Idefics2: sync added image tokens with transformers (#2080)

Before this change, the number of reserved image tokens was not the
same as the number of images. Fixes #2029.

While at it, also remove all the image token handling duplication
in `prepare_input`.

Cargo.lock

@@ -3832,6 +3832,7 @@ dependencies = [
  "hf-hub",
  "image",
  "init-tracing-opentelemetry",
+ "itertools 0.10.5",
  "jsonschema",
  "metrics 0.21.1",
  "metrics-exporter-prometheus",

integration-tests/models/__snapshots__/test_idefics2/test_flash_idefics2_next_simple.json

@@ -8,61 +8,61 @@
     "tokens": [
       {
         "id": 330,
-        "logprob": -0.13000488,
+        "logprob": -0.08660889,
         "special": false,
         "text": " A"
       },
       {
         "id": 13088,
-        "logprob": -0.6713867,
+        "logprob": -0.7089844,
         "special": false,
         "text": " chicken"
       },
       {
         "id": 349,
-        "logprob": -0.2980957,
+        "logprob": -0.32885742,
         "special": false,
         "text": " is"
       },
       {
         "id": 6398,
-        "logprob": -0.060638428,
+        "logprob": -0.05126953,
         "special": false,
         "text": " sitting"
       },
       {
         "id": 356,
-        "logprob": -0.27319336,
+        "logprob": -0.35229492,
         "special": false,
         "text": " on"
       },
       {
         "id": 264,
-        "logprob": -0.140625,
+        "logprob": -0.12561035,
         "special": false,
         "text": " a"
       },
       {
         "id": 17972,
-        "logprob": -0.040405273,
+        "logprob": -0.038085938,
         "special": false,
         "text": " pile"
       },
       {
         "id": 302,
-        "logprob": -0.0002708435,
+        "logprob": -0.00018656254,
         "special": false,
         "text": " of"
       },
       {
         "id": 2445,
-        "logprob": -0.095336914,
+        "logprob": -0.07293701,
         "special": false,
         "text": " money"
       },
       {
         "id": 28723,
-        "logprob": -0.0068359375,
+        "logprob": -0.004852295,
         "special": false,
         "text": "."
       }

integration-tests/models/__snapshots__/test_idefics2/test_flash_idefics2_two_images.json

@@ -8,115 +8,115 @@
     "tokens": [
       {
         "id": 415,
-        "logprob": -0.04421997,
+        "logprob": -0.039886475,
         "special": false,
         "text": " The"
       },
       {
         "id": 12072,
-        "logprob": -0.13500977,
+        "logprob": -0.1430664,
         "special": false,
         "text": " cow"
       },
       {
         "id": 349,
-        "logprob": -0.06750488,
+        "logprob": -0.056488037,
         "special": false,
         "text": " is"
       },
       {
         "id": 6328,
-        "logprob": -0.6352539,
+        "logprob": -0.6855469,
         "special": false,
         "text": " standing"
       },
       {
         "id": 356,
-        "logprob": -0.16186523,
+        "logprob": -0.1685791,
         "special": false,
         "text": " on"
       },
       {
         "id": 272,
-        "logprob": -0.5078125,
+        "logprob": -0.50097656,
         "special": false,
         "text": " the"
       },
       {
         "id": 10305,
-        "logprob": -0.017913818,
+        "logprob": -0.017303467,
         "special": false,
         "text": " beach"
       },
       {
         "id": 304,
-        "logprob": -1.5205078,
+        "logprob": -1.3564453,
         "special": false,
         "text": " and"
       },
       {
         "id": 272,
-        "logprob": -0.029174805,
+        "logprob": -0.017868042,
         "special": false,
         "text": " the"
       },
       {
         "id": 13088,
-        "logprob": -0.003479004,
+        "logprob": -0.0027103424,
         "special": false,
         "text": " chicken"
       },
       {
         "id": 349,
-        "logprob": -0.0035095215,
+        "logprob": -0.003156662,
         "special": false,
         "text": " is"
       },
       {
         "id": 6398,
-        "logprob": -0.3088379,
+        "logprob": -0.37304688,
         "special": false,
         "text": " sitting"
       },
       {
         "id": 356,
-        "logprob": -0.027755737,
+        "logprob": -0.034576416,
         "special": false,
         "text": " on"
       },
       {
         "id": 264,
-        "logprob": -0.31884766,
+        "logprob": -0.29418945,
         "special": false,
         "text": " a"
       },
       {
         "id": 17972,
-        "logprob": -0.047943115,
+        "logprob": -0.042877197,
         "special": false,
         "text": " pile"
       },
       {
         "id": 302,
-        "logprob": -0.0002925396,
+        "logprob": -0.00028443336,
         "special": false,
         "text": " of"
       },
       {
         "id": 2445,
-        "logprob": -0.02935791,
+        "logprob": -0.023223877,
         "special": false,
         "text": " money"
       },
       {
         "id": 28723,
-        "logprob": -0.031219482,
+        "logprob": -0.018157959,
         "special": false,
         "text": "."
       },
       {
         "id": 32002,
-        "logprob": -0.00034475327,
+        "logprob": -0.00018393993,
         "special": true,
         "text": "<end_of_utterance>"
       },

