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remove multimodal as this is completely dead code (#17750)

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This commit is contained in:
Simo Lin
2026-01-26 00:07:40 -08:00
committed by GitHub
parent 12f794e516
commit 6756cf18c6
25 changed files with 1 additions and 9188 deletions
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sgl-model-gateway/Cargo.toml
+1 -11
View File
@@ -40,7 +40,6 @@ serde_json = { version = "1.0", default-features = false, features = [
"std", "std",
"preserve_order", "preserve_order",
] } ] }
serde_bytes = "0.11"
bytes = "1.8.0" bytes = "1.8.0"
http-body = "1.0" http-body = "1.0"
rand = "0.9.2" rand = "0.9.2"
@@ -48,7 +47,6 @@ reqwest = { version = "0.12.8", features = ["stream", "blocking", "json", "rustl
futures-util = "0.3" futures-util = "0.3"
futures = "0.3" futures = "0.3"
dashmap = "6.1.0" dashmap = "6.1.0"
lru = "0.16.2"
blake3 = "1.5" blake3 = "1.5"
xxhash-rust = { version = "0.8", features = ["xxh3"] } xxhash-rust = { version = "0.8", features = ["xxh3"] }
bytemuck = { version = "1.21", features = ["derive"] } bytemuck = { version = "1.21", features = ["derive"] }
@@ -98,13 +96,10 @@ rmcp = { version = "0.8.3", features = ["client", "server",
"reqwest", "reqwest",
"auth"] } "auth"] }
serde_yaml = "0.9" serde_yaml = "0.9"
oracle = { version = "0.6.3", features = ["chrono"] }
subtle = "2.6" subtle = "2.6"
jsonwebtoken = { version = "9.3", default-features = false, features = ["use_pem"] } jsonwebtoken = { version = "9.3", default-features = false, features = ["use_pem"] }
num-traits = "0.2" num-traits = "0.2"
num-bigint = "0.4" num-bigint = "0.4"
image = { version = "0.25.4", default-features = false, features = ["png", "jpeg", "gif", "bmp", "ico", "tiff", "webp"] }
ndarray = "0.16"
base64 = "0.22" base64 = "0.22"
openai-harmony = { git = "https://github.com/openai/harmony", tag = "v0.0.4" } openai-harmony = { git = "https://github.com/openai/harmony", tag = "v0.0.4" }
openmetrics-parser = "0.4.4" openmetrics-parser = "0.4.4"
@@ -115,17 +110,12 @@ tonic = { version = "0.14.2", features = ["gzip", "transport"] }
prost = "0.14.1" prost = "0.14.1"
prost-types = "0.14.1" prost-types = "0.14.1"
tonic-prost = "0.14.2" tonic-prost = "0.14.2"
deadpool = { version = "0.12", features = ["managed", "rt_tokio_1"] }
backoff = { version = "0.4", features = ["tokio"] }
strum = { version = "0.26", features = ["derive"] }
bitflags = "2.10.0" bitflags = "2.10.0"
once_cell = "1.21.3" once_cell = "1.21.3"
tokio-postgres = { version = "0.7.15", features = ["runtime","with-chrono-0_4","with-serde_json-1","array-impls"] }
deadpool-postgres = "0.14.1"
# CRDT for Mesh state synchronization # CRDT for Mesh state synchronization
crdts = "7.3" crdts = "7.3"
redis = { version = "0.27.6", features = ["tokio-comp", "json", "connection-manager"] } redis = { version = "0.27.6", features = ["tokio-comp", "json", "connection-manager"] }
deadpool-redis = "0.18.0"
# wasm dependencies # wasm dependencies
sgl-model-gateway/src/lib.rs
-1
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@@ -7,7 +7,6 @@ pub mod grpc_client;
pub use smg_mcp as mcp; pub use smg_mcp as mcp;
pub mod mesh; pub mod mesh;
pub mod middleware; pub mod middleware;
pub mod multimodal;
pub mod observability; pub mod observability;
pub mod policies; pub mod policies;
pub use openai_protocol as protocols; pub use openai_protocol as protocols;
sgl-model-gateway/src/multimodal/error.rs
-47
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@@ -1,47 +0,0 @@
use std::time::Duration;
use thiserror::Error;
use super::types::Modality;
pub type MultiModalResult<T> = Result<T, MultiModalError>;
#[derive(Debug, Error)]
pub enum MediaConnectorError {
#[error("unsupported media scheme: {0}")]
UnsupportedScheme(String),
#[error("invalid media URL: {0}")]
InvalidUrl(String),
#[error("media domain '{0}' is not in the allow list")]
DisallowedDomain(String),
#[error("local media path is not allowed: {0}")]
DisallowedLocalPath(String),
#[error("HTTP error while fetching media: {0}")]
Http(#[from] reqwest::Error),
#[error("I/O error while reading media: {0}")]
Io(#[from] std::io::Error),
#[error("base64 decode error: {0}")]
Base64Decode(#[from] base64::DecodeError),
#[error("data URL parse error: {0}")]
DataUrl(String),
#[error("media decode task failed: {0}")]
Blocking(#[from] tokio::task::JoinError),
#[error("image decode error: {0}")]
Image(#[from] image::ImageError),
#[error("media fetch timed out after {0:?}")]
Timeout(Duration),
}
#[derive(Debug, Error)]
pub enum MultiModalError {
#[error(transparent)]
Media(#[from] MediaConnectorError),
#[error("unsupported content part: {0}")]
UnsupportedContent(&'static str),
#[error("too many {modality:?} items provided. limit={limit}")]
ModalityLimit { modality: Modality, limit: usize },
#[error("tracker task join error: {0}")]
Join(#[from] tokio::task::JoinError),
#[error("tracker validation error: {0}")]
Validation(String),
}
sgl-model-gateway/src/multimodal/media.rs
-206
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@@ -1,206 +0,0 @@
use std::{collections::HashSet, path::PathBuf, sync::Arc, time::Duration};
use base64::{engine::general_purpose::STANDARD as BASE64_STANDARD, Engine};
use bytes::Bytes;
use image::DynamicImage;
use reqwest::Client;
use tokio::{fs, task};
use url::Url;
use super::{
error::MediaConnectorError,
types::{ImageDetail, ImageFrame, ImageSource},
};
#[derive(Clone)]
pub struct MediaConnectorConfig {
pub allowed_domains: Option<Vec<String>>,
pub allowed_local_media_path: Option<PathBuf>,
pub fetch_timeout: Duration,
}
impl Default for MediaConnectorConfig {
fn default() -> Self {
Self {
allowed_domains: None,
allowed_local_media_path: None,
fetch_timeout: Duration::from_secs(10),
}
}
}
#[derive(Clone, Copy, Debug)]
pub struct ImageFetchConfig {
pub detail: ImageDetail,
}
impl Default for ImageFetchConfig {
fn default() -> Self {
Self {
detail: ImageDetail::Auto,
}
}
}
#[derive(Debug, Clone)]
pub enum MediaSource {
Url(String),
DataUrl(String),
InlineBytes(Vec<u8>),
File(PathBuf),
}
#[derive(Clone)]
pub struct MediaConnector {
client: Client,
allowed_domains: Option<HashSet<String>>,
allowed_local_media_path: Option<PathBuf>,
fetch_timeout: Duration,
}
impl MediaConnector {
pub fn new(client: Client, config: MediaConnectorConfig) -> Result<Self, MediaConnectorError> {
let allowed_domains = config.allowed_domains.map(|domains| {
domains
.into_iter()
.map(|d| d.to_ascii_lowercase())
.collect::<HashSet<_>>()
});
let allowed_local_media_path = if let Some(path) = config.allowed_local_media_path {
Some(std::fs::canonicalize(path)?)
} else {
None
};
Ok(Self {
client,
allowed_domains,
allowed_local_media_path,
fetch_timeout: config.fetch_timeout,
})
}
pub async fn fetch_image(
&self,
source: MediaSource,
cfg: ImageFetchConfig,
) -> Result<Arc<ImageFrame>, MediaConnectorError> {
match source {
MediaSource::Url(url) => self.fetch_http_image(url, cfg).await,
MediaSource::DataUrl(data_url) => self.fetch_data_url(data_url, cfg).await,
MediaSource::InlineBytes(bytes) => {
self.decode_image(bytes.into(), cfg.detail, ImageSource::InlineBytes)
.await
}
MediaSource::File(path) => self.fetch_file(path, cfg).await,
}
}
async fn fetch_http_image(
&self,
url: String,
cfg: ImageFetchConfig,
) -> Result<Arc<ImageFrame>, MediaConnectorError> {
let parsed = Url::parse(&url).map_err(|_| MediaConnectorError::InvalidUrl(url.clone()))?;
self.ensure_domain_allowed(&parsed)?;
let mut req = self.client.get(parsed.as_str());
if self.fetch_timeout > Duration::ZERO {
req = req.timeout(self.fetch_timeout);
}
let resp = req.send().await.map_err(|err| {
if err.is_timeout() {
MediaConnectorError::Timeout(self.fetch_timeout)
} else {
MediaConnectorError::Http(err)
}
})?;
let resp = resp.error_for_status()?;
let bytes = resp.bytes().await?;
self.decode_image(
bytes,
cfg.detail,
ImageSource::Url {
url: parsed.to_string(),
},
)
.await
}
async fn fetch_data_url(
&self,
data_url: String,
cfg: ImageFetchConfig,
) -> Result<Arc<ImageFrame>, MediaConnectorError> {
let (metadata, data) = data_url
.split_once(',')
.ok_or_else(|| MediaConnectorError::DataUrl("missing comma in data url".into()))?;
if !metadata.ends_with(";base64") {
return Err(MediaConnectorError::DataUrl(
"only base64 encoded data URLs are supported".into(),
));
}
let data = data.trim();
let decoded = BASE64_STANDARD.decode(data)?;
self.decode_image(decoded.into(), cfg.detail, ImageSource::DataUrl)
.await
}
async fn fetch_file(
&self,
path: PathBuf,
cfg: ImageFetchConfig,
) -> Result<Arc<ImageFrame>, MediaConnectorError> {
let allowed_root = self
.allowed_local_media_path
.as_ref()
.ok_or_else(|| MediaConnectorError::DisallowedLocalPath(path.display().to_string()))?;
let canonical = fs::canonicalize(&path).await?;
if !canonical.starts_with(allowed_root) {
return Err(MediaConnectorError::DisallowedLocalPath(
path.display().to_string(),
));
}
let bytes = fs::read(&canonical).await?;
self.decode_image(
bytes.into(),
cfg.detail,
ImageSource::File { path: canonical },
)
.await
}
fn ensure_domain_allowed(&self, url: &Url) -> Result<(), MediaConnectorError> {
if let Some(allowed) = &self.allowed_domains {
let host = url
.host_str()
.map(|h| h.to_ascii_lowercase())
.ok_or_else(|| MediaConnectorError::InvalidUrl(url.to_string()))?;
if !allowed.contains(&host) {
return Err(MediaConnectorError::DisallowedDomain(host));
}
}
Ok(())
}
async fn decode_image(
&self,
bytes: Bytes,
detail: ImageDetail,
source: ImageSource,
) -> Result<Arc<ImageFrame>, MediaConnectorError> {
let raw: Arc<Vec<u8>> = Arc::new(bytes.to_vec());
let raw_clone = raw.clone();
let image: DynamicImage =
task::spawn_blocking(move || image::load_from_memory(&raw_clone)).await??;
Ok(Arc::new(ImageFrame::new(image, raw, detail, source)))
}
}
sgl-model-gateway/src/multimodal/mod.rs
-17
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@@ -1,17 +0,0 @@
pub mod error;
pub mod media;
pub mod registry;
pub mod tracker;
pub mod types;
pub mod vision;
pub use error::{MediaConnectorError, MultiModalError, MultiModalResult};
pub use media::{ImageFetchConfig, MediaConnector, MediaConnectorConfig, MediaSource};
pub use registry::{ModelProcessorSpec, ModelRegistry};
pub use tracker::{AsyncMultiModalTracker, TrackerConfig, TrackerOutput};
pub use types::*;
// Re-export vision processing components
pub use vision::{
ImagePreProcessor, ImageProcessorRegistry, LlavaNextProcessor, LlavaProcessor,
PreProcessorConfig, PreprocessedImages, TransformError,
};
sgl-model-gateway/src/multimodal/registry.rs
-468
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@@ -1,468 +0,0 @@
use std::collections::HashMap;
use once_cell::sync::Lazy;
use serde_json::{json, Value};
use thiserror::Error;
use super::types::{ImageSize, Modality, PromptReplacement, TokenId};
use crate::tokenizer::traits::Tokenizer as TokenizerTrait;
#[derive(Debug, Error)]
pub enum ModelRegistryError {
#[error("unsupported model: {0}")]
UnsupportedModel(String),
#[error("token '{token}' not found in tokenizer vocabulary")]
TokenNotFound { token: String },
#[error("missing config field '{field}'")]
MissingConfigField { field: String },
}
pub type RegistryResult<T> = Result<T, ModelRegistryError>;
/// Metadata about the current model used to derive tokenizer/config dependent fields.
pub struct ModelMetadata<'a> {
pub model_id: &'a str,
pub tokenizer: &'a dyn TokenizerTrait,
pub config: &'a Value,
}
impl<'a> ModelMetadata<'a> {
pub fn token_id(&self, token: &str) -> RegistryResult<TokenId> {
self.tokenizer
.token_to_id(token)
.map(|id| id as TokenId)
.ok_or_else(|| ModelRegistryError::TokenNotFound {
token: token.to_string(),
})
}
pub fn config_u32(&self, path: &[&str]) -> Option<u32> {
Self::find_value(self.config, path).and_then(|value| value.as_u64().map(|v| v as u32))
}
fn find_value<'v>(value: &'v Value, path: &[&str]) -> Option<&'v Value> {
let mut current = value;
for key in path {
current = current.get(*key)?;
}
Some(current)
}
}
pub trait ModelProcessorSpec: Send + Sync {
fn name(&self) -> &'static str;
fn matches(&self, metadata: &ModelMetadata) -> bool;
fn placeholder_token(&self, metadata: &ModelMetadata) -> RegistryResult<String>;
fn placeholder_token_id(&self, metadata: &ModelMetadata) -> RegistryResult<TokenId>;
fn modality_limits(&self, metadata: &ModelMetadata)
-> RegistryResult<HashMap<Modality, usize>>;
fn processor_kwargs(&self, metadata: &ModelMetadata) -> RegistryResult<Value>;
fn prompt_replacements(
&self,
metadata: &ModelMetadata,
image_sizes: &[ImageSize],
) -> RegistryResult<Vec<PromptReplacement>>;
}
pub struct ModelRegistry {
specs: Vec<LazySpec>,
}
impl ModelRegistry {
pub fn new() -> Self {
Self {
specs: vec![
LazySpec::new("llava", || Box::new(LlavaSpec)),
LazySpec::new("qwen_vl", || Box::new(QwenVLVisionSpec)),
LazySpec::new("phi3_v", || Box::new(Phi3VisionSpec)),
],
}
}
pub fn lookup<'a>(&'a self, metadata: &ModelMetadata) -> Option<&'a dyn ModelProcessorSpec> {
for spec in &self.specs {
let spec_ref = spec.get();
if spec_ref.matches(metadata) {
return Some(spec_ref);
}
}
None
}
}
impl Default for ModelRegistry {
fn default() -> Self {
Self::new()
}
}
struct LazySpec {
inner: Lazy<Box<dyn ModelProcessorSpec>>,
}
impl LazySpec {
fn new(_id: &'static str, factory: fn() -> Box<dyn ModelProcessorSpec>) -> Self {
Self {
inner: Lazy::new(factory),
}
}
fn get(&self) -> &dyn ModelProcessorSpec {
self.inner.as_ref()
}
}
struct LlavaSpec;
impl LlavaSpec {
fn patch_size(metadata: &ModelMetadata) -> u32 {
metadata
.config_u32(&["vision_config", "patch_size"])
.unwrap_or(14)
}
fn tokens_per_image(metadata: &ModelMetadata, size: &ImageSize) -> usize {
let patch = Self::patch_size(metadata);
let cols = size.width.div_ceil(patch) as usize;
let rows = size.height.div_ceil(patch) as usize;
cols * rows
}
}
impl ModelProcessorSpec for LlavaSpec {
fn name(&self) -> &'static str {
"llava"
}
fn matches(&self, metadata: &ModelMetadata) -> bool {
metadata.model_id.to_ascii_lowercase().contains("llava")
}
fn placeholder_token(&self, _metadata: &ModelMetadata) -> RegistryResult<String> {
Ok("<image>".to_string())
}
fn placeholder_token_id(&self, metadata: &ModelMetadata) -> RegistryResult<TokenId> {
if let Some(value) = metadata.config_u32(&["image_token_index"]) {
return Ok(value as TokenId);
}
metadata.token_id("<image>")
}
fn modality_limits(
&self,
_metadata: &ModelMetadata,
) -> RegistryResult<HashMap<Modality, usize>> {
Ok(HashMap::from([(Modality::Image, 4)]))
}
fn processor_kwargs(&self, _metadata: &ModelMetadata) -> RegistryResult<Value> {
Ok(json!({}))
}
fn prompt_replacements(
&self,
metadata: &ModelMetadata,
image_sizes: &[ImageSize],
) -> RegistryResult<Vec<PromptReplacement>> {
let token_id = self.placeholder_token_id(metadata)?;
let token = self.placeholder_token(metadata)?;
Ok(image_sizes
.iter()
.map(|size| {
let count = Self::tokens_per_image(metadata, size);
PromptReplacement::repeated(Modality::Image, &token, token_id, count)
})
.collect())
}
}
struct QwenVLVisionSpec;
impl QwenVLVisionSpec {
fn pad_token_id(metadata: &ModelMetadata) -> RegistryResult<TokenId> {
metadata
.config_u32(&["vision_token_id"])
.map(|v| v as TokenId)
.ok_or_else(|| ModelRegistryError::MissingConfigField {
field: "vision_token_id".to_string(),
})
}
fn start_token_id(metadata: &ModelMetadata) -> RegistryResult<TokenId> {
metadata
.config_u32(&["vision_start_token_id"])
.map(|v| v as TokenId)
.ok_or_else(|| ModelRegistryError::MissingConfigField {
field: "vision_start_token_id".to_string(),
})
}
fn patch_grid(metadata: &ModelMetadata, size: &ImageSize) -> (usize, usize) {
let patch = metadata
.config_u32(&["vision_config", "patch_size"])
.unwrap_or(14);
let cols = size.width.div_ceil(patch) as usize;
let rows = size.height.div_ceil(patch) as usize;
(rows, cols)
}
}
impl ModelProcessorSpec for QwenVLVisionSpec {
fn name(&self) -> &'static str {
"qwen_vl"
}
fn matches(&self, metadata: &ModelMetadata) -> bool {
metadata.model_id.to_ascii_lowercase().contains("qwen")
&& metadata.model_id.to_ascii_lowercase().contains("vl")
}
fn placeholder_token(&self, _metadata: &ModelMetadata) -> RegistryResult<String> {
Ok("<image>".to_string())
}
fn placeholder_token_id(&self, metadata: &ModelMetadata) -> RegistryResult<TokenId> {
metadata
.config_u32(&["image_token_id"])
.map(|v| v as TokenId)
.ok_or_else(|| ModelRegistryError::MissingConfigField {
field: "image_token_id".to_string(),
})
}
fn modality_limits(
&self,
_metadata: &ModelMetadata,
) -> RegistryResult<HashMap<Modality, usize>> {
Ok(HashMap::from([(Modality::Image, 10)]))
}
fn processor_kwargs(&self, _metadata: &ModelMetadata) -> RegistryResult<Value> {
Ok(json!({}))
}
fn prompt_replacements(
&self,
metadata: &ModelMetadata,
image_sizes: &[ImageSize],
) -> RegistryResult<Vec<PromptReplacement>> {
let start_token_id = Self::start_token_id(metadata)?;
let pad_token_id = Self::pad_token_id(metadata)?;
let placeholder_token = self.placeholder_token(metadata)?;
Ok(image_sizes
.iter()
.map(|size| {
let (rows, cols) = Self::patch_grid(metadata, size);
let pad_len = rows * cols;
let mut tokens = Vec::with_capacity(pad_len + 1);
tokens.push(start_token_id);
tokens.extend(std::iter::repeat_n(pad_token_id, pad_len));
PromptReplacement::sequence(Modality::Image, &placeholder_token, tokens)
})
.collect())
}
}
struct Phi3VisionSpec;
impl Phi3VisionSpec {
fn tokens_per_image(metadata: &ModelMetadata) -> usize {
metadata
.config_u32(&["img_processor", "num_img_tokens"])
.unwrap_or(256) as usize
}
}
impl ModelProcessorSpec for Phi3VisionSpec {
fn name(&self) -> &'static str {
"phi3_v"
}
fn matches(&self, metadata: &ModelMetadata) -> bool {
let id = metadata.model_id.to_ascii_lowercase();
id.contains("phi") && id.contains("vision")
}
fn placeholder_token(&self, _metadata: &ModelMetadata) -> RegistryResult<String> {
Ok("<image>".to_owned())
}
fn placeholder_token_id(&self, metadata: &ModelMetadata) -> RegistryResult<TokenId> {
metadata.token_id("<image>")
}
fn modality_limits(
&self,
_metadata: &ModelMetadata,
) -> RegistryResult<HashMap<Modality, usize>> {
Ok(HashMap::from([(Modality::Image, 4)]))
}
fn processor_kwargs(&self, _metadata: &ModelMetadata) -> RegistryResult<Value> {
Ok(json!({}))
}
fn prompt_replacements(
&self,
metadata: &ModelMetadata,
image_sizes: &[ImageSize],
) -> RegistryResult<Vec<PromptReplacement>> {
let token_id = self.placeholder_token_id(metadata)?;
let token = self.placeholder_token(metadata)?;
let count = Self::tokens_per_image(metadata);
Ok(image_sizes
.iter()
.map(|_| PromptReplacement::repeated(Modality::Image, &token, token_id, count))
.collect())
}
}
#[cfg(test)]
mod tests {
use std::collections::HashMap;
use serde_json::json;
use super::*;
use crate::tokenizer::traits::{
Decoder, Encoder, Encoding, SpecialTokens, Tokenizer as TokenizerTrait,
};
struct TestTokenizer {
vocab: HashMap<String, u32>,
}
impl TestTokenizer {
fn new(pairs: &[(&str, u32)]) -> Self {
let vocab = pairs
.iter()
.map(|(token, id)| ((*token).to_string(), *id))
.collect();
Self { vocab }
}
}
impl Encoder for TestTokenizer {
fn encode(&self, _input: &str, _add_special_tokens: bool) -> anyhow::Result<Encoding> {
Ok(Encoding::Sp(Vec::new()))
}
fn encode_batch(
&self,
inputs: &[&str],
add_special_tokens: bool,
) -> anyhow::Result<Vec<Encoding>> {
inputs
.iter()
.map(|_| self.encode("", add_special_tokens))
.collect()
}
}
impl Decoder for TestTokenizer {
fn decode(&self, _token_ids: &[u32], _skip_special_tokens: bool) -> anyhow::Result<String> {
Ok(String::new())
}
}
impl TokenizerTrait for TestTokenizer {
fn vocab_size(&self) -> usize {
self.vocab.len()
}
fn get_special_tokens(&self) -> &SpecialTokens {
static TOKENS: Lazy<SpecialTokens> = Lazy::new(|| SpecialTokens {
bos_token: None,
eos_token: None,
unk_token: None,
sep_token: None,
pad_token: None,
cls_token: None,
mask_token: None,
additional_special_tokens: vec![],
});
&TOKENS
}
fn token_to_id(&self, token: &str) -> Option<u32> {
self.vocab.get(token).copied()
}
fn id_to_token(&self, _id: u32) -> Option<String> {
None
}
fn as_any(&self) -> &dyn std::any::Any {
self
}
}
#[test]
fn llava_prompt_replacement_uses_config_ids() {
let tokenizer = TestTokenizer::new(&[("<image>", 32000)]);
let config = json!({
"model_type": "llava",
"image_token_index": 32000,
"vision_config": {"patch_size": 14}
});
let metadata = ModelMetadata {
model_id: "llava-v1.5",
tokenizer: &tokenizer,
config: &config,
};
let registry = ModelRegistry::new();
let spec = registry.lookup(&metadata).expect("llava spec");
let replacements = spec
.prompt_replacements(&metadata, &[ImageSize::new(336, 336)])
.unwrap();
assert_eq!(replacements[0].tokens.len(), 576);
}
#[test]
fn qwen_vision_uses_config_token_ids() {
let tokenizer = TestTokenizer::new(&[("<image>", 999)]);
let config = json!({
"model_type": "qwen2_vl",
"vision_start_token_id": 151652,
"vision_token_id": 151654,
"image_token_id": 151655,
"vision_config": {"patch_size": 14}
});
let metadata = ModelMetadata {
model_id: "Qwen2-VL-7B",
tokenizer: &tokenizer,
config: &config,
};
let registry = ModelRegistry::new();
let spec = registry.lookup(&metadata).expect("qwen spec");
let replacements = spec
.prompt_replacements(&metadata, &[ImageSize::new(448, 448)])
.unwrap();
// 448/14 = 32 patches => 1024 pad tokens + 1 start token
assert_eq!(replacements[0].tokens.len(), 1025);
assert_eq!(replacements[0].tokens[0], 151652);
assert_eq!(replacements[0].tokens[1], 151654);
}
#[test]
fn phi3_uses_num_img_tokens() {
let tokenizer = TestTokenizer::new(&[("<image>", 555)]);
let config = json!({
"model_type": "phi3_v",
"img_processor": {"num_img_tokens": 144}
});
let metadata = ModelMetadata {
model_id: "Phi-3-vision",
tokenizer: &tokenizer,
config: &config,
};
let registry = ModelRegistry::new();
let spec = registry.lookup(&metadata).expect("phi3 spec");
let replacements = spec
.prompt_replacements(&metadata, &[ImageSize::new(336, 336)])
.unwrap();
assert_eq!(replacements[0].tokens.len(), 144);
assert_eq!(replacements[0].tokens[0], 555);
}
}
sgl-model-gateway/src/multimodal/tracker.rs
-176
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@@ -1,176 +0,0 @@
use std::{collections::HashMap, sync::Arc};
use tokio::task::JoinHandle;
use super::{
error::{MultiModalError, MultiModalResult},
media::{ImageFetchConfig, MediaConnector, MediaSource},
types::{
ChatContentPart, ConversationSegment, ImageDetail, Modality, MultiModalData,
MultiModalUUIDs, PlaceholderHandle, PlaceholderMap, TrackedMedia, DEFAULT_PLACEHOLDERS,
},
};
type PendingTask = JoinHandle<MultiModalResult<TrackedMedia>>;
#[derive(Debug, Clone)]
pub struct TrackerConfig {
pub placeholder_tokens: HashMap<Modality, String>,
pub modality_limits: HashMap<Modality, usize>,
}
impl Default for TrackerConfig {
fn default() -> Self {
Self {
placeholder_tokens: DEFAULT_PLACEHOLDERS
.iter()
.map(|(k, v)| (*k, (*v).to_string()))
.collect(),
modality_limits: HashMap::new(),
}
}
}
#[derive(Debug)]
pub struct TrackerOutput {
pub conversation: Vec<ConversationSegment>,
pub data: MultiModalData,
pub uuids: MultiModalUUIDs,
pub placeholders: PlaceholderMap,
}
pub struct AsyncMultiModalTracker {
media_connector: Arc<MediaConnector>,
config: TrackerConfig,
pending: HashMap<Modality, Vec<PendingTask>>,
placeholders: PlaceholderMap,
conversation: Vec<ConversationSegment>,
uuids: MultiModalUUIDs,
counts: HashMap<Modality, usize>,
}
impl AsyncMultiModalTracker {
pub fn new(media_connector: Arc<MediaConnector>, config: TrackerConfig) -> Self {
Self {
media_connector,
config,
pending: HashMap::new(),
placeholders: PlaceholderMap::new(),
conversation: Vec::new(),
uuids: HashMap::new(),
counts: HashMap::new(),
}
}
pub fn push_part(&mut self, part: ChatContentPart) -> MultiModalResult<()> {
match part {
ChatContentPart::Text { text } => {
if !text.is_empty() {
self.conversation.push(ConversationSegment::text(text));
}
Ok(())
}
ChatContentPart::ImageUrl { url, detail, uuid } => {
self.enqueue_image(MediaSource::Url(url), detail.unwrap_or_default(), uuid)
}
ChatContentPart::ImageData {
data,
mime_type: _,
uuid,
detail,
} => self.enqueue_image(
MediaSource::InlineBytes(data),
detail.unwrap_or_default(),
uuid,
),
ChatContentPart::ImageEmbeds { .. } => {
Err(MultiModalError::UnsupportedContent("image_embeds"))
}
}
}
pub async fn finalize(mut self) -> MultiModalResult<TrackerOutput> {
let mut data = MultiModalData::new();
for (modality, tasks) in self.pending.drain() {
let mut items = Vec::with_capacity(tasks.len());
for task in tasks {
let media = task.await??;
items.push(media);
}
data.insert(modality, items);
}
Ok(TrackerOutput {
conversation: self.conversation,
data,
uuids: self.uuids,
placeholders: self.placeholders,
})
}
fn placeholder_token(&self, modality: Modality) -> &str {
if let Some(token) = self.config.placeholder_tokens.get(&modality) {
token.as_str()
} else {
DEFAULT_PLACEHOLDERS
.get(&modality)
.copied()
.unwrap_or("<mm>")
}
}
fn next_index(&mut self, modality: Modality) -> MultiModalResult<usize> {
let count = self.counts.entry(modality).or_insert(0);
let next = *count;
let limit = self.config.modality_limits.get(&modality).copied();
if let Some(limit) = limit {
if next >= limit {
return Err(MultiModalError::ModalityLimit { modality, limit });
}
}
*count += 1;
Ok(next)
}
fn record_uuid(&mut self, modality: Modality, uuid: Option<String>) {
self.uuids.entry(modality).or_default().push(uuid);
}
fn add_placeholder(&mut self, token: String, handle: PlaceholderHandle) {
self.placeholders.entry(token).or_default().push(handle);
}
fn enqueue_image(
&mut self,
source: MediaSource,
detail: ImageDetail,
uuid: Option<String>,
) -> MultiModalResult<()> {
let modality = Modality::Image;
let idx = self.next_index(modality)?;
let token = self.placeholder_token(modality).to_string();
let text_position = self.conversation.len();
self.conversation
.push(ConversationSegment::placeholder(token.clone()));
self.add_placeholder(
token.clone(),
PlaceholderHandle {
modality,
item_index: idx,
text_position,
},
);
self.record_uuid(modality, uuid);
let connector = Arc::clone(&self.media_connector);
let handle = tokio::spawn(async move {
let frame = connector
.fetch_image(source, ImageFetchConfig { detail })
.await?;
Ok(TrackedMedia::Image(frame))
});
self.pending.entry(modality).or_default().push(handle);
Ok(())
}
}
sgl-model-gateway/src/multimodal/types.rs
-290
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@@ -1,290 +0,0 @@
use std::{
collections::{BTreeMap, HashMap},
fmt,
path::PathBuf,
sync::{Arc, LazyLock},
};
use image::DynamicImage;
use serde::{Deserialize, Serialize};
use serde_json::Value;
/// Supported multimodal modalities.
