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fix llava (#27909)

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* fix llava

* nits

* attention_mask was forgotten

* nice

* :)

* fixup
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Arthur 2023-12-08 17:32:34 +01:00 committed by GitHub
parent e0b617d192
commit aa7ab98e72
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1 changed files with 50 additions and 6 deletions
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src/transformers/models/llava/modeling_llava.py
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@ -22,6 +22,7 @@ from torch import nn
from ... import PreTrainedModel
from ...activations import ACT2FN
from ...cache_utils import Cache
from ...modeling_outputs import ModelOutput
from ...utils import (
add_start_docstrings,
@ -472,14 +473,57 @@ class LlavaForConditionalGeneration(LlavaPreTrainedModel):
)
def prepare_inputs_for_generation(
self, input_ids, past_key_values=None, inputs_embeds=None, pixel_values=None, **kwargs
self, input_ids, past_key_values=None, inputs_embeds=None, pixel_values=None, attention_mask=None, **kwargs
):
# Call `prepare_inputs_for_generation` from the LM
model_input = self.language_model.prepare_inputs_for_generation(
input_ids, past_key_values, inputs_embeds=inputs_embeds, **kwargs
if past_key_values is not None:
if isinstance(past_key_values, Cache):
cache_length = past_key_values.get_seq_length()
past_length = past_key_values.seen_tokens
else:
cache_length = past_length = past_key_values[0][0].shape[2]
# Keep only the unprocessed tokens:
# 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where
# some of the inputs are exclusivelly passed as part of the cache (e.g. when passing input_embeds as
# input)
if attention_mask is not None and attention_mask.shape[1] > input_ids.shape[1]:
input_ids = input_ids[:, -(attention_mask.shape[1] - past_length) :]
# 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard
# input_ids based on the past_length.
elif past_length < input_ids.shape[1]:
input_ids = input_ids[:, past_length:]
# 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens.
elif self.config.image_token_index in input_ids:
input_ids = input_ids[:, input_ids.shape[1] - 1 :]
# If the cache has seen more tokens than it can hold, then the cache has a size limit. Let's discard the
# older attention values, as their corresponding values are not part of the input.
if cache_length < past_length and attention_mask is not None:
attention_mask = attention_mask[:, -(cache_length + input_ids.shape[1]) :]
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_key_values:
position_ids = position_ids[:, -input_ids.shape[1] :]
# if `inputs_embeds` are passed, we only want to use them in the 1st generation step
if inputs_embeds is not None and past_key_values is None:
model_inputs = {"inputs_embeds": inputs_embeds}
else:
model_inputs = {"input_ids": input_ids}
model_inputs.update(
{
"position_ids": position_ids,
"past_key_values": past_key_values,
"use_cache": kwargs.get("use_cache"),
"attention_mask": attention_mask,
"pixel_values": pixel_values,
}
)
model_input.update({"pixel_values": pixel_values})
return model_input
return model_inputs
def _reorder_cache(self, *args, **kwargs):
return self.language_model._reorder_cache(*args, **kwargs)