Merge tag 'v2.0.2' into dev-rocm

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

+{
+  "details": {
+    "best_of_sequences": null,
+    "finish_reason": "length",
+    "generated_tokens": 10,
+    "prefill": [],
+    "seed": null,
+    "tokens": [
+      {
+        "id": 330,
+        "logprob": -0.13000488,
+        "special": false,
+        "text": " A"
+      },
+      {
+        "id": 13088,
+        "logprob": -0.6713867,
+        "special": false,
+        "text": " chicken"
+      },
+      {
+        "id": 349,
+        "logprob": -0.2980957,
+        "special": false,
+        "text": " is"
+      },
+      {
+        "id": 6398,
+        "logprob": -0.060638428,
+        "special": false,
+        "text": " sitting"
+      },
+      {
+        "id": 356,
+        "logprob": -0.27319336,
+        "special": false,
+        "text": " on"
+      },
+      {
+        "id": 264,
+        "logprob": -0.140625,
+        "special": false,
+        "text": " a"
+      },
+      {
+        "id": 17972,
+        "logprob": -0.040405273,
+        "special": false,
+        "text": " pile"
+      },
+      {
+        "id": 302,
+        "logprob": -0.0002708435,
+        "special": false,
+        "text": " of"
+      },
+      {
+        "id": 2445,
+        "logprob": -0.095336914,
+        "special": false,
+        "text": " money"
+      },
+      {
+        "id": 28723,
+        "logprob": -0.0068359375,
+        "special": false,
+        "text": "."
+      }
+    ],
+    "top_tokens": null
+  },
+  "generated_text": " A chicken is sitting on a pile of money."
+}

integration-tests/models/test_idefics2.py

+import pytest
+import base64
+
+
+# TODO fix the server parsser to count inline image tokens correctly
+def get_chicken():
+    with open("integration-tests/images/chicken_on_money.png", "rb") as image_file:
+        encoded_string = base64.b64encode(image_file.read())
+    return f"data:image/png;base64,{encoded_string.decode('utf-8')}"
+
+
+@pytest.fixture(scope="module")
+def flash_idefics2_next_handle(launcher):
+    with launcher(
+        "HuggingFaceM4/idefics2-8b",
+    ) as handle:
+        yield handle
+
+
+@pytest.fixture(scope="module")
+async def flash_idefics2_next(flash_idefics2_next_handle):
+    await flash_idefics2_next_handle.health(300)
+    return flash_idefics2_next_handle.client
+
+
+@pytest.mark.asyncio
+@pytest.mark.private
+async def test_flash_idefics2_next_simple(flash_idefics2_next, response_snapshot):
+    chicken = get_chicken()
+    response = await flash_idefics2_next.generate(
+        f"User:![]({chicken})Write me a short story<end_of_utterance> \nAssistant:",
+        max_new_tokens=10,
+    )
+    assert (
+        response.generated_text == " A chicken is sitting on a pile of money."
+    ), f"{repr(response.generated_text)}"
+    assert response.details.generated_tokens == 10
+    assert response == response_snapshot
+
+
+@pytest.mark.asyncio
+@pytest.mark.private
+async def test_flash_idefics2_next_all_params(flash_idefics2_next, response_snapshot):
+    response = await flash_idefics2_next.generate(
+        "Test request",
+        max_new_tokens=10,
+        repetition_penalty=1.2,
+        return_full_text=True,
+        stop_sequences=["test"],
+        temperature=0.5,
+        top_p=0.9,
+        top_k=10,
+        truncate=5,
+        typical_p=0.9,
+        watermark=True,
+        decoder_input_details=True,
+        seed=0,
+    )
+
+    assert response.details.generated_tokens == 10
+    assert response == response_snapshot
+
+
+@pytest.mark.asyncio
+@pytest.mark.private
+async def test_flash_idefics2_next_load(
+    flash_idefics2_next, generate_load, response_snapshot
+):
+    chicken = get_chicken()
+    responses = await generate_load(
+        flash_idefics2_next,
+        f"User:![]({chicken})Write me a short story<end_of_utterance> \nAssistant:",
+        max_new_tokens=10,
+        n=4,
+    )
+    generated_texts = [r.generated_text for r in responses]
+    assert generated_texts[0] == " A chicken is sitting on a pile of money."
+    assert len(generated_texts) == 4
+    assert all([r.generated_text == generated_texts[0] for r in responses])
+
+    assert responses == response_snapshot

launcher/src/env_runtime.rs

@@ -7,14 +7,17 @@ pub(crate) struct Env {
     git_sha: &'static str,
     docker_label: &'static str,
     nvidia_env: String,
+    xpu_env: String,
 }
 
 impl Env {
     pub fn new() -> Self {
         let nvidia_env = nvidia_smi();
+        let xpu_env = xpu_smi();
 
         Self {
             nvidia_env: nvidia_env.unwrap_or("N/A".to_string()),
+            xpu_env: xpu_env.unwrap_or("N/A".to_string()),
             cargo_target: env!("VERGEN_CARGO_TARGET_TRIPLE"),
             cargo_version: env!("VERGEN_RUSTC_SEMVER"),
             git_sha: option_env!("VERGEN_GIT_SHA").unwrap_or("N/A"),
@@ -31,7 +34,8 @@ impl fmt::Display for Env {
         writeln!(f, "Cargo version: {}", self.cargo_version)?;
         writeln!(f, "Commit sha: {}", self.git_sha)?;
         writeln!(f, "Docker label: {}", self.docker_label)?;
-        write!(f, "nvidia-smi:\n{}", self.nvidia_env)?;
+        writeln!(f, "nvidia-smi:\n{}", self.nvidia_env)?;
+        write!(f, "xpu-smi:\n{}", self.xpu_env)?;
 
         Ok(())
     }
@@ -43,3 +47,10 @@ fn nvidia_smi() -> Option<String> {
     let output = nvidia_smi.replace('\n', "\n   ");
     Some(output.trim().to_string())
 }
+
+fn xpu_smi() -> Option<String> {
+    let output = Command::new("xpu-smi").arg("discovery").output().ok()?;
+    let xpu_smi = String::from_utf8(output.stdout).ok()?;
+    let output = xpu_smi.replace('\n', "\n   ");
+    Some(output.trim().to_string())
+}

launcher/src/main.rs

@@ -251,7 +251,7 @@ struct Args {
     ///
     /// This setting is only applied if there is room in the batch
     /// as defined by `max_batch_total_tokens`.
-    #[clap(default_value = "1.2", long, env)]
+    #[clap(default_value = "0.3", long, env)]
     waiting_served_ratio: f32,
 
     /// Limits the number of tokens for the prefill operation.
@@ -448,6 +448,8 @@ fn shard_manager(
     cuda_memory_fraction: f32,
     rope_scaling: Option<RopeScaling>,
     rope_factor: Option<f32>,
+    max_total_tokens: usize,
+    max_batch_size: Option<usize>,
     otlp_endpoint: Option<String>,
     status_sender: mpsc::Sender<ShardStatus>,
     shutdown: Arc<AtomicBool>,
@@ -512,6 +514,7 @@ fn shard_manager(
         (Some(scaling), Some(factor)) => Some((scaling, factor)),
         (None, Some(factor)) => Some((RopeScaling::Linear, factor)),
     };
+
     // OpenTelemetry
     if let Some(otlp_endpoint) = otlp_endpoint {
         shard_args.push("--otlp-endpoint".to_string());
@@ -564,6 +567,14 @@ fn shard_manager(
         envs.push(("ROPE_FACTOR".into(), factor.to_string().into()));
     }
 
+    envs.push((
+        "MAX_TOTAL_TOKENS".into(),
+        max_total_tokens.to_string().into(),
+    ));
+    if let Some(max_batch_size) = max_batch_size {
+        envs.push(("MAX_BATCH_SIZE".into(), max_batch_size.to_string().into()));
+    }
+
     // If huggingface_hub_cache is some, pass it to the shard
     // Useful when running inside a docker container
     if let Some(huggingface_hub_cache) = huggingface_hub_cache {
@@ -672,9 +683,7 @@ fn shard_manager(
 
         // We received a shutdown signal
         if shutdown.load(Ordering::SeqCst) {
-            p.kill().unwrap();
-            let _ = p.wait();
-            tracing::info!("Shard terminated");
+            terminate("shard", p, Duration::from_secs(90)).unwrap();
             return;
         }
 
@@ -967,6 +976,7 @@ fn spawn_shards(
     num_shard: usize,
     args: &Args,
     cuda_graphs: Vec<usize>,
+    max_total_tokens: usize,
     shutdown: Arc<AtomicBool>,
     shutdown_receiver: &mpsc::Receiver<()>,
     shutdown_sender: mpsc::Sender<()>,
@@ -998,6 +1008,7 @@ fn spawn_shards(
         let cuda_memory_fraction = args.cuda_memory_fraction;
         let rope_scaling = args.rope_scaling;
         let rope_factor = args.rope_factor;
+        let max_batch_size = args.max_batch_size;
         thread::spawn(move || {
             shard_manager(
                 model_id,
@@ -1020,6 +1031,8 @@ fn spawn_shards(
                 cuda_memory_fraction,
                 rope_scaling,
                 rope_factor,
+                max_total_tokens,
+                max_batch_size,
                 otlp_endpoint,
                 status_sender,
                 shutdown,
@@ -1230,7 +1243,6 @@ fn terminate(process_name: &str, mut process: Child, timeout: Duration) -> io::R
     signal::kill(Pid::from_raw(process.id() as i32), Signal::SIGTERM).unwrap();
 
     tracing::info!("Waiting for {process_name} to gracefully shutdown");
-
     while terminate_time.elapsed() < timeout {
         if let Some(status) = process.try_wait()? {
             tracing::info!("{process_name} terminated");
@@ -1238,7 +1250,6 @@ fn terminate(process_name: &str, mut process: Child, timeout: Duration) -> io::R
         }
         sleep(Duration::from_millis(100));
     }
-
     tracing::info!("Killing {process_name}");
 
     process.kill()?;
@@ -1273,7 +1284,7 @@ fn main() -> Result<(), LauncherError> {
         tracing::info!("{}", env_runtime);
     }
 
-    tracing::info!("{:?}", args);
+    tracing::info!("{:#?}", args);
 
     let get_max_position_embeddings = || -> Result<usize, Box<dyn std::error::Error>> {
         let model_id = args.model_id.clone();
@@ -1306,7 +1317,12 @@ fn main() -> Result<(), LauncherError> {
             (Some(max_position_embeddings), _) | (None, Some(max_position_embeddings)) => {
                 if max_position_embeddings > max_default {
                     let max = max_position_embeddings;
+                    if args.max_input_tokens.is_none()
+                        && args.max_total_tokens.is_none()
+                        && args.max_batch_prefill_tokens.is_none()
+                    {
                         tracing::info!("Model supports up to {max} but tgi will now set its default to {max_default} instead. This is to save VRAM by refusing large prompts in order to allow more users on the same hardware. You can increase that size using `--max-batch-prefill-tokens={} --max-total-tokens={max} --max-input-tokens={}`.", max + 50, max - 1);
+                    }
                     max_default
                 } else {
                     max_position_embeddings
@@ -1378,7 +1394,7 @@ fn main() -> Result<(), LauncherError> {
     }
 
     let cuda_graphs = match (&args.cuda_graphs, &args.quantize) {
-        (Some(cuda_graphs), Some(_q)) => cuda_graphs.clone(),
+        (Some(cuda_graphs), _) => cuda_graphs.iter().cloned().filter(|&c| c > 0).collect(),
         #[allow(deprecated)]
         (
             None,
@@ -1475,6 +1491,7 @@ fn main() -> Result<(), LauncherError> {
         num_shard,
         &args,
         cuda_graphs,
+        max_total_tokens,
         shutdown.clone(),
         &shutdown_receiver,
         shutdown_sender,

load_tests/common.js

-import { check, randomSeed } from 'k6';
+import { check } from 'k6';
+import { scenario } from 'k6/execution';
 import http from 'k6/http';
 import { Trend, Counter } from 'k6/metrics';
-import { randomItem } from 'https://jslib.k6.io/k6-utils/1.2.0/index.js';
 
-const seed = 0;
-
-const host = __ENV.HOST || '127.0.0.1:8000';
+const host = __ENV.HOST;
+const model_id = __ENV.MODEL_ID;
 const timePerToken = new Trend('time_per_token', true);
 const tokens = new Counter('tokens');
 const new_tokens = new Counter('new_tokens');
 const input_tokens = new Counter('input_tokens');
+const max_new_tokens = 50;
 
-randomSeed(seed);
 // const shareGPT = JSON.parse(open("ShareGPT_V3_unfiltered_cleaned_split.json"))
 const shareGPT = JSON.parse(open("small.json"))
 
 
-export function get_options(reference_latency_ms){
+export function get_options() {
     return {
         thresholds: {
             http_req_failed: ['rate==0'],
-            time_per_token: [{
-                threshold: `p(50)<${5 * reference_latency_ms}`,
-                abortOnFail: true,
-                delayAbortEval: '10s'
-            }],
+            // time_per_token: [{
+            //     threshold: `p(50)<${5 * reference_latency_ms}`,
+            //     abortOnFail: true,
+            //     delayAbortEval: '10s'
+            // }],
         },
         scenarios: {
+            // single_user: {
+            //     executor: 'constant-arrival-rate',
+            //     duration: '60s',
+            //     preAllocatedVUs: 1,
+            //     rate: 20,
+            //     timeUnit: '1s',
+            // },
             load_test: {
                 executor: 'constant-arrival-rate',
                 duration: '60s',
-                preAllocatedVUs: 10,
-                rate: 10,
+                preAllocatedVUs: 100,
+                rate: 1,
                 timeUnit: '1s',
             },
+            // breakpoint: {
+            //     executor: 'ramping-arrival-rate', //Assure load increase if the system slows
+            //     preAllocatedVUs: 300,
+            //     stages: [
+            //         { duration: '60s', target: 100 }, // just slowly ramp-up to a HUGE load
+            //     ],
+            // },
+            // throughput: {
+            //     executor: 'shared-iterations',
+            //     vus: 100,
+            //     iterations: 200,
+            //     maxDuration: '40s',
+            // },
         },
     };
 }
 
+function generate_payload(gpt, max_new_tokens) {
+    const input = gpt["conversations"][0]["value"];
+    return { "messages": [{ "role": "user", "content": input }], "temperature": 0, "model": `${model_id}`, "max_tokens": max_new_tokens }
+}
+
+export const options = get_options();
 
-export function run(host, generate_payload, max_new_tokens) {
-    const headers = {'Content-Type': 'application/json'};
-    const query = randomItem(shareGPT);
-    const payload = JSON.stringify(generate_payload(query));
-    const res = http.post(`http://${host}/generate`, payload, {
+export default function run() {
+    const headers = { 'Content-Type': 'application/json' };
+    const query = shareGPT[scenario.iterationInTest % shareGPT.length];
+    const payload = JSON.stringify(generate_payload(query, max_new_tokens));
+    const res = http.post(`http://${host}/v1/chat/completions`, payload, {
         headers,
     });
-    if(res.status >= 400 && res.status < 500){
+    if (res.status >= 400 && res.status < 500) {
         return;
     }
 
 
     check(res, {
-        'Post status is 200': (r) => res.status === 200,
+        'Post status is 200': (res) => res.status === 200,
     });
     const duration = res.timings.duration;
 
     if (res.status === 200) {
         const body = res.json();
-        const n_tokens = body.details.tokens.length;
-        const latency_ms_per_token = duration / n_tokens;
+        const completion_tokens = body.usage.completion_tokens;
+        const latency_ms_per_token = duration / completion_tokens;
         timePerToken.add(latency_ms_per_token);
-        const latency_in_s = latency_ms_per_token / 1000;
-        const individual_throughput = 1 / latency_in_s;
-        const _input_tokens = body.details.prefill.length;
-        tokens.add(n_tokens + _input_tokens);
-        input_tokens.add(_input_tokens);
-        new_tokens.add(n_tokens);
+        const prompt_tokens = body.usage.prompt_tokens;
+        input_tokens.add(prompt_tokens);
+        new_tokens.add(completion_tokens);
+        tokens.add(completion_tokens + prompt_tokens);
     }
 }

load_tests/tgi.js

-import { get_options, run } from "./common.js";
-
-const reference_latency_ms = 70;
-const host = __ENV.HOST || '127.0.0.1:8000';
-const max_new_tokens = 50;
-
-
-function generate_payload(gpt){
-    const input = gpt["conversations"][0]["value"];
-    return {"inputs": input, "parameters": {"max_new_tokens": max_new_tokens, "decoder_input_details": true}}
-}
-
-export const options = get_options(reference_latency_ms);
-
-export default function(){
-    run(host, generate_payload, max_new_tokens);
-}

load_tests/vllm.js

-import { get_options, run } from "./common.js";
-
-const reference_latency_ms = 22;
-const host = __ENV.HOST || '127.0.0.1:8000';
-const max_new_tokens = 50;
-
-
-function generate_payload(gpt){
-    const input = gpt["conversations"][0]["value"];
-    return {"prompt": input, "temperature": 0.5, "ignore_eos": true}
-}
-
-export const options = get_options(reference_latency_ms);
-
-export default function(){
-    run(host, generate_payload, max_new_tokens);
-}

router/client/src/client.rs

@@ -114,8 +114,12 @@ impl Client {
             let truncate = min(max_input_length, max_prefill_tokens - n_tokens);
 
             let mut inputs = String::new();
-            inputs.push_str("![](data:image/jpeg;base64,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");
             inputs.push_str(&"_test ".to_string().repeat(max_input_length as usize));
+            if n_tokens == 0 {
+                // 1 request is enough to test vision heads.
+                // Sending images on other queries messes up easily with truncation.
+                inputs.push_str("![](data:image/jpeg;base64,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)");
+            }
 
             requests.push(Request {
                 id: 0,

router/src/config.rs

@@ -57,6 +57,31 @@ fn select_best_resolution(
     best_fit.unwrap_or((original_height, original_width))
 }
 
+fn get_unpadded_features(
+    height: usize,
+    width: usize,
+    npatches: usize,
+    num_patch_height: usize,
+    num_patch_width: usize,
+) -> (usize, usize) {
+    let current_height = npatches * num_patch_height;
+    let current_width = npatches * num_patch_width;
+
+    let aspect_ratio: f64 = width as f64 / height as f64;
+    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)
+    } else {
+        let new_width = (width * current_height) / height;
+        (current_height, new_width)
+    };
+
+    let unpadded_features = current_height * current_width;
+    let newline_features = current_height;
+    (unpadded_features, newline_features)
+}
+
 impl LlavaNext {
     pub fn get_number_of_features(&self, height: usize, width: usize) -> usize {
         let image_size = self.vision_config.image_size;
@@ -65,11 +90,9 @@ impl LlavaNext {
         let npatches = image_size / patch_size;
         let (num_patch_height, num_patch_width) =
             get_anyres_image_grid_shape(height, width, &self.image_grid_pinpoints, image_size);
-        // Ceil
-        let height_of_patch = (height * npatches + width - 1) / width;
-        let unpadded_features = npatches * height_of_patch * num_patch_height * num_patch_width;
-        // They are only added after width
-        let newline_features = height_of_patch * num_patch_width;
+
+        let (unpadded_features, newline_features) =
+            get_unpadded_features(height, width, npatches, num_patch_height, num_patch_width);
         // The base patch covers the entire image
         let base_features = npatches.pow(2);
         unpadded_features + newline_features + base_features
@@ -84,6 +107,17 @@ pub struct ClipVisionModel {
     patch_size: usize,
 }
 
+#[derive(Clone, Debug, Serialize, Deserialize)]
+#[serde(tag = "model_type")]
+#[serde(rename_all = "snake_case")]
+pub struct Idefics2 {}
+
+impl Idefics2 {
+    pub fn get_number_of_features(&self, _height: usize, _width: usize) -> usize {
+        320
+    }
+}
+
 #[derive(Clone, Debug, Serialize, Deserialize)]
 #[serde(tag = "model_type")]
 #[serde(rename_all = "snake_case")]
@@ -92,6 +126,7 @@ pub enum Config {
     ClipVisionModel(ClipVisionModel),
     Mistral,
     Idefics,
+    Idefics2(Idefics2),
     Ssm,
     GptBigcode,
     Santacoder,
@@ -146,13 +181,17 @@ mod test {
             ],
         };
 
