fix: gemma3 loss in forward pass (#2473) [skip ci]
* fix: gemma3 loss in forward pass * fix: lint * fix: move patch before plugins * Update src/axolotl/monkeypatch/gemma3.py Co-authored-by: salman <salman.mohammadi@outlook.com> --------- Co-authored-by: Wing Lian <wing.lian@gmail.com> Co-authored-by: salman <salman.mohammadi@outlook.com>
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src/axolotl/monkeypatch/gemma3.py
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238
src/axolotl/monkeypatch/gemma3.py
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"""Monkeypatch for gemma3 conditional generation forward to fix loss exploding"""
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# pylint: disable=duplicate-code
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from typing import Optional, Tuple, Union
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import torch
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from transformers.cache_utils import Cache
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from transformers.models.gemma3.modeling_gemma3 import (
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_CONFIG_FOR_DOC,
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GEMMA3_INPUTS_DOCSTRING,
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Gemma3CausalLMOutputWithPast,
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logger,
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)
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from transformers.utils import (
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add_start_docstrings_to_model_forward,
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is_torchdynamo_compiling,
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replace_return_docstrings,
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)
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from transformers.utils.deprecation import deprecate_kwarg
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@deprecate_kwarg("num_logits_to_keep", version="4.50", new_name="logits_to_keep")
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@add_start_docstrings_to_model_forward(GEMMA3_INPUTS_DOCSTRING)
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@replace_return_docstrings(
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output_type=Gemma3CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC
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)
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def new_forward(
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self,
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input_ids: torch.LongTensor = None,
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pixel_values: torch.FloatTensor = None,
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attention_mask: Optional[torch.Tensor] = None,
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position_ids: Optional[torch.LongTensor] = None,
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past_key_values: Optional[Union[list[torch.FloatTensor], Cache]] = None,
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token_type_ids: Optional[torch.LongTensor] = None,
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cache_position: Optional[torch.LongTensor] = None,
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inputs_embeds: Optional[torch.FloatTensor] = None,
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labels: Optional[torch.LongTensor] = None,
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use_cache: Optional[bool] = None,
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output_attentions: Optional[bool] = None,
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output_hidden_states: Optional[bool] = None,
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return_dict: Optional[bool] = None,
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logits_to_keep: Union[int, torch.Tensor] = 0,
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**lm_kwargs,
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) -> Union[Tuple, Gemma3CausalLMOutputWithPast]:
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r"""
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labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
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Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
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config.text_config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
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(masked), the loss is only computed for the tokens with labels in `[0, ..., config.text_config.vocab_size]`.
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logits_to_keep (`int` or `torch.Tensor`, *optional*):
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If an `int`, compute logits for the last `logits_to_keep` tokens. If `0`, calculate logits for all
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`input_ids` (special case). Only last token logits are needed for generation, and calculating them only for that
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token can save memory, which becomes pretty significant for long sequences or large vocabulary size.
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If a `torch.Tensor`, must be 1D corresponding to the indices to keep in the sequence length dimension.
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This is useful when using packed tensor format (single dimension for batch and sequence length).
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Returns:
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Example:
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```python
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>>> from PIL import Image
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>>> import requests
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>>> from transformers import AutoProcessor, Gemma3ForConditionalGeneration
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>>> model = Gemma3ForConditionalGeneration.from_pretrained("google/Gemma3-test-224px-hf")
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>>> processor = AutoProcessor.from_pretrained("google/Gemma3-test-224px-hf")
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>>> prompt = "answer en Where is the cow standing?"
