optimum/habana/transformers/models/paligemma/modeling_paligemma.py

# coding=utf-8
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#     http://www.apache.org/licenses/LICENSE-2.0
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"""PyTorch Paligemma model."""

from typing import List, Optional, Tuple, Union

import torch
import torch.utils.checkpoint
from torch import nn
from transformers.cache_utils import Cache
from transformers.modeling_outputs import CausalLMOutputWithPast
from transformers.models.paligemma.modeling_paligemma import (
    PaliGemmaCausalLMOutputWithPast,
    PaliGemmaForConditionalGeneration,
)
from transformers.utils import is_torchdynamo_compiling, logging


logger = logging.get_logger(__name__)


class GaudiPaliGemmaForConditionalGeneration(PaliGemmaForConditionalGeneration):
    def forward(
        self,
        input_ids: Optional[torch.LongTensor] = None,
        pixel_values: Optional[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,
        token_idx: Optional[torch.Tensor] = None,
        **lm_kwargs,
    ) -> Union[Tuple, PaliGemmaCausalLMOutputWithPast]:
        """
        Inherits from PaliGemmaForConditionalGeneration::forward https://github.com/huggingface/transformers/blob/v4.45.1/src/transformers/models/paligemma/modeling_paligemma.py#L402
        The only differences are:
        - add new args token_idx
        """

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

        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

        # Replace image id woth PAD if the image token if OOV, to avoid index-errors
        if input_ids is not None and self.config.image_token_index >= self.vocab_size:
            special_image_mask = input_ids == self.config.image_token_index
            llm_input_ids = input_ids.clone()
            llm_input_ids[special_image_mask] = 0
        else:
            llm_input_ids = input_ids

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

            if input_ids is None:
                special_image_mask = inputs_embeds == self.get_input_embeddings()(
                    torch.tensor(self.config.image_token_index, dtype=torch.long, device=inputs_embeds.device)
                )
            else:
                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 = (special_image_mask).sum(dim=1).sum(dim=0)[0]
                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: CausalLMOutputWithPast = self.language_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=True,
            cache_position=cache_position,
            # TODO: from Transformers v4.45, `generate` sets `num_logits_to_keep` to 1 if not given, which we don't want here
            # logits_to_keep=logits_to_keep,
            token_idx=token_idx,
            **lm_kwargs,
        )

        logits = outputs[0]
        loss = None
        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 = nn.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)

        output = PaliGemmaCausalLMOutputWithPast(
            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,
        )
        return output if return_dict else output.to_tuple()