+        let slots = config.get_number_of_features(20, 20);
+        assert_eq!(slots, 1176);
         let slots = config.get_number_of_features(640, 640);
         assert_eq!(slots, 2928);
         let slots = config.get_number_of_features(480, 640);
         assert_eq!(slots, 2340);
         let slots = config.get_number_of_features(899, 1024);
-        assert_eq!(slots, 2732);
+        assert_eq!(slots, 2634);
         let slots = config.get_number_of_features(1024, 899);
-        assert_eq!(slots, 3320);
+        assert_eq!(slots, 2640);
+        let slots = config.get_number_of_features(1067, 1600);
+        assert_eq!(slots, 2144);
     }
 }

router/src/lib.rs

@@ -73,9 +73,9 @@ pub struct HubTokenizerConfig {
 }
 
 impl HubTokenizerConfig {
-    pub fn from_file(filename: &std::path::Path) -> Self {
-        let content = std::fs::read_to_string(filename).unwrap();
-        serde_json::from_str(&content).unwrap_or_default()
+    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()
     }
 }
 
@@ -116,6 +116,7 @@ mod token_serde {
                     ))
                 }
             }
+            Value::Null => Ok(None),
             _ => Err(de::Error::custom("invalid token format")),
         }
     }
@@ -168,9 +169,12 @@ pub struct Info {
 
 #[derive(Clone, Debug, Deserialize, ToSchema, Default)]
 pub(crate) struct GenerateParameters {
+    /// Generate best_of sequences and return the one if the highest token logprobs.
     #[serde(default)]
     #[schema(exclusive_minimum = 0, nullable = true, default = "null", example = 1)]
     pub best_of: Option<usize>,
+
+    /// The value used to module the logits distribution.
     #[serde(default)]
     #[schema(
         exclusive_minimum = 0.0,
@@ -179,6 +183,9 @@ pub(crate) struct GenerateParameters {
         example = 0.5
     )]
     pub temperature: Option<f32>,
+
+    /// The parameter for repetition penalty. 1.0 means no penalty.
+    /// See [this paper](https://arxiv.org/pdf/1909.05858.pdf) for more details.
     #[serde(default)]
     #[schema(
         exclusive_minimum = 0.0,
@@ -187,6 +194,10 @@ pub(crate) struct GenerateParameters {
         example = 1.03
     )]
     pub repetition_penalty: Option<f32>,
+
+    /// The parameter for frequency penalty. 1.0 means no penalty
+    /// Penalize new tokens based on their existing frequency in the text so far,
+    /// decreasing the model's likelihood to repeat the same line verbatim.
     #[serde(default)]
     #[schema(
         exclusive_minimum = -2.0,
@@ -195,9 +206,13 @@ pub(crate) struct GenerateParameters {
         example = 0.1
     )]
     pub frequency_penalty: Option<f32>,
+
+    /// The number of highest probability vocabulary tokens to keep for top-k-filtering.
     #[serde(default)]
     #[schema(exclusive_minimum = 0, nullable = true, default = "null", example = 10)]
     pub top_k: Option<i32>,
+
+    /// Top-p value for nucleus sampling.
     #[serde(default)]
     #[schema(
         exclusive_minimum = 0.0,
@@ -207,6 +222,9 @@ pub(crate) struct GenerateParameters {
         example = 0.95
     )]
     pub top_p: Option<f32>,
+
+    /// Typical Decoding mass
+    /// See [Typical Decoding for Natural Language Generation](https://arxiv.org/abs/2202.00666) for more information.
     #[serde(default)]
     #[schema(
         exclusive_minimum = 0.0,
@@ -216,30 +234,48 @@ pub(crate) struct GenerateParameters {
         example = 0.95
     )]
     pub typical_p: Option<f32>,
+
+    /// Activate logits sampling.
     #[serde(default)]
     #[schema(default = "false", example = true)]
     pub do_sample: bool,
+
+    /// Maximum number of tokens to generate.
     #[serde(default = "default_max_new_tokens")]
     #[schema(nullable = true, default = "100", example = "20")]
     pub max_new_tokens: Option<u32>,
+
+    /// Whether to prepend the prompt to the generated text
     #[serde(default)]
     #[schema(nullable = true, default = "null", example = false)]
     pub return_full_text: Option<bool>,
+
+    /// Stop generating tokens if a member of `stop` is generated.
     #[serde(default)]
     #[schema(inline, max_items = 4, example = json ! (["photographer"]))]
     pub stop: Vec<String>,
+
+    /// Truncate inputs tokens to the given size.
     #[serde(default)]
     #[schema(nullable = true, default = "null", example = "null")]
     pub truncate: Option<usize>,
+
+    /// Watermarking with [A Watermark for Large Language Models](https://arxiv.org/abs/2301.10226).
     #[serde(default)]
     #[schema(default = "false", example = true)]
     pub watermark: bool,
+
+    /// Whether to return generation details.
     #[serde(default)]
     #[schema(default = "true")]
     pub details: bool,
+
+    /// Whether to return decoder input token logprobs and ids.
     #[serde(default)]
     #[schema(default = "false")]
     pub decoder_input_details: bool,
+
+    /// Random sampling seed.
     #[serde(default)]
     #[schema(
         exclusive_minimum = 0,
@@ -248,9 +284,13 @@ pub(crate) struct GenerateParameters {
         example = "null"
     )]
     pub seed: Option<u64>,
+
+    /// The number of highest probability vocabulary tokens to keep for top-n-filtering.
     #[serde(default)]
     #[schema(exclusive_minimum = 0, nullable = true, default = "null", example = 5)]
     pub top_n_tokens: Option<u32>,
+
+    /// Grammar constraints for the generation.
     #[serde(default)]
     #[schema(nullable = true, default = "null", example = "null")]
     pub grammar: Option<GrammarType>,
@@ -549,7 +589,9 @@ pub(crate) struct ChatCompletionChoice {
 #[derive(Clone, Debug, Deserialize, Serialize, ToSchema)]
 pub(crate) struct ChatCompletionDelta {
     #[schema(example = "user")]
-    pub role: String,
+    // TODO Modify this to a true enum.
+    #[serde(default, skip_serializing_if = "Option::is_none")]
+    pub role: Option<String>,
     #[serde(default, skip_serializing_if = "Option::is_none")]
     #[schema(example = "What is Deep Learning?")]
     pub content: Option<String>,
@@ -583,27 +625,40 @@ impl ChatCompletionChunk {
         logprobs: Option<ChatCompletionLogprobs>,
         finish_reason: Option<String>,
     ) -> Self {
-        Self {
-            id: String::new(),
-            object: "text_completion".to_string(),
-            created,
-            model,
-            system_fingerprint,
-            choices: vec![ChatCompletionChoice {
-                index: 0,
-                delta: ChatCompletionDelta {
-                    role: "assistant".to_string(),
-                    content: delta,
-                    tool_calls: tool_calls.map(|tc| DeltaToolCall {
+        let delta = match (delta, tool_calls) {
+            (Some(delta), _) => ChatCompletionDelta {
+                role: Some("assistant".to_string()),
+                content: Some(delta),
+                tool_calls: None,
+            },
+            (None, Some(tool_calls)) => ChatCompletionDelta {
+                role: Some("assistant".to_string()),
+                content: None,
+                tool_calls: Some(DeltaToolCall {
                     index: 0,
                     id: String::new(),
                     r#type: "function".to_string(),
                     function: Function {
                         name: None,
-                            arguments: tc[0].to_string(),
+                        arguments: tool_calls[0].to_string(),
                     },
                 }),
             },
+            (None, None) => ChatCompletionDelta {
+                role: None,
+                content: None,
+                tool_calls: None,
+            },
+        };
+        Self {
+            id: String::new(),
+            object: "text_completion".to_string(),
+            created,
+            model,
+            system_fingerprint,
+            choices: vec![ChatCompletionChoice {
+                index: 0,
+                delta,
                 logprobs,
                 finish_reason,
             }],
@@ -829,12 +884,75 @@ pub(crate) struct ToolCall {
     pub function: FunctionDefinition,
 }
 
