bahasa_model.py

import logging
from typing import Any, Dict, List, Optional, Set, Tuple, Union

import peft
import torch
import torch.nn as nn
import torch.nn.functional as F
import transformers
import transformers.activations
import transformers.modeling_outputs
import transformers.models
from transformers.models.whisper import modeling_whisper as whisper

from transformers.models.mllama.modeling_mllama import MllamaForConditionalGeneration
from transformers.models.mllama.modeling_mllama import _prepare_cross_attention_mask
from transformers.modeling_outputs import CausalLMOutputWithPast

# We must use relative import in this directory to allow uploading to HF Hub
# Even "from . import X" pattern doesn't work (undocumented and unclear why)
from .bahasa_config import LossConfig
from .bahasa_config import LossFunction
from .bahasa_config import BahasaConfig


class BahasaModel(transformers.LlamaPreTrainedModel, transformers.GenerationMixin):
    """
    The Bahasa model which consists of an audio encoder and a language model.

    Audio input is processed by the audio encoder, then every `stack_factor` frames are stacked together and
    projected to the language model's embedding space using a few linear layers.
    The text is embedded by the language model as usual and then the audio and text embeddings are merged together.

    A special token `<|audio|>` is used to indicate the start of the audio embeddings in the merged embeddings.

    Parameters:
        config: Model configuration class with all the parameters of the model.
    """

    config_class = BahasaConfig
    config: BahasaConfig  # for type hinting
    # We minimize the weights in state_dict in order to reduce the size of the checkpoint
    # The issue is that load_pretrained() uses state_dict() keys to know what keys are expected
    # As such we have to tell is to ignore some keys that are not always in the model
    _keys_to_ignore_on_load_unexpected = ["audio_tower.*", "language_model.*"]
    # Usually we load encoder weights from a pretrained model, so we don't want to load the decoder weights
    # Technically we never hit this issue because these keys are already removed from state_dict() however,
    # but there's no harm in keeping it here for when we change that behavior.
    _keys_to_ignore_on_load_missing = ["audio_tower.*"]

    def __init__(self, config: BahasaConfig):
        super().__init__(config)
        self._register_load_state_dict_pre_hook(self._pre_load_state_dict_hook)

        self.keep_params: Set[str] = set()
        self.vocab_size = config.vocab_size

        self.audio_tower = self._create_audio_tower(config)
        self.multi_modal_projector = self._create_multi_modal_projector(config)
        self.language_model = self._create_language_model(config)

        # Determine no_split_modules dynamically to use with FSDP auto_wrap policy.
        # FSDP throws an error if some of the layer types are not found in the model.
        # This would be something like ["LlamaDecoderLayer", "WhisperEncoderLayer"]
        self._no_split_modules = (self.language_model._no_split_modules or []) + (
            self.audio_tower._no_split_modules or []
        )

        self.loss_config = LossConfig()
        self.post_init()

    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 set_loss_config(self, loss_config: LossConfig):
        self.loss_config = loss_config

    def _setup_cache(
        self, cache_cls, max_batch_size: int, max_cache_len: Optional[int] = None
    ):
        self.language_model._setup_cache(cache_cls, max_batch_size, max_cache_len)

    def _reorder_cache(self, past_key_values, beam_idx):
        return self.language_model._reorder_cache(past_key_values, beam_idx)

    def resize_token_embeddings(
        self,
        new_num_tokens: Optional[int] = None,
        pad_to_multiple_of: Optional[int] = 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.config.vocab_size = model_embeds.num_embeddings
        self.vocab_size = model_embeds.num_embeddings
        return model_embeds

    def _compute_kl_loss(
        self,
        lm_output: transformers.modeling_outputs.CausalLMOutputWithPast,
        labels: Optional[torch.Tensor] = None,
        past_key_values: Optional[Union[Tuple, transformers.cache_utils.Cache]] = None,
        alt_input_ids: Optional[torch.Tensor] = None,
        alt_attention_mask: Optional[torch.Tensor] = None,
        alt_labels: Optional[torch.Tensor] = None,
        **kwargs,
    ):
        # disable gradient computation for the teacher model
        with torch.no_grad():
            # compute the teacher (text-only) model's distribution
            alt_inputs_embeds = self.get_input_embeddings().forward(alt_input_ids)
            alt_lm_output = self.language_model.forward(
                inputs_embeds=alt_inputs_embeds,
                labels=alt_labels,
                attention_mask=alt_attention_mask,
                past_key_values=past_key_values,
                **kwargs,
            )
        # compute the KL divergence loss between the two models
        kl_loss = F.kl_div(
            F.log_softmax(
                lm_output.logits[labels != -100] / self.loss_config.kl_temperature,
                dim=-1,
            ),
            F.softmax(
                alt_lm_output.logits[alt_labels != -100]
                / self.loss_config.kl_temperature,
                dim=-1,
            ),
            reduction="batchmean",
        )
        return {"loss": kl_loss}

