Merge pull request #1249 from ziliwang/master

fixed: hard coding for max and min number will out of range in fp16, which will cause nan.

pytorch_transformers/modeling_transfo_xl.py

@@ -231,7 +231,7 @@ class PositionwiseFF(nn.Module):
 
 
 class MultiHeadAttn(nn.Module):
-    def __init__(self, n_head, d_model, d_head, dropout, dropatt=0, 
+    def __init__(self, n_head, d_model, d_head, dropout, dropatt=0,
                  pre_lnorm=False, r_r_bias=None, r_w_bias=None, output_attentions=False):
         super(MultiHeadAttn, self).__init__()
 
@@ -451,11 +451,19 @@ class RelPartialLearnableMultiHeadAttn(RelMultiHeadAttn):
         if attn_mask is not None and torch.sum(attn_mask).item():
             attn_mask = (attn_mask == 1)  # Switch to bool
             if attn_mask.dim() == 2:
-                attn_score = attn_score.float().masked_fill(
-                    attn_mask[None,:,:,None], -1e30).type_as(attn_score)
+                if next(self.parameters()).dtype == torch.float16:
+                    attn_score = attn_score.float().masked_fill(
+                        attn_mask[None,:,:,None], -65000).type_as(attn_score)
+                else:
+                    attn_score = attn_score.float().masked_fill(
+                        attn_mask[None,:,:,None], -1e30).type_as(attn_score)
             elif attn_mask.dim() == 3:
-                attn_score = attn_score.float().masked_fill(
-                    attn_mask[:,:,:,None], -1e30).type_as(attn_score)
+                if next(self.parameters()).dtype == torch.float16:
+                    attn_score = attn_score.float().masked_fill(
+                        attn_mask[:,:,:,None], -65000).type_as(attn_score)
+                else:
+                    attn_score = attn_score.float().masked_fill(
+                        attn_mask[:,:,:,None], -1e30).type_as(attn_score)
 
         # [qlen x klen x bsz x n_head]
         attn_prob = F.softmax(attn_score, dim=1)
@@ -587,7 +595,7 @@ class DecoderLayer(nn.Module):
         super(DecoderLayer, self).__init__()
 
         self.dec_attn = MultiHeadAttn(n_head, d_model, d_head, dropout, **kwargs)
-        self.pos_ff = PositionwiseFF(d_model, d_inner, dropout, 
+        self.pos_ff = PositionwiseFF(d_model, d_inner, dropout,
                                      pre_lnorm=kwargs.get('pre_lnorm'))
 
     def forward(self, dec_inp, dec_attn_mask=None, mems=None, head_mask=None):
@@ -607,7 +615,7 @@ class RelLearnableDecoderLayer(nn.Module):
 
         self.dec_attn = RelLearnableMultiHeadAttn(n_head, d_model, d_head, dropout,
                                          **kwargs)
-        self.pos_ff = PositionwiseFF(d_model, d_inner, dropout, 
+        self.pos_ff = PositionwiseFF(d_model, d_inner, dropout,
                                      pre_lnorm=kwargs.get('pre_lnorm'))
 
     def forward(self, dec_inp, r_emb, r_w_bias, r_bias, dec_attn_mask=None, mems=None, head_mask=None):
@@ -628,7 +636,7 @@ class RelPartialLearnableDecoderLayer(nn.Module):
 
         self.dec_attn = RelPartialLearnableMultiHeadAttn(n_head, d_model,
                             d_head, dropout, **kwargs)
-        self.pos_ff = PositionwiseFF(d_model, d_inner, dropout, 
+        self.pos_ff = PositionwiseFF(d_model, d_inner, dropout,
                                      pre_lnorm=kwargs.get('pre_lnorm'))
 
     def forward(self, dec_inp, r, dec_attn_mask=None, mems=None, head_mask=None):
@@ -645,7 +653,7 @@ class RelPartialLearnableDecoderLayer(nn.Module):
 
 
 class AdaptiveEmbedding(nn.Module):
-    def __init__(self, n_token, d_embed, d_proj, cutoffs, div_val=1, 
+    def __init__(self, n_token, d_embed, d_proj, cutoffs, div_val=1,
                  sample_softmax=False):
         super(AdaptiveEmbedding, self).__init__()
 
@@ -683,7 +691,7 @@ class AdaptiveEmbedding(nn.Module):
         else:
             param = next(self.parameters())
             inp_flat = inp.view(-1)
-            emb_flat = torch.zeros([inp_flat.size(0), self.d_proj], 
+            emb_flat = torch.zeros([inp_flat.size(0), self.d_proj],
                 dtype=param.dtype, device=param.device)
             for i in range(len(self.cutoffs)):
                 l_idx, r_idx = self.cutoff_ends[i], self.cutoff_ends[i + 1]
@@ -852,7 +860,7 @@ class TransfoXLModel(TransfoXLPreTrainedModel):
         self.n_head = config.n_head
         self.d_head = config.d_head
 
-        self.word_emb = AdaptiveEmbedding(config.n_token, config.d_embed, config.d_model, config.cutoffs, 
+        self.word_emb = AdaptiveEmbedding(config.n_token, config.d_embed, config.d_model, config.cutoffs,
                                           div_val=config.div_val)
 
         self.drop = nn.Dropout(config.dropout)
@@ -1011,7 +1019,7 @@ class TransfoXLModel(TransfoXLPreTrainedModel):
         hids = []
         attentions = []
         if self.attn_type == 0: # default
-            pos_seq = torch.arange(klen-1, -1, -1.0, device=word_emb.device, 
+            pos_seq = torch.arange(klen-1, -1, -1.0, device=word_emb.device,
                                    dtype=word_emb.dtype)
             if self.clamp_len > 0:
                 pos_seq.clamp_(max=self.clamp_len)
@@ -1165,7 +1173,7 @@ class TransfoXLLMHeadModel(TransfoXLPreTrainedModel):
             self.sampler = LogUniformSampler(config.n_token, config.sample_softmax)
         # use adaptive softmax (including standard softmax)
         else:
-            self.crit = ProjectedAdaptiveLogSoftmax(config.n_token, config.d_embed, config.d_model, 
+            self.crit = ProjectedAdaptiveLogSoftmax(config.n_token, config.d_embed, config.d_model,
                                                     config.cutoffs, div_val=config.div_val)
         self.init_weights()
         self.tie_weights()

pytorch_transformers/modeling_utils.py

@@ -140,7 +140,7 @@ class PreTrainedModel(nn.Module):
         Arguments:
 
             new_num_tokens: (`optional`) int:
-                New number of tokens in the embedding matrix. Increasing the size will add newly initialized vectors at the end. Reducing the size will remove vectors from the end. 
+                New number of tokens in the embedding matrix. Increasing the size will add newly initialized vectors at the end. Reducing the size will remove vectors from the end.
                 If not provided or None: does nothing and just returns a pointer to the input tokens ``torch.nn.Embeddings`` Module of the model.
 
         Return: ``torch.nn.Embeddings``
@@ -434,7 +434,10 @@ class PoolerStartLogits(nn.Module):
         x = self.dense(hidden_states).squeeze(-1)
 
         if p_mask is not None:
-            x = x * (1 - p_mask) - 1e30 * p_mask
+            if next(self.parameters()).dtype == torch.float16:
+                x = x * (1 - p_mask) - 65500 * p_mask
+            else:
+                x = x * (1 - p_mask) - 1e30 * p_mask
 
         return x