feat: fix token padding, enable warmup and process basic request

router/src/config.rs

@@ -138,10 +138,39 @@ impl Paligemma {
     }
 }
 
+#[derive(Clone, Debug, Serialize, Deserialize)]
+#[serde(rename_all = "snake_case")]
+pub struct Qwen2VlVisionConfig {
+    pub(crate) depth: usize,
+    pub(crate) embed_dim: usize,
+    pub(crate) mlp_ratio: usize,
+    pub(crate) num_heads: usize,
+    pub(crate) in_chans: usize,
+    pub(crate) hidden_size: usize,
+    pub(crate) patch_size: usize,
+    pub(crate) spatial_merge_size: usize,
+    pub(crate) spatial_patch_size: usize,
+    pub(crate) temporal_patch_size: usize,
+}
+
+#[derive(Clone, Debug, Serialize, Deserialize)]
+#[serde(rename_all = "snake_case")]
+pub struct Qwen2Vl {
+    pub(crate) vision_config: Qwen2VlVisionConfig,
+}
+
+impl Qwen2Vl {
+    pub fn get_number_of_features(&self, _height: usize, _width: usize) -> usize {
+        // TODO: calculate number of features
+        6000 / 4
+    }
+}
+
 #[derive(Clone, Debug, Serialize, Deserialize)]
 #[serde(tag = "model_type")]
 #[serde(rename_all = "snake_case")]
 pub enum Config {
+    Qwen2Vl(Qwen2Vl),
     LlavaNext(LlavaNext),
     ClipVisionModel(ClipVisionModel),
     Mistral,

router/src/validation.rs

@@ -594,6 +594,10 @@ fn image_tokens(
         }
         Paligemma(config) => "<image>".repeat(config.get_number_of_features(height, width)),
         LlavaNext(config) => "<image>".repeat(config.get_number_of_features(height, width)),
+        Qwen2Vl(config) => format!(
+            "<|vision_start|>{:?}<|vision_end|>",
+            "<|image_pad|>".repeat(config.get_number_of_features(height, width))
+        ),
         _ => unimplemented!("Images tokens are not supported for this model configuration"),
     }
 }
@@ -620,7 +624,9 @@ fn prepare_input<T: TokenizerTrait>(
     use Config::*;
     static RE: Lazy<Regex> = Lazy::new(|| Regex::new(r"!\[\]\([^\)]*\)").unwrap());
     let (tokenizer_query, input_chunks) = match config {
-        Some(config @ (Idefics | Mllama | Idefics2(_) | Paligemma(_) | LlavaNext(_))) => {
+        Some(
+            config @ (Idefics | Mllama | Idefics2(_) | Paligemma(_) | LlavaNext(_) | Qwen2Vl(_)),
+        ) => {
             let mut input_chunks = Vec::new();
             let mut tokenizer_query = String::with_capacity(inputs.len());
             let mut start = 0;

server/text_generation_server/models/custom_modeling/flash_qwen2_modeling.py

@@ -348,6 +348,10 @@ class Qwen2Model(torch.nn.Module):
         # Get rotary cos and sin for this forward
         # Avoid to index in each layer
         # TODO: fix how N-D position_ids are handled
+
+        if position_ids.dim() == 2:
+            position_ids = position_ids.unsqueeze(0)
+
         cos, sin = self.layers[0].self_attn.rotary_emb.get_cos_sin_hack(
             position_ids, true_max_s, hidden_states.dtype
         )

server/text_generation_server/models/custom_modeling/qwen2_vl.py

@@ -34,6 +34,7 @@ from text_generation_server.layers.layernorm import (
 )
 from text_generation_server.layers import (
     TensorParallelColumnLinear,
+    TensorParallelRowLinear,
     FastLinear,
 )
 from text_generation_server.layers.attention import (
@@ -352,6 +353,7 @@ class Qwen2VisionModel(nn.Module):
 class Qwen2VLForConditionalGeneration(nn.Module):
     def __init__(self, prefix, config, weights):
         super().__init__()
+        self.config = config
         config.vision_config.quantize = None
         config.vision_config.speculator = config.speculator
         self.hidden_size = config.hidden_size
@@ -364,6 +366,10 @@ class Qwen2VLForConditionalGeneration(nn.Module):
             prefix="visual", config=config.vision_config, weights=weights
         )
         self.text_model = Qwen2Model(prefix=None, config=config, weights=weights)
+        self.lm_head = FastLinear.load(
+            prefix="lm_head", weights=weights, config=config, bias=False
+        )
+        self.device = weights.device
 
     def forward(
         self,
@@ -386,10 +392,10 @@ class Qwen2VLForConditionalGeneration(nn.Module):
         cross_attention_states: Optional[torch.Tensor] = None,
         image_indices=None,
     ):
-
-        # make an attention_mask that is the same size as the input_ids
-        attention_mask = torch.ones_like(input_ids, dtype=torch.bool)
-
+        # make an attention_mask that is (batch_size, sequence_length)
+        attention_mask = torch.ones_like(
+            input_ids, dtype=torch.bool, device=input_ids.device
+        )
         inputs_embeds = self.text_model.embed_tokens(input_ids)
 
         # apply the visual model to the pixel values if they are provided
@@ -525,7 +531,6 @@ class Qwen2VLForConditionalGeneration(nn.Module):
                 mrope_position_deltas, device=input_ids.device
             ).unsqueeze(1)
 
-        # TODO: adjust model to accept 2D position_ids
         outputs = self.text_model(
             input_ids=input_ids,
             position_ids=position_ids,
@@ -541,4 +546,5 @@ class Qwen2VLForConditionalGeneration(nn.Module):
             attention_mask=attention_mask,
         )
 
-        return outputs, None
+        logits = self.lm_head(outputs)
+        return logits, None

server/text_generation_server/models/vlm_causal_lm.py

@@ -68,7 +68,9 @@ def image_text_replacement(processor, image_input, config, image_id: int) -> str
     elif config.model_type == "paligemma":
         return "<image>" * config.text_config.num_image_tokens
     elif config.model_type == "qwen2_vl":
-        return "<image>"
+        num_pads = image_input.pixel_values.shape[0] // 4
+        padding = "<|image_pad|>" * num_pads
+        return f"<|vision_start|>{padding}<|vision_end|>"
     else:
         raise RuntimeError(f"Unknown config {config.model_type} for multimodal")
 
@@ -183,7 +185,8 @@ class VlmCausalLMBatch(FlashCausalLMBatch):
                     raise RuntimeError(f"Invalid chunk type {chunk_type}")
 
         if images:
-            # TODO: REMOVE (this is for debugging purposes)
+            if images[0][0].width <= 20:
+                # TODO: provide a better way to handle the issue of the prefill image being too small
                 images = images[0][0].resize(
                     (images[0][0].width * 2, images[0][0].height * 2)
                 )