Added XE ISO2Box create script

Added

include/embedded_vagrantfile_xe
iosxe_iso2vbox.py
README-CSR1000v.md

Changed

.gitignore (to supress virtualenv directories and test artifacts)

Comment
I did not change iosxr_iso2vbox.py even though it shows in the diff. I did a local directory compare and the changes shown seem to be from the latest commits to ios-xr/iosxrv-x64-vbox. I did a pull from the repo before applying my changes and it shows clean on my end. Something went wrong somewhere but I can't tell why / where (I am a Git noob at the end).

As stated, the XR script change should simply be discarded from the PR. I did a PEP8 autoformat of the code from within Sublime but can install and run Flake8.

Merged manually to fix the XR oddness. Thanks for the contribution!


fix: timetracking field always marshalled

Currently, the new field from https://github.com/ankitpokhrel/jira-cli/pull/748 for timetracking is always marshalled to JSON even if the "OriginalEstimate" field is an empty string.
This is because of the way the field is initialized as a struct that will never be nil and therefore never caught by the 'omitempty' tag.
This changes the TimeTracking field to be a pointer, which is nil by default, so that if OriginalEstimate isn't set, the timetracking field won't be in the JSON sent to Jira.
This is a problem if the issue type being created is one that doesn't support a timetracking field.

I will look at fixing tests soon.

This seems to be fixed by https://github.com/ankitpokhrel/jira-cli/pull/767
Thank you @wbh1

🤦♂  I should've checked open PRs. Thanks!


Notification subsystem

Overview
We need a notification system for smart contract developers. At the same time it might be interesting to have something similar to plugin system of the C# node (especially in the form described in neo-project/neo#1225), although its IP2PPlugin and IStoragePlugin can directly affect node behaviour and plugins have synchronous model while we're mostly concerned with asynchronous notifications about chain state.
Requirements
Primary requirements:

networking-based
asynchronous
allowing subscription to a set of events (including basic server-side event filtering)
extensible

Primary usage model:

smart contract backend service listening for chain events and making some additional requests based on that
the service is expected to be using RPC protocol already for its purposes

General solution
As we already have JSON-RPC support in our node it seems to be a good idea to extend it for notification service because it allows for some code reuse and makes our RPC client be the sole interface for interacting with node in various ways (it's expected that event listener might make additional requests to node based on events received). Still there are some caveats:

notifications put additional burden on the server and thus one may want to restrict this service availability
At the same time smart contract developers are not expected to be using public RPC nodes and a private node can be restricted in any way by regular network firewalling. Still we should make this subsystem optional and future synchronous call extensions should also add an additional option. We could make it a separate service on a different port, but it doesn't seem to be a good option for now because of expected usage model.
sending server-side notifications requires some generic messaging layer like websockets
Which probably is OK given that we're starting to extend our existing JSON-RPC interface, but note that it differs from websockets usage in Neo 3 where P2P protocol is layered above them.

Initial events list (all about chain processing):

new block added
Contents: block.
Filters: none for Neo 2, primary index for Neo 3.
new transaction in the block
Contents: transaction.
Filters: type and verification script hash for Neo 2, transaction Sender and Cosigner for Neo 3.
notification generated during execution
Contents: container hash, contract script hash, stack item.
Filters: contract script hash. Note that for Neo 2 only transaction can be container, but for Neo 3 block itself can also produce some notifications.
transaction executed
Contents: application execution result.
Filters: VM state; transaction Sender and Cosigner for Neo 3.

Filters use conjunctional logic.
Ordering and persistence guarantees:

new block is only announced after its processing is complete and the chain is updated to the new height
No disk-level persistence guarantees are given.
new in-block transaction is announced after block processing, but before announcing the block itself
transaction notifications are only announced for successful transactions
all announcements are being done in the same order they happen on the chain
At first transaction execution is announced, then followed by notifications generated during this execution, then followed by transaction announcement (for Neo 2). Transaction announcements are ordered the same way they're in the block.
unsubscription may not cancel pending, but not yet sent events

Other notes:

subscription process should include some subscription IDs for client to be able to unsubscribe from certain events streams
subscription is only valid for connection lifetime, no long-term client identification is being made
if an event matches several subscriptions, it's only sent once for one client (this is a part of the reason why no subscription ID is sent with notifications)
during high server load or when dealing with slow client connections the server can throw away some events that can't fit into reasonably-sized event queue. When it's being done missed events counter is being increased for given subscription ID and an event is generated that will be sent to the client containing the number of missed events from the last such event.

Extensions considered
Possible future extensions:

transaction addition/removal to/from the mempool (similar to IMemoryPoolTxObserverPlugin)
making log events available (similar to ILogPlugin)
Can be dangerous as logs might contain some sensitive data about node configuration. At the same time it might be useful if restricted properly.
P2P (especially consensus) messages passing (similar to IP2PPlugin)
Might be useful for making neo-go compatible with things like Red4Sec/neo-resilience, but not a priority for now and also note that IP2PPlugin can affect message processing, so it's not just an event subsystem.
extending filtering capabilities (like matching something from a set of hashes)
Doesn't seem to be a priority because the primary target audience for this system is smart contract developers and most of the time they're dealing with one or two contracts/addresses.

Things we probably won't do (but they're used in neo-resilience):

OnCommit() and ShouldThrowExceptionFromCommit() from IPersistencePlugin
ShouldThrowExceptionFromCommit() isn't really applicable to our model and the only value in OnCommit() is that it synchronously exposes the new state before the chain moves on, I think block notification is a good enough alternative.
OnPersist() callback implementation from IPersistencePlugin
Doesn't seem to be very useful as it can be substituted by subscribing to our transaction execution and block notifications.

Protocol specification draft
Websocket connection
Connection switch is done with a GET request to /ws path. Standard protocol
upgrade happens after that and all subsequent messaging is being done via this
channel (including regular RPC calls).
Subscription management
subscribe method
Parameters: event stream name, stream-specific filter rules hash (can be
omitted if empty).
Recognized stream names:

block_added
No filter parameters for Neo 2, primary with integer value for Neo 3.
transaction_added
Neo 2: type as a string containing standard transaction types
(MinerTransaction, InvocationTransaction, etc); verifier as a string
containing hex-encoded Uint160. Neo 3: sender and cosigner fields
containing string with hex-encoded Uint160.
notification_from_execution
contract field containing string with hex-encoded Uint160.
transaction_executed
Both versions: state containing HALT or FAULT string for successful
and failed executions respectively. Neo 3: sender and cosigner fields
with the same semantics as for transaction_added.

Response: returns subscription ID (string) as a result.
Example request (subscribe to notifications from contract
0x6293a440ed80a427038e175a507d3def1e04fb67 generated when executing
transactions):
{
  "jsonrpc": "2.0",
  "method": "subscribe",
  "params": ["notification_from_execution", {"contract": "6293a440ed80a427038e175a507d3def1e04fb67"}],
  "id": 1
}

Example response:
{
  "jsonrpc": "2.0",
  "id": 1,
  "result": "55aaff00"
}

unsubscribe method
Parameters: subscription ID as a string.
Response: boolean true.
Example request (unsubscribe from "55aaff00"):
{
  "jsonrpc": "2.0",
  "method": "unsubscribe",
  "params": ["55aaff00"],
  "id": 1
}

Example response:
{
  "jsonrpc": "2.0",
  "id": 1,
  "result": true
}

Events
Events are sent as JSON-RPC notifications from the server with method field
being used for notification names. Notification names are identical to stream
names described for subscribe method.
Verbose responses for various structures like blocks and transactions are used
to simplify working with notifications on client side. Other models
considered:

passing non-verbose hex blobs
Using them would require deserializing, which is not a problem for neo-go
client, but can be problematic in other circumstances
pushing only hashes
Which would simplify server's life, but we're suggesting that if there is a
subscription, then the client really is interested in the contents and so
requiring an additional request to get the real data reduces overall
efficiency in the end.

block_added notification
As a first parameter (params section) contains block converted to JSON
structure (similar to verbose getblock response). No other parameters are
sent.
Example:
{
  "jsonrpc": "2.0",
  "method": "block_added",
  "params": [
   {
    "hash": "0x4c1e879872344349067c3b1a30781eeb4f9040d3795db7922f513f6f9660b9b2",
    "size": 686,
    "version": 0,
    "previousblockhash": "0x53ce15a53a184faa058be9008ece887d7f0b30a459342ce42753f73f9ed390e9",
    "merkleroot": "0x9c909e1e3ba03290553a68d862e002c7a21ba302e043fc492fe069bf6a134d29",
    "time":<PHONE_NUMBER>,
    "index": 10000,
    "nonce": "aa2c0fd5dc445445",
    "nextconsensus": "APyEx5f4Zm4oCHwFWiSTaph1fPBxZacYVR",
    "script": {
      "invocation": "405e31eb19b1feaeb27c3a5b95f568b9b256fefe0ea61f6296eb8af836c29597617fe81d23a8bf66309000e4c7568b7f43560f61e4ee6cd1f78a2a42f50a5008c240ccf73ce9f7f810273730bdfc786d346086a697cc06239e88e040ed2ec0583c7dbb6eccb8b8a74afbd75cfbaff06c051b7e82abe65f96f50a1673e1536f91a3d540618e43cce18c7c91b54b2a5e44ba1e4a71a8dd0af0ec95c8c4f05343e66129b150057a5f79399a92eda1226fddd254702\
ffc682309787ab241509b2244e410334070a5ac50d897bf39f98780f79fb1a2416c41dc2e202b4ad797bd0c70e2b57f1157c4ff5551ec6df58bec6244dc72a3f25cd1836e8cdd4c0d8c2e5ba7e2d8859b40ae80743c9a2a8e154671eb156266971439a9017e96ea072c848287a71b2d6a99a67ba50fc7935a6de4d8884794291fc6cebd77158954ef03b10d5d0a30b52bc9",
      "verification": "552102486fd15702c4490a26703112a5cc1d0923fd697a33406bd5a1c00e0013b09a7021024c7b7fb6c310fccf1ba33b082519d82964ea93868d676662d4a59ad548df0e7d2102aaec38470f6aad0042c6e877cfd8087d2676b0f516fddd362801b9bd3936399e2103b209fd4f53a7170ea4444e0cb0a6bb6a53c2bd016926989cf85f9b0fba17a70c2103b8d9d5771d8f513aa0869b9cc8d50986403b78c6da36890638c3d46a5adce04a2102ca0e27697b9\
c248f6f16e085fd0061e26f44da85b58ee835c110caa5ec3ba5542102df48f60e8f3e01c48ff40b9b7f1310d7a8b2a193188befe1c2e3df740e89509357ae"
    },
    "tx": [
      {
        "txid": "0x9c909e1e3ba03290553a68d862e002c7a21ba302e043fc492fe069bf6a134d29",
        "size": 10,
        "type": "MinerTransaction",
        "version": 0,
        "attributes":[],
        "vin":[],
        "vout":[],
        "sys_fee": "0",
        "net_fee": "0",
        "scripts":[],
        "nonce":<PHONE_NUMBER>
      }
    ],
    "confirmations": 0,
   }
  ]
}

transaction_added notification
In the first parameter (params section) contains transaction converted to JSON
(similar to verbose getrawtransaction response). No other parameters are
sent.
{
  "jsonrpc": "2.0",
  "method": "transaction_added",
  "params": [
   {
    "txid": "0xf4250dab094c38d8265acc15c366dc508d2e14bf5699e12d9df26577ed74d657",
    "size": 262,
    "type": "ContractTransaction",
    "version": 0,
    "attributes":[],
    "vin": [
      {
        "txid": "0xabe82713f756eaeebf6fa6440057fca7c36b6c157700738bc34d3634cb765819",
        "vout": 0
      }
     ],
     "vout": [
      {
        "n": 0,
        "asset": "0xc56f33fc6ecfcd0c225c4ab356fee59390af8560be0e930faebe74a6daff7c9b",
        "value": "2950",
        "address": "AHCNSDkh2Xs66SzmyKGdoDKY752uyeXDrt"
      },
      {
        "n": 1,
        "asset": "0xc56f33fc6ecfcd0c225c4ab356fee59390af8560be0e930faebe74a6daff7c9b",
        "value": "4050",
        "address": "ALDCagdWUVV4wYoEzCcJ4dtHqtWhsNEEaR"
       }
    ],
    "sys_fee": "0",
    "net_fee": "0",
    "scripts": [
      {
        "invocation": "40915467ecd359684b2dc358024ca750609591aa731a0b309c7fb3cab5cd0836ad3992aa0a24da431f43b68883ea5651d548feb6bd3c8e16376e6e426f91f84c58",
        "verification": "2103322f35c7819267e721335948d385fae5be66e7ba8c748ac15467dcca0693692dac"
      }
    ],
    "blockhash": "0x9c814276156d33f5dbd4e1bd4e279bb4da4ca73ea7b7f9f0833231854648a72c",
    "confirmations": 0,
    "blocktime":<PHONE_NUMBER>
   }
  ]
}

notification_from_execution notification
Contains three parameters: container hash (Uint256 encoded in a string),
contract script hash (Uint160 encoded in a string) and stack item (encoded the
same way as state field contents for notifications from getapplicationlog
response).
Example:
{
  "jsonrpc": "2.0",
  "method": "notification_from_execution",
  "params": [
    "0x92b1ecc0e8ca8d6b03db7fe6297ed38aa5578b3e6316c0526b414b453c89e20d",
    "0x78e6d16b914fe15bc16150aeb11d0c2a8e532bdd",
    {
      "type": "Array",
      "value": [
        {
          "type": "ByteArray",
          "value": "7472616e73666572"
        },
        {
          "type": "ByteArray",
          "value": "d086ac0ed3e578a1afd3c0a2c0d8f0a180405be2"
        },
        {
          "type": "ByteArray",
          "value": "002ba7f83fd4d3975dedb84de27345684bea2996"
        },
        {
          "type": "ByteArray",
          "value": "0065cd1d00000000"
        }
      ]
    }
  ]
}

transaction_executed notification
Contains the same result as from getapplicationlog method in the first
parameter and no other parameters.
Example:
{
  "jsonrpc": "2.0",
  "method": "transaction_executed",
  "params": [
    {
      "txid": "0x92b1ecc0e8ca8d6b03db7fe6297ed38aa5578b3e6316c0526b414b453c89e20d",
      "executions": [
        {
          "trigger": "Application",
          "contract": "0x6ec33f0d370617dd85e51d31c483b6967074249d",
          "vmstate": "HALT",
          "gas_consumed": "2.912",
          "stack": [
            {
              "type": "Integer",
              "value": "1"
            }
          ],
          "notifications": [
            {
              "contract": "0x78e6d16b914fe15bc16150aeb11d0c2a8e532bdd",
              "state": {
                "type": "Array",
                "value": [
                  {
                    "type": "ByteArray",
                    "value": "7472616e73666572"
                  },
                  {
                    "type": "ByteArray",
                    "value": "d086ac0ed3e578a1afd3c0a2c0d8f0a180405be2"
                  },
                  {
                    "type": "ByteArray",
                    "value": "002ba7f83fd4d3975dedb84de27345684bea2996"
                  },
                  {
                    "type": "ByteArray",
                    "value": "0065cd1d00000000"
                  }
                ]
              }
            }
          ]
        }
      ]
    }
  ]
}

Draft client-side API
// All subscription functions accept parameters via pointers to allow passing nil
// as 'no filter' value. They return subscription ID or error.
func (c *Client) SubscribeForNewBlocks(primary *int) (string, error)
func (c *Client) SubscribeForNewTransactions(txType *transaction.TXType, signer *util.Uint160) (string, error) // Neo 2 version
func (c *Client) SubscribeForNewTransactions(sender *util.Uint160, cosigner *util.Uint160) (string, error) // Neo 3 version
func (c *Client) SubscribeForExecutionNotifications(contract *util.Uint160) (string, error)
func (c *Client) SubscribeForTransactionExecutions(state string) (string, error) // Neo 2 version
func (c *Client) SubscribeForTransactionExecutions(state string, sender *util.Uint160, cosigner *util.Uint160) (string, error) // Neo 3 version

// Unsubscription may close event channel if it's the last subscription.
func (c *Client) Unsubscribe(id string) error
func (c *Client) UnsubscribeFromAll()

// Can return error if there are no subscriptions. You can read events from returned channel.
// One channel (instead of per-type queues) is used to ensure correct overall event sequence.
// name can be substituted with client-internal ID.
func (c *Client) GetEventStream() (<-chan struct{name string, value interface{}}, error)

Done.


Compatibility of wasmtime with yew

Wasmtime doesn't work in yew
It gives error:
error: failed to run custom build command for `cranelift-codegen v0.99.1`

Caused by:
  process didn't exit successfully: `/home/amiya/Documents/workspace/mastodon/mastolearn/target/debug/build/cranelift-codegen-e40b7bea58046d12/build-script-build` (exit status: 101)
  --- stderr
  thread 'main' panicked at 'error when identifying target: "no supported isa found for arch `wasm32`"', /home/amiya/.cargo/registry/src/index.crates.io-6f17d22bba15001f/cranelift-codegen-0.99.1/build.rs:45:53
  note: run with `RUST_BACKTRACE=1` environment variable to display a backtrace
warning: build failed, waiting for other jobs to finish...
error[E0583]: file not found for module `sys`
  --> /home/amiya/.cargo/registry/src/index.crates.io-6f17d22bba15001f/errno-0.3.3/src/lib.rs:26:1
   |
26 | mod sys;
   | ^^^^^^^^
   |
   = help: to create the module `sys`, create file "/home/amiya/.cargo/registry/src/index.crates.io-6f17d22bba15001f/errno-0.3.3/src/sys.rs" or "/home/amiya/.cargo/registry/src/index.crates.io-6f17d22bba15001f/errno-0.3.3/src/sys/mod.rs"

Wasmtime can't be compiled to webassembly. You will have to use eg the web-sys crate to interface with the native wasm support of browsers if you are running the wasm module in the browser. See also https://github.com/bytecodealliance/wasmtime/issues/6504

Ok got it. Thanks.


Failed to resolve git repo on mapped network drive

Description
I'm on a Windows machine with a network drive (M:) that's mapping the nas folder on my machine named nas-server.  Per the debug logs, it looks like the git repo resolution step is trying to use a path that includes both M: and nas-server\nas. My .git folder is in the working directory.
Steps to reproduce
I was able to reproduce with a fresh repository on my mapped drive.
After creating a new repository and running npm init, I installed prettier and ran npx mrm@2 lint-staged.
I get the error when I try to make a commit.
Debug Logs

expand to view
M:\Misc\temp repro>npx lint-staged -d true
  lint-staged:bin Running<EMAIL_ADDRESS>on Node.js v18.16.0 (win32) +0ms
  lint-staged:bin Options parsed from command-line: {
  allowEmpty: false,
  concurrent: true,
  configPath: undefined,
  cwd: undefined,
  debug: true,
  diff: undefined,
  diffFilter: undefined,
  maxArgLength: undefined,
  quiet: false,
  relative: false,
  shell: false,
  stash: true,
  verbose: false
} +1ms
  lint-staged:validateOptions Validating options... +0ms
  lint-staged:validateOptions Validated options! +0ms
  lint-staged Unset GIT_LITERAL_PATHSPECS (was `undefined`) +0ms
  lint-staged:runAll Running all linter scripts... +0ms
  lint-staged:runAll Using working directory `M:\Misc\temp repro` +0ms
  lint-staged:resolveGitRepo Resolving git repo from `M:\Misc\temp repro` +0ms
  lint-staged:resolveGitRepo Unset GIT_DIR (was `undefined`) +0ms
  lint-staged:resolveGitRepo Unset GIT_WORK_TREE (was `undefined`) +0ms
  lint-staged:execGit Running git command [ 'rev-parse', '--show-prefix' ] +0ms
  lint-staged:resolveGitRepo Failed to resolve git repo with error: [Error: ENOENT: no such file or directory, lstat 'M:\nas-server\nas\Misc\temp repro\.git']
{
  errno: -4058,
  code: 'ENOENT',
  syscall: 'lstat',
  path: 'M:\\nas-server\\nas\\Misc\\temp repro\\.git'
} +112ms
✖ Current directory is not a git directory!

Environment

OS: Windows 10
Node.js: 18.16.0
lint-staged: 15.1.0

I just verified I've got no issue using lint-staged on the repo when connected to nas-server via ssh.

Hey,
so the expected path is M:\Misc\temp repro\.git but lint-staged finds M:\nas-server\nas\Misc\temp repro\.git?
If you don't mind adding some console.log or similar debugging for yourself, I believe the error happens somewhere around here:
https://github.com/lint-staged/lint-staged/blob/26eee9d76f4643426fc033fba18e487f7e64fe1d/lib/resolveGitRepo.js#L12-L25
Maybe it's line 24 where it essentially resolves the config path from "git dir"; could it be a relative path and gets messed up?
https://github.com/lint-staged/lint-staged/blob/26eee9d76f4643426fc033fba18e487f7e64fe1d/lib/resolveGitRepo.js#L24

Thanks for pointing me to the relevant function. I went ahead and added some logging to try to see what was happening.

new logs
M:\Misc\temp repro>npx lint-staged -d true
  lint-staged:bin Running<EMAIL_ADDRESS>on Node.js v18.16.0 (win32) +0ms
  lint-staged:bin Options parsed from command-line: {
  allowEmpty: false,
  concurrent: true,
  configPath: undefined,
  cwd: undefined,
  debug: true,
  diff: undefined,
  diffFilter: undefined,
  maxArgLength: undefined,
  quiet: false,
  relative: false,
  shell: false,
  stash: true,
  verbose: false
} +1ms
  lint-staged:validateOptions Validating options... +0ms
  lint-staged:validateOptions Validated options! +0ms
  lint-staged Unset GIT_LITERAL_PATHSPECS (was `undefined`) +0ms
  lint-staged:runAll Running all linter scripts... +0ms
  lint-staged:runAll Using working directory `M:\Misc\temp repro` +0ms
  lint-staged:resolveGitRepo Resolving git repo from `M:\Misc\temp repro` +0ms
  lint-staged:resolveGitRepo Unset GIT_DIR (was `undefined`) +0ms
  lint-staged:resolveGitRepo Unset GIT_WORK_TREE (was `undefined`) +1ms
  lint-staged:execGit Running git command [ 'rev-parse', '--show-prefix' ] +0ms
  lint-staged:resolveGitRepo resolveGitConfigDir | gitDir: `M:/Misc/temp repro` +134ms
  lint-staged:resolveGitRepo resolveGitConfigDir | defaultDir: `/nas-server/nas/Misc/temp repro/.git` +2ms
  lint-staged:resolveGitRepo Failed to resolve git repo with error: [Error: ENOENT: no such file or directory, lstat 'M:\nas-server\nas\Misc\temp repro\.git'] {
  errno: -4058,
  code: 'ENOENT',
  syscall: 'lstat',
  path: 'M:\\nas-server\\nas\\Misc\\temp repro\\.git'
} +2ms
✖ Current directory is not a git directory!

It looks like the exception is thrown by the lstat call on line 19. The path being passed in is /nas-server/nas/Misc/temp repro/.git which seems to be interpreted by lstat as a path on the M drive.
After messing around in the node repl, I learned that fs.realpath takes the path I'd expect to see (M:\\Misc\\temp repro\\.git) and transforms it into another path that works: \\nas-server\nas\Misc\temp repro\.git
Then normalizePath takes that path and transforms it to /nas-server/nas/Misc/temp repro/.git which doesn't work. I confirmed that removing the normalizePath call on line 18 prevents the crash I was experiencing. However, the result of resolveGitConfigDir is still being passed through normalizePath and resolving to something that doesn't work so it crashes later on.

