from typing import (Iterable, List, Literal, Mapping, Optional, Tuple, TypedDict, Union) import torch from loguru import logger from torch import nn from transformers import PaliGemmaConfig from aphrodite.attention import AttentionMetadata from aphrodite.common.config import CacheConfig, MultiModalConfig from aphrodite.common.sequence import IntermediateTensors, SamplerOutput from aphrodite.inputs import INPUT_REGISTRY, InputContext, LLMInputs from aphrodite.modeling.layers.logits_processor import LogitsProcessor from aphrodite.modeling.layers.sampler import Sampler from aphrodite.modeling.model_loader.weight_utils import default_weight_loader from aphrodite.modeling.models.gemma import GemmaModel from aphrodite.modeling.sampling_metadata import SamplingMetadata from aphrodite.multimodal import MULTIMODAL_REGISTRY from aphrodite.multimodal.image import cached_get_tokenizer from aphrodite.quantization.base_config import QuantizationConfig from .interfaces import SupportsMultiModal from .siglip import (SiglipVisionModel, dummy_image_for_siglip, dummy_seq_data_for_siglip, get_max_siglip_image_tokens) from .utils import merge_multimodal_embeddings _KEYS_TO_MODIFY_MAPPING = { "language_model.model": "language_model", } class PaliGemmaImagePixelInputs(TypedDict): type: Literal["pixel_values"] data: torch.Tensor """Shape: (batch_size, num_channels, height, width)""" class PaliGemmaImageEmbeddingInputs(TypedDict): type: Literal["image_embeds"] data: torch.Tensor """Shape: `(batch_size, image_feature_size, hidden_size)` `hidden_size` must match the hidden size of language model backbone. """ PaliGemmaImageInputs = Union[PaliGemmaImagePixelInputs, PaliGemmaImageEmbeddingInputs] def get_max_paligemma_image_tokens(ctx: InputContext): hf_config = ctx.get_hf_config(PaliGemmaConfig) vision_config = hf_config.vision_config return get_max_siglip_image_tokens(vision_config) def dummy_data_for_paligemma(ctx: InputContext, seq_len: int, mm_counts: Mapping[str, int]): hf_config = ctx.get_hf_config(PaliGemmaConfig) vision_config = hf_config.vision_config num_images = mm_counts["image"] seq_data = dummy_seq_data_for_siglip( vision_config, seq_len, num_images, image_token_id=hf_config.image_token_index, ) mm_data = dummy_image_for_siglip(vision_config, num_images) return seq_data, mm_data def input_processor_for_paligemma(ctx: InputContext, llm_inputs: LLMInputs): """ The correct prompt format needs to be: '' * image_feature_size + '' + prompt + '\n' See https://github.com/huggingface/transformers/blob/25245ec26dc29bcf6102e1b4ddd0dfd02e720cf5/src/transformers/models/paligemma/processing_paligemma.py#L55 """ # noqa multi_modal_data = llm_inputs.get("multi_modal_data") if multi_modal_data is None or "image" not in multi_modal_data: return llm_inputs model_config = ctx.model_config hf_config = ctx.get_hf_config(PaliGemmaConfig) tokenizer = cached_get_tokenizer(model_config.tokenizer) image_feature_size = hf_config.text_config.num_image_tokens image_token_str = tokenizer.decode(hf_config.image_token_index) bos_token = tokenizer.decode(hf_config.bos_token_id) image_token_str_pad = image_token_str * image_feature_size image_token_ids_pad = [hf_config.image_token_index] * image_feature_size orig_prompt = llm_inputs.get("prompt") orig_prompt_ids = llm_inputs.get("prompt_token_ids") if orig_prompt is not None and image_token_str in orig_prompt: logger.warning( f"The image token '{image_token_str}' was detected in the prompt " "and will be removed. Please follow the proper prompt format" " documented on HuggingFace.") orig_prompt = orig_prompt.replace(image_token_str, "") orig_prompt_ids.remove(hf_config.image_token_index) new_prompt = f"{image_token_str_pad}{bos_token}{orig_prompt}\n" new_token_ids = image_token_ids_pad + orig_prompt_ids + [108] #newline # NOTE: Create a defensive copy of the original inputs return LLMInputs(prompt_token_ids=new_token_ids, prompt=new_prompt, multi_modal_data=multi_modal_data) class PaliGemmaMultiModalProjector(nn.Module): def __init__(self, vision_hidden_size: int, projection_dim: int): super().__init__() self.linear = nn.Linear(vision_hidden_size, projection_dim, bias=True) def forward(self, image_features: torch.Tensor) -> torch.Tensor: hidden_states = self.linear(image_features) return hidden_states @MULTIMODAL_REGISTRY.register_image_input_mapper() @MULTIMODAL_REGISTRY.register_max_image_tokens(get_max_paligemma_image_tokens) @INPUT_REGISTRY.register_dummy_data(dummy_data_for_paligemma) @INPUT_REGISTRY.register_input_processor(input_processor_for_paligemma) class PaliGemmaForConditionalGeneration(nn.Module, SupportsMultiModal): def __init__(self, config: PaliGemmaConfig, multimodal_config: MultiModalConfig, cache_config: Optional[CacheConfig] = None, quant_config: Optional[QuantizationConfig] = None) -> None: super().__init__() self.config = config self.multimodal_config = multimodal_config # TODO: Port over SiglipVisionModel & TP self.vision_tower = SiglipVisionModel(config.vision_config) self.multi_modal_projector = PaliGemmaMultiModalProjector( vision_hidden_size=config.vision_config.hidden_size, projection_dim=config.vision_config.projection_dim) self.quant_config = quant_config self.language_model = GemmaModel(config.text_config, cache_config, quant_config) self.unpadded_vocab_size = config.text_config.vocab_size logit_scale = getattr(config, "logit_scale", 1.0) self.logits_processor = LogitsProcessor(self.unpadded_vocab_size, config.vocab_size, logit_scale) self.sampler = Sampler() def _validate_pixel_values(self, data: torch.Tensor) -> torch.Tensor: h = w = self.config.vision_config.image_size expected_dims = (3, h, w) actual_dims = tuple(data.shape[1:]) if actual_dims != expected_dims: expected_expr = ("batch_size", *map(str, expected_dims)) raise ValueError( f"The expected shape of pixel values is {expected_expr}. " f"You supplied {tuple(data.shape)}.") return data def _parse_and_validate_image_input( self, **kwargs: object) -> Optional[PaliGemmaImageInputs]: 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 pixel_values is not None: if not isinstance(pixel_values, torch.Tensor): raise ValueError("Incorrect type of pixel values. " f"Got type: {type(pixel_values)}") return PaliGemmaImagePixelInputs( type="pixel_values", data=self._validate_pixel_values(pixel_values), ) 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 PaliGemmaImageEmbeddingInputs( type="image_embeds", data=image_embeds, ) raise AssertionError("This line should be unreachable.") def _image_pixels_to_features( self, vision_tower: SiglipVisionModel, pixel_values: torch.Tensor, ) -> torch.Tensor: target_dtype = vision_tower.get_input_embeddings().weight.dtype image_features = vision_tower(pixel_values.to(dtype=target_dtype)) return image_features def _process_image_input( self, image_input: PaliGemmaImageInputs, ) -> torch.Tensor: if image_input["type"] == "image_embeds": return image_input["data"] assert self.vision_tower is not None pixel_values = image_input["data"] image_features = self._image_pixels_to_features( self.vision_tower, pixel_values, ) return self.multi_modal_projector(image_features) def forward(self, input_ids: torch.Tensor, positions: torch.Tensor, kv_caches: List[torch.Tensor], attn_metadata: AttentionMetadata, intermediate_tensors: Optional[IntermediateTensors] = None, **kwargs: object) -> SamplerOutput: parsed_image_input = self._parse_and_validate_image_input(**kwargs) if parsed_image_input is not None: vision_embeddings = self._process_image_input(parsed_image_input) # https://github.com/huggingface/transformers/blob/main/src/transformers/models/paligemma/modeling_paligemma.py#L294 # noqa vision_embeddings = vision_embeddings * (self.config.hidden_size** -0.5) inputs_embeds = self.language_model.get_input_embeddings(input_ids) inputs_embeds = merge_multimodal_embeddings( input_ids, inputs_embeds, vision_embeddings, self.config.image_token_index) input_ids = None else: inputs_embeds = None hidden_states = self.language_model(input_ids, positions, kv_caches, attn_metadata, None, inputs_embeds=inputs_embeds) return hidden_states # Copied from vllm/modeling/models/gemma.py def compute_logits( self, hidden_states: torch.Tensor, sampling_metadata: SamplingMetadata, ) -> Optional[torch.Tensor]: logits = self.logits_processor(self.language_model.embed_tokens, hidden_states, sampling_metadata) return logits # Copied from vllm/modeling/models/gemma.py def sample( self, logits: torch.Tensor, sampling_metadata: SamplingMetadata, ) -> Optional[SamplerOutput]: next_tokens = self.sampler(logits, sampling_metadata) return next_tokens # Adapted from vllm/modeling/models/gemma.py def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]): stacked_params_mapping = [ # (param_name, shard_name, shard_id) ("qkv_proj", "q_proj", "q"), ("qkv_proj", "k_proj", "k"), ("qkv_proj", "v_proj", "v"), ("gate_up_proj", "gate_proj", 0), ("gate_up_proj", "up_proj", 1), ] params_dict = dict(self.named_parameters()) loaded_params = set() for name, loaded_weight in weights: for key_to_modify, new_key in _KEYS_TO_MODIFY_MAPPING.items(): if key_to_modify in name: name = name.replace(key_to_modify, new_key) use_default_weight_loading = False if "vision" in name: if self.vision_tower is not None: # We only do sharding for language model and # not vision model for now. use_default_weight_loading = True else: for (param_name, shard_name, shard_id) in stacked_params_mapping: if shard_name not in name: continue name = name.replace(shard_name, param_name) # Skip loading extra bias for GPTQ models. if name.endswith(".bias") and name not in params_dict: continue param = params_dict[name] weight_loader = param.weight_loader weight_loader(param, loaded_weight, shard_id) break else: # lm_head is not used in vllm as it is tied with # embed_token. To prevent errors, skip loading # lm_head.weight. if "lm_head.weight" in name: continue # Skip loading extra bias for GPTQ models. if name.endswith(".bias") and name not in params_dict: continue use_default_weight_loading = True if use_default_weight_loading: param = params_dict[name] weight_loader = getattr(param, "weight_loader", default_weight_loader) weight_loader(param, loaded_weight) loaded_params.add(name) unloaded_params = params_dict.keys() - loaded_params if unloaded_params: logger.warning( "Some weights are not initialized from checkpoints: " f"{unloaded_params}")