diff --git a/docker/Dockerfile.rocm_base b/docker/Dockerfile.rocm_base
index 6f8c7222fdce..f6978dae00b0 100644
--- a/docker/Dockerfile.rocm_base
+++ b/docker/Dockerfile.rocm_base
@@ -9,7 +9,7 @@ ARG PYTORCH_AUDIO_BRANCH="v2.9.0"
 ARG PYTORCH_AUDIO_REPO="https://github.com/pytorch/audio.git"
 ARG FA_BRANCH="0e60e394"
 ARG FA_REPO="https://github.com/Dao-AILab/flash-attention.git"
-ARG AITER_BRANCH="6af8b687"
+ARG AITER_BRANCH="1f5a392"
 ARG AITER_REPO="https://github.com/ROCm/aiter.git"
 ARG MORI_BRANCH="2d02c6a9"
 ARG MORI_REPO="https://github.com/ROCm/mori.git"
diff --git a/vllm/v1/attention/backends/rocm_aiter_fa.py b/vllm/v1/attention/backends/rocm_aiter_fa.py
index 3febbe57a66f..c6d0301e975c 100644
--- a/vllm/v1/attention/backends/rocm_aiter_fa.py
+++ b/vllm/v1/attention/backends/rocm_aiter_fa.py
@@ -1143,8 +1143,25 @@ def forward(
                     return
                 assert attn_metadata.decode_metadata is not None
 
+                _, num_heads, head_size = query.shape
+                num_seqs = attn_metadata.seq_lens.shape[0]
+
                 if rocm_aiter_ops.is_shuffle_kv_cache_enabled():
-                    num_blocks, block_size, num_kv_heads, head_size = key_cache.shape
+                    max_num_partitions = (
+                        attn_metadata.max_seq_len + _PARTITION_SIZE_ROCM - 1
+                    ) // _PARTITION_SIZE_ROCM
+                    tmp_out = torch.empty(
+                        (num_seqs, num_heads, max_num_partitions, head_size),
+                        dtype=query.dtype,
+                        device=query.device,
+                    )
+                    exp_sums = torch.empty(
+                        (num_seqs, num_heads, max_num_partitions),
+                        dtype=torch.float32,
+                        device=query.device,
+                    )
+                    max_logits = torch.empty_like(exp_sums)
+                    num_blocks, block_size, num_kv_heads, _ = key_cache.shape
                     x = 16 // key_cache.element_size()
                     k_cache_template = torch.empty(
                         [num_blocks, num_kv_heads, head_size // x, block_size, x],
@@ -1158,18 +1175,36 @@ def forward(
                     )
                     new_key_cache = key_cache.view_as(k_cache_template)
                     new_value_cache = value_cache.view_as(v_cache_template)
-                    aiter.pa_fwd_asm(
+                    k_qscale_asm = (
+                        layer._k_scale
+                        if attn_metadata.k_scale is None
+                        else attn_metadata.k_scale
+                    )
+                    v_qscale_asm = (
+                        layer._v_scale
+                        if attn_metadata.v_scale is None
+                        else attn_metadata.v_scale
+                    )
+                    aiter.paged_attention_common(
                         Q=query[:num_decode_tokens],
                         K=new_key_cache,
                         V=new_value_cache,
+                        tmp_out=tmp_out,
+                        max_logits=max_logits,
+                        exp_sums=exp_sums,
+                        max_seq_len=attn_metadata.max_seq_len,
                         block_tables=attn_metadata.block_table[:num_decodes],
                         context_lens=attn_metadata.seq_lens[:num_decodes],
                         block_tables_stride0=attn_metadata.block_table[
                             :num_decodes
                         ].stride(0),
-                        K_QScale=attn_metadata.k_scale,
-                        V_QScale=attn_metadata.v_scale,
+                        scale=self.scale,
+                        K_QScale_hip=layer._k_scale,
+                        V_QScale_hip=layer._v_scale,
+                        K_QScale_asm=k_qscale_asm,
+                        V_QScale_asm=v_qscale_asm,
                         out_=output[:num_decode_tokens],
+                        kv_cache_dtype=self.kv_cache_dtype,
                     )
                 else:
                     _, num_heads, head_size = query.shape