modeling_magma.py

# coding=utf-8
# Copyright 2024 the HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
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"""PyTorch Magma model."""

import math
import re
import os
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch
import torch.utils.checkpoint
from torch import nn
import torch.distributed as dist
from transformers.modeling_utils import PreTrainedModel
from transformers.activations import ACT2FN
from transformers.cache_utils import Cache, DynamicCache
from transformers.utils import ModelOutput
from transformers.utils import (
    add_code_sample_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    logging,
    replace_return_docstrings,
)
from transformers import AutoConfig, AutoModelForCausalLM
from .configuration_magma import MagmaConfig
from .image_tower_magma import MagmaImageTower

logger = logging.get_logger(__name__)

_CONFIG_FOR_DOC = "MagmaConfig"
    
@dataclass
# Copied from transformers.models.idefics.modeling_idefics.IdeficsCausalLMOutputWithPast with Idefics->Magma
class MagmaCausalLMOutputWithPast(ModelOutput):
    """
    Base class for Magma causal language model (or autoregressive) outputs.

    Args:
        loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
            Language modeling loss (for next-token prediction).
        logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
            Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
        past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
            Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
            `(batch_size, num_heads, sequence_length, embed_size_per_head)`)

            Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see
            `past_key_values` input) to speed up sequential decoding.
        hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
            Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
            one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.

            Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
        attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
            Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
            sequence_length)`.

            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
            heads.
        image_hidden_states (`tuple(torch.FloatTensor)`, *optional*):
            Tuple of `torch.FloatTensor` (one for the output of the image embeddings, `(batch_size, num_images,
            sequence_length, hidden_size)`.

            image_hidden_states of the model produced by the vision encoder, and optionally by the perceiver
    """

    loss: Optional[torch.FloatTensor] = None
    logits: torch.FloatTensor = None
    past_key_values: Optional[List[torch.FloatTensor]] = None
    hidden_states: Optional[Tuple[torch.FloatTensor]] = None
    attentions: Optional[Tuple[torch.FloatTensor]] = None
    image_hidden_states: Optional[Tuple[torch.FloatTensor]] = None


class MagmaMultiModalProjector(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.config = config
        
        dim_vision = {'base': 640, 'large': 768, 'xxlarge': 1024}
        vision_backbone = config.get('vision_backbone', 'convnextxxlarge')
        vision_backbone_size = vision_backbone.replace('convnext', '')
        projector_type = config.get('mm_projector_type', 'linear')
        mlp_gelu_match = re.match(r'^mlp(\d+)x_gelu$', projector_type)
        if mlp_gelu_match:
            mlp_depth = int(mlp_gelu_match.group(1))
            modules = [nn.Linear(config['mm_hidden_size'], config['hidden_size'])]
            for _ in range(1, mlp_depth):
                modules.append(nn.GELU())
                modules.append(nn.Linear(config['hidden_size'], config['hidden_size']))
            self.proj = nn.Sequential(*modules)

        # define a row seperator
        self.row_seperator = nn.Parameter(torch.zeros(1, 1, config['hidden_size']))
        if config.get('mm_use_im_start_end', False):
            self.img_start_seperator = nn.Parameter(torch.zeros(1, config['hidden_size']))
            self.img_end_seperator = nn.Parameter(torch.zeros(1, config['hidden_size']))                        

    def forward(self, x):
        return self.proj(x)


MAGMA_START_DOCSTRING = r"""
    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
    etc.)

    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
    and behavior.

    Parameters:
        config ([`MagmaConfig`] or [`MagmaVisionConfig`]):
            Model configuration class with all the parameters of the model. Initializing with a config file does not
            load the weights associated with the model, only the configuration. Check out the
            [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""


@add_start_docstrings(
    "The bare LLaMA Model outputting raw hidden-states without any specific head on top.",
    MAGMA_START_DOCSTRING,
)

class MagmaPreTrainedModel(PreTrainedModel):
    config_class = MagmaConfig
    base_model_prefix = "model"
    supports_gradient_checkpointing = True
    _no_split_modules = ["MagmaImageTower"]
    _skip_keys_device_placement = "past_key_values"
    _supports_flash_attn_2 = True

    def _init_weights(self, module):
        std = (
            self.config.initializer_range
            if hasattr(self.config, "initializer_range")
            else self.config.text_config.initializer_range
        )

        if hasattr(module, "class_embedding"):
            module.class_embedding.data.normal_(mean=0.0, std=std)

        if isinstance(module, (nn.Linear, nn.Conv2d)):
            module.weight.data.normal_(mean=0.0, std=std)
            if module.bias is not None:
                module.bias.data.zero_()
        elif isinstance(module, nn.Embedding):
            module.weight.data.normal_(mean=0.0, std=std)
            if module.padding_idx is not None:
                module.weight.data[module.padding_idx].zero_()

    @property
    def _supports_sdpa(self):
        """
        Retrieve language_model's attribute to check whether the model supports
        SDPA or not.
        """
        return self.language_model._supports_sdpa


MAGMA_INPUTS_DOCSTRING = r"""
    Args:
        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
            Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
            it.

            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.

            [What are input IDs?](../glossary#input-ids)
        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)):
            The tensors corresponding to the input images. Pixel values can be obtained using
            [`AutoImageProcessor`]. See [`MagmaImageProcessor.__call__`] for details. [`MagmaProcessor`] uses
            [`MagmaImageProcessor`] for processing images.
        image_sizes (`torch.LongTensor` of shape `(batch_size, 2)`, *optional*):
            The sizes of the images in the batch, being (height, width) for each image.
        attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

            - 1 for tokens that are **not masked**,
            - 0 for tokens that are **masked**.

            [What are attention masks?](../glossary#attention-mask)

            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.

            If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
            `past_key_values`).

            If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
            and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
            information on the default strategy.

            - 1 indicates the head is **not masked**,
            - 0 indicates the head is **masked**.
        position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
            config.n_positions - 1]`. [What are position IDs?](../glossary#position-ids)
        past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
            Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
            `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
            `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.

            Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
            blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.

