atik-ahamed / timemachine Goto Github PK
View Code? Open in Web Editor NEWTimeMachine: A Time Series is Worth 4 Mambas for Long-term Forecasting
Home Page: https://arxiv.org/abs/2403.09898
License: Apache License 2.0
TimeMachine: A Time Series is Worth 4 Mambas for Long-term Forecasting
Home Page: https://arxiv.org/abs/2403.09898
License: Apache License 2.0
为什么这个代码运行的很慢了,是原本就这样吗?
I run the script "sh ./scripts/TimeMachine/weather.sh",and at the second training epoch, it occured nan training loss problem.
I print the per step loss, can you help me solve this problem?
How to install causal conv1d and mamba-sms
import torch
from mamba_ssm import Mamba
batch, length, dim = 2, 64, 16
x = torch.randn(batch, length, dim).to("cuda")
model = Mamba(
# This module uses roughly 3 * expand * d_model^2 parameters
d_model=dim, # Model dimension d_model
d_state=16, # SSM state expansion factor
d_conv=4, # Local convolution width
expand=2, # Block expansion factor
).to("cuda")
y = model(x)
assert y.shape == x.shape
This is the official introduction of mamba-ssm,so i think the input shape should be B L d,while in timemachine,the input data's shape is B M ni
I am confused at this about this,could you please provide some guidance for me.Thank you so much for your time and assistance.
First off, thank you for your excellent work on this project! I've encountered an issue while attempting to utilize the multi-GPU functionality. Specifically, the program runs successfully when GPUs are specified in a sequential order starting from zero using the --devices
parameter (e.g., "0,1,2"). However, errors occur when the GPUs are specified in a non-sequential order or do not start with zero (e.g., "0,2" or "1,2,3").
Full error message:
Traceback (most recent call last):
File "/home/mateus/repos/forks/TimeMachine/TimeMachine_supervised/run_longExp.py", line 117, in <module>
exp = Exp(args) # set experiments
File "/home/mateus/repos/forks/TimeMachine/TimeMachine_supervised/exp/exp_main.py", line 24, in __init__
super(Exp_Main, self).__init__(args)
File "/home/mateus/repos/forks/TimeMachine/TimeMachine_supervised/exp/exp_basic.py", line 10, in __init__
self.model = self._build_model().to(self.device)
File "/home/mateus/repos/forks/TimeMachine/TimeMachine_supervised/exp/exp_main.py", line 33, in _build_model
model = nn.DataParallel(model, device_ids=self.args.device_ids)
File "/home/mateus/miniconda3/envs/TimeMachine_supervised/lib/python3.10/site-packages/torch/nn/parallel/data_parallel.py", line 159, in __init__
_check_balance(self.device_ids)
File "/home/mateus/miniconda3/envs/TimeMachine_supervised/lib/python3.10/site-packages/torch/nn/parallel/data_parallel.py", line 26, in _check_balance
dev_props = _get_devices_properties(device_ids)
File "/home/mateus/miniconda3/envs/TimeMachine_supervised/lib/python3.10/site-packages/torch/_utils.py", line 734, in _get_devices_properties
return [_get_device_attr(lambda m: m.get_device_properties(i)) for i in device_ids]
File "/home/mateus/miniconda3/envs/TimeMachine_supervised/lib/python3.10/site-packages/torch/_utils.py", line 734, in <listcomp>
return [_get_device_attr(lambda m: m.get_device_properties(i)) for i in device_ids]
File "/home/mateus/miniconda3/envs/TimeMachine_supervised/lib/python3.10/site-packages/torch/_utils.py", line 713, in _get_device_attr
return get_member(torch.cuda)
File "/home/mateus/miniconda3/envs/TimeMachine_supervised/lib/python3.10/site-packages/torch/_utils.py", line 734, in <lambda>
return [_get_device_attr(lambda m: m.get_device_properties(i)) for i in device_ids]
File "/home/mateus/miniconda3/envs/TimeMachine_supervised/lib/python3.10/site-packages/torch/cuda/__init__.py", line 452, in get_device_properties
raise AssertionError("Invalid device id")
AssertionError: Invalid device id
Traceback (most recent call last):
File "/home/mateus/repos/forks/TimeMachine/TimeMachine_supervised/run_longExp.py", line 117, in <module>
exp = Exp(args) # set experiments
