Fix mask passed to flashinfer

Custom masks are padded to the shape `[batch_size, max_len, max_len]`.
However, flashinfer expects an unpadded mask of the shape
`[sum(q_len[i] * k_len[i] for i in range(batch_size)]`.

This change unpads the custom mask (currently only used by Gemma 3)
to this shape (assuming q_len == k_len, since we only use the custom
mask during prefill).

server/text_generation_server/layers/attention/flashinfer.py

@@ -1,6 +1,7 @@
 from typing import Optional
 from contextvars import ContextVar
 from contextlib import contextmanager
+import math
 
 import flashinfer
 import torch
@@ -20,6 +21,20 @@ decode_state: ContextVar[flashinfer.BatchDecodeWithPagedKVCacheWrapper] = Contex
 workspace: Optional[torch.Tensor] = None
 
 
+def unpad_2d_mask(
+    attention_mask: torch.Tensor, seq_lengths: torch.Tensor
+) -> torch.Tensor:
+    # Like torch unpad_sequence, but for 2D masks.
+    unpadded_tensors = []
+    for i, length in enumerate(seq_lengths):
+        unpadded_matrix = attention_mask[i, :length, :length]
+        unpadded_tensors.append(unpadded_matrix.flatten())
+
+    packed_tensor = torch.cat(unpadded_tensors)
+
+    return packed_tensor
+
+
 def get_workspace(device):
     """Get shared flashinfer workspace."""
     global workspace
@@ -83,6 +98,15 @@ def use_prefill_with_paged_kv_state(
         last_page_len += 1
 
     token = prefill_with_paged_kv_state.set(state)
+
+    # Attention masks are padded, unpad.
+    if custom_mask is not None:
+        bs = input_lengths.shape[0]
+        seq_len = math.isqrt(custom_mask.numel() // bs)
+        custom_mask = unpad_2d_mask(
+            custom_mask.reshape(bs, seq_len, seq_len), input_lengths
+        )
+
     try:
         state.plan(
             qo_indptr=cu_seqlens,