10.3: Can No Longer Build Loader, Incorrect stylesheet defined

Describe the bug
I can no longer build my loader. Ghidra 10.2.3 works, Ghidra 10.3 does no longer works.
To Reproduce
gradle -PGHIDRA_INSTALL_DIR=/home/poberoi/Desktop/ghidra_10.2.2_PUBLIC -> works
gradle -PGHIDRA_INSTALL_DIR=/home/poberoi/Desktop/ghidra_10.3_PUBLIC -> fails with the following error message:
> Task :buildHelp FAILED
INFO  Using log config file: jar:file:/home/poberoi/Desktop/ghidra_10.3_PUBLIC/Ghidra/Framework/Generic/lib/Generic.jar!/generic.log4j.xml (LoggingInitialization)
INFO  Using log file: /home/poberoi/.helpbuilder/.helpbuilder_0.1_NO_RELEASE/application.log (LoggingInitialization)
Exception in thread "main" ghidra.util.exception.AssertException: Errors parsing HTML file: help.html
Incorrect stylesheet defined - none match help/shared/DefaultStyle.css in file /home/poberoi/Ghidra-Loader/src/main/help/help/topics/loader/help.html
        Discovered stylesheets: [/home/poberoi/Ghidra-Loader/src/main/help/help/shared/Frontpage.css]

        at help.validator.model.HelpFile.parseLinks(HelpFile.java:106)
        at help.validator.model.HelpFile.<init>(HelpFile.java:43)
        at help.validator.model.HelpTopic$1.visitFile(HelpTopic.java:85)
        at help.validator.model.HelpTopic$1.visitFile(HelpTopic.java:71)
        at java.base/java.nio.file.Files.walkFileTree(Files.java:2811)
        at java.base/java.nio.file.Files.walkFileTree(Files.java:2882)
        at help.validator.model.HelpTopic.loadHelpFiles(HelpTopic.java:71)
        at help.validator.model.HelpTopic.<init>(HelpTopic.java:56)
        at help.validator.location.HelpModuleLocation.loadHelpTopics(HelpModuleLocation.java:62)
        at help.validator.location.HelpModuleLocation.<init>(HelpModuleLocation.java:41)
        at help.validator.location.DirectoryHelpModuleLocation.<init>(DirectoryHelpModuleLocation.java:29)
        at help.HelpBuildUtils.toLocation(HelpBuildUtils.java:68)
        at help.validator.location.HelpModuleCollection.toHelpLocations(HelpModuleCollection.java:95)
        at help.validator.location.HelpModuleCollection.fromFiles(HelpModuleCollection.java:79)
        at help.GHelpBuilder.collectAllHelp(GHelpBuilder.java:115)
        at help.GHelpBuilder.build(GHelpBuilder.java:89)
        at help.GHelpBuilder.main(GHelpBuilder.java:83)

FAILURE: Build failed with an exception.

* What went wrong:
Execution failed for task ':buildHelp'.
> Process 'command '/usr/lib/jvm/java-17-openjdk-amd64/bin/java'' finished with non-zero exit value 1

* Try:
> Run with --stacktrace option to get the stack trace.
> Run with --info or --debug option to get more log output.
> Run with --scan to get full insights.

* Get more help at https://help.gradle.org

BUILD FAILED in 3s
4 actionable tasks: 3 executed, 1 up-to-date

Expected behavior
I expect my loader to build.
Environment (please complete the following information):

Java Version: openjdk version "17.0.6" 2023-01-17
Ghidra Version: 10.3
Ghidra Origin: GitHub release

Additional context
I will try removing the help dir.

seems to be the same as https://github.com/NationalSecurityAgency/ghidra/issues/5328#issue-1708689142

Yes it was the same issue. Thank you!


IllegalArgumentException while opening edit

Describe the bug
When trying to open edit the app crashes as the value is neither 100g nor serving.
To Reproduce
Steps to reproduce the behavior:

Search Peanut Butter
Open very first product
Click on edit in SummaryFragment
See error

Expected behavior
ProductEditActivity must open.
Screenshots

I have fixed this issue. Will make a PR soon.

I have fixed this issue. Will make a PR soon.

@VaiTon Sometimes the value passed is 100.0 g. As the value is final I cannot convert 100.0 g to 100g. So, I created a new constant for 100.0 g. Is it the correct way ?

@VaiTon Sometimes the value passed is 100.0 g. As the value is final I cannot convert 100.0 g to 100g. So, I created a new constant for 100.0 g. Is it the correct way ?

@mrudultora I'm sorry it is not. I've already created an issue but i can't seem to find it. @teolemon do you remember this issue?

@mrudultora I'm sorry it is not. I've already created an issue but i can't seem to find it. @teolemon do you remember this issue?

@VaiTon That's why I asked you before creating a PR. Could you suggest me a way to do so. Because declaring the value as var instead of val is also the wrong way.

@VaiTon That's why I asked you before creating a PR. Could you suggest me a way to do so. Because declaring the value as var instead of val is also the wrong way.

It's an error with the server, not the application. We just have to wait for them

It's an error with the server, not the application. We just have to wait for them

Okay I am closing this issue :)

Okay I am closing this issue :)

@teolemon Its an issue from server side as stated by VaiTon. Please have a look into this.

It's an error with the server, not the application.

The bug reported is a crash in the application. Even if this crash only occurs after a malformed response from the server, the crash is still a bug in the application. Separately we may need to file a bug report against the server. However, the app should not crash. That is very poor user experience and results in users saying the app is "broken". If the app displayed a helpful error message to the user, such as, "Bad response from server", then at least the user would know what went wrong and the user could continue using the functionalities in the app that currently work.

I concur @TimMagee, we should have a perfectly working app (at least not crashing) even with the server down.
Would you be able to test the behavior in such cases ? Alternatively, we have a PR up for adoption about offline barcode scan.
For this bug: I'm going to close this one since I can't reproduce it.


Error Stack Showing Typescript Files

Search Terms

error stack, error stack typescript, error stack path
Suggestion

Can Typescript somehow return errors using the TS files? Stack errors being in the output folder is kinda annoying because based on tsconfig settings they can be massively different lines. I am not sure what the best way to achieve this behavior is or if its already possible. I have looked into it quite a bit but found no solution for trying to get errors reported to me based on the Typescript files.
Use Cases

For example, take a well-commented file in TS where tsconfig removes the comments and the lines are completely different. Imagine having a library with JSDocs or other ways that lines of code becomes changed because of the compiler.
It is quite annoying to have to go into the output folder which can be quite harder to read as compared to the TS files that most devs are used to looking at because that is what they code on daily.
Examples
Current Stack Error Response
filename.js:148:52

Suggested Stack Error Response
filename.ts:360:52

Checklist
My suggestion meets these guidelines:

[ ] This wouldn't be a breaking change in existing TypeScript/JavaScript code
[ ] This wouldn't change the runtime behavior of existing JavaScript code
[ ] This could be implemented without emitting different JS based on the types of the expressions
[ ] This isn't a runtime feature (e.g. library functionality, non-ECMAScript syntax with JavaScript output, etc.)
[ ] This feature would agree with the rest of TypeScript's Design Goals.

Thank You <3

This isn't something TypeScript (being a compiler) has any control over.  Errors are produced by the runtime and/or debugger; TypeScript can, however, generate source maps so you should have "sourceMap": true in your tsconfig and also check that your development environment supports them.

You probably want to emit sourceMaps as described above and then use https://www.npmjs.com/package/source-map-support at runtime to adjust the error stack.

Yes, TypeScript doesn't generate any runtime error messages.  Assuming you generate the source maps, most browsers will automatically remap the error stacks in the debug console and many of the bundlers/builders will remap source maps to the final bundle so you get source code positions.  VSCode when running the debugger will also allow you to set breaks and watches at source code.

Thank you source maps for the win!

Aftyer node 12,  the parameter --enable-source-maps is the best way to achieve  meanful stacktrace in typescript


AudioPlaybackStatus gets fired multiple times even after invoking startAudio()

Describe the bug
Im expecting audioPlaybackStatusChanged to not fire after calling room.startAudio() on user click. But audioPlaybackStatusChanged keeps firing on each new participant joined.
Reproduction
Happens in MS Edge Mobile on https://example.livekit.io/ whenever a new participant joins
Logs
chunk-vendors.d8055e62.js:316 room event {event: 'participantConnected', args: Array(1)}
chunk-vendors.d8055e62.js:316 room event {event: 'trackPublished', args: Array(2)}
chunk-vendors.d8055e62.js:316 room event {event: 'connectionQualityChanged', args: Array(2)}
chunk-vendors.d8055e62.js:316 received server offer {RTCSdpType: 'offer', signalingState: 'stable'}
chunk-vendors.d8055e62.js:316 room event {event: 'trackSubscriptionPermissionChanged', args: Array(3)}
chunk-vendors.d8055e62.js:316 room event {event: 'trackSubscribed', args: Array(3)}
app.a2ae327e.js:1 trackSubscribed Ba {_events: {…}, _eventsCount: 12, _maxListeners: undefined, audioLevel: 0, isSpeaking: false, …} Ha {_events: {…}, _eventsCount: 7, _maxListeners: undefined, metadataMuted: false, handleMuted: ƒ, …}
chunk-vendors.d8055e62.js:316 could not playback audio DOMException: play() can only be initiated by a user gesture.
 Ha {_events: {…}, _eventsCount: 7, _maxListeners: undefined, metadataMuted: false, handleMuted: ƒ, …}
chunk-vendors.d8055e62.js:316 could not playback audio DOMException: play() can only be initiated by a user gesture.
 From what I can gather, any new audio element that's created after the user has given autoplay consent will need to be granted explicitly again.
The following case does not require reconsent:

after approval, existing user leaves and rejoins
after approval, existing user leaves and another joins

These cases work because we recycle audio elements for this reason.
The following case does require reconsent:

after approval, an additional user joins (this involves a new audio element to be created)

Since this is an Edge security feature, I'm unsure if there's anything we can do about it.

maybe the work in https://github.com/livekit/client-sdk-js/pull/446 could serve as a workaround for this issue on edge, although not sure if that makes a difference at all if the autoplay is blocked on the element level.

that's a good idea! @karthikSirsikar Do you want to give this a try?

run against pre-release main
pass this option in when creating the room

expWebAudioMix: true

Yes, this seems to work on MS Edge android but not on IOS Safari

on which iOS version do you see this failing?

15.4.1 on IPad

This is the error: NotAllowedError: The request is not allowed by the user agent or the platform in the current context, possibly because the user denied permission

this sounds like you didn't allow the website to use microphone/camera!
make sure to test it on a secure host, it probably won't work for iOS if you use http instead of https

Its working actually. But when is disabled mic and camera both and join, in this case its not working

@karthikSirsikar does it work for you ?
just tried to reproduce the issue you mentioned (before deleting) when not publishing, but couldn't reproduce it

Sorry, that was misinterpreted. I had disabled expWebAudioMix for IOS and had only enabled it for android devices.

But now the issue is on android, the audio is going into loop. Should i skip attaching track to audio tag when expWebAudioMix is enabled

hm. it shouldn't create a feedback loop when calling attach. does the feedback loop also happen when running the sample ? (the sample also uses attach)

you mean sample hosted here https://example.livekit.io/ ?

Nope, feedback loop doesn't happen in the sample. what might i be doing wrong

Is expWebAudioMix set to true in the sample ?

Yes feedback loop is happening. I enabled expWebAudioMix in the sample and tried

for reference, this is the chromium bug (the issue mentions Android explicitly as well) https://bugs.chromium.org/p/chromium/issues/detail?id=687574#c60
sounds like the fix will be enabled by default soon in chrome!


Add shape checking for deformable_groups?

I found that only deform_conv on the master branch has shape checking for deformable_groups.
It may cause incomprehensible Cuda Runtime Error when the number of the input channel is divisible by deformable_groups.
Maybe adding some shape checkings is more friendly to use and there's no need to compile again when adding it in Python codes.
Changes can be seen at master branch and pytorch_1.0.0 branch in my repo.

Thanks for your attention and modification. Feel free to send a pull request.


Added exercises

accidentally sent pull request to wrong repo.
Added two exercises to the activity and edited the source for "DAMSELFISH"
Added COVID Mapping Exercise
Added hometown shape mapping exercise (in hopes that instructors can map all students' hometowns to show where people are from)

Closing without merging - we'll resolve issues with versioning and create new PR for that.


Error on confirmPayment

I got this error while trying to confirm the payment.
error :  There was an unexpected error -- try again in a few seconds
error :  StripeModule.failed
I am using
"react-native": "0.63.3"
"react-native-stripe-payments": "^1.0.6"
Do I need to test it in physical device instead of simulator?

Same Error for me also on stripe.confirmPayment
{"code":"StripeModule.failed","message":"There was an unexpected error -- try again in a few seconds","domain":"RCTErrorDomain","userInfo":null,"nativeStackIOS":["0   wavili                              0x000000010d082de7 RCTJSErrorFromCodeMessageAndNSError + 135","1   wavili                              0x000000010d00a623 __41-[RCTModuleMethod processMethodSignature]_block_invoke_2.129 + 179","2   wavili                              0x000000010d5e3786 __62-[StripePayments confirmPayment:cardParams:resolver:rejecter:]_block_invoke_2 + 198","3   libdispatch.dylib                   0x00007fff2010535b _dispatch_call_block_and_release + 12","4   libdispatch.dylib                   0x00007fff20106534 _dispatch_client_callout + 8","5   libdispatch.dylib                   0x00007fff20113011 _dispatch_main_queue_callback_4CF + 1045","6   CoreFoundation                      0x00007fff203a8276 CFRUNLOOP_IS_SERVICING_THE_MAIN_DISPATCH_QUEUE + 9","7   CoreFoundation                      0x00007fff203a2b06 __CFRunLoopRun + 2685","8   CoreFoundation                      0x00007fff203a1b9e CFRunLoopRunSpecific + 567","9   GraphicsServices                    0x00007fff2b793db3 GSEventRunModal + 139","10  UIKitCore                           0x00007fff2466d40f -[UIApplication _run] + 912","11  UIKitCore                           0x00007fff24672320 UIApplicationMain + 101","12  wavili                              0x000000010c99dc70 main + 112","13  libdyld.dylib                       0x00007fff20257409 start + 1","14  ???                                 0x0000000000000001 0x0 + 1"]}

@ericksondelacruz Have you verified your business with Stripe? I'm getting this too, but I'm wondering if it's because I haven't verified my business yet.

@ericksondelacruz Are you using a valid client_secret? I was getting this error passing a "dummy" string as the client_secret.


Increase batch size to 1 MB

Increasing the batch size to allow for higher bytes-per-second into Loki. Also allows podman or docker to be used to build image.

cc @eranra

Actually, hold off on this one. I've noticed some error.


REST Migration and Major UI Updates

Description

Removes previously used GraphQL backend code (all models, controllers, and Apollo client)
Migrates networking code to new REST backend
Integrates a new quote fetcher that updated Daily Vibe with a new quote each time app loads
Removes all unnecessary files and unneeded views
Restructures file naming convention (removed prefix as not needed with Swift) and reorganizes app file hierarchy
Adds app wide default colors and images and added dark mode compatibility across app
Applied UI customizations to all components (text fields, buttons, fonts, color tints) to be consistent across entire app
Implemented a local food log capability
Redesigned food search detail view to better match UX designers vision

Type of change

[x] Bug fix (non-breaking change which fixes an issue)
[x] New feature (non-breaking change which adds functionality)

Change Status

[x] Complete, tested, ready to review and merge

Checklist

[x] My code follows the style guidelines of this project
[x] I have performed a self-review of my own code
[x] My code has been reviewed by at least one peer
[x] I have commented my code, particularly in hard-to-understand areas
[x] My changes generate no new warnings
[x] New and existing unit tests pass locally with my changes
[x] There are no merge conflicts

 RC 11.4 - Enable Logging Foods


Add end to end scenario for vision: image classification

Addressed by https://github.com/microsoft/onnxruntime-inference-examples/pull/107


on getFrameSummary, go is not a function

can reproduce this issue by following these steps:
Admin --> Create Synthetic Frame

then Create

then View

then you see the getFrameSummary codecell, but no summary

broken at d1c36c02ee0d33dc2c91f13b985a27f67644f050 at end of day 12/21

working at c7de7203124572ff7a29f99b4d60a2b6a9b9d61e on 12/21

getFrameSummary should look like this

broken at 89e1c9525e729c7767ea34a8f545433b85d6a33d on 12/21

broken at e4fc7ff27186a9247a52b090b36569f3e6f0f577 on 12/21
looks like this is the commit where the issue is first introduced


BUG (High Prio): "Convert to CDM" feature has duplicate naming, causing error

Originally posted by @Zwky26 in https://github.com/OHDSI/Perseus/issues/4#issuecomment-1242061026
Error as originally posted here is caused by multiple entities being named "person_id". In a test mapping, I included only a single column and met the minimum requirements for pushing a "Convert to CDM" request.

Within the generated SQL preview, this means that the only mentions of "person_id" are those that are hardcoded within Perseus' logic. This might mean the error is independent of the mapping, and needs to be renamed to avoid ambiguity

@Zwky26 Could you please share etl file?

The configuration I used to get that error has proprietary data for some tables. When I removed those tables I get a different error.
I restructured the mapping to be very basic, with only a single table for now, and am getting new error messages. It first throws an error saying I need to include an "aou" table, even if aou is never referenced. (Err1.etl)

I then, add an aou table, and get another error saying "patient" table does not exist, even though it is never referenced.

I then add a patient table, and get another peculiar error. It seems that past queries, "Create View" tables, and aliases from a previous instance/mapping are still being called, without being defined or written. I am using a new instance and fresh login but the logic somehow references a column/table that is not specified. I've sent the .etl files via email as github does not allow sharing of that filetype

Fix has been added, closing issue!


fix: getRefs function to handle merge_group events

Fixes #456
Essentially, put https://github.com/actions/dependency-review-action/issues/456#issuecomment-1537840047 in the code.
To the best of my knowledge, this should work, because a merge_group event is a PullRequestEvent I think.

👋 @elireisman, sorry for the poke; I was hoping to get a review on this 😶. I think it's a quick win ;)
Feel free to redirect me to someone else if I'm barking at the wrong tree 😄
Thanks!

Thanks 🙌
I saw 47a6b35 :x: on https://github.com/actions/dependency-review-action/actions/runs/8933751781/job/24923072522
I'm assuming this was caused by my branch being a few commits behind main, so I rebased my branch onto main with 92ca1f7

👋 I think you need to update the PR one more time prior to re-review (see the check-dist entry)

👋 I think you need to update the PR one more time prior to re-review (see the check-dist entry)

My bad! I missed the npm run package 😅 before committing. Should be fixed in e7e99e1

Hi @elireisman do you know another possible reviewer than @pcarlisle?

👋 @nachtgold @louis-bompart apologies - looks like there wasn't time for anyone to circle back to this yet.
Looks like we need one more dist update (npm run package) pushed to the PR to get it updated before we can move forward. Unfortunately, if I check out the branch and do that myself, I won't be able to review the PR either 😄


Unsuccessful quantitative modeling using the MAIN method

log (computing)
(aqlm) root@f9f90a551b02:~/xinglin-data/AQLM# bash train.sh
wandb: Using wandb-core as the SDK backend. Please refer to https://wandb.me/wandb-core for more information.
wandb: (1) Create a W&B account
wandb: (2) Use an existing W&B account
wandb: (3) Don't visualize my results
wandb: Enter your choice: 3
wandb: You chose "Don't visualize my results"
wandb: Tracking run with wandb version 0.18.2
wandb: W&B syncing is set to offline in this directory.
wandb: Run wandb online or set WANDB_MODE=online to enable cloud syncing.
============ Load model... ============
Loading checkpoint shards: 100%|██████████████████████████████| 17/17 [00:01<00:00, 11.35it/s]
Loading pretrained model ...
Model loaded sucсessfully ...
============ Quantizing model... ============
Loading data ...
/root/xinglin-data/AQLM/src/datautils.py:219: FutureWarning: You are using torch.load with weights_only=False (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for weights_only will be flipped to True. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via torch.serialization.add_safe_globals. We recommend you start setting weights_only=True for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
data = torch.load(name)[:nsamples]
Loaded data from /root/xinglin-data/AQLM/train.pt; len(data)=1024 sequences
Starting AQ quantization ...
catching layer inputs from data
train.sh: line 23: 28722 Killed                  python main.py $MODEL_PATH $DATASET_PATH --nsamples=1024 --val_size=0 --num_codebooks=1 --nbits_per_codebook=16 --in_group_size=32 --relative_mse_tolerance=0.01 --finetune_batch_size=32 --finetune_max_epochs=10 --finetune_early_stop=3 --finetune_keep_best --local_batch_size=1 --offload_activations --wandb --resume --save $SAVE_PATH
configure
export CUDA_VISIBLE_DEVICES=0   # or e.g. 0,1,2,3
export MODEL_PATH=/root/xinglin-data/model/Qwen/Qwen2.5-32B-Instruct
export DATASET_PATH=/root/xinglin-data/AQLM/train.pt
export SAVE_PATH=/root/xinglin-data/Qwen2
export WANDB_PROJECT=MY_AQ_EXPS
export WANDB_NAME=COOL_EXP_NAME
python main.py $MODEL_PATH $DATASET_PATH
--nsamples=1024
--val_size=0
--num_codebooks=1
--nbits_per_codebook=16
--in_group_size=32
--relative_mse_tolerance=0.01
--finetune_batch_size=32
--finetune_max_epochs=10
--finetune_early_stop=3
--finetune_keep_best
--local_batch_size=1
--offload_activations
--wandb
--resume
--save $SAVE_PATH

Hello, @Jun-Howie!
Most likely you did not have enough RAM.
You are using nsamples=1024, the Qwen2.5-32B-Instruct model, and the --offload_activations key. Using the --offload_activations key means that the inps (of size [1024, 4096, 5120]) and outs (of size [1024, 4096, 5120]) tensors will be stored in RAM. Here 1024 is the value of nsamples, 4096 is the default model_seqlen value, 5120 is the hidden_size value of the Qwen2.5-32B-Instruct model.
Let's calculate how much RAM you will need for inps and outs. In your case, the data type of the inps and outs tensors will be bfloat16, i.e. 2 bytes per tensor parameter. Hence,

inps: 1024 * 4096 * 5120 * 2 / 1024 / 1024 = 40960 Mb,
outs: 1024 * 4096 * 5120 * 2 / 1024 / 1024 = 40960 Mb.

In total we get 40960 + 40960 = 81920 Mb. You use the --offload_activations key, so this memory will be used in RAM.
You can get around this problem by taking a smaller value of nsamples (for example, nsamples=512) or by using multiple GPU devices without the --offload_activations key.

Thank you , I use nsamples=512 which works for me!

A new problem has been encountered in quantizing Qwen2.5-7B with NVIDIA 4090.
(aqlm) root@Harvey-4090:/home/harvey/PM/AQLM# bash train.sh
wandb: Using wandb-core as the SDK backend. Please refer to https://wandb.me/wandb-core for more information.
wandb: (1) Create a W&B account
wandb: (2) Use an existing W&B account
wandb: (3) Don't visualize my results
wandb: Enter your choice: 3
wandb: You chose "Don't visualize my results"
wandb: Tracking run with wandb version 0.18.3
wandb: W&B syncing is set to offline in this directory.
wandb: Run wandb online or set WANDB_MODE=online to enable cloud syncing.
============ Load model... ============
Loading checkpoint shards: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 4/4 [00:00<00:00, 19.97it/s]
Loading pretrained model ...
Model loaded sucсessfully ...
============ Quantizing model... ============
Loading data ...
/home/harvey/PM/AQLM/src/datautils.py:219: FutureWarning: You are using torch.load with weights_only=False (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for weights_only will be flipped to True. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via torch.serialization.add_safe_globals. We recommend you start setting weights_only=True for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
data = torch.load(name)[:nsamples]
Loaded data from /home/harvey/PM/data/4096.pth; len(data)=256 sequences
Starting AQ quantization ...
catching layer inputs from data
Traceback (most recent call last):
File "/home/harvey/PM/AQLM/main.py", line 918, in
main()
File "/home/harvey/PM/AQLM/main.py", line 892, in main
quantize_model(model, args)
File "/home/harvey/PM/AQLM/main.py", line 59, in quantize_model
results = quantize_aq(model, train_data, val_data, args)
File "/root/miniconda3/envs/aqlm/lib/python3.10/site-packages/torch/utils/_contextlib.py", line 116, in decorate_context
return func(*args, **kwargs)
File "/home/harvey/PM/AQLM/main.py", line 168, in quantize_aq
inps, forward_args = get_inps(model, data, args.model_seqlen, args.devices, args.offload_activations)
File "/root/miniconda3/envs/aqlm/lib/python3.10/site-packages/torch/utils/_contextlib.py", line 116, in decorate_context
return func(*args, **kwargs)
File "/home/harvey/PM/AQLM/main.py", line 105, in get_inps
inps = [
File "/home/harvey/PM/AQLM/main.py", line 106, in
torch.zeros(
RuntimeError: CUDA error: out of memory
CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.
For debugging consider passing CUDA_LAUNCH_BLOCKING=1
Compile with TORCH_USE_CUDA_DSA to enable device-side assertions.
wandb:
wandb: You can sync this run to the cloud by running:
wandb: wandb sync /home/harvey/PM/AQLM/wandb/offline-run-20241009_213235-pykhgwzr
wandb: Find logs at: wandb/offline-run-20241009_213235-pykhgwzr/logs
(aqlm) root@Harvey-4090:/home/harvey/PM/AQLM#
train.sh
export CUDA_VISIBLE_DEVICES=0   # or e.g. 0,1,2,3
export MODEL_PATH=/home/harvey/PM/modelscope/hub/qwen/Qwen2.5-7B-Instruct
export DATASET_PATH=/home/harvey/PM/data/4096.pth
export SAVE_PATH=/home/harvey/PM/
export WANDB_PROJECT=MY_AQ_EXPS
export WANDB_NAME=COOL_EXP_NAME
python main.py $MODEL_PATH $DATASET_PATH
--nsamples=256
--val_size=32
--num_codebooks=1
--nbits_per_codebook=16
--in_group_size=8
--relative_mse_tolerance=0.01
--finetune_batch_size=32
--finetune_max_epochs=10
--finetune_early_stop=3
--finetune_keep_best
--local_batch_size=1
--offload_activations
--wandb
--resume
--save $SAVE_PATH

Hi, @Jun-Howie !
This looks very suspicious. You need more information to understand what the problem is. Can you send a screenshot of nvidia-smi or nvtop when you run bash train.sh again?

nvtop command No GPU to monitor.
I have a feeling that WSL2 is not recognizing the GPU correctly.
nvitop or nvidia-smi can read the GPU status correctly.
I may need to fix the system environment configuration first
Thanks for your help!

Hi, @Jun-Howie ! I see what the problem is.
You're using the WSL. You may find these sources helpful:

official documentation on WSL limitations
Does your WSL Linux allow you to pin more than 2GB of memory?.

Line 106 of main.py creates an inps tensor of size [256, 4096, 3584] (256 is the nsamples=256 value you specified, 4096 is the model_seqlen value, 3584 is the hidden_size value of the Qwen2.5-7B model.) with data type bfloat16, using pin_memory=True because you used the --offload_activations switch. In RAM, this tensor would take 256 * 4096 * 3584 * 2 / 1024 / 1024 = 7168 Mb. However, pin_memory=True is used, which will allocate some memory to the GPU.
You can do something simple to test in the same environment. For example
import torch
x = torch.zeros(256, 4096, 3584, pin_memory=True)


Can you share the IR model?

About gen2-mask-detection
Your OpenVINO version isn't compatible with my code
So can you share the IR model(.yml, .bin) which i can covert it to Blob model myself?
Many thank <3

Hello @huytuong010101,
I believe the mask detection model is sbd mask, you have the model in the onnx format on their github. You can convert onnx -> IR with the model optimizer.
And you can set the OpenVino model version with pipeline.setOpenVINOVersion(version=dai.OpenVINO.Version.VERSION_2020_1), since the models in the gen2-mask-detection were compiled with OpenVino 2020.1.
Thanks, Erik

Thank you for your reply

No problem, happy to help :)

Hey, do i need add any paramerter on command when convert from oonx to IR?
python3 mo.py --input_model <INPUT_MODEL>.onnx


-PhysicaldiskIds parameter

Server2019 has -PhysicalDisk  and  -PhysicalDiskGuid options

Document Details
⚠ Do not edit this section. It is required for docs.microsoft.com ➟ GitHub issue linking.

ID: 04c7a7fb-cf6d-3a63-a0d9-21f5f77b88c2
Version Independent ID: a8cb92af-9176-698b-bfcd-03c1900fbe53
Content: Set-ClusterStorageSpacesDirectDisk (failoverclusters)
Content Source: docset/windows/failoverclusters/Set-ClusterStorageSpacesDirectDisk.md
Product: w10
Technology: powershell-windows
GitHub Login: @andreabarr
Microsoft Alias: v-anbarr

Hello, @stefanie-msft ,
i've made some research, and found out following:

as it was already mentioned, PhysicalDiskIds parameter was replaced by PhysicalDisk and PhysicalDiskGuid. PhysicalDisk accepts values of CimInstance#MSFT_PhysicalDisk class (it is a class of output of Get-PhysicalDisk cmdlet), and PhysicalDiskGuid accepts GUID strings. These parameters cannot be used with each other.
Also, it has a new Reset parameter (without value)
There is also CacheUsage parameter, that accepts following values: NonHybrid, Capacity, Cache, Auto

So, the syntax is following:
Set-ClusterStorageSpacesDirectDisk -PhysicalDiskGuid <string[]> [-CanBeClaimed <Boolean>] [-Reset] [-CacheUsage {NonHybrid | Capacity | Cache | Auto}] [-CimSession <CimSession[]>] [-ThrottleLimit <Int32>] [-AsJob] [-WhatIf] [-Confirm] [<CommonParameters>]

Set-ClusterStorageSpacesDirectDisk -PhysicalDisk <CimInstance#MSFT_PhysicalDisk[]> [-CanBeClaimed <Boolean>] [-Reset] [-CacheUsage {NonHybrid | Capacity | Cache | Auto}] [-CimSession <CimSession[]>] [-ThrottleLimit <Int32>] [-AsJob] [-WhatIf] [-Confirm] [<CommonParameters>]

Dear @e0i, would it be possible to contact the author of the article. It needs to be rewritten, as syntax, examples and parameters sections are incorrect? I cannot do it myself, as information about these parameters is not available in public sources.
Thank you

Server 2019 content is not covered yet. Thank you.

Hello @e0i,
then we should just ignore it? Should I just close my PR?
Thank you

Hello @e0i,
may be I should add a note that WS 2019 is not covered yet? can we ask article owner about it?
Thank you


Proposal: Add support for comments powered by Discourse

For Home Assistant we use Discourse for commenting. Discourse is open source forum software.
Would a pull request be accepted that adds support for configuring Discourse comments for Docusaurus blog posts?
I would structure it similarly to how the existing Docusaurus FB comments feature is done (source).
[1]: Code looks like this:
  DiscourseEmbed = { discourseUrl: 'https://community.home-assistant.io/',
                     discourseEmbedUrl: 'URL OF PAGE THAT IS BEING EMBEDDED' };
  (function() {
    var d = document.createElement('script'); d.type = 'text/javascript'; d.async = true;
    d.src = DiscourseEmbed.discourseUrl + 'javascripts/embed.js';
    (document.getElementsByTagName('head')[0] || document.getElementsByTagName('body')[0]).appendChild(d);
  })();

Hi @balloob Thanks for the feature request. We have add other issues discuss this too:
https://github.com/facebook/Docusaurus/issues/667
https://github.com/facebook/Docusaurus/issues/219
I would definitely be willing to review a PR. Does it prevent spam well?

I will close this since we already have FB comment for v1. On v2 user can extends their own layout and write React component so this is up to the user


fix: export AttributedString and StringAttribute typings

Description of changes:
AttributedString and StringAttribute are exported by js file but missed in typescript definitions
By submitting this pull request, I confirm that my contribution is made under the terms of the Apache 2.0 license.

LGTM! Thanks for adding it.

:tada: This PR is included in version 1.7.1 :tada:
The release is available on:

npm package (@latest dist-tag)
GitHub release

Your semantic-release bot :package::rocket:


protocol: improve memcache parse performance

Improves memcache request parsing performance by replacing the
use of the pair of slice windows with a manual implementation
which will find specific byte sequences.
This results in better performance by reducing the need to scan
through a very long buffer to find matches for tokenization, as
we can just take the first match as check if it is the sequence
we were looking for or if we need to continue reading.
Adds benchmarks which show an improvement with this change.

What's old is new again 😉  It seems well worth it.


🐞 WorkVersionPolicy#new? checks to see if work is editable

@awead Yeah there was some reason why I had injected the latest version into this method, I think to avoid an n+1 query bug when we were backing the dashboard via activerecord? But you're right, that's not necessary anymore. I like your solution above better

@awead Our thinking about this issue had become very uptight, man.
We already have a method WorkPolicy#create_version? which answers the question "can a new version theoretically be created for this Work?" (i.e. is it editable, and does it already have a draft?).
The method we're discussing, WorkVersionPolicy#new? answers the question "can a new version be created from this specific version?"
I was able to re-write it to require no parameters, as you described above, but also to use the WorkPolicy for the editable and draft stuff.


Fix real-time update of color picker in pixel scene

Ensure the event listener for the Color Selector is correctly configured to update the preview panel immediately when a new color is selected.

Issue Resolved:
Real-Time Update: The lines $("#colorpicker").spectrum("set", newColor); and $("#colorpicker").spectrum("set", bgColor); are added to ensure that whenever the mouse enters or leaves a pixel, the color picker UI is immediately updated to reflect the current or background color. This should help in achieving the real-time feedback that is missing.