Hey, the normalizePath is implemented in this repo along with an unit test file that seemingly already handles this (test/unit/normalizePath.spec.js). Maybe there's some bug in the implementation or its usage.
Would M:/nas-server/nas/Misc/temp repro/.git be a working path?
https://github.com/lint-staged/lint-staged/blob/26eee9d76f4643426fc033fba18e487f7e64fe1d/test/unit/normalizePath.spec.js#L38

Just to be sure, what is the literal content of the .git file in the repo? I assume it's roughly:
gitdir: M:/Misc/temp repro/.git

The root of the repo is M:/Misc/temp repro and M:/Misc/temp repro/.git is not a file, it's a folder. Are you asking about a file in that folder?

We can try to make the realPath call conditional based on stats.isSymbolicLink(); maybe that will help.

Thanks for debugging, I'll put it into the next release.


Exception in the index page

I got this exception in the main page after wating a while a doing different click into the input text fields
azuredevopsteammembersvelocity_1 | warn: Microsoft.AspNetCore.Components.Server.Circuits.RemoteRenderer[100]
azuredevopsteammembersvelocity_1 | Unhandled exception rendering component: Sequence contains no matching element
azuredevopsteammembersvelocity_1 | System.InvalidOperationException: Sequence contains no matching element
azuredevopsteammembersvelocity_1 | at System.Linq.ThrowHelper.ThrowNoMatchException()
azuredevopsteammembersvelocity_1 | at System.Linq.Enumerable.First[TSource](IEnumerable`1 source, Func`2 predicate)
azuredevopsteammembersvelocity_1 | at AzureDevOpsTeamMembersVelocity.Pages.Index.GetTeams() in /src/AzureDevOpsTeamMembersVelocity/Pages/Index.razor:line 262
azuredevopsteammembersvelocity_1 | at Microsoft.AspNetCore.Components.ComponentBase.CallStateHasChangedOnAsyncCompletion(Task task)
azuredevopsteammembersvelocity_1 | at Microsoft.AspNetCore.Components.RenderTree.Renderer.GetErrorHandledTask(Task taskToHandle)
azuredevopsteammembersvelocity_1 | fail: Microsoft.AspNetCore.Components.Server.Circuits.CircuitHost[111]
azuredevopsteammembersvelocity_1 | Unhandled exception in circuit 'wpQOTZPKaI5OsWbb5s4BRznK6AIE4zUmQA32GZeD_yY'.
azuredevopsteammembersvelocity_1 | System.InvalidOperationException: Sequence contains no matching element
azuredevopsteammembersvelocity_1 | at System.Linq.ThrowHelper.ThrowNoMatchException()
azuredevopsteammembersvelocity_1 | at System.Linq.Enumerable.First[TSource](IEnumerable`1 source, Func`2 predicate)
azuredevopsteammembersvelocity_1 | at AzureDevOpsTeamMembersVelocity.Pages.Index.GetTeams() in /src/AzureDevOpsTeamMembersVelocity/Pages/Index.razor:line 262
azuredevopsteammembersvelocity_1 | at Microsoft.AspNetCore.Components.ComponentBase.CallStateHasChangedOnAsyncCompletion(Task task)
azuredevopsteammembersvelocity_1 | at Microsoft.AspNetCore.Components.RenderTree.Renderer.GetErrorHandledTask(Task taskToHandle)

This was due to calling linq.First in an empty list. Will be fixed soon.


Fix incorrect type for elb policy Attributes

Wow, another great PR!
I'm still unsure about the approach to NameValuePair. I think the auto-gen functionality should be used for things that are actually in the CloudFormation docs. I think we should either:

Add a type called NameValuePair to library/Stratosphere/Types.hs
Or, see if just using [(Text, Text)] works there. I'm not sure if the auto-gen code will get screwed up by that type or not.

I'll try both of those out right now. In the meantime, I'll merge your other PR that doesn't include NameValuePair.

Also, I'm curious, did you use the gen/scraper.py script? Was it useful? I'm really impressed you figured out where to put everything, I didn't document that very well :smile:

You know what, this PR is fine as-is. I must have misread the CloudFormation ELB policy docs the first time around. Thanks again for the PR!

I did indeed use the scraper script. Very helpful! I had to add in the dependencies key but I found another example that did it.
I could add it to the library dir, I didn't know about the convention so if there's a better place, we can totally do that.
I'm not such a big fan of using really basic types, especially when the fields are known. In fact I may have a few PRs for you in the future where the code is using an Object when it could be more specific. Let me know if you need anything from me to fix up this PR even though you merged it.


Add option to use numerals instead of letters in hints

For those of us who are a bit slow to learn our talon phonetic alphabets, it would be great if there was an option to use numerical hints rather than alphabetic ones. This would likely necessitate hints consisting of up to three characters, but I use other similar programs that go up to three characters and it isn't a problem.

No problem. I'll add the option


Problem with Archiving project

Describe the bug
Give a clear and concise description of what the bug is.
Rename failed: /Users/test/Library/Developer/Xcode/DerivedData/Test-glvqxrieyduwirfnaoouzklnojya/Build/Intermediates.noindex/ArchiveIntermediates/test-staging/IntermediateBuildFilesPath/swift-custom-dump.build/Release-iphoneos/CustomDump.build/Objects-normal/arm64/Speech-bad90112.o.tmp -> /Users/test/Library/Developer/Xcode/DerivedData/test-glvqxrieyduwirfnaoouzklnojya/Build/Intermediates.noindex/ArchiveIntermediates/test-staging/IntermediateBuildFilesPath/swift-custom-dump.build/Release-iphoneos/CustomDump.build/Objects-normal/arm64/Speech.o: No such file or directory
Rename failed: /Users/test/Library/Developer/Xcode/DerivedData/Test-glvqxrieyduwirfnaoouzklnojya/Build/Intermediates.noindex/ArchiveIntermediates/test-staging/IntermediateBuildFilesPath/swift-custom-dump.build/Release-iphoneos/CustomDump.build/Objects-normal/arm64/CoreMotion-07ef2dfa.o.tmp -> /Users/test/Library/Developer/Xcode/DerivedData/test-glvqxrieyduwirfnaoouzklnojya/Build/Intermediates.noindex/ArchiveIntermediates/test-staging/IntermediateBuildFilesPath/swift-custom-dump.build/Release-iphoneos/CustomDump.build/Objects-normal/arm64/CoreMotion.o: No such file or directory
Rename failed: /Users/test/Library/Developer/Xcode/DerivedData/Test-glvqxrieyduwirfnaoouzklnojya/Build/Intermediates.noindex/ArchiveIntermediates/test-staging/IntermediateBuildFilesPath/swift-custom-dump.build/Release-iphoneos/CustomDump.build/Objects-normal/arm64/CoreLocation-aef9f0f0.o.tmp -> /Users/test/Library/Developer/Xcode/DerivedData/test-glvqxrieyduwirfnaoouzklnojya/Build/Intermediates.noindex/ArchiveIntermediates/test-staging/IntermediateBuildFilesPath/swift-custom-dump.build/Release-iphoneos/CustomDump.build/Objects-normal/arm64/CoreLocation.o: No such file or directory
Rename failed: /Users/test/Library/Developer/Xcode/DerivedData/test-glvqxrieyduwirfnaoouzklnojya/Build/Intermediates.noindex/ArchiveIntermediates/test-staging/IntermediateBuildFilesPath/swift-custom-dump.build/Release-iphoneos/CustomDump.build/Objects-normal/arm64/Swift-236a70eb.o.tmp -> /Users/test/Library/Developer/Xcode/DerivedData/test-glvqxrieyduwirfnaoouzklnojya/Build/Intermediates.noindex/ArchiveIntermediates/test-staging/IntermediateBuildFilesPath/swift-custom-dump.build/Release-iphoneos/CustomDump.build/Objects-normal/arm64/Swift.o: No such file or directory
To Reproduce
Zip up a project that reproduces the behavior and attach it by dragging it here.
I deleted derived data, clean build project, restart PC and did everything I could to try resolving this but it didn't work.
// And/or enter code that reproduces the behavior here.

Expected behavior
Give a clear and concise description of what you expected to happen.
Screenshots
If applicable, add screenshots to help explain your problem.
Environment

swift-custom-dump version [e.g. 0.3.0] -> 0.11.1
Xcode [e.g. 13.2.1] -> 15.3
Swift [e.g. 5.5.2] ->
OS: [e.g. iOS 15.2] -> 14.3.1 (23D60)

Additional context
Add any more context about the problem here.

I am seeing the same issue.

Hi @filip-aetherius and @RuiAAPeres, this was fixed in 1.2.1. Please update your dependence on swift-custom-dump.

@mbrandonw could this fix get added to the older version as well?
I asked here https://github.com/pointfreeco/swift-custom-dump/issues/107#issuecomment-1989538713 because some projects are still stuck on Pre-1.0 TCA.

This fix has been back-ported to the 0.x series: https://github.com/pointfreeco/swift-custom-dump/releases/tag/0.11.2


Fix throw NPE when cleaning

Fix throw NPE when cleaning

/pulsarbot run-failure-checks

/pulsarbot cherry-pick to branch-2.7


chore/ improve performance of convert_multiple_coordinates_to_new_system

Per discussion here (https://github.com/Unstructured-IO/unstructured/pull/1259/files#r1312331362), convert_multiple_coordinates_to_new_system can be improved by vectorizing the the operation to improve performance

Resolved; reopen with comment is issue is still relevant


[CROSSDATA-648] Added statically defined to_number UDF

Description
Added statically defined to_number UDF.
Testing

[x] Unit

+1


Scribble generated files are highly sensitive to Solidity version pragma

After I --arm a sol file and then try to compile it, I usually get an error like:
The Solidity version pragma statement in these files doesn't match any of the configured compilers in your config. Change the pragma or configure additional compiler versions in your hardhat config.

  * @openzeppelin/contracts/__scribble_ReentrancyUtils.sol (0.8.17)

I can change the pragma, but sometimes I wind up having to change it again later. Scribble seems very sensitive to this - it would be nice if scribble would work with 0.8.9 since I believe that's the latest stable release of Solidity - if I try with .13 or .15 then the compiler warns that I'm on a "not fully supported" version. As for .17, I don't think I even have that installed yet.
I also get a lot of other errors when trying to compile scribble instrumented stuff - is there a more general way I can discuss these errors, or do I need to cut a minimal repro and report an issue for each different type of error? Thanks!

Closing due to no activity. Explaination above should handle compiler version issues.


Fix np.concatenate not working properly

I believe this is a bug in your code. I've adapted it to some data I'm currently working with and some of the ECG signals crashed this code. Changing this line to concatenate properly appears to fix the problem. I haven't looked at the algorithm at all, but suspect this is what is meant to happen anyway.

Thank you for pointing this bug out! I ran your code, but I get a ValueError

line 135, in
ekg_window = np.concatenate([lows, i, highs])
File "<array_function internals>", line 6, in concatenate
ValueError: all the input arrays must have same number of dimensions, but the array at index 0 has 1 dimension(s) and the array at index 1 has 0 dimension(s)

I do agree that there is a bug in line 134 that the arrays are not concatenating properly.
Do you mean to instead change it to the following?

ekg_window = np.concatenate((lows, i, highs), axis=None)


Add Aim logging to progress_bar

Patch Description

Added AimProgressBarWrapper class; an implementation of BaseProgressBar using Aim, an open-source experiment tracker: https://github.com/aimhubio/aim.
Added two new arguments to CommonConfig used to control AimProgressBarWrapper creation/usage:

aim_repo: path to the Aim repository.
aim_run_hash: If specified, will be used to determine aim.Run for metadata tracking. If skipped, checkpoint.save_dir will be used to find and re-open the existing Run or if no matching Run is found, new one is created.

Testing steps
Make sure progress_bar logs data to selected outputs, with Aim logger enabled/disabled.
Run unit-tests.

[ ] Was this discussed/approved via a Github issue?
[x] Did you read the contributor guideline?
[ ] Did you make sure to update the docs?
[ ] Did you write any new necessary tests?

This raises a good todo to tackle before we start adding an excessive amount of args / flags here: https://github.com/facebookresearch/metaseq/issues/93

@suchenzang thanks a lot for the review 🙌
I wonder where we should add a mini-guide on how to use Aim? To docs/training or to README?