    def generate(
        self,
        input_ids: torch.Tensor,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        audio_values: Optional[torch.FloatTensor] = None,
        audio_token_start_idx: Optional[torch.Tensor] = None,
        audio_token_len: Optional[torch.Tensor] = None,
        **kwargs,
    ):
        if inputs_embeds is None:
            # B x T  ->  B x T x D
            inputs_embeds = self.get_input_embeddings().forward(input_ids)

        if audio_values is not None:
            inputs_embeds = self._process_audio_input(
                inputs_embeds, audio_values, audio_token_start_idx, audio_token_len
            )

        # We need to pass input_ids, otherwise MllamaForConditionalGeneration won't know
        # if there was any image_token in the input_ids
        return self.language_model.generate(
            inputs_embeds=inputs_embeds, input_ids=input_ids, **kwargs
        )

    def _process_audio_input(
        self,
        inputs_embeds: torch.FloatTensor,
        audio_values: torch.FloatTensor,
        audio_token_start_idx: Optional[torch.Tensor],
        audio_token_len: Optional[torch.Tensor],
    ):
        assert (
            audio_token_start_idx is not None and audio_token_len is not None
        ), "audio_token_start_idx and audio_token_len must be provided if audio_values are provided."
        assert (
            len(audio_token_start_idx) == len(audio_token_len) == len(audio_values)
        ), "audio_token_start_idx, audio_token_len, and audio_values must have the same batch size."

        # B x A/3200 x D
        audio_tower_output = self.audio_tower.forward(
            audio_values.to(self.audio_tower.dtype)
        ).last_hidden_state
        audio_tower_output = audio_tower_output.to(inputs_embeds.dtype)

        audio_embeds = self.multi_modal_projector.forward(audio_tower_output)

        # combine audio and text embeddings
        for i, (audio, start, length) in enumerate(
            zip(audio_embeds, audio_token_start_idx, audio_token_len)
        ):
            length = min(length, audio.shape[0])
            inputs_embeds[i, start : start + length] = audio[:length]

        return inputs_embeds

    def forward(
        self,
        input_ids: torch.Tensor,
        audio_values: Optional[torch.FloatTensor] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        labels: Optional[torch.Tensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        audio_token_start_idx: Optional[torch.Tensor] = None,
        audio_token_len: Optional[torch.Tensor] = None,
        past_key_values: Optional[Union[Tuple, transformers.cache_utils.Cache]] = None,
        # Vision model arguments for Mllama. These are not used in text-only Llama. Handled through kwargs.
        # We need to include them, as the forward signature is used by the Trainer to determine the model inputs.
        pixel_values: Optional[torch.Tensor] = None,
        aspect_ratio_ids: Optional[torch.Tensor] = None,
        aspect_ratio_mask: Optional[torch.Tensor] = None,
        cross_attention_mask: Optional[torch.Tensor] = None,
        # the alt_* fields are needed for KL divergence loss
        alt_input_ids: Optional[torch.Tensor] = None,
        alt_attention_mask: Optional[torch.Tensor] = None,
        alt_labels: Optional[torch.Tensor] = None,
        **kwargs,
    ) -> Union[Tuple, transformers.modeling_outputs.CausalLMOutputWithPast]:
        """
        Forward pass for the Bahasa model.

        `input_ids` are the tokenized text input. They are embedded by the language model as usual.
        `audio_values` are processed by the audio encoder and then every `stack_factor` frames are stacked together and
        projected to the language model's embedding space using a few linear layers.
        The audio and text embeddings are merged together. A special token `<|audio|>` is used to indicate the start
        of the audio embeddings in the merged embeddings.

        Args:
            input_ids: The tokenized text input.
            audio_values: The processed audio values.
            inputs_embeds: The embeddings for the input tokens.
            labels: The tokenized text labels.
            attention_mask: The attention mask for the input.
            position_ids: The position ids for the input.
            past_key_values: The past key value cache for the language model attention layers.
            **kwargs: Additional keyword arguments. Passed directly to the language model.
        """
        if inputs_embeds is None:
            # B x T  ->  B x T x D
            inputs_embeds = self.get_input_embeddings().forward(input_ids)

        if audio_values is not None:
            inputs_embeds = self._process_audio_input(
                inputs_embeds, audio_values, audio_token_start_idx, audio_token_len
            )

        for key in [
            "pixel_values",
            "aspect_ratio_ids",
            "aspect_ratio_mask",
            "cross_attention_mask",
        ]:
            if locals()[key] is not None:
                kwargs[key] = locals()[key]

        lm_output = self.language_model.forward(
            inputs_embeds=inputs_embeds,
            labels=labels,
            attention_mask=attention_mask,
            past_key_values=past_key_values,
            **kwargs,
        )
        if self.training:
            if self.loss_config.loss_function == LossFunction.CrossEntropy:
                return lm_output
            elif self.loss_config.loss_function == LossFunction.KL_Divergence:
                return self._compute_kl_loss(
                    lm_output=lm_output,
                    labels=labels,
                    past_key_values=past_key_values,
                    alt_input_ids=alt_input_ids,
                    alt_attention_mask=alt_attention_mask,
                    alt_labels=alt_labels,
                    **kwargs,
                )
            else:
                raise ValueError(
                    f"Unsupported loss function: {self.loss_config.loss_function}"
                )
        else:
            return lm_output

    @classmethod
    def _create_multi_modal_projector(
        cls, config: BahasaConfig
    ) -> "BahasaProjector":
        projector = BahasaProjector(config)
        projector.to(config.torch_dtype)
        return projector

    @classmethod
    def _create_audio_tower(
        cls, config: BahasaConfig
    ) -> Union[transformers.Wav2Vec2Model, "BahasaAudioEncoder"]:
        if config.audio_model_id is not None:
            if "whisper" in config.audio_model_id is not None:
                audio_tower = BahasaAudioEncoder.from_pretrained(
                    config.audio_model_id, torch_dtype=config.torch_dtype
                )
            else:
                audio_tower = transformers.AutoModel.from_pretrained(
                    config.audio_model_id, torch_dtype=config.torch_dtype
                )
        else:
            if "whisper" in config.audio_config._name_or_path:
                audio_tower = BahasaAudioEncoder(config.audio_config)
            else:
                with transformers.modeling_utils.no_init_weights():
                    # we only ever use from_config if the weights are retrained, hence initializing is not
                    # required. This makes the model quite creation faster since init on CPU is quite slow.
                    audio_tower = transformers.AutoModel.from_config(
                        config.audio_config
                    )

        if isinstance(
            audio_tower,
            (transformers.Wav2Vec2BertModel, transformers.WhisperModel),
        ):
            # For these models we only need the encoder part
            # Wav2Vec2BertModel -> Wav2Vec2BertEncoder
            # WhisperModel -> WhisperEncoder
            audio_tower = audio_tower.encoder

        audio_tower = apply_lora(audio_tower, config.audio_model_lora_config)
        return audio_tower

    @classmethod
    def _create_language_model(
        cls, config: BahasaConfig
    ) -> Union[
        transformers.LlamaForCausalLM, transformers.MllamaForConditionalGeneration
    ]:
        base_classes: List[
            transformers.models.auto.auto_factory._BaseAutoModelClass
        ] = [
            BahasaVisionLanguageModel,
            transformers.AutoModelForPreTraining,
            transformers.AutoModelForCausalLM,
        ]
        if config.text_model_id is not None:
            for base_cls in base_classes:
                try:
                    language_model = base_cls.from_pretrained(
                        config.text_model_id,
                        attn_implementation=config._attn_implementation,
                        torch_dtype=config.torch_dtype,
                    )
                    break
                except ValueError:
                    pass
        else:
            # we only ever use from_config if the weights are retrained, hence initializing is not
            # required. This makes the model quite creation faster since init on CPU is quite slow.
            with transformers.modeling_utils.no_init_weights():
                for base_cls in base_classes:
                    try:
                        language_model = base_cls.from_config(
                            config._text_config,
                            attn_implementation=config._attn_implementation,
                            torch_dtype=config.torch_dtype,
                        )
                        break
                    except ValueError:
                        pass

        language_model = apply_lora(language_model, config.text_model_lora_config)
        return language_model

    def merge_and_unload(self):
        if isinstance(self.language_model, peft.PeftModel):
            self.language_model = self.language_model.merge_and_unload()
            # no need to download base language model weights anymore, so we can remove the id
            self.config.text_model_id = None
            self.keep_params.update(
                set(
                    [
                        f"language_model.{name}"
                        for name, _ in self.language_model.named_parameters()
                    ]
                )
            )

        if isinstance(self.audio_tower, peft.PeftModel):
            self.audio_tower = self.audio_tower.merge_and_unload()
            # no need to download base audio model weights anymore, so we can remove the id
            self.config.audio_model_id = None
            self.keep_params.update(
                set(
                    [
                        f"audio_tower.{name}"
                        for name, _ in self.audio_tower.named_parameters()
                    ]
                )
            )

        for param in ["text_model_lora_config", "audio_model_lora_config"]:
            if hasattr(self.config, param):
                delattr(self.config, param)

    def push_to_hub(self, *args, **kwargs):
        self.merge_and_unload()
        self.to(self.language_model.dtype)
        return super().push_to_hub(*args, **kwargs)

    def save_pretrained(
        self, *args, state_dict: Optional[Dict[str, Any]] = None, **kwargs
    ):
        if state_dict is None:
            state_dict = super().state_dict()

        named_params = dict(self.named_parameters())

        state_dict = {
            k: v
            for k, v in state_dict.items()
            if k in self.keep_params
            or (k in named_params and named_params[k].requires_grad)
        }

        super().save_pretrained(*args, state_dict=state_dict, **kwargs)

    def _pre_load_state_dict_hook(self, state_dict: Dict[str, Any], *args, **kwargs):
        self.keep_params.update(set(state_dict.keys()))

    def print_trainable_parameters(self):
        """
        Prints the number of trainable parameters in the model (reuses Peft model's method)
        """
        count_params = peft.peft_model.PeftModel.get_nb_trainable_parameters

        trainable_params, all_param = count_params(self)

        logging.info(
            f"trainable params: {trainable_params:,d} || all params: {all_param:,d}"
            f" || trainable%: {100 * trainable_params / all_param:.1f}%"
        )

        lm_trainable_params, lm_all_params = count_params(self.language_model)
        audio_trainable_params, audio_all_params = count_params(self.audio_tower)

        projector_trainable_params = (
            trainable_params - lm_trainable_params - audio_trainable_params
        )
        projector_all_params = all_param - lm_all_params - audio_all_params

        logging.info(
            f"Trainable%:   "
            f" LLM: {100 * lm_trainable_params / lm_all_params:.1f}%"
            f" || Audio Encoder: {100 * audio_trainable_params / audio_all_params:.1f}%"
            f" || Projector: {100 * projector_trainable_params / projector_all_params:.1f}%"
        )


def is_cache_empty(
    past_key_values: Optional[Union[Tuple, transformers.cache_utils.Cache]]
) -> bool:
    """
    Check if the cache is empty.
    """
    if past_key_values is None:
        return True
    if isinstance(past_key_values, tuple):
        return all(len(c) == 0 for c in past_key_values)
    return past_key_values.get_seq_length() == 0


def apply_lora(model: torch.nn.Module, lora_config: dict) -> torch.nn.Module:
    """
    Applies LoRA finetuning to the model. If the `r` parameter is set to 0, the model is frozen instead.
    """
    lora_config = peft.LoraConfig(**lora_config or {})

    if lora_config.r == 0:
        # freeze the model entirely
        for param in model.parameters():
            param.requires_grad = False
    else:
        model = peft.get_peft_model(model, lora_config)

    return model


class StackAudioFrames(nn.Module):
    """
    Stack the audio embedding frames to reduce the sequence length by a factor of `stack_factor`.

    The number of output frames will be `ceil(T / stack_factor) + 1` where `T` is the number of input frames.
    NOTE: the extra +1 is intentional: in case the number of audio tokens are over-estimated by the processor,
    we want to make sure `processor.audio_token_replacement` (i.e. EOS) doesn't get leaked into the middle of embeddings.
    In most cases this extra padding will get removed in the model's forward function so it has no effect.
    """

    def __init__(self, stack_factor: int = 8):
        super().__init__()
        self.stack_factor = stack_factor

    def forward(self, audio_embeds: torch.Tensor) -> torch.Tensor:
        B, T, C = audio_embeds.shape
        T_pad = (T + self.stack_factor - 1) // self.stack_factor * self.stack_factor
        audio_embeds = F.pad(audio_embeds, (0, 0, 0, T_pad - T + self.stack_factor))
        B, T, C = audio_embeds.shape
        audio_embeds = audio_embeds.view(
            B, T // self.stack_factor, C * self.stack_factor
        )
        return audio_embeds


class RMSNorm(transformers.models.llama.modeling_llama.LlamaRMSNorm):
    def __init__(self, hidden_size: int, init: float = 1, eps: float = 1e-6):
        super().__init__(hidden_size=hidden_size, eps=eps)
        self.weight.data.fill_(init)


class SwiGLU(nn.Module):
    def forward(self, x):
        x, gate = x.chunk(2, dim=-1)
        return F.silu(gate) * x


class BahasaProjector(nn.Sequential):
    def __init__(self, config: BahasaConfig):
        super().__init__()
        self.hidden_dim = config.hidden_size
        self._pad_and_stack = StackAudioFrames(config.stack_factor)
        dim = config.audio_config.hidden_size * config.stack_factor
        self.ln_pre = RMSNorm(dim, init=config.norm_init)
        self.linear_1 = nn.Linear(dim, self.hidden_dim, bias=False)
        dim = self.hidden_dim
        self.act = transformers.activations.get_activation(config.projector_act)
        dim = dim // 2 if config.projector_act == "swiglu" else dim
        self.linear_2 = nn.Linear(dim, config.text_config.hidden_size, bias=False)
        self.ln_post = RMSNorm(config.text_config.hidden_size, init=config.norm_init)

    def forward(self, audio_features: torch.Tensor) -> torch.Tensor:
        audio_features = self._pad_and_stack(audio_features)
        audio_features = self.ln_pre(audio_features)
        hidden_states = self.linear_1(audio_features)
        hidden_states = self.act(hidden_states)
        hidden_states = self.linear_2(hidden_states)
        hidden_states = self.ln_post(hidden_states)
        return hidden_states


class BahasaAudioEncoder(whisper.WhisperEncoder):
    """
    Encoder portion of OpenAI's Whisper model.

    This implementation is a slightly modified version of HF Transformers' Whisper Encoder, with only a few fixes:
    1. base_model_prefix updated to allow for doing `.from_pretrained` directly on the encoder
    2. allow less than 30 second of audio padding to be passed in:
        - relaxed ValueError check for `input_features` length to be less than or equal to `expected_seq_length` instead of strictly equal
        - embed_pos is now sliced to match the length of `inputs_embeds`

    Original: https://github.com/huggingface/transformers/blob/main/src/transformers/models/whisper/modeling_whisper.py
    """

    base_model_prefix = "model.encoder"
    _no_split_modules = ["WhisperEncoderLayer"]

    def forward(
        self,
        input_features,
        attention_mask=None,
        head_mask=None,
        output_attentions=None,
        output_hidden_states=None,
        return_dict=None,
    ):
        expected_seq_length = (
            self.config.max_source_positions
            * self.conv1.stride[0]
            * self.conv2.stride[0]
        )
        if input_features.shape[-1] > expected_seq_length:
            raise ValueError(
                f"Whisper expects the mel input features to be of length {expected_seq_length} or less, but found {input_features.shape[-1]}. Make sure to pad the input mel features to {expected_seq_length}."
            )

        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
        )
        inputs_embeds = nn.functional.gelu(self.conv1(input_features))
        inputs_embeds = nn.functional.gelu(self.conv2(inputs_embeds))

        inputs_embeds = inputs_embeds.permute(0, 2, 1)
        embed_pos = self.embed_positions.weight[: inputs_embeds.size(-2)]

        hidden_states = inputs_embeds + embed_pos
        hidden_states = nn.functional.dropout(
            hidden_states, p=self.dropout, training=self.training
        )

        encoder_states = () if output_hidden_states else None
        all_attentions = () if output_attentions else None

        # check if head_mask has a correct number of layers specified if desired
        if head_mask is not None:
            assert head_mask.size()[0] == (
                len(self.layers)
            ), f"The head_mask should be specified for {len(self.layers)} layers, but it is for {head_mask.size()[0]}."

        for idx, encoder_layer in enumerate(self.layers):
            if output_hidden_states:
                encoder_states = encoder_states + (hidden_states,)
            # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
            to_drop = False
            if self.training:
                dropout_probability = torch.rand([])
                if dropout_probability < self.layerdrop:  # skip the layer
                    to_drop = True

            if to_drop:
                layer_outputs = (None, None)
            else:
                if self.gradient_checkpointing and self.training:
                    layer_outputs = self._gradient_checkpointing_func(
                        encoder_layer.__call__,
                        hidden_states,
                        None,
                        (head_mask[idx] if head_mask is not None else None),
                        output_attentions,
                    )
                else:
                    layer_outputs = encoder_layer(
                        hidden_states,
                        None,
                        layer_head_mask=(
                            head_mask[idx] if head_mask is not None else None
                        ),
                        output_attentions=output_attentions,
                    )

                hidden_states = layer_outputs[0]

            if output_attentions:
                all_attentions = all_attentions + (layer_outputs[1],)

        hidden_states = self.layer_norm(hidden_states)
        if output_hidden_states:
            encoder_states = encoder_states + (hidden_states,)

        if not return_dict:
            return tuple(
                v
                for v in [hidden_states, encoder_states, all_attentions]
                if v is not None
            )
        return transformers.modeling_outputs.BaseModelOutput(
            last_hidden_state=hidden_states,
            hidden_states=encoder_states,
            attentions=all_attentions,
        )

class BahasaVisionLanguageModel(MllamaForConditionalGeneration):
    """
    Custom wrapper for MllamaForConditionalGeneration that keeps the original
    PreTrainedModel functionality but modifies the generation behavior
    """
    
    def __init__(self, config):
        super().__init__(config)
    
    @classmethod
    def from_pretrained(cls, pretrained_model_name_or_path, *args, **kwargs):
        # This will load the model using the original class's from_pretrained
        return super().from_pretrained(pretrained_model_name_or_path, *args, **kwargs)
    
    @classmethod
    def from_config(cls, config, *args, **kwargs):

        return super()._from_config(config, *args, **kwargs)
    
    def forward(
        self,
        input_ids: Optional[torch.LongTensor] = None,
        pixel_values: Optional[torch.FloatTensor] = None,
        aspect_ratio_mask: Optional[torch.Tensor] = None,
        aspect_ratio_ids: Optional[torch.Tensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        cross_attention_mask: Optional[torch.Tensor] = None,
        cross_attention_states: 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, CausalLMOutputWithPast]:
        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]`.

            num_logits_to_keep (`int`, *optional*):
                Calculate logits for the last `num_logits_to_keep` tokens. If `0`, calculate logits for all
                `input_ids` (special case). Only last token logits are needed for generation, and calculating them only for that
                token can save memory, which becomes pretty significant for long sequences or large vocabulary size.


        Returns:

        Example:

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

        >>> checkpoint = "meta-llama/Llama-3.2-11B-Vision"
        >>> model = MllamaForConditionalGeneration.from_pretrained(checkpoint)
        >>> processor = AutoProcessor.from_pretrained(checkpoint)

        >>> prompt = "<|image|>If I had to write a haiku for this one"
        >>> 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
        >>> output = model.generate(**inputs, max_new_tokens=15)

        >>> prompt_len = inputs.input_ids.shape[-1]
        >>> generated_ids = output[:, prompt_len:]
        >>> generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)
        >>> print(generated_text)
        [', it would be:.\\nA stop sign in Chinatown.\\n']
        ```
        """
        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 (input_ids is None) ^ (inputs_embeds is not None):
            raise ValueError(
                "You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one"
            )

        if pixel_values is not None and cross_attention_states is not None:
            raise ValueError("`pixel_values` and `cross_attention_states` cannot be provided simultaneously")

        if pixel_values is not None:
            if aspect_ratio_ids is None:
                raise ValueError("`aspect_ratio_ids` must be provided if `pixel_values` is provided")
            # get vision tokens from vision model
            vision_outputs = self.vision_model(
                pixel_values=pixel_values,
                aspect_ratio_ids=aspect_ratio_ids,
                aspect_ratio_mask=aspect_ratio_mask,
                output_hidden_states=output_hidden_states,
                output_attentions=output_attentions,
                return_dict=return_dict,
            )
            cross_attention_states = vision_outputs[0]
            cross_attention_states = self.multi_modal_projector(cross_attention_states).reshape(
                -1, cross_attention_states.shape[-2], self.hidden_size
            )

        if cross_attention_mask is not None:
            cross_attention_mask, full_text_row_masked_out_mask = _prepare_cross_attention_mask(
                cross_attention_mask,
                num_vision_tokens=self.vision_model.num_patches,
                dtype=self.dtype,
            )
        else:
            full_text_row_masked_out_mask = None

        if cross_attention_mask is not None and cache_position is not None:
            cross_attention_mask = cross_attention_mask[:, :, cache_position]
            full_text_row_masked_out_mask = full_text_row_masked_out_mask[:, :, cache_position]

        outputs = self.language_model(
            input_ids=input_ids,
            attention_mask=attention_mask,
            position_ids=position_ids,
            cross_attention_states=cross_attention_states,
            cross_attention_mask=cross_attention_mask,
            full_text_row_masked_out_mask=full_text_row_masked_out_mask,
            past_key_values=past_key_values,
            use_cache=use_cache,
            inputs_embeds=inputs_embeds,
            labels=labels,
            output_hidden_states=output_hidden_states,
            output_attentions=output_attentions,
            return_dict=return_dict,
            cache_position=cache_position,
            num_logits_to_keep=num_logits_to_keep,
        )

        return outputs

    def prepare_inputs_for_generation(
        self,
        input_ids=None,
        inputs_embeds=None,
        attention_mask=None,
        position_ids=None,
        pixel_values=None,
        aspect_ratio_ids=None,
        aspect_ratio_mask=None,
        cross_attention_mask=None,
        past_key_values=None,
        use_cache=False,
        cache_position=None,
        num_logits_to_keep=None,
        **kwargs,
    ):
        # If we have cache: let's slice `input_ids` through `cache_position`, to keep only the unprocessed tokens
        # Exception 1: when passing input_embeds, input_ids may be missing entries
        # Exception 2: some generation methods do special slicing of input_ids, so we don't need to do it here
        if past_key_values is not None:
            if inputs_embeds is not None:  # Exception 1
                input_ids = input_ids[:, -cache_position.shape[0] :]
            elif input_ids.shape[1] != cache_position.shape[0]:  # Default case (the "else", a no op, is Exception 2)
                input_ids = input_ids[:, cache_position]

        # TODO: we have no attention_mask so this won't work, check if we really won't need attention mask and find another way
        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] :]

                # This `clone` call is needed to avoid recapturing cuda graphs with `torch.compile`'s  `mode="reduce-overhead`, as otherwise the input `position_ids` would have various stride during the decoding. Here, simply using `.contiguous()` is not sufficient as in the batch size = 1 case, `position_ids` is already contiguous but with varying stride which retriggers a capture.
                position_ids = position_ids.clone(memory_format=torch.contiguous_format)

        # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
        if inputs_embeds is not None and (cache_position[0] == 0 or input_ids.shape[1] > 1): ## CHANGES MULTITURN
            if input_ids.shape[1] > 1:  ## CHANGES MULTITURN
                inputs_embeds = inputs_embeds[:, cache_position, :] ## CHANGES MULTITURN
            model_inputs = {"inputs_embeds": inputs_embeds, "input_ids": None}
        else:
            # The clone here is for the same reason as for `position_ids`.
            model_inputs = {"input_ids": input_ids.clone(memory_format=torch.contiguous_format), "inputs_embeds": None}

        if num_logits_to_keep is not None:
            model_inputs["num_logits_to_keep"] = num_logits_to_keep

        model_inputs.update(
            {
                "position_ids": position_ids,
                "cache_position": cache_position,
                "past_key_values": past_key_values,
                "use_cache": use_cache,
                "attention_mask": attention_mask,
                "cross_attention_mask": cross_attention_mask,
            }
        )

        # If we're in pre-fill or cacheless decoding step, then we need pixel_values and aspect ratios
        # to compute image hidden states, otherwise they are cached within each cross attn layer
        if (input_ids == self.config.image_token_index).any():
            model_inputs["pixel_values"] = pixel_values
            model_inputs["aspect_ratio_ids"] = aspect_ratio_ids
            model_inputs["aspect_ratio_mask"] = aspect_ratio_mask

        return model_inputs


BahasaConfig.register_for_auto_class()
BahasaModel.register_for_auto_class()

transformers.AutoConfig.register("bahasa", BahasaConfig)
transformers.AutoModel.register(BahasaConfig, BahasaModel)

transformers.activations.ACT2FN["swiglu"] = SwiGLU