#[derive(Debug, Clone, Copy, PartialEq, Eq, Hash, Serialize, Deserialize)]
#[serde(rename_all = "snake_case")]
pub enum Modality {
Image,
ImageEmbeds,
Audio,
Video,
}
impl fmt::Display for Modality {
fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result {
match self {
Modality::Image => write!(f, "image"),
Modality::ImageEmbeds => write!(f, "image_embeds"),
Modality::Audio => write!(f, "audio"),
Modality::Video => write!(f, "video"),
}
}
}
/// Simple helper used for default placeholder tokens per modality.
pub static DEFAULT_PLACEHOLDERS: LazyLock<HashMap<Modality, &'static str>> = LazyLock::new(|| {
HashMap::from([
(Modality::Image, "<image>"),
(Modality::ImageEmbeds, "<image_embeds>"),
(Modality::Audio, "<audio>"),
(Modality::Video, "<video>"),
])
});
/// Detail level passed by OpenAI style APIs.
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize, Default)]
#[serde(rename_all = "snake_case")]
pub enum ImageDetail {
#[default]
Auto,
Low,
High,
}
/// A normalized content part understood by the tracker.
#[derive(Debug, Clone, Serialize, Deserialize)]
#[serde(tag = "type", rename_all = "snake_case")]
pub enum ChatContentPart {
Text {
text: String,
},
ImageUrl {
url: String,
#[serde(skip_serializing_if = "Option::is_none")]
detail: Option<ImageDetail>,
#[serde(skip_serializing_if = "Option::is_none")]
uuid: Option<String>,
},
ImageData {
data: Vec<u8>,
#[serde(skip_serializing_if = "Option::is_none")]
mime_type: Option<String>,
#[serde(skip_serializing_if = "Option::is_none")]
uuid: Option<String>,
#[serde(skip_serializing_if = "Option::is_none")]
detail: Option<ImageDetail>,
},
ImageEmbeds {
payload: Value,
#[serde(skip_serializing_if = "Option::is_none")]
uuid: Option<String>,
},
}
/// Represents one segment in the conversation after placeholders are inserted.
#[derive(Debug, Clone, Serialize, Deserialize)]
#[serde(rename_all = "snake_case")]
pub enum ConversationSegment {
Text(String),
Placeholder { token: String },
}
impl ConversationSegment {
pub fn text<S: Into<String>>(text: S) -> Self {
ConversationSegment::Text(text.into())
}
pub fn placeholder<S: Into<String>>(token: S) -> Self {
ConversationSegment::Placeholder {
token: token.into(),
}
}
}
/// Metadata describing where placeholder tokens were inserted.
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PlaceholderHandle {
pub modality: Modality,
pub item_index: usize,
pub text_position: usize,
}
pub type PlaceholderMap = HashMap<String, Vec<PlaceholderHandle>>;
/// Image source metadata (useful for hashing & tracing).
#[derive(Debug, Clone, Serialize, Deserialize)]
#[serde(tag = "kind", rename_all = "snake_case")]
pub enum ImageSource {
Url { url: String },
DataUrl,
InlineBytes,
File { path: PathBuf },
}
/// Concrete image payload captured by the media connector.
#[derive(Debug)]
pub struct ImageFrame {
image: DynamicImage,
raw_bytes: Arc<Vec<u8>>,
pub detail: ImageDetail,
pub source: ImageSource,
}
impl ImageFrame {
pub fn new(
image: DynamicImage,
raw_bytes: Arc<Vec<u8>>,
detail: ImageDetail,
source: ImageSource,
) -> Self {
Self {
image,
raw_bytes,
detail,
source,
}
}
pub fn data(&self) -> &DynamicImage {
&self.image
}
pub fn raw_bytes(&self) -> &[u8] {
self.raw_bytes.as_slice()
}
pub fn source(&self) -> &ImageSource {
&self.source
}
pub fn size(&self) -> ImageSize {
ImageSize::new(self.image.width(), self.image.height())
}
}
/// Container for all supported multimodal media objects.
#[derive(Debug, Clone)]
pub enum TrackedMedia {
Image(Arc<ImageFrame>),
/// Placeholder variants for future modalities.
Audio,
Video,
Embeddings,
}
pub type MultiModalData = HashMap<Modality, Vec<TrackedMedia>>;
pub type MultiModalUUIDs = HashMap<Modality, Vec<Option<String>>>;
pub type TokenId = i32;
#[derive(Debug, Clone, Copy, Serialize, Deserialize, PartialEq, Eq)]
pub struct ImageSize {
pub width: u32,
pub height: u32,
}
impl ImageSize {
pub fn new(width: u32, height: u32) -> Self {
Self { width, height }
}
}
#[derive(Debug, Clone, Serialize, Deserialize, Default)]
pub struct PlaceholderRange {
pub offset: usize,
pub length: usize,
}
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct MultiModalTensor {
pub shape: Vec<usize>,
pub dtype: String,
#[serde(with = "serde_bytes")]
pub data: Vec<u8>,
}
#[derive(Debug, Clone, Serialize, Deserialize)]
#[serde(tag = "kind", rename_all = "snake_case")]
pub enum MultiModalValue {
Tensor(MultiModalTensor),
Json(Value),
}
#[derive(Debug, Clone, Serialize, Deserialize, Default)]
pub struct MultiModalInputs {
pub prompt_token_ids: Vec<u32>,
#[serde(default)]
pub mm_kwargs: BTreeMap<String, Vec<MultiModalValue>>,
#[serde(default)]
pub mm_hashes: BTreeMap<String, Vec<String>>,
#[serde(default)]
pub mm_placeholders: BTreeMap<String, Vec<PlaceholderRange>>,
#[serde(skip_serializing_if = "Option::is_none")]
pub cache_salt: Option<String>,
}
impl MultiModalInputs {
pub fn new(prompt_token_ids: Vec<u32>) -> Self {
Self {
prompt_token_ids,
..Default::default()
}
}
}
#[derive(Debug, Clone)]
pub struct PromptReplacement {
pub modality: Modality,
pub placeholder_token: String,
pub tokens: Vec<TokenId>,
}
impl PromptReplacement {
pub fn repeated(
modality: Modality,
placeholder_token: &str,
token_id: TokenId,
count: usize,
) -> Self {
Self {
modality,
placeholder_token: placeholder_token.to_string(),
tokens: vec![token_id; count],
}
}
pub fn sequence(modality: Modality, placeholder_token: &str, sequence: Vec<TokenId>) -> Self {
Self {
modality,
placeholder_token: placeholder_token.to_string(),
tokens: sequence,
}
}
}
#[cfg(test)]
mod tests {
use super::*;
#[test]
fn multimodal_inputs_defaults() {
let inputs = MultiModalInputs::new(vec![1, 2, 3]);
assert_eq!(inputs.prompt_token_ids, vec![1, 2, 3]);
assert!(inputs.mm_kwargs.is_empty());
}
#[test]
fn placeholder_range_serializes() {
let range = PlaceholderRange {
offset: 10,
length: 4,
};
let json = serde_json::to_string(&range).unwrap();
assert!(json.contains("offset"));
}
#[test]
fn prompt_replacement_builders() {
let rep = PromptReplacement::repeated(Modality::Image, "<image>", 100, 3);
assert_eq!(rep.tokens, vec![100, 100, 100]);
}
}
sgl-model-gateway/src/multimodal/vision/image_processor.rs
-491
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@@ -1,491 +0,0 @@
//! Image processor trait and output types.
//!
//! This module defines the interface for model-specific image processors
//! and the common output format for preprocessed images.
use std::{borrow::Cow, collections::HashMap};
use image::DynamicImage;
use ndarray::{Array4, ArrayD};
use super::{preprocessor_config::PreProcessorConfig, transforms::TransformError};
/// Model-specific output values that vary by architecture.
///
/// Different vision models require different auxiliary outputs beyond pixel_values.
/// This enum captures the common types of such outputs.
#[derive(Debug, Clone)]
pub enum ModelSpecificValue {
/// A tensor with shape information (data as flat vec, shape as dims)
Tensor { data: Vec<f32>, shape: Vec<usize> },
/// A tensor of integers (e.g., aspect_ratio_ids)
IntTensor { data: Vec<i64>, shape: Vec<usize> },
/// A tensor of unsigned integers (e.g., image_grid_thw)
UintTensor { data: Vec<u32>, shape: Vec<usize> },
/// Simple integer value
Int(i64),
/// Simple float value
Float(f64),
/// List of integers
IntVec(Vec<i64>),
/// List of unsigned integers
UintVec(Vec<u32>),
/// List of floats
FloatVec(Vec<f32>),
/// List of tuples (e.g., image sizes)
TupleVec(Vec<(u32, u32)>),
/// Boolean flag
Bool(bool),
}
impl ModelSpecificValue {
/// Create a 1D uint tensor from a vector.
pub fn uint_1d(data: Vec<u32>) -> Self {
let len = data.len();
Self::UintTensor {
data,
shape: vec![len],
}
}
/// Create a 2D uint tensor.
pub fn uint_2d(data: Vec<u32>, rows: usize, cols: usize) -> Self {
Self::UintTensor {
data,
shape: vec![rows, cols],
}
}
/// Create a 1D int tensor from a vector.
pub fn int_1d(data: Vec<i64>) -> Self {
let len = data.len();
Self::IntTensor {
data,
shape: vec![len],
}
}
}
/// Preprocessed images ready for model consumption.
///
/// This struct contains all the outputs needed by the SGLang scheduler
/// to construct `MultimodalInputs` for the model.
#[derive(Debug, Clone)]
pub struct PreprocessedImages {
/// Pixel values as a dynamic-dimensional float32 tensor.
///
/// This is the primary input to the vision encoder.
/// Shape varies by model:
/// - Standard: [B, C, H, W] (4D)
/// - Phi3-Vision: [B, num_crops+1, C, H, W] (5D)
pub pixel_values: ArrayD<f32>,
/// Number of image tokens per image in the batch.
///
/// Used to expand placeholder tokens in the text input.
/// For example, LLaVA with 336x336 and patch_size=14 produces 576 tokens.
pub num_img_tokens: Vec<usize>,
/// Original image sizes as (width, height) before preprocessing.
///
/// Some models need this for proper attention masking or position encoding.
pub image_sizes: Vec<(u32, u32)>,
/// Model-specific auxiliary outputs.
///
/// Examples:
/// - Qwen-VL: `image_grid_thw` for rotary position encoding
/// - LLaMA-Vision: `aspect_ratio_ids`, `aspect_ratio_mask`
/// - Phi3-Vision: `num_img_tokens` per crop
pub model_specific: HashMap<String, ModelSpecificValue>,
}
impl PreprocessedImages {
/// Create a new PreprocessedImages with required fields (4D pixel values).
pub fn new(
pixel_values: Array4<f32>,
num_img_tokens: Vec<usize>,
image_sizes: Vec<(u32, u32)>,
) -> Self {
Self {
pixel_values: pixel_values.into_dyn(),
num_img_tokens,
image_sizes,
model_specific: HashMap::new(),
}
}
/// Create a new PreprocessedImages with dynamic-dimensional pixel values.
///
/// Use this for models like Phi3-Vision that have 5D tensors.
pub fn new_dynamic(
pixel_values: ArrayD<f32>,
num_img_tokens: Vec<usize>,
image_sizes: Vec<(u32, u32)>,
) -> Self {
Self {
pixel_values,
num_img_tokens,
image_sizes,
model_specific: HashMap::new(),
}
}
/// Add a model-specific value.
pub fn with_extra(mut self, key: impl Into<String>, value: ModelSpecificValue) -> Self {
self.model_specific.insert(key.into(), value);
self
}
/// Get the batch size.
pub fn batch_size(&self) -> usize {
self.pixel_values.shape()[0]
}
/// Get the number of channels.
///
/// For 4D tensors [B, C, H, W], returns shape[1].
/// For 5D tensors [B, N, C, H, W] (Phi3-Vision), returns shape[2].
pub fn channels(&self) -> usize {
match self.pixel_values.ndim() {
4 => self.pixel_values.shape()[1],
5 => self.pixel_values.shape()[2],
ndim => panic!(
"Unsupported pixel_values dimension: {}, expected 4 or 5",
ndim
),
}
}
/// Get the height of processed images.
///
/// For 4D tensors [B, C, H, W], returns shape[2].
/// For 5D tensors [B, N, C, H, W] (Phi3-Vision), returns shape[3].
pub fn height(&self) -> usize {
match self.pixel_values.ndim() {
4 => self.pixel_values.shape()[2],
5 => self.pixel_values.shape()[3],
ndim => panic!(
"Unsupported pixel_values dimension: {}, expected 4 or 5",
ndim
),
}
}
/// Get the width of processed images.
///
/// For 4D tensors [B, C, H, W], returns shape[3].
/// For 5D tensors [B, N, C, H, W] (Phi3-Vision), returns shape[4].
pub fn width(&self) -> usize {
match self.pixel_values.ndim() {
4 => self.pixel_values.shape()[3],
5 => self.pixel_values.shape()[4],
ndim => panic!(
"Unsupported pixel_values dimension: {}, expected 4 or 5",
ndim
),
}
}
/// Get the number of dimensions of pixel_values.
pub fn ndim(&self) -> usize {
self.pixel_values.ndim()
}
/// Get total number of image tokens across all images.
pub fn total_tokens(&self) -> usize {
self.num_img_tokens.iter().sum()
}
/// Get pixel values as a flat f32 slice without copying if possible.
pub fn pixel_values_flat(&self) -> Cow<'_, [f32]> {
match self.pixel_values.as_slice() {
Some(slice) => Cow::Borrowed(slice),
None => Cow::Owned(self.pixel_values.iter().copied().collect()),
}
}
/// Get the shape of pixel values as a vector.
pub fn pixel_values_shape(&self) -> Vec<usize> {
self.pixel_values.shape().to_vec()
}
}
/// Trait for model-specific image preprocessors.
///
/// Each vision model (LLaVA, Qwen-VL, Phi3-Vision, etc.) implements this trait
/// to provide the correct preprocessing pipeline.
pub trait ImagePreProcessor: Send + Sync {
/// Default normalization mean for this model family.
fn default_mean(&self) -> [f64; 3];
/// Default normalization std for this model family.
fn default_std(&self) -> [f64; 3];
/// Preprocess a batch of images.
///
/// # Arguments
/// * `images` - Input images to preprocess
/// * `config` - Preprocessor configuration from HuggingFace
///
/// # Returns
/// Preprocessed images ready for the model, or an error.
fn preprocess(
&self,
images: &[DynamicImage],
config: &PreProcessorConfig,
) -> Result<PreprocessedImages, TransformError>;
/// Calculate the number of image tokens for a given image size.
///
/// This is used to determine how many placeholder tokens to insert
/// in the text input before the image has been fully processed.
///
/// # Arguments
/// * `width` - Image width after preprocessing
/// * `height` - Image height after preprocessing
/// * `config` - Preprocessor configuration
fn calculate_num_tokens(&self, width: u32, height: u32, config: &PreProcessorConfig) -> usize;
/// Get the model family name for identification.
fn model_name(&self) -> &'static str;
/// Get the expected image size after preprocessing.
///
/// Some models have fixed sizes, others are dynamic.
fn get_processed_size(&self, config: &PreProcessorConfig) -> Option<(u32, u32)> {
config.get_target_size()
}
}
/// Registry of available image processors.
pub struct ImageProcessorRegistry {
processors: HashMap<String, Box<dyn ImagePreProcessor>>,
}
impl ImageProcessorRegistry {
/// Create a new empty registry.
pub fn new() -> Self {
Self {
processors: HashMap::new(),
}
}
/// Register a processor for a model pattern.
pub fn register(&mut self, pattern: impl Into<String>, processor: Box<dyn ImagePreProcessor>) {
self.processors.insert(pattern.into(), processor);
}
/// Find a processor for the given model ID.
///
/// Matches by substring containment (case-insensitive).
pub fn find(&self, model_id: &str) -> Option<&dyn ImagePreProcessor> {
let model_lower = model_id.to_lowercase();
for (pattern, processor) in &self.processors {
if model_lower.contains(&pattern.to_lowercase()) {
return Some(processor.as_ref());
}
}
None
}
/// Check if a model has a registered processor.
pub fn has_processor(&self, model_id: &str) -> bool {
self.find(model_id).is_some()
}
/// Get list of supported model patterns.
pub fn supported_patterns(&self) -> Vec<&str> {
self.processors.keys().map(|s| s.as_str()).collect()
}
}
impl Default for ImageProcessorRegistry {
fn default() -> Self {
Self::new()
}
}
impl ImageProcessorRegistry {
/// Create a registry with all built-in processors registered.
///
/// Currently registers:
/// - `llava-next` -> LlavaNextProcessor
/// - `llava` -> LlavaProcessor (also matches llava-1.5, etc.)
/// - `qwen2-vl` -> Qwen2VLProcessor
/// - `qwen2.5-vl` -> Qwen2VLProcessor (same preprocessing as Qwen2-VL)
/// - `qwen3-vl` -> Qwen3VLProcessor (patch_size=16, [0.5,0.5,0.5] normalization)
/// - `phi-3-vision` -> Phi3VisionProcessor (HD transform with 336x336 tiles)
pub fn with_defaults() -> Self {
let mut registry = Self::new();
// Register LLaVA-NeXT first (more specific pattern)
registry.register(
"llava-next",
Box::new(super::processors::LlavaNextProcessor::new()),
);
registry.register(
"llava-v1.6",
Box::new(super::processors::LlavaNextProcessor::new()),
);
// Register standard LLaVA (matches llava-1.5, llava-v1.5, etc.)
registry.register("llava", Box::new(super::processors::LlavaProcessor::new()));
// Register Qwen3-VL first (more specific pattern - must match before qwen2)
registry.register(
"qwen3-vl",
Box::new(super::processors::Qwen3VLProcessor::new()),
);
registry.register(
"qwen3_vl",
Box::new(super::processors::Qwen3VLProcessor::new()),
);
// Register Qwen2-VL (matches Qwen/Qwen2-VL-*, etc.)
registry.register(
"qwen2-vl",
Box::new(super::processors::Qwen2VLProcessor::new()),
);
registry.register(
"qwen2_vl",
Box::new(super::processors::Qwen2VLProcessor::new()),
);
// Register Qwen2.5-VL (uses identical preprocessing to Qwen2-VL)
registry.register(
"qwen2.5-vl",
Box::new(super::processors::Qwen2VLProcessor::new()),
);
registry.register(
"qwen2_5-vl",
Box::new(super::processors::Qwen2VLProcessor::new()),
);
registry.register(
"qwen2_5_vl",
Box::new(super::processors::Qwen2VLProcessor::new()),
);
// Register Phi3-Vision
registry.register(
"phi-3-vision",
Box::new(super::processors::Phi3VisionProcessor::new()),
);
registry.register(
"phi3-vision",
Box::new(super::processors::Phi3VisionProcessor::new()),
);
registry
}
}
#[cfg(test)]
mod tests {
use ndarray::Array4;
use super::*;
#[test]
fn test_preprocessed_images_accessors() {
let pixel_values = Array4::<f32>::zeros((2, 3, 336, 336));
let images =
PreprocessedImages::new(pixel_values, vec![576, 576], vec![(640, 480), (800, 600)]);
assert_eq!(images.batch_size(), 2);
assert_eq!(images.channels(), 3);
assert_eq!(images.height(), 336);
assert_eq!(images.width(), 336);
assert_eq!(images.total_tokens(), 1152);
}
#[test]
fn test_preprocessed_images_with_extra() {
let pixel_values = Array4::<f32>::zeros((1, 3, 224, 224));
let images = PreprocessedImages::new(pixel_values, vec![196], vec![(224, 224)])
.with_extra(
"image_grid_thw",
ModelSpecificValue::uint_1d(vec![1, 16, 16]),
)
.with_extra("aspect_ratio_id", ModelSpecificValue::Int(0));
assert!(images.model_specific.contains_key("image_grid_thw"));
assert!(images.model_specific.contains_key("aspect_ratio_id"));
}
#[test]
fn test_model_specific_value_constructors() {
let uint_1d = ModelSpecificValue::uint_1d(vec![1, 2, 3]);
match uint_1d {
ModelSpecificValue::UintTensor { data, shape } => {
assert_eq!(data, vec![1, 2, 3]);
assert_eq!(shape, vec![3]);
}
_ => panic!("Expected UintTensor"),
}
let uint_2d = ModelSpecificValue::uint_2d(vec![1, 2, 3, 4], 2, 2);
match uint_2d {
ModelSpecificValue::UintTensor { data, shape } => {
assert_eq!(data, vec![1, 2, 3, 4]);
assert_eq!(shape, vec![2, 2]);
}
_ => panic!("Expected UintTensor"),
}
}
#[test]
fn test_pixel_values_flat() {
let mut pixel_values = Array4::<f32>::zeros((1, 1, 2, 2));
pixel_values[[0, 0, 0, 0]] = 1.0;
pixel_values[[0, 0, 0, 1]] = 2.0;
pixel_values[[0, 0, 1, 0]] = 3.0;
pixel_values[[0, 0, 1, 1]] = 4.0;
let images = PreprocessedImages::new(pixel_values, vec![4], vec![(2, 2)]);
let flat = images.pixel_values_flat();
assert_eq!(flat, vec![1.0, 2.0, 3.0, 4.0]);
}
#[test]
fn test_registry_with_defaults() {
let registry = ImageProcessorRegistry::with_defaults();
// Should find LLaVA processor
assert!(registry.has_processor("llava-hf/llava-1.5-7b-hf"));
assert!(registry.has_processor("liuhaotian/llava-v1.5-7b"));
// Should find LLaVA-NeXT processor
assert!(registry.has_processor("llava-hf/llava-v1.6-mistral-7b-hf"));
assert!(registry.has_processor("lmms-lab/llava-next-interleave-qwen-7b"));
// Get the processor and check model name
let processor = registry.find("llava-hf/llava-1.5-7b-hf").unwrap();
assert_eq!(processor.model_name(), "llava");
}
#[test]
fn test_registry_find() {
let mut registry = ImageProcessorRegistry::new();
// Create a mock processor using LlavaProcessor
registry.register(
"test-model",
Box::new(crate::multimodal::vision::processors::LlavaProcessor::new()),
);
assert!(registry.has_processor("test-model-7b"));
assert!(registry.has_processor("TEST-MODEL"));
assert!(!registry.has_processor("other-model"));
}
}
sgl-model-gateway/src/multimodal/vision/mod.rs
-46
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@@ -1,46 +0,0 @@
//! Pure Rust vision processing module for multimodal models.
//!
//! This module provides image preprocessing pipelines that match HuggingFace processor
//! outputs without requiring Python dependencies.
//!
//! # Architecture
//!
//! The vision module is structured as follows:
//!
//! - `transforms`: Core image transformations (resize, normalize, crop, etc.)
//! - `preprocessor_config`: HuggingFace config parsing
//! - `image_processor`: Trait and output types for processors
//! - `processors`: Model-specific implementations (LLaVA, Qwen-VL, etc.)
//!
//! # Usage
//!
//! ```rust,ignore
//! use smg::multimodal::vision::{
//! PreProcessorConfig,
//! processors::LlavaProcessor,
//! ImagePreProcessor,
//! };
//!
//! // Load config from HuggingFace
//! let config = PreProcessorConfig::from_json(config_json)?;
//!
//! // Create processor and preprocess images
//! let processor = LlavaProcessor::new();
//! let result = processor.preprocess(&images, &config)?;
//! ```
pub mod image_processor;
pub mod preprocessor_config;
pub mod processors;
pub mod transforms;
// Re-export commonly used types
pub use image_processor::{
ImagePreProcessor, ImageProcessorRegistry, ModelSpecificValue, PreprocessedImages,
};
pub use preprocessor_config::PreProcessorConfig;
pub use processors::{
Llama4VisionProcessor, LlavaNextProcessor, LlavaProcessor, Phi3VisionProcessor,
Phi4VisionProcessor, PixtralProcessor, Qwen2VLProcessor, Qwen3VLProcessor,
};
pub use transforms::TransformError;
sgl-model-gateway/src/multimodal/vision/preprocessor_config.rs
-470
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@@ -1,470 +0,0 @@
//! HuggingFace preprocessor_config.json parsing.
//!
//! This module parses the `preprocessor_config.json` files from HuggingFace model
//! repositories, providing the configuration needed for image preprocessing.
use std::collections::HashMap;
use image::imageops::FilterType;
use serde::{Deserialize, Deserializer};
use super::transforms;
/// Struct to represent patch_size as dict {"height": x, "width": y}
#[derive(Debug, Clone, Deserialize, Default)]
pub struct PatchSize {
pub height: Option<u32>,
pub width: Option<u32>,
}
/// Custom deserializer for patch_size that handles both integer and dict formats.
/// - Integer format: `"patch_size": 16` -> PatchSize { height: 16, width: 16 }
/// - Dict format: `"patch_size": {"height": 16, "width": 16}` -> PatchSize { height: 16, width: 16 }
fn deserialize_patch_size<'de, D>(deserializer: D) -> Result<Option<PatchSize>, D::Error>
where
D: Deserializer<'de>,
{
use std::fmt;
use serde::de::{self, MapAccess, Visitor};
struct PatchSizeVisitor;
impl<'de> Visitor<'de> for PatchSizeVisitor {
type Value = Option<PatchSize>;
fn expecting(&self, formatter: &mut fmt::Formatter) -> fmt::Result {
formatter.write_str("an integer, a dict with height/width, or null")
}
fn visit_none<E>(self) -> Result<Self::Value, E>
where
E: de::Error,
{
Ok(None)
}
fn visit_unit<E>(self) -> Result<Self::Value, E>
where
E: de::Error,
{
Ok(None)
}
fn visit_i64<E>(self, value: i64) -> Result<Self::Value, E>
where
E: de::Error,
{
let v = value as u32;
Ok(Some(PatchSize {
height: Some(v),
width: Some(v),
}))
}
fn visit_u64<E>(self, value: u64) -> Result<Self::Value, E>
where
E: de::Error,
{
let v = value as u32;
Ok(Some(PatchSize {
height: Some(v),
width: Some(v),
}))
}
fn visit_map<M>(self, mut map: M) -> Result<Self::Value, M::Error>
where
M: MapAccess<'de>,
{
let mut height = None;
let mut width = None;
while let Some(key) = map.next_key::<String>()? {
match key.as_str() {
"height" => height = Some(map.next_value::<u32>()?),
"width" => width = Some(map.next_value::<u32>()?),
_ => {
let _ = map.next_value::<de::IgnoredAny>()?;
}
}
}
Ok(Some(PatchSize { height, width }))
}
}
deserializer.deserialize_any(PatchSizeVisitor)
}
/// HuggingFace preprocessor_config.json structure.
///
/// This struct captures the common fields across different vision model processors.
/// Model-specific fields are accessed via the flexible `extra` field.
#[derive(Debug, Clone, Deserialize, Default)]
pub struct PreProcessorConfig {
/// Processor class name (e.g., "CLIPImageProcessor", "Qwen2VLImageProcessor")
#[serde(default)]
pub image_processor_type: Option<String>,
/// Whether to convert to RGB
#[serde(default)]
pub do_convert_rgb: Option<bool>,
/// Whether to normalize with mean/std
#[serde(default)]
pub do_normalize: Option<bool>,
/// Whether to pad images
#[serde(default)]
pub do_pad: Option<bool>,
/// Whether to rescale pixel values (typically by 1/255)
#[serde(default)]
pub do_rescale: Option<bool>,
/// Whether to resize images
#[serde(default)]
pub do_resize: Option<bool>,
/// Whether to center crop after resizing
#[serde(default)]
pub do_center_crop: Option<bool>,
/// Per-channel normalization mean
#[serde(default, alias = "norm_mean")]
pub image_mean: Option<Vec<f64>>,
/// Per-channel normalization std
#[serde(default, alias = "norm_std")]
pub image_std: Option<Vec<f64>>,
/// Rescale factor (typically 1/255 = 0.00392156862745098)
#[serde(default)]
pub rescale_factor: Option<f64>,
/// PIL resampling filter enum (0=Nearest, 1=Lanczos, 2=Bilinear, 3=Bicubic)
#[serde(default, alias = "resample")]
pub resampling: Option<usize>,
/// Target size for resizing
/// Can be {"height": H, "width": W} or {"shortest_edge": S}
#[serde(default)]
pub size: Option<HashMap<String, u32>>,
/// Target size for center cropping
#[serde(default)]
pub crop_size: Option<HashMap<String, u32>>,
// =====================
// Model-specific fields
// =====================
/// Vision encoder patch size (typically 14 or 16)
/// Can be an integer or a dict {"height": x, "width": y}
#[serde(default, deserialize_with = "deserialize_patch_size")]
pub patch_size: Option<PatchSize>,
/// Qwen-VL: merge size for token reduction
#[serde(default)]
pub merge_size: Option<usize>,
/// Qwen-VL: minimum total pixels
#[serde(default)]
pub min_pixels: Option<usize>,
/// Qwen-VL: maximum total pixels
#[serde(default)]
pub max_pixels: Option<usize>,
/// Qwen-VL: temporal patch size for video
#[serde(default)]
pub temporal_patch_size: Option<usize>,
/// Phi3-Vision: number of image crops
#[serde(default)]
pub num_crops: Option<usize>,
/// Phi4-Vision: dynamic HD max crops
#[serde(default)]
pub dynamic_hd: Option<usize>,
/// LLaMA-Vision: maximum image tiles
#[serde(default)]
pub max_image_tiles: Option<usize>,
/// Fixed number of image tokens (some models use this)
#[serde(default)]
pub num_img_tokens: Option<usize>,
// =====================
// Special tokens
// =====================
/// Image start token
#[serde(default)]
pub im_start_token: Option<String>,
/// Image end token
#[serde(default)]
pub im_end_token: Option<String>,
/// Slice start token (for multi-crop)
#[serde(default)]
pub slice_start_token: Option<String>,
/// Slice end token
#[serde(default)]
pub slice_end_token: Option<String>,
/// Vision start token (alternative naming)
#[serde(default)]
pub vision_start_token: Option<String>,
/// Vision end token
#[serde(default)]
pub vision_end_token: Option<String>,
/// Catch-all for model-specific fields not explicitly defined
#[serde(flatten)]
pub extra: HashMap<String, serde_json::Value>,
}
impl PreProcessorConfig {
/// Parse from JSON string.
pub fn from_json(json: &str) -> Result<Self, serde_json::Error> {
serde_json::from_str(json)
}
/// Parse from JSON value.
pub fn from_value(value: serde_json::Value) -> Result<Self, serde_json::Error> {
serde_json::from_value(value)
}
/// Get patch size as a simple usize.