router/Cargo.toml

@@ -22,6 +22,7 @@ text-generation-client = { path = "client" }
 clap = { version = "4.4.5", features = ["derive", "env"] }
 futures = "0.3.28"
 hf-hub = { workspace = true }
+itertools = "0.10"
 jsonschema = { version = "0.17.1", features = ["draft202012"] }
 metrics = "0.21.1"
 metrics-exporter-prometheus = { version = "0.15.1", features = [] }

router/src/config.rs

@@ -71,10 +71,12 @@ fn get_unpadded_features(
     let current_aspect_ratio: f64 = current_width as f64 / current_height as f64;
     let (current_height, current_width) = if aspect_ratio > current_aspect_ratio {
         let new_height = (height * current_width) / width;
-        (new_height, current_width)
+        let padding = (current_height - new_height) / 2;
+        (current_height - (2 * padding), current_width)
     } else {
         let new_width = (width * current_height) / height;
-        (current_height, new_width)
+        let padding = (current_width - new_width) / 2;
+        (current_height, current_width - (2 * padding))
     };
 
     let unpadded_features = current_height * current_width;
@@ -88,7 +90,9 @@ impl LlavaNext {
         let patch_size = self.vision_config.patch_size;
         assert!(image_size % patch_size == 0);
         let npatches = image_size / patch_size;
-        let (num_patch_height, num_patch_width) =
+        // Dimensions are intentionally swapped to be bug-compatible with
+        // upstream: https://github.com/LLaVA-VL/LLaVA-NeXT/issues/59
+        let (num_patch_width, num_patch_height) =
             get_anyres_image_grid_shape(height, width, &self.image_grid_pinpoints, image_size);
 
         let (unpadded_features, newline_features) =
@@ -112,7 +116,7 @@ pub struct Idefics2 {}
 
 impl Idefics2 {
     pub fn get_number_of_features(&self, _height: usize, _width: usize) -> usize {
-        320
+        64
     }
 }
 

router/src/lib.rs

@@ -70,6 +70,25 @@ impl HubTokenizerConfig {
     }
 }
 
+#[derive(Debug, Clone, Serialize, Deserialize)]
+#[serde(tag = "processor_class")]
+pub enum HubPreprocessorConfig {
+    Idefics2Processor(Idefics2Preprocessor),
+}
+
+impl HubPreprocessorConfig {
+    pub fn from_file<P: AsRef<std::path::Path>>(filename: P) -> Option<Self> {
+        let content = std::fs::read_to_string(filename).ok()?;
+        serde_json::from_str(&content).ok()
+    }
+}
+
+#[derive(Clone, Debug, Serialize, Deserialize)]
+pub struct Idefics2Preprocessor {
+    #[serde(default)]
+    do_image_splitting: bool,
+}
+
 #[derive(Debug, Clone, Deserialize, Default)]
 pub struct HubProcessorConfig {
     pub chat_template: Option<ChatTemplateVersions>,

router/src/main.rs

@@ -13,7 +13,9 @@ use std::io::BufReader;
 use std::net::{IpAddr, Ipv4Addr, SocketAddr};
 use std::path::{Path, PathBuf};
 use text_generation_router::config::Config;
-use text_generation_router::{server, HubModelInfo, HubProcessorConfig, HubTokenizerConfig};
+use text_generation_router::{
+    server, HubModelInfo, HubPreprocessorConfig, HubProcessorConfig, HubTokenizerConfig,
+};
 use thiserror::Error;
 use tokenizers::Tokenizer;
 use tower_http::cors::AllowOrigin;
@@ -214,6 +216,7 @@ async fn main() -> Result<(), RouterError> {
         tokenizer_filename,
         config_filename,
         tokenizer_config_filename,
+        preprocessor_config_filename,
         processor_config_filename,
         model_info,
     ) = match api {
@@ -221,6 +224,7 @@ async fn main() -> Result<(), RouterError> {
             Some(local_path.join("tokenizer.json")),
             Some(local_path.join("config.json")),
             Some(local_path.join("tokenizer_config.json")),
+            Some(local_path.join("preprocessor_config.json")),
             Some(local_path.join("processor_config.json")),
             None,
         ),
@@ -237,6 +241,7 @@ async fn main() -> Result<(), RouterError> {
             };
             let config_filename = api_repo.get("config.json").await.ok();
             let tokenizer_config_filename = api_repo.get("tokenizer_config.json").await.ok();
+            let preprocessor_config_filename = api_repo.get("preprocessor_config.json").await.ok();
             let processor_config_filename = api_repo.get("processor_config.json").await.ok();
 
             let model_info = if let Some(model_info) = get_model_info(&api_repo).await {
@@ -249,6 +254,7 @@ async fn main() -> Result<(), RouterError> {
                 tokenizer_filename,
                 config_filename,
                 tokenizer_config_filename,
+                preprocessor_config_filename,
                 processor_config_filename,
                 model_info,
             )
@@ -263,6 +269,7 @@ async fn main() -> Result<(), RouterError> {
                 repo.get("tokenizer.json"),
                 repo.get("config.json"),
                 repo.get("tokenizer_config.json"),
+                repo.get("preprocessor_config.json"),
                 repo.get("processor_config.json"),
                 None,
             )
@@ -300,6 +307,8 @@ async fn main() -> Result<(), RouterError> {
         HubTokenizerConfig::default()
     });
 
+    let preprocessor_config =
+        preprocessor_config_filename.and_then(HubPreprocessorConfig::from_file);
     let processor_config = processor_config_filename
         .and_then(HubProcessorConfig::from_file)
         .unwrap_or_default();
@@ -361,6 +370,7 @@ async fn main() -> Result<(), RouterError> {
         ngrok_authtoken,
         ngrok_edge,
         tokenizer_config,
+        preprocessor_config,
         processor_config,
         messages_api_enabled,
         disable_grammar_support,

router/src/server.rs

@@ -12,9 +12,9 @@ use crate::kserve::{
 use crate::validation::ValidationError;
 use crate::{
     BestOfSequence, Details, ErrorResponse, FinishReason, GenerateParameters, GenerateRequest,
-    GenerateResponse, GrammarType, HubModelInfo, HubProcessorConfig, HubTokenizerConfig, Info,
-    Message, PrefillToken, SimpleToken, StreamDetails, StreamResponse, Token, TokenizeResponse,
-    Usage, Validation,
+    GenerateResponse, GrammarType, HubModelInfo, HubPreprocessorConfig, HubProcessorConfig,
+    HubTokenizerConfig, Info, Message, PrefillToken, SimpleToken, StreamDetails, StreamResponse,
+    Token, TokenizeResponse, Usage, Validation,
 };
 use crate::{
     ChatCompletion, ChatCompletionChoice, ChatCompletionChunk, ChatCompletionComplete,
@@ -1423,6 +1423,7 @@ pub async fn run(
     _ngrok_authtoken: Option<String>,
     _ngrok_edge: Option<String>,
     tokenizer_config: HubTokenizerConfig,
+    preprocessor_config: Option<HubPreprocessorConfig>,
     processor_config: HubProcessorConfig,
     messages_api_enabled: bool,
     grammar_support: bool,
@@ -1636,6 +1637,7 @@ pub async fn run(
         validation_workers,
         tokenizer,
         config,
+        preprocessor_config,
         max_best_of,
         max_stop_sequences,
         max_top_n_tokens,

