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🔴 🔴 🔴 supersede paligemma forward to shift pos id indexing (#36859)
* supersede paligemma forward to shift pos id indexing * fix prepare_inputs_ as well * fix modular error --------- Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
This commit is contained in:
Pablo Montalvo
GitHub
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6321876b5b
commit
b8aadc31d5
@ -1313,9 +1313,6 @@ class Gemma3ForConditionalGeneration(Gemma3PreTrainedModel, GenerationMixin):
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past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
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)
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if position_ids is None:
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position_ids = cache_position.unsqueeze(0) + 1 # Gemma3 positions are 1-indexed
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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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@ -1363,7 +1360,7 @@ class Gemma3ForConditionalGeneration(Gemma3PreTrainedModel, GenerationMixin):
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**lm_kwargs,
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)
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logits = outputs[0]
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logits = outputs.logits
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loss = None
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if labels is not None:
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# Upcast to float if we need to compute the loss to avoid potential precision issues
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@ -1427,9 +1424,6 @@ class Gemma3ForConditionalGeneration(Gemma3PreTrainedModel, GenerationMixin):
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**kwargs,
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)
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# position_ids in Gemma3 are 1-indexed
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if model_inputs.get("position_ids") is not None:
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model_inputs["position_ids"] += 1
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# If we're in cached decoding stage, pixel values should be None because input ids do not contain special image token anymore
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# Otherwise we need pixel values to be passed to model. NOTE: use_cache=False needs pixel_values always
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if cache_position[0] == 0:
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@ -33,8 +33,12 @@ from ...modeling_rope_utils import rope_config_validation
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from ...modeling_utils import ALL_ATTENTION_FUNCTIONS
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from ...processing_utils import Unpack
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from ...utils import (
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add_start_docstrings_to_model_forward,
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is_torchdynamo_compiling,
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logging,
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replace_return_docstrings,
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)
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from ...utils.deprecation import deprecate_kwarg
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from ..bart.modeling_bart import BartScaledWordEmbedding
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from ..gemma2.configuration_gemma2 import Gemma2Config
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from ..gemma2.modeling_gemma2 import (
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@ -837,6 +841,217 @@ class Gemma3ForConditionalGeneration(PaliGemmaForConditionalGeneration):
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return causal_mask
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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(output_type=Gemma3CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
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def 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 = output_attentions if output_attentions is not None else self.config.output_attentions
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output_hidden_states = (
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output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
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)
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return_dict = return_dict if return_dict is not None else self.config.use_return_dict
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is_training = token_type_ids is not None and labels is not None
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# Replace image id woth PAD if the image token if 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 = past_key_values.get_seq_length() if past_key_values is not None else 0
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cache_position = torch.arange(
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past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], 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(self.config.image_token_index, dtype=torch.long, device=inputs_embeds.device)
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)
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else:
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special_image_mask = (input_ids == self.config.image_token_index).unsqueeze(-1)
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special_image_mask = special_image_mask.expand_as(inputs_embeds).to(inputs_embeds.device)
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if not is_torchdynamo_compiling() and inputs_embeds[special_image_mask].numel() != image_features.numel():
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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(input_ids == self.pad_token_id, self.config.ignore_index, labels)
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causal_mask = self._update_causal_mask(
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attention_mask, token_type_ids, past_key_values, cache_position, inputs_embeds, 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.logits
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loss = None
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if labels is not None:
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# Upcast to float if we need to compute the loss to avoid potential precision issues
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logits = logits.float()
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shift_logits = logits[..., :-1, :]
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shift_labels = labels[..., 1:]
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if attention_mask is not None:
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# we use the input attention mask to shift the logits and labels, because it is 2D.
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# we also crop attn mask in case it is longer, which happens in PrefixTuning with peft
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shift_attention_mask = attention_mask[:, -shift_logits.shape[1] :].to(logits.device)
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shift_logits = shift_logits[shift_attention_mask.to(logits.device) != 0].contiguous()
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shift_labels = shift_labels[shift_attention_mask.to(shift_labels.device) != 0].contiguous()
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else:
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shift_logits = shift_logits.contiguous()
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shift_labels = shift_labels.contiguous()
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# Flatten the tokens
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loss_fct = nn.CrossEntropyLoss()
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flat_logits = shift_logits.view(-1, self.config.text_config.vocab_size)
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flat_labels = shift_labels.view(-1).to(shift_logits.device)
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loss = loss_fct(flat_logits, flat_labels)
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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 prepare_inputs_for_generation(
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self,
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input_ids,
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past_key_values=None,
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inputs_embeds=None,
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cache_position=None,
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position_ids=None,
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pixel_values=None,
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attention_mask=None,
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token_type_ids=None,
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use_cache=True,
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logits_to_keep=None,
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labels=None,
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**kwargs,
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):
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# Overwritten -- custom `position_ids` and `pixel_values` handling
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model_inputs = self.language_model.prepare_inputs_for_generation(
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input_ids,
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past_key_values=past_key_values,
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inputs_embeds=inputs_embeds,
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attention_mask=attention_mask,
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position_ids=position_ids,
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cache_position=cache_position,
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use_cache=use_cache,
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logits_to_keep=logits_to_keep,
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token_type_ids=token_type_ids,
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**kwargs,
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)
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# If we're in cached decoding stage, pixel values should be None because input ids do not contain special image token anymore
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# Otherwise we need pixel values to be passed to model. NOTE: use_cache=False needs pixel_values always
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if cache_position[0] == 0:
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model_inputs["pixel_values"] = pixel_values
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is_training = token_type_ids is not None and labels is not None
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if cache_position[0] == 0 and isinstance(past_key_values, HybridCache):
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input_tensor = inputs_embeds if inputs_embeds is not None else input_ids
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causal_mask = self._update_causal_mask(
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attention_mask, token_type_ids, past_key_values, cache_position, input_tensor, is_training
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)
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model_inputs["attention_mask"] = causal_mask
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return model_inputs
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__all__ = [
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"Gemma3Config",
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