///
/// Returns the height value from PatchSize if available, falling back to provided default.
pub fn get_patch_size(&self, default: usize) -> usize {
self.patch_size
.as_ref()
.and_then(|p| p.height)
.map(|h| h as usize)
.unwrap_or(default)
}
/// Get image mean as fixed array, with fallback to CLIP defaults.
pub fn get_image_mean(&self) -> [f64; 3] {
self.image_mean
.as_ref()
.and_then(|v| {
if v.len() >= 3 {
Some([v[0], v[1], v[2]])
} else {
None
}
})
.unwrap_or(Self::CLIP_MEAN)
}
/// Get image std as fixed array, with fallback to CLIP defaults.
pub fn get_image_std(&self) -> [f64; 3] {
self.image_std
.as_ref()
.and_then(|v| {
if v.len() >= 3 {
Some([v[0], v[1], v[2]])
} else {
None
}
})
.unwrap_or(Self::CLIP_STD)
}
/// Get target size from various config formats.
///
/// Handles both `{"height": H, "width": W}` and `{"shortest_edge": S}` formats.
/// Returns (height, width).
pub fn get_target_size(&self) -> Option<(u32, u32)> {
self.size.as_ref().map(|s| {
// Try explicit height/width first
let h = s
.get("height")
.or_else(|| s.get("shortest_edge"))
.copied()
.unwrap_or(224);
let w = s
.get("width")
.or_else(|| s.get("shortest_edge"))
.copied()
.unwrap_or(224);
(h, w)
})
}
/// Get crop size.
///
/// Returns (height, width).
pub fn get_crop_size(&self) -> Option<(u32, u32)> {
self.crop_size.as_ref().map(|s| {
let h = s.get("height").copied().unwrap_or(224);
let w = s.get("width").copied().unwrap_or(224);
(h, w)
})
}
/// Get the interpolation filter for resizing.
pub fn get_filter(&self) -> FilterType {
transforms::pil_to_filter(self.resampling)
}
/// Check if normalization should be applied.
pub fn should_normalize(&self) -> bool {
self.do_normalize.unwrap_or(true)
}
/// Check if rescaling should be applied.
pub fn should_rescale(&self) -> bool {
self.do_rescale.unwrap_or(false)
}
/// Check if resizing should be applied.
pub fn should_resize(&self) -> bool {
self.do_resize.unwrap_or(true)
}
/// Check if center cropping should be applied.
pub fn should_center_crop(&self) -> bool {
self.do_center_crop.unwrap_or(false)
}
/// Get rescale factor with default.
pub fn get_rescale_factor(&self) -> f64 {
self.rescale_factor.unwrap_or(1.0 / 255.0)
}
/// Get a typed extra field.
pub fn get_extra<T: serde::de::DeserializeOwned>(&self, key: &str) -> Option<T> {
self.extra
.get(key)
.and_then(|v| serde_json::from_value(v.clone()).ok())
}
// Common default values
pub const CLIP_MEAN: [f64; 3] = [0.48145466, 0.4578275, 0.40821073];
pub const CLIP_STD: [f64; 3] = [0.26862954, 0.26130258, 0.27577711];
pub const IMAGENET_MEAN: [f64; 3] = [0.485, 0.456, 0.406];
pub const IMAGENET_STD: [f64; 3] = [0.229, 0.224, 0.225];
pub const SIGLIP_MEAN: [f64; 3] = [0.5, 0.5, 0.5];
pub const SIGLIP_STD: [f64; 3] = [0.5, 0.5, 0.5];
}
#[cfg(test)]
mod tests {
use super::*;
#[test]
fn test_parse_clip_config() {
let json = r#"{
"do_center_crop": true,
"do_normalize": true,
"do_resize": true,
"image_mean": [0.48145466, 0.4578275, 0.40821073],
"image_std": [0.26862954, 0.26130258, 0.27577711],
"resample": 3,
"size": {"shortest_edge": 224}
}"#;
let config = PreProcessorConfig::from_json(json).unwrap();
assert!(config.should_normalize());
assert!(config.should_center_crop());
assert!(config.should_resize());
assert_eq!(config.resampling, Some(3));
let (h, w) = config.get_target_size().unwrap();
assert_eq!(h, 224);
assert_eq!(w, 224);
let mean = config.get_image_mean();
assert!((mean[0] - 0.48145466).abs() < 1e-6);
}
#[test]
fn test_parse_qwen_vl_config() {
let json = r#"{
"do_normalize": true,
"do_rescale": true,
"do_resize": true,
"image_mean": [0.48145466, 0.4578275, 0.40821073],
"image_std": [0.26862954, 0.26130258, 0.27577711],
"min_pixels": 200704,
"max_pixels": 1003520,
"patch_size": 14,
"merge_size": 2,
"temporal_patch_size": 2,
"rescale_factor": 0.00392156862745098
}"#;
let config = PreProcessorConfig::from_json(json).unwrap();
assert_eq!(config.min_pixels, Some(200704));
assert_eq!(config.max_pixels, Some(1003520));
assert_eq!(config.get_patch_size(0), 14);
assert_eq!(config.merge_size, Some(2));
assert!((config.get_rescale_factor() - 1.0 / 255.0).abs() < 1e-10);
}
#[test]
fn test_parse_size_formats() {
// Height/width format
let json1 = r#"{"size": {"height": 336, "width": 336}}"#;
let config1 = PreProcessorConfig::from_json(json1).unwrap();
assert_eq!(config1.get_target_size(), Some((336, 336)));
// Shortest edge format
let json2 = r#"{"size": {"shortest_edge": 224}}"#;
let config2 = PreProcessorConfig::from_json(json2).unwrap();
assert_eq!(config2.get_target_size(), Some((224, 224)));
}
#[test]
fn test_defaults() {
let config = PreProcessorConfig::default();
// Should use CLIP defaults when not specified
let mean = config.get_image_mean();
assert!((mean[0] - PreProcessorConfig::CLIP_MEAN[0]).abs() < 1e-6);
// Default behaviors
assert!(config.should_normalize()); // true by default
assert!(!config.should_rescale()); // false by default
assert!(config.should_resize()); // true by default
assert!(!config.should_center_crop()); // false by default
}
#[test]
fn test_filter_conversion() {
let json = r#"{"resampling": 3}"#;
let config = PreProcessorConfig::from_json(json).unwrap();
assert!(matches!(config.get_filter(), FilterType::CatmullRom));
}
#[test]
fn test_extra_fields() {
let json = r#"{
"custom_field": 42,
"nested": {"foo": "bar"}
}"#;
let config = PreProcessorConfig::from_json(json).unwrap();
let custom: Option<i32> = config.get_extra("custom_field");
assert_eq!(custom, Some(42));
let nested: Option<HashMap<String, String>> = config.get_extra("nested");
assert_eq!(
nested.as_ref().unwrap().get("foo"),
Some(&"bar".to_string())
);
}
}
sgl-model-gateway/src/multimodal/vision/processors/llama4_vision.rs
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@@ -1,688 +0,0 @@
//! LLaMA 4 Vision image processor.
//!
//! This module implements the LLaMA 4 Vision (Llama-4-Scout, Llama-4-Maverick) image preprocessing
//! pipeline with tile-based processing similar to other dynamic resolution models.
//!
//! # Key Features
//!
//! | Feature | Value |
//! |---------|-------|
//! | Tile size | 336x336 |
//! | Default max_patches | 16 |
//! | Normalization | [0.5, 0.5, 0.5] mean/std |
//! | Interpolation | Bilinear |
//! | Global tile | Added when num_tiles > 1 |
//!
//! # Processing Pipeline
//!
//! 1. **Find supported resolutions**: Calculate valid tile configurations
//! 2. **Get best fit**: Find optimal resolution without distortion
//! 3. **Resize**: Scale to target resolution maintaining aspect ratio
//! 4. **Pad**: Add black padding (0) to reach target dimensions
//! 5. **Normalize**: Apply [0.5, 0.5, 0.5] mean/std normalization
//! 6. **Tile**: Split into (num_tiles_h * num_tiles_w, 3, 336, 336) tiles
//! 7. **Global tile**: If multiple tiles, add global view at the end
//!
//! # Token Count
//!
//! For LLaMA 4, tokens = num_tiles * (tile_size / patch_size)²
//! where patch_size is typically 14, giving 576 tokens per tile.
use std::collections::HashSet;
use image::{imageops::FilterType, DynamicImage, GenericImageView, Rgb, RgbImage};
use ndarray::{s, Array3, Array4, IxDyn};
use crate::multimodal::vision::{
image_processor::{ImagePreProcessor, ModelSpecificValue, PreprocessedImages},
preprocessor_config::PreProcessorConfig,
transforms::{self, TransformError},
};
/// Default normalization mean for LLaMA 4 Vision.
pub const LLAMA4_MEAN: [f64; 3] = [0.5, 0.5, 0.5];
/// Default normalization std for LLaMA 4 Vision.
pub const LLAMA4_STD: [f64; 3] = [0.5, 0.5, 0.5];
/// Default tile size for LLaMA 4 Vision.
pub const TILE_SIZE: u32 = 336;
/// Default maximum number of patches/tiles.
pub const DEFAULT_MAX_PATCHES: usize = 16;
/// Patch size used in vision encoder.
pub const PATCH_SIZE: usize = 14;
/// LLaMA 4 Vision image processor.
///
/// Implements tile-based processing with dynamic resolution selection.
#[derive(Debug, Clone)]
pub struct Llama4VisionProcessor {
/// Tile size (both height and width).
tile_size: u32,
/// Maximum number of tiles/patches.
max_patches: usize,
/// Whether to resize to max canvas (upscale aggressively).
resize_to_max_canvas: bool,
/// Normalization mean.
mean: [f64; 3],
/// Normalization std.
std: [f64; 3],
}
impl Default for Llama4VisionProcessor {
fn default() -> Self {
Self::new()
}
}
impl Llama4VisionProcessor {
/// Create a new LLaMA 4 Vision processor with default settings.
pub fn new() -> Self {
Self {
tile_size: TILE_SIZE,
max_patches: DEFAULT_MAX_PATCHES,
resize_to_max_canvas: false,
mean: LLAMA4_MEAN,
std: LLAMA4_STD,
}
}
/// Create a processor with custom max_patches setting.
pub fn with_max_patches(max_patches: usize) -> Self {
Self {
tile_size: TILE_SIZE,
max_patches,
resize_to_max_canvas: false,
mean: LLAMA4_MEAN,
std: LLAMA4_STD,
}
}
/// Create a processor from preprocessor config.
pub fn from_preprocessor_config(config: &PreProcessorConfig) -> Self {
Self {
tile_size: config
.size
.as_ref()
.and_then(|s| s.get("height").copied())
.unwrap_or(TILE_SIZE),
max_patches: config.max_image_tiles.unwrap_or(DEFAULT_MAX_PATCHES),
resize_to_max_canvas: false,
mean: config
.image_mean
.as_ref()
.map(|v| [v[0], v[1], v[2]])
.unwrap_or(LLAMA4_MEAN),
std: config
.image_std
.as_ref()
.map(|v| [v[0], v[1], v[2]])
.unwrap_or(LLAMA4_STD),
}
}
/// Get the tile size.
pub fn tile_size(&self) -> u32 {
self.tile_size
}
/// Get the max patches setting.
pub fn max_patches(&self) -> usize {
self.max_patches
}
/// Get all factors of a number.
fn get_factors(n: usize) -> HashSet<usize> {
let mut factors = HashSet::new();
for i in 1..=(n as f64).sqrt() as usize {
if n.is_multiple_of(i) {
factors.insert(i);
factors.insert(n / i);
}
}
factors
}
/// Find all supported resolutions for the given max_patches.
///
/// Returns list of (height, width) in pixels.
fn find_supported_resolutions(&self) -> Vec<(u32, u32)> {
let mut resolutions = Vec::new();
let tile = self.tile_size;
// For each possible number of chunks from max_patches down to 1
for chunk_size in (1..=self.max_patches).rev() {
let factors = Self::get_factors(chunk_size);
for &factor in &factors {
let h_tiles = factor;
let w_tiles = chunk_size / factor;
resolutions.push((h_tiles as u32 * tile, w_tiles as u32 * tile));
}
}
resolutions
}
/// Get the maximum resolution without distortion.
///
/// Given an image size and target size, compute the largest size
/// that fits within target while maintaining aspect ratio.
fn get_max_res_without_distortion(
image_size: (u32, u32),
target_size: (u32, u32),
) -> (u32, u32) {
let (orig_h, orig_w) = image_size;
let (target_h, target_w) = target_size;
let scale_w = target_w as f64 / orig_w as f64;
let scale_h = target_h as f64 / orig_h as f64;
if scale_w < scale_h {
let new_w = target_w;
let new_h = (orig_h as f64 * scale_w).floor() as u32;
(new_h.min(target_h), new_w)
} else {
let new_h = target_h;
let new_w = (orig_w as f64 * scale_h).floor() as u32;
(new_h, new_w.min(target_w))
}
}
/// Find the best fitting resolution from supported resolutions.
///
/// Selects resolution that:
/// - Minimizes upscaling if possible (unless resize_to_max_canvas)
/// - Minimizes downscaling if no upscaling possible
/// - Minimizes padding area when tied
fn get_best_fit(&self, image_size: (u32, u32)) -> (u32, u32) {
let resolutions = self.find_supported_resolutions();
let (orig_h, orig_w) = image_size;
// Calculate scaling factors for each resolution
let scales_and_resolutions: Vec<(f64, (u32, u32))> = resolutions
.iter()
.map(|&(target_h, target_w)| {
let scale_w = target_w as f64 / orig_w as f64;
let scale_h = target_h as f64 / orig_h as f64;
// Limiting scale is the minimum (the side that constrains)
let scale = scale_w.min(scale_h);
(scale, (target_h, target_w))
})
.collect();
// Separate upscaling and downscaling options
let upscaling: Vec<_> = scales_and_resolutions
.iter()
.filter(|(s, _)| *s >= 1.0)
.cloned()
.collect();
let selected_scale = if !upscaling.is_empty() {
if self.resize_to_max_canvas {
// Pick largest upscaling
upscaling
.iter()
.map(|(s, _)| *s)
.fold(f64::NEG_INFINITY, f64::max)
} else {
// Pick smallest upscaling (minimum distortion)
upscaling
.iter()
.map(|(s, _)| *s)
.fold(f64::INFINITY, f64::min)
}
} else {
// No upscaling possible, pick largest downscaling (minimum reduction)
scales_and_resolutions
.iter()
.filter(|(s, _)| *s < 1.0)
.map(|(s, _)| *s)
.fold(f64::NEG_INFINITY, f64::max)
};
// Get all resolutions with the selected scale
let candidates: Vec<_> = scales_and_resolutions
.iter()
.filter(|(s, _)| (*s - selected_scale).abs() < 1e-9)
.map(|(_, res)| *res)
.collect();
// If multiple candidates, pick the one with minimum area (less padding)
if candidates.len() > 1 {
*candidates
.iter()
.min_by_key(|(h, w)| h * w)
.unwrap_or(&candidates[0])
} else {
candidates[0]
}
}
/// Pad image to target dimensions with black padding.
fn pad_image(&self, image: &DynamicImage, target_w: u32, target_h: u32) -> DynamicImage {
let (w, h) = image.dimensions();
if w == target_w && h == target_h {
return image.clone();
}
// Create black background (LLaMA 4 uses 0 for padding)
let black = Rgb([0u8, 0, 0]);
let mut padded = RgbImage::from_pixel(target_w, target_h, black);
// Copy image to top-left using efficient overlay
image::imageops::overlay(&mut padded, &image.to_rgb8(), 0, 0);
DynamicImage::ImageRgb8(padded)
}
/// Split image tensor into tiles.
fn split_to_tiles(
&self,
tensor: &Array3<f32>,
num_tiles_h: usize,
num_tiles_w: usize,
) -> Array4<f32> {
let tile = self.tile_size as usize;
let num_tiles = num_tiles_h * num_tiles_w;
let mut tiles = Array4::<f32>::zeros((num_tiles, 3, tile, tile));
for h_idx in 0..num_tiles_h {
for w_idx in 0..num_tiles_w {
let tile_idx = h_idx * num_tiles_w + w_idx;
let y_start = h_idx * tile;
let x_start = w_idx * tile;
let tile_view =
tensor.slice(s![.., y_start..y_start + tile, x_start..x_start + tile]);
tiles.slice_mut(s![tile_idx, .., .., ..]).assign(&tile_view);
}
}
tiles
}
/// Create global image by bilinear interpolation to tile size.
fn create_global_image(&self, image: &DynamicImage) -> Array3<f32> {
let tile = self.tile_size;
let resized = image.resize_exact(tile, tile, FilterType::Triangle);
let mut tensor = transforms::to_tensor(&resized);
transforms::normalize(&mut tensor, &self.mean, &self.std);
tensor
}
/// Process a single image.
fn process_single_image(
&self,
image: &DynamicImage,
) -> Result<(Array4<f32>, (usize, usize)), TransformError> {
let (orig_w, orig_h) = image.dimensions();
let image_size = (orig_h, orig_w);
// Step 1: Find best fit resolution (canvas size for padding/tiling)
let target_size = self.get_best_fit(image_size);
let (target_h, target_w) = target_size;
// Step 2: Compute resize target - limit upscaling if not resize_to_max_canvas
// This limits how much we resize the image, but we still pad to target_size
let resize_target = if !self.resize_to_max_canvas {
let tile = self.tile_size;
let new_target_h = target_h.min(orig_h.max(tile));
let new_target_w = target_w.min(orig_w.max(tile));
(new_target_h, new_target_w)
} else {
target_size
};
// Step 3: Resize preserving aspect ratio to fit within resize_target
let new_size = Self::get_max_res_without_distortion(image_size, resize_target);
let (new_h, new_w) = (new_size.0.max(1), new_size.1.max(1));
let resized = image.resize_exact(new_w, new_h, FilterType::Triangle);
// Step 4: Pad to target_size (the canvas from get_best_fit, not resize_target)
let padded = self.pad_image(&resized, target_w, target_h);
// Step 5: Convert to tensor and normalize
let mut tensor = transforms::to_tensor(&padded);
transforms::normalize(&mut tensor, &self.mean, &self.std);
// Step 6: Calculate tile counts based on target_size (canvas size)
let tile = self.tile_size as usize;
let num_tiles_h = target_h as usize / tile;
let num_tiles_w = target_w as usize / tile;
// Step 7: Split into tiles
let tiles = self.split_to_tiles(&tensor, num_tiles_h, num_tiles_w);
let num_tiles = num_tiles_h * num_tiles_w;
// Step 8: Add global tile if there are multiple tiles
let output = if num_tiles > 1 {
let global_tile = self.create_global_image(image);
let mut combined = Array4::<f32>::zeros((num_tiles + 1, 3, tile, tile));
combined
.slice_mut(s![..num_tiles, .., .., ..])
.assign(&tiles);
combined
.slice_mut(s![num_tiles, .., .., ..])
.assign(&global_tile);
combined
} else {
tiles
};
Ok((output, (num_tiles_h, num_tiles_w)))
}
/// Calculate number of image tokens for a given aspect ratio.
pub fn calculate_num_tokens_for_aspect_ratio(&self, aspect_ratio: (usize, usize)) -> usize {
let (h_tiles, w_tiles) = aspect_ratio;
let num_tiles = h_tiles * w_tiles;
// Add 1 for global tile if num_tiles > 1
let total_tiles = if num_tiles > 1 {
num_tiles + 1
} else {
num_tiles
};
let tokens_per_tile = (self.tile_size as usize / PATCH_SIZE).pow(2);
total_tiles * tokens_per_tile
}
}
impl ImagePreProcessor for Llama4VisionProcessor {
fn default_mean(&self) -> [f64; 3] {
self.mean
}
fn default_std(&self) -> [f64; 3] {
self.std
}
fn preprocess(
&self,
images: &[DynamicImage],
config: &PreProcessorConfig,
) -> Result<PreprocessedImages, TransformError> {
if images.is_empty() {
return Err(TransformError::InvalidShape {
expected: "non-empty image batch".to_string(),
actual: vec![0],
});
}
let processor = if config.max_image_tiles.is_some()
|| config.image_mean.is_some()
|| config.image_std.is_some()
|| config.size.is_some()
{
Self::from_preprocessor_config(config)
} else {
self.clone()
};
let mut all_outputs = Vec::new();
let mut all_aspect_ratios = Vec::new();
let mut image_sizes = Vec::new();
let mut num_img_tokens = Vec::new();
for image in images {
let (output, aspect_ratio) = processor.process_single_image(image)?;
let tokens = processor.calculate_num_tokens_for_aspect_ratio(aspect_ratio);
all_outputs.push(output);
all_aspect_ratios.push(aspect_ratio);
image_sizes.push((image.height(), image.width()));
num_img_tokens.push(tokens);
}
// Find max tiles across batch for padding
let max_tiles = all_outputs.iter().map(|o| o.shape()[0]).max().unwrap();
let tile = self.tile_size as usize;
// Pad all outputs to max_tiles
let batch_size = images.len();
let mut pixel_values =
ndarray::ArrayD::<f32>::zeros(IxDyn(&[batch_size, max_tiles, 3, tile, tile]));
for (b, output) in all_outputs.iter().enumerate() {
let num_tiles = output.shape()[0];
for t in 0..num_tiles {
pixel_values
.slice_mut(s![b, t, .., .., ..])
.assign(&output.slice(s![t, .., .., ..]));
}
// Remaining tiles stay as zeros (padding)
}
// Store aspect ratios as model-specific data
let mut model_specific = std::collections::HashMap::new();
let aspect_ratios_flat: Vec<u32> = all_aspect_ratios
.iter()
.flat_map(|&(h, w)| vec![h as u32, w as u32])
.collect();
model_specific.insert(
"aspect_ratios".to_string(),
ModelSpecificValue::UintTensor {
data: aspect_ratios_flat,
shape: vec![batch_size, 2],
},
);
Ok(PreprocessedImages {
pixel_values: pixel_values.into_dyn(),
num_img_tokens,
image_sizes,
model_specific,
})
}
fn calculate_num_tokens(&self, width: u32, height: u32, config: &PreProcessorConfig) -> usize {
let processor = Self::from_preprocessor_config(config);
let image_size = (height, width);
// target_size from get_best_fit determines the canvas and tile count
let target_size = processor.get_best_fit(image_size);
let tile = processor.tile_size as usize;
let num_tiles_h = target_size.0 as usize / tile;
let num_tiles_w = target_size.1 as usize / tile;
processor.calculate_num_tokens_for_aspect_ratio((num_tiles_h, num_tiles_w))
}
fn model_name(&self) -> &'static str {
"llama4-vision"
}
fn get_processed_size(&self, config: &PreProcessorConfig) -> Option<(u32, u32)> {
// For LLaMA 4, the size depends on the input image
let _ = config;
None
}
}
#[cfg(test)]
mod tests {
use super::*;
fn create_test_image(width: u32, height: u32, color: Rgb<u8>) -> DynamicImage {
DynamicImage::from(RgbImage::from_pixel(width, height, color))
}
#[test]
fn test_llama4_vision_processor_default() {
let processor = Llama4VisionProcessor::new();
assert_eq!(processor.tile_size(), TILE_SIZE);
assert_eq!(processor.max_patches(), DEFAULT_MAX_PATCHES);
assert_eq!(processor.mean, LLAMA4_MEAN);
assert_eq!(processor.std, LLAMA4_STD);
}
#[test]
fn test_get_factors() {
let factors = Llama4VisionProcessor::get_factors(12);
assert!(factors.contains(&1));
assert!(factors.contains(&2));
assert!(factors.contains(&3));
assert!(factors.contains(&4));
assert!(factors.contains(&6));
assert!(factors.contains(&12));
assert_eq!(factors.len(), 6);
}
#[test]
fn test_find_supported_resolutions() {
let processor = Llama4VisionProcessor::with_max_patches(4);
let resolutions = processor.find_supported_resolutions();
// Should include 1x1, 1x2, 2x1, 1x3, 3x1, 2x2, 1x4, 4x1
let expected: Vec<(u32, u32)> = vec![
(336, 336), // 1x1
(336, 672), // 1x2
(672, 336), // 2x1
(336, 1008), // 1x3
(1008, 336), // 3x1
(672, 672), // 2x2
(336, 1344), // 1x4
(1344, 336), // 4x1
];
for exp in expected {
assert!(
resolutions.contains(&exp),
"Expected resolution {:?} not found",
exp
);
}
}
#[test]
fn test_get_best_fit_square() {
let processor = Llama4VisionProcessor::new();
let best = processor.get_best_fit((500, 500));
// Square image should get a square or near-square resolution
assert!(best.0 == best.1 || (best.0 as i32 - best.1 as i32).abs() <= 336);
}
#[test]
fn test_get_best_fit_wide() {
let processor = Llama4VisionProcessor::new();
let best = processor.get_best_fit((300, 900));
// Wide image should get wider resolution
assert!(best.1 >= best.0);
}
#[test]
fn test_get_best_fit_tall() {
let processor = Llama4VisionProcessor::new();
let best = processor.get_best_fit((900, 300));
// Tall image should get taller resolution
assert!(best.0 >= best.1);
}
#[test]
fn test_preprocess_square_image() {
let processor = Llama4VisionProcessor::new();
let config = PreProcessorConfig::default();
let image = create_test_image(500, 500, Rgb([128, 128, 128]));
let result = processor.preprocess(&[image], &config).unwrap();
assert_eq!(result.batch_size(), 1);
assert!(result.num_img_tokens[0] > 0);
// Check pixel values are normalized
let flat = result.pixel_values_flat();
assert!(flat.iter().all(|&v| (-1.5..=1.5).contains(&v)));
}
#[test]
fn test_preprocess_wide_image() {
let processor = Llama4VisionProcessor::new();
let config = PreProcessorConfig::default();
let image = create_test_image(1000, 300, Rgb([128, 128, 128]));
let result = processor.preprocess(&[image], &config).unwrap();
assert_eq!(result.batch_size(), 1);
// Wide image should have more tiles in width direction
let aspect_ratios = result.model_specific.get("aspect_ratios").unwrap();
if let ModelSpecificValue::UintTensor { data, .. } = aspect_ratios {
let h_tiles = data[0];
let w_tiles = data[1];
assert!(w_tiles >= h_tiles);
}
}
#[test]
fn test_preprocess_multiple_images() {
let processor = Llama4VisionProcessor::new();
let config = PreProcessorConfig::default();
let images = vec![
create_test_image(500, 500, Rgb([100, 100, 100])),
create_test_image(800, 400, Rgb([150, 150, 150])),
];
let result = processor.preprocess(&images, &config).unwrap();
assert_eq!(result.batch_size(), 2);
assert_eq!(result.image_sizes.len(), 2);
assert_eq!(result.num_img_tokens.len(), 2);
}
#[test]
fn test_global_tile_added_for_multiple_tiles() {
let processor = Llama4VisionProcessor::new();
let config = PreProcessorConfig::default();
// Large image that will require multiple tiles
let image = create_test_image(1000, 1000, Rgb([128, 128, 128]));
let result = processor.preprocess(&[image], &config).unwrap();
let aspect_ratios = result.model_specific.get("aspect_ratios").unwrap();
if let ModelSpecificValue::UintTensor { data, .. } = aspect_ratios {
let h_tiles = data[0] as usize;
let w_tiles = data[1] as usize;
let num_tiles = h_tiles * w_tiles;
if num_tiles > 1 {
// Output should have num_tiles + 1 (for global tile)
let shape = result.pixel_values.shape();
assert_eq!(shape[1], num_tiles + 1);
}
}
}
#[test]
fn test_model_name() {
let processor = Llama4VisionProcessor::new();
assert_eq!(processor.model_name(), "llama4-vision");
}
#[test]
fn test_normalization_values() {
let processor = Llama4VisionProcessor::new();
assert_eq!(processor.default_mean(), [0.5, 0.5, 0.5]);
assert_eq!(processor.default_std(), [0.5, 0.5, 0.5]);
}
#[test]
fn test_token_count_calculation() {
let processor = Llama4VisionProcessor::new();
// 1x1 tile: 576 tokens
assert_eq!(processor.calculate_num_tokens_for_aspect_ratio((1, 1)), 576);
// 2x2 tiles + 1 global: 5 * 576 = 2880 tokens
assert_eq!(
processor.calculate_num_tokens_for_aspect_ratio((2, 2)),
2880
);
// 1x2 tiles + 1 global: 3 * 576 = 1728 tokens
assert_eq!(
processor.calculate_num_tokens_for_aspect_ratio((1, 2)),
1728
);
}
}
sgl-model-gateway/src/multimodal/vision/processors/llava.rs
-869
View File
@@ -1,869 +0,0 @@
//! LLaVA family image processors.
//!
//! This module implements preprocessing for:
//! - LLaVA 1.5: CLIP-based preprocessing with configurable aspect ratio handling
//! - LLaVA-NeXT: Multi-crop anyres processing for higher resolution
//!
//! # Image Aspect Ratio Modes
//!
//! The processing behavior depends on the `image_aspect_ratio` config:
//!
//! - **None/Square**: Standard CLIP processing (resize shortest edge, center crop)
//! - **"pad"**: Expand to square with mean color padding, then resize
//! - **"anyres"**: Multi-crop processing for higher resolution (LLaVA-NeXT)
//!
