refactor code

internlm/model/modeling_moe.py

@@ -175,7 +175,7 @@ class PackedFlashBaseLayer1D(nn.Module):
                 hidden_size=hidden_size,
                 num_experts=num_experts,
                 ep_size=ep_size,
-                k=moe_gate_k,
+                topk=moe_gate_k,
                 capacity_factor=moe_capacity_factor,
                 eval_capacity_factor=moe_eval_capacity_factor,
                 min_capacity=moe_min_capacity,

internlm/model/moe.py

@@ -5,8 +5,7 @@ import torch
 from internlm.core.context import ParallelMode
 from internlm.core.context import global_context as gpc
 from internlm.model.linear import FeedForward
-from internlm.moe.experts import Experts
+from internlm.moe.sharded_moe import GShardMOELayer
-from internlm.moe.sharded_moe import GShardMOELayer, TopKGate
 from internlm.utils.logger import get_logger
 
 # global llm logger
@@ -40,7 +39,7 @@ class MoE(torch.nn.Module):
         hidden_size,
         num_experts=1,
         ep_size=1,
-        k=1,
+        topk=1,
         capacity_factor=1.0,
         eval_capacity_factor=1.0,
         min_capacity=4,
@@ -66,43 +65,21 @@ class MoE(torch.nn.Module):
             "Unsupported noisy_gate_policy: " + noisy_gate_policy
         )
 
-        # for elastic expert paralle, experts may have multiple groups
-        expert_group_name = f"moe_ep_size_{self.ep_size}"
-        if expert_group_name not in gpc.expert_parallel_group_names:
-            gpc.expert_parallel_group_names.append(expert_group_name)
-        experts = torch.nn.ModuleList(
-            [
-                FeedForward(
-                    hidden_size,
-                    int(hidden_size * gpc.config.model.mlp_ratio),
-                    out_features=hidden_size,
-                    process_group=gpc.get_group(ParallelMode.TENSOR),
-                    bias=False,
-                    device=device,
-                    dtype=dtype,
-                )
-                for _ in range(self.num_local_experts)
-            ]
-        )
-        experts = Experts(experts, self.num_local_experts, expert_group_name)
-
         if using_default_moe:
             self.moe_layer = GShardMOELayer(
-                TopKGate(
+                hidden_size,
-                    hidden_size,
-                    num_experts,
-                    k,
-                    capacity_factor,
-                    eval_capacity_factor,
-                    min_capacity,
-                    noisy_gate_policy,
-                    drop_tokens,
-                    use_rts,
-                ),
-                experts,
                 gpc.get_group(ParallelMode.EXPERT),
-                self.ep_size,
+                ep_size,
-                self.num_local_experts,
+                num_experts,
+                topk,
+                capacity_factor,
+                eval_capacity_factor,
+                min_capacity,
+                noisy_gate_policy,
+                drop_tokens,
+                use_rts,
+                device,
+                dtype,
             )
 
         # residual network, see https://arxiv.org/pdf/2201.05596.pdf, seems useful for convergence

internlm/moe/base_moe.py

@@ -1,7 +1,10 @@
-from typing import TYPE_CHECKING
+from typing import TYPE_CHECKING, Union
 
 from torch import Tensor
-from torch.nn import Module
+from torch.nn import Module, ModuleList
+
+from internlm.core.context import global_context as gpc
+from internlm.moe.experts import Experts
 
 if TYPE_CHECKING:
     Base = Module[Tensor]
@@ -14,10 +17,16 @@ class BaseMoELayer(Base):
     Base MoE Layer.
     """
 
-    def __init__(self, gate: Module, experts: Module, ep_group, ep_size, num_local_experts: int) -> None:
+    def __init__(
+        self, gate: Module, experts: Union[Module, ModuleList], ep_group, ep_size: int, num_local_experts: int
+    ) -> None:
         super().__init__()
+        # for elastic expert paralle, experts may have multiple groups
+        expert_group_name = f"moe_ep_size_{ep_size}"
+        if expert_group_name not in gpc.expert_parallel_group_names:
+            gpc.expert_parallel_group_names.append(expert_group_name)
         self.gate = gate
-        self.experts = experts
+        self.experts = Experts(experts, num_local_experts, expert_group_name)
         self.ep_group = ep_group
         self.ep_size = ep_size
         self.num_local_experts = num_local_experts

internlm/moe/sharded_moe.py

@@ -12,6 +12,9 @@ import torch.nn.functional as F
 from torch import Tensor
 from torch.nn import Module
 
+from internlm.core.context import ParallelMode
+from internlm.core.context import global_context as gpc
+from internlm.model.linear import FeedForward
 from internlm.utils.logger import get_logger
 from internlm.utils.megatron_timers import megatron_timer as timer
 
@@ -379,8 +382,52 @@ class GShardMOELayer(BaseMoELayer):
             expert network
     """
 
-    def __init__(self, gate: Module, experts: Module, ep_group, ep_size, num_local_experts: int) -> None:
+    def __init__(
-        super().__init__(gate, experts, ep_group, ep_size, num_local_experts)
+        self,
+        hidden_size,
+        ep_group,
+        ep_size: int,
+        num_experts: int,
+        topk,
+        capacity_factor,
+        eval_capacity_factor,
+        min_capacity,
+        noisy_gate_policy,
+        drop_tokens,
+        use_rts,
+        device=None,
+        dtype=None,
+    ) -> None:
+        super().__init__(
+            TopKGate(
+                hidden_size,
+                num_experts,
+                topk,
+                capacity_factor,
+                eval_capacity_factor,
+                min_capacity,
+                noisy_gate_policy,
+                drop_tokens,
+                use_rts,
+            ),
+            torch.nn.ModuleList(
+                [
+                    FeedForward(
+                        hidden_size,
+                        int(hidden_size * gpc.config.model.mlp_ratio),
+                        out_features=hidden_size,
+                        process_group=gpc.get_group(ParallelMode.TENSOR),
+                        bias=False,
+                        device=device,
+                        dtype=dtype,
+                    )
+                    for _ in range(num_experts // ep_size)
+                ]
+            ),
+            ep_group,
+            ep_size,
+            num_experts // ep_size,
+        )
 
         self.time_falltoall = 0.0
         self.time_salltoall = 0.0