            If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
            don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
            `decoder_input_ids` of shape `(batch_size, sequence_length)`.
        inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
            is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
            model's internal embedding lookup matrix.
        vision_feature_layer (`int`, *optional*, defaults to -2):
            The index of the layer to select the vision feature.
        vision_feature_select_strategy (`str`, *optional*, defaults to `"default"`):
            The feature selection strategy used to select the vision feature from the vision backbone.
            Can be one of `"default"` or `"full"`. If `"default"`, the CLS token is removed from the vision features.
            If `"full"`, the full vision features are used.
        use_cache (`bool`, *optional*):
            If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
            `past_key_values`).
        output_attentions (`bool`, *optional*):
            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
            tensors for more detail.
        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""

@add_start_docstrings(
    """The Magma model which consists of a vision backbone and a language model.""",
    MAGMA_START_DOCSTRING,
)
class MagmaForCausalLM(MagmaPreTrainedModel):
    def __init__(self, config: MagmaConfig):
        super().__init__(config)

        self.vision_tower = MagmaImageTower(config.vision_config, require_pretrained=False)
        config.vision_config['mm_hidden_size'] = config.vision_config['mm_hidden_size'] \
            if 'mm_hidden_size' in config.vision_config else self.vision_tower.hidden_size
        config.vision_config['hidden_size'] = config.vision_config['hidden_size'] \
            if 'hidden_size' in config.vision_config else self.config.text_config.hidden_size
        self.multi_modal_projector = MagmaMultiModalProjector(config.vision_config)

        self.vocab_size = config.text_config.vocab_size
        if hasattr(config.text_config, 'auto_map'):
            del config.text_config.auto_map

        try:
            self.language_model = AutoModelForCausalLM.from_config(
                config.text_config, 
                # attn_implementation=config._attn_implementation, 
                trust_remote_code=True
            )
        except:
            self.language_model = AutoModelForCausalLM.from_pretrained(
                config.text_config._name_or_path, 
                # attn_implementation=config._attn_implementation, 
                trust_remote_code=True
            )
        
        self.pad_token_id = self.config.pad_token_id if self.config.pad_token_id is not None else -1
        self._padding_side = "left"  # set it to left by default, user can use setter to change padding_sides

        self.post_init()
    
    # def from_pretrained(self, pretrained_model_name_or_path, *model_args, **kwargs):
    #     import pdb; pdb.set_trace()
    #     kwargs["_from_auto"] = True
    #     return super().from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)

    @property
    def padding_side(self):
        return self._padding_side

    @padding_side.setter
    def padding_side(self, padding_side: str):
        if padding_side not in ["left", "right"]:
            raise ValueError(f"{padding_side} is not `left` or `right`.")
        self._padding_side = padding_side

    def get_input_embeddings(self):
        return self.language_model.get_input_embeddings()

    def set_input_embeddings(self, value):
        self.language_model.set_input_embeddings(value)

    def get_output_embeddings(self):
        return self.language_model.get_output_embeddings()

    def set_output_embeddings(self, new_embeddings):
        self.language_model.set_output_embeddings(new_embeddings)

    def set_decoder(self, decoder):
        self.language_model.set_decoder(decoder)

    def get_decoder(self):
        return self.language_model.get_decoder()

    def tie_weights(self):
        return self.language_model.tie_weights()
        
    def resize_token_embeddings(self, new_num_tokens: Optional[int] = None, pad_to_multiple_of=None) -> nn.Embedding:
        model_embeds = self.language_model.resize_token_embeddings(new_num_tokens, pad_to_multiple_of)
        # update vocab size
        self.config.text_config.vocab_size = model_embeds.num_embeddings
        self.vocab_size = model_embeds.num_embeddings
        return model_embeds

    def _merge_input_ids_with_image_features(
        self,
        image_features,
        feature_lens,
        inputs_embeds,
        input_ids,
        attention_mask,
        position_ids=None,
        labels=None,
        image_token_index=None,
        ignore_index=-100,
    ):
        """
        Merge input_ids with with image features into final embeddings

        Args:
            image_features (`torch.Tensor` of shape `(all_feature_lens, embed_dim)`):
                All vision vectors of all images in the batch
            feature_lens (`torch.LongTensor` of shape `(num_images)`):
                The length of visual embeddings of each image as stacked in `image_features`
            inputs_embeds (`torch.Tensor` of shape `(batch_size, sequence_length, embed_dim)`):
                Token embeddings before merging with visual embeddings
            input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
                Input_ids of tokens, possibly filled with image token
            attention_mask (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
                Mask to avoid performing attention on padding token indices.
            position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
                Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
                config.n_positions - 1]`.
            labels (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*)
                :abels need to be recalculated to support training (if provided)
            image_token_index (`int`, *optional*)
                Token id used to indicate the special "image" token. Defaults to `config.image_token_index`
            ignore_index (`int`, *optional*)
                Value that is used to pad `labels` and will be ignored when calculated loss. Default: -100.
        Returns:
            final_embedding, final_attention_mask, position_ids, final_labels

        Explanation:
            each image has variable length embeddings, with length specified by feature_lens
            image_features is concatenation of all visual embed vectors
            task: fill each <image> with the correct number of visual embeddings
            Example:
                X (5 patches), Y (3 patches), Z (8)
                X, Y are in the same sequence (in-context learning)
            if right padding
                input_ids: [
                    a b c d e f X g h i j k Y l m
                    o p q r Z s t u v _ _ _ _ _ _
                ]
                input_ids should be: [
                    a b c d e f X X X X X g h i j k Y Y Y l m
                    o p q r Z Z Z Z Z Z Z Z s t u v _ _ _ _ _
                ]
                labels should be: [
                    a b c d e f _ _ _ _ _ g h i j k _ _ _ l m
                    o p q r _ _ _ _ _ _ _ _ s t u v _ _ _ _ _
                ]
            elif left padding
                input_ids: [
                    a b c d e f X g h i j k Y l m
                    _ _ _ _ _ _ o p q r Z s t u v
                ]
                input_ids should be: [
                    a b c d e f X X X X X g h i j k Y Y Y l m
                    _ _ _ _ _ o p q r Z Z Z Z Z Z Z Z s t u v
                ]
                labels should be: [
                    a b c d e f _ _ _ _ _ g h i j k _ _ _ l m
                    _ _ _ _ _ o p q r _ _ _ _ _ _ _ _ s t u v
                ]
            Edge cases:
                * If tokens are same but image token sizes are different, then cannot infer left or right padding

                input_ids: [
                    a b c d X g h
                    i j Y k l m n
                ]
                where X is 3 tokens while Y is 5, this mean after merge
                if left-padding (batched generation)
                    input_ids should be: [
                        _ _ a b c d X X X g h
                        i j Y Y Y Y Y k l m n
                    ]
                elif (right padding) (training)
                    input_ids should be: [
                        a b c d X X X g h _ _
                        i j Y Y Y Y Y k l m n
                    ]
        """
        image_token_index = image_token_index if image_token_index is not None else self.config.image_token_index
        ignore_index = ignore_index if ignore_index is not None else self.config.ignore_index

        with torch.no_grad():
            num_images = feature_lens.size(0)
            num_image_features, embed_dim = image_features.shape
            if feature_lens.sum() != num_image_features:
                raise ValueError(f"{feature_lens=} / {feature_lens.sum()} != {image_features.shape=}")
            batch_size = input_ids.shape[0]
            _left_padding = torch.any(attention_mask[:, 0] == 0)
            _right_padding = torch.any(attention_mask[:, -1] == 0)

            left_padding = True
            if batch_size > 1:
                if _left_padding and not _right_padding:
                    left_padding = True
                elif not _left_padding and _right_padding:
                    left_padding = False
                elif not _left_padding and not _right_padding:
                    # both side is 1, so cannot tell
                    left_padding = self.padding_side == "left"
                else:
                    # invalid attention_mask
                    raise ValueError(f"both side of attention_mask has zero, invalid. {attention_mask}")

            # Whether to turn off right padding
            # 1. Create a mask to know where special image tokens are
            special_image_token_mask = input_ids == image_token_index
            # special_image_token_mask: [bsz, seqlen]
            num_special_image_tokens = torch.sum(special_image_token_mask, dim=-1)
            # num_special_image_tokens: [bsz]
            # Reserve for padding of num_images
            total_num_special_image_tokens = torch.sum(special_image_token_mask)
            if total_num_special_image_tokens != num_images:
                raise ValueError(
                    f"Number of image tokens in input_ids ({total_num_special_image_tokens}) different from num_images ({num_images})."
                )
            # Compute the maximum embed dimension
            # max_image_feature_lens is max_feature_lens per batch
            feature_lens_batch = feature_lens.split(num_special_image_tokens.tolist(), dim=0)
            feature_lens_batch_sum = torch.tensor([x.sum() for x in feature_lens_batch], device=feature_lens.device)
            embed_sequence_lengths = (
                (attention_mask == 1).long().sum(-1) - num_special_image_tokens + feature_lens_batch_sum
            )
            max_embed_dim = embed_sequence_lengths.max()

            batch_indices, non_image_indices = torch.where((input_ids != image_token_index) & (attention_mask == 1))
            # 2. Compute the positions where text should be written
            # Calculate new positions for text tokens in merged image-text sequence.
            # `special_image_token_mask` identifies image tokens. Each image token will be replaced by `nb_text_tokens_per_images` text tokens.
            # `torch.cumsum` computes how each image token shifts subsequent text token positions.
            # - 1 to adjust for zero-based indexing, as `cumsum` inherently increases indices by one.
            # ! instead of special_image_token_mask * (num_image_patches - 1)
            #   special_image_token_mask * (num_feature_len - 1)
            special_image_token_mask = special_image_token_mask.long()
            special_image_token_mask[special_image_token_mask == 1] = feature_lens - 1
            new_token_positions = torch.cumsum((special_image_token_mask + 1), -1) - 1
            if left_padding:
                # shift right token positions so that they are ending at the same number
                # the below here was incorrect? new_token_positions += new_token_positions[:, -1].max() - new_token_positions[:, -1:]
                new_token_positions += max_embed_dim - 1 - new_token_positions[:, -1:]

            text_to_overwrite = new_token_positions[batch_indices, non_image_indices]

        # 3. Create the full embedding, already padded to the maximum position
        final_embedding = torch.zeros(
            batch_size, max_embed_dim, embed_dim, dtype=inputs_embeds.dtype, device=inputs_embeds.device
        )
        final_attention_mask = torch.zeros(
            batch_size, max_embed_dim, dtype=attention_mask.dtype, device=inputs_embeds.device
        )
        final_labels = None
        if labels is not None:
            # NOTE: this is a bug in the original code!!!
            final_labels = torch.full_like(final_attention_mask.long(), ignore_index).to(torch.long)
        # In case the Vision model or the Language model has been offloaded to CPU, we need to manually
        # set the corresponding tensors into their correct target device.
        target_device = inputs_embeds.device
        batch_indices, non_image_indices, text_to_overwrite = (
            batch_indices.to(target_device),
            non_image_indices.to(target_device),
            text_to_overwrite.to(target_device),
        )
        attention_mask = attention_mask.to(target_device)

        # 4. Fill the embeddings based on the mask. If we have ["hey" "<image>", "how", "are"]
        # we need to index copy on [0, 577, 578, 579] for the text and [1:576] for the image features
        final_embedding[batch_indices, text_to_overwrite] = inputs_embeds[batch_indices, non_image_indices]
        final_attention_mask[batch_indices, text_to_overwrite] = attention_mask[batch_indices, non_image_indices]
        if labels is not None:
            final_labels[batch_indices, text_to_overwrite] = labels[batch_indices, non_image_indices]

        # 5. Fill the embeddings corresponding to the images. Anything that is not `text_positions` needs filling (#29835)
        with torch.no_grad():
            image_to_overwrite = torch.full(
                (batch_size, max_embed_dim), True, dtype=torch.bool, device=inputs_embeds.device
            )
            image_to_overwrite[batch_indices, text_to_overwrite] = False
            embed_indices = torch.arange(max_embed_dim).unsqueeze(0).to(target_device)
            embed_indices = embed_indices.expand(batch_size, max_embed_dim)
            embed_seq_lens = embed_sequence_lengths[:, None].to(target_device)

            if left_padding:
                # exclude padding on the left
                val = (max_embed_dim - embed_indices) <= embed_seq_lens
            else:
                # exclude padding on the right
                val = embed_indices < embed_seq_lens
            image_to_overwrite &= val

            if image_to_overwrite.sum() != num_image_features:
                raise ValueError(
                    f"{image_to_overwrite.sum()=} != {num_image_features=} The input provided to the model are wrong. "
                    f"The number of image tokens is {torch.sum(special_image_token_mask)} while"
                    f" the number of image given to the model is {num_images}. "
                    f"This prevents correct indexing and breaks batch generation."
                )
        final_embedding[image_to_overwrite] = image_features.contiguous().reshape(-1, embed_dim).to(target_device)
        final_attention_mask |= image_to_overwrite
        position_ids = (final_attention_mask.cumsum(-1) - 1).masked_fill_((final_attention_mask == 0), 1)

        return final_embedding, final_attention_mask, position_ids, final_labels

    @add_start_docstrings_to_model_forward(MAGMA_INPUTS_DOCSTRING)
    @replace_return_docstrings(output_type=MagmaCausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
    def forward(
        self,
        input_ids: torch.LongTensor = None,
        pixel_values: Union[torch.FloatTensor, List[torch.FloatTensor], List[List[torch.FloatTensor]]] = None,
        image_sizes: Union[torch.LongTensor, List[torch.LongTensor], List[List[torch.LongTensor]]] = None,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_values: Optional[List[torch.FloatTensor]] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        vision_feature_layer: Optional[int] = None,
        vision_feature_select_strategy: Optional[str] = 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,
    ) -> Union[Tuple, MagmaCausalLMOutputWithPast]:
        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.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.vocab_size]`.

