[ROCm] Env variable to trigger custom PA (#15557)

Signed-off-by: Gregory Shtrasberg <Gregory.Shtrasberg@amd.com>

vllm/attention/backends/rocm_flash_attn.py

@@ -908,4 +908,5 @@ def _use_rocm_custom_paged_attention(qtype: torch.dtype, head_size: int,
             and (qtype == torch.half or qtype == torch.bfloat16)
             and (head_size == 64 or head_size == 128)
             and (block_size == 16 or block_size == 32)
-            and (gqa_ratio >= 1 and gqa_ratio <= 16) and max_seq_len <= 32768)
+            and (gqa_ratio >= 1 and gqa_ratio <= 16) and max_seq_len <= 32768
+            and envs.VLLM_ROCM_CUSTOM_PAGED_ATTN)

vllm/envs.py

@@ -78,6 +78,7 @@ if TYPE_CHECKING:
     VLLM_ROCM_USE_AITER_RMSNORM: bool = True
     VLLM_ROCM_FP8_PADDING: bool = True
     VLLM_ROCM_MOE_PADDING: bool = True
+    VLLM_ROCM_CUSTOM_PAGED_ATTN: bool = True
     VLLM_ENABLE_V1_MULTIPROCESSING: bool = True
     VLLM_LOG_BATCHSIZE_INTERVAL: float = -1
     VLLM_DISABLE_COMPILE_CACHE: bool = False
@@ -541,6 +542,11 @@ environment_variables: dict[str, Callable[[], Any]] = {
     "VLLM_ROCM_MOE_PADDING":
     lambda: bool(int(os.getenv("VLLM_ROCM_MOE_PADDING", "1"))),
 
+    # custom paged attention kernel for MI3* cards
+    "VLLM_ROCM_CUSTOM_PAGED_ATTN":
+    lambda: (os.getenv("VLLM_ROCM_CUSTOM_PAGED_ATTN", "True").lower() in
+             ("true", "1")),
+
     # Divisor for dynamic query scale factor calculation for FP8 KV Cache
     "Q_SCALE_CONSTANT":
     lambda: int(os.getenv("Q_SCALE_CONSTANT", "200")),