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rename the attributes in the Bert Layer

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Since the preloading of weights relies on the name of the class's
attributes changing the namespace breaks loading pretrained weights on
Bert and all related models. I reverted `self_attention` to `attention`
and us `crossattention` for the decoder instead.
This commit is contained in:
Rémi Louf
2019-10-10 12:51:14 +02:00
parent 51261167b4
commit d7092d592c
1 changed files with 9 additions and 10 deletions
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19
transformers/modeling_bert.py
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@@ -321,25 +321,24 @@ class BertOutput(nn.Module):
class BertLayer(nn.Module): class BertLayer(nn.Module):
def __init__(self, config): def __init__(self, config):
super(BertLayer, self).__init__() super(BertLayer, self).__init__()
self.self_attention = BertAttention(config)
if getattr(config, "is_decoder", False):
self.attention = BertAttention(config) self.attention = BertAttention(config)
if getattr(config, "is_decoder", False):
self.crossattention = BertAttention(config)
self.intermediate = BertIntermediate(config) self.intermediate = BertIntermediate(config)
self.output = BertOutput(config) self.output = BertOutput(config)
def forward(self, hidden_states, attention_mask=None, head_mask=None, encoder_hidden_state=None): def forward(self, hidden_states, attention_mask=None, head_mask=None, encoder_hidden_state=None):
self_attention_outputs = self.self_attention(hidden_states, attention_mask, head_mask) attention_outputs = self.attention(hidden_states, attention_mask, head_mask)
self_attention_output = self_attention_outputs[0] attention_output = attention_outputs[0]
attention_outputs = self_attention_outputs
if encoder_hidden_state: if encoder_hidden_state:
try: try:
attention_outputs = self.attention(self_attention_output, attention_mask, head_mask, encoder_hidden_state) crossattention_outputs = self.crossattention(attention_output, attention_mask, head_mask, encoder_hidden_state)
except AttributeError as ae: except AttributeError as ae:
raise ae("you need to set `is_encoder` to True in the configuration to instantiate an encoder layer") raise ae("you need to set `is_encoder` to True in the configuration to instantiate an encoder layer")
attention_output = attention_outputs[0] crossattention_output = crossattention_outputs[0]
intermediate_output = self.intermediate(attention_output) intermediate_output = self.intermediate(crossattention_output)
layer_output = self.output(intermediate_output, attention_output) layer_output = self.output(intermediate_output, attention_output)
outputs = (layer_output,) + attention_outputs[1:] # add attentions if we output them outputs = (layer_output,) + attention_outputs[1:] # add attentions if we output them
return outputs return outputs
@@ -633,7 +632,7 @@ class BertModel(BertPreTrainedModel):
See base class PreTrainedModel See base class PreTrainedModel
""" """
for layer, heads in heads_to_prune.items(): for layer, heads in heads_to_prune.items():
self.encoder.layer[layer].self_attention.prune_heads(heads) self.encoder.layer[layer].attention.prune_heads(heads)
def forward(self, input_ids, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None): def forward(self, input_ids, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None):
if attention_mask is None: if attention_mask is None:
@@ -737,7 +736,7 @@ class BertDecoderModel(BertPreTrainedModel):
""" """
for layer, heads in heads_to_prune.items(): for layer, heads in heads_to_prune.items():
self.decoder.layer[layer].attention.prune_heads(heads) self.decoder.layer[layer].attention.prune_heads(heads)
self.decoder.layer[layer].self_attention.prune_heads(heads) self.decoder.layer[layer].crossattention.prune_heads(heads)
def forward(self, input_ids, encoder_outputs, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None): def forward(self, input_ids, encoder_outputs, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None):
if attention_mask is None: if attention_mask is None: