modeling_llama3.py

# coding=utf-8
from typing import List, Optional, Tuple, Union

import torch
import torch.utils.checkpoint
from torch import nn

import transformers
from transformers import MllamaPreTrainedModel, MllamaVisionModel, MllamaForCausalLM, Wav2Vec2BertConfig, AutoModel, AutoModelForCausalLM
from transformers.cache_utils import Cache, StaticCache
from transformers.generation import GenerationMixin
from transformers.modeling_attn_mask_utils import AttentionMaskConverter
from transformers.modeling_outputs import CausalLMOutputWithPast, BaseModelOutputWithPast
from transformers.utils import logging
from transformers.models.mllama.modeling_mllama import _prepare_cross_attention_mask, MllamaCrossAttentionDecoderLayer, MllamaSelfAttentionDecoderLayer, MllamaTextRMSNorm, MllamaRotaryEmbedding
from transformers.models.mllama.configuration_mllama import MllamaTextConfig
from .configuration_llama3 import Llama3Config
from .mllama_audio_model import Llama3Embedding

    
logger = logging.get_logger(__name__)

class Llama3PreTrainedModel(MllamaPreTrainedModel):
    config_class = Llama3Config
    base_model_prefix = "model"

class Llama3TextModel(MllamaPreTrainedModel):
    config_class = MllamaTextConfig
    base_model_prefix = "language_model.model"

    def __init__(self, config: MllamaTextConfig):
        super().__init__(config)
        self.padding_idx = config.pad_token_id
        self.vocab_size = config.vocab_size
        #self.embed_tokens = Llama3Embedding(audio_config, config)
        self.cross_attention_layers = config.cross_attention_layers

        layers = []
        for layer_idx in range(config.num_hidden_layers):
            if layer_idx in self.cross_attention_layers:
                layers.append(MllamaCrossAttentionDecoderLayer(config, layer_idx))
            else:
                layers.append(MllamaSelfAttentionDecoderLayer(config, layer_idx))

        self.layers = nn.ModuleList(layers)
        self.norm = MllamaTextRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        self.rotary_emb = MllamaRotaryEmbedding(config=config)
        self.gradient_checkpointing = False
        self.post_init()

    def get_input_embeddings(self):
        #return self.embed_tokens.text_embeddings
        return None

    def set_input_embeddings(self, value):
        #self.embed_tokens.text_embeddings = value
        pass

    def forward(
        self,
        #input_ids: Optional[torch.LongTensor] = None,
        #audio_features: Optional[torch.Tensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        cross_attention_states: Optional[torch.FloatTensor] = None,
        cross_attention_mask: Optional[torch.Tensor] = None,
        full_text_row_masked_out_mask: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
        past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None,
        inputs_embeds: Optional[torch.FloatTensor] = 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,
    ) -> Union[Tuple, BaseModelOutputWithPast]:
        """

        Returns:

        Example:

        ```python
        >>> from transformers import AutoProcessor, MllamaTextModel

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

        >>> text = "<|image|>If I had to write a haiku for this one"
        >>> inputs = processor(text=text, return_tensors="pt")

        >>> output = model(**inputs)

        >>> print(output.last_hidden_state.shape)
        torch.Size([1, 13, 4096])
        ```
        """
        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
        )
        use_cache = use_cache if use_cache is not None else self.config.use_cache
        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 must specify exactly one of input_ids or inputs_embeds")

        if self.gradient_checkpointing and self.training and use_cache:
            logger.warning_once(
                "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`."
            )
            use_cache = False

        #if inputs_embeds is None:
        #    inputs_embeds = self.embed_tokens(input_ids=input_ids, audio_features=audio_features)
        

        hidden_states = inputs_embeds

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

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

        # create position embeddings to be shared across the decoder layers
        position_embeddings = self.rotary_emb(hidden_states, position_ids)

        # decoder layers
        all_hidden_states = () if output_hidden_states else None
        all_self_attns = () if output_attentions else None
        next_decoder_cache = None

        for idx, decoder_layer in enumerate(self.layers):
            if output_hidden_states:
                all_hidden_states += (hidden_states,)

            # For text-only path we should skip cross attention layers.
            # Let's check if the layer is cross attention layer and if we have cross attention states
            # or cached cross attention states.
            is_cross_attention_layer = idx in self.cross_attention_layers
            is_cross_attention_cache_empty = past_key_values is None or (
                past_key_values is not None and past_key_values.get_seq_length(idx) == 0
            )

            if is_cross_attention_layer and cross_attention_states is None and is_cross_attention_cache_empty:
                continue

            if self.gradient_checkpointing and self.training:
                layer_outputs = self._gradient_checkpointing_func(
                    decoder_layer.__call__,
                    hidden_states,
                    cross_attention_states,
                    cross_attention_mask,
                    causal_mask,
                    full_text_row_masked_out_mask,
                    position_ids,
                    past_key_values,
                    output_attentions,
                    use_cache,
                    cache_position,
                    position_embeddings,
                )
            else:
                layer_outputs = decoder_layer(
                    hidden_states,
                    cross_attention_states=cross_attention_states,
                    cross_attention_mask=cross_attention_mask,
                    attention_mask=causal_mask,
                    full_text_row_masked_out_mask=full_text_row_masked_out_mask,
                    position_ids=position_ids,
                    past_key_value=past_key_values,
                    output_attentions=output_attentions,
                    use_cache=use_cache,
                    cache_position=cache_position,
                    position_embeddings=position_embeddings,
                )

            hidden_states = layer_outputs[0]

            if use_cache:
                next_decoder_cache = layer_outputs[2 if output_attentions else 1]

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

        hidden_states = self.norm(hidden_states)

        # add hidden states from the last decoder layer
        if output_hidden_states:
            all_hidden_states += (hidden_states,)

        next_cache = next_decoder_cache if use_cache else None

        if not return_dict:
            return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
        return BaseModelOutputWithPast(
            last_hidden_state=hidden_states,
            past_key_values=next_cache,
            hidden_states=all_hidden_states,
            attentions=all_self_attns,
        )

    # Copied from transformers.models.llama.modeling_llama.LlamaModel._update_causal_mask
    def _update_causal_mask(
        self,
        attention_mask: torch.Tensor,
        input_tensor: torch.Tensor,
        cache_position: torch.Tensor,
        past_key_values: Cache,
        output_attentions: bool,
    ):
        if self.config._attn_implementation == "flash_attention_2":
            if attention_mask is not None and 0.0 in attention_mask:
                return attention_mask
            return None

        # For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument, in
        # order to dispatch on Flash Attention 2. This feature is not compatible with static cache, as SDPA will fail
        # to infer the attention mask.
        past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
        using_static_cache = isinstance(past_key_values, StaticCache)

        # When output attentions is True, sdpa implementation's forward method calls the eager implementation's forward
        if self.config._attn_implementation == "sdpa" and not using_static_cache and not output_attentions:
            if AttentionMaskConverter._ignore_causal_mask_sdpa(
                attention_mask,
                inputs_embeds=input_tensor,
                past_key_values_length=past_seen_tokens,
                is_training=self.training,
            ):
                return None

        dtype, device = input_tensor.dtype, input_tensor.device
        sequence_length = input_tensor.shape[1]
        if using_static_cache:
            target_length = past_key_values.get_max_cache_shape()
        else:
            target_length = (
                attention_mask.shape[-1]
                if isinstance(attention_mask, torch.Tensor)
                else past_seen_tokens + sequence_length + 1
            )

        # In case the provided `attention` mask is 2D, we generate a causal mask here (4D).
        causal_mask = self._prepare_4d_causal_attention_mask_with_cache_position(
            attention_mask,
            sequence_length=sequence_length,
            target_length=target_length,
            dtype=dtype,
            device=device,
            cache_position=cache_position,
            batch_size=input_tensor.shape[0],
        )

        if (
            self.config._attn_implementation == "sdpa"
            and attention_mask is not None
            and attention_mask.device.type == "cuda"
            and not output_attentions
        ):
            # Attend to all tokens in fully masked rows in the causal_mask, for example the relevant first rows when
            # using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path.
            # Details: https://github.com/pytorch/pytorch/issues/110213
            min_dtype = torch.finfo(dtype).min
            causal_mask = AttentionMaskConverter._unmask_unattended(causal_mask, min_dtype)

        return causal_mask

    @staticmethod
    # Copied from transformers.models.llama.modeling_llama.LlamaModel._prepare_4d_causal_attention_mask_with_cache_position
    def _prepare_4d_causal_attention_mask_with_cache_position(
        attention_mask: torch.Tensor,
        sequence_length: int,
        target_length: int,
        dtype: torch.dtype,
        device: torch.device,
        cache_position: torch.Tensor,
        batch_size: int,
        **kwargs,
    ):
        if attention_mask is not None and attention_mask.dim() == 4:
            # In this case we assume that the mask comes already in inverted form and requires no inversion or slicing.
            causal_mask = attention_mask
        else:
            min_dtype = torch.finfo(dtype).min
            causal_mask = torch.full(
                (sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=device
            )
            if sequence_length != 1:
                causal_mask = torch.triu(causal_mask, diagonal=1)
            causal_mask *= torch.arange(target_length, device=device) > cache_position.reshape(-1, 1)
            causal_mask = causal_mask[None, None, :, :].expand(batch_size, 1, -1, -1)
            if attention_mask is not None:
                causal_mask = causal_mask.clone()  # copy to contiguous memory for in-place edit
                mask_length = attention_mask.shape[-1]
                padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :]
                padding_mask = padding_mask == 0
                causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
                    padding_mask, min_dtype
                )