        Returns:

        Example:

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

        >>> model = MagmaForConditionalGeneration.from_pretrained("microsoft/magma-8b-hf")
        >>> processor = AutoProcessor.from_pretrained("microsoft/magma-8b-hf")

        >>> prompt = "[INST] <image>\nWhat is shown in this image? [/INST]"
        >>> url = "https://www.ilankelman.org/stopsigns/australia.jpg"
        >>> image = Image.open(requests.get(url, stream=True).raw)

        >>> inputs = processor(text=prompt, images=image, 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]
        "[INST]  \nWhat is shown in this image? [/INST] The image appears to be a radar chart, which is a type of multi-dimensional plot (...)"
        ```"""
        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
        vision_feature_layer = (
            vision_feature_layer if vision_feature_layer is not None else self.config.vision_config['vision_feature_layer']
        )
        
        use_cache = use_cache if use_cache is not None else self.config.use_cache
        
        if inputs_embeds is None:
            # 1. Extract the input embeddings
            # In case image_token_index is not in the embeddings (extra token but embedding don't have it)
            for_inputs_embeds_ids = input_ids.clone()
            for_inputs_embeds_ids[(input_ids == self.config.image_token_index)] = 0
            inputs_embeds = self.get_input_embeddings()(for_inputs_embeds_ids)

            # 2. Merge text and images
            if pixel_values is not None and input_ids.shape[1] != 1 and len(pixel_values) > 0:
                # ! infer image_num_patches from image_sizes
                if type(pixel_values) == list:
                    # nested list of pixel_values, each element is a list of pixel_values for each training instance, it could be multiple for video or interleaved setting
                    # e.g., pixel_values = [[img1, img2], [img1, img2, img3]]
                    n_imgs_per_sample = [len(pv) for pv in pixel_values]
                    pixels_values_list = sum(pixel_values, [])
                    image_sizes_list = sum(image_sizes, [])
                else:
                    image_num_patches = [(imsize[imsize.sum(1) > 0,0] * imsize[imsize.sum(1) > 0,1]).tolist() for imsize in image_sizes]       
                    # image_num_patches = [(imsize[:,0]*imsize[:,1]).tolist() for imsize in image_sizes]             
                    # figure out if pixel_values is concatenated or stacked
                    if pixel_values.dim() == 5:
                        # stacking when input is (batch_size, num_patches, num_channels, height, width)
                        _pixel_values_list = [
                            pix_val[:sum(num_patch)].split(num_patch, dim=0) for pix_val, num_patch in zip(pixel_values, image_num_patches)
                        ]
                        _image_sizes_list = [image_size[image_size.sum(-1) > 0].tolist() for image_size in image_sizes]
                    elif pixel_values.dim() != 4:
                        # otherwise has to be stacked from list of (num_patches, num_channels, height, width)
                        raise ValueError(f"pixel_values of shape {pixel_values.shape}, expect to be of 4 or 5 dimensions")

                if self.config.vision_config['img_anyres_strategy'] == "global":
                    selected_image_features = []
                    # NOTE: both _image_sizes_list and _pixel_values_list are lists of lists, each item represents an training instance with one or multiple images
                    for idx, (image_size_for_instance, pixel_values_for_instance) in enumerate(zip(_image_sizes_list, _pixel_values_list)):
                        assert len(image_size_for_instance) == len(pixel_values_for_instance), f"{len(image_size_for_instance)} != {len(pixel_values_for_instance)}"
                        for image_size, pixel_values_for_image in zip(image_size_for_instance, pixel_values_for_instance):
                            pixel_values_for_image = pixel_values_for_image.view(image_size[0], image_size[1], *pixel_values_for_image.shape[1:])
                            pixel_values_for_image = pixel_values_for_image.permute(2, 0, 3, 1, 4).flatten(3, 4).flatten(1, 2).unsqueeze(0)
                            image_features = self.vision_tower(pixel_values_for_image)
                            selected_image_feature = image_features[vision_feature_layer][0].permute(1, 2, 0)
                            selected_image_feature = self.multi_modal_projector(selected_image_feature)
                            selected_image_feature = torch.cat((selected_image_feature, self.multi_modal_projector.row_seperator.repeat(selected_image_feature.shape[0],1,1)), dim=1)
                            selected_image_features.append(selected_image_feature.flatten(0, 1))
                elif self.config.vision_config['img_anyres_strategy'] == "crop":
                    # calculate number of crops for each instance in the batch given _image_sizes_list
                    _image_sizes_list_temp = sum(_image_sizes_list, [])
                    # concate nate all images in _pixel_values_list
                    _pixel_values_list_temp = sum(_pixel_values_list, ())
                    _pixel_values_list_temp = torch.cat(_pixel_values_list_temp, dim=0)
                    image_features = self.vision_tower(_pixel_values_list_temp)[vision_feature_layer].permute(0, 2, 3, 1)
                    image_features = self.multi_modal_projector(image_features)

                    num_crops_list = [_image_size[0]*_image_size[1] for _image_size in _image_sizes_list_temp]
                    image_features_split = torch.split(image_features, num_crops_list, dim=0)
                    selected_image_features = []
                    for image_feature, image_size in zip(image_features_split, _image_sizes_list_temp):
                        image_feature = image_feature.view(image_size[0], image_size[1], *image_feature.shape[1:])
                        image_feature = image_feature.permute(0, 2, 1, 3, 4).flatten(2, 3).flatten(0, 1)
                        image_feature = torch.cat((image_feature, self.multi_modal_projector.row_seperator.repeat(image_feature.shape[0],1,1)), dim=1)
                        selected_image_features.append(image_feature.flatten(0, 1))

                    # raise NotImplementedError("crop strategy is not implemented yet")
                    # image_features = self.vision_tower(pixel_values)
                    # selected_image_feature = image_features[vision_feature_layer]            
                    # image_features = torch.split(image_features, image_num_patches, dim=0)

                # NOTE we only support multimodal_patch_merge_type == "spatial_unpad"
                feature_lens = [elem.shape[0] for elem in selected_image_features]        
                image_features = torch.cat(selected_image_features, 0)
                feature_lens = torch.tensor(feature_lens, dtype=torch.long, device=image_features.device)
                
                # inputs_embeds = inputs_embeds.to(image_features.dtype)
                inputs_embeds, attention_mask, position_ids, labels = self._merge_input_ids_with_image_features(
                    image_features,
                    feature_lens,
                    inputs_embeds,
                    input_ids,
                    attention_mask,
                    position_ids,
                    labels=labels,
                )

            # pixel_values is not None but is empty ---> text only cases
            elif pixel_values is not None and input_ids.shape[1] != 1 and pixel_values.size(0) == 0:
                # there are no images
                pass

            # In case input_ids.shape[1] == 1 & pixel_values==None & past_key_values != None, we are in the case of
            # generation with cache
            elif past_key_values is not None and pixel_values is not None and input_ids.shape[1] == 1:
                # Retrieve the first layer to inspect the logits and mask out the hidden states
                # that are set to 0
                first_layer_past_key_value = past_key_values[0][0][:, :, :, 0]

                # Sum all dimensions of head_dim (-2) to avoid random errors such as: https://github.com/huggingface/transformers/pull/28032#issuecomment-1863691941
                batch_index, non_attended_tokens = torch.where(first_layer_past_key_value.float().sum(-2) == 0)

                # Get the target length
                target_length = input_ids.shape[1]
                past_length = first_layer_past_key_value.shape[-1]

                extended_attention_mask = torch.ones(
                    (attention_mask.shape[0], past_length),
                    dtype=attention_mask.dtype,
                    device=attention_mask.device,
                )

                # Filter out only the tokens that can be un-attended, this can happen
                # if one uses Llava + Fused modules where the cache on the
                # first iteration is already big enough, or if one passes custom cache
                valid_indices = non_attended_tokens < extended_attention_mask.size(-1)
                new_batch_index = batch_index[valid_indices]
                new_non_attended_tokens = non_attended_tokens[valid_indices]

