feat: Support priorities

Signed-off-by: Masaru Hoshi<EMAIL_ADDRESS>Description
Add support to priorities
Testing plan

Follow the instructions about setup
Create an account on FireHydrant.io
Generate a Bot User and replace the token in examples/main.tf
Run examples/quick-redo.sh

Output
An execution plan has been generated and is shown below.
Resource actions are indicated with the following symbols:
  + create

Terraform will perform the following actions:

  # firehydrant_environment.production will be created
  + resource "firehydrant_environment" "production" {
      + id   = (known after apply)
      + name = "Production"
    }

  # firehydrant_priority.high will be created
  + resource "firehydrant_priority" "high" {
      + default     = false
      + description = "high priority"
      + id          = (known after apply)
      + slug        = "HIGH"
    }

  # firehydrant_priority.low will be created
  + resource "firehydrant_priority" "low" {
      + default     = true
      + description = "low priority"
      + id          = (known after apply)
      + slug        = "LOW"
    }

  # firehydrant_priority.medium will be created
  + resource "firehydrant_priority" "medium" {
      + default     = false
      + description = "medium priority"
      + id          = (known after apply)
      + slug        = "MEDIUM"
    }

  # firehydrant_runbook.default will be created
  + resource "firehydrant_runbook" "default" {
      + id   = (known after apply)
      + name = "Default Incident Process WOOHOO"
      + type = "incident"

      + steps {
          + action_id = "a3136370-9ebd-476f-94f6-3eedf93d7bda"
          + automatic = true
          + config    = {
              + "channel_name_format" = "inc-{{ number }}"
            }
          + name      = "Create incident channel in Slack"
          + repeats   = false
          + step_id   = (known after apply)
        }
      + steps {
          + action_id = "5a1b39d0-8e78-47b5-af64-947f661f9f7b"
          + automatic = true
          + config    = {
              + "message" = <<~EOT
                                Here's the documentation on successfully running an incident with FireHydrant's Slack bot: https://help.firehydrant.io/en/articles/3050697-incident-response-w-slack

                                Don't worry all of your messages and actions here are tracked into your incident on the FireHydrant UI.
                EOT
            }
          + name      = "Incident Preamble"
          + repeats   = false
          + step_id   = (known after apply)
        }
      + steps {
          + action_id = "df7b7a70-037f-4d9d-84e8-00c3c8afdefe"
          + automatic = true
          + config    = {
              + "email_address" =<EMAIL_ADDRESS>              + "subject"       = "{{ incident.severity }} - {{ incident.name }} incident has been started"
            }
          + name      = "Email stakeholders"
          + repeats   = false
          + step_id   = (known after apply)
        }
      + steps {
          + action_id = "3a59c9d4-57db-49a3-a886-01b6985785cb"
          + automatic = true
          + config    = {
              + "channels" = "#fh-incidents"
            }
          + name      = "Notify incidents channel that a new incident has been opened"
          + repeats   = false
          + step_id   = (known after apply)
        }
      + steps {
          + action_id = "b60654c8-2c15-4a25-b5e9-2ac2884f0b9b"
          + automatic = true
          + config    = {
              + "ticket_description" = "{{ incident.description }}"
              + "ticket_summary"     = "{{ incident.name }}"
            }
          + name      = "Create an incident ticket in Jira"
          + repeats   = false
          + step_id   = (known after apply)
        }
      + steps {
          + action_id = "5a1b39d0-8e78-47b5-af64-947f661f9f7b"
          + automatic = true
          + config    = {
              + "message" = <<~EOT
                                  Please check-in with your current status on this {{ incident.severity }} incident

                                  ```
                                  /firehydrant add note I'm calculating the power required by the flux capacitor
                                  ```
                EOT
            }
          + name      = "Remind Slack channel to update stakeholders"
          + repeats   = false
          + step_id   = (known after apply)
        }
      + steps {
          + action_id = "7125e149-6ac5-4887-a11b-4d11e88902b8"
          + automatic = true
          + name      = "Archive incident channel after retrospective completion"
          + repeats   = false
          + step_id   = (known after apply)
        }
    }

  # firehydrant_service.async will be created
  + resource "firehydrant_service" "async" {
      + alert_on_add = false
      + description  = "Sidekiq worker queue"
      + id           = (known after apply)
      + labels       = {
          + "language"  = "ruby"
          + "lifecycle" = "production"
          + "system"    = "main"
          + "tags"      = "foo"
          + "type"      = "user"
        }
      + name         = "async"
      + service_tier = 1
    }

  # firehydrant_service.backups will be created
  + resource "firehydrant_service" "backups" {
      + alert_on_add = false
      + description  = "Another AWS S3 bucket"
      + id           = (known after apply)
      + labels       = {
          + "lifecycle" = "production"
          + "system"    = "main"
          + "tags"      = "foo"
          + "type"      = "ops"
        }
      + name         = "backups"
      + service_tier = 2
    }

  # firehydrant_service.cron will be created
  + resource "firehydrant_service" "cron" {
      + alert_on_add = false
      + description  = "Scheduled tasks using clockwork"
      + id           = (known after apply)
      + labels       = {
          + "language"  = "ruby"
          + "lifecycle" = "production"
          + "system"    = "main"
          + "tags"      = "foo"
          + "type"      = "user"
        }
      + name         = "cron"
      + service_tier = 4
    }

  # firehydrant_service.db will be created
  + resource "firehydrant_service" "db" {
      + alert_on_add = false
      + description  = "Primary PG database"
      + id           = (known after apply)
      + labels       = {
          + "database"  = "postgresql"
          + "lifecycle" = "production"
          + "system"    = "main"
          + "tags"      = "foo"
          + "type"      = "ops"
        }
      + name         = "db"
      + service_tier = 1
    }

  # firehydrant_service.frontend will be created
  + resource "firehydrant_service" "frontend" {
      + alert_on_add = false
      + description  = "The main web UI for our company"
      + id           = (known after apply)
      + labels       = {
          + "language"  = "javascript"
          + "lifecycle" = "production"
          + "system"    = "main"
          + "tags"      = "foo; bar; baz"
          + "type"      = "user"
        }
      + name         = "frontend"
      + service_tier = 1
    }

  # firehydrant_service.logging will be created
  + resource "firehydrant_service" "logging" {
      + alert_on_add = false
      + description  = "Datadog"
      + id           = (known after apply)
      + labels       = {
          + "lifecycle" = "production"
          + "system"    = "main"
          + "tags"      = "foo"
          + "type"      = "ops"
        }
      + name         = "logging"
      + service_tier = 3
    }

  # firehydrant_service.uploads will be created
  + resource "firehydrant_service" "uploads" {
      + alert_on_add = false
      + description  = "AWS S3 bucket"
      + id           = (known after apply)
      + labels       = {
          + "lifecycle" = "production"
          + "system"    = "main"
          + "tags"      = "foo"
          + "type"      = "ops"
        }
      + name         = "uploads"
      + service_tier = 1
    }

  # firehydrant_service.web will be created
  + resource "firehydrant_service" "web" {
      + alert_on_add = false
      + description  = "The main web application for our company"
      + id           = (known after apply)
      + labels       = {
          + "language"  = "ruby"
          + "lifecycle" = "production"
          + "system"    = "main"
          + "tags"      = "foo; bar; baz"
          + "type"      = "user"
        }
      + name         = "web"
      + service_tier = 1
    }

  # firehydrant_team.firefighters will be created
  + resource "firehydrant_team" "firefighters" {
      + id   = (known after apply)
      + name = "Firefighters"
    }

Plan: 14 to add, 0 to change, 0 to destroy.

Do you want to perform these actions?
  Terraform will perform the actions described above.
  Only 'yes' will be accepted to approve.

  Enter a value: yes

make test output
go test -i $(go list ./... | grep -v 'vendor') || exit 1
go test: -i flag is deprecated
echo $(go list ./... | grep -v 'vendor') | xargs -t -n4 go test  -timeout=30s -parallel=4
go test -timeout=30s -parallel=4 github.com/firehydrant/terraform-provider-firehydrant github.com/firehydrant/terraform-provider-firehydrant/firehydrant github.com/firehydrant/terraform-provider-firehydrant/provider
?       github.com/firehydrant/terraform-provider-firehydrant   [no test files]
ok      github.com/firehydrant/terraform-provider-firehydrant/firehydrant       (cached)
ok      github.com/firehydrant/terraform-provider-firehydrant/provider  1.031s

Related links

API documentation

PR readiness

[x] Relevant documentation has been updated if this PR adds or updates attributes, resources, or data sources.
[x] An entry has been added to the changelog, if necessary.

@masaruhoshi Add it to change log as well?
https://github.com/firehydrant/terraform-provider-firehydrant/blob/main/CHANGELOG.md


my_interp存在必要性的问题

嗨，测了一下你的工作感觉效果很棒！但是有个疑问，就是my_interp的cuda实现是有必要的吗？不知道有没有pytorch/python的版本？有的话与cuda版本相差多远？

Hi, thank you very much for your work! I'm also very curious about this topic. Would it be possible to implement the functions in my_interp in Pytorch/Python?

@linghai06 @k8280627
my_interp主要是用来插值车道线坐标的，插值当然可以用pytorch或者python实现，但是实际工程中用python和pytorch实现会显著地慢（可能慢的速度远超你的想象），所以cuda只是加速了这个。这个就像卷积一样，也可以用python或pytorch实现卷积，但是这种情况下训练就会变得不现实。
my_interp is used to interpolate between lane points. Of course, you can use python or PyTorch to implement it, but it would be significantly slow (far slower than you might think). The Cuda implementation simply accelerates the speed. This is like the convolution, which you can also implement with python or PyTorch. But with such implementation, the training is impossible.

I see! Thank you very much for your answer. I'm also wondering, is it possible to use CUDA 11.4 for my_interp? Would you think there's going to be any incompatibility issue?
Thank you very much again!

@k8280627
It's a very simple CUDA program, and any CUDA version should be ok to run it.

Understood. Thank you for the quick reply!

谢谢你详细的解答！👍🏻


[MAUI] [Android] Sharpnado tabs become unusable in combination with MAUI Shell Tabbar

Platform (please complete the following information):

OS: Android
Device: Samsung Galaxy S21
Sdk version: Android SDK 34
MAUI: 8.0.72

Describe the bug
I see strange behavior of a Sharpnado tab bar inside a MAUI application with Shell Tabbar. In certain situations the Sharpnado tabs become unusable and don't react any more.
I'm not actually sure if this is a bug in the MAUI framework itself, or in Sharpnado.Tabs (could be both, I guess).
To Reproduce
Steps to reproduce the behavior:

Download the sample project from https://github.com/janusw/maui-samples/tree/Sharpnado.Tabs_bug/8.0/Navigation/ShellTabBarSample (this is a modified variant of the ShellTabBarSample in my fork of the maui-samples).
Build and run it.
On the shell tab 'One', click the button ("Click me").
A secondary page with a Sharpnado tab bar opens. The tabs have no content in this sample (for simplicity), but switching them works fine.
Switch to the shell tab 'Two'.
Switch back to 'One'.
The Sharpnado tabs are not usable any more. They don't react to tapping.

I verified that the problem occurs also with the latest MAUI version 8.0.90 and down to 8.0.7.

I verified that the problem occurs also with the latest MAUI version 8.0.90 and down to 8.0.7.

seems like a good first issue for the new contributor of the repo ;)

I can confirm this issue repro in my scenario

I verified that the problem occurs also with the latest MAUI version 8.0.90 and down to 8.0.7.

For completeness: It also happens with .NET 7 (and with 8.081, which was released in the meantime).
By now I suspect that it's rather a bug in Sharpnado.Tabs (and not in the MAUI framework).

I now also set up a reproducer directly in my fork of the Sharpnado.Tabs repo, so that one can debug it directly (together with the lib, and not via the nupkg):
https://github.com/janusw/Sharpnado.Tabs/commits/MauiShellSample/
From the log output and some debugging, I see that TabHostView.OnTabItemTapped is triggered correctly as long as one stays on the same shell tab. After switching shell tabs, it is not triggered any more, but I currently have no idea why.
@roubachof Do you have any ideas how to debug this? Or some suggestions what to try?

should be linked to this: #84 maybe?

Quite probable that this is related. Thanks for the pointer!
https://github.com/roubachof/Sharpnado.Tabs/issues/84#issuecomment-2165192578 mentions a solution concerning OnHandlerChanged. I did some debugging of the TabHostView events and found that:

the Unloaded event is triggered when navigating away from the shell tab that contains the Sharpnado tabs
the Loaded and HandlerChanged events are triggered when navigating back to the shell tab that contains the Sharpnado tabs

I tried the following in the OnHandlerChanged method of the TabHostView:
        protected override void OnHandlerChanged()
        {
            base.OnHandlerChanged();
            InternalLogger.Debug(Tag, () => $"HandlerChanged!");
            foreach (var tabItem in Tabs)
            {
                AddTapCommand(tabItem);
            }
        }

However, this does not seem to fix the problem. Any comments?
https://github.com/roubachof/Sharpnado.Tabs/issues/84#issuecomment-2165192578 says:

Fixed by reregistering the effect in the OnHandlerChanged override.

Am I doing the wrong thing here? Or maybe it is a different issue, after all ...?!?

well it's not linked to #84 since I cannot reproduce the issue with .net8

should be fixed by v3.1.0, please re-open if it doesn't.

Well, yes, I can confirm that v3.1.0 indeed fixes this problem on Android. Thanks! 👍
BUT: It looks like this release completely breaks iOS. The tabs do not react to tapping at all on iOS (observed with my own application, have not checked the sample yet). 😱

CAn you set UseMauiTapGesture to true on the TabHostView ?

CAn you set UseMauiTapGesture to true on the TabHostView ?

Yes, indeed this makes the tab on iOS react again. Maybe it should be the default then (on iOS)? Or are there any downsides?

I just tested on the sample and it's working fine :
https://github.com/user-attachments/assets/0bb263cf-396e-4fa7-bc34-242d0acdd9f3

I just tested on the sample and it's working fine :

Yes, it's working in principle (i.e. the tabs react to touching), but it's missing the touch effects on the main toolbar (where you set UseMauiTapGesture). This is the same problem as in my PR #126, which you rejected.

I don't understand you said:

It looks like this release completely breaks iOS. The tabs do not react to tapping at all on iOS (observed with my own application, have not checked the sample yet). 😱

But it does react at tapping, you also have the color feedback.
Do you say that you don't have the color feedback on the bottombar ?
If yes your PR didn't add color effect, on the contrary it removed it for all tab items.

I don't understand...

Ok, let's get this straight ....

It looks like this release completely breaks iOS. The tabs do not react to tapping at all on iOS (observed with my own application, have not checked the sample yet). 😱

This statement concerned my own app: The tabs on iOS did not react at all. This is fixed by using UseMauiTapGesture.

But it does react at tapping, you also have the color feedback. Do you say that you don't have the color feedback on the bottombar ?

Now you're talking about the MauiSample app. For this one, the tabs on iOS seems to react, even without UseMauiTapGesture (you added it for some of the TabHostViews there, e.g. on the MainPage, but not all of them).
However, the color effect when tapping seems to be removed by UseMauiTapGesture, e.g. on the MainPage. In your screen recording it is also missing for the main tab bar (but it was there before version 3.1.0).
Btw, I also checked that the tabs are reacting in the MauiShellSample. I'm not sure why it did not work in my case.

If yes your PR didn't add color effect, on the contrary it removed it for all tab items.

Right, I did not mean to say that my PR is better than what you did. 😉
The other way around: Your implementation has the same problem as my naive PR. 😆 (but at least you added a property to switch it).
Would be nice to have one solution that fully works everywhere instead of having workarounds for specific cases.
IIUC, you may need to get rid of the old effects anyway for .NET 9 (in MAUI they we're only kept for compatibility with Xamarin, but it seems like .NET 9 finally removes this compatibility) ...

I am so lost right now :)
Let's stop talking on a close issue and please open new issues if something is not working as expexted

I am so lost right now :) Let's stop talking on a closed issue and please open new issues if something is not working as expected

Alright 👍 (#130 seems related).
But you can confirm that the touch effect on the sample's main tab bar is missing right now? That's a "regression" in the sample, in a way.

When you are using UseMauiGesture, it uses maui vanilla gesturen which has no touch effect or tap sound


Incorrect behaviour for zero length arrays

>>> import numpy as np
>>> a = np.zeros((0,))
>>> a
array([], dtype=float64)
>>> a.shape
(0,)
>>> a[:] = a
>>> z = zarr.array((0,))
>>> z
Array((1,), int64, chunks=(1,), order=C)
  nbytes: 8; nbytes_stored: 311; ratio: 0.0; initialized: 1/1
  compressor: Blosc(cname='lz4', clevel=5, shuffle=1)
  store: dict
>>> z.shape
(1,)
>>> z[:] = a
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/home/jk/.local/lib/python3.4/site-packages/zarr/core.py", line 582, in __setitem__
    str(value.shape)))
ValueError: value has wrong shape, expecting (1,), found (0,)

Note the difference between the behaviour for numpy arrays and zarr arrays. It seems that the issue is that the shape gets changed from 0 to 1 in the zarr array?

This should be fixed in master, xref #150, #160.

Awesome, thanks @alimanfoo!

No probs.


Java docs for Google Cloud Java clients for older versions are not accessible

Recently, changes were done to migrate the Java doc page for Google Cloud client libraries. The latest version of the client libraries' API details can be found at https://googlecloudplatform.github.io/google-cloud-java/google-cloud-clients/apidocs/index.html However earlier we could just play with the version on the URL and get the API reference for a particular version like https://googlecloudplatform.github.io/google-cloud-java/0.46.0/apidocs/index.html but now we can't do that anymore. Whatever version we give, it is redirecting to the latest version's page. I think developers should still be able to view the API reference material for the older versions as well.

The space taken up by storing all versions was too high and it wasn't scalable to keep every prior version.
Note that we generate a javadocs jar for every build and that is available on maven; for example, you can get the javadocs for every previous version of spanner by doing a search on search.maven.org:
http://search.maven.org/#search|gav|1|g%3A"com.google.cloud" AND a%3A"google-cloud-spanner"


EMAILPLT-192: Add README and add link to Apereo contibuting guide

Ticket: https://issues.jasig.org/browse/EMAILPLT-192
Ported from: https://wiki.jasig.org/display/PLT/Email+Preview+Portlet

Requested in https://github.com/Jasig/email-preview/pull/71#pullrequestreview-20571763


#275 Add section for running validator on Spheron

Description: Updates to https://github.com/availproject/availproject.github.io/blob/main/docs/operate/deployment-options.md page

Add links to deploy validator node to Spheron in a couple minutes
Add additional steps to stake and activate validator node

Issue: #275
Contact<EMAIL_ADDRESS>Ran changes locally to verify:
http://localhost:8000/node/run/hosted-deployment

@skundu42 Could you take a look and let me know if you have any feedback?  Thanks!

We have already added Spheron Network as a Third Party Provider to run our node, closing this issue.
Link:https://docs.shardeum.org/node/run/providers#spheron-network


Simplify install

So we can just have one version of the installer from the user perspective
Summary by Sourcery
Simplify the installation process by modifying the install script to conditionally use sudo based on user permissions and update the README to reflect the unified installation method for both Linux and macOS.
Enhancements:

Simplify the installation script by allowing it to run with or without sudo, depending on user permissions.

Documentation:

Update the installation instructions in the README to unify the process for Linux and macOS.

LGTM


Is there a way to make multi song varables?

Describe your problem here.
Basically, is there a way to make a varable that can survive going from one song to another, so you can make songs affect other songs? I don't care how hacky it is, i just want a way that works, and won't be broken if the player uses a setting
Are you modding a build from source or with Lua?
Lua
What is your build target?
Windows x64
Did you edit anything in this build? If so, mention or summarize your changes.
No response

Lua supports save data

save data w

Pretty sure someone wrote a guide in issues, you can look it up

thanks, i'll close this now
also it's #8286 to anyone coming by later, also PLEASE UPDATE THE DAMN WIKI


[T4A2][W14-B3]Brehmer Chan Xi Quan

Ready for review

Accepting this PR without a review because another of your PRs for this tutorial was reviewed in detail.


OSError: Can't load tokenizer for 'daven3/k2-v1'

Hi,
When trying to load the K2-v1 model following the guidelines, I get this error :

Is this related to the missing tokenizer.json file in the Hugging Face hub ?

Issue closed as it has already been solved in a closed one.


vkCreateSwapchainKHR segfaults when VK_LAYER_LUNARG_standard_validation instance layer is active

Without this layer active, everything works fine
VKVALID (vk_debug_cb, line 25): OBJ[1] : CREATE VK_DEBUG_REPORT_OBJECT_TYPE_SURFACE_KHR_EXT object 0x1
VKVALID (vk_debug_cb, line 25): OBJ[2] : CREATE VK_DEBUG_REPORT_OBJECT_TYPE_PHYSICAL_DEVICE_EXT object 0x7f25a9ddb540
INFO (com_test_init, line 63): using physical device: GTX 980
VKVALID (vk_debug_cb, line 25): Insert device layer VK_LAYER_GOOGLE_unique_objects (libVkLayer_unique_objects.so)
VKVALID (vk_debug_cb, line 25): Insert device layer VK_LAYER_LUNARG_swapchain (libVkLayer_swapchain.so)
VKVALID (vk_debug_cb, line 25): Insert device layer VK_LAYER_LUNARG_core_validation (libVkLayer_core_validation.so)
VKVALID (vk_debug_cb, line 25): Insert device layer VK_LAYER_LUNARG_image (libVkLayer_image.so)
VKVALID (vk_debug_cb, line 25): Insert device layer VK_LAYER_LUNARG_object_tracker (libVkLayer_object_tracker.so)
VKVALID (vk_debug_cb, line 25): Insert device layer VK_LAYER_LUNARG_parameter_validation (libVkLayer_parameter_validation.so)
VKVALID (vk_debug_cb, line 25): Insert device layer VK_LAYER_GOOGLE_threading (libVkLayer_threading.so)
VKVALID (vk_debug_cb, line 25): OBJ[3] : CREATE VK_DEBUG_REPORT_OBJECT_TYPE_DEVICE_EXT object 0x7f25a9c21000
VKVALID (vk_debug_cb, line 25): OBJ[4] : CREATE VK_DEBUG_REPORT_OBJECT_TYPE_QUEUE_EXT object 0x7f25a9c447e0
VKVALID (vk_debug_cb, line 25): OBJ[5] : CREATE VK_DEBUG_REPORT_OBJECT_TYPE_QUEUE_EXT object 0x7f25a9c44700
zsh: segmentation fault (core dumped)

#0  0x00007ffff2c374d3 in ?? () from /usr/lib/libVkLayer_swapchain.so
#1  0x00007ffff2c38573 in swapchain::CreateSwapchainKHR(VkDevice_T*, VkSwapchainCreateInfoKHR const*, VkAllocationCallbacks const*, VkSwapchainKHR_T**) () from /usr/lib/libVkLayer_swapchain.so
#2  0x00007ffff296723b in core_validation::CreateSwapchainKHR(VkDevice_T*, VkSwapchainCreateInfoKHR const*, VkAllocationCallbacks const*, VkSwapchainKHR_T**) ()
   from /usr/lib/libVkLayer_core_validation.so
#3  0x00007ffff22d2090 in object_tracker::CreateSwapchainKHR(VkDevice_T*, VkSwapchainCreateInfoKHR const*, VkAllocationCallbacks const*, VkSwapchainKHR_T**) ()
   from /usr/lib/libVkLayer_object_tracker.so
#4  0x000000000040430a in com_vk_swapchain_init (dev=dev@entry=0x60c180 <dev>) at ../src/com_vk_swapchain.c:85
#5  0x0000000000400ff1 in com_init () at ../src/com.c:61
#6  0x0000000000409439 in main () at ../src/entry.c:19

Can you provide a repro case?