//! # Processing Pipeline
//!
//! ## LLaVA 1.5 (Standard - no expand_to_square)
//! Used for `llava-hf/*` models where `image_aspect_ratio` is not set:
//! 1. Resize so shortest edge = target_size (preserving aspect ratio)
//! 2. Center crop to target_size x target_size
//! 3. Rescale by 1/255
//! 4. Normalize with CLIP mean/std
//!
//! ## LLaVA 1.5 (Pad mode - with expand_to_square)
//! Used for `liuhaotian/llava-*` models where `image_aspect_ratio = "pad"`:
//! 1. Expand image to square by padding with mean color
//! 2. Resize to target size (typically 336x336)
//! 3. Normalize with CLIP mean/std
//!
//! ## LLaVA-NeXT
//! 1. Select best resolution from grid pinpoints
//! 2. Resize and pad to best resolution
//! 3. Divide into crops
//! 4. Process each crop + original resized image
//! 5. Stack all processed patches
use image::{DynamicImage, GenericImageView};
use ndarray::Array3;
use crate::multimodal::vision::{
image_processor::{ImagePreProcessor, PreprocessedImages},
preprocessor_config::PreProcessorConfig,
transforms::{
center_crop, expand_to_square, mean_to_rgb, normalize, pil_to_filter, resize, stack_batch,
to_tensor, TransformError,
},
};
/// CLIP normalization mean values used by LLaVA models.
pub const CLIP_MEAN: [f64; 3] = [0.48145466, 0.4578275, 0.40821073];
/// CLIP normalization std values used by LLaVA models.
pub const CLIP_STD: [f64; 3] = [0.26862954, 0.26130258, 0.27577711];
/// Image aspect ratio handling mode.
#[derive(Debug, Clone, Default, PartialEq, Eq)]
pub enum ImageAspectRatio {
/// Standard CLIP processing: resize shortest edge, center crop.
/// Used for llava-hf/* models where image_aspect_ratio is not set.
#[default]
Square,
/// Expand to square with mean color padding, then resize.
/// Used for liuhaotian/llava-* models where image_aspect_ratio = "pad".
Pad,
/// Multi-crop anyres processing (handled by LlavaNextProcessor).
Anyres,
}
impl std::str::FromStr for ImageAspectRatio {
type Err = std::convert::Infallible;
/// Parse from string value (e.g., from config).
fn from_str(s: &str) -> Result<Self, Self::Err> {
Ok(match s.to_lowercase().as_str() {
"pad" => Self::Pad,
"anyres" => Self::Anyres,
_ if s.contains("anyres") => Self::Anyres, // anyres_max_12, etc.
_ => Self::Square,
})
}
}
/// LLaVA 1.5 image processor.
///
/// Implements CLIP-based preprocessing with configurable aspect ratio handling.
/// This processor is used for LLaVA 1.5 and similar models that expect fixed-size
/// square inputs.
///
/// # Aspect Ratio Modes
///
/// - `Square` (default): Standard CLIP processing (llava-hf/*)
/// - `Pad`: Expand to square with mean padding (liuhaotian/llava-*)
///
/// # Token Calculation
///
/// For LLaVA 1.5, the number of image tokens is:
/// ```text
/// num_tokens = (image_size / patch_size)²
/// ```
/// With default settings (336x336, patch_size=14): 576 tokens
#[derive(Debug, Clone)]
pub struct LlavaProcessor {
/// Patch size for token calculation (typically 14)
pub patch_size: u32,
/// Target image size after processing (typically 336)
pub image_size: u32,
/// Image aspect ratio handling mode
pub aspect_ratio: ImageAspectRatio,
}
impl Default for LlavaProcessor {
fn default() -> Self {
Self::new()
}
}
impl LlavaProcessor {
/// Create a new LLaVA 1.5 processor with default settings.
///
/// Default: patch_size=14, image_size=336, aspect_ratio=Square
/// This matches the llava-hf/* model behavior.
pub fn new() -> Self {
Self {
patch_size: 14,
image_size: 336,
aspect_ratio: ImageAspectRatio::Square,
}
}
/// Create a processor with "pad" aspect ratio mode.
///
/// This matches the liuhaotian/llava-* model behavior where
/// images are expanded to square before processing.
pub fn new_with_pad() -> Self {
Self {
patch_size: 14,
image_size: 336,
aspect_ratio: ImageAspectRatio::Pad,
}
}
/// Create a processor with custom settings.
pub fn with_config(patch_size: u32, image_size: u32, aspect_ratio: ImageAspectRatio) -> Self {
Self {
patch_size,
image_size,
aspect_ratio,
}
}
/// Create a processor from model config JSON.
///
/// Extracts patch_size, image_size, and image_aspect_ratio from config.
pub fn from_config(config: &serde_json::Value) -> Self {
let patch_size = config
.get("vision_config")
.and_then(|v| v.get("patch_size"))
.and_then(|v| v.as_u64())
.map(|v| v as u32)
.unwrap_or(14);
let image_size = config
.get("vision_config")
.and_then(|v| v.get("image_size"))
.and_then(|v| v.as_u64())
.map(|v| v as u32)
.unwrap_or(336);
let aspect_ratio = config
.get("image_aspect_ratio")
.and_then(|v| v.as_str())
.and_then(|s| s.parse().ok())
.unwrap_or_default();
Self {
patch_size,
image_size,
aspect_ratio,
}
}
/// Process a single image through the LLaVA 1.5 pipeline.
///
/// The processing flow depends on `self.aspect_ratio`:
/// - `Square`: Standard CLIP (resize shortest edge, center crop)
/// - `Pad`: Expand to square with mean padding, then resize
fn process_one_image(
&self,
image: &DynamicImage,
config: &PreProcessorConfig,
) -> Result<Array3<f32>, TransformError> {
let mean = config.get_image_mean();
let std = config.get_image_std();
let filter = pil_to_filter(config.resampling);
// Get target size from config or use default
let target_size = config
.get_target_size()
.map(|(h, _w)| h)
.unwrap_or(self.image_size);
// Get crop size (may be different from target_size)
let crop_size = config
.get_crop_size()
.map(|(h, _w)| h)
.unwrap_or(target_size);
let processed = match self.aspect_ratio {
ImageAspectRatio::Pad => {
// Pad mode: expand to square with mean color padding, then resize
let mean_color = mean_to_rgb(&mean);
let squared = expand_to_square(image, mean_color);
// Resize to target size (maintaining square)
if config.do_resize.unwrap_or(true) {
resize(&squared, target_size, target_size, filter)
} else {
squared
}
}
ImageAspectRatio::Square | ImageAspectRatio::Anyres => {
// Square mode: Standard CLIP processing
// 1. Resize so shortest edge = target_size (preserving aspect ratio)
// 2. Center crop to crop_size x crop_size
let resized = if config.do_resize.unwrap_or(true) {
// Resize so shortest edge = target_size
let (w, h) = image.dimensions();
let scale = if w < h {
target_size as f32 / w as f32
} else {
target_size as f32 / h as f32
};
let new_w = (w as f32 * scale).round() as u32;
let new_h = (h as f32 * scale).round() as u32;
resize(image, new_w, new_h, filter)
} else {
image.clone()
};
// Center crop to crop_size
if config.do_center_crop.unwrap_or(true) {
center_crop(&resized, crop_size, crop_size)
} else {
resized
}
}
};
// Convert to tensor [C, H, W] normalized to [0, 1]
let mut tensor = to_tensor(&processed);
// Normalize with mean/std
if config.do_normalize.unwrap_or(true) {
normalize(&mut tensor, &mean, &std);
}
Ok(tensor)
}
}
impl ImagePreProcessor for LlavaProcessor {
fn default_mean(&self) -> [f64; 3] {
CLIP_MEAN
}
fn default_std(&self) -> [f64; 3] {
CLIP_STD
}
fn preprocess(
&self,
images: &[DynamicImage],
config: &PreProcessorConfig,
) -> Result<PreprocessedImages, TransformError> {
if images.is_empty() {
return Err(TransformError::EmptyBatch);
}
// Store original sizes
let image_sizes: Vec<(u32, u32)> = images.iter().map(|img| img.dimensions()).collect();
// Process each image
let tensors: Vec<Array3<f32>> = images
.iter()
.map(|img| self.process_one_image(img, config))
.collect::<Result<Vec<_>, _>>()?;
// Stack into batch
let pixel_values = stack_batch(&tensors)?;
// Calculate token counts
let num_img_tokens: Vec<usize> = images
.iter()
.map(|_| self.calculate_num_tokens(self.image_size, self.image_size, config))
.collect();
Ok(PreprocessedImages::new(
pixel_values,
num_img_tokens,
image_sizes,
))
}
fn calculate_num_tokens(
&self,
_width: u32,
_height: u32,
config: &PreProcessorConfig,
) -> usize {
// For LLaVA 1.5, token count is based on processed image size and patch size
let patch_size = config.get_patch_size(self.patch_size as usize) as u32;
let image_size = config
.get_target_size()
.map(|(h, _w)| h)
.unwrap_or(self.image_size);
let patches_per_side = image_size / patch_size;
(patches_per_side * patches_per_side) as usize
}
fn model_name(&self) -> &'static str {
"llava"
}
fn get_processed_size(&self, config: &PreProcessorConfig) -> Option<(u32, u32)> {
let size = config
.get_target_size()
.map(|(h, _w)| h)
.unwrap_or(self.image_size);
Some((size, size))
}
}
// ============================================================================
// LLaVA-NeXT (Anyres) Support
// ============================================================================
/// LLaVA-NeXT image processor with anyres (multi-crop) support.
///
/// LLaVA-NeXT processes high-resolution images by:
/// 1. Selecting the best resolution from predefined grid pinpoints
/// 2. Resizing and padding the image to that resolution
/// 3. Dividing into crops
/// 4. Processing each crop plus the original resized image
///
/// # Token Calculation
///
/// For LLaVA-NeXT, the number of tokens depends on the selected resolution:
/// ```text
/// base_tokens = (image_size / patch_size)²
/// grid_shape = (best_width / patch_size, best_height / patch_size)
/// unpad_shape = adjusted for aspect ratio
/// total_tokens = base_tokens + (unpad_w + 1) * unpad_h
/// ```
#[derive(Debug, Clone)]
pub struct LlavaNextProcessor {
/// Base processor for individual patches
pub base: LlavaProcessor,
/// Grid pinpoints for resolution selection [(width, height), ...]
pub image_grid_pinpoints: Vec<(u32, u32)>,
}
impl Default for LlavaNextProcessor {
fn default() -> Self {
Self::new()
}
}
impl LlavaNextProcessor {
/// Create a new LLaVA-NeXT processor with default settings.
///
/// Default grid pinpoints are common LLaVA-NeXT resolutions.
pub fn new() -> Self {
Self {
base: LlavaProcessor::new(),
// Common LLaVA-NeXT grid pinpoints
image_grid_pinpoints: vec![
(336, 672),
(672, 336),
(672, 672),
(1008, 336),
(336, 1008),
],
}
}
/// Create a processor with custom grid pinpoints.
pub fn with_grid_pinpoints(grid_pinpoints: Vec<(u32, u32)>) -> Self {
Self {
base: LlavaProcessor::new(),
image_grid_pinpoints: grid_pinpoints,
}
}
/// Create a processor from model config.
pub fn from_config(config: &serde_json::Value) -> Self {
let base = LlavaProcessor::from_config(config);
let grid_pinpoints = config
.get("image_grid_pinpoints")
.and_then(|v| v.as_array())
.map(|arr| {
arr.iter()
.filter_map(|p| {
let pair = p.as_array()?;
let w = pair.first()?.as_u64()? as u32;
let h = pair.get(1)?.as_u64()? as u32;
Some((w, h))
})
.collect()
})
.unwrap_or_else(|| vec![(336, 672), (672, 336), (672, 672), (1008, 336), (336, 1008)]);
Self {
base,
image_grid_pinpoints: grid_pinpoints,
}
}
/// Select the best resolution from grid pinpoints for the given image.
///
/// Minimizes wasted pixels while maximizing effective resolution.
pub fn select_best_resolution(&self, original_size: (u32, u32)) -> (u32, u32) {
select_best_resolution(original_size, &self.image_grid_pinpoints)
}
/// Get the grid shape (in patches) for anyres processing.
pub fn get_anyres_grid_shape(&self, image_size: (u32, u32)) -> (u32, u32) {
let (width, height) = self.select_best_resolution(image_size);
(width / self.base.patch_size, height / self.base.patch_size)
}
/// Calculate unpad dimensions based on original aspect ratio.
pub fn calculate_unpad(&self, grid_shape: (u32, u32), original_size: (u32, u32)) -> (u32, u32) {
calculate_unpad(grid_shape, original_size)
}
/// Resize and pad image to target resolution, maintaining aspect ratio.
fn resize_and_pad_image(&self, image: &DynamicImage, target: (u32, u32)) -> DynamicImage {
resize_and_pad_image(image, target)
}
/// Divide image into crops of specified size.
fn divide_to_samples(&self, image: &DynamicImage, crop_size: (u32, u32)) -> Vec<DynamicImage> {
divide_to_samples(image, crop_size)
}
/// Process a single patch/crop.
fn process_patch(
&self,
image: &DynamicImage,
config: &PreProcessorConfig,
) -> Result<Array3<f32>, TransformError> {
let mean = config.get_image_mean();
let std = config.get_image_std();
let filter = pil_to_filter(config.resampling);
// Get target size for patches
let target_size = config
.get_target_size()
.map(|(h, _w)| h)
.unwrap_or(self.base.image_size);
// Resize patch to target size
let resized = if config.do_resize.unwrap_or(true) {
resize(image, target_size, target_size, filter)
} else {
image.clone()
};
// Center crop if configured
let cropped = if config.do_center_crop.unwrap_or(true) {
if let Some((crop_h, crop_w)) = config.get_crop_size() {
center_crop(&resized, crop_w, crop_h)
} else {
resized
}
} else {
resized
};
// Convert to tensor
let mut tensor = to_tensor(&cropped);
// Normalize
if config.do_normalize.unwrap_or(true) {
normalize(&mut tensor, &mean, &std);
}
Ok(tensor)
}
}
impl ImagePreProcessor for LlavaNextProcessor {
fn default_mean(&self) -> [f64; 3] {
CLIP_MEAN
}
fn default_std(&self) -> [f64; 3] {
CLIP_STD
}
fn preprocess(
&self,
images: &[DynamicImage],
config: &PreProcessorConfig,
) -> Result<PreprocessedImages, TransformError> {
if images.is_empty() {
return Err(TransformError::EmptyBatch);
}
let mut all_patches = Vec::new();
let mut num_img_tokens = Vec::with_capacity(images.len());
let mut image_sizes = Vec::with_capacity(images.len());
let filter = pil_to_filter(config.resampling);
let target_size = config
.get_target_size()
.map(|(h, _w)| h)
.unwrap_or(self.base.image_size);
let crop_size = config.get_crop_size().unwrap_or((target_size, target_size));
for image in images {
let original_size = image.dimensions();
image_sizes.push(original_size);
let best_resolution = self.select_best_resolution(original_size);
let image_padded = self.resize_and_pad_image(image, best_resolution);
let image_original_resize = resize(image, target_size, target_size, filter);
let mut samples = vec![image_original_resize];
samples.extend(self.divide_to_samples(&image_padded, crop_size));
for sample in samples {
all_patches.push(self.process_patch(&sample, config)?);
}
num_img_tokens.push(self.calculate_num_tokens(
original_size.0,
original_size.1,
config,
));
}
let pixel_values = stack_batch(&all_patches)?;
Ok(PreprocessedImages::new(
pixel_values,
num_img_tokens,
image_sizes,
))
}
fn calculate_num_tokens(&self, width: u32, height: u32, _config: &PreProcessorConfig) -> usize {
let original_size = (width, height);
// Base tokens (from original resized image)
let patches_per_side = self.base.image_size / self.base.patch_size;
let base_tokens = (patches_per_side * patches_per_side) as usize;
// Grid tokens (from crops)
let grid_shape = self.get_anyres_grid_shape(original_size);
let unpad_shape = self.calculate_unpad(grid_shape, original_size);
// Total: base + unpadded area
base_tokens + (unpad_shape.0 as usize + 1) * unpad_shape.1 as usize
}
fn model_name(&self) -> &'static str {
"llava-next"
}
fn get_processed_size(&self, config: &PreProcessorConfig) -> Option<(u32, u32)> {
// LLaVA-NeXT has variable output size based on crops
// Return the base patch size
let size = config
.get_target_size()
.map(|(h, _w)| h)
.unwrap_or(self.base.image_size);
Some((size, size))
}
}
// ============================================================================
// Helper Functions (ported from mistral.rs)
// ============================================================================
/// Select the best resolution from possible resolutions for the given image size.
///
/// Minimizes wasted pixels while maximizing effective resolution.
fn select_best_resolution(
original_size: (u32, u32),
possible_resolutions: &[(u32, u32)],
) -> (u32, u32) {
let (original_width, original_height) = original_size;
let mut best_fit = (0, 0);
let original_width_f = original_width as f32;
let original_height_f = original_height as f32;
let mut max_effective_resolution = 0_u32;
let mut min_wasted_resolution = u32::MAX;
for &(width, height) in possible_resolutions {
let width_f = width as f32;
let height_f = height as f32;
let scale = (width_f / original_width_f).min(height_f / original_height_f);
let (downscaled_width, downscaled_height) = (
(original_width_f * scale) as u32,
(original_height_f * scale) as u32,
);
let effective_resolution =
std::cmp::min(width * height, downscaled_width * downscaled_height);
let wasted_resolution = width * height - effective_resolution;
if effective_resolution > max_effective_resolution
|| (effective_resolution == max_effective_resolution
&& wasted_resolution < min_wasted_resolution)
{
best_fit = (width, height);
max_effective_resolution = effective_resolution;
min_wasted_resolution = wasted_resolution;
}
}
best_fit
}
/// Calculate unpad dimensions based on aspect ratio.
fn calculate_unpad(size: (u32, u32), original_size: (u32, u32)) -> (u32, u32) {
let (original_width, original_height) = original_size;
let (current_width, current_height) = size;
let original_aspect_ratio = original_width as f32 / original_height as f32;
let current_aspect_ratio = current_width as f32 / current_height as f32;
if original_aspect_ratio > current_aspect_ratio {
let scale_factor = current_width as f32 / original_width as f32;
let new_height = (original_height as f32 * scale_factor).floor() as u32;
let padding = (current_height - new_height) / 2;
(current_width, current_height - 2 * padding)
} else {
let scale_factor = current_height as f32 / original_height as f32;
let new_width = (original_width as f32 * scale_factor).floor() as u32;
let padding = (current_width - new_width) / 2;
(current_width - 2 * padding, current_height)
}
}
/// Resize and pad image to target resolution, centering the image.
fn resize_and_pad_image(image: &DynamicImage, target: (u32, u32)) -> DynamicImage {
let (original_width, original_height) = image.dimensions();
let (target_width, target_height) = target;
let scale_w = target_width as f32 / original_width as f32;
let scale_h = target_height as f32 / original_height as f32;
let (new_width, new_height) = if scale_w < scale_h {
(
target_width,
std::cmp::min(
(original_height as f32 * scale_w).ceil() as u32,
target_height,
),
)
} else {
(
std::cmp::min(
(original_width as f32 * scale_h).ceil() as u32,
target_width,
),
target_height,
)
};
let resized = image.resize_exact(
new_width,
new_height,
image::imageops::FilterType::CatmullRom,
);
let mut new_image = DynamicImage::new_rgb8(target_width, target_height);
let paste_x = (target_width - new_width) as i64 / 2;
let paste_y = (target_height - new_height) as i64 / 2;
image::imageops::overlay(&mut new_image, &resized, paste_x, paste_y);
new_image
}
/// Divide image into crops of specified size.
fn divide_to_samples(image: &DynamicImage, crop_size: (u32, u32)) -> Vec<DynamicImage> {
let (width, height) = image.dimensions();
let mut samples = Vec::new();
for y in (0..height).step_by(crop_size.1 as usize) {
for x in (0..width).step_by(crop_size.0 as usize) {
let patch = image.crop_imm(x, y, crop_size.0, crop_size.1);
samples.push(patch);
}
}
samples
}
#[cfg(test)]
mod tests {
use image::{Rgb, RgbImage};
use super::*;
fn create_test_image(width: u32, height: u32, color: Rgb<u8>) -> DynamicImage {
DynamicImage::from(RgbImage::from_pixel(width, height, color))
}
#[test]
fn test_llava_processor_default() {
let processor = LlavaProcessor::new();
assert_eq!(processor.patch_size, 14);
assert_eq!(processor.image_size, 336);
assert_eq!(processor.aspect_ratio, ImageAspectRatio::Square);
}
#[test]
fn test_llava_processor_with_pad() {
let processor = LlavaProcessor::new_with_pad();
assert_eq!(processor.patch_size, 14);
assert_eq!(processor.image_size, 336);
assert_eq!(processor.aspect_ratio, ImageAspectRatio::Pad);
}
#[test]
fn test_llava_token_calculation() {
let processor = LlavaProcessor::new();
let config = PreProcessorConfig::default();
// 336 / 14 = 24, 24 * 24 = 576
let tokens = processor.calculate_num_tokens(336, 336, &config);
assert_eq!(tokens, 576);
}
#[test]
fn test_llava_preprocess_square() {
let processor = LlavaProcessor::new();
let config = PreProcessorConfig {
do_resize: Some(true),
do_center_crop: Some(true),
do_normalize: Some(true),
image_mean: Some(CLIP_MEAN.to_vec()),
image_std: Some(CLIP_STD.to_vec()),
..Default::default()
};
let image = create_test_image(336, 336, Rgb([128, 128, 128]));
let result = processor.preprocess(&[image], &config).unwrap();
assert_eq!(result.batch_size(), 1);
assert_eq!(result.height(), 336);
assert_eq!(result.width(), 336);
assert_eq!(result.num_img_tokens[0], 576);
}
#[test]
fn test_llava_preprocess_rectangular_square_mode() {
// Square mode (default): resize shortest edge, center crop
let processor = LlavaProcessor::new();
let config = PreProcessorConfig {
do_resize: Some(true),
do_center_crop: Some(true),
do_normalize: Some(true),
size: Some([("shortest_edge".to_string(), 336)].into_iter().collect()),
crop_size: Some(
[("height".to_string(), 336), ("width".to_string(), 336)]
.into_iter()
.collect(),
),
..Default::default()
};
// Tall image - should be resized so shortest edge = 336, then center cropped
let image = create_test_image(200, 400, Rgb([128, 128, 128]));
let result = processor.preprocess(&[image], &config).unwrap();
assert_eq!(result.batch_size(), 1);
assert_eq!(result.height(), 336);
assert_eq!(result.width(), 336);
}
#[test]
fn test_llava_preprocess_rectangular_pad_mode() {
// Pad mode: expand to square with mean padding, then resize
let processor = LlavaProcessor::new_with_pad();
let config = PreProcessorConfig {
do_resize: Some(true),
do_center_crop: Some(false),
do_normalize: Some(true),
..Default::default()
};
// Tall image should be padded to square first
let image = create_test_image(200, 400, Rgb([128, 128, 128]));
let result = processor.preprocess(&[image], &config).unwrap();
assert_eq!(result.batch_size(), 1);
// After expand_to_square: 400x400, then resize to 336x336
assert_eq!(result.height(), 336);
assert_eq!(result.width(), 336);
}
#[test]
fn test_select_best_resolution() {
let pinpoints = vec![(336, 672), (672, 336), (672, 672), (1008, 336), (336, 1008)];
// Square image should pick square resolution
let best = select_best_resolution((500, 500), &pinpoints);
assert_eq!(best, (672, 672));
// Wide image should pick wide resolution
let best = select_best_resolution((800, 400), &pinpoints);
assert_eq!(best, (672, 336));
// Tall image should pick tall resolution
let best = select_best_resolution((400, 800), &pinpoints);
assert_eq!(best, (336, 672));
}
#[test]
fn test_calculate_unpad() {
// Square grid, wide original -> should reduce width padding
let unpad = calculate_unpad((24, 24), (800, 400));
assert!(unpad.0 >= unpad.1); // Width should be >= height
// Square grid, tall original -> should reduce height padding
let unpad = calculate_unpad((24, 24), (400, 800));
assert!(unpad.1 >= unpad.0); // Height should be >= width
}
#[test]
fn test_llava_next_processor_default() {
let processor = LlavaNextProcessor::new();
assert!(!processor.image_grid_pinpoints.is_empty());
assert_eq!(processor.base.patch_size, 14);
}
#[test]
fn test_llava_next_preprocess() {
let processor = LlavaNextProcessor::new();
let config = PreProcessorConfig {
do_resize: Some(true),
do_center_crop: Some(false),
do_normalize: Some(true),
..Default::default()
};
let image = create_test_image(500, 500, Rgb([128, 128, 128]));
let result = processor.preprocess(&[image], &config).unwrap();
// Should have multiple patches (original + crops)
assert!(result.batch_size() > 1);
}
#[test]
fn test_divide_to_samples() {
let image = create_test_image(672, 672, Rgb([128, 128, 128]));
let samples = divide_to_samples(&image, (336, 336));
// 672x672 / 336x336 = 2x2 = 4 patches
assert_eq!(samples.len(), 4);
for sample in &samples {
assert_eq!(sample.width(), 336);
assert_eq!(sample.height(), 336);
}
}
}
sgl-model-gateway/src/multimodal/vision/processors/mod.rs
-33
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@@ -1,33 +0,0 @@
//! Model-specific image processors.
//!
//! This module contains implementations of `ImagePreProcessor` for various
//! vision-language model families.
//!
//! # Supported Models
//!
//! - **LLaVA 1.5** (`llava`): CLIP-based preprocessing with configurable aspect ratio
//! - **LLaVA-NeXT** (`llava`): Multi-crop anyres processing
//! - **Qwen2-VL** (`qwen2_vl`): Dynamic resolution with smart resizing
//! - **Qwen2.5-VL** (`qwen2_vl`): Same processor as Qwen2-VL (identical preprocessing)
//! - **Qwen3-VL** (`qwen3_vl`): Similar to Qwen2-VL but with patch_size=16 and [0.5,0.5,0.5] normalization
//! - **Phi3-Vision** (`phi3_vision`): Dynamic HD transform with 336x336 tiles
//! - **Phi4-Vision** (`phi4_vision`): Dynamic HD transform with 448x448 tiles and SiGLIP encoder
//! - **LLaMA 4 Vision** (`llama4_vision`): Tile-based processing with 336x336 tiles and global tile
//! - **Pixtral/Mistral3** (`pixtral`): CLIP-based preprocessing with dynamic resolution
pub mod llama4_vision;
pub mod llava;
pub mod phi3_vision;
pub mod phi4_vision;
pub mod pixtral;
pub mod qwen2_vl;
pub mod qwen3_vl;
pub mod qwen_vl_base;
pub use llama4_vision::Llama4VisionProcessor;
pub use llava::{ImageAspectRatio, LlavaNextProcessor, LlavaProcessor};
pub use phi3_vision::Phi3VisionProcessor;
pub use phi4_vision::Phi4VisionProcessor;
pub use pixtral::PixtralProcessor;
pub use qwen2_vl::Qwen2VLProcessor;
pub use qwen3_vl::Qwen3VLProcessor;
sgl-model-gateway/src/multimodal/vision/processors/phi3_vision.rs
-551
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@@ -1,551 +0,0 @@
//! Phi3-Vision image processor.
//!
//! This module implements the Phi3-Vision image preprocessing pipeline with
//! Dynamic High Definition (HD) transform, which tiles images into 336x336 crops.
//!
//! # Processing Pipeline
//!
//! 1. **HD Transform**: Resize and pad image to multiples of 336
//! 2. **Normalize**: Apply CLIP normalization
//! 3. **Create Global Image**: Bicubic interpolate to 336x336
//! 4. **Tile**: Reshape into (num_tiles, 3, 336, 336)
//! 5. **Concatenate**: [global_image, tiles...]
//! 6. **Pad**: Zero-pad to (num_crops+1, 3, 336, 336)
//!
//! # Key Features
//!
//! - Dynamic resolution via HD transform
//! - Default num_crops: 16
//! - CLIP normalization: mean=[0.48145466, 0.4578275, 0.40821073], std=[0.26862954, 0.26130258, 0.27577711]
//! - Token count formula: `((h//336)*(w//336)+1)*144 + 1 + (h//336+1)*12`
use image::{imageops::FilterType, DynamicImage, GenericImageView, Rgb, RgbImage};
use ndarray::{s, Array3, Array4, IxDyn};
use crate::multimodal::vision::{
image_processor::{ImagePreProcessor, PreprocessedImages},
preprocessor_config::PreProcessorConfig,
transforms::{self, TransformError},
};
/// CLIP normalization mean values.
pub const CLIP_MEAN: [f64; 3] = [0.48145466, 0.4578275, 0.40821073];
/// CLIP normalization std values.
pub const CLIP_STD: [f64; 3] = [0.26862954, 0.26130258, 0.27577711];
/// Default number of crops for HD transform.
pub const DEFAULT_NUM_CROPS: usize = 16;
/// Default number of image tokens per crop (144 per tile + base).
pub const DEFAULT_NUM_IMG_TOKENS: usize = 144;
/// Tile size used in Phi3-Vision (336x336).
pub const TILE_SIZE: u32 = 336;
/// Phi3-Vision image processor.
///
/// Implements Dynamic HD transform with tile-based processing.
#[derive(Debug, Clone)]
pub struct Phi3VisionProcessor {
/// Maximum number of HD crops (not including global).
num_crops: usize,
/// Normalization mean.
mean: [f64; 3],
/// Normalization std.
std: [f64; 3],
}
impl Default for Phi3VisionProcessor {
fn default() -> Self {
Self::new()
}
}
impl Phi3VisionProcessor {
/// Create a new Phi3-Vision processor with default settings.
pub fn new() -> Self {
Self {
num_crops: DEFAULT_NUM_CROPS,
mean: CLIP_MEAN,
std: CLIP_STD,
}
}
/// Create a processor with custom settings.
pub fn with_config(num_crops: usize) -> Self {
Self {
num_crops,
mean: CLIP_MEAN,
std: CLIP_STD,
}
}
/// Create a processor from preprocessor config.
pub fn from_preprocessor_config(config: &PreProcessorConfig) -> Self {
Self {
num_crops: config.num_crops.unwrap_or(DEFAULT_NUM_CROPS),
mean: config
.image_mean
.as_ref()
.map(|v| [v[0], v[1], v[2]])
.unwrap_or(CLIP_MEAN),
std: config
.image_std
.as_ref()
.map(|v| [v[0], v[1], v[2]])
.unwrap_or(CLIP_STD),
}
}
/// Get the number of crops.
pub fn num_crops(&self) -> usize {
self.num_crops
}
/// HD transform: resize and pad image to multiples of 336.
