Created all the logic server side (with image download on the fly too).

This commit is contained in:
Nicolas Patry 2024-04-09 11:26:30 +00:00
parent 215030ac88
commit 2283562bfc
9 changed files with 818 additions and 49 deletions

546
Cargo.lock generated
View File

@ -40,6 +40,12 @@ dependencies = [
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version = "0.3.7"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "d66dc143e6b11c1eddc06d5c423cfc97062865baf299914ab64caa38182078fe"
[[package]]
name = "simd_helpers"
version = "0.1.0"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "95890f873bec569a0362c235787f3aca6e1e887302ba4840839bcc6459c42da6"
dependencies = [
"quote",
]
[[package]]
name = "sketches-ddsketch"
version = "0.2.2"
@ -2817,6 +3234,9 @@ name = "spin"
version = "0.9.8"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "6980e8d7511241f8acf4aebddbb1ff938df5eebe98691418c4468d0b72a96a67"
dependencies = [
"lock_api",
]
[[package]]
name = "spm_precompiled"
@ -2933,6 +3353,19 @@ dependencies = [
"libc",
]
[[package]]
name = "system-deps"
version = "6.2.2"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "a3e535eb8dded36d55ec13eddacd30dec501792ff23a0b1682c38601b8cf2349"
dependencies = [
"cfg-expr",
"heck 0.5.0",
"pkg-config",
"toml",
"version-compare",
]
[[package]]
name = "tabled"
version = "0.14.0"
@ -2957,6 +3390,12 @@ dependencies = [
"syn 1.0.109",
]
[[package]]
name = "target-lexicon"
version = "0.12.14"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "e1fc403891a21bcfb7c37834ba66a547a8f402146eba7265b5a6d88059c9ff2f"
[[package]]
name = "tempfile"
version = "3.10.1"
@ -3029,10 +3468,12 @@ dependencies = [
"async-stream",
"axum",
"axum-tracing-opentelemetry",
"base64 0.22.0",
"clap",
"futures",
"futures-util",
"hf-hub",
"image",
"init-tracing-opentelemetry",
"jsonschema",
"metrics",
@ -3092,6 +3533,17 @@ dependencies = [
"once_cell",
]
[[package]]
name = "tiff"
version = "0.9.1"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "ba1310fcea54c6a9a4fd1aad794ecc02c31682f6bfbecdf460bf19533eed1e3e"
dependencies = [
"flate2",
"jpeg-decoder",
"weezl",
]
[[package]]
name = "time"
version = "0.3.34"
@ -3295,6 +3747,40 @@ dependencies = [
"tracing",
]
[[package]]
name = "toml"
version = "0.8.12"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "e9dd1545e8208b4a5af1aa9bbd0b4cf7e9ea08fabc5d0a5c67fcaafa17433aa3"
dependencies = [
"serde",
"serde_spanned",
"toml_datetime",
"toml_edit",
]
[[package]]
name = "toml_datetime"
version = "0.6.5"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "3550f4e9685620ac18a50ed434eb3aec30db8ba93b0287467bca5826ea25baf1"
dependencies = [
"serde",
]
[[package]]
name = "toml_edit"
version = "0.22.9"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "8e40bb779c5187258fd7aad0eb68cb8706a0a81fa712fbea808ab43c4b8374c4"
dependencies = [
"indexmap 2.2.6",
"serde",
"serde_spanned",
"toml_datetime",
"winnow",
]
[[package]]
name = "tonic"
version = "0.9.2"
@ -3699,6 +4185,17 @@ version = "1.8.0"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "a183cf7feeba97b4dd1c0d46788634f6221d87fa961b305bed08c851829efcc0"
[[package]]
name = "v_frame"
version = "0.3.8"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "d6f32aaa24bacd11e488aa9ba66369c7cd514885742c9fe08cfe85884db3e92b"
dependencies = [
"aligned-vec",
"num-traits",
"wasm-bindgen",
]
[[package]]
name = "valuable"
version = "0.1.0"
@ -3727,6 +4224,12 @@ dependencies = [
"time",
]
[[package]]
name = "version-compare"
version = "0.2.0"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "852e951cb7832cb45cb1169900d19760cfa39b82bc0ea9c0e5a14ae88411c98b"
[[package]]
name = "version_check"
version = "0.9.4"
@ -3853,6 +4356,12 @@ dependencies = [
"rustls-pki-types",
]
[[package]]
name = "weezl"
version = "0.1.8"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "53a85b86a771b1c87058196170769dd264f66c0782acf1ae6cc51bfd64b39082"
[[package]]
name = "which"
version = "4.4.2"
@ -4113,6 +4622,15 @@ version = "0.52.4"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "32b752e52a2da0ddfbdbcc6fceadfeede4c939ed16d13e648833a61dfb611ed8"
[[package]]
name = "winnow"
version = "0.6.5"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "dffa400e67ed5a4dd237983829e66475f0a4a26938c4b04c21baede6262215b8"
dependencies = [
"memchr",
]
[[package]]
name = "winreg"
version = "0.50.0"
@ -4160,3 +4678,27 @@ dependencies = [
"crossbeam-utils",
"flate2",
]
[[package]]
name = "zune-core"
version = "0.4.12"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "3f423a2c17029964870cfaabb1f13dfab7d092a62a29a89264f4d36990ca414a"
[[package]]
name = "zune-inflate"
version = "0.2.54"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "73ab332fe2f6680068f3582b16a24f90ad7096d5d39b974d1c0aff0125116f02"
dependencies = [
"simd-adler32",
]
[[package]]
name = "zune-jpeg"
version = "0.4.11"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "ec866b44a2a1fd6133d363f073ca1b179f438f99e7e5bfb1e33f7181facfe448"
dependencies = [
"zune-core",
]

View File

@ -44,10 +44,12 @@ utoipa = { version = "3.5.0", features = ["axum_extras"] }
utoipa-swagger-ui = { version = "3.1.5", features = ["axum"] }
ngrok = { version = "0.13.1", features = ["axum"], optional = true }
init-tracing-opentelemetry = { version = "0.14.1", features = ["opentelemetry-otlp"] }
minijinja = { git = "https://github.com/mitsuhiko/minijinja.git", branch = "main", commit = "5cd4efb" }
minijinja = { git = "https://github.com/mitsuhiko/minijinja.git", rev = "5cd4efb" }
futures-util = "0.3.30"
regex = "1.10.3"
once_cell = "1.19.0"
image = "0.25.1"
base64 = "0.22.0"
[build-dependencies]
vergen = { version = "8.2.5", features = ["build", "git", "gitcl"] }

View File

@ -112,8 +112,11 @@ impl Client {
// Create requests
while n_tokens < max_prefill_tokens {
let truncate = min(max_input_length, max_prefill_tokens - n_tokens);
let mut inputs = "_test ".to_string().repeat(max_input_length as usize);
let mut inputs = String::new();
inputs.push_str("![](data:image/jpeg;base64,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");
inputs.push_str(&"_test ".to_string().repeat(max_input_length as usize));
requests.push(Request {
id: 0,
// We truncate the input on the server side to be sure that it has the correct size

99
router/src/config.rs Normal file
View File

@ -0,0 +1,99 @@
use serde::{Deserialize, Serialize};
#[derive(Clone, Debug, Serialize, Deserialize)]
#[serde(tag = "model_type")]
#[serde(rename_all = "snake_case")]
pub struct LlavaNext {
text_config: Box<Config>,
vision_config: VisionConfig,
image_grid_pinpoints: Vec<(usize, usize)>,
}
fn get_anyres_image_grid_shape(
height: usize,
width: usize,
grid_pinpoints: &[(usize, usize)],
patch_size: usize,
) -> (usize, usize) {
let (height, width) = select_best_resolution(height, width, grid_pinpoints);
(height / patch_size, width / patch_size)
}
/// Selects the best resolution from a list of possible resolutions based on the original size.
/// This is done by calculating the effective and wasted resolution for each possible resolution.
/// The best fit resolution is the one that maximizes the effective resolution and minimizes the wasted resolution.
fn select_best_resolution(
original_height: usize,
original_width: usize,
possible_resolutions: &[(usize, usize)],
) -> (usize, usize) {
let mut best_fit = None;
let mut max_effective_resolution = 0;
let mut min_wasted_resolution = f32::NEG_INFINITY;
for (height, width) in possible_resolutions {
// let scale = std::cmp::min(width / original_width, height / original_height);
let downscaled_width = width / original_width * original_width;
let downscaled_height = height / original_height * original_height;
let effective_resolution = std::cmp::min(
downscaled_width * downscaled_height,
original_width * original_height,
);
let wasted_resolution = (width * height) - effective_resolution;
if effective_resolution > max_effective_resolution
|| (effective_resolution == max_effective_resolution
&& (wasted_resolution as f32) < min_wasted_resolution)
{
max_effective_resolution = effective_resolution;
min_wasted_resolution = wasted_resolution as f32;
best_fit = Some((*height, *width));
}
}
best_fit.expect("Expect a resolution to exist")
}
impl LlavaNext {
pub fn get_number_of_features(&self, height: usize, width: usize) -> usize {
let image_size = self.vision_config.image_size;
let patch_size = self.vision_config.patch_size;
assert!(image_size % patch_size == 0);
let npatches = image_size / patch_size;
let (num_patch_height, num_patch_width) =
get_anyres_image_grid_shape(height, width, &self.image_grid_pinpoints, image_size);
// Ceil
let height_of_patch = (height * npatches + width - 1) / width;
let unpadded_features = npatches * height_of_patch * num_patch_height * num_patch_width;
// They are only added after width
let newline_features = height_of_patch * num_patch_width;
// The base patch covers the entire image
let base_features = npatches.pow(2);
unpadded_features + newline_features + base_features
}
}
#[derive(Clone, Debug, Serialize, Deserialize)]
#[serde(tag = "model_type")]
#[serde(rename_all = "snake_case")]
pub struct ClipVisionModel {
image_size: usize,
patch_size: usize,
}
#[derive(Clone, Debug, Serialize, Deserialize)]
#[serde(tag = "model_type")]
#[serde(rename_all = "snake_case")]
pub enum Config {
LlavaNext(LlavaNext),
ClipVisionModel(ClipVisionModel),
Mistral,
Idefics,
}
#[derive(Clone, Debug, Serialize, Deserialize)]
#[serde(rename_all = "snake_case")]
pub struct VisionConfig {
image_size: usize,
patch_size: usize,
}

View File

@ -1,3 +1,4 @@
pub mod config;
mod health;
/// Text Generation Inference Webserver
mod infer;

View File

@ -13,6 +13,7 @@ use std::io::BufReader;
use std::net::{IpAddr, Ipv4Addr, SocketAddr};
use std::path::Path;
use text_generation_client::{ClientError, ShardedClient};
use text_generation_router::config::Config;
use text_generation_router::{server, HubModelInfo, HubTokenizerConfig};
use thiserror::Error;
use tokenizers::Tokenizer;
@ -191,15 +192,19 @@ async fn main() -> Result<(), RouterError> {
};
// Load tokenizer and model info
let (tokenizer, model_info) = if local_model {
let (tokenizer, model_info, config) = if local_model {
let tokenizer = Tokenizer::from_file(local_path.join("tokenizer.json")).ok();
let model_info = HubModelInfo {
model_id: tokenizer_name.to_string(),
sha: None,
pipeline_tag: None,
};
let config: Option<Config> = std::fs::read_to_string(local_path.join("config.json"))
.ok()
.as_ref()
.and_then(|c| serde_json::from_str(c).ok());
(tokenizer, model_info)
(tokenizer, model_info, config)
} else if let Some(api) = api.clone() {
let api_repo = api.repo(Repo::with_revision(
tokenizer_name.to_string(),
@ -212,6 +217,18 @@ async fn main() -> Result<(), RouterError> {
Err(_) => get_base_tokenizer(&api, &api_repo).await,
};
let config: Option<Config> = api_repo.get("config.json").await.ok().and_then(|filename| {
tracing::info!("Config filename {filename:?}");
std::fs::read_to_string(filename)
.ok()
.as_ref()
.and_then(|c| {
let config: Result<Config, _> = serde_json::from_str(c);
tracing::info!("Config parse {config:?}");
config.ok()
})
});
let model_info = get_model_info(&api_repo).await.unwrap_or_else(|| {
tracing::warn!("Could not retrieve model info from the Hugging Face hub.");
HubModelInfo {
@ -221,7 +238,7 @@ async fn main() -> Result<(), RouterError> {
}
});
(tokenizer, model_info)
(tokenizer, model_info, config)
} else {
// No API and no local model
return Err(RouterError::ArgumentValidation(
@ -229,6 +246,8 @@ async fn main() -> Result<(), RouterError> {
));
};
tracing::info!("Using config {config:?}");
// Load tokenizer config if found locally, or check if we can get it from the API if needed
let tokenizer_config = if let Some(path) = tokenizer_config_path {
tracing::info!("Using local tokenizer config from user specified path");
@ -363,6 +382,7 @@ async fn main() -> Result<(), RouterError> {
max_batch_size,
sharded_client,
tokenizer,
config,
validation_workers,
addr,
cors_allow_origin,

View File

@ -1,3 +1,4 @@
use crate::config::Config;
/// HTTP Server logic
use crate::health::Health;
use crate::infer::{InferError, InferResponse, InferStreamResponse};
@ -1155,6 +1156,7 @@ pub async fn run(
max_batch_size: Option<usize>,
client: ShardedClient,
tokenizer: Option<Tokenizer>,
config: Option<Config>,
validation_workers: usize,
addr: SocketAddr,
allow_origin: Option<AllowOrigin>,
@ -1237,6 +1239,7 @@ pub async fn run(
let validation = Validation::new(
validation_workers,
tokenizer,
config,
max_best_of,
max_stop_sequences,
max_top_n_tokens,

View File

@ -1,15 +1,19 @@
use crate::config::Config;
/// Payload validation logic
use crate::validation::ValidationError::{BestOfSampling, BestOfSeed, EmptyInput};
use crate::{GenerateParameters, GenerateRequest, GrammarType};
use jsonschema::{Draft, JSONSchema};
use rand::{thread_rng, Rng};
use serde_json::Value;
use std::io::Cursor;
use text_generation_client::{
GrammarType as ProtoGrammarType, NextTokenChooserParameters, StoppingCriteriaParameters,
};
use thiserror::Error;
use tokenizers::tokenizer::Tokenizer;
use tokenizers::TruncationDirection;
// use tokenizers::TruncationDirection;
use base64::{engine::general_purpose::STANDARD, Engine};
use image::{io::Reader as ImageReader, ImageFormat};
use tokio::sync::mpsc;
use tokio::sync::oneshot;
use tracing::{instrument, Span};
@ -34,6 +38,7 @@ impl Validation {
pub(crate) fn new(
workers: usize,
tokenizer: Option<Tokenizer>,
config: Option<Config>,
max_best_of: usize,
max_stop_sequences: usize,
max_top_n_tokens: u32,
@ -50,12 +55,13 @@ impl Validation {
// Create workers
for _ in 0..workers {
let tokenizer_clone = tokenizer.clone();
let config_clone = config.clone();
let (tokenizer_sender, tokenizer_receiver) = mpsc::unbounded_channel();
senders.push(tokenizer_sender);
// Spawn worker
tokio::task::spawn_blocking(move || {
tokenizer_worker(tokenizer_clone, tokenizer_receiver)
tokenizer_worker(tokenizer_clone, config_clone, tokenizer_receiver)
});
}
@ -408,54 +414,137 @@ async fn round_robin_task(
}
/// Start tokenization workers
fn tokenizer_worker(tokenizer: Tokenizer, mut receiver: mpsc::UnboundedReceiver<TokenizerRequest>) {
fn tokenizer_worker(
tokenizer: Tokenizer,
config: Option<Config>,
mut receiver: mpsc::UnboundedReceiver<TokenizerRequest>,
) {
// Loop over requests
let is_multimodal = {
let vocab = tokenizer.get_vocab(true);
vocab.contains_key("<image>")
};
while let Some(((inputs, truncate), response_tx, parent_span)) = receiver.blocking_recv() {
parent_span.in_scope(|| {
response_tx
.send(prepare_input(inputs, truncate, &tokenizer, is_multimodal))
.send(prepare_input(inputs, truncate, &tokenizer, &config))
.unwrap_or(())
})
}
}
fn format_from_mimetype(mimetype: &str) -> Option<ImageFormat> {
match mimetype {
"image/png" => Some(ImageFormat::Png),
"image/jpeg" => Some(ImageFormat::Jpeg),
"image/jpg" => Some(ImageFormat::Jpeg),
"image/gif" => Some(ImageFormat::Gif),
"image/webp" => Some(ImageFormat::WebP),
"image/tiff" => Some(ImageFormat::Tiff),
// "image/pnm"=>Some(ImageFormat::Pnm),
// "image/tga"=>Some(ImageFormat::Tga),