-#[derive(Clone, Deserialize, ToSchema, Serialize)]
+#[derive(Clone, Deserialize, Serialize, ToSchema, Default, Debug)]
+pub(crate) struct Text {
+    #[serde(default)]
+    pub text: String,
+}
+
+#[derive(Clone, Deserialize, Serialize, ToSchema, Default, Debug)]
+pub(crate) struct ImageUrl {
+    #[serde(default)]
+    pub url: String,
+}
+
+#[derive(Clone, Deserialize, Serialize, ToSchema, Default, Debug)]
+pub(crate) struct Content {
+    pub r#type: String,
+    #[serde(default, skip_serializing_if = "Option::is_none")]
+    pub text: Option<String>,
+    #[serde(default, skip_serializing_if = "Option::is_none")]
+    pub image_url: Option<ImageUrl>,
+}
+
+mod message_content_serde {
+    use super::*;
+    use serde::de;
+    use serde::Deserializer;
+    use serde_json::Value;
+
+    pub fn deserialize<'de, D>(deserializer: D) -> Result<Option<String>, D::Error>
+    where
+        D: Deserializer<'de>,
+    {
+        let value = Value::deserialize(deserializer)?;
+        match value {
+            Value::String(s) => Ok(Some(s)),
+            Value::Array(arr) => {
+                let results: Result<Vec<String>, _> = arr
+                    .into_iter()
+                    .map(|v| {
+                        let content: Content =
+                            serde_json::from_value(v).map_err(de::Error::custom)?;
+                        match content.r#type.as_str() {
+                            "text" => Ok(content.text.unwrap_or_default()),
+                            "image_url" => {
+                                if let Some(url) = content.image_url {
+                                    Ok(format!("![]({})", url.url))
+                                } else {
+                                    Ok(String::new())
+                                }
+                            }
+                            _ => Err(de::Error::custom("invalid content type")),
+                        }
+                    })
+                    .collect();
+
+                results.map(|strings| Some(strings.join("")))
+            }
+            Value::Null => Ok(None),
+            _ => Err(de::Error::custom("invalid token format")),
+        }
+    }
+}
+
+#[derive(Clone, Deserialize, ToSchema, Serialize, Debug)]
 pub(crate) struct Message {
     #[schema(example = "user")]
     pub role: String,
     #[serde(skip_serializing_if = "Option::is_none")]
     #[schema(example = "My name is David and I")]
+    #[serde(deserialize_with = "message_content_serde::deserialize")]
     pub content: Option<String>,
     #[serde(default, skip_serializing_if = "Option::is_none")]
     #[schema(example = "\"David\"")]

router/src/main.rs

 use axum::http::HeaderValue;
 use clap::Parser;
 use hf_hub::api::tokio::{Api, ApiBuilder, ApiRepo};
-use hf_hub::{Repo, RepoType};
+use hf_hub::{Cache, Repo, RepoType};
 use opentelemetry::sdk::propagation::TraceContextPropagator;
 use opentelemetry::sdk::trace;
 use opentelemetry::sdk::trace::Sampler;
@@ -11,7 +11,7 @@ use opentelemetry_otlp::WithExportConfig;
 use std::fs::File;
 use std::io::BufReader;
 use std::net::{IpAddr, Ipv4Addr, SocketAddr};
-use std::path::Path;
+use std::path::{Path, PathBuf};
 use text_generation_client::{ClientError, ShardedClient};
 use text_generation_router::config::Config;
 use text_generation_router::{server, HubModelInfo, HubTokenizerConfig};
@@ -162,7 +162,6 @@ async fn main() -> Result<(), RouterError> {
     // Tokenizer instance
     // This will only be used to validate payloads
     let local_path = Path::new(&tokenizer_name);
-    let local_model = local_path.exists() && local_path.is_dir();
 
     // Shared API builder initialization
     let api_builder = || {
@@ -181,46 +180,83 @@ async fn main() -> Result<(), RouterError> {
     let use_api = revision.is_some() || !local_path.exists() || !local_path.is_dir();
 
     // Initialize API if needed
+    #[derive(Clone)]
+    enum Type {
+        Api(Api),
+        Cache(Cache),
+        None,
+    }
     let api = if use_api {
+        if std::env::var("HF_HUB_OFFLINE") == Ok("1".to_string()) {
+            let cache = Cache::default();
+            tracing::warn!("Offline mode active using cache defaults");
+            Type::Cache(cache)
+        } else {
             tracing::info!("Using the Hugging Face API");
             match api_builder().build() {
-            Ok(api) => Some(api),
+                Ok(api) => Type::Api(api),
                 Err(_) => {
                     tracing::warn!("Unable to build the Hugging Face API");
-                None
+                    Type::None
+                }
             }
         }
     } else {
-        None
+        Type::None
     };
 
     // Load tokenizer and model info
-    let (tokenizer, model_info, config) = if local_model {
-        let tokenizer = Tokenizer::from_file(local_path.join("tokenizer.json")).ok();
-        let model_info = HubModelInfo {
-            model_id: tokenizer_name.to_string(),
-            sha: None,
-            pipeline_tag: None,
-        };
-        let config: Option<Config> = std::fs::read_to_string(local_path.join("config.json"))
-            .ok()
-            .as_ref()
-            .and_then(|c| serde_json::from_str(c).ok());
-
-        (tokenizer, model_info, config)
-    } else if let Some(api) = api.clone() {
+    let (tokenizer_filename, config_filename, tokenizer_config_filename, model_info) = match api {
+        Type::None => (
+            Some(local_path.join("tokenizer.json")),
+            Some(local_path.join("config.json")),
+            Some(local_path.join("tokenizer_config.json")),
+            None,
+        ),
+        Type::Api(api) => {
             let api_repo = api.repo(Repo::with_revision(
                 tokenizer_name.to_string(),
                 RepoType::Model,
                 revision.clone().unwrap_or_else(|| "main".to_string()),
             ));
 
-        let tokenizer = match api_repo.get("tokenizer.json").await {
-            Ok(tokenizer_filename) => Tokenizer::from_file(tokenizer_filename).ok(),
+            let tokenizer_filename = match api_repo.get("tokenizer.json").await {
+                Ok(tokenizer_filename) => Some(tokenizer_filename),
                 Err(_) => get_base_tokenizer(&api, &api_repo).await,
             };
+            let config_filename = api_repo.get("config.json").await.ok();
+            let tokenizer_config_filename = api_repo.get("tokenizer_config.json").await.ok();
 
-        let config: Option<Config> = api_repo.get("config.json").await.ok().and_then(|filename| {
+            let model_info = if let Some(model_info) = get_model_info(&api_repo).await {
+                Some(model_info)
+            } else {
+                tracing::warn!("Could not retrieve model info from the Hugging Face hub.");
+                None
+            };
+            (
+                tokenizer_filename,
+                config_filename,
+                tokenizer_config_filename,
+                model_info,
+            )
+        }
+        Type::Cache(cache) => {
+            let repo = cache.repo(Repo::with_revision(
+                tokenizer_name.to_string(),
+                RepoType::Model,
+                revision.clone().unwrap_or_else(|| "main".to_string()),
+            ));
+            (
+                repo.get("tokenizer.json"),
+                repo.get("config.json"),
+                repo.get("tokenizer_config.json"),
+                None,
+            )
+        }
+    };
+    let tokenizer: Option<Tokenizer> =
+        tokenizer_filename.and_then(|filename| Tokenizer::from_file(filename).ok());
+    let config: Option<Config> = config_filename.and_then(|filename| {
         std::fs::read_to_string(filename)
             .ok()
             .as_ref()
@@ -232,58 +268,25 @@ async fn main() -> Result<(), RouterError> {
                 config.ok()
             })
     });
-
-        let model_info = get_model_info(&api_repo).await.unwrap_or_else(|| {
-            tracing::warn!("Could not retrieve model info from the Hugging Face hub.");
-            HubModelInfo {
+    let model_info = model_info.unwrap_or_else(|| HubModelInfo {
         model_id: tokenizer_name.to_string(),
         sha: None,
         pipeline_tag: None,
-            }
     });
 
-        (tokenizer, model_info, config)
+    // Read the JSON contents of the file as an instance of 'HubTokenizerConfig'.
+    let tokenizer_config: Option<HubTokenizerConfig> = if let Some(filename) = tokenizer_config_path
+    {
+        HubTokenizerConfig::from_file(filename)
     } else {
-        // No API and no local model
-        return Err(RouterError::ArgumentValidation(
-            "No local model found and no revision specified".to_string(),
-        ));
+        tokenizer_config_filename.and_then(HubTokenizerConfig::from_file)
     };
-
-    tracing::info!("Using config {config:?}");
-
-    // Load tokenizer config if found locally, or check if we can get it from the API if needed
-    let tokenizer_config = if let Some(path) = tokenizer_config_path {
-        tracing::info!("Using local tokenizer config from user specified path");
-        HubTokenizerConfig::from_file(&std::path::PathBuf::from(path))
-    } else if local_model {
-        tracing::info!("Using local tokenizer config");
-        HubTokenizerConfig::from_file(&local_path.join("tokenizer_config.json"))
-    } else {
-        match api {
-            Some(api) => {
-                tracing::info!("Using the Hugging Face API to retrieve tokenizer config");
-                let repo = Repo::with_revision(
-                    tokenizer_name.to_string(),
-                    RepoType::Model,
-                    revision.unwrap_or("main".to_string()),
-                );
-                get_tokenizer_config(&api.repo(repo))
-                    .await
-                    .unwrap_or_else(|| {
-                        tracing::warn!(
-                            "Could not retrieve tokenizer config from the Hugging Face hub."
-                        );
-                        HubTokenizerConfig::default()
-                    })
-            }
-            None => {
+    let tokenizer_config = tokenizer_config.unwrap_or_else(|| {
         tracing::warn!("Could not find tokenizer config locally and no API specified");
         HubTokenizerConfig::default()
-            }
-        }
-    };
+    });
 
+    tracing::info!("Using config {config:?}");
     if tokenizer.is_none() {
         tracing::warn!("Could not find a fast tokenizer implementation for {tokenizer_name}");
         tracing::warn!("Rust input length validation and truncation is disabled");
@@ -480,7 +483,7 @@ pub async fn get_model_info(api: &ApiRepo) -> Option<HubModelInfo> {
 }
 
 /// get base tokenizer
-pub async fn get_base_tokenizer(api: &Api, api_repo: &ApiRepo) -> Option<Tokenizer> {
+pub async fn get_base_tokenizer(api: &Api, api_repo: &ApiRepo) -> Option<PathBuf> {
     let config_filename = api_repo.get("config.json").await.ok()?;
 
     // Open the file in read-only mode with buffer.
@@ -497,8 +500,7 @@ pub async fn get_base_tokenizer(api: &Api, api_repo: &ApiRepo) -> Option<Tokeniz
             "main".to_string(),
         ));
 
-        let tokenizer_filename = api_base_repo.get("tokenizer.json").await.ok()?;
-        Tokenizer::from_file(tokenizer_filename).ok()
+        api_base_repo.get("tokenizer.json").await.ok()
     } else {
         None
     }

router/src/server.rs

@@ -1000,6 +1000,7 @@ async fn chat_completions(
         tools,
         tool_choice,
         tool_prompt,
+        temperature,
         ..
     } = req;
 
@@ -1008,6 +1009,11 @@ async fn chat_completions(
     let logprobs = logprobs.unwrap_or(false);
     let tool_prompt = tool_prompt.unwrap_or_default();
     let stop = stop.unwrap_or_default();
+    // enable greedy only when temperature is 0
+    let (do_sample, temperature) = match temperature {
+        Some(temperature) if temperature == 0.0 => (false, None),
+        other => (true, other),
+    };
 
     // extract tool grammar if present
     let tool_grammar = match ToolGrammar::apply(tools, tool_choice) {
@@ -1054,13 +1060,13 @@ async fn chat_completions(
         inputs: inputs.to_string(),
         parameters: GenerateParameters {
             best_of: None,
-            temperature: req.temperature,
+            temperature,
             repetition_penalty,
             frequency_penalty: req.frequency_penalty,
             top_k: None,
             top_p: req.top_p,
             typical_p: None,
-            do_sample: true,
+            do_sample,
             max_new_tokens,
             return_full_text: None,
             stop,
@@ -1097,7 +1103,13 @@ async fn chat_completions(
             let (content, tool_calls) = if tool_grammar.is_some() {
                 (None, Some(vec![stream_token.token.text]))
             } else {
-                (Some(stream_token.token.text), None)
+                let content = if !stream_token.token.special {
+                    Some(stream_token.token.text)
+                } else {
+                    None
+                };
+
+                (content, None)
             };
 
             event

router/src/validation.rs

@@ -540,7 +540,57 @@ fn prepare_input(
             inputs = modified_inputs;
             tokenizer_query
         }
-        Some(Config::Idefics) => RE.replace_all(&inputs, "<image>").into(),
+        Some(Config::Idefics2(config)) => {
+            let mut modified_inputs = String::with_capacity(inputs.len());
+            let mut tokenizer_query = String::with_capacity(inputs.len());
+            let mut start = 0;
+            for chunk in RE.find_iter(&inputs) {
+                let chunk_start = chunk.start();
+                let chunk_end = chunk.end();
+                if chunk_start != start {
+                    modified_inputs.push_str(&inputs[start..chunk_start]);
+                    tokenizer_query.push_str(&inputs[start..chunk_start]);
+                }
+                let (image_uri, height, width) = fetch_image(&inputs[chunk_start..chunk_end])?;
+                let slots = config.get_number_of_features(height, width);
+                tokenizer_query.push_str("<fake_token_around_image>");
+                tokenizer_query.push_str(&"<image>".repeat(slots));
+                tokenizer_query.push_str("<fake_token_around_image>");
+
+                modified_inputs.push_str(&image_uri);
+                start = chunk_end;
+            }
+            if start != inputs.len() - 1 {
+                modified_inputs.push_str(&inputs[start..]);
+                tokenizer_query.push_str(&inputs[start..]);
+            }
+            inputs = modified_inputs;
+            tokenizer_query
+        }
+        Some(Config::Idefics) => {
+            let mut modified_inputs = String::with_capacity(inputs.len());
+            let mut tokenizer_query = String::with_capacity(inputs.len());
+            let mut start = 0;
+            for chunk in RE.find_iter(&inputs) {
+                let chunk_start = chunk.start();
+                let chunk_end = chunk.end();
+                if chunk_start != start {
+                    modified_inputs.push_str(&inputs[start..chunk_start]);
+                    tokenizer_query.push_str(&inputs[start..chunk_start]);
+                }
+                let (image_uri, _height, _width) = fetch_image(&inputs[chunk_start..chunk_end])?;
+                let slots = 1;
+                tokenizer_query.push_str(&"<image>".repeat(slots));
+                modified_inputs.push_str(&image_uri);
+                start = chunk_end;
+            }
+            if start != inputs.len() - 1 {
+                modified_inputs.push_str(&inputs[start..]);
+                tokenizer_query.push_str(&inputs[start..]);
+            }
+            inputs = modified_inputs;
+            tokenizer_query
+        }
         _ => inputs.clone(),
     };
 

server/Makefile-vllm

 vllm-cuda:
     # Clone vllm
 	pip install -U ninja packaging --no-cache-dir
-	git clone https://github.com/OlivierDehaene/vllm.git vllm
+	git clone https://github.com/Narsil/vllm.git vllm
 
 build-vllm-cuda: vllm-cuda
-	cd vllm && git fetch && git checkout 4bec8cee87f6bb8cebaec297029713cd2082e0b2
+	cd vllm && git fetch && git checkout b5dfc61db88a81069e45b44f7cc99bd9e62a60fa
 	cd vllm && python setup.py build
 
 install-vllm-cuda: build-vllm-cuda

server/pyproject.toml

 [tool.poetry]
 name = "text-generation-server"
-version = "2.0.1"
+version = "2.0.2"
 description = "Text Generation Inference Python gRPC Server"
 authors = ["Olivier Dehaene <olivier@huggingface.co>"]
 
@@ -31,10 +31,12 @@ einops = "^0.6.1"
 texttable = { version = "^1.6.7", optional = true }
 datasets = { version = "^2.14.0", optional = true }
 peft = { version = "^0.10", optional = true }
-torch = { version = "^2.1.1", optional = true }
+torch = { version = "^2.3.0", optional = true }
 scipy = "^1.11.1"
 pillow = "^10.0.0"
 outlines= { version = "^0.0.36", optional = true }
+prometheus-client = "^0.20.0"
+py-cpuinfo = "^9.0.0"
 
 [tool.poetry.extras]
 torch = ["torch"]

server/requirements_cuda.txt

@@ -5,13 +5,13 @@ click==8.1.7 ; python_version >= "3.9" and python_version < "3.13"
 colorama==0.4.6 ; python_version >= "3.9" and python_version < "3.13" and (sys_platform == "win32" or platform_system == "Windows")
 deprecated==1.2.14 ; python_version >= "3.9" and python_version < "3.13"
 einops==0.6.1 ; python_version >= "3.9" and python_version < "3.13"
-filelock==3.13.4 ; python_version >= "3.9" and python_version < "3.13"
-fsspec==2024.2.0 ; python_version >= "3.9" and python_version < "3.13"
+filelock==3.14.0 ; python_version >= "3.9" and python_version < "3.13"
+fsspec==2024.3.1 ; python_version >= "3.9" and python_version < "3.13"
 googleapis-common-protos==1.63.0 ; python_version >= "3.9" and python_version < "3.13"
 grpc-interceptor==0.15.4 ; python_version >= "3.9" and python_version < "3.13"
-grpcio-reflection==1.62.1 ; python_version >= "3.9" and python_version < "3.13"
-grpcio-status==1.62.1 ; python_version >= "3.9" and python_version < "3.13"
-grpcio==1.62.1 ; python_version >= "3.9" and python_version < "3.13"
+grpcio-reflection==1.62.2 ; python_version >= "3.9" and python_version < "3.13"
+grpcio-status==1.62.2 ; python_version >= "3.9" and python_version < "3.13"
+grpcio==1.62.2 ; python_version >= "3.9" and python_version < "3.13"
 hf-transfer==0.1.6 ; python_version >= "3.9" and python_version < "3.13"
 huggingface-hub==0.19.4 ; python_version >= "3.9" and python_version < "3.13"
 idna==3.7 ; python_version >= "3.9" and python_version < "3.13"
@@ -28,9 +28,11 @@ opentelemetry-sdk==1.15.0 ; python_version >= "3.9" and python_version < "3.13"
 opentelemetry-semantic-conventions==0.36b0 ; python_version >= "3.9" and python_version < "3.13"
 packaging==24.0 ; python_version >= "3.9" and python_version < "3.13"
 pillow==10.3.0 ; python_version >= "3.9" and python_version < "3.13"
+prometheus-client==0.20.0 ; python_version >= "3.9" and python_version < "3.13"
 protobuf==4.25.3 ; python_version >= "3.9" and python_version < "3.13"
+py-cpuinfo==9.0.0 ; python_version >= "3.9" and python_version < "3.13"
 pyyaml==6.0.1 ; python_version >= "3.9" and python_version < "3.13"
-regex==2024.4.16 ; python_version >= "3.9" and python_version < "3.13"
+regex==2024.4.28 ; python_version >= "3.9" and python_version < "3.13"
 requests==2.31.0 ; python_version >= "3.9" and python_version < "3.13"
 safetensors==0.4.3 ; python_version >= "3.9" and python_version < "3.13"
 scipy==1.13.0 ; python_version >= "3.9" and python_version < "3.13"
@@ -38,7 +40,7 @@ sentencepiece==0.1.99 ; python_version >= "3.9" and python_version < "3.13"
 setuptools==69.5.1 ; python_version >= "3.9" and python_version < "3.13"
 tokenizers==0.19.1 ; python_version >= "3.9" and python_version < "3.13"
 tqdm==4.66.2 ; python_version >= "3.9" and python_version < "3.13"
-transformers==4.40.0 ; python_version >= "3.9" and python_version < "3.13"
+transformers==4.40.1 ; python_version >= "3.9" and python_version < "3.13"
 typer==0.6.1 ; python_version >= "3.9" and python_version < "3.13"
 typing-extensions==4.11.0 ; python_version >= "3.9" and python_version < "3.13"
 urllib3==2.2.1 ; python_version >= "3.9" and python_version < "3.13"

