[ROCm][Perf] New design on ROCm AITER MHA backend Implementation (#25763)

Signed-off-by: ganyi <ygan@amd.com>

vllm/v1/attention/backends/utils.py

@@ -728,6 +728,73 @@ def subclass_attention_backend(
     )
 
 
+def split_decodes_prefills_and_extends(
+    common_attn_metadata: CommonAttentionMetadata,
+    decode_threshold: int = 1,
+) -> tuple[int, int, int, int, int, int]:
+    """
+    Assuming a reordered batch, finds the boundary between prefill and decode
+    requests.
+
+    Args:
+        common_attn_metadata: CommonAttentionMetadata object containing the
+            batch metadata.
+        decode_threshold: The maximum query length to be considered a decode.
+
+    Returns:
+        num_decodes: The number of decode requests.
+        num_extends: The number of extend requests.
+        num_prefills: The number of prefill requests.
+        num_decode_tokens: The number of tokens in the decode requests.
+        num_extend_tokens: The number of tokens in the extend requests.
+        num_prefill_tokens: The number of tokens in the prefill requests.
+    """
+    max_query_len = common_attn_metadata.max_query_len
+    num_reqs = common_attn_metadata.num_reqs
+    num_tokens = common_attn_metadata.num_actual_tokens
+    query_start_loc = common_attn_metadata.query_start_loc_cpu
+    seq_lens = common_attn_metadata.seq_lens_cpu
+
+    if max_query_len <= decode_threshold:
+        return num_reqs, 0, 0, num_tokens, 0, 0
+
+    query_lens = query_start_loc[1:] - query_start_loc[:-1]
+    is_prefill_or_extend = query_lens > decode_threshold
+    is_prefill = (seq_lens == query_lens) & is_prefill_or_extend
+    first_extend = is_prefill_or_extend.int().argmax(dim=-1).item()
+    first_prefill = is_prefill.int().argmax(dim=-1).item()
+    num_decodes = first_extend
+    num_decode_tokens = query_start_loc[first_extend].item()
+    if not torch.any(is_prefill_or_extend):
+        return (num_decodes, 0, 0, num_decode_tokens, 0, 0)
+
+    num_prefills_or_extends = num_reqs - num_decodes
+    num_prefill_or_extend_tokens = num_tokens - num_decode_tokens
+    if not torch.any(is_prefill):
+        return (
+            num_decodes,
+            num_prefills_or_extends,
+            0,
+            num_decode_tokens,
+            num_prefill_or_extend_tokens,
+            0,
+        )
+
+    num_extends = first_prefill - num_decodes
+    num_prefills = num_reqs - first_prefill
+
+    num_prefill_tokens = num_tokens - query_start_loc[first_prefill]
+    num_extend_tokens = num_prefill_or_extend_tokens - num_prefill_tokens
+    return (
+        num_decodes,
+        num_extends,
+        num_prefills,
+        num_decode_tokens,
+        num_extend_tokens,
+        num_prefill_tokens,
+    )
+
+
 def split_decodes_and_prefills(
     common_attn_metadata: CommonAttentionMetadata,
     decode_threshold: int = 1,