File size: 4,586 Bytes
94e46db | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 | from transformers.models.gemma3.configuration_gemma3 import Gemma3TextConfig
from transformers.models.gemma3.modeling_gemma3 import (
Gemma3TextModel,
Gemma3PreTrainedModel,
GEMMA3_INPUTS_DOCSTRING,
)
from transformers.modeling_outputs import (
SequenceClassifierOutputWithPast,
BaseModelOutputWithPast,
)
from transformers.utils.doc import add_start_docstrings_to_model_forward
from transformers.utils.generic import can_return_tuple
from transformers.utils import logging
from transformers.cache_utils import Cache
from typing import Optional
from torch import nn
import torch
logger = logging.get_logger(__name__)
class Gemma3ForSequenceClassification(Gemma3PreTrainedModel):
config_class = Gemma3TextConfig
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.model = Gemma3TextModel(config)
self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
self.post_init()
def get_input_embeddings(self):
return self.model.embed_tokens
def set_input_embeddings(self, value):
self.model.embed_tokens = value
@can_return_tuple
@add_start_docstrings_to_model_forward(GEMMA3_INPUTS_DOCSTRING)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Cache] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
) -> SequenceClassifierOutputWithPast:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
"""
transformer_outputs: BaseModelOutputWithPast = self.model(
input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
)
hidden_states = transformer_outputs.last_hidden_state
logits = self.score(hidden_states)
if input_ids is not None:
batch_size = input_ids.shape[0]
else:
batch_size = inputs_embeds.shape[0]
if self.config.pad_token_id is None and batch_size != 1:
raise ValueError(
"Cannot handle batch sizes > 1 if no padding token is defined."
)
if self.config.pad_token_id is None:
last_non_pad_token = -1
elif input_ids is not None:
# To handle both left- and right- padding, we take the rightmost token that is not equal to pad_token_id
non_pad_mask = (input_ids != self.config.pad_token_id).to(
logits.device, torch.int32
)
token_indices = torch.arange(
input_ids.shape[-1], device=logits.device, dtype=torch.int32
)
last_non_pad_token = (token_indices * non_pad_mask).argmax(-1)
else:
last_non_pad_token = -1
logger.warning_once(
f"{self.__class__.__name__} will not detect padding tokens in `inputs_embeds`. Results may be "
"unexpected if using padding tokens in conjunction with `inputs_embeds.`"
)
pooled_logits = logits[
torch.arange(batch_size, device=logits.device), last_non_pad_token
]
loss = None
if labels is not None:
loss = self.loss_function(
logits=logits,
labels=labels,
pooled_logits=pooled_logits,
config=self.config,
)
return SequenceClassifierOutputWithPast(
loss=loss,
logits=pooled_logits,
past_key_values=transformer_outputs.past_key_values,
hidden_states=transformer_outputs.hidden_states,
attentions=transformer_outputs.attentions,
)
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