phi4mm.py 44.2 KB
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# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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import math
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from collections.abc import Iterable, Mapping, Sequence
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from typing import Annotated, Any, Literal, TypeAlias
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import numpy as np
import torch
import torch.nn as nn
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from transformers import (
    BatchFeature,
    PretrainedConfig,
    ProcessorMixin,
    SequenceFeatureExtractor,
    SiglipVisionConfig,
)
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from vllm.config import VllmConfig
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from vllm.config.multimodal import BaseDummyOptions
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from vllm.distributed import get_pp_group
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from vllm.model_executor.layers.logits_processor import LogitsProcessor
from vllm.model_executor.layers.quantization import QuantizationConfig
from vllm.model_executor.layers.vocab_parallel_embedding import (
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    ParallelLMHead,
)
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from vllm.model_executor.models.llama import LlamaModel
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from vllm.model_executor.models.module_mapping import MultiModelKeys
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from vllm.multimodal import MULTIMODAL_REGISTRY
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from vllm.multimodal.inputs import (
    MultiModalDataDict,
    MultiModalFieldConfig,
    MultiModalKwargsItems,
    NestedTensors,
)
from vllm.multimodal.parse import (
    AudioProcessorItems,
    ImageEmbeddingItems,
    ImageProcessorItems,
    ImageSize,
    MultiModalDataItems,
    MultiModalDataParser,
)
from vllm.multimodal.processing import (
    BaseMultiModalProcessor,
    BaseProcessingInfo,
    PromptReplacement,
    PromptUpdate,
    ResolvedPromptUpdate,
)
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from vllm.multimodal.profiling import BaseDummyInputsBuilder
from vllm.sequence import IntermediateTensors
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from vllm.utils.tensor_schema import TensorSchema, TensorShape
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from .idefics2_vision_model import Idefics2VisionTransformer
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from .interfaces import MultiModalEmbeddings, SupportsLoRA, SupportsMultiModal
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from .phi4mm_audio import AudioEmbedding
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from .utils import AutoWeightsLoader, WeightsMapper, maybe_prefix
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# <|endoftext10|> (see vocab.json in hf model)
_IMAGE_PLACEHOLDER_TOKEN_ID = 200010
# <|endoftext11|>
_AUDIO_PLACEHOLDER_TOKEN_ID = 200011

_AUDIO_MAX_SOUNDFILE_SIZE = 241_000

SIGLIP_NAME = "siglip-so400m-patch14-448"
VISION_ENCODER_TO_PROCESSING_CONFIG = {
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    "siglip-so400m-patch14-448": {
        "vit_image_size": 448,
        "vit_patch_size": 14,
        "token_compression_factor": 2,
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    },
}


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def _get_padding_size(
    orig_width: int, orig_height: int, target_height: int, target_width: int
):
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    ratio_width = target_width / orig_width
    ratio_height = target_height / orig_height

    if ratio_width < ratio_height:
        padding_width = 0
        padding_height = target_height - int(orig_height * ratio_width)
    else:
        padding_width = target_width - int(orig_width * ratio_height)
        padding_height = 0
    return padding_height, padding_width


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def get_navit_vision_model(layer_idx: int = -1, **kwargs):
    vision_config = {
        "hidden_size": 1152,
        "image_size": 448,
        "intermediate_size": 4304,
        "model_type": "siglip_vision_model",
        "num_attention_heads": 16,
        "num_hidden_layers": 27,
        "patch_size": 14,
    }

    model_config = SiglipVisionConfig(**vision_config, **kwargs)
    if layer_idx < 0:
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        num_hidden_layers = model_config.num_hidden_layers + layer_idx + 1
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    else:
        num_hidden_layers = layer_idx + 1

    vision_model = Idefics2VisionTransformer(
        config=model_config,
        require_post_norm=False,
        num_hidden_layers_override=num_hidden_layers,
    )

    return vision_model


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class Phi4MMImageEncoder(nn.Module):
    """Image embedding."""

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    def __init__(
        self,
        config: PretrainedConfig,
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        quant_config: QuantizationConfig | None,
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        prefix: str = "",
        model_dir: str = "",
    ) -> None:
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        super().__init__()

        # n_embed or hidden_size
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        hidden_size = config.n_embd if hasattr(config, "n_embd") else config.hidden_size
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        # layer_idx to output the img features
        if isinstance(config.img_processor, dict):
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            self.layer_idx = config.img_processor.get("layer_idx", -2)
            self.type_feature = config.img_processor.get("type_feature", "patch")
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        else:
            self.layer_idx = -2
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            self.type_feature = "patch"
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        self.img_processor = get_navit_vision_model(layer_idx=self.layer_idx)
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        pe_weight = self.img_processor.embeddings.position_embedding.weight
        L, D = pe_weight.size()
        H = int(math.sqrt(L))
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        assert H**2 == L, f"position embedding size {L} is not square"
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        if H % 2 != 0:
            self.img_processor_padding = nn.ReflectionPad2d((0, 1, 0, 1))
            H += 1
        image_dim_out = D
        # ((448/14)//2)**2
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        self.num_img_tokens = (H // 2) ** 2
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        self.base_feat_height_target = H

        self.image_dim_out = image_dim_out
        self.img_sizes = None
        self.image_attention_mask = None

        # global_gn and sub_gn for hd transform, serves as line separator
        self.use_hd_transform = True
        self.with_learnable_separator = True
        self.hd_transform_order = "sub_glb"
        self.freeze_img_processor = False
        self.crop_size = 448

        # image token compression
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        self.image_token_compression_cls = "avg_pool_2d"
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        self.image_token_compression = nn.AvgPool2d(kernel_size=2, stride=2)
        self.base_feat_height_reduction = 1
        self.base_feat_height_target = self.base_feat_height_target // 2

        # with_hd_transform and with_learnable_separator should have same value
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        assert self.use_hd_transform == self.with_learnable_separator, (
            "use_hd_transform and with_learnable_separator should have same value"
        )
        assert self.use_hd_transform, "learnable separator is only for hd transform"
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        # 1024 * 4, merge spatial to channel dimension
        self.glb_GN = nn.Parameter(
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            torch.zeros([1, 1, self.image_dim_out * self.base_feat_height_reduction**2])
        )
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        self.sub_GN = nn.Parameter(
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            torch.zeros(
                [1, 1, 1, self.image_dim_out * self.base_feat_height_reduction**2]
            )
        )
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        dim_projection = hidden_size
        depth = 2
        layers = [
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            nn.Linear(
                image_dim_out * self.base_feat_height_reduction**2, dim_projection
            )
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        ]
        for _ in range(1, depth):
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            layers.extend([nn.GELU(), nn.Linear(dim_projection, dim_projection)])
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        self.img_projection = nn.Sequential(*layers)

        self.vocab_size = config.vocab_size
        self.img_features = None

        self.use_out_place_operations = False

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    def get_img_features(
        self, img_embeds: torch.FloatTensor, attention_mask=None
    ) -> torch.FloatTensor:
        img_feature = self.img_processor(
            img_embeds, patch_attention_mask=attention_mask
        )
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        if self.type_feature == "patch":
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            patch_feature = img_feature

            use_token_compression = self.image_token_compression is not None
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            use_padding = getattr(self, "img_processor_padding", None) is not None
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            if use_token_compression or use_padding:
                # reshape to 2D tensor
                width = int(math.sqrt(patch_feature.size(1)))
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                patch_feature = patch_feature.view(
                    -1, width, width, patch_feature.size(-1)
                )
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                # convert to NCHW
                patch_feature = patch_feature.permute(0, 3, 1, 2)

                if use_padding:
                    patch_feature = self.img_processor_padding(patch_feature)
                if use_token_compression:
                    patch_feature = self.image_token_compression(patch_feature)

                # convert to NHWC
                patch_feature = patch_feature.permute(0, 2, 3, 1)
                patch_feature = patch_feature.view(
                    -1,
                    patch_feature.size(1) * patch_feature.size(2),
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                    patch_feature.size(-1),
                )
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            return patch_feature

        raise NotImplementedError

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    def forward(
        self,
        pixel_values: torch.FloatTensor,
        image_sizes: torch.Tensor,
        image_attention_mask: torch.Tensor,
    ) -> list[torch.FloatTensor]:
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        """
        process image and return vision embeddings.

        pixel_values: (num_images, num_crops, c, h, w)
        image_sizes: [[h1, w1], [h2, w2]]
        image_attention_mask: num_images x num_crops x 32 x 32
        output: (num_images, num_img_tokens, hidden_size)
        """

        # eg
        # pixel_values: torch.Size([1, 7, 3, 448, 448])
        # image_sizes: tensor([[ 896, 1344]], device='cuda:0')
        # output: torch.Size([1, 1841, 3072])

        if isinstance(self.img_projection, nn.Sequential):
            target_device = self.img_projection[0].bias.device
            target_dtype = self.img_projection[0].bias.dtype
        else:  # It's a single nn.Linear layer
            target_device = self.img_projection.bias.device
            target_dtype = self.img_projection.bias.dtype

        img_sizes = image_sizes
        num_images, num_crops, c, h, w = pixel_values.shape
        bs = num_images
        pixel_values = pixel_values.flatten(0, 1)

        img_features = self.get_img_features(
            pixel_values,
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            image_attention_mask.type(torch.BoolTensor).flatten(0, 1).to(target_device),
        )
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        base_feat_height_target = self.base_feat_height_target
        base_resolution = self.crop_size
        base_feat_height_reduction = self.base_feat_height_reduction

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        base_feat_height = base_feat_width = int(np.sqrt(img_features.shape[1]))
        assert (
            base_feat_height == base_feat_height_target
            and base_feat_width == base_feat_height_target
        ), (
            f"base_feat_height: {base_feat_height}, "
            f"base_feat_width: {base_feat_width}, "
            f"expect {base_feat_height_target} features for hd transform"
        )
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        # bs x max_num_crops x (24x24) x C
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        img_features = img_features.view(
            bs, -1, base_feat_height * base_feat_width, self.image_dim_out
        )
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        C = self.image_dim_out
        H = base_feat_height

        output_imgs = []
        output_len = []
        # training is tensor, inference is list
        if isinstance(img_sizes, torch.Tensor):
            img_sizes = img_sizes.view(-1, 2)
        for _bs in range(bs):
            h, w = img_sizes[_bs]
            h = h // base_resolution
            w = w // base_resolution
            B_ = h * w

            # 1 x (24x24) x 1024
            global_img_feature = img_features[_bs, :1]

            # 1 x 12 x 12 x 4096
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            glb_img = (
                global_img_feature.reshape(1, H, H, C)
                .reshape(
                    1,
                    H // base_feat_height_reduction,
                    base_feat_height_reduction,
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                    H // base_feat_height_reduction,
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                    base_feat_height_reduction,
                    C,
                )
                .contiguous()
                .permute(0, 1, 3, 2, 4, 5)
                .reshape(
                    1,
                    H // base_feat_height_reduction,
                    H // base_feat_height_reduction,
                    base_feat_height_reduction * base_feat_height_reduction * C,
                )
                .contiguous()
            )
            temp_glb_GN = self.sub_GN.repeat(1, H // base_feat_height_reduction, 1, 1)
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            # 1 x 156 x 4096
            glb_img = torch.cat([glb_img, temp_glb_GN], dim=2).reshape(
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                1, -1, base_feat_height_reduction * base_feat_height_reduction * C
            )
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            # (max_num_crops-1) x (12x12) x C
            sub_img = img_features[_bs, 1:]
            # 16x574x1024
            # get rid of padding sub_img
            sub_img = sub_img[:B_]

            # (num_crops, 12, 2, 12, 2, 1024) ->
            # (num_crops, 12, 12, 2, 2, 1024) -> (num_crops, 12*12, 4*1024)
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            sub_img = (
                sub_img.reshape(B_, H, H, C)
                .reshape(
                    B_,
                    H // base_feat_height_reduction,
                    base_feat_height_reduction,
                    H // base_feat_height_reduction,
                    base_feat_height_reduction,
                    C,
                )
                .contiguous()
                .permute(0, 1, 3, 2, 4, 5)
                .reshape(
                    B_, -1, base_feat_height_reduction * base_feat_height_reduction * C
                )
                .contiguous()
            )
            sub_img = (
                sub_img.reshape(
                    1,
                    h,
                    w,
                    base_feat_height // base_feat_height_reduction,
                    base_feat_width // base_feat_height_reduction,
                    -1,
                )
                .permute(0, 1, 3, 2, 4, 5)
                .reshape(
                    1,
                    h * base_feat_height // base_feat_height_reduction,
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                    w * base_feat_width // base_feat_height_reduction,
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                    base_feat_height_reduction * base_feat_height_reduction * C,
                )
            )

            if image_attention_mask is not None and len(image_attention_mask) > 0:
                reshaped_image_attention_mask = (
                    image_attention_mask[_bs, 1 : B_ + 1, 0::2, 0::2]
                    .reshape(
                        1,
                        h,
                        w,
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                        base_feat_height // base_feat_height_reduction,
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                        base_feat_width // base_feat_height_reduction,
                    )
                    .permute(0, 1, 3, 2, 4)
                    .reshape(
                        1,
                        h * base_feat_height // base_feat_height_reduction,
                        w * base_feat_width // base_feat_height_reduction,
                    )
                )
                useful_height = int(reshaped_image_attention_mask[0, :, 0].sum().item())
                useful_width = int(reshaped_image_attention_mask[0, 0, :].sum().item())
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                sub_img = sub_img[:, :useful_height, :useful_width]
                temp_sub_GN = self.sub_GN.repeat(1, useful_height, 1, 1)
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                temp_len = (
                    int(image_attention_mask[_bs, : B_ + 1, 0::2, 0::2].sum().item())
                    + (useful_height + 1)
                    + base_feat_height // base_feat_height_reduction
                )
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            else:
                temp_sub_GN = self.sub_GN.repeat(
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                    1, h * base_feat_height // base_feat_height_reduction, 1, 1
                )
                temp_len = int(
                    (h * w + 1) * self.num_img_tokens
                    + 1
                    + (h + 1) * base_feat_height // base_feat_height_reduction
                )
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            sub_img = torch.cat([sub_img, temp_sub_GN], dim=2).reshape(
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                1, -1, base_feat_height_reduction * base_feat_height_reduction * C
            )
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            # (1, num_img_tokens, 1024*4)

            # glb + sub
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            if self.hd_transform_order == "glb_sub":
                output_imgs.append(torch.cat([glb_img, self.glb_GN, sub_img], dim=1))
            elif self.hd_transform_order == "sub_glb":
                output_imgs.append(torch.cat([sub_img, self.glb_GN, glb_img], dim=1))
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            else:
                raise NotImplementedError(
                    f'hd_transform_order = {self.hd_transform_order}, "\
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                        "not implemented'
                )
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            # temp_len = int((h*w+1)*144 + 1 + (h+1)*12)
            assert temp_len == output_imgs[-1].shape[1], (
                f'temp_len: {temp_len}, output_imgs[-1].shape[1]: "\
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                    "{output_imgs[-1].shape[1]}'
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            )
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            output_len.append(temp_len)

        img_set_tensor = []
        for _output_img in output_imgs:
            img_feature_proj = self.img_projection(
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                _output_img.to(target_device).to(target_dtype)
            )
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            img_set_tensor.append(img_feature_proj.squeeze(0))
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        return img_set_tensor


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class Phi4MMImagePixelInputs(TensorSchema):
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    """
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    Dimensions:
        - bn: Batch size * number of images
        - p: Number of patches (1 + num_patches)
        - c: Number of channels (3)
        - h: Height of each image patch
        - w: Width of each image patch
        - nc: Number of crops
        - H_mask: Height of attention mask
        - W_mask: Width of attention mask
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    """
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    type: Literal["pixel_values"]
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    pixel_values: Annotated[
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        torch.Tensor | list[torch.Tensor],
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        TensorShape(
            "bn", "p", 3, "h", "w", dynamic_dims={"p"}
        ),  # may be different per batch and image
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    ]

    image_sizes: Annotated[
        torch.Tensor,
        TensorShape("bn", 2),  # (height, width)
    ]
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    num_img_tokens: Annotated[
        list[int],
        TensorShape("bn"),
    ]
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    image_attention_mask: Annotated[
        torch.Tensor,
        TensorShape("bn", "nc", 32, 32),  # H_mask, W_mask
    ]
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class Phi4MMAudioFeatureInputs(TensorSchema):
    """
    Dimensions:
        - bn: Batch size * number of audios
        - t: Time frames (M)
    """

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    type: Literal["audio_features"]
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    audio_features: Annotated[
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        torch.Tensor | list[torch.Tensor],
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        TensorShape("bn", "t", 80, dynamic_dims={"t"}),
    ]
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class Phi4MMAudioEmbeddingInputs(TensorSchema):
    """
    Dimensions:
        - b: Batch size
        - n: Number of audios
        - f: Audio feature size
        - h: Hidden size (must match language model backbone)
    """
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    type: Literal["audio_embeds"]
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    data: Annotated[
        NestedTensors,
        TensorShape("b", "n", "f", "h"),
    ]
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Phi4MMAudioInputs: TypeAlias = Phi4MMAudioFeatureInputs | Phi4MMAudioEmbeddingInputs
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def cat_with_pad(tensors, dim, padding_value=0):
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    """
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    cat along dim, while pad to max for all other dims
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    """
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    ndim = tensors[0].dim()
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    assert all(t.dim() == ndim for t in tensors[1:]), (
        "All tensors must have the same number of dimensions"
    )
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    out_size = [max(t.shape[i] for t in tensors) for i in range(ndim)]
    out_size[dim] = sum(t.shape[dim] for t in tensors)
    output = tensors[0].new_full(out_size, padding_value)
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    index = 0
    for t in tensors:
        # Create a slice list where every dimension except dim is full slice
        slices = [slice(0, t.shape[d]) for d in range(ndim)]
        # Update only the concat dimension slice
        slices[dim] = slice(index, index + t.shape[dim])
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        output[slices] = t
        index += t.shape[dim]
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    return output
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class Phi4MMProcessingInfo(BaseProcessingInfo):
    @property
    def image_tokens(self) -> list[str]:
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        return [f"<|image_{i + 1}|>" for i in range(100)]
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    @property
    def audio_tokens(self) -> list[str]:
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        return [f"<|audio_{i + 1}|>" for i in range(100)]
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    def get_dynamic_hd(
        self,
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        processor: ProcessorMixin | None = None,
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    ) -> int:
        if processor is None:
            processor = self.get_hf_processor()
        image_processor = processor.image_processor
        return image_processor.dynamic_hd
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    def get_feature_extractor(self, **kwargs: object) -> SequenceFeatureExtractor:
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        return self.get_hf_processor(**kwargs).audio_processor
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    def get_supported_mm_limits(self) -> Mapping[str, int | None]:
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        return {"audio": None, "image": None}
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    def _find_target_aspect_ratio(
        self,
        orig_width: int,
        orig_height: int,
        image_size: int,
        max_num: int,
        min_num: int,
    ):
        w_crop_num = math.ceil(orig_width / float(image_size))
        h_crop_num = math.ceil(orig_height / float(image_size))
        if w_crop_num * h_crop_num > max_num:
            aspect_ratio = orig_width / orig_height

            # calculate the existing image aspect ratio
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            target_ratios = set(
                (i, j)
                for i in range(1, max_num + 1)
                for j in range(1, max_num + 1)
                if i * j <= max_num and i * j >= min_num
            )
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            target_ratios = sorted(target_ratios, key=lambda x: x[0] * x[1])

            # find the closest aspect ratio to the target
            image_processor = self.get_hf_processor().image_processor
            target_aspect_ratio = image_processor.find_closest_aspect_ratio(
                aspect_ratio,
                target_ratios,
                orig_width,
                orig_height,
                image_size,
            )

            # calculate the target width and height
            target_width = image_size * target_aspect_ratio[0]
            target_height = image_size * target_aspect_ratio[1]
        else:
            target_width = image_size * w_crop_num
            target_height = image_size * h_crop_num
            target_aspect_ratio = (w_crop_num, h_crop_num)
        return target_aspect_ratio, target_height, target_width
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    def _compute_num_image_tokens(
        self,
        orig_width: int,
        orig_height: int,
        dynamic_hd_size: int,
        vit_image_size: int,
        vit_patch_size: int,
        token_compression_factor: int = 2,
    ):
        """
        compute the number of tokens an image is expected to take up considering
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        the image encoder architecture and exclude output features containing
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        only padding pixels
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        for siglip, vit_image_size=448, vit_patch_size=14, so output will be
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        32x32 feature map
        NOTE right now, Phi4MM uses hard-coded token_compression_factor=2
        """
        assert vit_image_size % vit_patch_size == 0, (
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            "vit_image_size must be divisible by vit_patch_size"
        )
        assert vit_image_size // vit_patch_size % token_compression_factor == 0, (
            "vit_image_size // vit_patch_size must be divisible by "
            "token_compression_factor"
        )
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        target_aspect_ratio, target_height, target_width = (
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            self._find_target_aspect_ratio(
                orig_width, orig_height, vit_image_size, dynamic_hd_size, min_num=1
            )
        )
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        assert target_aspect_ratio[0] * vit_image_size == target_width, (
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            f"{target_aspect_ratio[0]} * {vit_image_size} != {target_width}"
        )
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        assert target_aspect_ratio[1] * vit_image_size == target_height, (
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            f"{target_aspect_ratio[1]} * {vit_image_size} != {target_height}"
        )
        assert (
            target_height % vit_image_size == 0 and target_width % vit_image_size == 0
        )
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        padding_height, padding_width = _get_padding_size(
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            orig_width, orig_height, target_height, target_width
        )
        assert padding_width == 0 or padding_height == 0, (
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            "padding_width or padding_height must be 0"
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        )
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        target_feat_width = target_width // vit_patch_size
        target_feat_height = target_height // vit_patch_size
        if padding_width >= vit_patch_size:
            assert padding_height == 0, "padding_height not 0"
            non_pad_feat_width = target_feat_width - math.floor(
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                padding_width / vit_patch_size
            )
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            non_pad_feat_height = target_feat_height
        elif padding_height >= vit_patch_size:
            assert padding_width == 0, "padding_width not 0"
            non_pad_feat_height = target_feat_height - math.floor(
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                padding_height / vit_patch_size
            )
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            non_pad_feat_width = target_feat_width
        else:
            # small padding shorter than a vit patch
            non_pad_feat_width = target_feat_width
            non_pad_feat_height = target_feat_height

        feat_width = non_pad_feat_width // token_compression_factor
        feat_height = non_pad_feat_height // token_compression_factor
        # NOTE it's possible that the non-padding feature is not divisible
        if non_pad_feat_width % token_compression_factor != 0:
            feat_width += 1
        if non_pad_feat_height % token_compression_factor != 0:
            feat_height += 1
        num_hd_patch_tokens = feat_width * feat_height
        num_hd_newline_tokens = feat_height
        vit_feature_size = vit_image_size // vit_patch_size
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        num_global_image_tokens = (vit_feature_size // token_compression_factor) ** 2
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        num_sep_tokens = 1
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        num_global_image_newline_tokens = vit_feature_size // token_compression_factor

        return (
            num_global_image_tokens
            + num_sep_tokens
            + num_hd_patch_tokens
            + num_hd_newline_tokens
            + num_global_image_newline_tokens
        )
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    def get_num_image_tokens(
        self,
        *,
        image_width: int,
        image_height: int,
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        processor: ProcessorMixin | None = None,
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    ) -> int:
        hf_config = self.get_hf_config()
        vision_encoder_name = hf_config.img_processor
        if vision_encoder_name is None:
            vision_encoder_name = SIGLIP_NAME
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        prepro_config = VISION_ENCODER_TO_PROCESSING_CONFIG[vision_encoder_name]
        vit_image_size = prepro_config["vit_image_size"]
        vit_patch_size = prepro_config["vit_patch_size"]
        token_compression_factor = prepro_config["token_compression_factor"]
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        dynamic_hd_size = self.get_dynamic_hd(processor=processor)

        image_num_tokens = self._compute_num_image_tokens(
            image_width,
            image_height,
            dynamic_hd_size=dynamic_hd_size,
            vit_image_size=vit_image_size,
            vit_patch_size=vit_patch_size,
            token_compression_factor=token_compression_factor,
        )
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        return image_num_tokens
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    def get_image_size_with_most_features(
        self,
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        processor: ProcessorMixin | None = None,
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    ) -> ImageSize:
        hf_config = self.get_hf_config()
        vision_encoder_name = hf_config.img_processor
        if vision_encoder_name is None:
            vision_encoder_name = SIGLIP_NAME
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        prepro_config = VISION_ENCODER_TO_PROCESSING_CONFIG[vision_encoder_name]
        vit_image_size = prepro_config["vit_image_size"]
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        max_side = vit_image_size * self.get_dynamic_hd(processor=processor)
        return ImageSize(height=max_side, width=vit_image_size)

    def get_audio_num_frames(self, audio_len: int, sr: float) -> int:
        """
        Compute the output size of the `extract_features` method.
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        Args:
            audio_len (int): Length of the input waveform in samples.
            sr (float): Sampling rate of the waveform, either 16000 or 8000.
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        Returns:
            tuple (int, int): Output size as (T, D), where:
                T: Number of time frames.
                D: Number of Mel filterbank bins (80).
        """
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        # Resample to 16000 or 8000 if needed
        if sr > 16000:
            audio_len //= sr // 16000
        elif 8000 <= sr < 16000:
            # We'll resample to 16K from 8K
            audio_len *= 2
        elif sr < 8000:
            raise RuntimeError(f"Unsupported sample rate {sr}")

        # Spectrogram parameters for 16 kHz
        win_length = 400  # Frame length in samples
        hop_length = 160  # Frame shift in samples

        # Calculate number of frames (T)
        num_frames = (audio_len - win_length) // hop_length + 1
        if num_frames < 1:
            raise ValueError("Waveform too short for given parameters.")

        # Return time frames (T)
        return num_frames

    def _compute_audio_embed_size(self, audio_frames: int) -> int:
        """
        Compute the audio embedding size based on the audio frames and
        compression rate.
        """
        hf_config = self.get_hf_config()
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        compression_rate = hf_config.embd_layer["audio_embd_layer"]["compression_rate"]
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        # NOTE: this is a hard-coded value but might be configurable
        # in the future
        qformer_compression_rate = 1
        integer = audio_frames // compression_rate
        remainder = audio_frames % compression_rate
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        result = integer if remainder == 0 else integer + 1
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        integer = result // qformer_compression_rate
        remainder = result % qformer_compression_rate
        # qformer compression
        result = integer if remainder == 0 else integer + 1
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        return result
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class Phi4MMDummyInputsBuilder(BaseDummyInputsBuilder[Phi4MMProcessingInfo]):
    def get_dummy_text(self, mm_counts: Mapping[str, int]) -> str:
        num_audios = mm_counts.get("audio", 0)
        num_images = mm_counts.get("image", 0)
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        image_tokens: list[str] = self.info.image_tokens[:num_images]
        audio_tokens: list[str] = self.info.audio_tokens[:num_audios]
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        return "".join(image_tokens + audio_tokens)
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    def get_dummy_mm_data(
        self,
        seq_len: int,
        mm_counts: Mapping[str, int],
820
        mm_options: Mapping[str, BaseDummyOptions] | None = None,
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    ) -> MultiModalDataDict:
        num_audios = mm_counts.get("audio", 0)
        num_images = mm_counts.get("image", 0)
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        target_width, target_height = self.info.get_image_size_with_most_features()
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        image_overrides = mm_options.get("image") if mm_options else None
        audio_overrides = mm_options.get("audio") if mm_options else None

830
        mm_data = {
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            "image": self._get_dummy_images(
                width=target_width,
                height=target_height,
                num_images=num_images,
                overrides=image_overrides,
            ),
            "audio": self._get_dummy_audios(
                length=_AUDIO_MAX_SOUNDFILE_SIZE,
                num_audios=num_audios,
                overrides=audio_overrides,
            ),
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        }

844
        return mm_data
845
846


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class Phi4MMMultiModalProcessor(BaseMultiModalProcessor[Phi4MMProcessingInfo]):
    def _get_data_parser(self) -> MultiModalDataParser:
        feature_extractor = self.info.get_feature_extractor()
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        return MultiModalDataParser(
            target_sr=feature_extractor.sampling_rate, audio_resample_method="scipy"
        )
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    def _call_hf_processor(
        self,
        prompt: str,
        mm_data: Mapping[str, object],
        mm_kwargs: Mapping[str, object],
859
        tok_kwargs: Mapping[str, object],
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    ) -> BatchFeature:
        if not mm_data:
            prompt_ids = self.info.get_tokenizer().encode(prompt)
            prompt_ids = self._apply_hf_processor_tokens_only(prompt_ids)
            return BatchFeature(dict(input_ids=[prompt_ids]), tensor_type="pt")

866
        sr = self.info.get_feature_extractor(**mm_kwargs).sampling_rate
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        if audio_data := mm_data.get("audios", []):
            mm_data["audios"] = [(data, sr) for data in audio_data]
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        processed_outputs = super()._call_hf_processor(
            prompt, mm_data, mm_kwargs, tok_kwargs
        )
873
874

        num_img_tokens = [
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            self.info.get_num_image_tokens(
                image_width=img_size[0], image_height=img_size[1]
            )
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880
            for img_size in processed_outputs["image_sizes"]
        ]
        processed_outputs["num_img_tokens"] = num_img_tokens
881

882
        audio_features = processed_outputs["input_audio_embeds"]
883
        feature_sizes = [
884
            self.info.get_audio_num_frames(len(audio), sr) for audio in audio_data
885
        ]
886
887
        processed_outputs["input_audio_embeds"] = [
            audio_features[idx, :size] for idx, size in enumerate(feature_sizes)
888
        ]
889

890
        return processed_outputs
891

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    def _get_mm_fields_config(
        self,
        hf_inputs: BatchFeature,
        hf_processor_mm_kwargs: Mapping[str, object],
    ) -> Mapping[str, MultiModalFieldConfig]:
        return dict(
            input_image_embeds=MultiModalFieldConfig.batched("image"),
            image_attention_mask=MultiModalFieldConfig.batched("image"),
            image_sizes=MultiModalFieldConfig.batched("image"),
            num_img_tokens=MultiModalFieldConfig.batched("image"),
            input_audio_embeds=MultiModalFieldConfig.batched("audio"),
        )

    def _get_prompt_updates(
        self,
        mm_items: MultiModalDataItems,
        hf_processor_mm_kwargs: Mapping[str, Any],
909
        out_mm_kwargs: MultiModalKwargsItems,
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    ) -> Sequence[PromptUpdate]:
        image_tokens: list[str] = self.info.image_tokens  # type: ignore
        audio_tokens: list[str] = self.info.audio_tokens  # type: ignore
913
        feature_extractor = self.info.get_feature_extractor(**hf_processor_mm_kwargs)
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917
        hf_processor = self.info.get_hf_processor(**hf_processor_mm_kwargs)

        def get_image_replacement_phi4mm(item_idx: int):
            images = mm_items.get_items(
918
919
                "image", (ImageEmbeddingItems, ImageProcessorItems)
            )
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930

            if isinstance(images, ImageEmbeddingItems):
                num_image_tokens = images.get_feature_size(item_idx)
            else:
                image_size = images.get_image_size(item_idx)
                num_image_tokens = self.info.get_num_image_tokens(
                    image_width=image_size.width,
                    image_height=image_size.height,
                    processor=hf_processor,
                )

931
            return [_IMAGE_PLACEHOLDER_TOKEN_ID] * num_image_tokens
932
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934
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936
937

        def get_audio_replacement_phi4mm(item_idx: int):
            audios = mm_items.get_items("audio", AudioProcessorItems)
            # TODO(Isotr0py): support embedding inputs
            audio_len = audios.get_audio_length(item_idx)
            audio_frames = self.info.get_audio_num_frames(
938
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940
                audio_len, feature_extractor.sampling_rate
            )
            audio_embed_size = self.info._compute_audio_embed_size(audio_frames)
941

942
            return [_AUDIO_PLACEHOLDER_TOKEN_ID] * audio_embed_size
943

944
        return [
945
946
            PromptReplacement(
                modality="image",
947
                target=image_tokens.__getitem__,
948
                replacement=get_image_replacement_phi4mm,
949
            ),
950
951
            PromptReplacement(
                modality="audio",
952
                target=audio_tokens.__getitem__,
953
                replacement=get_audio_replacement_phi4mm,
954
            ),
955
        ]
956

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    def _recompute_cached_prompt_update(
        self,
        cached_update: ResolvedPromptUpdate,
        new_item_idx: int,
    ) -> ResolvedPromptUpdate:
        new_update = super()._recompute_cached_prompt_update(
            cached_update,
            new_item_idx,
        )

        if cached_update.modality == "image":
            image_tokens: list[str] = self.info.image_tokens  # type: ignore
            new_update = new_update.with_target(image_tokens[new_item_idx])
        elif cached_update.modality == "audio":
            audio_tokens: list[str] = self.info.audio_tokens  # type: ignore
            new_update = new_update.with_target(audio_tokens[new_item_idx])

        return new_update

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982
@MULTIMODAL_REGISTRY.register_processor(
    Phi4MMMultiModalProcessor,
    info=Phi4MMProcessingInfo,
    dummy_inputs=Phi4MMDummyInputsBuilder,
)
class Phi4MMForCausalLM(nn.Module, SupportsLoRA, SupportsMultiModal):
983
    """
984
    Implements the Phi-4-multimodal-instruct model in vLLM.
985
    """
986

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995
    packed_modules_mapping = {
        "qkv_proj": [
            "qkv_proj",
        ],
        "gate_up_proj": [
            "gate_up_proj",
        ],
    }

996
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1000
    hf_to_vllm_mapper = WeightsMapper(
        orig_to_new_substr={
            "base_layer.": "",
        },
        orig_to_new_prefix={
1001
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            "model.embed_tokens_extend.audio_embed.audio_projection.vision.": "embed_tokens_extend.audio_projection_for_vision.",  # noqa: E501
            "model.embed_tokens_extend.audio_embed.audio_projection.speech.": "embed_tokens_extend.audio_projection.",  # noqa: E501
1003
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1007
            "model.embed_tokens_extend.audio_embed.": "embed_tokens_extend.",
            "model.embed_tokens_extend.image_embed.": "vision_encoder.",
        },
    )

1008
    @classmethod
1009
    def get_placeholder_str(cls, modality: str, i: int) -> str | None:
1010
1011
1012
1013
1014
1015
1016
        if modality.startswith("image"):
            return f"<|image_{i}|>"
        if modality.startswith("audio"):
            return f"<|audio_{i}|>"

        raise ValueError("Only image or audio modality is supported")

1017
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    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
        super().__init__()
        config = vllm_config.model_config.hf_config
        multimodal_config = vllm_config.model_config.multimodal_config
        assert multimodal_config, "multimodal_config is required"
        quant_config = vllm_config.quant_config

        self.config = config
        self.multimodal_config = multimodal_config
        self.quant_config = quant_config

        # Tensor/Pipeline parallel not supported for now.
1029
        assert get_pp_group().world_size == 1, "pipeline parallel is not supported"
1030
1031
1032
1033
1034

        self.vision_encoder = Phi4MMImageEncoder(
            config,
            quant_config,
            prefix="model.vision_embed_tokens",
1035
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            model_dir=config._name_or_path,
        )
1037
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1039

        if isinstance(config.embd_layer["audio_embd_layer"], dict):
            embedding_config = {
1040
                "embedding_cls": config.embd_layer["audio_embd_layer"]["embedding_cls"],
1041
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1043
1044
1045
1046
1047
1048
                **config.embd_layer["audio_embd_layer"],
            }
        else:
            embedding_config = {
                "embedding_cls": self.config.embd_layer["embedding_cls"]
            }

        self.embed_tokens_extend = AudioEmbedding(config, **embedding_config)
1049
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1051
        self.model = LlamaModel(
            vllm_config=vllm_config, prefix=maybe_prefix(prefix, "model")
        )
1052
1053

        self.lm_head = ParallelLMHead(
1054
            config.vocab_size,
1055
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            config.hidden_size,
            quant_config=quant_config,
1057
            prefix=maybe_prefix(prefix, "lm_head"),
1058
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1060
1061
        )
        if config.tie_word_embeddings:
            self.lm_head = self.lm_head.tie_weights(self.model.embed_tokens)
        logit_scale = getattr(config, "logit_scale", 1.0)
1062
        self.logits_processor = LogitsProcessor(config.vocab_size, scale=logit_scale)
1063
1064

    def _parse_and_validate_audio_input(
1065
        self, **kwargs: object
1066
    ) -> Phi4MMAudioInputs | None:
1067
        """
1068
        Parse and validate the audio input to the model.  This handles both
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1073
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        audio features and audio embeddings, but only the former is used for
        now.

        Args:
            kwargs (object): Keyword arguments.

        Returns:
            Optional[Phi4MMAudioInputs]: Parsed and validated audio inputs.
        """
1078
        audio_features = kwargs.pop("input_audio_embeds", None)
1079
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1084
        audio_embeds = kwargs.pop("audio_embeds", None)

        if audio_features is None and audio_embeds is None:
            return None

        if audio_features is not None:
1085
            return Phi4MMAudioFeatureInputs(
1086
1087
                type="audio_features",
                audio_features=audio_features,
1088
            )
1089
1090

        if audio_embeds is not None:
1091
            return Phi4MMAudioEmbeddingInputs(type="audio_embeds", data=audio_embeds)
1092
1093
1094

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

1095
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1097
    def _process_audio_input(
        self, audio_input: Phi4MMAudioInputs, audio_projection_mode: str
    ) -> NestedTensors:
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1111
        """
        Create the audio embeddings from the audio input, where the audio input
        is pairs of audio features and audio embed lengths.  The audio input is
        created by `input_mapper_for_phi4mm_audio`.

        Args:
            audio_input (Phi4MMAudioInputs): Audio input.

        Returns:
            NestedTensors: Audio embeddings
        """
        if audio_input["type"] == "audio_embeds":
            return audio_input["data"]

1112
        audio_features = audio_input["audio_features"]
1113
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1115
        # (e.g. multiple examples) and the second dim is the multi-audio dim
        # (e.g. multiple audios in the same example)

1116
1117
1118
1119
1120
        dtype = next(self.embed_tokens_extend.parameters()).dtype
        audio_embeds = [
            self.embed_tokens_extend(
                features.to(dtype),
                audio_projection_mode=audio_projection_mode,
1121
1122
            )
            for features in audio_features
1123
1124
        ]
        return audio_embeds
1125

1126
    def _parse_and_validate_image_input(
1127
        self, **kwargs: object
1128
    ) -> Phi4MMImagePixelInputs | None:
1129
1130
        pixel_values = kwargs.get("input_image_embeds")
        if pixel_values is None:
1131
1132
1133
1134
1135
            return None

        image_sizes = kwargs.get("image_sizes")
        image_attention_mask = kwargs.get("image_attention_mask")
        num_img_tokens = kwargs.get("num_img_tokens")
1136
1137
1138
1139
1140
        assert (
            image_sizes is not None
            and image_attention_mask is not None
            and num_img_tokens is not None
        ), "Missing image inputs"
1141

1142
1143
        return Phi4MMImagePixelInputs(
            type="pixel_values",
1144
            pixel_values=pixel_values,
1145
1146
1147
1148
1149
1150
1151
1152
1153
1154
1155
            image_sizes=image_sizes,
            image_attention_mask=image_attention_mask,
            num_img_tokens=num_img_tokens,
        )

    def _parse_and_validate_multimodal_inputs(self, **kwargs: object) -> dict:
        modalities = {}

        # Preserve the order of modalities if there are multiple of them
        # from the order of kwargs.
        for input_key in kwargs:
1156
1157
1158
1159
1160
1161
1162
1163
1164
1165
            if (
                input_key in ("input_image_embeds", "image_embeds")
                and "images" not in modalities
            ):
                modalities["images"] = self._parse_and_validate_image_input(**kwargs)
            if (
                input_key in ("input_audio_embeds", "audio_embeds")
                and "audios" not in modalities
            ):
                modalities["audios"] = self._parse_and_validate_audio_input(**kwargs)
1166
1167
1168
1169

        return modalities

    def _process_image_input(
1170
1171
        self, image_input: Phi4MMImagePixelInputs
    ) -> list[torch.Tensor]:
1172
        dtype = next(self.vision_encoder.parameters()).dtype
1173
        pixel_values = image_input["pixel_values"].to(dtype)
1174
1175
1176
1177
1178
        image_sizes = image_input["image_sizes"]
        image_attention_mask = image_input["image_attention_mask"]
        image_embeds = self.vision_encoder(
            pixel_values, image_sizes, image_attention_mask
        )
1179
1180
        return image_embeds

1181
    def embed_multimodal(self, **kwargs: object) -> MultiModalEmbeddings:
1182
1183
        modalities = self._parse_and_validate_multimodal_inputs(**kwargs)
        if not modalities:
1184
            return []
1185

1186
        # The result multimodal_embeddings is tuple of tensors, with each
1187
        # tensor corresponding to a multimodal data item (image or video).
1188
1189
1190
1191
        multimodal_embeddings: tuple[torch.Tensor, ...] = ()

        # NOTE: It is important to iterate over the keys in this dictionary
        # to preserve the order of the modalities.
1192
        audio_projection_mode = "speech"
1193
1194
1195
1196
1197
        for modality in modalities:
            # make sure process images first
            if modality == "images":
                audio_projection_mode = "vision"
                image_input = modalities["images"]
1198
1199
                image_embeddings = self._process_image_input(image_input)
                multimodal_embeddings += tuple(image_embeddings)
1200
1201
1202
            if modality == "audios":
                audio_input = modalities["audios"]
                audio_embeddings = self._process_audio_input(
1203
1204
                    audio_input, audio_projection_mode=audio_projection_mode
                )
1205
1206
1207
1208
                multimodal_embeddings += tuple(audio_embeddings)

        return multimodal_embeddings

1209
1210
1211
1212
    def forward(
        self,
        input_ids: torch.Tensor,
        positions: torch.Tensor,
1213
1214
        intermediate_tensors: IntermediateTensors | None = None,
        inputs_embeds: torch.Tensor | None = None,
1215
1216
1217
1218
        **kwargs: object,
    ) -> torch.Tensor:
        if intermediate_tensors is not None:
            inputs_embeds = None
1219

1220
1221
1222
1223
1224
1225
1226
1227
1228
1229
1230
1231
        hidden_states = self.model(
            input_ids,
            positions,
            intermediate_tensors,
            inputs_embeds=inputs_embeds,
        )

        return hidden_states

    def compute_logits(
        self,
        hidden_states: torch.Tensor,
1232
    ) -> torch.Tensor | None:
1233
        logits = self.logits_processor(self.lm_head, hidden_states)
1234
1235
        return logits

1236
    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> None:
1237
        loader = AutoWeightsLoader(self, skip_substrs=["lora"])
1238
1239
        return loader.load_weights(weights, mapper=self.hf_to_vllm_mapper)

1240
1241
1242
1243
1244
1245
1246
1247
    def get_mm_mapping(self) -> MultiModelKeys:
        """
        Get the module prefix in multimodal models
        """
        return MultiModelKeys.from_string_field(
            language_model="model.",
            connector=["audio_projection_for_vision", "audio_projection"],
            tower_model=["vision_encoder", "embed_tokens_extend"],
1248
        )
1249
1250
1251

    def get_language_model(self) -> torch.nn.Module:
        return self.model