[gpt-oss] Add gpt-oss bf16 support

vllm/model_executor/models/deepseek_vl2.py

@@ -0,0 +1,648 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+# adapted from https://github.com/deepseek-ai/DeepSeek-VL2/blob/faf18023f24b962b32d9f0a2d89e402a8d383a78/deepseek_vl2/models/modeling_deepseek_vl_v2.py
+"""Inference-only Deepseek-VL2 model compatible with HuggingFace weights."""
+import math
+from collections.abc import Iterable, Mapping, Sequence
+from typing import Literal, Optional, TypedDict, Union
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from einops import rearrange, repeat
+from transformers import BatchFeature
+
+from vllm.config import VllmConfig
+from vllm.model_executor import SamplingMetadata
+from vllm.model_executor.layers.quantization import QuantizationConfig
+from vllm.model_executor.model_loader.utils import set_default_torch_dtype
+from vllm.multimodal import MULTIMODAL_REGISTRY
+from vllm.multimodal.inputs import (MultiModalDataDict, MultiModalFieldConfig,
+                                    MultiModalKwargs, NestedTensors)
+from vllm.multimodal.parse import (ImageEmbeddingItems, ImageProcessorItems,
+                                   ImageSize, MultiModalDataItems)
+from vllm.multimodal.processing import (BaseMultiModalProcessor,
+                                        BaseProcessingInfo, MultiModalHashes,
+                                        PromptReplacement, PromptUpdate)
+from vllm.multimodal.profiling import BaseDummyInputsBuilder
+from vllm.sequence import IntermediateTensors
+from vllm.transformers_utils.configs.deepseek_vl2 import (DeepseekVLV2Config,
+                                                          MlpProjectorConfig,
+                                                          VisionEncoderConfig)
+from vllm.transformers_utils.processors.deepseek_vl2 import (
+    DeepseekVLV2Processor)
+from vllm.transformers_utils.tokenizer import cached_tokenizer_from_config
+from vllm.utils import is_list_of
+
+from .interfaces import MultiModalEmbeddings, SupportsMultiModal, SupportsPP
+from .utils import (AutoWeightsLoader, WeightsMapper, flatten_bn,
+                    init_vllm_registered_model, maybe_prefix,
+                    merge_multimodal_embeddings)
+
+# The image token id may be various
+_IMAGE_TOKEN = "<image>"
+
+
+class DeepseekVL2ImagePixelInputs(TypedDict):
+    type: Literal["pixel_values"]
+    data: Union[torch.Tensor, list[torch.Tensor]]
+    """
+    Shape: `(batch_size * num_images, num_channels, height, width)`
+    """
+    images_spatial_crop: torch.Tensor
+    """
+    Shape: `(batch_size * num_images, 2)`
+    """
+
+
+class DeepseekVL2VImageEmbeddingInputs(TypedDict):
+    type: Literal["image_embeds"]
+    data: Union[torch.Tensor, list[torch.Tensor]]
+    """Shape: `(batch_size * num_images, image_feature_size, hidden_size)`
+
+    `hidden_size` must match the hidden size of language model backbone.
+    """
+
+
+DeepseekVL2ImageInputs = Union[DeepseekVL2ImagePixelInputs,
+                               DeepseekVL2VImageEmbeddingInputs]
+
+
+class MlpProjector(nn.Module):
+
+    def __init__(self, cfg: MlpProjectorConfig):
+
+        super().__init__()
+
+        self.cfg = cfg
+        assert not cfg.token_pooling, (
+            "Token pooling is not supported currently.")
+
+        if cfg.projector_type == "downsample_mlp_gelu":
+            mlp_depth = cfg.depth
+            mlp_ratio = cfg.mlp_ratio
+            modules = [
+                nn.Linear(
+                    cfg.input_dim * cfg.downsample_ratio *
+                    cfg.downsample_ratio, cfg.n_embed * mlp_ratio)
+            ]
+            for _ in range(1, mlp_depth - 1):
+                modules.append(nn.GELU())
+                modules.append(
+                    nn.Linear(cfg.n_embed * mlp_ratio,
+                              cfg.n_embed * mlp_ratio))
+            modules.append(nn.GELU())
+            modules.append(nn.Linear(cfg.n_embed * mlp_ratio, cfg.n_embed))
+            modules = nn.Sequential(*modules)
+
+        else:
+            raise NotImplementedError(
+                f"Unsupported projector type: {cfg.projector_type}")
+
+        self.layers = modules
+
+    def forward(self, x):
+        bs, hw, input_dim = x.shape
+        h = w = int((hw)**0.5)
+        """compute padding"""
+        if h % self.cfg.downsample_ratio:
+            pad = self.cfg.downsample_ratio - h % self.cfg.downsample_ratio
+        else:
+            pad = 0
+        x = x.reshape(bs, h, w, input_dim)
+        if pad > 0:
+            x = F.pad(x, (0, 0, 0, pad, 0, pad), "constant", 0)
+        """4 to 1 concat"""
+        x = x.permute(0, 3, 1, 2)  # B, C, H, W
+        x = F.unfold(x,
+                     kernel_size=self.cfg.downsample_ratio,
+                     stride=self.cfg.downsample_ratio,
+                     padding=0)  # B, C*4, HW // 4
+        x = x.permute(0, 2, 1)
+
+        return self.layers(x)
+
+
+class DeepseekVL2ProcessingInfo(BaseProcessingInfo):
+
+    def get_hf_config(self):
+        return self.ctx.get_hf_config(DeepseekVLV2Config)
+
+    def get_hf_processor(self, **kwargs: object):
+        return self.ctx.get_hf_processor(DeepseekVLV2Processor, **kwargs)
+
+    def get_supported_mm_limits(self) -> Mapping[str, Optional[int]]:
+        return {"image": None}
+
+    def get_num_image_tokens(self,
+                             *,
+                             image_width: int,
+                             image_height: int,
+                             cropping: bool = True) -> int:
+        hf_processor = self.get_hf_processor()
+        image_size = hf_processor.image_size
+        patch_size = hf_processor.patch_size
+        downsample_ratio = hf_processor.downsample_ratio
+
+        if cropping:
+            best_width, best_height = hf_processor.select_best_resolution(
+                (image_width, image_height))
+            num_width_tiles, num_height_tiles = (best_width // image_size,
+                                                 best_height // image_size)
+        else:
+            num_width_tiles = num_height_tiles = 1
+
+        h = w = math.ceil((image_size // patch_size) / downsample_ratio)
+
+        global_views_tokens = h * (w + 1)
+        local_views_tokens = (num_height_tiles * h) * (num_width_tiles * w + 1)
+        return global_views_tokens + local_views_tokens + 1
+
+    def get_image_size_with_most_features(self) -> ImageSize:
+        hf_config = self.get_hf_config()
+        candidate_resolutions = hf_config.candidate_resolutions
+        height, width = max(candidate_resolutions,
+                            key=lambda x: self.get_num_image_tokens(
+                                image_width=x[1], image_height=x[0]))
+        return ImageSize(width=width, height=height)
+
+
+class DeepseekVL2DummyInputsBuilder(
+        BaseDummyInputsBuilder[DeepseekVL2ProcessingInfo]):
+
+    def get_dummy_text(self, mm_counts: Mapping[str, int]) -> str:
+        num_images = mm_counts.get("image", 0)
+
+        processor = self.info.get_hf_processor()
+        image_token = processor.image_token
+
+        return image_token * num_images
+
+    def get_dummy_mm_data(
+        self,
+        seq_len: int,
+        mm_counts: Mapping[str, int],
+    ) -> MultiModalDataDict:
+        num_images = mm_counts.get("image", 0)
+
+        max_image_size = self.info.get_image_size_with_most_features()
+
+        return {
+            "image":
+            self._get_dummy_images(width=max_image_size.width,
+                                   height=max_image_size.height,
+                                   num_images=num_images)
+        }
+
+
+class DeepseekVL2MultiModalProcessor(
+        BaseMultiModalProcessor[DeepseekVL2ProcessingInfo]):
+
+    def _call_hf_processor(
+        self,
+        prompt: str,
+        mm_data: Mapping[str, object],
+        mm_kwargs: Mapping[str, object],
+    ) -> BatchFeature:
+        if mm_data:
+            processed_outputs = self.info.ctx.call_hf_processor(
+                self.info.get_hf_processor(**mm_kwargs),
+                dict(prompt=prompt, **mm_data),
+                mm_kwargs,
+            )
+            pixel_values = processed_outputs["pixel_values"]
+            # split pixel values into patches corresponding to each image
+            images_spatial_crop = processed_outputs["images_spatial_crop"]
+            patches_per_image = [
+                x.prod().item() + 1 for x in images_spatial_crop
+            ]
+            pixel_values = pixel_values.split(patches_per_image)
+            processed_outputs["pixel_values"] = pixel_values
+        else:
+            tokenizer = self.info.get_tokenizer()
+            processed_outputs = tokenizer(prompt,
+                                          add_special_tokens=True,
+                                          return_tensors="pt")
+
+        return processed_outputs
+
+    def _get_mm_fields_config(
+        self,
+        hf_inputs: BatchFeature,
+        hf_processor_mm_kwargs: Mapping[str, object],
+    ) -> Mapping[str, MultiModalFieldConfig]:
+        return dict(
+            pixel_values=MultiModalFieldConfig.batched("image"),
+            images_spatial_crop=MultiModalFieldConfig.batched("image"),
+            image_embeds=MultiModalFieldConfig.batched("image"),
+        )
+
+    def _get_prompt_updates(
+        self,
+        mm_items: MultiModalDataItems,
+        hf_processor_mm_kwargs: Mapping[str, object],
+        out_mm_kwargs: MultiModalKwargs,
+    ) -> Sequence[PromptUpdate]:
+        hf_processor = self.info.get_hf_processor(**hf_processor_mm_kwargs)
+
+        image_token_id = hf_processor.image_token_id
+        assert isinstance(image_token_id, int)
+
+        def get_replacement_deepseek_vl2(item_idx: int):
+            images = mm_items.get_items(
+                "image", (ImageEmbeddingItems, ImageProcessorItems))
+
+            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,
+                    cropping=len(images) <= 2,
+                )
+            return [image_token_id] * num_image_tokens
+
+        return [
+            PromptReplacement(
+                modality="image",
+                target=[image_token_id],
+                replacement=get_replacement_deepseek_vl2,
+            )
+        ]
+
+    def _cached_apply_hf_processor(
+        self,
+        prompt: Union[str, list[int]],
+        mm_data_items: MultiModalDataItems,
+        hf_processor_mm_kwargs: Mapping[str, object],
+        *,
+        return_mm_hashes: bool,
+    ) -> tuple[list[int], MultiModalKwargs, Optional[MultiModalHashes], bool]:
+        # The processor logic is different for len(images) <= 2 vs > 2
+        # Since the processing cache assumes that the processor output is
+        # invariant of how many images are passed per prompt, we only
+        # perform caching for the most common case
+        if mm_data_items.get_count("image", strict=False) > 2:
+            return self._apply_hf_processor(
+                prompt=prompt,
+                mm_data_items=mm_data_items,
+                hf_processor_mm_kwargs=hf_processor_mm_kwargs,
+                return_mm_hashes=return_mm_hashes,
+            )
+
+        return super()._cached_apply_hf_processor(
+            prompt=prompt,
+            mm_data_items=mm_data_items,
+            hf_processor_mm_kwargs=hf_processor_mm_kwargs,
+            return_mm_hashes=return_mm_hashes,
+        )
+
+
+@MULTIMODAL_REGISTRY.register_processor(
+    DeepseekVL2MultiModalProcessor,
+    info=DeepseekVL2ProcessingInfo,
+    dummy_inputs=DeepseekVL2DummyInputsBuilder)
+class DeepseekVLV2ForCausalLM(nn.Module, SupportsMultiModal, SupportsPP):
+
+    hf_to_vllm_mapper = WeightsMapper(orig_to_new_prefix={
+        "language.": "language_model.",
+    })
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+        config: DeepseekVLV2Config = vllm_config.model_config.hf_config
+        quant_config = vllm_config.quant_config
+        multimodal_config = vllm_config.model_config.multimodal_config
+
+        self.config = config
+        self.multimodal_config = multimodal_config
+
+        self.vision_config = config.vision_config
+        self.projector_config = config.projector_config
+        self.text_config = config.text_config
+
+        model_config = vllm_config.model_config
+        tokenizer = cached_tokenizer_from_config(model_config)
+        self.image_token_id = tokenizer.vocab[_IMAGE_TOKEN]
+
+        self.vision = self._init_vision_module(self.vision_config,
+                                               quant_config,
+                                               maybe_prefix(prefix, "vision"))
+
+        self.projector = MlpProjector(self.projector_config)
+        self.tile_tag = config.tile_tag
+        self.global_view_pos = config.global_view_pos
+
+        # special token for image token sequence format
+        embed_std = 1 / torch.sqrt(
+            torch.tensor(self.projector_config.n_embed, dtype=torch.float32))
+        if self.tile_tag == "2D":
+            # <|view_separator|>, <|\n|>
+            self.image_newline = nn.Parameter(
+                torch.randn(self.projector_config.n_embed) * embed_std)
+            # This is a typo in original implementation
+            self.view_seperator = nn.Parameter(
+                torch.randn(self.projector_config.n_embed) * embed_std)
+        else:
+            raise ValueError(
+                f"Only 2D tile_tag is supported currently, got: {self.tile_tag}"
+            )
+
+        if self.text_config.topk_method == "noaux_tc":
+            architectures = ["DeepseekV3ForCausalLM"]
+        elif not self.text_config.use_mla:
+            architectures = ["DeepseekForCausalLM"]
+        else:
+            architectures = ["DeepseekV2ForCausalLM"]
+
+        self.language_model = init_vllm_registered_model(
+            vllm_config=vllm_config,
+            hf_config=self.text_config,
+            prefix=maybe_prefix(prefix, "language"),
+            architectures=architectures,
+        )
+
+        self.make_empty_intermediate_tensors = (
+            self.language_model.make_empty_intermediate_tensors)
+
+    def _init_vision_module(
+        self,
+        vision_config: VisionEncoderConfig,
+        quant_config: Optional[QuantizationConfig],
+        prefix: str = "",
+    ) -> nn.Module:
+        # TODO: refactor vision model through timm wrapper from transformers
+        try:
+            import timm
+        except ImportError:
+            raise ImportError("Please install timm") from ImportError
+
+        with set_default_torch_dtype(torch.float16):
+            model = timm.create_model(
+                "vit_so400m_patch14_siglip_384.webli",
+                pretrained=False,
+                num_classes=0,
+                dynamic_img_size=True,
+                dynamic_img_pad=True,
+            )
+
+        model = model.to(dtype=torch.get_default_dtype())
+        return model
+
+    def _validate_pixel_values(
+        self, data: Union[torch.Tensor, list[torch.Tensor]]
+    ) -> Union[torch.Tensor, list[torch.Tensor]]:
+
+        h = w = self.vision_config.image_size
+        expected_dims = (3, h, w)
+
+        def _validate_shape(d: torch.Tensor):
+            actual_dims = tuple(d.shape[1:])
+
+            if actual_dims != expected_dims:
+                expected_expr = ("num_patches", *map(str, expected_dims))
+                raise ValueError(
+                    "The expected shape of pixel values per image per batch "
+                    f"is {expected_expr}. You supplied {tuple(d.shape)}.")
+
+        for d in data:
+            _validate_shape(d)
+
+        return data
+
+    def _validate_images_spatial_crop(
+        self, data: Union[torch.Tensor, list[torch.Tensor]]
+    ) -> Union[torch.Tensor, list[torch.Tensor]]:
+        expected_dims = 2
+
+        def _validate_shape(d: torch.Tensor):
+            actual_dims = d.size(-1)
+
+            if actual_dims != expected_dims:
+                expected_expr = str(expected_dims)
+                raise ValueError(
+                    f"The expected shape of image sizes per image per batch "
+                    f"is {expected_expr}. You supplied {tuple(d.shape)}.")
+
+        for d in data:
+            _validate_shape(d)
+
+        return data
+
+    def _parse_and_validate_image_input(
+            self, **kwargs: object) -> Optional[DeepseekVL2ImageInputs]:
+        pixel_values = kwargs.pop("pixel_values", None)
+        images_spatial_crop = kwargs.pop("images_spatial_crop", None)
+        image_embeds = kwargs.pop("image_embeds", None)
+
+        if pixel_values is None and image_embeds is None:
+            return None
+
+        if pixel_values is not None:
+            if not isinstance(pixel_values, (torch.Tensor, list)):
+                raise ValueError("Incorrect type of pixel values. "
+                                 f"Got type: {type(pixel_values)}")
+
+            if not isinstance(images_spatial_crop, (torch.Tensor, list)):
+                raise ValueError("Incorrect type of image sizes. "
+                                 f"Got type: {type(images_spatial_crop)}")
+
+            return DeepseekVL2ImagePixelInputs(
+                type="pixel_values",
+                data=self._validate_pixel_values(flatten_bn(pixel_values)),
+                images_spatial_crop=self._validate_images_spatial_crop(
+                    flatten_bn(images_spatial_crop, concat=True)))
+
+        if image_embeds is not None:
+            if not isinstance(image_embeds, (torch.Tensor, list)):
+                raise ValueError("Incorrect type of image embeddings. "
+                                 f"Got type: {type(image_embeds)}")
+
+            return DeepseekVL2VImageEmbeddingInputs(
+                type="image_embeds",
+                data=flatten_bn(image_embeds),
+            )
+
+        raise AssertionError("This line should be unreachable.")
+
+    def _pixel_values_to_embedding(
+        self,
+        pixel_values: NestedTensors,
+        images_spatial_crop: torch.Tensor,
+    ) -> NestedTensors:
+        # Pixel_values: n_image * batch_size * [patch_per_img, 3, height, width]
+        total_tiles = [x for x in pixel_values]
+
+        # [batch_all_tiles, 3, height, width]
+        total_tiles = torch.cat(total_tiles, dim=0)
+
+        # [batch_all_tiles, vit_seq_len, c]
+        images_feature = self.vision.forward_features(total_tiles)
+
+        # [batch_all_tiles, hw, D]
+        images_embeds = self.projector(images_feature)
+
+        _, hw, n_dim = images_embeds.shape
+        h = w = int(hw**0.5)
+
+        # fill image token based on self.tile_tag & self.global_view_pos
+        tile_index = 0
+        vision_embeddings = []
+        for jdx in range(images_spatial_crop.size(0)):
+            # extra global & local features
+            num_width_tiles, num_height_tiles = images_spatial_crop[jdx]
+            if num_width_tiles == 0 or num_height_tiles == 0:
+                break
+            num_tiles_in_image = num_width_tiles * num_height_tiles
+
+            # [hw, D]
+            global_features = images_embeds[tile_index]
+
+            # [num_height_tiles * num_width_tiles, hw, D]
+            local_features = images_embeds[tile_index + 1:tile_index + 1 +
+                                           num_tiles_in_image]
+            tile_index += num_tiles_in_image + 1
+
+            # format global and local features
+            # ----------------- global view add newline -----------------
+            # [hw, D] -> [h, w, D]
+            global_features = global_features.view(h, w, n_dim)
+
+            # [D]     -> [h, 1, D]
+            new_lines_in_global = repeat(self.image_newline, "d -> h 1 d", h=h)
+
+            # cat([h, w, D], [h, 1, D], dim=1) -> [h, w + 1, D]
+            global_features = torch.cat([global_features, new_lines_in_global],
+                                        dim=1)
+
+            # [h, w + 1, D] -> [h * (w + 1), D]
+            global_features = global_features.view(-1, n_dim)
+
+            # ----------------- local view add newline -----------------
+            # [num_height_tiles * num_width_tiles, h * w, D] ->
+            # [num_height_tiles * h, num_width_tiles * w, D]
+            local_features = rearrange(local_features,
+                                       "(th tw) (h w) d -> (th h) (tw w) d",
+                                       th=num_height_tiles,
+                                       tw=num_width_tiles,
+                                       h=h,
+                                       w=w)
+
+            # [D] -> [num_height_tiles * h, 1, D]
+            new_lines_in_local = repeat(self.image_newline,
+                                        "d -> (th h) 1 d",
+                                        th=num_height_tiles,
+                                        h=h)
+
+            # [num_height_tiles * h, num_width_tiles * w + 1, D]
+            local_features = torch.cat([local_features, new_lines_in_local],
+                                       dim=1)
+
+            # [num_height_tiles * h, num_width_tiles * w + 1, D]
+            #   --> [(num_height_tiles * h) * (num_width_tiles * w + 1), D]
+            local_features = local_features.view(-1, n_dim)
+
+            # merge global and local tiles
+            if self.global_view_pos == "head":
+                global_local_features = torch.cat([
+                    global_features,
+                    self.view_seperator[None, :],
+                    local_features,
+                ])
+            else:
+                global_local_features = torch.cat([
+                    local_features,
+                    self.view_seperator[None, :],
+                    global_features,
+                ])
+
+            vision_embeddings.append(global_local_features)
+        return vision_embeddings
+
+    def _process_image_input(
+            self, image_input: DeepseekVL2ImageInputs) -> torch.Tensor:
+        if image_input["type"] == "image_embeds":
+            image_data = image_input["data"]
+            if is_list_of(image_data, torch.Tensor):
+                # it's already a list of tensors
+                return image_data
+            if len(image_data.shape) == 3:
+                # 3D tensor
+                return list(torch.unbind(image_data, dim=0))
+            raise ValueError(
+                "We expect batched 2D tensors; "
+                "this can be either a list of 2D tensors or a single 3D tensor."
+            )
+
+        pixel_values = image_input["data"]
+        images_spatial_crop = image_input["images_spatial_crop"]
+
+        return self._pixel_values_to_embedding(
+            pixel_values=pixel_values, images_spatial_crop=images_spatial_crop)
+
+    def get_language_model(self) -> torch.nn.Module:
+        return self.language_model
+
+    def get_multimodal_embeddings(
+            self, **kwargs: object) -> Optional[MultiModalEmbeddings]:
+        image_input = self._parse_and_validate_image_input(**kwargs)
+        if image_input is None:
+            return None
+        vision_embeddings = self._process_image_input(image_input)
+        return vision_embeddings
+
+    def get_input_embeddings(
+        self,
+        input_ids: torch.Tensor,
+        multimodal_embeddings: Optional[MultiModalEmbeddings] = None,
+    ) -> torch.Tensor:
+        inputs_embeds = self.language_model.get_input_embeddings(input_ids)
+        if multimodal_embeddings is not None:
+            inputs_embeds = merge_multimodal_embeddings(
+                input_ids, inputs_embeds, multimodal_embeddings,
+                self.image_token_id)
+        return inputs_embeds
+
+    def forward(self,
+                input_ids: torch.Tensor,
+                positions: torch.Tensor,
+                intermediate_tensors: Optional[IntermediateTensors] = None,
+                inputs_embeds: Optional[torch.Tensor] = None,
+                **kwargs: object):
+
+        if intermediate_tensors is not None:
+            inputs_embeds = None
+
+        # NOTE: In v1, inputs_embeds is always generated at model runner, this
+        # condition is for v0 compatibility
+        elif inputs_embeds is None:
+            vision_embeddings = self.get_multimodal_embeddings(**kwargs)
+            inputs_embeds = self.get_input_embeddings(input_ids,
+                                                      vision_embeddings)
+            input_ids = None
+
+        hidden_states = self.language_model(input_ids,
+                                            positions,
+                                            intermediate_tensors,
+                                            inputs_embeds=inputs_embeds)
+
+        return hidden_states
+
+    def compute_logits(
+        self,
+        hidden_states: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> Optional[torch.Tensor]:
+        return self.language_model.compute_logits(hidden_states,
+                                                  sampling_metadata)
+
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+
+        loader = AutoWeightsLoader(self)
+        autoloaded_weights = loader.load_weights(weights,
+                                                 mapper=self.hf_to_vllm_mapper)
+        return autoloaded_weights