# 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 functools import cached_property from typing import (Iterable, List, Literal, Mapping, Optional, Set, Tuple, TypedDict, Union) import torch import torch.nn as nn from transformers import (BatchFeature, CLIPVisionConfig, PretrainedConfig, ProcessorMixin) from vllm.attention import AttentionMetadata from vllm.config import VllmConfig from vllm.inputs import InputContext from vllm.logger import init_logger from vllm.model_executor.layers.quantization import QuantizationConfig from vllm.model_executor.layers.sampler import SamplerOutput, get_sampler from vllm.model_executor.layers.vocab_parallel_embedding import ( VocabParallelEmbedding) from vllm.model_executor.models.clip import CLIPVisionModel from vllm.model_executor.sampling_metadata import SamplingMetadata from vllm.multimodal import MULTIMODAL_REGISTRY from vllm.multimodal.image import cached_get_image_processor from vllm.multimodal.inputs import MultiModalKwargs, NestedTensors from vllm.multimodal.processing import (BaseMultiModalProcessor, InputProcessingContext, ModalityProcessingMetadata, MultiModalDataDict, MultiModalProcessingMetadata, PromptReplacement) from vllm.sequence import IntermediateTensors from vllm.utils import is_list_of from .clip import dummy_image_for_clip from .interfaces import SupportsMultiModal, SupportsPP 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 # Result in the max possible feature size (h:w = 16:1) MAX_IMAGE_FEATURE_SIZE_HEIGHT = 8000 MAX_IMAGE_FEATURE_SIZE_WIDTH = 50 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(TypedDict): type: Literal["pixel_values"] data: Union[torch.Tensor, List[torch.Tensor]] """ Shape: `(batch_size * num_images, 1 + num_patches, num_channels, height, width)` Note that `num_patches` may be different per batch and image, in which case the data is passed as a list instead of a batched tensor. """ image_sizes: torch.Tensor """ Shape: `(batch_size * num_images, 2)` This should be in `(height, width)` format. """ class Phi3VImageEmbeddingInputs(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. """ 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 def get_max_phi3v_image_tokens(ctx: InputContext, *, num_crops: Optional[int] = None): mm_processor_kwargs = {} if num_crops is not None: mm_processor_kwargs["num_crops"] = num_crops model_config = ctx.model_config image_processor = cached_get_image_processor( model_config.model, trust_remote_code=model_config.trust_remote_code, **mm_processor_kwargs, ) num_tokens = image_processor.calc_num_image_tokens_from_image_size( width=MAX_IMAGE_FEATURE_SIZE_WIDTH, height=MAX_IMAGE_FEATURE_SIZE_HEIGHT, ) return num_tokens def dummy_mm_kwargs_for_phi3v(ctx: InputProcessingContext, mm_counts: Mapping[str, int]): num_images = mm_counts["image"] data = dummy_image_for_clip( CLIP_VIT_LARGE_PATCH14_336_CONFIG, num_images, image_width_override=MAX_IMAGE_FEATURE_SIZE_WIDTH, image_height_override=MAX_IMAGE_FEATURE_SIZE_HEIGHT, ) hf_processor = ctx.get_hf_processor() image_processor = hf_processor.image_processor # type: ignore hf_inputs = image_processor.preprocess(data['image'], return_tensors="pt") return MultiModalKwargs(**hf_inputs) def create_metadata_for_phi3v( ctx: InputProcessingContext) -> MultiModalProcessingMetadata: return { "image": ModalityProcessingMetadata(prompt_repls=[ PromptReplacement(target=[_IMAGE_TOKEN_ID], repl_unit=[_IMAGE_TOKEN_ID], repl_count=get_max_phi3v_image_tokens(ctx)), ]), } class Phi3VProcessor(BaseMultiModalProcessor): def __init__(self, ctx: InputProcessingContext) -> None: super().__init__( ctx=ctx, metadata=create_metadata_for_phi3v(ctx), ) def _get_hf_processor( self, *, num_crops: Optional[int] = None, ) -> ProcessorMixin: if num_crops is not None: return self.ctx.get_hf_processor(num_crops=num_crops) return self.ctx.get_hf_processor() def _apply_hf_processor( self, prompt: str, mm_data: MultiModalDataDict, mm_processor_kwargs: Mapping[str, object], ) -> BatchFeature: processed_outputs = super()._apply_hf_processor( prompt, mm_data, mm_processor_kwargs) # 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 token_ids = processed_outputs['input_ids'] token_ids[token_ids < 0] = _IMAGE_TOKEN_ID processed_outputs['input_ids'] = token_ids return processed_outputs def _get_dummy_mm_kwargs( self, mm_counts: Mapping[str, int], ) -> MultiModalKwargs: return dummy_mm_kwargs_for_phi3v(self.ctx, mm_counts) @MULTIMODAL_REGISTRY.register_max_image_tokens(get_max_phi3v_image_tokens) @MULTIMODAL_REGISTRY.register_processor(Phi3VProcessor) class Phi3VForCausalLM(nn.Module, SupportsMultiModal, SupportsPP): def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""): super().__init__() config = 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.image_token_id = _IMAGE_TOKEN_ID self.embed_tokens = VocabParallelEmbedding( config.vocab_size, config.hidden_size, org_num_embeddings=config.vocab_size, quant_config=quant_config, prefix=maybe_prefix(prefix, "model.embed_tokens"), ) # TODO: Optionally initializes this for supporting input embeddings. self.vision_embed_tokens = Phi3HDImageEmbedding( config, 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) @cached_property def sampler(self): if hasattr(self.language_model, "sampler"): return self.language_model.sampler return get_sampler() def _validate_image_sizes(self, data: torch.Tensor) -> torch.Tensor: expected_dims = (2, ) def _validate_shape(d: torch.Tensor): actual_dims = tuple(d.shape) 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 _validate_pixel_values( self, data: Union[torch.Tensor, List[torch.Tensor]] ) -> Union[torch.Tensor, List[torch.Tensor]]: h = w = CLIP_VIT_LARGE_PATCH14_336_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 _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: if not isinstance(pixel_values, (torch.Tensor, list)): raise ValueError("Incorrect type of pixel values. " f"Got type: {type(pixel_values)}") if not isinstance(image_sizes, (torch.Tensor, list)): raise ValueError("Incorrect type of image sizes. " f"Got type: {type(image_sizes)}") return Phi3VImagePixelInputs( type="pixel_values", data=self._validate_pixel_values(flatten_bn(pixel_values)), image_sizes=self._validate_image_sizes( flatten_bn(image_sizes, concat=True))) if image_embeds is not None: if not isinstance(image_embeds, torch.Tensor): raise ValueError("Incorrect type of image embeddings. " f"Got type: {type(image_embeds)}") 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_multimodal_embeddings(self, **kwargs) -> Optional[NestedTensors]: 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[NestedTensors] = None, ) -> torch.Tensor: inputs_embeds = self.embed_tokens(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, kv_caches: List[torch.Tensor], attn_metadata: AttentionMetadata, 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, kv_caches, attn_metadata, 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 sample( self, logits: torch.Tensor, sampling_metadata: SamplingMetadata, ) -> Optional[SamplerOutput]: return self.language_model.sample(logits, sampling_metadata) def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]) -> Set[str]: 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.", }) loader = AutoWeightsLoader(self) autoloaded_weights = loader.load_weights(weights, mapper=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