                # Zero-out the places where we don't need to attend
                extended_attention_mask[new_batch_index, new_non_attended_tokens] = 0

                attention_mask = torch.cat((extended_attention_mask, attention_mask[:, -target_length:]), dim=1)

                position_ids = torch.sum(attention_mask, dim=1).unsqueeze(-1) - 1
        
        # outputs = self.language_model(
        #     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,
        #     return_dict=return_dict,
        # )

        # logits = outputs[0]
        # loss = None
        # if labels is not None:
        #     # Shift so that tokens < n predict n
        #     if attention_mask is not None:
        #         shift_attention_mask = attention_mask[..., 1:]
        #         shift_logits = logits[..., :-1, :][shift_attention_mask.to(logits.device) != 0].contiguous()
        #         shift_labels = labels[..., 1:][shift_attention_mask.to(labels.device) != 0].contiguous()
        #     else:
        #         shift_logits = logits[..., :-1, :].contiguous()
        #         shift_labels = labels[..., 1:].contiguous()
        #     # Flatten the tokens
        #     loss_fct = nn.CrossEntropyLoss()
        #     loss = loss_fct(
        #         shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1).to(shift_logits.device)
        #     )

        outputs = self.language_model.model(
            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,
            return_dict=return_dict
        )

        hidden_states = outputs[0]

        loss = None

        if labels is not None and self.training:
            valid_mask = labels[..., 1:] != -100
            shift_logits = self.language_model.lm_head(hidden_states[:,:-1][valid_mask]).contiguous()
            shift_logits = shift_logits.view(-1, self.language_model.config.vocab_size)
            logits = shift_logits # dummy logits
            shift_labels = labels[..., 1:][valid_mask].contiguous()
            shift_labels = shift_labels.to(shift_logits.device)
            loss_fct = nn.CrossEntropyLoss()
            loss = loss_fct(shift_logits, shift_labels)

            # localize the positions for shift_labels where the id is in betweek [config.tokenizer_vocab_size-256, config.tokenizer_vocab_size]
            valid_indices = (shift_labels<self.config.tokenizer_vocab_size) & (shift_labels>=self.config.tokenizer_vocab_size-256)
            if valid_indices.sum() > 0:
                action_labels = shift_labels[valid_indices]
                action_logits = shift_logits[valid_indices]
                # calcualte the accuracy
                action_accuracy = (action_logits.argmax(-1) == action_labels).float().mean()
                # log the action accuracy
            else:
                action_accuracy = torch.tensor(0.0).to(shift_logits.device)
            # torch distributed gather the action accuracy across all devices     
            action_accuracy = action_accuracy.unsqueeze(0)
            # gather the action accuracy across all devices
            action_accuracy_gather = [torch.zeros_like(action_accuracy) for _ in range(dist.get_world_size())]
            dist.all_gather(action_accuracy_gather, action_accuracy)
            # concatenate the action accuracy across all devices
            action_accuracy = torch.cat(action_accuracy_gather)            
            
        else:
            logits = self.language_model.lm_head(hidden_states)
            logits = logits.float()

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

        return MagmaCausalLMOutputWithPast(
            loss=loss,
            logits=logits,
            past_key_values=outputs.past_key_values,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )

    def prepare_inputs_for_generation(
        self,
        input_ids,
        past_key_values=None,
        inputs_embeds=None,
        pixel_values=None,
        image_sizes=None,
        attention_mask=None,
        **kwargs,
    ):
        if past_key_values is not None:
            if isinstance(past_key_values, Cache):
                cache_length = past_key_values.get_seq_length()
                past_length = past_key_values.seen_tokens
            else:
                cache_length = past_length = past_key_values[0][0].shape[2]

            # Keep only the unprocessed tokens:
            # 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where
            # some of the inputs are exclusively passed as part of the cache (e.g. when passing input_embeds as
            # input)
            if attention_mask is not None and attention_mask.shape[1] > input_ids.shape[1]:
                input_ids = input_ids[:, -(attention_mask.shape[1] - past_length) :]
            # 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard
            # input_ids based on the past_length.
            elif past_length < input_ids.shape[1]:
                input_ids = input_ids[:, past_length:]
            # 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens.
            elif self.config.image_token_index in input_ids:
                input_ids = input_ids[:, input_ids.shape[1] - 1 :]
            # If the cache has seen more tokens than it can hold, then the cache has a size limit. Let's discard the
            # older attention values, as their corresponding values are not part of the input.
            if cache_length < past_length and attention_mask is not None:
                attention_mask = attention_mask[:, -(cache_length + input_ids.shape[1]) :]

        position_ids = kwargs.get("position_ids", None)
        if attention_mask is not None and position_ids is None:
            # create position_ids on the fly for batch generation
            position_ids = attention_mask.long().cumsum(-1) - 1
            position_ids.masked_fill_(attention_mask == 0, 1)
            if past_key_values:
                position_ids = position_ids[:, -input_ids.shape[1] :]

        # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
        if inputs_embeds is not None and past_key_values is None:
            model_inputs = {"inputs_embeds": inputs_embeds}
        else:
            model_inputs = {"input_ids": input_ids}

        model_inputs.update(
            {
                "position_ids": position_ids,
                "past_key_values": past_key_values,
                "use_cache": kwargs.get("use_cache"),
                "attention_mask": attention_mask,
                "pixel_values": pixel_values,
                "image_sizes": image_sizes,
            }
        )
        return model_inputs

    def _reorder_cache(self, *args, **kwargs):
        return self.language_model._reorder_cache(*args, **kwargs)

@add_start_docstrings(
    """The Magma model which consists of a vision backbone and a language model.""",
    MAGMA_START_DOCSTRING,
)
class MagmaForConditionalGeneration(MagmaPreTrainedModel):
    def __init__(self, config: MagmaConfig):
        super().__init__(config)

        self.vision_tower = MagmaImageTower(config.vision_config, require_pretrained=('magma' not in config.name_or_path))
        self.multi_modal_projector = MagmaMultiModalProjector(config.vision_config)

        self.vocab_size = config.text_config.vocab_size
        self.language_model = AutoModelForCausalLM.from_config(
            config.text_config, 
            # attn_implementation=config._attn_implementation, 
            trust_remote_code=True
        )
        
        self.pad_token_id = self.config.pad_token_id if self.config.pad_token_id is not None else -1
        self._padding_side = "left"  # set it to left by default, user can use setter to change padding_sides

        self.post_init()

    @property
    def padding_side(self):
        return self._padding_side

    @padding_side.setter
    def padding_side(self, padding_side: str):
        if padding_side not in ["left", "right"]:
            raise ValueError(f"{padding_side} is not `left` or `right`.")
        self._padding_side = padding_side

    def get_input_embeddings(self):
        return self.language_model.get_input_embeddings()

    def set_input_embeddings(self, value):
        self.language_model.set_input_embeddings(value)

    def get_output_embeddings(self):
        return self.language_model.get_output_embeddings()

    def set_output_embeddings(self, new_embeddings):
        self.language_model.set_output_embeddings(new_embeddings)

    def set_decoder(self, decoder):
        self.language_model.set_decoder(decoder)

    def get_decoder(self):
        return self.language_model.get_decoder()

    def tie_weights(self):
        return self.language_model.tie_weights()

    def resize_token_embeddings(self, new_num_tokens: Optional[int] = None, pad_to_multiple_of=None) -> nn.Embedding:
        model_embeds = self.language_model.resize_token_embeddings(new_num_tokens, pad_to_multiple_of)
        # update vocab size
        self.config.text_config.vocab_size = model_embeds.num_embeddings
        self.vocab_size = model_embeds.num_embeddings
        return model_embeds

    def _merge_input_ids_with_image_features(
        self,
        image_features,
        feature_lens,
        inputs_embeds,
        input_ids,
        attention_mask,
        position_ids=None,
        labels=None,
        image_token_index=None,
        ignore_index=-100,
    ):
        """
        Merge input_ids with with image features into final embeddings

        Args:
            image_features (`torch.Tensor` of shape `(all_feature_lens, embed_dim)`):
                All vision vectors of all images in the batch
            feature_lens (`torch.LongTensor` of shape `(num_images)`):
                The length of visual embeddings of each image as stacked in `image_features`
            inputs_embeds (`torch.Tensor` of shape `(batch_size, sequence_length, embed_dim)`):
                Token embeddings before merging with visual embeddings
            input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
                Input_ids of tokens, possibly filled with image token
            attention_mask (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
                Mask to avoid performing attention on padding token indices.
            position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
                Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
                config.n_positions - 1]`.
            labels (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*)
                :abels need to be recalculated to support training (if provided)
            image_token_index (`int`, *optional*)
                Token id used to indicate the special "image" token. Defaults to `config.image_token_index`
            ignore_index (`int`, *optional*)
                Value that is used to pad `labels` and will be ignored when calculated loss. Default: -100.
        Returns:
            final_embedding, final_attention_mask, position_ids, final_labels

        Explanation:
            each image has variable length embeddings, with length specified by feature_lens
            image_features is concatenation of all visual embed vectors
            task: fill each <image> with the correct number of visual embeddings
            Example:
                X (5 patches), Y (3 patches), Z (8)
                X, Y are in the same sequence (in-context learning)
            if right padding
                input_ids: [
                    a b c d e f X g h i j k Y l m
                    o p q r Z s t u v _ _ _ _ _ _
                ]
                input_ids should be: [
                    a b c d e f X X X X X g h i j k Y Y Y l m
                    o p q r Z Z Z Z Z Z Z Z s t u v _ _ _ _ _
                ]
                labels should be: [
                    a b c d e f _ _ _ _ _ g h i j k _ _ _ l m
                    o p q r _ _ _ _ _ _ _ _ s t u v _ _ _ _ _
                ]
            elif left padding
                input_ids: [
                    a b c d e f X g h i j k Y l m
                    _ _ _ _ _ _ o p q r Z s t u v
                ]
                input_ids should be: [
                    a b c d e f X X X X X g h i j k Y Y Y l m
                    _ _ _ _ _ o p q r Z Z Z Z Z Z Z Z s t u v
                ]
                labels should be: [
                    a b c d e f _ _ _ _ _ g h i j k _ _ _ l m
                    _ _ _ _ _ o p q r _ _ _ _ _ _ _ _ s t u v
                ]
            Edge cases:
                * If tokens are same but image token sizes are different, then cannot infer left or right padding

                input_ids: [
                    a b c d X g h
                    i j Y k l m n
                ]
                where X is 3 tokens while Y is 5, this mean after merge
                if left-padding (batched generation)
                    input_ids should be: [
                        _ _ a b c d X X X g h
                        i j Y Y Y Y Y k l m n
                    ]
                elif (right padding) (training)
                    input_ids should be: [
                        a b c d X X X g h _ _
                        i j Y Y Y Y Y k l m n
                    ]
        """
        image_token_index = image_token_index if image_token_index is not None else self.config.image_token_index
        ignore_index = ignore_index if ignore_index is not None else self.config.ignore_index

        with torch.no_grad():
            num_images = feature_lens.size(0)
            num_image_features, embed_dim = image_features.shape
            if feature_lens.sum() != num_image_features:
                raise ValueError(f"{feature_lens=} / {feature_lens.sum()} != {image_features.shape=}")
            batch_size = input_ids.shape[0]
            _left_padding = torch.any(attention_mask[:, 0] == 0)
            _right_padding = torch.any(attention_mask[:, -1] == 0)

            left_padding = True
            if batch_size > 1:
                if _left_padding and not _right_padding:
                    left_padding = True
                elif not _left_padding and _right_padding:
                    left_padding = False
                elif not _left_padding and not _right_padding:
                    # both side is 1, so cannot tell
                    left_padding = self.padding_side == "left"
                else:
                    # invalid attention_mask
                    raise ValueError(f"both side of attention_mask has zero, invalid. {attention_mask}")

            # Whether to turn off right padding
            # 1. Create a mask to know where special image tokens are
            special_image_token_mask = input_ids == image_token_index
            # special_image_token_mask: [bsz, seqlen]
            num_special_image_tokens = torch.sum(special_image_token_mask, dim=-1)
            # num_special_image_tokens: [bsz]
            # Reserve for padding of num_images
            total_num_special_image_tokens = torch.sum(special_image_token_mask)
            if total_num_special_image_tokens != num_images:
                raise ValueError(
                    f"Number of image tokens in input_ids ({total_num_special_image_tokens}) different from num_images ({num_images})."
                )
            # Compute the maximum embed dimension
            # max_image_feature_lens is max_feature_lens per batch
            feature_lens_batch = feature_lens.split(num_special_image_tokens.tolist(), dim=0)
            feature_lens_batch_sum = torch.tensor([x.sum() for x in feature_lens_batch], device=feature_lens.device)
            embed_sequence_lengths = (
                (attention_mask == 1).long().sum(-1) - num_special_image_tokens + feature_lens_batch_sum
            )
            max_embed_dim = embed_sequence_lengths.max()

            batch_indices, non_image_indices = torch.where((input_ids != image_token_index) & (attention_mask == 1))
            # 2. Compute the positions where text should be written
            # Calculate new positions for text tokens in merged image-text sequence.
            # `special_image_token_mask` identifies image tokens. Each image token will be replaced by `nb_text_tokens_per_images` text tokens.
            # `torch.cumsum` computes how each image token shifts subsequent text token positions.
            # - 1 to adjust for zero-based indexing, as `cumsum` inherently increases indices by one.
            # ! instead of special_image_token_mask * (num_image_patches - 1)
            #   special_image_token_mask * (num_feature_len - 1)
            special_image_token_mask = special_image_token_mask.long()
            special_image_token_mask[special_image_token_mask == 1] = feature_lens - 1
            new_token_positions = torch.cumsum((special_image_token_mask + 1), -1) - 1
            if left_padding:
                # shift right token positions so that they are ending at the same number
                # the below here was incorrect? new_token_positions += new_token_positions[:, -1].max() - new_token_positions[:, -1:]
                new_token_positions += max_embed_dim - 1 - new_token_positions[:, -1:]

            text_to_overwrite = new_token_positions[batch_indices, non_image_indices]

        # 3. Create the full embedding, already padded to the maximum position
        final_embedding = torch.zeros(
            batch_size, max_embed_dim, embed_dim, dtype=inputs_embeds.dtype, device=inputs_embeds.device
        )
        final_attention_mask = torch.zeros(
            batch_size, max_embed_dim, dtype=attention_mask.dtype, device=inputs_embeds.device
        )
        final_labels = None
        if labels is not None:
            final_labels = torch.full_like(final_attention_mask, ignore_index).to(torch.long)
        # In case the Vision model or the Language model has been offloaded to CPU, we need to manually
        # set the corresponding tensors into their correct target device.
        target_device = inputs_embeds.device
        batch_indices, non_image_indices, text_to_overwrite = (
            batch_indices.to(target_device),
            non_image_indices.to(target_device),
            text_to_overwrite.to(target_device),
        )
        attention_mask = attention_mask.to(target_device)

        # 4. Fill the embeddings based on the mask. If we have ["hey" "<image>", "how", "are"]
        # we need to index copy on [0, 577, 578, 579] for the text and [1:576] for the image features
        final_embedding[batch_indices, text_to_overwrite] = inputs_embeds[batch_indices, non_image_indices]
        final_attention_mask[batch_indices, text_to_overwrite] = attention_mask[batch_indices, non_image_indices]
        if labels is not None:
            final_labels[batch_indices, text_to_overwrite] = labels[batch_indices, non_image_indices]

        # 5. Fill the embeddings corresponding to the images. Anything that is not `text_positions` needs filling (#29835)
        with torch.no_grad():
            image_to_overwrite = torch.full(
                (batch_size, max_embed_dim), True, dtype=torch.bool, device=inputs_embeds.device
            )
            image_to_overwrite[batch_indices, text_to_overwrite] = False
            embed_indices = torch.arange(max_embed_dim).unsqueeze(0).to(target_device)
            embed_indices = embed_indices.expand(batch_size, max_embed_dim)
            embed_seq_lens = embed_sequence_lengths[:, None].to(target_device)

            if left_padding:
                # exclude padding on the left
                val = (max_embed_dim - embed_indices) <= embed_seq_lens
            else:
                # exclude padding on the right
                val = embed_indices < embed_seq_lens
            image_to_overwrite &= val

            if image_to_overwrite.sum() != num_image_features:
                raise ValueError(
                    f"{image_to_overwrite.sum()=} != {num_image_features=} The input provided to the model are wrong. "
                    f"The number of image tokens is {torch.sum(special_image_token_mask)} while"
                    f" the number of image given to the model is {num_images}. "
                    f"This prevents correct indexing and breaks batch generation."
                )
        final_embedding[image_to_overwrite] = image_features.contiguous().reshape(-1, embed_dim).to(target_device)
        final_attention_mask |= image_to_overwrite
        position_ids = (final_attention_mask.cumsum(-1) - 1).masked_fill_((final_attention_mask == 0), 1)

        return final_embedding, final_attention_mask, position_ids, final_labels

    @add_start_docstrings_to_model_forward(MAGMA_INPUTS_DOCSTRING)
    @replace_return_docstrings(output_type=MagmaCausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
    def forward(
        self,
        input_ids: torch.LongTensor = None,
        pixel_values: torch.FloatTensor = None,
        image_sizes: Optional[torch.LongTensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_values: Optional[List[torch.FloatTensor]] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        vision_feature_layer: Optional[int] = None,
        vision_feature_select_strategy: Optional[str] = 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,
    ) -> Union[Tuple, MagmaCausalLMOutputWithPast]:
        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.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.vocab_size]`.

        Returns:

        Example:

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

        >>> model = MagmaForConditionalGeneration.from_pretrained("microsoft/magma-8b-hf")
        >>> processor = AutoProcessor.from_pretrained("microsoft/magma-8b-hf")

        >>> prompt = "[INST] <image>\nWhat is shown in this image? [/INST]"
        >>> url = "https://www.ilankelman.org/stopsigns/australia.jpg"
        >>> image = Image.open(requests.get(url, stream=True).raw)

        >>> inputs = processor(text=prompt, images=image, 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]
        "[INST]  \nWhat is shown in this image? [/INST] The image appears to be a radar chart, which is a type of multi-dimensional plot (...)"
        ```"""
        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
        vision_feature_layer = (
            vision_feature_layer if vision_feature_layer is not None else self.config.vision_config['vision_feature_layer']
        )

        if inputs_embeds is None:
            # 1. Extract the input embeddings
            # In case image_token_index is not in the embeddings (extra token but embedding don't have it)
            for_inputs_embeds_ids = input_ids.clone()
            for_inputs_embeds_ids[(input_ids == self.config.image_token_index)] = 0
            inputs_embeds = self.get_input_embeddings()(for_inputs_embeds_ids)

            # 2. Merge text and images
            if pixel_values is not None and input_ids.shape[1] != 1 and pixel_values.size(0) > 0:
                # ! infer image_num_patches from image_sizes
                # figure out if pixel_values is concatenated or stacked
                if pixel_values.dim() == 5:
                    image_num_patches = [(imsize[:,0]*imsize[:,1]).tolist() for imsize in image_sizes]
                    # stacking when input is (batch_size, num_patches, num_channels, height, width)
                    _pixel_values_list = [
                        pix_val[:num_patch] for pix_val, num_patch in zip(pixel_values, image_num_patches)
                    ]
                    pixel_values = torch.cat(_pixel_values_list, dim=0)
                elif pixel_values.dim() != 4:
                    # otherwise has to be stacked from list of (num_patches, num_channels, height, width)
                    raise ValueError(f"pixel_values of shape {pixel_values.shape}, expect to be of 4 or 5 dimensions")

