[BugFix]add all2all when dp_size > 1 && downgrade npu_dequant_swiglu_quant (#819)

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### What this PR does / why we need it?
1. This PR introduces native `all_to_all` communication operator to fix
`allgather` bugs when dp_size > 1. Besides, it adds a naive
implementation of force-load-balance when doing profile runs.
2. The operator `npu_dequant_swiglu_quant` only supports input
hidden_states with dtype `torch.int32`. This tensor occupies space of
`global_bs * seq_len * topk * hidden_size`, which might be very large as
`ep_size` grows. Therefore we need to disable this operator and use
original `swiglu` && `quantize`.

### Does this PR introduce _any_ user-facing change?
No.

### How was this patch tested?
By performing offline inference:

![image](https://github.com/user-attachments/assets/e003d5dc-0753-41ae-9303-e87f73ac6828)

---------

Signed-off-by: angazenn <zengyanjia@huawei.com>
Co-authored-by: angazenn <zengyanjia@huawei.com>

vllm_ascend/models/deepseek_v2.py

@@ -205,50 +205,66 @@ class CustomDeepseekV2MoE(nn.Module):
             )
         CustomDeepseekV2MoE.top_k = config.num_experts_per_tok
 
-        vllm_config = get_current_vllm_config()
         self.dp_size = get_dp_group().world_size
-        batch_size = vllm_config.scheduler_config.max_num_seqs
 
-        params_dtype = torch.get_default_dtype()
-        self.final_hidden_states = torch.zeros(
-            [batch_size, config.hidden_size], dtype=params_dtype, device="npu")
         self.tp_group = get_tp_group().device_group
+        self.tp_rank = get_tp_group().rank_in_group
 
     def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
         attn_metadata = get_forward_context().attn_metadata
+        # when profile runs, force experts to load balanced tokens
+        # to avoid high memory consumption on a single rank.
+        # TODO: need a better flag to indicate whether in profile run or not.
         if attn_metadata is None:
             # for profile run
             is_prefill = True
+            enable_force_load_balance = True
         else:
             is_prefill = attn_metadata.num_prefills > 0
+            enable_force_load_balance = False
         num_tokens, hidden_dim = hidden_states.shape
-        hidden_states = hidden_states.view(-1, hidden_dim)
 
-        if (self.tp_size > 1 and VLLM_ENABLE_MC2 and not is_prefill):
-            chunks = torch.chunk(hidden_states,
-                                 get_tp_group().world_size,
-                                 dim=0)
-            hidden_states = chunks[get_tp_group().rank_in_group]
-
-        # router_logits: (num_tokens, n_experts)
-        router_logits, _ = self.gate(hidden_states)
-
-        final_hidden_states = self.experts(
-            hidden_states=hidden_states,
-            router_logits=router_logits,
-            is_prefill=is_prefill,
-            top_k=CustomDeepseekV2MoE.top_k) * self.routed_scaling_factor
-
-        if self.tp_size > 1:
-            if VLLM_ENABLE_MC2 and not is_prefill:
-                dist.all_gather_into_tensor(self.final_hidden_states,
-                                            final_hidden_states, self.tp_group)
-                final_hidden_states = self.final_hidden_states
-            else:
-                final_hidden_states = tensor_model_parallel_all_reduce(
-                    final_hidden_states)
         if self.n_shared_experts is not None:
             shared_output = self.shared_experts(hidden_states)
+
+        if self.tp_size > 1:
+            # pass
+            num_tokens, hidden_size = hidden_states.shape
+            if num_tokens < self.tp_size:
+                target_size = self.tp_size
+                new_hidden_states = torch.empty([target_size, hidden_size],
+                                                dtype=hidden_states.dtype,
+                                                device=hidden_states.device)
+                new_hidden_states[:num_tokens] = hidden_states
+                hidden_states = new_hidden_states
+            chunk_hidden_states = torch.tensor_split(hidden_states,
+                                                     self.tp_size,
+                                                     dim=0)
+            local_hidden_states = chunk_hidden_states[self.tp_rank]
+        else:
+            local_hidden_states = hidden_states
+
+        # router_logits: (num_tokens, n_experts)
+        router_logits, _ = self.gate(local_hidden_states)
+
+        router_hidden_states = self.experts(
+            hidden_states=local_hidden_states,
+            router_logits=router_logits,
+            is_prefill=is_prefill,
+            top_k=CustomDeepseekV2MoE.top_k,
+            enable_force_load_balance=enable_force_load_balance,
+        ) * self.routed_scaling_factor
+
+        if self.tp_size > 1:
+            dist.all_gather(list(chunk_hidden_states), router_hidden_states,
+                            self.tp_group)
+            final_hidden_states = torch.cat(chunk_hidden_states, dim=0)
+            if num_tokens < self.tp_size:
+                final_hidden_states = final_hidden_states[:num_tokens]
+        else:
+            final_hidden_states = router_hidden_states
+
+        if shared_output is not None:
             final_hidden_states = final_hidden_states + shared_output
 
         return final_hidden_states.view(num_tokens, hidden_dim)