This is a very small thrown together program that reproduces the issue.
2 lines in here are commented with "IF THIS LINE IS REMOVED, THERE IS NO SEGFAULT", the removal of these lines also cause the validation layer to print a warning message. It would appear it only segfaults if it doesn't have to print any warning messages.
#include <stdlib.h>
#include <dlfcn.h>
#define VK_NO_PROTOTYPES
#define VK_USE_PLATFORM_XCB_KHR
#include <vulkan/vulkan.h>
#include <assert.h>

static xcb_connection_t * com_xcb_connection = NULL;
static xcb_window_t com_xcb_window = 0;
static xcb_intern_atom_cookie_t delete_cookie;
static xcb_intern_atom_reply_t * delete_reply = NULL;

static void xcb_init() {
	com_xcb_connection = xcb_connect( NULL, NULL );
	xcb_setup_t const * xcb_setup  = xcb_get_setup( com_xcb_connection );
	xcb_screen_iterator_t xcb_screen_iter   = xcb_setup_roots_iterator( xcb_setup );
	xcb_screen_t * xcb_screen = xcb_screen_iter.data;
	com_xcb_window = xcb_generate_id( com_xcb_connection );
	uint32_t value_list[] = { 0x00400040, XCB_EVENT_MASK_EXPOSURE | XCB_EVENT_MASK_KEY_PRESS | XCB_EVENT_MASK_STRUCTURE_NOTIFY };
	xcb_create_window( com_xcb_connection, XCB_COPY_FROM_PARENT, com_xcb_window, xcb_screen->root, 0, 0, 800, 600, 0, XCB_WINDOW_CLASS_INPUT_OUTPUT, xcb_screen->root_visual, XCB_CW_BACK_PIXEL | XCB_CW_EVENT_MASK, value_list );

	xcb_intern_atom_cookie_t protocols_cookie = xcb_intern_atom( com_xcb_connection, 1, 12, "WM_PROTOCOLS" );
	xcb_intern_atom_reply_t * protocols_reply = xcb_intern_atom_reply( com_xcb_connection, protocols_cookie, 0 );
	delete_cookie = xcb_intern_atom( com_xcb_connection, 0, 16, "WM_DELETE_WINDOW" );
	delete_reply = xcb_intern_atom_reply( com_xcb_connection, delete_cookie, 0 );
	xcb_change_property( com_xcb_connection, XCB_PROP_MODE_REPLACE, com_xcb_window, (*protocols_reply).atom, 4, 32, 1, &(*delete_reply).atom );
	free( protocols_reply );

	xcb_map_window(com_xcb_connection, com_xcb_window);
	xcb_flush(com_xcb_connection);
}

static void * vk_handle;
static VkInstance vk_instance;
static VkSurfaceKHR vk_surface;
static VkDevice vk_device;
static VkSwapchainKHR vk_swapchain;

static void vk_init() {
	vk_handle = dlopen("libvulkan.so", RTLD_LOCAL | RTLD_NOW);
	PFN_vkGetInstanceProcAddr vkGetInstanceProcAddr = dlsym(vk_handle, "vkGetInstanceProcAddr");
	PFN_vkCreateInstance vkCreateInstance = (PFN_vkCreateInstance)vkGetInstanceProcAddr(NULL, "vkCreateInstance");
	VkApplicationInfo application_info = {
		.sType = VK_STRUCTURE_TYPE_APPLICATION_INFO,
		.pNext = NULL,
		.pApplicationName = "test",
		.applicationVersion = VK_MAKE_VERSION(0, 0, 1),
		.pEngineName = "test",
		.engineVersion = VK_MAKE_VERSION(0, 0, 1),
		.apiVersion = VK_MAKE_VERSION(1, 0, 21),
	};
	static char const * iex [] = {"VK_KHR_surface", "VK_KHR_xcb_surface"};
	static char const * ilay [] = {"VK_LAYER_LUNARG_standard_validation"};
	VkInstanceCreateInfo instance_create_info = {
		.sType = VK_STRUCTURE_TYPE_INSTANCE_CREATE_INFO,
		.pNext = NULL,
		.flags = 0,
		.pApplicationInfo = &application_info,
		.enabledLayerCount = 1,
		.ppEnabledLayerNames = ilay,
		.enabledExtensionCount = 2,
		.ppEnabledExtensionNames = iex,
	};
	assert(vkCreateInstance(&instance_create_info, NULL, &vk_instance) == VK_SUCCESS);
	PFN_vkCreateXcbSurfaceKHR vkCreateXcbSurfaceKHR = (PFN_vkCreateXcbSurfaceKHR)vkGetInstanceProcAddr(vk_instance, "vkCreateXcbSurfaceKHR");
    VkXcbSurfaceCreateInfoKHR surface_create_info = {
		.sType = VK_STRUCTURE_TYPE_XCB_SURFACE_CREATE_INFO_KHR,
		.pNext = NULL,
		.flags = 0,
		.connection = com_xcb_connection,
		.window = com_xcb_window,
    };
	assert(vkCreateXcbSurfaceKHR(vk_instance, &surface_create_info, NULL, &vk_surface) == VK_SUCCESS);
	float qp = 1;
	VkDeviceQueueCreateInfo device_queue_create = {
		.sType = VK_STRUCTURE_TYPE_DEVICE_QUEUE_CREATE_INFO,
		.pNext = NULL,
		.flags = 0,
		.queueFamilyIndex = 0,
		.queueCount = 1,
		.pQueuePriorities = &qp,
	};
	PFN_vkEnumeratePhysicalDevices vkEnumeratePhysicalDevices = (PFN_vkEnumeratePhysicalDevices)vkGetInstanceProcAddr(vk_instance, "vkEnumeratePhysicalDevices");
	uint32_t one = 1;
	VkPhysicalDevice pdev;
	vkEnumeratePhysicalDevices(vk_instance, &one, &pdev);

	PFN_vkGetPhysicalDeviceQueueFamilyProperties vkGetPhysicalDeviceQueueFamilyProperties = (PFN_vkGetPhysicalDeviceQueueFamilyProperties)vkGetInstanceProcAddr(vk_instance, "vkGetPhysicalDeviceQueueFamilyProperties");
	VkQueueFamilyProperties qfam;
	uint32_t al;

	vkGetPhysicalDeviceQueueFamilyProperties(pdev, &al, NULL); //IF THIS LINE IS REMOVED, THERE IS NO SEGFAULT
	assert(al);

	vkGetPhysicalDeviceQueueFamilyProperties(pdev, &one, &qfam);
	assert(qfam.queueFlags & VK_QUEUE_GRAPHICS_BIT);
	PFN_vkGetPhysicalDeviceSurfaceSupportKHR vkGetPhysicalDeviceSurfaceSupportKHR = (PFN_vkGetPhysicalDeviceSurfaceSupportKHR)vkGetInstanceProcAddr(vk_instance, "vkGetPhysicalDeviceSurfaceSupportKHR");
	VkBool32 sup_pres;
	vkGetPhysicalDeviceSurfaceSupportKHR(pdev, 0, vk_surface, &sup_pres);
	assert(sup_pres);
	PFN_vkGetPhysicalDeviceSurfaceFormatsKHR vkGetPhysicalDeviceSurfaceFormatsKHR = (PFN_vkGetPhysicalDeviceSurfaceFormatsKHR)vkGetInstanceProcAddr(vk_instance, "vkGetPhysicalDeviceSurfaceFormatsKHR");

	vkGetPhysicalDeviceSurfaceFormatsKHR(pdev, vk_surface, &al, NULL); //IF THIS LINE IS REMOVED, THERE IS NO SEGFAULT
	assert(al);

	VkSurfaceFormatKHR asd;
	vkGetPhysicalDeviceSurfaceFormatsKHR(pdev, vk_surface, &one, &asd);

	char const * dex [] = {"VK_KHR_swapchain"};
	VkDeviceCreateInfo device_create_info = {
		.sType = VK_STRUCTURE_TYPE_DEVICE_CREATE_INFO,
		.pNext = NULL,
		.flags = 0,
		.queueCreateInfoCount = 1,
		.pQueueCreateInfos = &device_queue_create,
		.enabledLayerCount = 1,
		.ppEnabledLayerNames = ilay,
		.enabledExtensionCount = 1,
		.ppEnabledExtensionNames = dex,
		.pEnabledFeatures = NULL,
	};
	PFN_vkCreateDevice vkCreateDevice = (PFN_vkCreateDevice)vkGetInstanceProcAddr(vk_instance, "vkCreateDevice");
	assert(vkCreateDevice(pdev, &device_create_info, NULL, &vk_device) == VK_SUCCESS);
	PFN_vkGetPhysicalDeviceSurfaceCapabilitiesKHR vkGetPhysicalDeviceSurfaceCapabilitiesKHR = (PFN_vkGetPhysicalDeviceSurfaceCapabilitiesKHR)vkGetInstanceProcAddr(vk_instance, "vkGetPhysicalDeviceSurfaceCapabilitiesKHR");
	VkSurfaceCapabilitiesKHR scap;
	vkGetPhysicalDeviceSurfaceCapabilitiesKHR(pdev, vk_surface, &scap);
	VkSwapchainCreateInfoKHR swap_chain_create_info = {
		.sType = VK_STRUCTURE_TYPE_SWAPCHAIN_CREATE_INFO_KHR,
		.pNext = NULL,
		.flags = 0,
		.surface = vk_surface,
		.minImageCount = 2,
		.imageFormat = VK_FORMAT_B8G8R8A8_UNORM,
		.imageColorSpace = VK_COLORSPACE_SRGB_NONLINEAR_KHR,
		.imageExtent = scap.currentExtent,
		.imageArrayLayers = 1,
		.imageUsage = VK_IMAGE_USAGE_COLOR_ATTACHMENT_BIT | VK_IMAGE_USAGE_TRANSFER_DST_BIT,
		.imageSharingMode = VK_SHARING_MODE_EXCLUSIVE,
		.queueFamilyIndexCount = 0,
		.pQueueFamilyIndices = NULL,
		.preTransform = scap.currentTransform,
		.compositeAlpha = VK_COMPOSITE_ALPHA_OPAQUE_BIT_KHR,
		.presentMode = VK_PRESENT_MODE_FIFO_KHR,
		.clipped = VK_TRUE,
		.oldSwapchain = NULL,
	};
	PFN_vkGetDeviceProcAddr vkGetDeviceProcAddr = (PFN_vkGetDeviceProcAddr)vkGetInstanceProcAddr(vk_instance, "vkGetDeviceProcAddr");
	PFN_vkCreateSwapchainKHR vkCreateSwapchainKHR = (PFN_vkCreateSwapchainKHR)vkGetDeviceProcAddr(vk_device, "vkCreateSwapchainKHR");
	assert(vkCreateSwapchainKHR(vk_device, &swap_chain_create_info, NULL, &vk_swapchain) == VK_SUCCESS);
}

int main() {
	xcb_init();
	vk_init();
	sleep(1);
}

I'll try to take a look at this, later today.

I just created pull request #882 to address this.  I haven't copied your test app into a separate file for testing with, but I can see what it was doing that was causing the crash.
Thanks for making the validation code stronger.  I am hopeful that this is not how your production application code is written:-).

The hardcoded 1s are only in the above test code, but removing them did fix the segfault on that test code. It looks like my actual project was segfaulting for a different but probably related reason: I had done vkGetPhysicalDeviceSurfacePresentModesKHR to get the number to fill an array with, but I never actually made a second call. When I added a second call to vkGetPhysicalDeviceSurfacePresentModesKHR to fill an array, the segfault on my actual project was fixed as well.
Thanks for looking into it.

Are you good (i.e. no segfaults), without the pull request I made?

Yes, but I feel the validation layers should still be hardened against those kinds if simple mistakes.

I agree--we need hardened layers.  I'm just glad that you're not blocked.  Thanks!

The pull-request change has been pushed to master.  Thanks again.


SSC-245 convert to monorepo

Issue ticket number and link from Jira
Please add a link to the issue here:
https://front-finance.atlassian.net/browse/SCS-245
If there isn't one just a short description of where this change came from.
Type of change
Please tick the relevant options (with an "x").

[ ] Bug fix (non-breaking change which fixes an issue)
[ ] New feature (non-breaking change which adds functionality)
[ ] Breaking change (fix or feature that would cause existing functionality not to work as expected)
[X] Refactor (none of the core functionality has changed)
[X] Chore (adding new tests, build processes, tooling, performance)
[ ] This change requires a documentation update

Checklist:
Please tick the options (with an "x") of the actions which you have done. For example, it may be that an issue doesn't require new tests to be written, so you would leave option 5 unchecked:

[X] I have performed a self-review of my code and it meets the criteria specified by the ticket/issue
[X] I have commented on my code, particularly in hard-to-understand areas
[ ] I have made corresponding changes to the documentation if necessary
[ ] I have added tests that prove my fix is effective or that my feature works
[X] I have not changed existing tests (if so please add a description in the PR)
[X] New and existing unit tests pass locally with my changes

closing in favor to #59


claim attestations with uuid

change api to use uuid instead of dpid for claiming attestations, enabling draft nodes to claim attestations
Closes https://github.com/desci-labs/nodes-web-v2/issues/729
Description of the Problem / Feature

need to allow users to claim attestations while in draft mode (prior to publish)

Explanation of the solution

use uuid as primary key for managing attestation claims instead of dpid, which is only available after publish

Instructions on making this work
Use the PR in nodes-web-v2 https://github.com/desci-labs/nodes-web-v2/pull/753 and create a node and go to attestations pane and claim/unclaim

@hubsmoke After publishing a node all draft claims should be updated to include the nodeDpid10 field. This is necessary to be able to differentiate valid claims from draft claims and also when we're migrating to ceramic

IMO we should not be storing nodeDpid10 and uuid inside the nodeAttestation table -- just node.id is sufficient because the node table has both fields.
I added support for this for now, but will prob remove these columns from nodeAttestation soon @shadrach-tayo

closing in favor of https://github.com/desci-labs/nodes/pull/366


reallocating new Headers and Streams on Frames is slow

Additionally the existing pooling support doesn't scale well so it is actually slower than not pooling.  Proposal is to add an option to cache the Headers/Streams on the Frame object and remove current pooling scheme.

Related: https://github.com/aspnet/KestrelHttpServer/issues/754

@cesarbs Could you dupe this?

Dupe of #754.


fix: use GetStringUTFLength when converting Java UTF16 to UTF8 C-strings

The Android JNI bindings query jniEnv->GetStringLength to determine the size of the allocated std::string but then copy UTF8 bytes which could be longer should non-ASCII input occur. jniEnv->GetStringUTFLength should be used instead.

Hi all,
the travis-ci build fails with:

To run the demo, you must provide a Mapzen API key as an environment    variable named 'MAPZEN_API_KEY' or a Gradle property named 'mapzenApiKey'. Expression: (apiKey != null). Values: apiKey = null

so nothing related to this patch

@cleeus
Thanks for the PR (number 1600 :D). This is good.
Yeah CI builds fail for any external contribution precisely because of the reason you mentioned. I created #1602 with your commit which takes care of the CI. Again thanks for catching this issue.

Closing this in favor of #1602


[Bugfix] Fix guided_decode LogitsProcessor FSM_state missing error

FILL IN THE PR DESCRIPTION HERE
FIX https://github.com/vllm-project/vllm/issues/7827

What is the bug

For tool_calls handling, tested with ISSUE_7827, returned tool_calls is not in correct format.
After investigating the codes, error is caused by _fsm_states object being refreshed everytime with new model_execute. While the logit_processor initial purpose is using a consistent _fsm_state crossing requests.

What is the fix

Pass sampling_metadata_cache in model_execute, and use existing sampling_param in the cache.

How it tested

Tested with same script indicated in ISSUE_7827
Before fixing, output is
tool_calls: [ChatCompletionMessageToolCall(id='chatcmpl-tool-a081c6fb632c4d7bbfd3ed00ac5a4399', function=Function(arguments='{ ', name='get_current_weather'), type='function')]

After fixing, output is
tool_calls: [ChatCompletionMessageToolCall(id='chatcmpl-tool-b58a97994c5d4de7bc817a36dcf5e95d', function=Function(arguments='{ "location": "Boston, MA", "unit": "celsius" }', name='get_current_weather'), type='function')]

BEFORE SUBMITTING, PLEASE READ THE CHECKLIST BELOW AND FILL IN THE DESCRIPTION ABOVE

 PR Checklist (Click to Expand)
Thank you for your contribution to vLLM! Before submitting the pull request, please ensure the PR meets the following criteria. This helps vLLM maintain the code quality and improve the efficiency of the review process.
PR Title and Classification
Only specific types of PRs will be reviewed. The PR title is prefixed appropriately to indicate the type of change. Please use one of the following:

[Bugfix] for bug fixes.
[CI/Build] for build or continuous integration improvements.
[Doc] for documentation fixes and improvements.
[Model] for adding a new model or improving an existing model. Model name should appear in the title.
[Frontend] For changes on the vLLM frontend (e.g., OpenAI API server, LLM class, etc.)
[Kernel] for changes affecting CUDA kernels or other compute kernels.
[Core] for changes in the core vLLM logic (e.g., LLMEngine, AsyncLLMEngine, Scheduler, etc.)
[Hardware][Vendor] for hardware-specific changes. Vendor name should appear in the prefix (e.g., [Hardware][AMD]).
[Misc] for PRs that do not fit the above categories. Please use this sparingly.

Note: If the PR spans more than one category, please include all relevant prefixes.
Code Quality
The PR need to meet the following code quality standards:

We adhere to Google Python style guide and Google C++ style guide.
Pass all linter checks. Please use format.sh to format your code.
The code need to be well-documented to ensure future contributors can easily understand the code.
Include sufficient tests to ensure the project to stay correct and robust. This includes both unit tests and integration tests.
Please add documentation to docs/source/ if the PR modifies the user-facing behaviors of vLLM. It helps vLLM user understand and utilize the new features or changes.

Notes for Large Changes
Please keep the changes as concise as possible. For major architectural changes (>500 LOC excluding kernel/data/config/test), we would expect a GitHub issue (RFC) discussing the technical design and justification. Otherwise, we will tag it with rfc-required and might not go through the PR.
What to Expect for the Reviews
The goal of the vLLM team is to be a transparent reviewing machine. We would like to make the review process transparent and efficient and make sure no contributor feel confused or frustrated. However, the vLLM team is small, so we need to prioritize some PRs over others. Here is what you can expect from the review process:

 After the PR is submitted, the PR will be assigned to a reviewer. Every reviewer will pick up the PRs based on their expertise and availability.
 After the PR is assigned, the reviewer will provide status update every 2-3 days. If the PR is not reviewed within 7 days, please feel free to ping the reviewer or the vLLM team.
 After the review, the reviewer will put an  action-required label on the PR if there are changes required. The contributor should address the comments and ping the reviewer to re-review the PR.
 Please respond to all comments within a reasonable time frame. If a comment isn't clear or you disagree with a suggestion, feel free to ask for clarification or discuss the suggestion.

Thank You
 Finally, thank you for taking the time to read these guidelines and for your interest in contributing to vLLM. Your contributions make vLLM a great tool for everyone!

@zhouyuan, may you help me to review this PR and @ who may as reviewers. Thanks


Fix: Docker builds x ICA genesis state

Several improvements to catch minor issues with our Docker and ICA init from genesis

Updates docker build to catch missed dependency change (wasmvm 1.0.0 -> 1.1.0)
Enables cosmwasm1_1 in app features
Adds missing genesis state in docker builds by default using a new cmd

@JakeHartnell / @dimiandre / @faddat this is ready to go I think


jQuery in JS only version docs

FYI, you have some jQuery in the JS version docs for example 7:
<script type="text/javascript" src="dist/InstagramFeed.min.js"></script>
<script type="text/javascript">
    (function(){
        new InstagramFeed({
            'username': 'instagram',
            'callback': function(data){
                $('#jsonHere').html(JSON.stringify(data, null, 2));
            }
        });
    })();
</script>

However, in your source you're using it correctly:
            new InstagramFeed({
                'username': 'instagram',
                'callback': function(data) {
                    document.getElementById("jsonHere").innerHTML = JSON.stringify(data, null, 2);
                }
            });

Just thought I'd let you know in case you wanted to update accordingly.

You are right, I will update it when I have time or if you want feel free to send a pull request :)

I'd be happy to send a PR. I'm not seeing the site in your repos, however.

Yes, it is the index.html of this repo


Added missing Pages product to README

Closes #1078
Issue:
I do not see the Pages product listing in the README.md.
Summary of change:
I have updated the README.md to add the missing Pages product listing.

@dcpena would you mind reviewing this PR? It looks like we are missing Pages from the list of products.


Decoding artifacts when using ingress and WHIP

We have experienced decoding artifacts like pixelation and even green screens on browsers receiving video tracks from an ingress that is using WHIP.
We verified that the video that is fed to ingress using whip was correctly encoded and in fact we could record it and see that the video at whip ingress was correct. However at the same time, the video received in all browsers subscribed to the track show decoding artifacts. We have been able to reproduce the problem using H264 and VP8. and if many browser subscribe to the tracks, all of the show the same problem at the same time
We have analysed the ingress and we think we have found possible causes for this problem: basically on ingress frames may be dropped to several causes, that may happen when media comes from WHIP. The problem seems to be that dropping samples in the middle of an encoded video stream casuses gaps in the stream that will make the video player impossible to correctly decode the stream until a new keyframe appears on the stream. The main problem is that after a sample is dropped subsequent samples are still pushed downstream even to the RemoteTrack that without sequence numbers has no way for livekit server or the browser  to detect that there is a gap on the stream. Thus, when this issue happens, the decoder on the browser cannot detect the gap and just tries to decode frames causing decoding artifacts.
This happens in two places:

The jitter buffer used at the reception of WHIP packets may drop late ones. It is true that in this case, jitter buffer dropping samples will also cause a PLI request that theoretically should generate a key frame at some point in the near future, but until that happens, subsequent frames that has lost their previous frame reference will be pushed downstream, making the decoder to cause decoding artifact that may last from just a flash to a few seconds depending on when the keyframe appears
Also the output synchronizer may drop samples https://github.com/livekit/ingress/blob/2204d43619ec2f839e911c54a6f00349e8dafaf0/pkg/whip/sdk_media_sink.go#L271 and in this case apart from pushing downstream subsequent frames, the point is that no PLI request is generated, so the publisher has no way to know that a keyframe is needed and the decoding artifact may last for many seconds, even minutes, in fact until for any other reason a keyframe is generated.

We have created a fix for this that basically whenever a sample is dropped, it just drops all subsequent frames until a keyframe appears. Also for the sdk_media_sink.go the fix includes that if a sample is dropped a PLI is requested upstream.
We have verified that with this fix the decoding artifacts completely dissapear, so we are pretty sure that the issue is as we described. However, I am afraid that the fix also impacts the output synchronizer  causing freezings longer than what theoretically should appear. We haven't gone further on that analysis, but it seems that as when a sample is dropped a sequence of samples is dropped, this may be impacting the output synchronizer that handle sample durations thus having more difficult to correctly synchronize when not short gaps appear on the stream.

The jitter buffer and output synchronizer have been removed from the bypass_transcoding path. Are you still experiencing these issues with the current ingress server?

I haven't tried, the feature was urgent to us, so we decided implementing our own ingress feature. If I find time to test I will let you know


Deepl translator

first of all, hello, thank you for making this great program.
i have a deepl pro membership. but the only key is giving a deepl error in translation. it never translates, unfortunately

During my test, it still works well. The DEEL API is offered by the official service.
Are you sure you can access the url and do you input the correct key token?
_DEEPL_SERVER_URL = "https://api.deepl.com"
_DEEPL_SERVER_URL_FREE = "https://api-free.deepl.com"

yes, I can't translate files with deepl pro

yes, I can't translate files with deepl pro

Can you offer the log file?

i'm entering the api key, but it's not working.

Try v2.5.1 which upgrade the deepl api

i'm getting this error;
ImportError: attempted relative import with no known parent package

i'm getting this error;
ImportError: attempted relative import with no known parent package

If you are trying to write a custom api, you need to write import inside the function , otherwise will cause NameError
You can refer to the template caiyun.py and baidu.py (the api name must be tranlate_single or tranlate_queue)

i don't know how to import

please help me to use deepl pro translation

please help me to use deepl pro translation

It's weird,  during my test, it works well for free deepl api and it should work for pro version
The api is from the official website : https://developers.deepl.com/docs/getting-started/your-first-api-request

Do you have any request errors？
like
Traceback (most recent call last):
File "D:\PycharmProjects\renpy_translator\src\deepl_translate.py", line 53, in translate_limit
result = self.translator.translate_text(

File "C:\Users\Administrator\AppData\Local\Programs\Python\Python38\lib\site-packages\deepl\translator.py", line 446, in translate_text
self._raise_for_status(status, content, json)

File "C:\Users\Administrator\AppData\Local\Programs\Python\Python38\lib\site-packages\deepl\translator.py", line 183, in _raise_for_status
raise AuthorizationException(

deepl.exceptions.AuthorizationException: Authorization failure, check auth_key

i have no idea about the codes. please add deepl pro to the translation engines in the new version

i have no idea about the codes. please add deepl pro to the translation engines in the new version

I try the deepl pro key, it works well
Can you access the following webite ?
https://www.deepl.com/en/your-account/keys

It seems like that your key is invalid?  Try to get a new one

he made the same mistake again. i guess I can't do it

he made the same mistake again. i guess I can't do it

you may need  to ask deepl for help due to the account problem,  active deepl pro key is required

the api keys part does not appear to me

the api keys part does not appear to me

Try to create a new key?

to me deepl.py can you send the file?

 to me deepl.py can you send the file?

Maybe later, I am busy now, tomorrow

ok bro

ok bro
Try the pre test version
You can download it from https://github.com/anonymousException/renpy-translator/actions/runs/10748889698
https://github.com/anonymousException/renpy-translator/blob/main/src/custom_engine/deepl_custom.py

thank you, brother, the problem is solved)

can you make an anime translation program like this? :)


Falsey values don't get rendered by a MediaType

If you have an media type
class BlogMT < Praxis::MediaType
  attributes do
    attribute :promoted_blog, Attributor::Boolean
    attribute :name, String
  end

  view :link do
    attribute :promoted_blog
    attribute :name
  end
end

and an object like this
class BlogObject
  def promoted_blog
    false
  end

  def name
    nil
  end
end

When you rendered the Media type:
BlogMt.new(BlogObject).render

The result would be an empty hash.

@shaley91 It appears that this issue was resolved by #10. Can you close this issue if you agree? Thanks!


PromptTuning and PTuning with SEQ_2_SEQ_LM

I'm trying to use Parameter-Efficient Fine-tuning approaches for a SEQ_2_SEQ_LM tasks (ought/raft: twitter_complaints, tweet_eval_hate). I'm mainly testing with models such as google/flan-t5-base, bigscience/T0_3B, google/t5-base-lm-adapt. I am using a dataset tokenized with model's tokenizer, DataCollatorForSeq2Seq, as well as a regular train/eval loops. I'm getting very good results (both metrics as well as the decoded sequence) when using PrefixTuning, however, I cannot get results any close to these when using PromptTuning or PTuning.
I've tried several changes to mitigate these problems, however, the problem still persists.
PromptTuning:

Using num_transformer_submodules=1

PTuning, PromptTuning:

Removing num_virtual_tokens tokens from the left side of the model outputs during evaluations (as mentioned in Caveats 2.)

Is there anything else I'm missing?

Hi! I have an additional question to your experiments: aren't you getting a NotImplementedError due to this line with PromptTuning?
I have the same issue when using Prompt Tuning on the google/t5-small-lm-adapt model, task_type='CausalLM', and get really bad results.
Perhaps there is a deeper problem here w/ encoder-decoder models pre-trained on mask problems?

Hi @fotinidelig! I was getting NotImplementedError when decoding, however, that's why I used logits of the classification labels from outputs = model(**inputs).

I have the same issue when using Prompt Tuning on the google/t5-small-lm-adapt model, task_type='CausalLM', and get really bad results.
That sounds like the same problem.

Perhaps there is a deeper problem here w/ encoder-decoder models pre-trained on mask problems?
It's just unclear to me how PrefixTuning works properly.

It seems that the promptTuning does not work with "google/flan-t5-small". The following code block will raise NotImplementedError. Any comments or thoughts?
from peft import get_peft_model
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
from peft import LoraConfig, TaskType, PromptTuningConfig, PromptTuningInit
import torch

model_name_or_path = "google/flan-t5-small"
tokenizer_name_or_path = "google/flan-t5-small"
model = AutoModelForSeq2SeqLM.from_pretrained(model_name_or_path)

peft_config = PromptTuningConfig(
    task_type=TaskType.SEQ_2_SEQ_LM,
    prompt_tuning_init=PromptTuningInit.TEXT,
    num_virtual_tokens=8,
    prompt_tuning_init_text="Choose the best option to answer the question:",
    tokenizer_name_or_path=model_name_or_path,
)

model = get_peft_model(model, peft_config)
model.print_trainable_parameters()

tokenizer = AutoTokenizer.from_pretrained(tokenizer_name_or_path)

device = 'cuda:0'
model = model.to(device)
model.eval()
inputs = tokenizer("Tweet text : @HondaCustSvc Your customer service has been horrible during the recall process. I will never purchase a Honda again. Label :", return_tensors="pt")["input_ids"]
print('inputs: ', inputs.shape)

with torch.no_grad():
    outputs = model.generate(input_ids=inputs.to(device))
    print(tokenizer.batch_decode(outputs.detach().cpu().numpy(), skip_special_tokens=True)[0])

Thank you!

Hi! I have an additional question to your experiments: aren't you getting a NotImplementedError due to this line with PromptTuning?
I have the same issue when using Prompt Tuning on the google/t5-small-lm-adapt model, task_type='CausalLM', and get really bad results.
Perhaps there is a deeper problem here w/ encoder-decoder models pre-trained on mask problems?

I have the same issue. I don't think generate is supported for seq2seqLM w/ p tuning and prompt tuning. I also mentioned this issue here. When do the team plan to support this feature?
Pinging first two contributors of this repo. @pacman100 @younesbelkada

Hello everyone, PR https://github.com/huggingface/peft/pull/519 resolves this issue


test and inference

Hello, I've noticed that there seems to be an issue with the testing in the code. When I ran your tests, the output was just noise images. Additionally, I couldn't find the inference files for the code. Could you please upload them?

I am sure it is right. You may forget to set the checkpoint path in the file.

path
path:
pretrain_network_g: E:\code\model\LighTDiff\experiments\training_archived_20241216_092250\models\net_g_latest.pth
param_key_g: params
strict_load_g: False
pretrain_network_d: ~
resume_state: ~
ignore_resume_networks: ['network_identity']
Is this setup incorrect

Hmmm, it looks good to me. That's weird. But this code is replicable. You may need to find the bug.

Thank you very much for your help, I have already solved the problem


:seedling: Fixes write permissions for ok-to-test

Please check if the PR fulfills these requirements

[ ] Tests for the changes have been added (for bug fixes / features)
[x] PR title follows the guidelines defined in  https://github.com/ossf/scorecard/blob/main/CONTRIBUTING.md#pr-process

What kind of change does this PR introduce? (Bug fix, feature, docs update, ...)
Bug fix

What is the current behavior? (You can also link to an open issue here)
#432

What is the new behavior (if this is a feature change)?

Allowed write permissions to action for commenting on the status of the PR.

Does this PR introduce a breaking change? (What changes might users need to make in their application due to this PR?)
None

Other information:
None

I can't test this locally, so probably going to be multiple PR's for this fix

The checks are going to fail because they don't have permission.


Detector groups

Adds support for detector groups in the experimental section

The tests are going to fail until we rollout the backend change


Perf: EQUAL_WAVE_WRAPPER generates the msg string for each mode and evaluates the result afterwards

The check for CHECK_EQUAL_WAVES is slow because we generate the detailed message before we evaluate the result.
Look into that, if the performance can be increased by not creating output strings that are not output in the end.

Looking into the code it doesn't seem we create detailed reports before evaluating the result. Yes, we combine some strings but not the whole report.
The function CHECK_EQUAL_WAVES calls IUTF_Wrapper#EQUAL_WAVE_WRAPPER. The second function does some checks with the provided arguments (like tolerances and comparison modes). The detailed comparison is done in IUTF_Checks#AreWavesEqual, which does a basic EqualWaves first and only in case of a mismatch a detailed report is generated.
Yes, IUTF_Wrapper#EQUAL_WAVE_WRAPPER does some additional stuff like fetching wave names or combining the empty details into an error message, which is not required if we have no error. And I think this is the only thing that can be improved.

The code at https://github.com/byte-physics/igortest/blob/33200cf27241cf94ff1013c3c59f362bbb62af0b/procedures/igortest-assertion-wrappers.ipf#L799-L805
takes some time as GetWaveNameInDFStr is expensive.
For this case it could be as easy as moving the message generation into AreWavesEqual.

I don't think there is a need to move these lines into another function. We can just wrap them in an if-statement that checks result and only calls the expensive functions when needed.


Add org Center on Long-Term Risk

Check https://longtermrisk.org/

I added 42 positions. Ready for review.

I added 42 positions. Ready for review.

https://longtermrisk.org/summer-research-fellowship/ lists past summer fellows


High latency doc

Hi,
I added a note to the documentation of a Stream's desired latency indicating that 'high' may not be sufficient to obtain stable audio.
In my commit history there's a revert.  Does this impact you in any way?  Or do my commits just get merged into one at your end?
Cheers,
Matthew

Oh, for reference, we discussed this in #262.

Thanks for this PR!
I think I would like this to be a larger change.
This sentence is copied from the PortAudio docs, but it is quite useless in the sounddevice context:
        Where practical, implementations should configure their
        latency based on this parameter, otherwise they may choose
        the closest viable latency instead.  Unless the suggested
        latency is greater than the absolute upper limit for the
        device, implementations should round the *latency* up to the
        next practical value -- i.e. to provide an equal or higher
        latency  wherever possible.

Isn't it?
Even if the information is useful, it's written from the wrong point of view.
Would you like to fix this?
Also, I don't like that there is divergent information between the latency argument to the Stream constructor and sd.default.latency.
I think I'd prefer to document only one and link to it from the other one.
That's also what I've done with sd.default.blocksize.

In my commit history there's a revert. Does this impact you in any way? Or do my commits just get merged into one at your end?

No problem, I can squash the commits in the end.


[BUG] Table title's padding has a background color by default in some tmux configs.

A friend of mine showed me that tables generated by rich has a background for the padding of the title if he uses tmux. On the left you can see how it looks in tmux and on the right how it looks without tmux.

Code:
summary = Table(title='[not italic bold magenta]Encoding summary')
summary.add_column('[bold cyan]Version\nLatest', style='green')
summary.add_column(f'[bold color(231)]{prog_version}\n{latest_version}', style='color(231)')

summary.add_row('[bold yellow]Output')
summary.add_row('Format', 'TrueHD' if aformat == 'thd' else aformat.upper())
summary.add_row('Channels', channel_number_to_name(outchannels))
summary.add_row('Bitrate', 'N/A' if aformat == 'thd' else f'{str(bitrate)} kbps')
summary.add_row('Dialnorm', 'auto (0)' if args.dialnorm == 0 else f'{str(args.dialnorm)} dB', end_section=True)

summary.add_row('[bold yellow]Input')
summary.add_row('Channels', channel_number_to_name(channels))
summary.add_row('Bit depth', str(bit_depth), end_section=True)
summary.add_row('[bold yellow]Other')
summary.add_row('Files', str(len(filelist)))
summary.add_row('Max threads', str(threads))
summary.add_row('Delay', delay_print)
print(summary)

platform: Linux (WSL)
version: latest

tmux interprets italic as reverse video for some reason. Not much Rich can do about that, but you could not use italic by setting header_style="" on the table.

Isn't it possible to set non italic for every space character?

No

Oh, so the line is italic, and the non italic is only applied to the string if i understand correctly. For some reason I thought the non italic tag is applied after the padding. Thanks for the feedback


Unable to use nsys profile while running cpp benchmark (Failed, NCCL error)

I strictly followed the document and compiled the Docker and subsequent executable programs, successfully running the CPP version of the benchmark.
However, I found that when running the CPP version of the benchmark, adding an nsys profile will result in an error.
Only CPP benchmark (Successful):
root@ip-10-210-100-179-devel:/dcache/zhanglimin/TensorRT-LLM# mpirun  -n 8 --allow-run-as-root --oversubscribe ./cpp/build/benchmarks/gptSessionBenchmark --engine_dir ./benchmarks/cpp/temp/engine_out_builddocker_tp8/ --warm_up 1 --batch_size "64" --duration 0 --num_runs 1 --input_output_len "128,128"

Benchmarking done. Iteration: 1, duration: 1.99 sec.
Latencies: [1990.18]
[BENCHMARK] batch_size 64 input_length 128 output_length 128 latency(ms) 1990.18 tokensPerSec 4116.21 generation_time(ms) 1729.03 generationTokensPerSec 4737.92 gpu_peak_mem(gb) 31.17
Benchmarking done. Iteration: 1, duration: 1.99 sec.
Latencies: [1990.17]
Benchmarking done. Iteration: 1, duration: 1.99 sec.
Latencies: [1990.16]
Benchmarking done. Iteration: 1, duration: 1.99 sec.
Latencies: [1990.41]
Benchmarking done. Iteration: 1, duration: 1.99 sec.
Latencies: [1990.20]
Benchmarking done. Iteration: 1, duration: 1.99 sec.
Latencies: [1990.44]
Benchmarking done. Iteration: 1, duration: 1.99 sec.
Latencies: [1990.22]
Benchmarking done. Iteration: 1, duration: 1.99 sec.
Latencies: [1990.48]

CPP benchmark with  nsys profile  (Error):
root@ip-10-210-100-179-devel:/dcache/zhanglimin/TensorRT-LLM# FT_NVTX=ON nsys profile mpirun  -n 8 --allow-run-as-root --oversubscribe ./cpp/build/benchmarks/gptSessionBenchmark --engine_dir ./benchmarks/cpp/temp/engine_out_builddocker_tp8/ --warm_up 1 --batch_size "64" --duration 0 --num_runs 1 --input_output_len "128,128"
Failed, NCCL error /code/tensorrt_llm/cpp/tensorrt_llm/plugins/common/plugin.cpp:86 'internal error - please report this issue to the NCCL developers'
Failed, NCCL error /code/tensorrt_llm/cpp/tensorrt_llm/plugins/common/plugin.cpp:86 'internal error - please report this issue to the NCCL developers'
Failed, NCCL error /code/tensorrt_llm/cpp/tensorrt_llm/plugins/common/plugin.cpp:86 'internal error - please report this issue to the NCCL developers'
Failed, NCCL error /code/tensorrt_llm/cpp/tensorrt_llm/plugins/common/plugin.cpp:86 'internal error - please report this issue to the NCCL developers'
Failed, NCCL error /code/tensorrt_llm/cpp/tensorrt_llm/plugins/common/plugin.cpp:86 'internal error - please report this issue to the NCCL developers'
Failed, NCCL error /code/tensorrt_llm/cpp/tensorrt_llm/plugins/common/plugin.cpp:86 'internal error - please report this issue to the NCCL developers'
Failed, NCCL error /code/tensorrt_llm/cpp/tensorrt_llm/plugins/common/plugin.cpp:86 'internal error - please report this issue to the NCCL developers'

Why did this error occur? Why can't nsys profile be used when running cpp benchamrk?

Note: When referring to the Python version of the benchmark, I can use the nsys profile.

Could you share the TRT LLM version and nsight system version you use?

I strictly followed the document and compiled the Docker:

TRT LLM version ： [TensorRT-LLM] TensorRT-LLM version: 0.10.0.dev2024042300
nsys version: 2024.2.1

error log:
root@ip-10-210-100-179-devel:/dcache/zhanglimin/TensorRT-LLM/examples/quantization# nsys profile mpirun -n 8 --allow-run-as-root  python3 ../run.py --max_output_len=1 \
                  --tokenizer_dir /dcache/zhanglimin/Llama-2-70b-chat-hf/ \
                  --engine_dir=./engine_outputs_temp \
                  --log_level "warning"
[TensorRT-LLM] TensorRT-LLM version: 0.10.0.dev2024042300
[TensorRT-LLM] TensorRT-LLM version: 0.10.0.dev2024042300
[TensorRT-LLM] TensorRT-LLM version: 0.10.0.dev2024042300
[TensorRT-LLM] TensorRT-LLM version: 0.10.0.dev2024042300
[TensorRT-LLM] TensorRT-LLM version: 0.10.0.dev2024042300
[TensorRT-LLM] TensorRT-LLM version: 0.10.0.dev2024042300
[TensorRT-LLM] TensorRT-LLM version: 0.10.0.dev2024042300
[TensorRT-LLM] TensorRT-LLM version: 0.10.0.dev2024042300
[TensorRT-LLM][INFO] Engine version 0.10.0.dev2024042300 found in the config file, assuming engine(s) built by new builder API.
[TensorRT-LLM][WARNING] Parameter layer_types cannot be read from json:
[TensorRT-LLM][WARNING] [json.exception.out_of_range.403] key 'layer_types' not found
[TensorRT-LLM][WARNING] [json.exception.out_of_range.403] key 'num_medusa_heads' not found
[TensorRT-LLM][WARNING] Optional value for parameter num_medusa_heads will not be set.
[TensorRT-LLM][WARNING] [json.exception.out_of_range.403] key 'max_draft_len' not found
[TensorRT-LLM][WARNING] Optional value for parameter max_draft_len will not be set.
[TensorRT-LLM][INFO] MPI size: 8, rank: 5
[TensorRT-LLM][INFO] Engine version 0.10.0.dev2024042300 found in the config file, assuming engine(s) built by new builder API.
[TensorRT-LLM][INFO] Engine version 0.10.0.dev2024042300 found in the config file, assuming engine(s) built by new builder API.
[TensorRT-LLM][WARNING] Parameter layer_types cannot be read from json:
[TensorRT-LLM][WARNING] [json.exception.out_of_range.403] key 'layer_types' not found
[TensorRT-LLM][WARNING] Parameter layer_types cannot be read from json:
[TensorRT-LLM][WARNING] [json.exception.out_of_range.403] key 'layer_types' not found
[TensorRT-LLM][WARNING] [json.exception.out_of_range.403] key 'num_medusa_heads' not found
[TensorRT-LLM][WARNING] Optional value for parameter num_medusa_heads will not be set.
[TensorRT-LLM][WARNING] [json.exception.out_of_range.403] key 'max_draft_len' not found
[TensorRT-LLM][WARNING] [json.exception.out_of_range.403] key 'num_medusa_heads' not found
[TensorRT-LLM][WARNING] Optional value for parameter num_medusa_heads will not be set.
[TensorRT-LLM][WARNING] [json.exception.out_of_range.403] key 'max_draft_len' not found
[TensorRT-LLM][WARNING] Optional value for parameter max_draft_len will not be set.
[TensorRT-LLM][WARNING] Optional value for parameter max_draft_len will not be set.
[TensorRT-LLM][INFO] MPI size: 8, rank: 3
[TensorRT-LLM][INFO] Engine version 0.10.0.dev2024042300 found in the config file, assuming engine(s) built by new builder API.
[TensorRT-LLM][WARNING] Parameter layer_types cannot be read from json:
[TensorRT-LLM][WARNING] [json.exception.out_of_range.403] key 'layer_types' not found
[TensorRT-LLM][INFO] Engine version 0.10.0.dev2024042300 found in the config file, assuming engine(s) built by new builder API.
[TensorRT-LLM][WARNING] [json.exception.out_of_range.403] key 'num_medusa_heads' not found
[TensorRT-LLM][WARNING] Optional value for parameter num_medusa_heads will not be set.
[TensorRT-LLM][WARNING] [json.exception.out_of_range.403] key 'max_draft_len' not found
[TensorRT-LLM][WARNING] Optional value for parameter max_draft_len will not be set.
[TensorRT-LLM][INFO] MPI size: 8, rank: 7
[TensorRT-LLM][WARNING] Parameter layer_types cannot be read from json:
[TensorRT-LLM][WARNING] [json.exception.out_of_range.403] key 'layer_types' not found
[TensorRT-LLM][INFO] MPI size: 8, rank: 2
[TensorRT-LLM][WARNING] [json.exception.out_of_range.403] key 'num_medusa_heads' not found
[TensorRT-LLM][WARNING] Optional value for parameter num_medusa_heads will not be set.
[TensorRT-LLM][WARNING] [json.exception.out_of_range.403] key 'max_draft_len' not found
[TensorRT-LLM][WARNING] Optional value for parameter max_draft_len will not be set.
[TensorRT-LLM][INFO] MPI size: 8, rank: 0
[TensorRT-LLM][INFO] Engine version 0.10.0.dev2024042300 found in the config file, assuming engine(s) built by new builder API.
[TensorRT-LLM][WARNING] Parameter layer_types cannot be read from json:
[TensorRT-LLM][WARNING] [json.exception.out_of_range.403] key 'layer_types' not found
[TensorRT-LLM][WARNING] [json.exception.out_of_range.403] key 'num_medusa_heads' not found
[TensorRT-LLM][WARNING] Optional value for parameter num_medusa_heads will not be set.
[TensorRT-LLM][WARNING] [json.exception.out_of_range.403] key 'max_draft_len' not found
[TensorRT-LLM][WARNING] Optional value for parameter max_draft_len will not be set.
[TensorRT-LLM][INFO] MPI size: 8, rank: 4
[TensorRT-LLM][INFO] Engine version 0.10.0.dev2024042300 found in the config file, assuming engine(s) built by new builder API.
[TensorRT-LLM][WARNING] Parameter layer_types cannot be read from json:
[TensorRT-LLM][WARNING] [json.exception.out_of_range.403] key 'layer_types' not found
[TensorRT-LLM][WARNING] [json.exception.out_of_range.403] key 'num_medusa_heads' not found
[TensorRT-LLM][WARNING] Optional value for parameter num_medusa_heads will not be set.
[TensorRT-LLM][WARNING] [json.exception.out_of_range.403] key 'max_draft_len' not found
[TensorRT-LLM][WARNING] Optional value for parameter max_draft_len will not be set.
[TensorRT-LLM][INFO] MPI size: 8, rank: 6
[TensorRT-LLM][INFO] Engine version 0.10.0.dev2024042300 found in the config file, assuming engine(s) built by new builder API.
[TensorRT-LLM][WARNING] Parameter layer_types cannot be read from json:
[TensorRT-LLM][WARNING] [json.exception.out_of_range.403] key 'layer_types' not found
[TensorRT-LLM][WARNING] [json.exception.out_of_range.403] key 'num_medusa_heads' not found
[TensorRT-LLM][WARNING] Optional value for parameter num_medusa_heads will not be set.
[TensorRT-LLM][WARNING] [json.exception.out_of_range.403] key 'max_draft_len' not found
[TensorRT-LLM][WARNING] Optional value for parameter max_draft_len will not be set.
[TensorRT-LLM][INFO] MPI size: 8, rank: 1
[TensorRT-LLM][INFO] Loaded engine size: 62 MiB
[TensorRT-LLM][INFO] Loaded engine size: 62 MiB
[TensorRT-LLM][INFO] Loaded engine size: 62 MiB
[TensorRT-LLM][INFO] Loaded engine size: 62 MiB
[TensorRT-LLM][INFO] Loaded engine size: 62 MiB
[TensorRT-LLM][INFO] Loaded engine size: 62 MiB
[TensorRT-LLM][INFO] Loaded engine size: 62 MiB
[TensorRT-LLM][WARNING] Using an engine plan file across different models of devices is not recommended and is likely to affect performance or even cause errors.
[TensorRT-LLM][WARNING] Using an engine plan file across different models of devices is not recommended and is likely to affect performance or even cause errors.
[TensorRT-LLM][WARNING] Using an engine plan file across different models of devices is not recommended and is likely to affect performance or even cause errors.
[TensorRT-LLM][WARNING] Using an engine plan file across different models of devices is not recommended and is likely to affect performance or even cause errors.
[TensorRT-LLM][WARNING] Using an engine plan file across different models of devices is not recommended and is likely to affect performance or even cause errors.
[TensorRT-LLM][INFO] Loaded engine size: 62 MiB
[TensorRT-LLM][WARNING] Using an engine plan file across different models of devices is not recommended and is likely to affect performance or even cause errors.
[TensorRT-LLM][WARNING] Using an engine plan file across different models of devices is not recommended and is likely to affect performance or even cause errors.
[TensorRT-LLM][WARNING] Using an engine plan file across different models of devices is not recommended and is likely to affect performance or even cause errors.
  This could lead to a segmentation fault at exit
Failed, NCCL error /code/tensorrt_llm/cpp/tensorrt_llm/plugins/common/plugin.cpp:86 'internal error - please report this issue to the NCCL developers'
Failed, NCCL error /code/tensorrt_llm/cpp/tensorrt_llm/plugins/common/plugin.cpp:86 'internal error - please report this issue to the NCCL developers'
/opt/rapids/src/cudf/cpp/build/_deps/arrow-src/cpp/src/arrow/filesystem/s3fs.cc:2904:  arrow::fs::FinalizeS3 was not called even though S3 was initialized.  This could lead to a segmentation fault at exit
/opt/rapids/src/cudf/cpp/build/_deps/arrow-src/cpp/src/arrow/filesystem/s3fs.cc:2904:  arrow::fs::FinalizeS3 was not called even though S3 was initialized.  This could lead to a segmentation fault at exit
Failed, NCCL error /code/tensorrt_llm/cpp/tensorrt_llm/plugins/common/plugin.cpp:86 'internal error - please report this issue to the NCCL developers'
/opt/rapids/src/cudf/cpp/build/_deps/arrow-src/cpp/src/arrow/filesystem/s3fs.cc:2904:  arrow::fs::FinalizeS3 was not called even though S3 was initialized.  This could lead to a segmentation fault at exit
Failed, NCCL error /code/tensorrt_llm/cpp/tensorrt_llm/plugins/common/plugin.cpp:86 'internal error - please report this issue to the NCCL developers'
Failed, NCCL error /code/tensorrt_llm/cpp/tensorrt_llm/plugins/common/plugin.cpp:86 'internal error - please report this issue to the NCCL developers'
/opt/rapids/src/cudf/cpp/build/_deps/arrow-src/cpp/src/arrow/filesystem/s3fs.cc:2904:  arrow::fs::FinalizeS3 was not called even though S3 was initialized.  This could lead to a segmentation fault at exit
/opt/rapids/src/cudf/cpp/build/_deps/arrow-src/cpp/src/arrow/filesystem/s3fs.cc:2904:  arrow::fs::FinalizeS3 was not called even though S3 was initialized.  This could lead to a segmentation fault at exit
Failed, NCCL error /code/tensorrt_llm/cpp/tensorrt_llm/plugins/common/plugin.cpp:86 'internal error - please report this issue to the NCCL developers'
/opt/rapids/src/cudf/cpp/build/_deps/arrow-src/cpp/src/arrow/filesystem/s3fs.cc:2904:  arrow::fs::FinalizeS3 was not called even though S3 was initialized.  This could lead to a segmentation fault at exit
--------------------------------------------------------------------------
Primary job  terminated normally, but 1 process returned
a non-zero exit code. Per user-direction, the job has been aborted.
--------------------------------------------------------------------------
--------------------------------------------------------------------------
mpirun detected that one or more processes exited with non-zero status, thus causing
the job to be terminated. The first process to do so was:

  Process name: [[16944,1],6]
  Exit code:    1
--------------------------------------------------------------------------
The target application terminated. One or more process it created re-parented.
Waiting for termination of re-parented processes.
Use the `--wait` option to modify this behavior.
Generating '/tmp/nsys-report-59d1.qdstrm'
[1/1] [========================100%] report51.nsys-rep
Generated:
    /dcache/zhanglimin/TensorRT-LLM/examples/quantization/report51.nsys-rep

Could you share the full nsys version like
root@97df2d66e6fb:/home/scratch.bhsueh_sw_1/workspace/TensorRT-LLM/tllm_nvbug_3# nsys --version
NVIDIA Nsight Systems version 2<IP_ADDRESS>-242133975141v0

If you use the default nsight system of the docker, could you try installing the latest nsight system from the official website?

@byshiue yes，you are right，thanks！！
the default nsight system of the docker is too old:
root@ip-10-210-100-179-devel:/code/tensorrt_llm# nsys --version
NVIDIA Nsight Systems version 2<IP_ADDRESS>-234133557503v0       # default nsight system
root@ip-10-210-100-179-devel:/code/tensorrt_llm# /opt/nvidia/nsight-systems/2024.2.1/bin/nsys --version
NVIDIA Nsight Systems version 2<IP_ADDRESS>-242134037904v0     #  latest nsight system

After I switched to the latest nsight system, I was able to successfully use the nsys profile command (previously, I mistakenly used the default nsight system in Docker):
FT_NVTX=ON /opt/nvidia/nsight-systems/2024.2.1/bin/nsys profile mpirun  -n 8 --allow-run-as-root --oversubscribe ./cpp/build/benchmarks/gptSessionBenchmark --engine_dir ./benchmarks/cpp/temp/engine_out_builddocker_tp8/ --warm_up 1 --batch_size "64" --duration 0 --num_runs 1 --input_output_len "128,128"

Benchmarking done. Iteration: 1, duration: 2.11 sec.
Latencies: [2105.28]
Benchmarking done. Iteration: 1, duration: 2.10 sec.
Latencies: [2104.66]
[BENCHMARK] batch_size 64 input_length 128 output_length 128 latency(ms) 2104.66 tokensPerSec 3892.31 generation_time(ms) 1842.98 generationTokensPerSec 4444.98 gpu_peak_mem(gb) 31.41
Benchmarking done. Iteration: 1, duration: 2.10 sec.
Latencies: [2104.99]
Benchmarking done. Iteration: 1, duration: 2.10 sec.
Latencies: [2104.71]
Benchmarking done. Iteration: 1, duration: 2.10 sec.
Latencies: [2104.65]
Benchmarking done. Iteration: 1, duration: 2.10 sec.
Latencies: [2104.71]
Benchmarking done. Iteration: 1, duration: 2.11 sec.
Latencies: [2105.27]
Benchmarking done. Iteration: 1, duration: 2.10 sec.
Latencies: [2104.63]
Generating '/tmp/nsys-report-5c38.qdstrm'
[1/1] [========================100%] report13.nsys-rep
Generated:
    /code/tensorrt_llm/report13.nsys-rep

It works, thank you!!
And I suggest upgrading the default nsight system version within Docker to avoid unnecessary errors.


Implement all effects

Changelog:

Implement and provide types for all effects from redux-saga
Ported the actual library code from TS to JS (see explanation below)
As a consequence, build the project using babel instead of tsc
Setup ESLint
Add typing tests and linting through dtslint

Fixes #1
Fixes #3
Fixes #8
Note that this is a ~major~ complete rewrite of the whole library and I would completely understand should you say to me that I should maintain my own fork.
As to why I migrated the code from TS to JS: TS was complaining about conflicting method definitions. Making the code in JS is actually simpler, but if you know how to fix this I'm happy to revise.
I'm not sure how far we could get regarding automation. I think the JS code generation is "good-enough", but the typedefs are just a wild copy-paste from redux-saga's typedefs. I just changed the return value of every effect to the SagaGenerator stuff and tried to ensure all the return types were correct. However, some return types ended up being quite complicated (like the all and race effects).
Finally, unless I'm missing something regarding automation, I don't see this getting merged into redux-saga's codebase anytime soon unfortunately. IMHO, the only acceptable way would be to drop support for yield effect in redux-saga, which is a huge breaking change to satisfy only a few typescript users. It's unlikely the community would accept this. :disappointed: Maybe the maintenance would be reduced if we just did a fork of redux-saga. This way the only job of the mainteners would be to merge upstream from time to time, updating type definitions as they change.
Before I forget:

[ ] Task type from Redux-Saga could be improved (see FIXME in index.d.ts)
[ ] I feel like it's unconvenient for the race and all yield* effects to take raw redux-saga's effects. Is it possible and/or desirable to pass yield* effects to them? If so, it would be quite easy to add a simple linter rule that bans redux-saga/import. I would kind of like that. WDYT?

And now I need some sleep :smile:
ping @danielnixon @Andarist

I'm not opposed to merging this into redux-saga itself
@Andarist can you elaborate on what's the downside of this being the default? Would the main issue be the requirement to use yield* instead of yield?


After the cluster is successfully installed, some core components restart indefinitely.

Sealos Version
v4.3.7
How to reproduce the bug?

组件正常
What do you see instead?
组件无限重启
Operating environment
- Sealos version: v4.3.7
- Docker version: v1.6.24（containerd）
- Kubernetes version: v1.26.10
- Operating system: CentOS8.2
- Runtime environment: master(2C2G)、node(8C16G)
- Cluster size:1 master 1node
- Additional information:

Additional information
查看宿主机监控，发现 master 节点内存占用稳定在85%左右，是否和内存占用高有关？

主节点太小了， kubectl logs 看下calico pod日志排查下


Can't create cluster

"""Stand up the infrastructure for the application."""

import pulumi
import pulumiverse_cockroach as cockroach

project = pulumi.get_project()
p = project + "-"

cockroach_cluster = cockroach.Cluster(
    resource_name=p+"cockroach-cluster",
    name=p+"cockroach-cluster",
    cloud_provider="AWS",
    regions=[cockroach.ClusterRegionArgs(
        name="us-east-1",
        # node_count=0,
        # primary=True,
    )],
    # parent_id="root",
    # cockroach_version="24.1",
    serverless=cockroach.ClusterServerlessArgs(
        spend_limit=0,
    )
    )

pulumi.export("cluster_id", cockroach_cluster.id)

Results in
2024/08/30 20:15:06 [DEBUG] Waiting for state to become: [success]
2024/08/30 20:15:07 [TRACE] Waiting 500ms before next try
2024/08/30 20:15:07 [TRACE] Waiting 1s before next try
panic: fatal: An assertion has failed: Resource state did not contain an 'id' property. This is an error in the provider. Special identity handling may be needed. Consider setting ResourceInfo.ComputeID for the cockroach_cluster resource
goroutine 38 [running]:
github.com/pulumi/pulumi/sdk/v3/go/common/util/contract.failfast(...)
/home/runner/go/pkg/mod/github.com/pulumi/pulumi/sdk/v3@v3.116.1/go/common/util/contract/failfast.go:23
github.com/pulumi/pulumi/sdk/v3/go/common/util/contract.Assertf(0x0?, {0x10181701b?, 0x1017d3ccb?}, {0x14000673258?, 0x1?, 0x1400013b590?})
/home/runner/go/pkg/mod/github.com/pulumi/pulumi/sdk/v3@v3.116.1/go/common/util/contract/assert.go:35 +0xe0
github.com/pulumi/pulumi-terraform-bridge/pf/tfbridge.extractID({0x101d77fa8?, 0x1400013aa50?}, {0x1017e5062?, 0x140006c3ea0?}, 0x0?, 0x0?)
/home/runner/go/pkg/mod/github.com/pulumi/pulumi-terraform-bridge/pf@v0.36.0/tfbridge/ids.go:41 +0x104
github.com/pulumi/pulumi-terraform-bridge/pf/tfbridge.(*provider).CreateWithContext(0x1400001ee00, {0x101d77fa8?, 0x1400013a570?}, {0x1400004a230, 0x63}, 0x0?, 0x0?, 0x0)
/home/runner/go/pkg/mod/github.com/pulumi/pulumi-terraform-bridge/pf@v0.36.0/tfbridge/provider_create.go:126 +0x5e4
github.com/pulumi/pulumi-terraform-bridge/pf/internal/plugin.(*providerServer).Create(0x1400017c330, {0x101d77fa8, 0x1400013a570}, 0x140001feb90)
/home/runner/go/pkg/mod/github.com/pulumi/pulumi-terraform-bridge/pf@v0.36.0/internal/plugin/provider_server.go:351 +0x12c
github.com/pulumi/pulumi/sdk/v3/proto/go._ResourceProvider_Create_Handler.func1({0x101d77fa8, 0x1400013a570}, {0x101c93ba0?, 0x140001feb90})
/home/runner/go/pkg/mod/github.com/pulumi/pulumi/sdk/v3@v3.116.1/proto/go/provider_grpc.pb.go:593 +0x74
github.com/grpc-ecosystem/grpc-opentracing/go/otgrpc.OpenTracingServerInterceptor.func1({0x101d77fa8, 0x1400013a1b0}, {0x101c93ba0, 0x140001feb90}, 0x1400003ae20, 0x14000226648)
/home/runner/go/pkg/mod/github.com/grpc-ecosystem/grpc-opentracing@v0.0.0-20180507213350-8e809c8a8645/go/otgrpc/server.go:57 +0x2e8
github.com/pulumi/pulumi/sdk/v3/proto/go._ResourceProvider_Create_Handler({0x101d24500?, 0x1400017c330}, {0x101d77fa8, 0x1400013a1b0}, 0x140003f4a00, 0x1400025e460)
/home/runner/go/pkg/mod/github.com/pulumi/pulumi/sdk/v3@v3.116.1/proto/go/provider_grpc.pb.go:595 +0x12c
google.golang.org/grpc.(*Server).processUnaryRPC(0x14000276400, {0x101d77fa8, 0x1400013a120}, {0x101d82520, 0x140000fe480}, 0x140004098c0, 0x1400017c3c0, 0x10270dfa0, 0x0)
/home/runner/go/pkg/mod/google.golang.org/grpc@v1.63.2/server.go:1369 +0xba0
google.golang.org/grpc.(*Server).handleStream(0x14000276400, {0x101d82520, 0x140000fe480}, 0x140004098c0)
/home/runner/go/pkg/mod/google.golang.org/grpc@v1.63.2/server.go:1780 +0xc80
google.golang.org/grpc.(*Server).serveStreams.func2.1()
/home/runner/go/pkg/mod/google.golang.org/grpc@v1.63.2/server.go:1019 +0x8c
created by google.golang.org/grpc.(*Server).serveStreams.func2 in goroutine 61
/home/runner/go/pkg/mod/google.golang.org/grpc@v1.63.2/server.go:1030 +0x150
error: update failed

Did you manage to resolve this?

@plainspoken-chris could you retry with provider version v0.4.0 or up?

@plainspoken-chris could you retry with provider version v0.4.0 or up?

Same error:
    2024/10/31 21:05:34 [TRACE] Waiting 10s before next try
    2024/10/31 21:05:44 [TRACE] Waiting 10s before next try
    2024/10/31 21:05:55 [TRACE] Waiting 10s before next try
    2024/10/31 21:06:05 [TRACE] Waiting 10s before next try
    2024/10/31 21:06:16 [TRACE] Waiting 10s before next try
    2024/10/31 21:06:26 [TRACE] Waiting 10s before next try
    2024/10/31 21:06:36 [TRACE] Waiting 10s before next try

    error: update failed

  cockroach:index:Cluster (clusterResource):
    error: Could not find required property 'id' in state. This is an error in cockroach resource provider, please report at https://github.com/pulumiverse/pulumi-cockroach

@adinhodovic I changed something in the mapping of the Cluster resource from the underlying TF provider:
https://github.com/pulumiverse/pulumi-cockroach/commit/de88ea497eeba4f5d1245134f1df240e995d4548#diff-34c57e622183cb0d8dd0d3f9eaa0861b3340120e9b2ad811bac7ac7be4cea4b1
I accidentally remapped the id to clusterId for the Cluster resource itself. While this is required for resources taking a cluster ID as an input, it prevented the Cluster resource to expose its own ID as a proper output.
Included in release v0.6.0.
https://github.com/pulumiverse/pulumi-cockroach/releases/tag/v0.6.0
Can you retest with this version?

@adinhodovic I changed something in the mapping of the Cluster resource from the underlying TF provider:
de88ea4#diff-34c57e622183cb0d8dd0d3f9eaa0861b3340120e9b2ad811bac7ac7be4cea4b1
I accidentally remapped the id to clusterId for the Cluster resource itself. While this is required for resources taking a cluster ID as an input, it prevented the Cluster resource to expose its own ID as a proper output.
Included in release v0.6.0. https://github.com/pulumiverse/pulumi-cockroach/releases/tag/v0.6.0
Can you retest with this version?

That worked. Thanks!
However, need the same fix for the Prometheus config resource.
  provider/metric_export_prometheus_config_resource.go:140: provider: enable prometheus metric export response: {
 "cluster_id": "redacted",
 "status": "ENABLED",
 "targets": {
   "us-east-1": "redacted"
 },
 "user_message": "This integration is active."
}<EMAIL_ADDRESS>error: Could not find required property 'id' in state. This is an error in cockroach resource provider, please report at https://github.com/pulumiverse/pulumi-cockroach
error: update failed

Does #51 fix this for remainder of resources?

Also this fails in a similar way:
 cockroach:index:UserRoleGrant (abc):
    error: Could not find required property 'id' in state. This is an error in cockroach resource provider, please report at https://github.com/pulumiverse/pulumi-cockroach

The last error we hit are:
@ Updating........
    pulumi:pulumi:Stack <stack> running 2024/11/25 10:51:30 [DEBUG] Waiting for state to become: [success]
@ Updating....
    pulumi:pulumi:Stack <stack> running 2024/11/25 10:51:31 [DEBUG] Waiting for state to become: [success]
 +  cockroach:index:UserRoleGrant <stack> creating (5s) error: Could not find required property 'id' in state. This is an error in cockroach resource provider, please re
port at https://github.com/pulumiverse/pulumi-cockroach
 +  cockroach:index:UserRoleGrant <stack> **creating failed** error: Could not find required property 'id' in state. This is an error in cockroach resource provider, ple
ase report at https://github.com/pulumiverse/pulumi-cockroach
    pulumi:pulumi:Stack <stack> running 2024/11/25 10:51:31 [TRACE] Waiting 500ms before next try
@ Updating....
    pulumi:pulumi:Stack <stack> running 2024/11/25 10:51:32 [TRACE] Waiting 1s before next try
@ Updating.....
    pulumi:pulumi:Stack <stack> running 2024/11/25 10:51:34 [TRACE] Waiting 2s before next try
@ Updating.....
 +  cockroach:index:MetricExportPrometheusConfig <stack> creating (11s) error: Could not find required property 'id' in state. This is an error in cockroach resource provider, please report at https://github.com/pulumiverse/pulumi-cockroach
 +  cockroach:index:MetricExportPrometheusConfig <stack> **creating failed** error: Could not find required property 'id' in state. This is an error in cockroach resource provider,
 please report at https://github.com/pulumiverse/pulumi-cockroach

Also, debug/trace logs seem to be on by default?


Use 'sphinx_rtd_theme' as theme

Since end of 2023, the default theme at ReadTheDocs has been changed to Alabaster.
This change will make sure the classic ReadTheDocs theme is used again.
If you want to check the document creation locally, you can do that my running
TZ=UTC make html
(TZ=UTC was needed on my system due to a timezone error)
When building locally, make sure to install the packages sphinx_rtd_theme.
via pip install sphinx_rtd_theme.
When building via ReadTheDocs, this is not needed as their standard image for building includes that package

I tried building API docs at Read The Docs but it failed with this error:

Extension error:
Could not import extension sphinx_rtd_theme (exception: No module named 'sphinx_rtd_theme')
The full traceback and some more additional info can be see here.

Could it be that this theme isn't available automatically?

Wondering if that's the root cause. They only mention that new projects
created on readthedocs since August 2023 won't have that theme installed by
default.
https://blog.readthedocs.com/python-core-requirements-changed/
Starting on August 7, 2023 all new projects imported on Read the Docs will
install only sphinx, mkdocs and readthedocs-sphinx-ext as “core
requirements”. The default behavior will be to install the latest version
of these requirements.
Note that previously Read the Docs was installing also jinja,
sphinx-rtd-theme, pillow, mock, alabaster, commonmark and recommonmark
specifying particular versions depending on different factors that were
confusing for users and hard to debug.
A possible solution is posted here:
https://stackoverflow.com/questions/76924291/error-on-sphinxs-generated-documentation-on-readthedocs
You need to install the Sphinx theme dependency on Read the Docs. To do so,
you have to create a requirements.txt file with the following content:
sphinx-rtd-theme==1.2.2
Then, modify your .readthedocs.yaml file to tell Read the Docs to install
these requirements:
python:
install:
- requirements: "requirements.txt
Pekka Klärck @.***> schrieb am So., 28. Jan. 2024,
11:20:

I tried building API docs at Read The Docs but it failed with this error:
Extension error:
Could not import extension sphinx_rtd_theme (exception: No module named
'sphinx_rtd_theme')
The full traceback and some more additional info can be see here
https://readthedocs.org/projects/robot-framework/builds/23257790/.
Could it be that this theme isn't available automatically?
—
Reply to this email directly, view it on GitHub
https://github.com/robotframework/robotframework/pull/5034#issuecomment-1913545561,
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You are receiving this because you authored the thread.Message ID:
@.***>


[Manager] Remove alloc for first/last layer during inference

Remove the allocation of input of the first layer and label of the last
layer during inference. This is because those tensors are overridden at
the call to inference by the tensors provided externally by the user.
Note that the output of the last layer will still be allocated by the manager.
Also refactor initializeTensors() in manager into two functions
separated for inference and training, along with a couple of bug fixes inside them.
See Also #974 #888
Self evaluation:

Build test: [x]Passed [ ]Failed [ ]Skipped
Run test: [x]Passed [ ]Failed [ ]Skipped

Signed-off-by: Parichay Kapoor<EMAIL_ADDRESS>cc. @zhoonit Please check if this resolves the second issue in #888.

:memo: TAOS-CI Version: 1.4.20191203. Thank you for submitting PR #990. Please a submit 1commit/1PR (one commit per one PR) policy to get comments quickly from reviewers. Your PR must pass all verificiation processes of cibot before starting a review process from reviewers. If you are new member to join this project, please read manuals in documentation folder and wiki page. In order to monitor a progress status of your PR in more detail, visit http://nnsuite.mooo.com/.

forwarding and backwarding are not supposed to be exposed as the API.

If we are to allow users to make, something complex training flow, don't we need to expose the forwarding, backwarding as an api?

If we are to allow users to make, something complex training flow, don't we need to expose the forwarding, backwarding as an api?

IMO a better way would be to create a customLayer/customModel which does the complex training part. Opening forwarding/backwarding means that all the inits/allocations/batchsize verification/etc would have to be done by user, inducing a lot of bugs.
Creating customModel is not yet possible but will be after #986 is done to some extent.

I think we need some solid baseline how forwarding, backwarding works beforehand. Some of the logics are tangled and prone to fail.

Please make some PRs for this, as this PR and others related to #974 are focused towards manager managing the memory and not related for forwarding/backwarding related issues.

True that, forwarding, backwarding issue is a bit orthogonal to this.


Adding transformers, exporting MonadTrans and MonadIO

This is probably going to cause a wide range of "Redundant Import" type compiler warnings (as errors) regarding lift and liftIO.
Ironically though, the more errors it causes the more its inclusion is justified probably however.....
I.e There will be a phase where we have to work to undo the work that this change will save in the future.
So yeah.

@nickhibberd Absolutely I'll export all the useful functions, i'll do bifunctors as well quickly

So exporting EitherT and its ilk will probably also introduce a simliar world of pain, so i think its enough for one PR
Then again, i suppose this is already enough to cost me all my friends here so..... ;)

@charleso @nhibberd @etorreborre @markhibberd @michaelneale I'm going to merge this in soon unless i get some really vocal objection, I dont see no :+1:, but I can't say I could have possibly expected much enthusiasm for the consequences of this PR

:8ball:

:hankey:

:shaved_ice:

@charleso please dont hate me

(made a minor change to eitherTFromMaybe with permission from @tmcgilchrist)


WIP: feat(back): #695 allow input args

Adapt make search paths to allow
passing args to sources

Also, do you know why considering sets is also required?

On Nix, pretty much everything is not a derivation, but packages normally are a set with metadata and outPath:

nix-repl> builtins.typeOf nixpkgs.bash
"set"
nix-repl> builtins.typeOf nixpkgs.bash.outPath
"string"
nix-repl> nixpkgs.bash.outPath
"/nix/store/wv35g5lff84rray15zlzarcqi9fxzz84-bash-4.4-p23"


Save button remains inactive after editing navigation config

I was able to create my navigation structure in the UI Navigation tab that this plugin adds to Strapi admin. However, when I make changes to my navigation (adding new navigation items or changing existing ones) the "Save" button remains inactive, so I cannot save my changes.
Plugin version: 2.2.1
Strapi version: 4.3.8

@nimame We recommend to use version2.2.2 where this issue being fixed.

Thank you @cyp3rius. I upgraded to 2.2.2 but still get the same behavior. Now I also get a warning message when navigating to the "UI Navigation" tab: "Warning: Relations provided in some items are incorrect". I don't know in what way relations are incorrect. Any other suggestions?

@nimame Do you have any internal items in your navigation? If so check if every item has a proper related field provided. This error is displayed when for some reason one of the navigation items doesn't have a related field but should have one. If this isn't the case in your navigation I will investigate further this behaviour.

@ltsNotMike yes all navigation items where "Navigation item type" is "internal source" have a content-type and entity selected. I can't find anything looking suspicious. Your help is much appreciated.


Fix test dependencies

Based on https://github.com/kevin1024/vcrpy/issues/645#issuecomment-1561933604 .

This is very helpful for our future selves :)

Codecov Report

Merging #705 (13af8ca) into master (c41bd2b) will decrease coverage by 0.29%.
The diff coverage is n/a.

:exclamation: Your organization is not using the GitHub App Integration. As a result you may experience degraded service beginning May 15th. Please install the Github App Integration for your organization. Read more.
@@            Coverage Diff             @@
##           master     #705      +/-   ##
==========================================
- Coverage   90.15%   89.86%   -0.29%
==========================================
  Files          27       27
  Lines        1726     1726
  Branches      308      308
==========================================
- Hits         1556     1551       -5
- Misses        135      138       +3
- Partials       35       37       +2

see 3 files with indirect coverage changes
:mega: We’re building smart automated test selection to slash your CI/CD build times. Learn more


(maint) pinned to Cake 1.3.0 and bumped to Cake.Recipe 3

according to https://cake-contrib.github.io/Cake.Recipe/docs/upgrading/2.x-to-3.x

 Thank you for your submission! We really appreciate it. Like many open source projects, we ask that you sign our Contributor License Agreement before we can accept your contribution.You have signed the CLA already but the status is still pending? Let us recheck it.


Add "ask" to the Ask interface

Hmm.. I added a description field that I intended to serve that purpose. I like "ask" better. Can you delete the description field?

@ericelliott done

Thanks!


istio: expose federation-discovery-service on AUTO_PASSTHROUGH port

Closing in favor of https://github.com/openshift-service-mesh/federation/pull/84