Support qwen2 vl

docs/source/supported_models.md

@@ -24,6 +24,7 @@ Text Generation Inference enables serving optimized models. The following sectio
 - [Falcon](https://huggingface.co/tiiuae/falcon-7b-instruct)
 - [StarCoder 2](https://huggingface.co/bigcode/starcoder2-15b-instruct-v0.1)
 - [Qwen 2](https://huggingface.co/collections/Qwen/qwen2-6659360b33528ced941e557f)
+- [Qwen 2 VL](https://huggingface.co/collections/Qwen/qwen2-vl-66cee7455501d7126940800d)
 - [Opt](https://huggingface.co/facebook/opt-6.7b)
 - [T5](https://huggingface.co/google/flan-t5-xxl)
 - [Galactica](https://huggingface.co/facebook/galactica-120b)

integration-tests/models/__snapshots__/test_flash_qwen2_vl/test_flash_qwen2_vl_simple.json

@@ -0,0 +1,26 @@
+{
+  "choices": [
+    {
+      "finish_reason": "stop",
+      "index": 0,
+      "logprobs": null,
+      "message": {
+        "content": "The image depicts an anthropomorphic rabbit, wearing a futuristic spacesuit, in an extraterrestrial environment. The setting appears to be a red planet resembling Mars, with rugged terrain and rocky formations in the background. The moon is visible in the distant sky, adding to the lunar landscape.",
+        "name": null,
+        "role": "assistant",
+        "tool_calls": null
+      },
+      "usage": null
+    }
+  ],
+  "created": 1730164250,
+  "id": "",
+  "model": "Qwen/Qwen2-VL-7B-Instruct",
+  "object": "chat.completion",
+  "system_fingerprint": "2.4.1-dev0-native",
+  "usage": {
+    "completion_tokens": 58,
+    "prompt_tokens": 349,
+    "total_tokens": 407
+  }
+}

integration-tests/models/test_flash_qwen2_vl.py

@@ -0,0 +1,42 @@
+import pytest
+
+
+@pytest.fixture(scope="module")
+def flash_qwen2_vl_handle(launcher):
+    with launcher("Qwen/Qwen2-VL-7B-Instruct", cuda_graphs=[0]) as handle:
+        yield handle
+
+
+@pytest.fixture(scope="module")
+async def flash_qwen2(flash_qwen2_vl_handle):
+    await flash_qwen2_vl_handle.health(300)
+    return flash_qwen2_vl_handle.client
+
+
+@pytest.mark.private
+async def test_flash_qwen2_vl_simple(flash_qwen2, response_snapshot):
+    response = await flash_qwen2.chat(
+        max_tokens=100,
+        seed=42,
+        messages=[
+            {
+                "role": "user",
+                "content": [
+                    {
+                        "type": "image_url",
+                        "image_url": {
+                            "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/rabbit.png"
+                        },
+                    },
+                    {"type": "text", "text": "Describe this image."},
+                ],
+            },
+        ],
+    )
+
+    assert (
+        response.choices[0].message.content
+        == "The image depicts an anthropomorphic rabbit, wearing a futuristic spacesuit, in an extraterrestrial environment. The setting appears to be a red planet resembling Mars, with rugged terrain and rocky formations in the background. The moon is visible in the distant sky, adding to the lunar landscape."
+    )
+
+    assert response == response_snapshot

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 {
+        let num_pixels = height * width;
+        num_pixels / self.vision_config.patch_size.pow(2)
+    }
+}
+
 #[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/layers/rotary.py

@@ -89,6 +89,8 @@ def static(cls, config, dim, base, device):
 
             if rope_type == "linear":
                 pass
+            elif rope_type == "default":
+                pass
             elif rope_type == "dynamic":
                 scaling_factor = rope_scaling["factor"]
                 return DynamicPositionRotaryEmbedding(

server/text_generation_server/models/__init__.py

@@ -146,6 +146,9 @@
     from text_generation_server.models.custom_modeling.idefics2 import (
         Idefics2ForConditionalGeneration,
     )
+    from text_generation_server.models.custom_modeling.qwen2_vl import (
+        Qwen2VLForConditionalGeneration,
+    )
     from text_generation_server.layers.attention import SUPPORTS_WINDOWING
 except ImportError as e:
     log_master(logger.warning, f"Could not import Flash Attention enabled models: {e}")
@@ -275,6 +278,11 @@ class ModelType(enum.Enum):
         "name": "Qwen 2",
         "url": "https://huggingface.co/collections/Qwen/qwen2-6659360b33528ced941e557f",
     }
+    QWEN2_VL = {
+        "type": "qwen2_vl",
+        "name": "Qwen 2 VL",
+        "url": "https://huggingface.co/collections/Qwen/qwen2-vl-66cee7455501d7126940800d",
+    }
     OPT = {
         "type": "opt",
         "name": "Opt",
@@ -1193,6 +1201,18 @@ def get_model(
             )
         else:
             raise NotImplementedError(FLASH_ATT_ERROR_MESSAGE.format("Idefics"))
+    if model_type == QWEN2_VL:
+        return VlmCausalLM(
+            model_id=model_id,
+            model_class=Qwen2VLForConditionalGeneration,
+            revision=revision,
+            quantize=quantize,
+            speculator=speculator,
+            dtype=dtype,
+            kv_cache_dtype=kv_cache_dtype,
+            trust_remote_code=trust_remote_code,
+            lora_adapter_ids=lora_adapter_ids,
+        )
     if model_type == MLLAMA:
         if FLASH_ATTENTION:
             return MllamaCausalLM(

server/text_generation_server/models/custom_modeling/flash_pali_gemma_modeling.py

@@ -80,6 +80,7 @@ def forward(
         pixel_attention_mask: Optional[torch.BoolTensor] = None,
         image_sizes: Optional[torch.Tensor] = None,
         adapter_data: Optional[torch.Tensor] = None,
+        image_grid_thw: Optional[torch.LongTensor] = None,
     ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
         inputs_embeds = self.text_model.embed_tokens(input_ids)
         # TODO This is odd but apparently pali gemma position ids start at 1.

server/text_generation_server/models/custom_modeling/flash_qwen2_modeling.py

@@ -61,6 +61,11 @@ def __init__(
             config.sliding_window if config.sliding_window is not None else -1
         )
         self.num_heads = config.num_attention_heads
+        self.mrope_section = (
+            config.rope_scaling.get("mrope_section", None)
+            if config.rope_scaling is not None
+            else None
+        )
         self.hidden_size = config.hidden_size
         self.head_size = self.hidden_size // self.num_heads
 
@@ -122,6 +127,17 @@ def forward(
         query = query.view(-1, self.num_heads, self.head_size)
         kv = kv.view(-1, 2, self.num_key_value_heads, self.head_size)
 
+        if self.mrope_section is not None:
+            # if mrope_section is set, we need to split the cos and sin into 3 parts and concatenate them in a specific order
+            cos = torch.cat(
+                [m[i % 3] for i, m in enumerate(cos.split(self.mrope_section, dim=-1))],
+                dim=-1,
+            )
+            sin = torch.cat(
+                [m[i % 3] for i, m in enumerate(sin.split(self.mrope_section, dim=-1))],
+                dim=-1,
+            )
+
         self.rotary_emb(query, torch.select(kv, dim=1, index=0), cos, sin)
 
         if prefill_cache_indices is not None:
@@ -270,9 +286,6 @@ def __init__(self, prefix: str, config, weights):
         process_group = weights.process_group
         self.tp_rank = process_group.rank()
         self.tp_world_size = process_group.size()
-        self.embed_tokens = TensorParallelEmbedding(
-            prefix=f"{prefix}.embed_tokens", weights=weights
-        )
         self.layers = nn.ModuleList(
             [
                 Qwen2Layer(
@@ -296,7 +309,7 @@ def __init__(self, prefix: str, config, weights):
 
     def forward(
         self,
-        input_ids: torch.Tensor,
+        inputs_embeds: torch.Tensor,
         position_ids: torch.Tensor,
         cu_seqlen_prefill: Optional[torch.Tensor],
         kv_cache: List[Tuple[torch.Tensor, torch.Tensor]],
@@ -307,13 +320,16 @@ def forward(
         true_max_s: int,
         prefill_cache_indices: Optional[torch.Tensor],
     ) -> torch.Tensor:
-        hidden_states = self.embed_tokens(input_ids)
+        hidden_states = inputs_embeds
 
-        # Get rotary cos and sin for this forward
-        # Avoid to index in each layer
+        # flatten position ids from 2D to 1D
         cos, sin = self.layers[0].self_attn.rotary_emb.get_cos_sin(
-            position_ids, true_max_s, hidden_states.dtype
+            position_ids.flatten(), true_max_s, hidden_states.dtype
         )
+        # reshape back to 2D if the position_ids were 2D
+        if position_ids.size(0) != cos.size(0):
+            cos = cos.view(position_ids.size(0), position_ids.size(-1), -1).unsqueeze(2)
+            sin = sin.view(position_ids.size(0), position_ids.size(-1), -1).unsqueeze(2)
 
         residual = None
         for i, layer in enumerate(self.layers):
@@ -352,6 +368,12 @@ def __init__(self, prefix: str, config, weights):
             prefix=f"{prefix}.{suffix}" if prefix else suffix,
             weights=weights,
         )
+
+        self.embed_tokens = TensorParallelEmbedding(
+            prefix=f"{prefix}.embed_tokens" if prefix else "model.embed_tokens",
+            weights=weights,
+        )
+
         self.max_past = config.sliding_window
         self.max_past_tensor = (
             torch.tensor(config.sliding_window, device=weights.device)
@@ -382,8 +404,10 @@ def forward(
             # kernel requires the true values
             seqlen = seqlen.clamp(max=self.max_past_tensor)
 
+        inputs_embeds = self.embed_tokens(input_ids)
+
         hidden_states = self.model(
-            input_ids,
+            inputs_embeds,
             position_ids,
             cu_seqlen_prefill,
             kv_cache,

server/text_generation_server/models/custom_modeling/idefics2.py

@@ -750,6 +750,7 @@ def forward(
         # Unused here
         image_sizes: Optional[torch.Tensor] = None,
         adapter_data: Optional[torch.Tensor] = None,
+        image_grid_thw: Optional[torch.LongTensor] = None,
     ):
         inputs_embeds = self.text_model.embed_tokens(input_ids)
         if pixel_values is not None:

server/text_generation_server/models/custom_modeling/llava_next.py

@@ -180,6 +180,7 @@ def forward(
         pixel_attention_mask=None,
         image_sizes: Optional[torch.LongTensor] = None,
         adapter_data: Optional[torch.Tensor] = None,
+        image_grid_thw: Optional[torch.LongTensor] = None,
     ):
         inputs_embeds = self.text_model.embed_tokens(input_ids)
         if pixel_values is not None and len(pixel_values) > 0: