implement post training

src/axolotl/monkeypatch/moe/moe.py

@@ -21,14 +21,37 @@ def __init__(self, experts, gate, hidden_dim, ffn_dim, num_experts, top_k):
         )
 
     def _post_training(self, model, name):
-        # get original weights back: reverse the concat + stack in the fused experts
+        # Get original weights back: reverse the concat + stack in the fused experts
         w1s, w3s = torch.split(torch.unbind(self.experts.experts.weight, dim=0), 2, dim=1)
         w2s = torch.unbind(self.experts.output_experts.weight, dim=0)
 
-        # TODO: recreate MoE class with original weights
-        experts = []
-        for i in range(self.num_experts):
-            pass
+        # Recreate the structure of the original MixtralSparseMoeBlock
+        original_moe = nn.Module()
+        original_moe.hidden_dim = self.hidden_dim
+        original_moe.ffn_dim = self.ffn_dim
+        original_moe.num_experts = self.num_experts
+        original_moe.top_k = self.top_k
+
+        # Recreate the gating module
+        original_moe.gate = nn.Linear(self.hidden_dim, self.num_experts, bias=False)
+        original_moe.gate.weight.data = self.gate.weight.data
+
+        # Recreate the experts as a ModuleList
+        original_moe.experts = nn.ModuleList()
+        for expert_idx in range(self.num_experts):
+            expert = nn.Module()
+            expert.w1 = nn.Linear(self.hidden_dim, 2 * self.ffn_dim, bias=False)
+            expert.w2 = nn.Linear(self.ffn_dim, self.hidden_dim, bias=False)
+            expert.w3 = nn.Linear(self.hidden_dim, 2 * self.ffn_dim, bias=False)
+            expert.act_fn = self.experts.activation
+
+            expert.w1.weight.data = torch.cat([w1s[expert_idx], w3s[expert_idx]], dim=0)
+            expert.w2.weight.data = w2s[expert_idx]
+
+            original_moe.experts.append(expert)
+
+        # Replace the SparseMoeBlock with the recreated MixtralSparseMoeBlock structure
+        setattr(model, name, original_moe)
 
     def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
         batch_size, sequence_length, hidden_dim = hidden_states.shape