Fix E266 flake8 warning (x90).

transformers/convert_xlm_original_pytorch_checkpoint_to_pytorch.py

@@ -70,7 +70,7 @@ def convert_xlm_checkpoint_to_pytorch(xlm_checkpoint_path, pytorch_dump_folder_p
 
 if __name__ == "__main__":
     parser = argparse.ArgumentParser()
-    ## Required parameters
+    # Required parameters
     parser.add_argument(
         "--xlm_checkpoint_path", default=None, type=str, required=True, help="Path the official PyTorch dump."
     )

transformers/convert_xlnet_original_tf_checkpoint_to_pytorch.py

@@ -82,7 +82,7 @@ def convert_xlnet_checkpoint_to_pytorch(
 
 if __name__ == "__main__":
     parser = argparse.ArgumentParser()
-    ## Required parameters
+    # Required parameters
     parser.add_argument(
         "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
     )

transformers/modeling_distilbert.py

@@ -47,7 +47,7 @@ DISTILBERT_PRETRAINED_MODEL_ARCHIVE_MAP = {
 }
 
 
-### UTILS AND BUILDING BLOCKS OF THE ARCHITECTURE ###
+# UTILS AND BUILDING BLOCKS OF THE ARCHITECTURE #
 def gelu(x):
     return 0.5 * x * (1.0 + torch.erf(x / math.sqrt(2.0)))
 
@@ -327,7 +327,7 @@ class Transformer(nn.Module):
         return outputs  # last-layer hidden state, (all hidden states), (all attentions)
 
 
-### INTERFACE FOR ENCODER AND TASK SPECIFIC MODEL ###
+# INTERFACE FOR ENCODER AND TASK SPECIFIC MODEL #
 class DistilBertPreTrainedModel(PreTrainedModel):
     """ An abstract class to handle weights initialization and
         a simple interface for downloading and loading pretrained models.

transformers/modeling_tf_distilbert.py

@@ -42,7 +42,7 @@ TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_MAP = {
 }
 
 
-### UTILS AND BUILDING BLOCKS OF THE ARCHITECTURE ###
+# UTILS AND BUILDING BLOCKS OF THE ARCHITECTURE #
 def gelu(x):
     """ Gaussian Error Linear Unit.
     Original Implementation of the gelu activation function in Google Bert repo when initially created.
@@ -463,7 +463,7 @@ class TFDistilBertMainLayer(tf.keras.layers.Layer):
         return tfmr_output  # last-layer hidden-state, (all hidden_states), (all attentions)
 
 
-### INTERFACE FOR ENCODER AND TASK SPECIFIC MODEL ###
+# INTERFACE FOR ENCODER AND TASK SPECIFIC MODEL #
 class TFDistilBertPreTrainedModel(TFPreTrainedModel):
     """ An abstract class to handle weights initialization and
         a simple interface for downloading and loading pretrained models.

transformers/modeling_tf_pytorch_utils.py

@@ -67,7 +67,8 @@ def convert_tf_weight_name_to_pt_weight_name(tf_name, start_prefix_to_remove="")
 
 
 #####################
-### PyTorch => TF 2.0
+# PyTorch => TF 2.0 #
+#####################
 
 
 def load_pytorch_checkpoint_in_tf2_model(tf_model, pytorch_checkpoint_path, tf_inputs=None, allow_missing_keys=False):
@@ -197,7 +198,8 @@ def load_pytorch_weights_in_tf2_model(tf_model, pt_state_dict, tf_inputs=None, a
 
 
 #####################
-### TF 2.0 => PyTorch
+# TF 2.0 => PyTorch #
+#####################
 
 
 def load_tf2_checkpoint_in_pytorch_model(pt_model, tf_checkpoint_path, tf_inputs=None, allow_missing_keys=False):

transformers/modeling_tf_transfo_xl.py

@@ -79,23 +79,23 @@ class TFPositionwiseFF(tf.keras.layers.Layer):
 
     def call(self, inp, training=False):
         if self.pre_lnorm:
-            ##### layer normalization + positionwise feed-forward
+            # layer normalization + positionwise feed-forward
             core_out = self.layer_norm(inp)
             core_out = self.layer_1(core_out)
             core_out = self.drop_1(core_out, training=training)
             core_out = self.layer_2(core_out)
             core_out = self.drop_2(core_out, training=training)
 
-            ##### residual connection
+            # residual connection
             output = core_out + inp
         else:
-            ##### positionwise feed-forward
+            # positionwise feed-forward
             core_out = self.layer_1(inp)
             core_out = self.drop_1(core_out, training=training)
             core_out = self.layer_2(core_out)
             core_out = self.drop_2(core_out, training=training)
 
-            ##### residual connection + layer normalization
+            # residual connection + layer normalization
             output = self.layer_norm(inp + core_out)
 
         return output
@@ -206,7 +206,7 @@ class TFRelPartialLearnableMultiHeadAttn(tf.keras.layers.Layer):
 
         r_head_k = tf.reshape(r_head_k, (rlen, self.n_head, self.d_head))  # qlen x n_head x d_head
 
-        #### compute attention score
+        # compute attention score
         rw_head_q = w_head_q + self.r_w_bias  # qlen x bsz x n_head x d_head
         AC = tf.einsum("ibnd,jbnd->ijbn", rw_head_q, w_head_k)  # qlen x klen x bsz x n_head
 
@@ -218,7 +218,7 @@ class TFRelPartialLearnableMultiHeadAttn(tf.keras.layers.Layer):
         attn_score = AC + BD
         attn_score = attn_score * self.scale
 
-        #### compute attention probability
+        # compute attention probability
         if attn_mask is not None:
             attn_mask_t = attn_mask[:, :, None, None]
             attn_score = attn_score * (1 - attn_mask_t) - 1e30 * attn_mask_t
@@ -231,22 +231,22 @@ class TFRelPartialLearnableMultiHeadAttn(tf.keras.layers.Layer):
         if head_mask is not None:
             attn_prob = attn_prob * head_mask
 
-        #### compute attention vector
+        # compute attention vector
         attn_vec = tf.einsum("ijbn,jbnd->ibnd", attn_prob, w_head_v)
 
         # [qlen x bsz x n_head x d_head]
         attn_vec_sizes = shape_list(attn_vec)
         attn_vec = tf.reshape(attn_vec, (attn_vec_sizes[0], attn_vec_sizes[1], self.n_head * self.d_head))
 
-        ##### linear projection
+        # linear projection
         attn_out = self.o_net(attn_vec)
         attn_out = self.drop(attn_out, training=training)
 
         if self.pre_lnorm:
-            ##### residual connection
+            # residual connection
             outputs = [w + attn_out]
         else:
-            ##### residual connection + layer normalization
+            # residual connection + layer normalization
             outputs = [self.layer_norm(w + attn_out)]
 
         if self.output_attentions:

transformers/modeling_tf_xlnet.py

@@ -190,7 +190,7 @@ class TFXLNetRelativeAttention(tf.keras.layers.Layer):
         (h, g, attn_mask_h, attn_mask_g, r, seg_mat, mems, target_mapping, head_mask) = inputs
 
         if g is not None:
-            ###### Two-stream attention with relative positional encoding.
+            # Two-stream attention with relative positional encoding.
             # content based attention score
             if mems is not None and len(shape_list(mems)) > 1:
                 cat = tf.concat([mems, h], axis=0)
@@ -206,7 +206,7 @@ class TFXLNetRelativeAttention(tf.keras.layers.Layer):
             # position-based key head
             k_head_r = tf.einsum("ibh,hnd->ibnd", r, self.r)
 
-            ##### h-stream
+            # h-stream
             # content-stream query head
             q_head_h = tf.einsum("ibh,hnd->ibnd", h, self.q)
 
@@ -221,7 +221,7 @@ class TFXLNetRelativeAttention(tf.keras.layers.Layer):
             # post processing
             output_h = self.post_attention([h, attn_vec_h], training=training)
 
-            ##### g-stream
+            # g-stream
             # query-stream query head
             q_head_g = tf.einsum("ibh,hnd->ibnd", g, self.q)
 
@@ -251,7 +251,7 @@ class TFXLNetRelativeAttention(tf.keras.layers.Layer):
                 attn_prob = attn_prob_h, attn_prob_g
 
         else:
-            ###### Multi-head attention with relative positional encoding
+            # Multi-head attention with relative positional encoding
             if mems is not None and len(shape_list(mems)) > 1:
                 cat = tf.concat([mems, h], axis=0)
             else:
@@ -552,7 +552,7 @@ class TFXLNetMainLayer(tf.keras.layers.Layer):
 
         dtype_float = tf.bfloat16 if self.use_bfloat16 else tf.float32
 
-        ##### Attention mask
+        # Attention mask
         # causal attention mask
         if self.attn_type == "uni":
             attn_mask = self.create_mask(qlen, mlen)
@@ -597,7 +597,7 @@ class TFXLNetMainLayer(tf.keras.layers.Layer):
         else:
             non_tgt_mask = None
 
-        ##### Word embeddings and prepare h & g hidden states
+        # Word embeddings and prepare h & g hidden states
         if inputs_embeds is not None:
             word_emb_k = inputs_embeds
         else:
@@ -612,7 +612,7 @@ class TFXLNetMainLayer(tf.keras.layers.Layer):
         else:
             output_g = None
 
-        ##### Segment embedding
+        # Segment embedding
         if token_type_ids is not None:
             # Convert `token_type_ids` to one-hot `seg_mat`
             mem_pad = tf.zeros([mlen, bsz], dtype=tf.int32)
@@ -624,7 +624,7 @@ class TFXLNetMainLayer(tf.keras.layers.Layer):
         else:
             seg_mat = None
 
-        ##### Positional encoding
+        # Positional encoding
         pos_emb = self.relative_positional_encoding(qlen, klen, bsz=bsz, dtype=dtype_float)
         pos_emb = self.dropout(pos_emb, training=training)
 

transformers/modeling_transfo_xl.py

@@ -213,16 +213,16 @@ class PositionwiseFF(nn.Module):
 
     def forward(self, inp):
         if self.pre_lnorm:
-            ##### layer normalization + positionwise feed-forward
+            # layer normalization + positionwise feed-forward
             core_out = self.CoreNet(self.layer_norm(inp))
 
-            ##### residual connection
+            # residual connection
             output = core_out + inp
         else:
-            ##### positionwise feed-forward
+            # positionwise feed-forward
             core_out = self.CoreNet(inp)
 
-            ##### residual connection + layer normalization
+            # residual connection + layer normalization
             output = self.layer_norm(inp + core_out)
 
         return output
@@ -316,7 +316,7 @@ class RelPartialLearnableMultiHeadAttn(nn.Module):
 
         r_head_k = r_head_k.view(rlen, self.n_head, self.d_head)  # qlen x n_head x d_head
 
-        #### compute attention score
+        # compute attention score
         rw_head_q = w_head_q + self.r_w_bias  # qlen x bsz x n_head x d_head
         AC = torch.einsum("ibnd,jbnd->ijbn", (rw_head_q, w_head_k))  # qlen x klen x bsz x n_head
 
@@ -328,7 +328,7 @@ class RelPartialLearnableMultiHeadAttn(nn.Module):
         attn_score = AC + BD
         attn_score.mul_(self.scale)
 
-        #### compute attention probability
+        # compute attention probability
         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:
@@ -352,21 +352,21 @@ class RelPartialLearnableMultiHeadAttn(nn.Module):
         if head_mask is not None:
             attn_prob = attn_prob * head_mask
 
-        #### compute attention vector
+        # compute attention vector
         attn_vec = torch.einsum("ijbn,jbnd->ibnd", (attn_prob, w_head_v))
 
