Closed pepinu closed 1 year ago
oh, I forgot to attach, I've updated all the kv_cache stuff from unpruned function and removed the syntax errors (? init function arg list looked like copy pasted by mistake), here it is:
class PrunedGPT2InferenceModel(GPT2PreTrainedModel):
def __init__(self, config, gpt, text_pos_emb, embeddings, norm, linear, kv_cache):
super().__init__(config)
self.transformer = gpt
self.text_pos_embedding = text_pos_emb
self.embeddings = embeddings
self.lm_head = nn.Sequential(norm, linear)
self.kv_cache = kv_cache
def store_mel_emb(self, mel_emb):
self.cached_mel_emb = mel_emb
def prepare_inputs_for_generation(self, input_ids, past=None, **kwargs):
token_type_ids = kwargs.get("token_type_ids", None)
if not self.kv_cache: past_key_values = None
# only last token for inputs_ids if past is defined in kwargs
if past:
input_ids = input_ids[:, -1].unsqueeze(-1)
if token_type_ids is not None:
token_type_ids = token_type_ids[:, -1].unsqueeze(-1)
attention_mask = kwargs.get("attention_mask", None)
position_ids = kwargs.get("position_ids", None)
if attention_mask is not None and position_ids is None:
# create position_ids on the fly for batch generation
position_ids = attention_mask.long().cumsum(-1) - 1
position_ids.masked_fill_(attention_mask == 0, 1)
if past:
position_ids = position_ids[:, -1].unsqueeze(-1)
else:
position_ids = None
return {
"input_ids": input_ids,
"past_key_values": past,
"use_cache": kwargs.get("use_cache"),
"position_ids": position_ids,
"attention_mask": attention_mask,
"token_type_ids": token_type_ids,
}
def forward(
self,
input_ids=None,
past_key_values=None
attention_mask=None,
position_ids=None, **kwargs
token_type_ids=None
head_mask=None
inputs_embeds=None
encoder_hidden_states=None
encoder_attention_mask=None
labels=None
use_cache=True
output_attentions=False
output_hidden_states=False
return_dict=True
):
assert self.cached_mel_emb is not None
assert inputs_embeds is None # Not supported by this inference model.
assert labels is None # Training not supported by this inference model.
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
# Create embedding
mel_len = self.cached_mel_emb.shape[1]
text_inputs = input_ids[:, mel_len:]
text_emb = self.embeddings(text_inputs)
text_emb = text_emb + self.text_pos_embedding(text_emb)
mel_emb = self.cached_mel_emb.repeat_interleave(text_emb.shape[0]//self.cached_mel_emb.shape[0], 0)
emb = torch.cat([mel_emb, text_emb], dim=1)
transformer_outputs = self.transformer(
inputs_embeds=emb,
past_key_values=past_key_values,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
hidden_states = transformer_outputs[0]
lm_logits = self.lm_head(hidden_states)
if not return_dict:
return (lm_logits,) + transformer_outputs[1:]
return CausalLMOutputWithCrossAttentions(
loss=None,
logits=lm_logits,
past_key_values=transformer_outputs.past_key_values,
hidden_states=transformer_outputs.hidden_states,
attentions=transformer_outputs.attentions,
cross_attentions=transformer_outputs.cross_attentions,
)
@staticmethod
def _reorder_cache(past, beam_idx):
"""
This function is used to re-order the :obj:`past_key_values` cache if
:meth:`~transformers.PreTrainedModel.beam_search` or :meth:`~transformers.PreTrainedModel.beam_sample` is
called. This is required to match :obj:`past_key_values` with the correct beam_idx at every generation step.
"""
return tuple(
tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past)
for layer_past in past
)
However, it would be cool to rewrite this with inheritance
Committing the prunedgpt class code was a mistake on my part. It was a part of my attempt to create a proper huggingface class for the model that wasn't poorly inherited off of the base transformers.GPT2Model
. Ultimately I made some false assumptions about what would be required; I do not have any actual pruned model atm.
I've looked through the PrunedGPT2 class in the autoregressive.py side by side, and I did not see too much difference than in embedding creation. I wonder, do you have a pruned model somewhere available? If not what's the strategy for pruning?