import moe multi-stream

Signed-off-by: whx-sjtu <2952154980@qq.com>

Author: whx-sjtu

vllm_ascend/models/deepseek_v2.py

@@ -390,7 +390,7 @@ def forward(self,
 
         # router_logits: (num_tokens, n_experts)
         router_logits = None
-        if not self.rm_router_logits:
+        if not self.rm_router_logits and not self.enable_multistream_moe:
             router_logits, _ = self.gate(hidden_states)
 
         experts_hidden_states = self.experts(

vllm_ascend/ops/fused_moe.py

@@ -1298,6 +1298,21 @@ def forward(self,
 
         fused_moe_state = get_fused_moe_state(self.moe_parallel_config.ep_size,
                                               is_prefill, is_deepseek_v3_r1)
+        # For w8a8 dynamic we can do npu_dynamic_quant and gate in parallel.
+        quantized_x_for_share, dynamic_scale_for_share = None, None
+        from vllm_ascend.quantization.w8a8_dynamic import \
+            AscendW8A8DynamicFusedMoEMethod
+        if self.enable_multistream_moe:
+            if not self.rm_router_logits:
+                router_logits, _ = gate(hidden_states)
+            if hasattr(self.quant_method, "quant_method") and \
+               isinstance(self.quant_method.quant_method,
+                          AscendW8A8DynamicFusedMoEMethod
+                          ) and fused_moe_state == FusedMoEState.MC2:
+                with npu_stream_switch("moe_secondary", 0):
+                    quantized_x_for_share, dynamic_scale_for_share = torch_npu.npu_dynamic_quant(
+                        hidden_states)
+
         if shared_experts:
             if not self.enable_multistream_moe or fused_moe_state != FusedMoEState.MC2:
                 # When all_reduce_merge is in progress, shared_experts does not do all_reduce in mlp, but waits until shared_experts+router_experts are completed before doing all_reduce
@@ -1378,6 +1393,8 @@ def forward(self,
             global_redundant_expert_num=self.global_redundant_expert_num,
             shared_experts=shared_experts if self.torchair_graph_enabled
             and self.enable_multistream_moe and not is_prefill else None,
+            quantized_x_for_share=quantized_x_for_share,
+            dynamic_scale_for_share=dynamic_scale_for_share,
         )
 
         if shared_experts:

vllm_ascend/quantization/w8a8_dynamic.py

@@ -15,7 +15,7 @@
 # limitations under the License.
 #
 
-from typing import Any, Callable, Dict, Optional, Tuple, Union
+from typing import Any, Callable, Dict, List, Optional, Tuple, Union
 
 import torch
 import torch.distributed as dist
@@ -31,6 +31,80 @@
                                npu_stream_switch, npu_wait_tensor)
 
 
+def apply_mlp_decode(hidden_states_wrapper: List[torch.Tensor],
+                     w1: torch.Tensor,
+                     w1_scale: torch.Tensor,
+                     w2: torch.Tensor,
+                     w2_scale: torch.Tensor,
+                     group_list: torch.Tensor,
+                     dynamic_scale: torch.Tensor = None,
+                     group_list_type: int = 1) -> torch.Tensor:
+    """
+    apply MLP: gate_up_proj -> swiglu -> down_proj
+    Args:
+        hidden_states_wrapper: wrapper of input hidden states with shape (num_tokens, hidden_size).
+        w1: expert weights1 with shape
+            (num_experts, hidden_size, intermediate_size * 2)
+        w1_scale: weights1 scale with shape (num_experts, intermediate_size * 2)
+        w2: expert weights2 with shape
+            (num_experts, intermediate_size, hidden_size)
+        w2_scale: weights2 scale with shape (num_experts, hidden_size)
+        group_list: number of tokens for each expert, follow cumsum mode, and
+            with shape (num_experts).
+        transpose_weight:
+            w1: (num_experts, intermediate_size * 2, hidden_size) ->
+                    (num_experts, hidden_size, intermediate_size * 2)
+            w2: (num_experts, hidden_size, intermediate_size) ->
+                    (num_experts, intermediate_size, hidden_size)
+    Returns:
+        hidden_states: output hidden states after MLP.
+    """
+
+    assert len(hidden_states_wrapper) == 1
+    hidden_states = hidden_states_wrapper.pop()
+    if dynamic_scale is None:
+        hidden_states, pertoken_scale = torch_npu.npu_dynamic_quant(
+            hidden_states)
+    else:
+        pertoken_scale = dynamic_scale
+
+    # gmm1: gate_up_proj
+    hidden_states = torch_npu.npu_grouped_matmul(
+        x=[hidden_states],
+        weight=[w1],
+        split_item=3,
+        group_list_type=group_list_type,
+        group_type=0,
+        group_list=group_list,
+        output_dtype=torch.int32)[0]
+
+    # act_fn: swiglu
+    hidden_states, swiglu_out_scale = torch_npu.npu_dequant_swiglu_quant(
+        x=hidden_states,
+        weight_scale=w1_scale,
+        activation_scale=pertoken_scale,
+        bias=None,
+        quant_scale=None,
+        quant_offset=None,
+        group_index=group_list,
+        activate_left=True,
+        quant_mode=1,
+    )
+
+    # gmm2: down_proj
+    hidden_states = torch_npu.npu_grouped_matmul(
+        x=[hidden_states],
+        weight=[w2],
+        scale=[w2_scale],
+        per_token_scale=[swiglu_out_scale],
+        split_item=2,
+        group_list_type=group_list_type,
+        group_type=0,
+        group_list=group_list,
+        output_dtype=w2_scale.dtype)[0]
+    return hidden_states
+
+
 def apply_mlp(hidden_states: torch.Tensor,
               w1: torch.Tensor,
               w1_scale: torch.Tensor,
@@ -118,6 +192,8 @@ def fused_experts_with_mc2(
     log2phy: torch.Tensor = None,
     global_redundant_expert_num: int = 0,
     shared_experts: Optional[Any] = None,
+    quantized_x_for_share: Optional[Any] = None,
+    dynamic_scale_for_share: Optional[Any] = None,
 ) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
     if log2phy is not None:
         topk_ids = log2phy[topk_ids]
@@ -165,19 +241,19 @@ def fused_experts_with_mc2(
 