         # [qlen x bsz x n_head x d_head]
         attn_vec = attn_vec.contiguous().view(attn_vec.size(0), attn_vec.size(1), self.n_head * self.d_head)
 
-        ##### linear projection
+        # linear projection
         attn_out = self.o_net(attn_vec)
         attn_out = self.drop(attn_out)
 
         if self.pre_lnorm:
-            ##### residual connection
+            # residual connection
             outputs = [w + attn_out]
         else:
-            ##### residual connection + layer normalization
+            # residual connection + layer normalization
             outputs = [self.layer_norm(w + attn_out)]
 
         if self.output_attentions:

transformers/modeling_xlnet.py

@@ -330,7 +330,7 @@ class XLNetRelativeAttention(nn.Module):
 
     def forward(self, h, g, attn_mask_h, attn_mask_g, r, seg_mat, mems=None, target_mapping=None, head_mask=None):
         if g is not None:
-            ###### Two-stream attention with relative positional encoding.
+            # Two-stream attention with relative positional encoding.
             # content based attention score
             if mems is not None and mems.dim() > 1:
                 cat = torch.cat([mems, h], dim=0)
@@ -346,7 +346,7 @@ class XLNetRelativeAttention(nn.Module):
             # position-based key head
             k_head_r = torch.einsum("ibh,hnd->ibnd", r, self.r)
 
-            ##### h-stream
+            # h-stream
             # content-stream query head
             q_head_h = torch.einsum("ibh,hnd->ibnd", h, self.q)
 
@@ -361,7 +361,7 @@ class XLNetRelativeAttention(nn.Module):
             # post processing
             output_h = self.post_attention(h, attn_vec_h)
 
-            ##### g-stream
+            # g-stream
             # query-stream query head
             q_head_g = torch.einsum("ibh,hnd->ibnd", g, self.q)
 
@@ -391,7 +391,7 @@ class XLNetRelativeAttention(nn.Module):
                 attn_prob = attn_prob_h, attn_prob_g
 
         else:
-            ###### Multi-head attention with relative positional encoding
+            # Multi-head attention with relative positional encoding
             if mems is not None and mems.dim() > 1:
                 cat = torch.cat([mems, h], dim=0)
             else:
@@ -804,7 +804,7 @@ class XLNetModel(XLNetPreTrainedModel):
         dtype_float = next(self.parameters()).dtype
         device = next(self.parameters()).device
 
-        ##### Attention mask
+        # Attention mask
         # causal attention mask
         if self.attn_type == "uni":
             attn_mask = self.create_mask(qlen, mlen)
@@ -849,7 +849,7 @@ class XLNetModel(XLNetPreTrainedModel):
         else:
             non_tgt_mask = None
 
-        ##### Word embeddings and prepare h & g hidden states
+        # Word embeddings and prepare h & g hidden states
         if inputs_embeds is not None:
             word_emb_k = inputs_embeds
         else:
@@ -864,7 +864,7 @@ class XLNetModel(XLNetPreTrainedModel):
         else:
             output_g = None
 
-        ##### Segment embedding
+        # Segment embedding
         if token_type_ids is not None:
             # Convert `token_type_ids` to one-hot `seg_mat`
             if mlen > 0:
@@ -879,7 +879,7 @@ class XLNetModel(XLNetPreTrainedModel):
         else:
             seg_mat = None
 
-        ##### Positional encoding
+        # Positional encoding
         pos_emb = self.relative_positional_encoding(qlen, klen, bsz=bsz)
         pos_emb = self.dropout(pos_emb)
 

transformers/optimization_tf.py

@@ -178,7 +178,7 @@ class AdamWeightDecay(tf.keras.optimizers.Adam):
         return True
 
 
-## Inspired from https://github.com/OpenNMT/OpenNMT-tf/blob/master/opennmt/optimizers/utils.py
+# Inspired from https://github.com/OpenNMT/OpenNMT-tf/blob/master/opennmt/optimizers/utils.py
 class GradientAccumulator(object):
     """Distribution strategies-aware gradient accumulation utility."""