server/requirements_rocm.txt

@@ -5,13 +5,13 @@ click==8.1.7 ; python_version >= "3.9" and python_version < "3.13"
 colorama==0.4.6 ; python_version >= "3.9" and python_version < "3.13" and (sys_platform == "win32" or platform_system == "Windows")
 deprecated==1.2.14 ; python_version >= "3.9" and python_version < "3.13"
 einops==0.6.1 ; python_version >= "3.9" and python_version < "3.13"
-filelock==3.13.4 ; python_version >= "3.9" and python_version < "3.13"
-fsspec==2024.2.0 ; python_version >= "3.9" and python_version < "3.13"
+filelock==3.14.0 ; python_version >= "3.9" and python_version < "3.13"
+fsspec==2024.3.1 ; python_version >= "3.9" and python_version < "3.13"
 googleapis-common-protos==1.63.0 ; python_version >= "3.9" and python_version < "3.13"
 grpc-interceptor==0.15.4 ; python_version >= "3.9" and python_version < "3.13"
-grpcio-reflection==1.62.1 ; python_version >= "3.9" and python_version < "3.13"
-grpcio-status==1.62.1 ; python_version >= "3.9" and python_version < "3.13"
-grpcio==1.62.1 ; python_version >= "3.9" and python_version < "3.13"
+grpcio-reflection==1.62.2 ; python_version >= "3.9" and python_version < "3.13"
+grpcio-status==1.62.2 ; python_version >= "3.9" and python_version < "3.13"
+grpcio==1.62.2 ; python_version >= "3.9" and python_version < "3.13"
 hf-transfer==0.1.6 ; python_version >= "3.9" and python_version < "3.13"
 huggingface-hub==0.19.4 ; python_version >= "3.9" and python_version < "3.13"
 idna==3.7 ; python_version >= "3.9" and python_version < "3.13"
@@ -28,9 +28,11 @@ opentelemetry-sdk==1.15.0 ; python_version >= "3.9" and python_version < "3.13"
 opentelemetry-semantic-conventions==0.36b0 ; python_version >= "3.9" and python_version < "3.13"
 packaging==24.0 ; python_version >= "3.9" and python_version < "3.13"
 pillow==10.3.0 ; python_version >= "3.9" and python_version < "3.13"
+prometheus-client==0.20.0 ; python_version >= "3.9" and python_version < "3.13"
 protobuf==4.25.3 ; python_version >= "3.9" and python_version < "3.13"
+py-cpuinfo==9.0.0 ; python_version >= "3.9" and python_version < "3.13"
 pyyaml==6.0.1 ; python_version >= "3.9" and python_version < "3.13"
-regex==2024.4.16 ; python_version >= "3.9" and python_version < "3.13"
+regex==2024.4.28 ; python_version >= "3.9" and python_version < "3.13"
 requests==2.31.0 ; python_version >= "3.9" and python_version < "3.13"
 safetensors==0.4.3 ; python_version >= "3.9" and python_version < "3.13"
 scipy==1.13.0 ; python_version >= "3.9" and python_version < "3.13"
@@ -38,7 +40,7 @@ sentencepiece==0.1.99 ; python_version >= "3.9" and python_version < "3.13"
 setuptools==69.5.1 ; python_version >= "3.9" and python_version < "3.13"
 tokenizers==0.19.1 ; python_version >= "3.9" and python_version < "3.13"
 tqdm==4.66.2 ; python_version >= "3.9" and python_version < "3.13"
-transformers==4.40.0 ; python_version >= "3.9" and python_version < "3.13"
+transformers==4.40.1 ; python_version >= "3.9" and python_version < "3.13"
 typer==0.6.1 ; python_version >= "3.9" and python_version < "3.13"
 typing-extensions==4.11.0 ; python_version >= "3.9" and python_version < "3.13"
 urllib3==2.2.1 ; python_version >= "3.9" and python_version < "3.13"

server/text_generation_server/models/__init__.py

@@ -68,6 +68,7 @@ try:
     )
     from text_generation_server.models.idefics import IDEFICSSharded
     from text_generation_server.models.llava_next import LlavaNext
+    from text_generation_server.models.idefics2 import Idefics2
     from text_generation_server.models.flash_mistral import FlashMistral
     # from text_generation_server.models.flash_mixtral import FlashMixtral
     from text_generation_server.models.flash_phi import FlashPhi
@@ -327,7 +328,7 @@ def get_model(
                 trust_remote_code=trust_remote_code,
             )
 
-    elif model_type == "llama" or model_type == "baichuan":
+    elif model_type == "llama" or model_type == "baichuan" or model_type == "phi3":
         if FLASH_ATTENTION:
             return FlashLlama(
                 model_id,
@@ -579,6 +580,18 @@ def get_model(
             )
         else:
             raise NotImplementedError(FLASH_ATT_ERROR_MESSAGE.format("Idefics"))
+    if model_type == "idefics2":
+        if FLASH_ATTENTION:
+            return Idefics2(
+                model_id,
+                revision,
+                quantize=quantize,
+                use_medusa=use_medusa,
+                dtype=dtype,
+                trust_remote_code=trust_remote_code,
+            )
+        else:
+            raise NotImplementedError(FLASH_ATT_ERROR_MESSAGE.format("Idefics"))
 
     if model_type == "llava_next":
         if FLASH_ATTENTION: