vllm.model_executor.models.minicpmv ¶

@MULTIMODAL_REGISTRY.register_processor(
    MiniCPMVMultiModalProcessor,
    info=MiniCPMVProcessingInfo,
    dummy_inputs=MiniCPMVDummyInputsBuilder,
)
class MiniCPMV(MiniCPMVBaseModel, SupportsMultiModal, SupportsLoRA):
    """
    Different versions of MiniCPMV use different visual encoders and LLMs,
    which is not conducive to the current integration logic of LoRA and
    bitsandbytes in vLLM. Therefore, it is necessary to separate them.
    """

    def __new__(cls, *, vllm_config: VllmConfig, prefix: str = ""):
        config = vllm_config.model_config.hf_config
        if not hasattr(config, "version"):
            if config.hidden_size == 2304 and config.query_num == 64:
                version = (2, 0)
            else:
                version = (2, 5)
        else:
            version = str(config.version).split(".")
            version = tuple([int(x) for x in version])
        # Dispatch class based on version
        instance_cls = _SUPPORT_VERSION.get(version)
        if instance_cls is None:
            supported_versions = ", ".join(
                [f"{v[0]}.{v[1]}" for v in sorted(_SUPPORT_VERSION.keys())]
            )
            raise ValueError(
                f"Currently, MiniCPMV only supports versions "
                f"{supported_versions}. Got version: {version}"
            )

        # quant_config references base class members,
        # so update values before init is called
        cls.packed_modules_mapping.update(instance_cls.packed_modules_mapping)
        cls.embedding_modules.update(instance_cls.embedding_modules)
        return instance_cls(vllm_config=vllm_config, prefix=prefix)

class MiniCPMVBaseModel(nn.Module, SupportsMultiModal, SupportsPP):
    """
    The abstract class of MiniCPMV can only be inherited, but cannot be
    instantiated.
    """

    supports_encoder_tp_data = True

    @classmethod
    def get_placeholder_str(cls, modality: str, i: int) -> str | None:
        if modality.startswith("image"):
            return "(<image>./</image>)"
        if modality.startswith("video"):
            return "(<video>./</video>)"

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

    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
        config = vllm_config.model_config.hf_config
        multimodal_config = vllm_config.model_config.multimodal_config
        quant_config = vllm_config.quant_config
        self.use_data_parallel = multimodal_config.mm_encoder_tp_mode == "data"
        super().__init__()
        # All MiniCPM-V models disable `tie_word_embeddings` but
        # `PretrainedConfig.tie_word_embeddings` defaults to True; we cannot
        # check `tie_word_embeddings` until vLLM integrate MiniCPM-V model
        # and config class
        self.config = config
        self.multimodal_config = multimodal_config

        self.version = get_version_by_config(self.config)

        with self._mark_language_model(vllm_config):
            self.llm = self.init_llm(
                vllm_config=vllm_config, prefix=maybe_prefix(prefix, "llm")
            )

        with self._mark_tower_model(vllm_config, {"image", "video"}):
            self.vpm = self.init_vision_module(
                config, quant_config, prefix=maybe_prefix(prefix, "vpm")
            )
            self.vision_dim = (
                self.vpm.embed_dim
                if self.version == (2, 0)
                else self.vpm.embeddings.embed_dim
            )
            self.embed_dim = self.config.hidden_size

            self.resampler = self.init_resampler(
                self.embed_dim,
                self.vision_dim,
                quant_config=quant_config,
                prefix=maybe_prefix(prefix, "resampler"),
            )
            self._resampler_moved = False

        self.make_empty_intermediate_tensors = self.llm.make_empty_intermediate_tensors

    def _ensure_resampler_device(self) -> None:
        if self._resampler_moved:
            return
        # Only move device, DO NOT touch dtype (fp8 quant needs its own dtype)
        self.resampler.to(current_platform.device_type)
        self._resampler_moved = True

    def _parse_and_validate_vision_input(
        self,
        modality: str,
        **kwargs: object,
    ) -> MiniCPMVImageInputs | None:
        pixel_values = kwargs.pop("pixel_values", None)
        image_embeds = kwargs.pop("image_embeds", None)

        if pixel_values is None and image_embeds is None:
            return None

        if image_embeds is not None:
            return MiniCPMVImageEmbeddingInputs(
                type="image_embeds",
                image_embeds=image_embeds,
            )

        tgt_sizes = kwargs.pop("tgt_sizes")

        num_slices_flat = torch.tensor([len(ps) for ps in pixel_values])
        pixel_values_flat = flatten_bn(pixel_values)
        tgt_sizes_flat = flatten_bn(tgt_sizes, concat=True)

        return MiniCPMVImagePixelInputs(
            type="pixel_values",
            pixel_values=pixel_values_flat,
            tgt_sizes=tgt_sizes_flat,
            num_slices=num_slices_flat,
        )

    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:
            if (
                input_key in ("pixel_values", "image_embeds")
                and "images" not in modalities
            ):
                modalities["images"] = self._parse_and_validate_vision_input(
                    "images", **kwargs
                )
            if (
                input_key in ("video_pixel_values", "video_embeds")
                and "videos" not in modalities
            ):
                modalities["videos"] = self._parse_and_validate_vision_input(
                    "videos", **{k.removeprefix("video_"): v for k, v in kwargs.items()}
                )

        return modalities

    def _process_vision_input(
        self,
        image_input: MiniCPMVImageInputs,
    ) -> torch.Tensor | list[torch.Tensor] | tuple[torch.Tensor, ...]:
        if image_input["type"] == "image_embeds":
            return image_input["image_embeds"]

        image_features_flat = self.get_vision_hidden_states(image_input)

        num_slices = image_input["num_slices"]
        return [e.flatten(0, 1) for e in image_features_flat.split(num_slices.tolist())]

    def _process_multimodal_inputs(self, modalities: dict):
        # The result multimodal_embeddings is tuple of tensors, with each
        # tensor corresponding to a multimodal data item (image or video).
        multimodal_embeddings: tuple[torch.Tensor, ...] = ()

        # NOTE: It is important to iterate over the keys in this dictionary
        # to preserve the order of the modalities.
        for modality in modalities:
            if modality == "images":
                image_input = modalities["images"]
                image_embeddings = self._process_vision_input(image_input)
                multimodal_embeddings += tuple(image_embeddings)
            if modality == "videos":
                video_input = modalities["videos"]
                video_embeddings = self._process_vision_input(video_input)
                multimodal_embeddings += tuple(video_embeddings)

        return multimodal_embeddings

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

        return self._process_multimodal_inputs(modalities)

    def forward(
        self,
        input_ids: torch.Tensor | None,
        positions: torch.Tensor,
        intermediate_tensors: IntermediateTensors | None = None,
        inputs_embeds: torch.Tensor | None = None,
        **kwargs: Any,
    ) -> torch.Tensor:
        if intermediate_tensors is not None:
            inputs_embeds = None

        hidden_states = self.llm.model(
            input_ids=input_ids,
            positions=positions,
            intermediate_tensors=intermediate_tensors,
            inputs_embeds=inputs_embeds,
        )
        return hidden_states

    def compute_logits(
        self,
        hidden_states: torch.Tensor,
    ) -> torch.Tensor | None:
        return self.llm.compute_logits(hidden_states)

    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
        loader = AutoWeightsLoader(self)
        loaded = loader.load_weights(weights)
        self._ensure_resampler_device()
        return loaded

    def get_mm_mapping(self) -> MultiModelKeys:
        """
        Get the module prefix in multimodal models
        """
        return MultiModelKeys.from_string_field(
            language_model="llm", connector="resampler", tower_model="vpm"
        )

    def init_llm(
        self,
        vllm_config: VllmConfig,
        prefix: str = "",
    ) -> nn.Module:
        raise NotImplementedError

    def init_vision_module(
        self,
        config: PretrainedConfig,
        quant_config: QuantizationConfig | None,
        prefix: str = "",
    ) -> nn.Module:
        raise NotImplementedError

    def init_resampler(
        self,
        embed_dim: int,
        vision_dim: int,
        quant_config: QuantizationConfig | None = None,
        prefix: str = "",
    ) -> nn.Module:
        raise NotImplementedError

    def get_vision_hidden_states(self, data: MiniCPMVImagePixelInputs) -> torch.Tensor:
        raise NotImplementedError

class MiniCPMVImageEmbeddingInputs(TensorSchema):
    """
    Dimensions:
        - bn: Batch size * number of images
        - ns: Number of slices
        - hs: Hidden size (must match language model backbone)
    """

    type: Literal["image_embeds"]
    image_embeds: Annotated[
        torch.Tensor | list[torch.Tensor],
        TensorShape("bn", "ns", "hs", dynamic_dims={"ns"}),
    ]

class MiniCPMVImagePixelInputs(TensorSchema):
    """
    Dimensions:
        - bns: Batch size * number of images * number of slices
        - bn: Batch size * number of images
        - c: Number of channels
        - h: Height
        - w: Width
    """

    type: Literal["pixel_values"] = "pixel_values"

    # Note that the patch size may vary, so we pass it as a list instead of a
    # batched tensor.
    pixel_values: Annotated[
        list[torch.Tensor],
        TensorShape("bns", "c", "h", "w", dynamic_dims={"h", "w"}),
    ]
    tgt_sizes: Annotated[
        torch.Tensor,
        TensorShape("bns", 2),  # This should be in `(height, width)` format.
    ]
    num_slices: Annotated[
        torch.Tensor,
        TensorShape("bn"),
    ]

class MiniCPMVMultiModalProcessor(BaseMultiModalProcessor[_I]):
    def get_image_prompt_texts(self, image_size: ImageSize, image_idx: int = 0) -> str:
        return self.info.get_slice_image_placeholder(
            image_size,
            image_idx=image_idx,
        )

    def get_video_prompt_texts(self, image_size: ImageSize, num_frames: int) -> str:
        return (
            self.info.get_slice_image_placeholder(
                image_size=image_size,
                image_idx=0,
                max_slice_nums=self.info.get_video_max_slice_num(),
                use_image_id=False,
            )
            * num_frames
        )

    def process_images(
        self,
        mm_data: Mapping[str, object],
        mm_kwargs: Mapping[str, object],
    ) -> Mapping[str, NestedTensors]:
        if (images := mm_data.get("images")) is None:
            return {}

        mm_items = self.info.parse_mm_data({"image": images}, validate=False)
        parsed_images = mm_items.get_items(
            "image", (MiniCPMVImageEmbeddingItems, ImageProcessorItems)
        )

        if isinstance(parsed_images, MiniCPMVImageEmbeddingItems):
            image_inputs = {}
        else:
            image_inputs = self._call_hf_processor_on_prompts(
                prompts=[self.info.image_pattern] * len(parsed_images),
                mm_data={"images": [[image] for image in parsed_images]},
                mm_kwargs=mm_kwargs,
                out_keys={"pixel_values", "image_sizes", "tgt_sizes"},
            )

        return image_inputs

    def process_videos(
        self,
        mm_data: Mapping[str, object],
        mm_kwargs: Mapping[str, object],
    ) -> Mapping[str, NestedTensors]:
        if (videos := mm_data.get("videos")) is None:
            return {}

        mm_items = self.info.parse_mm_data({"video": videos}, validate=False)
        parsed_videos = mm_items.get_items(
            "video", (MiniCPMVVideoEmbeddingItems, VideoProcessorItems)
        )

        if isinstance(parsed_videos, MiniCPMVVideoEmbeddingItems):
            video_inputs = {}
        else:
            video_inputs = self._call_hf_processor_on_prompts(
                prompts=[
                    self.info.image_pattern * len(video) for video in parsed_videos
                ],
                mm_data={"images": list(parsed_videos)},
                mm_kwargs={
                    **mm_kwargs,
                    "max_slice_nums": self.info.get_video_max_slice_num(),
                },
                out_keys={"pixel_values", "image_sizes", "tgt_sizes"},
            )

        video_inputs = {f"video_{k}": v for k, v in video_inputs.items()}

        return video_inputs

    def process_mm_inputs(
        self,
        mm_data: Mapping[str, object],
        mm_kwargs: Mapping[str, object],
    ) -> Mapping[str, NestedTensors]:
        return {
            **self.process_images(mm_data, mm_kwargs),
            **self.process_videos(mm_data, mm_kwargs),
        }

    def _apply_prompt_updates(
        self,
        token_ids: list[int],
        mm_prompt_updates: MultiModalPromptUpdates,
    ) -> tuple[list[int], Mapping[str, list[PlaceholderFeaturesInfo]]]:
        """Apply multi-modal prompt updates to token IDs."""
        tokenizer = self.info.get_tokenizer()

        new_token_ids, match_result = self._apply_token_matches(
            token_ids,
            mm_prompt_updates,
        )

        # If the search text does not represent a special token,
        # it may have different token IDs in the prompt, because
        # the tokens may go across the boundaries of the search text.
        # ----
        # e.g. when searching for "foo" in "food", if "food" itself makes
        # up a token, then the token ID of "foo" will not appear at all
        # ----
        # Since it is inefficient to search for all possible tokenizations
        # of the search text in the prompt, we instead perform string-based
        # updates on the decoded token IDs, then encode them back.
        if not all(
            all(update_idx is not None for update_idx in update_idxs)
            for update_idxs in match_result.values()
        ):
            new_token_ids, match_result = self._apply_text_matches_as_segmented_tokens(
                _seq2text(tokenizer, token_ids, use_cache=False),
                mm_prompt_updates,
            )

        matched_updates = defaultdict[str, list[Sequence[ResolvedPromptUpdate]]](list)
        for modality, update_idxs in match_result.items():
            for item_idx, update_idx in enumerate(update_idxs):
                assert update_idx is not None, (
                    "Failed to apply prompt replacement for "
                    f"mm_items[{modality!r}][{item_idx}]"
                )

                matched_updates[modality].append(
                    [mm_prompt_updates[modality][item_idx][update_idx]]
                )

        placeholders = self._find_mm_placeholders(
            new_token_ids,
            dict(matched_updates),
        )

        return new_token_ids, placeholders

    def _call_hf_processor_on_prompts(
        self,
        prompts: list[str],
        mm_data: Mapping[str, Sequence[object]],
        mm_kwargs: Mapping[str, object],
        *,
        out_keys: set[str],
    ) -> dict[str, NestedTensors]:
        # This processor supports zipping prompt and mm_data together
        if self.info.get_model_version() in {(2, 6), (4, 0), (4, 5), (4, 6)}:
            inputs = self.info.ctx.call_hf_processor(
                self.info.get_hf_processor(**mm_kwargs),
                dict(text=prompts, **mm_data),
                mm_kwargs,
            )
        else:
            inputs = defaultdict[str, list[torch.Tensor]](list)

            for i, prompt in enumerate(prompts):
                inputs_one = self.info.ctx.call_hf_processor(
                    self.info.get_hf_processor(**mm_kwargs),
                    dict(text=prompt, **{k: v[i] for k, v in mm_data.items()}),
                    mm_kwargs,
                )

                for k, v in inputs_one.items():
                    assert len(v) == 1, (k, len(v))
                    inputs[k].append(v[0])

        return {k: inputs[k] for k in out_keys}

    def _apply_hf_processor_main(
        self,
        mm_items: MultiModalDataItems,
        hf_processor_mm_kwargs: Mapping[str, object],
    ) -> BatchFeature:
        valid_mm_items = mm_items.select(
            {k for k, c in mm_items.get_all_counts().items() if c > 0}
        )
        mm_data, passthrough_data = self._get_hf_mm_data(valid_mm_items)

        prompt_text = self.dummy_inputs.get_dummy_text(mm_items.get_all_counts())

        tokenizer = self.info.get_tokenizer()

        input_ids = torch.tensor([tokenizer.encode(prompt_text)])
        mm_inputs = self.process_mm_inputs(mm_data, hf_processor_mm_kwargs)

        processed_data = BatchFeature(
            {
                "input_ids": input_ids,
                **mm_inputs,
            }
        )
        processed_data.update(passthrough_data)
        return processed_data

    def _get_prompt_updates(
        self,
        mm_items: MultiModalDataItems,
        hf_processor_mm_kwargs: Mapping[str, object],
        out_mm_kwargs: MultiModalKwargsItems,
    ) -> Sequence[PromptUpdate]:
        placeholders = [
            ("image", self.info.image_pattern),
            ("video", self.info.video_pattern),
        ]

        # hard code for inconsistency of encode-decode image_pattern
        additional_placeholders = []
        tokenizer = self.info.get_tokenizer()
        for modality, pattern in placeholders:
            sub_pattern = tokenizer.decode(
                tokenizer.encode(pattern, add_special_tokens=False)
            )
            if sub_pattern != pattern:
                additional_placeholders.append((modality, sub_pattern))
        placeholders += additional_placeholders

        def get_image_replacement(item_idx: int):
            images = mm_items.get_items(
                "image", (MiniCPMVImageEmbeddingItems, ImageProcessorItems)
            )

            image_size = images.get_image_size(item_idx)

            return PromptUpdateDetails.select_text(
                self.get_image_prompt_texts(image_size, item_idx),
                "<unk>",
            )

        def get_video_replacement(item_idx: int):
            videos = mm_items.get_items(
                "video", (MiniCPMVVideoEmbeddingItems, VideoProcessorItems)
            )

            frame_size = videos.get_frame_size(item_idx)
            num_frames = videos.get_num_frames(item_idx)

            return PromptUpdateDetails.select_text(
                self.get_video_prompt_texts(frame_size, num_frames),
                "<unk>",
            )

        get_replacement = {
            "image": get_image_replacement,
            "video": get_video_replacement,
        }

        return [
            PromptReplacement(
                modality=modality, target=pattern, replacement=get_replacement[modality]
            )
            for modality, pattern in placeholders
        ]

    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":
            tokenizer = self.info.get_tokenizer()
            image_processor = self.info.get_image_processor()
            version = self.info.get_model_version()

            text = _seq2text(tokenizer, cached_update.content.full)
            prev_item_idx = cached_update.item_idx

            if version == (2, 0) or version == (2, 5):
                im_start = image_processor.im_start_token
                im_end = image_processor.im_end_token
            elif hasattr(image_processor, "im_id_start"):
                im_start = image_processor.im_id_start
                im_end = image_processor.im_id_end
            else:
                # transformers v5.7+ keeps im_id tokens on the tokenizer.
                im_start = getattr(tokenizer, "image_id_start_token", "<image_id>")
                im_end = getattr(tokenizer, "image_id_end_token", "</image_id>")

            embed_text = getattr(tokenizer, "image_token", "<unk>")
            new_update = new_update.with_content(
                PromptUpdateDetails.select_text(
                    text.replace(
                        f"{im_start}{prev_item_idx}{im_end}",
                        f"{im_start}{new_item_idx}{im_end}",
                        1,
                    ),
                    embed_text,
                )
            )

        return new_update

    def _get_mm_fields_config(
        self,
        hf_inputs: BatchFeature,
        hf_processor_mm_kwargs: Mapping[str, object],
    ) -> Mapping[str, MultiModalFieldConfig]:
        return _minicpmv_field_config(hf_inputs)

class Resampler4_5(Resampler2_5):
    def __init__(
        self,
        num_queries: int,
        embed_dim: int,
        num_heads: int,
        kv_dim: int | None = None,
        norm_layer: Callable[[int], nn.LayerNorm] = DEFAULT_LN,
        max_size: tuple[int, int] = (70, 70),
        max_temporal_size: int = 36000,
        quant_config: QuantizationConfig | None = None,
        prefix: str = "",
    ) -> None:
        super().__init__(
            num_queries,
            embed_dim,
            num_heads,
            kv_dim,
            norm_layer,
            max_size,
            quant_config=quant_config,
            prefix=prefix,
        )

        trunc_normal_(self.query, std=0.02)
        self.max_temporal_size = max_temporal_size
        self._set_temporal_pos_cache(self.max_temporal_size)
        self.apply(self._init_weights)

    def get_1d_sincos_pos_embed_from_temporal_size(
        self, embed_dim: int, pos: np.ndarray
    ):
        """
        embed_dim: output dimension for each position
        pos: a list of positions to be encoded: size (M,)
        out: (M, D)
        """
        assert embed_dim % 2 == 0
        omega = np.arange(embed_dim // 2, dtype=np.float32)
        omega /= embed_dim / 2.0
        omega = 1.0 / 10000**omega  # (D/2,)

        pos = pos.reshape(-1)  # (M,)
        out = np.einsum("m,d->md", pos, omega)  # (M, D/2), outer product

        emb_sin = np.sin(out)  # (M, D/2)
        emb_cos = np.cos(out)  # (M, D/2)

        emb = np.concatenate([emb_sin, emb_cos], axis=1)  # (M, D)
        return emb

    def _set_temporal_pos_cache(
        self, max_temporal_size: int, device: torch.types.Device = "cpu"
    ) -> None:
        temporal_size = np.arange(max_temporal_size, dtype=np.float32)
        pos_embed = (
            torch.from_numpy(
                self.get_1d_sincos_pos_embed_from_temporal_size(
                    self.embed_dim, temporal_size
                )
            )
            .float()
            .to(device)
        )
        self.register_buffer("temporal_pos_embed", pos_embed, persistent=False)

    def _adjust_temporal_pos_cache(
        self, max_temporal_size: int, device: torch.types.Device = "cpu"
    ):
        if max_temporal_size > self.max_temporal_size:
            self.max_temporal_size = max_temporal_size
            self._set_temporal_pos_cache(self.max_temporal_size, device)

    def _init_weights(self, m: nn.Linear | nn.LayerNorm):
        if isinstance(m, nn.Linear):
            trunc_normal_(m.weight, std=0.02)
            if isinstance(m, nn.Linear) and m.bias is not None:
                nn.init.constant_(m.bias, 0)
        elif isinstance(m, nn.LayerNorm):
            nn.init.constant_(m.bias, 0)
            nn.init.constant_(m.weight, 1.0)

    def forward(
        self,
        x: torch.Tensor,
        tgt_sizes: torch.Tensor,
        # temporal_ids for high refresh rate videos
        temporal_ids=None,
    ) -> torch.Tensor:
        assert x.shape[0] == tgt_sizes.shape[0]
        bs = x.shape[0]

        device = x.device
        dtype = x.dtype

        patch_len = tgt_sizes[:, 0] * tgt_sizes[:, 1]

        self._adjust_pos_cache(tgt_sizes, device=device)

        temporal_pos_emb = False
        temporal_ids_flatten = None
        if temporal_ids is not None:
            # example: [[-1], [-1], [2, 6, 9]]
            temporal_ids_flatten = list(chain.from_iterable(temporal_ids))
            max_temporal_size = max(temporal_ids_flatten, default=0)
            if max_temporal_size > -1:
                temporal_pos_emb = True
            if max_temporal_size > self.max_temporal_size:
                self._adjust_temporal_pos_cache(max_temporal_size, device)

        max_patch_len = patch_len.max().item()
        assert isinstance(max_patch_len, int)

        key_padding_mask = torch.zeros(
            (bs, max_patch_len), dtype=torch.bool, device=device
        )

        x, _ = self.kv_proj(x)  # B * L * D
        x = self.ln_kv(x).permute(1, 0, 2)  # L * B * D
        q = self.ln_q(self.query)  # Q * D

        pos_embed_2d = []
        pos_embed_temporal = []
        for i in range(bs):
            tgt_h, tgt_w = tgt_sizes[i]
            if temporal_pos_emb:
                if temporal_ids_flatten[i] == -1:
                    pos_embed_temporal.append(
                        torch.zeros(self.embed_dim, dtype=dtype, device=device)
                    )
                else:
                    pos_embed_temporal.append(
                        self.temporal_pos_embed[temporal_ids_flatten[i]].to(dtype)
                    )  # D

            pos_embed_2d.append(
                self.pos_embed[:tgt_h, :tgt_w, :]
                .reshape((tgt_h * tgt_w, -1))
                .to(device=device, dtype=dtype)
            )  # patches * D
            key_padding_mask[i, patch_len[i] :] = True

        pos_embed_2d = torch.nn.utils.rnn.pad_sequence(
            pos_embed_2d, batch_first=True, padding_value=0.0
        ).permute(1, 0, 2)  # BLD => L * B * D

        k = x + pos_embed_2d
        v = x
        if pos_embed_temporal:
            k += torch.stack(pos_embed_temporal, dim=0)
            bs = len(temporal_ids)
            merge_k = []
            merge_v = []
            merge_key_padding_mask = []

            start = 0
            for tp in temporal_ids:
                end = start + len(tp)
                # L * (end-start) * D -> (end-start) * L * D
                # -> 1 * L*(end-start) * D
                merge_k.append(
                    k[:, start:end, :].permute(1, 0, 2).reshape(-1, self.embed_dim)
                )
                merge_v.append(
                    v[:, start:end, :].permute(1, 0, 2).reshape(-1, self.embed_dim)
                )
                merge_key_padding_mask.append(
                    key_padding_mask[start:end, :].reshape(-1, 1)
                )

                start = end

            k = torch.nn.utils.rnn.pad_sequence(
                merge_k, batch_first=True, padding_value=0.0
            ).permute(1, 0, 2)  # L*(end-start)
            v = torch.nn.utils.rnn.pad_sequence(
                merge_v, batch_first=True, padding_value=0.0
            ).permute(1, 0, 2)  # L*(end-start)
            key_padding_mask = torch.nn.utils.rnn.pad_sequence(
                merge_key_padding_mask, batch_first=True, padding_value=True
            ).squeeze(-1)

        out = self.attn(
            self._repeat(q, bs),  # Q * B * D
            k,  # L * B * D +  L * B * D
            v,
            key_padding_mask=key_padding_mask,
        )[0]
        #  out: Q * B * D
        x = out.permute(1, 0, 2)  # B * Q * D

        x = self.ln_post(x)
        x = x @ self.proj
        return x