     if shared_experts is not None:
         with npu_stream_switch("moe_secondary", 0):
-            npu_wait_tensor(hidden_states, topk_weights)
-            shared_gate_up, _ = shared_experts.gate_up_proj(hidden_states)
-            npu_wait_tensor(shared_gate_up[0], expand_x)
-            shared_act = shared_experts.act_fn(shared_gate_up)
+            npu_wait_tensor(quantized_x_for_share, expand_x)
+            shared_act_out = shared_experts.act_fn(
+                (quantized_x_for_share, dynamic_scale_for_share))
+            shared_act, swiglu_out_scale = shared_act_out[0], shared_act_out[1]
 
     # `expand_x` will be disposed in the `apply_mlp` function
-    down_out_list = apply_mlp(expand_x,
-                              w1,
-                              w1_scale,
-                              w2,
-                              w2_scale,
-                              expert_token_nums,
-                              dynamic_scale=dynamic_scale)
+    down_out_list = apply_mlp_decode([expand_x],
+                                    w1,
+                                    w1_scale,
+                                    w2,
+                                    w2_scale,
+                                    expert_token_nums,
+                                    dynamic_scale=dynamic_scale)
 
     # moeCombine
     kwargs_mc2 = {
@@ -213,8 +289,9 @@ def fused_experts_with_mc2(
         return hidden_states
     else:
         with npu_stream_switch("moe_secondary", 0):
-            npu_wait_tensor(shared_act[0], down_out_list)
-            shared_output, _ = shared_experts.down_proj(shared_act)
+            npu_wait_tensor(shared_act, down_out_list)
+            shared_output, _ = shared_experts.down_proj(
+                (shared_act, swiglu_out_scale))
         return hidden_states, shared_output
 
 
@@ -708,6 +785,8 @@ def apply(
         log2phy: torch.Tensor = None,
         global_redundant_expert_num: int = 0,
         shared_experts: Optional[Any] = None,
+        quantized_x_for_share: Optional[Any] = None,
+        dynamic_scale_for_share: Optional[Any] = None,
         **kwargs,
     ) -> torch.Tensor:
         assert router_logits.shape[
@@ -744,6 +823,15 @@ def apply(
                 e_score_correction_bias=e_score_correction_bias,
             )
 
+        shared_gate_up, shared_dequant_scale = None, None
+        if shared_experts is not None and fused_moe_state == FusedMoEState.MC2:
+            with npu_stream_switch("moe_secondary", 0):
+                npu_wait_tensor(quantized_x_for_share, router_logits)
+                share_up_out, _ = shared_experts.gate_up_proj(
+                    (quantized_x_for_share, dynamic_scale_for_share))
+                shared_gate_up, shared_dequant_scale = share_up_out[
+                    0], share_up_out[1]
+
         # this is a naive implementation for experts load balance so as
         # to avoid accumulating too much tokens on a single rank.
         # currently it is only activated when doing profile runs.
@@ -770,7 +858,7 @@ def apply(
                 hidden_states=x,
                 w1=layer.w13_weight,
                 w2=layer.w2_weight,
-                w1_scale=layer.w13_weight_scale,
+                w1_scale=layer.w13_weight_scale_fp32,
                 w2_scale=layer.w2_weight_scale,
                 topk_weights=topk_weights,
                 topk_ids=topk_ids,
@@ -779,7 +867,9 @@ def apply(
                 moe_all_to_all_group_name=self.moe_all_to_all_group_name,
                 log2phy=log2phy,
                 global_redundant_expert_num=global_redundant_expert_num,
-                shared_experts=shared_experts)
+                shared_experts=shared_experts,
+                quantized_x_for_share=shared_gate_up,
+                dynamic_scale_for_share=shared_dequant_scale)
         elif fused_moe_state in [
                 FusedMoEState.AllGather, FusedMoEState.NaiveMulticast
         ]:
@@ -822,6 +912,8 @@ def process_weights_after_loading(self, layer):
             torch_npu.npu_format_cast_(layer.w2_weight, ACL_FORMAT_FRACTAL_NZ)
         layer.w13_weight_scale.data = layer.w13_weight_scale.data.view(
             layer.w13_weight_scale.data.shape[0], -1)
+        layer.w13_weight_scale_fp32 = layer.w13_weight_scale.data.to(
+            torch.float32)
         layer.w13_weight_offset.data = layer.w13_weight_offset.data.view(
             layer.w13_weight_offset.data.shape[0], -1)
         layer.w2_weight_scale.data = layer.w2_weight_scale.data.view(