Fixing rocm. (#2164)

server/text_generation_server/layers/attention/rocm.py

@@ -2,6 +2,7 @@ import os
 import torch
 from text_generation_server.utils.import_utils import SYSTEM
 from text_generation_server.models.globals import FLASH_DECODING
+from text_generation_server.layers.attention import Seqlen
 from loguru import logger
 
 major, minor = torch.cuda.get_device_capability()
@@ -45,8 +46,7 @@ def paged_attention(
     kv_head_mapping: torch.Tensor,
     softmax_scale: float,
     block_tables: torch.Tensor,
-    cu_seqlen_q: torch.Tensor,
-    cu_seqlen_k: torch.Tensor,
+    input_lengths: Seqlen,
     max_s: int,
 ):
     # Adapted from: https://github.com/vllm-project/vllm/blob/f8a1e39fae05ca610be8d5a78be9d40f5274e5fc/vllm/model_executor/layers/attention.py
@@ -70,7 +70,7 @@ def paged_attention(
     block_size = value_cache.shape[3]
     num_seqs, num_heads, head_size = query.shape
     max_num_partitions = (max_s + _PARTITION_SIZE - 1) // _PARTITION_SIZE
-    input_lengths = cu_seqlen_k
+    input_lengths = input_lengths.input_lengths
 
     # NOTE(woosuk): We use a simple heuristic to decide whether to use
     # PagedAttention V1 or V2. If the number of partitions is 1, we use