[main][refactor] Refactoring forward_context and model_runner_v1 (#1979)

### What this PR does / why we need it?

A refactoring of forward_context and model_runner_v1, add some context
which is necessary in model inference into forward_context, and refactor
dummy_run logic, make it more reasonable.
Some details for this PR:

Add `ascend_forward_context`;
Update mc2_v2 op, and support `active_mask` param;
Update scripts in examples dir;
refactor `dummy_run` logic;
Add soc_version for A2 and A3;

### Does this PR introduce _any_ user-facing change?

No change at user-facing.

### How was this patch tested?


- vLLM version: v0.10.0
- vLLM main:
57c22e57f9

Signed-off-by: zzzzwwjj <1183291235@qq.com>

vllm_ascend/attention/attention_v1_torchair.py

@@ -127,8 +127,6 @@ class AscendTorchairMetadata:
     query_start_loc: torch.Tensor
     query_lens: torch.Tensor
     seq_lens: torch.Tensor
-    # max value of number of tokens across dp group
-    max_num_tokens_across_dp: int = 0
     # Maximum query length in the batch. None for decoding.
     max_query_len: Optional[int] = None
     # (num_tokens,). The indices of the token slots that input tokens will be
@@ -139,7 +137,7 @@ class AscendTorchairMetadata:
     # Current state of this attention run.
     attn_state: AscendAttentionState = AscendAttentionState.ChunkedPrefill
     attn_mask: Optional[torch.Tensor] = None
-    with_prefill_across_dp: bool = False
+
     decode: Optional[AscendDecodeMetadata] = None
 
 
@@ -178,8 +176,9 @@ class AscendAttentionTorchairMetadataBuilder:
 
         return graph_block_tables[:num_seqs, :max_blocks]
 
-    def build_dummy(self, num_reqs: int,
-                    num_actual_tokens: int) -> AscendTorchairMetadata:
+    def build_torchair_graph_dummy(
+            self, num_reqs: int,
+            num_actual_tokens: int) -> AscendTorchairMetadata:
         device = self.runner.device
         _, max_blocks = self.runner.graph_block_tables.shape
         block_table = torch.zeros((num_reqs, max_blocks),
@@ -214,7 +213,6 @@ class AscendAttentionTorchairMetadataBuilder:
             seq_lens=seq_lens,
             slot_mapping=slot_mapping,
             attn_state=AscendAttentionState.DecodeOnly,
-            max_num_tokens_across_dp=num_reqs,
             decode=decode_metadata)
         return attn_metadata
 
@@ -222,9 +220,7 @@ class AscendAttentionTorchairMetadataBuilder:
               num_reqs,
               num_actual_tokens,
               max_query_len,
-              graph_pad_size: int = -1,
-              max_num_tokens_across_dp: int = 0,
-              with_prefill_across_dp: bool = False):
+              graph_pad_size: int = -1):
 
         device = self.runner.device
 
@@ -263,7 +259,6 @@ class AscendAttentionTorchairMetadataBuilder:
                 pad_value = 1
                 padded_seq_lens = seq_lens.tolist() + [pad_value
                                                        ] * graph_pad_size
-                max_num_tokens_across_dp = len(padded_seq_lens)
 
                 seq_lens = torch.from_numpy(
                     np.array(padded_seq_lens).astype(np.int32))
@@ -303,9 +298,7 @@ class AscendAttentionTorchairMetadataBuilder:
             max_query_len=max_query_len,
             slot_mapping=slot_mapping,
             attn_mask=attn_mask,
-            attn_state=attn_state,
-            max_num_tokens_across_dp=max_num_tokens_across_dp,
-            with_prefill_across_dp=with_prefill_across_dp)
+            attn_state=attn_state)
         return attn_metadata