modeling_aria.py

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from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import torch
import torch.nn as nn
from torch import nn
from transformers import GenerationMixin, PreTrainedModel
from transformers.modeling_outputs import ModelOutput
from transformers.utils import logging

from .configuration_aria import AriaConfig
from .moe_lm import AriaMoELMForCausalLM
from .projector import AriaProjector
from .vision_encoder import AriaVisionModel

logger = logging.get_logger(__name__)


class AriaPretrainedModel(PreTrainedModel):
    """
    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models.
    """

    config_class = AriaConfig
    base_model_prefix = "model"
    _no_split_modules = []
    supports_gradient_checkpointing = True
    _skip_keys_device_placement = "past_key_values"
    _supports_flash_attn_2 = True
    _supports_cache_class = True
    _supports_static_cache = True

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


@dataclass
# Copied from transformers.models.llava.modeling_llava.LlavaCausalLMOutputWithPast with Llava->Aria
class AriaCausalLMOutputWithPast(ModelOutput):
    """
    Base class for Aria 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


def build_mm_projector(config: AriaConfig):
    """
    Builds and returns an AriaProjector instance based on the provided configuration.

    Args:
        config (AriaConfig): The configuration object containing necessary parameters.

    Returns:
        AriaProjector: An instance of the AriaProjector class.
    """
    return AriaProjector(
        patch_to_query_dict=config.projector_patch_to_query_dict,
        embed_dim=config.vision_config.hidden_size,
        num_heads=config.vision_config.num_attention_heads,
        kv_dim=config.vision_config.hidden_size,
        ff_dim=config.text_config.hidden_size,
        output_dim=config.text_config.hidden_size,
    )


# adapted from transformers.models.llava.modeling_llava.LlavaForConditionalGeneration
class AriaForConditionalGeneration(AriaPretrainedModel, GenerationMixin):
    """
    Aria model for conditional generation tasks.

    This model combines a vision tower, a multi-modal projector, and a language model
    to perform tasks that involve both image and text inputs.
    """

    def __init__(self, config: AriaConfig):
        super().__init__(config)

        self.vision_tower = AriaVisionModel(config.vision_config)
        self.multi_modal_projector = build_mm_projector(config)
        self.vocab_size = config.text_config.vocab_size
        self.language_model = AriaMoELMForCausalLM(config.text_config)
        self.pad_token_id = (
            self.config.pad_token_id if self.config.pad_token_id is not None else -1
        )
        self.post_init()

    def freeze_vit(self):
        """Freeze the parameters of the vision tower."""
        for param in self.vision_tower.parameters():
            param.requires_grad = False

    def freeze_projector(self):
        """Freeze the parameters of the multi-modal projector."""
        for param in self.multi_modal_projector.parameters():
            param.requires_grad = False

    def freeze_llm(self):
        """Freeze the parameters of the language model."""
        for param in self.language_model.parameters():
            param.requires_grad = False

    def get_input_embeddings(self) -> nn.Module:
        """Retrieve the input embeddings from the language model."""
        return self.language_model.get_input_embeddings()

    def set_input_embeddings(self, value):
        """Set the input embeddings for the language model."""
        self.language_model.set_input_embeddings(value)

    def set_moe_z_loss_coeff(self, value):
        """
        Set the z-loss coefficient for Mixture of Experts (MoE) models.

        Args:
            value: The z-loss coefficient value to set.
        """
        self.language_model.set_z_loss_coeff(value)

    def set_moe_aux_loss_coeff(self, value):
        """
        Set the auxiliary loss coefficient for Mixture of Experts (MoE) models.

        Args:
            value: The auxiliary loss coefficient value to set.
        """
        self.language_model.set_aux_loss_coeff(value)

    def forward(
        self,
        input_ids: torch.LongTensor = None,
        pixel_values: torch.FloatTensor = None,
        pixel_mask: 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,
        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,
        cache_position: Optional[torch.LongTensor] = None,
        num_logits_to_keep: int = 0,
    ) -> Union[Tuple, AriaCausalLMOutputWithPast]:
        """
        Forward pass of the AriaForConditionalGeneration model.

        This method processes both text and image inputs, merges them if necessary,
        and generates output using the language model.

        Args:
            input_ids (torch.LongTensor, optional): Input token ids.
            pixel_values (torch.FloatTensor, optional): Pixel values of the images.
            pixel_mask (torch.LongTensor, optional): Mask for the pixel values.
            attention_mask (torch.Tensor, optional): Attention mask.
            position_ids (torch.LongTensor, optional): Position ids.
            past_key_values (List[torch.FloatTensor], optional): Past key values for efficient processing.
            inputs_embeds (torch.FloatTensor, optional): Input embeddings.
            labels (torch.LongTensor, optional): Labels for computing the language modeling loss.
            use_cache (bool, optional): Whether to use the model's cache mechanism.
            output_attentions (bool, optional): Whether to output attention weights.
            output_hidden_states (bool, optional): Whether to output hidden states.
            return_dict (bool, optional): Whether to return a ModelOutput object.

        Returns:
            Union[Tuple, AriaCausalLMOutputWithPast]: Model outputs.
        """
        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
        )

        if inputs_embeds is None:
            # 1. Extra the input embeddings
            inputs_embeds = self.get_input_embeddings()(input_ids)

        image_features = None
        if pixel_values is not None:
            image_outputs, image_attn_mask = self.vision_tower(
                pixel_values,
                pixel_mask=pixel_mask,
            )

            selected_image_feature = image_outputs.last_hidden_state
            image_features = self.multi_modal_projector(
                selected_image_feature, attn_mask=image_attn_mask
            )

        if image_features is not None:
            n_image_tokens = (input_ids == self.config.image_token_index).sum().item()
            n_image_features = image_features.shape[0] * image_features.shape[1]

            if n_image_tokens != n_image_features:
                raise ValueError(
                    f"Image features and image tokens do not match: tokens: {n_image_tokens}, features {n_image_features}"
                )
            special_image_mask = (
                (input_ids == self.config.image_token_index)
                .unsqueeze(-1)
                .expand_as(inputs_embeds)
                .to(inputs_embeds.device)
            )
            image_features = image_features.to(
                inputs_embeds.device, inputs_embeds.dtype
            )
            inputs_embeds = inputs_embeds.masked_scatter(
                special_image_mask, image_features
            )

        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,
            cache_position=cache_position,
            num_logits_to_keep=num_logits_to_keep,
        )

        logits = outputs[0]

        loss = None
        if labels is not None:
            # Shift so that tokens < n predict n
            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[:, -(logits.shape[1] - 1) :].to(
                    logits.device
                )
                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 AriaCausalLMOutputWithPast(
            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,
        attention_mask=None,
        cache_position=None,
        num_logits_to_keep=None,
        **kwargs,
    ):
        model_inputs = self.language_model.prepare_inputs_for_generation(
            input_ids,
            past_key_values=past_key_values,
            inputs_embeds=inputs_embeds,
            attention_mask=attention_mask,
            cache_position=cache_position,
            num_logits_to_keep=num_logits_to_keep,
            **kwargs,
        )

        if cache_position[0] == 0:
            # If we're in cached decoding stage, pixel values should be None because input ids do not contain special image token anymore
            # Otherwise we need pixel values to be passed to model
            model_inputs["pixel_values"] = pixel_values

        return model_inputs