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Merge pull request #628 from OptimalScale/lianqing/multi_modal_training
[Features] Support multi_modal training
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{ | ||
"fp16": { | ||
"enabled": false | ||
"enabled": "auto", | ||
"loss_scale": 0, | ||
"loss_scale_window": 1000, | ||
"initial_scale_power": 16, | ||
"hysteresis": 2, | ||
"min_loss_scale": 1 | ||
}, | ||
"bf16": { | ||
"enabled": false | ||
"enabled": "auto" | ||
}, | ||
"comms_logger": { | ||
"enabled": false, | ||
"verbose": false, | ||
"prof_all": false, | ||
"debug": false | ||
}, | ||
"steps_per_print": 20000000000000000, | ||
"train_micro_batch_size_per_gpu": 1, | ||
"wall_clock_breakdown": false | ||
} | ||
"train_micro_batch_size_per_gpu": "auto", | ||
"train_batch_size": "auto", | ||
"gradient_accumulation_steps": "auto", | ||
"zero_optimization": { | ||
"stage": 2, | ||
"overlap_comm": true, | ||
"contiguous_gradients": true, | ||
"sub_group_size": 1e9, | ||
"reduce_bucket_size": "auto" | ||
} | ||
} |
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Original file line number | Diff line number | Diff line change |
---|---|---|
@@ -0,0 +1,17 @@ | ||
{ | ||
"fp16": { | ||
"enabled": false | ||
}, | ||
"bf16": { | ||
"enabled": false | ||
}, | ||
"comms_logger": { | ||
"enabled": false, | ||
"verbose": false, | ||
"prof_all": false, | ||
"debug": false | ||
}, | ||
"steps_per_print": 20000000000000000, | ||
"train_micro_batch_size_per_gpu": 1, | ||
"wall_clock_breakdown": false | ||
} |
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#!/usr/bin/env python | ||
# coding=utf-8 | ||
# Copyright 2023 Statistics and Machine Learning Research Group at HKUST. All rights reserved. | ||
# FIXME should merge with finetune.py | ||
"""A one-line summary of the module or program, terminated by a period. | ||
Leave one blank line. The rest of this docstring should contain an | ||
overall description of the module or program. Optionally, it may also | ||
contain a brief description of exported classes and functions and/or usage | ||
examples. | ||
Typical usage example: | ||
foo = ClassFoo() | ||
bar = foo.FunctionBar() | ||
""" | ||
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import sys | ||
import os | ||
sys.path.remove(os.path.abspath(os.path.dirname(sys.argv[0]))) | ||
from transformers import HfArgumentParser | ||
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from lmflow.args import ( | ||
VisModelArguments, | ||
MultiModalDatasetArguments, | ||
AutoArguments, | ||
) | ||
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from lmflow.datasets.dataset import Dataset | ||
from lmflow.models.auto_model import AutoModel | ||
from lmflow.pipeline.auto_pipeline import AutoPipeline | ||
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from lmflow.models.vision2seq_model import CustomAutoVision2SeqModel | ||
from lmflow.models.vision_encoder import build_vision_tower | ||
from lmflow.datasets.multi_modal_dataset import DataCollatorForSupervisedDataset | ||
from torch.utils.data import DataLoader | ||
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def main(): | ||
# Parses arguments | ||
pipeline_name = "finetuner" | ||
PipelineArguments = AutoArguments.get_pipeline_args_class(pipeline_name) | ||
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parser = HfArgumentParser((VisModelArguments, MultiModalDatasetArguments, PipelineArguments)) | ||
if len(sys.argv) == 2 and sys.argv[1].endswith(".json"): | ||
# If we pass only one argument to the script and it's the path to a json file, | ||
# let's parse it to get our arguments. | ||
model_args, data_args, pipeline_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1])) | ||
else: | ||
model_args, data_args, pipeline_args = parser.parse_args_into_dataclasses() | ||
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# Initialization | ||
finetuner = AutoPipeline.get_pipeline( | ||
pipeline_name=pipeline_name, | ||
model_args=model_args, | ||
data_args=data_args, | ||
pipeline_args=pipeline_args, | ||
) | ||
# do not resiger deepspeed in the model. | ||
# with_deepspeed flag may be removed | ||
# by modifying the tune strategy in the future. | ||
model = AutoModel.get_model(model_args, tune_strategy='none', | ||
ds_config=pipeline_args.deepspeed, | ||
custom_model=True, | ||
with_deepspeed=False) | ||
# FIXME check if need to move this part to hf_encoder_decoder.py | ||
for param in model.backend_model.parameters(): | ||
param.requires_grad = False | ||
if "language_projection" in pipeline_args.finetune_part: | ||
for param in model.backend_model.language_projection.parameters(): | ||
param.requires_grad = True | ||
if "language_model" in pipeline_args.finetune_part: | ||
for param in model.backend_model.language_model.parameters(): | ||
param.requires_grad = True | ||
if "vision_model" in pipeline_args.finetune_part: | ||
for param in model.backend_model.vision_model.parameters(): | ||
param.requires_grad = True | ||
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dataset = Dataset(data_args, backend="custom_multi_modal") | ||
data_collator = DataCollatorForSupervisedDataset(tokenizer=model.tokenizer) | ||
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# Finetuning | ||
tuned_model = finetuner.tune( | ||
model=model, dataset=dataset, data_collator=data_collator) | ||
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if __name__ == '__main__': | ||
main() |
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