File "/home/mateus/repos/forks/TimeMachine/TimeMachine_supervised/exp/exp_main.py", line 24, in __init__
super(Exp_Main, self).__init__(args)
File "/home/mateus/repos/forks/TimeMachine/TimeMachine_supervised/exp/exp_basic.py", line 10, in __init__
self.model = self._build_model().to(self.device)
File "/home/mateus/repos/forks/TimeMachine/TimeMachine_supervised/exp/exp_main.py", line 33, in _build_model
model = nn.DataParallel(model, device_ids=self.args.device_ids)
File "/home/mateus/miniconda3/envs/TimeMachine_supervised/lib/python3.10/site-packages/torch/nn/parallel/data_parallel.py", line 159, in __init__
_check_balance(self.device_ids)
File "/home/mateus/miniconda3/envs/TimeMachine_supervised/lib/python3.10/site-packages/torch/nn/parallel/data_parallel.py", line 26, in _check_balance
dev_props = _get_devices_properties(device_ids)
File "/home/mateus/miniconda3/envs/TimeMachine_supervised/lib/python3.10/site-packages/torch/_utils.py", line 734, in _get_devices_properties
return [_get_device_attr(lambda m: m.get_device_properties(i)) for i in device_ids]
File "/home/mateus/miniconda3/envs/TimeMachine_supervised/lib/python3.10/site-packages/torch/_utils.py", line 734, in <listcomp>
return [_get_device_attr(lambda m: m.get_device_properties(i)) for i in device_ids]
File "/home/mateus/miniconda3/envs/TimeMachine_supervised/lib/python3.10/site-packages/torch/_utils.py", line 713, in _get_device_attr
return get_member(torch.cuda)
File "/home/mateus/miniconda3/envs/TimeMachine_supervised/lib/python3.10/site-packages/torch/_utils.py", line 734, in <lambda>
return [_get_device_attr(lambda m: m.get_device_properties(i)) for i in device_ids]
File "/home/mateus/miniconda3/envs/TimeMachine_supervised/lib/python3.10/site-packages/torch/cuda/__init__.py", line 452, in get_device_properties
raise AssertionError("Invalid device id")
AssertionError: Invalid device id
Traceback (most recent call last):
File "/home/mateus/repos/forks/TimeMachine/TimeMachine_supervised/run_longExp.py", line 117, in <module>
exp = Exp(args) # set experiments
File "/home/mateus/repos/forks/TimeMachine/TimeMachine_supervised/exp/exp_main.py", line 24, in __init__
super(Exp_Main, self).__init__(args)
File "/home/mateus/repos/forks/TimeMachine/TimeMachine_supervised/exp/exp_basic.py", line 10, in __init__
self.model = self._build_model().to(self.device)
File "/home/mateus/repos/forks/TimeMachine/TimeMachine_supervised/exp/exp_main.py", line 33, in _build_model
model = nn.DataParallel(model, device_ids=self.args.device_ids)
File "/home/mateus/miniconda3/envs/TimeMachine_supervised/lib/python3.10/site-packages/torch/nn/parallel/data_parallel.py", line 159, in __init__
_check_balance(self.device_ids)
File "/home/mateus/miniconda3/envs/TimeMachine_supervised/lib/python3.10/site-packages/torch/nn/parallel/data_parallel.py", line 26, in _check_balance
dev_props = _get_devices_properties(device_ids)
File "/home/mateus/miniconda3/envs/TimeMachine_supervised/lib/python3.10/site-packages/torch/_utils.py", line 734, in _get_devices_properties
return [_get_device_attr(lambda m: m.get_device_properties(i)) for i in device_ids]
File "/home/mateus/miniconda3/envs/TimeMachine_supervised/lib/python3.10/site-packages/torch/_utils.py", line 734, in <listcomp>
return [_get_device_attr(lambda m: m.get_device_properties(i)) for i in device_ids]
File "/home/mateus/miniconda3/envs/TimeMachine_supervised/lib/python3.10/site-packages/torch/_utils.py", line 713, in _get_device_attr
return get_member(torch.cuda)
File "/home/mateus/miniconda3/envs/TimeMachine_supervised/lib/python3.10/site-packages/torch/_utils.py", line 734, in <lambda>
return [_get_device_attr(lambda m: m.get_device_properties(i)) for i in device_ids]
File "/home/mateus/miniconda3/envs/TimeMachine_supervised/lib/python3.10/site-packages/torch/cuda/__init__.py", line 452, in get_device_properties
raise AssertionError("Invalid device id")
AssertionError: Invalid device id
Traceback (most recent call last):
File "/home/mateus/repos/forks/TimeMachine/TimeMachine_supervised/run_longExp.py", line 117, in <module>
exp = Exp(args) # set experiments
File "/home/mateus/repos/forks/TimeMachine/TimeMachine_supervised/exp/exp_main.py", line 24, in __init__
super(Exp_Main, self).__init__(args)
File "/home/mateus/repos/forks/TimeMachine/TimeMachine_supervised/exp/exp_basic.py", line 10, in __init__
self.model = self._build_model().to(self.device)
File "/home/mateus/repos/forks/TimeMachine/TimeMachine_supervised/exp/exp_main.py", line 33, in _build_model
model = nn.DataParallel(model, device_ids=self.args.device_ids)
File "/home/mateus/miniconda3/envs/TimeMachine_supervised/lib/python3.10/site-packages/torch/nn/parallel/data_parallel.py", line 159, in __init__
_check_balance(self.device_ids)
File "/home/mateus/miniconda3/envs/TimeMachine_supervised/lib/python3.10/site-packages/torch/nn/parallel/data_parallel.py", line 26, in _check_balance
dev_props = _get_devices_properties(device_ids)
File "/home/mateus/miniconda3/envs/TimeMachine_supervised/lib/python3.10/site-packages/torch/_utils.py", line 734, in _get_devices_properties
return [_get_device_attr(lambda m: m.get_device_properties(i)) for i in device_ids]
File "/home/mateus/miniconda3/envs/TimeMachine_supervised/lib/python3.10/site-packages/torch/_utils.py", line 734, in <listcomp>
return [_get_device_attr(lambda m: m.get_device_properties(i)) for i in device_ids]
File "/home/mateus/miniconda3/envs/TimeMachine_supervised/lib/python3.10/site-packages/torch/_utils.py", line 713, in _get_device_attr
return get_member(torch.cuda)
File "/home/mateus/miniconda3/envs/TimeMachine_supervised/lib/python3.10/site-packages/torch/_utils.py", line 734, in <lambda>
return [_get_device_attr(lambda m: m.get_device_properties(i)) for i in device_ids]
File "/home/mateus/miniconda3/envs/TimeMachine_supervised/lib/python3.10/site-packages/torch/cuda/__init__.py", line 452, in get_device_properties
raise AssertionError("Invalid device id")
AssertionError: Invalid device id
My etth1.sh
file:
if [ ! -d "./logs" ]; then
mkdir ./logs
fi
if [ ! -d "./logs/LongForecasting" ]; then
mkdir ./logs/LongForecasting
fi
if [ ! -d "./csv_results" ]; then
mkdir ./csv_results
fi
if [ ! -d "./results" ]; then
mkdir ./results
fi
if [ ! -d "./test_results" ]; then
mkdir ./test_results
fi
model_name=TimeMachine
root_path_name=../data/ETT-small
data_path_name=ETTh1.csv
model_id_name=ETTh1
data_name=ETTh1
rin=1
random_seed=2024
one=96
two=192
three=336
four=720
residual=1
fc_drop=0.7
dstate=256
dconv=2
for seq_len in 96
do
for pred_len in 96 192 336 720
do
for e_fact in 1
do
if [ $pred_len -eq $one ]
then
n1=512
n2=32
fi
if [ $pred_len -eq $two ]
then
n1=512
n2=64
fi
if [ $pred_len -eq $three ]
then
n1=512
n2=128
fi
if [ $pred_len -eq $four ]
then
n1=128
n2=16
fi
python -u run_longExp.py \
--random_seed $random_seed \
--is_training 1 \
--root_path $root_path_name \
--data_path $data_path_name \
--model_id $model_id_name_$seq_len'_'$pred_len \
--model $model_name \
--data $data_name \
--features M \
--seq_len $seq_len \
--pred_len $pred_len \
--enc_in 7 \
--n1 $n1 \
--n2 $n2 \
--dropout $fc_drop\
--revin 1\
--ch_ind 1\
--residual $residual\
--dconv $dconv \
--d_state $dstate\
--e_fact $e_fact\
--des 'Exp' \
--train_epochs 100\
--itr 1 --batch_size 64 --learning_rate 0.001 --use_multi_gpu --devices "1,2" >logs/LongForecasting/$model_name'_'$model_id_name'_'$seq_len'_'$pred_len'_'$n1'_'$n2'_'$fc_drop'_'$rin'_'$residual'_'$dstate'_'$dconv'_'$e_fact.log
done
done
done
I appreciate any guidance or updates you can provide on this issue. Thank you!
Hi, I recently run the code and the MSE results(and so on) is OK. However, although the model costs relatively low gpu memory, its training speed is really really slow, have you ever encountered a similar situation? Take Weather_96_96 as an example, TimeMachine costs 380s for one epoch while PatchTST only needs 40s...
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.