polypush135
Livebook cuda 12.2 XLA out of memory at 11006MiB
How to restrict memory usage on EXLA?
This is my memory usage before loading EXLA.
nvidia-smi
Sun Sep 3 13:13:11 2023
+---------------------------------------------------------------------------------------+
| NVIDIA-SMI 535.104.05 Driver Version: 535.104.05 CUDA Version: 12.2 |
|-----------------------------------------+----------------------+----------------------+
| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|=========================================+======================+======================|
| 0 NVIDIA GeForce RTX 3060 On | 00000000:01:00.0 On | N/A |
| 45% 53C P5 30W / 170W | 398MiB / 12288MiB | 0% Default |
| | | N/A |
+-----------------------------------------+----------------------+----------------------+
+---------------------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=======================================================================================|
| 0 N/A N/A 1914 G /usr/lib/xorg/Xorg 131MiB |
| 0 N/A N/A 2128 G /usr/bin/gnome-shell 63MiB |
| 0 N/A N/A 7334 G ...irefox/3068/usr/lib/firefox/firefox 193MiB |
+---------------------------------------------------------------------------------------+
And after.
❯ nvidia-smi
Sun Sep 3 13:18:13 2023
+---------------------------------------------------------------------------------------+
| NVIDIA-SMI 535.104.05 Driver Version: 535.104.05 CUDA Version: 12.2 |
|-----------------------------------------+----------------------+----------------------+
| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|=========================================+======================+======================|
| 0 NVIDIA GeForce RTX 3060 On | 00000000:01:00.0 On | N/A |
| 0% 43C P3 30W / 170W | 11446MiB / 12288MiB | 2% Default |
| | | N/A |
+-----------------------------------------+----------------------+----------------------+
+---------------------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=======================================================================================|
| 0 N/A N/A 1914 G /usr/lib/xorg/Xorg 131MiB |
| 0 N/A N/A 2128 G /usr/bin/gnome-shell 65MiB |
| 0 N/A N/A 7334 G ...irefox/3068/usr/lib/firefox/firefox 229MiB |
| 0 N/A N/A 15749 C ...ang/25.2.1/erts-13.1.4/bin/beam.smp 11006MiB |
+---------------------------------------------------------------------------------------+
As you can see Firefox and gnome bumped a little bit and the beam is trying to use the rest.
The issue is I get it to run once, but if I re evaluate to say change the seed or steps I then run out of memory.
13:20:48.865 [info] Total bytes in pool: 11350867968 memory_limit_: 11350867968 available bytes: 0 curr_region_allocation_bytes_: 22701735936
13:20:48.865 [info] Stats:
Limit: 11350867968
InUse: 11229623808
MaxInUse: 11261113856
NumAllocs: 6164
MaxAllocSize: 3514198016
Reserved: 0
PeakReserved: 0
LargestFreeBlock: 0
13:20:48.865 [warning] ****************************************************************************************************
13:20:48.865 [error] Execution of replica 0 failed: RESOURCE_EXHAUSTED: Out of memory while trying to allocate 6553600 bytes.
BufferAssignment OOM Debugging.
BufferAssignment stats:
parameter allocation: 6.25MiB
constant allocation: 0B
maybe_live_out allocation: 6.25MiB
preallocated temp allocation: 0B
total allocation: 12.50MiB
total fragmentation: 0B (0.00%)
Peak buffers:
Buffer 1:
Size: 6.25MiB
Entry Parameter Subshape: f32[1638400]
==========================
Buffer 2:
Size: 6.25MiB
XLA Label: copy
Shape: f32[1280,1280]
==========================
Buffer 3:
Size: 8B
XLA Label: tuple
Shape: (f32[1280,1280])
==========================
So is there a way to limit the MaxInUse for xla from the start? 10 should be enough right? Its trying to grab 11 now.
Marked As Solved
jonatanklosko
XLA reserves memory upfront and then allocates within that reservation as needed. This behaviour can be customized with client options preallocate: false or other :memory_fraction. However, I don’t think this will help with the OOM error.
We are still yet to do more optimisations for stable diffusion, but two things you can try this:
- Load the parameters into the CPU with
Bumblebee.load_model(..., backend: {EXLA.Backend, client: :host}) - Enable lazy transfers in serving defn options:
defn_options: [compiler: EXLA, lazy_transfers: :always]
This way, instead of placing all parameters on the GPU, they will be transferred as needed.
Also make sure to try with batch size 1.
Also Liked
jonatanklosko
@nutheory they aren’t conflicting, but aim to do the same thing in a slightly different way. :preallocate_params is a recent addition, I updated the docs to mention it instead. Recently I’ve also added this docs page that goes into more details on how to minimise memory usage by trading off inference time : )
nutheory
its my bad obviously but its a easy trip up…
when you do BB.load_model … its BB.load_model(hf_params, repo_params) … which makes total sense…
with defn_options … why is one (ie. “lazy transfers”) in the keyword list as where “preallocate_params: true” is another parameter all together?
jonatanklosko
with defn_options … why is one (ie. “lazy transfers”) in the keyword list as where “preallocate_params: true” is another parameter all together?
All the top-level options (including :preallocate_params are handled by Bumblebee). The keyword list given as :defn_options is what we pass to Nx.Defn.jit/compile, where compiler: EXLA specifies the compiler and the remaining options are passed to the compiler.
So in other words :preallocate_params is for Bumblebee, :lazy_transfers is for the EXLA compiler : )
jonatanklosko
@vgrechin to reduce memory usage you can set the number of images to 1. Another thing you can do is converting smart cell to code, and trying the changes in this comment.
How much RAM does your GPU have? If it’s 4GB then IIRC that’s not enough to run the Stable Diffusion.
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