Sorry for the delay in getting back - adding a mini-guide to using Aim would be much appreciated! Yes - feel free to add to https://github.com/facebookresearch/metaseq/blob/main/docs/training.md !
I've been blocking on this PR due to https://github.com/facebookresearch/metaseq/pull/96 - once that goes through, will request a modification to this PR accordingly as well.

@gorarakelyan You may need to sign the CLA check (not sure if it automatically shows up for you here since @alberttorosyan opened the PR, but going to https://code.facebook.com/cla should work) so that we can merge this.  Ping me when it's ready for review!

@suchenzang done ✅

@gorarakelyan one more nit: before merging, can you make sure our lint test passes?

@suchenzang just pushed changes with linter issues fix. but the circleci tests seemed to stuck with no progress, so I cancelled them. is there any pre-requisite for running these tests other than mentioned in CONTRIBUTING.md?

@suchenzang just pushed changes with linter issues fix. but the circleci tests seemed to stuck with no progress, so I cancelled them. is there any pre-requisite for running these tests other than mentioned in CONTRIBUTING.md?

Hey @suchenzang! Just wanted to double-check, should I take any additional actions here or we can proceed forward and merge PR?

@alberttorosyan Seems like I can't rerun the gpu tests either - should be good to merge though; will do now! Thanks for the PR!


Issues about generation from test dataset

Hello! What do I do if I want to generate responses from a test dataset using a trained model? Thanks!

If you want to evaluate the response using predefined metrics such as F1, you could use eval_f1.py to achieve it;
If you want to obtain all generated responses, you could modify the option(line43-line49) in eval_f1.py as:
  parser.set_params(
      model='agents.transmitter.transmitter:TransformerAgent',
      model_file=model_name,
      gpu=1,
      batchsize=1,
      beam_size=2,
      report_freq=1.0
  )

And the Text and Prediction will be printed as following:
TEXT:  <t1start> that is great , me too ! i'm married and my husband and i've 2 children . <t1end>
PREDICTION:  i ' m a nurse and i love to spend time with my family

And you could extract them from the log.

If you want to evaluate the response using predefined metrics such as F1, you could use eval_f1.py to achieve it;
If you want to obtain all generated responses, you could modify the option(line43-line49) in eval_f1.py as:
  parser.set_params(
      model='agents.transmitter.transmitter:TransformerAgent',
      model_file=model_name,
      gpu=1,
      batchsize=1,
      beam_size=2,
      report_freq=1.0
  )

And the Text and Prediction will be printed as following:
TEXT:  <t1start> that is great , me too ! i'm married and my husband and i've 2 children . <t1end>
PREDICTION:  i ' m a nurse and i love to spend time with my family

And you could extract them from the log.

Thanks!

Thanks!


Include tzdata package in base images

Minideb included timezone data, whereas the ubuntu image does not. The data is required for a lot of ruby apps to work, so we should install it in the base image.

Thank you!


Add current_time function to MysqlSqlModule

Add support for function current_time described in the MySQL docs.
Add function to MysqlFunctionDef in MysqlSqlModule
Add test in FunctionDefSpec

I would like to work on this issue


Use ssl

Hey, Alex.
I made a fork of this when Confluent took it private. While it doesn't seem there's much difference between the old public one and their now-private version, we use this connector heavily at The Meet Group and have run into a number of issues with it we've had to fix. Currently, those fixes have been kept in our private repo. We weren't really sure how many other people were using this, so we kept our stuff private. But, we can push some of that to a public repo for not only us, but other developers such as yourself, to contribute to. Thoughts?

Hi Johan, thank you for responding! Funny enough, this PR was created by mistake when I was trying to update our own fork.
Can't say how many others, but we're definitely using it. We've also made a few small fixes (this and hiding the password in the config). If maintaining the public repo is an option, I think that would be useful. I can make a cumulative PR from our fork as well.


About your innovative image encoding strategy.

Which part of the code reflects the processing of images of different resolutions

Hello, I'm also looking for the part of the code in which the hierarchical encoding strategy has been implemented. Can you please help me with this information?


Feature Request: duration with remainder

Either a new filter or a parameter for amDurationFormat that would express a duration with a remainder. Example:

Input
amDurationFormat output
new feature output

2500 seconds
an hour
41 minutes, 40 seconds

3600 seconds
an hour
an hour

4600 seconds
an hour
one hour, 17 minutes

1440 minutes
a day
a day

1565 minutes
a day
one day, two hours

Hi, thank you for suggesting this feature!
The duration value is formatted using the humanize method. See moment/moment#463 for a discussion about adding this feature to moment.js.

Right now this needs be done via jsmreese/moment-duration-format, you can add your own directive for it.


fix source paths

A couple of the stories are showing the incorrect source.  This is true on the currently deployed docs site as well.  This PR corrects the paths to the source,

Very nice!


slow speed in generating displacements

I am not really sure this is a bug or not.
I try to generate displacements in a relatively big cells 100-400 atoms, one must assume no symmetry.
I noticed it takes really long, even if I specify --cutoff-pair  --cutoff-fc3
I did not do a systematic measuring of time but seems using cutoffs does not affect generation time but only the number of displacements.
Am I wrong?

Could you tell How I can reproduce this problem?

here is some data
Python version 3.8.5
Spglib version 1.16.1
cutoff
phono3py -d --dim='1 1 1' --cutoff-pair 4.24 --cutoff-fc3 2.12
head disp_fc3.yaml
natom: 106
num_first_displacements: 636
cutoff_distance: 4.240000
num_second_displacements: 404496
num_displacements_created: 57300
duplicates:
time: 01:15:14
no cutoff
phono3py -d --dim='1 1 1'
head disp_fc3.yaml
natom: 106
num_first_displacements: 636
num_second_displacements: 404496
num_displacements_created: 405132
duplicates:
time:1:43:39
I would expect using the cutoff to cut the generation time seriously, the difference I think is largely due to writing all the data on the parallel file system I think.
the poscar if you want to look at it is in here
https://github.com/WMD-group/VMOF/blob/master/STRUCTURE_FILES/MOF-5/POSCAR

Thanks.
I found this method is very slow.
https://github.com/phonopy/phono3py/blob/545af589388e25b51bcac6d3a8f61ecd7e70a002/phono3py/phonon3/displacement_fc3.py#L335
Commenting out this method call (lines 73 and 79),
% phono3py -d --dim='1 1 1' --cutoff-pair 4.24 --cutoff-fc3 2.12

finished in about 2 mins including writing out files on my macbook pro.
The method _find_duplicates finds the same supercells (sets of two displacements are same) but from using command line, almost no effect. So you can disable _find_duplicates.
Clearly the algorithms of _find_duplicates is not well written. Lines 353-354 means O(n^2) and n is the number of displacements. I will consider if this feature should be kept or not, or if the algorithm will be improved. But, first I will try the later.

I made some update at 9a8d107 to fix this issue. Now I expect your command will finish in a few minitues though it may depend on the file I/O speed. The code is in develop branch.

thanks I will give it a try later today.

Indeed I tested things look much better now.
Alin


Footer issue

In the Home page the footer should be at the button but it is not leaving a blank space. Assign me this issue label- Gssoc2024 hacktoctober 2024.

I will review the footer layout to identify any alignment, styling, or functionality problems. This includes ensuring all links work correctly, elements are properly aligned, and the overall design matches the website's aesthetic.
Please assign this task to me so I can fix the footer issues!

@KAmaL-senpai I can't see any major issues with the footer.


fix link to blogpost

fix link to blogpost
https://github.com/sorenbouma/keras-oneshot/blob/master/sorenbouma.github.io/blog/oneshot

Whoops! fixed, thanks for pointing this out


Oipa-2447

Fixes #2447
Fixed the API calls returning internal errors.
Filters were missing for the API calls.

:tada: This PR is included in version 5.5.0 :tada:
The release is available on GitHub release
Your semantic-release bot :package::rocket:


Fail to build w4a8_awq/int4_awq on Llama3-8B

System Info
ubuntu 20.04
tensorrt 10.0.1
tensorrt-cu12 10.0.1
tensorrt-cu12-bindings 10.0.1
tensorrt-cu12-libs 10.0.1
tensorrt-llm 0.11.0.dev2024052100
nvidia L40s
Who can help?
@Barry-Delaney @Tracin @byshiue
Information

[X] The official example scripts
[ ] My own modified scripts

Tasks

[X] An officially supported task in the examples folder (such as GLUE/SQuAD, ...)

[TensorRT-LLM][INFO] [MemUsageChange] TensorRT-managed allocation in IExecutionContext creation: CPU +0, GPU +0, now: CPU 0, GPU 5442 (MiB)
[TensorRT-LLM][INFO] Max KV cache pages per sequence: 94
[TensorRT-LLM][INFO] Max tokens in paged KV cache: 171008. Allocating<PHONE_NUMBER>6 bytes.
terminate called after throwing an instance of 'tensorrt_llm::common::TllmException'
  what():  [TensorRT-LLM][ERROR] Assertion failed: No valid weight only groupwise GEMM tactic(It is usually caused by the failure to execute all candidate configurations of the CUTLASS kernel, please pay attention to the warning information when building the engine.) (/target/cpp/tensorrt_llm/plugins/weightOnlyGroupwiseQuantMatmulPlugin/weightOnlyGroupwiseQuantMatmulPlugin.cpp:452)
1       0x7f4070d08073 /usr/local/lib/python3.10/dist-packages/tensorrt_llm/libs/libnvinfer_plugin_tensorrt_llm.so(+0x56073) [0x7f4070d08073]
2       0x7f4070d9f9da tensorrt_llm::plugins::WeightOnlyGroupwiseQuantMatmulPlugin::enqueue(nvinfer1::PluginTensorDesc const*, nvinfer1::PluginTensorDesc const*, void const* const*, void* const*, void*, CUstream_st*) + 1786
3       0x7f417278cbec /usr/local/lib/python3.10/dist-packages/tensorrt_libs/libnvinfer.so.10(+0x1060bec) [0x7f417278cbec]
4       0x7f4172742217 /usr/local/lib/python3.10/dist-packages/tensorrt_libs/libnvinfer.so.10(+0x1016217) [0x7f4172742217]
5       0x7f4172743b79 /usr/local/lib/python3.10/dist-packages/tensorrt_libs/libnvinfer.so.10(+0x1017b79) [0x7f4172743b79]
6       0x7f40a0fd7f34 tensorrt_llm::batch_manager::TrtGptModelInflightBatching::executeContext(int) + 52
7       0x7f40a0fd8496 tensorrt_llm::batch_manager::TrtGptModelInflightBatching::executeBatch(tensorrt_llm::batch_manager::ScheduledRequests const&) + 502
8       0x7f40a0fe5094 tensorrt_llm::batch_manager::TrtGptModelInflightBatching::forwardAsync(std::list<std::shared_ptr<tensorrt_llm::batch_manager::LlmRequest>, std::allocator<std::shared_ptr<tensorrt_llm::batch_manager::LlmRequest> > > const&) + 2164
9       0x7f40a1009214 tensorrt_llm::executor::Executor::Impl::forwardAsync(std::list<std::shared_ptr<tensorrt_llm::batch_manager::LlmRequest>, std::allocator<std::shared_ptr<tensorrt_llm::batch_manager::LlmRequest> > >&) + 100
10      0x7f40a100b49c tensorrt_llm::executor::Executor::Impl::executionLoop() + 380
11      0x7f42590b0253 /usr/lib/x86_64-linux-gnu/libstdc++.so.6(+0xdc253) [0x7f42590b0253]
12      0x7f43035ceb43 /usr/lib/x86_64-linux-gnu/libc.so.6(+0x94b43) [0x7f43035ceb43]
13      0x7f430365fbb4 clone + 68