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>>> url = "https://huggingface.co/gv-hf/Gemma3-test-224px-hf/resolve/main/cow_beach_1.png"
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>>> image = Image.open(requests.get(url, stream=True).raw)
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>>> inputs = processor(images=image, text=prompt, return_tensors="pt")
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>>> # Generate
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>>> generate_ids = model.generate(**inputs, max_length=30)
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>>> processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
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"answer en Where is the cow standing?\nbeach"
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```"""
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if (input_ids is None) ^ (inputs_embeds is not None):
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raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
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output_attentions = (
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output_attentions
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if output_attentions is not None
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else self.config.output_attentions
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)
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output_hidden_states = (
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output_hidden_states
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if output_hidden_states is not None
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else self.config.output_hidden_states
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)
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return_dict = (
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return_dict if return_dict is not None else self.config.use_return_dict
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)
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is_training = token_type_ids is not None and labels is not None
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# Replace image id with PAD if the image token is OOV, to avoid index-errors
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if input_ids is not None and self.config.image_token_index >= self.vocab_size:
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special_image_mask = input_ids == self.config.image_token_index
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llm_input_ids = input_ids.clone()
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llm_input_ids[special_image_mask] = 0
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else:
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llm_input_ids = input_ids
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if inputs_embeds is None:
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inputs_embeds = self.get_input_embeddings()(llm_input_ids)
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if cache_position is None:
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past_seen_tokens = (
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past_key_values.get_seq_length() if past_key_values is not None else 0
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)
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cache_position = torch.arange(
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past_seen_tokens,
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past_seen_tokens + inputs_embeds.shape[1],
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device=inputs_embeds.device,
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)
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# Merge text and images
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if pixel_values is not None:
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image_features = self.get_image_features(pixel_values)
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if input_ids is None:
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special_image_mask = inputs_embeds == self.get_input_embeddings()(
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torch.tensor(
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self.config.image_token_index,
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dtype=torch.long,
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device=inputs_embeds.device,
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)
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)
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else:
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special_image_mask = (input_ids == self.config.image_token_index).unsqueeze(
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-1
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)
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special_image_mask = special_image_mask.expand_as(inputs_embeds).to(
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inputs_embeds.device
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)
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if (
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not is_torchdynamo_compiling()
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and inputs_embeds[special_image_mask].numel() != image_features.numel()
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):
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image_tokens_in_text = (special_image_mask).sum(dim=1).sum(dim=0)[0]
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raise ValueError(
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f"Number of images does not match number of special image tokens in the input text. "
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f"Got {image_tokens_in_text} image tokens in the text but {image_features.shape[0] * image_features.shape[1]} "
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"tokens from image embeddings."
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)
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image_features = image_features.to(inputs_embeds.device, inputs_embeds.dtype)
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inputs_embeds = inputs_embeds.masked_scatter(special_image_mask, image_features)
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# mask out pad-token-ids in labels for BC
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if labels is not None and self.pad_token_id in labels:
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logger.warning_once(
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"`labels` contains `pad_token_id` which will be masked with `config.ignore_index`. "
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"You have to mask out `pad_token_id` when preparing `labels`, this behavior will be removed in v.4.46.",
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)
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labels = torch.where(
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input_ids == self.pad_token_id, self.config.ignore_index, labels
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)
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causal_mask = self._update_causal_mask( # pylint: disable=protected-access
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attention_mask,
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token_type_ids,
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past_key_values,
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cache_position,
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inputs_embeds,
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is_training,
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)
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outputs = self.language_model(
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attention_mask=causal_mask,
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position_ids=position_ids,
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past_key_values=past_key_values,
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inputs_embeds=inputs_embeds,
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use_cache=use_cache,
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output_attentions=output_attentions,
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output_hidden_states=output_hidden_states,
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return_dict=return_dict,
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cache_position=cache_position,
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logits_to_keep=logits_to_keep,
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**lm_kwargs,
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)
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logits = outputs[0]
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loss = None
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if labels is not None:
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if attention_mask is not None:
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# Get the shifted attention mask
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shift_attention_mask = attention_mask[:, -logits.shape[1] + 1 :].to(
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logits.device
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) # +1 for shift
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# Filter logits and labels based on attention mask
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valid_indices = shift_attention_mask != 0
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filtered_logits = logits[..., :-1, :][valid_indices]
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filtered_labels = labels[..., 1:][valid_indices.to(labels.device)]
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# TODO: do we need to handle num_items_in_batch given we filter the logits and labels?
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loss = self.loss_function(
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logits=filtered_logits,
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labels=None, # we pass shift_labels
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shift_labels=filtered_labels,
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vocab_size=self.config.text_config.vocab_size,
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**lm_kwargs,
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)
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else:
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# Standard case without filtering
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loss = self.loss_function(
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logits=logits,
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labels=labels,
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vocab_size=self.config.text_config.vocab_size,
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**lm_kwargs,
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)
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if not return_dict:
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output = (logits,) + outputs[1:]
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return (loss,) + output if loss is not None else output
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return Gemma3CausalLMOutputWithPast(
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loss=loss,
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logits=logits,
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past_key_values=outputs.past_key_values,
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hidden_states=outputs.hidden_states,
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attentions=outputs.attentions,
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image_hidden_states=image_features if pixel_values is not None else None,
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)
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def patch_gemma3conditionalgeneration_forward():
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from transformers.models.gemma3.modeling_gemma3 import (
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Gemma3ForConditionalGeneration,
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)
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Gemma3ForConditionalGeneration.forward = new_forward
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@@ -535,6 +535,15 @@ class ModelLoader:
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self.auto_model_loader = AutoModelForCausalLM # pylint: disable=invalid-name
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def apply_patches(self) -> None:
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# patch gemma3 conditional generation forward before loading plugins
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# as it could be overridden by plugins
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if self.cfg.model_config_type == "gemma3":
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from axolotl.monkeypatch.gemma3 import (
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patch_gemma3conditionalgeneration_forward,
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)
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patch_gemma3conditionalgeneration_forward()
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# load any patches from plugins
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from axolotl.integrations.base import PluginManager
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