[Main] [Refactor] Enable MoECommMethod in Eager Mode (#2791)

### What this PR does / why we need it?
1. Replace prepare/finalize operation in fused_moe.py by
moe_comm_method.prepare()/finalize()
2. Replace unified_fused_experts by moe_comm_method.fused_experts() in
fused_moe.py/w8a8_dynamic.py/w4a8_dynamic.py
3. Add calling _select_moe_comm_method in spec-decode proposers.
4. Currently, w4a8_dynamic does not support gatherep, use all2allv
instead.
5. Remove redundant code.
### Does this PR introduce _any_ user-facing change?
AllgatherEP switch is disabled in aclgraph/eager mode, just follow the
rules in modelrunner_v1._select_moe_comm_method()
### How was this patch tested?
e2e & ut


- vLLM version: v0.10.2
- vLLM main:
7f6f2c1182

Signed-off-by: Pr0Wh1teGivee <calvin_zhu0210@outlook.com>
Co-authored-by: weijinqian0 <12153182+weijinqian0@users.noreply.github.com>

vllm_ascend/spec_decode/eagle_proposer.py

@@ -117,8 +117,11 @@ class EagleProposer(Proposer):
                   skip_attn: bool = False,
                   num_reqs: int = 0,
                   num_tokens_across_dp: Optional[torch.Tensor] = None):
+        moe_comm_method = self.runner._select_moe_comm_method(
+            num_tokens, with_prefill)
         with set_ascend_forward_context(None,
                                         self.vllm_config,
+                                        moe_comm_method=moe_comm_method,
                                         num_tokens=num_tokens):
             self.model(
                 input_ids=self.input_ids[:num_tokens],
@@ -447,12 +450,20 @@ class EagleProposer(Proposer):
             num_input_tokens = self.vllm_config.pad_for_cudagraph(num_tokens)
         else:
             num_input_tokens = num_tokens
+
+        with_prefill = attn_metadata.attn_state not in [
+            AscendAttentionState.DecodeOnly, AscendAttentionState.SpecDecoding
+        ]
+        moe_comm_method = self.runner._select_moe_comm_method(
+            num_input_tokens, with_prefill)
+
         # copy inputs to buffer for cudagraph
         self.positions[:num_tokens] = target_positions.to(device)
         self.hidden_states[:num_tokens] = target_hidden_states
         attn_metadata.block_tables = block_table.to(device)
         with set_ascend_forward_context(attn_metadata,
                                         self.vllm_config,
+                                        moe_comm_method=moe_comm_method,
                                         num_tokens=num_input_tokens):
             last_hidden_states, hidden_states = self.model(
                 input_ids=self.input_ids[:num_input_tokens],
@@ -483,6 +494,10 @@ class EagleProposer(Proposer):
             input_batch_size = self.vllm_config.pad_for_cudagraph(batch_size)
         else:
             input_batch_size = batch_size
+
+        moe_comm_method = self.runner._select_moe_comm_method(
+            input_batch_size, False)
+
         attn_metadata.num_actual_tokens = batch_size
         attn_metadata.max_query_len = 1
         attn_metadata.query_start_loc = self.arange[:batch_size + 1]
@@ -553,6 +568,7 @@ class EagleProposer(Proposer):
             # Run the model.
             with set_ascend_forward_context(attn_metadata,
                                             self.vllm_config,
+                                            moe_comm_method=moe_comm_method,
                                             num_tokens=input_batch_size):
 
                 last_hidden_states, hidden_states = self.model(