router/src/validation.rs

 /// Payload validation logic
 use crate::config::Config;
 use crate::validation::ValidationError::{BestOfSampling, BestOfSeed, EmptyInput};
-use crate::{GenerateParameters, GenerateRequest, GrammarType};
+use crate::{
+    GenerateParameters, GenerateRequest, GrammarType, HubPreprocessorConfig, Idefics2Preprocessor,
+};
 use base64::{engine::general_purpose::STANDARD, Engine};
 use image::{io::Reader as ImageReader, ImageFormat};
 use jsonschema::{Draft, JSONSchema};
 use rand::{thread_rng, Rng};
 use serde_json::Value;
 use std::io::Cursor;
+use std::iter;
 use text_generation_client::{Chunk, Image, InputChunk};
 use thiserror::Error;
 use tokenizers::tokenizer::Tokenizer;
@@ -36,6 +39,7 @@ impl Validation {
         workers: usize,
         tokenizer: Option<Tokenizer>,
         config: Option<Config>,
+        preprocessor_config: Option<HubPreprocessorConfig>,
         max_best_of: usize,
         max_stop_sequences: usize,
         max_top_n_tokens: u32,
@@ -53,12 +57,18 @@ impl Validation {
             for _ in 0..workers {
                 let tokenizer_clone = tokenizer.clone();
                 let config_clone = config.clone();
+                let preprocessor_config_clone = preprocessor_config.clone();
                 let (tokenizer_sender, tokenizer_receiver) = mpsc::unbounded_channel();
                 senders.push(tokenizer_sender);
 
                 // Spawn worker
                 tokio::task::spawn_blocking(move || {
-                    tokenizer_worker(tokenizer_clone, config_clone, tokenizer_receiver)
+                    tokenizer_worker(
+                        tokenizer_clone,
+                        config_clone,
+                        preprocessor_config_clone,
+                        tokenizer_receiver,
+                    )
                 });
             }
 
@@ -422,13 +432,20 @@ async fn round_robin_task(
 fn tokenizer_worker(
     tokenizer: Tokenizer,
     config: Option<Config>,
+    preprocessor_config: Option<HubPreprocessorConfig>,
     mut receiver: mpsc::UnboundedReceiver<TokenizerRequest>,
 ) {
     // Loop over requests
     while let Some(((inputs, truncate), response_tx, parent_span)) = receiver.blocking_recv() {
         parent_span.in_scope(|| {
             response_tx
-                .send(prepare_input(inputs, truncate, &tokenizer, &config))
+                .send(prepare_input(
+                    inputs,
+                    truncate,
+                    &tokenizer,
+                    config.as_ref(),
+                    preprocessor_config.as_ref(),
+                ))
                 .unwrap_or(())
         })
     }
@@ -508,16 +525,67 @@ fn fetch_image(input: &str) -> Result<(Vec<u8>, String, usize, usize), Validatio
     }
 }
 
+fn image_tokens(
+    config: &Config,
+    preprocessor_config: Option<&HubPreprocessorConfig>,
+    height: usize,
+    width: usize,
+) -> String {
+    use Config::*;
+    use HubPreprocessorConfig::*;
+    match config {
+        Idefics => "<image>".to_string(),
+        Idefics2(config) => {
+            const FAKE: &str = "<fake_token_around_image>";
+            const IMAGE: &str = "<image>";
+
+            let slots = config.get_number_of_features(height, width);
+
+            let mut image_string = String::with_capacity(2 * FAKE.len() + slots * IMAGE.len());
+            image_string.push_str(FAKE);
+            image_string.extend(iter::repeat(IMAGE).take(slots));
+            image_string.push_str(FAKE);
+
+            if matches!(
+                preprocessor_config,
+                Some(Idefics2Processor(Idefics2Preprocessor {
+                    do_image_splitting: true,
+                    ..
+                }))
+            ) {
+                image_string = image_string.repeat(5);
+            };
+
+            image_string
+        }
+        Paligemma(config) => "<image>".repeat(config.get_number_of_features(height, width)),
+        LlavaNext(config) => "<image>".repeat(config.get_number_of_features(height, width)),
+        _ => unimplemented!("Images tokens are not supported for this model configuration"),
+    }
+}
+
+fn image_tokens_fixup(config: &Config, text: String) -> String {
+    match config {
+        Config::Idefics2(_) => {
+            const FAKE: &str = "<fake_token_around_image>";
+            text.replace(&format!("{FAKE}{FAKE}"), FAKE)
+        }
+        _ => text,
+    }
+}
+
 /// Get input length and optionally truncate it
 fn prepare_input(
     inputs: String,
     _truncate: Option<usize>,
     tokenizer: &Tokenizer,
-    config: &Option<Config>,
+    config: Option<&Config>,
+    preprocessor_config: Option<&HubPreprocessorConfig>,
 ) -> Result<(tokenizers::Encoding, Vec<InputChunk>), ValidationError> {
+    use Config::*;
     static RE: Lazy<Regex> = Lazy::new(|| Regex::new(r"!\[\]\([^\)]*\)").unwrap());
     let (tokenizer_query, input_chunks) = match config {
-        Some(Config::LlavaNext(config)) => {
+        Some(config @ (Idefics | Idefics2(_) | Paligemma(_) | LlavaNext(_))) => {
             let mut input_chunks = Vec::new();
             let mut tokenizer_query = String::with_capacity(inputs.len());
             let mut start = 0;
@@ -529,88 +597,17 @@ fn prepare_input(
                     tokenizer_query.push_str(&inputs[start..chunk_start]);
                 }
                 let (data, mimetype, height, width) = fetch_image(&inputs[chunk_start..chunk_end])?;
-                let slots = config.get_number_of_features(height, width);
                 input_chunks.push(Chunk::Image(Image { data, mimetype }).into());
-                tokenizer_query.push_str(&"<image>".repeat(slots));
+                tokenizer_query.push_str(&image_tokens(config, preprocessor_config, height, width));
                 start = chunk_end;
             }
             if start != inputs.len() {
                 input_chunks.push(Chunk::Text(inputs[start..].to_string()).into());
                 tokenizer_query.push_str(&inputs[start..]);
             }
-            (tokenizer_query, input_chunks)
-        }
-        Some(Config::Paligemma(config)) => {
-            let mut input_chunks = Vec::new();
-            let mut tokenizer_query = String::with_capacity(inputs.len());
-            let mut start = 0;
-            for chunk in RE.find_iter(&inputs) {
-                let chunk_start = chunk.start();
-                let chunk_end = chunk.end();
-                if chunk_start != start {
-                    input_chunks.push(Chunk::Text(inputs[start..chunk_start].to_string()).into());
-                    tokenizer_query.push_str(&inputs[start..chunk_start]);
-                }
-                let (data, mimetype, height, width) = fetch_image(&inputs[chunk_start..chunk_end])?;
-                let slots = config.get_number_of_features(height, width);
-                input_chunks.push(Chunk::Image(Image { data, mimetype }).into());
-                tokenizer_query.push_str(&"<image>".repeat(slots));
-                start = chunk_end;
-            }
-            if start != inputs.len() {
-                input_chunks.push(Chunk::Text(inputs[start..].to_string()).into());
-                tokenizer_query.push_str(&inputs[start..]);
-            }
-            (tokenizer_query, input_chunks)
-        }
-        Some(Config::Idefics2(config)) => {
-            let mut input_chunks = Vec::new();
-            let mut tokenizer_query = String::with_capacity(inputs.len());
-            let mut start = 0;
-            for chunk in RE.find_iter(&inputs) {
-                let chunk_start = chunk.start();
-                let chunk_end = chunk.end();
-                if chunk_start != start {
-                    input_chunks.push(Chunk::Text(inputs[start..chunk_start].to_string()).into());
-                    tokenizer_query.push_str(&inputs[start..chunk_start]);
-                }
-                let (data, mimetype, height, width) = fetch_image(&inputs[chunk_start..chunk_end])?;
-                let slots = config.get_number_of_features(height, width);
-                tokenizer_query.push_str("<fake_token_around_image>");
-                tokenizer_query.push_str(&"<image>".repeat(slots));
-                tokenizer_query.push_str("<fake_token_around_image>");
 
-                input_chunks.push(Chunk::Image(Image { data, mimetype }).into());
-                start = chunk_end;
-            }
-            if start != inputs.len() {
-                input_chunks.push(Chunk::Text(inputs[start..].to_string()).into());
-                tokenizer_query.push_str(&inputs[start..]);
-            }
-            (tokenizer_query, input_chunks)
-        }
-        Some(Config::Idefics) => {
-            let mut input_chunks = Vec::new();
-            let mut tokenizer_query = String::with_capacity(inputs.len());
-            let mut start = 0;
-            for chunk in RE.find_iter(&inputs) {
-                let chunk_start = chunk.start();
-                let chunk_end = chunk.end();
-                if chunk_start != start {
-                    input_chunks.push(Chunk::Text(inputs[start..chunk_start].to_string()).into());
-                    tokenizer_query.push_str(&inputs[start..chunk_start]);
-                }
-                let (data, mimetype, _height, _width) =
-                    fetch_image(&inputs[chunk_start..chunk_end])?;
-                let slots = 1;
-                tokenizer_query.push_str(&"<image>".repeat(slots));
-                input_chunks.push(Chunk::Image(Image { data, mimetype }).into());
-                start = chunk_end;
-            }
-            if start != inputs.len() {
-                input_chunks.push(Chunk::Text(inputs[start..].to_string()).into());
-                tokenizer_query.push_str(&inputs[start..]);
-            }
+            tokenizer_query = image_tokens_fixup(config, tokenizer_query);
+
             (tokenizer_query, input_chunks)
         }
         _ => (inputs.clone(), vec![Chunk::Text(inputs).into()]),
@@ -750,7 +747,7 @@ pub enum ValidationError {
 #[cfg(test)]
 mod tests {
     use super::*;
-    use crate::config::{PaliTextConfig, Paligemma};
+    use crate::config::{Idefics2, PaliTextConfig, Paligemma};
     use crate::default_parameters;
     use crate::tests::get_tokenizer;
 
@@ -769,6 +766,7 @@ mod tests {
             workers,
             tokenizer,
             config,
+            None,
             max_best_of,
             max_stop_sequence,
             max_top_n_tokens,
@@ -803,6 +801,7 @@ mod tests {
             workers,
             tokenizer,
             config,
+            None,
             max_best_of,
             max_stop_sequence,
             max_top_n_tokens,
@@ -836,6 +835,7 @@ mod tests {
             workers,
             tokenizer,
             config,
+            None,
             max_best_of,
             max_stop_sequence,
             max_top_n_tokens,
@@ -874,6 +874,7 @@ mod tests {
             workers,
             tokenizer,
             config,
+            None,
             max_best_of,
             max_stop_sequence,
             max_top_n_tokens,
@@ -941,6 +942,7 @@ mod tests {
             workers,
             tokenizer,
             config,
+            None,
             max_best_of,
             max_stop_sequences,
             max_top_n_tokens,
@@ -1026,6 +1028,7 @@ mod tests {
             workers,
             tokenizer,
             Some(config),
+            None,
             max_best_of,
             max_stop_sequence,
             max_top_n_tokens,
@@ -1058,4 +1061,83 @@ mod tests {
             "Failed to process images",
         );
     }
+
+    #[tokio::test]
+    async fn test_idefics2_correct_n_fake_tokens() {
+        let pixel_data = STANDARD.decode(PIXEL_GIF).unwrap();
+
+        let tokenizer = Some(get_tokenizer().await);
+
+        let max_best_of = 2;
+        let max_stop_sequence = 3;
+        let max_top_n_tokens = 4;
+        let max_input_length = 5;
+        let max_total_tokens = 6;
+        let disable_grammar_support = true;
+        let workers = 1;
+        let config = Config::Idefics2(Idefics2 {});
+        let validation = Validation::new(
+            workers,
+            tokenizer,
+            Some(config),
+            Some(HubPreprocessorConfig::Idefics2Processor(
+                Idefics2Preprocessor {
+                    do_image_splitting: true,
+                },
+            )),
+            max_best_of,
+            max_stop_sequence,
+            max_top_n_tokens,
+            max_input_length,
+            max_total_tokens,
+            disable_grammar_support,
+        );
+
+        let (encoding, chunks) = match validation
+            .tokenize(
+                format!(
+                    "test![](data:image/gif;base64,{})![](data:image/gif;base64,{})",
+                    PIXEL_GIF, PIXEL_GIF
+                ),
+                None,
+            )
+            .await
+        {
+            Ok(Some((encoding, chunks))) => (encoding, chunks),
+            _ => panic!("Unexpected tokenization failure"),
+        };
+
+        assert!(
+            chunks
+                == vec![
+                    Chunk::Text("test".to_string()).into(),
+                    Chunk::Image(Image {
+                        data: pixel_data.clone(),
+                        mimetype: "image/gif".to_string()
+                    })
+                    .into(),
+                    Chunk::Image(Image {
+                        data: pixel_data.clone(),
+                        mimetype: "image/gif".to_string()
+                    })
+                    .into()
+                ],
+            "Failed to process images",
+        );
+
+        // Verify the number of fake tokens:
+        //
+        // - Two images surrounded/separated by a fake token = 3.
+        // - Both are split in 5 subimages, separated by a fake token: 2 * 4
+        //
+        // Fake tokens get split up by the testing tokenizer, but we don't care.
+        assert_eq!(
+            encoding
+                .get_tokens()
+                .iter()
+                .filter(|t| *t == "fake")
+                .count(),
+            11
+        );
+    }
 }

server/text_generation_server/models/custom_modeling/llava_next.py

@@ -39,7 +39,7 @@ def get_anyres_image_grid_shape(image_size, grid_pinpoints, patch_size):
 
     Args:
         image_size (`tuple`):
-            The size of the input image in the format (width, height).
+            The size of the input image in the format (height, width).
         grid_pinpoints (`List`):
             A list containing possible resolutions. Each item in the list should be a tuple or list
             of the form `(height, width)`.
@@ -47,7 +47,7 @@ def get_anyres_image_grid_shape(image_size, grid_pinpoints, patch_size):
             The size of each image patch.
 
     Returns:
-        tuple: The shape of the image patch grid in the format (width, height).
+        tuple: The shape of the image patch grid in the format (height, width).
     """
     if not isinstance(grid_pinpoints, list):
         raise ValueError("grid_pinpoints should be a list of tuples or lists")
@@ -230,7 +230,10 @@ class LlavaNextForConditionalGeneration(nn.Module):
                         raise ValueError(
                             "The number of patches is not consistent with the image size."
                         )
-                    num_patch_height, num_patch_width = get_anyres_image_grid_shape(
+
+                    # Dimensions are intentionally swapped to be bug-compatible with
+                    # upstream: https://github.com/LLaVA-VL/LLaVA-NeXT/issues/59
+                    num_patch_width, num_patch_height = get_anyres_image_grid_shape(
                         image_sizes[image_idx],
                         self.config.image_grid_pinpoints,
                         self.config.vision_config.image_size,

server/text_generation_server/models/pali_gemma.py

@@ -39,7 +39,9 @@ class PaliGemmaBatch(VlmCausalLMBatch):
                     # TODO do_convert_RGB should be on by default ?
                     image = image.convert("RGB")
                     image_input = processor.image_processor(image, return_tensors="pt")
-                    full_text += image_text_replacement(image_input, config, image_id)
+                    full_text += image_text_replacement(
+                        processor, image_input, config, image_id
+                    )
                     image_inputs.append(image_input)
                 else:
                     raise RuntimeError(f"Invalid chunk type {chunk_type}")

server/text_generation_server/models/vlm_causal_lm.py

+from itertools import repeat
 import torch
 from PIL import Image
 from io import BytesIO
@@ -15,6 +16,9 @@ from text_generation_server.models.flash_mistral import (
 
 tracer = trace.get_tracer(__name__)
 
+IDEFICS2_FAKE_TOKEN = "<fake_token_around_image>"
+IDEFICS2_IMAGE_TOKEN = "<image>"
+
 
 def get_anyres_image_grid_shape(image_size, grid_pinpoints, patch_size):
     """
@@ -22,7 +26,7 @@ def get_anyres_image_grid_shape(image_size, grid_pinpoints, patch_size):
 
     Args:
         image_size (`tuple`):
-            The size of the input image in the format (width, height).
+            The size of the input image in the format (height, width).
         grid_pinpoints (`List`):
             A list containing possible resolutions. Each item in the list should be a tuple or list
             of the form `(height, width)`.
@@ -39,15 +43,13 @@ def get_anyres_image_grid_shape(image_size, grid_pinpoints, patch_size):
     return height // patch_size, width // patch_size
 
 
-def image_text_replacement(image_input, config, image_id) -> str:
+def image_text_replacement(processor, image_input, config, image_id: int) -> str:
     if config.model_type == "idefics2":
-        # TODO technically depends on image splitting which is not implemented.
-        num_features = 320
-        return (
-            "<fake_token_around_image>"
-            + "<image>" * num_features
-            + "<fake_token_around_image>"
-        )
+        image_seq_len = 64
+        image_str = f"{IDEFICS2_FAKE_TOKEN}{IDEFICS2_IMAGE_TOKEN * image_seq_len}{IDEFICS2_FAKE_TOKEN}"
+        if processor.image_processor.do_image_splitting:
+            image_str *= 5
+        return image_str
     elif config.model_type == "llava_next":
         height, width = image_input["image_sizes"][image_id]
         num_features = get_number_of_features(height, width, config)
@@ -64,20 +66,35 @@ def image_text_replacement(image_input, config, image_id) -> str:
         raise RuntimeError(f"Unknown config {config.model_type} for multimodal")
 
 
+def image_text_replacement_fixup(config, text: str) -> str:
+    if config.model_type == "idefics2":
+        return text.replace(
+            f"{IDEFICS2_FAKE_TOKEN}{IDEFICS2_FAKE_TOKEN}", IDEFICS2_FAKE_TOKEN
+        )
+    return text
+
+
 def get_unpadded_features(
-    height: int, width: int, npatches: int, num_patch_height: int, num_patch_width: int
+    original_height: int,
+    original_width: int,
+    npatches: int,
+    num_patch_height: int,
+    num_patch_width: int,
 ) -> Tuple[int, int]:
     current_height = npatches * num_patch_height
     current_width = npatches * num_patch_width
 
-    aspect_ratio: float = width / height
+    aspect_ratio: float = original_width / original_height
     current_aspect_ratio: float = current_width / current_height
+
     if aspect_ratio > current_aspect_ratio:
-        new_height = (height * current_width) // width
-        current_height = new_height
+        new_height = (original_height * current_width) // original_width
+        padding = (current_height - new_height) // 2
+        current_height = current_height - (2 * padding)
     else:
-        new_width = (width * current_height) // height
-        current_width = new_width
+        new_width = (original_width * current_height) // original_height
+        padding = (current_width - new_width) // 2
+        current_width = current_width - (2 * padding)
 
     unpadded_features = current_height * current_width
     newline_features = current_height
@@ -96,7 +113,9 @@ def get_number_of_features(height: int, width: int, config) -> int:
 
     npatches = image_size // patch_size
 
-    num_patch_height, num_patch_width = get_anyres_image_grid_shape(
+    # Dimensions are intentionally swapped to be bug-compatible with
+    # upstream: https://github.com/LLaVA-VL/LLaVA-NeXT/issues/59
+    num_patch_width, num_patch_height = get_anyres_image_grid_shape(
         [height, width],
         image_grid_pinpoints,
         image_size,
@@ -168,9 +187,13 @@ class VlmCausalLMBatch(FlashCausalLMBatch):
                 if chunk_type == "text":
                     full_text += chunk.text
                 elif chunk_type == "image":
-                    full_text += image_text_replacement(image_inputs, config, image_id)
+                    full_text += image_text_replacement(
+                        processor, image_inputs, config, image_id
+                    )
                     image_id += 1
 
+            full_text = image_text_replacement_fixup(config, full_text)
+
             batch_inputs.append(full_text)
             max_truncation = max(max_truncation, r.truncate)