change expert_fn structure

fmoe/layers.py

@@ -112,8 +112,8 @@ def _fmoe_general_global_forward(inp, gate, expert_fn, num_expert, world_size):
 
 class FMoE(nn.Module):
     r'''
-    A general moe implementation that supports an arbitrary module as the expert
-    Either `expert` or `expert_fn` is required.
+    A general moe implementation that supports an arbitrary module as the
+    expert.
     * `num_expert` stands for the number of experts on **each** worker.
     * `world_size` stands for the total number of workers that contains
     different experts.
@@ -126,12 +126,9 @@ class FMoE(nn.Module):
     * `gate` is a gate class which can found in `fmoe.gates`.
     * `expert` can be specified as a module class, it is used to generate
     `num_expert` expert modules.
-    * `expert_fn` is specified as a callable object or a function, it will be
-    called during forward, giving the input tensor (contiguous) and the array of
-    the number of input feature to each expert as input.
     '''
     def __init__(self, num_expert=32, d_model=1024, world_size=1, mp_group=None,
-            top_k=2, gate=NaiveGate, expert=None, expert_fn=None):
+            top_k=2, gate=NaiveGate, expert=None):
         super().__init__()
         self.num_expert = num_expert
         self.d_model = d_model
@@ -145,10 +142,12 @@ class FMoE(nn.Module):
             self.mp_rank = mp_group.rank()
         self.top_k = top_k
         self.gate = gate(d_model, num_expert, world_size, top_k)
-        if expert_fn is None:
-            assert expert is not None, 'Either expert or expert_fn should be set'
+        if expert is not None:
             self.experts = [expert(d_model) for _ in range(num_expert)]
-            def expert_fn(inp, fwd_expert_count):
+
+    def expert_fn(self, inp, fwd_expert_count):
+        if isinstance(self.experts, nn.Module):
+            return self.experts(inp, fwd_expert_count)
         outputs = []
         base_idx = 0
         for i in range(self.num_expert):
@@ -157,7 +156,6 @@ class FMoE(nn.Module):
             outputs.append(self.experts[i](inp_slice))
             base_idx += batch_size
         return torch.cat(outputs, dim=0)
-        self.expert_fn = expert_fn
 
     def mark_parallel_comm(self):
         r'''
@@ -193,7 +191,8 @@ class FMoE(nn.Module):
         gate_top_k_idx, gate_score = self.gate(inp)
         # to: (BxLxtop_k) x d_model
         inp = inp.repeat_interleave(repeats=self.top_k, dim=0)
-        x = _fmoe_general_global_forward(inp, gate_top_k_idx, self.expert_fn,
+        expert_fn = lambda inp, fec: self.expert_fn(inp, fec)
+        x = _fmoe_general_global_forward(inp, gate_top_k_idx, expert_fn,
                 self.num_expert, self.world_size)
         # to: (BxL) x top_k x d_model
         x = x.view(-1, self.top_k, self.d_model)

fmoe/transformer.py

@@ -49,11 +49,8 @@ class FMoETransformerMLP(FMoE):
         top_k=2,
         pre_lnorm=False
     ):
-        def expert_fn(inp, gate):
-            return self.experts(inp, gate)
         super().__init__(num_expert=num_expert, d_model=d_model, gate=gate,
-                top_k=top_k, world_size=world_size, mp_group=mp_group,
-                expert_fn=expert_fn)
+                top_k=top_k, world_size=world_size, mp_group=mp_group)
         self.experts = _Expert(num_expert, d_model, d_hidden, activation,
                 rank=self.mp_rank)
         self.pre_lnorm = pre_lnorm