Revert "[Refactor] Unify full-graph parameter update logic (#6041)" (#6227)

This reverts commit 8966a99710.

It breaks the test
`tests/e2e/singlecard/spec_decode/test_mtp_eagle_correctness.py::test_deepseek_mtp_correctness[True-FULL_DECODE_ONLY-2-wemaster/deepseek_mtp_main_random_bf16]`

- vLLM version: v0.14.0
- vLLM main:
d68209402d

vllm_ascend/attention/context_parallel/mla_cp.py

@@ -284,85 +284,6 @@ class AscendMlaCPImpl(AscendMLAImpl):
         self.dcp_rank = get_decode_context_model_parallel_rank() if self.dcp_size > 1 else 0
         self.dcp_group = get_dcp_group().device_group if self.dcp_size > 1 else None
 
-    @staticmethod
-    def update_graph_params(
-        update_stream,
-        forward_context,
-        num_tokens,
-        vllm_config=None,
-        speculative_config=None,
-        num_dcp_pcp_tokens=None,
-    ):
-        if forward_context.is_draft_model:
-            graph_params = get_draft_graph_params()
-        else:
-            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.
-        with torch.npu.stream(update_stream):
-            for key, param, handle, event in zip(
-                forward_context.attn_metadata,
-                graph_params.attn_params[num_tokens],
-                graph_params.handles[num_tokens],
-                graph_params.events[num_tokens],
-            ):
-                (
-                    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,
-                    actual_seq_lengths,
-                    actual_seq_lengths_kv,
-                    attn_output,
-                    softmax_lse,
-                ) = param
-
-                decode_meta = forward_context.attn_metadata[key].decode
-                seq_len = decode_meta.cp_seq_len
-                if isinstance(seq_len, torch.Tensor):
-                    seq_len = seq_len.tolist()
-                actual_seq_lengths_kv = seq_len
-
-                pad_length = num_tokens - len(actual_seq_lengths_kv)
-                if pad_length > 0:
-                    actual_seq_lengths_kv = actual_seq_lengths_kv + [0] * (num_tokens - len(actual_seq_lengths_kv))
-
-                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,
-                    softmax_lse_flag=True,
-                    block_table=block_table,
-                    block_size=block_size,
-                    actual_seq_lengths_kv=actual_seq_lengths_kv,
-                    actual_seq_lengths=actual_seq_lengths,
-                    workspace=graph_params.workspaces.get(num_tokens),
-                    out=[attn_output, softmax_lse],
-                )
-                torch.npu.graph_task_update_end(update_stream)
-
-                event.record(update_stream)
-
     def get_num_actual_tokens(self, attn_metadata: M):
         if self.pcp_size > 1:
             return attn_metadata.num_actual_tokens_pcp_padded // self.pcp_size