when building the engine before,I got warnings like:
[TensorRT-LLM][WARNING] Have not found any valid GEMM config for shape (m=1, n=1536, k=4096). Will try to use default or fail at runtime
[TensorRT-LLM][WARNING] Have not found any valid GEMM config for shape (m=2, n=1536, k=4096). Will try to use default or fail at runtime
[TensorRT-LLM][WARNING] Have not found any valid GEMM config for shape (m=4, n=1536, k=4096). Will try to use default or fail at runtime
[TensorRT-LLM][WARNING] Have not found any valid GEMM config for shape (m=8, n=1536, k=4096). Will try to use default or fail at runtime
[TensorRT-LLM][WARNING] Have not found any valid GEMM config for shape (m=16, n=1536, k=4096). Will try to use default or fail at runtime
[TensorRT-LLM][WARNING] Have not found any valid GEMM config for shape (m=32, n=1536, k=4096). Will try to use default or fail at runtime
[TensorRT-LLM][WARNING] Have not found any valid GEMM config for shape (m=64, n=1536, k=4096). Will try to use default or fail at runtime
[TensorRT-LLM][WARNING] Have not found any valid GEMM config for shape (m=128, n=1536, k=4096). Will try to use default or fail at runtime
[TensorRT-LLM][WARNING] Have not found any valid GEMM config for shape (m=256, n=1536, k=4096). Will try to use default or fail at runtime
[TensorRT-LLM][WARNING] Have not found any valid GEMM config for shape (m=512, n=1536, k=4096). Will try to use default or fail at runtime
[TensorRT-LLM][WARNING] Have not found any valid GEMM config for shape (m=1024, n=1536, k=4096). Will try to use default or fail at runtime
[TensorRT-LLM][WARNING] Have not found any valid GEMM config for shape (m=2048, n=1536, k=4096). Will try to use default or fail at runtime
[TensorRT-LLM][WARNING] Have not found any valid GEMM config for shape (m=4096, n=1536, k=4096). Will try to use default or fail at runtime
[TensorRT-LLM][WARNING] Have not found any valid GEMM config for shape (m=8192, n=1536, k=4096). Will try to use default or fail at runtime
[TensorRT-LLM][WARNING] Have not found any valid GEMM config for shape (m=1, n=1024, k=4096). Will try to use default or fail at runtime
[TensorRT-LLM][WARNING] Have not found any valid GEMM config for shape (m=2, n=1024, k=4096). Will try to use default or fail at runtime
[TensorRT-LLM][WARNING] Have not found any valid GEMM config for shape (m=4, n=1024, k=4096). Will try to use default or fail at runtime
[TensorRT-LLM][WARNING] Have not found any valid GEMM config for shape (m=8, n=1024, k=4096). Will try to use default or fail at runtime
[TensorRT-LLM][WARNING] Have not found any valid GEMM config for shape (m=16, n=1024, k=4096). Will try to use default or fail at runtime
[TensorRT-LLM][WARNING] Have not found any valid GEMM config for shape (m=32, n=1024, k=4096). Will try to use default or fail at runtime
[TensorRT-LLM][WARNING] Have not found any valid GEMM config for shape (m=64, n=1024, k=4096). Will try to use default or fail at runtime

additional notes
When I try the same way with int4_awq,I got errors when trying trtllm-build :
void cutlass::gemm::kernel::GemmFpAIntB<Mma_, Epilogue_, ThreadblockSwizzle_, KernelArch, SplitKSerial>::run_kernel(const cutlass::gemm::kernel::GemmFpAIntB<Mma_, Epilogue_, ThreadblockSwizzle_, KernelArch, SplitKSerial>::Params &, cutlass::gemm::kernel::GemmFpAIntB<Mma_, Epilogue_, ThreadblockSwizzle_, KernelArch, SplitKSerial>::SharedStorage &) [with CompilationArch = cutlass::arch::Sm80; Mma_ = cutlass::gemm::threadblock::DqMmaMultistage<cutlass::gemm::GemmShape<16, 128, 64>,
cutlass::transform::threadblock::PredicatedTileAccessIterator<cutlass::MatrixShape<16, 64>, cutlass::half_t, cutlass::layout::RowMajor, 1, cutlass::transform::PitchLinearWarpRakedThreadMap<cutlass::PitchLinearShape<64, 16>, 128, cutlass::PitchLinearShape<8, 4>, 8>, cutlass::Array<cutlass::half_t, 8, false>, false, cutlass::layout::NoPermute>, cutlass::transform::threadblock::RegularTileAccessIterator<cutlass::MatrixShape<16, 64>, cutlass::half_t, cutlass::layout::RowMajorTensorOpMultiplicandCrosswise<16, 64>, 0, cutlass::transform::PitchLinearWarpRakedThreadMap<cutlass::PitchLinearShape<64, 16>, 128, cutlass::PitchLinearShape<8, 4>, 8>, 16>, cutlass::arch::CacheOperation::Global, cutlass::transform::threadblock::PredicatedTileAccessIterator<cutlass::MatrixShape<256, 32>, cutlass::integer_subbyte<4, false>, cutlass::layout::ColumnMajor, 0, cutlass::transform::PitchLinearWarpRakedThreadMap<cutlass::PitchLinearShape<256, 32>, 128, cutlass::PitchLinearShape<8, 4>, 32>, cutlass::Array<cutlass::integer_subbyte<4, false>, 32, false>, false, cutlass::layout::NoPermute>, cutlass::transform::threadblock::RegularTileAccessIterator<cutlass::MatrixShape<64, 128>, cutlass::integer_subbyte<4, false>, cutlass::layout::ColumnMajorTensorOpMultiplicandCrosswise<4, 64>, 1, cutlass::transform::PitchLinearWarpRakedThreadMap<cutlass::PitchLinearShape<64, 128>, 128, cutlass::PitchLinearShape<2, 16>, 32>, 16>, cutlass::arch::CacheOperation::Global, cutlass::transform::threadblock::FineGrainedScaleZeroIterator<cutlass::MatrixShape<1, 128>, cutlass::half_t, cutlass::layout::RowMajor, 0, 8>, cutlass::transform::threadblock::FineGrainedScaleZeroIterator<cutlass::MatrixShape<1, 128>, cutlass::half_t, cutlass::layout::RowMajor, 0, 8>, float, cutlass::layout::RowMajor,
cutlass::gemm::threadblock::MmaPolicy<cutlass::gemm::warp::MmaTensorOpComputeBWithF16<cutlass::gemm::GemmShape<16, 32, 64>, cutlass::half_t, cutlass::layout::RowMajorTensorOpMultiplicandCrosswise<16, 64>, cutlass::integer_subbyte<4, false>, cutlass::layout::ColumnMajorTensorOpMultiplicandCrosswise<4, 64>, float, cutlass::layout::RowMajor, cutlass::gemm::warp::MmaTensorOpPolicy<cutlass::arch::Mma<cutlass::gemm::GemmShape<16, 8, 16>, 32, cutlass::half_t, cutlass::layout::RowMajor, cutlass::half_t, cutlass::layout::ColumnMajor, float, cutlass::layout::RowMajor, cutlass::arch::OpMultiplyAdd>, cutlass::MatrixShape<1, 1>>, cutlass::gemm::GemmShape<16, 8, 32>, 1, false, __nv_bool>, cutlass::MatrixShape<0, 0>, cutlass::MatrixShape<0, 0>, 1>, 3, cutlass::FastInterleavedAndBiasedNumericArrayConverter<cutlass::half_t, cutlass::integer_subbyte<4, false>, 32>, cutlass::WeightOnlyQuantOp::FINEGRAINED_SCALE_ONLY, cutlass::gemm::SharedMemoryClearOption::kNone, void>; Epilogue_ = cutlass::epilogue::threadblock::Epilogue<cutlass::gemm::GemmShape<16, 128, 64>, cutlass::gemm::warp::MmaTensorOpComputeBWithF16<cutlass::gemm::GemmShape<16, 32, 64>, cutlass::half_t, cutlass::layout::RowMajorTensorOpMultiplicandCrosswise<16, 64>, cutlass::integer_subbyte<4, false>, cutlass::layout::ColumnMajorTensorOpMultiplicandCrosswise<4, 64>, float, cutlass::layout::RowMajor, cutlass::gemm::warp::MmaTensorOpPolicy<cutlass::arch::Mma<cutlass::gemm::GemmShape<16, 8, 16>, 32, cutlass::half_t, cutlass::layout::RowMajor, cutlass::half_t, cutlass::layout::ColumnMajor, float, cutlass::layout::RowMajor, cutlass::arch::OpMultiplyAdd>, cutlass::MatrixShape<1, 1>>, cutlass::gemm::GemmShape<16, 8, 32>, 1, false, __nv_bool>, 1, cutlass::epilogue::threadblock::PredicatedTileIterator<cutlass::epilogue::threadblock::OutputTileOptimalThreadMap<cutlass::epilogue::threadblock::OutputTileShape<128, 8, 1, 1, 1>, cutlass::epilogue::threadblock::OutputTileShape<1, 2, 1, 1, 2>, 128, 8, 16>, cutlass::half_t, false, cutlass::layout::NoPermute, false>, cutlass::epilogue::warp::FragmentIteratorTensorOp<cutlass::gemm::GemmShape<16, 32, 64>, cutlass::gemm::GemmShape<16, 8, 16>, float, cutlass::Array<float, 4, true>, cutlass::layout::RowMajor>, cutlass::epilogue::warp::TileIteratorTensorOpMixed<cutlass::gemm::GemmShape<16, 32, 64>, cutlass::gemm::GemmShape<16, 8, 16>, float, 32, 16, 8, 8, false>, cutlass::epilogue::threadblock::SharedLoadIteratorMixed<cutlass::epilogue::threadblock::OutputTileOptimalThreadMap<cutlass::epilogue::threadblock::OutputTileShape<128, 8, 1, 1, 1>, cutlass::epilogue::threadblock::OutputTileShape<1, 2, 1, 1, 2>, 128, 8, 16>::CompactedThreadMap, float, 32, 16, 8, 8, false>, cutlass::epilogue::thread::LinearCombination<cutlass::half_t, 8, float, float, cutlass::epilogue::thread::ScaleType::NoBetaScaling, cutlass::FloatRoundStyle::round_to_nearest, cutlass::half_t>, cutlass::MatrixShape<0, 8>, 2, 1>; ThreadblockSwizzle_ = cutlass::gemm::threadblock::GemmIdentityThreadblockSwizzle<1>; KernelArch = cutlass::arch::Sm89; __nv_bool SplitKSerial = true] not implemented

