paligemma.py

from typing import List
from typing import Optional
from typing import Tuple
from typing import Union

import torch

from torch.nn import CrossEntropyLoss
from transformers.cache_utils import Cache
from transformers.utils import is_torchdynamo_compiling
from transformers.utils import logging

from liger_kernel.transformers.model.loss_utils import LigerForCausalLMLoss
from liger_kernel.transformers.model.loss_utils import unpack_cross_entropy_result
from liger_kernel.transformers.model.output_classes import LigerPaliGemmaCausalLMOutputWithPast

logger = logging.get_logger(__name__)


def lce_forward(
    self,
    input_ids: torch.LongTensor = None,
    pixel_values: torch.FloatTensor = None,
    attention_mask: Optional[torch.Tensor] = None,
    position_ids: Optional[torch.LongTensor] = None,
    past_key_values: Optional[Union[List[torch.FloatTensor], Cache]] = None,
    token_type_ids: Optional[torch.LongTensor] = None,
    cache_position: Optional[torch.LongTensor] = 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,
    return_dict: Optional[bool] = None,
    logits_to_keep: Union[int, torch.Tensor] = 0,
    skip_logits: Optional[bool] = None,
    **lm_kwargs,
) -> Union[Tuple, LigerPaliGemmaCausalLMOutputWithPast]:
    r"""
    Args:
        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
            Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
            config.text_config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
            (masked), the loss is only computed for the tokens with labels in `[0, ..., config.text_config.vocab_size]`.

        logits_to_keep (`int` or `torch.Tensor`, *optional*):
            If an `int`, compute logits for the last `logits_to_keep` tokens. If `0`, calculate logits for all
            `input_ids` (special case). Only last token logits are needed for generation, and calculating them only for that
            token can save memory, which becomes pretty significant for long sequences or large vocabulary size.
            If a `torch.Tensor`, must be 1D corresponding to the indices to keep in the sequence length dimension.
            This is useful when using packed tensor format (single dimension for batch and sequence length).

    Returns:

    Example:

    ```python
    >>> from PIL import Image
    >>> import requests
    >>> from transformers import AutoProcessor, PaliGemmaForConditionalGeneration

    >>> model = PaliGemmaForConditionalGeneration.from_pretrained("google/PaliGemma-test-224px-hf")
    >>> processor = AutoProcessor.from_pretrained("google/PaliGemma-test-224px-hf")

    >>> prompt = "answer en Where is the cow standing?"
    >>> url = "https://huggingface.co/gv-hf/PaliGemma-test-224px-hf/resolve/main/cow_beach_1.png"
    >>> image = Image.open(requests.get(url, stream=True).raw)

    >>> inputs = processor(images=image, text=prompt,  return_tensors="pt")

    >>> # Generate
    >>> generate_ids = model.generate(**inputs, max_length=30)
    >>> processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
    "answer en Where is the cow standing?\nbeach"
    ```"""

    if (input_ids is None) ^ (inputs_embeds is not None):
        raise ValueError("You must specify exactly one of input_ids or inputs_embeds")

    if pixel_values is not None and inputs_embeds is not None:
        raise ValueError(
            "You cannot specify both pixel_values and inputs_embeds at the same time, and must specify either one"
        )

    output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
    output_hidden_states = (
        output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
    )
    return_dict = return_dict if return_dict is not None else self.config.use_return_dict

    is_training = token_type_ids is not None and labels is not None

    if inputs_embeds is None:
        inputs_embeds = self.get_input_embeddings()(input_ids)

    if cache_position is None:
        past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
        cache_position = torch.arange(
            past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
        )

    if position_ids is None:
        position_ids = cache_position.unsqueeze(0) + 1  # Paligemma positions are 1-indexed

    # Merge text and images
    if pixel_values is not None:
        image_features = self.get_image_features(pixel_values)

        special_image_mask = (input_ids == self.config.image_token_index).unsqueeze(-1)
        special_image_mask = special_image_mask.expand_as(inputs_embeds).to(inputs_embeds.device)
        if not is_torchdynamo_compiling() and inputs_embeds[special_image_mask].numel() != image_features.numel():
            image_tokens_in_text = torch.sum(input_ids == self.config.image_token_index)
            raise ValueError(
                f"Number of images does not match number of special image tokens in the input text. "
                f"Got {image_tokens_in_text} image tokens in the text but {image_features.shape[0] * image_features.shape[1]} "
                "tokens from image embeddings."
            )
        image_features = image_features.to(inputs_embeds.device, inputs_embeds.dtype)
        inputs_embeds = inputs_embeds.masked_scatter(special_image_mask, image_features)

