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[InstructBlip] Add accelerate support for instructblip #24488

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merged 5 commits into from
Jun 26, 2023
Merged

[InstructBlip] Add accelerate support for instructblip #24488

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7ee03df
add accelerate support for instructblip
younesbelkada Jun 26, 2023
838db9b
add `_keep_in_fp32_modules`
younesbelkada Jun 26, 2023
88ccd18
dynamically adapt `_no_split_modules`
younesbelkada Jun 26, 2023
802b536
better fix
younesbelkada Jun 26, 2023
1f4fd81
same logic for `_keep_in_fp32_modules`
younesbelkada Jun 26, 2023
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7 changes: 6 additions & 1 deletion src/transformers/models/instructblip/modeling_instructblip.py
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Original file line number Diff line number Diff line change
Expand Up @@ -281,6 +281,8 @@ class InstructBlipPreTrainedModel(PreTrainedModel):
r"language_model.decoder.embed_tokens.weight",
r"language_model.lm_head.weight",
]
_keep_in_fp32_modules = ["wo"]
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Last comment: this is the same issue here (this comes from T5 if I'm not mistaken). This key should also be build dynamically.

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Makes sense, just updated it!

_no_split_modules = ["InstructBlipAttention", "InstructBlipQFormerMultiHeadAttention"]

# Copied from transformers.models.blip_2.modeling_blip_2.Blip2PreTrainedModel._init_weights with Blip2->InstructBlip
def _init_weights(self, module):
Expand Down Expand Up @@ -1264,10 +1266,13 @@ def __init__(self, config: InstructBlipConfig):
self.qformer = InstructBlipQFormerModel(config.qformer_config)

self.language_projection = nn.Linear(config.qformer_config.hidden_size, config.text_config.hidden_size)

if config.use_decoder_only_language_model:
language_model = AutoModelForCausalLM.from_config(config.text_config)
self._no_split_modules.append("LlamaDecoderLayer")
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No, should language_model._no_split_modules here.

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I think language_model._no_split_modules should already contain LlamaDecoderLayer, the purpose of adding it in self._no_split_modules is that in from_pretrained we never look at all the child modules that contain that attribute: https://github.com/huggingface/transformers/blob/195a9e5bdb1faa58cd58b47a23a47734d2b90d8c/src/transformers/modeling_utils.py#L2807C13-L2807C29 | then gets passed to accelerate this way:

transformers/src/transformers/modeling_utils.py

Line 2814 in 195a9e5

kwargs = {"no_split_module_classes": no_split_modules}
right? So not sure we should add it to language_model. What do you think?

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I don't understand your comment. The code loads any model from the Hub (granted it should be llama models in the actual checkpoints) but you add the specific Llama no split blocks. This will stop working if someone adds an instructblip-2 model that loads a different language model.

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@younesbelkada younesbelkada Jun 26, 2023 •
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A cleaner implementation maybe would be that in post_init we should add an utility method to look at all child modules that contain _no_split_modules and dynamically append them to self._no_split_modules

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If you think that's better, happy to change the PR to add these changes

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Ah I see yes, sorry I misunderstood your comment, will update that now

else:
language_model = AutoModelForSeq2SeqLM.from_config(config.text_config)
self._no_split_modules.append("T5Block")
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Same there.


self.language_model = language_model

Expand Down Expand Up @@ -1422,7 +1427,7 @@ def forward(

if attention_mask is None:
attention_mask = torch.ones_like(input_ids)
attention_mask = torch.cat([language_model_attention_mask, attention_mask], dim=1)
attention_mask = torch.cat([language_model_attention_mask.to(attention_mask.device), attention_mask], dim=1)

if self.config.use_decoder_only_language_model:
outputs = self.language_model(
Expand Down