label_classification":
loss_fct = nn.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,
)
class Gemma3EncoderForTokenClassification(Gemma3PreTrainedModel):
"""Gemma3 Encoder with a token classification head for NER/POS tagging."""
config_class = Gemma3TextConfig
base_model_prefix = "encoder"
def __init__(self, config: Gemma3TextConfig):
cfg = copy.deepcopy(config)
if hasattr(cfg, "use_bidirectional_attention"):
cfg.use_bidirectional_attention = True
cfg.use_cache = False
super().__init__(cfg)
self.num_labels = getattr(cfg, "num_labels", 2)
self.encoder = Gemma3TextModel(cfg)
classifier_dropout = getattr(cfg, "classifier_dropout", 0.0)
self.dropout = nn.Dropout(classifier_dropout)
self.classifier = nn.Linear(cfg.hidden_size, self.num_labels)
self.post_init()
def get_input_embeddings(self):
return self.encoder.embed_tokens
def set_input_embeddings(self, new_embeddings):
self.encoder.embed_tokens = new_embeddings
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = True,
**kwargs,
) -> Union[TokenClassifierOutput, Tuple[torch.Tensor, ...]]:
outputs = self.encoder(
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
inputs_embeds=inputs_embeds,
use_cache=False,
is_causal=False,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
**kwargs,
)
hidden_states = outputs.last_hidden_state
hidden_states = self.dropout(hidden_states)
logits = self.classifier(hidden_states)
loss = None
if labels is not None:
loss_fct = nn.CrossEntropyLoss()
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
if not return_dict:
output = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return TokenClassifierOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
) |