Comments (8)
I did more tests, changing the default compiler optimization option from -O2
to -O1
, and I am able to use the same configurations I used with transformers-neuronx==0.6.106
: batch_size=1
and n_positions=2048
.
During inference, the device memory is at 64 Gb for the 13B model and 22 Gb for the 7B model.
I also tested with -O3
but got the same kind of errors.
from transformers-neuronx.
Thank you for reporting the issue. We are replicating the issue on our end and will get back with a fix.
from transformers-neuronx.
Hi
What’s the throughput tokens/sec did u get on 7 billion model ?
from transformers-neuronx.
With the 2.14.1
compiler (neuronx-cc
), I am able to compile the llama2 7B
model with -O1
for different batch sizes.
I tested several combinations of cores / batch size with the default maximum sequence length for llama model (2048
).
Here are the results:
| cores/batch | 128 tokens | 512 tokens | 1024 tokens | 2048 tokens | Throughput |
|-------------|------------|------------|-------------|-------------|--------------|
| 2c / bs2 | 8.5 s | 34 s | 69 s | 143 s | 29 tokens/s |
| 2c / bs4 | 8.6 s | 35 s | 72 s | 150 s | 55 tokens/s |
| 24c / bs2 | 1.3 s | 5.4 s | 11.5 s | 22.8 s | 180 tokens/s |
| 24c / bs4 | 1.4 s | 5.8 s | 11.5 s | 24 s | 341 tokens/s |
Note: I experienced extremely long compilation times for batch size 4 (more than 3 hours), even with -O1
, when it takes only minutes for batch size 1 or 2.
from transformers-neuronx.
@dacorvo thank you for confirming. Yes, batch 4 compilation time is an issue, we are working on it and it's been tracked elsewhere. I'm closing this one.
from transformers-neuronx.
closing
from transformers-neuronx.
On most open-source projects, issues are closed only when they have been resolved, so that users:
- users reporting the issue can be notified when a fix is pushed,
- new users facing the issues later can be redirected to the proper version.
How can we track progress on these compilation errors now that you've closed this one ? Can you link it to the relevant issues ?
from transformers-neuronx.
Hi @dacorvo:
We confirmed that the Llama 7B compilation error you reported is fixed in the 2.15.2 Release. Can you install the latest Neuron SDK and try re-running your script to confirm that you no longer see compilation issues for this model?
from transformers-neuronx.
Related Issues (20)
- llama-2/codellama benchmark for inf2.xlarge HOT 4
- Mixtral Model support HOT 2
- Vicuna13B model support
- Inf2 Modified Llama 2 Loading Issue HOT 11
- Skipping generation for useless tokens, and modiying cacheids HOT 3
- How to use generate() with inputs_embeds HOT 2
- Mixtral config issue -- not handling null well HOT 8
- Generate Llama 2 from Embeddings HOT 5
- Infering logits from `model.forward` for the entire batch instead of the last forward's output. HOT 5
- Support for MPT model HOT 1
- `stopping_criteria_list(input_ids, probs)` does not check for the correct sequence. HOT 4
- User feedback when compiling and reloading a large model HOT 1
- Issue while compiling Mistral 7B 0.2 Instruct HOT 5
- Backward compatibility with saved llama 2 compiled artifacts HOT 1
- NaN outputs when masking llama model inputs HOT 6
- Improve Neuron model loading time HOT 4
- Add support for `gemma` models HOT 1
- Add support for Baichuan-13B model
- Latest changes introduced for continuous batching break Mixtral model HOT 3
- llava support HOT 2
Recommend Projects
-
React
A declarative, efficient, and flexible JavaScript library for building user interfaces.
-
Vue.js
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
-
Typescript
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
-
TensorFlow
An Open Source Machine Learning Framework for Everyone
-
Django
The Web framework for perfectionists with deadlines.
-
Laravel
A PHP framework for web artisans
-
D3
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
-
Recommend Topics
-
javascript
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
-
web
Some thing interesting about web. New door for the world.
-
server
A server is a program made to process requests and deliver data to clients.
-
Machine learning
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
-
Visualization
Some thing interesting about visualization, use data art
-
Game
Some thing interesting about game, make everyone happy.
Recommend Org
-
Facebook
We are working to build community through open source technology. NB: members must have two-factor auth.
-
Microsoft
Open source projects and samples from Microsoft.
-
Google
Google ❤️ Open Source for everyone.
-
Alibaba
Alibaba Open Source for everyone
-
D3
Data-Driven Documents codes.
-
Tencent
China tencent open source team.
from transformers-neuronx.