///
/// Algorithm:
/// 1. If width < height, transpose (flip over main diagonal)
/// 2. Calculate scale: while scale * ceil(scale/ratio) <= hd_num: scale++
/// 3. Resize to new_w = scale * 336, new_h = new_w / ratio
/// 4. Pad height to multiple of 336 (centered, white padding)
/// 5. If transposed, transpose back
pub fn hd_transform(&self, image: &DynamicImage) -> DynamicImage {
let (width, height) = image.dimensions();
let (img, transposed) = if width < height {
// Transpose (PIL's Image.TRANSPOSE): equivalent to fliph + rotate270 (ccw 90°)
// This swaps x and y coordinates: pixel at (x, y) goes to (y, x)
(image.fliph().rotate270(), true)
} else {
(image.clone(), false)
};
let (width, height) = img.dimensions();
let ratio = width as f64 / height as f64;
// Calculate scale factor
let mut scale = 1.0f64;
while scale * (scale / ratio).ceil() <= self.num_crops as f64 {
scale += 1.0;
}
scale -= 1.0;
let new_w = (scale * TILE_SIZE as f64) as u32;
let new_h = (new_w as f64 / ratio) as u32;
// Resize using bilinear filter (matching torchvision's bilinear+antialias)
// HuggingFace uses torchvision.transforms.functional.resize with
// BILINEAR interpolation and antialias=True. PIL's BILINEAR includes
// implicit antialiasing that closely matches torchvision.
let resized = img.resize_exact(new_w, new_h, FilterType::Triangle);
// Pad height to multiple of 336
let padded = self.padding_336(&resized);
// Transpose back if needed (transpose is self-inverse)
if transposed {
padded.fliph().rotate270()
} else {
padded
}
}
/// Pad image height to multiple of 336 (centered, white padding).
fn padding_336(&self, image: &DynamicImage) -> DynamicImage {
let (width, height) = image.dimensions();
let target_h = ((height as f64 / TILE_SIZE as f64).ceil() * TILE_SIZE as f64) as u32;
if height == target_h {
return image.clone();
}
let top_padding = (target_h - height) / 2;
// Create white-padded image
let mut new_image =
DynamicImage::from(RgbImage::from_pixel(width, target_h, Rgb([255, 255, 255])));
// Copy original image to center
image::imageops::overlay(&mut new_image, image, 0, top_padding as i64);
new_image
}
/// Create global image by bicubic interpolation to 336x336.
///
/// Uses the shared `bicubic_resize` which matches PyTorch's
/// `torch.nn.functional.interpolate(mode='bicubic', align_corners=False)`.
fn create_global_image(&self, tensor: &Array3<f32>) -> Array3<f32> {
transforms::bicubic_resize(tensor, TILE_SIZE as usize, TILE_SIZE as usize)
}
/// Reshape HD image into tiles.
///
/// Transforms [3, H, W] -> [num_tiles, 3, 336, 336]
/// where H and W are multiples of 336.
fn reshape_to_tiles(&self, tensor: &Array3<f32>) -> Vec<Array3<f32>> {
let (_c, h, w) = (tensor.shape()[0], tensor.shape()[1], tensor.shape()[2]);
let grid_h = h / TILE_SIZE as usize;
let grid_w = w / TILE_SIZE as usize;
let mut tiles = Vec::with_capacity(grid_h * grid_w);
for gh in 0..grid_h {
for gw in 0..grid_w {
let y_start = gh * TILE_SIZE as usize;
let x_start = gw * TILE_SIZE as usize;
let y_end = y_start + TILE_SIZE as usize;
let x_end = x_start + TILE_SIZE as usize;
let tile_view = tensor.slice(s![.., y_start..y_end, x_start..x_end]);
tiles.push(tile_view.to_owned());
}
}
tiles
}
/// Calculate number of image tokens for given HD size.
///
/// Formula: `((h//336)*(w//336)+1)*144 + 1 + (h//336+1)*12`
pub fn calculate_num_tokens(&self, h: usize, w: usize) -> usize {
let grid_h = h / TILE_SIZE as usize;
let grid_w = w / TILE_SIZE as usize;
// ((h//336)*(w//336)+1)*144 + 1 + (h//336+1)*12
(grid_h * grid_w + 1) * 144 + 1 + (grid_h + 1) * 12
}
/// Process a single image through the full pipeline.
#[allow(clippy::type_complexity)]
fn process_single_image(
&self,
image: &DynamicImage,
config: &PreProcessorConfig,
) -> Result<(Array4<f32>, (usize, usize), usize), TransformError> {
// 1. Convert to RGB
let image = DynamicImage::ImageRgb8(image.to_rgb8());
// 2. HD transform
let hd_image = self.hd_transform(&image);
let (hd_w, hd_h) = hd_image.dimensions();
// 3. To tensor [0, 1] and normalize
let mut tensor = transforms::to_tensor(&hd_image);
let mean = config
.image_mean
.as_ref()
.map(|v| [v[0], v[1], v[2]])
.unwrap_or(self.mean);
let std = config
.image_std
.as_ref()
.map(|v| [v[0], v[1], v[2]])
.unwrap_or(self.std);
transforms::normalize(&mut tensor, &mean, &std);
// 4. Create global image (336x336)
let global_image = self.create_global_image(&tensor);
// 5. Reshape HD image into tiles
let tiles = self.reshape_to_tiles(&tensor);
// 6. Concatenate global + tiles
let max_crops = self.num_crops + 1; // num_crops + 1 for global
// Create output tensor [max_crops, 3, 336, 336]
let mut output =
Array4::<f32>::zeros((max_crops, 3, TILE_SIZE as usize, TILE_SIZE as usize));
// Copy global image (first position)
output.slice_mut(s![0, .., .., ..]).assign(&global_image);
// Copy tiles (positions 1..num_actual_crops)
for (i, tile) in tiles.iter().enumerate() {
if i + 1 < max_crops {
output.slice_mut(s![i + 1, .., .., ..]).assign(tile);
}
}
// Calculate token count
let num_tokens = self.calculate_num_tokens(hd_h as usize, hd_w as usize);
// image_sizes is the HD-transformed size
Ok((output, (hd_h as usize, hd_w as usize), num_tokens))
}
}
impl ImagePreProcessor for Phi3VisionProcessor {
fn default_mean(&self) -> [f64; 3] {
self.mean
}
fn default_std(&self) -> [f64; 3] {
self.std
}
fn preprocess(
&self,
images: &[DynamicImage],
config: &PreProcessorConfig,
) -> Result<PreprocessedImages, TransformError> {
if images.is_empty() {
return Err(TransformError::InvalidShape {
expected: "at least one image".to_string(),
actual: vec![0],
});
}
let mut all_pixel_values = Vec::with_capacity(images.len());
let mut all_image_sizes = Vec::with_capacity(images.len());
let mut all_num_tokens = Vec::with_capacity(images.len());
for image in images {
let (pixel_values, image_size, num_tokens) =
self.process_single_image(image, config)?;
all_pixel_values.push(pixel_values);
all_image_sizes.push((image_size.1 as u32, image_size.0 as u32)); // (width, height)
all_num_tokens.push(num_tokens);
}
// Stack into batch [B, num_crops+1, 3, 336, 336]
let max_crops = self.num_crops + 1;
let batch_size = images.len();
let mut batch_tensor = ndarray::Array5::<f32>::zeros((
batch_size,
max_crops,
3,
TILE_SIZE as usize,
TILE_SIZE as usize,
));
for (i, pv) in all_pixel_values.iter().enumerate() {
batch_tensor.slice_mut(s![i, .., .., .., ..]).assign(pv);
}
// Convert to dynamic array for storage
let shape = batch_tensor.shape().to_vec();
let (flat_data, _offset) = batch_tensor.into_raw_vec_and_offset();
// Store image_sizes as model-specific data
let mut model_specific = std::collections::HashMap::new();
// image_sizes as [batch, 2] tensor (h, w for each image)
let image_sizes_data: Vec<u32> = all_image_sizes
.iter()
.flat_map(|(w, h)| [*h, *w]) // [h, w] for each image
.collect();
model_specific.insert(
"image_sizes".to_string(),
crate::multimodal::vision::image_processor::ModelSpecificValue::UintTensor {
data: image_sizes_data,
shape: vec![batch_size, 2],
},
);
// num_img_tokens as list
model_specific.insert(
"num_img_tokens".to_string(),
crate::multimodal::vision::image_processor::ModelSpecificValue::UintVec(
all_num_tokens.iter().map(|&t| t as u32).collect(),
),
);
// Convert 5D tensor to appropriate format
// Phi3-Vision expects [B, num_crops+1, C, H, W]
let pixel_values = ndarray::ArrayD::<f32>::from_shape_vec(IxDyn(&shape), flat_data)
.map_err(|e| TransformError::InvalidShape {
expected: format!("valid 5D shape, but failed with error: {}", e),
actual: shape.clone(),
})?;
Ok(PreprocessedImages {
pixel_values,
num_img_tokens: all_num_tokens,
image_sizes: all_image_sizes,
model_specific,
})
}
fn calculate_num_tokens(&self, width: u32, height: u32, _config: &PreProcessorConfig) -> usize {
// First apply HD transform to get the actual size
let image = DynamicImage::new_rgb8(width, height);
let hd_image = self.hd_transform(&image);
let (_, hd_h) = hd_image.dimensions();
let hd_w = hd_image.width();
self.calculate_num_tokens(hd_h as usize, hd_w as usize)
}
fn model_name(&self) -> &'static str {
"phi3-vision"
}
fn get_processed_size(&self, _config: &PreProcessorConfig) -> Option<(u32, u32)> {
// Phi3-Vision has dynamic size based on HD transform
None
}
}
#[cfg(test)]
mod tests {
use image::RgbImage;
use super::*;
fn create_test_image(width: u32, height: u32, color: Rgb<u8>) -> DynamicImage {
DynamicImage::from(RgbImage::from_pixel(width, height, color))
}
#[test]
fn test_phi3_vision_processor_default() {
let processor = Phi3VisionProcessor::new();
assert_eq!(processor.num_crops(), 16);
assert_eq!(processor.default_mean(), CLIP_MEAN);
assert_eq!(processor.default_std(), CLIP_STD);
}
#[test]
fn test_hd_transform_square() {
let processor = Phi3VisionProcessor::new();
let image = create_test_image(504, 504, Rgb([128, 128, 128]));
let hd_image = processor.hd_transform(&image);
let (w, h) = hd_image.dimensions();
// Should be multiple of 336
assert_eq!(h % 336, 0);
assert_eq!(w % 336, 0);
// Should respect num_crops limit
let num_tiles = (h / 336) * (w / 336);
assert!(num_tiles <= processor.num_crops() as u32);
}
#[test]
fn test_hd_transform_tall() {
let processor = Phi3VisionProcessor::new();
let image = create_test_image(400, 600, Rgb([100, 100, 100]));
let hd_image = processor.hd_transform(&image);
let (w, h) = hd_image.dimensions();
// Should be multiple of 336
assert_eq!(h % 336, 0);
assert_eq!(w % 336, 0);
}
#[test]
fn test_hd_transform_wide() {
let processor = Phi3VisionProcessor::new();
let image = create_test_image(600, 400, Rgb([150, 150, 150]));
let hd_image = processor.hd_transform(&image);
let (w, h) = hd_image.dimensions();
// Should be multiple of 336
assert_eq!(h % 336, 0);
assert_eq!(w % 336, 0);
}
#[test]
fn test_calculate_num_tokens() {
let processor = Phi3VisionProcessor::new();
// 1344x1344 -> 4x4 grid -> (16+1)*144 + 1 + (4+1)*12 = 2448 + 1 + 60 = 2509
let tokens = processor.calculate_num_tokens(1344, 1344);
assert_eq!(tokens, 2509);
// 1008x1344 -> 3x4 grid -> (12+1)*144 + 1 + (3+1)*12 = 1872 + 1 + 48 = 1921
let tokens = processor.calculate_num_tokens(1008, 1344);
assert_eq!(tokens, 1921);
// 1344x1008 -> 4x3 grid -> (12+1)*144 + 1 + (4+1)*12 = 1872 + 1 + 60 = 1933
let tokens = processor.calculate_num_tokens(1344, 1008);
assert_eq!(tokens, 1933);
}
#[test]
fn test_phi3_vision_preprocess() {
let processor = Phi3VisionProcessor::new();
let config = PreProcessorConfig::default();
let image = create_test_image(504, 504, Rgb([128, 128, 128]));
let result = processor.preprocess(&[image], &config).unwrap();
assert_eq!(result.batch_size(), 1);
// Check output shape is [1, num_crops+1, 3, 336, 336]
let shape = result.pixel_values.shape();
assert_eq!(shape.len(), 5);
assert_eq!(shape[0], 1); // batch
assert_eq!(shape[1], 17); // num_crops + 1
assert_eq!(shape[2], 3); // channels
assert_eq!(shape[3], 336); // height
assert_eq!(shape[4], 336); // width
// Check model-specific outputs
assert!(result.model_specific.contains_key("image_sizes"));
assert!(result.model_specific.contains_key("num_img_tokens"));
}
#[test]
fn test_phi3_vision_preprocess_multiple() {
let processor = Phi3VisionProcessor::new();
let config = PreProcessorConfig::default();
let images = vec![
create_test_image(504, 504, Rgb([100, 100, 100])),
create_test_image(400, 600, Rgb([150, 150, 150])),
];
let result = processor.preprocess(&images, &config).unwrap();
assert_eq!(result.batch_size(), 2);
assert_eq!(result.image_sizes.len(), 2);
assert_eq!(result.num_img_tokens.len(), 2);
}
#[test]
fn test_model_name() {
let processor = Phi3VisionProcessor::new();
assert_eq!(processor.model_name(), "phi3-vision");
}
#[test]
fn test_from_config() {
let config = PreProcessorConfig {
num_crops: Some(8),
image_mean: Some(vec![0.5, 0.5, 0.5]),
image_std: Some(vec![0.5, 0.5, 0.5]),
..Default::default()
};
let processor = Phi3VisionProcessor::from_preprocessor_config(&config);
assert_eq!(processor.num_crops(), 8);
}
#[test]
fn test_transpose_equivalence() {
// Test that fliph().rotate270() correctly implements PIL's Image.TRANSPOSE
// TRANSPOSE swaps x and y coordinates: pixel at (x, y) goes to (y, x)
use image::{GenericImageView, Rgb, RgbImage};
let mut img = RgbImage::new(100, 200);
img.put_pixel(0, 0, Rgb([255, 0, 0])); // Top-left = red
img.put_pixel(99, 0, Rgb([0, 255, 0])); // Top-right = green
img.put_pixel(0, 199, Rgb([0, 0, 255])); // Bottom-left = blue
img.put_pixel(99, 199, Rgb([255, 255, 0])); // Bottom-right = yellow
let img = DynamicImage::ImageRgb8(img);
let transposed = img.fliph().rotate270();
// After TRANSPOSE: (x, y) -> (y, x)
assert_eq!(transposed.get_pixel(0, 0).0[0..3], [255, 0, 0]); // (0,0) -> (0,0)
assert_eq!(transposed.get_pixel(0, 99).0[0..3], [0, 255, 0]); // (99,0) -> (0,99)
assert_eq!(transposed.get_pixel(199, 0).0[0..3], [0, 0, 255]); // (0,199) -> (199,0)
assert_eq!(transposed.get_pixel(199, 99).0[0..3], [255, 255, 0]); // (99,199) -> (199,99)
}
}
sgl-model-gateway/src/multimodal/vision/processors/phi4_vision.rs
-724
View File
@@ -1,724 +0,0 @@
//! Phi4-Vision (Phi4-Multimodal) image processor.
//!
//! This module implements the Phi4-Vision image preprocessing pipeline with
//! Dynamic HD transform using a 448x448 base resolution.
//!
//! # Key Differences from Phi3-Vision
//!
//! | Feature | Phi3-Vision | Phi4-Vision |
//! |---------|-------------|-------------|
//! | Base resolution | 336 | 448 |
//! | Normalization | CLIP | [0.5, 0.5, 0.5] |
//! | Default max crops | 16 | 36 |
//! | Aspect ratio selection | Simple scale | Target ratio matching |
//! | Attention mask | No | Yes |
//!
//! # Processing Pipeline
//!
//! 1. **Dynamic Preprocess**: Calculate target resolution based on aspect ratio
//! 2. **Resize**: Scale to target resolution maintaining aspect ratio
//! 3. **Pad**: Add white padding to reach exact target dimensions
//! 4. **Normalize**: Apply [0.5, 0.5, 0.5] mean/std normalization
//! 5. **Create Global Image**: Bilinear interpolate to 448x448
//! 6. **Tile**: Reshape HD image into (h_crops * w_crops, 3, 448, 448) tiles
//! 7. **Concatenate**: [global_image, tiles...]
//! 8. **Generate Attention Mask**: Track valid (non-padding) regions
//!
//! # Token Count Formula
//!
//! `num_tokens = 256 + 1 + mask_sum + mask_col0_sum + 16`
//!
//! Where:
//! - 256: base global tokens
//! - mask_sum: sum of downsampled attention mask
//! - mask_col0_sum: sum of first column of mask (row separators)
use std::collections::HashSet;
use image::{imageops::FilterType, DynamicImage, GenericImageView, Rgb, RgbImage};
use ndarray::{s, Array2, Array3, Array4, IxDyn};
use crate::multimodal::vision::{
image_processor::{ImagePreProcessor, ModelSpecificValue, PreprocessedImages},
preprocessor_config::PreProcessorConfig,
transforms::{self, TransformError},
};
/// Simple normalization mean for Phi4-Vision.
pub const PHI4_MEAN: [f64; 3] = [0.5, 0.5, 0.5];
/// Simple normalization std for Phi4-Vision.
pub const PHI4_STD: [f64; 3] = [0.5, 0.5, 0.5];
/// Default dynamic_hd value (max crops) for Phi4-Vision.
pub const DEFAULT_DYNAMIC_HD: usize = 36;
/// Base resolution for Phi4-Vision (448x448).
pub const BASE_RESOLUTION: u32 = 448;
/// Mask resolution (base_resolution / patch_size = 448 / 14).
pub const MASK_RESOLUTION: usize = 32;
/// Patch size used in Phi4-Vision.
pub const PATCH_SIZE: usize = 14;
/// Result type for single image processing.
/// Contains (pixel_values, attention_mask, (height, width), num_tokens).
type SingleImageResult = (Array4<f32>, Array3<u32>, (u32, u32), usize);
/// Phi4-Vision image processor.
///
/// Implements Dynamic HD transform with aspect ratio matching.
#[derive(Debug, Clone)]
pub struct Phi4VisionProcessor {
/// Maximum number of crops (dynamic_hd parameter).
dynamic_hd: usize,
/// Base resolution for tiles.
base_resolution: u32,
/// Mask resolution (base_resolution / patch_size).
mask_resolution: usize,
/// Normalization mean.
mean: [f64; 3],
/// Normalization std.
std: [f64; 3],
}
impl Default for Phi4VisionProcessor {
fn default() -> Self {
Self::new()
}
}
impl Phi4VisionProcessor {
/// Create a new Phi4-Vision processor with default settings.
pub fn new() -> Self {
Self {
dynamic_hd: DEFAULT_DYNAMIC_HD,
base_resolution: BASE_RESOLUTION,
mask_resolution: MASK_RESOLUTION,
mean: PHI4_MEAN,
std: PHI4_STD,
}
}
/// Create a processor with custom dynamic_hd setting.
pub fn with_dynamic_hd(dynamic_hd: usize) -> Self {
Self {
dynamic_hd,
base_resolution: BASE_RESOLUTION,
mask_resolution: MASK_RESOLUTION,
mean: PHI4_MEAN,
std: PHI4_STD,
}
}
/// Create a processor from preprocessor config.
pub fn from_preprocessor_config(config: &PreProcessorConfig) -> Self {
Self {
dynamic_hd: config.dynamic_hd.unwrap_or(DEFAULT_DYNAMIC_HD),
base_resolution: BASE_RESOLUTION,
mask_resolution: MASK_RESOLUTION,
mean: config
.image_mean
.as_ref()
.map(|v| [v[0], v[1], v[2]])
.unwrap_or(PHI4_MEAN),
std: config
.image_std
.as_ref()
.map(|v| [v[0], v[1], v[2]])
.unwrap_or(PHI4_STD),
}
}
/// Get the dynamic_hd (max crops) value.
pub fn dynamic_hd(&self) -> usize {
self.dynamic_hd
}
/// Get the base resolution.
pub fn base_resolution(&self) -> u32 {
self.base_resolution
}
/// Compute valid target aspect ratios for the given crop range.
///
/// Returns sorted list of (width_crops, height_crops) tuples where
/// min_num <= w * h <= max_num.
fn compute_target_ratios(&self, min_num: usize, max_num: usize) -> Vec<(usize, usize)> {
let mut ratios: HashSet<(usize, usize)> = HashSet::new();
for n in min_num..=max_num {
// Find factor pairs by iterating up to sqrt(n)
for i in 1..=(n as f64).sqrt() as usize {
if n % i == 0 {
ratios.insert((i, n / i));
ratios.insert((n / i, i));
}
}
}
let mut sorted_ratios: Vec<(usize, usize)> = ratios.into_iter().collect();
sorted_ratios.sort_by_key(|&(i, j)| i * j);
sorted_ratios
}
/// Find the closest aspect ratio from the target ratios.
///
/// Selects the ratio that minimizes the difference from the original
/// aspect ratio. When tied, prefers ratios where the resized area
/// exceeds half the base area product.
fn find_closest_aspect_ratio(
&self,
aspect_ratio: f64,
target_ratios: &[(usize, usize)],
width: u32,
height: u32,
) -> (usize, usize) {
let mut best_ratio_diff = f64::INFINITY;
let mut best_ratio = (1, 1);
let area = (width * height) as f64;
let base_area = (self.base_resolution * self.base_resolution) as f64;
for &(w_ratio, h_ratio) in target_ratios {
let target_aspect_ratio = w_ratio as f64 / h_ratio as f64;
let ratio_diff = (aspect_ratio - target_aspect_ratio).abs();
if ratio_diff < best_ratio_diff {
best_ratio_diff = ratio_diff;
best_ratio = (w_ratio, h_ratio);
} else if (ratio_diff - best_ratio_diff).abs() < 1e-6 {
// Tie-breaker: prefer ratio if area > 0.5 * base_area * w * h
if area > 0.5 * base_area * (w_ratio * h_ratio) as f64 {
best_ratio = (w_ratio, h_ratio);
}
}
}
best_ratio
}
/// Dynamic preprocess: calculate target dimensions and create attention mask.
///
/// Returns (processed_image, attention_mask, target_h_crops, target_w_crops)
fn dynamic_preprocess(
&self,
image: &DynamicImage,
) -> Result<(DynamicImage, Array2<u32>, usize, usize), TransformError> {
let (orig_w, orig_h) = image.dimensions();
let base_res = self.base_resolution as f64;
// Calculate natural crop numbers
let w_crop_num = (orig_w as f64 / base_res).ceil() as usize;
let h_crop_num = (orig_h as f64 / base_res).ceil() as usize;
let (target_w_crops, target_h_crops, target_width, target_height) =
if w_crop_num * h_crop_num > self.dynamic_hd {
// Image exceeds max crops, need to find best aspect ratio
let aspect_ratio = orig_w as f64 / orig_h as f64;
let target_ratios = self.compute_target_ratios(1, self.dynamic_hd);
let (w_ratio, h_ratio) =
self.find_closest_aspect_ratio(aspect_ratio, &target_ratios, orig_w, orig_h);
let target_width = self.base_resolution * w_ratio as u32;
let target_height = self.base_resolution * h_ratio as u32;
(w_ratio, h_ratio, target_width, target_height)
} else {
// Image fits within max crops
let target_width = self.base_resolution * w_crop_num as u32;
let target_height = self.base_resolution * h_crop_num as u32;
(w_crop_num, h_crop_num, target_width, target_height)
};
// Calculate resize ratios
let ratio_width = target_width as f64 / orig_w as f64;
let ratio_height = target_height as f64 / orig_h as f64;
let (new_w, new_h, padding_width, padding_height) = if ratio_width < ratio_height {
// Width is the limiting factor
let new_w = target_width;
let new_h = (orig_h as f64 * ratio_width) as u32;
(new_w, new_h, 0u32, target_height - new_h)
} else {
// Height is the limiting factor
let new_h = target_height;
let new_w = (orig_w as f64 * ratio_height) as u32;
(new_w, new_h, target_width - new_w, 0u32)
};
// Create attention mask (tracks valid regions)
let mask_h = self.mask_resolution * target_h_crops;
let mask_w = self.mask_resolution * target_w_crops;
let mut attention_mask = Array2::<u32>::ones((mask_h, mask_w));
// Mark padding regions as 0 in mask
if padding_width >= PATCH_SIZE as u32 {
let padding_mask_cols = (padding_width as usize) / PATCH_SIZE;
for row in 0..mask_h {
for col in (mask_w - padding_mask_cols)..mask_w {
attention_mask[[row, col]] = 0;
}
}
}
if padding_height >= PATCH_SIZE as u32 {
let padding_mask_rows = (padding_height as usize) / PATCH_SIZE;
for row in (mask_h - padding_mask_rows)..mask_h {
for col in 0..mask_w {
attention_mask[[row, col]] = 0;
}
}
}
// Resize image with bilinear interpolation (matching HuggingFace torchvision)
// HuggingFace uses torchvision.transforms.functional.resize with BILINEAR + antialias=True.
// FilterType::Triangle (bilinear) closely matches this behavior.
let resized = image.resize_exact(new_w, new_h, FilterType::Triangle);
// Pad to target dimensions (white padding on right/bottom)
let padded = self.pad_image(&resized, target_width, target_height);
Ok((padded, attention_mask, target_h_crops, target_w_crops))
}
/// Pad image to target dimensions with white padding.
fn pad_image(&self, image: &DynamicImage, target_w: u32, target_h: u32) -> DynamicImage {
let (w, h) = image.dimensions();
if w == target_w && h == target_h {
return image.clone();
}
// Create white background
let white = Rgb([255u8, 255, 255]);
let mut padded = RgbImage::from_pixel(target_w, target_h, white);
// Copy image to top-left using efficient overlay
image::imageops::overlay(&mut padded, &image.to_rgb8(), 0, 0);
DynamicImage::ImageRgb8(padded)
}
/// Create global image by bicubic interpolation to base resolution.
///
/// Uses the shared `bicubic_resize` which matches PyTorch's
/// `torch.nn.functional.interpolate(mode='bicubic', align_corners=False)`.
fn create_global_image(&self, tensor: &Array3<f32>) -> Array3<f32> {
let target = self.base_resolution as usize;
transforms::bicubic_resize(tensor, target, target)
}
/// Tile the HD image into crops of base_resolution x base_resolution.
fn tile_image(&self, tensor: &Array3<f32>, h_crops: usize, w_crops: usize) -> Array4<f32> {
let base = self.base_resolution as usize;
let num_tiles = h_crops * w_crops;
let mut tiles = Array4::<f32>::zeros((num_tiles, 3, base, base));
for h_idx in 0..h_crops {
for w_idx in 0..w_crops {
let tile_idx = h_idx * w_crops + w_idx;
let y_start = h_idx * base;
let x_start = w_idx * base;
for c in 0..3 {
for y in 0..base {
for x in 0..base {
tiles[[tile_idx, c, y, x]] = tensor[[c, y_start + y, x_start + x]];
}
}
}
}
}
tiles
}
/// Downsample attention mask by factor of 2.
fn downsample_mask(&self, mask: &Array2<u32>, h_crops: usize, w_crops: usize) -> Array2<u32> {
let half_res = self.mask_resolution / 2;
let out_h = h_crops * half_res;
let out_w = w_crops * half_res;
let mut downsampled = Array2::<u32>::zeros((out_h, out_w));
for y in 0..out_h {
for x in 0..out_w {
// Sample every other pixel
let src_y = y * 2;
let src_x = x * 2;
if src_y < mask.shape()[0] && src_x < mask.shape()[1] {
downsampled[[y, x]] = mask[[src_y, src_x]];
}
}
}
downsampled
}
/// Calculate number of image tokens.
///
/// Formula: 256 + 1 + mask_sum + mask_col0_sum + 16
/// - 256: global image tokens
/// - 1: separator
/// - mask_sum: sum of downsampled attention mask (valid HD tokens)
/// - mask_col0_sum: sum of first column (row separators)
/// - 16: additional fixed tokens
fn calculate_num_tokens(&self, downsampled_mask: &Array2<u32>) -> usize {
let mask_sum: u32 = downsampled_mask.iter().sum();
let mask_col0_sum: u32 = downsampled_mask.column(0).iter().sum();
256 + 1 + mask_sum as usize + mask_col0_sum as usize + 16
}
/// Process a single image.
fn process_single_image(
&self,
image: &DynamicImage,
) -> Result<SingleImageResult, TransformError> {
// Step 1: Dynamic preprocess (resize, pad, create attention mask)
let (hd_image, attention_mask, h_crops, w_crops) = self.dynamic_preprocess(image)?;
let hd_h = hd_image.height();
let hd_w = hd_image.width();
// Step 2: Convert to tensor and normalize
let mut hd_tensor = transforms::to_tensor(&hd_image);
transforms::normalize(&mut hd_tensor, &self.mean, &self.std);
// Step 3: Create global image
let global_tensor = self.create_global_image(&hd_tensor);
// Step 4: Tile HD image
let tiles = self.tile_image(&hd_tensor, h_crops, w_crops);
let num_hd_tiles = h_crops * w_crops;
// Step 5: Concatenate global + tiles
// Output shape: [num_hd_tiles + 1, 3, base_resolution, base_resolution]
let base = self.base_resolution as usize;
let total_crops = num_hd_tiles + 1;
let mut output = Array4::<f32>::zeros((total_crops, 3, base, base));
// First slot is global image
output.slice_mut(s![0, .., .., ..]).assign(&global_tensor);
// Remaining slots are HD tiles
if num_hd_tiles > 0 {
output.slice_mut(s![1.., .., .., ..]).assign(&tiles);
}
// Step 6: Create combined attention mask [total_crops, mask_resolution, mask_resolution]
let mask_res = self.mask_resolution;
let mut combined_mask = Array3::<u32>::zeros((total_crops, mask_res, mask_res));
// Global mask is all ones
combined_mask.slice_mut(s![0, .., ..]).fill(1);
// Tile attention masks
for h_idx in 0..h_crops {
for w_idx in 0..w_crops {
let tile_idx = h_idx * w_crops + w_idx + 1; // +1 for global
let mask_y_start = h_idx * mask_res;
let mask_x_start = w_idx * mask_res;
let tile_mask = attention_mask.slice(s![
mask_y_start..mask_y_start + mask_res,
mask_x_start..mask_x_start + mask_res
]);
combined_mask
.slice_mut(s![tile_idx, .., ..])
.assign(&tile_mask);
}
}
// Step 7: Calculate token count
let downsampled = self.downsample_mask(&attention_mask, h_crops, w_crops);
let num_tokens = self.calculate_num_tokens(&downsampled);
Ok((output, combined_mask, (hd_h, hd_w), num_tokens))
}
}
impl ImagePreProcessor for Phi4VisionProcessor {
fn default_mean(&self) -> [f64; 3] {
self.mean
}
fn default_std(&self) -> [f64; 3] {
self.std
}
fn preprocess(
&self,
images: &[DynamicImage],
config: &PreProcessorConfig,
) -> Result<PreprocessedImages, TransformError> {
if images.is_empty() {
return Err(TransformError::InvalidShape {
expected: "non-empty image batch".to_string(),
actual: vec![0],
});
}
let processor = if config.dynamic_hd.is_some() || config.image_mean.is_some() {
Self::from_preprocessor_config(config)
} else {
self.clone()
};
let mut all_outputs = Vec::new();
let mut all_masks = Vec::new();
let mut image_sizes = Vec::new();
let mut num_img_tokens = Vec::new();
for image in images {
let (output, mask, size, tokens) = processor.process_single_image(image)?;
all_outputs.push(output);
all_masks.push(mask);
image_sizes.push(size);
num_img_tokens.push(tokens);
}
// Find max crops across batch for padding
let max_crops = all_outputs.iter().map(|o| o.shape()[0]).max().unwrap();
let base = self.base_resolution as usize;
let mask_res = self.mask_resolution;
// Pad all outputs to max_crops
let batch_size = images.len();
let mut pixel_values =
ndarray::ArrayD::<f32>::zeros(IxDyn(&[batch_size, max_crops, 3, base, base]));
let mut attention_masks =
ndarray::ArrayD::<u32>::zeros(IxDyn(&[batch_size, max_crops, mask_res, mask_res]));
for (b, (output, mask)) in all_outputs.iter().zip(all_masks.iter()).enumerate() {
let num_crops = output.shape()[0];
for t in 0..num_crops {
for c in 0..3 {
for y in 0..base {
for x in 0..base {
pixel_values[[b, t, c, y, x]] = output[[t, c, y, x]];
}
}
}
for y in 0..mask_res {
for x in 0..mask_res {
attention_masks[[b, t, y, x]] = mask[[t, y, x]];
}
}
}
// Remaining crops stay as zeros (padding)
}
// Convert to standard format
let mut model_specific = std::collections::HashMap::new();
// Store attention mask as model-specific data
let mask_flat: Vec<u32> = attention_masks.iter().copied().collect();
model_specific.insert(
"pixel_attention_mask".to_string(),
ModelSpecificValue::UintTensor {
data: mask_flat,
shape: vec![batch_size, max_crops, mask_res, mask_res],
},
);
// Store image sizes (H, W after HD transform)
let sizes_flat: Vec<u32> = image_sizes.iter().flat_map(|&(h, w)| vec![h, w]).collect();
model_specific.insert(
"image_sizes".to_string(),
ModelSpecificValue::UintTensor {
data: sizes_flat,
shape: vec![batch_size, 2],
},
);
Ok(PreprocessedImages {
pixel_values: pixel_values.into_dyn(),
num_img_tokens,
image_sizes,
model_specific,
})
}
fn calculate_num_tokens(&self, width: u32, height: u32, config: &PreProcessorConfig) -> usize {
let processor = Self::from_preprocessor_config(config);
let base_res = processor.base_resolution as f64;
let w_crop_num = (width as f64 / base_res).ceil() as usize;
let h_crop_num = (height as f64 / base_res).ceil() as usize;
let (target_w_crops, target_h_crops) = if w_crop_num * h_crop_num > processor.dynamic_hd {
let aspect_ratio = width as f64 / height as f64;
let target_ratios = processor.compute_target_ratios(1, processor.dynamic_hd);
processor.find_closest_aspect_ratio(aspect_ratio, &target_ratios, width, height)
} else {
(w_crop_num, h_crop_num)
};
// Approximate token count (without actual mask)
// Full mask would have target_w_crops * target_h_crops * (mask_res/2)^2 tokens
let half_res = processor.mask_resolution / 2;
let mask_area = target_h_crops * target_w_crops * half_res * half_res;
let mask_col0 = target_h_crops * half_res;
256 + 1 + mask_area + mask_col0 + 16
}
fn model_name(&self) -> &'static str {
"phi4-vision"
}
fn get_processed_size(&self, config: &PreProcessorConfig) -> Option<(u32, u32)> {
// For Phi4, the size depends on the input image
let _ = config;
None
}
}
#[cfg(test)]
mod tests {
use super::*;
fn create_test_image(width: u32, height: u32, color: Rgb<u8>) -> DynamicImage {
DynamicImage::from(RgbImage::from_pixel(width, height, color))
}
#[test]
fn test_phi4_vision_processor_default() {
let processor = Phi4VisionProcessor::new();
assert_eq!(processor.dynamic_hd(), DEFAULT_DYNAMIC_HD);
assert_eq!(processor.base_resolution(), BASE_RESOLUTION);
assert_eq!(processor.mean, PHI4_MEAN);
assert_eq!(processor.std, PHI4_STD);
}
#[test]
fn test_compute_target_ratios() {
let processor = Phi4VisionProcessor::new();
let ratios = processor.compute_target_ratios(1, 4);
// Should include (1,1), (1,2), (2,1), (1,3), (3,1), (2,2), (1,4), (4,1)
assert!(ratios.contains(&(1, 1)));
assert!(ratios.contains(&(2, 2)));
assert!(ratios.contains(&(1, 4)));
assert!(ratios.contains(&(4, 1)));
}
#[test]
fn test_find_closest_aspect_ratio_square() {
let processor = Phi4VisionProcessor::new();
let ratios = processor.compute_target_ratios(1, 36);
// Square image should get close to (1,1) or similar square ratio
let result = processor.find_closest_aspect_ratio(1.0, &ratios, 500, 500);
assert_eq!(result.0, result.1); // Should be square
}
#[test]
fn test_find_closest_aspect_ratio_wide() {
let processor = Phi4VisionProcessor::new();
let ratios = processor.compute_target_ratios(1, 36);
// Wide image (2:1 aspect ratio)
let result = processor.find_closest_aspect_ratio(2.0, &ratios, 1000, 500);
assert!(result.0 > result.1); // Width crops > height crops
}
#[test]
fn test_find_closest_aspect_ratio_tall() {
let processor = Phi4VisionProcessor::new();
let ratios = processor.compute_target_ratios(1, 36);
// Tall image (1:2 aspect ratio)
let result = processor.find_closest_aspect_ratio(0.5, &ratios, 500, 1000);
assert!(result.0 < result.1); // Width crops < height crops
}
#[test]
fn test_pad_image() {
let processor = Phi4VisionProcessor::new();
let image = create_test_image(300, 200, Rgb([100, 100, 100]));
let padded = processor.pad_image(&image, 448, 448);
assert_eq!(padded.width(), 448);
assert_eq!(padded.height(), 448);
// Check original content is preserved
let p = padded.get_pixel(100, 100);
assert_eq!(p.0[0], 100);
// Check padding is white
let p = padded.get_pixel(400, 400);
assert_eq!(p.0[0], 255);
}
#[test]
fn test_preprocess_square_image() {
let processor = Phi4VisionProcessor::new();
let config = PreProcessorConfig::default();
let image = create_test_image(500, 500, Rgb([128, 128, 128]));
let result = processor.preprocess(&[image], &config).unwrap();
assert_eq!(result.batch_size(), 1);
assert!(result.num_img_tokens[0] > 256); // At least global tokens
// Check pixel values are normalized
let flat = result.pixel_values_flat();
assert!(flat.iter().all(|&v| (-1.5..=1.5).contains(&v)));
}
#[test]
fn test_preprocess_wide_image() {
let processor = Phi4VisionProcessor::new();
let config = PreProcessorConfig::default();
let image = create_test_image(1000, 500, Rgb([128, 128, 128]));
let result = processor.preprocess(&[image], &config).unwrap();
assert_eq!(result.batch_size(), 1);
// Wide image should have more crops in width direction
assert!(result.image_sizes[0].1 >= result.image_sizes[0].0);
}
#[test]
fn test_preprocess_multiple_images() {
let processor = Phi4VisionProcessor::new();
let config = PreProcessorConfig::default();
let images = vec![
create_test_image(500, 500, Rgb([100, 100, 100])),
create_test_image(800, 400, Rgb([150, 150, 150])),
];
let result = processor.preprocess(&images, &config).unwrap();
assert_eq!(result.batch_size(), 2);
assert_eq!(result.image_sizes.len(), 2);
assert_eq!(result.num_img_tokens.len(), 2);
}
#[test]
fn test_model_name() {
let processor = Phi4VisionProcessor::new();
assert_eq!(processor.model_name(), "phi4-vision");
}
#[test]
fn test_normalization_values() {
let processor = Phi4VisionProcessor::new();
assert_eq!(processor.default_mean(), [0.5, 0.5, 0.5]);
assert_eq!(processor.default_std(), [0.5, 0.5, 0.5]);
}
#[test]
fn test_phi4_vs_phi3_differences() {
// Verify key differences from Phi3
let processor = Phi4VisionProcessor::new();
// Phi4 uses 448 base resolution (vs 336 in Phi3)
assert_eq!(processor.base_resolution(), 448);
// Phi4 uses simple 0.5 normalization (vs CLIP in Phi3)
assert_eq!(processor.mean, [0.5, 0.5, 0.5]);
assert_eq!(processor.std, [0.5, 0.5, 0.5]);
// Phi4 default dynamic_hd is 36 (vs 16 num_crops in Phi3)
assert_eq!(processor.dynamic_hd(), 36);
}
}
sgl-model-gateway/src/multimodal/vision/processors/pixtral.rs
-411
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@@ -1,411 +0,0 @@
//! Pixtral/Mistral3 Vision image processor implementation.
//!
//! This module implements the image preprocessing for Pixtral/Mistral3 models,
//! matching the behavior of HuggingFace's `PixtralImageProcessor`.
//!
//! Key characteristics:
//! - CLIP normalization: mean [0.48145466, 0.4578275, 0.40821073], std [0.26862954, 0.26130258, 0.27577711]
//! - Bicubic resampling for resize
//! - Images resized to fit within longest_edge (default 1024)
//! - Output dimensions are multiples of patch_size (default 16)
//! - No tiling - single image output per input
use std::collections::HashMap;
use image::{imageops::FilterType, DynamicImage};
use ndarray::{Array4, IxDyn};
use crate::multimodal::vision::{
image_processor::{ImagePreProcessor, ModelSpecificValue, PreprocessedImages},
preprocessor_config::PreProcessorConfig,
transforms::{self, TransformError},
};
/// Default normalization mean values (CLIP)
const DEFAULT_IMAGE_MEAN: [f64; 3] = [0.48145466, 0.4578275, 0.40821073];
/// Default normalization std values (CLIP)
const DEFAULT_IMAGE_STD: [f64; 3] = [0.26862954, 0.26130258, 0.27577711];
/// Default longest edge for resize
const DEFAULT_LONGEST_EDGE: u32 = 1024;
/// Default patch size
const DEFAULT_PATCH_SIZE: u32 = 16;
/// Pixtral/Mistral3 Vision image processor.
///
/// This processor handles image preprocessing for Pixtral and Mistral3 vision models.
/// Unlike tile-based processors (Phi3, LLaMA4), Pixtral processes images at their
/// natural resolution (up to a maximum), preserving aspect ratio.
#[derive(Debug, Clone)]
pub struct PixtralProcessor {
/// Maximum dimension for the longest edge
longest_edge: u32,
/// Patch size for calculating output dimensions
patch_size: u32,
/// Normalization mean values
image_mean: [f64; 3],
/// Normalization std values
image_std: [f64; 3],
}
impl Default for PixtralProcessor {
fn default() -> Self {
Self::new()
}
}
impl PixtralProcessor {
/// Creates a new Pixtral processor with default settings.
pub fn new() -> Self {
Self {
longest_edge: DEFAULT_LONGEST_EDGE,
patch_size: DEFAULT_PATCH_SIZE,
image_mean: DEFAULT_IMAGE_MEAN,
image_std: DEFAULT_IMAGE_STD,
}
}
/// Creates a processor from a HuggingFace preprocessor config.
pub fn from_preprocessor_config(config: &PreProcessorConfig) -> Self {
let longest_edge = config
.size
.as_ref()
.and_then(|s| s.get("longest_edge").copied())
.unwrap_or(DEFAULT_LONGEST_EDGE);
// Patch size uses the new PatchSize type from config
let patch_size = config.get_patch_size(DEFAULT_PATCH_SIZE as usize) as u32;
let image_mean = config
.image_mean
.as_ref()
.filter(|m| m.len() >= 3)
.map(|m| [m[0], m[1], m[2]])
.unwrap_or(DEFAULT_IMAGE_MEAN);
let image_std = config
.image_std
.as_ref()
.filter(|s| s.len() >= 3)
.map(|s| [s[0], s[1], s[2]])
.unwrap_or(DEFAULT_IMAGE_STD);
Self {
longest_edge,
patch_size,
image_mean,
image_std,
}
}
/// Calculates the target output size for an image.
///
/// The image is resized to fit within `longest_edge` while preserving aspect ratio.
/// The output dimensions are then adjusted to be multiples of `patch_size`.
fn get_resize_output_size(&self, height: u32, width: u32) -> (u32, u32) {
let max_size = self.longest_edge;
let patch_size = self.patch_size;
// Calculate ratio for scaling down (only if larger than max_size)
let ratio = f64::max(
height as f64 / max_size as f64,
width as f64 / max_size as f64,
);
let (new_height, new_width) = if ratio > 1.0 {
// Scale down using floor to ensure we don't exceed max_size
let new_height = (height as f64 / ratio).floor() as u32;
let new_width = (width as f64 / ratio).floor() as u32;
(new_height, new_width)
} else {
(height, width)
};
// Calculate number of patches in each dimension
// Using: num_tokens = (dim - 1) / patch_size + 1 (i.e., ceiling division)
let num_height_tokens = (new_height.max(1) - 1) / patch_size + 1;
let num_width_tokens = (new_width.max(1) - 1) / patch_size + 1;
// Final size is patches * patch_size
(
num_height_tokens * patch_size,
num_width_tokens * patch_size,
)
}
/// Processes a single image through the Pixtral pipeline.
fn process_single_image(
&self,
image: &DynamicImage,
) -> Result<(Array4<f32>, (usize, usize)), TransformError> {
let (orig_width, orig_height) = (image.width(), image.height());
// Step 1: Calculate output size
let (target_h, target_w) = self.get_resize_output_size(orig_height, orig_width);
// Step 2: Resize image using bicubic interpolation
let resized = image.resize_exact(target_w, target_h, FilterType::CatmullRom);
// Step 3: Convert to tensor (0-1 range) and normalize
let mut tensor = transforms::to_tensor(&resized);
transforms::normalize(&mut tensor, &self.image_mean, &self.image_std);
// Step 4: Reshape to (1, C, H, W)
let (c, h, w) = (tensor.shape()[0], tensor.shape()[1], tensor.shape()[2]);
let output = tensor
.into_shape_with_order((1, c, h, w))
.map_err(|e| TransformError::ShapeError(e.to_string()))?;
Ok((output, (target_h as usize, target_w as usize)))
}
}
impl ImagePreProcessor for PixtralProcessor {
fn default_mean(&self) -> [f64; 3] {
self.image_mean
}
fn default_std(&self) -> [f64; 3] {
self.image_std
}
fn preprocess(
&self,
images: &[DynamicImage],
config: &PreProcessorConfig,
) -> Result<PreprocessedImages, TransformError> {
if images.is_empty() {
return Err(TransformError::InvalidShape {
expected: "non-empty image batch".to_string(),
actual: vec![0],
});
}
// Apply config overrides if present
let processor = if config.size.is_some()
|| config.patch_size.is_some()
|| config.image_mean.is_some()
|| config.image_std.is_some()
{
Self::from_preprocessor_config(config)
} else {
self.clone()
};
let mut all_pixel_values = Vec::new();
let mut all_image_sizes = Vec::new();
let mut original_sizes = Vec::new();
let mut num_img_tokens = Vec::new();
for image in images {
let (pixels, size) = processor.process_single_image(image)?;
let tokens = processor.calculate_num_tokens(image.width(), image.height(), config);
all_pixel_values.push(pixels);
all_image_sizes.push(size);
original_sizes.push((image.height(), image.width()));
num_img_tokens.push(tokens);
}
// Pad images to the same size for batching
let max_height = all_image_sizes.iter().map(|(h, _)| *h).max().unwrap_or(0);
let max_width = all_image_sizes.iter().map(|(_, w)| *w).max().unwrap_or(0);
// Create batch tensor with padding
let batch_size = all_pixel_values.len();
let channels = 3;
let mut batch_tensor =
ndarray::ArrayD::<f32>::zeros(IxDyn(&[batch_size, channels, max_height, max_width]));
for (i, (pixels, (h, w))) in all_pixel_values
.iter()
.zip(all_image_sizes.iter())
.enumerate()
{
// Copy the image data into the batch (top-left aligned, zero-padded)
for c in 0..channels {
for y in 0..*h {
for x in 0..*w {
batch_tensor[[i, c, y, x]] = pixels[[0, c, y, x]];
}
}
}
}
// Store image sizes as model-specific data
let mut model_specific = HashMap::new();
let image_sizes_flat: Vec<i64> = all_image_sizes
.iter()
.flat_map(|&(h, w)| vec![h as i64, w as i64])
.collect();
model_specific.insert(
"image_sizes".to_string(),
ModelSpecificValue::IntTensor {
data: image_sizes_flat,
shape: vec![batch_size, 2],
},
);
Ok(PreprocessedImages {
pixel_values: batch_tensor,
num_img_tokens,
image_sizes: original_sizes,
model_specific,
})
}
fn calculate_num_tokens(&self, width: u32, height: u32, config: &PreProcessorConfig) -> usize {
let processor = Self::from_preprocessor_config(config);
let (target_h, target_w) = processor.get_resize_output_size(height, width);
let patch_size = processor.patch_size;
// Number of tokens = num_patches_h * num_patches_w
let num_patches_h = target_h / patch_size;
let num_patches_w = target_w / patch_size;
(num_patches_h * num_patches_w) as usize
}
fn model_name(&self) -> &'static str {
"pixtral"
}
fn get_processed_size(&self, _config: &PreProcessorConfig) -> Option<(u32, u32)> {
// Pixtral has dynamic size based on input
None
}
}
#[cfg(test)]
mod tests {
use image::{Rgb, RgbImage};
use super::*;
fn create_test_image(width: u32, height: u32) -> DynamicImage {
let mut img = RgbImage::new(width, height);
for y in 0..height {
for x in 0..width {
let r = ((x * 255) / width.max(1)) as u8;
let g = ((y * 255) / height.max(1)) as u8;
let b = (((x + y) * 128) / (width + height).max(1)) as u8;
img.put_pixel(x, y, Rgb([r, g, b]));
}
}
DynamicImage::ImageRgb8(img)
}
#[test]
fn test_resize_output_size_small_image() {
let processor = PixtralProcessor::new();
// Small image that doesn't need resizing - just pad to patch boundary
// 100x100 -> patches: ceil(100/16) = 7, output: 7*16 = 112
let (h, w) = processor.get_resize_output_size(100, 100);
assert_eq!((h, w), (112, 112));
}
#[test]
fn test_resize_output_size_large_image() {
let processor = PixtralProcessor::new();
// Large image that needs resizing
// 2048x1024: ratio = 2048/1024 = 2.0
// scaled: 2048/2 = 1024, 1024/2 = 512
// patches h: ceil(1024/16) = 64, patches w: ceil(512/16) = 32
// output: 64*16 = 1024, 32*16 = 512
let (h, w) = processor.get_resize_output_size(2048, 1024);
assert_eq!((h, w), (1024, 512));
}
#[test]
fn test_resize_output_size_at_limit() {
let processor = PixtralProcessor::new();
// Image exactly at limit
// 1024x768: ratio = max(1024/1024, 768/1024) = 1.0
// No resize needed
// patches h: ceil(1024/16) = 64, patches w: ceil(768/16) = 48
// output: 64*16 = 1024, 48*16 = 768
let (h, w) = processor.get_resize_output_size(1024, 768);
assert_eq!((h, w), (1024, 768));
}
#[test]
fn test_process_single_image() {
let processor = PixtralProcessor::new();
let image = create_test_image(200, 150);
let (tensor, size) = processor.process_single_image(&image).unwrap();
// 200x150 -> patches h: ceil(150/16) = 10, patches w: ceil(200/16) = 13
// output: 10*16 = 160, 13*16 = 208
assert_eq!(size, (160, 208));
assert_eq!(tensor.shape(), &[1, 3, 160, 208]);
}
#[test]
fn test_preprocess_batch() {
let processor = PixtralProcessor::new();
let config = PreProcessorConfig::default();
let images = vec![create_test_image(200, 150), create_test_image(300, 100)];
let result = processor.preprocess(&images, &config).unwrap();
// First image: 150x200 -> 160x208
// Second image: 100x300 -> 112x304 (ceil(100/16)=7, ceil(300/16)=19)
// Batch padded to max: 160x304
assert_eq!(result.pixel_values.shape()[0], 2); // batch size
assert_eq!(result.pixel_values.shape()[1], 3); // channels
}
#[test]
fn test_normalization_values() {
let processor = PixtralProcessor::new();
// Verify CLIP normalization values
assert!((processor.image_mean[0] - 0.48145466).abs() < 1e-6);
assert!((processor.image_mean[1] - 0.4578275).abs() < 1e-6);
assert!((processor.image_mean[2] - 0.40821073).abs() < 1e-6);
assert!((processor.image_std[0] - 0.26862954).abs() < 1e-6);
assert!((processor.image_std[1] - 0.26130258).abs() < 1e-6);
assert!((processor.image_std[2] - 0.27577711).abs() < 1e-6);
}
#[test]
fn test_from_config() {
let mut size = HashMap::new();
size.insert("longest_edge".to_string(), 2048u32);
let config = PreProcessorConfig {
size: Some(size),
patch_size: Some(crate::multimodal::vision::preprocessor_config::PatchSize {
height: Some(14),
width: Some(14),
}),
image_mean: Some(vec![0.5, 0.5, 0.5]),
image_std: Some(vec![0.5, 0.5, 0.5]),
..Default::default()
};
let processor = PixtralProcessor::from_preprocessor_config(&config);
assert_eq!(processor.longest_edge, 2048);
assert_eq!(processor.patch_size, 14);
assert_eq!(processor.image_mean, [0.5, 0.5, 0.5]);
assert_eq!(processor.image_std, [0.5, 0.5, 0.5]);
}
#[test]
fn test_calculate_num_tokens() {
let processor = PixtralProcessor::new();
let config = PreProcessorConfig::default();
// 200x150 -> 208x160 -> 13*10 = 130 patches
let tokens = processor.calculate_num_tokens(200, 150, &config);
assert_eq!(tokens, 130);
}
}
sgl-model-gateway/src/multimodal/vision/processors/qwen2_vl.rs
-458
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@@ -1,458 +0,0 @@
//! Qwen2-VL family image processors.
//!
//! This module provides the Qwen2-VL processor which wraps the shared
//! `QwenVLProcessorBase` with Qwen2-VL specific default parameters.
//!
//! # Key Features
//!
//! - **Smart Resize**: Resizes images to fit within min/max pixel bounds while
//! preserving aspect ratio and aligning to patch boundaries
//! - **Dynamic Token Count**: Token count depends on actual image dimensions
//! - **image_grid_thw**: Returns (T, H, W) grid dimensions for position encoding
//!
//! # Qwen2-VL Parameters
//!
//! - patch_size: 14
//! - merge_size: 2
//! - factor: 28 (patch_size * merge_size)
//! - normalization: CLIP mean/std
use std::ops::Deref;
use image::DynamicImage;
use ndarray::Array3;
use super::qwen_vl_base::{QwenVLConfig, QwenVLProcessorBase};
use crate::multimodal::vision::{
image_processor::{ImagePreProcessor, PreprocessedImages},
preprocessor_config::PreProcessorConfig,
transforms::TransformError,
};
/// CLIP normalization mean values used by Qwen2-VL models.
pub const CLIP_MEAN: [f64; 3] = [0.48145466, 0.4578275, 0.40821073];
/// CLIP normalization std values used by Qwen2-VL models.
pub const CLIP_STD: [f64; 3] = [0.26862954, 0.26130258, 0.27577711];
/// Default minimum pixels (256 * 28 * 28 = 200,704)
pub const DEFAULT_MIN_PIXELS: usize = 256 * 28 * 28;
/// Default maximum pixels (1280 * 28 * 28 = 1,003,520)
pub const DEFAULT_MAX_PIXELS: usize = 1280 * 28 * 28;
/// Default patch size
pub const DEFAULT_PATCH_SIZE: usize = 14;
/// Default merge size for token reduction
pub const DEFAULT_MERGE_SIZE: usize = 2;
/// Default temporal patch size (for video frames)
pub const DEFAULT_TEMPORAL_PATCH_SIZE: usize = 2;
/// Qwen2-VL image processor.
///
/// This is a thin wrapper around `QwenVLProcessorBase` with Qwen2-VL
/// specific default parameters:
/// - patch_size: 14
/// - merge_size: 2
/// - CLIP normalization mean/std
#[derive(Debug, Clone)]
pub struct Qwen2VLProcessor {
inner: QwenVLProcessorBase,
}
impl Default for Qwen2VLProcessor {
fn default() -> Self {
Self::new()
}
}
impl Qwen2VLProcessor {
/// Create a new Qwen2-VL processor with default settings.
///
/// Defaults:
/// - patch_size: 14
/// - merge_size: 2
/// - min_pixels: 200,704 (256 * 28 * 28)
/// - max_pixels: 1,003,520 (1280 * 28 * 28)
/// - temporal_patch_size: 2
/// - normalization: CLIP mean/std
pub fn new() -> Self {
Self {
inner: QwenVLProcessorBase::new(QwenVLConfig {
patch_size: DEFAULT_PATCH_SIZE,
merge_size: DEFAULT_MERGE_SIZE,
min_pixels: DEFAULT_MIN_PIXELS,
max_pixels: DEFAULT_MAX_PIXELS,
temporal_patch_size: DEFAULT_TEMPORAL_PATCH_SIZE,
mean: CLIP_MEAN,
std: CLIP_STD,
model_name: "qwen2-vl",
}),
}
}
/// Create a processor with custom settings.
pub fn with_config(
patch_size: usize,
merge_size: usize,
min_pixels: usize,
max_pixels: usize,
temporal_patch_size: usize,
) -> Self {
Self {
inner: QwenVLProcessorBase::new(QwenVLConfig {
patch_size,
merge_size,
min_pixels,
max_pixels,
temporal_patch_size,
mean: CLIP_MEAN,
std: CLIP_STD,
model_name: "qwen2-vl",
}),
}
}
/// Create a processor from preprocessor config.
pub fn from_preprocessor_config(config: &PreProcessorConfig) -> Self {
Self {
inner: QwenVLProcessorBase::new(QwenVLConfig {
patch_size: config.get_patch_size(DEFAULT_PATCH_SIZE),
merge_size: config.merge_size.unwrap_or(DEFAULT_MERGE_SIZE),
min_pixels: config.min_pixels.unwrap_or(DEFAULT_MIN_PIXELS),
max_pixels: config.max_pixels.unwrap_or(DEFAULT_MAX_PIXELS),
temporal_patch_size: config
.temporal_patch_size
.unwrap_or(DEFAULT_TEMPORAL_PATCH_SIZE),
mean: CLIP_MEAN,
std: CLIP_STD,
model_name: "qwen2-vl",
}),
}
}
/// Get the patch size.
pub fn patch_size(&self) -> usize {
self.inner.patch_size()
}
/// Get the merge size.
pub fn merge_size(&self) -> usize {
self.inner.merge_size()
}
/// Get the minimum pixels.
pub fn min_pixels(&self) -> usize {
self.inner.min_pixels()
}
/// Get the maximum pixels.
pub fn max_pixels(&self) -> usize {
self.inner.max_pixels()
}
/// Get the temporal patch size.
pub fn temporal_patch_size(&self) -> usize {
self.inner.temporal_patch_size()
}
/// Get the factor for dimension alignment.
