# SPDX-License-Identifier: Apache-2.0 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project # Copyright 2024 The vLLM team. # Copyright 2024 Microsoft and the HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from collections.abc import Iterable, Mapping, Sequence from typing import Annotated, Any, Literal, Optional, Union import regex as re import torch import torch.nn as nn from transformers import (BatchFeature, CLIPVisionConfig, PretrainedConfig, ProcessorMixin) from vllm.config import VllmConfig from vllm.logger import init_logger from vllm.model_executor.layers.quantization import QuantizationConfig from vllm.model_executor.layers.vocab_parallel_embedding import ( VocabParallelEmbedding) from vllm.model_executor.sampling_metadata import SamplingMetadata from vllm.multimodal import MULTIMODAL_REGISTRY from vllm.multimodal.inputs import (MultiModalDataDict, MultiModalFieldConfig, MultiModalKwargs) from vllm.multimodal.parse import (ImageEmbeddingItems, ImageProcessorItems, ImageSize, MultiModalDataItems) # yapf conflicts with isort for this block # yapf: disable from vllm.multimodal.processing import (BaseMultiModalProcessor, BaseProcessingInfo, BoundPromptUpdate, PlaceholderFeaturesInfo, PromptReplacement, PromptUpdate) # yapf: enable from vllm.multimodal.profiling import BaseDummyInputsBuilder from vllm.sequence import IntermediateTensors from vllm.utils import is_list_of from vllm.utils.tensor_schema import TensorSchema, TensorShape from .clip import CLIPVisionModel from .interfaces import (MultiModalEmbeddings, SupportsMultiModal, SupportsPP, SupportsQuant) from .utils import (AutoWeightsLoader, WeightsMapper, flatten_bn, init_vllm_registered_model, maybe_prefix, merge_multimodal_embeddings) logger = init_logger(__name__) # Cannot find the following 2 numbers from hf config. _IMAGE_TOKEN_ID = 32044 CLIP_VIT_LARGE_PATCH14_336_CONFIG = CLIPVisionConfig(dropout=0.0, hidden_act="quick_gelu", hidden_size=1024, image_size=336, intermediate_size=4096, num_attention_heads=16, num_channels=3, num_hidden_layers=24, patch_size=14, projection_dim=768) def _init_img_processor(hf_config: PretrainedConfig, quant_config: Optional[QuantizationConfig], prefix: str = "") -> CLIPVisionModel: clip_config = CLIP_VIT_LARGE_PATCH14_336_CONFIG layer_idx = hf_config.img_processor.get('layer_idx', -2) # Initialize the CLIP only up to the required feature layer if layer_idx < 0: num_hidden_layers = clip_config.num_hidden_layers + \ layer_idx + 1 else: num_hidden_layers = layer_idx + 1 img_processor = CLIPVisionModel( clip_config, quant_config, num_hidden_layers_override=num_hidden_layers, prefix=prefix, ) return img_processor class Phi3VImagePixelInputs(TensorSchema): """ Dimensions: - b: Batch size - n: Number of images - p: Number of patches - h: Height of each patch - w: Width of each patch """ type: Literal["pixel_values", "image_embeds"] = "pixel_values" # Supports either a stacked tensor or a list of (p, 3, h, w) tensors data: Annotated[ Union[torch.Tensor, list[torch.Tensor]], TensorShape("bn", "p", 3, "h", "w", dynamic_dims={"p"} ), # 'p' may vary across items ] # Stacked tensor with height and width for each image image_sizes: Annotated[Optional[torch.Tensor], TensorShape("bn", 2)] class Phi3VImageEmbeddingInputs(TensorSchema): """ Dimensions: - b: Batch size - n: Number of images - f: Image feature size (e.g., number of tokens per image) - h: Hidden size (must match language model backbone) """ type: Literal["image_embeds"] = "image_embeds" data: Annotated[ Union[torch.Tensor, list[torch.Tensor]], TensorShape("bn", "f", "h"), ] Phi3VImageInputs = Union[Phi3VImagePixelInputs, Phi3VImageEmbeddingInputs] class Phi3ImageEmbeddingBase(nn.Module): def __init__(self) -> None: super().__init__() self.layer_idx: int self.type_feature: str self.img_processor: CLIPVisionModel def get_img_features(self, img_embeds: torch.FloatTensor) -> torch.FloatTensor: TYPE_FEATURE = self.type_feature # NOTE: we skip the step to select the vision feature layer since # this is already done inside the img_processor img_feature = self.img_processor(img_embeds) if TYPE_FEATURE == "patch": patch_feature = img_feature[:, 1:] return patch_feature if TYPE_FEATURE == "cls_patch": return img_feature raise NotImplementedError # adapted from https://huggingface.co/microsoft/Phi-3-vision-128k-instruct/blob/main/image_embedding_phi3_v.py class Phi3HDImageEmbedding(Phi3ImageEmbeddingBase): """Phi3 Image embedding with HD transform.""" def __init__(self, config: PretrainedConfig, quant_config: Optional[QuantizationConfig], prefix: str = "") -> None: super().__init__() # n_embed or hidden_size hidden_size = config.n_embd if hasattr( config, 'n_embd') else config.hidden_size self.img_processor = _init_img_processor( config, quant_config, prefix=f"{prefix}.img_processor") image_dim_out = config.img_processor['image_dim_out'] self.num_img_tokens = config.img_processor['num_img_tokens'] self.image_dim_out = image_dim_out # global_gn and sub_gn for hd transform, serves as line separator self.use_hd_transform = config.embd_layer.get('use_hd_transform', False) self.with_learnable_separator = config.embd_layer.get( 'with_learnable_separator', False) self.hd_transform_order = config.embd_layer.get( 'hd_transform_order', 'glb_sub') # with_hd_transform and with_learnable_separator should have same value assert self.use_hd_transform and self.with_learnable_separator # 1024 * 4, merge spatial to channel dimension self.glb_GN = nn.Parameter(torch.empty([1, 1, self.image_dim_out * 4])) self.sub_GN = nn.Parameter( torch.empty([1, 1, 1, self.image_dim_out * 4])) dim_projection = hidden_size depth = 2 layers = [nn.Linear(image_dim_out * 4, dim_projection)] for _ in range(1, depth): layers.extend( [nn.GELU(), nn.Linear(dim_projection, dim_projection)]) self.img_projection = nn.Sequential(*layers) self.type_feature = config.img_processor.get('type_feature', 'patch') def forward(self, pixel_values: torch.FloatTensor, image_sizes: torch.Tensor) -> torch.FloatTensor: """ process image and return vision embeddings. pixel_values: (num_images, num_crops, c, h, w) output: (num_images, num_img_tokens, hidden_size) """ num_images, num_crops, c, h, w = pixel_values.shape pixel_values = pixel_values.flatten(0, 1) img_features = self.get_img_features(pixel_values) img_features = img_features.reshape(num_images, num_crops, -1, self.image_dim_out) image_features_proj = self.hd_feature_transform( img_features, image_sizes) return image_features_proj def hd_feature_transform(self, image_features, image_sizes): """ image_features: (num_images, num_crops+1, 24*24, 1024) """ assert ( self.hd_transform_order == 'sub_glb' ), f'hd_transform_order `{self.hd_transform_order}` not implemented' 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 global_image_features = image_features[:, 0] # (num_images, 24*24, 1024) # global feature can be viewed as a special HD case with num_crops 1x1 global_image_features_hd = self.reshape_hd_patches_2x2merge( global_image_features, 1, 1) global_image_features_hd_newline = self.add_image_newline( global_image_features_hd) batch_image_features_proj = [] # need a for loop to process each image because of different image sizes # (patch arrangement is different for each image) for i, img_size in enumerate(image_sizes): h, w = img_size h_crop = h // 336 w_crop = w // 336 num_crops = h_crop * w_crop # NOTE: real num_crops is padded # (num_crops, 24*24, 1024) sub_image_features = image_features[i, 1:1 + num_crops] sub_image_features_hd = self.reshape_hd_patches_2x2merge( sub_image_features, h_crop, w_crop) sub_image_features_hd_newline = self.add_image_newline( sub_image_features_hd) # [sub features, separator, global features] image_embeddings = torch.cat([ sub_image_features_hd_newline.squeeze( 0), # (h_crop*12*(w_crop*12+1), 4096) self.glb_GN.squeeze(0), global_image_features_hd_newline[i], ]) img_proj = self.img_projection( image_embeddings.to(target_device, target_dtype)) batch_image_features_proj.append(img_proj) return batch_image_features_proj def reshape_hd_patches_2x2merge(self, image_features, h_crop, w_crop): """ image_features: (num_images*num_crops, 24*24, 1024) output: (num_images, h_crop*12, w_crop*12, 4096) where h_crop*w_crop == num_crops """ N, L, C = image_features.shape assert L == 576 and C == 1024 and N % (h_crop * w_crop) == 0 num_images = N // (h_crop * w_crop) H = int(L**0.5) image_features_hd = ( image_features.reshape(N, H, H, C) # N, 24, 24, 1024 .reshape(N, H // 2, 2, H // 2, 2, C) # N, 12, 2, 12, 2, 1024 .permute(0, 1, 3, 2, 4, 5) # N, 12, 12, 2, 2, 1024 .reshape(N, -1, 4 * C) # N, 144, 4096 .reshape(num_images, h_crop, w_crop, H // 2, H // 2, -1) # n_img, h_crop, w_crop, 12, 12, 4096 .permute(0, 1, 3, 2, 4, 5) # n_img, h_crop, 12, w_crop, 12, 4096 .reshape(num_images, h_crop * H // 2, w_crop * H // 2, 4 * C) # n_img, h_crop*12, w_crop*12, 4096 ) return image_features_hd def add_image_newline(self, image_features_hd): """ image_features_hd: (num_images, h_crop*12, w_crop*12, 4096) output: (num_images, (h_crop*12) * (w_crop*12+1), 4096) """ num_images, h, w, hid_dim = image_features_hd.shape # add the newline token to the HD image feature patches newline_embeddings = self.sub_GN.expand(num_images, h, -1, -1) # (n_img, h, 1, hid_dim) image_features_hd_newline = torch.cat( [image_features_hd, newline_embeddings], dim=2).reshape(num_images, -1, hid_dim) return image_features_hd_newline class Phi3VProcessingInfo(BaseProcessingInfo): 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, processor: Optional[ProcessorMixin] = None, ) -> int: if processor is None: processor = self.get_hf_processor() return processor.calc_num_image_tokens_from_image_size( # type: ignore width=image_width, height=image_height, ) def get_image_size_with_most_features(self) -> ImageSize: # Result in the max possible feature size (h:w = 16:1) return ImageSize(height=8000, width=50) class Phi3VDummyInputsBuilder(BaseDummyInputsBuilder[Phi3VProcessingInfo]): def get_dummy_text(self, mm_counts: Mapping[str, int]) -> str: num_images = mm_counts.get("image", 0) hf_processor = self.info.get_hf_processor() image_tokens: list[str] = hf_processor.img_tokens # type: ignore return "".join(image_tokens[:num_images]) def get_dummy_mm_data( self, seq_len: int, mm_counts: Mapping[str, int], ) -> MultiModalDataDict: num_images = mm_counts.get("image", 0) target_width, target_height = \ self.info.get_image_size_with_most_features() return { "image": self._get_dummy_images(width=target_width, height=target_height, num_images=num_images) } class Phi3VMultiModalProcessor(BaseMultiModalProcessor[Phi3VProcessingInfo]): def _call_hf_processor( self, prompt: str, mm_data: Mapping[str, object], mm_kwargs: Mapping[str, object], tok_kwargs: Mapping[str, object], ) -> BatchFeature: processed_outputs = super()._call_hf_processor( prompt=prompt, mm_data=mm_data, mm_kwargs=mm_kwargs, tok_kwargs=tok_kwargs, ) input_ids = processed_outputs["input_ids"] assert isinstance(input_ids, torch.Tensor) # Phi3v processor has inserted -1, -2 etc as placeholder in prompt_ids, # which will cause OverflowError when decoding the prompt_ids. # Therefore, we need to do an early replacement here input_ids.masked_fill_(input_ids < 0, _IMAGE_TOKEN_ID) 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"), image_sizes=MultiModalFieldConfig.batched("image"), image_embeds=MultiModalFieldConfig.batched("image"), ) def _get_prompt_updates( self, mm_items: MultiModalDataItems, hf_processor_mm_kwargs: Mapping[str, Any], out_mm_kwargs: MultiModalKwargs, ) -> Sequence[PromptUpdate]: hf_processor = self.info.get_hf_processor(**hf_processor_mm_kwargs) image_tokens: list[str] = hf_processor.img_tokens # type: ignore def get_replacement_phi3v(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, processor=hf_processor, ) return [_IMAGE_TOKEN_ID] * num_image_tokens num_images = mm_items.get_count("image", strict=False) return [ PromptReplacement( modality="image", target=image_token, replacement=get_replacement_phi3v, ) for image_token in image_tokens[:num_images] ] def _apply_prompt_updates( self, token_ids: list[int], mm_prompt_updates: Mapping[str, Sequence[BoundPromptUpdate]], mm_item_counts: Mapping[str, int], ) -> tuple[list[int], str, Mapping[str, list[PlaceholderFeaturesInfo]]]: # align to hf behavior when there are images if len(mm_item_counts): tokenizer = self.info.get_tokenizer() # to decode token_ids to the original text, we need to # 1. remove the first bos token # 2. remove space after each special token # introduced by the tokenizer if len(token_ids) and token_ids[0] == tokenizer.bos_token_id: token_ids = token_ids[1:] text = tokenizer.decode(token_ids) for special_tokens in tokenizer.special_tokens_map.values(): if isinstance(special_tokens, str): text = text.replace(f"{special_tokens} ", special_tokens) elif isinstance(special_tokens, list): for special_token in special_tokens: text = text.replace(f"{special_token} ", special_token) # perform hf behavior # https://huggingface.co/microsoft/Phi-3.5-vision-instruct/blob/64f88b6/processing_phi3_v.py#L407 pattern = r"<\|image_\d+\|>" prompt_chunks = [ tokenizer(chunk).input_ids for chunk in re.split(pattern, text) ] image_tags = [ tokenizer(chunk, add_special_tokens=False).input_ids for chunk in re.findall(pattern, text) ] if len(prompt_chunks) > len(image_tags): image_tags.append([]) token_ids = [ e for sublist in zip(prompt_chunks, image_tags) for ele in sublist for e in ele ] token_ids, text, placeholders = super()._apply_prompt_updates( token_ids=token_ids, mm_prompt_updates=mm_prompt_updates, mm_item_counts=mm_item_counts, ) # Keep the behavior in line with HF processor if text.startswith(" <|image|>"): text = text.replace(" <|image|>", "<|image|>", 1) token_ids = [token_ids[0], *token_ids[2:]] placeholders = { modality: [ PlaceholderFeaturesInfo( modality=p.modality, item_idx=p.item_idx, start_idx=p.start_idx - 1, tokens=p.tokens, is_embed=p.is_embed, ) for p in ps ] for modality, ps in placeholders.items() } return token_ids, text, placeholders @MULTIMODAL_REGISTRY.register_processor(Phi3VMultiModalProcessor, info=Phi3VProcessingInfo, dummy_inputs=Phi3VDummyInputsBuilder) class Phi3VForCausalLM(nn.Module, SupportsMultiModal, SupportsPP, SupportsQuant): hf_to_vllm_mapper = WeightsMapper( orig_to_new_prefix={ "model.vision_embed_tokens.wte": "embed_tokens", "model.vision_embed_tokens.": "vision_embed_tokens.", "lm_head.": "language_model.lm_head.", "model.": "language_model.model.", }) @classmethod def get_placeholder_str(cls, modality: str, i: int) -> Optional[str]: if modality.startswith("image"): return f"<|image_{i}|>" raise ValueError("Only image modality is supported") 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 self.config = config self.multimodal_config = multimodal_config self.image_token_id = _IMAGE_TOKEN_ID self.embed_tokens = VocabParallelEmbedding( config.vocab_size, config.hidden_size, org_num_embeddings=config.vocab_size, quant_config=self.quant_config, prefix=maybe_prefix(prefix, "model.embed_tokens"), ) # TODO: Optionally initializes this for supporting input embeddings. self.vision_embed_tokens = Phi3HDImageEmbedding( config, self.quant_config, prefix=maybe_prefix(prefix, "model.vision_embed_tokens")) self.language_model = init_vllm_registered_model( vllm_config=vllm_config, # The prefix is empty intentionally because default prefix of # LlamaForCausalLM is "model" prefix="", # We don't directly initialize vLLM's LlamaForCausalLM so we # can automatically apply embedding wrapper if this model is # initialized as an embedding model architectures=["LlamaForCausalLM"], ) self.make_empty_intermediate_tensors = ( self.language_model.make_empty_intermediate_tensors) def _parse_and_validate_image_input( self, **kwargs: object) -> Optional[Phi3VImageInputs]: pixel_values = kwargs.pop("pixel_values", None) image_sizes = kwargs.pop("image_sizes", 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: return Phi3VImagePixelInputs( type="pixel_values", data=flatten_bn(pixel_values), image_sizes=flatten_bn(image_sizes, concat=True), resolve_bindings={ "h": CLIP_VIT_LARGE_PATCH14_336_CONFIG.image_size, "w": CLIP_VIT_LARGE_PATCH14_336_CONFIG.image_size }) if image_embeds is not None: return Phi3VImageEmbeddingInputs( type="image_embeds", data=flatten_bn(image_embeds), ) raise AssertionError("This line should be unreachable.") def _process_image_input( self, image_input: Phi3VImageInputs, ) -> 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." ) assert self.vision_embed_tokens is not None image_embeds = self.vision_embed_tokens(image_input["data"], image_input["image_sizes"]) return image_embeds def get_language_model(self) -> torch.nn.Module: return self.language_model def get_multimodal_embeddings(self, **kwargs: object) -> MultiModalEmbeddings: image_input = self._parse_and_validate_image_input(**kwargs) if image_input is None: return [] 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.embed_tokens(input_ids) if multimodal_embeddings is not None \ and len(multimodal_embeddings) != 0: 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.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) # The HF config doesn't specify whether these are tied, # so we detect it this way if "embed_tokens.weight" not in autoloaded_weights: self.embed_tokens = self.language_model.model.embed_tokens autoloaded_weights.add("embed_tokens.weight") return autoloaded_weights