[Feat][Graph]Support FULL_DECEDE_ONLY mode for MLA models (#3125)

### What this PR does / why we need it?
Adds support for capturing the Multi-Layer Attention (MLA) decode
operation into an ACL graph. This improves performance by compiling the
attention kernel for single-token decoding.

Key changes include:
- Implementing the graph capture logic for the MLA kernel, including
workspace management and parameter updates.
- Modifying the rotary embedding (RoPE) handling to use pre-allocated
tensors, which is a requirement for graph capture.
- Adding a `build_for_graph_capture` method to the MLA metadata builder
to create dummy metadata during the graph compilation phase.

Known issues:
- Currently, MTP is not supported in FULL_DECEDE_ONLY mode -- we're
working on a fix
- We are preparing to remove update_mla_attn_params with
auto_dispatch_capture

### Does this PR introduce _any_ user-facing change?
compilation_config={
    "cudagraph_mode": "FULL_DECODE_ONLY",
},
### How was this patch tested?


- vLLM version: v0.11.0

---------

Signed-off-by: panchao-hub <315134829@qq.com>
Signed-off-by: p00465316 <panchao13@huawei.com>
Co-authored-by: p00465316 <panchao13@huawei.com>
Co-authored-by: Yizhou Liu <liu_yizhou@outlook.com>

vllm_ascend/compilation/acl_graph.py

@@ -229,6 +229,52 @@ def update_attn_params(update_stream, forward_context, runtime_shape):
             event.record(update_stream)
 
 
+def update_mla_attn_params(update_stream, forward_context, runtime_shape):
+    graph_params = get_graph_params()
+    # FIXME: Behold! We are using a temporary hack here to update the args
+    # for each layer's attention op in the graph.
+    for key, param, handle, event in zip(
+            forward_context.attn_metadata,
+            graph_params.attn_params[runtime_shape],
+            graph_params.handles[runtime_shape],
+            graph_params.events[runtime_shape],
+    ):
+        (q_nope, k_nope, q_pe, k_pe, num_heads, num_kv_heads, input_layout,
+         spec_attn_mask, sparse_mode, scale, block_table, block_size,
+         seq_lens_list, actual_seq_lengths, workspace, attn_output,
+         softmax_lse) = param
+        seq_lens_list = forward_context.attn_metadata[key].decode.seq_lens_list
+        seq_lens_list = seq_lens_list + [0] * (runtime_shape -
+                                               len(seq_lens_list))
+
+        with torch.npu.stream(update_stream):
+            torch.npu.graph_task_update_begin(update_stream, handle)
+
+            torch_npu.npu_fused_infer_attention_score.out(
+                q_nope,
+                k_nope,
+                k_nope,
+                query_rope=q_pe,
+                key_rope=k_pe,
+                num_heads=num_heads,
+                num_key_value_heads=num_kv_heads,
+                input_layout=input_layout,
+                atten_mask=spec_attn_mask,
+                sparse_mode=sparse_mode,
+                scale=scale,
+                antiquant_mode=0,
+                antiquant_scale=None,
+                block_table=block_table,
+                block_size=block_size,
+                actual_seq_lengths_kv=seq_lens_list,
+                actual_seq_lengths=actual_seq_lengths,
+                workspace=workspace,
+                out=[attn_output, softmax_lse])
+            torch.npu.graph_task_update_end(update_stream)
+
+            event.record(update_stream)
+
+
 @dataclass
 class GraphParams:
     events: dict[int, list[torch.npu.ExternalEvent]]