#[inline]
pub fn get_factor(&self) -> usize {
self.inner.get_factor()
}
/// Smart resize algorithm for Qwen2-VL.
pub fn smart_resize(
&self,
height: usize,
width: usize,
) -> Result<(usize, usize), TransformError> {
self.inner.smart_resize(height, width)
}
/// Calculate the grid dimensions (T, H, W) for an image.
pub fn calculate_grid_thw(
&self,
height: usize,
width: usize,
num_frames: usize,
) -> (usize, usize, usize) {
self.inner.calculate_grid_thw(height, width, num_frames)
}
/// Calculate the number of image tokens after merge.
pub fn calculate_tokens_from_grid(&self, grid_t: usize, grid_h: usize, grid_w: usize) -> usize {
self.inner
.calculate_tokens_from_grid(grid_t, grid_h, grid_w)
}
/// Reshape pixel values from [C, H, W] to flattened patches format.
pub fn reshape_to_patches(
&self,
tensor: &Array3<f32>,
grid_t: usize,
grid_h: usize,
grid_w: usize,
) -> Vec<f32> {
self.inner
.reshape_to_patches(tensor, grid_t, grid_h, grid_w)
}
}
impl Deref for Qwen2VLProcessor {
type Target = QwenVLProcessorBase;
fn deref(&self) -> &Self::Target {
&self.inner
}
}
impl ImagePreProcessor for Qwen2VLProcessor {
fn default_mean(&self) -> [f64; 3] {
self.inner.default_mean()
}
fn default_std(&self) -> [f64; 3] {
self.inner.default_std()
}
fn preprocess(
&self,
images: &[DynamicImage],
config: &PreProcessorConfig,
) -> Result<PreprocessedImages, TransformError> {
self.inner.preprocess(images, config)
}
fn calculate_num_tokens(&self, width: u32, height: u32, config: &PreProcessorConfig) -> usize {
self.inner.calculate_num_tokens(width, height, config)
}
fn model_name(&self) -> &'static str {
self.inner.model_name()
}
fn get_processed_size(&self, config: &PreProcessorConfig) -> Option<(u32, u32)> {
self.inner.get_processed_size(config)
}
}
#[cfg(test)]
mod tests {
use image::{Rgb, RgbImage};
use super::*;
use crate::multimodal::vision::{
image_processor::ModelSpecificValue, preprocessor_config::PatchSize,
};
fn create_test_image(width: u32, height: u32, color: Rgb<u8>) -> DynamicImage {
DynamicImage::from(RgbImage::from_pixel(width, height, color))
}
#[test]
fn test_qwen2_vl_processor_default() {
let processor = Qwen2VLProcessor::new();
assert_eq!(processor.patch_size(), 14);
assert_eq!(processor.merge_size(), 2);
assert_eq!(processor.min_pixels(), DEFAULT_MIN_PIXELS);
assert_eq!(processor.max_pixels(), DEFAULT_MAX_PIXELS);
assert_eq!(processor.get_factor(), 28); // 14 * 2
}
#[test]
fn test_smart_resize_within_bounds() {
let processor = Qwen2VLProcessor::new();
// Image that's already within bounds
let (h, w) = processor.smart_resize(500, 500).unwrap();
// Should be aligned to factor (28)
assert_eq!(h % 28, 0);
assert_eq!(w % 28, 0);
// Should be within bounds
assert!(h * w >= processor.min_pixels());
assert!(h * w <= processor.max_pixels());
}
#[test]
fn test_smart_resize_too_large() {
let processor = Qwen2VLProcessor::new();
// Very large image
let (h, w) = processor.smart_resize(3000, 3000).unwrap();
// Should be scaled down
assert!(h * w <= processor.max_pixels());
assert_eq!(h % 28, 0);
assert_eq!(w % 28, 0);
}
#[test]
fn test_smart_resize_too_small() {
let processor = Qwen2VLProcessor::new();
// Small image (but above minimum dimension)
let (h, w) = processor.smart_resize(100, 100).unwrap();
// Should be scaled up to min_pixels
assert!(h * w >= processor.min_pixels());
assert_eq!(h % 28, 0);
assert_eq!(w % 28, 0);
}
#[test]
fn test_smart_resize_aspect_ratio_preserved() {
let processor = Qwen2VLProcessor::new();
// 2:1 aspect ratio
let (h, w) = processor.smart_resize(400, 800).unwrap();
// Aspect ratio should be approximately preserved
let original_ratio = 800.0 / 400.0;
let new_ratio = w as f64 / h as f64;
assert!((new_ratio - original_ratio).abs() < 0.5);
}
#[test]
fn test_smart_resize_extreme_aspect_ratio_error() {
let processor = Qwen2VLProcessor::new();
// 300:1 aspect ratio - should fail
let result = processor.smart_resize(100, 30000);
assert!(result.is_err());
}
#[test]
fn test_smart_resize_too_small_dimension_error() {
let processor = Qwen2VLProcessor::new();
// Dimension smaller than factor
let result = processor.smart_resize(10, 100);
assert!(result.is_err());
}
#[test]
fn test_calculate_grid_thw_image() {
let processor = Qwen2VLProcessor::new();
// 448x448 image (16x16 grid patches)
let (t, h, w) = processor.calculate_grid_thw(448, 448, 1);
assert_eq!(t, 1); // Single image
assert_eq!(h, 448 / 14); // 32
assert_eq!(w, 448 / 14); // 32
}
#[test]
fn test_calculate_tokens() {
let processor = Qwen2VLProcessor::new();
// With merge_size=2, tokens = (t * h * w) / 4
let tokens = processor.calculate_tokens_from_grid(1, 32, 32);
assert_eq!(tokens, (32 * 32) / 4); // 256
}
#[test]
fn test_qwen2_vl_preprocess() {
let processor = Qwen2VLProcessor::new();
let config = PreProcessorConfig {
do_resize: Some(true),
do_normalize: Some(true),
image_mean: Some(CLIP_MEAN.to_vec()),
image_std: Some(CLIP_STD.to_vec()),
patch_size: Some(PatchSize {
height: Some(14),
width: Some(14),
}),
merge_size: Some(2),
min_pixels: Some(DEFAULT_MIN_PIXELS),
max_pixels: Some(DEFAULT_MAX_PIXELS),
..Default::default()
};
let image = create_test_image(600, 400, Rgb([128, 128, 128]));
let result = processor.preprocess(&[image], &config).unwrap();
assert_eq!(result.batch_size(), 1);
// Check pixel values are normalized
let flat = result.pixel_values_flat();
// After normalization with CLIP mean/std, gray (0.5) should be near 0
// (0.5 - 0.48) / 0.27 ≈ 0.07
assert!(flat.iter().all(|&v| v.abs() < 1.0)); // Should be normalized
// Check image_grid_thw is present
assert!(result.model_specific.contains_key("image_grid_thw"));
// Verify token count is reasonable
assert!(result.num_img_tokens[0] > 0);
}
#[test]
fn test_qwen2_vl_preprocess_multiple() {
let processor = Qwen2VLProcessor::new();
let config = PreProcessorConfig::default();
let images = vec![
create_test_image(600, 400, Rgb([100, 100, 100])),
create_test_image(400, 600, Rgb([150, 150, 150])),
];
let result = processor.preprocess(&images, &config).unwrap();
// Both images processed
assert_eq!(result.image_sizes.len(), 2);
assert_eq!(result.num_img_tokens.len(), 2);
// Check grid_thw shape
if let Some(ModelSpecificValue::UintTensor { data, shape }) =
result.model_specific.get("image_grid_thw")
{
assert_eq!(shape, &[2, 3]); // 2 images, 3 values (T, H, W) each
assert_eq!(data.len(), 6);
} else {
panic!("Expected image_grid_thw to be UintTensor");
}
}
#[test]
fn test_qwen2_vl_from_config() {
let config = PreProcessorConfig {
patch_size: Some(PatchSize {
height: Some(16),
width: Some(16),
}),
merge_size: Some(4),
min_pixels: Some(100000),
max_pixels: Some(500000),
temporal_patch_size: Some(4),
..Default::default()
};
let processor = Qwen2VLProcessor::from_preprocessor_config(&config);
assert_eq!(processor.patch_size(), 16);
assert_eq!(processor.merge_size(), 4);
assert_eq!(processor.min_pixels(), 100000);
assert_eq!(processor.max_pixels(), 500000);
assert_eq!(processor.temporal_patch_size(), 4);
}
#[test]
fn test_model_name() {
let processor = Qwen2VLProcessor::new();
assert_eq!(processor.model_name(), "qwen2-vl");
}
#[test]
fn test_default_mean_std() {
let processor = Qwen2VLProcessor::new();
assert_eq!(processor.default_mean(), CLIP_MEAN);
assert_eq!(processor.default_std(), CLIP_STD);
}
}
sgl-model-gateway/src/multimodal/vision/processors/qwen3_vl.rs
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@@ -1,470 +0,0 @@
//! Qwen3-VL family image processors.
//!
//! This module provides the Qwen3-VL processor which wraps the shared
//! `QwenVLProcessorBase` with Qwen3-VL specific default parameters.
//!
//! # Key Differences from Qwen2-VL
//!
//! - **Patch Size**: 16 (vs 14 in Qwen2-VL)
//! - **Factor**: 32 (patch_size * merge_size) (vs 28 in Qwen2-VL)
//! - **Normalization**: [0.5, 0.5, 0.5] mean/std (vs CLIP in Qwen2-VL)
//!
//! # Qwen3-VL Parameters
//!
//! - patch_size: 16
//! - merge_size: 2
//! - factor: 32 (patch_size * merge_size)
//! - normalization: [0.5, 0.5, 0.5] mean/std
use std::ops::Deref;
use image::DynamicImage;
use ndarray::Array3;
use super::qwen_vl_base::{QwenVLConfig, QwenVLProcessorBase};
use crate::multimodal::vision::{
image_processor::{ImagePreProcessor, PreprocessedImages},
preprocessor_config::PreProcessorConfig,
transforms::TransformError,
};
/// Qwen3-VL normalization mean values (simple [0.5, 0.5, 0.5]).
pub const QWEN3_MEAN: [f64; 3] = [0.5, 0.5, 0.5];
/// Qwen3-VL normalization std values (simple [0.5, 0.5, 0.5]).
pub const QWEN3_STD: [f64; 3] = [0.5, 0.5, 0.5];
/// Default minimum pixels for Qwen3-VL
/// This corresponds to shortest_edge = 65536 from HF config
pub const DEFAULT_MIN_PIXELS: usize = 65536;
/// Default maximum pixels for Qwen3-VL
/// This corresponds to longest_edge = 16777216 from HF config
pub const DEFAULT_MAX_PIXELS: usize = 16777216;
/// Default patch size for Qwen3-VL (16, vs 14 in Qwen2-VL)
pub const DEFAULT_PATCH_SIZE: usize = 16;
/// Default merge size for token reduction
pub const DEFAULT_MERGE_SIZE: usize = 2;
/// Default temporal patch size (for video frames)
pub const DEFAULT_TEMPORAL_PATCH_SIZE: usize = 2;
/// Qwen3-VL image processor.
///
/// This is a thin wrapper around `QwenVLProcessorBase` with Qwen3-VL
/// specific default parameters:
/// - patch_size: 16
/// - merge_size: 2
/// - [0.5, 0.5, 0.5] normalization mean/std
#[derive(Debug, Clone)]
pub struct Qwen3VLProcessor {
inner: QwenVLProcessorBase,
}
impl Default for Qwen3VLProcessor {
fn default() -> Self {
Self::new()
}
}
impl Qwen3VLProcessor {
/// Create a new Qwen3-VL processor with default settings.
///
/// Defaults:
/// - patch_size: 16
/// - merge_size: 2
/// - min_pixels: 65,536
/// - max_pixels: 16,777,216
/// - temporal_patch_size: 2
/// - normalization: [0.5, 0.5, 0.5] mean/std
pub fn new() -> Self {
Self {
inner: QwenVLProcessorBase::new(QwenVLConfig {
patch_size: DEFAULT_PATCH_SIZE,
merge_size: DEFAULT_MERGE_SIZE,
min_pixels: DEFAULT_MIN_PIXELS,
max_pixels: DEFAULT_MAX_PIXELS,
temporal_patch_size: DEFAULT_TEMPORAL_PATCH_SIZE,
mean: QWEN3_MEAN,
std: QWEN3_STD,
model_name: "qwen3-vl",
}),
}
}
/// Create a processor with custom settings.
pub fn with_config(
patch_size: usize,
merge_size: usize,
min_pixels: usize,
max_pixels: usize,
temporal_patch_size: usize,
) -> Self {
Self {
inner: QwenVLProcessorBase::new(QwenVLConfig {
patch_size,
merge_size,
min_pixels,
max_pixels,
temporal_patch_size,
mean: QWEN3_MEAN,
std: QWEN3_STD,
model_name: "qwen3-vl",
}),
}
}
/// Create a processor from preprocessor config.
pub fn from_preprocessor_config(config: &PreProcessorConfig) -> Self {
Self {
inner: QwenVLProcessorBase::new(QwenVLConfig {
patch_size: config.get_patch_size(DEFAULT_PATCH_SIZE),
merge_size: config.merge_size.unwrap_or(DEFAULT_MERGE_SIZE),
min_pixels: config.min_pixels.unwrap_or(DEFAULT_MIN_PIXELS),
max_pixels: config.max_pixels.unwrap_or(DEFAULT_MAX_PIXELS),
temporal_patch_size: config
.temporal_patch_size
.unwrap_or(DEFAULT_TEMPORAL_PATCH_SIZE),
mean: QWEN3_MEAN,
std: QWEN3_STD,
model_name: "qwen3-vl",
}),
}
}
/// Get the patch size.
pub fn patch_size(&self) -> usize {
self.inner.patch_size()
}
/// Get the merge size.
pub fn merge_size(&self) -> usize {
self.inner.merge_size()
}
/// Get the minimum pixels.
pub fn min_pixels(&self) -> usize {
self.inner.min_pixels()
}
/// Get the maximum pixels.
pub fn max_pixels(&self) -> usize {
self.inner.max_pixels()
}
/// Get the temporal patch size.
pub fn temporal_patch_size(&self) -> usize {
self.inner.temporal_patch_size()
}
/// Get the factor for dimension alignment.
#[inline]
pub fn get_factor(&self) -> usize {
self.inner.get_factor()
}
/// Smart resize algorithm for Qwen3-VL.
pub fn smart_resize(
&self,
height: usize,
width: usize,
) -> Result<(usize, usize), TransformError> {
self.inner.smart_resize(height, width)
}
/// Calculate the grid dimensions (T, H, W) for an image.
pub fn calculate_grid_thw(
&self,
height: usize,
width: usize,
num_frames: usize,
) -> (usize, usize, usize) {
self.inner.calculate_grid_thw(height, width, num_frames)
}
/// Calculate the number of image tokens after merge.
pub fn calculate_tokens_from_grid(&self, grid_t: usize, grid_h: usize, grid_w: usize) -> usize {
self.inner
.calculate_tokens_from_grid(grid_t, grid_h, grid_w)
}
/// Reshape pixel values from [C, H, W] to flattened patches format.
pub fn reshape_to_patches(
&self,
tensor: &Array3<f32>,
grid_t: usize,
grid_h: usize,
grid_w: usize,
) -> Vec<f32> {
self.inner
.reshape_to_patches(tensor, grid_t, grid_h, grid_w)
}
}
impl Deref for Qwen3VLProcessor {
type Target = QwenVLProcessorBase;
fn deref(&self) -> &Self::Target {
&self.inner
}
}
impl ImagePreProcessor for Qwen3VLProcessor {
fn default_mean(&self) -> [f64; 3] {
self.inner.default_mean()
}
fn default_std(&self) -> [f64; 3] {
self.inner.default_std()
}
fn preprocess(
&self,
images: &[DynamicImage],
config: &PreProcessorConfig,
) -> Result<PreprocessedImages, TransformError> {
self.inner.preprocess(images, config)
}
fn calculate_num_tokens(&self, width: u32, height: u32, config: &PreProcessorConfig) -> usize {
self.inner.calculate_num_tokens(width, height, config)
}
fn model_name(&self) -> &'static str {
self.inner.model_name()
}
fn get_processed_size(&self, config: &PreProcessorConfig) -> Option<(u32, u32)> {
self.inner.get_processed_size(config)
}
}
#[cfg(test)]
mod tests {
use image::{Rgb, RgbImage};
use super::*;
use crate::multimodal::vision::{
image_processor::ModelSpecificValue, preprocessor_config::PatchSize,
};
fn create_test_image(width: u32, height: u32, color: Rgb<u8>) -> DynamicImage {
DynamicImage::from(RgbImage::from_pixel(width, height, color))
}
#[test]
fn test_qwen3_vl_processor_default() {
let processor = Qwen3VLProcessor::new();
assert_eq!(processor.patch_size(), 16);
assert_eq!(processor.merge_size(), 2);
assert_eq!(processor.min_pixels(), DEFAULT_MIN_PIXELS);
assert_eq!(processor.max_pixels(), DEFAULT_MAX_PIXELS);
assert_eq!(processor.get_factor(), 32); // 16 * 2
}
#[test]
fn test_smart_resize_within_bounds() {
let processor = Qwen3VLProcessor::new();
// Image that's within bounds
let (h, w) = processor.smart_resize(500, 500).unwrap();
// Should be aligned to factor (32)
assert_eq!(h % 32, 0);
assert_eq!(w % 32, 0);
// Should be within bounds
assert!(h * w >= processor.min_pixels());
assert!(h * w <= processor.max_pixels());
}
#[test]
fn test_smart_resize_aspect_ratio_preserved() {
let processor = Qwen3VLProcessor::new();
// 2:1 aspect ratio
let (h, w) = processor.smart_resize(400, 800).unwrap();
// Aspect ratio should be approximately preserved
let original_ratio = 800.0 / 400.0;
let new_ratio = w as f64 / h as f64;
assert!((new_ratio - original_ratio).abs() < 0.5);
}
#[test]
fn test_smart_resize_extreme_aspect_ratio_error() {
let processor = Qwen3VLProcessor::new();
// 300:1 aspect ratio - should fail
let result = processor.smart_resize(100, 30000);
assert!(result.is_err());
}
#[test]
fn test_smart_resize_too_small_dimension_error() {
let processor = Qwen3VLProcessor::new();
// Dimension smaller than factor (32)
let result = processor.smart_resize(10, 100);
assert!(result.is_err());
}
#[test]
fn test_calculate_grid_thw_image() {
let processor = Qwen3VLProcessor::new();
// 480x640 image
let (t, h, w) = processor.calculate_grid_thw(480, 640, 1);
assert_eq!(t, 1); // Single image
assert_eq!(h, 480 / 16); // 30
assert_eq!(w, 640 / 16); // 40
}
#[test]
fn test_calculate_tokens() {
let processor = Qwen3VLProcessor::new();
// With merge_size=2, tokens = (t * h * w) / 4
let tokens = processor.calculate_tokens_from_grid(1, 30, 40);
assert_eq!(tokens, (30 * 40) / 4); // 300
}
#[test]
fn test_qwen3_vl_preprocess() {
let processor = Qwen3VLProcessor::new();
let config = PreProcessorConfig {
do_resize: Some(true),
do_normalize: Some(true),
image_mean: Some(QWEN3_MEAN.to_vec()),
image_std: Some(QWEN3_STD.to_vec()),
patch_size: Some(PatchSize {
height: Some(16),
width: Some(16),
}),
merge_size: Some(2),
min_pixels: Some(DEFAULT_MIN_PIXELS),
max_pixels: Some(DEFAULT_MAX_PIXELS),
..Default::default()
};
let image = create_test_image(640, 480, Rgb([128, 128, 128]));
let result = processor.preprocess(&[image], &config).unwrap();
assert_eq!(result.batch_size(), 1);
// Check pixel values are normalized
let flat = result.pixel_values_flat();
// After normalization with [0.5, 0.5, 0.5] mean/std:
// (0.5 - 0.5) / 0.5 = 0.0 for gray
// Values should be in [-1, 1] range
assert!(flat.iter().all(|&v| (-1.5..=1.5).contains(&v)));
// Check image_grid_thw is present
assert!(result.model_specific.contains_key("image_grid_thw"));
// Verify token count is reasonable
assert!(result.num_img_tokens[0] > 0);
}
#[test]
fn test_qwen3_vl_preprocess_multiple() {
let processor = Qwen3VLProcessor::new();
let config = PreProcessorConfig {
image_mean: Some(QWEN3_MEAN.to_vec()),
image_std: Some(QWEN3_STD.to_vec()),
..Default::default()
};
let images = vec![
create_test_image(640, 480, Rgb([100, 100, 100])),
create_test_image(480, 640, Rgb([150, 150, 150])),
];
let result = processor.preprocess(&images, &config).unwrap();
// Both images processed
assert_eq!(result.image_sizes.len(), 2);
assert_eq!(result.num_img_tokens.len(), 2);
// Check grid_thw shape
if let Some(ModelSpecificValue::UintTensor { data, shape }) =
result.model_specific.get("image_grid_thw")
{
assert_eq!(shape, &[2, 3]); // 2 images, 3 values (T, H, W) each
assert_eq!(data.len(), 6);
} else {
panic!("Expected image_grid_thw to be UintTensor");
}
}
#[test]
fn test_qwen3_vl_from_config() {
let config = PreProcessorConfig {
patch_size: Some(PatchSize {
height: Some(16),
width: Some(16),
}),
merge_size: Some(4),
min_pixels: Some(100000),
max_pixels: Some(500000),
temporal_patch_size: Some(4),
..Default::default()
};
let processor = Qwen3VLProcessor::from_preprocessor_config(&config);
assert_eq!(processor.patch_size(), 16);
assert_eq!(processor.merge_size(), 4);
assert_eq!(processor.min_pixels(), 100000);
assert_eq!(processor.max_pixels(), 500000);
assert_eq!(processor.temporal_patch_size(), 4);
}
#[test]
fn test_model_name() {
let processor = Qwen3VLProcessor::new();
assert_eq!(processor.model_name(), "qwen3-vl");
}
#[test]
fn test_default_mean_std() {
let processor = Qwen3VLProcessor::new();
assert_eq!(processor.default_mean(), QWEN3_MEAN);
assert_eq!(processor.default_std(), QWEN3_STD);
}
#[test]
fn test_qwen3_vs_qwen2_differences() {
// Verify the key differences from Qwen2-VL
let processor = Qwen3VLProcessor::new();
// Qwen3-VL uses patch_size=16 (vs 14 in Qwen2)
assert_eq!(processor.patch_size(), 16);
// Factor is 32 (vs 28 in Qwen2)
assert_eq!(processor.get_factor(), 32);
// Mean/std are [0.5, 0.5, 0.5] (vs CLIP values in Qwen2)
assert_eq!(processor.default_mean(), [0.5, 0.5, 0.5]);
assert_eq!(processor.default_std(), [0.5, 0.5, 0.5]);
}
#[test]
fn test_smart_resize_grayscale_400x300() {
// grayscale.jpg is 400x300
// 400/32 = 12.5 -> rounds to 12 (banker's rounding) -> 384
// 300/32 = 9.375 -> rounds to 9 -> 288
// Expected: 384x288, giving grid [1, 18, 24]
let processor = Qwen3VLProcessor::new();
// smart_resize takes (height, width)
let (h, w) = processor.smart_resize(300, 400).unwrap();
// Expected from HuggingFace: 288x384 -> grid [1, 18, 24]
assert_eq!(h, 288, "Height should be 288");
assert_eq!(w, 384, "Width should be 384");
}
}
sgl-model-gateway/src/multimodal/vision/processors/qwen_vl_base.rs
-500
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@@ -1,500 +0,0 @@
//! Shared base implementation for Qwen VL family image processors.
//!
//! This module provides a generic processor that handles the common logic
//! for Qwen2-VL, Qwen2.5-VL, and Qwen3-VL models. The specific variants
//! differ only in their default parameters (patch_size, normalization values).
//!
//! # Processing Pipeline
//!
//! 1. Validate aspect ratio (must be < 200:1)
//! 2. Smart resize to fit within min/max pixel bounds
//! 3. Align dimensions to (patch_size * merge_size) boundary
//! 4. Convert to tensor and normalize
//! 5. Reshape into patches for the vision encoder
//!
//! # Token Calculation
//!
//! ```text
//! grid_t = 1 (for images, temporal dimension is 1)
//! grid_h = resized_height / patch_size
//! grid_w = resized_width / patch_size
//! num_tokens = (grid_t * grid_h * grid_w) / merge_size²
//! ```
use image::{DynamicImage, GenericImageView};
use ndarray::Array3;
use crate::multimodal::vision::{
image_processor::{ImagePreProcessor, ModelSpecificValue, PreprocessedImages},
preprocessor_config::PreProcessorConfig,
transforms::{normalize, pil_to_filter, resize, stack_batch, to_tensor, TransformError},
};
/// Python-compatible rounding (banker's rounding / round half to even).
///
/// This matches Python's `round()` behavior where 0.5 is rounded to the nearest
/// even number, unlike Rust's `f64::round()` which rounds half away from zero.
///
/// Examples:
/// - round_half_to_even(12.5) = 12 (not 13)
/// - round_half_to_even(13.5) = 14 (not 14)
/// - round_half_to_even(12.4) = 12
/// - round_half_to_even(12.6) = 13
#[inline]
fn round_half_to_even(x: f64) -> f64 {
let rounded = x.round();
// Check if we're exactly at a .5 case
if (x - x.floor() - 0.5).abs() < 1e-9 {
// Round to nearest even
if rounded as i64 % 2 != 0 {
return rounded - 1.0;
}
}
rounded
}
/// Configuration for a Qwen VL processor variant.
#[derive(Debug, Clone)]
pub struct QwenVLConfig {
/// Vision encoder patch size
pub patch_size: usize,
/// Merge size for token reduction
pub merge_size: usize,
/// Minimum total pixels allowed
pub min_pixels: usize,
/// Maximum total pixels allowed
pub max_pixels: usize,
/// Temporal patch size for video
pub temporal_patch_size: usize,
/// Normalization mean values
pub mean: [f64; 3],
/// Normalization std values
pub std: [f64; 3],
/// Model name for identification
pub model_name: &'static str,
}
/// Generic Qwen VL image processor.
///
/// This struct implements the shared preprocessing logic for all Qwen VL
/// model variants. Each variant (Qwen2-VL, Qwen3-VL, etc.) uses this with
/// different configuration values.
#[derive(Debug, Clone)]
pub struct QwenVLProcessorBase {
config: QwenVLConfig,
}
impl QwenVLProcessorBase {
/// Create a new processor with the given configuration.
pub fn new(config: QwenVLConfig) -> Self {
Self { config }
}
/// Get the patch size.
pub fn patch_size(&self) -> usize {
self.config.patch_size
}
/// Get the merge size.
pub fn merge_size(&self) -> usize {
self.config.merge_size
}
/// Get the minimum pixels.
pub fn min_pixels(&self) -> usize {
self.config.min_pixels
}
/// Get the maximum pixels.
pub fn max_pixels(&self) -> usize {
self.config.max_pixels
}
/// Get the temporal patch size.
pub fn temporal_patch_size(&self) -> usize {
self.config.temporal_patch_size
}
/// Get the factor for dimension alignment.
///
/// Dimensions must be divisible by (patch_size * merge_size).
#[inline]
pub fn get_factor(&self) -> usize {
self.config.patch_size * self.config.merge_size
}
/// Smart resize algorithm for Qwen VL models.
///
/// Resizes image dimensions to fit within min/max pixel bounds while:
/// - Preserving aspect ratio
/// - Aligning to (patch_size * merge_size) boundaries
///
/// # Arguments
/// * `height` - Original image height
/// * `width` - Original image width
///
/// # Returns
/// (new_height, new_width) or error if aspect ratio is too extreme
///
/// # Errors
/// - If height or width is smaller than the factor
/// - If aspect ratio exceeds 200:1
pub fn smart_resize(
&self,
height: usize,
width: usize,
) -> Result<(usize, usize), TransformError> {
let factor = self.get_factor();
// Validate minimum dimensions
if height < factor || width < factor {
return Err(TransformError::InvalidShape {
expected: format!("dimensions >= {} (patch_size * merge_size)", factor),
actual: vec![height, width],
});
}
// Validate aspect ratio
let max_dim = height.max(width) as f64;
let min_dim = height.min(width) as f64;
let aspect_ratio = max_dim / min_dim;
if aspect_ratio > 200.0 {
return Err(TransformError::InvalidShape {
expected: "aspect ratio < 200:1".to_string(),
actual: vec![height, width],
});
}
// Round to nearest factor multiple using Python-compatible rounding
// Python uses banker's rounding (round half to even), which affects
// edge cases like 400/32 = 12.5 -> 12 (not 13)
let mut h_bar = round_half_to_even(height as f64 / factor as f64) as usize * factor;
let mut w_bar = round_half_to_even(width as f64 / factor as f64) as usize * factor;
// Ensure minimum size
h_bar = h_bar.max(factor);
w_bar = w_bar.max(factor);
// Scale down if exceeding max_pixels
if h_bar * w_bar > self.config.max_pixels {
let beta = ((height * width) as f64 / self.config.max_pixels as f64).sqrt();
h_bar = ((height as f64 / beta / factor as f64).floor() as usize) * factor;
w_bar = ((width as f64 / beta / factor as f64).floor() as usize) * factor;
// Ensure minimum size after scaling down
h_bar = h_bar.max(factor);
w_bar = w_bar.max(factor);
}
// Scale up if below min_pixels
else if h_bar * w_bar < self.config.min_pixels {
let beta = (self.config.min_pixels as f64 / (height * width) as f64).sqrt();
h_bar = ((height as f64 * beta / factor as f64).ceil() as usize) * factor;
w_bar = ((width as f64 * beta / factor as f64).ceil() as usize) * factor;
}
Ok((h_bar, w_bar))
}
/// Calculate the grid dimensions (T, H, W) for an image.
///
/// For single images, T=1. For video, T = num_frames / temporal_patch_size.
///
/// # Arguments
/// * `height` - Resized image height
/// * `width` - Resized image width
/// * `num_frames` - Number of frames (1 for images)
///
/// # Returns
/// (grid_t, grid_h, grid_w)
pub fn calculate_grid_thw(
&self,
height: usize,
width: usize,
num_frames: usize,
) -> (usize, usize, usize) {
let grid_t =
num_frames.max(self.config.temporal_patch_size) / self.config.temporal_patch_size;
let grid_h = height / self.config.patch_size;
let grid_w = width / self.config.patch_size;
(grid_t, grid_h, grid_w)
}
/// Calculate the number of image tokens after merge.
///
/// tokens = (grid_t * grid_h * grid_w) / merge_size²
pub fn calculate_tokens_from_grid(&self, grid_t: usize, grid_h: usize, grid_w: usize) -> usize {
(grid_t * grid_h * grid_w) / (self.config.merge_size * self.config.merge_size)
}
/// Reshape pixel values from [C, H, W] to flattened patches format.
///
/// This matches the HuggingFace Qwen2VLImageProcessor output format:
/// `(num_patches, patch_features)` where:
/// - num_patches = grid_t * grid_h * grid_w
/// - patch_features = C * temporal_patch_size * patch_size * patch_size
///
/// The transformation follows these steps (matching HuggingFace exactly):
/// 1. Start with [C, H, W] tensor, expand to [temporal, C, H, W]
/// 2. Reshape to [grid_t, temporal, C, grid_h/merge, merge, patch, grid_w/merge, merge, patch]
/// 3. Permute to [grid_t, grid_h/merge, grid_w/merge, merge, merge, C, temporal, patch, patch]
/// 4. Flatten to [num_patches, patch_features]
///
/// # Arguments
/// * `tensor` - Input tensor of shape [C, H, W]
/// * `grid_t` - Temporal grid size (1 for images)
/// * `grid_h` - Height grid size (H / patch_size)
/// * `grid_w` - Width grid size (W / patch_size)
///
/// # Returns
/// Flattened patches as Vec<f32> with shape semantics (num_patches, patch_features)
pub fn reshape_to_patches(
&self,
tensor: &Array3<f32>,
grid_t: usize,
grid_h: usize,
grid_w: usize,
) -> Vec<f32> {
use ndarray::IxDyn;
let channel = tensor.shape()[0];
let height = tensor.shape()[1];
let width = tensor.shape()[2];
let patch_size = self.config.patch_size;
let merge_size = self.config.merge_size;
let temporal_patch_size = self.config.temporal_patch_size;
// Verify dimensions match expected grid
debug_assert_eq!(
height,
grid_h * patch_size,
"Height must match grid_h * patch_size"
);
debug_assert_eq!(
width,
grid_w * patch_size,
"Width must match grid_w * patch_size"
);
// Step 1: Expand temporal dimension by replicating the frame
// [C, H, W] -> [temporal_patch_size, C, H, W]
let expanded = tensor
.view()
.insert_axis(ndarray::Axis(0))
.broadcast((temporal_patch_size, channel, height, width))
.expect("Broadcast failed")
.to_owned();
// Step 2: Reshape to split spatial dimensions into grid and patch components
// [temporal, C, H, W] -> [grid_t, temporal, C, grid_h/merge, merge, patch, grid_w/merge, merge, patch]
let grid_h_merged = grid_h / merge_size;
let grid_w_merged = grid_w / merge_size;
// Use IxDyn for 9-dimensional reshape (ndarray only supports up to Ix6 for fixed dims)
let shape_9d = IxDyn(&[
grid_t,
temporal_patch_size,
channel,
grid_h_merged,
merge_size,
patch_size,
grid_w_merged,
merge_size,
patch_size,
]);
let reshaped = expanded
.into_shape_with_order(shape_9d)
.expect("Reshape failed");
// Step 3: Permute axes to match HuggingFace output order
// From: [grid_t, temporal, C, grid_h/merge, merge, patch, grid_w/merge, merge, patch]
// [ 0 , 1 , 2, 3 , 4 , 5 , 6 , 7 , 8 ]
// To: [grid_t, grid_h/merge, grid_w/merge, merge, merge, C, temporal, patch, patch]
// [ 0 , 3 , 6 , 4 , 7 , 2, 1 , 5 , 8 ]
let permuted = reshaped.permuted_axes(&[0, 3, 6, 4, 7, 2, 1, 5, 8][..]);
// Step 4: Flatten to [num_patches, patch_features]
let num_patches = grid_t * grid_h * grid_w;
let patch_features = channel * temporal_patch_size * patch_size * patch_size;
// Make contiguous and flatten
let contiguous = permuted.as_standard_layout().into_owned();
let flat = contiguous
.into_shape_with_order(IxDyn(&[num_patches, patch_features]))
.expect("Final reshape failed");
let (vec, _offset) = flat.into_raw_vec_and_offset();
vec
}
}
impl ImagePreProcessor for QwenVLProcessorBase {
fn default_mean(&self) -> [f64; 3] {
self.config.mean
}
fn default_std(&self) -> [f64; 3] {
self.config.std
}
fn preprocess(
&self,
images: &[DynamicImage],
config: &PreProcessorConfig,
) -> Result<PreprocessedImages, TransformError> {
if images.is_empty() {
return Err(TransformError::EmptyBatch);
}
// Store original sizes
let image_sizes: Vec<(u32, u32)> = images.iter().map(|img| img.dimensions()).collect();
// First pass: calculate target dimensions for each image
let mut target_sizes = Vec::with_capacity(images.len());
for image in images {
let (w, h) = image.dimensions();
let (new_h, new_w) = self.smart_resize(h as usize, w as usize)?;
target_sizes.push((new_h, new_w));
}
// Find max height and width across all images
let max_height = target_sizes.iter().map(|(h, _)| *h).max().unwrap_or(0);
let max_width = target_sizes.iter().map(|(_, w)| *w).max().unwrap_or(0);
// Process each image with uniform max dimensions
let mean = config.get_image_mean();
let std = config.get_image_std();
let filter = pil_to_filter(config.resampling);
let mut tensors = Vec::with_capacity(images.len());
let mut grid_thw_data = Vec::with_capacity(images.len() * 3);
let mut num_img_tokens = Vec::with_capacity(images.len());
for (i, image) in images.iter().enumerate() {
let (target_h, target_w) = target_sizes[i];
// Resize to the target size for this image
let resized = if config.do_resize.unwrap_or(true) {
// For batching: resize to max dimensions to enable stacking
resize(image, max_width as u32, max_height as u32, filter)
} else {
image.clone()
};
// Convert to tensor
let mut tensor = to_tensor(&resized);
// Normalize
if config.do_normalize.unwrap_or(true) {
normalize(&mut tensor, &mean, &std);
}
tensors.push(tensor);
// Grid dimensions are based on the individual image's target size
let (grid_t, grid_h, grid_w) = self.calculate_grid_thw(target_h, target_w, 1);
grid_thw_data.push(grid_t as u32);
grid_thw_data.push(grid_h as u32);
grid_thw_data.push(grid_w as u32);
// Token count is based on individual grid
let tokens = self.calculate_tokens_from_grid(grid_t, grid_h, grid_w);
num_img_tokens.push(tokens);
}
// Stack tensors into batch (now all same size)
let pixel_values = stack_batch(&tensors)?;
// Create result with model-specific image_grid_thw
let result = PreprocessedImages::new(pixel_values, num_img_tokens, image_sizes).with_extra(
"image_grid_thw",
ModelSpecificValue::uint_2d(grid_thw_data, images.len(), 3),
);
Ok(result)
}
fn calculate_num_tokens(&self, width: u32, height: u32, _config: &PreProcessorConfig) -> usize {
// Calculate resized dimensions
let (new_height, new_width) = match self.smart_resize(height as usize, width as usize) {
Ok((h, w)) => (h, w),
Err(_) => {
// Fallback: use minimum size
let factor = self.get_factor();
(factor, factor)
}
};
// Calculate grid and tokens
let (grid_t, grid_h, grid_w) = self.calculate_grid_thw(new_height, new_width, 1);
self.calculate_tokens_from_grid(grid_t, grid_h, grid_w)
}
fn model_name(&self) -> &'static str {
self.config.model_name
}
fn get_processed_size(&self, _config: &PreProcessorConfig) -> Option<(u32, u32)> {
// Qwen VL models have dynamic sizing, no fixed output size
None
}
}
#[cfg(test)]
mod tests {
use super::*;
fn create_test_config() -> QwenVLConfig {
QwenVLConfig {
patch_size: 14,
merge_size: 2,
min_pixels: 256 * 28 * 28,
max_pixels: 1280 * 28 * 28,
temporal_patch_size: 2,
mean: [0.5, 0.5, 0.5],
std: [0.5, 0.5, 0.5],
model_name: "test-qwen-vl",
}
}
#[test]
fn test_qwen_vl_base_factor() {
let processor = QwenVLProcessorBase::new(create_test_config());
assert_eq!(processor.get_factor(), 28); // 14 * 2
}
#[test]
fn test_smart_resize_within_bounds() {
let processor = QwenVLProcessorBase::new(create_test_config());
let (h, w) = processor.smart_resize(500, 500).unwrap();
assert_eq!(h % 28, 0);
assert_eq!(w % 28, 0);
assert!(h * w >= processor.min_pixels());
assert!(h * w <= processor.max_pixels());
}
#[test]
fn test_smart_resize_extreme_aspect_ratio_error() {
let processor = QwenVLProcessorBase::new(create_test_config());
let result = processor.smart_resize(100, 30000);
assert!(result.is_err());
}
#[test]
fn test_calculate_grid_thw() {
let processor = QwenVLProcessorBase::new(create_test_config());
let (t, h, w) = processor.calculate_grid_thw(448, 448, 1);
assert_eq!(t, 1);
assert_eq!(h, 448 / 14);
assert_eq!(w, 448 / 14);
}
#[test]
fn test_calculate_tokens() {
let processor = QwenVLProcessorBase::new(create_test_config());
let tokens = processor.calculate_tokens_from_grid(1, 32, 32);
assert_eq!(tokens, (32 * 32) / 4);
}
}
sgl-model-gateway/src/multimodal/vision/transforms.rs
-499
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@@ -1,499 +0,0 @@
//! Image transformation functions for vision preprocessing.
//!
//! This module provides composable transforms that match HuggingFace image processor
//! behavior, enabling pure Rust preprocessing without Python dependencies.
use image::{imageops::FilterType, DynamicImage, GenericImageView, Rgb, RgbImage};
use ndarray::{s, Array3, Array4};
use thiserror::Error;
/// Errors that can occur during image transformations.
#[derive(Error, Debug)]
pub enum TransformError {
#[error("Invalid tensor shape: expected {expected}, got {actual:?}")]
InvalidShape {
expected: String,
actual: Vec<usize>,
},
#[error("Image operation failed: {0}")]
ImageError(#[from] image::ImageError),
#[error("Empty batch: cannot stack zero tensors")]
EmptyBatch,
#[error("Inconsistent tensor shapes in batch")]
InconsistentShapes,
#[error("Shape error: {0}")]
ShapeError(String),
}
pub type Result<T> = std::result::Result<T, TransformError>;
/// Convert image to tensor [C, H, W] normalized to [0, 1].
///
/// This matches the default behavior of `torchvision.transforms.ToTensor()`.
pub fn to_tensor(image: &DynamicImage) -> Array3<f32> {
let rgb = image.to_rgb8();
let (w, h) = (rgb.width() as usize, rgb.height() as usize);
let mut arr = Array3::<f32>::zeros((3, h, w));
for (x, y, pixel) in rgb.enumerate_pixels() {
let (x, y) = (x as usize, y as usize);
arr[[0, y, x]] = pixel[0] as f32 / 255.0;
arr[[1, y, x]] = pixel[1] as f32 / 255.0;
arr[[2, y, x]] = pixel[2] as f32 / 255.0;
}
arr
}
/// Convert image to tensor [C, H, W] without normalization (keeps [0, 255]).
///
/// Some models expect unnormalized pixel values.
pub fn to_tensor_no_norm(image: &DynamicImage) -> Array3<f32> {
let rgb = image.to_rgb8();
let (w, h) = (rgb.width() as usize, rgb.height() as usize);
let mut arr = Array3::<f32>::zeros((3, h, w));
for (x, y, pixel) in rgb.enumerate_pixels() {
let (x, y) = (x as usize, y as usize);
arr[[0, y, x]] = pixel[0] as f32;
arr[[1, y, x]] = pixel[1] as f32;
arr[[2, y, x]] = pixel[2] as f32;
}
arr
}
/// Normalize tensor per channel: (x - mean) / std.
///
/// This matches `torchvision.transforms.Normalize(mean, std)`.
///
/// # Arguments
/// * `tensor` - Input tensor of shape [C, H, W]
/// * `mean` - Per-channel mean values
/// * `std` - Per-channel standard deviation values
pub fn normalize(tensor: &mut Array3<f32>, mean: &[f64; 3], std: &[f64; 3]) {
for c in 0..3 {
let mean_c = mean[c] as f32;
let std_c = std[c] as f32;
tensor
.slice_mut(s![c, .., ..])
.mapv_inplace(|v| (v - mean_c) / std_c);
}
}
/// Rescale tensor by a constant factor.
///
/// Used when `do_rescale=True` in HuggingFace configs (typically 1/255).
pub fn rescale(tensor: &mut Array3<f32>, factor: f64) {
let factor = factor as f32;
tensor.mapv_inplace(|v| v * factor);
}
/// Resize image to exact dimensions.
///
/// # Arguments
/// * `image` - Input image
/// * `width` - Target width
/// * `height` - Target height
/// * `filter` - Interpolation filter (Nearest, Triangle/Bilinear, CatmullRom/Bicubic, Lanczos3)
pub fn resize(image: &DynamicImage, width: u32, height: u32, filter: FilterType) -> DynamicImage {
image.resize_exact(width, height, filter)
}
/// Resize image preserving aspect ratio, fitting within max dimensions.
pub fn resize_to_fit(
image: &DynamicImage,
max_width: u32,
max_height: u32,
filter: FilterType,
) -> DynamicImage {
image.resize(max_width, max_height, filter)
}
/// Center crop image to specified dimensions.
///
/// If the crop size is larger than the image, the image is returned unchanged.
pub fn center_crop(image: &DynamicImage, crop_w: u32, crop_h: u32) -> DynamicImage {
let (w, h) = image.dimensions();
if crop_w >= w && crop_h >= h {
return image.clone();
}
let left = (w.saturating_sub(crop_w)) / 2;
let top = (h.saturating_sub(crop_h)) / 2;
let actual_w = crop_w.min(w);
let actual_h = crop_h.min(h);
image.crop_imm(left, top, actual_w, actual_h)
}
/// Expand image to square by padding with background color.
///
/// This is used by LLaVA models which expect square inputs. The image is
/// centered and padded with the mean color on the shorter dimension.
pub fn expand_to_square(image: &DynamicImage, background: Rgb<u8>) -> DynamicImage {
let (w, h) = image.dimensions();
match w.cmp(&h) {
std::cmp::Ordering::Equal => image.clone(),
std::cmp::Ordering::Less => {
// Height > Width: pad horizontally
let mut new_image = DynamicImage::from(RgbImage::from_pixel(h, h, background));
image::imageops::overlay(&mut new_image, image, ((h - w) / 2) as i64, 0);
new_image
}
std::cmp::Ordering::Greater => {
// Width > Height: pad vertically
let mut new_image = DynamicImage::from(RgbImage::from_pixel(w, w, background));
image::imageops::overlay(&mut new_image, image, 0, ((w - h) / 2) as i64);
new_image
}
}
}
/// Pad image to specified dimensions with background color.
///
/// Image is placed at top-left corner.
pub fn pad_to_size(
image: &DynamicImage,
target_w: u32,
target_h: u32,
background: Rgb<u8>,
) -> DynamicImage {
let (w, h) = image.dimensions();
if w >= target_w && h >= target_h {
return image.clone();
}
let new_w = w.max(target_w);
let new_h = h.max(target_h);
let mut new_image = DynamicImage::from(RgbImage::from_pixel(new_w, new_h, background));
image::imageops::overlay(&mut new_image, image, 0, 0);
new_image
}
/// Stack multiple [C, H, W] tensors into [B, C, H, W].
///
/// All tensors must have the same shape.
pub fn stack_batch(tensors: &[Array3<f32>]) -> Result<Array4<f32>> {
if tensors.is_empty() {
return Err(TransformError::EmptyBatch);
}
let shape = tensors[0].shape();
let (c, h, w) = (shape[0], shape[1], shape[2]);
// Verify all tensors have the same shape
for tensor in tensors.iter().skip(1) {
if tensor.shape() != shape {
return Err(TransformError::InvalidShape {
expected: format!("[{}, {}, {}]", c, h, w),
actual: tensor.shape().to_vec(),
});
}
}
let mut batch = Array4::<f32>::zeros((tensors.len(), c, h, w));
for (i, tensor) in tensors.iter().enumerate() {
batch.slice_mut(s![i, .., .., ..]).assign(tensor);
}
Ok(batch)
}
/// Convert PIL/HuggingFace resampling enum to image crate filter.
///
/// PIL resampling constants:
/// - 0: NEAREST
/// - 1: LANCZOS (also ANTIALIAS)
/// - 2: BILINEAR
/// - 3: BICUBIC
/// - 4: BOX
/// - 5: HAMMING
pub fn pil_to_filter(resampling: Option<usize>) -> FilterType {
match resampling {
Some(0) => FilterType::Nearest,
Some(1) => FilterType::Lanczos3,
Some(2) | None => FilterType::Triangle, // Bilinear (default)
Some(3) => FilterType::CatmullRom, // Bicubic
// Box and Hamming don't have direct equivalents, use Triangle
Some(4) | Some(5) => FilterType::Triangle,
_ => FilterType::Triangle,
}
}
/// Calculate mean color of an image as RGB.
pub fn calculate_mean_color(image: &DynamicImage) -> Rgb<u8> {
let rgb = image.to_rgb8();
let (w, h) = (rgb.width() as u64, rgb.height() as u64);
let total_pixels = w * h;
if total_pixels == 0 {
return Rgb([128, 128, 128]);
}
let (mut r_sum, mut g_sum, mut b_sum) = (0u64, 0u64, 0u64);
for pixel in rgb.pixels() {
r_sum += pixel[0] as u64;
g_sum += pixel[1] as u64;
b_sum += pixel[2] as u64;
}
Rgb([
(r_sum / total_pixels) as u8,
(g_sum / total_pixels) as u8,
(b_sum / total_pixels) as u8,
])
}
/// Convert normalized mean values [0, 1] to RGB bytes.
pub fn mean_to_rgb(mean: &[f64; 3]) -> Rgb<u8> {
Rgb([
(mean[0] * 255.0).round() as u8,
(mean[1] * 255.0).round() as u8,
(mean[2] * 255.0).round() as u8,
])
}
/// Cubic interpolation weight function (Keys bicubic kernel with a=-0.5).
///
/// This matches PyTorch's bicubic interpolation used in
/// `torch.nn.functional.interpolate(mode='bicubic')`.
#[inline]
pub fn cubic_weight(x: f32) -> f32 {
let x = x.abs();
if x < 1.0 {
(1.5 * x - 2.5) * x * x + 1.0
} else if x < 2.0 {
((-0.5 * x + 2.5) * x - 4.0) * x + 2.0
} else {
0.0
}
}
/// Perform bicubic interpolation at a single point in a tensor.
///
/// Uses a 4x4 kernel with Keys bicubic weights (a=-0.5) to match PyTorch's
/// `torch.nn.functional.interpolate(mode='bicubic')`.
///
/// # Arguments
/// * `tensor` - Input tensor of shape [C, H, W]
/// * `c` - Channel index
/// * `src_y` - Source Y coordinate (can be fractional)
/// * `src_x` - Source X coordinate (can be fractional)
/// * `h` - Height of the tensor
/// * `w` - Width of the tensor
///
/// # Returns
/// The interpolated value at the specified position.
pub fn bicubic_interpolate(
tensor: &Array3<f32>,
c: usize,
src_y: f32,
src_x: f32,
h: usize,
w: usize,
) -> f32 {
let y_int = src_y.floor() as i32;
let x_int = src_x.floor() as i32;
let y_frac = src_y - y_int as f32;
let x_frac = src_x - x_int as f32;
let mut result = 0.0f32;
// Sample 4x4 neighborhood
for dy in -1..=2 {
let y_idx = (y_int + dy).clamp(0, h as i32 - 1) as usize;
let y_weight = cubic_weight(y_frac - dy as f32);
for dx in -1..=2 {
let x_idx = (x_int + dx).clamp(0, w as i32 - 1) as usize;
let x_weight = cubic_weight(x_frac - dx as f32);
result += tensor[[c, y_idx, x_idx]] * y_weight * x_weight;
}
}
result
}
/// Resize a tensor using bicubic interpolation.
///
/// This matches PyTorch's `torch.nn.functional.interpolate(mode='bicubic', align_corners=False)`.
///
/// # Arguments
/// * `tensor` - Input tensor of shape [C, H, W]
/// * `target_h` - Target height
/// * `target_w` - Target width
///
/// # Returns
/// Resized tensor of shape [C, target_h, target_w].
pub fn bicubic_resize(tensor: &Array3<f32>, target_h: usize, target_w: usize) -> Array3<f32> {
let (c, h, w) = (tensor.shape()[0], tensor.shape()[1], tensor.shape()[2]);
if h == target_h && w == target_w {
return tensor.clone();
}
let mut result = Array3::<f32>::zeros((c, target_h, target_w));
// PyTorch align_corners=False coordinate mapping
let scale_h = h as f32 / target_h as f32;
let scale_w = w as f32 / target_w as f32;
for ch in 0..c {
for y in 0..target_h {
for x in 0..target_w {
// PyTorch align_corners=False: src = (dst + 0.5) * scale - 0.5
let src_y = (y as f32 + 0.5) * scale_h - 0.5;
let src_x = (x as f32 + 0.5) * scale_w - 0.5;
result[[ch, y, x]] = bicubic_interpolate(tensor, ch, src_y, src_x, h, w);
}
}
}
result
}
#[cfg(test)]
mod tests {
use super::*;
fn create_test_image(width: u32, height: u32, color: Rgb<u8>) -> DynamicImage {
DynamicImage::from(RgbImage::from_pixel(width, height, color))
}
#[test]
fn test_to_tensor_shape() {
let img = create_test_image(10, 20, Rgb([255, 128, 0]));
let tensor = to_tensor(&img);
assert_eq!(tensor.shape(), &[3, 20, 10]); // [C, H, W]
}
#[test]
fn test_to_tensor_values() {
let img = create_test_image(2, 2, Rgb([255, 128, 0]));
let tensor = to_tensor(&img);
// Check normalization to [0, 1]
assert!((tensor[[0, 0, 0]] - 1.0).abs() < 1e-6); // R=255 -> 1.0
assert!((tensor[[1, 0, 0]] - 0.502).abs() < 0.01); // G=128 -> ~0.5
assert!((tensor[[2, 0, 0]] - 0.0).abs() < 1e-6); // B=0 -> 0.0
}
#[test]
fn test_to_tensor_no_norm() {
let img = create_test_image(2, 2, Rgb([255, 128, 64]));
let tensor = to_tensor_no_norm(&img);
assert!((tensor[[0, 0, 0]] - 255.0).abs() < 1e-6);
assert!((tensor[[1, 0, 0]] - 128.0).abs() < 1e-6);
assert!((tensor[[2, 0, 0]] - 64.0).abs() < 1e-6);
}
#[test]
fn test_normalize() {
let mut tensor = Array3::<f32>::from_elem((3, 2, 2), 0.5);
let mean = [0.5, 0.5, 0.5];
let std = [0.5, 0.5, 0.5];
normalize(&mut tensor, &mean, &std);
// (0.5 - 0.5) / 0.5 = 0.0
for val in tensor.iter() {
assert!(val.abs() < 1e-6);
}
}
#[test]
fn test_rescale() {
let mut tensor = Array3::<f32>::from_elem((3, 2, 2), 255.0);
rescale(&mut tensor, 1.0 / 255.0);
for val in tensor.iter() {
assert!((val - 1.0).abs() < 1e-6);
}
}
#[test]
fn test_resize() {
let img = create_test_image(100, 50, Rgb([128, 128, 128]));
let resized = resize(&img, 50, 25, FilterType::Triangle);
assert_eq!(resized.width(), 50);
assert_eq!(resized.height(), 25);
}
#[test]
fn test_center_crop() {
let img = create_test_image(100, 100, Rgb([128, 128, 128]));
let cropped = center_crop(&img, 50, 50);
assert_eq!(cropped.width(), 50);
assert_eq!(cropped.height(), 50);
}
#[test]
fn test_expand_to_square_horizontal() {
let img = create_test_image(100, 50, Rgb([255, 0, 0]));
let background = Rgb([0, 0, 0]);
let squared = expand_to_square(&img, background);
assert_eq!(squared.width(), 100);
assert_eq!(squared.height(), 100);
}
#[test]
fn test_expand_to_square_vertical() {
let img = create_test_image(50, 100, Rgb([255, 0, 0]));
let background = Rgb([0, 0, 0]);
let squared = expand_to_square(&img, background);
assert_eq!(squared.width(), 100);
assert_eq!(squared.height(), 100);
}
#[test]
fn test_expand_to_square_already_square() {
let img = create_test_image(100, 100, Rgb([255, 0, 0]));
let background = Rgb([0, 0, 0]);
let squared = expand_to_square(&img, background);
assert_eq!(squared.width(), 100);
assert_eq!(squared.height(), 100);
}
#[test]
fn test_stack_batch() {
let t1 = Array3::<f32>::zeros((3, 10, 10));
let t2 = Array3::<f32>::ones((3, 10, 10));
let batch = stack_batch(&[t1, t2]).unwrap();
assert_eq!(batch.shape(), &[2, 3, 10, 10]);
}
#[test]
fn test_stack_batch_empty() {
let result = stack_batch(&[]);
assert!(matches!(result, Err(TransformError::EmptyBatch)));
}
#[test]
fn test_pil_to_filter() {
assert!(matches!(pil_to_filter(Some(0)), FilterType::Nearest));
assert!(matches!(pil_to_filter(Some(1)), FilterType::Lanczos3));
assert!(matches!(pil_to_filter(Some(2)), FilterType::Triangle));
assert!(matches!(pil_to_filter(Some(3)), FilterType::CatmullRom));
assert!(matches!(pil_to_filter(None), FilterType::Triangle));
}
#[test]
fn test_mean_to_rgb() {
let mean = [0.5, 0.25, 1.0];
let rgb = mean_to_rgb(&mean);
assert_eq!(rgb[0], 128);
assert_eq!(rgb[1], 64);
assert_eq!(rgb[2], 255);
}
}
sgl-model-gateway/tests/multimodal/mod.rs
-4
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@@ -1,4 +0,0 @@
//! Multimodal and vision integration tests
mod multimodal_tracker_test;
mod vision_golden_tests;
sgl-model-gateway/tests/multimodal/multimodal_tracker_test.rs
-151
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@@ -1,151 +0,0 @@
use std::{collections::HashMap, path::PathBuf, sync::Arc, time::Duration};
use base64::{engine::general_purpose::STANDARD as BASE64_STANDARD, Engine};
use reqwest::Client;
use smg::multimodal::{
AsyncMultiModalTracker, ChatContentPart, ConversationSegment, ImageFetchConfig, ImageSource,
MediaConnector, MediaConnectorConfig, MediaSource, Modality, TrackerConfig,
};
use tempfile::tempdir;
const TINY_PNG_BASE64: &str =
"iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAQAAAC1HAwCAAAAC0lEQVR42mNgYAAAAAMAASsJTYQAAAAASUVORK5CYII=";
fn tiny_png_bytes() -> Vec<u8> {
BASE64_STANDARD
.decode(TINY_PNG_BASE64)
.expect("decode tiny png fixture")
}
fn test_connector(allowed_path: Option<PathBuf>) -> MediaConnector {
let client = Client::builder()
.timeout(Duration::from_secs(5))
.no_proxy()
.build()
.expect("client");
MediaConnector::new(
client,
MediaConnectorConfig {
allowed_domains: None,
allowed_local_media_path: allowed_path,
fetch_timeout: Duration::from_secs(5),
},
)
.expect("media connector")
}
#[tokio::test]
async fn fetch_image_from_inline_bytes() {
let connector = test_connector(None);
let bytes = tiny_png_bytes();
let frame = connector
.fetch_image(
MediaSource::InlineBytes(bytes.clone()),
ImageFetchConfig::default(),
)
.await
.expect("inline image");
assert_eq!(frame.data().width(), 1);
assert_eq!(frame.data().height(), 1);
assert_eq!(frame.raw_bytes(), bytes.as_slice());
}
#[tokio::test]
async fn fetch_image_from_data_url() {
let connector = test_connector(None);
let bytes = tiny_png_bytes();
let data_url = format!(
"data:image/png;base64,{}",
BASE64_STANDARD.encode(bytes.clone())
);
let frame = connector
.fetch_image(MediaSource::DataUrl(data_url), ImageFetchConfig::default())
.await
.expect("data url");
assert_eq!(frame.data().width(), 1);
assert_eq!(frame.raw_bytes(), bytes.as_slice());
}
#[tokio::test]
async fn fetch_image_from_file() {
let tmp = tempdir().expect("tempdir");
let allowed_root = std::fs::canonicalize(tmp.path()).expect("canonical tmp path");
let file_path = allowed_root.join("tiny.png");
std::fs::write(&file_path, tiny_png_bytes()).expect("write png");
let connector = test_connector(Some(allowed_root));
let frame = connector
.fetch_image(
MediaSource::File(file_path.clone()),
ImageFetchConfig::default(),
)
.await
.expect("file png");
assert_eq!(frame.data().width(), 1);
let expected = std::fs::canonicalize(&file_path).expect("canonical path");
match frame.source() {
ImageSource::File { path } => assert_eq!(path, &expected),
other => panic!("expected file source, got {:?}", other),
}
}
#[tokio::test]
async fn tracker_collects_conversation_and_placeholders() {
let connector = Arc::new(test_connector(None));
let mut tracker = AsyncMultiModalTracker::new(
connector,
TrackerConfig {
placeholder_tokens: Default::default(),
modality_limits: HashMap::from([(Modality::Image, 2)]),
},
);
tracker
.push_part(ChatContentPart::Text {
text: "before".into(),
})
.expect("text part");
tracker
.push_part(ChatContentPart::ImageData {
data: tiny_png_bytes(),
mime_type: Some("image/png".into()),
uuid: Some("img-1".into()),
detail: None,
})
.expect("image part");
tracker
.push_part(ChatContentPart::Text {
text: "after".into(),
})
.expect("text part");
let output = tracker.finalize().await.expect("tracker finalize");
assert_eq!(output.conversation.len(), 3);
assert!(matches!(
&output.conversation[0],
ConversationSegment::Text(text) if text == "before"
));
assert!(matches!(
&output.conversation[1],
ConversationSegment::Placeholder { token } if token == "<image>"
));
assert!(matches!(
&output.conversation[2],
ConversationSegment::Text(text) if text == "after"
));
let images = output.data.get(&Modality::Image).expect("image entry");
assert_eq!(images.len(), 1);
let uuids = output.uuids.get(&Modality::Image).expect("uuid entry");
assert_eq!(uuids, &vec![Some("img-1".into())]);
let placeholders = output
.placeholders
.get("<image>")
.expect("placeholder entry");
assert_eq!(placeholders.len(), 1);
assert_eq!(placeholders[0].text_position, 1);
assert_eq!(placeholders[0].item_index, 0);
}
sgl-model-gateway/tests/multimodal/vision_golden_tests.rs
-1601
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sgl-model-gateway/tests/multimodal_tests.rs
-6
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@@ -1,6 +0,0 @@
//! Multimodal integration tests
#[path = "common/mod.rs"]
pub mod common;
mod multimodal;