// "image/dds"=>Some(ImageFormat::Dds),
// "image/bmp"=>Some(ImageFormat::Bmp),
// "image/ico"=>Some(ImageFormat::Ico),
// "image/x-exr"=>Some(ImageFormat::OpenExr),
_ => None,
}
}
fn format_to_mimetype(format: ImageFormat) -> String {
match format {
ImageFormat::Png => "image/png",
ImageFormat::Jpeg => "image/jpeg",
ImageFormat::Gif => "image/gif",
ImageFormat::WebP => "image/webp",
ImageFormat::Tiff => "image/tiff",
_ => "application/octet-stream",
}
.to_string()
}
fn fetch_image(input: &str) -> Result<(String, usize, usize), ValidationError> {
if input.starts_with("![](http://") || input.starts_with("![](https://") {
let url = &input["![](".len()..input.len() - 1];
let data = reqwest::blocking::get(url)?.bytes()?;
let format = image::guess_format(&data)?;
// TODO Remove this clone
let img = ImageReader::with_format(Cursor::new(data.clone()), format).decode()?;
let height: usize = img.height().try_into()?;
let width: usize = img.width().try_into()?;
let mimetype = format_to_mimetype(format);
let encoded = STANDARD.encode(data);
let data_uri = format!("![](data:{mimetype};base64,{encoded})");
Ok((data_uri, height, width))
} else if input.starts_with("![](data:") {
// Remove ![](....)
let content = &input["![](data:".len()..input.len() - 1];
let tokens: Vec<_> = content.split(';').collect();
if tokens.len() != 2 {
return Err(ValidationError::InvalidImageContent(content.to_string()));
}
let mimetype = tokens[0];
let content = tokens[1];
if !content.starts_with("base64,") {
return Err(ValidationError::InvalidImageContent(content.to_string()));
}
let data = STANDARD.decode(content["base64,".len()..].as_bytes())?;
let img = if let Some(format) = format_from_mimetype(mimetype) {
ImageReader::with_format(Cursor::new(data), format).decode()?
} else {
ImageReader::new(Cursor::new(data))
.with_guessed_format()
.map_err(|_io_error| ValidationError::InvalidImageContent(content.to_string()))?
.decode()?
};
let height: usize = img.height().try_into()?;
let width: usize = img.width().try_into()?;
Ok((input.to_string(), height, width))
} else {
Err(ValidationError::InvalidImageContent(input.to_string()))
}
}
/// Get input length and optionally truncate it
fn prepare_input(
mut inputs: String,
truncate: Option<usize>,
_truncate: Option<usize>,
tokenizer: &Tokenizer,
is_multimodal: bool,
config: &Option<Config>,
) -> Result<(tokenizers::Encoding, String), ValidationError> {
let simplified_query = if is_multimodal {
static RE: Lazy<Regex> = Lazy::new(|| Regex::new(r"!\[\]\([^\)]*\)").unwrap());
// HACK: Llava uses arbitrary number of tokens (between 576 and ~576 * 5).
// Idefics uses a sequence encoder which doesn't take as much space in the KV cache, but
// there is still some encoder values.
// This hacks just forces more "allocation" for the given
RE.replace_all(&inputs, "<image>".repeat(576)).into()
} else {
inputs.clone()
let tokenizer_query = match config {
Some(Config::LlavaNext(config)) => {
let mut modified_inputs = String::with_capacity(inputs.len());
let mut tokenizer_query = String::with_capacity(inputs.len());
let mut start = 0;
for chunk in RE.find_iter(&inputs) {
let chunk_start = chunk.start();
let chunk_end = chunk.end();
if chunk_start != start {
modified_inputs.push_str(&inputs[start..chunk_start]);
tokenizer_query.push_str(&inputs[start..chunk_start]);
}
let (image_uri, height, width) = fetch_image(&inputs[chunk_start..chunk_end])?;
let slots = config.get_number_of_features(height, width);
tokenizer_query.push_str(&"<image>".repeat(slots));
modified_inputs.push_str(&image_uri);
start = chunk_end;
}
if start != inputs.len() - 1 {
modified_inputs.push_str(&inputs[start..]);
tokenizer_query.push_str(&inputs[start..]);
}
inputs = modified_inputs;
tokenizer_query
}
Some(Config::Idefics) => RE.replace_all(&inputs, "<image>").into(),
_ => inputs.clone(),
};
// Get the number of tokens in the input
let mut encoding = tokenizer
.encode(simplified_query, true)
.map_err(|err| ValidationError::Tokenizer(err.to_string()))?;
// Optionally truncate
if let Some(truncate) = truncate {
// XXX: Critical to keep the multimodal check otherwise this modifies the original string
// Which we really don't want.
if truncate < encoding.len() && !is_multimodal {
encoding.truncate(truncate, 0, TruncationDirection::Left);
inputs = tokenizer
.decode(encoding.get_ids(), false)
// Get the number of tokens in the input
let encoding = tokenizer
.encode(tokenizer_query, true)
.map_err(|err| ValidationError::Tokenizer(err.to_string()))?;
}
}
Ok((encoding, inputs))
}
@ -529,6 +618,16 @@ pub enum ValidationError {
Grammar,
#[error("grammar is not valid: {0}")]
InvalidGrammar(String),
#[error("base64 encoding is invalid: {0}")]
InvalidBase64(#[from] base64::DecodeError),
#[error("invalid image: {0}")]
InvalidImage(#[from] image::ImageError),
#[error("invalid integer: {0}")]
InvalidInt(#[from] core::num::TryFromIntError),
#[error("invalid image content: {0}")]
InvalidImageContent(String),
#[error("Could not fetch image: {0}")]
FailedFetchImage(#[from] reqwest::Error),
}
#[cfg(test)]

View File

@ -90,9 +90,6 @@ def get_number_of_features(height: int, width: int, config) -> int:
# The base patch covers the entire image
base_features = npatches**2
return unpadded_features + newline_features + base_features
if height == 640 and width == 640:
return 2928
return 2634
def load_data_uri(image_uri: str) -> Image.Image:
@ -138,13 +135,16 @@ class VlmCausalLMBatch(FlashMistralBatch):
full_text += chunk["content"]
elif chunk["type"] == "image":
image = chunk["content"]
if image.startswith("https://") or image.startswith("http://"):
image = processor.image_processor.fetch_images(image)
elif image.startswith("data:"):
# Should never receive URLs anymore, processing should be done
# On the rust layer.
# This avoid making n queries per TP
# if image.startswith("https://") or image.startswith("http://"):
# image = processor.image_processor.fetch_images(image)
if image.startswith("data:"):
image = load_data_uri(image)
else:
raise RuntimeError(
"Cannot process input image not starting with http(s):// nor data:"
"Cannot process input image not starting with data:"
)
image_input = processor.image_processor(image, return_tensors="pt")
height, width = image_input["image_sizes"][0]