Added patch to remaining models

pytorch_transformers/modeling_bert.py

@@ -337,12 +337,14 @@ class BertAttention(nn.Module):
         super(BertAttention, self).__init__()
         self.self = BertSelfAttention(config)
         self.output = BertSelfOutput(config)
+        self.pruned_heads = []
 
     def prune_heads(self, heads):
         if len(heads) == 0:
             return
         mask = torch.ones(self.self.num_attention_heads, self.self.attention_head_size)
         for head in heads:
+            head -= len(list(filter(lambda h: h < head, self.pruned_heads)))
             mask[head] = 0
         mask = mask.view(-1).contiguous().eq(1)
         index = torch.arange(len(mask))[mask].long()
@@ -354,6 +356,7 @@ class BertAttention(nn.Module):
         # Update hyper params
         self.self.num_attention_heads = self.self.num_attention_heads - len(heads)
         self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads
+        self.pruned_heads.extend(heads)
 
     def forward(self, input_tensor, attention_mask, head_mask=None):
         self_outputs = self.self(input_tensor, attention_mask, head_mask)

pytorch_transformers/modeling_openai.py

@@ -249,12 +249,14 @@ class Attention(nn.Module):
         self.c_proj = Conv1D(n_state, nx)
         self.attn_dropout = nn.Dropout(config.attn_pdrop)
         self.resid_dropout = nn.Dropout(config.resid_pdrop)
+        self.pruned_heads = []
 
     def prune_heads(self, heads):
         if len(heads) == 0:
             return
         mask = torch.ones(self.n_head, self.split_size // self.n_head)
         for head in heads:
+            head -= len(list(filter(lambda h: h < head, self.pruned_heads)))
             mask[head] = 0
         mask = mask.view(-1).contiguous().eq(1)
         index = torch.arange(len(mask))[mask].long()
@@ -265,6 +267,7 @@ class Attention(nn.Module):
         # Update hyper params
         self.split_size = (self.split_size // self.n_head) * (self.n_head - len(heads))
         self.n_head = self.n_head - len(heads)
+        self.pruned_heads.extend(heads)
 
     def _attn(self, q, k, v, head_mask=None):
         w = torch.matmul(q, k)

pytorch_transformers/modeling_xlm.py

@@ -271,6 +271,7 @@ class MultiHeadAttention(nn.Module):
         self.k_lin = nn.Linear(dim, dim)
         self.v_lin = nn.Linear(dim, dim)
         self.out_lin = nn.Linear(dim, dim)
+        self.pruned_heads = []
 
     def prune_heads(self, heads):
         attention_head_size = self.dim // self.n_heads
@@ -278,6 +279,7 @@ class MultiHeadAttention(nn.Module):
             return
         mask = torch.ones(self.n_heads, attention_head_size)
         for head in heads:
+            head -= len(list(filter(lambda h: h < head, self.pruned_heads)))
             mask[head] = 0
         mask = mask.view(-1).contiguous().eq(1)
         index = torch.arange(len(mask))[mask].long()
@@ -289,6 +291,7 @@ class MultiHeadAttention(nn.Module):
         # Update hyper params
         self.n_heads = self.n_heads - len(heads)
         self.dim = attention_head_size * self.n_heads
+        self.pruned_heads.extend(heads)
 
     def forward(self, input, mask, kv=None, cache=None, head_mask=None):
         """