[BugFix] Fix accuracy bugs for unquantized deepseekv3 models (#897)

### What this PR does / why we need it?
This PR fixes two accuracy bugs incurred by PR #819 when running
deepseekv3 series models:
1. #819 adds `all_to_all` communication in quantized cases, but
`all_gather` && `reduce_scatter` are removed in both of quantized and
unquantized cases. When running unquantized deepseekv3 models with
`ep_size == world_size`, the moe modules fail to communicate. Therefore,
this PR adds `all_to_all` communication on unquantized situation to
solve this accuracy issue.
2. Use `ep_size` rather than `dp_size` to decide whether to use
`all_to_all` in moe.

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

### How was this patch tested?
CI passed with new added/existing test.

---------

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

vllm_ascend/ops/fused_moe.py

@@ -18,9 +18,11 @@
 from typing import Callable, Optional
 
 import torch
+import torch.distributed as dist
 import torch_npu
 from vllm.config import get_current_vllm_config
-from vllm.distributed import (get_tensor_model_parallel_world_size,
+from vllm.distributed import (GroupCoordinator,
+                              get_tensor_model_parallel_world_size,
                               tensor_model_parallel_all_reduce)
 from vllm.distributed.parallel_state import get_dp_group
 from vllm.model_executor.layers.fused_moe.layer import (
@@ -154,6 +156,143 @@ def fused_experts_with_mc2(
     return hidden_states
 
 
+# currently expert parallelism implemented with all2all
+# is under-optimized.
+def fused_experts_with_all2all(
+    hidden_states: torch.Tensor,
+    w1: torch.Tensor,
+    w2: torch.Tensor,
+    topk_weights: torch.Tensor,
+    topk_ids: torch.Tensor,
+    top_k: int,
+    expert_map: torch.Tensor = None,
+    ep_group: GroupCoordinator = None,
+):
+    original_shape = hidden_states.shape
+    if len(original_shape) == 3:
+        hidden_states = hidden_states.view(-1, hidden_states.shape[-1])
+
+    num_tokens, _ = hidden_states.shape
+    num_experts = w1.shape[0]
+    device = hidden_states.device
+
+    if expert_map is not None:
+        global_num_experts = len(expert_map)
+        local_num_experts = global_num_experts // ep_group.world_size
+        row_idx_len = num_tokens * top_k
+        row_idx = (torch.arange(0,
+                                row_idx_len,
+                                dtype=torch.int32,
+                                device=device).view(top_k, -1).permute(
+                                    1, 0).contiguous())
+        hidden_states, expanded_row_idx, expanded_expert_idx = torch_npu.npu_moe_init_routing(
+            hidden_states,
+            row_idx=row_idx,
+            expert_idx=topk_ids,
+            active_num=num_tokens)
+
+        global_expert_tokens = torch.bincount(expanded_expert_idx,
+                                              minlength=global_num_experts)
+        scatter_sizes = global_expert_tokens.view(ep_group.world_size,
+                                                  -1).sum(-1)
+
+        gather_sizes = torch.empty_like(scatter_sizes)
+        dist.all_to_all_single(gather_sizes,
+                               scatter_sizes,
+                               group=ep_group.device_group)
+        scatter_size_list = scatter_sizes.cpu().tolist()
+        gather_size_list = gather_sizes.cpu().tolist()
+
+        expanded_expert_idx = expanded_expert_idx % local_num_experts
+        hidden_states = ep_group.all_to_all(hidden_states, 0, 0,
+                                            scatter_size_list,
+                                            gather_size_list)
+        local_expert_idx = ep_group.all_to_all(expanded_expert_idx, 0, 0,
+                                               scatter_size_list,
+                                               gather_size_list)
+
+        sorted_local_expert_idx, sorted_idx = torch.sort(local_expert_idx)
+
+        expert_tokens = torch_npu.npu_moe_compute_expert_tokens(
+            sorted_local_expert_idx, local_num_experts).to(torch.int64)
+
+        hidden_states = hidden_states[sorted_idx]
+    else:
+        row_idx_len = num_tokens * top_k
+        row_idx = torch.arange(0,
+                               row_idx_len,
+                               dtype=torch.int32,
+                               device=topk_weights.device).view(
+                                   top_k, -1).permute(1, 0).contiguous()
+        hidden_states, expanded_row_idx, expanded_expert_idx = torch_npu.npu_moe_init_routing(
+            hidden_states,
+            row_idx=row_idx,
+            expert_idx=topk_ids,
+            active_num=num_tokens)
+
+        expert_tokens = torch_npu.npu_moe_compute_expert_tokens(
+            expanded_expert_idx, num_experts)
+        expert_tokens = expert_tokens.to(torch.int64)
+
+    w1 = w1.transpose(1, 2)
+    gate_up_out_list = torch_npu.npu_grouped_matmul(
+        x=[hidden_states],
+        weight=[w1],
+        split_item=2,
+        group_list_type=0,
+        group_type=0,
+        group_list=expert_tokens,
+    )
+
+    # TODO: Remove this in the future.
+    hidden_states = torch.cat(gate_up_out_list, dim=0)
+    hidden_states = torch_npu.npu_swiglu(hidden_states)
+
+    w2 = w2.transpose(1, 2)
+    down_out_list = torch_npu.npu_grouped_matmul(
+        x=[hidden_states],
+        weight=[w2],
+        split_item=2,
+        group_list_type=0,
+        group_type=0,
+        group_list=expert_tokens,
+    )
+
+    hidden_states = torch.cat(down_out_list, dim=0)
+
+    if expert_map is not None:
+        resorted_idx = torch.argsort(sorted_idx)
+        hidden_states = hidden_states[resorted_idx]
+        hidden_states = ep_group.all_to_all(hidden_states, 0, 0,
+                                            gather_size_list,
+                                            scatter_size_list)
+
+        final_hidden_states = torch_npu.npu_moe_finalize_routing(
+            hidden_states,
+            skip1=None,
+            skip2=None,
+            bias=None,
+            scales=topk_weights,
+            expanded_src_to_dst_row=expanded_row_idx,
+            export_for_source_row=topk_ids,
+        )
+    else:
+        # TODO: Reorder device memory 2 times here, replace the current
+        # implementation here when suitable operators become available.
+        final_hidden_states = torch_npu.npu_moe_finalize_routing(
+            hidden_states,
+            skip1=None,
+            skip2=None,
+            bias=None,
+            scales=topk_weights,
+            expanded_src_to_dst_row=expanded_row_idx,
+            export_for_source_row=topk_ids,
+        )
+    if len(original_shape) == 3:
+        final_hidden_states = final_hidden_states.view(original_shape)
+    return final_hidden_states
+
+
 def fused_experts(
     hidden_states: torch.Tensor,
     w1: torch.Tensor,
@@ -494,7 +633,7 @@ class AscendUnquantizedFusedMoEMethod(UnquantizedFusedMoEMethod):
         custom_routing_function: Optional[Callable] = None,
         scoring_func: str = "softmax",
         e_score_correction_bias: Optional[torch.Tensor] = None,
-        is_prefill=False,
+        is_prefill: bool = False,
         **kwargs,
     ):
         # NOTE: now npu_moe_gating_top_k can only support `group_count=256` pattern
@@ -536,7 +675,7 @@ class AscendUnquantizedFusedMoEMethod(UnquantizedFusedMoEMethod):
                 top_k=top_k,
                 expert_map=expert_map,
                 moe_all_to_all_group_name=self.moe_all_to_all_group_name)
-        else:
+        elif get_ep_group().world_size == 1:
             return fused_experts(hidden_states=x,
                                  w1=layer.w13_weight,
                                  w2=layer.w2_weight,
@@ -544,6 +683,19 @@ class AscendUnquantizedFusedMoEMethod(UnquantizedFusedMoEMethod):
                                  topk_ids=topk_ids,
                                  top_k=top_k,
                                  expert_map=expert_map)
+        else:
+            # The current implementation of deepseek moe splits hidden_states
+            # according to tp_size before they are feed into fused_moe module.
+            # Therefore, all2all is needed no matter how dp/tp is set so as to
+            # dispatch/combine tokens.
+            return fused_experts_with_all2all(hidden_states=x,
+                                              w1=layer.w13_weight,
+                                              w2=layer.w2_weight,
+                                              topk_weights=topk_weights,
+                                              topk_ids=topk_ids,
+                                              top_k=top_k,
+                                              expert_map=expert_map,
+                                              ep_group=get_ep_group())
 
 
 class AscendFusedMoE(FusedMoE):
@@ -721,8 +873,7 @@ class AscendFusedMoE(FusedMoE):
             scoring_func=self.scoring_func,
             e_score_correction_bias=self.e_score_correction_bias,
             is_prefill=is_prefill,
-            enable_force_load_balance=enable_force_load_balance,
+            enable_force_load_balance=enable_force_load_balance)
-            dp_size=self.dp_size)
 
         if VLLM_ENABLE_MC2 and not is_prefill:
             ...

vllm_ascend/quantization/quant_config.py

@@ -323,14 +323,13 @@ class AscendFusedMoEMethod(FusedMoEMethodBase):
         e_score_correction_bias: Optional[torch.Tensor] = None,
         is_prefill: bool = True,
         enable_force_load_balance: bool = False,
-        dp_size: int = 1,
         **kwargs,
     ) -> torch.Tensor:
         return self.quant_method.apply(
             layer, x, router_logits, top_k, renormalize, use_grouped_topk,
             global_num_experts, expert_map, topk_group, num_expert_group,
             custom_routing_function, scoring_func, e_score_correction_bias,
-            is_prefill, enable_force_load_balance, dp_size)
+            is_prefill, enable_force_load_balance)
 
     def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
         if hasattr(self.quant_method, "process_weights_after_loading"):

vllm_ascend/quantization/w8a8_dynamic.py

@@ -582,7 +582,6 @@ class AscendW8A8DynamicFusedMoEMethod:
         e_score_correction_bias: Optional[torch.Tensor] = None,
         is_prefill: bool = True,
         enable_force_load_balance: bool = True,
-        dp_size: int = 1,
         **kwargs,
     ) -> torch.Tensor:
         assert router_logits.shape[
@@ -635,7 +634,7 @@ class AscendW8A8DynamicFusedMoEMethod:
                 top_k=top_k,
                 expert_map=expert_map,
                 moe_all_to_all_group_name=self.moe_all_to_all_group_name)
-        elif dp_size == 1:
+        elif self.ep_group.world_size == 1:
             return fused_experts(hidden_states=x,
                                  w1=layer.w13_weight,
                                  w1_scale=layer.w13_weight_scale,
@@ -646,6 +645,10 @@ class AscendW8A8DynamicFusedMoEMethod:
                                  top_k=top_k,
                                  expert_map=expert_map)
         else:
+            # The current implementation of deepseek moe splits hidden_states
+            # according to tp_size before they are feed into fused_moe module.
+            # Therefore, all2all is needed no matter how dp/tp is set so as to
+            # dispatch/combine tokens.
             return fused_experts_with_all2all(hidden_states=x,
                                               w1=layer.w13_weight,
                                               w1_scale=layer.w13_weight_scale,