diff --git a/models/s3rec.py b/models/s3rec.py
index 263c52c..06f513a 100644
--- a/models/s3rec.py
+++ b/models/s3rec.py
@@ -1,3 +1,4 @@
+import math
 import torch
 import torch.nn as nn
 
@@ -16,7 +17,7 @@ def __init__(self, cfg, num_items, attributes_count):
         self.positional_encoding = nn.Parameter(torch.rand(self.cfg.max_seq_len, self.cfg.embed_size))
 
         self.multihead_attns = nn.ModuleList(
-            [nn.MultiheadAttention(self.cfg.embed_size, self.cfg.num_heads, batch_first=True) for _ in range(self.cfg.num_blocks)])
+            [MultiHeadAttention(self.cfg.embed_size, self.cfg.num_heads) for _ in range(self.cfg.num_blocks)])
         self.layernorm1s = nn.ModuleList(
             [nn.LayerNorm(self.cfg.embed_size) for _ in range(self.cfg.num_blocks)])
         
@@ -55,11 +56,12 @@ def _embedding_layer(self, X):
     def _self_attention_block(self, X, padding_mask, attn_mask):
         for multihead_attn, ffn1, ffn2, layernorm1, layernorm2 in zip(
                 self.multihead_attns, self.ffn1s, self.ffn2s, self.layernorm1s, self.layernorm2s):
-            # multi-head self-attention
-            merged_mask,_ = multihead_attn.merge_masks(attn_mask, padding_mask, X)
-            attn_output, attn_output_weights = multihead_attn(
-                X, X, X, #key_padding_mask=padding_mask, 
-                is_causal=True, attn_mask=attn_mask)
+            # # multi-head self-attention
+            # merged_mask,_ = multihead_attn.merge_masks(attn_mask, padding_mask, X)
+            # attn_output, attn_output_weights = multihead_attn(
+            #     X, X, X, #key_padding_mask=padding_mask, 
+            #     is_causal=True, attn_mask=attn_mask)
+            attn_output = multihead_attn(X, X, X, padding_mask, attn_mask)
             # dropout
             attn_output = self.dropout(attn_output)
             # add & norm
@@ -161,3 +163,48 @@ def sp(self, context_attention_output, subsequence_attention_output):
         s_tilde = subsequence_attention_output[:, -1, :] # [ B H ]
         SW = self.sp_weight(s)
         return torch.einsum('bi,bi->b', SW, s_tilde) # [ B ]
+
+
+class MultiHeadAttention(nn.Module):
+    def __init__(self, embed_size, num_heads):
+        super().__init__()
+        # self.multihead_attns = nn.ModuleList(
+        # [nn.MultiheadAttention(self.cfg.embed_size, self.cfg.num_heads, batch_first=True) for _ in range(self.cfg.num_blocks)])
+        self.embed_size = embed_size
+        self.num_heads = num_heads
+        self.q_weights = nn.ModuleList(
+            [nn.Linear(self.embed_size, self.embed_size, bias=False) for _ in range(self.num_heads)])
+        self.k_weights = nn.ModuleList(
+            [nn.Linear(self.embed_size, self.embed_size, bias=False) for _ in range(self.num_heads)])
+        self.v_weights = nn.ModuleList(
+            [nn.Linear(self.embed_size, self.embed_size, bias=False) for _ in range(self.num_heads)])
+        self.output = nn.Linear(num_heads * embed_size, embed_size)
+
+    def forward(self, q, k, v, padding_mask, attn_mask):
+        # merged_mask,_ = multihead_attn.merge_masks(attn_mask, padding_mask, X)
+        # attn_output, attn_output_weights = multihead_attn(
+        #     X, X, X, #key_padding_mask=padding_mask, 
+        #     is_causal=True, attn_mask=attn_mask)
+        # # dropout
+        attention_outputs = []
+        for q_weight, k_weight, v_weight in zip(self.q_weights, self.k_weights, self.v_weights):
+            Q = q_weight(q)
+            K = k_weight(k) # (B, L, E)
+            K = K * padding_mask.unsqueeze(2) # (B, L)
+            logger.info(K)
+            V = v_weight(v)
+
+            attention_score = torch.matmul(Q, K.permute(0,2,1).contiguous()) # (B, L, L)
+            attention_score /= math.sqrt(self.embed_size) 
+            attention_score = attention_score * attn_mask.unsqueeze(0)
+            logger.info(attention_score)
+            attention_score = torch.nn.functional.softmax(attention_score)
+            logger.info(attention_score)
+            attention_score = torch.matmul(attention_score, V) # (B, L, E)
+            # torch.einsum('bi,bi->b', Q, K) # (batch, seq_len, embed_size)
+            attention_outputs.append(attention_score) # (H, B, L, E)
+        
+        attention_scores = torch.cat(attention_outputs, dim=-1) # (B, L, E*H)
+
+        return self.output(attention_scores) # (B, L, E)
+        
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