[Model] Add PaliGemma

docs/source/models/supported_models.rst

@@ -186,6 +186,10 @@ Vision Language Models
     - LLaVA-NeXT
     - :code:`llava-hf/llava-v1.6-mistral-7b-hf`, :code:`llava-hf/llava-v1.6-vicuna-7b-hf`, etc.
     -
+  * - :code:`PaliGemmaForConditionalGeneration`
+    - PaliGemma
+    - :code:`google/paligemma-3b-pt-224`, :code:`google/paligemma-3b-mix-224`, etc.
+    - 
   * - :code:`Phi3VForCausalLM`
     - Phi-3-Vision
     - :code:`microsoft/Phi-3-vision-128k-instruct`, etc.

examples/paligemma_example.py

@@ -0,0 +1,52 @@
+import os
+import subprocess
+
+from PIL import Image
+
+from vllm import LLM
+
+# The assets are located at `s3://air-example-data-2/vllm_opensource_llava/`.
+# You can use `.buildkite/download-images.sh` to download them
+
+
+def run_paligemma():
+    llm = LLM(model="google/paligemma-3b-mix-224")
+
+    prompt = "caption es"
+
+    image = Image.open("images/stop_sign.jpg")
+
+    outputs = llm.generate({
+        "prompt": prompt,
+        "multi_modal_data": {
+            "image": image
+        },
+    })
+
+    for o in outputs:
+        generated_text = o.outputs[0].text
+        print(generated_text)
+
+
+def main():
+    run_paligemma()
+
+
+if __name__ == "__main__":
+    # Download from s3
+    s3_bucket_path = "s3://air-example-data-2/vllm_opensource_llava/"
+    local_directory = "images"
+
+    # Make sure the local directory exists or create it
+    os.makedirs(local_directory, exist_ok=True)
+
+    # Use AWS CLI to sync the directory, assume anonymous access
+    subprocess.check_call([
+        "aws",
+        "s3",
+        "sync",
+        s3_bucket_path,
+        local_directory,
+        "--no-sign-request",
+    ])
+    main()

tests/models/test_paligemma.py

@@ -0,0 +1,147 @@
+from typing import List, Optional, Tuple, Type
+
+import pytest
+from transformers import AutoTokenizer
+
+from vllm.multimodal.utils import rescale_image_size
+from vllm.sequence import SampleLogprobs
+
+from ..conftest import IMAGE_ASSETS, HfRunner, VllmRunner, _ImageAssets
+from .utils import check_logprobs_close
+
+pytestmark = pytest.mark.vlm
+
+HF_IMAGE_PROMPTS = IMAGE_ASSETS.prompts({
+    "stop_sign": "caption es",
+    "cherry_blossom": "What is in the picture?",
+    "boardwalk": "What is in the picture?",
+})
+
+IMAGE_TOKEN_ID = 257152
+
+models = ["google/paligemma-3b-mix-224"]
+
+
+def vllm_to_hf_output(vllm_output: Tuple[List[int], str,
+                                         Optional[SampleLogprobs]],
+                      model: str):
+    """Sanitize vllm output to be comparable with hf output."""
+    output_ids, output_str, out_logprobs = vllm_output
+
+    tokenizer = AutoTokenizer.from_pretrained(model)
+    eos_token_id = tokenizer.eos_token_id
+
+    hf_output_ids = [
+        token_id for idx, token_id in enumerate(output_ids)
+        if token_id != IMAGE_TOKEN_ID or output_ids[idx - 1] != IMAGE_TOKEN_ID
+    ]
+
+    hf_output_str = output_str
+
+    if hf_output_ids[-1] == eos_token_id:
+        hf_output_str = hf_output_str + tokenizer.decode(eos_token_id)
+
+    return hf_output_ids, hf_output_str, out_logprobs
+
+
+def run_test(
+    hf_runner: Type[HfRunner],
+    vllm_runner: Type[VllmRunner],
+    image_assets: _ImageAssets,
+    model: str,
+    *,
+    size_factors: List[float],
+    dtype: str,
+    max_tokens: int,
+    num_logprobs: int,
+    tensor_parallel_size: int,
+    distributed_executor_backend: Optional[str] = None,
+):
+    """Inference result should be the same between hf and vllm.
+
+    All the image fixtures for the test is under tests/images.
+    For huggingface runner, we provide the PIL images as input.
+    For vllm runner, we provide MultiModalDataDict objects 
+    and corresponding vision language config as input.
+    Note, the text input is also adjusted to abide by vllm contract.
+    The text output is sanitized to be able to compare with hf.
+    """
+    images = [asset.pil_image for asset in image_assets]
+
+    inputs_per_image = [(
+        [prompt for _ in size_factors],
+        [rescale_image_size(image, factor) for factor in size_factors],
+    ) for image, prompt in zip(images, HF_IMAGE_PROMPTS)]
+
+    # NOTE: take care of the order. run vLLM first, and then run HF.
+    # vLLM needs a fresh new process without cuda initialization.
+    # if we run HF first, the cuda initialization will be done and it
+    # will hurt multiprocessing backend with fork method (the default method).
+
+    # max_model_len should be greater than image_feature_size
+    with vllm_runner(model,
+                     dtype=dtype,
+                     tensor_parallel_size=tensor_parallel_size,
+                     distributed_executor_backend=distributed_executor_backend,
+                     enforce_eager=True) as vllm_model:
+        vllm_outputs_per_image = [
+            vllm_model.generate_greedy_logprobs(prompts,
+                                                max_tokens,
+                                                num_logprobs=num_logprobs,
+                                                images=images)
+            for prompts, images in inputs_per_image
+        ]
+
+    with hf_runner(model, dtype=dtype, is_vision_model=True) as hf_model:
+        hf_outputs_per_image = [
+            hf_model.generate_greedy_logprobs_limit(prompts,
+                                                    max_tokens,
+                                                    num_logprobs=num_logprobs,
+                                                    images=images)
+            for prompts, images in inputs_per_image
+        ]
+
+    for hf_outputs, vllm_outputs in zip(hf_outputs_per_image,
+                                        vllm_outputs_per_image):
+
+        check_logprobs_close(
+            outputs_0_lst=hf_outputs,
+            outputs_1_lst=[
+                vllm_to_hf_output(vllm_output, model)
+                for vllm_output in vllm_outputs
+            ],
+            name_0="hf",
+            name_1="vllm",
+        )
+
+
+@pytest.mark.parametrize("model", models)
+@pytest.mark.parametrize(
+    "size_factors",
+    [
+        # No image
+        [],
+        # Single-scale
+        [1.0],
+        # Single-scale, batched
+        [1.0, 1.0, 1.0],
+        # Multi-scale
+        [0.25, 0.5, 1.0],
+    ],
+)
+@pytest.mark.parametrize("dtype", ["float"])
+@pytest.mark.parametrize("max_tokens", [128])
+@pytest.mark.parametrize("num_logprobs", [5])
+def test_models(hf_runner, vllm_runner, image_assets, model, size_factors,
+                dtype: str, max_tokens: int, num_logprobs: int) -> None:
+    run_test(
+        hf_runner,
+        vllm_runner,
+        image_assets,
+        model,
+        size_factors=size_factors,
+        dtype=dtype,
+        max_tokens=max_tokens,
+        num_logprobs=num_logprobs,
+        tensor_parallel_size=1,
+    )

vllm/model_executor/models/__init__.py

@@ -49,6 +49,8 @@
     "OlmoForCausalLM": ("olmo", "OlmoForCausalLM"),
     "OPTForCausalLM": ("opt", "OPTForCausalLM"),
     "OrionForCausalLM": ("orion", "OrionForCausalLM"),
+    "PaliGemmaForConditionalGeneration":
+    ("paligemma", "PaliGemmaForConditionalGeneration"),
     "PhiForCausalLM": ("phi", "PhiForCausalLM"),
     "Phi3ForCausalLM": ("llama", "LlamaForCausalLM"),
     "Phi3VForCausalLM": ("phi3v", "Phi3VForCausalLM"),

vllm/model_executor/models/gemma.py

@@ -268,16 +268,22 @@ def __init__(
         normalizer = self.config.hidden_size**0.5
         self.register_buffer("normalizer", torch.tensor(normalizer))
 
+    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.embed_tokens(input_ids)
+
     def forward(
         self,
         input_ids: torch.Tensor,
         positions: torch.Tensor,
         kv_caches: List[torch.Tensor],
         attn_metadata: AttentionMetadata,
+        inputs_embeds: Optional[torch.Tensor] = None,
     ) -> torch.Tensor:
-        hidden_states = self.embed_tokens(input_ids)
+        if inputs_embeds is not None:
+            hidden_states = inputs_embeds
+        else:
+            hidden_states = self.get_input_embeddings(input_ids)
         hidden_states *= self.normalizer
-
         residual = None
         for i in range(len(self.layers)):
             layer = self.layers[i]

vllm/model_executor/models/paligemma.py

@@ -0,0 +1,344 @@
+from typing import Iterable, List, Literal, Optional, Tuple, TypedDict
+
+import torch
+from PIL import Image
+from torch import nn
+from transformers import PaliGemmaConfig, SiglipVisionConfig, SiglipVisionModel
+
+from vllm.attention import AttentionMetadata
+from vllm.config import CacheConfig, MultiModalConfig
+from vllm.inputs import INPUT_REGISTRY, InputContext, LLMInputs
+from vllm.logger import init_logger
+from vllm.model_executor.layers.linear import ColumnParallelLinear
+from vllm.model_executor.layers.logits_processor import LogitsProcessor
+from vllm.model_executor.layers.quantization.base_config import (
+    QuantizationConfig)
+from vllm.model_executor.layers.sampler import Sampler
+from vllm.model_executor.model_loader.weight_utils import default_weight_loader
+from vllm.model_executor.models.gemma import GemmaModel
+from vllm.model_executor.sampling_metadata import SamplingMetadata
+from vllm.multimodal import MULTIMODAL_REGISTRY
+from vllm.multimodal.image import cached_get_tokenizer
+from vllm.sequence import SamplerOutput, SequenceData
+
+from .interfaces import SupportsVision
+from .utils import merge_vision_embeddings
+
+logger = init_logger(__name__)
+
+_KEYS_TO_MODIFY_MAPPING = {
+    "language_model.model": "language_model",
+}
+
+
+def get_max_paligemma_image_tokens(ctx: InputContext):
+    hf_config = ctx.get_hf_config(PaliGemmaConfig)
+    text_config = hf_config.text_config
+
+    return text_config.num_image_tokens
+
+
+def dummy_seq_data_for_paligemma(
+    hf_config: PaliGemmaConfig,
+    seq_len: int,
+    *,
+    image_token_id: int,
+    image_feature_size_override: Optional[int] = None,
+):
+    if image_feature_size_override is None:
+        image_feature_size = hf_config.text_config.num_image_tokens
+    else:
+        image_feature_size = image_feature_size_override
+
+    token_ids = [image_token_id] * image_feature_size
+    token_ids += [0] * (seq_len - image_feature_size)
+    return SequenceData(token_ids)
+
+
+def dummy_image_for_paligemma(
+    hf_config: SiglipVisionConfig,
+    *,
+    image_width_override: Optional[int] = None,
+    image_height_override: Optional[int] = None,
+):
+    width = height = hf_config.image_size
+    if image_width_override is not None:
+        width = image_width_override
+    if image_height_override is not None:
+        height = image_height_override
+
+    image = Image.new("RGB", (width, height), color=0)
+    return {"image": image}
+
+
+def dummy_data_for_paligemma(ctx: InputContext, seq_len: int):
+    hf_config = ctx.get_hf_config(PaliGemmaConfig)
+    vision_config = hf_config.vision_config
+
+    seq_data = dummy_seq_data_for_paligemma(
+        hf_config,
+        seq_len,
+        image_token_id=hf_config.image_token_index,
+    )
+
+    mm_data = dummy_image_for_paligemma(vision_config)
+    return seq_data, mm_data
+
+
+def input_processor_for_paligemma(ctx: InputContext, llm_inputs: LLMInputs):
+
+    """
+    The correct prompt format needs to be:
+    '<image>' * image_feature_size + '<bos>' + 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 image_token_str in orig_prompt:
+        logger.warning(
+            "The image token '%s' was detected in the prompt and "
+            "will be removed. Please follow the proper prompt format"
+            " documented on HuggingFace.", image_token_str)
+        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 = ColumnParallelLinear(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
+
+
+class PaliGemmaImagePixelInputs(TypedDict):
+    type: Literal["pixel_values"]
+    data: torch.Tensor
+    """Shape: (batch_size, num_channels, height, width)"""
+
+
+PaliGemmaImageInputs = PaliGemmaImagePixelInputs
+
+
+@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, SupportsVision):
+
+    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(ywang96): 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)
+
+        if pixel_values is None:
+            return 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),
+        )
+
+    def _image_pixels_to_features(self, vision_tower: SiglipVisionModel,
+                                  pixel_values: torch.Tensor) -> torch.Tensor:
+
+        image_outputs = vision_tower(pixel_values, output_hidden_states=True)
+
+        selected_image_features = image_outputs.last_hidden_state
+
+        return selected_image_features
+
+    def _process_image_pixels(
+            self, inputs: PaliGemmaImagePixelInputs) -> torch.Tensor:
+        assert self.vision_tower is not None
+
+        pixel_values = inputs["data"]
+
+        return self._image_pixels_to_features(self.vision_tower, pixel_values)
+
+    def _process_image_input(
+            self, image_input: PaliGemmaImageInputs) -> torch.Tensor:
+
+        assert self.vision_tower is not None
+        image_features = self._process_image_pixels(image_input)
+
+        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,
+                **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_vision_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,
+                                            inputs_embeds=inputs_embeds)
+
+        return hidden_states
+
+    # Copied from vllm/model_executor/models/gemma.py
+    def compute_logits(self, hidden_states: torch.Tensor,
+                       sampling_metadata: SamplingMetadata) -> torch.Tensor:
+        logits = self.logits_processor(self.language_model.embed_tokens,
+                                       hidden_states, sampling_metadata)
+        return logits
+
+    # Copied from vllm/model_executor/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/model_executor/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:
+            raise RuntimeError(
+                "Some weights are not initialized from checkpoints: "
+                f"{unloaded_params}")