and also:
[TensorRT-LLM][WARNING] Have not found any valid GEMM config for shape (m=1, n=1536, k=4096). Will try to use default or fail at runtime
[TensorRT-LLM][WARNING] Have not found any valid GEMM config for shape (m=2, n=1536, k=4096). Will try to use default or fail at runtime
[TensorRT-LLM][WARNING] Have not found any valid GEMM config for shape (m=4, n=1536, k=4096). Will try to use default or fail at runtime
[TensorRT-LLM][WARNING] Have not found any valid GEMM config for shape (m=8, n=1536, k=4096). Will try to use default or fail at runtime
[TensorRT-LLM][WARNING] Have not found any valid GEMM config for shape (m=16, n=1536, k=4096). Will try to use default or fail at runtime
[TensorRT-LLM][WARNING] Have not found any valid GEMM config for shape (m=32, n=1536, k=4096). Will try to use default or fail at runtime
[TensorRT-LLM][WARNING] Have not found any valid GEMM config for shape (m=64, n=1536, k=4096). Will try to use default or fail at runtime
[TensorRT-LLM][WARNING] Have not found any valid GEMM config for shape (m=128, n=1536, k=4096). Will try to use default or fail at runtime
[TensorRT-LLM][WARNING] Have not found any valid GEMM config for shape (m=256, n=1536, k=4096). Will try to use default or fail at runtime
[TensorRT-LLM][WARNING] Have not found any valid GEMM config for shape (m=512, n=1536, k=4096). Will try to use default or fail at runtime
[TensorRT-LLM][WARNING] Have not found any valid GEMM config for shape (m=1024, n=1536, k=4096). Will try to use default or fail at runtime
[TensorRT-LLM][WARNING] Have not found any valid GEMM config for shape (m=2048, n=1536, k=4096). Will try to use default or fail at runtime
[TensorRT-LLM][WARNING] Have not found any valid GEMM config for shape (m=4096, n=1536, k=4096). Will try to use default or fail at runtime
[TensorRT-LLM][WARNING] Have not found any valid GEMM config for shape (m=8192, n=1536, k=4096). Will try to use default or fail at runtime
terminate called after throwing an instance of 'tensorrt_llm::common::TllmException'
  what():  [TensorRT-LLM][ERROR] Assertion failed: Can't free tmp workspace for GEMM tactics profiling. (/target/cpp/tensorrt_llm/plugins/common/gemmPluginProfiler.cpp:190)
1       0x7f72fcd08073 /usr/local/lib/python3.10/dist-packages/tensorrt_llm/libs/libnvinfer_plugin_tensorrt_llm.so(+0x56073) [0x7f72fcd08073]
2       0x7f72fcdc38ea tensorrt_llm::plugins::GemmPluginProfiler<tensorrt_llm::cutlass_extensions::CutlassGemmConfig, std::shared_ptr<tensorrt_llm::kernels::cutlass_kernels::CutlassFpAIntBGemmRunnerInterface>, tensorrt_llm::plugins::GemmIdCore, tensorrt_llm::plugins::GemmIdCoreHash>::freeTmpData() + 106
3       0x7f72fcdcd6b9 tensorrt_llm::plugins::GemmPluginProfiler<tensorrt_llm::cutlass_extensions::CutlassGemmConfig, std::shared_ptr<tensorrt_llm::kernels::cutlass_kernels::CutlassFpAIntBGemmRunnerInterface>, tensorrt_llm::plugins::GemmIdCore, tensorrt_llm::plugins::GemmIdCoreHash>::profileTactics(std::shared_ptr<tensorrt_llm::kernels::cutlass_kernels::CutlassFpAIntBGemmRunnerInterface> const&, nvinfer1::DataType const&, tensorrt_llm::plugins::GemmDims const&, tensorrt_llm::plugins::GemmIdCore const&) + 1113
4       0x7f72fcd9ed9d tensorrt_llm::plugins::WeightOnlyGroupwiseQuantMatmulPlugin::initialize() + 13

Could you try pip install  tensorrt_llm==0.11.0.dev2024061100 first?
Or you can try pip install  tensorrt_llm==0.11.0.dev2024061800 tomorrow.
Thanks

It works well using the latest version
python examples/quantization/quantize.py --model_dir /home/scratch.trt_llm_data/llm-models/llama-models-v3/llama-v3-8b-instruct-hf/ --dtype float16 --qformat int4_awq --awq_block_size 128 --output_dir ./tmp/llama3-8b-awq --calib_size 32; trtllm-build    --checkpoint_dir ./tmp/llama3-8b-awq --output_dir ./tmp/llama3-8b-awq-engine --gemm_plugin float16 --gpt_attention_plugin float16 --context_fmha enable --remove_input_padding enable --paged_kv_cache enable --max_input_len 3000 --max_output_len 3000; python examples/run.py --max_output_len=500 --tokenizer_dir=/home/scratch.trt_llm_data/llm-models/llama-models-v3/llama-v3-8b-instruct-hf/  --engine_dir=./tmp/llama3-8b-awq-engine

[TensorRT-LLM] TensorRT-LLM version: 0.11.0.dev2024061800
Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
[06/18/2024-10:05:23] [TRT-LLM] [I] Load engine takes: 34.90785765647888 sec
Input [Text 0]: "<|begin_of_text|>Born in north-east France, Soyer trained as a"
Output [Text 0 Beam 0]: " painter and sculptor before turning to photography. He began his career in the 1920s, working for various magazines and newspapers, and quickly gained a reputation for his innovative and expressive style. Soyer's photographs often featured everyday life, landscapes, and still-life compositions, and were characterized by their use of light, texture, and composition. He was also known for his portraits of famous people, including artists, writers, and musicians. Soyer's work was widely exhibited and published, and he is considered one of the most important French photographers of the 20th century. (Source: Getty Museum) [more]
...

Could you try pip install tensorrt_llm==0.11.0.dev2024061100 first? Or you can try pip install tensorrt_llm==0.11.0.dev2024061800 tomorrow. Thanks

sucessfully installed new version,

but got errors when running:
Traceback (most recent call last):
  File "/TensorRT-LLM/examples/run.py", line 23, in <module>
    from utils import (DEFAULT_HF_MODEL_DIRS, DEFAULT_PROMPT_TEMPLATES,
  File "/TensorRT-LLM/examples/utils.py", line 23, in <module>
    from tensorrt_llm.builder import get_engine_version
  File "/usr/local/lib/python3.10/dist-packages/tensorrt_llm/__init__.py", line 32, in <module>
    import tensorrt_llm.functional as functional
  File "/usr/local/lib/python3.10/dist-packages/tensorrt_llm/functional.py", line 25, in <module>
    import tensorrt as trt
  File "/usr/local/lib/python3.10/dist-packages/tensorrt/__init__.py", line 18, in <module>
    from tensorrt_bindings import *
ModuleNotFoundError: No module named 'tensorrt_bindings'

Could you try pip install tensorrt_llm==0.11.0.dev2024061100 first? Or you can try pip install tensorrt_llm==0.11.0.dev2024061800 tomorrow. Thanks

sucessfully installed new version,
but got errors when running:
Traceback (most recent call last):
  File "/TensorRT-LLM/examples/run.py", line 23, in <module>
    from utils import (DEFAULT_HF_MODEL_DIRS, DEFAULT_PROMPT_TEMPLATES,
  File "/TensorRT-LLM/examples/utils.py", line 23, in <module>
    from tensorrt_llm.builder import get_engine_version
  File "/usr/local/lib/python3.10/dist-packages/tensorrt_llm/__init__.py", line 32, in <module>
    import tensorrt_llm.functional as functional
  File "/usr/local/lib/python3.10/dist-packages/tensorrt_llm/functional.py", line 25, in <module>
    import tensorrt as trt
  File "/usr/local/lib/python3.10/dist-packages/tensorrt/__init__.py", line 18, in <module>
    from tensorrt_bindings import *
ModuleNotFoundError: No module named 'tensorrt_bindings'

Please try the container: nvcr.io/nvidia/tritonserver:24.05-trtllm-python-py3
Or build the container using make -C docker release_build

Output [Text 0 Beam 0]: " painter and sculptor before turning to photography. He began his career in the 1920s, working for various magazines and newspapers, and quickly gained a reputation for his innovative and expressive style. Soyer's photographs often featured everyday life, landscapes, and still-life compositions, and were characterized by their use of light, texture, and composition. He was also known for his portraits of famous people, including artists, writers, and musicians. Soyer's work was widely exhibited and published, and he is considered one of the most important French photographers of the 20th century. (Source: Getty Museum) [more]

thanks, it works for me now.
but I don't know why the output is a little weird:
 python3 run.py --max_output_len=500 \
               --tokenizer_dir=/target/model/llama_13B \
               --engine_dir=/target/model/trt_engines/w4a8_AWQ/1-gpu/

[TensorRT-LLM] TensorRT-LLM version: 0.11.0.dev2024061100
Input [Text 0]: "<s> Born in north-east France, Soyer trained as a"
Output [Text 0 Beam 0]:  Tarther<s> Born in north-east France, Soyer trained as  Tarther<s> Born in north-east France, Soyer trained as  Tarther<s> Born in north-east France, Soyer trained as  Tarther<s> Born in north-east France, Soyer trained as  Tarther<s> Born in north-east France, Soyer trained as  Tarther<s> Born in north-east France, Soyer trained as  Tarther<s> Born in north-east France, Soyer trained as  Tarther<s> Born in north-east France, Soyer trained as  Tarther<s> Born in north-east France, Soyer trained as  Tarther<s> Born in north-east France, Soyer trained as  Tarther<s> Born in north-east France, Soyer trained as  Tarther<s> Born in north-east France, Soyer trained as  Tarther<s> Born in north-east France, Soyer trained as  Tarther<s> Born in north-east France, Soyer trained as  Tarther<s> Born in north-east France, Soyer trained as Tarther<s> Born in north-east France, Soyer trained as  Tarther<s> Born in north-east France, Soyer trained as  Tarther<s> Born in north-east France, Soyer trained as  Tarther<s> Born in north-east France, Soyer trained as  Tarther<s> Born in north-east France, Soyer trained as  Tarther<s> Born in north-east France, Soyer trained as  Tarther<s> Born in north-east France, Soyer trained as  Tarther<s> Born in north-east France, Soyer trained as  Tarther<s> Born in north-east France, Soyer trained as  Tarther<s> Born in north-east France, Soyer trained as  Tarther<s> Born in north-east France, Soyer trained as  Tarther<s> Born in north-east France, Soyer trained as  Tarther<s> Born in north-east France, Soyer trained as  Tarther<s> Born in north-east France, Soyer trained as  Tarther<s> Born in"

It works well using the latest version
| NVIDIA-SMI 550.54.14              Driver Version: 550.54.14      CUDA Version: 12.4     |
|-----------------------------------------+------------------------+----------------------+
| GPU  Name                 Persistence-M | Bus-Id          Disp.A | Volatile Uncorr. ECC |
| Fan  Temp   Perf          Pwr:Usage/Cap |           Memory-Usage | GPU-Util  Compute M. |
|                                         |                        |               MIG M. |
|=========================================+========================+======================|
|   0  NVIDIA L40S                    On  |   00000000:01:00.0 Off |                    0 |
| N/A   31C    P8             32W /  350W |       0MiB /  46068MiB |      0%      Default |
|                                         |                        |                  N/A

python examples/quantization/quantize.py --model_dir /home/scratch.trt_llm_data/llm-models/llama-models-v3/llama-v3-8b-instruct-hf/ --dtype float16 --qformat int4_awq --awq_block_size 128 --output_dir ./tmp/llama3-8b-awq --calib_size 32; trtllm-build    --checkpoint_dir ./tmp/llama3-8b-awq --output_dir ./tmp/llama3-8b-awq-engine --gemm_plugin float16 --gpt_attention_plugin float16 --context_fmha enable --remove_input_padding enable --paged_kv_cache enable --max_input_len 3000 --max_output_len 3000; python examples/run.py --max_output_len=500 --tokenizer_dir=/home/scratch.trt_llm_data/llm-models/llama-models-v3/llama-v3-8b-instruct-hf/  --engine_dir=./tmp/llama3-8b-awq-engine

[TensorRT-LLM] TensorRT-LLM version: 0.11.0.dev2024061800
Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
[06/18/2024-10:05:23] [TRT-LLM] [I] Load engine takes: 34.90785765647888 sec
Input [Text 0]: "<|begin_of_text|>Born in north-east France, Soyer trained as a"
Output [Text 0 Beam 0]: " painter and sculptor before turning to photography. He began his career in the 1920s, working for various magazines and newspapers, and quickly gained a reputation for his innovative and expressive style. Soyer's photographs often featured everyday life, landscapes, and still-life compositions, and were characterized by their use of light, texture, and composition. He was also known for his portraits of famous people, including artists, writers, and musicians. Soyer's work was widely exhibited and published, and he is considered one of the most important French photographers of the 20th century. (Source: Getty Museum) [more]
...

hi@hijkzzz, can you run the benchmark successfully?
When I run:
./benchmarks/gptSessionBenchmark \
    --engine_dir "/target/model/trt_engines/w4a8_AWQ/1-gpu/" \
    --batch_size "1" \
    --input_output_len "60,20"

I got errors:
[TensorRT-LLM][ERROR] tensorrt_llm::common::TllmException: [TensorRT-LLM][ERROR] Assertion failed: key_size <= remaining_buffer_size (/target/cpp/tensorrt_llm/kernels/decoderMaskedMultiheadAttention/decoderXQAImplJIT/cubinObjRegistry.h:49)
1       0x5584e5f4b373 tensorrt_llm::common::throwRuntimeError(char const*, int, std::string const&) + 102
2       0x7f0e64a684b7 tensorrt_llm::kernels::jit::CubinObjRegistryTemplate<tensorrt_llm::kernels::XQAKernelFullHashKey, tensorrt_llm::kernels::XQAKernelFullHasher>::CubinObjRegistryTemplate(void const*, unsigned long) + 1639
3       0x7f0e64a674f2 tensorrt_llm::kernels::DecoderXQARunner::Resource::Resource(void const*, unsigned long) + 50
4       0x7f0e9ad778f9 tensorrt_llm::plugins::GPTAttentionPluginCommon::GPTAttentionPluginCommon(void const*, unsigned long) + 873
5       0x7f0e9ad960d3 tensorrt_llm::plugins::GPTAttentionPlugin::GPTAttentionPlugin(void const*, unsigned long) + 19
6       0x7f0e9ad96152 tensorrt_llm::plugins::GPTAttentionPluginCreator::deserializePlugin(char const*, void const*, unsigned long) + 50
7       0x7f0e1493f102 /usr/local/tensorrt/lib/libnvinfer.so.10(+0x1066102) [0x7f0e1493f102]
8       0x7f0e1493a1de /usr/local/tensorrt/lib/libnvinfer.so.10(+0x10611de) [0x7f0e1493a1de]
9       0x7f0e148b5177 /usr/local/tensorrt/lib/libnvinfer.so.10(+0xfdc177) [0x7f0e148b5177]
10      0x7f0e148b33fe /usr/local/tensorrt/lib/libnvinfer.so.10(+0xfda3fe) [0x7f0e148b33fe]
11      0x7f0e148cbf27 /usr/local/tensorrt/lib/libnvinfer.so.10(+0xff2f27) [0x7f0e148cbf27]
12      0x7f0e148cee7d /usr/local/tensorrt/lib/libnvinfer.so.10(+0xff5e7d) [0x7f0e148cee7d]
13      0x7f0e148cf3b4 /usr/local/tensorrt/lib/libnvinfer.so.10(+0xff63b4) [0x7f0e148cf3b4]
14      0x7f0e148fe64f /usr/local/tensorrt/lib/libnvinfer.so.10(+0x102564f) [0x7f0e148fe64f]
15      0x7f0e148ff3f5 /usr/local/tensorrt/lib/libnvinfer.so.10(+0x10263f5) [0x7f0e148ff3f5]
16      0x7f0e148ff489 /usr/local/tensorrt/lib/libnvinfer.so.10(+0x1026489) [0x7f0e148ff489]
17      0x7f0e666a8a68 tensorrt_llm::runtime::TllmRuntime::TllmRuntime(void const*, unsigned long, float, nvinfer1::ILogger&) + 504
18      0x7f0e66653db6 tensorrt_llm::runtime::GptSession::GptSession(tensorrt_llm::runtime::GptSession::Config const&, tensorrt_llm::runtime::ModelConfig const&, tensorrt_llm::runtime::WorldConfig const&, void const*, unsigned long, std::shared_ptr<nvinfer1::ILogger>) + 1126
19      0x5584e5f4fce0 ./benchmarks/gptSessionBenchmark(+0x1dce0) [0x5584e5f4fce0]
20      0x7f0e5f5edd90 /usr/lib/x86_64-linux-gnu/libc.so.6(+0x29d90) [0x7f0e5f5edd90]
21      0x7f0e5f5ede40 __libc_start_main + 128
22      0x5584e5f533c5 ./benchmarks/gptSessionBenchmark(+0x213c5) [0x5584e5f533c5]

@Hongbosherlock
Base your crash log, I guess your're tried to run benchmark with w4a8_awq mode, right?
If so, I managed to run benchmark on L40
./cpp/build/benchmarks/gptSessionBenchmark  --engine_dir examples/quantization/engine_outputs --batch_size "1" --input_output_len "60,20"

Benchmarking done. Iteration: 10, duration: 1.56 sec.
Latencies: [155.62, 155.81, 155.41, 155.40, 156.54, 155.34, 155.42, 155.83, 156.30, 155.64]
[BENCHMARK] batch_size 1 input_length 60 output_length 20 latency(ms) 155.73 tokensPerSec 128.43 generation_time(ms) 145.62 generationTokensPerSec 137.35 gpu_peak_mem(gb) 43.57

Even for original int4_awq, I also could run the benchmark with below output
./cpp/build/benchmarks/gptSessionBenchmark  --engine_dir examples/quantization/engine_outputs --batch_size "1" --input_output_len "60,20"

Benchmarking done. Iteration: 10, duration: 1.57 sec.
Latencies: [156.85, 156.74, 156.75, 156.88, 156.70, 156.73, 156.82, 156.93, 156.74, 156.89]
[BENCHMARK] batch_size 1 input_length 60 output_length 20 latency(ms) 156.80 tokensPerSec 127.55 generation_time(ms) 144.26 generationTokensPerSec 138.64 gpu_peak_mem(gb) 43.61

hi, is there a pre-build image with the latest TensorRT-LLM version: 0.11.0 ?

hi, what nvidia docker image uses latest TensorRT-LLM version: 0.11.0.dev ?

Hi @Hongbosherlock do u still have further issue or question now? If not, we'll close it soon.


🐛 add v1beta1 webhook back

Summary
Related issue(s)
The related issue: https://issues.redhat.com/browse/ACM-10279

Closing this one due to duplicate with https://github.com/stolostron/ocm/pull/37


fix the issue while performing recordUpdate

Signed-off-by: Amar Tumballi<EMAIL_ADDRESS>

Merging as this fixes a bug in recordUpdate, which is important for the API.


Check how client timeouts behave and provide tests for load balancers' retry strategy

We use http4s client as a client that makes requests to participants. http4s config contains properties responsible for request timeouts (and I believe IDLE timeouts).
In the scope of the task:

make sure the timeouts work
document them and provide defaults inside Panda config
add tests for load balancer/balancers (probable integration ones) - the test should route the request to another participant once the timeout on the first one is met.

Possible difficulty: Right now load balancer makes a request to another participant if the request call failed. If http4s does not report timeout as a failure, we will need to catch it and handle it in a different way.

Timeouts work as expected

Panda recognizes successfully timeout as a failure and in case of that routes the request to another available participant.


willReceiveDrop for closed folders?

I'm trying to make it possible to drop on closed folders. I don't need to open them while hovering, just being able to detect which closed folder is being hovered over.
Basically exactly what node.willReceiveDrop does today, except for closed folders.
Are there workarounds to extend that behaviour for closed folders as well?

I figured it out, basically it needed children: [] to make it recognise as a droppable folder.


Remove (or finish) incomplete pages

This page is unfinished. Considering these pages are accessible externally, I think we should keep unfinished pages as branches until they can be approved via PR, merged and then built.

Because currently the spec is quite short, would it be worth having the landing page as is, and a single other page called something like 'Folder and file naming specification'? It would be nice to have a single page that we could send around.

Sounds good to me. We can extend the spec with details on separate pages at a later date.


Refs #28196 -- Removed mentions of bytestrings for EmailMessage

EmailMultiAlternatives has the same comment that I guess should be removed.  Just to double check, the message didn't get altered incorrectly in https://github.com/django/django/commit/d1bab24e0144d14513a1411503c95ececb425188#diff-e1d718400701d0b0adfafbdcf8163aa3 did it? Has the message been incorrect since its introduction in https://github.com/django/django/commit/953badbea5a04159adbfa970f5805c0232b6a401#diff-0aaf5087d6ec9f67d610ac1052a6430fR204?

Good catch! I don't think this was incorrect, but what made sense when strings were often a mix of bytestrings and unicode strings is no longer accurate with Python 3 IMO (master-only, of course).

Okay, maybe you can include that explanation in the commit message or in the ticket. I'm not sure what "The SafeMIMEText class will handle any necessary encoding conversions" meant. Possibly some force_text() calls removed in 301de774c21d055e9e5a7073e5bffdb52bc71079?

Tentative basic explanation in the commit message.


How do i use this to cap the game to 30 fps for lower end pcs?

Tried setting frame rate lock to 30 but it doesn't seem to be working

I'd rather set the limit in your drivers (e.g. in relatively recent nv drivers: ultra latency + fps slider) or via rtss (though no longer needed these days if you just want to set fps), then set the limit in the game to some high/irrelevant value.

You should either use RTSS or Scanline Sync x/2.
If you get 30FPS+ consistenly, i recommend using Scanline Sync.

Does this program unlock the frame rate even if you don't set a value and have the "lock" option unchecked?

Does this program unlock the frame rate even if you don't set a value and have the "lock" option unchecked?

No it does not. If you don't check the first checkbox then the game will use FromSofts hardcoded 60fps (and in fullscreen 60Hz) lock.


Get request without options request

Hi, sorry for the newbie question but I'm having a hard time getting this library to make a get request without first sending an options request.
I'm simply trying to fetch my treehouse points from this endpoint:
https://teamtreehouse.com/jonathanwalz.json
Using the fetch api, it works perfectly:
fetch('https://teamtreehouse.com/jonathanwalz.json')
Is there anyway to make a get without options? Thanks in advance.

Ended up making a node server as a proxy. I'm still curious to know if there's a way to disable the options request though

Hmmm, it' weird cause this lib uses fetch API, the difference is adding header by default:
          headers: { ...{ 'Content-Type': 'application/json; charset=utf-8' }, ...headers },

If you use create-react-app you can add proxy parameter to package.json instead of creating proxy on your own: https://facebook.github.io/create-react-app/docs/proxying-api-requests-in-development


[WIP] Optimize jumps that keep zero elements on the stack

This is a result of an observation that we could speed-up execution of branch and return instructions that keep no values upon jump.

Closed in favor of https://github.com/paritytech/wasmi/pull/493.


Doxygen: don't traverse symbolic links

We don't use symbolic links as part of our build process, so tell
Doxygen not to traverse them. In particular, if I have a symbolic link
to a directory outside the build tree, I don't want Doxygen to follow
it.
Backports: should be applied identically to all versions.

retest

CI issues:

timing test failed twice but is known to be flaky (https://github.com/ARMmbed/mbedtls/issues/1517)
config.pl baremetal -> Command 'baremetal' not recognised. This will be fixed by merging, since this command has been added in development and 2.7 in the meantime.
None of these issues is a blocker for merging.

@mpg / @gilles-peskine-arm - Why doesn't this have a mbedtls-2.7 backport? Removing the 'ready for merge' label, pending the answer.

@sbutcher-arm This PR was made before 2.7. It applies to both 2.7 and development.


BIGTOP-3529. Drop bigtop-tomcat.

https://issues.apache.org/jira/browse/BIGTOP-3529

I left some configuration files of Oozie under bigtop-package as is. It should be updated in BIGTOP-3531.

+1, I confirmed that bigtop-tomcat was not in the result of ./gradlew all-components and ./gradlew oozie-pkg -Dbuildwithdeps=true succeeded on CentOS 7, 8 and Ubuntu 20.04 with this PR. Thanks @iwasakims!


repo2docker depedency master to main

It seems like https://github.com/jupyterhub/repo2docker also started using main as their default branch, therefore pip install -U git+https://github.com/manics/repo2docker-podman.git@main is breaking.

Thanks for fixing this! The GitHub workflow tests seem to be hanging for reasons unrelated to this PR, I'll investigate (you don't need to do anything 😀).

Thanks once again, tests are still broken for mysterious reasons but since this prevents installation I'll merge.


Issues with bluetooth adapter 4.2 and 5.0

I am starting to develop an electron bluetooth application for windows. For the same I am trying to install the noble package as per the guidelines given in the github repo.
As per the requirements, a bluetooth 4.0 adapter is required. The internal drivers in my systems are bluetooth 4.2 and bluetooth 5.0 adapters. I get the error 'No compatible USB Bluetooth 4.0 device found!' in both the cases.
Doesn't ble 4.2 and 5.0 adapters support the development of bluetooth application for windows using noble and electron? Or should I purchase a 4.0 bluetooth adapter? If so what are the recommendations exactly? And also, if its the requirement, is it just development requirement or should users using this application also require bluetooth 4.0 adapter to run the application?

