saareliad / ftpipe Goto Github PK
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FTPipe and related pipeline model parallelism research.
Hi~Thanks for your neat project!
I was trying to partition T5-3B model. When partitioning it on 8 GPUs, I used
python -m autopipe.partition t5 --model_name_or_path t5-3b --t5_task squad1 --lmhead --n_iter 10 --analysis_batch_size 4 --partitioning_batch_size 4 --ct trace_cache_t53b_512_4_op --cp prof_cache_t53b_512_4_op_ftpipe --precompute_masks --stateless_tied --lmhead --n_partitions 8 --L 8 16 24 --max_seq_length 512 --answer_max_seq_length 4 --partitioning_method mpipe --preset ftpipe --dont_use_async_meta_alg --save_memory_mode --special_blocks T5Block
and got a pretty balanced plan:
.
But when partitioning it on 16 GPUs, I used
python -m autopipe.partition t5 --model_name_or_path t5-3b --t5_task squad1 --lmhead --n_iter 10 --analysis_batch_size 4 --partitioning_batch_size 4 --ct trace_cache_t53b_512_4_op --cp prof_cache_t53b_512_4_op_ftpipe --precompute_masks --stateless_tied --lmhead --n_partitions 16 --L 16 32 48 --max_seq_length 512 --answer_max_seq_length 4 --partitioning_method mpipe --preset ftpipe --dont_use_async_meta_alg --save_memory_mode --special_blocks T5Block
and got a less balanced plan:
I understand that T5-3B model has 24 encoders and 24 decoders, so when partitioning on 8 GPUs, each GPU can be assigned 3 encoders and 3 decoders, which is very similar to what FTPipe has done. When partitioning on 16 GPUs, however, by reading the partitioned model generated by FTPipe, I found that each GPU is roughly assigned 3 encoders or 3 decoders, resulting in a less balanced plan.
Question: Is it possible to get a more balanced partitioning plan for T5-3B-16GPUs using FTPipe?
exec: python -m pipe.data.download_glue_data,
will encounter some errors such as: 400 HTTP error. I reviewed the code such as:
TASK2PATH = {"CoLA":'https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FCoLA.zip?alt=media&token=46d5e637-3411-4188-bc44-5809b5bfb5f4'}, the url is really wrong.
Can you provide more detailed Readme about training including training dataset.
When I run the command
python -m pipe.main --help
That error appeared, and here is the whole error information.
Traceback (most recent call last):
File "/home/sun/.conda/envs/nompi/lib/python3.8/runpy.py", line 194, in _run_module_as_main
return _run_code(code, main_globals, None,
File "/home/sun/.conda/envs/nompi/lib/python3.8/runpy.py", line 87, in _run_code
exec(code, run_globals)
File "/home/sun/data/FTPipe/pipe/main.py", line 16, in <module>
from pipe.data import add_dataset_argument
File "/home/sun/data/FTPipe/pipe/data/__init__.py", line 6, in <module>
from .from_args_and_kw import *
File "/home/sun/data/FTPipe/pipe/data/from_args_and_kw.py", line 7, in <module>
from pipe.models.simple_partitioning_config import PipelineConfig
File "/home/sun/data/FTPipe/pipe/models/__init__.py", line 1, in <module>
from . import transformers_utils
File "/home/sun/data/FTPipe/pipe/models/transformers_utils.py", line 3, in <module>
from .transformers_cfg import MODEL_TYPES
File "/home/sun/data/FTPipe/pipe/models/transformers_cfg.py", line 883, in <module>
from pipe.models.oldt5 import oldt5_functions_list
ModuleNotFoundError: No module named 'pipe.models.oldt5'
It seems lacking a source file in the dir pipe/models/
.
Hi, I wonder why the P2P communication in FTPipe is implemented by CUDA-Aware MPI instead of NCCL?
Maybe perform better or anything else?
BTW, can I run this repo without re-compiling CUDA-Aware MPI and PyTorch?
Hi, very neat project.
Question: is it possible to use FTPipe with massively parallel CPU clusters? Say for example 256 VMs?
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