From f559b96af8773ddee9ed930c0b44f7c0dec75734 Mon Sep 17 00:00:00 2001
From: Mihirsinh Chauhan <112346682+MihirsinhChauhan@users.noreply.github.com>
Date: Sun, 23 Feb 2025 17:22:31 +0530
Subject: [PATCH] MOE implementation

---
 model/config.py |  2 ++
 model/model.py  | 63 ++++++++++++++++++++++++++++++++++++++++++++++---
 2 files changed, 62 insertions(+), 3 deletions(-)

diff --git a/model/config.py b/model/config.py
index 4bc7750..0f1173e 100644
--- a/model/config.py
+++ b/model/config.py
@@ -14,3 +14,5 @@ class Config:
     multiple_of: int = 128
     eps: float = 1e-6
     flash: bool = True
+    num_experts=8,
+    num_expert_per_tok=2
\ No newline at end of file
diff --git a/model/model.py b/model/model.py
index 1170cef..391ae34 100644
--- a/model/model.py
+++ b/model/model.py
@@ -177,6 +177,61 @@ def forward(self,x):
 
         return self.dropout(self.w2(F.silu(self.w1(x))*self.w3(x)))
 
+class MoEFFN(nn.Module):
+    def __init__(self,d_model, hidden_dim, num_experts, num_expert_per_tok,multiple_of, dropout):
+        super().__init__()
+        self.d_model = d_model
+        self.num_experts = num_experts
+        self.num_expert_per_tok = num_expert_per_tok  # Number of experts to route each token to
+        
+        if hidden_dim is None:
+            hidden_dim = 4 * d_model
+            hidden_dim = int(2/3 * hidden_dim)
+            hidden_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of)
+        
+        # Gate network to route tokens to experts
+        self.gate = nn.Linear(d_model, num_experts, bias=False)
+        
+        # Create multiple expert FFNs
+        self.experts = nn.ModuleList([
+            FFN(d_model, hidden_dim, multiple_of, dropout)
+            for _ in range(num_experts)
+        ])
+    def forward(self, x):
+        B, T, C = x.shape
+        
+        # Get routing probabilities
+        route_logits = self.gate(x)  # [B, T, num_experts]
+        
+        # Select top-k experts for each token
+        route_probs = F.softmax(route_logits, dim=-1)
+        scores, indices = torch.topk(route_probs, k=self.num_expert_per_tok, dim=-1)
+        
+        # Normalize the routing probabilities
+        scores = scores / scores.sum(dim=-1, keepdim=True)
+        
+        # Initialize output tensor
+        final_output = torch.zeros_like(x)
+        
+        # Process tokens through selected experts
+        for expert_idx in range(self.num_experts):
+            # Find which positions need this expert
+            expert_mask = (indices == expert_idx).any(dim=-1)  # [B, T]
+            if not expert_mask.any():
+                continue
+            
+            # Get the scores for this expert where it was selected
+            expert_scores = torch.zeros_like(route_probs[..., 0])  # [B, T]
+            expert_scores[indices == expert_idx] = scores[indices == expert_idx]
+            
+            # Process tokens through this expert
+            expert_output = self.experts[expert_idx](x)  # [B, T, C]
+            
+            # Add weighted output to final result
+            final_output += expert_output * expert_scores.unsqueeze(-1)
+            
+        return final_output
+
 class RMSNorm(nn.Module):
     def __init__(self,d_model,norm_eps=1e-6):
         super().__init__()
@@ -196,11 +251,13 @@ class TransformerBlock(nn.Module):
     def __init__(self,layer_id, config):
         super().__init__()
         self.attn = Attention(config)
-        self.ffn = FFN(
+        self.moe = MoEFFN(
             config.d_model,
             config.hidden_dim,
-            config.multiple_of,
-            config.dropout
+            num_experts=config.num_experts,
+            num_expert_per_tok=config.num_expert_per_tok,
+            multiple_of=config.multiple_of,
+            dropout=config.dropout
         )
         self.layer_id = layer_id
         self.attn_norm = RMSNorm(config.d_model, config.eps)