        return causal_mask

class Llama3ForCausalLM(MllamaPreTrainedModel, GenerationMixin):
    config_class = MllamaTextConfig
    base_model_prefix = "model"
    #_tied_weights_keys = ["lm_head.weight"]

    def __init__(self, config: MllamaTextConfig):
        super().__init__(config.get_text_config())
        self.text_config = config.get_text_config()
        self.vocab_size = self.text_config.vocab_size
        self.model = Llama3TextModel._from_config(self.text_config, attn_implementation=config._attn_implementation)
        self.lm_head = nn.Linear(self.text_config.hidden_size, self.vocab_size, bias=False)

        self.post_init()

    def get_input_embeddings(self):
        #return self.model.embed_tokens.text_embeddings
        return None

    def set_input_embeddings(self, value):
        #self.model.embed_tokens.text_embeddings = value
        pass

    def get_output_embeddings(self):
        return self.lm_head

    def set_output_embeddings(self, new_embeddings):
        self.lm_head = new_embeddings

    def set_decoder(self, decoder):
        self.model = decoder

    def get_decoder(self):
        return self.model

    def forward(
        self,
        #input_ids: torch.LongTensor = None,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        cross_attention_states: Optional[torch.LongTensor] = None,
        cross_attention_mask: Optional[torch.LongTensor] = None,
        full_text_row_masked_out_mask: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
        past_key_values: Optional[Union[Cache, 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,
        **loss_kwargs,
    ) -> Union[Tuple, CausalLMOutputWithPast]:
        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

        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
        outputs = self.model(
            #input_ids=input_ids,
            cross_attention_states=cross_attention_states,
            attention_mask=attention_mask,
            position_ids=position_ids,
            cross_attention_mask=cross_attention_mask,
            full_text_row_masked_out_mask=full_text_row_masked_out_mask,
            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,
        )

        hidden_states = outputs[0]
        logits = self.lm_head(hidden_states[:, -num_logits_to_keep:, :]).float()

        loss = None
        if labels is not None:
            loss = self.loss_function(logits, labels, self.vocab_size, **loss_kwargs)

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

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

#AutoModelForCausalLM.register(MllamaTextConfig, Llama3ForCausalLM)
#transformers.Llama3ForCausalLM = Llama3ForCausalLM

class Llama3ForConditionalGeneration(MllamaPreTrainedModel, GenerationMixin):
    config_class = Llama3Config
    base_model_prefix = "model"
    _supports_quantized_cache = False  # quant cache not supported in encoder-decoder setting

    def __init__(self, config: Llama3Config):
        super().__init__(config)
        self.vocab_size = config.text_config.vocab_size
        self.hidden_size = config.text_config.hidden_size
        self.max_num_tiles = config.vision_config.max_num_tiles
        self.vision_output_dim = config.vision_config.vision_output_dim
        self.pad_token_id = self.config.pad_token_id if self.config.pad_token_id is not None else -1

        self.vision_model = MllamaVisionModel._from_config(config.vision_config)
        self.language_model = MllamaForCausalLM._from_config(config.text_config)
        self.language_model.get_input_embeddings().weight.required_grad = False
        self.embed_tokens = Llama3Embedding(config.audio_config, config.text_config)
        self.multi_modal_projector = nn.Linear(
            config.vision_config.vision_output_dim,
            config.text_config.hidden_size,
            bias=True,
        )
        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 forward(
        self,
        input_ids: Optional[torch.LongTensor] = None,
        audio_features: Optional[torch.FloatTensor] = 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.text_config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.text_config.output_hidden_states
        )
        return_dict = return_dict if return_dict is not None else self.config.text_config.use_return_dict

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

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

        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]

        if inputs_embeds is None:
            inputs_embeds = self.embed_tokens(input_ids=input_ids, audio_features=audio_features)
            
        outputs = self.language_model(
            #input_ids=input_ids,
            #audio_features=audio_features,
            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,
        audio_features=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,
    ):
        # Overwritten -- in specific circumstances we don't want to forward image inputs to the model

        # 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:
            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 cache_position[0] == 0:
            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

    def _update_model_kwargs_for_generation(self, outputs, model_kwargs, is_encoder_decoder, **kwargs):
        cross_attention_mask_prev = model_kwargs.get("cross_attention_mask", None)
        model_kwargs = super()._update_model_kwargs_for_generation(
            outputs=outputs,
            model_kwargs=model_kwargs,
            is_encoder_decoder=is_encoder_decoder,
            **kwargs,
        )

        # add cross-attn mask for new token
        if cross_attention_mask_prev is not None:
            model_kwargs["cross_attention_mask"] = torch.cat(
                [cross_attention_mask_prev, cross_attention_mask_prev[:, -1:, ...]], dim=1
            )
        return model_kwargs

#AutoModel.register(Llama3Config, Llama3ForConditionalGeneration)
#transformers.Llama3ForConditionalGeneration = Llama3ForConditionalGeneration