                if self.config.vision_config['img_anyres_strategy'] == "global":
                    num_patches_for_images = [(imsize[0]*imsize[1]).item() for imsize in image_sizes]
                    pixel_values_for_images = pixel_values.split(num_patches_for_images, dim=0)
                    selected_image_features = []
                    for idx, (image_size, pixel_values_for_image) in enumerate(zip(image_sizes, pixel_values_for_images)):
                        pixel_values_for_image = pixel_values_for_image.view(image_size[0], image_size[1], *pixel_values_for_image.shape[1:])
                        pixel_values_for_image = pixel_values_for_image.permute(2, 0, 3, 1, 4).flatten(3, 4).flatten(1, 2).unsqueeze(0)
                        image_features = self.vision_tower(pixel_values_for_image)
                        selected_image_feature = image_features[vision_feature_layer][0].permute(1, 2, 0)
                        selected_image_feature = self.multi_modal_projector(selected_image_feature)
                        selected_image_feature = torch.cat((selected_image_feature, self.multi_modal_projector.row_seperator.repeat(selected_image_feature.shape[0],1,1)), dim=1)
                        selected_image_features.append(selected_image_feature)
                elif self.config.vision_config['img_anyres_strategy'] == "crop":
                    image_features = self.vision_tower(pixel_values)[vision_feature_layer].permute(0, 2, 3, 1)
                    image_features = self.multi_modal_projector(image_features)
                    num_patches_for_images = [(imsize[0]*imsize[1]).item() for imsize in image_sizes]
                    image_features_split = torch.split(image_features, num_patches_for_images, dim=0)
                    selected_image_features = []
                    for image_feature, image_size in zip(image_features_split, image_sizes):
                        image_feature = image_feature.view(image_size[0], image_size[1], *image_feature.shape[1:])
                        image_feature = image_feature.permute(0, 2, 1, 3, 4).flatten(2, 3).flatten(0, 1)
                        image_feature = torch.cat((image_feature, self.multi_modal_projector.row_seperator.repeat(image_feature.shape[0],1,1)), dim=1)
                        selected_image_features.append(image_feature)

                # NOTE we only support multimodal_patch_merge_type == "spatial_unpad"
                feature_lens = [elem.shape[0]*elem.shape[1] for elem in selected_image_features]        
                image_features = torch.cat([elem.flatten(0, 1) for elem in selected_image_features], 0)
                feature_lens = torch.tensor(feature_lens, dtype=torch.long, device=image_features.device)

                # inputs_embeds = inputs_embeds.to(image_features.dtype)
                inputs_embeds, attention_mask, position_ids, labels = self._merge_input_ids_with_image_features(
                    image_features,
                    feature_lens,
                    inputs_embeds,
                    input_ids,
                    attention_mask,
                    position_ids,
                    labels=labels,
                )

            # pixel_values is not None but is empty ---> text only cases
            elif pixel_values is not None and input_ids.shape[1] != 1 and pixel_values.size(0) == 0:
                # there are no images
                pass

            # In case input_ids.shape[1] == 1 & pixel_values==None & past_key_values != None, we are in the case of
            # generation with cache
            elif past_key_values is not None and pixel_values is not None and input_ids.shape[1] == 1:
                # Retrieve the first layer to inspect the logits and mask out the hidden states
                # that are set to 0
                first_layer_past_key_value = past_key_values[0][0][:, :, :, 0]

                # Sum all dimensions of head_dim (-2) to avoid random errors such as: https://github.com/huggingface/transformers/pull/28032#issuecomment-1863691941
                batch_index, non_attended_tokens = torch.where(first_layer_past_key_value.float().sum(-2) == 0)

                # Get the target length
                target_length = input_ids.shape[1]
                past_length = first_layer_past_key_value.shape[-1]

                extended_attention_mask = torch.ones(
                    (attention_mask.shape[0], past_length),
                    dtype=attention_mask.dtype,
                    device=attention_mask.device,
                )

                # Filter out only the tokens that can be un-attended, this can happen
                # if one uses Llava + Fused modules where the cache on the
                # first iteration is already big enough, or if one passes custom cache
                valid_indices = non_attended_tokens < extended_attention_mask.size(-1)
                new_batch_index = batch_index[valid_indices]
                new_non_attended_tokens = non_attended_tokens[valid_indices]

                # Zero-out the places where we don't need to attend
                extended_attention_mask[new_batch_index, new_non_attended_tokens] = 0

                attention_mask = torch.cat((extended_attention_mask, attention_mask[:, -target_length:]), dim=1)

                position_ids = torch.sum(attention_mask, dim=1).unsqueeze(-1) - 1

        outputs = self.language_model(
            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,
            return_dict=return_dict,
        )

        logits = outputs[0]

        loss = None
        if labels is not None:
            # Shift so that tokens < n predict n
            if attention_mask is not None:
                shift_attention_mask = attention_mask[..., 1:]
                shift_logits = logits[..., :-1, :][shift_attention_mask.to(logits.device) != 0].contiguous()
                shift_labels = labels[..., 1:][shift_attention_mask.to(labels.device) != 0].contiguous()
            else:
                shift_logits = logits[..., :-1, :].contiguous()
                shift_labels = labels[..., 1:].contiguous()
            # Flatten the tokens
            loss_fct = nn.CrossEntropyLoss()
            loss = loss_fct(
                shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1).to(shift_logits.device)
            )

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

        return MagmaCausalLMOutputWithPast(
            loss=loss,
            logits=logits,
            past_key_values=outputs.past_key_values,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )

    def prepare_inputs_for_generation(
        self,
        input_ids,
        past_key_values=None,
        inputs_embeds=None,
        pixel_values=None,
        image_sizes=None,
        attention_mask=None,
        **kwargs,
    ):
        if past_key_values is not None:
            if isinstance(past_key_values, Cache):
                cache_length = past_key_values.get_seq_length()
                past_length = past_key_values.seen_tokens
            else:
                cache_length = past_length = past_key_values[0][0].shape[2]

            # Keep only the unprocessed tokens:
            # 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where
            # some of the inputs are exclusively passed as part of the cache (e.g. when passing input_embeds as
            # input)
            if attention_mask is not None and attention_mask.shape[1] > input_ids.shape[1]:
                input_ids = input_ids[:, -(attention_mask.shape[1] - past_length) :]
            # 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard
            # input_ids based on the past_length.
            elif past_length < input_ids.shape[1]:
                input_ids = input_ids[:, past_length:]
            # 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens.
            elif self.config.image_token_index in input_ids:
                input_ids = input_ids[:, input_ids.shape[1] - 1 :]
            # If the cache has seen more tokens than it can hold, then the cache has a size limit. Let's discard the
            # older attention values, as their corresponding values are not part of the input.
            if cache_length < past_length and attention_mask is not None:
                attention_mask = attention_mask[:, -(cache_length + input_ids.shape[1]) :]

        position_ids = kwargs.get("position_ids", None)
        if attention_mask is not None and position_ids is None:
            # create position_ids on the fly for batch generation
            position_ids = attention_mask.long().cumsum(-1) - 1
            position_ids.masked_fill_(attention_mask == 0, 1)
            if past_key_values:
                position_ids = position_ids[:, -input_ids.shape[1] :]

        # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
        if inputs_embeds is not None and past_key_values is None:
            model_inputs = {"inputs_embeds": inputs_embeds}
        else:
            model_inputs = {"input_ids": input_ids}

        model_inputs.update(
            {
                "position_ids": position_ids,
                "past_key_values": past_key_values,
                "use_cache": kwargs.get("use_cache"),
                "attention_mask": attention_mask,
                "pixel_values": pixel_values,
                "image_sizes": image_sizes,
            }
        )
        return model_inputs

    def _reorder_cache(self, *args, **kwargs):
        return self.language_model._reorder_cache(*args, **kwargs)

AutoConfig.register("magma", MagmaConfig)
AutoModelForCausalLM.register(MagmaConfig, MagmaForConditionalGeneration)