    # mask out pad-token-ids in labels for BC
    if labels is not None and self.pad_token_id in labels:
        logger.warning_once(
            "`labels` contains `pad_token_id` which will be masked with `config.ignore_index`. "
            "You have to mask out `pad_token_id` when preparing `labels`, this behavior will be removed in v.4.46.",
        )
        labels = torch.where(input_ids == self.pad_token_id, self.config.ignore_index, labels)

    causal_mask = self._update_causal_mask(
        attention_mask, token_type_ids, past_key_values, cache_position, inputs_embeds, is_training
    )

    outputs = self.language_model.model(
        attention_mask=causal_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,
        return_dict=return_dict,
        cache_position=cache_position,
        logits_to_keep=logits_to_keep,
        **lm_kwargs,
    )

    shift_labels = lm_kwargs.pop("shift_labels", None)
    hidden_states = outputs[0]

    loss = None
    logits = None
    token_accuracy = None
    predicted_tokens = None

    if skip_logits and labels is None:
        raise ValueError("skip_logits is True, but labels is None")

    if skip_logits is None:
        skip_logits = self.training and (labels is not None)

    if skip_logits:
        shift_hidden_states = hidden_states[..., :-1, :]
        shift_labels = labels[..., 1:]

        hidden_device = shift_hidden_states.device

        if attention_mask is not None:
            # we use the input attention mask to shift the hidden_states and labels, because it is 2D.
            # we also crop attn mask in case it is longer, which happens in PrefixTuning with peft
            shift_attention_mask = attention_mask[:, -shift_hidden_states.shape[1] :].to(hidden_device)
            shift_hidden_states = shift_hidden_states[shift_attention_mask.to(hidden_device) != 0].contiguous()
            shift_labels = shift_labels[shift_attention_mask.to(shift_labels.device) != 0].contiguous()
        else:
            shift_hidden_states = shift_hidden_states.contiguous()
            shift_labels = shift_labels.contiguous()

        # Flatten hidden state
        shift_hidden_states = shift_hidden_states.view(-1, self.config.text_config.hidden_size)
        shift_labels = shift_labels.view(-1).to(hidden_device)

        # Use LigerForCausalLMLoss with accuracy support and pass already shifted labels
        result = LigerForCausalLMLoss(
            hidden_states=shift_hidden_states,
            lm_head_weight=self.language_model.lm_head.weight,
            labels=None,
            shift_labels=shift_labels,
            hidden_size=self.config.text_config.hidden_size,
            **lm_kwargs,
        )
        loss, _, token_accuracy, predicted_tokens = unpack_cross_entropy_result(result)
    else:
        logits = self.language_model.lm_head(hidden_states)
        if labels is not None:
            # Upcast to float if we need to compute the loss to avoid potential precision issues
            logits = logits.float()
            shift_logits = logits[..., :-1, :]
            shift_labels = labels[..., 1:]
            if attention_mask is not None:
                # we use the input attention mask to shift the logits and labels, because it is 2D.
                # we also crop attn mask in case it is longer, which happens in PrefixTuning with peft
                shift_attention_mask = attention_mask[:, -shift_logits.shape[1] :].to(logits.device)
                shift_logits = shift_logits[shift_attention_mask.to(logits.device) != 0].contiguous()
                shift_labels = shift_labels[shift_attention_mask.to(shift_labels.device) != 0].contiguous()
            else:
                shift_logits = shift_logits.contiguous()
                shift_labels = shift_labels.contiguous()
            # Flatten the tokens
            loss_fct = CrossEntropyLoss()

            flat_logits = shift_logits.view(-1, self.config.text_config.vocab_size)
            flat_labels = shift_labels.view(-1).to(shift_logits.device)
            loss = loss_fct(flat_logits, flat_labels)
        elif shift_labels is not None:
            # Upcast to float if we need to compute the loss to avoid potential precision issues
            logits = logits.float()
            shift_logits = logits[..., :-1, :]
            if attention_mask is not None:
                # we use the input attention mask to shift the logits and labels, because it is 2D.
                # we also crop attn mask in case it is longer, which happens in PrefixTuning with peft
                shift_attention_mask = attention_mask[:, -shift_logits.shape[1] :].to(logits.device)
                shift_logits = shift_logits[shift_attention_mask.to(logits.device) != 0].contiguous()
                shift_labels = shift_labels[shift_attention_mask.to(shift_labels.device) != 0].contiguous()
            else:
                shift_logits = shift_logits.contiguous()
                shift_labels = shift_labels.contiguous()
            # Flatten the tokens
            loss_fct = CrossEntropyLoss()

            flat_logits = shift_logits.view(-1, self.config.text_config.vocab_size)
            flat_labels = shift_labels.view(-1).to(shift_logits.device)
            loss = loss_fct(flat_logits, flat_labels)

    if not return_dict:
        output = (logits,) + outputs[1:]
        output = (loss,) + output if loss is not None else output
        output = output + (token_accuracy,) if token_accuracy is not None else output
        output = output + (predicted_tokens,) if predicted_tokens is not None else output
        return output

    # Return PaliGemma output with token_accuracy field
    return LigerPaliGemmaCausalLMOutputWithPast(
        loss=loss,
        logits=logits,
        past_key_values=outputs.past_key_values,
        hidden_states=outputs.hidden_states,
        attentions=outputs.attentions,
        image_hidden_states=image_features if pixel_values is not None else None,
        token_accuracy=token_accuracy,
        predicted_tokens=predicted_tokens,
    )