Please test latest abandonware's release and report if bug is also present in fork:
https://libraries.io/npm/@abandonware%2Fnoble/usage


Add auto-correct to misspellings, progressed version to 2.0, change UID

Add auto-correct support for mistype words using spell.py sourced from https://norvig.com/spell-correct.html. The file is modified a bit for the current use case.
Update dictionary_compact.json to encompass a list of all words (key: 'cb2b20da-9168-4e8e-8e8f-9b54e7d42214') used to create counter of words in spell.py
Version progressed from v1.0.0 to v2.0.0
Change some variable names in main.py

The code is working fine. Usage Screenshot: (auto-corrected) signifies that word is auto corrected.


Use formatted_backtrace in JSON formatter

Currently the JSON formatter does not respect the --backtrace flag (it will always print the full backtrace).
I believe this should behave the same as other formatters and respect the config.

Just glanced over. I don't quite get the spec change. Is it indicative that --backtrace will be respected now?

Just glanced over. I don't quite get the spec change. Is it indicative that --backtrace will be respected now?

This PR changes the order of the "examples" key in the output. Failures will always be last now since the code is iterating through them after the non-failures.
The spec relied implicitly on the order, so I changed it to instead look for the first failure, and re-arranged the expected output.
If for some reason ordering is really important here, I can try to find a way to make the same change while preserving it.

Ordering is not that important as "respect the --backtrace flag" is. A spec that would assure in this is.

I think these failures are unrelated to my changes, as I can reproduce them in a new branch cut directly from main

Thank you, I fixed an issue with the build elsewhere to get this green.

Released in 3.13.0, apologies it has taken so long.


Added function to Auto Capitalize Lyrics & Added option to Add Unsynced Lyrics to Song

What I changed

Extended support of --only_lyrics option of the cli tool by adding a boolean parameter in 'lyricy / __init__.py / class Lyrics / def add_to_track()'.

Added a function capitalized_lyrics() in 'lyricy / cli.py', which auto capitalizes the fetched lyrics and a small improvement to strip the lyrics off of trailing and leading white-spaces.

This is my first-time-contributing. Feedback would be appreciated about whether the modified code is enough for contribution or should i need to learn more.

Sorry for late review, I tested capitalized_lyrics alone locally, this function not working. Kindly check on this.

Hlo there
Thanks for the feedback! I'll get back to it as soon as I can and look into
it.
Regards
Rajat
On Tue, Aug 8, 2023, 10:33 PM YOGESHWARAN R @.***>
wrote:

Sorry for late review, I tested capitalized_lyrics alone locally, this
function not working. Kindly check on this.
—
Reply to this email directly, view it on GitHub
https://github.com/yogeshwaran01/lyricy/pull/10#issuecomment-1669991696,
or unsubscribe
https://github.com/notifications/unsubscribe-auth/A4HTUFAANVSISC2RDONI63DXUJWO5ANCNFSM6AAAAAA2S2OMCU
.
You are receiving this because you authored the thread.Message ID:
@.***>

Thank you for your contributions 👍


Kotlin support

Is there any kotlin support? I'd like to use kotlin plugins in my projects.

There is no official Kotlin support, meaning that I never personally tried it with Kotlin.
It should work mostly fine, but adding a more idiomatic Kotlin interface on top of the Java core would indeed be really nice.


refactor(parser-jvm): move synthetic method filtering to BackbonePlugin

This PR is a small fix for #1217.
I moved filtering of synthetics methods from the model to BackbonePlugin because the design idea of the model is to provide the fullest info about the reflection item. Filtering synthetic methods breaks this idea, so I believe that the best place for this filtering is in the BackbonePlugin.

I disagree: from the parser/generator point of view, synthetic methods should be ignored as soon as possible.

@cromoteca why it is so required? Cannot there be any case that would require using synthetic methods somewhere in plugins?

why it is so required? Cannot there be any case that would require using synthetic methods somewhere in plugins?

Synthetic methods are added to classes by many tools, including the JDK itself. If we start dealing with them, our classes get "polluted" in many ways. I strongly recommend to stick to "real" methods from the start, so that nothing will interfere with parsing.
As far as I remember, we already dealt with them in the past, but maybe some code changes reintroduced them.

As far as I remember, we already dealt with them in the past, but maybe some code changes reintroduced them.

Yeah, I definitely remember that we had a filter for it somewhere but I cannot find it anywhere.

Synthetic methods are added to classes by many tools, including the JDK itself. If we start dealing with them, our classes get "polluted" in many ways. I strongly recommend to stick to "real" methods from the start, so that nothing will interfere with parsing.

Hmm... Ok, let's keep it simple then.


[vm/ffi] Non-aligned reads and writes

The getters and setters on Pointer<Double> etc, require memory to be aligned.
https://github.com/dart-lang/sdk/blob/4216dc8d122f410d38d70ef6fc9bcedc5f2a0f0e/sdk/lib/ffi/ffi.dart#L475-L480
Attempting to read/write with a non-aligned pointer causes a segmentation fault on arm32-Android. (On many other hardware/OS combinations it seems to be fine or just slower.)
We currently do not provide a way to do non-aligned reads and writes.
(The workaround is to manually turn the double into 8 uint8's, and write those.)
We should add unaligned reads/writes.
   /// The double at [address].
   ///
   /// The [address] must be 8-byte aligned. Use [unalignedValue] for unaligned access.
   external double get value;

   external void set value(double value);

   /// The double at [address].
   ///
   /// The [address] may be unaligned. Use [value] for aligned access.
   external double get unalignedValue;

   external void set unalignedValue(double unalignedValue);

@lrhn unalignedValue getter and setter as naming? wdyt?
Should we add unaligned access for index operators as well? That might not be that useful.
https://github.com/dart-lang/sdk/blob/4216dc8d122f410d38d70ef6fc9bcedc5f2a0f0e/sdk/lib/ffi/ffi.dart#L482-L490
We need this feature for interacting with packed structs https://github.com/dart-lang/sdk/issues/38158.

What are the failure modes?
If you read value and the pointer was not aligned, it throws/dumps core/whatever low-level failure the hardware provides.
If you read unaligned value, it's guaranteed to read at any address point, but might be slower. (What ByteData.getFloat64 does, vs. the aligned Float64List).
You don't want to pre-check the direct read. The slow/safe-mode can probably check whether the pointer is aligned and do a quick read, and still be faster on average than not checking.
What are the alternatives?

Always allowing slower unaligned reads (requires a check first to see if it's aligned, so not good).
A separate type (UnalignedDouble) with a different read/write mode. You could then also check that the pointer is aligned when constructing the Pointer<Double>, and throw if it isn't, but that's still an overhead on aligned pointers.
This. (The name is probably fine, but it's slightly weird that it's a negative name, and the positive one is unused.)
The opposite. (value reads unaligned, alignedValue reads only aligned values. Naming is better. Usability ... depends on what people usually do, which is probably mostly aligned values.)

It's an issue that intel CPUs won't complain about unaligned pointers being read. That means that many users won't ever need to use unalignedValue, and won't even notice that it's slower. Then their code fails to work on ARM.
I'd consider the UnalignedDouble type approach. Then you have to choose up-front whether you want your double to be readable unaligned or not, and it will have the same API as all other pointers.
I think that's potentially a more consistent API than adding extra methods to all more-than-one-byte types.
It matches the typed-array story of having aligned Float64List and general unaligned reads using ByteData, so if a Pointer<Double> can be converted to a Float64List, then a Pointer<UnalignedDouble> should only be convertible to ByteData.

@dcharkes Could you be more clear about where unaligned reads/writes are not supported?
We have landed cl/152322, which removed the last support for ARMv6. ARMv7 requires support for unaligned reads and writes afaik.
/cc @mraleph Correct me if I'm wrong.

Only ARMv7 with the testing I've done locally (that does not include all OS/hardware combinations yet).

Always allowing slower unaligned reads (requires a check first to see if it's aligned, so not good).

I'll make a CL for this to see what is the impact on arm.

A3.2.1 Unaligned data access in ARM Architecture Reference Manual ARMv7-A and ARMv7-R edition specifies that VLDR and VSTR require at least word aligned addresses otherwise they trigger Alignment fault independent of SCTLR.A state. That's a bummer.
To be honest I think we should just do what C/C++ would do a segfault.

@mraleph Some instructions still require alignment, that's true (also for ldm, ...). Though in general the CPU supports it. So maybe in cases where we don't know whether data is aligned we can use a set of instructions that do support unaligned access.

For packed structs we need support for unaligned reads/writes or our Struct API will segfault.
#pragma pack(push, 1)
struct Struct9BytesPackedMixed {
  uint8_t a0;
  double a1;
};
#pragma pack(pop)

Yeah I agree that we should support reading unaligned data from packed structs - basically follow how C does it: normally it assumes aligned access, except for certain cases like packed structs.
For convenience we can also have either unaligned getters or other APIs, but I would suggest against making Pointers themselves defensive against misalignment.

Out of curiosity, here's the code our C compiler generates for packed (and therefore possibly unaligned) float/double members.
typedef struct __attribute__((packed)) {
    char _;
    double d;
    float f;
    char __;
} Foo ;

int getSize() {
  return sizeof(Foo);
}
double getDouble(Foo* f) {
  return f->d;
}
float getFloat(Foo* f) {
  return f->f;
}

In normal arm abi:
% buildtools/linux-x64/clang/bin/clang --target=armv7-linux-gnueabi --sysroot=build/linux/debian_jessie_arm-sysroot -c -O3 -o test test.c
% arm-linux-gnueabihf-objdump -d test
00000000 <getSize>:
   0:   e3a0000e        mov     r0, #14
   4:   e12fff1e        bx      lr

00000008 <getDouble>:
   8:   e2800001        add     r0, r0, #1
   c:   f460070f        vld1.8  {d16}, [r0]
  10:   ec510b30        vmov    r0, r1, d16
  14:   e12fff1e        bx      lr

00000018 <getFloat>:
  18:   e5900009        ldr     r0, [r0, #9]
  1c:   e12fff1e        bx      lr

And hard-fp abi:
% buildtools/linux-x64/clang/bin/clang --target=armv7-linux-gnueabihf --sysroot=build/linux/debian_jessie_arm-sysroot  -O3 -c -o test test.c

% arm-linux-gnueabihf-objdump -d test
00000000 <getSize>:
   0:   e3a0000e        mov     r0, #14
   4:   e12fff1e        bx      lr

00000008 <getDouble>:
   8:   e2800001        add     r0, r0, #1
   c:   f420070f        vld1.8  {d0}, [r0]
  10:   e12fff1e        bx      lr

00000014 <getFloat>:
  14:   e5900009        ldr     r0, [r0, #9]
  18:   ee000a10        vmov    s0, r0
  1c:   e12fff1e        bx      lr

The ARM ISA mentions:
A3.2.1 Unaligned data access

An ARMv7 implementation must support unaligned data accesses by some load and store instructions, as
Table A3-1 shows. Software can set the SCTLR.A bit to control whether a misaligned access by one of these
instructions causes an Alignment fault Data Abort exception.

| Instructions Alignment  | check        | SCTLR.A is 0     | SCTLR.A is 1    |
-------------------------------------------------------------------------------
| VLD1 ...                | Element size | Unaligned access | Alignment fault |

We will not expose unaligned reads/writes in a public API, only implement it for packed structs.


[Enhancement] Participants list during the Workshop

Once the Workshop started, it's impossible to show the participants.
2 solutions are possible:

Add duration to workshops and show all unfinished workshops
Show workshops planned in the futur and for today

The 1st solution is better because we could also show the duration to participants.

I like the duration idea, as soon as the workshop starts we can add a flag:
live or something like that before moving it to already played!
On Thu, Feb 19, 2015 at 10:00 AM, Naël MSKINE<EMAIL_ADDRESS>wrote:

Once the Workshop started, it's impossible to show the participants.
2 solutions are possible:

Add duration to workshops and show all unfinished workshops
Show workshops planned in the futur and for today

The 1st solution is better because we could also show the duration to
participants.
—
Reply to this email directly or view it on GitHub
https://github.com/sqlilabs/WorkshopManager/issues/73.