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@ -3,7 +3,7 @@ PyTorch BLOOM model that implements several memory-efficient modes.
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Based on https://github.com/huggingface/transformers/commit/ca2a55e9dfb245527b5e1c954fec6ffbb7aef07b
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See commit history for authorship.
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"""
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from typing import Tuple
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from typing import Tuple, Union
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import torch
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import torch.nn.functional as F
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@ -319,16 +319,16 @@ class BloomForCausalLM(BloomPreTrainedModel):
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def __init__(self, config):
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super().__init__(config)
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self.transformer = BloomModel(config)
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self.lm_head = LMHeadForCausalLM(config, self.transformer.word_embeddings)
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self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
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# Initialize weights and apply final processing
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self.post_init()
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def get_output_embeddings(self):
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return self.lm_head.word_embeddings
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return self.lm_head
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def set_output_embeddings(self, new_embeddings):
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self.lm_head.word_embeddings.weight = new_embeddings.weight
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self.lm_head = new_embeddings
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def prepare_inputs_for_generation(self, input_ids, past=None, **kwargs):
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# only last token for inputs_ids if past is defined in kwargs
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@ -361,7 +361,20 @@ class BloomForCausalLM(BloomPreTrainedModel):
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output_type=CausalLMOutputWithCrossAttentions,
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config_class=_CONFIG_FOR_DOC,
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)
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def forward(self, input_ids=None, labels=None, return_dict=None, **kwargs):
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def forward(
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self,
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input_ids=None,
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past_key_values=None,
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attention_mask=None,
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position_ids=None,
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head_mask=None,
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inputs_embeds=None,
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labels=None,
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use_cache=None,
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output_attentions=None,
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output_hidden_states=None,
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return_dict=None,
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) -> Union[Tuple[torch.Tensor], CausalLMOutputWithCrossAttentions]:
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r"""
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labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
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Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set
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@ -369,8 +382,21 @@ class BloomForCausalLM(BloomPreTrainedModel):
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are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]`
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"""
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return_dict = return_dict if return_dict is not None else self.config.use_return_dict
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transformer_outputs = self.transformer.forward(input_ids=input_ids, return_dict=return_dict, **kwargs)
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transformer_outputs = self.transformer(
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input_ids,
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past_key_values=past_key_values,
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attention_mask=attention_mask,
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position_ids=position_ids,
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head_mask=head_mask,
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inputs_embeds=inputs_embeds,
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use_cache=use_cache,
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output_attentions=output_attentions,
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output_hidden_states=output_hidden_states,
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return_dict=return_dict,
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)
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hidden_states = transformer_outputs[0]
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lm_logits = self.lm_head(hidden_states)
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loss = None
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@ -415,7 +441,7 @@ class BloomForCausalLM(BloomPreTrainedModel):
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""",
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BLOOM_START_DOCSTRING,
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)
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class LMHeadForCausalLM(nn.Module):
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class LMHead(nn.Module):
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def __init__(self, config, word_embeddings: nn.Embedding):
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super().__init__()
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self.word_embeddings = word_embeddings
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