Support flashinfer for Gemma3 prefill

integration-tests/models/__snapshots__/test_flash_gemma3/test_flash_gemma3_image_base64_rgb_png.json

@@ -1,26 +1,26 @@
 {
   "choices": [
     {
-      "finish_reason": "stop",
+      "finish_reason": "length",
       "index": 0,
       "logprobs": null,
       "message": {
-        "content": "Okay, let's analyze the image. \n\nThe image is entirely white, with a very subtle, faint outline of a stylized, cartoonish figure. It appears to be a simplified depiction of a person, likely a child, with a wide-eyed expression and a small, rounded body. \n\nIt's almost like a minimalist, iconic representation. \n\nDo you want me to try and describe it in more detail or perhaps speculate about the context of the image?",
+        "content": "Okay, let's analyze the image. \n\nThe image is entirely white, with a very subtle, faint outline of a stylized, cartoonish figure. It appears to be a simplified depiction of a person, likely a child, with a wide-eyed expression and a small, rounded body. \n\nIt's almost like a minimalist, iconic representation. \n\nDo you want me to try and describe it in more detail, or perhaps suggest what this image might represent (e.g",
         "name": null,
         "role": "assistant",
         "tool_calls": null
       },
       "usage": null
     }
   ],
-  "created": 1741965892,
+  "created": 1744396706,
   "id": "",
   "model": "google/gemma-3-4b-it",
   "object": "chat.completion",
-  "system_fingerprint": "3.2.1-dev0-native",
+  "system_fingerprint": "3.2.3-dev0-native",
   "usage": {
-    "completion_tokens": 98,
+    "completion_tokens": 100,
     "prompt_tokens": 277,
-    "total_tokens": 375
+    "total_tokens": 377
   }
 }

integration-tests/models/__snapshots__/test_flash_gemma3/test_flash_gemma3_image_base64_rgba.json

@@ -5,22 +5,22 @@
       "index": 0,
       "logprobs": null,
       "message": {
-        "content": "Okay, let's analyze the image. \n\nThe transparent image reveals a stylized depiction of **a human head**. It's a minimalist, geometric representation, showing the basic shapes of the skull, eye sockets, and head outline. \n\nDo you want me to describe any specific element of the image in more detail?",
+        "content": "Okay, let's analyze the image. \n\nThe transparent image reveals a stylized depiction of **a human head**. It's a minimalist, geometric representation, showing the basic shapes of the skull, eye sockets, and head outline. \n\nIf you'd like, you can give me more details about the image or ask me to focus on a specific aspect of it.",
         "name": null,
         "role": "assistant",
         "tool_calls": null
       },
       "usage": null
     }
   ],
-  "created": 1741966313,
+  "created": 1744396703,
   "id": "",
   "model": "google/gemma-3-4b-it",
   "object": "chat.completion",
-  "system_fingerprint": "3.2.1-dev0-native",
+  "system_fingerprint": "3.2.3-dev0-native",
   "usage": {
-    "completion_tokens": 67,
+    "completion_tokens": 78,
     "prompt_tokens": 277,
-    "total_tokens": 344
+    "total_tokens": 355
   }
 }

integration-tests/models/__snapshots__/test_flash_gemma3/test_flash_gemma3_image_cow.json

@@ -13,11 +13,11 @@
       "usage": null
     }
   ],
-  "created": 1741964480,
+  "created": 1744396699,
   "id": "",
   "model": "google/gemma-3-4b-it",
   "object": "chat.completion",
-  "system_fingerprint": "3.2.1-dev0-native",
+  "system_fingerprint": "3.2.3-dev0-native",
   "usage": {
     "completion_tokens": 74,
     "prompt_tokens": 275,

integration-tests/models/__snapshots__/test_flash_gemma3/test_flash_gemma3_image_cow_dog.json

@@ -13,11 +13,11 @@
       "usage": null
     }
   ],
-  "created": 1741964477,
+  "created": 1744396697,
   "id": "",
   "model": "google/gemma-3-4b-it",
   "object": "chat.completion",
-  "system_fingerprint": "3.2.1-dev0-native",
+  "system_fingerprint": "3.2.3-dev0-native",
   "usage": {
     "completion_tokens": 75,
     "prompt_tokens": 279,

launcher/src/main.rs

@@ -260,11 +260,22 @@ struct Config {
 
 impl Config {
     fn get_head_dim(&self) -> Option<usize> {
-        self.head_dim.or_else(|| {
-            self.text_config
-                .as_ref()
-                .and_then(|text_config| text_config.head_dim)
-        })
+        if let Some(head_dim) = self.head_dim {
+            return Some(head_dim);
+        }
+
+        let text_config = self.text_config.as_ref()?;
+        if let Some(head_size) = text_config.head_dim {
+            return Some(head_size);
+        }
+
+        match self.model_type.as_deref() {
+            // We special-case gemma3 here, since we need flashinfer for
+            // handling bidirectional masks. And flashinfer can only be
+            // used when the head size is known.
+            Some("gemma3") => Some(256),
+            _ => None,
+        }
     }
 
     fn flop(&self) -> Option<u64> {

server/text_generation_server/layers/attention/flashinfer.py

@@ -45,6 +45,7 @@ def use_prefill_with_paged_kv_state(
     state: flashinfer.BatchPrefillWithPagedKVCacheWrapper,
     block_tables: torch.Tensor,
     cu_seqlens: torch.Tensor,
+    custom_mask: Optional[torch.Tensor],
     input_lengths: torch.Tensor,
     num_heads: int,
     num_kv_heads: int,
@@ -88,6 +89,7 @@ def use_prefill_with_paged_kv_state(
             paged_kv_indptr=indptr,
             paged_kv_indices=block_tables,
             paged_kv_last_page_len=last_page_len,
+            custom_mask=custom_mask,
             num_qo_heads=num_heads,
             num_kv_heads=num_kv_heads,
             head_dim=head_size,

server/text_generation_server/models/custom_modeling/flash_gemma3_modeling.py

@@ -45,6 +45,7 @@
 from text_generation_server.layers.layernorm import (
     FastRMSNorm,
 )
+from text_generation_server.models.globals import ATTENTION
 from text_generation_server.utils.weights import UnquantizedWeight
 from transformers.activations import ACT2FN
 from text_generation_server.layers.attention import (
@@ -248,7 +249,7 @@ def forward(
 
         # Prefill
         if cu_seqlen_prefill is not None:
-            if attention_mask is None:
+            if attention_mask is None or ATTENTION == "flashinfer":
                 # flash attention
                 attn_output = attention(
                     query=query,
@@ -701,8 +702,16 @@ def __init__(self, prefix, config, weights):
         )
 
     def get_attention_mask(
-        self, input_ids, max_s, cu_seqlen_prefill, dtype, image_token_mask
+        self,
+        input_ids: torch.Tensor,
+        cu_seqlen_prefill: Optional[torch.Tensor],
+        dtype: torch.dtype,
+        bool_mask: bool = False,
     ):
+        image_token_mask = (input_ids == self.config.image_token_index).to(
+            input_ids.device
+        )
+
         device = input_ids.device
         min_dtype = torch.finfo(dtype).min
 
@@ -748,9 +757,10 @@ def get_attention_mask(
         )
         full_attention_mask[:, :, :, :sequence_length] = combined_mask
 
-        final_attention_mask = torch.where(full_attention_mask, 0, min_dtype).to(device)
-
-        return final_attention_mask
+        if bool_mask:
+            return full_attention_mask
+        else:
+            return torch.where(full_attention_mask, 0, min_dtype).to(device)
 
     def forward(
         self,
@@ -793,10 +803,8 @@ def forward(
             )
             attention_mask = self.get_attention_mask(
                 input_ids,
-                max_s,
                 cu_seqlen_prefill,
                 inputs_embeds.dtype,
-                image_token_mask,
             )
         # Use flash attention for text-only input
         # else:

server/text_generation_server/models/flash_causal_lm.py

@@ -2434,6 +2434,7 @@ def _forward_context(
         input_lengths_tensor: torch.Tensor,
         cache_lengths_tensor: torch.Tensor,
         state: Optional[Any] = None,
+        attention_mask: Optional[torch.Tensor] = None,
     ) -> ContextManager:
         if ATTENTION != "flashinfer":
             return nullcontext()
@@ -2450,6 +2451,7 @@ def _forward_context(
                 ),
                 block_tables=block_tables,
                 cu_seqlens=cu_seqlen_prefill,
+                custom_mask=attention_mask,
                 input_lengths=input_lengths_tensor + cache_lengths_tensor,
                 num_heads=self.num_heads,
                 num_kv_heads=self.num_kv_heads,

server/text_generation_server/models/vlm_causal_lm.py

@@ -485,6 +485,14 @@ def forward(
                 )
                 batch.position_ids = position_ids
 
+        if self.model.config.model_type == "gemma3" and cu_seqlen_prefill is not None:
+            # Get the mask, needed for flashinfer.
+            attention_mask = self.model.get_attention_mask(
+                input_ids, cu_seqlen_prefill, self.dtype, bool_mask=True
+            ).reshape(-1)
+        else:
+            attention_mask = None
+
         # Try to find an associated cuda graph
         bs = input_ids.shape[0]
         sorted_padded_bs = sorted([k for k in self.cuda_graphs.keys() if k >= bs])
@@ -508,6 +516,7 @@ def forward(
                 cu_seqlen_prefill=cu_seqlen_prefill,
                 input_lengths_tensor=input_lengths,
                 cache_lengths_tensor=cache_lengths_tensor,
+                attention_mask=attention_mask,
             ):
                 seqlen = Seqlen(
                     input_lengths=input_lengths,