mllama.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
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""PyTorch Mllama model."""
import math
from typing import (Iterable, List, Literal, Mapping, Optional, Tuple,
                    TypedDict, Union)

import numpy as np
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
import transformers.models.mllama.configuration_mllama as config_mllama
from PIL import Image
from torch import nn
from transformers.modeling_outputs import (BaseModelOutput,
                                           CausalLMOutputWithPast)
from transformers.models.mllama.image_processing_mllama import (
    get_optimal_tiled_canvas)
from transformers.models.mllama.processing_mllama import (
    get_cross_attention_token_mask)

import vllm.distributed.parallel_state as ps
from vllm.attention import Attention, AttentionMetadata, AttentionType
from vllm.attention.ops.paged_attn import PagedAttention
from vllm.config import CacheConfig, MultiModalConfig
from vllm.distributed import get_tensor_model_parallel_world_size
from vllm.inputs import (INPUT_REGISTRY, DecoderOnlyInputs,
                         EncoderDecoderInputs, InputContext)
from vllm.logger import init_logger
from vllm.model_executor.layers.layernorm import RMSNorm
from vllm.model_executor.layers.linear import (ColumnParallelLinear,
                                               QKVParallelLinear,
                                               RowParallelLinear)
from vllm.model_executor.layers.logits_processor import LogitsProcessor
from vllm.model_executor.layers.quantization import QuantizationConfig
from vllm.model_executor.layers.sampler import Sampler, SamplerOutput
from vllm.model_executor.layers.vocab_parallel_embedding import (
    DEFAULT_VOCAB_PADDING_SIZE, ParallelLMHead, VocabParallelEmbedding)
from vllm.model_executor.model_loader.weight_utils import default_weight_loader
from vllm.model_executor.sampling_metadata import SamplingMetadata
from vllm.multimodal import MULTIMODAL_REGISTRY
from vllm.sequence import SequenceData

from .clip import CLIPMLP
from .interfaces import SupportsMultiModal
from .llama import LlamaDecoderLayer, LlamaMLP

logger = init_logger(__name__)
MLLAMA_IMAGE_TOKEN_ID = 128256
MLLAMA_IMAGE_TOKEN = "<|image|>"


class MllamaImagePixelInputs(TypedDict):
    type: Literal["pixel_values"]
    data: torch.Tensor
    """Shape: """
    """(batch_size, max_num_image, max_num_chunk, num_channel, height, width)"""
    aspect_ratio_ids: torch.Tensor
    """Shape: `(batch_size, max_num_image)`"""
    aspect_ratio_mask: torch.Tensor
    """Shape: `(batch_size, max_num_image, max_num_tiles)`"""


# TODO: support LlamaImageEmbeddingInputs


def _get_num_image_in_last_group(prompt_token_ids: List[int]) -> int:
    num_images = 0
    for token_id in prompt_token_ids[::-1]:
        if token_id == MLLAMA_IMAGE_TOKEN_ID:
            num_images += 1
        elif num_images > 0:
            break
    return num_images


def input_processor_for_mllama(ctx: InputContext,
                               inputs: Union[DecoderOnlyInputs,
                                             EncoderDecoderInputs]):
    # move encoder_prompt to prompt
    if inputs.get("prompt") is None:
        inputs["prompt"] = inputs["encoder_prompt"]
        inputs["prompt_token_ids"] = inputs["encoder_prompt_token_ids"]

    # process multi-modal data
    multi_modal_data = inputs.get("encoder_multi_modal_data")

    if multi_modal_data is None or "image" not in multi_modal_data \
        or multi_modal_data["image"] is None:
        # text-only
        inputs["encoder_prompt"] = ""
        inputs["encoder_prompt_token_ids"] = []
        inputs["encoder_multi_modal_data"] = {}
        return inputs

    if isinstance(multi_modal_data['image'], Image.Image):
        multi_modal_data['image'] = [multi_modal_data['image']]
    # Since only the last group of consecutive images
    # are attended by the decoded tokens, we only need to
    # get the number of tiles for those images.
    num_decode_images = _get_num_image_in_last_group(
        inputs["prompt_token_ids"])
    hf_config = ctx.model_config.hf_config
    num_tiles = 0
    for image in multi_modal_data["image"][::-1]:
        width, height = image.size
        tile_size = hf_config.vision_config.image_size
        canvas_height, canvas_width = get_optimal_tiled_canvas(
            image_height=height,
            image_width=width,
            max_image_tiles=hf_config.vision_config.max_num_tiles,
            tile_size=tile_size,
        )
        num_tiles_height = canvas_height // tile_size
        num_tiles_width = canvas_width // tile_size
        num_tiles += num_tiles_height * num_tiles_width
        num_decode_images -= 1
        if num_decode_images == 0:
            break

    # Set encoder prompt length based on the number of tiles.
    # This tells the block manager to allocate correct number
    # of slots for encoder tokens.
    assert hf_config.vision_config.image_size % 14 == 0, \
        "chunk size should be multiple of 14"
    token_per_chunk = (hf_config.vision_config.image_size // 14)**2 + 1
    num_tokens = num_tiles * token_per_chunk
    inputs["encoder_prompt"] = MLLAMA_IMAGE_TOKEN * num_tokens
    inputs["encoder_prompt_token_ids"] = [MLLAMA_IMAGE_TOKEN_ID] * num_tokens

    return inputs


def get_max_mllama_image_tokens(ctx: InputContext) -> int:
    hf_config = ctx.model_config.hf_config
    token_per_chunk = (hf_config.vision_config.image_size // 14)**2 + 1
    return hf_config.vision_config.max_num_tiles * token_per_chunk


def dummy_decoder_seq_data(seq_len: int, num_images: int):
    # <|image|> * num_images + 0 * (seq_len - num_images)
    assert seq_len >= num_images, \
        "seq_len should be greater than or equal to num_images"

    return SequenceData.from_prompt_token_counts(
        (MLLAMA_IMAGE_TOKEN_ID, num_images),
        (0, seq_len - num_images),
    )


def dummy_encoder_seq_data(ctx: InputContext, num_images: int):
    num_tokens = get_max_mllama_image_tokens(ctx) * num_images

    return SequenceData.from_prompt_token_counts(
        (MLLAMA_IMAGE_TOKEN_ID, num_tokens))


def dummy_image(num_images: int, ):
    width = height = 1024
    image = Image.new("RGB", (width, height), color=0)
    return {"image": image if num_images == 1 else [image] * num_images}


def dummy_decoder_data_for_mllama(ctx: InputContext, seq_len: int,
                                  mm_counts: Mapping[str, int]):
    num_images = mm_counts["image"]
    return dummy_decoder_seq_data(seq_len, num_images), None


def dummy_encoder_data_for_mllama(ctx: InputContext, seq_len: int,
                                  mm_counts: Mapping[str, int]):
    num_images = mm_counts["image"]
    return dummy_encoder_seq_data(ctx, num_images), dummy_image(num_images)


def _prepare_aspect_ratio_attention_mask(
    aspect_ratio_mask: torch.Tensor,
    num_patches: int,
    target_length: int,
    dtype: torch.dtype,
) -> torch.Tensor:
    # Expand aspect ratio mask to target_length
    batch_size, max_num_tiles = aspect_ratio_mask.shape
    attention_mask = aspect_ratio_mask.view(batch_size, max_num_tiles, 1,
                                            1).to(dtype)
    attention_mask = attention_mask.repeat(1, 1, target_length, 1)

    # Mask padding patches
    pad_patches = target_length - num_patches
    attention_mask[:, :, -pad_patches:] = 0

    # Invert the mask (0 -> 1, 1 -> 0)
    attention_mask = 1 - attention_mask

    # Reshape to 2D and create 4D attention mask
    # (batch_size, 1, max_num_tiles*target_length, max_num_tiles*target_length)
    attention_mask = attention_mask.reshape(batch_size,
                                            max_num_tiles * target_length, 1)
    attention_mask = attention_mask @ attention_mask.transpose(
        -1, -2) * torch.finfo(dtype).min
    attention_mask = attention_mask.unsqueeze(1)

    return attention_mask


class ColumnParallelConv2dPatch(torch.nn.Module):
    """Conv2D Patching layer with model parallelism.
    Column parallel over unfolded input.
    Arguments:
        in_channels: Input channels.
        out_channels: Output channels.
        kernel_size: Size of convolution kernel.
        stride (default 1): Stride for convolution.
        bias (default False): Use bias in Conv2d.
    Input: (bsz, in_channels, width, height)
    Output: (bsz, num_tokens, out_channels)
    """

    def __init__(
        self,
        in_channels: int,
        out_channels: int,
        kernel_size: Union[int, Tuple[int, int]],
        stride: Union[int, Tuple[int, int]],
        bias: bool = False,
    ) -> None:
        super().__init__()
        if isinstance(kernel_size, int):
            kernel_size = (kernel_size, kernel_size)
        self._unfold = torch.nn.Unfold(kernel_size=kernel_size, stride=stride)
        self._linear = ColumnParallelLinear(
            in_channels * kernel_size[0] * kernel_size[1],
            out_channels,
            bias=bias,
        )

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        x = self._unfold(x)
        x = x.permute(0, 2, 1)
        x, _ = self._linear(x)
        return x


class MllamaPrecomputedAspectRatioEmbedding(nn.Module):

    def __init__(self,
                 config: config_mllama.MllamaVisionConfig,
                 is_gated: bool = True):
        super().__init__()
        self.max_num_tiles = config.max_num_tiles
        self.hidden_size = config.hidden_size
        self.max_aspect_ratio_id = config.max_aspect_ratio_id
        self.is_gated = is_gated

        self.embedding = nn.Embedding(self.max_aspect_ratio_id + 1,
                                      self.max_num_tiles * self.hidden_size)
        if is_gated:
            self.gate = nn.Parameter(torch.zeros(1))

    def forward(self, hidden_state: torch.Tensor,
                aspect_ratio_ids: torch.Tensor) -> torch.Tensor:
        embeddings = self.embedding(aspect_ratio_ids)
        embeddings = embeddings.reshape(-1, self.max_num_tiles, 1,
                                        self.hidden_size)

        if self.is_gated:
            embeddings = embeddings * self.gate.tanh()

        hidden_state = hidden_state + embeddings
        return hidden_state


class MllamaPrecomputedPositionEmbedding(nn.Module):

    def __init__(self, config: config_mllama.MllamaVisionConfig):
        super().__init__()
        self.max_num_tiles = config.max_num_tiles
        self.max_aspect_ratio_id = config.max_aspect_ratio_id
        self.num_patches = (config.image_size // config.patch_size)**2 + 1
        self.hidden_size = config.hidden_size
        self.scale = config.hidden_size**-0.5

        self.gate = nn.Parameter(torch.zeros(1))

        # position embedding
        position_embedding = torch.randn(self.num_patches, self.hidden_size)
        self.embedding = nn.Parameter(self.scale * position_embedding)

        # tile position embedding
        self.tile_embedding = nn.Embedding(
            self.max_aspect_ratio_id + 1,
            self.max_num_tiles * self.num_patches * self.hidden_size)

    def forward(self, hidden_state: torch.Tensor,
                aspect_ratio_ids: torch.Tensor) -> torch.Tensor:
        # position embeddings
        gated_position_embedding = (1 - self.gate.tanh()) * self.embedding
        hidden_state = hidden_state + gated_position_embedding.view(
            1, 1, self.num_patches, self.hidden_size)

        # precomputed tile position embeddings
        tile_position_embedding = self.tile_embedding(aspect_ratio_ids)
        batch_size = hidden_state.shape[0]
        tile_position_embedding = tile_position_embedding.reshape(
            batch_size, self.max_num_tiles, self.num_patches, self.hidden_size)
        gated_tile_position_embedding = self.gate.tanh(
        ) * tile_position_embedding
        hidden_state = hidden_state + gated_tile_position_embedding

        return hidden_state


# TODO: support other attention backends for attention in vision model
class MllamaVisionSdpaAttention(nn.Module):

    def __init__(self, config: config_mllama.MllamaVisionConfig):
        super().__init__()

        model_parallel_size = get_tensor_model_parallel_world_size()
        self.embed_dim = config.hidden_size
        self.num_heads = config.attention_heads
        self.head_dim = config.hidden_size // config.attention_heads
        self.num_local_heads = self.num_heads // model_parallel_size
        self.q_size = self.num_local_heads * self.head_dim
        self.kv_size = self.num_local_heads * self.head_dim

        self.qkv_proj = QKVParallelLinear(
            self.embed_dim,
            self.head_dim,
            self.num_heads,
            bias=False,
        )
        self.o_proj = RowParallelLinear(
            self.num_heads * self.head_dim,
            self.embed_dim,
            bias=False,
            input_is_parallel=True,
        )

    def forward(
        self,
        hidden_state: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
    ) -> torch.Tensor:
        qkv, _ = self.qkv_proj(hidden_state)
        q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
        q = q.view(q.shape[0], q.shape[1], self.num_local_heads,
                   self.head_dim).transpose(1, 2)
        k = k.view(k.shape[0], k.shape[1], self.num_local_heads,
                   self.head_dim).transpose(1, 2)
        v = v.view(v.shape[0], v.shape[1], self.num_local_heads,
                   self.head_dim).transpose(1, 2)

        # TODO: remove padding in image encoder
        attn_output = F.scaled_dot_product_attention(q,
                                                     k,
                                                     v,
                                                     attn_mask=attention_mask,
                                                     dropout_p=0.0)

        attn_output = attn_output.transpose(1, 2).contiguous()
        attn_output = attn_output.reshape(attn_output.shape[0],
                                          attn_output.shape[1], -1)
        output, _ = self.o_proj(attn_output)
        return output


class MllamaVisionEncoderLayer(nn.Module):

    def __init__(
        self,
        config: config_mllama.MllamaVisionConfig,
        quant_config: Optional[QuantizationConfig],
        prefix: str = "",
        is_gated: bool = False,
    ) -> None:
        super().__init__()

        self.hidden_size = config.hidden_size
        self.num_attention_heads = config.attention_heads
        self.is_gated = is_gated
        self.intermediate_size = config.intermediate_size

        self.self_attn = MllamaVisionSdpaAttention(config)
        self.mlp = CLIPMLP(config,
                           quant_config=quant_config,
                           prefix=f"{prefix}.mlp")

        self.input_layernorm = nn.LayerNorm(self.hidden_size,
                                            eps=config.norm_eps)
        self.post_attention_layernorm = nn.LayerNorm(self.hidden_size,
                                                     eps=config.norm_eps)

        # there used to be an if else here, no code path
        if is_gated:
            self.gate_attn = nn.Parameter(torch.ones(1) * math.pi / 4)
            self.gate_ffn = nn.Parameter(torch.ones(1) * math.pi / 4)

    def forward(
        self,
        hidden_state: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
    ):
        # Self Attention
        residual = hidden_state
        hidden_state = self.input_layernorm(hidden_state)
        hidden_state = self.self_attn(hidden_state,
                                      attention_mask=attention_mask)
        gate_attn = 1 if not self.is_gated else self.gate_attn.tanh()
        hidden_state = residual + gate_attn * hidden_state

        # Feed forward
        residual = hidden_state
        hidden_state = self.post_attention_layernorm(hidden_state)
        hidden_state = self.mlp(hidden_state)
        gate_ffn = 1 if not self.is_gated else self.gate_ffn.tanh()
        hidden_state = residual + gate_ffn * hidden_state

        return hidden_state


class MllamaVisionEncoder(nn.Module):

    def __init__(
        self,
        config: config_mllama.MllamaVisionConfig,
        quant_config: Optional[QuantizationConfig],
        num_layers: int = 32,
        is_gated: bool = False,
        output_hidden_states=None,
        prefix: str = "",
    ) -> None:
        super().__init__()
        self.config = config
        self.layers = nn.ModuleList([
            MllamaVisionEncoderLayer(config,
                                     quant_config=quant_config,
                                     is_gated=is_gated,
                                     prefix=f"{prefix}.layers.{layer_idx}")
            for layer_idx in range(num_layers)
        ])
        self.output_hidden_states = output_hidden_states or []

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
    ) -> Union[Tuple, BaseModelOutput]:
        encoder_states = ()

        for i, encoder_layer in enumerate(self.layers):
            if i in self.output_hidden_states:
                encoder_states = encoder_states + (hidden_states, )
            hidden_states = encoder_layer(
                hidden_states,
                attention_mask,
            )

        if len(self.layers) - 1 in self.output_hidden_states:
            encoder_states = encoder_states + (hidden_states, )

        return hidden_states, encoder_states


class MllamaVisionModel(nn.Module):

    def __init__(
        self,
        config: config_mllama.MllamaVisionConfig,
        quant_config: Optional[QuantizationConfig],
        prefix: str = "",
    ) -> None:
        super().__init__()

        self.image_size = config.image_size
        self.patch_size = config.patch_size
        self.max_num_tiles = config.max_num_tiles
        self.hidden_size = config.hidden_size
        self.in_channels = config.num_channels
        self.intermediate_layers_indices = config.intermediate_layers_indices

        self.num_patches = (self.image_size // self.patch_size)**2 + 1
        self.scale = config.hidden_size**-0.5

        self.patch_embedding = ColumnParallelConv2dPatch(
            in_channels=config.num_channels,
            out_channels=self.hidden_size,
            kernel_size=self.patch_size,
            stride=self.patch_size,
            bias=False,
        )

        self.class_embedding = nn.Parameter(self.scale *
                                            torch.randn(self.hidden_size))
        self.gated_positional_embedding = MllamaPrecomputedPositionEmbedding(
            config)

        self.pre_tile_positional_embedding = \
            MllamaPrecomputedAspectRatioEmbedding(config, is_gated=True)
        self.post_tile_positional_embedding = \
            MllamaPrecomputedAspectRatioEmbedding(config, is_gated=True)

        # layer norms
        self.layernorm_pre = nn.LayerNorm(self.hidden_size)
        self.layernorm_post = nn.LayerNorm(self.hidden_size)

        # encoders
        self.transformer = MllamaVisionEncoder(
            config,
            quant_config,
            config.num_hidden_layers,
            is_gated=False,
            output_hidden_states=config.intermediate_layers_indices,
            prefix=f"{prefix}.transformer",
        )
        self.global_transformer = MllamaVisionEncoder(
            config,
            quant_config,
            config.num_global_layers,
            is_gated=True,
            prefix=f"{prefix}.global_transformer",
        )

    def apply_class_embedding(self,
                              hidden_state: torch.Tensor) -> torch.Tensor:
        batch_size, _, hidden_size = hidden_state.shape
        class_embedding = self.class_embedding.expand(batch_size, 1,
                                                      hidden_size)
        hidden_state = torch.cat([class_embedding, hidden_state], dim=1)
        return hidden_state

    def forward(self, pixel_values: torch.Tensor,
                aspect_ratio_ids: torch.Tensor,
                aspect_ratio_mask: torch.Tensor) -> torch.Tensor:
        batch_size, num_concurrent_media, num_tiles, num_channels, \
            height, width = pixel_values.shape

        pixel_values = pixel_values.reshape(
            batch_size * num_concurrent_media * num_tiles, num_channels,
            height, width)
        aspect_ratio_ids = aspect_ratio_ids.reshape(
            batch_size * num_concurrent_media, -1)

        # patch embedding
        patch_embeds = self.patch_embedding(
            pixel_values.to(self.layernorm_pre.weight.dtype))
        hidden_state = patch_embeds
        hidden_state = ps.get_tp_group().all_gather(hidden_state)

        # tile embeddings
        _, num_patches, dim = hidden_state.shape
        hidden_state = hidden_state.reshape(batch_size * num_concurrent_media,
                                            num_tiles, -1, dim)
        hidden_state = self.pre_tile_positional_embedding(
            hidden_state, aspect_ratio_ids)

        # apply cls token
        hidden_state = hidden_state.reshape(
            batch_size * num_concurrent_media * num_tiles, num_patches, dim)
        hidden_state = self.apply_class_embedding(hidden_state)
        num_patches += 1

        # apply position embeddings
        hidden_state = hidden_state.reshape(batch_size * num_concurrent_media,
                                            num_tiles, num_patches, dim)
        hidden_state = self.gated_positional_embedding(hidden_state,
                                                       aspect_ratio_ids)

        # apply encoder
        hidden_state = self.layernorm_pre(hidden_state)

        # Compute the number of tokens to pad
        num_padding_patches = (8 - (hidden_state.shape[-2] % 8)) % 8
        # Compute padding tuple for pad function
        padding = (
            0, 0, 0, num_padding_patches
        )  # (pad_left, pad_right, pad_left for dim -2, pad_right for dim -2)
        # Pad the tensor
        hidden_state = F.pad(hidden_state, padding, mode="constant", value=0)
        slice_index = -num_padding_patches if num_padding_patches > 0 else None

        attention_mask = aspect_ratio_mask.reshape(
            batch_size * num_concurrent_media, -1)
        attention_mask = _prepare_aspect_ratio_attention_mask(
            aspect_ratio_mask=attention_mask,
            num_patches=self.num_patches,
            target_length=hidden_state.shape[2],
            dtype=self.layernorm_pre.weight.dtype,
        )

        hidden_state = hidden_state.view(batch_size * num_concurrent_media, -1,
                                         dim)
        output = self.transformer(
            hidden_state,
            attention_mask=attention_mask,
        )
        hidden_state, intermediate_hidden_states = output[0], output[1]
        intermediate_hidden_states = torch.stack(intermediate_hidden_states,
                                                 dim=-1)

        # apply global encoder
        hidden_state = self.layernorm_post(hidden_state)
        hidden_state = hidden_state.reshape(batch_size * num_concurrent_media,
                                            num_tiles,
                                            num_patches + num_padding_patches,
                                            dim)
        hidden_state = self.post_tile_positional_embedding(
            hidden_state, aspect_ratio_ids)
        hidden_state = hidden_state.reshape(
            batch_size * num_concurrent_media,
            num_tiles * (num_patches + num_padding_patches), dim)
        hidden_state = self.global_transformer(
            hidden_state, attention_mask=attention_mask)[0]
        hidden_state = hidden_state.reshape(batch_size * num_concurrent_media,
                                            num_tiles,
                                            num_patches + num_padding_patches,
                                            dim)
        hidden_state = hidden_state[:, :, :slice_index]

        # adding intermediate layer outputs
        hidden_state = hidden_state.reshape(batch_size, num_concurrent_media,
                                            num_tiles, num_patches, dim)
        intermediate_hidden_states = intermediate_hidden_states.reshape(
            batch_size * num_concurrent_media, num_tiles,
            num_patches + num_padding_patches, -1)
        intermediate_hidden_states = intermediate_hidden_states[:, :, :
                                                                slice_index]
        intermediate_hidden_states = intermediate_hidden_states.reshape(
            batch_size, num_concurrent_media, num_tiles, num_patches, -1)
        hidden_state = torch.cat([hidden_state, intermediate_hidden_states],
                                 dim=-1)
        return hidden_state


class MllamaTextRMSNorm(nn.Module):

    def __init__(self, hidden_size, eps=1e-6):
        """
        MllamaTextRMSNorm is equivalent to T5LayerNorm
        """
        super().__init__()
        self.weight = nn.Parameter(torch.ones(hidden_size))
        self.variance_epsilon = eps

    def forward(self, hidden_states):
        input_dtype = hidden_states.dtype
        hidden_states = hidden_states.to(torch.float32)
        variance = hidden_states.pow(2).mean(-1, keepdim=True)
        hidden_states = hidden_states * torch.rsqrt(variance +
                                                    self.variance_epsilon)
        return self.weight * hidden_states.to(input_dtype)

    def extra_repr(self):
        return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}"


class MllamaTextCrossAttention(nn.Module):
    """Multi-headed attention from 'Attention Is All You Need' paper"""

    def __init__(
        self,
        config: Optional[config_mllama.MllamaTextConfig] = None,
        layer_idx: Optional[int] = None,
        quant_config: Optional[QuantizationConfig] = None,
        prefix: str = "",
    ):
        super().__init__()
        self.config = config
        self.model_parallel_size = get_tensor_model_parallel_world_size()
        self.num_heads = self.config.num_attention_heads
        self.num_local_heads = self.num_heads // self.model_parallel_size
        self.num_key_value_heads = self.config.num_key_value_heads
        self.num_local_key_value_heads = \
            self.num_key_value_heads // self.model_parallel_size
        self.dropout = config.dropout
        self.hidden_size = config.hidden_size
        self.head_dim = config.hidden_size // self.num_heads
        self.layer_idx = layer_idx
        self.num_key_value_groups = self.num_heads // self.num_key_value_heads
        self.q_local_size = self.num_local_heads * self.head_dim
        self.kv_local_size = self.num_local_key_value_heads * self.head_dim

        # TODO: change to Q/KV separate linear after #7448 is merged
        self.qkv_proj = QKVParallelLinear(
            self.hidden_size,
            self.head_dim,
            self.num_heads,
            self.num_key_value_heads,
            bias=False,
            quant_config=quant_config,
            prefix=f"{prefix}.qkv_proj",
        )
        self.o_proj = RowParallelLinear(
            self.num_heads * self.head_dim,
            self.hidden_size,
            bias=False,
            input_is_parallel=True,
            quant_config=quant_config,
            prefix=f"{prefix}.o_proj",
        )
        # vllm.model_executor.layers.layernorm.RMSNorm has precision issue,
        # use huggingface's instead
        self.q_norm = MllamaTextRMSNorm(self.head_dim, eps=config.rms_norm_eps)
        self.k_norm = MllamaTextRMSNorm(self.head_dim, eps=config.rms_norm_eps)
        self.scaling = self.head_dim**-0.5

        self.attn = Attention(
            self.num_local_heads,
            self.head_dim,
            self.scaling,
            self.num_local_key_value_heads,
            prefix=f"{prefix}.attn",
        )

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.Tensor],
        kv_range_for_decode: Optional[List[Tuple[int, int]]],
        cross_attention_states: Optional[torch.Tensor],
        kv_cache: torch.Tensor,
        attn_metadata: AttentionMetadata,
    ) -> torch.Tensor:
        qkv_dec, _ = self.qkv_proj(hidden_states)
        q, _, _ = qkv_dec.split(
            [self.q_local_size, self.kv_local_size, self.kv_local_size],
            dim=-1)
        if cross_attention_states is None:
            k = None
            v = None
        else:
            qkv_enc, _ = self.qkv_proj(cross_attention_states)
            _, k, v = qkv_enc.split(
                [self.q_local_size, self.kv_local_size, self.kv_local_size],
                dim=-1)
            k = k.view(-1, self.num_local_key_value_heads, self.head_dim)
            v = v.view(-1, self.num_local_key_value_heads, self.head_dim)
            k = self.k_norm(k)
        q = q.view(-1, self.num_local_heads, self.head_dim)
        q = self.q_norm(q)

        if attention_mask is not None:
            output = self.attention_with_mask(q, k, v, kv_cache,
                                              attention_mask,
                                              kv_range_for_decode,
                                              attn_metadata)
        else:
            output = self.attn(q,
                               k,
                               v,
                               kv_cache,
                               attn_metadata,
                               attn_type=AttentionType.ENCODER_DECODER)
        out, _ = self.o_proj(output)
        return out

    def attention_with_mask(
        self,
        q: torch.Tensor,
        k: torch.Tensor,
        v: torch.Tensor,
        kv_cache: torch.Tensor,
        attention_mask: torch.Tensor,
        kv_range_for_decode: List[Tuple[int, int]],
        attn_metadata: AttentionMetadata,
    ) -> torch.Tensor:
        # Skip writing kv-cache for the initial profiling run.
        if len(kv_cache.shape) == 3:
            key_cache, value_cache = PagedAttention.split_kv_cache(
                kv_cache, self.num_local_key_value_heads, self.head_dim)
            cached_k = torch.cat([k[s:e] for s, e in kv_range_for_decode])
            cached_v = torch.cat([v[s:e] for s, e in kv_range_for_decode])
            PagedAttention.write_to_paged_cache(
                cached_k, cached_v, key_cache, value_cache,
                attn_metadata.cross_slot_mapping, "auto", 1.0, 1.0)
        # We have to call torch.sdpa for prefill when using a
        # custom cross-attention mask. Because the mask is not a
        # standard causal mask, neither a block diagonal mask which
        # can be optimized by xformers.BlockDiagonalMask.
        # The mask is specially calculated for supporting multi
        # images and interleaved images.
        q_len = q.shape[0]
        kv_len = k.shape[0]
        q = q.transpose(0, 1).view(self.num_local_key_value_heads,
                                   self.num_key_value_groups, q_len,
                                   self.head_dim).contiguous()
        k = k.transpose(0,
                        1)[:,
                           None, :, :].expand(self.num_local_key_value_heads,
                                              self.num_key_value_groups,
                                              kv_len,
                                              self.head_dim).contiguous()
        v = v.transpose(0,
                        1)[:,
                           None, :, :].expand(self.num_local_key_value_heads,
                                              self.num_key_value_groups,
                                              kv_len,
                                              self.head_dim).contiguous()
        attention_mask = attention_mask.view(1, 1, q_len, kv_len)
        output = F.scaled_dot_product_attention(q,
                                                k,
                                                v,
                                                attn_mask=attention_mask,
                                                is_causal=False)
        output = output.permute(2, 0, 1, 3).reshape(
            q_len, self.num_local_heads * self.head_dim)
        return output


class MllamaCrossAttentionDecoderLayer(torch.nn.Module):
    """Cross-attention transformer block with tanh-gated attention
    and feedforward."""

    def __init__(
        self,
        config: config_mllama.MllamaTextConfig,
        layer_idx: int,
        quant_config: Optional[QuantizationConfig],
        prefix: str = "",
    ) -> None:
        super().__init__()

        self.layer_idx = layer_idx
        self.cross_attn = MllamaTextCrossAttention(
            config=config,
            layer_idx=layer_idx,
            quant_config=quant_config,
            prefix=f"{prefix}.cross_attn",
        )

        self.input_layernorm = RMSNorm(config.hidden_size,
                                       eps=config.rms_norm_eps)
        self.cross_attn_attn_gate = torch.nn.Parameter(torch.zeros(1))

        self.mlp = LlamaMLP(
            hidden_size=config.hidden_size,
            intermediate_size=config.intermediate_size,
            hidden_act=config.hidden_act,
            quant_config=quant_config,
            prefix=f"{prefix}.mlp",
        )
        self.post_attention_layernorm = RMSNorm(config.hidden_size,
                                                eps=config.rms_norm_eps)
        self.cross_attn_mlp_gate = torch.nn.Parameter(torch.zeros(1))

    def forward(
        self,
        hidden_states: torch.Tensor,
        cross_attention_states: torch.Tensor,
        cross_attention_mask: torch.Tensor,
        kv_range_for_decode: Optional[List[Tuple[int, int]]],
        full_text_row_masked_out_mask: torch.Tensor,
        kv_cache: List[torch.Tensor],
        attn_metadata: AttentionMetadata,
    ) -> torch.Tensor:
        residual = hidden_states
        hidden_states = self.input_layernorm(hidden_states)

        hidden_states = self.cross_attn(
            hidden_states=hidden_states,
            attention_mask=cross_attention_mask,
            kv_range_for_decode=kv_range_for_decode,
            cross_attention_states=cross_attention_states,
            kv_cache=kv_cache,
            attn_metadata=attn_metadata,
        )
        hidden_states = full_text_row_masked_out_mask * hidden_states
        hidden_states = residual + self.cross_attn_attn_gate.tanh(
        ) * hidden_states

        residual = hidden_states
        hidden_states = self.post_attention_layernorm(hidden_states)
        hidden_states = self.mlp(hidden_states)
        hidden_states = full_text_row_masked_out_mask * hidden_states
        hidden_states = residual + self.cross_attn_mlp_gate.tanh(
        ) * hidden_states
        return hidden_states


class MllamaTextModel(nn.Module):
    config_class = config_mllama.MllamaTextConfig
    base_model_prefix = "model"

    def __init__(
        self,
        config: config_mllama.MllamaTextConfig,
        cache_config: Optional[CacheConfig],
        quant_config: Optional[QuantizationConfig],
        prefix: str = "",
    ) -> None:
        super().__init__()

        self.padding_idx = config.pad_token_id
        self.vocab_size = config.vocab_size
        self.embed_tokens = VocabParallelEmbedding(config.vocab_size + 8,
                                                   config.hidden_size)
        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,
                        quant_config=quant_config,
                        prefix=f"{prefix}.layers.{layer_idx}",
                    ))
            else:
                # TODO: force LlamaDecoderLayer to config.attention_bias=False
                layers.append(
                    LlamaDecoderLayer(
                        config,
                        cache_config=cache_config,
                        quant_config=quant_config,
                        prefix=f"{prefix}.layers.{layer_idx}",
                    ))

        self.layers = nn.ModuleList(layers)
        self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)

    def forward(
        self,
        input_ids: torch.LongTensor,
        positions: Optional[torch.LongTensor],
        cross_attention_states: Optional[torch.LongTensor],
        cross_attention_mask: Optional[torch.LongTensor],
        kv_range_for_decode: Optional[List[Tuple[int, int]]],
        full_text_row_masked_out_mask: Optional[Tuple[torch.Tensor,
                                                      torch.Tensor]],
        kv_caches: List[torch.Tensor],
        attn_metadata: AttentionMetadata,
        skip_cross_attention: bool,
    ) -> torch.Tensor:
        inputs_embeds = self.embed_tokens(input_ids)
        hidden_states = inputs_embeds

        for idx, decoder_layer in enumerate(self.layers):
            if isinstance(decoder_layer, MllamaCrossAttentionDecoderLayer):
                if not skip_cross_attention:
                    hidden_states = decoder_layer(
                        hidden_states=hidden_states,
                        cross_attention_states=cross_attention_states,
                        cross_attention_mask=cross_attention_mask,
                        kv_range_for_decode=kv_range_for_decode,
                        full_text_row_masked_out_mask=
                        full_text_row_masked_out_mask,
                        kv_cache=kv_caches[idx],
                        attn_metadata=attn_metadata,
                    )
            elif isinstance(decoder_layer, LlamaDecoderLayer):
                hidden_states, residual = decoder_layer(
                    positions=positions,
                    hidden_states=hidden_states,
                    kv_cache=kv_caches[idx],
                    attn_metadata=attn_metadata,
                    residual=None,
                )
                hidden_states = hidden_states + residual
            else:
                raise ValueError(
                    f"Unknown decoder layer type {type(decoder_layer)}")
        hidden_states = self.norm(hidden_states)
        return hidden_states


class MllamaForCausalLM(nn.Module):
    config_class = config_mllama.MllamaTextConfig
    base_model_prefix = "language_model"
    _no_split_modules = [
        "MllamaCrossAttentionDecoderLayer", "MllamaSelfAttentionDecoderLayer"
    ]

    def __init__(
        self,
        config: config_mllama.MllamaTextConfig,
        cache_config: Optional[CacheConfig],
        quant_config: Optional[QuantizationConfig],
        prefix: str = "",
    ) -> None:
        super().__init__()
        self.vocab_size = config.vocab_size
        self.model = MllamaTextModel(config,
                                     cache_config,
                                     quant_config,
                                     prefix=f"{prefix}.model")
        self.lm_head = ParallelLMHead(
            config.vocab_size,
            config.hidden_size,
            org_num_embeddings=config.vocab_size,
            padding_size=DEFAULT_VOCAB_PADDING_SIZE,
            quant_config=quant_config,
        )

    def forward(
        self,
        input_ids: torch.LongTensor,
        positions: Optional[torch.LongTensor],
        cross_attention_states: Optional[torch.LongTensor],
        cross_attention_mask: Optional[torch.LongTensor],
        kv_range_for_decode: Optional[List[Tuple[int, int]]],
        full_text_row_masked_out_mask: Optional[Tuple[torch.Tensor,
                                                      torch.Tensor]],
        kv_caches: List[torch.Tensor],
        attn_metadata: AttentionMetadata,
        skip_cross_attention: bool,
    ) -> torch.Tensor:
        hidden_states = self.model(
            input_ids=input_ids,
            positions=positions,
            cross_attention_states=cross_attention_states,
            cross_attention_mask=cross_attention_mask,
            kv_range_for_decode=kv_range_for_decode,
            full_text_row_masked_out_mask=full_text_row_masked_out_mask,
            kv_caches=kv_caches,
            attn_metadata=attn_metadata,
            skip_cross_attention=skip_cross_attention,
        )
        return hidden_states


@MULTIMODAL_REGISTRY.register_image_input_mapper()
@MULTIMODAL_REGISTRY.register_max_image_tokens(get_max_mllama_image_tokens)
@INPUT_REGISTRY.register_dummy_data(dummy_decoder_data_for_mllama)
@INPUT_REGISTRY.register_dummy_encoder_data(dummy_encoder_data_for_mllama)
@INPUT_REGISTRY.register_input_processor(input_processor_for_mllama)
class MllamaForConditionalGeneration(nn.Module, SupportsMultiModal):

    def __init__(self,
                 config: config_mllama.MllamaConfig,
                 multimodal_config: MultiModalConfig,
                 cache_config: Optional[CacheConfig] = None,
                 quant_config: Optional[QuantizationConfig] = None):
        super().__init__()
        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 = \
            config.pad_token_id if config.pad_token_id is not None else -1
        self.image_size = config.vision_config.image_size

        self.vision_model = MllamaVisionModel(config.vision_config,
                                              quant_config,
                                              prefix="vision_model")
        self.language_model = MllamaForCausalLM(
            config.text_config,
            cache_config=cache_config,
            quant_config=quant_config,
            prefix="language_model",
        )
        self.multi_modal_projector = nn.Linear(
            config.vision_config.vision_output_dim,
            config.text_config.hidden_size,
            bias=True,
        )
        self.logits_processor = LogitsProcessor(config.output_hidden_states,
                                                config.text_config.vocab_size)
        self.sampler = Sampler()

    def compute_logits(
        self,
        hidden_states: torch.Tensor,
        sampling_metadata: SamplingMetadata,
    ) -> Optional[torch.Tensor]:
        logits = self.logits_processor(self.language_model.lm_head,
                                       hidden_states, sampling_metadata)
        return logits

    def sample(
        self,
        logits: torch.Tensor,
        sampling_metadata: SamplingMetadata,
    ) -> Optional[SamplerOutput]:
        next_tokens = self.sampler(logits, sampling_metadata)
        return next_tokens

    def _parse_and_validate_image_input(self, **kwargs: object):
        # tensor with the same shape will be batched together by
        # MultiModalInputs.batch, so pixel_values here can be:
        #   - List[List[torch.Tensor]]:
        #       with shape (num_tiles, 3, image_res, image_res)
        #   - List[torch.Tensor]:
        #       with shape (num_image, num_tiles, 3, image_res, image_res)
        #   - torch.Tensor:
        #       with shape (bs, num_image, num_tiles, 3, image_res, image_res)
        pixel_values: Optional[Union[List[List[torch.Tensor]],
                                     List[torch.Tensor],
                                     torch.Tensor]] = kwargs.pop(
                                         "pixel_values", None)
        image_embeds: Optional[Union[List[List[torch.Tensor]],
                                     List[torch.Tensor],
                                     torch.Tensor]] = kwargs.pop(
                                         "image_embeds", None)
        aspect_ratio_ids: Optional[Union[List[List[torch.Tensor]],
                                         List[torch.Tensor],
                                         torch.Tensor]] = kwargs.pop(
                                             "aspect_ratio_ids", None)
        aspect_ratio_mask: Optional[Union[List[List[torch.Tensor]],
                                          List[torch.Tensor],
                                          torch.Tensor]] = kwargs.pop(
                                              "aspect_ratio_mask", None)

        if pixel_values is None and image_embeds is None:
            return None

        if pixel_values is not None and image_embeds is not None:
            raise ValueError(
                "Both pixel values and image embeds are provided.")

        if pixel_values is not None:
            assert aspect_ratio_ids is not None
            assert aspect_ratio_mask is not None
            max_num_images = max([len(x[0]) for x in pixel_values])
            if max_num_images == 0:
                raise ValueError("No images provided.")
            max_num_tiles = max(
                max([len(x) for x in y[0]]) for y in pixel_values)
            device = self.multi_modal_projector.weight.device
            bsz = len(pixel_values)
            out_num_tiles = []
            out_images = torch.zeros(
                bsz,
                max_num_images,
                max_num_tiles,
                3,
                self.image_size,
                self.image_size,
                dtype=torch.float32,
                device=device,
            )
            out_ar_ids = torch.ones(bsz,
                                    max_num_images,
                                    dtype=torch.int64,
                                    device=device)
            out_ar_mask = torch.zeros(bsz,
                                      max_num_images,
                                      max_num_tiles,
                                      dtype=torch.int64,
                                      device=device)
            for b in range(len(pixel_values)):
                _num_tiles = []
                for i in range(len(pixel_values[b][0])):
                    img = pixel_values[b][0][i]
                    out_images[b, i, :img.shape[0]] = img
                    out_ar_ids[b, i] = aspect_ratio_ids[b][0][i]
                    out_ar_mask[b, i] = aspect_ratio_mask[b][0][i]
                    _num_tiles.append(img.shape[0])
                out_num_tiles.append(_num_tiles)

            return MllamaImagePixelInputs(
                type="pixel_values",
                data=out_images,
                aspect_ratio_ids=out_ar_ids,
                aspect_ratio_mask=out_ar_mask,
            )

        if image_embeds is not None:
            raise NotImplementedError

        raise AssertionError("This line should be unreachable.")

    def flat_encoder_result(self, cross_attention_states: torch.Tensor,
                            attn_metadata: AttentionMetadata,
                            actual_encoder_seq_lens: List[int]):

        cross_attention_states_flat = torch.zeros(
            sum(actual_encoder_seq_lens),
            cross_attention_states.shape[-1],
            device=cross_attention_states.device,
            dtype=cross_attention_states.dtype)
        start_pos = 0
        for seq_len, vision_token_in_batch in zip(actual_encoder_seq_lens,
                                                  cross_attention_states):
            end_pos = start_pos + seq_len
            cross_attention_states_flat[
                start_pos:end_pos] = vision_token_in_batch[:seq_len]
            start_pos = end_pos
        cross_attention_states = cross_attention_states_flat
        return cross_attention_states

    def get_cross_attention_states(
        self,
        image_inputs: MllamaImagePixelInputs,
        attn_metadata: AttentionMetadata,
        actual_encoder_seq_lens: List[int],
    ) -> Tuple[torch.Tensor]:
        # NOTE: llama's reference implementation runs vision model on CPU
        pixel_values = image_inputs['data']
        aspect_ratio_ids = image_inputs['aspect_ratio_ids']
        aspect_ratio_mask = image_inputs['aspect_ratio_mask']
        cross_attention_states = self.vision_model(pixel_values,
                                                   aspect_ratio_ids,
                                                   aspect_ratio_mask)
        cross_attention_states = self.multi_modal_projector(
            cross_attention_states)

        bsz, _, _, _, image_token_dim = tuple(cross_attention_states.shape)
        cross_attention_states = cross_attention_states.view(
            bsz, -1, image_token_dim)

        cross_attention_states = self.flat_encoder_result(
            cross_attention_states, attn_metadata, actual_encoder_seq_lens)

        return cross_attention_states

    def get_cross_attention_mask(
        self,
        input_ids: torch.Tensor,
        attn_metadata: AttentionMetadata,
        num_tiles: List[List[int]],
        num_tokens_per_tile: int,
        dtype: torch.dtype,
    ) -> Tuple[torch.Tensor, torch.Tensor]:
        token_ids = input_ids.tolist()
        start = 0
        batch_token_ids = []
        for seq_len in attn_metadata.seq_lens:
            batch_token_ids.append(token_ids[start:start + seq_len])
            start += seq_len
        sparse_mask = [
            get_cross_attention_token_mask(t, MLLAMA_IMAGE_TOKEN_ID)
            for t in batch_token_ids
        ]

        # Skip generating cross-attention mask if all samples
        # are text-only or have only 1 leading image.
        if skip_attention_mask(sparse_mask):
            return None, None

        dense_mask, tile_range_for_decode = \
            convert_sparse_cross_attention_mask_to_dense(
                sparse_mask, num_tiles, attn_metadata.seq_lens)
        cross_attention_mask = \
            convert_dense_cross_attention_mask_to_tensor(
                dense_mask, num_tokens_per_tile, input_ids.device, dtype)
        kv_range_for_decode = [[
            t[0] * num_tokens_per_tile, t[1] * num_tokens_per_tile
        ] for t in tile_range_for_decode]

        return cross_attention_mask, kv_range_for_decode

    def get_full_text_row_masked_out_mask(
        self,
        attn_metadata: AttentionMetadata,
        device: torch.device,
    ) -> torch.Tensor:
        full_text_row_masked_out_mask = torch.ones(
            (attn_metadata.num_prefill_tokens, 1), dtype=torch.bool)
        start_pos = 0
        for seq_len, encoder_seq_len in zip(attn_metadata.seq_lens,
                                            attn_metadata.encoder_seq_lens):
            if encoder_seq_len == 0:
                full_text_row_masked_out_mask[start_pos:start_pos +
                                              seq_len] = False
            start_pos += seq_len
        full_text_row_masked_out_mask = full_text_row_masked_out_mask.to(
            device)
        return full_text_row_masked_out_mask

    def forward(
        self,
        input_ids: torch.Tensor,
        positions: torch.Tensor,
        kv_caches: List[torch.Tensor],
        attn_metadata: AttentionMetadata,
        **kwargs: object,
    ) -> Union[Tuple, CausalLMOutputWithPast]:
        if attn_metadata.num_prefill_tokens > 0 and \
            attn_metadata.num_decode_tokens > 0:
            raise ValueError("Chunk prefill not supported")
        image_inputs = self._parse_and_validate_image_input(**kwargs)
        cross_attention_states = None
        cross_attention_mask = None
        kv_range_for_decode = None

        # For 1) text-only prefill and decode, 2) image-present decode.
        if image_inputs is None:
            full_text_row_masked_out_mask = (
                attn_metadata.encoder_seq_lens_tensor != 0).reshape(-1, 1).to(
                    input_ids.device)
            skip_cross_attention = max(attn_metadata.encoder_seq_lens) == 0

        # For image-present prefill.
        else:
            skip_cross_attention = False

            # Get the actual number of encoder tokens for each sample.
            # Because attn_metadata.encoder_seq_lens only counts the last
            # group of images for each sample, which is used to cheat the
            # block manager to allocate blocks for those images only.
            # See input_processor_for_mllama() for more details.
            num_tiles_tensor = kwargs.pop("num_tiles")
            num_tiles = [t[0].tolist() for t in num_tiles_tensor]
            num_tokens_per_tile = (self.image_size // 14)**2 + 1
            actual_encoder_seq_lens = [
                sum(num_tile) * num_tokens_per_tile for num_tile in num_tiles
            ]
            for actual_len, last_group_len in zip(
                    actual_encoder_seq_lens, attn_metadata.encoder_seq_lens):
                assert actual_len >= last_group_len

            cross_attention_states = self.get_cross_attention_states(
                image_inputs, attn_metadata, actual_encoder_seq_lens)

            full_text_row_masked_out_mask = \
                self.get_full_text_row_masked_out_mask(
                    attn_metadata, input_ids.device)

            cross_attention_mask, kv_range_for_decode = \
                self.get_cross_attention_mask(
                    input_ids, attn_metadata, num_tiles,
                    num_tokens_per_tile, cross_attention_states.dtype)

        outputs = self.language_model(
            input_ids=input_ids,
            positions=positions,
            cross_attention_states=cross_attention_states,
            cross_attention_mask=cross_attention_mask,
            kv_range_for_decode=kv_range_for_decode,
            full_text_row_masked_out_mask=full_text_row_masked_out_mask,
            kv_caches=kv_caches,
            attn_metadata=attn_metadata,
            skip_cross_attention=skip_cross_attention,
        )

        return outputs

    def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
        stacked_params_mapping = [
            # (param_name, shard_name, shard_id)
            (".qkv_proj", ".q_proj", "q"),
            (".qkv_proj", ".k_proj", "k"),
            (".qkv_proj", ".v_proj", "v"),
            (".gate_up_proj", ".gate_proj", 0),
            (".gate_up_proj", ".up_proj", 1),
        ]
        params_dict = dict(self.named_parameters())
        updated_params = set()
        for name, loaded_weight in weights:
            if 'patch_embedding.weight' in name:
                name = name.replace('patch_embedding.weight',
                                    'patch_embedding._linear.weight')
                loaded_weight = loaded_weight.view(loaded_weight.shape[0], -1)
            for (param_name, weight_name, shard_id) in stacked_params_mapping:
                if weight_name not in name:
                    continue
                name = name.replace(weight_name, param_name)
                param = params_dict[name]
                updated_params.add(name)
                weight_loader = param.weight_loader
                weight_loader(param, loaded_weight, shard_id)
                break
            else:
                param = params_dict.pop(name)
                weight_loader = getattr(param, "weight_loader",
                                        default_weight_loader)
                weight_loader(param, loaded_weight)


def skip_attention_mask(sparse_mask: List[List[int]]) -> bool:
    for mask in sparse_mask:
        # Skip text-only samples.
        if len(mask) == 0:
            continue
        # If the sample contains more than 1 images,
        # we can't skip mask.
        if len(mask) != 1:
            return False
        # If the sample contains only 1 image,
        # but the image is not the leading one,
        # we can't skip mask.
        if mask[0][0] != 0 or mask[0][1] != -1:
            return False
    return True


def convert_sparse_cross_attention_mask_to_dense(
    sparse_mask: List[List[List[int]]],
    num_tiles: List[List[int]],
    lengths: List[int],
) -> Tuple[np.ndarray, List[Tuple[int, int]]]:
    total_length = sum(lengths)
    total_tiles = sum([sum(tiles) for tiles in num_tiles])
    dense_mask = np.zeros(shape=(total_length, total_tiles), dtype=np.int64)
    # A list of ranges, range[i] = [start, end] means
    # if the i-th sample has N tiles in total, the tiles[start, end]
    # will be used for cross-attention decoding.
    tile_range_for_decode = []

    seq_start = 0
    tile_start = 0
    for masks, tiles, length in zip(sparse_mask, num_tiles, lengths):
        ts, td = -1, 0
        for mask, tile in zip(masks, tiles):
            if len(mask) != 2:
                continue
            start, end = mask
            end = min(end, length)
            if end == -1:
                end = length
            if end == length:
                if ts == -1:
                    ts = tile_start
                td += tile
            dense_mask[seq_start + start:seq_start + end,
                       tile_start:tile_start + tile] = 1
            tile_start += tile
        tile_range_for_decode.append((ts, ts + td))
        seq_start += length

    return dense_mask, tile_range_for_decode


def convert_dense_cross_attention_mask_to_tensor(
    cross_attention_token_mask: np.ndarray,
    num_tokens_per_tile: int,
    device: torch.device,
    dtype: torch.dtype,
) -> torch.Tensor:
    mask = torch.tensor(cross_attention_token_mask, dtype=dtype, device=device)
    mask = mask.repeat_interleave(num_tokens_per_tile, dim=1)

    mask = 1.0 - mask
    mask = mask.masked_fill(mask.to(torch.bool), torch.finfo(dtype).min)

    ninf = torch.finfo(dtype).min
    full_text_mask = ((mask != ninf).any(dim=-1).type_as(mask)[..., None])
    mask *= full_text_mask
    # (num_prompt_tokens, num_encoder_tokens)
    return mask